[pysal] 01/02: Imported Upstream version 1.9.1
Johan Van de Wauw
johanvdw-guest at moszumanska.debian.org
Fri Jun 12 20:58:01 UTC 2015
This is an automated email from the git hooks/post-receive script.
johanvdw-guest pushed a commit to branch master
in repository pysal.
commit 919fbbd032459f1a112d3bd2a2894f14389644cf
Author: Johan Van de Wauw <johan.vandewauw at gmail.com>
Date: Thu Feb 5 21:45:06 2015 +0100
Imported Upstream version 1.9.1
---
.gitignore | 79 +
.travis.yml | 46 +
CHANGELOG.txt | 1993 +++++++
INSTALL.txt | 242 +
LICENSE.txt | 30 +
MANIFEST.in | 2 +
Makefile | 25 +
README.md | 101 +
README.txt | 101 +
THANKS.txt | 43 +
Vagrantfile | 72 +
authors.txt | 22 +
distribute_setup.py | 485 ++
doc/Makefile | 99 +
doc/source/_static/favicon.png | Bin 0 -> 4808 bytes
doc/source/_static/images/bugs.png | Bin 0 -> 6442 bytes
doc/source/_static/images/documentation.png | Bin 0 -> 1868 bytes
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doc/source/_static/images/tutorial.png | Bin 0 -> 7119 bytes
doc/source/_static/images/yi_jing_01_chien.jpg | Bin 0 -> 9498 bytes
doc/source/_static/mydoc.css | 3 +
doc/source/_static/pysalgraph.png | Bin 0 -> 46397 bytes
doc/source/_static/pysalsmall.png | Bin 0 -> 15755 bytes
doc/source/_templates/ganalytics_layout.html | 213 +
doc/source/_templates/layout.html | 24 +
doc/source/conf.py | 230 +
doc/source/contents.txt | 16 +
doc/source/developers/docs/index.rst | 252 +
doc/source/developers/guidelines.rst | 130 +
doc/source/developers/index.rst | 18 +
doc/source/developers/known-issues.rst | 42 +
doc/source/developers/pep/index.rst | 18 +
doc/source/developers/pep/pep-0001.rst | 74 +
doc/source/developers/pep/pep-0002.rst | 56 +
doc/source/developers/pep/pep-0003.rst | 55 +
doc/source/developers/pep/pep-0004.rst | 56 +
doc/source/developers/pep/pep-0005.rst | 56 +
doc/source/developers/pep/pep-0006.rst | 60 +
doc/source/developers/pep/pep-0007.rst | 76 +
doc/source/developers/pep/pep-0008.rst | 55 +
doc/source/developers/pep/pep-0009.rst | 57 +
doc/source/developers/pep/pep-0010.rst | 46 +
doc/source/developers/pep/pep-0011.rst | 120 +
doc/source/developers/projects.rst | 25 +
doc/source/developers/py3k.rst | 111 +
doc/source/developers/release.rst | 93 +
doc/source/developers/testing.rst | 127 +
doc/source/funding.rst | 16 +
doc/source/index.rst | 87 +
doc/source/index.txt | 110 +
doc/source/library/cg/index.rst | 16 +
doc/source/library/cg/kdtree.rst | 10 +
doc/source/library/cg/locators.rst | 14 +
doc/source/library/cg/rtree.rst | 10 +
doc/source/library/cg/shapes.rst | 10 +
doc/source/library/cg/sphere.rst | 10 +
doc/source/library/cg/standalone.rst | 11 +
doc/source/library/contrib/index.rst | 73 +
doc/source/library/core/FileIO.rst | 15 +
doc/source/library/core/IOHandlers/arcgis_dbf.rst | 13 +
doc/source/library/core/IOHandlers/arcgis_swm.rst | 13 +
doc/source/library/core/IOHandlers/arcgis_txt.rst | 10 +
doc/source/library/core/IOHandlers/csvWrapper.rst | 13 +
doc/source/library/core/IOHandlers/dat.rst | 10 +
doc/source/library/core/IOHandlers/gal.rst | 13 +
doc/source/library/core/IOHandlers/geobugs_txt.rst | 13 +
doc/source/library/core/IOHandlers/geoda_txt.rst | 10 +
doc/source/library/core/IOHandlers/gwt.rst | 10 +
doc/source/library/core/IOHandlers/index.rst | 8 +
doc/source/library/core/IOHandlers/mat.rst | 10 +
doc/source/library/core/IOHandlers/mtx.rst | 10 +
doc/source/library/core/IOHandlers/pyDbfIO.rst | 11 +
doc/source/library/core/IOHandlers/pyShpIO.rst | 12 +
doc/source/library/core/IOHandlers/stata_txt.rst | 12 +
doc/source/library/core/IOHandlers/wk1.rst | 13 +
doc/source/library/core/IOHandlers/wkt.rst | 15 +
doc/source/library/core/Tables.rst | 12 +
doc/source/library/core/index.rst | 8 +
doc/source/library/esda/gamma.rst | 10 +
doc/source/library/esda/geary.rst | 10 +
doc/source/library/esda/getis-ord.rst | 11 +
doc/source/library/esda/index.rst | 13 +
doc/source/library/esda/join_counts.rst | 9 +
doc/source/library/esda/mapclassify.rst | 10 +
doc/source/library/esda/moran.rst | 11 +
doc/source/library/esda/smoothing.rst | 9 +
doc/source/library/index.rst | 34 +
doc/source/library/inequality/gini.rst | 12 +
doc/source/library/inequality/index.rst | 8 +
doc/source/library/inequality/theil.rst | 12 +
doc/source/library/network/index.rst | 7 +
doc/source/library/network/network.rst | 13 +
doc/source/library/region/index.rst | 9 +
doc/source/library/region/maxp.rst | 8 +
doc/source/library/region/randomregion.rst | 8 +
.../library/spatial_dynamics/directional.rst | 8 +
doc/source/library/spatial_dynamics/ergodic.rst | 8 +
doc/source/library/spatial_dynamics/index.rst | 13 +
.../library/spatial_dynamics/interaction.rst | 8 +
doc/source/library/spatial_dynamics/markov.rst | 8 +
doc/source/library/spatial_dynamics/rank.rst | 8 +
doc/source/library/spreg/diagnostics.rst | 12 +
doc/source/library/spreg/diagnostics_sp.rst | 12 +
doc/source/library/spreg/diagnostics_tsls.rst | 12 +
doc/source/library/spreg/error_sp.rst | 12 +
doc/source/library/spreg/error_sp_het.rst | 12 +
doc/source/library/spreg/error_sp_het_regimes.rst | 12 +
doc/source/library/spreg/error_sp_hom.rst | 12 +
doc/source/library/spreg/error_sp_hom_regimes.rst | 12 +
doc/source/library/spreg/error_sp_regimes.rst | 12 +
doc/source/library/spreg/index.rst | 29 +
doc/source/library/spreg/ml_error.rst | 12 +
doc/source/library/spreg/ml_error_regimes.rst | 12 +
doc/source/library/spreg/ml_lag.rst | 12 +
doc/source/library/spreg/ml_lag_regimes.rst | 12 +
doc/source/library/spreg/ols.rst | 12 +
doc/source/library/spreg/ols_regimes.rst | 12 +
doc/source/library/spreg/probit.rst | 12 +
doc/source/library/spreg/regimes.rst | 12 +
doc/source/library/spreg/twosls.rst | 12 +
doc/source/library/spreg/twosls_regimes.rst | 12 +
doc/source/library/spreg/twosls_sp.rst | 12 +
doc/source/library/spreg/twosls_sp_regimes.rst | 12 +
doc/source/library/weights/Contiguity.rst | 11 +
doc/source/library/weights/Distance.rst | 11 +
doc/source/library/weights/Wsets.rst | 9 +
doc/source/library/weights/index.rst | 12 +
doc/source/library/weights/spatial_lag.rst | 14 +
doc/source/library/weights/user.rst | 10 +
doc/source/library/weights/util.rst | 10 +
doc/source/library/weights/weights.rst | 13 +
doc/source/license.rst | 33 +
doc/source/news.rst | 55 +
doc/source/users/index.rst | 11 +
doc/source/users/installation.rst | 160 +
doc/source/users/introduction.rst | 68 +
doc/source/users/tutorials/autocorrelation.rst | 830 +++
doc/source/users/tutorials/dynamics.rst | 805 +++
doc/source/users/tutorials/econometrics.rst | 23 +
doc/source/users/tutorials/examples.rst | 142 +
doc/source/users/tutorials/fileio.rst | 429 ++
doc/source/users/tutorials/index.rst | 20 +
doc/source/users/tutorials/intro.rst | 108 +
doc/source/users/tutorials/next.rst | 17 +
doc/source/users/tutorials/region.rst | 211 +
doc/source/users/tutorials/shapely.rst | 94 +
doc/source/users/tutorials/smoothing.rst | 405 ++
doc/source/users/tutorials/weights.rst | 944 +++
pysal/COPYING | 25 +
pysal/__init__.py | 94 +
pysal/cg/__init__.py | 10 +
pysal/cg/kdtree.py | 246 +
pysal/cg/locators.py | 990 ++++
pysal/cg/rtree.py | 639 ++
pysal/cg/segmentLocator.py | 403 ++
pysal/cg/shapes.py | 1913 ++++++
pysal/cg/sphere.py | 502 ++
pysal/cg/standalone.py | 913 +++
pysal/cg/tests/__init__.py | 0
pysal/cg/tests/test_geoJSON.py | 26 +
pysal/cg/tests/test_locators.py | 65 +
pysal/cg/tests/test_rtree.py | 68 +
pysal/cg/tests/test_segmentLocator.py | 48 +
pysal/cg/tests/test_shapes.py | 758 +++
pysal/cg/tests/test_standalone.py | 601 ++
pysal/common.py | 25 +
pysal/contrib/README | 57 +
pysal/contrib/__init__.py | 0
pysal/contrib/clusterpy/README.md | 15 +
pysal/contrib/clusterpy/__init__.py | 12 +
pysal/contrib/clusterpy/clusterpy.ipynb | 3320 +++++++++++
pysal/contrib/clusterpy/clusterpy_ext.py | 284 +
pysal/contrib/db/PGDump.py | 104 +
pysal/contrib/db/README | 125 +
pysal/contrib/db/README_PLPYTHON | 10 +
pysal/contrib/network/__init__.py | 0
pysal/contrib/network/access.py | 56 +
pysal/contrib/network/crimes.dbf | Bin 0 -> 5551 bytes
pysal/contrib/network/crimes.shp | Bin 0 -> 8136 bytes
pysal/contrib/network/crimes.shx | Bin 0 -> 2396 bytes
pysal/contrib/network/distances.csv | 65 +
pysal/contrib/network/kernel.py | 184 +
pysal/contrib/network/kfuncs.py | 75 +
pysal/contrib/network/klincs.py | 233 +
pysal/contrib/network/lincs.py | 349 ++
pysal/contrib/network/network.py | 599 ++
pysal/contrib/network/priordict.py | 80 +
pysal/contrib/network/simulator.py | 235 +
pysal/contrib/network/streets.dbf | Bin 0 -> 11525 bytes
pysal/contrib/network/streets.shp | Bin 0 -> 26044 bytes
pysal/contrib/network/streets.shx | Bin 0 -> 2444 bytes
pysal/contrib/network/test_access.py | 53 +
pysal/contrib/network/test_kernel.py | 44 +
pysal/contrib/network/test_kfuncs.py | 40 +
pysal/contrib/network/test_klincs.py | 136 +
pysal/contrib/network/test_lincs.py | 89 +
pysal/contrib/network/test_network.py | 186 +
pysal/contrib/network/test_weights.py | 22 +
pysal/contrib/network/weights.py | 80 +
pysal/contrib/opendata/__init__.py | 0
pysal/contrib/opendata/google.py | 287 +
pysal/contrib/shapely_ext.py | 301 +
pysal/contrib/shared_perimeter_weights.py | 33 +
pysal/contrib/spatialnet/__init__.py | 0
pysal/contrib/spatialnet/beth_roads.shp | Bin 0 -> 77660 bytes
pysal/contrib/spatialnet/beth_roads.shx | Bin 0 -> 3164 bytes
pysal/contrib/spatialnet/cleanNetShp.py | 141 +
pysal/contrib/spatialnet/eberly.shp | Bin 0 -> 2652 bytes
pysal/contrib/spatialnet/eberly.shx | Bin 0 -> 332 bytes
pysal/contrib/spatialnet/spatialnet.py | 107 +
pysal/contrib/spatialnet/util.py | 221 +
pysal/contrib/viz/__init__.py | 0
pysal/contrib/viz/mapping.py | 598 ++
pysal/contrib/viz/mapping_guide.ipynb | 335 ++
pysal/contrib/viz/taz_example.ipynb | 454 ++
pysal/contrib/weights_viewer/__init__.py | 0
pysal/contrib/weights_viewer/transforms.py | 208 +
pysal/contrib/weights_viewer/weights_viewer.py | 179 +
pysal/core/FileIO.py | 346 ++
pysal/core/IOHandlers/__init__.py | 20 +
pysal/core/IOHandlers/arcgis_dbf.py | 231 +
pysal/core/IOHandlers/arcgis_swm.py | 212 +
pysal/core/IOHandlers/arcgis_txt.py | 208 +
pysal/core/IOHandlers/csvWrapper.py | 97 +
pysal/core/IOHandlers/dat.py | 132 +
pysal/core/IOHandlers/gal.py | 218 +
pysal/core/IOHandlers/geobugs_txt.py | 254 +
pysal/core/IOHandlers/geoda_txt.py | 91 +
pysal/core/IOHandlers/gwt.py | 286 +
pysal/core/IOHandlers/mat.py | 175 +
pysal/core/IOHandlers/mtx.py | 241 +
pysal/core/IOHandlers/pyDbfIO.py | 306 +
pysal/core/IOHandlers/pyShpIO.py | 189 +
pysal/core/IOHandlers/stata_txt.py | 236 +
pysal/core/IOHandlers/template.py | 145 +
pysal/core/IOHandlers/tests/__init__.py | 0
pysal/core/IOHandlers/tests/test_arcgis_dbf.py | 57 +
pysal/core/IOHandlers/tests/test_arcgis_swm.py | 44 +
pysal/core/IOHandlers/tests/test_arcgis_txt.py | 60 +
pysal/core/IOHandlers/tests/test_csvWrapper.py | 61 +
pysal/core/IOHandlers/tests/test_dat.py | 44 +
pysal/core/IOHandlers/tests/test_gal.py | 49 +
pysal/core/IOHandlers/tests/test_geobugs_txt.py | 57 +
pysal/core/IOHandlers/tests/test_geoda_txt.py | 26 +
pysal/core/IOHandlers/tests/test_gwt.py | 54 +
pysal/core/IOHandlers/tests/test_mat.py | 49 +
pysal/core/IOHandlers/tests/test_mtx.py | 57 +
pysal/core/IOHandlers/tests/test_pyDbfIO.py | 111 +
pysal/core/IOHandlers/tests/test_pyShpIO.py | 85 +
pysal/core/IOHandlers/tests/test_stata_txt.py | 61 +
pysal/core/IOHandlers/tests/test_wk1.py | 44 +
pysal/core/IOHandlers/tests/test_wkt.py | 31 +
pysal/core/IOHandlers/wk1.py | 330 ++
pysal/core/IOHandlers/wkt.py | 98 +
pysal/core/Tables.py | 167 +
pysal/core/__init__.py | 1 +
pysal/core/tests/__init__.py | 0
pysal/core/tests/test_FileIO.py | 0
pysal/core/util/__init__.py | 2 +
pysal/core/util/shapefile.py | 758 +++
pysal/core/util/tests/test_shapefile.py | 362 ++
pysal/core/util/tests/test_weight_converter.py | 137 +
pysal/core/util/tests/test_wkt.py | 52 +
pysal/core/util/weight_converter.py | 243 +
pysal/core/util/wkt.py | 125 +
pysal/esda/__init__.py | 12 +
pysal/esda/gamma.py | 204 +
pysal/esda/geary.py | 161 +
pysal/esda/getisord.py | 394 ++
pysal/esda/join_counts.py | 144 +
pysal/esda/mapclassify.py | 1867 ++++++
pysal/esda/mixture_smoothing.py | 311 +
pysal/esda/moran.py | 793 +++
pysal/esda/smoothing.py | 1573 +++++
pysal/esda/tests/__init__.py | 0
pysal/esda/tests/test_gamma.py | 68 +
pysal/esda/tests/test_geary.py | 61 +
pysal/esda/tests/test_getisord.py | 59 +
pysal/esda/tests/test_join_counts.py | 41 +
pysal/esda/tests/test_mapclassify.py | 373 ++
pysal/esda/tests/test_mixture_smoothing.py | 43 +
pysal/esda/tests/test_moran.py | 94 +
pysal/esda/tests/test_smoothing.py | 248 +
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pysal/examples/10740.shx | Bin 0 -> 1660 bytes
pysal/examples/10740_queen.gal | 391 ++
pysal/examples/10740_rook.gal | 391 ++
pysal/examples/Chicago77.dbf | Bin 0 -> 16787 bytes
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pysal/examples/Line.dbf | Bin 0 -> 621 bytes
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pysal/examples/Point.dbf | Bin 0 -> 1236 bytes
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pysal/examples/Point.shx | Bin 0 -> 172 bytes
pysal/examples/Polygon.dbf | Bin 0 -> 498 bytes
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pysal/examples/Polygon.shx | Bin 0 -> 124 bytes
pysal/examples/README.txt | 5 +
pysal/examples/__init__.py | 9 +
pysal/examples/arcgis_ohio.dbf | Bin 0 -> 21876 bytes
pysal/examples/arcgis_txt.txt | 9 +
pysal/examples/baltim.dbf | Bin 0 -> 31595 bytes
pysal/examples/baltim.shp | Bin 0 -> 6008 bytes
pysal/examples/baltim.shx | Bin 0 -> 1788 bytes
pysal/examples/baltim_k4.gwt | 845 +++
pysal/examples/baltim_q.gal | 423 ++
pysal/examples/book.gal | 33 +
pysal/examples/book.txt | 18 +
pysal/examples/burkitt.dbf | Bin 0 -> 7558 bytes
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pysal/examples/burkitt.shx | Bin 0 -> 1604 bytes
pysal/examples/calempdensity.csv | 59 +
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pysal/examples/columbus.gal | 99 +
pysal/examples/columbus.html | 132 +
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pysal/examples/columbus.shx | Bin 0 -> 492 bytes
pysal/examples/desmith.gal | 21 +
pysal/examples/desmith.txt | 12 +
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pysal/examples/eberly_net.shp | Bin 0 -> 2652 bytes
pysal/examples/eberly_net.shx | Bin 0 -> 332 bytes
pysal/examples/eberly_net_pts_offnetwork.dbf | Bin 0 -> 2197 bytes
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pysal/examples/eberly_net_pts_offnetwork.shx | Bin 0 -> 900 bytes
pysal/examples/eberly_net_pts_onnetwork.dbf | Bin 0 -> 1275 bytes
pysal/examples/eberly_net_pts_onnetwork.shp | Bin 0 -> 3180 bytes
pysal/examples/eberly_net_pts_onnetwork.shx | Bin 0 -> 980 bytes
pysal/examples/examples.txt | 141 +
pysal/examples/geobugs_scot | 66 +
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pysal/examples/geodanet/crimes.shp | Bin 0 -> 8136 bytes
pysal/examples/geodanet/crimes.shp.xml | 3 +
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pysal/examples/geodanet/schools.dbf | Bin 0 -> 146 bytes
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pysal/examples/geodanet/schools.shp | Bin 0 -> 324 bytes
pysal/examples/geodanet/schools.shp.xml | 546 ++
pysal/examples/geodanet/schools.shx | Bin 0 -> 164 bytes
pysal/examples/geodanet/streets.dbf | Bin 0 -> 11525 bytes
pysal/examples/geodanet/streets.prj | 1 +
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pysal/examples/geodanet/streets.shx | Bin 0 -> 2444 bytes
pysal/examples/juvenile.dbf | Bin 0 -> 4834 bytes
pysal/examples/juvenile.gwt | 2803 +++++++++
pysal/examples/juvenile.html | 63 +
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pysal/examples/lattice10x10.shp | Bin 0 -> 13700 bytes
pysal/examples/lattice10x10.shx | Bin 0 -> 900 bytes
pysal/examples/mexico.csv | 33 +
pysal/examples/mexico.gal | 65 +
pysal/examples/nat_queen.gal | 6171 ++++++++++++++++++++
pysal/examples/nat_queen_old.gal | 6171 ++++++++++++++++++++
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pysal/examples/nonplanarsegments.dbf | Bin 0 -> 87 bytes
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pysal/examples/ohio.swm | Bin 0 -> 6978 bytes
pysal/examples/rook31.dbf | Bin 0 -> 161 bytes
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pysal/examples/sacramentot2.dbf | Bin 0 -> 98149 bytes
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pysal/examples/sids2.dbf | Bin 0 -> 23810 bytes
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pysal/examples/snow_maps/SohoWater.shp | Bin 0 -> 464 bytes
pysal/examples/snow_maps/SohoWater.shx | Bin 0 -> 204 bytes
pysal/examples/snow_maps/Soho_Network.dbf | Bin 0 -> 892 bytes
pysal/examples/snow_maps/Soho_Network.prj | 1 +
pysal/examples/snow_maps/Soho_Network.sbn | Bin 0 -> 1292 bytes
pysal/examples/snow_maps/Soho_Network.sbx | Bin 0 -> 228 bytes
pysal/examples/snow_maps/Soho_Network.shp | Bin 0 -> 11620 bytes
pysal/examples/snow_maps/Soho_Network.shx | Bin 0 -> 1044 bytes
pysal/examples/south.dbf | Bin 0 -> 1145962 bytes
pysal/examples/south.shp | Bin 0 -> 737448 bytes
pysal/examples/south.shx | Bin 0 -> 11396 bytes
pysal/examples/south_q.gal | 2825 +++++++++
pysal/examples/spat-sym-us.mat | Bin 0 -> 416 bytes
pysal/examples/spat-sym-us.wk1 | Bin 0 -> 2221 bytes
pysal/examples/spdep_listw2WB_columbus | 59 +
pysal/examples/spi_download.csv | 66 +
pysal/examples/stata_full.txt | 57 +
pysal/examples/stata_sparse.txt | 57 +
pysal/examples/states48.gal | 97 +
pysal/examples/stl.gal | 157 +
pysal/examples/stl_hom.csv | 79 +
pysal/examples/stl_hom.dbf | Bin 0 -> 23280 bytes
pysal/examples/stl_hom.html | 143 +
pysal/examples/stl_hom.shp | Bin 0 -> 28276 bytes
pysal/examples/stl_hom.shx | Bin 0 -> 724 bytes
pysal/examples/stl_hom.txt | 80 +
pysal/examples/stl_hom.wkt | 78 +
pysal/examples/stl_hom_rook.gal | 157 +
pysal/examples/street_net_pts.dbf | Bin 0 -> 3398 bytes
pysal/examples/street_net_pts.prj | 1 +
pysal/examples/street_net_pts.qpj | 1 +
pysal/examples/street_net_pts.shp | Bin 0 -> 8584 bytes
pysal/examples/street_net_pts.shx | Bin 0 -> 2524 bytes
pysal/examples/taz.dbf | Bin 0 -> 485344 bytes
pysal/examples/taz.shp | Bin 0 -> 5774828 bytes
pysal/examples/taz.shx | Bin 0 -> 32972 bytes
pysal/examples/us48.dbf | Bin 0 -> 4274 bytes
pysal/examples/us48.shp | Bin 0 -> 186476 bytes
pysal/examples/us48.shx | Bin 0 -> 484 bytes
pysal/examples/usjoin.csv | 49 +
pysal/examples/virginia.dbf | Bin 0 -> 11410 bytes
pysal/examples/virginia.gal | 273 +
pysal/examples/virginia.prj | 1 +
pysal/examples/virginia.shp | Bin 0 -> 71416 bytes
pysal/examples/virginia.shx | Bin 0 -> 1188 bytes
pysal/examples/virginia_rook.gal | 273 +
pysal/examples/wmat.dat | 232 +
pysal/examples/wmat.mtx | 237 +
pysal/inequality/__init__.py | 8 +
pysal/inequality/_indices.py | 579 ++
pysal/inequality/gini.py | 167 +
pysal/inequality/tests/__init__.py | 0
pysal/inequality/tests/test_theil.py | 42 +
pysal/inequality/theil.py | 201 +
pysal/meta/akern1.wmd | 15 +
pysal/meta/chain.wmd | 14 +
pysal/meta/chain2.wmd | 14 +
pysal/meta/kernel.wmd | 17 +
pysal/meta/knn.wmd | 14 +
pysal/meta/taz_block.wmd | 11 +
pysal/meta/taz_intersection.wmd | 16 +
pysal/meta/taz_rook.wmd | 10 +
pysal/meta/wmd.py | 476 ++
pysal/meta/wrook1.wmd | 9 +
pysal/meta/wrooko1.wmd | 12 +
pysal/meta/wrooko2.wmd | 12 +
pysal/meta/wrooko2l.wmd | 12 +
pysal/network/Network Usage.ipynb | 644 ++
pysal/network/__init__.py | 7 +
pysal/network/analysis.py | 186 +
pysal/network/network.py | 1162 ++++
pysal/network/util.py | 155 +
pysal/region/__init__.py | 9 +
pysal/region/components.py | 160 +
pysal/region/maxp.py | 588 ++
pysal/region/randomregion.py | 527 ++
pysal/region/tests/__init__.py | 0
pysal/region/tests/test_components.py | 25 +
pysal/region/tests/test_maxp.py | 56 +
pysal/region/tests/test_randomregion.py | 177 +
pysal/spatial_dynamics/__init__.py | 10 +
pysal/spatial_dynamics/directional.py | 187 +
pysal/spatial_dynamics/ergodic.py | 179 +
pysal/spatial_dynamics/interaction.ipynb | 4151 +++++++++++++
pysal/spatial_dynamics/interaction.py | 641 ++
pysal/spatial_dynamics/markov.py | 1548 +++++
pysal/spatial_dynamics/rank.py | 443 ++
pysal/spatial_dynamics/tests/__init__.py | 0
pysal/spatial_dynamics/tests/test_directional.py | 62 +
pysal/spatial_dynamics/tests/test_ergodic.py | 55 +
pysal/spatial_dynamics/tests/test_interaction.py | 80 +
pysal/spatial_dynamics/tests/test_markov.py | 190 +
pysal/spatial_dynamics/tests/test_rank.py | 73 +
pysal/spatial_dynamics/tests/test_util.py | 41 +
pysal/spatial_dynamics/util.py | 79 +
pysal/spreg/__init__.py | 20 +
pysal/spreg/diagnostics.py | 1424 +++++
pysal/spreg/diagnostics_sp.py | 826 +++
pysal/spreg/diagnostics_tsls.py | 337 ++
pysal/spreg/error_sp.py | 1133 ++++
pysal/spreg/error_sp_het.py | 1509 +++++
pysal/spreg/error_sp_het_regimes.py | 1481 +++++
pysal/spreg/error_sp_hom.py | 1522 +++++
pysal/spreg/error_sp_hom_regimes.py | 1496 +++++
pysal/spreg/error_sp_regimes.py | 1374 +++++
pysal/spreg/ml_error.py | 511 ++
pysal/spreg/ml_error_regimes.py | 462 ++
pysal/spreg/ml_lag.py | 593 ++
pysal/spreg/ml_lag_regimes.py | 484 ++
pysal/spreg/ols.py | 471 ++
pysal/spreg/ols_regimes.py | 541 ++
pysal/spreg/probit.py | 684 +++
pysal/spreg/regimes.py | 689 +++
pysal/spreg/robust.py | 169 +
pysal/spreg/summary_output.py | 1235 ++++
pysal/spreg/tests/test_diagnostics.py | 128 +
pysal/spreg/tests/test_diagnostics_sp.py | 179 +
pysal/spreg/tests/test_diagnostics_tsls.py | 66 +
pysal/spreg/tests/test_error_sp.py | 317 +
pysal/spreg/tests/test_error_sp_het.py | 407 ++
pysal/spreg/tests/test_error_sp_het_regimes.py | 307 +
pysal/spreg/tests/test_error_sp_het_sparse.py | 414 ++
pysal/spreg/tests/test_error_sp_hom.py | 315 +
pysal/spreg/tests/test_error_sp_hom_regimes.py | 307 +
pysal/spreg/tests/test_error_sp_hom_sparse.py | 320 +
pysal/spreg/tests/test_error_sp_regimes.py | 305 +
pysal/spreg/tests/test_error_sp_sparse.py | 332 ++
pysal/spreg/tests/test_ml_error.py | 71 +
pysal/spreg/tests/test_ml_error_regimes.py | 138 +
pysal/spreg/tests/test_ml_lag.py | 68 +
pysal/spreg/tests/test_ml_lag_regimes.py | 117 +
pysal/spreg/tests/test_ols.py | 125 +
pysal/spreg/tests/test_ols_regimes.py | 144 +
pysal/spreg/tests/test_ols_sparse.py | 107 +
pysal/spreg/tests/test_probit.py | 108 +
pysal/spreg/tests/test_twosls.py | 254 +
pysal/spreg/tests/test_twosls_regimes.py | 276 +
pysal/spreg/tests/test_twosls_sp.py | 369 ++
pysal/spreg/tests/test_twosls_sp_regimes.py | 352 ++
pysal/spreg/tests/test_twosls_sp_sparse.py | 379 ++
pysal/spreg/tests/test_twosls_sparse.py | 258 +
pysal/spreg/twosls.py | 475 ++
pysal/spreg/twosls_regimes.py | 515 ++
pysal/spreg/twosls_sp.py | 540 ++
pysal/spreg/twosls_sp_regimes.py | 705 +++
pysal/spreg/user_output.py | 642 ++
pysal/spreg/utils.py | 839 +++
pysal/spreg/w_utils.py | 27 +
pysal/test_NameSpace.py | 51 +
pysal/version.py | 1 +
pysal/weights/Contiguity.py | 97 +
pysal/weights/Distance.py | 532 ++
pysal/weights/Wsets.py | 506 ++
pysal/weights/__init__.py | 12 +
pysal/weights/_contW_binning.py | 389 ++
pysal/weights/_contW_rtree.py | 116 +
pysal/weights/spatial_lag.py | 84 +
pysal/weights/tests/__init__.py | 0
pysal/weights/tests/test_Contiguity.py | 27 +
pysal/weights/tests/test_Distance.py | 149 +
pysal/weights/tests/test_Wsets.py | 64 +
pysal/weights/tests/test__contW_binning.py | 132 +
pysal/weights/tests/test__contW_rtree.py | 138 +
pysal/weights/tests/test_spatial_lag.py | 41 +
pysal/weights/tests/test_user.py | 161 +
pysal/weights/tests/test_util.py | 186 +
pysal/weights/tests/test_weights.py | 451 ++
pysal/weights/user.py | 1123 ++++
pysal/weights/util.py | 1197 ++++
pysal/weights/weights.py | 1066 ++++
pysal/weights/weights_from_geojson.ipynb | 311 +
requirements.txt | 1 +
rtd.txt | 1 +
setup.cfg | 6 +
setup.py | 120 +
tools/cron.py | 16 +
tools/docs.sh | 39 +
tools/github_stats.py | 193 +
tools/py3tool.py | 345 ++
tools/test.sh | 69 +
travis.txt | 6 +
592 files changed, 112811 insertions(+)
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..b3b303c
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,79 @@
+*.py[cod]
+*.bak
+.ipynb_checkpoints/
+# C extensions
+*.so
+
+# Packages
+*.egg
+*.egg-info
+dist
+build
+eggs
+parts
+bin
+var
+sdist
+develop-eggs
+.installed.cfg
+lib
+lib64
+__pycache__
+
+# Installer logs
+pip-log.txt
+
+# Unit test / coverage reports
+.coverage
+.tox
+nosetests.xml
+
+# Translations
+*.mo
+
+# Mr Developer
+.mr.developer.cfg
+.project
+.pydevproject
+
+# OS generated files #
+######################
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+Icon?
+ehthumbs.db
+Thumbs.db
+
+
+# pysal
+#
+lattice.*
+.vagrant/
+pysal/contrib/viz/.ipynb_checkpoints/
+pysal/contrib/viz/bp.png
+pysal/contrib/viz/fj.png
+pysal/contrib/viz/fj_classless.png
+pysal/contrib/viz/lmet.tex
+pysal/contrib/viz/lmp.tex
+pysal/contrib/viz/lmplot.png
+pysal/contrib/viz/lmss.tex
+pysal/contrib/viz/lmt.tex
+pysal/contrib/viz/out.png
+pysal/contrib/viz/p.tex
+pysal/contrib/viz/quantiles.png
+pysal/contrib/viz/quantiles_HR60.png
+pysal/contrib/viz/quantiles_HR70.png
+pysal/contrib/viz/quantiles_HR80.png
+pysal/contrib/viz/quantiles_HR90.png
+pysal/contrib/viz/quatiles.png
+pysal/contrib/viz/region.ipynb
+pysal/contrib/viz/south_base.html
+pysal/contrib/viz/sp.tex
+pysal/contrib/viz/sss.tex
+pysal/examples/south.prj
+
+#Vi
+*.swp
diff --git a/.travis.yml b/.travis.yml
new file mode 100644
index 0000000..e5d1168
--- /dev/null
+++ b/.travis.yml
@@ -0,0 +1,46 @@
+language: python
+branches:
+only:
+ - master
+python:
+ - "2.7"
+ #- "2.6"
+ #
+env:
+ - SCIPY_VERSION="deb http://us.archive.ubuntu.com/ubuntu/ precise main universe"
+ - SCIPY_VERSION="deb http://us.archive.ubuntu.com/ubuntu/ trusty main universe"
+
+virtualenv:
+ system_site_packages: true
+
+before_install:
+ - sudo apt-get update -qq
+ - sudo apt-get install -qq libgeos-3.2.2 libgeos-dev python-numpy python-scipy
+ - sudo apt-add-repository "$SCIPY_VERSION"
+ - sudo apt-get update -qq
+ - sudo apt-get --only-upgrade install -qq python-numpy python-scipy
+
+install:
+ - pip install -q -r travis.txt
+ #- sudo pip install http://sourceforge.net/projects/pychecker/files/pychecker/0.8.19/pychecker-0.8.19.tar.gz/download
+ #- pip install coveralls
+ - sudo rm -rf /dev/shm && sudo ln -s /run/shm /dev/shm
+
+script:
+ - python -c 'import numpy; print numpy.__version__'
+ - python -c 'import scipy; print scipy.__version__'
+ - python setup.py install >/dev/null
+ - python -c 'import pysal; print pysal.version'
+ #- python -c 'import multiprocessing as mp; print mp.cpu_count()'
+ #- pychecker --limit 1000 pysal
+ - python setup.py sdist >/dev/null
+ #- nosetests --processes=-1 --process-timeout=60 --verbosity=1 --ignore-files=collection --exclude-dir=pysal/contrib
+ - nosetests
+ - cd doc; make pickle; make doctest
+notifications:
+ email:
+ recipients:
+ - sjsrey at gmail.com
+ - phil.stphns at gmail.com
+ on_success: always
+ on_failure: always
diff --git a/CHANGELOG.txt b/CHANGELOG.txt
new file mode 100644
index 0000000..17a0e90
--- /dev/null
+++ b/CHANGELOG.txt
@@ -0,0 +1,1993 @@
+v<1.9.1>, 2015-01-31
+
+GitHub stats for 2015/01/30 - 2015/01/31
+
+These lists are automatically generated, and may be incomplete or contain duplicates.
+
+The following 4 authors contributed 14 commits.
+
+* Dani Arribas-Bel
+* Serge Rey
+* Sergio Rey
+* jlaura
+
+
+We closed a total of 8 issues, 3 pull requests and 5 regular issues;
+this is the full list (generated with the script
+:file:`tools/github_stats.py`):
+
+Pull Requests (3):
+
+* :ghpull:`566`: Fix for 1.9.0 missing file in setup.py
+* :ghpull:`563`: Updating release instructions
+* :ghpull:`561`: Rolling over to 1.10
+
+Issues (5):
+
+* :ghissue:`566`: Fix for 1.9.0 missing file in setup.py
+* :ghissue:`565`: Bsetup
+* :ghissue:`563`: Updating release instructions
+* :ghissue:`562`: adjustments to release management
+* :ghissue:`561`: Rolling over to 1.10
+
+
+v<1.9.0>, 2015-01-30
+
+GitHub stats for 2014/07/25 - 2015/01/30
+
+These lists are automatically generated, and may be incomplete or contain duplicates.
+
+The following 12 authors contributed 131 commits.
+
+* Andy Reagan
+* Dani Arribas-Bel
+* Jay
+* Levi John Wolf
+* Philip Stephens
+* Qunshan
+* Serge Rey
+* jlaura
+* ljwolf
+* luc
+
+
+We closed a total of 113 issues, 44 pull requests and 69 regular issues;
+this is the full list (generated with the script
+:file:`tools/github_stats.py`):
+
+Pull Requests (44):
+
+* :ghpull:`560`: modifying import scheme for network module
+* :ghpull:`559`: Network2
+* :ghpull:`558`: Network2
+* :ghpull:`557`: Network2
+* :ghpull:`556`: Added analytical functions and edge segmentation
+* :ghpull:`550`: Network2
+* :ghpull:`553`: correction in denominator of spatial tau.
+* :ghpull:`547`: Updates to get network integrated
+* :ghpull:`544`: update .gitignore
+* :ghpull:`543`: k nearest neighbor gwt example file for baltimore points (with k=4) added to examples directory
+* :ghpull:`542`: new format nat_queen.gal file added to examples directory
+* :ghpull:`541`: Update tutorial docs for new book
+* :ghpull:`540`: doc: updating instructions for anaconda and enthought
+* :ghpull:`539`: doc: pysal is now on sagemathcloud
+* :ghpull:`538`: Clean up of cg and fixes of other doctests/formats
+* :ghpull:`536`: adding entry for getis ord module
+* :ghpull:`537`: new opendata module for contrib
+* :ghpull:`535`: Add method for extracting data columns as Numpy array rather than list
+* :ghpull:`534`: added geogrid to __all__ in sphere.py
+* :ghpull:`533`: added geogrid function to create a grid of points on a sphere
+* :ghpull:`532`: new functions to deal with spherical geometry: lat-lon conversion, degre...
+* :ghpull:`530`: I390
+* :ghpull:`528`: Replacing 0 by min value in choropleths
+* :ghpull:`526`: B166
+* :ghpull:`525`: copyright update
+* :ghpull:`524`: New homogeneity tests for general case and spatial markov as a special case
+* :ghpull:`523`: pointing to github.io pages
+* :ghpull:`520`: Same typo. Toolkit.
+* :ghpull:`518`: Update util.py
+* :ghpull:`519`: Typo
+* :ghpull:`517`: Documentation correction for Prais Conditional Mobility Index
+* :ghpull:`516`: ENH for https://github.com/PySAL/PySAL.github.io/issues/17
+* :ghpull:`515`: BUG: conditional check for extension of lower bound of colorbar to conta...
+* :ghpull:`514`: ENH: adding the user_defined classification
+* :ghpull:`513`: rewriting to not use ipython notebook --pylab=line
+* :ghpull:`512`: Viz
+* :ghpull:`508`: Adding barebones pysal2matplotlib options in viz
+* :ghpull:`511`: DOC updating news
+* :ghpull:`507`: Sched
+* :ghpull:`510`: BUG: fix for #509
+* :ghpull:`506`: 1.9dev
+* :ghpull:`505`: REL bumping master to 1.9.0dev
+* :ghpull:`504`: Release prep 1.8
+* :ghpull:`503`: Grid for landing page
+
+Issues (69):
+
+* :ghissue:`560`: modifying import scheme for network module
+* :ghissue:`559`: Network2
+* :ghissue:`558`: Network2
+* :ghissue:`557`: Network2
+* :ghissue:`556`: Added analytical functions and edge segmentation
+* :ghissue:`555`: Added edge segmentation by distance
+* :ghissue:`550`: Network2
+* :ghissue:`553`: correction in denominator of spatial tau.
+* :ghissue:`549`: Network2
+* :ghissue:`547`: Updates to get network integrated
+* :ghissue:`548`: Installation Issues
+* :ghissue:`546`: Network2
+* :ghissue:`545`: Network
+* :ghissue:`544`: update .gitignore
+* :ghissue:`543`: k nearest neighbor gwt example file for baltimore points (with k=4) added to examples directory
+* :ghissue:`542`: new format nat_queen.gal file added to examples directory
+* :ghissue:`541`: Update tutorial docs for new book
+* :ghissue:`540`: doc: updating instructions for anaconda and enthought
+* :ghissue:`539`: doc: pysal is now on sagemathcloud
+* :ghissue:`538`: Clean up of cg and fixes of other doctests/formats
+* :ghissue:`536`: adding entry for getis ord module
+* :ghissue:`537`: new opendata module for contrib
+* :ghissue:`535`: Add method for extracting data columns as Numpy array rather than list
+* :ghissue:`534`: added geogrid to __all__ in sphere.py
+* :ghissue:`533`: added geogrid function to create a grid of points on a sphere
+* :ghissue:`532`: new functions to deal with spherical geometry: lat-lon conversion, degre...
+* :ghissue:`390`: add option to have local moran quadrant codes align with geoda
+* :ghissue:`530`: I390
+* :ghissue:`528`: Replacing 0 by min value in choropleths
+* :ghissue:`526`: B166
+* :ghissue:`176`: contrib module for proj 4
+* :ghissue:`178`: contrib module for gdal/org
+* :ghissue:`203`: implement network class in spatialnet
+* :ghissue:`204`: pysal-networkx util functions
+* :ghissue:`209`: csv reader enhancement
+* :ghissue:`215`: Add a tutorial for the spreg module
+* :ghissue:`244`: ps.knnW_from_shapefile returns wrong W ids when idVariable specified
+* :ghissue:`246`: Only use idVariable in W when writing out to file
+* :ghissue:`283`: Create new nodes at intersections of edges
+* :ghissue:`291`: Enum links around regions hangs
+* :ghissue:`292`: Handle multiple filaments within a region in the Wed construction
+* :ghissue:`302`: Handle hole polygons when constructing wed
+* :ghissue:`309`: Develop consistent solution for precision induced errors in doctests across platforms
+* :ghissue:`350`: reading/writing weights file with spaces in the ids
+* :ghissue:`450`: x_name and summary method not consistent in ols
+* :ghissue:`521`: Nosetests don't accept setup.cfg
+* :ghissue:`509`: ESDA bin type inconsistency
+* :ghissue:`525`: copyright update
+* :ghissue:`524`: New homogeneity tests for general case and spatial markov as a special case
+* :ghissue:`523`: pointing to github.io pages
+* :ghissue:`520`: Same typo. Toolkit.
+* :ghissue:`522`: Nosetests for python3 porting
+* :ghissue:`518`: Update util.py
+* :ghissue:`519`: Typo
+* :ghissue:`517`: Documentation correction for Prais Conditional Mobility Index
+* :ghissue:`516`: ENH for https://github.com/PySAL/PySAL.github.io/issues/17
+* :ghissue:`515`: BUG: conditional check for extension of lower bound of colorbar to conta...
+* :ghissue:`514`: ENH: adding the user_defined classification
+* :ghissue:`513`: rewriting to not use ipython notebook --pylab=line
+* :ghissue:`512`: Viz
+* :ghissue:`508`: Adding barebones pysal2matplotlib options in viz
+* :ghissue:`511`: DOC updating news
+* :ghissue:`507`: Sched
+* :ghissue:`510`: BUG: fix for #509
+* :ghissue:`502`: spreg.ml_lag.ML_Lag is very very very time-consuming?
+* :ghissue:`506`: 1.9dev
+* :ghissue:`505`: REL bumping master to 1.9.0dev
+* :ghissue:`504`: Release prep 1.8
+* :ghissue:`503`: Grid for landing page
+
+
+v<1.8.0>, 2014-07-25
+
+GitHub stats for 2014/01/29 - 2014/07/25
+
+These lists are automatically generated, and may be incomplete or contain duplicates.
+
+The following 8 authors contributed 281 commits.
+
+* Dani Arribas-Bel
+* Jay
+* Philip Stephens
+* Serge Rey
+* Sergio Rey
+* jlaura
+* pedrovma
+* sjsrey
+
+
+We closed a total of 160 issues, 60 pull requests and 100 regular issues;
+this is the full list (generated with the script
+:file:`tools/github_stats.py`):
+
+Pull Requests (60):
+
+* :ghpull:`503`: Grid for landing page
+* :ghpull:`501`: Two figs rather than three
+* :ghpull:`500`: More efficient higher order operations
+* :ghpull:`499`: renamed nat_queen.gal for #452
+* :ghpull:`497`: ENH Deprecation warning for regime_weights #486
+* :ghpull:`494`: Enables testing against two versions of SciPy shipped with the last two Ubuntu LTS versions.
+* :ghpull:`490`: Fix for #487
+* :ghpull:`492`: BUG cleaning up temporary files for #398
+* :ghpull:`493`: Phil: Skipping several tests that fail due to precision under older scipy
+* :ghpull:`489`: test suite fixes
+* :ghpull:`488`: More tests to skip if scipy less than 11
+* :ghpull:`484`: ENH: cleaning up more test generated files
+* :ghpull:`483`: Forwarding Phil's commit: skipping doctests, conditional skip of unit tests
+* :ghpull:`482`: DOC cleaning up files after running doctests #398
+* :ghpull:`481`: DOC contrib updates and links
+* :ghpull:`480`: DOC cleaning up doctests
+* :ghpull:`479`: ENH Changing regime_weights to block_weights for #455
+* :ghpull:`478`: DOC: link fixes
+* :ghpull:`477`: cKDTree for #460
+* :ghpull:`476`: redefining w.remap_ids to take only a single arg
+* :ghpull:`475`: Adding docstrings and error check to fix #471
+* :ghpull:`470`: fixing order of args for api consistency.
+* :ghpull:`469`: Idfix for #449
+* :ghpull:`463`: updating gitignore
+* :ghpull:`462`: ENH: handle the case of an ergodic distribution where one state has 0 probability
+* :ghpull:`458`: ENH: Vagrantfile for PySAL devs and workshops
+* :ghpull:`447`: Clusterpy
+* :ghpull:`456`: BUG: fix for #451 handling W or WSP in higher_order_sp
+* :ghpull:`454`: Foobar
+* :ghpull:`443`: Updating spreg: several minor bug and documentation fixes.
+* :ghpull:`453`: Resolving conflicts
+* :ghpull:`448`: Wsp
+* :ghpull:`445`: ENH: unique qualitative color ramp. Also refactoring for future ipython deprecation of --pylab=inline
+* :ghpull:`446`: Wmd
+* :ghpull:`444`: Scipy dependency
+* :ghpull:`442`: Wmd
+* :ghpull:`441`: fixed kernel wmd for updated wmd structure
+* :ghpull:`440`: ENH: sidebar for Releases and installation doc update
+* :ghpull:`439`: - events
+* :ghpull:`438`: ENH: pruning to respect flake8
+* :ghpull:`437`: BUG: fix for removal of scipy.stat._support #436
+* :ghpull:`433`: Rank markov
+* :ghpull:`424`: testing
+* :ghpull:`431`: FOSS4G
+* :ghpull:`430`: Network
+* :ghpull:`429`: moving analytics out of wed class and into their own module
+* :ghpull:`428`: Network
+* :ghpull:`427`: devel docs
+* :ghpull:`425`: Viz2contrib
+* :ghpull:`423`: Update news.rst
+* :ghpull:`422`: ENH: Update doc instructions for napoleon dependency
+* :ghpull:`421`: Adding files used in some examples as per Luc's request.
+* :ghpull:`419`: Doc fixes 1.7
+* :ghpull:`393`: Doc fixes 1.7
+* :ghpull:`417`: ENH hex lattice W for #416
+* :ghpull:`415`: Temporarily commenting out tests that are blocking Travis.
+* :ghpull:`407`: Viz: Moving into contrib/viz in master
+* :ghpull:`404`: version change
+* :ghpull:`401`: fixes #388
+* :ghpull:`402`: release changes
+
+Issues (100):
+
+* :ghissue:`503`: Grid for landing page
+* :ghissue:`501`: Two figs rather than three
+* :ghissue:`500`: More efficient higher order operations
+* :ghissue:`452`: nat_queen.gal example file
+* :ghissue:`499`: renamed nat_queen.gal for #452
+* :ghissue:`486`: add a deprecation warning on regime_weights
+* :ghissue:`497`: ENH Deprecation warning for regime_weights #486
+* :ghissue:`449`: Lower order neighbor included in higher order
+* :ghissue:`487`: Issue with w.weights when row-standardizing
+* :ghissue:`398`: running test suite generates files
+* :ghissue:`358`: Graph weights
+* :ghissue:`338`: ENH: Move Geary's C calculations to Cython.
+* :ghissue:`494`: Enables testing against two versions of SciPy shipped with the last two Ubuntu LTS versions.
+* :ghissue:`490`: Fix for #487
+* :ghissue:`492`: BUG cleaning up temporary files for #398
+* :ghissue:`493`: Phil: Skipping several tests that fail due to precision under older scipy
+* :ghissue:`489`: test suite fixes
+* :ghissue:`485`: Revert "ENH: cleaning up more test generated files"
+* :ghissue:`488`: More tests to skip if scipy less than 11
+* :ghissue:`484`: ENH: cleaning up more test generated files
+* :ghissue:`483`: Forwarding Phil's commit: skipping doctests, conditional skip of unit tests
+* :ghissue:`482`: DOC cleaning up files after running doctests #398
+* :ghissue:`481`: DOC contrib updates and links
+* :ghissue:`480`: DOC cleaning up doctests
+* :ghissue:`455`: regime weights vs block weights
+* :ghissue:`479`: ENH Changing regime_weights to block_weights for #455
+* :ghissue:`478`: DOC: link fixes
+* :ghissue:`460`: Optimize KDTree
+* :ghissue:`477`: cKDTree for #460
+* :ghissue:`472`: Check for any side effects from new id remapping in w.sparse
+* :ghissue:`473`: update all user space functions for new w.remap_ids
+* :ghissue:`476`: redefining w.remap_ids to take only a single arg
+* :ghissue:`263`: Transition to scipy.spatial.cKDTree from scipy.spatial.KDTree
+* :ghissue:`414`: Travis build is killing nosetests
+* :ghissue:`335`: Weights transformation docs
+* :ghissue:`471`: add docstring example for w.remap_ids
+* :ghissue:`475`: Adding docstrings and error check to fix #471
+* :ghissue:`405`: ENH: Handling ids in W (Leave open for discussion)
+* :ghissue:`470`: fixing order of args for api consistency.
+* :ghissue:`469`: Idfix for #449
+* :ghissue:`467`: redirect pysal.org to new dynamic landing page
+* :ghissue:`466`: design the grid for the notebooks
+* :ghissue:`464`: design new dynamic landing page for github.io
+* :ghissue:`465`: move news out of docs and into dynamic landing page
+* :ghissue:`468`: Move dynamic items out of sphinx docs and into dynamic landing page
+* :ghissue:`463`: updating gitignore
+* :ghissue:`451`: docs for higher_order_sp have wrong argument types
+* :ghissue:`462`: ENH: handle the case of an ergodic distribution where one state has 0 probability
+* :ghissue:`458`: ENH: Vagrantfile for PySAL devs and workshops
+* :ghissue:`447`: Clusterpy
+* :ghissue:`456`: BUG: fix for #451 handling W or WSP in higher_order_sp
+* :ghissue:`457`: This is a test to see if pull request notifications get sent out to the list
+* :ghissue:`454`: Foobar
+* :ghissue:`443`: Updating spreg: several minor bug and documentation fixes.
+* :ghissue:`453`: Resolving conflicts
+* :ghissue:`412`: On travis and darwin test_ml_error_regimes.py hangs
+* :ghissue:`448`: Wsp
+* :ghissue:`435`: Will spatial durbin model be added in the near future?
+* :ghissue:`445`: ENH: unique qualitative color ramp. Also refactoring for future ipython deprecation of --pylab=inline
+* :ghissue:`446`: Wmd
+* :ghissue:`444`: Scipy dependency
+* :ghissue:`442`: Wmd
+* :ghissue:`441`: fixed kernel wmd for updated wmd structure
+* :ghissue:`440`: ENH: sidebar for Releases and installation doc update
+* :ghissue:`439`: - events
+* :ghissue:`438`: ENH: pruning to respect flake8
+* :ghissue:`436`: Scipy 0.14 induced breakage
+* :ghissue:`437`: BUG: fix for removal of scipy.stat._support #436
+* :ghissue:`408`: Use of `platform.system()` to determine platform
+* :ghissue:`403`: Scipy dependency
+* :ghissue:`434`: W Object Metadata Attribute
+* :ghissue:`433`: Rank markov
+* :ghissue:`424`: testing
+* :ghissue:`432`: Implementation of rank Markov classes
+* :ghissue:`431`: FOSS4G
+* :ghissue:`430`: Network
+* :ghissue:`429`: moving analytics out of wed class and into their own module
+* :ghissue:`420`: Local Moran's I, I Attribute Undefined
+* :ghissue:`418`: Extended pysal.weights.user.build_lattice_shapefile
+* :ghissue:`428`: Network
+* :ghissue:`427`: devel docs
+* :ghissue:`426`: dev docs
+* :ghissue:`425`: Viz2contrib
+* :ghissue:`423`: Update news.rst
+* :ghissue:`422`: ENH: Update doc instructions for napoleon dependency
+* :ghissue:`421`: Adding files used in some examples as per Luc's request.
+* :ghissue:`419`: Doc fixes 1.7
+* :ghissue:`393`: Doc fixes 1.7
+* :ghissue:`416`: Add hexagonal lattice option for lat2W
+* :ghissue:`417`: ENH hex lattice W for #416
+* :ghissue:`409`: add wiki page on viz module design
+* :ghissue:`413`: Temporary fix for https://github.com/pysal/pysal/issues/412
+* :ghissue:`415`: Temporarily commenting out tests that are blocking Travis.
+* :ghissue:`407`: Viz: Moving into contrib/viz in master
+* :ghissue:`406`: Viz: pruning old code and adding more examples for TAZ paper
+* :ghissue:`380`: Pep 8 and Line Length
+* :ghissue:`404`: version change
+* :ghissue:`401`: fixes #388
+* :ghissue:`388`: update testing procedures docs
+* :ghissue:`402`: release changes
+
+v<1.7.0>, 2014-01-29
+
+36d268f Philip Stephens -Merge pull request #400 from sjsrey/mldoc
+c2c4741 Serge Rey -Formatting ml docs
+685f5e3 Sergio Rey -Merge pull request #399 from sjsrey/master
+481ccb4 Serge Rey -correct thanks
+4a5cce3 Sergio Rey -Update index.txt
+1fe7aeb Philip Stephens -Merge pull request #396 from sjsrey/mldoc
+e731278 Serge Rey -EHN: fixing link to bleeding edge docs.
+e4e9930 Serge Rey -ENH: adding ml docs to api
+9b3c77e Serge Rey -Merge branch 'master' of github.com:pysal/pysal
+dda3c01 Philip Stephens -Merge pull request #389 from dfolch/master
+74b26d5 Philip Stephens -Merge pull request #392 from pedrovma/spreg17
+b47ba84 pedrovma -Bump.
+3d8504c Sergio Rey -Merge pull request #386 from pastephens/master
+f9b59ea Philip Stephens -Merge branch 'master' of https://github.com/pysal/pysal
+429e19e pedrovma -Upgrading to spreg 1.7.
+c698747 David Folch -removing legacy speedup hack that is no longer relevant
+88177d0 Sergio Rey -Merge pull request #387 from sjsrey/scipy13
+64a4089 Serge Rey -BUG: sorting ijs for asymmetries
+5539ef5 Sergio Rey -Merge pull request #1 from sjsrey/scipy13
+8a86951 Serge Rey -BUG: fixes for scipy .0.9.0 to 0.13.0 induced errors
+fe02796 Philip Stephens -tweaking travis to only run master commits
+8c1fbe8 jlaura -Merge pull request #385 from sjsrey/docupdate
+b71aedc Serge Rey -ENH: update date
+4f237e4 Sergio Rey -Merge pull request #384 from sjsrey/moran
+01da3be Serge Rey -ENH: Analytical p-values for Moran are two-tailed by default #337
+918fe60 Philip Stephens -further travis tweaks
+3920d73 Sergio Rey -Merge pull request #382 from sjsrey/st_docs
+d90bc70 Serge Rey -DOC: updating refs for concordance algorithm
+0db2790 Philip Stephens -tweaks to travis
+063e057 Philip Stephens -upgrading scipy on travis
+f90e742 Philip Stephens -Merge branch 'master' of https://github.com/pysal/pysal
+edc9c07 Dani Arribas-Bel -Merge pull request #379 from sjsrey/b244
+82479bb Serge Rey -BUG: fix for the comment https://github.com/pysal/pysal/issues/244#issuecomment-30055558
+57ba485 jlaura -Update README.md
+981ed31 Sergio Rey -Merge pull request #377 from darribas/master
+3320c39 darribas -Changing cmap default in plot_choropleth so every type defaults to its own adecuate colormap
+e063bee darribas -Fixing ignorance of argument cmap in base_choropleth_unique
+1f10906 Dani Arribas-Bel -Merge pull request #375 from sjsrey/viz
+94aa3e7 Dani Arribas-Bel -Merge pull request #376 from pedrovma/baltim_data
+7568b0b pedrovma -Adding Baltimore example dataset for use with LM models.
+5b23f89 Serge Rey -greys for classless map
+d4eae1e Dani Arribas-Bel -Merge pull request #374 from sjsrey/viz
+652440d Serge Rey -shrinking colorbar
+c17bf67 Sergio Rey -Merge pull request #373 from darribas/master
+a71c3cb darribas -Fixing minor conflict to merge darribas viz branch into darribas master
+ec27e30 Dani Arribas-Bel -Merge pull request #372 from sjsrey/viz
+8c03170 Serge Rey -option for resolution of output figs
+3fc5bd4 Philip Stephens -Merge branch 'master' of https://github.com/pysal/pysal
+2b5cb23 jlaura -Merge pull request #371 from sjsrey/geopandas
+469afa7 Serge Rey -fix for #370
+59cdafc jlaura -Merge pull request #369 from pedrovma/south_data
+6b88e13 jlaura -Merge pull request #368 from schmidtc/issue367
+40fe928 pedrovma -Adding south data to be used in ML doctests.
+bcc257e schmidtc -fixes #367
+87e057f jlaura -Merge pull request #366 from sjsrey/ml_lag
+a64eb27 Serge Rey -queen contiguity for nat.shp
+77add5c Sergio Rey -Merge pull request #365 from sjsrey/news
+82464ef Serge Rey -narsc workshop
+fd79424 Sergio Rey -Merge pull request #364 from sjsrey/news
+bc7f25a Serge Rey -Merge branch 'master' of https://github.com/sjsrey/pysal
+d669913 David Folch -Merge pull request #363 from sjsrey/maxp
+22f9e36 Serge Rey -update example for bug fix #362
+fac3b8a Serge Rey -- update tests for bug fix #362
+44b4b06 Sergio Rey -Merge pull request #1 from sjsrey/maxp
+1e6f1e5 Serge Rey -- fix for #362
+68ab3e9 Sergio Rey -Merge pull request #361 from sjsrey/components
+aa27c7e Serge Rey -doc test fix
+7c08208 Serge Rey -putting Graph class back in for component checking
+003b519 Serge Rey -alternative efficient component checker
+2080e62 Serge Rey -- fixing doc
+4fda442 Serge Rey -Merge branch 'components' of github.com:sjsrey/pysal into components
+e9e613b Serge Rey -reverting back to old component check
+83d855e Serge Rey -updating example
+9defd86 jlaura -Merge pull request #360 from sjsrey/components
+6f92335 Serge Rey -more efficient connectivity test
+ebde3d1 Dani Arribas-Bel -Adding try/except for ogr since it's only used to reprojection methods but not on the plotting toolkit
+5b170eb Sergio Rey -Merge pull request #356 from sjsrey/classification
+c9dac41 Serge Rey -- update unit tests for reshaping jenks caspal
+d9b06e2 Sergio Rey -Merge pull request #355 from sjsrey/cleanup/moran
+dc589e8 darribas -Adding caution note when plotting points to the notebook. Ideally, we wanna be able to build a PathCollection out of the XYs, but for now we rely on plt.scatter, which gets the job done but has some problems.
+2224b95 darribas -Including support for points in base_choropleth_unique and base_choropleth_classless
+ac2d08a darribas -Modifying example to show how to do choropleth mapping on points
+270786e darribas -Adding support for choropleth plotting on point map objects (this may come from map_point_shp or from a simple matplotlib scatter
+e56697c Sergio Rey -Merge pull request #357 from jlaura/newstyle_wed
+4c67c2f Jay -errors in segmentation fixed
+512cc76 Serge Rey -have Jenks-Caspal bins be a one dimensional array - to be consistent with all other classifiers
+5254859 Philip Stephens -Merge branch 'master' of https://github.com/pysal/pysal
+788ecab Serge -pruning
+5b6b7b6 Serge -pruning
+eb7e9a1 Jay -bug fix and all pointers filled for external edges
+e47aa7a Jay -Node insertion, precursor to segmentation.
+18a44d1 darribas -*Replacing shp by map_obj in medium layer functionality. *Bringing everything else in line with it *Adding example for line colorig and mixing overlaying of points.
+bd041b1 darribas -Replacing shp_link by shp as input for medium and low-level layers. This brings much more flexibility and opens the door to plot formats other than shapefiles (e.g. geojson)
+c74a361 darribas -Adding IP notebook to exemplify and keep track of development of mapping module
+d23c882 darribas -Minor fixes
+4b82a76 darribas -New commit message* Replacing map_poly_shp_lonlat for map_poly_shp in base_choropleth_classif/unique/classless * removed 'projection' from base_choropleth_classif/unique/classless * Allow base_choropleth_classif/unique/classless to plot multi-part polygons properly * changes streamlined to generic plot_choropleth * Added dependency on pandas for rapid reindexing (this is done externally on the method _expand_values to it is easy to drop the dependency when neccesary/tim [...]
+7a0eaec darribas -Merge branch 'viz' of github.com:darribas/pysal into viz
+5536424 darribas -Merge branch 'master' of github.com:darribas/pysal
+e54ce16 Sergio Rey -Merge pull request #353 from darribas/master
+819ee60 darribas -Adding immediate todo on head of the file
+946772d darribas -Passing k to base_choropleth_classif from plot_choropleth. This should fix Issue #352
+f299b45 darribas -Merge branch 'master' of https://github.com/pysal/pysal
+f044f43 Jay -Added W generation
+5f48446 jlaura -Merge pull request #348 from sjsrey/master
+938a1ae Serge Rey -- adding nn stats to point based methods
+a86a051 Philip Stephens -removing dependency tracking service, it was ruby only
+1e24fde Philip Stephens -testing dependency tracking service
+3aa410c Philip Stephens -Merge pull request #347 from pedrovma/w_silence_island
+03990f6 pedrovma -Extending PR #310 (silence island warnings) to include w.transform.
+160001a Sergio Rey -Merge pull request #346 from jlaura/newstyle_wed
+44989f9 Sergio Rey -Merge pull request #345 from sjsrey/master
+2fd99b8 Sergio Rey -Update README.md
+bdcc6a8 Jay -NCSR with uniform distribution
+769aa03 Jay -Fixed snapping
+2561071 Jay -saved notebook and updated readme
+3784783 Jay -ReadMe for Changes
+019e16b Sergio Rey -Merge pull request #334 from jseabold/fix-build-example-dirs
+1889885 Skipper Seabold -BLD: Correctly install package_data dirs.
+ff4e355 Serge Rey -- assignments
+c5b0cc0 Serge Rey -- reorg
+a4f5642 Serge Rey -Merge branch 'network' of github.com:pysal/pysal into network
+a95fec8 jlaura -Update README.md
+1713145 Serge Rey -Merge branch 'master' of github.com:pysal/pysal into network
+ede75c0 Sergio Rey -Merge pull request #329 from jlaura/wed_polar
+7399cf2 Jay -Single-source shortest path notebook
+9eb3fc1 Philip Stephens -Merge pull request #331 from sjsrey/docfix
+ef9c82a Serge Rey -- sphinx doctest markup fix
+1e2b6b3 jlaura -Update README.md
+e19bffa jlaura -Merge pull request #330 from pysal/b328
+6afc30b Serge Rey -- tutorial doc fixes for #328
+c7239f1 Serge Rey -- b328 fix
+d5fec13 Serge Rey -- fix for #328 making all p-values one-tailed
+16b5e6e Jay -enumeration working with filaments
+9507bbc jlaura -Update README.md
+eef8eec Serge Rey -- stub for design of module
+2707d60 Jay -Filaments in polar coordinates
+b64f9e2 Serge Rey -Documentation for the development of network module
+b90876e Serge Rey -Merge branch 'network' of github.com:pysal/pysal into network
+ddad2a5 Philip Stephens -Merge pull request #326 from sjsrey/doc
+6b0cd08 Serge Rey -- update release schedule
+4cc7bca Jay -bisecting for single point working
+79c77d9 jlaura -Merge pull request #324 from pysal/bf_id
+9f4c7c9 Serge Rey -id is a keyword
+72b1f85 Sergio Rey -Merge pull request #323 from jlaura/network
+b5cdae0 Jay -fix to shp2graph
+846dce2 Jay -Brute force for point outside network
+d6c2ef4 Jay -Added length computation, alter global morans
+b7e1465 Jay -Added new pointer to reader/writer
+616d62d Jay -LISA and Global Morans on the network
+16f84d6 Jay -Added explicit point external to network warning
+34f4d8e Jay -update to the ipython notebook
+e359e59 Jay -JSON and cPickle Bianry WED Reader/Writer
+5373c82 Sergio Rey -Merge pull request #322 from jlaura/network
+059d99c Jay -wed into class, tests added
+aa5969d Sergio Rey -Merge pull request #320 from pastephens/master
+a18000b Philip Stephens -version added info
+5b8d490 Philip Stephens -typo
+d31a22a Philip Stephens -stubs for cg docs
+4dbdfe3 schmidtc -fixes #318
+35a0317 Jay -Merge branch 'master' of https://github.com/pysal/pysal into network
+77e8387 Jay -Merge branch 'geojson' of https://github.com/pysal/pysal into network
+ad670c5 Sergio Rey -Merge pull request #317 from pastephens/master
+628f27e Philip Stephens -merging local changes
+f9dcb3e Philip Stephens -simplified install instructions
+f2fab4c Serge Rey -- notebook on w construction for geojson
+830826b Serge Rey -prototyping W from geojson
+b10240d Serge Rey -created with "ogr2ogr -lco WRITE_BBOX=YES -f "GeoJSON" columbus.json columbus.shp"
+d546926 Philip Stephens -merging with pull
+d711011 darribas -Merge branch 'rod'
+8bef782 darribas -Merge branch 'rod' of https://github.com/pysal/pysal into rod
+03c1003 pedrovma -Merge pull request #315 from sjsrey/rod
+950fe8b Serge Rey -Replacing ROD with regular dictionary
+b1f009f Philip Stephens -Changes to release docs.
+028364a Sergio Rey -Update THANKS.txt
+94f5916 Sergio Rey -Update INSTALL.txt
+
+v<1.6.0>, 2013-07-31
+
+5fa9d09 darribas -silent_island_warning implemented for w_union
+6526c62 Sergio Rey -Update README.md
+ea826c1 darribas -silent_island_warning implemented for w_intersection
+335540a darribas -silent_island_warning implemented for w_difference
+0a156cb darribas -silent_island_warning implemented for w_symmetric_difference. Previous commit included support of silent_island_warning for WSP2W as well
+34d20d7 darribas -silent_island_warning implemented for w_clip
+499815d pedrovma -Test fixing...
+8778f75 pedrovma -Test fixing...
+a799a13 pedrovma -Test fixing...
+6482d81 pedrovma -Test fixing...
+2752b1b pedrovma -Test fixing...
+0c0a5bf pedrovma -Test fixing...
+bbf9dcb pedrovma -Test fixing...
+05c34ff pedrovma -Test fixing...
+8a3986a Serge Rey -- preparing for release, version updates
+9106cfe pedrovma -Matching travis results reg. precision issues.
+3cd0ce1 Serge Rey -- updating changelog
+74dadd6 pedrovma -Bump.
+c7774fb Serge Rey -- update THANKS.txt - testing travis for timing out
+cd98057 Serge Rey -- travis fix for multiprocessing permission error
+86702f8 Serge Rey -- start of changelog for 1.6
+3ee686d pedrovma -Reloading to check new results from Travis.
+2de1d21 Serge Rey -- docs
+ef72edc Serge Rey -- update docs
+0716581 Serge Rey -- deal with multiprocessing on travis
+b508c88 Serge Rey -- excluding network from 1.6 release
+ff13e31 pedrovma -Matching Travis results. Multiprocessing errors still an issue.
+5b916ba pedrovma -Adding Chow test on lambda and updating dynamics of regime_err_sep and regime_lag_sep in combom models.
+b6e687f darribas -Patch to include switch for island warning as proposed in #295. The method is modified as well to include the switch
+7ea5f35 pedrovma -Fixing defaults
+62ca76b pedrovma -Updating documentation and checking if there are more than 2 regimes when regimes methods are used.
+3212249 pedrovma -Fixing documentation on 'name_regimes'
+a782d50 pedrovma -Updating tests for integration with pysal 1.6
+14f9181 pedrovma -Merging spreg_1.6 with my pysal fork.
+817f2c2 Serge Rey -- having build_lattice_shapefile also create the associated dbf file - useful for testing our contiguity builders against geoda since dbf is required by the latter
+41d59a4 Serge Rey -- adding diagonal option to kernel weights in user.py
+506d808 Serge Rey -update when added
+b2ec3d4 Serge Rey -- updating api docs
+9d45496 Serge Rey -- example and doctests for spatial gini
+95635bb Serge Rey -updating release docs
+bd2f924 darribas -Fixing doctest of towsp method by including isinstance(wsp, ps.weights.weights.WSP)
+76183d7 darribas -Fixing doctest of towsp method by including type(wsp)
+0c54181 darribas -Adding method in W that calls WSP class for convenience and elegance. Related to issue #226
+f3b23e8 Philip Stephens -adding source build to travis-ci
+60930e7 Philip Stephens -adding new url for downloads
+9bf7f5b Philip Stephens -modified release docs.
+f98d4a9 Philip Stephens -interim ci
+aa19028 Philip Stephens -Adding docs about installing in develop mode.
+674112f Philip Stephens -starting rewrite of install docs
+af0d9b3 Philip Stephens -working on doc tickets
+200e77e Serge Rey -handle ties in knnW in doctest
+d0d2dd2 Serge Rey -resetting README for pysal/pysal
+6afb6ac Serge Rey -- updating docs for new api in interation.py
+4c5572f Serge Rey -- updating tests for new api
+fabd16a Serge Rey -- refactored signatures to use numpy arrays rather than event class
+6367947 Serge Rey -- refactor knox for large samples
+5fad3b2 Serge Rey -- updating travis test
+06894d8 Serge Rey -- updated README
+8b06e63 Serge Rey -- so only i get email when i commit locally
+efbb7ff Serge Rey -- removing google pysal-dev circle
+9859bda Serge Rey -- turning off gmail circle
+51f6d3e Serge Rey -- fixing
+46b1084 Serge Rey --docos
+4e2c27a Philip Stephens -missing if statement added
+d1a83fd Serge Rey -- fixing docs
+8275d76 Serge Rey -- fix precision
+87ea5cc Philip Stephens -adding to authors and quick test fix for linux
+1cfb67f Serge Rey -cant easily remove idVariable, reverting
+5933d1e Serge Rey -removing idvariable from Distance - causes too many issues
+05f2573 Philip Stephens -removing coverage tests
+fcb8c6f Philip Stephens -Knox using KDTree.
+2237173 Serge Rey -with tests against previous implementation removed
+233e59a Serge Rey -speed comparison for change to query_pairs in kdtree
+fb78ea9 Serge Rey -removing test file
+4d04575 Philip Stephens -testing
+357a184 Serge Rey -second great idea
+1fafc2b Serge Rey -on a plane commit 1
+fef6eae Philip Stephens -fix
+86c17ac Serge Rey -- test file
+a619f62 Philip Stephens -interim ci
+1a9d881 Serge Rey -- knox test using kdtrees
+7459c44 Serge Rey -Fixing reference to missing shapefile Fixing one rounding error induced test
+5616b12 Serge Rey -refactored to avoid second loop in explicit queen or rook check
+d3d2f71 Philip Stephens -Revert "Changed doctest path calls to account for modified shapefile."
+da1d8a1 Philip Stephens -Changed doctest path calls to account for modified shapefile.
+f591c99 Philip Stephens -progress on permutations of knox for larger datasets
+8d31cde Serge Rey -Testing integration of spatialnet creation and reading into wed
+11de6f3 Jay -Fixed wed_modular.py
+077658a Serge Rey -adding new test case for wed extraction from a spatialnet shapefile
+bbb10b4 Philip Stephens -saving state of development
+44076b7 Serge Rey -- update doc test
+6fdd94d Serge Rey -- moved regions_from_graph into wed_modular - documented all functions and cleaned up
+5bd27c3 Serge Rey -- wrapping in functions
+3ad162f Serge Rey -- working version of wed_modular module - starting point for clean up
+2380f15 Philip Stephens -Copy of sphinx install docs. Closes #251
+5687700 Philip Stephens -tweaks to install instructions
+9ffd432 Serge Rey -- updating for switch from svn to git
+fdaf521 Philip Stephens -Fixing 250
+5ba4fdf Serge Rey -Fixes #249 Closes #249
+d89944d Pedro -Adding docs for each regimes estimator
+f03bb63 Serge Rey -- updating docs for spatial regimes in spreg
+a49d0f7 Philip Stephens -Adding info to setup script.
+1f27605 Philip Stephens -mainly docs
+04f8a31 Philip Stephens -Adding test coverage with nose, data collected and presented on coveralls.io
+6db978b Philip Stephens -last changes
+137e088 Philip Stephens -added bigdata parameter
+7ca81c2 Philip Stephens -got Knox stat working in alt form
+24c1fcc Philip Stephens -workign on refactoring the space-time matrices for the Knox test [ci-skip]
+28013f0 Serge Rey -- enumeration of cw edges for faces
+baa8f60 Serge Rey -- hole is now included and enumeration of links (cw) around nodes works for all nodes. - isolated nodes also handled in enumeration of links around nodes.
+33741c8 Serge Rey -- filaments inserted and pointers updated - have to add hole polygon and isolated nodes, but almost there!!!!!!!!!
+416d3db Serge Rey -- pointers updated for edges of connected components
+c34e274 Serge Rey -- convex/between edge test as start of testing for insertion of multiple internal filaments in one region.
+78d96b1 Serge Rey -- filament insertion and pointer updates
+ced2c5b Serge Rey -- filament insertion (inc)
+ba4263f Jay -Logic roughed in for filaments [ci skip]
+cf3b0bc Jay -updated wed ipynb [ci skip]
+33ce81e Serge Rey -- refactoring of wed construction (incomplete)
+0fc16fc Jay -modular WED Pulled Apart 2 funcs in 1 cell
+bf73b90 Jay -modular WED
+3163377 Serge Rey -- new modular wed construction
+e50b31d Jay -added test_wed additions to test_wed2
+1cbc941 Serge Rey -- isolated nodes handled
+d28b97f Serge Rey -- isolated filament handled
+6188fd5 Serge Rey -- hole component handled
+a96040b Serge Rey -- getting connected components (current 14,15,16 and 25,26,27 are not included)
+3aa31a5 Jay -Added boolean arg to include or exclude holes [ci skip]
+d07876d Jay -Filament identification [ci skip]
+0139ea5 Philip Stephens -Slight speed improvement getting rid of append calls in reading shapefile and building x,y lists.
+43010b5 Serge Rey -- fixed logic problem with enum for v1, starting on components
+8737918 Pedro -Adding more meaningful error message to inverse distance weights
+01f52f6 Serge Rey -- replacing code that got deleted previously
+7c4c6e1 Philip Stephens -Replacing deleted files.
+a8da725 Philip Stephens -added date support to spacetimeevents class, a date column to example dbf.
+90c4730 Philip Stephens -logic works, numeric test still failing
+b8e43e1 Philip Stephens -saving progress on interaction
+81f2408 Serge Rey -- handling external end-node-filament
+7de6253 Serge Rey -- adding end node filament handling - edge enumeration around node working
+f542b9a Serge -- adding end node filament handling - edge enumeration around node working
+d7e3a57 Philip Stephens -[ci skip] disabling nose-progressive so travis output looks best
+fe03013 Dani Arribas-Bel -Adding set of diversity indices to inequality module under _indices.py for now. Still lacks doctests, unittests, and a few others will be added
+951b6f5 Dani Arribas-Bel -Adding try/except to the import of Basemap to allow the use of the module when there is no Basemap installation
+89003eb Serge Rey -- adding wed for eberly example
+665ef22 Serge Rey -- fixed 7,2 failure
+71fc9ad Serge Rey -start of adding gini and other inequality measures
+f7b7bcc Phil Stephens -Adding nose-progressive plugin to test suite. Devs can run test suite with 'make test'.
+f5db7bf Serge Rey -- updating copyright
+07574b5 Serge Rey -- docs
+478d2cb Philip Stephens -Adding requirement. Removing redundancy.
+916a6ca Serge Rey -- more island check updates
+edd9960 Serge Rey -- more island check doctest changes
+ad1a91c Serge Rey -- updating doctests for island check
+ce77772 Serge Rey -- fixing doctests to incorporate new island warning
+554a30b Serge Rey -- silencing floating point warning
+4f76862 Serge Rey -- moving default contiguity builder back to binning from rtree
+b99665b Jay -Eberly
+d911344 Jay -mp removed, passing nosetests on my machine serial
+f005675 Serge Rey -improved binning algorithm for contiguity builder
+4a69557 Serge Rey -- double checking threshold in Distance Band - new example to show functionality
+7256f13 Serge Rey -- fix handling of idVariable for knnW
+31bb36e Jay -bug fixes [ci skip]
+a2d2dd4 Jay -WEberly - WED Building [ci skip]
+3abc55e Serge Rey -- fixing doctests for new check/reporting for islands
+756ac05 Serge Rey -- adding warning if islands exist upon W instantiation
+db097a6 Jay -Weberly, bug fix, c and cc link remaining
+d5cc6f9 Jay -All but start / end working
+033963d Jay -Integration to WEberly error fixed [ci skip]
+22b931a Serge Rey -- removing main for doc tests which can be run from nosetests. - updating testing docs
+bf753e9 Jay -Integration to WEberly started [ci skip]
+6506e07 Serge Rey -- typo
+aede375 Serge Rey -- replacing double quotes around multi word ids with strings joined with underscores
+cf029e8 Serge Rey -- changes to wrap string ids in gwt writer - see https://github.com/pysal/pysal/issues/244#issuecomment-16707353
+626ac08 Serge Rey -- adding shapefile and variable name to gwt objects created in user space
+3c84bb0 Jay -Working version 4.19 [ci skip]
+7d77da9 darribas -Include warning in sp_att when rho is outside (-1, 1), ammends #243 although the true problem (pearsonr in diagnostics_tsls) will still raise an error
+3719d21 Jay -working WED [ci skip]
+b4ce294 Serge Rey -checking edges
+f4bb412 Jay -excessive print statements removed. ci skip
+9f7dee6 Jay -SUCCESS! ci skip
+9077615 Phil Stephens -Note, [ci skip] anywhere in your commit message causes Travis to NOT build a test run.
+cb072c4 Jay -getting there
+d3b36bc Serge Rey -correcting typo user told me about
+19ea051 Jay -trivial working
+b9ea577 Jay -eberly cycles - edge issue still
+d5153e3 Serge Rey -more refinement of wed from plannar graph
+edff44b Philip Stephens -adding git ignore file
+8093f21 Serge Rey -wed from minimum cycle basis
+b5bcead Serge Rey -handle filaments
+9a8927a Serge Rey -face extraction using horton algorithm
+10d66c1 Serge Rey -updating readme formatting
+59f3750 schmidtc -adding Universal newline support to csvReader, fixes #235
+09e813f Serge Rey -- updating notifications
+f8b0a26 Serge Rey -- fixing Distance.py and testing travis message
+d1ec0f2 Phil Stephens -quieting pip output and fix one doctest
+927e799 Phil Stephens -adding networkx, tweaks to travis config
+5971bb1 Serge Rey -neighbors from wed
+28f0e55 Serge Rey -adding robust segment intersection tests
+3bcac73 Serge Rey -adding doubly connected edge list to network module
+86f0fea darribas -Adding methods to read line and point shapefiles and improving the method to append different collections to one axes. Still in progress
+b61cb55 Serge Rey -- fixing introduced bug in knnW_arc
+801e78d Serge Rey -Handle point sets with large percentage of duplicate points
+dbafbc4 serge -update pointer to github
+427a620 Serge Rey -dealing with filaments
+23216ef Serge Rey -Fixed cw enumeration of links incident to a node
+0a51a53 Serge Rey -- readme
+5f4cab4 sjsrey -cw enumeration not working for all nodes
+f2e65d3 Serge Rey -- cw traversal of edges incident with a node
+90d150c sjsrey -- version debug for travis
+24598a8 sjsrey -- noting move to org
+9fb8a17 sjsrey -- fixing tutorial tests
+5a14f9e serge -- cleaning up weights tests
+6265b3b Serge Rey -- fixing doc tests
+7e8c4fe Serge Rey -- testing after move to org
+37fc8d4 Serge Rey -- testing post commit emails
+bed7f6e Phil Stephens -removed files
+eab2895 Phil Stephens -removed virginia_queen files
+bcef010 Serge Rey -- adding diagonal argument to Kernel weights - adding doctest evaluation to Distance.py
+02d27e9 Phil Stephens -adding libgeos-dev
+1126d71 Phil Stephens -pipe build output to null
+37dbb35 Phil Stephens -adding -y flag to pip uninstall
+06d56e9 Phil Stephens -adding libgeos_c install, pysal from pip
+4c53277 Phil Stephens -trying to quiet output, using Makefile
+74448e8 Phil Stephens -find setup.py
+4634fb1 Phil Stephens -test install in venv and build
+5d58723 Phil Stephens -working out travis-ci doctest configuration
+5e905d3 Phil Stephens -adding numpydoc
+33a5298 Phil Stephens -tweaks travis config
+5c85f50 Phil Stephens -tweaking service configs
+4ed1201 Josh Kalderimis -use the correct syntax for sysytem_site_packages
+954b6d2 Phil Stephens -stop!
+311eca8 Phil Stephens -ssp=true
+c601bca Phil Stephens -numpy first
+54b0afe Phil Stephens -ok, so travis is serious about not using system site packages.
+2b912cc Phil Stephens -doh
+28994df Phil Stephens -better yaml
+ce1d89e Phil Stephens -testing
+b535d3e Phil Stephens -testing
+440a772 Phil Stephens -tweaking pip requirements file
+34a74e2 Phil Stephens -tweaking travis file
+33b13aa Serge Rey -- new links
+8e09d7b Serge Rey -- setting up travis
+d33001e Sergio Rey -Update CHANGELOG.txt
+9d4de66 Serge Rey -- added authors
+ab672c9 Serge Rey -- modified knnW to speed up dict construction
+4edd2ab Serge Rey -- update cr
+39e6564 Phil Stephens -syncing install instructions with docs
+9e98db9 Phil Stephens -adding website favicon; chrome does not empty cache properly!!
+
+ * migration to github from svn
+ svn2git http://pysal.googlecode.com/svn --authors ~/Dropbox/pysal/src/pysal/authors.txt --verbose
+
+v<1.5.0>, 2013-01-31
+
+2013-01-29 20:36 phil.stphns
+
+ * doc/source/users/installation.txt: updating and simplifying user
+ install instructions.
+
+2013-01-18 16:17 sjsrey
+
+ * Adding regime classes for all GM methods and OLS available in
+ pysal.spreg, i.e. OLS, TSLS, spatial lag models, spatial error models
+ and SARAR models. All tests and heteroskedasticity
+ corrections/estimators currently available in pysal.spreg apply to
+ regime models (e.g. White, HAC and KP-HET). With the regimes, it is
+ possible to estimate models that have:
+ -- Common or regime-specific error variance;
+ -- Common or regime-specific coefficients for all variables or for a
+ selection of variables;
+ -- Common or regime-specific constant term;
+ - Various refactoring to streamline code base and improve long term
+ maintainability
+ - Contributions from Luc Anselin, Pedro Amaral, Daniel Arribas-Bel
+ and David Folch
+
+2013-01-18 14:08 schmidtc
+
+ * pysal/common.py: implemented deepcopy for ROD, see #237
+
+2013-01-08 12:28 dreamessence
+
+ * pysal/contrib/spatialnet/__init__.py: Adding __init__.py to make it importable
+
+2012-12-31 22:53 schmidtc
+
+ * pysal/core/IOHandlers/gwt.py: adding kwt support, see #232
+
+2012-12-21 20:53 sjsrey at gmail.com
+
+ * pysal/__init__.py, pysal/cg/rtree.py,
+ pysal/contrib/weights_viewer/weights_viewer.py,
+ pysal/weights/weights.py: - turning off randomization in rtree
+
+2012-12-06 16:34 dfolch
+
+ * pysal/contrib/shapely_ext.py: adding unary_union() to shapely
+ contrib; note this only works with shapely version 1.2.16 or higher
+
+
+2012-11-29 13:39 dreamessence
+
+ * pysal/contrib/viz/mapping.py: Added option in setup_ax to pass
+ pre-existing axes object to append. It is optional and it enables,
+ for instance, to embed several different maps in one single figure
+
+2012-11-20 00:23 dfolch
+
+ * pysal/contrib/shapely_ext.py: adding shapely's cascaded_union
+ function to contrib
+
+2012-11-12 18:08 dreamessence
+
+ * pysal/contrib/viz/mapping.py: -Adding transCRS method to convert
+ points from one prj to another arbitrary one -Adding map_poly_shp to
+ be able to plot shapefiles in arbitrary projections, not needing to
+ be in lonlat and not depending on Basemap
+
+2012-11-09 15:40 sjsrey at gmail.com
+
+ * pysal/weights/weights.py:
+ - distinguish between intrinsic symmetry and general symmetry
+
+2012-11-02 17:48 schmidtc
+
+ * pysal/weights/user.py, pysal/weights/util.py: Adding Minkowski
+ p-norm to min_threshold_dist_from_shapefile, see issue #221
+
+2012-10-19 22:35 sjsrey at gmail.com
+
+ * pysal/weights/weights.py:
+ explicitly prohibit chaining of transformations - all
+ transformations are only applied to the original weights at
+ instantiation
+
+2012-10-19 17:38 sjsrey at gmail.com
+
+ * pysal/spatial_dynamics/markov.py:
+ - fixing bug in permutation matrix to reorder kronecker product in
+ the join test
+
+2012-10-17 17:55 sjsrey at gmail.com
+
+ * pysal/weights/util.py:
+ -
+ higher order contiguity for WSP objects
+
+2012-10-17 15:43 sjsrey at gmail.com
+
+ * pysal/weights/user.py:
+ -
+ id_order attribute was always NONE for wsp created from
+
+ queen/rook_from_shapefile with sparse=True
+
+2012-10-16 19:25 schmidtc
+
+ * pysal/weights/util.py: improving memory usage of
+ get_points_array_from_shapefile, no need to read entire shapefile
+ into memory.
+
+2012-10-15 00:44 dreamessence
+
+ * pysal/contrib/viz/mapping.py: First attempt to refactor Serge's code
+ for choropleth mapping. It now offers a more general and flexible
+ architecture. Still lots of work and extensions left. The module
+ is explained in a notebook available as a gist at
+ https://gist.github.com/3890284 and viewable at
+ http://nbviewer.ipython.org/3890284/
+
+2012-10-12 18:34 schmidtc
+
+ * pysal/contrib/spatialnet/spatialnet.py: modified SpatialNetwork.snap
+ to calculate and return the snapped point
+
+2012-10-12 17:05 dfolch
+
+ * pysal/contrib/viz/mapping.py: made edits to unique_values_map to
+ allow for unlimited number of categories; I commented out the
+ previous code so these changes can easily be rolled back if it
+ breaks something somewhere else
+
+2012-10-12 15:03 schmidtc
+
+ * pysal/cg/segmentLocator.py: Fixing issue with segmentLocator, when
+ query point is extreamly far from the grid boundary, overflow errors
+ were causing the KDTree to not return any results. Changed both
+ KDtree's to use Float64 and share the same data. Previously,
+ cKDTree was using float64 and KDtree was using int32.
+
+2012-10-11 08:12 dreamessence
+
+ * pysal/contrib/viz/__init__.py: Adding __init__.py to viz module to
+ make it importable
+
+2012-08-31 02:57 phil.stphns
+
+ * pysal/spreg/tests/test_diagnostics.py,
+ pysal/spreg/tests/test_diagnostics_sp.py,
+ pysal/spreg/tests/test_diagnostics_tsls.py,
+ pysal/spreg/tests/test_error_sp.py,
+ pysal/spreg/tests/test_error_sp_het.py,
+ pysal/spreg/tests/test_error_sp_het_sparse.py,
+ pysal/spreg/tests/test_error_sp_hom.py,
+ pysal/spreg/tests/test_error_sp_hom_sparse.py,
+ pysal/spreg/tests/test_error_sp_sparse.py,
+ pysal/spreg/tests/test_ols.py,
+ pysal/spreg/tests/test_ols_sparse.py,
+ pysal/spreg/tests/test_probit.py,
+ pysal/spreg/tests/test_twosls.py,
+ pysal/spreg/tests/test_twosls_sp.py,
+ pysal/spreg/tests/test_twosls_sp_sparse.py,
+ pysal/spreg/tests/test_twosls_sparse.py:
+ - autopep8 -iv spreg/tests/*.py - nosetests pysal - no fixes needed
+
+2012-08-31 01:16 phil.stphns
+
+ * pysal/spreg/diagnostics.py,
+ pysal/spreg/diagnostics_sp.py,
+ pysal/spreg/diagnostics_tsls.py,
+ pysal/spreg/error_sp.py,
+ pysal/spreg/error_sp_het.py,
+ pysal/spreg/error_sp_hom.py,
+ pysal/spreg/ols.py,
+ pysal/spreg/probit.py,
+ pysal/spreg/robust.py,
+ pysal/spreg/summary_output.py,
+ pysal/spreg/twosls.py,
+ pysal/spreg/twosls_sp.py,
+ pysal/spreg/user_output.py,
+ pysal/spreg/utils.py:
+ -
+ autopep8 -iv spreg/*.py - fixed autopep8-introduced doctest failures
+ - fixed lingering scientific notation test failures
+
+2012-08-31 00:26 phil.stphns
+
+ * pysal/esda/gamma.py,
+ pysal/esda/join_counts.py,
+ pysal/esda/mapclassify.py,
+ pysal/esda/mixture_smoothing.py,
+ pysal/esda/moran.py,
+ pysal/esda/smoothing.py:
+ -
+ autopep8 fixes - make sure to run unit and doc tests before
+ committing - one autofix breaks long lines, and thus breaks some
+ doctests; must be
+
+ fixed manually
+
+2012-08-31 00:10 phil.stphns
+
+ * pysal/esda/getisord.py:
+ -
+ using autopep8 module - call: autopep8 -vi getisord.py
+
+2012-08-30 23:18 phil.stphns
+
+ * pysal/esda/geary.py:
+ -
+ pep8 clear - removed wildcard import
+
+2012-08-26 22:53 phil.stphns
+
+ * pysal/spatial_dynamics/directional.py,
+ pysal/spatial_dynamics/ergodic.py,
+ pysal/spatial_dynamics/interaction.py,
+ pysal/spatial_dynamics/markov.py,
+ pysal/spatial_dynamics/rank.py,
+ pysal/spatial_dynamics/util.py:
+ -pep8 and pylint fixes -clean wildcard imports
+
+2012-08-26 21:03 phil.stphns
+
+ * pysal/region/maxp.py,
+ pysal/region/randomregion.py:
+ -
+ cleaning up imports
+
+2012-08-26 18:16 phil.stphns
+
+ * pysal/region/maxp.py:
+ -
+ style fixes with pep8 - cmd line call: pep8 --show-source
+ --ignore=E128,E302,E501,E502,W293,W291
+
+ region/maxp.py
+
+2012-08-26 17:47 phil.stphns
+
+ * pysal/common.py,
+ pysal/examples/README.txt,
+ pysal/region/components.py,
+ pysal/region/randomregion.py:
+ -
+ using pep8 module
+
+2012-08-24 20:47 schmidtc
+
+ * pysal/network,
+ pysal/network/__init__.py: adding network module
+
+2012-08-21 22:53 phil.stphns
+
+ * doc/source/_templates/ganalytics_layout.html:
+ -
+ updating analytics tracker
+
+2012-08-17 17:11 sjsrey at gmail.com
+
+ * pysal/contrib/spatialnet/util.py:
+ -
+ more utility functions for pysal
+ -
+ networkx interop
+
+2012-08-16 23:44 phil.stphns
+
+ * setup.py:
+ -
+ tweak for build names
+
+2012-08-12 13:15 dreamessence
+
+ * doc/source/index.txt:
+ Adding announcement links to landing page
+
+2012-08-11 17:38 sjsrey
+
+ * LICENSE.txt:
+ -
+ update
+
+2012-08-09 17:19 phil.stphns
+
+ * doc/source/developers/pep/pep-0008.txt:
+ updating
+ spatial
+ db
+ pep
+
+2012-08-08 17:22 schmidtc
+
+ * pysal/weights/Distance.py:
+ Fixing bug in Kernel weights that causes erroneous results when
+ using ArcDistances. See issue #218.
+
+2012-08-04 21:14 sjsrey
+
+ * doc/source/developers/docs/index.txt:
+ -
+ fixed
+ links
+
+2012-08-04 21:03 sjsrey
+
+ * doc/source/developers/docs/index.txt:
+ -
+ hints
+ on
+ editing
+ docs
+
+
+2012-08-04 20:14 phil.stphns
+
+ * doc/source/developers/pep/pep-0011.txt:
+ note
+ about
+ travis-ci
+ and
+ github
+
+2012-08-04 16:24 sjsrey
+
+ * doc/source/developers/pep/pep-0011.txt:
+ PEP-0011
+
+
+2012-08-04 16:22 sjsrey
+
+ * doc/source/developers/pep/index.txt:
+ -
+ PEP 0011 Move from Google Code to Github
+
+2012-08-04 04:42 sjsrey
+
+ * doc/source/index.txt:
+ - broken link
+
+2012-08-04 04:35 sjsrey
+
+ * doc/source/index.txt:
+ - news updates
+
+2012-08-04 04:24 sjsrey
+
+ * doc/source/index.txt:
+ - reorg
+
+2012-08-02 02:32 sjsrey
+
+ * pysal/examples/__init__.py:
+ -
+ moving back to r1049 but leaving r1310 in history for ideas on
+ moving forward - we need to distinguish between using examples in
+ the doctests (which the users see) and for the developers since we
+ are no longer distributing examples with the source
+
+2012-08-02 01:49 sjsrey
+
+ * pysal/examples/__init__.py:
+ -
+ correct conditional this time (i hope)
+
+2012-08-02 01:36 sjsrey
+
+ * pysal/examples/__init__.py:
+ -
+ compromise
+ -
+ returns pth rather than None if file does not exist
+
+2012-08-02 00:58 sjsrey
+
+ * pysal/examples/__init__.py:
+ -
+ link to examples download
+
+2012-08-02 00:42 sjsrey
+
+ * pysal/examples/__init__.py:
+ -
+ explicit check if examples are actually present
+
+
+
+
+
+v<1.4.0>, 2012-07-31
+
+2013-01-31
+
+
+2012-07-31 21:30 sjsrey at gmail.com
+
+ * pysal/spatial_dynamics/ergodic.py,
+ pysal/spatial_dynamics/rank.py:
+ - docs/example
+
+2012-07-31 20:47 sjsrey at gmail.com
+
+ * pysal/spreg/tests/test_error_sp_hom.py:
+ - rounding/precision issue
+
+2012-07-31 20:27 sjsrey at gmail.com
+
+ * pysal/spatial_dynamics/directional.py,
+ pysal/spatial_dynamics/tests/test_directional.py:
+ - fixing pvalue bug
+
+2012-07-31 20:24 sjsrey at gmail.com
+
+ * doc/source/users/tutorials/dynamics.txt:
+ - fixed rounding problem
+
+2012-07-31 19:58 sjsrey at gmail.com
+
+ * doc/source/index.txt,
+ doc/source/users/tutorials/autocorrelation.txt,
+ doc/source/users/tutorials/dynamics.txt,
+ doc/source/users/tutorials/econometrics.txt,
+ doc/source/users/tutorials/fileio.txt,
+ doc/source/users/tutorials/index.txt,
+ doc/source/users/tutorials/intro.txt,
+ doc/source/users/tutorials/region.txt,
+ doc/source/users/tutorials/smoothing.txt,
+ doc/source/users/tutorials/weights.txt:
+ - adding links to API for more details
+
+2012-07-31 19:05 sjsrey at gmail.com
+
+ * pysal/spatial_dynamics/directional.py:
+ - consistency on pvalues for randomization
+
+2012-07-31 19:02 sjsrey at gmail.com
+
+ * pysal/weights/Distance.py:
+ - docs
+
+2012-07-31 18:58 sjsrey at gmail.com
+
+ * doc/source/users/tutorials/dynamics.txt:
+ - seed issue
+
+2012-07-31 18:36 sjsrey at gmail.com
+
+ * doc/source/users/tutorials/autocorrelation.txt:
+ - closing issue 214
+
+2012-07-31 18:19 sjsrey at gmail.com
+
+ * doc/source/users/tutorials/autocorrelation.txt:
+ - fixing random.seed issues in doctests
+
+2012-07-31 17:31 schmidtc
+
+ * pysal/cg/shapes.py,
+ pysal/cg/tests/test_shapes.py:
+ Fixing small bugs with VerticleLines and testing
+
+2012-07-31 16:26 sjsrey at gmail.com
+
+ * doc/source/developers/guidelines.txt,
+ doc/source/users/installation.txt:
+ - updating docs
+
+
+
+2012-07-26 15:24 schmidtc
+
+ * pysal/core/FileIO.py,
+ pysal/core/Tables.py:
+ Fixing issue #190
+
+2012-07-24 16:32 schmidtc
+
+ * pysal/cg/sphere.py:
+ Allowing linear2arcdist function to maintin 'inf', this allows compatability with Scipy's KDTree and addresses issue 208.
+
+2012-07-24 16:07 schmidtc
+
+ * pysal/cg/locators.py,
+ pysal/core/FileIO.py,
+ pysal/core/Tables.py:
+ Addressing issue 212, renaming nested and private classes to begin with an underscore. By default sphinx does not try to document private object, which avoids what appears to be a a bug in Sphinx.
+
+2012-07-17 22:06 sjsrey at gmail.com
+
+ * pysal/spreg/probit.py: pedro doc fixes
+
+2012-07-17 15:07 schmidtc
+
+ * pysal/cg/segmentLocator.py,
+ pysal/cg/tests/test_segmentLocator.py: Cleaned up fix for Issue 211
+
+2012-07-13 22:50 sjsrey at gmail.com
+
+ * doc/source/users/tutorials/autocorrelation.txt: fixing sphinx weirdness in footnotes
+
+2012-07-13 22:37 sjsrey at gmail.com
+
+ * doc/source/users/tutorials/autocorrelation.txt: update for new default parameter values
+
+2012-07-13 22:13 sjsrey at gmail.com
+
+ * pysal/esda/geary.py,
+ pysal/esda/tests/test_geary.py: consistency on transformation and permutation args
+
+2012-07-13 19:59 sjsrey at gmail.com
+
+ * doc/source/users/tutorials/dynamics.txt,
+ pysal/__init__.py,
+ pysal/spatial_dynamics/rank.py: - update user tutorial and __init__
+
+2012-07-13 19:33 sjsrey at gmail.com
+
+ * pysal/spatial_dynamics/rank.py,
+ pysal/spatial_dynamics/tests/test_rank.py: - O(n log n) algorithm for spatial tau (old one was O(n^2)) - closing ticket http://code.google.com/p/pysal/issues/detail?id=83
+
+2012-07-13 17:57 schmidtc
+
+ * pysal/core/IOHandlers/pyDbfIO.py,
+ pysal/core/IOHandlers/tests/test_pyDbfIO.py: Adding better support for writing Null values to DBF. See issue #193
+
+2012-07-13 15:55 schmidtc
+
+ * pysal/core/util/shapefile.py,
+ pysal/core/util/tests/test_shapefile.py: Cleaning up support for ZM points, polylines and polygons in the shapefile reader. Added unit tests for same.
+
+2012-07-13 15:42 sjsrey at gmail.com
+
+ * doc/source/library/esda/gamma.txt: - update version info
+
+2012-07-13 15:37 sjsrey at gmail.com
+
+ * doc/source/library/esda/gamma.txt,
+ doc/source/library/esda/index.txt: - adding gamma to api docs
+
+2012-07-13 00:21 sjsrey at gmail.com
+
+ * pysal/esda/gamma.py: optimizations
+
+2012-07-12 21:28 schmidtc
+
+ * pysal/core/IOHandlers/pyDbfIO.py: Disabling mising value warning for DBF files. See issue #185
+
+2012-07-12 21:07 schmidtc
+
+ * pysal/cg/segmentLocator.py,
+ pysal/cg/shapes.py,
+ pysal/cg/tests/test_segmentLocator.py,
+ pysal/contrib/spatialnet/spatialnet.py: Adding unittests for segmentLocator (including one that fails see #211). Added VerticalLine class to represent verticle LineSegments. Updated __all__ in segmentLocator. Minor comment formatting in spatialnet.
+
+2012-07-12 19:41 lanselin at gmail.com
+
+ * doc/source/users/tutorials/autocorrelation.txt: tutorial for gamma index
+
+2012-07-12 19:40 lanselin at gmail.com
+
+ * pysal/esda/gamma.py,
+ pysal/esda/tests/test_gamma.py: gamma with generic function
+
+2012-07-12 14:17 sjsrey at gmail.com
+
+ * pysal/__init__.py: - gamma index added
+
+2012-07-12 03:14 lanselin at gmail.com
+
+ * pysal/esda/tests/test_gamma.py: tests for gamma
+
+2012-07-12 03:13 lanselin at gmail.com
+
+ * pysal/esda/gamma.py: gamma index of spatial autocorrelation
+
+2012-07-12 03:11 lanselin at gmail.com
+
+ * pysal/esda/__init__.py: gamma index
+
+2012-07-11 21:32 lanselin at gmail.com
+
+ * pysal/esda/join_counts.py,
+ pysal/esda/tests/test_join_counts.py: join counts without analytical results, new permutation
+
+2012-07-11 21:32 lanselin at gmail.com
+
+ * doc/source/users/tutorials/autocorrelation.txt: updated docs for join counts
+
+2012-07-10 21:13 lanselin at gmail.com
+
+ * doc/source/users/tutorials/autocorrelation.txt: docs for join count in autocorrelation
+
+2012-07-10 21:12 lanselin at gmail.com
+
+ * pysal/esda/join_counts.py,
+ pysal/esda/tests/test_join_counts.py: additional test in join counts, docs added
+
+2012-07-10 19:24 lanselin at gmail.com
+
+ * pysal/esda/join_counts.py,
+ pysal/esda/tests/test_join_counts.py: join counts with permutations for BB, updated tests to include permutations
+
+2012-07-09 04:22 sjsrey
+
+ * pysal/weights/weights.py:
+ - fixing bug luc identified with regard to mean_neighbor property. wrong key name was used in cache dictionary.
+
+2012-07-07 17:00 sjsrey
+
+ * pysal/__init__.py: update for spreg and contrib inclusion
+
+
+2012-07-07 16:51 sjsrey
+
+ * pysal/spatial_dynamics/markov.py: - updating doc strings
+
+2012-07-07 16:17 sjsrey
+
+ * pysal/spreg/probit.py:
+ - fixing doc string and refs
+
+2012-07-06 21:58 dfolch
+
+ * doc/source/library/spreg/probit.txt: txt file to include probit in the HTML docs
+
+2012-07-06 21:11 dfolch
+
+ * pysal/spreg/tests/test_ols_sparse.py: fixing unittest error; still no solution to scientific notation formatting in doctests
+
+2012-07-06 20:24 dfolch
+
+ * pysal/spreg/__init__.py,
+ pysal/spreg/diagnostics.py,
+ pysal/spreg/diagnostics_sp.py,
+ pysal/spreg/diagnostics_tsls.py,
+ pysal/spreg/error_sp.py,
+ pysal/spreg/error_sp_het.py,
+ pysal/spreg/error_sp_hom.py,
+ pysal/spreg/ols.py,
+ pysal/spreg/probit.py,
+ pysal/spreg/robust.py,
+ pysal/spreg/summary_output.py,
+ pysal/spreg/tests/test_diagnostics.py,
+ pysal/spreg/tests/test_diagnostics_sp.py,
+ pysal/spreg/tests/test_diagnostics_tsls.py,
+ pysal/spreg/tests/test_error_sp.py,
+ pysal/spreg/tests/test_error_sp_het.py,
+ pysal/spreg/tests/test_error_sp_het_sparse.py,
+ pysal/spreg/tests/test_error_sp_hom.py,
+ pysal/spreg/tests/test_error_sp_hom_sparse.py,
+ pysal/spreg/tests/test_error_sp_sparse.py,
+ pysal/spreg/tests/test_ols.py,
+ pysal/spreg/tests/test_ols_sparse.py,
+ pysal/spreg/tests/test_probit.py,
+ pysal/spreg/tests/test_twosls.py,
+ pysal/spreg/tests/test_twosls_sp.py,
+ pysal/spreg/tests/test_twosls_sp_sparse.py,
+ pysal/spreg/tests/test_twosls_sparse.py,
+ pysal/spreg/twosls.py,
+ pysal/spreg/twosls_sp.py,
+ pysal/spreg/user_output.py,
+ pysal/spreg/utils.py: -Adding classic probit regression class -Adding spatial diagnostics for probit -Allowing x parameter to be either a numpy array or scipy sparse matrix in all regression classes -Adding additional unit tests -Various refactoring to streamline code base and improve long term maintainability -Contributions from Luc Anselin, Pedro Amaral, Daniel Arribas-Bel, David Folch and Nicholas Malizia
+
+2012-07-03 18:59 sjsrey
+
+ * pysal/spatial_dynamics/markov.py,
+ pysal/spatial_dynamics/tests/test_markov.py: - refactor significant move_types for clarity and fixing a logic bug
+
+
+2012-06-20 04:50 sjsrey at gmail.com
+
+ * doc/source/developers/docs/index.txt:
+ - added section for how to write a tutorial for new modules
+2012-06-20 02:45 sjsrey
+
+ * doc/source/developers/docs/index.txt:
+ - updating doc building instructions
+
+2012-06-06 18:58 phil.stphns
+
+ * .build-osx10.6-py26.sh,
+ .build-osx10.6-py27.sh:
+ - local modifications for Frameworks builds
+
+2012-06-05 20:56 phil.stphns
+
+ * .build-osx10.6-py26.sh,
+ .build-osx10.6-py27.sh,
+ .build-osx10.7-py27.sh, .runTests.sh:
+ - adding experimental build and test scripts.
+
+2012-06-05 16:43 schmidtc
+
+ * pysal/cg/segmentLocator.py,
+ pysal/cg/shapes.py,
+ pysal/contrib/spatialnet/spatialnet.py:
+ initial snap function for spatialnet
+
+2012-06-05 16:38 schmidtc
+
+ * pysal/core/IOHandlers/pyShpIO.py,
+ pysal/core/util/shapefile.py,
+ pysal/core/util/tests/test_shapefile.py: Adding PolygonZ support to Shapefile IO
+
+2012-05-24 21:57 sjsrey
+
+ * pysal/esda/mapclassify.py: - truncate option for fisher_jenks sampling
+
+2012-05-15 20:08 schmidtc
+
+ * pysal/cg/segmentLocator.py,
+ pysal/cg/shapes.py: Added query to SegmentLocator
+
+2012-05-11 22:17 sjsrey
+
+ * pysal/esda/mapclassify.py: - added Fisher_Jenks_Sampled
+
+2012-05-11 00:45 mhwang4
+
+ * pysal/contrib/network/distances.csv,
+ pysal/contrib/network/simulator.py,
+ pysal/contrib/network/test_lincs.py,
+ pysal/contrib/network/test_weights.py,
+ pysal/contrib/network/weights.py: adding test code for distance-file-based weight generator; updates on simulator
+
+2012-05-10 22:37 mhwang4
+
+ * pysal/contrib/network/klincs.py,
+ pysal/contrib/network/lincs.py,
+ pysal/contrib/network/test_klincs.py,
+ pysal/contrib/network/test_lincs.py: adding test code for network-constrained lisa
+
+2012-05-10 21:11 mhwang4
+
+ * pysal/contrib/network/crimes.dbf,
+ pysal/contrib/network/crimes.shp,
+ pysal/contrib/network/crimes.shx,
+ pysal/contrib/network/test_klincs.py: test code for local K function
+
+2012-05-08 18:05 mhwang4
+
+ * pysal/contrib/network/streets.dbf,
+ pysal/contrib/network/streets.shp,
+ pysal/contrib/network/streets.shx,
+ pysal/contrib/network/test_network.py: adding a test data set
+
+2012-05-08 16:34 schmidtc
+
+ * pysal/cg/segmentLocator.py,
+ pysal/cg/shapes.py,
+ pysal/core/FileIO.py: Adding start of segmentLocator, adding minimal slicing support to FileIO
+
+
+2012-05-03 17:03 schmidtc
+
+ * pysal/cg/shapes.py,
+ pysal/cg/tests/test_shapes.py: Adding solve for x support to Line. Cleaning up LineSegment's Line method.
+
+2012-04-20 17:48 schmidtc
+
+ * pysal/cg/shapes.py: adding arclen method to Chain object.
+
+2012-04-19 16:37 dfolch
+
+ * pysal/weights/Distance.py: reducing number of distance queries in Kernel from n^2 to n
+
+2012-04-17 21:20 schmidtc
+
+ * pysal/contrib/spatialnet/spatialnet.py: adding distance
+
+2012-04-17 19:46 schmidtc
+
+ * pysal/contrib/spatialnet/cleanNetShp.py,
+ pysal/contrib/spatialnet/spatialnet.py: Adding FNODE/TNODE to dbf when cleaning shapefiles. Added util function createSpatialNetworkShapefile Added SpatialNetwork class
+
+2012-04-17 15:32 schmidtc
+
+ * pysal/contrib/weights_viewer/weights_viewer.py: "revert back to the background when the point is outside of any unit" - request from serge
+
+2012-04-11 02:50 schmidtc
+
+ * pysal/cg/kdtree.py: Fixing user submitted bug,issue #206.
+
+2012-04-10 22:00 dreamessence
+
+ * pysal/weights/Wsets.py: Including w_clip in __all__
+
+2012-04-10 21:58 dreamessence
+
+ * pysal/weights/Wsets.py: Adding w_clip method to clip W matrices (sparse and/or pysal.W) with a second (binary) matrix
+
+2012-04-10 21:57 schmidtc
+
+ * pysal/contrib/spatialnet/beth_roads.shp,
+ pysal/contrib/spatialnet/beth_roads.shx,
+ pysal/contrib/spatialnet/cleanNetShp.py: Adding network shapefile cleaning tools and temporary sample data.
+
+2012-04-10 21:48 sjsrey
+
+ * pysal/contrib/spatialnet/util.py: - more stubs for util mod
+
+2012-04-10 19:58 sjsrey
+
+ * pysal/contrib/spatialnet/util.py: - start of util module
+
+2012-04-03 20:43 sjsrey
+
+ * pysal/contrib/spatialnet: - new contrib module - integrate geodanet functional (move over from network) - wrap networkx
+
+2012-04-03 01:21 schmidtc
+
+ * pysal/cg/rtree.py: Adding pickle support to RTree
+
+
+2012-03-28 23:27 mhwang4
+ * pysal/contrib/network/kernel.py,
+ pysal/contrib/network/kfuncs.py,
+ pysal/contrib/network/test_access.py,
+ pysal/contrib/network/test_kernel.py,
+ pysal/contrib/network/test_kfuncs.py,
+ pysal/contrib/network/test_network.py: adding examples for network-related modules
+
+2012-03-19 15:33 schmidtc
+
+ * pysal/core/IOHandlers/pyDbfIO.py: Adding support for writing Null dates
+
+2012-03-14 21:04 phil.stphns
+
+ * doc/source/developers/testing.txt,
+ doc/source/users/installation.txt: Small changes to user install instructions to highlight the ease with which pysal can be installed ;-> And, developer instructions for running the test suite from within a session if desired.
+
+2012-03-03 00:00 phil.stphns
+
+ * pysal/spatial_dynamics/markov.py: Potential source of dev docs pngmath latex fail.
+
+2012-02-24 23:29 mhwang4
+
+ * pysal/contrib/network/network.py: fixing bug in network.py
+
+2012-02-20 19:50 phil.stphns
+
+ * doc/source/developers/py3k.txt: Developer doc to explain setting up PySAL for Python3.
+
+2012-02-20 16:18 schmidtc
+
+ * pysal/esda/__init__.py: removing invalid __all__ from esda's init. See #194
+
+2012-02-16 23:15 phil.stphns
+
+ * pysal/__init__.py,
+ pysal/core/util/shapefile.py: Minor changes to imports that cause py3tool to stumble.
+
+2012-02-15 23:16 phil.stphns
+
+ * doc/source/developers/py3k.txt,
+ doc/source/users/installation.txt: Modified links in user installation instructions. Added more steps for developers setting up Python3 dev environments on OSX.
+
+2012-02-14 21:55 schmidtc
+
+ * pysal/esda/getisord.py: fixing side effect caused when changing the shape of y, creating a new view with reshape instead.
+
+2012-02-14 21:21 schmidtc
+
+ * pysal/esda/getisord.py: optimizing G_Local
+
+2012-02-14 20:37 schmidtc
+
+ * pysal/esda/getisord.py: optimizing G
+
+2012-02-14 00:21 phil.stphns
+
+ * doc/source/developers/index.txt,
+ doc/source/developers/py3k.txt,
+ doc/source/developers/release.txt: Adding early docs on Python 3 support. Modifying release instructions.
+
+v<1.3.0>, 2012-01-31
+
+ * core/IOHandlers/pyDbfIO.py: Addressing issue #186
+
+ * cg/shapes.py: fixing small bug in polygon constructor that causes
+ an exception when an empty list is passed in for the holes.
+
+ * cg/standalone.py: removing standalone centroid method. see issue
+ #138.
+
+ * esda/mapclassify.py, esda/tests/test_mapclassify.py: - new
+ implementation of fisher jenks
+
+ * spreg/__init__.py, spreg/diagnostics_sp.py,
+ spreg/diagnostics_tsls.py, spreg/error_sp.py,
+ spreg/error_sp_het.py, spreg/error_sp_hom.py, spreg/ols.py,
+ spreg/robust.py, spreg/tests, spreg/twosls.py,
+ spreg/twosls_sp.py, spreg/user_output.py, spreg/utils.py: Adding
+ the following non-spatial/spatial regression modules:
+ * Two Stage Least Squares
+ * Spatial Two Stage Least Squares
+ * GM Error (KP 98-99)
+ * GM Error Homoskedasticity (Drukker et. al, 2010)
+ * GM Error Heteroskedasticity (Arraiz et. al, 2010)
+ * Anselin-Kelejian test for residual spatial autocorrelation of
+ residuals from IV regression
+
+ Adding also utility functions and other helper classes.
+
+ * cg/standalone.py: slight improvment to get_shared_segments, in
+ part to make it more readable.
+
+ * cg/shapes.py, cg/tests/test_standalone.py: adding <,<=,>,>= tests
+ to Point, this fixes a bug in the get_shared_segments function
+ that was causing some LineSegments to be incorectly ordered
+ because the default memory address was being used instead of the
+ points location.
+
+ * core/IOHandlers/tests/test_wkt.py, core/IOHandlers/wkt.py,
+ core/util/tests/test_wkt.py, core/util/wkt.py,
+ weights/tests/test_Distance.py, weights/tests/test_user.py,
+ weights/user.py: Fixing small numerical errors n testing that
+ resulted from changing the centroid algorithm.
+
+ * esda/moran.py: another optimization for __crand see issue #188
+
+ * weights/util.py: Added option for row-standardized SW in lat2SW.
+ Implementing suggestion from Charlie in Issue 181 from
+ StackOverflow
+
+ * esda/moran.py: another optimization to __crand, see issue #188
+ for details.
+
+ * esda/moran.py: Optimized __crand in Local_Moran
+
+ * cg/shapes.py, cg/standalone.py, contrib/shapely_ext.py:
+ Adddressing issue #138, centroids for polygons with holes
+ Fixing some issues with the shapely wrapper and out implemenation
+ of __geo_interface__
+
+ * weights/Distance.py: previous 'fix' to uniform kernel did not
+ have correct dimensions
+
+ * core/IOHandlers/arcgis_txt.py, core/IOHandlers/dat.py,
+ weights/user.py: fixing rounding errors with docstrings
+
+ * contrib/README, contrib/shared_perimeter_weights.py: Adding
+ shared perimeter weights, see Issue #46
+
+ * contrib/README, contrib/shapely_ext.py: moving shapely_ext into
+ contrib
+
+ * core/IOHandlers/pyDbfIO.py: Fixing issue with scientific notation
+ is DBF files. #182
+
+ * core/IOHandlers/pyShpIO.py: clockwise testing should only be
+ performed on Polygons. #183
+
+ * spreg/diagnostics_sp.py: Switching ints to floats in variance of
+ Morans I for residuals to get correct results
+
+ * core/util/shapefile.py, examples/__init__.py: Add a "get_path"
+ function to examples module.
+ pysal.examples.get_path('stl_hom.shp') will always return the
+ correct system path to stl_hom.shp, no matter where it's run
+ from. This is useful for testing.
+ Modified shapefile tests to use the new function.
+
+ * spreg/diagnostics.py: Adding check on condition_index to pick OLS
+ (xtx) or IV (hth) model
+
+ * core/IOHandlers/template.py: Updating template to pass unit
+ testing.
+
+ * core/util/shapefile.py: Fixing issue #180. Making shapefile
+ opener case insensitive.
+
+ * spatial_dynamics/interaction.py,
+ spatial_dynamics/tests/test_interaction.py: Adding modified Knox
+ and changes to existing tests in spatial_dynamics.
+
+ * core/IOHandlers/arcgis_txt.py,
+ core/IOHandlers/tests/test_arcgis_txt.py: fixing arcgis_txt.py so
+ that it ignores self-neighbors with zero weights
+
+ * core/FileIO.py: Updating library README.
+ Removing docstrings from FileIO module.
+
+ * contrib/README: adding contrib to installer and adding initial
+ README
+
+ * core/IOHandlers/gwt.py: rewrote GWT reader to avoid list appends.
+ resulted in speed up of about 12x.
+
+ * core/IOHandlers/pyDbfIO.py: implementing _get_col for dbf files.
+
+ * core/IOHandlers/gwt.py: Adding a small fix to gwt reader, if the
+ ids cannot be found in the associated DBF, they will be read in
+ order from the GWT file.
+
+ * contrib/weights_viewer/weights_viewer.py: Small change to
+ identify polygons that are their own neighbor.
+
+ * weights/Distance.py: removing incorrect kernel functions and
+ fixing bug in uniform kernel
+
+ * weights/util.py: refactoring insert_diagonal so that it can add
+ or overwrite the diagonal weights
+
+ * contrib, contrib/README, contrib/__init__.py,
+ contrib/weights_viewer, contrib/weights_viewer/__init__.py,
+ contrib/weights_viewer/transforms.py,
+ contrib/weights_viewer/weights_viewer.py: Adding 1st contrib, a
+ wxPython based Weights file viewer.
+
+
+ * spatial_dynamics/markov.py: - handle case of zero transitions in
+ spatial markov, consistent with treatment
+ in classic markov
+
+ * core/FileIO.py, core/IOHandlers/pyShpIO.py: Changes to allow
+ reading of null polygons.
+
+ * core/util/shapefile.py, core/util/tests/test_shapefile.py:
+ refactoring shapefile reader, see issue #89
+
+ * core/FileIO.py: small change to FileIO to allow FileFormat
+ argument to be passed through
+
+ * esda/getisord.py: fixing bug in local Z values for integer data
+
+ * cg/__init__.py, weights/user.py, weights/util.py: adding radius
+ option to user weights methods
+
+ * cg/kdtree.py, common.py, weights/Distance.py,
+ weights/tests/test_Distance.py: Distance weights can not be
+ passed an instnace of KDTree instead of an array. If the KDTree
+ is of type ArcKDTree, the weights returns will be based on
+ ArcDistances. Adding tests for Arc cases off KNN and
+ DistanceBand.
+
+ * weights/util.py: - added function for local clustering
+ coefficient - summary for W as a graph
+
+ * cg/kdtree.py, cg/sphere.py: finishing up Arc_KDTree
+
+ * weights/Distance.py: More doctest fixes.
+
+ * region/maxp.py, spreg/diagnostics.py, weights/Distance.py,
+ weights/user.py: Fixing the doctests for dusty python setup.
+
+ * cg/kdtree.py, cg/sphere.py: adding spherical wrapper around scipy
+ kdtree
+
+ * cg/__init__.py, cg/sphere.py: Adding spherical distance tools to
+ cg. Related to issue #168
+
+ * core/IOHandlers/gwt.py, core/IOHandlers/tests/test_gwt.py:
+ re-enabled gwt writing.
+ 'o' transform is used on all GWTs for writing (w is returned to
+ existing transform on exit)
+ Also, setting '_shpName' and '_varName' attributes on W's which
+ are read in through gwt.
+ the writer will check if these vars exist and use them for the
+ header, this prevents metadata loss on simple copies
+
+ * esda/join_counts.py: - fix for handling int array type
+
+ * spreg/diagnostics.py: Adding more efficient constant check for
+ spreg.
+
+ * cg/shapes.py: adding __geo_interface__ and asShape adapter for
+ Point, LineString and Polygon
+
+ * spreg/diagnostics.py: minor change to t-stat function to
+ accommodate future regression models
+
+ * esda/mapclassify.py: - more general fix for #166
+
+
+v<1.2.0>, 2011-07-31
+
+ * pysal/spreg/user_output.py: Fix for bug 162
+
+ * pysal/spatial_dynamics/markov.py: Added markov mobility measures; addresses issue 137
+
+ * pysal/weights/weights.py: Partially addressed issue 160 by removing the shimbel, order, and higher_order methods from W.
+
+ * doc/source/users/installation.txt: Adding known issue regarding GNU/Linux testing and random seeds; see ticket 52.
+
+ * pysal/esda/geary.py: Adding sparse implementation of Geary's C; substantial gains on larger datasets.
+
+ * pysal/core/IOHandlers/mtx.py: Adding WSP2W function for fast conversion of sparse weights object (WSP) to pysal W.
+
+ * pysal/esda/getisord.py: Adding Getis-Ord G test module
+
+ * pysal/weights/util.py: Added function that inserts values along the main diagonal of a weights object
+
+ * doc/source/users/tutorials: Fixed issue 76.
+
+ * pysal/core/IOHandlers/mtx.py: Added an IOHandler for MatrixMarket MTX files
+
+ * pysal/esda/moran.py: Optimized conditional randomization
+
+ * pysal/weights/util.py: Re-adding full2W() method to convert full arrays into W objects; related to issue #136.
+
+ * pysal/core/IOHandlers/gal.py: Added sparse WSP (thin W); gal reader can return W or WSP
+
+ * pysal/core/IOHandlers/pyDbfIO.py: Bug Fix, DBF files are not properly closed when opened in 'r' mode. See issue #155.
+
+ * pysal/core/IOHandlers/stata_txt.py: Adding FileIO handlers for STATA text files
+
+ * pysal/weights/user.py: Fixed issue #154, adding k option to User Kernel weights functions.
+
+ * pysal/core/IOHandlers/mat.py: Adding an IOHandler for MATLAB mat file
+
+ * pysal/core/IOHandlers/wk1.py: Adding an IO handler for wk1 file
+
+ * pysal/core/IOHandlers/geobugs_txt.py: Adding an IO handler for geobugs text file.
+
+ * pysal/core/IOHandlers/arcgis_swm.py: Added ArcGIS SWM file handler
+
+ * pysal/core/IOHandlers/arcgis_dbf.py: Adding a spatial weights file in the (ArcGIS-style) DBF format.
+
+ * pysal/core/IOHandlers/arcgis_txt.py: Added ArcGIS ASCII file IO handler.
+
+ * pysal/core/IOHandlers/dat.py: Added DAT file handler.
+
+ * pysal/cg/locators.py: Added point in polygon method for Polygon and PolygonLocator
+
+ * pysal/weights/Distance.py: Optimized Kernel() method to run much faster for the case of adaptive bandwidths
+
+ * pysal/weights/user.py: Added helper function in user.py to create scipy sparse matrix from a gal file
+
+ * pysal/common.py: Added shallow copy method to Read-Only Dict to support multiprocessing.
+
+ * pysal/spatial_dynamics/rank.py: More efficient regime weights
+
+ * pysal/weights/Distance.py: Adding epanechnikov and bisquare kernel funtions
+
+ * pysal/core/IOHandlers/pyDbfIO.py: Adding NULL support to numerical DBF fields; modifying PointLocator API to match PolygonLocator API
+
+ * pysal/cg/locators.py: Handles case when query rectangle is completely inside a polygon
+
+ * pysal/cg/locators.py: Explicit polygon overlap hit test
+
+ * pysal/cg/standalone.py: Adding point-polygon intersection support for polygons with holes.
+
+ * pysal/spatial_dynamics/markov.py: Added homogeneity test.
+
+ * pysal/spatial_dynamics/markov.py: Added spillover test in LISA_Markov.
+
+ * pysal/cg/locators.py: Added Rtree based spatial index for polygonlocator.
+
+ * pysal/cg/rtree.py: Added pure python Rtree module.
+
+ * doc/source/developers/pep/pep-0010.txt: Added PEP 0010: Rtree module in pure python.
+
+ * pysal/esda/geary.py: Fixed bug 144.
+
+ * pysal/spatial_dynamics/markov.py: Added significance filtering of LISA markov.
+
+ * doc/source/developers/pep/pep-0009.txt: Added new PEP, "PEP 0009: Add Python 3.x Support."
+
+ * doc/source/developers/guidelines.txt: New release cycle schedules for 1.2 and 1.3.
+
+ * doc/source/developers/release.txt: Updated pypi instructions; PySAL available on the Python Package Index via download, easy_install, and pip.
+
+v<1.1.0>, 2011-01-31
+
+ * pysal/core/FileIO.py, pysal/core/IOHandlers/pyDbfIO.py: Added missing value support to FileIO. Warnings will be issued when missing values are found and the value will be set to pysal.MISSINGVALUE, currently None, but the user can change it as needed.
+
+ * pysal/spreg/: Added Spatial Regression module, spreg, and tests. Added non-spatial diagnostic tests for OLS regression.
+
+ * pysal/core/IOHandlers/gwt.py: Fixing bottle neck in gwt reader, adding support for GeoDa Style ID's and DBF id_order.
+
+ * pysal/cg/standalone.py: adding, distance_matrix, full distance matrix calculation using sparse matrices
+
+ * pysal/core/util: Moved "converters" into core.util, allows them to be used independently of FileIO.
+
+ * pysal/weights/Distance.py: Adding work around for bug in scipy spatial, see pysal issue #126
+
+ * pysal/weights/user.py: Added build_lattice_shapefile in weights.user, which writes an ncol by nrow grid to a shapefile.
+
+ * pysal/weights/Distance.py: fixed coincident point problem in knnW and made sure it returns k neighbors
+ * pysal/spatial_dynamics/interaction.py: Added a suite of spatio-temporal interaction tests including the Knox, Mantel, and Jacquez tests.
+
+ * pysal/weights/util.py: Added lat2SW, allows to create a sparse W matrix for a regular lattice.
+
+ * pysal/tests/tests.py: - new 1.1 integration testing scheme.
+
+ * pysal/esda/interaction.py: added standardized Mantel test and improved readability.
+
+ * pysal/spatial_dynamics/directional.py: - adding directional LISA analytics
+
+ * pysal/esda/mapclassify.py: Natural_Breaks will lower k for data with fewer than k unique values, prints warning.
+
+ * pysal/region/randomregion.py: improvements to spatially constrained random region algorithm
+
+ * pysal/esda/smoothing.py: Adding choynowski probabilities and SMR to smoothing.py
+
+ * doc/source/developers/release.txt: - updating release cycle - release management
+
+v<1.0.0>, 2010-07-31 -- Initial release.
diff --git a/INSTALL.txt b/INSTALL.txt
new file mode 100644
index 0000000..9fca779
--- /dev/null
+++ b/INSTALL.txt
@@ -0,0 +1,242 @@
+.. _installation:
+
+================
+Installing PySAL
+================
+
+.. contents::
+
+Prerequisites
+=============
+
+
+Before installing PySAL, make sure the following libraries are properly
+installed on your machine:
+
+
+* `Python <http://www.python.org/>`_ 2.5 or later
+* `Numpy <http://new.scipy.org/download.html>`_ 1.3 or later
+* `Scipy <http://new.scipy.org/download.html>`_ 0.7 or later
+
+
+*Optional, bundled installation:*
+
+The Anaconda Python distribution ships with PySAL. Opting for this distro means you don't have to read any further!
+Check it out at http://continuum.io/downloads.html
+
+With the `Enthought Python Distribution (EPD Free) <http://www.enthought.com/products/epd_free.php>`_,
+you will install all of PySAL's required dependencies, as well as iPython and a lot more, rather than installing dependencies one by one.
+The full-blown EPD package is free for academics, and downloadable `here <http://www.enthought.com/products/edudownload.php>`_.
+
+* Optional, tools to compile our documentation:*
+
+* `Sphinx <http://sphinx.pocoo.org/>`_
+* `numpydoc <http://pypi.python.org/pypi/numpydoc/0.4>`_ extension to Sphinx (*not included in EPD*)
+
+*Optional, recommended additions:*
+
+* `iPython <http://ipython.scipy.org/moin/Download>`_
+
+Getting PySAL
+=============
+
+PySAL is available on the `Python Package Index
+<http://pypi.python.org/pypi/PySAL>`_, which means it can be
+downloaded and installed manually or from the command line using pip or easy_install, as
+follows::
+
+ $ pip install pysal
+ #or
+ $ easy_install pysal
+
+Alternatively, grab the source distribution (.tar.gz) and decompress it to your selected destination. Open a command shell and navigate to the decompressed PySAL folder. Type::
+
+ $ python setup.py install
+
+Development version on GitHub
+-----------------------------
+
+Developers can checkout PySAL using **git**::
+
+ $ git clone https://github.com/pysal/pysal.git pysal
+
+
+This will create a folder called 'pysal' containing the source
+code in trunk. This will allow you to easily update any change
+to the developer code base by running::
+
+ $ git pull
+
+Since PySAL is in active development, changes are made frequently. For that
+reason, we recommend you 'tell' Python to look for PySAL in that folder
+rather than properly install it as a package. You can do this by adding the
+PySAL folder to the PYTHONPATH, a UNIX environment variable.
+To do that, create or edit your command shell profile (.bash_profile if using
+BASH) and add the following line, substituting where appropriate::
+
+
+ $ export PYTHONPATH=${PYTHONPATH}:"/path_to_desired/folder/pysal/"
+
+Save and quit the file, then source it as follows::
+
+ $ source ~/.bash_profile
+
+You are all set. To test your setup, start a Python session and type::
+
+ >>> import pysal; pysal.open.check()
+
+Your terminal should reply with the following::
+
+ PySAL File I/O understands the following file extensions:
+ Ext: '.shp', Modes: ['r', 'wb', 'w', 'rb']
+ Ext: '.mtx', Modes: ['r', 'w']
+ Ext: '.swm', Modes: ['r', 'w']
+ Ext: '.mat', Modes: ['r', 'w']
+ Ext: '.shx', Modes: ['r', 'wb', 'w', 'rb']
+ Ext: '.stata_text', Modes: ['r', 'w']
+ Ext: '.geoda_txt', Modes: ['r']
+ Ext: '.dbf', Modes: ['r', 'w']
+ Ext: '.dat', Modes: ['r', 'w']
+ Ext: '.gwt', Modes: ['r']
+ Ext: '.gal', Modes: ['r', 'w']
+ Ext: '.arcgis_text', Modes: ['r', 'w']
+ Ext: '.wk1', Modes: ['r', 'w']
+ Ext: '.arcgis_dbf', Modes: ['r', 'w']
+ Ext: '.geobugs_text', Modes: ['r', 'w']
+ Ext: '.csv', Modes: ['r']
+ Ext: '.wkt', Modes: ['r']
+ >>>
+
+
+Windows
+~~~~~~~~
+
+To keep up to date with PySAL development, you will need a Git client that allows you to access and
+update the code from our repository. We recommend
+`GitHub Windows <http://windows.github.com/>`_.
+
+After using GitHub Windows to install a local copy of Pysal, you need to tell
+your Python distribution where to find PySAL. There are several ways to do this. Here we
+will use a simple one that requires you to create a text file
+called ``sitecustomize.py`` in the
+``site-packages`` folder of your Python distribution. That path looks more or
+less like this::
+
+ C:\PythonXX\Lib\site-packages\sitecustomize.py
+
+,where XX corresponds to the version of the Python distribution you are using
+(25 for 2.5, for example).
+Edit ``sitecustomize.py`` with the following two lines of text::
+
+ import sys
+ sys.path.append("C:/PySALsvn/pysal-read-only")
+
+Save and close the file.
+You are all set. To test your setup, start a Python session and type::
+
+ >>> import pysal; pysal.open.check()
+
+Your terminal should reply with the following::
+
+
+ PySAL File I/O understands the following file extensions:
+ Ext: '.shp', Modes: ['r', 'wb', 'w', 'rb']
+ Ext: '.mtx', Modes: ['r', 'w']
+ Ext: '.swm', Modes: ['r', 'w']
+ Ext: '.mat', Modes: ['r', 'w']
+ Ext: '.shx', Modes: ['r', 'wb', 'w', 'rb']
+ Ext: '.stata_text', Modes: ['r', 'w']
+ Ext: '.geoda_txt', Modes: ['r']
+ Ext: '.dbf', Modes: ['r', 'w']
+ Ext: '.dat', Modes: ['r', 'w']
+ Ext: '.gwt', Modes: ['r']
+ Ext: '.gal', Modes: ['r', 'w']
+ Ext: '.arcgis_text', Modes: ['r', 'w']
+ Ext: '.wk1', Modes: ['r', 'w']
+ Ext: '.arcgis_dbf', Modes: ['r', 'w']
+ Ext: '.geobugs_text', Modes: ['r', 'w']
+ Ext: '.csv', Modes: ['r']
+ Ext: '.wkt', Modes: ['r']
+ >>>
+
+
+Known Issues
+============
+
+1.5 install fails with scipy 11.0 on Mac OS X
+---------------------------------------------
+
+Running `python setup.py install` results in::
+
+ from _cephes import *
+ ImportError:
+ dlopen(/Users/serge/Documents/p/pysal/virtualenvs/python1.5/lib/python2.7/site-packages/scipy/special/_cephes.so,
+ 2): Symbol not found: _aswfa_
+ Referenced from:
+ /Users/serge/Documents/p/pysal/virtualenvs/python1.5/lib/python2.7/site-packages/scipy/special/_cephes.so
+ Expected in: dynamic lookup
+
+This occurs when your scipy on Mac OS X was complied with gnu95 and not
+gfortran. See `this thread <http://mail.scipy.org/pipermail/scipy-user/2010-November/027548.html>`_ for possible solutions.
+
+weights.DistanceBand failing
+----------------------------
+
+This occurs due to a bug in scipy.sparse prior to version 0.8. If you are running such a version see `Issue 73 <http://code.google.com/p/pysal/issues/detail?id=73&sort=milestone>`_ for a fix.
+
+doc tests and unit tests under Linux
+------------------------------------
+
+Some Linux machines return different results for the unit and doc tests. We suspect this has to do with the way random seeds are set. See `Issue 52 <http://code.google.com/p/pysal/issues/detail?id=52&sort=milestone>`_
+
+LISA Markov missing a transpose
+-------------------------------
+In versions of PySAL < 1.1 there is a bug in the LISA Markov, resulting in
+incorrect values. For a fix and more details see `Issue 115 <http://code.google.com/p/pysal/issues/detail?id=115>`_.
+
+
+PIP Install Fails
+-----------------
+
+
+Having numpy and scipy specified in pip requiretments.txt causes PIP install of pysal to fail. For discussion and suggested fixes see `Issue 207 <http://code.google.com/p/pysal/issues/detail?id=207&sort=milestone>`_.
+
+Troubleshooting
+===============
+
+If you experience problems when building, installing, or testing PySAL, ask for
+help on the
+`OpenSpace <http://geodacenter.asu.edu/support/community>`_
+list or
+browse the archives of the
+`pysal-dev <http://groups.google.com/group/pysal-dev?pli=1>`_
+google group.
+
+Please include the output of the following commands in your message:
+
+1) Platform information::
+
+ python -c 'import os,sys;print os.name, sys.platform'
+ uname -a
+
+2) Python version::
+
+ python -c 'import sys; print sys.version'
+
+3) SciPy version::
+
+ python -c 'import scipy; print scipy.__version__'
+
+3) NumPy version::
+
+ python -c 'import numpy; print numpy.__version__'
+
+4) Feel free to add any other relevant information.
+ For example, the full output (both stdout and stderr) of the PySAL
+ installation command can be very helpful. Since this output can be
+ rather large, ask before sending it into the mailing list (or
+ better yet, to one of the developers, if asked).
+
+
+
+
diff --git a/LICENSE.txt b/LICENSE.txt
new file mode 100644
index 0000000..8ca191d
--- /dev/null
+++ b/LICENSE.txt
@@ -0,0 +1,30 @@
+Copyright (c) 2007-2014, PySAL Developers
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+ list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright
+ notice, this list of conditions and the following disclaimer in the
+ documentation and/or other materials provided with the distribution.
+
+* Neither the name of the GeoDa Center for Geospatial Analysis and Computation
+ nor the names of its contributors may be used to endorse or promote products
+ derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
+CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
+INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
+CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF
+USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 0000000..f7f610c
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1,2 @@
+include authors.txt INSTALL.txt LICENSE.txt THANKS.txt CHANGELOG.txt MANIFEST.in pysal/COPYING
+
diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000..cb93325
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,25 @@
+# developer Makefile for repeated tasks
+#
+.PHONY: clean
+
+test:
+ nosetests
+
+doctest:
+ cd doc; make pickle; make doctest
+
+install:
+ python setup.py install >/dev/null
+
+src:
+ python setup.py sdist >/dev/null
+
+win:
+ python setup.py bdist_wininst >/dev/null
+
+clean:
+ find . -name "*.pyc" -exec rm '{}' ';'
+ find pysal -name "__pycache__" -exec rm -rf '{}' ';'
+ rm -rf dist
+ rm -rf build
+ rm -rf PySAL.egg-info
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..b0504df
--- /dev/null
+++ b/README.md
@@ -0,0 +1,101 @@
+Python Spatial Analysis Library
+==============================
+
+What is PySAL
+--------------
+[](https://travis-ci.org/pysal/pysal)
+
+PySAL is an open source cross-platform library of spatial analysis functions
+written in Python. It is intended to support the development of high level
+applications for spatial analysis.
+
+It is important to underscore what PySAL is, and is not, designed to do. First
+and foremost, PySAL is a library in the fullest sense of the word. Developers
+looking for a suite of spatial analytical methods that they can incorporate
+into application development should feel at home using PySAL. Spatial analysts
+who may be carrying out research projects requiring customized scripting,
+extensive simulation analysis, or those seeking to advance the state of the art
+in spatial analysis should also find PySAL to be a useful foundation for their
+work.
+
+End users looking for a user friendly graphical user interface for spatial
+analysis should not turn to PySAL directly. Instead, we would direct them to
+projects like STARS and the GeoDaX suite of software products which wrap PySAL
+functionality in GUIs. At the same time, we expect that with developments such
+as the Python based plug-in architectures for QGIS, GRASS, and the toolbox
+extensions for ArcGIS, that end user access to PySAL functionality will be
+widening in the near future.
+
+PySAL package structure
+-----------------------
+
+Currently PySAL consists of the following files and directories:
+
+ LICENSE.txt
+ PySAL license.
+
+ INSTALL.txt
+ PySAL prerequisites, installation, testing, and troubleshooting.
+
+ THANKS.txt
+ PySAL developers and contributors.
+
+ README.txt
+ PySAL structure (this document).
+
+ setup.py
+ Script for building and installing PySAL.
+
+ MANIFEST.in
+ Additions to distutils-generated PySAL tar-balls.
+
+ CHANGELOG.txt
+ Changes since the last release
+
+ pysal/
+ Contains PySAL __init__.py and the directories of PySAL modules.
+
+ doc/
+ Contains PySAL documentation using the Sphinx framework.
+
+PySAL modules
+-------------
+
+* pysal.cg — Computational geometry
+* pysal.contrib - Contributed modules
+* pysal.core — Core data structures and IO
+* pysal.esda — Exploratory spatial data analysis
+* pysal.examples — Data sets
+* pysal.inequality — Spatial inequality analysis
+* pysal.network - Spatial analysis on networks
+* pysal.region — Spatially constrained clustering
+* pysal.spatial_dynamics — Spatial dynamics
+* pysal.spreg — Regression and diagnostics
+* pysal.weights — Spatial weights
+
+Website
+-------
+All things PySAL can be found here
+ http://pysal.org/
+
+Mailing Lists
+-------------
+Please see the developer list here
+ http://groups.google.com/group/pysal-dev
+
+Help for users is here
+ http://groups.google.com/group/openspace-list
+
+Bug reports
+-----------
+To search for or report bugs, please see
+ http://github.com/pysal/pysal/issues
+
+License information
+-------------------
+See the file "LICENSE.txt" for information on the history of this
+software, terms & conditions for usage, and a DISCLAIMER OF ALL
+WARRANTIES.
+
+
diff --git a/README.txt b/README.txt
new file mode 100644
index 0000000..b0504df
--- /dev/null
+++ b/README.txt
@@ -0,0 +1,101 @@
+Python Spatial Analysis Library
+==============================
+
+What is PySAL
+--------------
+[](https://travis-ci.org/pysal/pysal)
+
+PySAL is an open source cross-platform library of spatial analysis functions
+written in Python. It is intended to support the development of high level
+applications for spatial analysis.
+
+It is important to underscore what PySAL is, and is not, designed to do. First
+and foremost, PySAL is a library in the fullest sense of the word. Developers
+looking for a suite of spatial analytical methods that they can incorporate
+into application development should feel at home using PySAL. Spatial analysts
+who may be carrying out research projects requiring customized scripting,
+extensive simulation analysis, or those seeking to advance the state of the art
+in spatial analysis should also find PySAL to be a useful foundation for their
+work.
+
+End users looking for a user friendly graphical user interface for spatial
+analysis should not turn to PySAL directly. Instead, we would direct them to
+projects like STARS and the GeoDaX suite of software products which wrap PySAL
+functionality in GUIs. At the same time, we expect that with developments such
+as the Python based plug-in architectures for QGIS, GRASS, and the toolbox
+extensions for ArcGIS, that end user access to PySAL functionality will be
+widening in the near future.
+
+PySAL package structure
+-----------------------
+
+Currently PySAL consists of the following files and directories:
+
+ LICENSE.txt
+ PySAL license.
+
+ INSTALL.txt
+ PySAL prerequisites, installation, testing, and troubleshooting.
+
+ THANKS.txt
+ PySAL developers and contributors.
+
+ README.txt
+ PySAL structure (this document).
+
+ setup.py
+ Script for building and installing PySAL.
+
+ MANIFEST.in
+ Additions to distutils-generated PySAL tar-balls.
+
+ CHANGELOG.txt
+ Changes since the last release
+
+ pysal/
+ Contains PySAL __init__.py and the directories of PySAL modules.
+
+ doc/
+ Contains PySAL documentation using the Sphinx framework.
+
+PySAL modules
+-------------
+
+* pysal.cg — Computational geometry
+* pysal.contrib - Contributed modules
+* pysal.core — Core data structures and IO
+* pysal.esda — Exploratory spatial data analysis
+* pysal.examples — Data sets
+* pysal.inequality — Spatial inequality analysis
+* pysal.network - Spatial analysis on networks
+* pysal.region — Spatially constrained clustering
+* pysal.spatial_dynamics — Spatial dynamics
+* pysal.spreg — Regression and diagnostics
+* pysal.weights — Spatial weights
+
+Website
+-------
+All things PySAL can be found here
+ http://pysal.org/
+
+Mailing Lists
+-------------
+Please see the developer list here
+ http://groups.google.com/group/pysal-dev
+
+Help for users is here
+ http://groups.google.com/group/openspace-list
+
+Bug reports
+-----------
+To search for or report bugs, please see
+ http://github.com/pysal/pysal/issues
+
+License information
+-------------------
+See the file "LICENSE.txt" for information on the history of this
+software, terms & conditions for usage, and a DISCLAIMER OF ALL
+WARRANTIES.
+
+
diff --git a/THANKS.txt b/THANKS.txt
new file mode 100644
index 0000000..577d2b0
--- /dev/null
+++ b/THANKS.txt
@@ -0,0 +1,43 @@
+PySAL is an open source library of routines for exploratory spatial data
+analysis using Python. It is a community project sponsored by the GeoDa
+Center for Geospatial Analysis and Computation at Arizona State University.
+PySAL originated with code contributions by Luc Anselin and Serge Rey.
+
+Since then many people have contributed to PySAL, in code development,
+suggestions, and financial support. Below is a partial list. If you've
+been left off, please email the "PySAL Developers List" <pysal-dev at googlegroups.com>
+
+Pedro Amaral
+Luc Anselin
+Daniel Arribas-Bel
+David C. Folch
+Myunghwa Hwang
+Julia Koschinsky
+Jason Laura
+Xun Li
+Nicholas Malizia
+Mark McCann
+Serge Rey
+Charles R. Schmidt
+Philip Stephens
+Bohumil Svoma
+Ran Wei
+Andrew Winslow
+Jing Yao
+Xinyue Ye
+
+Funding from the following sources has supported PySAL development:
+
+National Science Foundation CyberGIS Software Integration for Sustained Geospatial Innovation
+
+National Institute of Justice Flexible Geospatial Visual Analytics and
+Simulation Technologies to Enhance Criminal Justice Decision Support Systems
+
+National Institutes of Health Geospatial Factors and Impacts: Measurement and
+Use (R01CA126858-02)
+
+National Science Foundation An Exploratory Space-Time Data Analysis Toolkit for
+Spatial Social Science Research (0433132)
+
+National Science Foundation Hedonic Models of Location Decisions with
+Applications to Geospatial Microdata (0852261)
diff --git a/Vagrantfile b/Vagrantfile
new file mode 100644
index 0000000..79dcc08
--- /dev/null
+++ b/Vagrantfile
@@ -0,0 +1,72 @@
+# -*- mode: ruby -*-
+# vi: set ft=ruby :
+
+MACHINE_NAME = "PySALvm"
+# Vagrantfile API/syntax version. Don't touch unless you know what you're doing!
+VAGRANTFILE_API_VERSION = "2"
+
+Vagrant.configure(VAGRANTFILE_API_VERSION) do |config|
+ # All Vagrant configuration is done here. The most common configuration
+ # options are documented and commented below. For a complete reference,
+ # please see the online documentation at vagrantup.com.
+
+ # Every Vagrant virtual environment requires a box to build off of.
+ config.vm.box = "ubuntu/trusty32"
+ config.ssh.forward_agent = true
+ config.ssh.forward_x11 = true
+ config.vm.network :forwarded_port, host: 8888, guest: 8888
+
+$requirements = <<END
+apt-get update -qq
+apt-get install -y build-essential
+apt-get install -y git-core
+apt-get install -y python-dev
+apt-get install -y python-pip
+apt-get install -y python-numpy
+apt-get install -y python-scipy
+apt-get install -y python-matplotlib
+apt-get install -y python-pandas
+apt-get install -y python-networkx
+apt-get install -y python-qt4
+apt-get install -y qt4-dev-tools
+apt-get install -y python-sip
+apt-get install -y python-sip-dev
+apt-get install -y python-tk
+pip install ipython[notebook]
+pip install -U pyzmq
+pip install -U jinja2
+pip install -U tornado
+pip install -U pygments
+pip install -U pysal
+pip install -U clusterpy
+END
+
+$ipython_notebook = <<CONF_SCRIPT
+ipython profile create
+echo "c.NotebookApp.ip = '0.0.0.0'" >> /home/vagrant/.ipython/profile_default/ipython_notebook_config.py
+echo "c.IPKernelApp.pylab = 'inline'" >> /home/vagrant/.ipython/profile_default/ipython_notebook_config.py
+mkdir -p /home/vagrant/.config/matplotlib
+echo "backend: Qt4AGG" >> /home/vagrant/.config/matplotlib/matplotlibrc
+CONF_SCRIPT
+
+_bashrc = 'echo -e "force_color_prompt=yes" >> /home/vagrant/.bashrc;'
+_bashrc << 'echo -e "red_color=\'\e[1;31m\'" >> /home/vagrant/.bashrc;'
+_bashrc << 'echo -e "end_color=\'\e[0m\'" >> /home/vagrant/.bashrc;'
+_bashrc << "echo -e 'PS1=\"[\${red_color}#{MACHINE_NAME}\${end_color}]$ \"' >> /home/vagrant/.bashrc;"
+_bashrc << 'echo -e alias netebook=\"ipython notebook\" >> /home/vagrant/.bashrc;'
+_bashrc << 'echo -e export EDITOR=\"vi\" >> /home/vagrant/.bashrc;'
+_bashrc << 'echo -e export PYTHONPATH=\"/vagrant\" >> /home/vagrant/.bashrc;'
+
+_bash_login = 'echo -e "cd /vagrant" >> /home/vagrant/.bash_login;'
+_bash_login << 'echo -e "source ~/.bashrc" >> /home/vagrant/.bash_login;'
+
+
+
+ config.vm.provision :shell, :inline => $requirements
+ config.vm.provision :shell, :inline => $ipython_notebook, :privileged => false
+ config.vm.provision :shell, :inline => _bashrc
+ config.vm.provision :shell, :inline => _bash_login
+ config.vm.provision :shell, :inline => "touch ~/.huslogin", :privileged => false
+
+
+end
diff --git a/authors.txt b/authors.txt
new file mode 100644
index 0000000..3cfe054
--- /dev/null
+++ b/authors.txt
@@ -0,0 +1,22 @@
+andrewwinslow = Andrew Winslow <andrewwinslow at gmail.com>
+bohumil.svoma at asu.edu = bohumul <bohumil.svoma at asu.edu>
+dfolch = David Folch <dfolch at gmail.com>
+dfolch at gmail.com = David Folch <dfolch at gmail.com>
+dreamessence = Dani Arribas <dreamessence at gmail.com>
+dreamessence at gmail.com = Dani Arribas <dreamessence at gmail.com>
+drferling at gmail.com = Jay Laura <drferling at gmail.com>
+kx.knight at gmail.com = Xing Kang <kx.knight at gmail.com>
+lanselin = Luc Anselin <lanselin at gmail.com>
+lanselin at gmail.com = Luc Anselin <lanselin at gmail.com>
+mhwang4 = Myhungha Hwang <mhwang4 at gmail.com>
+mhwang4 at gmail.com = Myhungha Hwang <mhwang4 at gmail.com>
+nmalizia = Nick Malizia <nmalizia at gmail.com>
+nmalizia at gmail.com = Nick Malizia <nmalizia at gmail.com>
+phil.stphns = Phil Stephens <phil.stphns at gmail.com>
+phil.stphns at gmail.com = Phil Stephens <phil.stphns at gmail.com>
+schmidtc = Charles Schimdt <schmidtc at gmail.com>
+schmidtc at gmail.com = Charles Schimdt <schmidtc at gmail.com>
+sjsrey = Serge Rey <sjsrey at gmail.com>
+sjsrey at gmail.com = Serge Rey <sjsrey at gmail.com>
+xinyueye = Xinyue Ye <xinyueye at gmail.com>
+(no author) = Serge Rey <sjsrey at gmail.com>
diff --git a/distribute_setup.py b/distribute_setup.py
new file mode 100644
index 0000000..bbb6f3c
--- /dev/null
+++ b/distribute_setup.py
@@ -0,0 +1,485 @@
+#!python
+"""Bootstrap distribute installation
+
+If you want to use setuptools in your package's setup.py, just include this
+file in the same directory with it, and add this to the top of your setup.py::
+
+ from distribute_setup import use_setuptools
+ use_setuptools()
+
+If you want to require a specific version of setuptools, set a download
+mirror, or use an alternate download directory, you can do so by supplying
+the appropriate options to ``use_setuptools()``.
+
+This file can also be run as a script to install or upgrade setuptools.
+"""
+import os
+import sys
+import time
+import fnmatch
+import tempfile
+import tarfile
+from distutils import log
+
+try:
+ from site import USER_SITE
+except ImportError:
+ USER_SITE = None
+
+try:
+ import subprocess
+
+ def _python_cmd(*args):
+ args = (sys.executable,) + args
+ return subprocess.call(args) == 0
+
+except ImportError:
+ # will be used for python 2.3
+ def _python_cmd(*args):
+ args = (sys.executable,) + args
+ # quoting arguments if windows
+ if sys.platform == 'win32':
+ def quote(arg):
+ if ' ' in arg:
+ return '"%s"' % arg
+ return arg
+ args = [quote(arg) for arg in args]
+ return os.spawnl(os.P_WAIT, sys.executable, *args) == 0
+
+DEFAULT_VERSION = "0.6.19"
+DEFAULT_URL = "http://pypi.python.org/packages/source/d/distribute/"
+SETUPTOOLS_FAKED_VERSION = "0.6c11"
+
+SETUPTOOLS_PKG_INFO = """\
+Metadata-Version: 1.0
+Name: setuptools
+Version: %s
+Summary: xxxx
+Home-page: xxx
+Author: xxx
+Author-email: xxx
+License: xxx
+Description: xxx
+""" % SETUPTOOLS_FAKED_VERSION
+
+
+def _install(tarball):
+ # extracting the tarball
+ tmpdir = tempfile.mkdtemp()
+ log.warn('Extracting in %s', tmpdir)
+ old_wd = os.getcwd()
+ try:
+ os.chdir(tmpdir)
+ tar = tarfile.open(tarball)
+ _extractall(tar)
+ tar.close()
+
+ # going in the directory
+ subdir = os.path.join(tmpdir, os.listdir(tmpdir)[0])
+ os.chdir(subdir)
+ log.warn('Now working in %s', subdir)
+
+ # installing
+ log.warn('Installing Distribute')
+ if not _python_cmd('setup.py', 'install'):
+ log.warn('Something went wrong during the installation.')
+ log.warn('See the error message above.')
+ finally:
+ os.chdir(old_wd)
+
+
+def _build_egg(egg, tarball, to_dir):
+ # extracting the tarball
+ tmpdir = tempfile.mkdtemp()
+ log.warn('Extracting in %s', tmpdir)
+ old_wd = os.getcwd()
+ try:
+ os.chdir(tmpdir)
+ tar = tarfile.open(tarball)
+ _extractall(tar)
+ tar.close()
+
+ # going in the directory
+ subdir = os.path.join(tmpdir, os.listdir(tmpdir)[0])
+ os.chdir(subdir)
+ log.warn('Now working in %s', subdir)
+
+ # building an egg
+ log.warn('Building a Distribute egg in %s', to_dir)
+ _python_cmd('setup.py', '-q', 'bdist_egg', '--dist-dir', to_dir)
+
+ finally:
+ os.chdir(old_wd)
+ # returning the result
+ log.warn(egg)
+ if not os.path.exists(egg):
+ raise IOError('Could not build the egg.')
+
+
+def _do_download(version, download_base, to_dir, download_delay):
+ egg = os.path.join(to_dir, 'distribute-%s-py%d.%d.egg'
+ % (version, sys.version_info[0], sys.version_info[1]))
+ if not os.path.exists(egg):
+ tarball = download_setuptools(version, download_base,
+ to_dir, download_delay)
+ _build_egg(egg, tarball, to_dir)
+ sys.path.insert(0, egg)
+ import setuptools
+ setuptools.bootstrap_install_from = egg
+
+
+def use_setuptools(version=DEFAULT_VERSION, download_base=DEFAULT_URL,
+ to_dir=os.curdir, download_delay=15, no_fake=True):
+ # making sure we use the absolute path
+ to_dir = os.path.abspath(to_dir)
+ was_imported = 'pkg_resources' in sys.modules or \
+ 'setuptools' in sys.modules
+ try:
+ try:
+ import pkg_resources
+ if not hasattr(pkg_resources, '_distribute'):
+ if not no_fake:
+ _fake_setuptools()
+ raise ImportError
+ except ImportError:
+ return _do_download(version, download_base, to_dir, download_delay)
+ try:
+ pkg_resources.require("distribute>="+version)
+ return
+ except pkg_resources.VersionConflict:
+ e = sys.exc_info()[1]
+ if was_imported:
+ sys.stderr.write(
+ "The required version of distribute (>=%s) is not available,\n"
+ "and can't be installed while this script is running. Please\n"
+ "install a more recent version first, using\n"
+ "'easy_install -U distribute'."
+ "\n\n(Currently using %r)\n" % (version, e.args[0]))
+ sys.exit(2)
+ else:
+ del pkg_resources, sys.modules['pkg_resources'] # reload ok
+ return _do_download(version, download_base, to_dir,
+ download_delay)
+ except pkg_resources.DistributionNotFound:
+ return _do_download(version, download_base, to_dir,
+ download_delay)
+ finally:
+ if not no_fake:
+ _create_fake_setuptools_pkg_info(to_dir)
+
+def download_setuptools(version=DEFAULT_VERSION, download_base=DEFAULT_URL,
+ to_dir=os.curdir, delay=15):
+ """Download distribute from a specified location and return its filename
+
+ `version` should be a valid distribute version number that is available
+ as an egg for download under the `download_base` URL (which should end
+ with a '/'). `to_dir` is the directory where the egg will be downloaded.
+ `delay` is the number of seconds to pause before an actual download
+ attempt.
+ """
+ # making sure we use the absolute path
+ to_dir = os.path.abspath(to_dir)
+ try:
+ from urllib.request import urlopen
+ except ImportError:
+ from urllib2 import urlopen
+ tgz_name = "distribute-%s.tar.gz" % version
+ url = download_base + tgz_name
+ saveto = os.path.join(to_dir, tgz_name)
+ src = dst = None
+ if not os.path.exists(saveto): # Avoid repeated downloads
+ try:
+ log.warn("Downloading %s", url)
+ src = urlopen(url)
+ # Read/write all in one block, so we don't create a corrupt file
+ # if the download is interrupted.
+ data = src.read()
+ dst = open(saveto, "wb")
+ dst.write(data)
+ finally:
+ if src:
+ src.close()
+ if dst:
+ dst.close()
+ return os.path.realpath(saveto)
+
+def _no_sandbox(function):
+ def __no_sandbox(*args, **kw):
+ try:
+ from setuptools.sandbox import DirectorySandbox
+ if not hasattr(DirectorySandbox, '_old'):
+ def violation(*args):
+ pass
+ DirectorySandbox._old = DirectorySandbox._violation
+ DirectorySandbox._violation = violation
+ patched = True
+ else:
+ patched = False
+ except ImportError:
+ patched = False
+
+ try:
+ return function(*args, **kw)
+ finally:
+ if patched:
+ DirectorySandbox._violation = DirectorySandbox._old
+ del DirectorySandbox._old
+
+ return __no_sandbox
+
+def _patch_file(path, content):
+ """Will backup the file then patch it"""
+ existing_content = open(path).read()
+ if existing_content == content:
+ # already patched
+ log.warn('Already patched.')
+ return False
+ log.warn('Patching...')
+ _rename_path(path)
+ f = open(path, 'w')
+ try:
+ f.write(content)
+ finally:
+ f.close()
+ return True
+
+_patch_file = _no_sandbox(_patch_file)
+
+def _same_content(path, content):
+ return open(path).read() == content
+
+def _rename_path(path):
+ new_name = path + '.OLD.%s' % time.time()
+ log.warn('Renaming %s into %s', path, new_name)
+ os.rename(path, new_name)
+ return new_name
+
+def _remove_flat_installation(placeholder):
+ if not os.path.isdir(placeholder):
+ log.warn('Unkown installation at %s', placeholder)
+ return False
+ found = False
+ for file in os.listdir(placeholder):
+ if fnmatch.fnmatch(file, 'setuptools*.egg-info'):
+ found = True
+ break
+ if not found:
+ log.warn('Could not locate setuptools*.egg-info')
+ return
+
+ log.warn('Removing elements out of the way...')
+ pkg_info = os.path.join(placeholder, file)
+ if os.path.isdir(pkg_info):
+ patched = _patch_egg_dir(pkg_info)
+ else:
+ patched = _patch_file(pkg_info, SETUPTOOLS_PKG_INFO)
+
+ if not patched:
+ log.warn('%s already patched.', pkg_info)
+ return False
+ # now let's move the files out of the way
+ for element in ('setuptools', 'pkg_resources.py', 'site.py'):
+ element = os.path.join(placeholder, element)
+ if os.path.exists(element):
+ _rename_path(element)
+ else:
+ log.warn('Could not find the %s element of the '
+ 'Setuptools distribution', element)
+ return True
+
+_remove_flat_installation = _no_sandbox(_remove_flat_installation)
+
+def _after_install(dist):
+ log.warn('After install bootstrap.')
+ placeholder = dist.get_command_obj('install').install_purelib
+ _create_fake_setuptools_pkg_info(placeholder)
+
+def _create_fake_setuptools_pkg_info(placeholder):
+ if not placeholder or not os.path.exists(placeholder):
+ log.warn('Could not find the install location')
+ return
+ pyver = '%s.%s' % (sys.version_info[0], sys.version_info[1])
+ setuptools_file = 'setuptools-%s-py%s.egg-info' % \
+ (SETUPTOOLS_FAKED_VERSION, pyver)
+ pkg_info = os.path.join(placeholder, setuptools_file)
+ if os.path.exists(pkg_info):
+ log.warn('%s already exists', pkg_info)
+ return
+
+ log.warn('Creating %s', pkg_info)
+ f = open(pkg_info, 'w')
+ try:
+ f.write(SETUPTOOLS_PKG_INFO)
+ finally:
+ f.close()
+
+ pth_file = os.path.join(placeholder, 'setuptools.pth')
+ log.warn('Creating %s', pth_file)
+ f = open(pth_file, 'w')
+ try:
+ f.write(os.path.join(os.curdir, setuptools_file))
+ finally:
+ f.close()
+
+_create_fake_setuptools_pkg_info = _no_sandbox(_create_fake_setuptools_pkg_info)
+
+def _patch_egg_dir(path):
+ # let's check if it's already patched
+ pkg_info = os.path.join(path, 'EGG-INFO', 'PKG-INFO')
+ if os.path.exists(pkg_info):
+ if _same_content(pkg_info, SETUPTOOLS_PKG_INFO):
+ log.warn('%s already patched.', pkg_info)
+ return False
+ _rename_path(path)
+ os.mkdir(path)
+ os.mkdir(os.path.join(path, 'EGG-INFO'))
+ pkg_info = os.path.join(path, 'EGG-INFO', 'PKG-INFO')
+ f = open(pkg_info, 'w')
+ try:
+ f.write(SETUPTOOLS_PKG_INFO)
+ finally:
+ f.close()
+ return True
+
+_patch_egg_dir = _no_sandbox(_patch_egg_dir)
+
+def _before_install():
+ log.warn('Before install bootstrap.')
+ _fake_setuptools()
+
+
+def _under_prefix(location):
+ if 'install' not in sys.argv:
+ return True
+ args = sys.argv[sys.argv.index('install')+1:]
+ for index, arg in enumerate(args):
+ for option in ('--root', '--prefix'):
+ if arg.startswith('%s=' % option):
+ top_dir = arg.split('root=')[-1]
+ return location.startswith(top_dir)
+ elif arg == option:
+ if len(args) > index:
+ top_dir = args[index+1]
+ return location.startswith(top_dir)
+ if arg == '--user' and USER_SITE is not None:
+ return location.startswith(USER_SITE)
+ return True
+
+
+def _fake_setuptools():
+ log.warn('Scanning installed packages')
+ try:
+ import pkg_resources
+ except ImportError:
+ # we're cool
+ log.warn('Setuptools or Distribute does not seem to be installed.')
+ return
+ ws = pkg_resources.working_set
+ try:
+ setuptools_dist = ws.find(pkg_resources.Requirement.parse('setuptools',
+ replacement=False))
+ except TypeError:
+ # old distribute API
+ setuptools_dist = ws.find(pkg_resources.Requirement.parse('setuptools'))
+
+ if setuptools_dist is None:
+ log.warn('No setuptools distribution found')
+ return
+ # detecting if it was already faked
+ setuptools_location = setuptools_dist.location
+ log.warn('Setuptools installation detected at %s', setuptools_location)
+
+ # if --root or --preix was provided, and if
+ # setuptools is not located in them, we don't patch it
+ if not _under_prefix(setuptools_location):
+ log.warn('Not patching, --root or --prefix is installing Distribute'
+ ' in another location')
+ return
+
+ # let's see if its an egg
+ if not setuptools_location.endswith('.egg'):
+ log.warn('Non-egg installation')
+ res = _remove_flat_installation(setuptools_location)
+ if not res:
+ return
+ else:
+ log.warn('Egg installation')
+ pkg_info = os.path.join(setuptools_location, 'EGG-INFO', 'PKG-INFO')
+ if (os.path.exists(pkg_info) and
+ _same_content(pkg_info, SETUPTOOLS_PKG_INFO)):
+ log.warn('Already patched.')
+ return
+ log.warn('Patching...')
+ # let's create a fake egg replacing setuptools one
+ res = _patch_egg_dir(setuptools_location)
+ if not res:
+ return
+ log.warn('Patched done.')
+ _relaunch()
+
+
+def _relaunch():
+ log.warn('Relaunching...')
+ # we have to relaunch the process
+ # pip marker to avoid a relaunch bug
+ if sys.argv[:3] == ['-c', 'install', '--single-version-externally-managed']:
+ sys.argv[0] = 'setup.py'
+ args = [sys.executable] + sys.argv
+ sys.exit(subprocess.call(args))
+
+
+def _extractall(self, path=".", members=None):
+ """Extract all members from the archive to the current working
+ directory and set owner, modification time and permissions on
+ directories afterwards. `path' specifies a different directory
+ to extract to. `members' is optional and must be a subset of the
+ list returned by getmembers().
+ """
+ import copy
+ import operator
+ from tarfile import ExtractError
+ directories = []
+
+ if members is None:
+ members = self
+
+ for tarinfo in members:
+ if tarinfo.isdir():
+ # Extract directories with a safe mode.
+ directories.append(tarinfo)
+ tarinfo = copy.copy(tarinfo)
+ tarinfo.mode = 448 # decimal for oct 0700
+ self.extract(tarinfo, path)
+
+ # Reverse sort directories.
+ if sys.version_info < (2, 4):
+ def sorter(dir1, dir2):
+ return cmp(dir1.name, dir2.name)
+ directories.sort(sorter)
+ directories.reverse()
+ else:
+ directories.sort(key=operator.attrgetter('name'), reverse=True)
+
+ # Set correct owner, mtime and filemode on directories.
+ for tarinfo in directories:
+ dirpath = os.path.join(path, tarinfo.name)
+ try:
+ self.chown(tarinfo, dirpath)
+ self.utime(tarinfo, dirpath)
+ self.chmod(tarinfo, dirpath)
+ except ExtractError:
+ e = sys.exc_info()[1]
+ if self.errorlevel > 1:
+ raise
+ else:
+ self._dbg(1, "tarfile: %s" % e)
+
+
+def main(argv, version=DEFAULT_VERSION):
+ """Install or upgrade setuptools and EasyInstall"""
+ tarball = download_setuptools()
+ _install(tarball)
+
+
+if __name__ == '__main__':
+ main(sys.argv[1:])
diff --git a/doc/Makefile b/doc/Makefile
new file mode 100644
index 0000000..964792d
--- /dev/null
+++ b/doc/Makefile
@@ -0,0 +1,99 @@
+# Makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS =
+SPHINXBUILD = sphinx-build
+PAPER =
+
+# Internal variables.
+PAPEROPT_a4 = -D latex_paper_size=a4
+PAPEROPT_letter = -D latex_paper_size=letter
+ALLSPHINXOPTS = -d build/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
+
+.PHONY: help clean html dirhtml pickle json htmlhelp qthelp latex changes linkcheck doctest
+
+help:
+ @echo "Please use \`make <target>' where <target> is one of"
+ @echo " html to make standalone HTML files"
+ @echo " dirhtml to make HTML files named index.html in directories"
+ @echo " pickle to make pickle files"
+ @echo " json to make JSON files"
+ @echo " htmlhelp to make HTML files and a HTML help project"
+ @echo " qthelp to make HTML files and a qthelp project"
+ @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
+ @echo " changes to make an overview of all changed/added/deprecated items"
+ @echo " linkcheck to check all external links for integrity"
+ @echo " doctest to run all doctests embedded in the documentation (if enabled)"
+
+clean:
+ -rm -rf build/*
+
+html:
+ $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) build/html
+ @echo
+ @echo "Build finished. The HTML pages are in build/html."
+
+dirhtml:
+ $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) build/dirhtml
+ @echo
+ @echo "Build finished. The HTML pages are in build/dirhtml."
+
+pickle:
+ $(SPHINXBUILD) -Q -b pickle $(ALLSPHINXOPTS) build/pickle
+ @echo
+ @echo "Build finished; now you can process the pickle files."
+
+json:
+ $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) build/json
+ @echo
+ @echo "Build finished; now you can process the JSON files."
+
+htmlhelp:
+ $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) build/htmlhelp
+ @echo
+ @echo "Build finished; now you can run HTML Help Workshop with the" \
+ ".hhp project file in build/htmlhelp."
+
+qthelp:
+ $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) build/qthelp
+ @echo
+ @echo "Build finished; now you can run "qcollectiongenerator" with the" \
+ ".qhcp project file in build/qthelp, like this:"
+ @echo "# qcollectiongenerator build/qthelp/pysal.qhcp"
+ @echo "To view the help file:"
+ @echo "# assistant -collectionFile build/qthelp/pysal.qhc"
+
+latex:
+ $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) build/latex
+ @echo
+ @echo "Build finished; the LaTeX files are in build/latex."
+ @echo "Run \`make all-pdf' or \`make all-ps' in that directory to" \
+ "run these through (pdf)latex."
+
+changes:
+ $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) build/changes
+ @echo
+ @echo "The overview file is in build/changes."
+
+linkcheck:
+ $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) build/linkcheck
+ @echo
+ @echo "Link check complete; look for any errors in the above output " \
+ "or in build/linkcheck/output.txt."
+
+doctest:
+ $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) build/doctest
+ @echo "Testing of doctests in the sources finished, look at the " \
+ "results in build/doctest/output.txt."
+ rm ../pysal/examples/sids2.swm
+ rm ../pysal/examples/virginia_queen
+ rm ../pysal/examples/virginia_queen.dat
+ rm ../pysal/examples/virginia_queen.dbf
+ rm ../pysal/examples/virginia_queen.gal
+ rm ../pysal/examples/virginia_queen.mat
+ rm ../pysal/examples/virginia_queen.mtx
+ rm ../pysal/examples/virginia_queen.swm
+ rm ../pysal/examples/virginia_queen.txt
+ rm ../pysal/examples/virginia_queen.wk1
+
diff --git a/doc/source/_static/favicon.png b/doc/source/_static/favicon.png
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diff --git a/doc/source/_static/images/socal_1.jpg b/doc/source/_static/images/socal_1.jpg
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diff --git a/doc/source/_static/images/socal_2.jpg b/doc/source/_static/images/socal_2.jpg
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diff --git a/doc/source/_static/images/socal_3.jpg b/doc/source/_static/images/socal_3.jpg
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diff --git a/doc/source/_static/images/tutorial.png b/doc/source/_static/images/tutorial.png
new file mode 100644
index 0000000..b9766d3
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diff --git a/doc/source/_static/images/yi_jing_01_chien.jpg b/doc/source/_static/images/yi_jing_01_chien.jpg
new file mode 100644
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diff --git a/doc/source/_static/mydoc.css b/doc/source/_static/mydoc.css
new file mode 100644
index 0000000..a3df02c
--- /dev/null
+++ b/doc/source/_static/mydoc.css
@@ -0,0 +1,3 @@
+ at import url("default.css");
+
+span.strike {text-decoration: line-through; }
diff --git a/doc/source/_static/pysalgraph.png b/doc/source/_static/pysalgraph.png
new file mode 100644
index 0000000..92462a8
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diff --git a/doc/source/_static/pysalsmall.png b/doc/source/_static/pysalsmall.png
new file mode 100644
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diff --git a/doc/source/_templates/ganalytics_layout.html b/doc/source/_templates/ganalytics_layout.html
new file mode 100644
index 0000000..351b2bb
--- /dev/null
+++ b/doc/source/_templates/ganalytics_layout.html
@@ -0,0 +1,213 @@
+{%- block doctype -%}
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
+ "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+{%- endblock %}
+{%- set reldelim1 = reldelim1 is not defined and ' »' or reldelim1 %}
+{%- set reldelim2 = reldelim2 is not defined and ' |' or reldelim2 %}
+
+{%- macro relbar() %}
+ <div class="related">
+ <h3>{{ _('Navigation') }}</h3>
+ <ul>
+ {%- for rellink in rellinks %}
+ <li class="right" {% if loop.first %}style="margin-right: 10px"{% endif %}>
+ <a href="{{ pathto(rellink[0]) }}" title="{{ rellink[1]|striptags }}"
+ {{ accesskey(rellink[2]) }}>{{ rellink[3] }}</a>
+ {%- if not loop.first %}{{ reldelim2 }}{% endif %}</li>
+ {%- endfor %}
+ {%- block rootrellink %}
+ <li><a href="{{ pathto(master_doc) }}">{{ shorttitle|e }}</a>{{ reldelim1 }}</li>
+ {%- endblock %}
+ {%- for parent in parents %}
+ <li><a href="{{ parent.link|e }}" {% if loop.last %}{{ accesskey("U") }}{% endif %}>{{ parent.title }}</a>{{ reldelim1 }}</li>
+ {%- endfor %}
+ {%- block relbaritems %} {% endblock %}
+ </ul>
+ </div>
+{%- endmacro %}
+
+{%- macro sidebar() %}
+ {%- if not embedded %}{% if not theme_nosidebar|tobool %}
+ <div class="sphinxsidebar">
+ <div class="sphinxsidebarwrapper">
+ {%- block sidebarlogo %}
+ {%- if logo %}
+ <p class="logo"><a href="{{ pathto(master_doc) }}">
+ <img class="logo" src="{{ pathto('_static/' + logo, 1) }}" alt="Logo"/>
+ </a></p>
+ {%- endif %}
+ {%- endblock %}
+ {%- block sidebartoc %}
+ {%- if display_toc %}
+ <h3><a href="{{ pathto(master_doc) }}">{{ _('Table Of Contents') }}</a></h3>
+ {{ toc }}
+ {%- endif %}
+ {%- endblock %}
+ {%- block sidebarrel %}
+ {%- if prev %}
+ <h4>{{ _('Previous topic') }}</h4>
+ <p class="topless"><a href="{{ prev.link|e }}"
+ title="{{ _('previous chapter') }}">{{ prev.title }}</a></p>
+ {%- endif %}
+ {%- if next %}
+ <h4>{{ _('Next topic') }}</h4>
+ <p class="topless"><a href="{{ next.link|e }}"
+ title="{{ _('next chapter') }}">{{ next.title }}</a></p>
+ {%- endif %}
+ {%- endblock %}
+ {%- block sidebarsourcelink %}
+ {%- if show_source and has_source and sourcename %}
+ <h3>{{ _('This Page') }}</h3>
+ <ul class="this-page-menu">
+ <li><a href="{{ pathto('_sources/' + sourcename, true)|e }}"
+ rel="nofollow">{{ _('Show Source') }}</a></li>
+ </ul>
+ {%- endif %}
+ {%- endblock %}
+ {%- if customsidebar %}
+ {% include customsidebar %}
+ {%- endif %}
+ {%- block sidebarsearch %}
+ {%- if pagename != "search" %}
+ <div id="searchbox" style="display: none">
+ <h3>{{ _('Quick search') }}</h3>
+ <form class="search" action="{{ pathto('search') }}" method="get">
+ <input type="text" name="q" size="18" />
+ <input type="submit" value="{{ _('Go') }}" />
+ <input type="hidden" name="check_keywords" value="yes" />
+ <input type="hidden" name="area" value="default" />
+ </form>
+ <p class="searchtip" style="font-size: 90%">
+ {{ _('Enter search terms or a module, class or function name.') }}
+ </p>
+ </div>
+ <script type="text/javascript">$('#searchbox').show(0);</script>
+ {%- endif %}
+ {%- endblock %}
+ </div>
+ </div>
+ {%- endif %}{% endif %}
+{%- endmacro %}
+
+<html xmlns="http://www.w3.org/1999/xhtml">
+ <head>
+ <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+ {{ metatags }}
+ {%- if not embedded %}
+ {%- set titlesuffix = " — "|safe + docstitle|e %}
+ {%- else %}
+ {%- set titlesuffix = "" %}
+ {%- endif %}
+ <title>{{ title|striptags }}{{ titlesuffix }}</title>
+ <link rel="stylesheet" href="{{ pathto('_static/' + style, 1) }}" type="text/css" />
+ <link rel="stylesheet" href="{{ pathto('_static/pygments.css', 1) }}" type="text/css" />
+ {%- if not embedded %}
+ <script type="text/javascript">
+ var DOCUMENTATION_OPTIONS = {
+ URL_ROOT: '{{ pathto("", 1) }}',
+ VERSION: '{{ release|e }}',
+ COLLAPSE_MODINDEX: false,
+ FILE_SUFFIX: '{{ file_suffix }}',
+ HAS_SOURCE: {{ has_source|lower }}
+ };
+ </script>
+ {%- for scriptfile in script_files %}
+ <script type="text/javascript" src="{{ pathto(scriptfile, 1) }}"></script>
+ {%- endfor %}
+ {%- if use_opensearch %}
+ <link rel="search" type="application/opensearchdescription+xml"
+ title="{% trans docstitle=docstitle|e %}Search within {{ docstitle }}{% endtrans %}"
+ href="{{ pathto('_static/opensearch.xml', 1) }}"/>
+ {%- endif %}
+ {%- if favicon %}
+ <link rel="shortcut icon" href="{{ pathto('_static/' + favicon, 1) }}"/>
+ {%- endif %}
+ {%- endif %}
+{%- block linktags %}
+ {%- if hasdoc('about') %}
+ <link rel="author" title="{{ _('About these documents') }}" href="{{ pathto('about') }}" />
+ {%- endif %}
+ {%- if hasdoc('genindex') %}
+ <link rel="index" title="{{ _('Index') }}" href="{{ pathto('genindex') }}" />
+ {%- endif %}
+ {%- if hasdoc('search') %}
+ <link rel="search" title="{{ _('Search') }}" href="{{ pathto('search') }}" />
+ {%- endif %}
+ {%- if hasdoc('copyright') %}
+ <link rel="copyright" title="{{ _('Copyright') }}" href="{{ pathto('copyright') }}" />
+ {%- endif %}
+ <link rel="top" title="{{ docstitle|e }}" href="{{ pathto('index') }}" />
+ {%- if parents %}
+ <link rel="up" title="{{ parents[-1].title|striptags }}" href="{{ parents[-1].link|e }}" />
+ {%- endif %}
+ {%- if next %}
+ <link rel="next" title="{{ next.title|striptags }}" href="{{ next.link|e }}" />
+ {%- endif %}
+ {%- if prev %}
+ <link rel="prev" title="{{ prev.title|striptags }}" href="{{ prev.link|e }}" />
+ {%- endif %}
+{%- endblock %}
+{%- block extrahead %} {% endblock %}
+ </head>
+ <body>
+{%- block header %}{% endblock %}
+
+{%- block relbar1 %}{{ relbar() }}{% endblock %}
+
+{%- block sidebar1 %} {# possible location for sidebar #} {% endblock %}
+
+ <div class="document">
+{%- block document %}
+ <div class="documentwrapper">
+ {%- if not embedded %}{% if not theme_nosidebar|tobool %}
+ <div class="bodywrapper">
+ {%- endif %}{% endif %}
+ <div class="body">
+ {% block body %} {% endblock %}
+ </div>
+ {%- if not embedded %}{% if not theme_nosidebar|tobool %}
+ </div>
+ {%- endif %}{% endif %}
+ </div>
+{%- endblock %}
+
+{%- block sidebar2 %}{{ sidebar() }}{% endblock %}
+ <div class="clearer"></div>
+ </div>
+
+{%- block relbar2 %}{{ relbar() }}{% endblock %}
+
+{%- block footer %}
+ <div class="footer">
+ {%- if hasdoc('copyright') %}
+ {% trans path=pathto('copyright'), copyright=copyright|e %}© <a href="{{ path }}">Copyright</a> {{ copyright }}.{% endtrans %}
+ {%- else %}
+ {% trans copyright=copyright|e %}© Copyright {{ copyright }}.{% endtrans %}
+ {%- endif %}
+ {%- if last_updated %}
+ {% trans last_updated=last_updated|e %}Last updated on {{ last_updated }}.{% endtrans %}
+ {%- endif %}
+ {%- if show_sphinx %}
+ {% trans sphinx_version=sphinx_version|e %}Created using <a href="http://sphinx.pocoo.org/">Sphinx</a> {{ sphinx_version }}.{% endtrans %}
+ {%- endif %}
+ </div>
+{%- endblock %}
+
+<!--Begin Google Analytics Code -->
+<script type="text/javascript">
+
+ var _gaq = _gaq || [];
+ _gaq.push(['_setAccount', 'UA-34241241-1']);
+ _gaq.push(['_trackPageview']);
+
+ (function() {
+ var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
+ ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
+ var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
+ })();
+
+</script>
+
+<!--End Google Analytics Code -->
+ </body>
+</html>
diff --git a/doc/source/_templates/layout.html b/doc/source/_templates/layout.html
new file mode 100644
index 0000000..f5b4b6d
--- /dev/null
+++ b/doc/source/_templates/layout.html
@@ -0,0 +1,24 @@
+{% extends "ganalytics_layout.html" %}
+
+
+{% block rootrellink %}
+ <li><a href="{{ pathto('index') }}">home</a>| </li>
+ <li><a href="{{ pathto('search') }}">search</a>| </li>
+ <li><a href="{{ pathto('contents') }}">documentation </a> »</li>
+{% endblock %}
+
+
+{% block relbar1 %}
+
+<div style= "background-color: white; text-align: left; vertical-align:middle">
+ <a href="{{ pathto('index') }}"><img src="{{
+pathto("_static/pysalsmall.png", 1) }}" border="0" alt="pysal"/></a>
+</div>
+
+{{ super() }}
+{% endblock %}
+
+{# put the sidebar before the body #}
+{% block sidebar1 %}{% endblock %}
+{% block sidebar2 %}{{ sidebar() }}{% endblock %}
+
diff --git a/doc/source/conf.py b/doc/source/conf.py
new file mode 100644
index 0000000..df9e4c9
--- /dev/null
+++ b/doc/source/conf.py
@@ -0,0 +1,230 @@
+# -*- coding: utf-8 -*-
+#
+# pysal documentation build configuration file, created by
+# sphinx-quickstart on Wed Aug 26 19:58:20 2009.
+#
+# This file is execfile()d with the current directory set to its containing dir.
+#
+# Note that not all possible configuration values are present in this
+# autogenerated file.
+#
+# All configuration values have a default; values that are commented out
+# serve to show the default.
+
+import sys
+import os
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#sys.path.append(os.path.abspath('sphinxext'))
+#sys.path.append(os.path.abspath('../../'))
+sys.path.append(os.path.abspath('~/anaconda/lib/python2.7/site-packages/'))
+
+# -- General configuration -----------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be extensions
+# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
+extensions = ['sphinx.ext.autodoc',
+'sphinx.ext.doctest','sphinx.ext.graphviz', 'sphinx.ext.intersphinx',
+'sphinx.ext.pngmath',
+'sphinx.ext.autosummary','sphinx.ext.viewcode', 'sphinxcontrib.napoleon']
+
+#doctest extension config values
+doctest_path = '/Users/stephens/code/pysal/doc/source/users/tutorials/'
+doctest_test_doctest_blocks = ''
+#doctest_global_setup =
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# The suffix of source filenames.
+source_suffix = '.rst'
+#source_suffix = '.txt'
+
+# The encoding of source files.
+#source_encoding = 'utf-8'
+
+# The master toctree document.
+master_doc = 'index'
+
+# General information about the project.
+project = u'pysal'
+copyright = u'2014-, PySAL Developers; 2009-13 Sergio Rey'
+
+# The version info for the project you're documenting, acts as replacement for
+# |version| and |release|, also used in various other places throughout the
+# built documents.
+#
+# The short X.Y version.
+version = '1.9.1'
+# The full version, including alpha/beta/rc tags.
+release = '1.9.1'
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#language = None
+
+# There are two options for replacing |today|: either, you set today to some
+# non-false value, then it is used:
+#today = ''
+# Else, today_fmt is used as the format for a strftime call.
+#today_fmt = '%B %d, %Y'
+
+# List of documents that shouldn't be included in the build.
+#unused_docs = []
+
+# List of directories, relative to source directory, that shouldn't be searched
+# for source files.
+exclude_trees = []
+
+
+# The reST default role (used for this markup: `text`) to use for all documents.
+#default_role = None
+
+# If true, '()' will be appended to :func: etc. cross-reference text.
+add_function_parentheses = False
+
+# If true, the current module name will be prepended to all description
+# unit titles (such as .. function::).
+#add_module_names = True
+
+# If true, sectionauthor and moduleauthor directives will be shown in the
+# output. They are ignored by default.
+#show_authors = False
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = 'sphinx'
+
+# A list of ignored prefixes for module index sorting.
+#modindex_common_prefix = []
+
+
+# -- Options for HTML output ---------------------------------------------------
+#custom strikethrough setup
+#html_style = 'mydoc.css'
+
+# The theme to use for HTML and HTML Help pages. Major themes that come with
+# Sphinx are currently 'default' and 'sphinxdoc'.
+html_theme = 'default'
+#html_theme = 'sphinxdoc'
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further. For a list of options available for each theme, see the
+# documentation.
+html_theme_options = {
+ "rightsidebar": "true",
+ "relbarbgcolor": "CornflowerBlue",
+ "sidebartextcolor": "black",
+ "sidebarlinkcolor": "#355f7c",
+ "sidebarbgcolor": "#F2F2F2",
+ "codebgcolor": "AliceBlue",
+ "footerbgcolor": "Black",
+ "externalrefs": "false",
+ "bodyfont": "Optima",
+ "headfont": "Optima "
+ }
+
+# Add any paths that contain custom themes here, relative to this directory.
+#html_theme_path = []
+
+# The name for this set of Sphinx documents. If None, it defaults to
+# "<project> v<release> documentation".
+#html_title = "%s v%s Reference Guide" % (project, version)
+html_title = "Python Spatial Analysis Library"
+
+# A shorter title for the navigation bar. Default is the same as html_title.
+html_short_title = "PySAL"
+
+# The name of an image file (relative to this directory) to place at the top
+# of the sidebar.
+#html_logo = None
+
+# The name of an image file (within the static path) to use as favicon of the
+# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
+# pixels large.
+html_favicon = 'favicon.png'
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
+# using the given strftime format.
+html_last_updated_fmt = '%b %d, %Y'
+
+# If true, SmartyPants will be used to convert quotes and dashes to
+# typographically correct entities.
+#html_use_smartypants = True
+
+# Custom sidebar templates, maps document names to template names.
+#html_sidebars = {}
+
+# Additional templates that should be rendered to pages, maps page names to
+# template names.
+#html_additional_pages = {}
+
+# If false, no module index is generated.
+#html_use_modindex = True
+
+# If false, no index is generated.
+#html_use_index = True
+
+# If true, the index is split into individual pages for each letter.
+#html_split_index = False
+
+# If true, links to the reST sources are added to the pages.
+#html_show_sourcelink = True
+
+# If true, an OpenSearch description file will be output, and all pages will
+# contain a <link> tag referring to it. The value of this option must be the
+# base URL from which the finished HTML is served.
+#html_use_opensearch = ''
+
+# If nonempty, this is the file name suffix for HTML files (e.g. ".xhtml").
+#html_file_suffix = ''
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = 'pysaldoc'
+
+
+# -- Options for LaTeX output --------------------------------------------------
+
+# The paper size ('letter' or 'a4').
+#latex_paper_size = 'letter'
+
+# The font size ('10pt', '11pt' or '12pt').
+#latex_font_size = '10pt'
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title, author, documentclass [howto/manual]).
+latex_documents = [
+ ('index', 'pysal.tex', u'pysal Documentation',
+ u'PySAL Developers', 'manual'),
+]
+
+# The name of an image file (relative to this directory) to place at the top of
+# the title page.
+#latex_logo = None
+
+# For "manual" documents, if this is true, then toplevel headings are parts,
+# not chapters.
+#latex_use_parts = False
+
+# Additional stuff for the LaTeX preamble.
+#latex_preamble = ''
+
+# Documents to append as an appendix to all manuals.
+#latex_appendices = []
+
+# If false, no module index is generated.
+#latex_use_modindex = True
+
+
+# Example configuration for intersphinx: refer to the Python standard library.
+intersphinx_mapping = {'http://docs.python.org/': None}
+
+#numpydoc option
+numpydoc_show_class_members = True
+numpydoc_class_members_toctree = False
diff --git a/doc/source/contents.txt b/doc/source/contents.txt
new file mode 100644
index 0000000..b9bdf05
--- /dev/null
+++ b/doc/source/contents.txt
@@ -0,0 +1,16 @@
+.. _contents:
+
+===================
+PySAL Documentation
+===================
+
+ :Release: |version|
+ :Date: |today|
+
+
+.. toctree::
+ :maxdepth: 1
+
+ users/index
+ developers/index
+ library/index
diff --git a/doc/source/developers/docs/index.rst b/doc/source/developers/docs/index.rst
new file mode 100644
index 0000000..147f1fb
--- /dev/null
+++ b/doc/source/developers/docs/index.rst
@@ -0,0 +1,252 @@
+.. role:: strike
+
+*******************
+PySAL Documentation
+*******************
+.. contents::
+
+.. _compiling-doc-label:
+
+
+Writing Documentation
+=====================
+
+The PySAL project contains two distinct forms of documentation: inline and
+non-inline. Inline docs are contained in the source
+code itself, in what are known as *docstrings*. Non-inline documentation is in the
+doc folder in the trunk.
+
+Inline documentation is processed with an extension to Sphinx called napoleon.
+We have adopted the community standard outlined `here`_.
+
+PySAL makes use of the built-in Sphinx extension *viewcode*, which allows the
+reader to quicky toggle between docs and source code. To use it,
+the source code module requires at least one properly formatted docstring.
+
+Non-inline documentation editors can opt to strike-through older documentation rather than
+delete it with the custom "role" directive as
+follows. Near the top of the document, add the role directive. Then, to strike through old text, add the :strike:
+directive and offset the text with back-ticks. This :strike:`strikethrough` is produced
+like this::
+
+ .. role:: strike
+
+ ...
+ ...
+
+ This :strike:`strikethrough` is produced like this:
+
+Compiling Documentation
+=======================
+
+PySAL documentation is built using `Sphinx`_ and the Sphinx extension `napoleon`_, which formats PySAL's docstrings.
+
+Note
+----
+If you're using Sphinx version 1.3 or newer, napoleon is included and should be called in the main conf.py as sphinx.ext.napoleon rather than installing it as we show below.
+
+If you're using a version of Sphinx that does not ship with napoleon ( Sphinx < 1.3), you'll need napoleon version 0.2.4 or later and Sphinx version 1.0 or later to compile the documentation.
+Both modules are available at the Python Package Index, and can be downloaded and installed
+from the command line using *pip* or *easy_install*.::
+
+ $ easy_install sphinx
+ $ easy_install sphinxcontrib-napoleon
+
+If you get a permission error, trying using 'sudo'.
+
+The source for the docs is in `doc`. Building the documentation is
+done as follows (assuming sphinx and napoleon are already installed)::
+
+ $ cd doc; ls
+ build Makefile source
+
+ $ make clean
+ $ make html
+
+To see the results in a browser open `build/html/index.html`. To make
+changes, edit (or add) the relevant files in `source` and rebuild the
+docs using the 'make html' (or 'make clean' if you're adding new documents) command.
+Consult the `Sphinx markup guide`_ for details on the syntax and structure of the files in `source`.
+
+Once you're happy with your changes, check-in the `source` files. Do not
+add or check-in files under `build` since they are dynamically built.
+
+Changes checked in to `Github`_ will be propogated to `readthedocs`_ within a few minutes.
+
+
+Lightweight Editing with rst2html.py
+------------------------------------
+
+Because the doc build process can sometimes be lengthy, you may want to avoid
+having to do a full build until after you are done with your major edits on
+one particular document. As part of the
+`docutils`_ package,
+the file `rs2html.py` can take an `rst` document and generate the html file.
+This will get most of the work done that you need to get a sense if your edits
+are good, *without* having to rebuild all the PySAL docs. As of version 0.8 it
+also understands LaTeX. It will cough on some sphinx directives, but those can
+be dealt with in the final build.
+
+To use this download the doctutils tarball and put `rst2html.py` somewhere in
+your path. In vim (on Mac OS X) you can then add something like::
+
+ map ;r ^[:!rst2html.py % > ~/tmp/tmp.html; open ~/tmp/tmp.html^M^M
+
+which will render the html in your default browser.
+
+Things to watch out for
+------------------------
+
+If you encounter a failing tutorial doctest that does not seem to be in error, it could be
+a difference in whitespace between the expected and received output. In that case, add an
+'options' line as follows::
+
+ .. doctest::
+ :options: +NORMALIZE_WHITESPACE
+
+ >>> print 'a b c'
+ abc
+
+Adding a new package and modules
+================================
+
+To include the docstrings of a new module in the :doc:`API docs </library/index>` the following steps are required:
+
+ 1. In the directory `/doc/source/library` add a directory with the name of
+ the new package. You can skip to step 3 if the package exists and you are
+ just adding new modules to this package.
+ 2. Within `/doc/source/library/packageName` add a file `index.rst`
+ 3. For each new module in this package, add a file `moduleName.rst` and
+ update the `index.rst` file to include `modulename`.
+
+
+Adding a new tutorial: spreg
+============================
+
+While the :doc:`API docs </library/index>` are automatically generated when
+compiling with Sphinx, tutorials that demonstrate use cases for new modules
+need to be crafted by the developer. Below we use the case of one particular
+module that currently does not have a tutorial as a guide for how to add
+tutorials for new modules.
+
+As of PySAL 1.3 there are API docs for
+:doc:`spreg </library/spreg/index>`
+but no :doc:`tutorial </users/tutorials/index>` currently exists for this module.
+
+We will fix this and add a tutorial for
+:doc:`spreg </library/spreg/index>`.
+
+
+Requirements
+------------
+
+ - sphinx
+ - napoleon
+ - pysal sources
+
+
+You can install `sphinx` or `napoleon` using `easy_install` as described
+above in :ref:`compiling-doc-label`.
+
+Where to add the tutorial content
+---------------------------------
+
+Within the PySAL source the docs live in::
+
+ pysal/doc/source
+
+This directory has the source `reStructuredText`_ files used to render the html
+pages. The tutorial pages live under::
+
+ pysal/doc/source/users/tutorials
+
+As of PySAL 1.3, the content of this directory is::
+
+ autocorrelation.rst fileio.rst next.rst smoothing.rst
+ dynamics.rst index.rst region.rst weights.rst
+ examples.rst intro.rst shapely.rst
+
+The body of the `index.rst` file lists the sections for the tutorials::
+
+ Introduction to the Tutorials <intro>
+ File Input and Output <fileio>
+ Spatial Weights <weights>
+ Spatial Autocorrelation <autocorrelation>
+ Spatial Smoothing <smoothing>
+ Regionalization <region>
+ Spatial Dynamics <dynamics>
+ Shapely Extension <shapely>
+ Next Steps <next>
+ Sample Datasets <examples>
+
+In order to add a tutorial for `spreg` we need the to change this to read::
+
+ Introduction to the Tutorials <intro>
+ File Input and Output <fileio>
+ Spatial Weights <weights>
+ Spatial Autocorrelation <autocorrelation>
+ Spatial Smoothing <smoothing>
+ Spatial Regression <spreg>
+ Regionalization <region>
+ Spatial Dynamics <dynamics>
+ Shapely Extension <shapely>
+ Next Steps <next>
+ Sample Datasets <examples>
+
+So we are adding a new section that will show up as `Spatial Regression` and
+its contents will be found in the file `spreg.rst`. To create the latter
+file simpy copy say `dynamics.rst` to `spreg.rst` and then modify `spreg.rst`
+to have the correct content.
+
+Once this is done, move back up to the top level doc directory::
+
+ pysal/doc
+
+Then::
+
+ $ make clean
+ $ make html
+
+Point your browser to `pysal/doc/build/html/index.html`
+
+and check your work. You can then make changes to the `spreg.rst` file and
+recompile until you are set with the content.
+
+Proper Reference Formatting
+---------------------------
+
+For proper hypertext linking of reference material, each unique reference in a
+single python module can only be explicitly named once. Take the following example for
+instance::
+
+ References
+ ----------
+
+ .. [1] Kelejian, H.R., Prucha, I.R. (1998) "A generalized spatial
+ two-stage least squares procedure for estimating a spatial autoregressive
+ model with autoregressive disturbances". The Journal of Real State
+ Finance and Economics, 17, 1.
+
+It is "named" as "1". Any other references (even the same paper) with the same "name" will cause a
+Duplicate Reference error when Sphinx compiles the document. Several
+work-arounds are available but no concensus has emerged.
+
+One possible solution is to use an anonymous reference on any subsequent
+duplicates, signified by a single underscore with no brackets. Another solution
+is to put all document references together at the bottom of the document, rather
+than listing them at the bottom of each class, as has been done in some modules.
+
+
+
+.. _tutorial: /users/tutorials/index
+.. _docutils: http://docutils.sourceforge.net/docs/user/tools.html
+.. _API docs: /library/index
+.. _spreg: /library/spreg/index
+.. _Sphinx: http://pypi.python.org/pypi/Sphinx/
+.. _here: https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt
+.. _Github: http://github.com/pysal
+.. _spreg: /library/spreg/index
+.. _reStructuredText: http://sphinx.pocoo.org/rest.html
+.. _Sphinx markup guide: http://sphinx.pocoo.org/contents.html
+.. _napoleon: http://sphinxcontrib-napoleon.readthedocs.org/en/latest/sphinxcontrib.napoleon.html
+.. _readthedocs: http://pysal.readthedocs.org/en/latest
diff --git a/doc/source/developers/guidelines.rst b/doc/source/developers/guidelines.rst
new file mode 100644
index 0000000..31bb86a
--- /dev/null
+++ b/doc/source/developers/guidelines.rst
@@ -0,0 +1,130 @@
+.. _guidelines:
+
+==========
+Guidelines
+==========
+.. contents::
+
+PySAL is adopting many of the conventions in the larger scientific computing
+in Python community and we ask that anyone interested in joining the project
+please review the following documents:
+
+ * `Documentation standards <http://projects.scipy.org/numpy/wiki/CodingStyleGuidelines>`_
+ * `Coding guidelines <http://www.python.org/dev/peps/pep-0008/>`_
+ * :doc:`Testing guidelines <testing>`
+
+
+-----------------------
+Open Source Development
+-----------------------
+
+PySAL is an open source project and we invite any interested user who wants to
+contribute to the project to contact one of the
+`team members <https://github.com/pysal?tab=members>`_. For users who
+are new to open source development you may want to consult the following
+documents for background information:
+
+ * `Contributing to Open Source Projects HOWTO
+ <http://www.kegel.com/academy/opensource.html>`_
+
+
+
+
+-----------------------
+Source Code
+-----------------------
+
+
+PySAL uses `git <http://git-scm.com/>`_ and github for our `code repository <https://github.com/pysal/pysal.git/>`_.
+
+
+You can setup PySAL for local development following the :doc:`installation instructions </users/installation>`.
+
+
+------------------------
+Development Mailing List
+------------------------
+
+Development discussions take place on `pysal-dev
+<http://groups.google.com/group/pysal-dev>`_.
+
+
+-----------------------
+Release Schedule
+-----------------------
+
+PySAL development follows a six-month release schedule that is aligned with
+the academic calendar.
+
+1.10 Cycle
+=========
+
+======== ======== ================= ====================================================
+Start End Phase Notes
+======== ======== ================= ====================================================
+2/1/15 2/14/15 Module Proposals Developers draft PEPs and prototype
+2/15/15 2/15/15 Developer vote All developers vote on PEPs
+2/16/15 2/16/15 Module Approval BDFL announces final approval
+2/17/15 6/30/15 Development Implementation and testing of approved modules
+7/1/15 7/27/15 Code Freeze APIs fixed, bug and testing changes only
+7/23/15 7/30/15 Release Prep Test release builds, updating svn
+7/31/15 7/31/15 Release Official release of 1.10
+======== ======== ================= ====================================================
+
+
+1.11 Cycle
+=========
+
+======== ======== ================= ====================================================
+Start End Phase Notes
+======== ======== ================= ====================================================
+8/1/15 8/14/15 Module Proposals Developers draft PEPs and prototype
+8/15/15 8/15/15 Developer vote All developers vote on PEPs
+8/16/15 8/16/15 Module Approval BDFL announces final approval
+8/17/15 12/30/15 Development Implementation and testing of approved modules
+1/1/16 1/1/16 Code Freeze APIs fixed, bug and testing changes only
+1/23/16 1/30/16 Release Prep Test release builds, updating svn
+1/31/16 1/31/16 Release Official release of 1.11
+======== ======== ================= ====================================================
+
+
+
+
+
+
+-----------------------
+Governance
+-----------------------
+
+PySAL is organized around the Benevolent Dictator for Life (BDFL) model of project management.
+The BDFL is responsible for overall project management and direction. Developers have a critical role in shaping that
+direction. Specific roles and rights are as follows:
+
+========= ================ ===================================================
+Title Role Rights
+========= ================ ===================================================
+BDFL Project Director Commit, Voting, Veto, Developer Approval/Management
+Developer Development Commit, Voting
+========= ================ ===================================================
+
+-----------------------
+Voting and PEPs
+-----------------------
+
+During the initial phase of a release cycle, new functionality for PySAL should be described in a PySAL Enhancment
+Proposal (PEP). These should follow the
+`standard format <http://www.python.org/dev/peps/pep-0009/>`_
+used by the Python project. For PySAL, the PEP process is as follows
+
+#. Developer prepares a plain text PEP following the guidelines
+
+#. Developer sends PEP to the BDFL
+
+#. Developer posts PEP to the PEP index
+
+#. All developers consider the PEP and vote
+
+#. PEPs receiving a majority approval become priorities for the release cycle
+
+
+
diff --git a/doc/source/developers/index.rst b/doc/source/developers/index.rst
new file mode 100644
index 0000000..3fb20dd
--- /dev/null
+++ b/doc/source/developers/index.rst
@@ -0,0 +1,18 @@
+===============
+Developer Guide
+===============
+
+Go to our issues queue on `GitHub NOW!
+<http://github.com/pysal/pysal/issues?state=open>`_
+
+.. toctree::
+ :maxdepth: 1
+
+ guidelines
+ testing
+ pep/index
+ docs/index
+ release
+ py3k
+ projects
+ known-issues
diff --git a/doc/source/developers/known-issues.rst b/doc/source/developers/known-issues.rst
new file mode 100644
index 0000000..0f1ec27
--- /dev/null
+++ b/doc/source/developers/known-issues.rst
@@ -0,0 +1,42 @@
+
+Known Issues
+============
+
+1.5 install fails with scipy 11.0 on Mac OS X
+---------------------------------------------
+
+Running `python setup.py install` results in::
+
+ from _cephes import *
+ ImportError:
+ dlopen(/Users/serge/Documents/p/pysal/virtualenvs/python1.5/lib/python2.7/site-packages/scipy/special/_cephes.so,
+ 2): Symbol not found: _aswfa_
+ Referenced from:
+ /Users/serge/Documents/p/pysal/virtualenvs/python1.5/lib/python2.7/site-packages/scipy/special/_cephes.so
+ Expected in: dynamic lookup
+
+This occurs when your scipy on Mac OS X was complied with gnu95 and not
+gfortran. See `this thread <http://mail.scipy.org/pipermail/scipy-user/2010-November/027548.html>`_ for possible solutions.
+
+weights.DistanceBand failing
+----------------------------
+
+This occurs due to a bug in scipy.sparse prior to version 0.8. If you are running such a version see `Issue 73 <http://code.google.com/p/pysal/issues/detail?id=73&sort=milestone>`_ for a fix.
+
+doc tests and unit tests under Linux
+------------------------------------
+
+Some Linux machines return different results for the unit and doc tests. We suspect this has to do with the way random seeds are set. See `Issue 52 <http://code.google.com/p/pysal/issues/detail?id=52&sort=milestone>`_
+
+LISA Markov missing a transpose
+-------------------------------
+In versions of PySAL < 1.1 there is a bug in the LISA Markov, resulting in
+incorrect values. For a fix and more details see `Issue 115 <http://code.google.com/p/pysal/issues/detail?id=115>`_.
+
+
+PIP Install Fails
+-----------------
+
+
+Having numpy and scipy specified in pip requiretments.txt causes PIP install of pysal to fail. For discussion and suggested fixes see `Issue 207 <http://code.google.com/p/pysal/issues/detail?id=207&sort=milestone>`_.
+
diff --git a/doc/source/developers/pep/index.rst b/doc/source/developers/pep/index.rst
new file mode 100644
index 0000000..a8d6489
--- /dev/null
+++ b/doc/source/developers/pep/index.rst
@@ -0,0 +1,18 @@
+*********************************
+PySAL Enhancement Proposals (PEP)
+*********************************
+
+.. toctree::
+ :maxdepth: 1
+
+ pep-0001
+ pep-0002
+ pep-0003
+ pep-0004
+ pep-0005
+ pep-0006
+ pep-0007
+ pep-0008
+ pep-0009
+ pep-0010
+ pep-0011
diff --git a/doc/source/developers/pep/pep-0001.rst b/doc/source/developers/pep/pep-0001.rst
new file mode 100644
index 0000000..36581df
--- /dev/null
+++ b/doc/source/developers/pep/pep-0001.rst
@@ -0,0 +1,74 @@
+********************************
+PEP 0001 Spatial Dynamics Module
+********************************
+
+======== =================================
+Author Serge Rey <sjsrey at gmail.com>,
+ Xinyue Ye <xinyue.ye at gmail.com>
+Status Approved 1.0
+Created 18-Jan-2010
+Updated 09-Feb-2010
+======== =================================
+
+Abstract
+========
+
+With the increasing availability of spatial longitudinal data sets there
+is an growing demand for exploratory methods that integrate both the
+spatial and temporal dimensions of the data. The spatial dynamics
+module combines a number of previously developed and to-be-developed
+classes for the analysis of spatial dynamics. It will include classes
+for the following statistics for spatial dynamics, Markov, spatial
+Markov, rank mobility, spatial rank mobility, space-time LISA.
+
+Motivation
+==========
+
+Rather than having each of the spatial dynamics as separate modules in
+PySAL, it makes sense to move them all within the same module. This would
+facilitate common signatures for constructors and similar forms of data
+structures for space-time analysis (and generation of results).
+
+The module would implement some of the ideas for extending LISA statistics
+to a dynamic context ([Anselin2000]_ [ReyJanikas2006]_),
+and recent work developing empirics and summary
+measures for comparative space time analysis ([ReyYe2010]_).
+
+
+Reference Implementation
+========================
+
+We suggest adding the module ``pysal.spatialdynamics`` which in turn would
+encompass the following modules:
+
+* rank mobility
+ rank concordance (relative mobility or internal mixing)
+ Kendall's index
+
+* spatial rank mobility
+ add a spatial dimension into rank mobility investigate the extent to
+ which the relative mobility is spatially dependent
+ use various types of spatial weight matrix
+
+* Markov
+ empirical transition probability matrix (mobility across class)
+ Shorrock's index
+
+* Spatial Markov
+ adds a spatial dimension (regional conditioning) into classic Markov models
+ a trace statistic from a modified Markov transition matrix
+ investigate the extent to which the inter-class mobility are spatially dependent
+
+* Space-Time LISA
+ extends LISA measures to integrate the time dimension
+ combined with cg (computational geometry) module to develop comparative measurements
+
+References
+==========
+
+.. [Anselin2000] Anselin, Luc (2000) Computing environments for spatial data analysis. *Journal of Geographical Systems* 2: 201-220
+
+.. [ReyJanikas2006] Rey, S.J. and M.V. Janikas (2006) STARS: Space-Time Analysis of Regional Systems, *Geographical Analysis* 38: 67-86.
+
+.. [ReyYe2010] Rey, S.J. and X. Ye (2010) Comparative spatial dyanmics of regional systems. In Paez, A. et al. (eds) *Progress in Spatial Analysis: Methods and Applications*. Springer: Berlin, 441-463.
+
diff --git a/doc/source/developers/pep/pep-0002.rst b/doc/source/developers/pep/pep-0002.rst
new file mode 100644
index 0000000..d2d19b2
--- /dev/null
+++ b/doc/source/developers/pep/pep-0002.rst
@@ -0,0 +1,56 @@
+***************************************
+PEP 0002 Residential Segregation Module
+***************************************
+
+======== =================================
+Author David C. Folch <david.folch at asu.edu>
+ Serge Rey <srey at asu.edu>
+Status Draft
+Created 10-Feb-2010
+Updated
+======== =================================
+
+
+Abstract
+========
+
+The segregation module combines a number of previously developed and
+to-be-developed measures for the analysis of residential segregation. It will
+include classes for two-group and multi-group aspatial (classic) segregation
+indices along with their spatialized counterparts. Local segregation indices
+will also be included.
+
+
+Motivation
+==========
+
+The study of residential segregation continues to be a popular field in
+empirical social science and public policy development. While some of the
+classic measures are relatively simple to implement, the spatial versions are
+not nearly as straightforward for the average user. Furthermore, there does
+not appear to be a Python implementation of residential segregation measures
+currently available. There is a standalone C#.Net GUI implementation
+(http://www.ucs.inrs.ca/inc/Groupes/LASER/Segregation.zip) containing many of
+the measures to be implanted via this PEP but this is Windows only and I could
+not get it to run easily (it is not open source but the author sent me the
+code).
+
+It has been noted that there is no one-size-fits-all segregation index;
+however, some are clearly more popular than others. This module would bring
+together a wide variety of measures to allow users to easily compare the
+results from different indices.
+
+
+Reference Implementation
+========================
+
+We suggest adding the module ``pysal.segregation`` which in turn would
+encompass the following modules:
+
+* globalSeg
+* localSeg
+
+
+References
+==========
+
diff --git a/doc/source/developers/pep/pep-0003.rst b/doc/source/developers/pep/pep-0003.rst
new file mode 100644
index 0000000..3d677d7
--- /dev/null
+++ b/doc/source/developers/pep/pep-0003.rst
@@ -0,0 +1,55 @@
+*********************************
+PEP 0003 Spatial Smoothing Module
+*********************************
+
+======== =================================
+Author Myunghwa Hwang <mhwang4 at gmail.com>
+ Luc Anselin <luc.anselin at asu.edu>
+ Serge Rey <srey at asu.edu>
+Status Approved 1.0
+Created 11-Feb-2010
+Updated
+======== =================================
+
+
+Abstract
+========
+
+Spatial smoothing techniques aim to adjust problems with applying simple
+normalization to rate computation. Geographic studies of disease widely
+adopt these techniques to better summarize spatial patterns of disease occurrences.
+The smoothing module combines a number of previously developed and to-be-developed
+classes for carrying out spatial smoothing. It will include classes for
+the following techniques: mean and median based smoothing, nonparametric smoothing,
+and empirical Bayes smoothing.
+
+
+Motivation
+==========
+
+Despite wide usage of spatial smoothing techniques in epidemiology,
+there are only few software libraries that include a range of different smoothing
+techniques at one place.
+Since spatial smoothing is a subtype of exploratory data analysis method,
+PySAL is the best place that host multiple smoothing techniques.
+
+The smoothing module will mainly implement the techniques reported in [Anselin2006].
+
+
+Reference Implementation
+========================
+
+We suggest adding the module ``pysal.esda.smoothing`` which in turn would
+encompass the following modules:
+
+* locally weighted averages, locally weighted median, headbanging
+* spatial rate smoothing
+* excess risk, empricial Bayes smoothing, spatial empirical Bayes smoothing
+* headbanging
+
+
+References
+==========
+
+[Anselin2006] Anselin, L., N. Lozano, and J. Koschinsky (2006) Rate Transformations and Smoothing, GeoDa Center Research Report.
+
diff --git a/doc/source/developers/pep/pep-0004.rst b/doc/source/developers/pep/pep-0004.rst
new file mode 100644
index 0000000..4533e0f
--- /dev/null
+++ b/doc/source/developers/pep/pep-0004.rst
@@ -0,0 +1,56 @@
+**********************************************************************
+PEP 0004 Geographically Nested Inequality based on the Geary Statistic
+**********************************************************************
+
+======== =================================
+Author Boris Dev <boris.dev at gmail.com>
+ Charles Schmidt <schmidtc at gmail.com>
+Status Draft
+Created 9-Aug-2010
+Updated
+======== =================================
+
+
+Abstract
+========
+
+
+I propose to extend the Geary statistic to describe inequality
+patterns between people in the same geographic zones. Geographically nested
+associations can be represented with a spatial weights matrix defined jointly
+using both geographic and social positions. The key class in the proposed
+geographically nested inequality module would sub-class from class
+``pysal.esda.geary`` with 2 extensions: 1) as an additional argument, an array
+of regimes to represent social space; and 2) for the output, spatially nested
+randomizations will be performed for pseudo-significance tests.
+
+Motivation
+==========
+
+Geographically nested measures may reveal inequality patterns that are masked
+by conventional aggregate approaches. Aggregate human inequality statistics
+summarize the size of the gaps in variables such as mortality rate or income
+level between different different groups of people. A geographically nested
+measure is computed using only a pairwise subset of the values defined by
+common location in the same geographic zone. For example, this type of
+measure was proposed in my dissertation to assess changes in income inequality
+between nearby blocks of different school attendance zones or different racial
+neighborhoods within the same cities. Since there are no standard statistical
+packages to do this sort of analysis, currently such a pairwise approach to
+inequality analysis across many geographic zones is tedious for researchers
+who are non-hackers. Since it will take advantage of the currently existing
+``pysal.esda.geary`` and ``pysal.weights.regime_weights()``, the proposed
+module should be readable for hackers.
+
+Reference Implementation
+========================
+
+I suggest adding the module ``pysal.inequality.nested``.
+
+
+References
+==========
+
+[Dev2010] Dev, B. (2010) "Assessing Inequality using Geographic Income Distributions: Spatial Data Analysis of States,
+Neighborhoods, and School Attendance Zones" http://dl.dropbox.com/u/408103/dissertation.pdf.
+
diff --git a/doc/source/developers/pep/pep-0005.rst b/doc/source/developers/pep/pep-0005.rst
new file mode 100644
index 0000000..a8c69ea
--- /dev/null
+++ b/doc/source/developers/pep/pep-0005.rst
@@ -0,0 +1,56 @@
+*************************************************
+PEP 0005 Space Time Event Clustering Module
+*************************************************
+
+======== ======================================
+Author Nicholas Malizia <nmalizia at gmail.com>,
+ Serge Rey <sjsrey at gmail.com>
+Status Approved 1.1
+Created 13-Jul-2010
+Updated 06-Oct-2010
+======== ======================================
+
+Abstract
+========
+
+The space-time event clustering module will be an addition (in the form of a sub-module) to the spatial dynamics module. The purpose of this module will be to house all methods concerned with identifying clusters within spatio-temporal event data. The module will include classes for the major methods for spatio-temporal event clustering, including: the Knox, Mantel, Jacquez k Nearest Neighbors, and the Space-Time K Function. Although these methods are tests of global spatio-temporal clus [...]
+
+Motivation
+==========
+
+While the methods of the parent module are concerned with the dynamics of aggregate lattice-based data, the methods encompassed in this sub-module will focus on exploring the dynamics of individual events. The methods suggested here have historically been utilized by researchers looking for clusters of events in the fields of epidemiology and criminology. Currently, the methods presented here are not widely implemented in an open source context. Although the Knox, Mantel, and Jacquez met [...]
+
+Reference Implementation
+========================
+
+We suggest adding the module ``pysal.spatialdynamics.events`` which in turn would encompass the following modules:
+
+Knox
+ The Knox test for space-time interaction sets critical distances in space and time; if the data are clustered, numerous pairs of events will be located within both of these critical distances and the test statistic will be large [3]_. Significance will be established using a Monte Carlo method. This means that either the time stamp or location of the events is scrambled and the statistic is calculated again. This procedure is permuted to generate a distribution of statistics (for the [...]
+
+Mantel
+ Akin to the Knox test in its simplicity, the Mantel test keeps the distance information discarded by the Knox test. The Mantel statistic is calculated by summing the product of the distances between all the pairs of events [4]_. Again, significance will be determined through a Monte Carlo approach.
+
+Jacquez
+ This test tallies the number of event pairs that are within k-nearest neighbors of each other in *both* space and time. Significance of this count is established using a Monte Carlo permutation method [5]_. Again, the permutation is done by randomizing either the time or location of the events and then running the statistic again. The test should be implemented with the additional descriptives as suggested by [6]_.
+
+SpaceTimeK
+ The space-time K function takes the K function which has been used to detect clustering in spatial point patterns and expands it to the realm of spatio-temporal data. Essentially, the method calculates K functions in space and time independently and then compares the product of these functions with a K function which takes both dimensions of space and time into account from the start [7]_. Significance is established through Monte Carlo methods and the construction of confidence envelopes.
+
+References
+==========
+
+.. [1] G. Jacquez, D. Greiling, H. Durbeck, L. Estberg, E. Do, A. Long, and B. Rommel. ClusterSeer User Guide 2: Software for Identifying Disease Clusters. Ann Arbor, MI: TerraSeer Press, 2002.
+
+.. [2] B. Rowlingson and P. Diggle. splancs: Spatial and Space-Time Point Pattern Analysis. R Package. Version 2.01-25, 2009.
+
+.. [3] E. Knox. The detection of space-time interactions. Journal of the Royal Statistical Society. Series C (Applied Statistics), 13(1):25–30, 1964.
+
+.. [4] N. Mantel. The detection of disease clustering and a generalized regression approach. Cancer Research, 27(2):209–220, 1967.
+
+.. [5] G. Jacquez. A k nearest neighbour test for space-time interaction. Statistics in Medicine, 15(18):1935– 1949, 1996.
+
+.. [6] E. Mack and N. Malizia. Enhancing the results of the Jacquez *k* Nearest Neighbor test for space-time interaction. *In Preparation*
+
+.. [7] P. Diggle, A. Chetwynd, R. Haggkvist, and S. Morris. Second-order analysis of space-time clustering. Statistical Methods in Medical Research, 4(2):124, 1995.
+
diff --git a/doc/source/developers/pep/pep-0006.rst b/doc/source/developers/pep/pep-0006.rst
new file mode 100644
index 0000000..9959b97
--- /dev/null
+++ b/doc/source/developers/pep/pep-0006.rst
@@ -0,0 +1,60 @@
+**********************************
+PEP 0006 Kernel Density Estimation
+**********************************
+
+======== ======================================
+Author Serge Rey <sjsrey at gmail.com>
+ Charles Schmidt <schmidtc at gmail.com>
+
+Status Draft
+Created 11-Oct-2010
+Updated 11-Oct-2010
+======== ======================================
+
+Abstract
+========
+
+The kernel density estimation module will provide a uniform interface to a set
+of kernel density estimation (KDE) methods. Currently KDE is used in various places
+within PySAL
+(e.g., :class:`~pysal.weights.Distance.Kernel`,
+:class:`~pysal.esda.smoothing.Kernel_Smoother`) as well as in STARS and
+various projects within the GeoDA Center, but these implementations were done
+separately. This module would centralize KDE within PySAL as well as extend
+the suite of KDE methods and related measures available in PySAL.
+
+Motivation
+==========
+
+KDE is widely used throughout spatial analysis, from estimation of process
+intensity in point pattern analysis, deriving spatial weights, geographically
+weighted regression, rate smoothing, to hot spot detection, among others.
+
+
+Reference Implementation
+========================
+
+
+Since KDE would be used throughout existing (and likely future) modules in
+PySAL, it makes sense to implement it as a top level module in PySAL.
+
+Core KDE methods that would be implemented include:
+
+ * triangular
+ * uniform
+ * quadratic
+ * quartic
+ * gaussian
+
+Additional classes and methods to deal with KDE on restricted spaces would
+also be implemented.
+
+A unified KDE api would be developed for use of the module.
+
+Computational optimization would form a significant component of the effort
+for this PEP.
+
+References
+==========
+
+in progress
diff --git a/doc/source/developers/pep/pep-0007.rst b/doc/source/developers/pep/pep-0007.rst
new file mode 100644
index 0000000..021896b
--- /dev/null
+++ b/doc/source/developers/pep/pep-0007.rst
@@ -0,0 +1,76 @@
+*****************************
+PEP 0007 Spatial Econometrics
+*****************************
+
+======== ======================================
+Author Luc Anselin <luc.anselin at asu.edu>
+ Serge Rey <sjsrey at gmail.com>,David Folch <dfolch at asu.edu>,Daniel Arribas-Bel <daniel.arribas.bel at gmail.com>,Pedro Amaral <pvmda2 at cam.ac.uk>,Nicholas Malizia <nmalizia at gmail.com>,Ran Wei <rwei5 at asu.edu>,Jing Yao <jyao13 at asu.edu>,Elizabeth Mack <Elizabeth.A.Mack at asu.edu>
+Status Approved 1.1
+Created 12-Oct-2010
+Updated 12-Oct-2010
+======== ======================================
+
+Abstract
+========
+
+The spatial econometrics module will provide a uniform interface to the
+spatial econometric functionality contained in the former PySpace and
+current GeoDaSpace efforts. This module would centralize all specification,
+estimation, diagnostic testing and prediction/simulation for spatial
+econometric models.
+
+
+Motivation
+==========
+
+Spatial econometric methodology is at the core of GeoDa and
+GeoDaSpace. This module would allow access to state of the art
+methods at the source code level.
+
+
+Reference Implementation
+========================
+
+We suggest adding the module ``pysal.spreg``. As development
+progresses, there may be a need for submodules dealing with
+pure cross sectional regression, spatial panel models and spatial probit.
+
+Core methods to be implemented include:
+
+ * OLS estimation with diagnostics for spatial effects
+ * 2SLS estimation with diagnostics for spatial effects
+ * spatial 2SLS for spatial lag model (with endogeneity)
+ * GM and GMM estimation for spatial error model
+ * GMM spatial error with heteroskedasticity
+ * spatial HAC estimation
+
+A significant component of the effort for this PEP would consist of
+implementing methods with good performance on very large data
+sets, exploiting sparse matrix operations in scipy.
+
+References
+==========
+
+[1] Anselin, L. (1988). Spatial Econometrics, Methods and Models. Kluwer, Dordrecht.
+
+[2] Anselin, L. (2006). Spatial econometrics. In Mills, T. and Patterson, K., editors,
+ Palgrave Handbook of Econometrics, Volume I, Econometric Theory, pp. 901-969.
+ Palgrave Macmillan, Basingstoke.
+
+[3] Arraiz, I., Drukker, D., Kelejian H.H., and Prucha, I.R. (2010). A spatial Cliff-Ord-type
+ model with heteroskedastic innovations: small and large sample results.
+ Journal of Regional Science 50: 592-614.
+
+[4] Kelejian, H.H. and Prucha, I.R. (1998). A generalized spatial two stage least squares
+ procedure for estimationg a spatial autoregressive model with autoregressive
+ disturbances. Journal of Real Estate Finance and Economics 17: 99-121.
+
+[5] Kelejian, H.H. and Prucha, I.R. (1999). A generalized moments estimator for the
+ autoregressive parameter in a spatial model. International Economic Review 40: 509-533.
+
+[6] Kelejian, H.H. and Prucha, I.R. (2007). HAC estimation in a spatial framework.
+ Journal of Econometrics 140: 131-154.
+
+[7] Kelejian, H.H. and Prucha, I.R. (2010). Specification and estimation of spatial autoregressive
+ models with autoregressive and heteroskedastic disturbances. Journal of Econometrics
+ (forthcoming).
diff --git a/doc/source/developers/pep/pep-0008.rst b/doc/source/developers/pep/pep-0008.rst
new file mode 100644
index 0000000..00ddfba
--- /dev/null
+++ b/doc/source/developers/pep/pep-0008.rst
@@ -0,0 +1,55 @@
+**************************************************
+PEP 0008 Spatial Database Module
+**************************************************
+
+======== =======================================
+Author Phil Stephens <phil.stphns at gmail.com>,
+ Serge Rey <sjsrey at gmail.com>
+Status Draft
+Created 09-Sep-2010
+Updated 31-Aug-2012
+======== =======================================
+
+Abstract
+========
+
+A spatial database module will extend PySAL file I/O capabilities to spatial
+database software, allowing PySAL
+users to connect to and perform geographic lookups and queries on spatial
+databases.
+
+Motivation
+==========
+
+PySAL currently reads and writes geometry in only the Shapefile data
+structure.
+Spatially-indexed databases permit queries on the
+geometric relations between objects [1]_.
+
+Reference Implementation
+========================
+
+We propose to add the module ``pysal.contrib.spatialdb``, hereafter
+referred to simply as spatialdb.
+spatialdb will leverage the Python Object Relational Mapper (ORM) libraries SQLAlchemy [2]_ and GeoAlchemy [3]_, MIT-licensed software that
+provides a database-agnostic SQL layer for several different databases and
+spatial database extensions
+including PostgreSQL/PostGIS, Oracle Spatial, Spatialite, MS SQL Server, MySQL Spatial, and others.
+These lightweight libraries manage database connections, transactions, and SQL expression translation.
+
+Another option to research is the GeoDjango package. It provides a large
+number of spatial lookups [5]_ and geo queries for PostGIS databases, and a
+smaller set of lookups / queries for Oracle, MySQL, and SpatiaLite.
+
+References
+==========
+
+.. [1] OpenGeo (2010) `Spatial Database Tips and Tricks <http://workshops.opengeo.org/postgis-spatialdbtips/introduction.html>`_. Accessed September 9, 2010.
+
+.. [2] SQLAlchemy (2010) `SQLAlchemy 0.6.5 Documentation <http://www.sqlalchemy.org/docs/orm/index.html>`_. Accessed October 4, 2010.
+
+.. [3] GeoAlchemy (2010) `GeoAlchemy 0.4.1 Documentation <http://geoalchemy.org/index.html>`_. Accessed October 4, 2010.
+
+.. [4] GeoAlchemy (2012) `GeoAlchemy on GitHub <http://https://github.com/geoalchemy/geoalchemy>`_. Accessed August 9, 2012.
+
+.. [5] GeoDjango (2012) `GeoDjango Compatibility Tables <http://docs.djangoproject.com/en/dev/ref/contrib/gis/db-api>`_. Accessed August 31, 2012.
diff --git a/doc/source/developers/pep/pep-0009.rst b/doc/source/developers/pep/pep-0009.rst
new file mode 100644
index 0000000..129c06a
--- /dev/null
+++ b/doc/source/developers/pep/pep-0009.rst
@@ -0,0 +1,57 @@
+*******************************
+PEP 0009 Add Python 3.x Support
+*******************************
+
+======== =================================
+Author Charles Schmidt <schmidtc at gmail.com>
+Status Approved 1.2
+Created 02-Feb-2011
+Updated 02-Feb-2011
+======== =================================
+
+Abstract
+========
+
+Python 2.x is being phased out in favor of the backwards incompatible Python 3
+line. In order to stay relevant to the python community as a whole PySAL needs
+to support the latest production releases of Python. With the release of Numpy
+1.5 and the pending release of SciPy 0.9, all PySAL dependencies support Python
+3. This PEP proposes porting the code base to support both the 2.x and 3.x lines
+of Python.
+
+Motivation
+==========
+
+Python 2.7 is the final major release in the 2.x line. The Python 2.x line will
+continue to receive bug fixes, however only the 3.x line will receive new
+features ([Python271]_). Python 3.x introduces many backward incompatible
+changes to Python ([PythonNewIn3]_). Numpy added support for Python 3.0 in
+version 1.5 ([NumpyANN150]_). Scipy 0.9.0 is currently in the release candidate
+stage and supports Python 3.0 ([SciPyRoadmap]_, [SciPyANN090rc2]_). Many of the
+new features in Python 2.7 were back ported from 3.0, allowing us to start using
+some of the new feature of the language without abandoning our 2.x users.
+
+Reference Implementation
+========================
+
+Since python 2.6 the interpreter has included a '-3' command line switch to
+"warn about Python 3.x incompatibilities that 2to3 cannot trivially fix"
+([Python2to3]_). Running PySAL tests with this switch produces no warnings
+internal to PySAL. This suggests porting to 3.x will require only trivial
+changes to the code. A porting strategy is provided by [PythonNewIn3]_.
+
+References
+==========
+
+.. [Python271] http://www.python.org/download/releases/2.7.1/
+
+.. [PythonNewIn3] http://docs.python.org/release/3.0.1/whatsnew/3.0.html
+
+.. [Python2to3] http://docs.python.org/release/3.0.1/library/2to3.html#to3-reference
+
+.. [NumpyANN150] http://mail.scipy.org/pipermail/numpy-discussion/2010-August/052522.html
+
+.. [SciPyRoadmap] http://projects.scipy.org/scipy/roadmap#python-3
+
+.. [SciPyANN090rc2] http://mail.scipy.org/pipermail/scipy-dev/2011-January/015927.html
+
diff --git a/doc/source/developers/pep/pep-0010.rst b/doc/source/developers/pep/pep-0010.rst
new file mode 100644
index 0000000..f66c5b3
--- /dev/null
+++ b/doc/source/developers/pep/pep-0010.rst
@@ -0,0 +1,46 @@
+*******************************
+PEP 0010 Add pure Python rtree
+*******************************
+
+======== ============================
+Author Serge Rey <sjsrey at gmail.com>
+Status Approved 1.2
+Created 12-Feb-2011
+Updated 12-Feb-2011
+======== ============================
+
+Abstract
+========
+
+A pure Python implementation of an Rtree will be developed for use in the
+construction of spatial weights matrices based on contiguity relations in
+shapefiles as well as supporting a spatial index that can be used by GUI based
+applications built with PySAL requiring brushing and linking.
+
+
+Motivation
+==========
+
+As of 1.1 PySAL checks if the external library ([Rtree]_) is installed. If it
+is not, then an internal binning algorithm is used to determine contiguity
+relations in shapefiles for the construction of certain spatial weights. A
+pure Python implementation of Rtrees may provide for improved cross-platform
+efficiency when the external Rtree library is not present. At the same time,
+such an implementation can be relied on by application developers using PySAL
+who wish to build visualization applications supporting brushing, linking and
+other interactions requiring spatial indices for object selection.
+
+
+Reference Implementation
+========================
+
+A pure Python implementation of Rtrees has recently been implemented ([pyrtree]_) and
+is undergoing testing for possible inclusion in PySAL. It appears that this
+module can be integrated into PySAL with modest effort.
+
+References
+==========
+
+.. [Rtree] http://pypi.python.org/pypi/Rtree/
+
+.. [pyrtree] http://code.google.com/p/pyrtree/
diff --git a/doc/source/developers/pep/pep-0011.rst b/doc/source/developers/pep/pep-0011.rst
new file mode 100644
index 0000000..aca7719
--- /dev/null
+++ b/doc/source/developers/pep/pep-0011.rst
@@ -0,0 +1,120 @@
+****************************************
+PEP 0011 Move from Google Code to Github
+****************************************
+
+======== ============================
+Author Serge Rey <sjsrey at gmail.com>
+Status Draft
+Created 04-Aug-2012
+Updated 04-Aug-2012
+======== ============================
+
+Abstract
+========
+
+This proposal is to move the PySAL code repository from Google Code to Github.
+
+
+Motivation
+==========
+
+`Git`_ is a decentralized version control system that
+brings a number of benefits:
+
+ - distributed development
+ - off-line development
+ - elegant and lightweight branching
+ - fast operations
+ - flexible workflows
+
+among `many others.`_
+
+The two main PySAL dependencies, SciPy and NumPy, made the switch to GitHub
+roughly two years ago. In discussions with members of those development teams
+and related projects (pandas, statsmodels) it is clear that git is gaining
+widespread adoption in the Python scientific computing community. By moving to
+git and GitHub, PySAL would benefit by facilitating interaction with developers in this
+community. Discussions with developers at SciPy 2012 indicated that all
+projects experienced significant growth in community involvement after the
+move to Github. Other projects considering such a move have been `discussing`_ similar issues.
+
+Moving to GitHub would also streamline the administration of project updates,
+documentation and related tasks. The Google Code infrastructure requires
+updates in multiple locations which results in either additional work, or
+neglected changes during releases. GitHub understands markdown and reStructured
+text formats, the latter is heavily used in PySAL documentation and the former
+is clearly preferred to wiki markup on Google Code.
+
+Although there is a learning curve to Git, it is relatively minor for
+developers familiar with Subversion, as all PySAL developers are. Moreover,
+several of the developers have been using Git and GitHub for other projects
+and have expressed interest in such a move. There are excellent on-line
+resources for learning more about git, such as this
+`book.`_
+
+Reference Implementation
+========================
+
+Moving code and history
+-----------------------
+
+There are utilities, such as
+`svn2git`_
+that can be used to convert an SVN repo to a git repo.
+
+The converted git repo would then be pushed to a `GitHub`_ account.
+
+Setting up post-(commit|push|pull) hooks
+----------------------------------------
+Migration of the current integration testing will be required. Github has
+support for `Post-Receive Hooks`_
+that can be used for this aspect of the migration.
+
+
+Moving issues tracking over
+---------------------------
+
+A decision about whether to move the issue tracking over to Github will have
+to be considered. This has been handled in different ways:
+
+ - keep using Google Code for issue tracking
+ - move all issues (even closed ones) over to Github
+ - freeze tickets at Google Code and have a breadcrumb for active tickets
+ pointing to issue tracker at Github
+
+If we decide to move the issues over we may look at
+`tratihubus`_
+as well as other possibilities.
+
+Continuous integration with travis-ci
+-------------------------------------
+
+`Travis-CI`_ is a hosted Continuous Integration (CI) service that is integrated
+with GitHub. This sponsored service provides:
+
+ - testing with multiple versions of Python
+ - testing with multiple versions of project dependencies (numpy and scipy)
+ - `build history`_
+ - integrated GitHub commit hooks
+ - testing against multiple `database services`_
+
+Configuration is achieved with a single YAML file, reducing development
+overhead, maintenance, and monitoring.
+
+Code Sprint for GitHub migration
+--------------------------------
+The proposal is to organize a future sprint to focus on this migration.
+
+
+
+.. _discussing: http://groups.google.com/group/networkx-devel/browse_thread/thread/6b82286cdd5e434a
+.. _tratihubus: https://GitHub.com/roskakori/tratihubis
+.. _Git: http://git-scm.com
+.. _many others.: http://www.youtube.com/watch?v=4XpnKHJAok8
+.. _GitHub: https://GitHub.com
+.. _svn2git: https://help.GitHub.com/articles/importing-from-subversion
+.. _Post-Receive Hooks: https://help.GitHub.com/articles/post-receive-hooks/
+.. _book.: http://git-scm.com/book
+.. _Travis-CI: http://travis-ci.org/
+.. _build history: http://travis-ci.org/#!/zendframework/zf2
+.. _database services: http://about.travis-ci.org/docs/user/database-setup/
diff --git a/doc/source/developers/projects.rst b/doc/source/developers/projects.rst
new file mode 100644
index 0000000..742885f
--- /dev/null
+++ b/doc/source/developers/projects.rst
@@ -0,0 +1,25 @@
+.. _projectsusingpysal:
+
+=========================
+Projects Using PySAL
+=========================
+
+This page lists other software projects making use of PySAL. If your project is
+not listed here, contact one of the `team members <http://code.google.com/p/pysal/people/list>`_ and we'll add it.
+
+----------------------
+GeoDa Center Projects
+----------------------
+
+ * `GeoDaNet <http://geodacenter.asu.edu/software>`_
+ * `GeoDaSpace <http://geodacenter.asu.edu/software>`_
+ * `GeoDaWeights <http://geodacenter.asu.edu/software>`_
+ * `STARS <http://geodacenter.asu.edu/software>`_
+
+
+-----------------
+Related Projects
+-----------------
+ * `Anaconda <http://continuum.io/downloads>`_
+ * `StatsModels <http://statsmodels.sourceforge.net/related.html#related>`_
+ * `PythonAnywhere <http://pythonanywhere.com>`_ includes latest PySAL release
diff --git a/doc/source/developers/py3k.rst b/doc/source/developers/py3k.rst
new file mode 100644
index 0000000..057265f
--- /dev/null
+++ b/doc/source/developers/py3k.rst
@@ -0,0 +1,111 @@
+=================
+PySAL and Python3
+=================
+.. contents::
+
+Background
+----------
+
+PySAL Enhancement Proposal #9 was approved February 2, 2011. It called for
+adapting the code base to support both Python 2.x and 3.x releases.
+
+Setting up for development
+--------------------------
+
+First install `Python3 <http://python.org/download/releases/3.2.2/>`_.
+Once Python3 is installed, you have the choice of downloading the following
+files as pure source code from PyPi and running "python3 setup.py install" for
+each, or follow the instructions below to setup useful helpers: easy_install and
+pip.
+
+To get setuptools and pip, first get distribute from PyPi::
+
+ curl -O http://python-distribute.org/distribute_setup.py
+ python3 distribute_setup.py
+ # Now you have easy_install
+ # It may be useful to setup an alias to this version of easy_install in your shell profile
+ alias easy_install3='/Library/Frameworks/Python.framework/Versions/3.2/bin/easy_install'
+
+After distribute is installed, get pip::
+
+ curl -O https://raw.github.com/pypa/pip/master/contrib/get-pip.py
+ python3 get-pip.py
+ # It may be useful to setup an alias to this version of pip in your shell profile
+ alias pip3='/Library/Frameworks/Python.framework/Versions/3.2/bin/pip'
+
+
+NumPy and SciPy require extensive refactoring on installation. We recommend
+`downloading the source code <http://new.scipy.org/download.html>`_, unzipping,
+and running::
+
+ cd numpy<dir>
+ python3 setup.py install
+ # If all looks good, cd outside of the source directory, and verify import
+ cd
+ python3 -c 'import numpy'
+
+Be sure to install NumPy first since SciPy depends on it. Now install SciPy in
+the same manner::
+
+ cd scipy<dir>
+ python3 setup.py install
+ # After extensive building, if all looks good, cd outside of the source directory, and verify import
+ cd
+ python3 -c 'import scipy'
+
+Post any installation-related issues to the pysal-dev mailing list.
+If python complains about not finding gcc-4.2, and you're sure it is installed,
+(run "gcc --version" to verify), you may create an alias to satisfy this::
+
+ cd /usr/bin/
+ sudo ln -s gcc gcc-4.2
+
+
+Now for PySAL. Get the bleeding edge repository version of PySAL and pass in
+this call::
+
+ cd pysal/trunk
+ python3 setup.py install
+
+You'll be able to watch the dynamic refactoring taking place. If all goes well,
+PySAL will be installed into your Python3 site-packages directory. Confirm
+success with::
+
+ cd
+ python3 -c 'import pysal; pysal.open.check()'
+
+
+Optional Installations
+-----------------------
+
+Now that you have pip, get iPython::
+
+ # Use pip from the Python3 distribution on your system, or with the alias above
+ pip3 install iPython
+
+The first time you launch iPython3, you may receive a warning about the Python
+library readline. The warning makes it clear that pip does not work to install
+readline, so use easy_install, which was installed with distribute above::
+
+ /Library/Frameworks/Python.framework/Versions/3.2/bin/easy_install readline
+
+If when launching iPython3 you receive another warning about kernmagic, note
+that iPython 0.12 and newer use an alternate config file from previous versions.
+Since I had not extensively customized my iPython profile, I just deleted the
+~/.iPython directory and relaunched iPython3.
+
+Now let's get our testing and documentation suites::
+
+ pip3 install nose nose-exclude sphinx numpydoc
+
+Now that nose is installed, let's run the test suite. Since the refactored code
+only exists in the Python3 site-packages directory, cd into it and run nose.
+First, however, copy our nose config files to the installed pysal so that nose
+finds them::
+
+ cp <path to local pysal svn>/nose-exclude.txt /Library/Frameworks/Python.frameworks/Versions/3.2/lib/python3.2/site-packages/pysal/
+ cp <path to local pysal svn>/setup.cfg /Library/Frameworks/Python.frameworks/Versions/3.2/lib/python3.2/site-packages/pysal/
+ cd /Library/Frameworks/Python.frameworks/Versions/3.2/lib/python3.2/site-packages
+ /Library/Frameworks/Python.framework/Versions/3.2/bin/nosetests pysal > ~/Desktop/nose-output.txt 2>&1
+
+
diff --git a/doc/source/developers/release.rst b/doc/source/developers/release.rst
new file mode 100644
index 0000000..7f7c9eb
--- /dev/null
+++ b/doc/source/developers/release.rst
@@ -0,0 +1,93 @@
+.. _release:
+.. role:: strike
+
+************************
+PySAL Release Management
+************************
+.. contents::
+
+Prepare the release
+-------------------
+
+- Check all tests pass.
+- Update CHANGELOG::
+
+ $ python tools/github_stats.py >> chglog
+
+- Prepend `chglog` to `CHANGELOG` and edit
+- Edit THANKS and README and README.md if needed.
+- Change MAJOR, MINOR version in setup script.
+- Change pysal/version.py to non-dev number
+- Change the docs version from X.xdev to X.x by editing doc/source/conf.py in two places.
+- Change docs/index.rst to update Stable version and date, and Development version
+- Commit all changes.
+
+Tag
+---
+
+Make the Tag::
+
+ $ git tag -a v1.4 -m 'my version 1.4'
+
+ $ git push upstream v1.4
+
+On each build machine, clone and checkout the newly created tag::
+
+ $ git clone http://github.com/pysal/pysal.git
+ $ git fetch --tags
+ $ git checkout v1.4
+
+Make docs
+---------
+
+As of verison 1.6, docs are automatically compiled and hosted_.
+
+Make and Upload distributions
+-------------------------------
+
+- Make and upload_ to the Python Package Index in one shot!::
+
+ $ python setup.py sdist (to test it)
+ $ python setup.py sdist upload
+
+ - if not registered_, do so. Follow the prompts. You can save the
+ login credentials in a dot-file, .pypirc
+
+- Make and upload the Windows installer to SourceForge.
+ - On a Windows box, build the installer as so::
+
+ $ python setup.py bdist_wininst
+
+Announce
+--------
+
+- Draft and distribute press release on geodacenter.asu.edu, openspace-list, and pysal.org
+
+ - On GeoDa center website, do this:
+
+ - Login and expand the wrench icon to reveal the Admin menu
+ - Click "Administer", "Content Management", "Content"
+ - Next, click "List", filter by type, and select "Featured Project".
+ - Click "Filter"
+
+ Now you will see the list of Featured Projects. Find "PySAL".
+
+ - Choose to 'edit' PySAL and modify the short text there. This changes the text users see on the homepage slider.
+ - Clicking on the name "PySAL" allows you to edit the content of the PySAL project page, which is also the "About PySAL" page linked to from the homepage slider.
+
+Put master back to dev
+----------------------
+
+- Change MAJOR, MINOR version in setup script.
+- Change pysal/version.py to dev number
+- Change the docs version from X.x to X.xdev by editing doc/source/conf.py in two places.
+- Update the release schedule in doc/source/developers/guidelines.rst
+
+
+Update the `github.io news page <https://github.com/pysal/pysal.github.io/blob/master/_includes/news.md>`_
+to announce the release.
+
+.. _upload: http://docs.python.org/2.7/distutils/uploading.html
+.. _registered: http://docs.python.org/2.7/distutils/packageindex.html
+.. _source: http://docs.python.org/distutils/sourcedist.html
+.. _hosted: http://pysal.readthedocs.org
diff --git a/doc/source/developers/testing.rst b/doc/source/developers/testing.rst
new file mode 100644
index 0000000..94e5b1d
--- /dev/null
+++ b/doc/source/developers/testing.rst
@@ -0,0 +1,127 @@
+.. _testing:
+.. role:: strike
+
+************************
+PySAL Testing Procedures
+************************
+.. contents::
+
+As of PySAL release 1.6, continuous integration testing was ported to the
+Travis-CI hosted testing framework (http://travis-ci.org). There is integration within GitHub that
+provides Travis-CI test results included in a pending Pull Request page, so
+developers can know before merging a Pull Request that the changes will or will
+not induce breakage.
+
+Take a moment to read about the Pull Request development model on our wiki at
+https://github.com/pysal/pysal/wiki/GitHub-Standard-Operating-Procedures
+
+PySAL relies on two different modes of testing [1] integration (regression)
+testing and [2] doctests. All developers responsible for given packages shall
+utilize both modes.
+
+Integration Testing
+===================
+
+Each package shall have a directory `tests` in which unit test scripts for
+each module in the package directory are required.
+For example, in the directory `pysal/esda` the module `moran.py` requires a
+unittest script named `test_moran.py`. This path for this script needs to be
+`pysal/esda/tests/test_moran.py`.
+
+To ensure that any changes made to one package/module do not introduce breakage in the wider project,
+developers should run the package wide test suite using nose before making any
+commits. As of release version 1.5, all tests must pass using a 64-bit
+version of Python.
+To run the new test suite, install nose, nose-progressive, and nose-exclude into your
+working python installation. If you're using EPD, nose is already available::
+
+ pip install -U nose
+ pip install nose-progressive
+ pip install nose-exclude
+
+Then::
+
+ cd trunk/
+ nosetests pysal/
+
+You can also run the test suite from within a Python session. At the
+conclusion of the test, Python will, however, exit::
+
+ import pysal
+ import nose
+ nose.runmodule('pysal')
+
+
+The file setup.cfg (added in revision 1050) in trunk holds nose configuration variables. When nosetests
+is run from trunk, nose reads those configuration parameters into its operation,
+so developers do not need to specify the optional flags on the command line as
+shown below.
+
+To specify running just a subset of the tests, you can also run::
+
+ nosetests pysal/esda/
+
+or any other directory, for instance, to run just those tests.
+To run the entire unittest test suite plus all of the doctests, run::
+
+ nosetests --with-doctest pysal/
+
+To exclude a specific directory or directories, install nose-exclude from PyPi
+(pip install nose-exclude). Then run it like this::
+
+ nosetests -v --exclude-dir=pysal/contrib --with-doctest pysal/
+
+
+Note that you'll probably run into an IOError complaining about too many open
+files. To fix that, pass this via the command line::
+
+ ulimit -S -n 1024
+
+That changes the machine's open file limit for just the current terminal
+session.
+
+The trunk should most always be in a state where all tests are passed.
+
+
+Generating Unit Tests
+=====================
+
+A useful development companion is the package `pythoscope <http://pythoscope.org>`_. It scans
+package folders and produces test script stubs for your modules that fail until
+you write the tests -- a pesky but useful trait. Using pythoscope in the most
+basic way requires just two simple command line calls::
+
+ pythoscope --init
+
+ pythoscope <my_module>.py
+
+
+:strike:`One caveat: pythoscope does not name your test classes in a PySAL-friendly way
+so you'll have to rename each test class after the test scripts are generated.`
+Nose finds tests!
+
+Docstrings and Doctests
+=======================
+
+All public classes and functions should include examples in their docstrings. Those examples serve two purposes:
+
+#. Documentation for users
+#. Tests to ensure code behavior is aligned with the documentation
+
+Doctests will be executed when building PySAL documentation with Sphinx.
+
+
+Developers *should* run tests manually before committing any changes that
+may potentially effect usability. Developers can run doctests (docstring
+tests) manually from the command line using nosetests ::
+
+ nosetests --with-doctest pysal/
+
+Tutorial Doctests
+=================
+
+All of the tutorials are tested along with the overall test suite. Developers
+can test their changes against the tutorial docstrings by cd'ing into
+/doc/ and running::
+
+ make doctest
diff --git a/doc/source/funding.rst b/doc/source/funding.rst
new file mode 100644
index 0000000..7bb8856
--- /dev/null
+++ b/doc/source/funding.rst
@@ -0,0 +1,16 @@
+.. funding:
+
+Funding
+=======
+
+National Science Foundation `CyberGIS Software Integration for Sustained Geospatial Innovation <http://geodacenter.asu.edu/about/news/NSF_Cyber_Award>`_
+
+National Institute of Justice `Flexible Geospatial Visual Analytics and Simulation Technologies to Enhance Criminal Justice Decision Support Systems <http://geoplan.asu.edu/node/3855>`_
+
+National Institutes of Health `Geospatial Factors and Impacts: Measurement and Use (R01CA126858-02) <http://geodacenter.asu.edu/projects/rti/content/geospatial-fact>`_
+
+National Science Foundation `An Exploratory Space-Time Data Analysis Toolkit for Spatial Social Science Research (0433132) <http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0433132>`_
+
+National Science Foundation `Hedonic Models of Location Decisions with Applications to Geospatial Microdata (0852261) <http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0852261>`_
+
+
diff --git a/doc/source/index.rst b/doc/source/index.rst
new file mode 100644
index 0000000..fba27f9
--- /dev/null
+++ b/doc/source/index.rst
@@ -0,0 +1,87 @@
+.. pysal documentation master file, created by
+ sphinx-quickstart on Wed Aug 26 19:58:20 2009.
+
+.. raw:: html
+
+ <style type="text/css">
+ .section h1 { display: none; }
+ </style>
+
+PySAL
+=====
+
+.. raw:: html
+
+ <div class="section" id="showcase">
+ <a href="http://nbviewer.ipython.org/github/pysal/pysal/blob/master/pysal/contrib/viz/taz_example.ipynb?create=1"><img src="_static/images/socal_2.jpg" border="0" alt="So. Cal. taz network"/></a>
+ <a href="http://nbviewer.ipython.org/github/pysal/pysal/blob/master/pysal/contrib/viz/taz_example.ipynb?create=1"><img src="_static/images/socal_3.jpg" border="0" alt="County components"/></a>
+ </div>
+
+
+.. sidebar:: Releases
+
+ - `Stable 1.9.1 - January 2015 <users/installation.html>`_
+ - `Development 1.10.0dev <http://github.com/pysal/pysal/>`_
+
+PySAL is an open source library of spatial analysis functions written in
+Python intended to support the development of high level applications.
+PySAL is open source under the `BSD License`_.
+
+.. _BSD License: license.html
+
+
+.. toctree::
+ :maxdepth: 1
+
+ News <http://pysal.github.io/news.html>
+ Events <http://pysal.github.io/upcoming_events.html>
+ Funding <http://pysal.github.io/funding.html>
+ Gallery <http://pysal.github.io/grid.html>
+ users/index
+ developers/index
+ library/index
+
+.. raw:: html
+
+ <style type="text/css">
+ table.linktable {
+ margin: 10px;
+ margin-bottom: 20px;
+ margin-left:auto;
+ margin-right: auto;
+ }
+ table.icontable {
+ margin: 10px;
+ }
+ table.linktable td {
+ padding-left: 15px;
+ padding-right: 15px;
+ padding-bottom: 5px;
+ text-align: center;
+ }
+ </style>
+
+ <table class="linktable">
+ <tr>
+ <td>
+ <a href="users/installation.html">
+ <img alt="Download" src="_static/images/download2.png" title="Download" height="80" style="display: block; margin-left: auto; margin-right: auto;"></a> </td>
+ <td>
+ <a href="users/tutorials/index.html">
+ <img alt="Getting Started" src="_static/images/tutorial.png" title="Getting Started" height="80" style="display: block; margin-left: auto; margin-right: auto;"></a> </td>
+ <td>
+ <a href="library/index.html">
+ <img alt="Documentation" src="_static/images/documentation.png" title="Documentation" height="80" style="display: block; margin-left: auto; margin-right: auto;"></a> </td>
+ <td>
+ <a href="http://github.com/pysal/pysal/issues?state=open">
+ <img alt="Bug Report" src="_static/images/bugs.png" title="Bug Report" height="80" style="display: block; margin-left: auto; margin-right: auto;"></a> </td>
+ </tr>
+
+ <tr>
+ <td><strong><small><a href="users/installation.html">Install</a></small></strong></td>
+ <td><strong><small><a href="users/tutorials/index.html">Getting Started</a></small></strong></td>
+ <td><strong><small><a href="library/index.html">Documentation</a></small></strong></td>
+ <td><strong><small><a href="http://github.com/pysal/pysal/issues?state=open">Report Bugs</a></small></strong></td>
+ </tr>
+ </table>
+
diff --git a/doc/source/index.txt b/doc/source/index.txt
new file mode 100644
index 0000000..27bc1d5
--- /dev/null
+++ b/doc/source/index.txt
@@ -0,0 +1,110 @@
+.. pysal documentation master file, created by
+ sphinx-quickstart on Wed Aug 26 19:58:20 2009.
+
+*******
+Welcome
+*******
+
+PySAL is a cross-platform library of spatial analysis functions written in
+Python. It is intended to support the development of high level applications
+for spatial analysis.
+
+PySAL is Open Source and licensed under the `BSD License`_.
+
+.. _BSD License: http://opensource.org/licenses/bsd-license.php
+
+Getting Started
+===============
+
+If you are new to PySAL you might want to read :ref:`users-tutorials`.
+
+Downloads
+=========
+
+`Source downloads <http://pypi.python.org/pypi/PySAL>`_ are hosted on the
+Python Package Index.
+
+Graphical installers for recent releases are hosted on the `GeoDa Center
+website <http://geodacenter.asu.edu/software>`_.
+
+Legacy builds (<=1.5) are still available on the old source code repository at
+Google Code, `here <http://code.google.com/p/pysal>`_. You can also find old
+versions of this documentation there as well.
+
+Getting Involved
+================
+
+Prospective developers can learn more in the :doc:`developer guide
+</developers/index>`.
+
+You can track PySAL development, submit bug reports, and make feature requests
+`here <https://github.com/pysal/pysal/issues>`_.
+
+
+Documentation
+=============
+
+ :Release: |version|
+ :Date: |today|
+
+.. toctree::
+ :maxdepth: 1
+
+ users/index
+ developers/index
+ library/index
+
+Bleeding edge documentation is available for developers by selecting `latest` from the drop-down in the lower right-hand corner of this page.
+
+News
+====
+
+(2013-11-13) `PySAL Workshop at North American Regional Science Association Meetings, Atlanta, GA <http://www.narsc.org/newsite/?page_id=2547>`_
+
+
+(2013-07-31) `PySAL 1.6.0 released <https://github.com/pysal/pysal/wiki/PySAL-1.6-Released-(2013-07-31)>`_ (`Install <http://pysal.geodacenter.org/1.6/users/installation.html>`_)
+
+
+(2013-01-30) `PySAL 1.5.0 released <http://code.google.com/p/pysal/wiki/Announce1_5>`_ (`Install <http://pysal.geodacenter.org/1.5/users/installation.html>`_)
+
+(2012-10-24) `PySAL short course at OSGRS 2012 <https://twitter.com/OGRS2012/status/261106998861504512>`_
+
+(2012-09-18) `PySAL short course at GIScience 2012 <http://www.giscience.org/workshops.html>`_
+
+(2012-07-31) `PySAL 1.4.0 released <http://code.google.com/p/pysal/wiki/Announce1_4>`_ (`Install <http://pysal.geodacenter.org/1.4/users/installation.html>`_)
+
+(2012-07-30) `Short course on PySAL for Spatial Regression <https://www.geodapress.com/workshops/spatial-regression#description>`_
+
+(2012-07-30) `PySAL presentation at Joint Statistical Meetings
+<https://www.amstat.org/meetings/jsm/2012/onlineprogram/AbstractDetails.cfm?abstractid=303498>`_
+
+(2012-07-18) `PySAL at SciPy 2012 <http://conference.scipy.org/scipy2012/schedule/conf_schedule_1.php>`_
+
+(2012-01-31) `PySAL 1.3.0 released <http://code.google.com/p/pysal/wiki/Announce1_3>`_ (`Install <http://pysal.geodacenter.org/1.3/users/installation.html>`_)
+
+(2011-07-31) `PySAL 1.2.0 released <http://code.google.com/p/pysal/wiki/Announce1_2>`_ (`Install <http://pysal.geodacenter.org/1.2/users/installation.html>`_)
+
+(2011-01-31) `PySAL 1.1.0 released <http://code.google.com/p/pysal/wiki/Announce1_1>`_ (`Install <http://pysal.geodacenter.org/1.1/users/installation.html>`_)
+
+(2010-07-31) `PySAL 1.0.0 released <http://code.google.com/p/pysal/wiki/Announce1_0>`_ (`Install <http://pysal.geodacenter.org/1.0/users/installation.html>`_)
+
+
+(2010-01-6) New Website for PySAL!
+
+(2009-11-03) `National Institute of Justice funding <http://geoplan.asu.edu/node/3855>`_
+
+
+Funding
+=======
+
+National Science Foundation `CyberGIS Software Integration for Sustained Geospatial Innovation <http://geodacenter.asu.edu/about/news/NSF_Cyber_Award>`_
+
+National Institute of Justice `Flexible Geospatial Visual Analytics and Simulation Technologies to Enhance Criminal Justice Decision Support Systems <http://geoplan.asu.edu/node/3855>`_
+
+National Institutes of Health `Geospatial Factors and Impacts: Measurement and Use (R01CA126858-02) <http://geodacenter.asu.edu/rti>`_
+
+National Science Foundation `An Exploratory Space-Time Data Analysis Toolit for Spatial Social Science Research (0433132) <http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0433132>`_
+
+National Science Foundation `Hedonic Models of Location Decisions with Applications to Geospatial Microdata (0852261) <http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0852261>`_
+
+
diff --git a/doc/source/library/cg/index.rst b/doc/source/library/cg/index.rst
new file mode 100644
index 0000000..e3229d3
--- /dev/null
+++ b/doc/source/library/cg/index.rst
@@ -0,0 +1,16 @@
+:mod:`pysal.cg` -- Computational Geometry
+==========================================
+
+.. toctree::
+ :maxdepth: 1
+
+ locators
+ shapes
+ standalone
+ rtree
+ kdtree
+ sphere
+
+
+
+
diff --git a/doc/source/library/cg/kdtree.rst b/doc/source/library/cg/kdtree.rst
new file mode 100644
index 0000000..8f4fd91
--- /dev/null
+++ b/doc/source/library/cg/kdtree.rst
@@ -0,0 +1,10 @@
+:mod:`cg.kdtree` --- KDTree
+===========================
+
+The :mod:`cg.kdtree` module provides kdtree data structures for PySAL.
+
+.. versionadded:: 1.3
+
+.. automodule:: pysal.cg.kdtree
+ :members:
+
diff --git a/doc/source/library/cg/locators.rst b/doc/source/library/cg/locators.rst
new file mode 100644
index 0000000..e846ff1
--- /dev/null
+++ b/doc/source/library/cg/locators.rst
@@ -0,0 +1,14 @@
+:mod:`cg.locators` --- Locators
+===============================
+
+The :mod:`cg.locators` module provides ....
+
+.. versionadded:: 1.0
+
+
+.. automodule:: pysal.cg.locators
+ :synopsis: Computational geometry code for PySAL: Python Spatial Analysis Library
+ :members:
+ :inherited-members:
+ :undoc-members:
+
diff --git a/doc/source/library/cg/rtree.rst b/doc/source/library/cg/rtree.rst
new file mode 100644
index 0000000..a1eb2a3
--- /dev/null
+++ b/doc/source/library/cg/rtree.rst
@@ -0,0 +1,10 @@
+:mod:`cg.rtree` --- rtree
+===========================
+
+The :mod:`cg.rtree` module provides a pure python rtree.
+
+.. versionadded:: 1.2
+
+.. automodule:: pysal.cg.rtree
+ :members:
+
diff --git a/doc/source/library/cg/shapes.rst b/doc/source/library/cg/shapes.rst
new file mode 100644
index 0000000..776efc1
--- /dev/null
+++ b/doc/source/library/cg/shapes.rst
@@ -0,0 +1,10 @@
+:mod:`cg.shapes` --- Shapes
+===========================
+
+The :mod:`cg.shapes` module provides basic data structures.
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.cg.shapes
+ :members:
+
diff --git a/doc/source/library/cg/sphere.rst b/doc/source/library/cg/sphere.rst
new file mode 100644
index 0000000..43154f0
--- /dev/null
+++ b/doc/source/library/cg/sphere.rst
@@ -0,0 +1,10 @@
+:mod:`cg.sphere` --- Sphere
+===========================
+
+The :mod:`cg.sphere` module provides tools for working with spherical distances.
+
+.. versionadded:: 1.3
+
+.. automodule:: pysal.cg.sphere
+ :members:
+
diff --git a/doc/source/library/cg/standalone.rst b/doc/source/library/cg/standalone.rst
new file mode 100644
index 0000000..9e5ba1a
--- /dev/null
+++ b/doc/source/library/cg/standalone.rst
@@ -0,0 +1,11 @@
+:mod:`cg.standalone` --- Standalone
+===================================
+
+The :mod:`cg.standalone` module provides ...
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.cg.standalone
+ :synopsis: Computational geometry code for PySAL: Python Spatial Analysis Library
+ :members:
+
diff --git a/doc/source/library/contrib/index.rst b/doc/source/library/contrib/index.rst
new file mode 100644
index 0000000..7fe43d8
--- /dev/null
+++ b/doc/source/library/contrib/index.rst
@@ -0,0 +1,73 @@
+:mod:`pysal.contrib` -- Contributed Modules
+===========================================
+
+**Intro**
+
+The PySAL Contrib library contains user contributions that enhance PySAL, but
+are not fit for inclusion in the general library. The primary reason a
+contribution would not be allowed in the general library is external
+dependencies. PySAL has a strict no dependency policy (aside from Numpy/Scipy).
+This helps ensure the library is easy to install and maintain.
+
+However, this policy often limits our ability to make use of existing code or
+exploit performance enhancements from C-extensions. This contrib module is
+designed to alleviate this problem. There are no restrictions on external
+dependencies in contrib.
+
+**Ground Rules**
+
+ 1. Contribs must not be used within the general library.
+ 2. *Explicit imports*: each contrib must be imported manually.
+ 3. *Documentation*: each contrib must be documented, dependencies especially.
+
+**Contribs**
+
+Currently the following contribs are available:
+
+ 1. World To View Transform -- A class for modeling viewing windows, used by Weights Viewer.
+
+ - .. versionadded:: 1.3
+ - Path: pysal.contrib.weights_viewer.transforms
+ - Requires: None
+
+ 2. Weights Viewer -- A Graphical tool for examining spatial weights.
+
+ - .. versionadded:: 1.3
+ - Path: pysal.contrib.weights_viewer.weights_viewer
+ - Requires: `wxPython`_
+
+ 3. Shapely Extension -- Exposes shapely methods as standalone functions
+
+ - .. versionadded:: 1.3
+ - Path: pysal.contrib.shapely_ext
+ - Requires: `shapely`_
+
+ 4. Shared Perimeter Weights -- calculate shared perimeters weights.
+
+ - .. versionadded:: 1.3
+ - Path: pysal.contrib.shared_perimeter_weights
+ - Requires: `shapely`_
+
+ 5. Visualization -- Lightweight visualization layer (`Project page`_).
+
+ - .. versionadded:: 1.7
+ - Path: pysal.contrib.viz
+ - Requires: `matplotlib`_
+
+ 6. Clusterpy -- Spatially constrained clustering.
+
+ - .. versionadded:: 1.8
+ - Path: pysal.contrib.clusterpy
+ - Requires: `clusterpy`_
+
+
+
+
+
+
+
+.. _clusterpy: https://pypi.python.org/pypi/clusterPy/0.9.9
+.. _matplotlib: http://matplotlib.org/
+.. _project page: https://github.com/pysal/pysal/wiki/PySAL-Visualization-Project
+.. _shapely: https://pypi.python.org/pypi/Shapely
+.. _wxPython: http://www.wxpython.org/
diff --git a/doc/source/library/core/FileIO.rst b/doc/source/library/core/FileIO.rst
new file mode 100644
index 0000000..78d0d42
--- /dev/null
+++ b/doc/source/library/core/FileIO.rst
@@ -0,0 +1,15 @@
+:mod:`FileIO` -- File Input/Output System
+===============================================
+
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.core.FileIO
+ :synopsis: File Input/Output System for PySAL
+ :members:
+ :undoc-members:
+ :inherited-members:
+
+
+
+
diff --git a/doc/source/library/core/IOHandlers/arcgis_dbf.rst b/doc/source/library/core/IOHandlers/arcgis_dbf.rst
new file mode 100644
index 0000000..880b03a
--- /dev/null
+++ b/doc/source/library/core/IOHandlers/arcgis_dbf.rst
@@ -0,0 +1,13 @@
+:mod:`IOHandlers.arcgis_dbf` -- ArcGIS DBF plugin
+========================================================================
+
+.. versionadded:: 1.2
+
+.. automodule:: pysal.core.IOHandlers.arcgis_dbf
+ :synopsis: ArcGIS DBF weights file plugin
+ :members:
+ :undoc-members:
+ :inherited-members:
+
+
+
diff --git a/doc/source/library/core/IOHandlers/arcgis_swm.rst b/doc/source/library/core/IOHandlers/arcgis_swm.rst
new file mode 100644
index 0000000..9fe2b0c
--- /dev/null
+++ b/doc/source/library/core/IOHandlers/arcgis_swm.rst
@@ -0,0 +1,13 @@
+:mod:`IOHandlers.arcgis_swm` --- ArcGIS SWM plugin
+==================================================
+
+.. versionadded:: 1.2
+
+.. automodule:: pysal.core.IOHandlers.arcgis_swm
+ :synopsis: ArcGIS SWM weights file plugin
+ :members:
+ :undoc-members:
+ :inherited-members:
+
+
+
diff --git a/doc/source/library/core/IOHandlers/arcgis_txt.rst b/doc/source/library/core/IOHandlers/arcgis_txt.rst
new file mode 100644
index 0000000..3c0c28f
--- /dev/null
+++ b/doc/source/library/core/IOHandlers/arcgis_txt.rst
@@ -0,0 +1,10 @@
+:mod:`IOHandlers.arcgis_txt` -- ArcGIS ASCII plugin
+=================================================================
+
+.. versionadded:: 1.2
+
+.. automodule:: pysal.core.IOHandlers.arcgis_txt
+ :synopsis: ArcGIS ASCII text weights file plugin
+ :members:
+ :undoc-members:
+ :inherited-members:
diff --git a/doc/source/library/core/IOHandlers/csvWrapper.rst b/doc/source/library/core/IOHandlers/csvWrapper.rst
new file mode 100644
index 0000000..bd5ecbb
--- /dev/null
+++ b/doc/source/library/core/IOHandlers/csvWrapper.rst
@@ -0,0 +1,13 @@
+:mod:`IOHandlers.csvWrapper` --- CSV plugin
+===========================================
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.core.IOHandlers.csvWrapper
+ :synopsis: CSV Plugin
+ :members:
+ :undoc-members:
+ :inherited-members:
+
+
+
diff --git a/doc/source/library/core/IOHandlers/dat.rst b/doc/source/library/core/IOHandlers/dat.rst
new file mode 100644
index 0000000..b0a54e2
--- /dev/null
+++ b/doc/source/library/core/IOHandlers/dat.rst
@@ -0,0 +1,10 @@
+:mod:`IOHandlers.dat` --- DAT plugin
+====================================
+
+.. versionadded:: 1.2
+
+.. automodule:: pysal.core.IOHandlers.dat
+ :synopsis: DAT Plugin for PySAL FileIO System
+ :members:
+ :undoc-members:
+ :inherited-members:
diff --git a/doc/source/library/core/IOHandlers/gal.rst b/doc/source/library/core/IOHandlers/gal.rst
new file mode 100644
index 0000000..14cb409
--- /dev/null
+++ b/doc/source/library/core/IOHandlers/gal.rst
@@ -0,0 +1,13 @@
+:mod:`IOHandlers.gal` --- GAL plugin
+====================================
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.core.IOHandlers.gal
+ :synopsis: GAL Plugin for PySAL FileIO System
+ :members:
+ :undoc-members:
+ :inherited-members:
+
+
+
diff --git a/doc/source/library/core/IOHandlers/geobugs_txt.rst b/doc/source/library/core/IOHandlers/geobugs_txt.rst
new file mode 100644
index 0000000..11c0b24
--- /dev/null
+++ b/doc/source/library/core/IOHandlers/geobugs_txt.rst
@@ -0,0 +1,13 @@
+:mod:`IOHandlers.geobugs_txt` --- GeoBUGS plugin
+================================================
+
+.. versionadded:: 1.2
+
+.. automodule:: pysal.core.IOHandlers.geobugs_txt
+ :synopsis: GeoBUGS Plugin for PySAL FileIO System
+ :members:
+ :undoc-members:
+ :inherited-members:
+
+
+
diff --git a/doc/source/library/core/IOHandlers/geoda_txt.rst b/doc/source/library/core/IOHandlers/geoda_txt.rst
new file mode 100644
index 0000000..68cee8c
--- /dev/null
+++ b/doc/source/library/core/IOHandlers/geoda_txt.rst
@@ -0,0 +1,10 @@
+:mod:`IOHandlers.geoda_txt` -- Geoda text plugin
+================================================
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.core.IOHandlers.geoda_txt
+ :synopsis: Geoda_txt Plugin for PySAL FileIO System
+ :members:
+ :undoc-members:
+ :inherited-members:
diff --git a/doc/source/library/core/IOHandlers/gwt.rst b/doc/source/library/core/IOHandlers/gwt.rst
new file mode 100644
index 0000000..96b6cff
--- /dev/null
+++ b/doc/source/library/core/IOHandlers/gwt.rst
@@ -0,0 +1,10 @@
+:mod:`IOHandlers.gwt` --- GWT plugin
+====================================
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.core.IOHandlers.gwt
+ :synopsis: GWT Plugin for PySAL FileIO System
+ :members:
+ :undoc-members:
+ :inherited-members:
diff --git a/doc/source/library/core/IOHandlers/index.rst b/doc/source/library/core/IOHandlers/index.rst
new file mode 100644
index 0000000..7f806d8
--- /dev/null
+++ b/doc/source/library/core/IOHandlers/index.rst
@@ -0,0 +1,8 @@
+:mod:`pysal.core.IOHandlers` --- Input Output Handlers
+======================================================
+
+.. toctree::
+ :glob:
+
+ *
+
diff --git a/doc/source/library/core/IOHandlers/mat.rst b/doc/source/library/core/IOHandlers/mat.rst
new file mode 100644
index 0000000..ffefa56
--- /dev/null
+++ b/doc/source/library/core/IOHandlers/mat.rst
@@ -0,0 +1,10 @@
+:mod:`IOHandlers.mat` --- MATLAB Level 4-5 plugin
+======================================================
+
+.. versionadded:: 1.2
+
+.. automodule:: pysal.core.IOHandlers.mat
+ :synopsis: MATLAB Level 4-5 file plugin
+ :members:
+ :undoc-members:
+ :inherited-members:
diff --git a/doc/source/library/core/IOHandlers/mtx.rst b/doc/source/library/core/IOHandlers/mtx.rst
new file mode 100644
index 0000000..a5b69bc
--- /dev/null
+++ b/doc/source/library/core/IOHandlers/mtx.rst
@@ -0,0 +1,10 @@
+:mod:`IOHandlers.mtx` --- Matrix Market MTX plugin
+=======================================================
+
+.. versionadded:: 1.2
+
+.. automodule:: pysal.core.IOHandlers.mtx
+ :synopsis: Matrix Market MTX file plugin
+ :members:
+ :undoc-members:
+ :inherited-members:
diff --git a/doc/source/library/core/IOHandlers/pyDbfIO.rst b/doc/source/library/core/IOHandlers/pyDbfIO.rst
new file mode 100644
index 0000000..491cc58
--- /dev/null
+++ b/doc/source/library/core/IOHandlers/pyDbfIO.rst
@@ -0,0 +1,11 @@
+:mod:`IOHandlers.pyDbfIO` -- PySAL DBF plugin
+=============================================
+
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.core.IOHandlers.pyDbfIO
+ :synopsis: DBF Plugin
+ :members:
+ :undoc-members:
+ :inherited-members:
diff --git a/doc/source/library/core/IOHandlers/pyShpIO.rst b/doc/source/library/core/IOHandlers/pyShpIO.rst
new file mode 100644
index 0000000..800ea9e
--- /dev/null
+++ b/doc/source/library/core/IOHandlers/pyShpIO.rst
@@ -0,0 +1,12 @@
+:mod:`IOHandlers.pyShpIO` -- Shapefile plugin
+==============================================
+
+The :mod:`IOHandlers.pyShpIO` Shapefile Plugin for PySAL's FileIO System
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.core.IOHandlers.pyShpIO
+ :synopsis: PySAL Shapefile Plugin
+ :members:
+ :undoc-members:
+ :inherited-members:
diff --git a/doc/source/library/core/IOHandlers/stata_txt.rst b/doc/source/library/core/IOHandlers/stata_txt.rst
new file mode 100644
index 0000000..eb14b0c
--- /dev/null
+++ b/doc/source/library/core/IOHandlers/stata_txt.rst
@@ -0,0 +1,12 @@
+:mod:`IOHandlers.stata_txt` --- STATA plugin
+============================================================
+.. versionadded:: 1.2
+
+.. automodule:: pysal.core.IOHandlers.stata_txt
+ :synopsis: PySAL STATA spatial weights plugin
+ :members:
+ :undoc-members:
+ :inherited-members:
+
+
+
diff --git a/doc/source/library/core/IOHandlers/wk1.rst b/doc/source/library/core/IOHandlers/wk1.rst
new file mode 100644
index 0000000..97b84f9
--- /dev/null
+++ b/doc/source/library/core/IOHandlers/wk1.rst
@@ -0,0 +1,13 @@
+:mod:`IOHandlers.wk1` --- Lotus WK1 plugin
+===============================================
+
+.. versionadded:: 1.2
+
+.. automodule:: pysal.core.IOHandlers.wk1
+ :synopsis: PySAL plugin for WK1 files
+ :members:
+ :undoc-members:
+ :inherited-members:
+
+
+
diff --git a/doc/source/library/core/IOHandlers/wkt.rst b/doc/source/library/core/IOHandlers/wkt.rst
new file mode 100644
index 0000000..ab6126d
--- /dev/null
+++ b/doc/source/library/core/IOHandlers/wkt.rst
@@ -0,0 +1,15 @@
+:mod:`IOHandlers.wkt` -- Well Known Text (geometry) plugin
+===========================================================
+
+.. versionadded:: 1.0
+
+PySAL plugin for Well Known Text (geometry)
+
+.. automodule:: pysal.core.IOHandlers.wkt
+ :synopsis: PySAL plugin for Well Known Text (geometry)
+ :members:
+ :undoc-members:
+ :inherited-members:
+
+
+
diff --git a/doc/source/library/core/Tables.rst b/doc/source/library/core/Tables.rst
new file mode 100644
index 0000000..9e1a894
--- /dev/null
+++ b/doc/source/library/core/Tables.rst
@@ -0,0 +1,12 @@
+:mod:`Tables` -- DataTable Extension
+==========================================
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.core.Tables
+ :synopsis: DataTable Extension for PySAL FileIO System
+ :members:
+ :undoc-members:
+
+
+
diff --git a/doc/source/library/core/index.rst b/doc/source/library/core/index.rst
new file mode 100644
index 0000000..9ab7868
--- /dev/null
+++ b/doc/source/library/core/index.rst
@@ -0,0 +1,8 @@
+:mod:`pysal.core` --- Core Data Structures and IO
+=================================================
+
+.. toctree::
+
+ Tables
+ FileIO
+ IOHandlers/index
diff --git a/doc/source/library/esda/gamma.rst b/doc/source/library/esda/gamma.rst
new file mode 100644
index 0000000..c2c377b
--- /dev/null
+++ b/doc/source/library/esda/gamma.rst
@@ -0,0 +1,10 @@
+:mod:`esda.gamma` --- Gamma statistics for spatial autocorrelation
+======================================================================
+
+
+.. versionadded:: 1.4
+
+.. automodule:: pysal.esda.gamma
+ :synopsis: Global measures of autocorrelation based on Gamma statistics
+ :members:
+
diff --git a/doc/source/library/esda/geary.rst b/doc/source/library/esda/geary.rst
new file mode 100644
index 0000000..1dd8ab4
--- /dev/null
+++ b/doc/source/library/esda/geary.rst
@@ -0,0 +1,10 @@
+:mod:`esda.geary` --- Geary's C statistics for spatial autocorrelation
+======================================================================
+
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.esda.geary
+ :synopsis: Global measures of autocorrelation based on Geary's C
+ :members:
+
diff --git a/doc/source/library/esda/getis-ord.rst b/doc/source/library/esda/getis-ord.rst
new file mode 100644
index 0000000..36b3923
--- /dev/null
+++ b/doc/source/library/esda/getis-ord.rst
@@ -0,0 +1,11 @@
+
+:mod:`esda.getisord` --- Getis-Ord statistics for spatial association
+=====================================================================
+
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.esda.getisord
+ :synopsis: Getis-Ord global and local measures of spatial association
+ :members:
+
diff --git a/doc/source/library/esda/index.rst b/doc/source/library/esda/index.rst
new file mode 100644
index 0000000..36697eb
--- /dev/null
+++ b/doc/source/library/esda/index.rst
@@ -0,0 +1,13 @@
+:mod:`pysal.esda` --- Exploratory Spatial Data Analysis
+=========================================================
+
+.. toctree::
+
+ gamma
+ geary
+ getis-ord
+ join_counts
+ mapclassify
+ moran
+ smoothing
+
diff --git a/doc/source/library/esda/join_counts.rst b/doc/source/library/esda/join_counts.rst
new file mode 100644
index 0000000..6b74d26
--- /dev/null
+++ b/doc/source/library/esda/join_counts.rst
@@ -0,0 +1,9 @@
+:mod:`esda.join_counts` --- Spatial autocorrelation statistics for binary attributes
+====================================================================================
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.esda.join_counts
+ :synopsis: Spatial autocorrelation statistics for binary attributes
+ :members:
+
diff --git a/doc/source/library/esda/mapclassify.rst b/doc/source/library/esda/mapclassify.rst
new file mode 100644
index 0000000..1b42dc1
--- /dev/null
+++ b/doc/source/library/esda/mapclassify.rst
@@ -0,0 +1,10 @@
+:mod:`esda.mapclassify` --- Choropleth map classification
+=========================================================
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.esda.mapclassify
+ :synopsis: Choropleth map classification
+ :members:
+ :inherited-members:
+ :undoc-members:
diff --git a/doc/source/library/esda/moran.rst b/doc/source/library/esda/moran.rst
new file mode 100644
index 0000000..bd6d253
--- /dev/null
+++ b/doc/source/library/esda/moran.rst
@@ -0,0 +1,11 @@
+:mod:`esda.moran` --- Moran's I measures of spatial autocorrelation
+====================================================================
+
+.. versionadded:: 1.0
+
+Moran's I global and local measures of spatial autocorrelation
+
+.. automodule:: pysal.esda.moran
+ :synopsis: Moran's I global and local measures of spatial autocorrelation
+ :members:
+
diff --git a/doc/source/library/esda/smoothing.rst b/doc/source/library/esda/smoothing.rst
new file mode 100644
index 0000000..0608362
--- /dev/null
+++ b/doc/source/library/esda/smoothing.rst
@@ -0,0 +1,9 @@
+:mod:`esda.smoothing` --- Smoothing of spatial rates
+====================================================
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.esda.smoothing
+ :synopsis: Smoothing of spatial rates
+ :members:
+
diff --git a/doc/source/library/index.rst b/doc/source/library/index.rst
new file mode 100644
index 0000000..7fdccf4
--- /dev/null
+++ b/doc/source/library/index.rst
@@ -0,0 +1,34 @@
+.. _library-index
+
+#################
+Library Reference
+#################
+
+:Release: |version|
+:Date: |today|
+
+Python Spatial Analysis Library
+===============================
+
+The Python Spatial Analysis Library consists of several sub-packages each addressing a different area of spatial analysis. In addition to these sub-packages PySAL includes some general utilities used across all modules.
+
+
+Sub-packages
+-------------
+
+.. toctree::
+ :maxdepth: 1
+
+ cg/index
+ core/index
+ esda/index
+ inequality/index
+ region/index
+ spatial_dynamics/index
+ spreg/index
+ weights/index
+ network/index
+ contrib/index
+
+
+
diff --git a/doc/source/library/inequality/gini.rst b/doc/source/library/inequality/gini.rst
new file mode 100644
index 0000000..6c4f450
--- /dev/null
+++ b/doc/source/library/inequality/gini.rst
@@ -0,0 +1,12 @@
+:mod:`inequality.gini` -- Gini inequality and decomposition measures
+======================================================================
+
+The :mod:`inequality.gini` module provides Gini inequality based measures
+
+
+.. versionadded:: 1.6
+
+.. automodule:: pysal.inequality.gini
+ :synopsis: Gini based inequality measures
+ :members:
+
diff --git a/doc/source/library/inequality/index.rst b/doc/source/library/inequality/index.rst
new file mode 100644
index 0000000..77e7a2c
--- /dev/null
+++ b/doc/source/library/inequality/index.rst
@@ -0,0 +1,8 @@
+:mod:`pysal.inequality` --- Spatial Inequality Analysis
+=========================================================
+
+.. toctree::
+ :maxdepth: 1
+ :glob:
+
+ *
diff --git a/doc/source/library/inequality/theil.rst b/doc/source/library/inequality/theil.rst
new file mode 100644
index 0000000..466b42b
--- /dev/null
+++ b/doc/source/library/inequality/theil.rst
@@ -0,0 +1,12 @@
+:mod:`inequality.theil` -- Theil inequality and decomposition measures
+======================================================================
+
+The :mod:`inequality.theil` module provides Theil inequality based measures
+
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.inequality.theil
+ :synopsis: Theil based inequality measures
+ :members:
+
diff --git a/doc/source/library/network/index.rst b/doc/source/library/network/index.rst
new file mode 100644
index 0000000..2675074
--- /dev/null
+++ b/doc/source/library/network/index.rst
@@ -0,0 +1,7 @@
+:mod:`pysal.network` --- Network Constrained Analysis
+=====================================================
+
+.. toctree::
+
+ network
+
diff --git a/doc/source/library/network/network.rst b/doc/source/library/network/network.rst
new file mode 100644
index 0000000..b280d44
--- /dev/null
+++ b/doc/source/library/network/network.rst
@@ -0,0 +1,13 @@
+:mod:`pysal.network` --- Network Constrained Analysis
+======================================================
+
+The :mod:`network` Network Analysis for PySAL
+
+.. versionadded:: 1.9
+
+.. automodule:: pysal.network.network
+ :synopsis: Network Analysis for PySAL
+ :members:
+
+
+
diff --git a/doc/source/library/region/index.rst b/doc/source/library/region/index.rst
new file mode 100644
index 0000000..3495897
--- /dev/null
+++ b/doc/source/library/region/index.rst
@@ -0,0 +1,9 @@
+:mod:`pysal.region` --- Spatially Constrained Clustering
+=============================================================
+
+.. toctree::
+
+ maxp
+ randomregion
+
+
diff --git a/doc/source/library/region/maxp.rst b/doc/source/library/region/maxp.rst
new file mode 100644
index 0000000..c7d952e
--- /dev/null
+++ b/doc/source/library/region/maxp.rst
@@ -0,0 +1,8 @@
+:mod:`region.maxp` -- maxp regionalization
+===========================================================
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.region.maxp
+ :synopsis: Regionalization based on contiguity constraints and LISA seeds
+ :members:
diff --git a/doc/source/library/region/randomregion.rst b/doc/source/library/region/randomregion.rst
new file mode 100644
index 0000000..4eb2e9e
--- /dev/null
+++ b/doc/source/library/region/randomregion.rst
@@ -0,0 +1,8 @@
+:mod:`region.randomregion` -- Random region creation
+===========================================================
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.region.randomregion
+ :synopsis: Creation of random regions subject to various constraints
+ :members:
diff --git a/doc/source/library/spatial_dynamics/directional.rst b/doc/source/library/spatial_dynamics/directional.rst
new file mode 100644
index 0000000..4ca2860
--- /dev/null
+++ b/doc/source/library/spatial_dynamics/directional.rst
@@ -0,0 +1,8 @@
+:mod:`spatial_dynamics.directional` -- Directional LISA Analytics
+=================================================================
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.spatial_dynamics.directional
+ :synopsis:
+ :members:
diff --git a/doc/source/library/spatial_dynamics/ergodic.rst b/doc/source/library/spatial_dynamics/ergodic.rst
new file mode 100644
index 0000000..2374701
--- /dev/null
+++ b/doc/source/library/spatial_dynamics/ergodic.rst
@@ -0,0 +1,8 @@
+:mod:`spatial_dynamics.ergodic` -- Summary measures for ergodic Markov chains
+=============================================================================
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.spatial_dynamics.ergodic
+ :synopsis: summary measures for ergodic Markov chains
+ :members:
diff --git a/doc/source/library/spatial_dynamics/index.rst b/doc/source/library/spatial_dynamics/index.rst
new file mode 100644
index 0000000..5fc133f
--- /dev/null
+++ b/doc/source/library/spatial_dynamics/index.rst
@@ -0,0 +1,13 @@
+:mod:`pysal.spatial_dynamics` --- Spatial Dynamics
+==================================================
+
+.. toctree::
+ :maxdepth: 1
+
+ directional
+ ergodic
+ interaction
+ markov
+ rank
+
+
diff --git a/doc/source/library/spatial_dynamics/interaction.rst b/doc/source/library/spatial_dynamics/interaction.rst
new file mode 100644
index 0000000..6e6c138
--- /dev/null
+++ b/doc/source/library/spatial_dynamics/interaction.rst
@@ -0,0 +1,8 @@
+:mod:`spatial_dynamics.interaction` -- Space-time interaction tests
+===================================================================
+
+.. versionadded:: 1.1
+
+.. automodule:: pysal.spatial_dynamics.interaction
+ :synopsis:
+ :members:
diff --git a/doc/source/library/spatial_dynamics/markov.rst b/doc/source/library/spatial_dynamics/markov.rst
new file mode 100644
index 0000000..e740558
--- /dev/null
+++ b/doc/source/library/spatial_dynamics/markov.rst
@@ -0,0 +1,8 @@
+:mod:`spatial_dynamics.markov` -- Markov based methods
+======================================================
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.spatial_dynamics.markov
+ :synopsis:
+ :members:
diff --git a/doc/source/library/spatial_dynamics/rank.rst b/doc/source/library/spatial_dynamics/rank.rst
new file mode 100644
index 0000000..cf6fb82
--- /dev/null
+++ b/doc/source/library/spatial_dynamics/rank.rst
@@ -0,0 +1,8 @@
+:mod:`spatial_dynamics.rank` -- Rank and spatial rank mobility measures
+=======================================================================
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.spatial_dynamics.rank
+ :synopsis:
+ :members:
diff --git a/doc/source/library/spreg/diagnostics.rst b/doc/source/library/spreg/diagnostics.rst
new file mode 100644
index 0000000..db94b0e
--- /dev/null
+++ b/doc/source/library/spreg/diagnostics.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.diagnostics`- Diagnostics
+=========================================
+
+
+The :mod:`spreg.diagnostics` module provides a set of standard non-spatial diagnostic tests.
+
+.. versionadded:: 1.1
+
+
+.. automodule:: pysal.spreg.diagnostics
+ :synopsis: Code for spreg diagnostics
+ :members:
diff --git a/doc/source/library/spreg/diagnostics_sp.rst b/doc/source/library/spreg/diagnostics_sp.rst
new file mode 100644
index 0000000..abc17db
--- /dev/null
+++ b/doc/source/library/spreg/diagnostics_sp.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.diagnostics_sp` --- Spatial Diagnostics
+====================================================
+
+The :mod:`spreg.diagnostics_sp` module provides spatial diagnostic tests.
+
+.. versionadded:: 1.1
+
+
+.. automodule:: pysal.spreg.diagnostics_sp
+ :synopsis: Code for spreg spatial diagnostics
+ :members:
+
diff --git a/doc/source/library/spreg/diagnostics_tsls.rst b/doc/source/library/spreg/diagnostics_tsls.rst
new file mode 100644
index 0000000..6780bf0
--- /dev/null
+++ b/doc/source/library/spreg/diagnostics_tsls.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.diagnostics_tsls` --- Diagnostics for 2SLS
+======================================================
+
+The :mod:`spreg.diagnostics_tsls` module provides diagnostic tests for two stage least squares based models.
+
+.. versionadded:: 1.3
+
+
+.. automodule:: pysal.spreg.diagnostics_tsls
+ :synopsis: Code for spreg 2SLS diagnostics
+ :members:
+
diff --git a/doc/source/library/spreg/error_sp.rst b/doc/source/library/spreg/error_sp.rst
new file mode 100644
index 0000000..1be4f3a
--- /dev/null
+++ b/doc/source/library/spreg/error_sp.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.error_sp` --- GM/GMM Estimation of Spatial Error and Spatial Combo Models
+=====================================================================================
+
+The :mod:`spreg.error_sp` module provides spatial error and spatial combo (spatial lag with spatial error) regression estimation with and without endogenous variables; based on Kelejian and Prucha (1998 and 1999).
+
+.. versionadded:: 1.3
+
+
+.. automodule:: pysal.spreg.error_sp
+ :synopsis: Code for spreg spatial error and spatial combo regression
+ :members:
+
diff --git a/doc/source/library/spreg/error_sp_het.rst b/doc/source/library/spreg/error_sp_het.rst
new file mode 100644
index 0000000..eb36114
--- /dev/null
+++ b/doc/source/library/spreg/error_sp_het.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.error_sp_het` --- GM/GMM Estimation of Spatial Error and Spatial Combo Models with Heteroskedasticity
+=================================================================================================================
+
+The :mod:`spreg.error_sp_het` module provides spatial error and spatial combo (spatial lag with spatial error) regression estimation with and without endogenous variables, and allowing for heteroskedasticity; based on Arraiz et al (2010) and Anselin (2011).
+
+.. versionadded:: 1.3
+
+
+.. automodule:: pysal.spreg.error_sp_het
+ :synopsis: Code for spreg spatial error and spatial combo regression with heteroskedasticity
+ :members:
+
diff --git a/doc/source/library/spreg/error_sp_het_regimes.rst b/doc/source/library/spreg/error_sp_het_regimes.rst
new file mode 100644
index 0000000..3520622
--- /dev/null
+++ b/doc/source/library/spreg/error_sp_het_regimes.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.error_sp_het_regimes` --- GM/GMM Estimation of Spatial Error and Spatial Combo Models with Heteroskedasticity with Regimes
+======================================================================================================================================
+
+The :mod:`spreg.error_sp_het_regimes` module provides spatial error and spatial combo (spatial lag with spatial error) regression estimation with regimes and with and without endogenous variables, and allowing for heteroskedasticity; based on Arraiz et al (2010) and Anselin (2011).
+
+.. versionadded:: 1.5
+
+
+.. automodule:: pysal.spreg.error_sp_het_regimes
+ :synopsis: Code for spreg spatial error and spatial combo regression with heteroskedasticity with regimes
+ :members:
+
diff --git a/doc/source/library/spreg/error_sp_hom.rst b/doc/source/library/spreg/error_sp_hom.rst
new file mode 100644
index 0000000..ec5c730
--- /dev/null
+++ b/doc/source/library/spreg/error_sp_hom.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.error_sp_hom` --- GM/GMM Estimation of Spatial Error and Spatial Combo Models
+=========================================================================================
+
+The :mod:`spreg.error_sp_hom` module provides spatial error and spatial combo (spatial lag with spatial error) regression estimation with and without endogenous variables, and includes inference on the spatial error parameter (lambda); based on Drukker et al. (2010) and Anselin (2011).
+
+.. versionadded:: 1.3
+
+
+.. automodule:: pysal.spreg.error_sp_hom
+ :synopsis: Code for spreg spatial error and spatial combo regression with inference on lambda
+ :members:
+
diff --git a/doc/source/library/spreg/error_sp_hom_regimes.rst b/doc/source/library/spreg/error_sp_hom_regimes.rst
new file mode 100644
index 0000000..9a1796c
--- /dev/null
+++ b/doc/source/library/spreg/error_sp_hom_regimes.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.error_sp_hom_regimes` --- GM/GMM Estimation of Spatial Error and Spatial Combo Models with Regimes
+==============================================================================================================
+
+The :mod:`spreg.error_sp_hom_regimes` module provides spatial error and spatial combo (spatial lag with spatial error) regression estimation with regimes and with and without endogenous variables, and includes inference on the spatial error parameter (lambda); based on Drukker et al. (2010) and Anselin (2011).
+
+.. versionadded:: 1.5
+
+
+.. automodule:: pysal.spreg.error_sp_hom_regimes
+ :synopsis: Code for spreg spatial error and spatial combo regression with inference on lambda and with regimes
+ :members:
+
diff --git a/doc/source/library/spreg/error_sp_regimes.rst b/doc/source/library/spreg/error_sp_regimes.rst
new file mode 100644
index 0000000..e8e96d4
--- /dev/null
+++ b/doc/source/library/spreg/error_sp_regimes.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.error_sp_regimes` --- GM/GMM Estimation of Spatial Error and Spatial Combo Models with Regimes
+==========================================================================================================
+
+The :mod:`spreg.error_sp_regimes` module provides spatial error and spatial combo (spatial lag with spatial error) regression estimation with regimes and with and without endogenous variables; based on Kelejian and Prucha (1998 and 1999).
+
+.. versionadded:: 1.5
+
+
+.. automodule:: pysal.spreg.error_sp_regimes
+ :synopsis: Code for spreg spatial error and spatial combo with regimes regression
+ :members:
+
diff --git a/doc/source/library/spreg/index.rst b/doc/source/library/spreg/index.rst
new file mode 100644
index 0000000..1116b6d
--- /dev/null
+++ b/doc/source/library/spreg/index.rst
@@ -0,0 +1,29 @@
+:mod:`pysal.spreg` --- Regression and Diagnostics
+=================================================
+
+.. toctree::
+ :maxdepth: 1
+
+ ols
+ ols_regimes
+ probit
+ twosls
+ twosls_regimes
+ twosls_sp
+ twosls_sp_regimes
+ diagnostics
+ diagnostics_sp
+ diagnostics_tsls
+ error_sp
+ error_sp_regimes
+ error_sp_het
+ error_sp_het_regimes
+ error_sp_hom
+ error_sp_hom_regimes
+ regimes
+ ml_error
+ ml_error_regimes
+ ml_lag
+ ml_lag_regimes
+
+
diff --git a/doc/source/library/spreg/ml_error.rst b/doc/source/library/spreg/ml_error.rst
new file mode 100644
index 0000000..2ad22b1
--- /dev/null
+++ b/doc/source/library/spreg/ml_error.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.ml_error` --- ML Estimation of Spatial Error Model
+=====================================================================================
+
+The :mod:`spreg.ml_error` module provides spatial error model estimation with maximum likelihood following Anselin (1988).
+
+.. versionadded:: 1.7
+
+
+.. automodule:: pysal.spreg.ml_error
+ :synopsis: Code for spreg spatial error estimation via ML
+ :members:
+
diff --git a/doc/source/library/spreg/ml_error_regimes.rst b/doc/source/library/spreg/ml_error_regimes.rst
new file mode 100644
index 0000000..41ca555
--- /dev/null
+++ b/doc/source/library/spreg/ml_error_regimes.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.ml_error_regimes` --- ML Estimation of Spatial Error Model with Regimes
+=====================================================================================
+
+The :mod:`spreg.ml_error_regimes` module provides spatial error model with regimes estimation with maximum likelihood following Anselin (1988).
+
+.. versionadded:: 1.7
+
+
+.. automodule:: pysal.spreg.ml_error_regimes
+ :synopsis: Code for spreg spatial error model with regimes estimation via ML
+ :members:
+
diff --git a/doc/source/library/spreg/ml_lag.rst b/doc/source/library/spreg/ml_lag.rst
new file mode 100644
index 0000000..601c134
--- /dev/null
+++ b/doc/source/library/spreg/ml_lag.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.ml_lag` --- ML Estimation of Spatial Lag Model
+=====================================================================================
+
+The :mod:`spreg.ml_lag` module provides spatial lag model estimation with maximum likelihood following Anselin (1988).
+
+.. versionadded:: 1.7
+
+
+.. automodule:: pysal.spreg.ml_lag
+ :synopsis: Code for spreg spatial lag estimation via ML
+ :members:
+
diff --git a/doc/source/library/spreg/ml_lag_regimes.rst b/doc/source/library/spreg/ml_lag_regimes.rst
new file mode 100644
index 0000000..5f32e56
--- /dev/null
+++ b/doc/source/library/spreg/ml_lag_regimes.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.ml_lag_regimes` --- ML Estimation of Spatial Lag Model with Regimes
+=====================================================================================
+
+The :mod:`spreg.ml_lag_regimes` module provides spatial lag model with regimes estimation with maximum likelihood following Anselin (1988).
+
+.. versionadded:: 1.7
+
+
+.. automodule:: pysal.spreg.ml_lag_regimes
+ :synopsis: Code for spreg spatial lag model with regimes estimation via ML
+ :members:
+
diff --git a/doc/source/library/spreg/ols.rst b/doc/source/library/spreg/ols.rst
new file mode 100644
index 0000000..ca8a3f1
--- /dev/null
+++ b/doc/source/library/spreg/ols.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.ols` --- Ordinary Least Squares
+============================================
+
+The :mod:`spreg.ols` module provides OLS regression estimation.
+
+.. versionadded:: 1.1
+
+
+.. automodule:: pysal.spreg.ols
+ :synopsis: Code for spreg OLS regression
+ :members:
+
diff --git a/doc/source/library/spreg/ols_regimes.rst b/doc/source/library/spreg/ols_regimes.rst
new file mode 100644
index 0000000..1286ea5
--- /dev/null
+++ b/doc/source/library/spreg/ols_regimes.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.ols_regimes` --- Ordinary Least Squares with Regimes
+================================================================
+
+The :mod:`spreg.ols_regimes` module provides OLS with regimes regression estimation.
+
+.. versionadded:: 1.5
+
+
+.. automodule:: pysal.spreg.ols_regimes
+ :synopsis: Code for spreg OLS with regimes regression
+ :members:
+
diff --git a/doc/source/library/spreg/probit.rst b/doc/source/library/spreg/probit.rst
new file mode 100644
index 0000000..07461a3
--- /dev/null
+++ b/doc/source/library/spreg/probit.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.probit` --- Probit
+===============================================
+
+The :mod:`spreg.probit` module provides probit regression estimation.
+
+.. versionadded:: 1.4
+
+
+.. automodule:: pysal.spreg.probit
+ :synopsis: Code for spreg probit regression
+ :members:
+
diff --git a/doc/source/library/spreg/regimes.rst b/doc/source/library/spreg/regimes.rst
new file mode 100644
index 0000000..141f379
--- /dev/null
+++ b/doc/source/library/spreg/regimes.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.regimes` --- Spatial Regimes
+============================================
+
+The :mod:`spreg.regimes` module provides different spatial regime estimation procedures.
+
+.. versionadded:: 1.5
+
+
+.. automodule:: pysal.spreg.regimes
+ :synopsis: Code for spatial regimes
+ :members:
+
diff --git a/doc/source/library/spreg/twosls.rst b/doc/source/library/spreg/twosls.rst
new file mode 100644
index 0000000..9a47e7b
--- /dev/null
+++ b/doc/source/library/spreg/twosls.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.twosls` --- Two Stage Least Squares
+===============================================
+
+The :mod:`spreg.twosls` module provides 2SLS regression estimation.
+
+.. versionadded:: 1.3
+
+
+.. automodule:: pysal.spreg.twosls
+ :synopsis: Code for spreg 2SLS regression
+ :members:
+
diff --git a/doc/source/library/spreg/twosls_regimes.rst b/doc/source/library/spreg/twosls_regimes.rst
new file mode 100644
index 0000000..95f2400
--- /dev/null
+++ b/doc/source/library/spreg/twosls_regimes.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.twosls_regimes` --- Two Stage Least Squares with Regimes
+====================================================================
+
+The :mod:`spreg.twosls_regimes` module provides 2SLS with regimes regression estimation.
+
+.. versionadded:: 1.5
+
+
+.. automodule:: pysal.spreg.twosls_regimes
+ :synopsis: Code for spreg 2SLS with regimes regression
+ :members:
+
diff --git a/doc/source/library/spreg/twosls_sp.rst b/doc/source/library/spreg/twosls_sp.rst
new file mode 100644
index 0000000..e0052d5
--- /dev/null
+++ b/doc/source/library/spreg/twosls_sp.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.twosls_sp` --- Spatial Two Stage Least Squares
+==========================================================
+
+The :mod:`spreg.twosls_sp` module provides S2SLS regression estimation.
+
+.. versionadded:: 1.3
+
+
+.. automodule:: pysal.spreg.twosls_sp
+ :synopsis: Code for spreg S2SLS regression
+ :members:
+
diff --git a/doc/source/library/spreg/twosls_sp_regimes.rst b/doc/source/library/spreg/twosls_sp_regimes.rst
new file mode 100644
index 0000000..bc30c61
--- /dev/null
+++ b/doc/source/library/spreg/twosls_sp_regimes.rst
@@ -0,0 +1,12 @@
+:mod:`spreg.twosls_sp_regimes` --- Spatial Two Stage Least Squares with Regimes
+===============================================================================
+
+The :mod:`spreg.twosls_sp_regimes` module provides S2SLS with regimes regression estimation.
+
+.. versionadded:: 1.5
+
+
+.. automodule:: pysal.spreg.twosls_sp_regimes
+ :synopsis: Code for spreg S2SLS with regimes regression
+ :members:
+
diff --git a/doc/source/library/weights/Contiguity.rst b/doc/source/library/weights/Contiguity.rst
new file mode 100644
index 0000000..b35faa0
--- /dev/null
+++ b/doc/source/library/weights/Contiguity.rst
@@ -0,0 +1,11 @@
+:mod:`weights.Contiguity` --- Contiguity based spatial weights
+==============================================================
+
+The :mod:`weights.Contiguity.` module provides for the construction and manipulation of spatial weights matrices based on contiguity criteria.
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.weights.Contiguity
+ :synopsis: Spatial weights for PySAL: Python Spatial Analysis Library
+ :members:
+
diff --git a/doc/source/library/weights/Distance.rst b/doc/source/library/weights/Distance.rst
new file mode 100644
index 0000000..0976586
--- /dev/null
+++ b/doc/source/library/weights/Distance.rst
@@ -0,0 +1,11 @@
+:mod:`weights.Distance` --- Distance based spatial weights
+==========================================================
+
+The :mod:`weights.Distance` module provides for spatial weights defined
+on distance relationships.
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.weights.Distance
+ :synopsis:
+ :members:
diff --git a/doc/source/library/weights/Wsets.rst b/doc/source/library/weights/Wsets.rst
new file mode 100644
index 0000000..f3f9dea
--- /dev/null
+++ b/doc/source/library/weights/Wsets.rst
@@ -0,0 +1,9 @@
+:mod:`weights.Wsets` --- Set operations on spatial weights
+==========================================================
+
+The :mod:`weights.user` module provides for set operations on weights objects
+.. versionadded:: 1.0
+
+.. automodule:: pysal.weights.Wsets
+ :synopsis:
+ :members:
diff --git a/doc/source/library/weights/index.rst b/doc/source/library/weights/index.rst
new file mode 100644
index 0000000..f97b7c9
--- /dev/null
+++ b/doc/source/library/weights/index.rst
@@ -0,0 +1,12 @@
+:mod:`pysal.weights` --- Spatial Weights
+==========================================
+
+.. toctree::
+
+ weights
+ util
+ user
+ Contiguity
+ Distance
+ Wsets
+ spatial_lag
diff --git a/doc/source/library/weights/spatial_lag.rst b/doc/source/library/weights/spatial_lag.rst
new file mode 100644
index 0000000..ae528af
--- /dev/null
+++ b/doc/source/library/weights/spatial_lag.rst
@@ -0,0 +1,14 @@
+:mod:`weights.spatial_lag` --- Spatial lag operators
+====================================================
+
+The :mod:`weights.spatial_lag` Spatial lag operators for PySAL
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.weights.spatial_lag
+ :synopsis: Spatial lag operators for PySAL
+ :members:
+ :inherited-members:
+
+
+
diff --git a/doc/source/library/weights/user.rst b/doc/source/library/weights/user.rst
new file mode 100644
index 0000000..0a40f39
--- /dev/null
+++ b/doc/source/library/weights/user.rst
@@ -0,0 +1,10 @@
+
+:mod:`weights.user` --- Convenience functions for spatial weights
+=================================================================
+
+The :mod:`weights.user` module provides convenience functions for spatial weights
+.. versionadded:: 1.0
+
+.. automodule:: pysal.weights.user
+ :synopsis:
+ :members:
diff --git a/doc/source/library/weights/util.rst b/doc/source/library/weights/util.rst
new file mode 100644
index 0000000..7f5bcd8
--- /dev/null
+++ b/doc/source/library/weights/util.rst
@@ -0,0 +1,10 @@
+
+:mod:`weights.util` --- Utility functions on spatial weights
+===============================================================
+
+The :mod:`weights.util` module provides utility functions on spatial weights
+.. versionadded:: 1.0
+
+.. automodule:: pysal.weights.util
+ :synopsis:
+ :members:
diff --git a/doc/source/library/weights/weights.rst b/doc/source/library/weights/weights.rst
new file mode 100644
index 0000000..ffeb9f4
--- /dev/null
+++ b/doc/source/library/weights/weights.rst
@@ -0,0 +1,13 @@
+:mod:`pysal.weights` --- Spatial weights matrices
+=================================================
+
+The :mod:`weights` Spatial weights for PySAL
+
+.. versionadded:: 1.0
+
+.. automodule:: pysal.weights.weights
+ :synopsis: Spatial weights for PySAL
+ :members:
+
+
+
diff --git a/doc/source/license.rst b/doc/source/license.rst
new file mode 100644
index 0000000..024fe49
--- /dev/null
+++ b/doc/source/license.rst
@@ -0,0 +1,33 @@
+PySAL License
+=============
+
+| Copyright (c) 2007-2014, PySAL Developers
+| All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+ list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright
+ notice, this list of conditions and the following disclaimer in the
+ documentation and/or other materials provided with the distribution.
+
+* Neither the name of the GeoDa Center for Geospatial Analysis and Computation
+ nor the names of PySAL contributors may be used to endorse or promote products
+ derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
+CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
+INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
+CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF
+USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
diff --git a/doc/source/news.rst b/doc/source/news.rst
new file mode 100644
index 0000000..71ea07f
--- /dev/null
+++ b/doc/source/news.rst
@@ -0,0 +1,55 @@
+.. news:
+
+Upcoming Events
+===============
+
+`2014-11-12: PySAL Workshop at the North American Meetings of the Regional Science Association Meetings, Bethesda <http://www.narsc.org/newsite/?page_id=67>`_
+
+
+News
+====
+
+(2014-09-23) `PySAL Tutorial at GIScience 2014,
+Vienna <http://www.giscience.org/workshops_tutorials.html>`__
+
+(2014-09-08) `PySAL Workshop at FOSS4G 2014,
+Portland <https://2014.foss4g.org/schedule/workshops/#wshop-content-568>`__
+
+(2014-08-08) `National Science Foundation Funding for New Approaches to Spatial Distribution Dynamics <https://geoplan.asu.edu/research-projects/new-approaches-spatial-distribution-dynamics>`_
+
+(2014-07-25) `PySAL 1.8.0 released <http://PySAL.readthedocs.org/en/v1.8/users/installation.html>`_
+
+(2014-01-31) `PySAL 1.7.0 released <https://github.com/pysal/pysal/wiki/PySAL-1.7-Release-(2014-01-31)>`_
+
+(2013-11-13) `PySAL Workshop at North American Regional Science Association Meetings, Atlanta, GA <http://www.narsc.org/newsite/?page_id=2547>`_
+
+(2013-07-31) `PySAL 1.6.0 released <https://github.com/pysal/pysal/wiki/PySAL-1.6-Released-(2013-07-31)>`_
+
+(2013-01-30) `PySAL 1.5.0 released <http://code.google.com/p/pysal/wiki/Announce1_5>`_
+
+(2012-10-24) `PySAL short course at OSGRS 2012 <https://twitter.com/OGRS2012/status/261106998861504512>`_
+
+(2012-09-18) `PySAL short course at GIScience 2012 <http://www.giscience.org/workshops.html>`_
+
+(2012-07-31) `PySAL 1.4.0 released <http://code.google.com/p/pysal/wiki/Announce1_4>`_
+
+(2012-07-30) `Short course on PySAL for Spatial Regression <https://www.geodapress.com/workshops/spatial-regression#description>`_
+
+(2012-07-30) `PySAL presentation at Joint Statistical Meetings
+<https://www.amstat.org/meetings/jsm/2012/onlineprogram/AbstractDetails.cfm?abstractid=303498>`_
+
+(2012-07-18) `PySAL at SciPy 2012 <http://conference.scipy.org/scipy2012/schedule/conf_schedule_1.php>`_
+
+(2012-01-31) `PySAL 1.3.0 released <http://code.google.com/p/pysal/wiki/Announce1_3>`_
+
+(2011-07-31) `PySAL 1.2.0 released <http://code.google.com/p/pysal/wiki/Announce1_2>`_
+
+(2011-01-31) `PySAL 1.1.0 released <http://code.google.com/p/pysal/wiki/Announce1_1>`_
+
+(2010-07-31) `PySAL 1.0.0 released <http://code.google.com/p/pysal/wiki/Announce1_0>`_
+
+
+(2010-01-6) New Website for PySAL!
+
+(2009-11-03) `National Institute of Justice funding <http://geoplan.asu.edu/node/3855>`_
+
diff --git a/doc/source/users/index.rst b/doc/source/users/index.rst
new file mode 100644
index 0000000..a68eca0
--- /dev/null
+++ b/doc/source/users/index.rst
@@ -0,0 +1,11 @@
+==========
+User Guide
+==========
+
+
+.. toctree::
+ :maxdepth: 1
+
+ What is PySAL? <introduction>
+ Installing PySAL <installation>
+ Getting Started with PySAL <tutorials/index>
diff --git a/doc/source/users/installation.rst b/doc/source/users/installation.rst
new file mode 100644
index 0000000..3658886
--- /dev/null
+++ b/doc/source/users/installation.rst
@@ -0,0 +1,160 @@
+.. _installation:
+
+==============
+Install PySAL
+==============
+
+Windows users can download an .exe installer `here on
+Sourceforge <http://sourceforge.net/projects/pysal/files/?source=navbar>`_.
+
+
+PySAL is built upon the Python scientific stack including numpy and
+scipy. While these libraries are packaged for several platforms, the
+Anaconda and Enthought Python distributions include them along with the core
+Python library.
+
+- `Anaconda Python distribution <http://continuum.io/downloads.html>`_
+- `Enthought Canopy <https://www.enthought.com/downloads>`_
+
+Note that while both Anaconda and Enthought Canopy will satisfy the
+dependencies for PySAL, the version of PySAL included in these distributions
+might be behind the latest stable release of PySAL. You can update to the latest
+stable version of PySAL with either of these distributions as follows:
+
+1. In a terminal start the python version associated with the distribution.
+ Make sure you are not using a different (system) version of Python. To
+ check this use `which python` from a terminal to see if Anaconda or
+ Enthought appear in the output.
+2. `pip install -U pysal`
+
+
+
+
+If you do not wish to use either Anaconda or Enthought, ensure the following software packages are available on your machine:
+
+* `Python <http://www.python.org/download>`_ 2.6, or 2.7
+* `numpy <http://new.scipy.org/download.html>`_ 1.3 or later
+* `scipy <http://new.scipy.org/download.html>`_ 0.11 or later
+
+Getting your feet wet
+----------------------
+
+You can start using PySAL right away on the web with Wakari,
+PythonAnywhere, or SageMathCloud.
+
+wakari http://continuum.io/wakari
+
+PythonAnywhere https://www.pythonanywhere.com/
+
+SageMathCloud https://cloud.sagemath.com/
+
+
+Download and install
+--------------------
+
+PySAL is available on the `Python Package Index
+<http://pypi.python.org/pypi/pysal>`_, which means it can be
+downloaded and installed manually or from the command line using
+`pip`, as follows::
+
+ $ pip install pysal
+
+Alternatively, grab the source distribution (.tar.gz) and decompress it to your selected destination. Open a command shell and navigate to the decompressed pysal folder. Type::
+
+ $ python setup.py install
+
+
+Development version on GitHub
+-----------------------------
+
+Developers can checkout PySAL using **git**::
+
+ $ git clone https://github.com/pysal/pysal.git
+
+Open a command shell and navigate to the cloned pysal
+directory. Type::
+
+ $ python setup.py develop
+
+The 'develop' subcommand builds the modules in place
+and modifies sys.path to include the code.
+The advantage of this method is that you get the latest code
+but don't have to fuss with editing system environment variables.
+
+To test your setup, start a Python session and type::
+
+ >>> import pysal
+
+Keep up to date with pysal development by 'pulling' the latest
+changes::
+
+ $ git pull
+
+Windows
+~~~~~~~~
+
+To keep up to date with PySAL development, you will need a Git client that allows you to access and
+update the code from our repository. We recommend
+`GitHub Windows <http://windows.github.com/>`_ for a more graphical client, or
+`Git Bash <https://code.google.com/p/msysgit/downloads/list?q=label:Featured>`_ for a
+command line client. This one gives you a nice Unix-like shell with
+familiar commands. Here is `a nice tutorial
+<http://openhatch.org/missions/windows-setup/>`_ on getting going with Open
+Source software on Windows.
+
+After cloning pysal, install it in develop mode so Python knows where to find it.
+
+Open a command shell and navigate to the cloned pysal
+directory. Type::
+
+ $ python setup.py develop
+
+To test your setup, start a Python session and type::
+
+ >>> import pysal
+
+Keep up to date with pysal development by 'pulling' the latest
+changes::
+
+ $ git pull
+
+
+Troubleshooting
+===============
+
+If you experience problems when building, installing, or testing pysal, ask for
+help on the
+`OpenSpace <http://geodacenter.asu.edu/support/community>`_
+list or
+browse the archives of the
+`pysal-dev <http://groups.google.com/group/pysal-dev?pli=1>`_
+google group.
+
+Please include the output of the following commands in your message:
+
+1) Platform information::
+
+ python -c 'import os,sys;print os.name, sys.platform'
+ uname -a
+
+2) Python version::
+
+ python -c 'import sys; print sys.version'
+
+3) SciPy version::
+
+ python -c 'import scipy; print scipy.__version__'
+
+3) NumPy version::
+
+ python -c 'import numpy; print numpy.__version__'
+
+4) Feel free to add any other relevant information.
+ For example, the full output (both stdout and stderr) of the pysal
+ installation command can be very helpful. Since this output can be
+ rather large, ask before sending it into the mailing list (or
+ better yet, to one of the developers, if asked).
+
+
+
+
diff --git a/doc/source/users/introduction.rst b/doc/source/users/introduction.rst
new file mode 100644
index 0000000..2194b59
--- /dev/null
+++ b/doc/source/users/introduction.rst
@@ -0,0 +1,68 @@
+.. _introduction:
+
+============
+Introduction
+============
+
+.. contents::
+
+History
+*******
+
+PySAL grew out of a collaborative effort between Luc Anselin's group
+previously located at the University of Illinois, Champaign-Urbana, and Serge
+Rey who was at San Diego State University. It was born out of a recognition that
+the respective projects at the two institutions, `PySpace (now GeoDaSpace)
+<http://geodacenter.asu.edu/pyspaceimg>`_ and `STARS
+<http://regionalanalysislab.org/index.php/Main/STARS>`_ - Space Time Analysis
+of Regional Systems, could benefit from a shared analytical core, since
+this would limit code duplication and free up additional developer time to
+focus on enhancements of the respective applications.
+
+This recognition also came at a time when Python was starting to make major
+inroads in geographic information systems as represented by projects such as
+the `Python Cartographic Library <http://zmapserver.sourceforge.net/PCL/>`_,
+`Shapely <http://trac.gispython.org/lab/wiki/Shapely>`_ and ESRI's adoption of
+Python as a scripting language, among others. At the same time there was a
+dearth of Python modules for spatial statistics, spatial econometrics, location
+modeling and other areas of spatial analysis, and the role for PySAL was then
+expanded beyond its support of STARS and GeoDaSpace to provide a library of core
+spatial analytical functions that could support the next generation of spatial
+analysis applications.
+
+In 2008 the home for PySAL moved to the `GeoDa Center for Geospatial Analysis
+and Computation <http://geodacenter.asu.edu/>`_ at Arizona State University.
+
+Scope
+*****
+
+It is important to underscore what PySAL is, and is not, designed to do. First
+and foremost, PySAL is a **library** in the fullest sense of the word.
+Developers looking for a suite of spatial analytical methods that they can
+incorporate into application development should feel at home using PySAL.
+Spatial analysts who may be carrying out research projects requiring customized
+scripting, extensive simulation analysis, or those seeking to advance the state
+of the art in spatial analysis should also find PySAL to be a useful
+foundation for their work.
+
+End users looking for a user friendly graphical user interface for spatial
+analysis should not turn to PySAL directly. Instead, we would direct them to
+projects like STARS and the GeoDaX suite of software products which wrap PySAL
+functionality in GUIs. At the same time, we expect that with developments such
+as the Python based plug-in architectures for `QGIS
+<http://www.qgis.org/wiki/Python_Plugin_Repositories>`_, `GRASS
+<http://grass.osgeo.org/wiki/GRASS_and_Python>`_, and the toolbox extensions
+for `ArcGIS
+<http://training.esri.com/gateway/index.cfm?fa=catalog.courseDetail&CourseID=50089911_9.X>`_,
+that end user access to PySAL functionality will be widening in the near
+future.
+
+
+Research Papers and Presentations
+*********************************
+ * Rey, Sergio J. (2012) `PySAL: A Python Library for Exploratory Spatial Data Analysis and Geocomputation <http://www.youtube.com/watch?v=FN1nH4Fkd_Y>`_ (Movie) SciPy 2012.
+ * Rey, Sergio J. and Luc Anselin. (2010) `PySAL: A Python Library of
+ Spatial Analytical Methods. <http://books.google.com/books?hl=en&lr=&id=c0EP_6eYsjAC&oi=fnd&pg=PA174&dq=pysal&ots=JzI8vk8D4T&sig=J6FEAnbG5Wzw2nn2-0nfj4B6c3Q#v=onepage&q=pysal&f=false>`_ In M. Fischer and A. Getis (eds.) Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications. Springer, Berlin.
+ * Rey, Sergio J. and Luc Anselin. (2009) `PySAL: A Python Library for Spatial Analysis and Geocomputation <http://www.archive.org/details/scipy09_day2_10-Serge_Rey>`_. (Movie) Python for Scientific Computing. Caltech, Pasadena, CA August 2009.
+ * Rey, Sergio J. (2009). `Show Me the Code: Spatial Analysis and Open Source <http://www.springerlink.com/content/91u84l471h043282/>`_. *Journal of Geographical Systems* 11: 191-2007.
+ * Rey, S.J., Anselin, L., & M. Hwang. (2008). `Dynamic Manipulation of Spatial Weights Using Web Services. <http://geodacenter.asu.edu/node/174>`_ GeoDa Center Working Paper 2008-12.
diff --git a/doc/source/users/tutorials/autocorrelation.rst b/doc/source/users/tutorials/autocorrelation.rst
new file mode 100644
index 0000000..1b16c4c
--- /dev/null
+++ b/doc/source/users/tutorials/autocorrelation.rst
@@ -0,0 +1,830 @@
+.. testsetup:: *
+
+ import pysal
+ import numpy as np
+ np.random.seed(12345)
+
+#######################
+Spatial Autocorrelation
+#######################
+
+.. contents::
+
+Introduction
+============
+
+Spatial autocorrelation pertains to the non-random pattern of attribute values
+over a set of spatial units. This can take two general forms: positive
+autocorrelation which reflects value similarity in space, and negative
+autocorrelation or value dissimilarity in space. In either case the
+autocorrelation arises when the observed spatial pattern is different from what would
+be expected under a random process operating in space.
+
+Spatial autocorrelation can be analyzed from two different perspectives. Global
+autocorrelation analysis involves the study of the entire map pattern and
+generally asks the question as to whether the pattern displays clustering or
+not. Local autocorrelation, on the other hand, shifts the focus to explore
+within the global pattern to identify clusters or so called hot spots that may be
+either driving the overall clustering pattern, or that reflect heterogeneities
+that depart from global pattern.
+
+In what follows, we first highlight the global spatial autocorrelation classes
+in PySAL. This is followed by an illustration of the analysis of local spatial
+autocorrelation.
+
+Global Autocorrelation
+======================
+
+PySAL implements five different tests for global spatial autocorrelation:
+the Gamma index of spatial autocorrelation, join count statistics,
+Moran's I, Geary's C, and Getis and Ord's G.
+
+Gamma Index of Spatial Autocorrelation
+--------------------------------------
+
+The Gamma Index of spatial autocorrelation consists of the application of the principle
+behind a general cross-product statistic to measuring spatial autocorrelation. [#]_
+The idea is to assess whether two similarity matrices for n objects, i.e., n by n
+matrices A and B measure the same type of similarity. This is reflected in a so-called
+Gamma Index :math:`\Gamma = \sum_i \sum_j a_{ij}.b_{ij}`. In other words, the statistic
+consists of the sum over all cross-products of matching elements (i,j) in the two
+matrices.
+
+The application of this principle to spatial autocorrelation consists of turning
+the first similarity matrix into a measure of attribute similarity and the second
+matrix into a measure of locational similarity. Naturally, the second matrix is the
+a spatial :doc:`weight <weights>` matrix. The first matrix can be any reasonable measure of attribute
+similarity or dissimilarity, such as a cross-product, squared difference or absolute
+difference.
+
+Formally, then, the Gamma index is:
+
+.. math::
+
+ \Gamma = \sum_i \sum_j a_{ij}.w_{ij}
+
+where the :math:`w_{ij}` are the elements of the weights matrix and
+:math:`a_{ij}` are corresponding measures of attribute similarity.
+
+Inference for this statistic is based on a permutation approach in which the values
+are shuffled around among the locations and the statistic is recomputed each
+time. This creates a reference distribution for the statistic under the null
+hypothesis of spatial randomness. The observed statistic is then compared to this
+reference distribution and a pseudo-significance computed as
+
+.. math::
+
+ p = (m + 1) / (n + 1)
+
+where m is the number of values from the reference distribution that are equal to
+or greater than the observed join count and n is the number of permutations.
+
+The Gamma test is a two-sided test in the sense that both extremely high values (e.g.,
+larger than any value in the reference distribution) and extremely low values
+(e.g., smaller than any value in the reference distribution) can be considered
+to be significant. Depending on how the measure of attribute similarity is defined,
+a high value will indicate positive or negative spatial autocorrelation, and vice
+versa. For example, for a cross-product measure of attribute similarity, high values
+indicate positive spatial autocorrelation and low values negative spatial autocorrelation.
+For a squared difference measure, it is the reverse. This is similar to the
+interpretation of the :ref:`moran` statistic and :ref:`geary` statistic respectively.
+
+Many spatial autocorrelation test statistics can be shown to be special cases of the
+Gamma index. In most instances, the Gamma index is an unstandardized version of the
+commonly used statistics. As such, the Gamma index is scale dependent, since no
+normalization is carried out (such as deviations from the mean or rescaling by the
+variance). Also, since the sum is over all the elements, the value of a Gamma
+statistic will grow with the sample size, everything else being the same.
+
+PySAL implements four forms of the Gamma index. Three of these are pre-specified
+and one allows the user to pass any function that computes a measure of attribute
+similarity. This function should take three parameters: the vector of observations,
+an index i and an index j.
+
+We will illustrate the Gamma index using the same small artificial example
+as we use for the :ref:`moran1` in order to illustrate the similarities
+and differences between them. The data consist of a regular 4 by 4 lattice with
+values of 0 in the top half and values of 1 in the bottom half. We start with the usual
+imports, and set the random seed to 12345 in order
+to be able to replicate the results of the permutation approach.
+
+
+ >>> import pysal
+ >>> import numpy as np
+ >>> np.random.seed(12345)
+
+We create the binary weights matrix for the 4 x 4 lattice and generate the
+observation vector y:
+
+.. doctest::
+
+ >>> w=pysal.lat2W(4,4)
+ >>> y=np.ones(16)
+ >>> y[0:8]=0
+
+The Gamma index function has five arguments, three of which are optional.
+The first two arguments are the vector of observations (y) and the spatial
+weights object (w). Next are ``operation``, the measure of attribute similarity,
+the default of which is ``operation = 'c'`` for cross-product similarity,
+:math:`a_{ij} = y_i.y_j`. The other two built-in options are ``operation = 's'`` for
+squared difference, :math:`a_{ij} = (y_i - y_j)^2` and ``operation = 'a'`` for
+absolute difference, :math:`a_{ij} = | y_i - y_j |`. The fourth option is to
+pass an arbitrary attribute similarity function, as in ``operation = func``, where ``func``
+is a function with three arguments, ``def func(y,i,j)`` with y as the vector
+of observations, and i and j as indices. This function should return a single
+value for attribute similarity.
+
+The fourth argument allows the observed values to be standardized before the
+calculation of the Gamma index. To some extent, this addresses the scale dependence
+of the index, but not its dependence on the number of observations. The default
+is no standardization, ``standardize = 'no'``. To force standardization,
+set ``standardize = 'yes'`` or ``'y'``. The final argument is the number of
+permutations, ``permutations`` with the default set to 999.
+
+As a first illustration, we invoke the Gamma index using all the default
+values, i.e. cross-product similarity, no standardization, and permutations
+set to 999. The interesting statistics are the magnitude of the Gamma index ``g``,
+the standardized Gamma index using the mean and standard deviation from the
+reference distribution, ``g_z`` and the pseudo-p value obtained from the
+permutation, ``g_sim_p``. In addition, the minimum (``min_g``), maximum (``max_g``)
+and mean (``mean_g``) of the reference distribution are available as well.
+
+.. doctest::
+
+ >>> g = pysal.Gamma(y,w)
+ >>> g.g
+ 20.0
+ >>> "%.3f"%g.g_z
+ '3.188'
+ >>> g.p_sim_g
+ 0.0030000000000000001
+ >>> g.min_g
+ 0.0
+ >>> g.max_g
+ 20.0
+ >>> g.mean_g
+ 11.093093093093094
+
+Note that the value for Gamma is exactly twice the BB statistic obtained in the
+example below, since the attribute similarity criterion is identical, but Gamma is
+not divided by 2.0. The observed value is very extreme, with only two replications
+from the permutation equalling the value of 20.0. This indicates significant
+positive spatial autocorrelation.
+
+As a second illustration, we use the squared difference criterion, which
+corresponds to the BW Join Count statistic. We reset the random seed to
+keep comparability of the results.
+
+.. doctest::
+
+ >>> np.random.seed(12345)
+ >>> g1 = pysal.Gamma(y,w,operation='s')
+ >>> g1.g
+ 8.0
+ >>> "%.3f"%g1.g_z
+ '-3.706'
+ >>> g1.p_sim_g
+ 0.001
+ >>> g1.min_g
+ 14.0
+ >>> g1.max_g
+ 48.0
+ >>> g1.mean_g
+ 25.623623623623622
+
+The Gamma index value of 8.0 is exactly twice the value of the BW statistic for
+this example. However, since the Gamma index is used for a two-sided test, this
+value is highly significant, and with a negative z-value, this suggests positive
+spatial autocorrelation (similar
+to Geary's C). In other words, this result is consistent with the finding for the
+Gamma index that used cross-product similarity.
+
+As a third example, we use the absolute difference for attribute similarity.
+The results are identical to those for squared difference since these two
+criteria are equivalent for 0-1 values.
+
+.. doctest::
+
+ >>> np.random.seed(12345)
+ >>> g2 = pysal.Gamma(y,w,operation='a')
+ >>> g2.g
+ 8.0
+ >>> "%.3f"%g2.g_z
+ '-3.706'
+ >>> g2.p_sim_g
+ 0.001
+ >>> g2.min_g
+ 14.0
+ >>> g2.max_g
+ 48.0
+ >>> g2.mean_g
+ 25.623623623623622
+
+We next illustrate the effect of standardization, using the default operation.
+As shown, the value of the statistic is quite different from the unstandardized
+form, but the inference is equivalent.
+
+.. doctest::
+
+ >>> np.random.seed(12345)
+ >>> g3 = pysal.Gamma(y,w,standardize='y')
+ >>> g3.g
+ 32.0
+ >>> "%.3f"%g3.g_z
+ '3.706'
+ >>> g3.p_sim_g
+ 0.001
+ >>> g3.min_g
+ -48.0
+ >>> g3.max_g
+ 20.0
+ >>> "%.3f"%g3.mean_g
+ '-3.247'
+
+Note that all the tests shown here have used the weights matrix in binary form.
+However, since the Gamma index is perfectly general,
+any standardization can be applied to the weights.
+
+Finally, we illustrate the use of an arbitrary attribute similarity function.
+In order to compare to the results above, we will define a function that
+produces a cross product similarity measure. We will then pass this function
+to the ``operation`` argument of the Gamma index.
+
+.. doctest::
+
+ >>> np.random.seed(12345)
+ >>> def func(z,i,j):
+ ... q = z[i]*z[j]
+ ... return q
+ ...
+ >>> g4 = pysal.Gamma(y,w,operation=func)
+ >>> g4.g
+ 20.0
+ >>> "%.3f"%g4.g_z
+ '3.188'
+ >>> g4.p_sim_g
+ 0.0030000000000000001
+
+As expected, the results are identical to those obtained with the default
+operation.
+
+
+.. _moran1:
+
+Join Count Statistics
+---------------------
+
+The join count statistics measure global spatial autocorrelation for binary data, i.e.,
+with observations coded as 1 or B (for Black) and 0 or W (for White). They follow the
+very simple principle of counting joins, i.e., the arrangement of values between
+pairs of observations where the pairs correspond to neighbors. The three resulting
+join count statistics are BB, WW and BW. Both BB and WW are measures of positive
+spatial autocorrelation, whereas BW is an indicator of negative spatial autocorrelation.
+
+To implement the join count statistics, we need the spatial weights matrix in
+binary (not row-standardized) form. With :math:`y` as the vector of observations
+and the spatial :doc:`weight <weights>` as :math:`w_{i,j}`, the three statistics can be expressed as:
+
+.. math::
+
+ BB = (1/2) \sum_{i}\sum_{j} y_i y_j w_{ij}
+
+.. math::
+
+ WW = (1/2) \sum_{i}\sum_{j} (1 - y_i)(1 - y_j) w_{ij}
+
+.. math::
+
+ BW = (1/2) \sum_{i}\sum_{j} (y_i - y_j)^2 w_{ij}
+
+By convention, the join counts are divided by 2 to avoid double counting. Also, since
+the three joins exhaust all the possibilities, they sum to one half (because of the
+division by 2) of the total sum of weights :math:`J = (1/2)S_0 = (1/2)\sum_{i}\sum_{j} w_{ij}`.
+
+Inference for the join count statistics can be based on either an analytical approach
+or a computational approach. The analytical approach starts from the binomial distribution
+and derives the moments of the statistics under the assumption of free sampling
+and non-free sampling. The resulting mean and variance are used to construct a
+standardized z-variable which can be approximated as a standard normal variate. [#]_
+However, the approximation is often poor in practice. We therefore only implement the
+computational approach.
+
+Computational inference is based on a permutation approach in which the values of y
+are randomly reshuffled many times to obtain a reference distribution of the statistics
+under the null hypothesis of spatial randomness. The observed join count is then
+compared to this reference distribution and a pseudo-significance computed as
+
+.. math::
+
+ p = (m + 1) / (n + 1)
+
+where m is the number of values from the reference distribution that are equal to
+or greater than the observed join count and n is the number of permutations. Note
+that the join counts are a one sided-test. If the counts are extremely smaller
+than the reference distribution, this is not an indication of significance. For
+example, if the BW counts are extremely small, this is not an indication of
+*negative* BW autocorrelation, but instead points to the presence of BB or WW
+autocorrelation.
+
+We will illustrate the join count statistics with a simple artificial example
+of a 4 by 4 square lattice with values of 0 in the top half and values of 1 in
+the bottom half.
+
+We start with the usual imports, and set the random seed to 12345 in order
+to be able to replicate the results of the permutation approach.
+
+.. doctest::
+
+ >>> import pysal
+ >>> import numpy as np
+ >>> np.random.seed(12345)
+
+We create the binary weights matrix for the 4 x 4 lattice and generate the
+observation vector y:
+
+.. doctest::
+
+ >>> w=pysal.lat2W(4,4)
+ >>> y=np.ones(16)
+ >>> y[0:8]=0
+
+We obtain an instance of the joint count statistics BB, BW and WW as (J is
+half the sum of all the weights and should equal the sum of BB, WW and BW):
+
+.. doctest::
+
+ >>> jc=pysal.Join_Counts(y,w)
+ >>> jc.bb
+ 10.0
+ >>> jc.bw
+ 4.0
+ >>> jc.ww
+ 10.0
+ >>> jc.J
+ 24.0
+
+The number of permutations is set to 999 by default. For other values, this parameter
+needs to be passed explicitly, as in:
+
+
+ >>> jc=pysal.Join_Counts(y,w,permutations=99)
+
+The results in our simple example show that the BB counts are 10. There are
+in fact 3 horizontal joins in each of the bottom rows of the lattice as well as
+4 vertical joins, which makes for bb = 3 + 3 + 4 = 10. The BW joins are 4, matching the
+separation between the bottom and top part.
+
+The permutation results give a pseudo-p value for BB of 0.003, suggesting highly
+significant positive spatial autocorrelation. The average BB count
+for the sample of 999 replications is 5.5, quite a bit lower than the count of 10
+we obtain. Only two instances of the replicated samples yield a value equal to 10,
+none is greater (the randomly permuted samples yield bb values between 0 and 10).
+
+.. doctest::
+
+ >>> len(jc.sim_bb)
+ 999
+ >>> jc.p_sim_bb
+ 0.0030000000000000001
+ >>> np.mean(jc.sim_bb)
+ 5.5465465465465469
+ >>> np.max(jc.sim_bb)
+ 10.0
+ >>> np.min(jc.sim_bb)
+ 0.0
+
+The results for BW (negative spatial autocorrelation) show a probability of 1.0
+under the null hypothesis. This means that all the values of BW from the randomly
+permuted data sets were larger than the observed value of 4. In fact the range
+of these values is between 7 and 24. In other words, this again strongly points
+towards the presence of positive spatial autocorrelation. The observed number of
+BB and WW joins (10 each) is so high that there are hardly any BW joins (4).
+
+.. doctest::
+
+ >>> len(jc.sim_bw)
+ 999
+ >>> jc.p_sim_bw
+ 1.0
+ >>> np.mean(jc.sim_bw)
+ 12.811811811811811
+ >>> np.max(jc.sim_bw)
+ 24.0
+ >>> np.min(jc.sim_bw)
+ 7.0
+
+.. _moran:
+
+Moran's I
+---------
+
+Moran's I measures the global spatial autocorrelation in an attribute :math:`y` measured over :math:`n` spatial units and is given as:
+
+.. math::
+
+ I = n/S_0 \sum_{i}\sum_j z_i w_{i,j} z_j / \sum_i z_i z_i
+
+where :math:`w_{i,j}` is a spatial :doc:`weight <weights>`, :math:`z_i = y_i - \bar{y}`, and :math:`S_0=\sum_i\sum_j w_{i,j}`. We illustrate the use of Moran's I with a case study of homicide rates for a group of 78 counties surrounding St. Louis over the period 1988-93. [#]_
+We start with the usual imports:
+
+
+ >>> import pysal
+ >>> import numpy as np
+
+Next, we read in the homicide rates:
+
+.. doctest::
+
+ >>> f = pysal.open(pysal.examples.get_path("stl_hom.txt"))
+ >>> y = np.array(f.by_col['HR8893'])
+
+To calculate Moran's I we first need to read in a GAL file for a rook weights
+matrix and create an instance of W:
+
+.. doctest::
+
+ >>> w = pysal.open(pysal.examples.get_path("stl.gal")).read()
+
+The instance of Moran's I can then be obtained with:
+
+.. doctest::
+
+ >>> mi = pysal.Moran(y, w, two_tailed=False)
+ >>> "%.3f"%mi.I
+ '0.244'
+ >>> mi.EI
+ -0.012987012987012988
+ >>> "%.5f"%mi.p_norm
+ '0.00014'
+
+From these results, we see that the observed value for I is significantly above its expected value, under the assumption of normality for the homicide rates.
+
+If we peek inside the mi object to learn more:
+
+ >>> help(mi)
+
+which generates::
+
+ Help on instance of Moran in module pysal.esda.moran:
+
+ class Moran
+ | Moran's I Global Autocorrelation Statistic
+ |
+ | Parameters
+ | ----------
+ |
+ | y : array
+ | variable measured across n spatial units
+ | w : W
+ | spatial weights instance
+ | permutations : int
+ | number of random permutations for calculation of pseudo-p_values
+ |
+ |
+ | Attributes
+ | ----------
+ | y : array
+ | original variable
+ | w : W
+ | original w object
+ | permutations : int
+ | number of permutations
+ | I : float
+ | value of Moran's I
+ | EI : float
+ | expected value under normality assumption
+ | VI_norm : float
+ | variance of I under normality assumption
+ | seI_norm : float
+ | standard deviation of I under normality assumption
+ | z_norm : float
+ | z-value of I under normality assumption
+ | p_norm : float
+ | p-value of I under normality assumption (one-sided)
+ | for two-sided tests, this value should be multiplied by 2
+ | VI_rand : float
+ | variance of I under randomization assumption
+ | seI_rand : float
+ | standard deviation of I under randomization assumption
+ | z_rand : float
+ | z-value of I under randomization assumption
+ | p_rand : float
+ | p-value of I under randomization assumption (1-tailed)
+ | sim : array (if permutations>0)
+
+we see that we can base the inference not only on the normality assumption, but also on random permutations of the values on the spatial units to generate a reference distribution for I under the null:
+
+.. doctest::
+
+ >>> np.random.seed(10)
+ >>> mir = pysal.Moran(y, w, permutations = 9999)
+
+The pseudo p value based on these permutations is:
+
+.. doctest::
+
+ >>> print mir.p_sim
+ 0.0022
+
+in other words there were 14 permutations that generated values for I that
+were as extreme as the original value, so the p value becomes (14+1)/(9999+1). [#]_
+Alternatively, we could use the realized values for I from the permutations and
+compare the original I using a z-transformation to get:
+
+.. doctest::
+
+ >>> print mir.EI_sim
+ -0.0118217511619
+ >>> print mir.z_sim
+ 4.55451777821
+ >>> print mir.p_z_sim
+ 2.62529422013e-06
+
+When the variable of interest (:math:`y`) is rates based on populations with different sizes,
+the Moran's I value for :math:`y` needs to be adjusted to account for the differences among populations. [#]_
+To apply this adjustment, we can create an instance of the Moran_Rate class rather than the Moran class.
+For example, let's assume that we want to estimate the Moran's I for the rates of newborn infants who died of
+Sudden Infant Death Syndrome (SIDS). We start this estimation by reading in the total number of newborn infants (BIR79)
+and the total number of newborn infants who died of SIDS (SID79):
+
+.. doctest::
+
+ >>> f = pysal.open(pysal.examples.get_path("sids2.dbf"))
+ >>> b = np.array(f.by_col('BIR79'))
+ >>> e = np.array(f.by_col('SID79'))
+
+Next, we create an instance of W:
+
+.. doctest::
+
+ >>> w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
+
+Now, we create an instance of Moran_Rate:
+
+.. doctest::
+
+ >>> mi = pysal.esda.moran.Moran_Rate(e, b, w, two_tailed=False)
+ >>> "%6.4f" % mi.I
+ '0.1662'
+ >>> "%6.4f" % mi.EI
+ '-0.0101'
+ >>> "%6.4f" % mi.p_norm
+ '0.0042'
+
+From these results, we see that the observed value for I is significantly higher than its expected value,
+after the adjustment for the differences in population.
+
+.. _geary:
+
+Geary's C
+---------
+The fourth statistic for global spatial autocorrelation implemented in PySAL is Geary's C:
+
+.. math::
+
+ C=\frac{(n-1)}{2S_0} \sum_i\sum_j w_{i,j} (y_i-y_j)^2 / \sum_i z_i^2
+
+with all the terms defined as above. Applying this to the St. Louis data:
+
+.. doctest::
+
+ >>> np.random.seed(12345)
+ >>> f = pysal.open(pysal.examples.get_path("stl_hom.txt"))
+ >>> y = np.array(f.by_col['HR8893'])
+ >>> w = pysal.open(pysal.examples.get_path("stl.gal")).read()
+ >>> gc = pysal.Geary(y, w)
+ >>> "%.3f"%gc.C
+ '0.597'
+ >>> gc.EC
+ 1.0
+ >>> "%.3f"%gc.z_norm
+ '-5.449'
+
+we see that the statistic :math:`C` is significantly lower than its expected
+value :math:`EC`. Although the sign of the standardized statistic is negative (in contrast to what held for :math:`I`, the interpretation is the same, namely evidence of strong positive spatial autocorrelation in the homicide rates.
+
+Similar to what we saw for Moran's I, we can base inference on Geary's :math:`C` using
+random spatial permutations, which are actually run as a default with the
+number of permutations=999 (this is why we set the seed of the random number
+generator to 12345 to replicate the result):
+
+.. doctest::
+
+ >>> gc.p_sim
+ 0.001
+
+which indicates that none of the C values from the permuted samples was as extreme as our observed value.
+
+Getis and Ord's G
+-----------------
+The last statistic for global spatial autcorrelation implemented in PySAL is Getis and Ord's G:
+
+.. math::
+
+ G(d)=\frac{\sum_i\sum_j w_{i,j}(d) y_i y_j}{\sum_i\sum_j y_i y_j}
+
+where :math:`d` is a threshold distance used to define a spatial :doc:`weight <weights>`.
+Only :class:`pysal.weights.Distance.DistanceBand` weights objects are applicable to Getis and Ord's G.
+Applying this to the St. Louis data:
+
+.. doctest::
+
+ >>> dist_w = pysal.threshold_binaryW_from_shapefile('../pysal/examples/stl_hom.shp',0.6)
+ >>> dist_w.transform = "B"
+ >>> from pysal.esda.getisord import G
+ >>> g = G(y, dist_w)
+ >>> print g.G
+ 0.103483215873
+ >>> print g.EG
+ 0.0752580752581
+ >>> print g.z_norm
+ 3.28090342959
+ >>> print g.p_norm
+ 0.000517375830488
+
+Although we switched the contiguity-based weights object into another distance-based one,
+we see that the statistic :math:`G` is significantly higher than its expected
+value :math:`EG` under the assumption of normality for the homicide rates.
+
+Similar to what we saw for Moran's I and Geary's C, we can base inference on Getis and Ord's G using random spatial permutations:
+
+.. doctest::
+
+ >>> np.random.seed(12345)
+ >>> g = G(y, dist_w, permutations=9999)
+ >>> print g.p_z_sim
+ 0.000564384586974
+ >>> print g.p_sim
+ 0.0065
+
+with the first p-value based on a z-transform of the observed G relative to the
+distribution of values obtained in the permutations, and the second based on
+the cumulative probability of the observed value in the empirical distribution.
+
+Local Autocorrelation
+=====================
+
+.. _lisa:
+
+To measure local autocorrelation quantitatively,
+PySAL implements Local Indicators of Spatial Association (LISAs) for Moran's I and Getis and Ord's G.
+
+Local Moran's I
+----------------
+
+PySAL implements local Moran's I as follows:
+
+.. math::
+
+ I_i = \sum_j z_i w_{i,j} z_j / \sum_i z_i z_i
+
+which results in :math:`n` values of local spatial autocorrelation, 1 for each spatial unit. Continuing on with the St. Louis example, the LISA statistics are obtained with:
+
+.. doctest::
+
+ >>> f = pysal.open(pysal.examples.get_path("stl_hom.txt"))
+ >>> y = np.array(f.by_col['HR8893'])
+ >>> w = pysal.open(pysal.examples.get_path("stl.gal")).read()
+ >>> np.random.seed(12345)
+ >>> lm = pysal.Moran_Local(y,w)
+ >>> lm.n
+ 78
+ >>> len(lm.Is)
+ 78
+
+thus we see 78 LISAs are stored in the vector lm.Is. Inference about these values is obtained through conditional randomization [#]_ which leads to pseudo p-values for each LISA:
+
+.. doctest::
+
+ >>> lm.p_sim
+ array([ 0.176, 0.073, 0.405, 0.267, 0.332, 0.057, 0.296, 0.242,
+ 0.055, 0.062, 0.273, 0.488, 0.44 , 0.354, 0.415, 0.478,
+ 0.473, 0.374, 0.415, 0.21 , 0.161, 0.025, 0.338, 0.375,
+ 0.285, 0.374, 0.208, 0.3 , 0.373, 0.411, 0.478, 0.414,
+ 0.009, 0.429, 0.269, 0.015, 0.005, 0.002, 0.077, 0.001,
+ 0.088, 0.459, 0.435, 0.365, 0.231, 0.017, 0.033, 0.04 ,
+ 0.068, 0.101, 0.284, 0.309, 0.113, 0.457, 0.045, 0.269,
+ 0.118, 0.346, 0.328, 0.379, 0.342, 0.39 , 0.376, 0.467,
+ 0.357, 0.241, 0.26 , 0.401, 0.185, 0.172, 0.248, 0.4 ,
+ 0.482, 0.159, 0.373, 0.455, 0.083, 0.128])
+
+To identify the significant [#]_ LISA values we can use numpy indexing:
+
+.. doctest::
+
+ >>> sig = lm.p_sim<0.05
+ >>> lm.p_sim[sig]
+ array([ 0.025, 0.009, 0.015, 0.005, 0.002, 0.001, 0.017, 0.033,
+ 0.04 , 0.045])
+
+and then use this indexing on the q attribute to find out which quadrant of the Moran scatter plot each of the significant values is contained in:
+
+.. doctest::
+
+ >>> lm.q[sig]
+ array([4, 1, 3, 1, 3, 1, 1, 3, 3, 3])
+
+As in the case of global Moran's I, when the variable of interest is rates based on populations with different sizes,
+we need to account for the differences among population to estimate local Moran's Is.
+Continuing on with the SIDS example above, the adjusted local Moran's Is are obtained with:
+
+.. doctest::
+
+ >>> f = pysal.open(pysal.examples.get_path("sids2.dbf"))
+ >>> b = np.array(f.by_col('BIR79'))
+ >>> e = np.array(f.by_col('SID79'))
+ >>> w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
+ >>> np.random.seed(12345)
+ >>> lm = pysal.esda.moran.Moran_Local_Rate(e, b, w)
+ >>> lm.Is[:10]
+ array([-0.13452366, -1.21133985, 0.05019761, 0.06127125, -0.12627466,
+ 0.23497679, 0.26345855, -0.00951288, -0.01517879, -0.34513514])
+
+As demonstrated above, significant Moran's Is can be identified by using numpy indexing:
+
+.. doctest::
+
+ >>> sig = lm.p_sim<0.05
+ >>> lm.p_sim[sig]
+ array([ 0.021, 0.04 , 0.047, 0.015, 0.001, 0.017, 0.032, 0.031,
+ 0.019, 0.014, 0.004, 0.048, 0.003])
+
+
+Local G and G*
+--------------
+
+Getis and Ord's G can be localized in two forms: :math:`G_i` and :math:`G^*_i`.
+
+.. math::
+
+ G_i(d) = \frac{\sum_j w_{i,j}(d) y_j - W_i\bar{y}(i)}{s(i)\{[(n-1)S_{1i} - W^2_i]/(n-2)\}^(1/2)}, j \neq i
+
+.. math::
+
+ G^*_i(d) = \frac{\sum_j w_{i,j}(d) y_j - W^*_i\bar{y}}{s\{[(nS^*_{1i}) - (W^*_i)^2]/(n-1)\}^(1/2)}, j = i
+
+where we have :math:`W_i = \sum_{j \neq i} w_{i,j}(d)`, :math:`\bar{y}(i) = \frac{\sum_j y_j}{(n-1)}`, :math:`s^2(i) = \frac{\sum_j y^2_j}{(n-1)} - [\bar{y}(i)]^2`, :math:`W^*_i = W_i + w{i,i}`, :math:`S_{1i} = \sum_j w^2_{i,j} (j \neq i)`, and :math:`S^*_{1i} = \sum_j w^2_{i,j} (\forall j)`, :math:`\bar{y}` and :math:`s^2` denote the usual sample mean and variance of :math:`y`.
+
+Continuing on with the St. Louis example, the :math:`G_i` and :math:`G^*_i` statistics are obtained with:
+
+.. doctest::
+
+ >>> from pysal.esda.getisord import G_Local
+ >>> np.random.seed(12345)
+ >>> lg = G_Local(y, dist_w)
+ >>> lg.n
+ 78
+ >>> len(lg.Gs)
+ 78
+ >>> lgstar = G_Local(y, dist_w, star=True)
+ >>> lgstar.n
+ 78
+ >>> len(lgstar.Gs)
+ 78
+
+thus we see 78 :math:`G_i` and :math:`G^*_i` are stored in the vector lg.Gs and lgstar.Gs, respectively. Inference about these values is obtained through conditional randomization as in the case of local Moran's I:
+
+.. doctest::
+
+ >>> lg.p_sim
+ array([ 0.301, 0.037, 0.457, 0.011, 0.062, 0.006, 0.094, 0.163,
+ 0.075, 0.078, 0.419, 0.286, 0.138, 0.443, 0.36 , 0.484,
+ 0.434, 0.251, 0.415, 0.21 , 0.177, 0.001, 0.304, 0.042,
+ 0.285, 0.394, 0.208, 0.089, 0.244, 0.493, 0.478, 0.433,
+ 0.006, 0.429, 0.037, 0.105, 0.005, 0.216, 0.23 , 0.023,
+ 0.105, 0.343, 0.395, 0.305, 0.264, 0.017, 0.033, 0.01 ,
+ 0.001, 0.115, 0.034, 0.225, 0.043, 0.312, 0.045, 0.092,
+ 0.118, 0.428, 0.258, 0.379, 0.408, 0.39 , 0.475, 0.493,
+ 0.357, 0.298, 0.232, 0.454, 0.149, 0.161, 0.226, 0.4 ,
+ 0.482, 0.159, 0.27 , 0.423, 0.083, 0.128])
+
+
+To identify the significant :math:`G_i` values we can use numpy indexing:
+
+.. doctest::
+
+
+ >>> sig = lg.p_sim<0.05
+ >>> lg.p_sim[sig]
+ array([ 0.037, 0.011, 0.006, 0.001, 0.042, 0.006, 0.037, 0.005,
+ 0.023, 0.017, 0.033, 0.01 , 0.001, 0.034, 0.043, 0.045])
+
+Further Information
+====================
+
+For further details see the :doc:`ESDA API <../../library/esda/index>`.
+
+
+
+
+.. rubric:: Footnotes
+
+
+.. [#] Hubert, L., R. Golledge and C.M. Costanzo (1981). Generalized procedures for evaluating spatial autocorrelation. Geographical Analysis 13, 224-233.
+.. [#] Technical details and derivations can be found in A.D. Cliff and J.K. Ord (1981). Spatial Processes, Models and Applications. London, Pion, pp. 34-41.
+.. [#] Messner, S., L. Anselin, D. Hawkins, G. Deane, S. Tolnay, R. Baller (2000). An Atlas of the Spatial Patterning of County-Level Homicide, 1960-1990. Pittsburgh, PA, National Consortium on Violence Research (NCOVR)
+.. [#] Because the permutations are random, results from those presented here may vary if you replicate this example.
+.. [#] Assuncao, R. E. and Reis, E. A. 1999. A new proposal to adjust Moran's I for population density. Statistics in Medicine. 18, 2147-2162.
+.. [#] The n-1 spatial units other than i are used to generate the empirical distribution of the LISA statistics for each i.
+.. [#] Caution is required in interpreting the significance of the LISA statistics due to difficulties with multiple comparisons and a lack of independence across the individual tests. For further discussion see Anselin, L. (1995). "Local indicators of spatial association – LISA". Geographical Analysis, 27, 93-115.
+
diff --git a/doc/source/users/tutorials/dynamics.rst b/doc/source/users/tutorials/dynamics.rst
new file mode 100644
index 0000000..cd57dfe
--- /dev/null
+++ b/doc/source/users/tutorials/dynamics.rst
@@ -0,0 +1,805 @@
+.. testsetup:: *
+
+ import pysal
+ import numpy as np
+
+################
+Spatial Dynamics
+################
+
+.. contents::
+
+Introduction
+============
+
+PySAL implements a number of exploratory approaches to analyze the
+dynamics of longitudinal spatial data, or observations on fixed areal
+units over multiple time periods. Examples could include time series
+of voting patterns in US Presidential elections, time series of remote
+sensing images, labor market dynamics, regional business cycles, among
+many others. Two broad sets of spatial dynamics methods are implemented
+to analyze these data types. The first are Markov based methods, while
+the second are based on Rank dynamics.
+
+Additionally, methods are included in this module to analyze patterns of individual events which have spatial and temporal coordinates associated with them. Examples include locations and times of individual cases of disease or crimes. Methods are included here to determine if these event patterns exhibit space-time interaction.
+
+Markov Based Methods
+====================
+
+The Markov based methods include classic Markov chains and extensions of
+these approaches to deal with spatially referenced data. In what follows
+we illustrate the functionality of these Markov methods. Readers
+interested in the methodological foundations of these approaches are
+directed to [#]_.
+
+Classic Markov
+--------------
+
+We start with a look at a simple example of classic Markov methods
+implemented in PySAL. A Markov chain may be in one of :math:`k` different
+states at any point in time. These states are exhaustive and mutually
+exclusive. For example, if one had a time series of remote sensing images
+used to develop land use classifications, then the states could be defined
+as the specific land use classes and interest would center on the
+transitions in and out of different classes for each pixel.
+
+For example, let's construct a small artificial chain consisting of 3 states
+(a,b,c) and 5 different pixels at three different points in time:
+
+
+.. doctest::
+ :options: +NORMALIZE_WHITESPACE
+
+ >>> import pysal
+ >>> import numpy as np
+ >>> c = np.array([['b','a','c'],['c','c','a'],['c','b','c'],['a','a','b'],['a','b','c']])
+ >>> c
+ array([['b', 'a', 'c'],
+ ['c', 'c', 'a'],
+ ['c', 'b', 'c'],
+ ['a', 'a', 'b'],
+ ['a', 'b', 'c']],
+ dtype='|S1')
+
+
+So the first pixel was in class 'b' in period 1, class 'a' in period 2,
+and class 'c' in period 3. We can summarize the overall transition
+dynamics for the set of pixels by treating it as a Markov chain:
+
+.. doctest::
+ :options: +NORMALIZE_WHITESPACE
+
+ >>> m = pysal.Markov(c)
+ >>> m.classes
+ array(['a', 'b', 'c'],
+ dtype='|S1')
+
+
+The Markov instance m has an attribute class extracted from the chain -
+the assumption is that the observations are on the rows of the input and
+the different points in time on the columns. In addition to extracting the
+classes as an attribute, our Markov instance will also have a transitions
+matrix:
+
+.. doctest::
+
+ >>> m.transitions
+ array([[ 1., 2., 1.],
+ [ 1., 0., 2.],
+ [ 1., 1., 1.]])
+
+indicating that of the four pixels that began a transition interval in
+class 'a', 1 remained in that class, 2 transitioned to class 'b' and 1
+transitioned to class 'c'.
+
+This simple example illustrates the basic creation of a Markov instance,
+but the small sample size makes it unrealistic for the more advanced
+features of this approach. For a larger example, we will look at an
+application of Markov methods to understanding regional income dynamics in
+the US. Here we will load in data on per capita income observed annually
+from 1929 to 2010 for the lower 48 US states:
+
+.. doctest::
+
+ >>> f = pysal.open("../pysal/examples/usjoin.csv")
+ >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)])
+ >>> pci.shape
+ (81, 48)
+
+The first row of the array is the per capita income for the first year:
+
+.. doctest::
+
+ >>> pci[0, :]
+ array([ 323, 600, 310, 991, 634, 1024, 1032, 518, 347, 507, 948,
+ 607, 581, 532, 393, 414, 601, 768, 906, 790, 599, 286,
+ 621, 592, 596, 868, 686, 918, 410, 1152, 332, 382, 771,
+ 455, 668, 772, 874, 271, 426, 378, 479, 551, 634, 434,
+ 741, 460, 673, 675])
+
+In order to apply the classic Markov approach to this series, we first
+have to discretize the distribution by defining our classes. There are
+many ways to do this, but here we will use the quintiles for each annual
+income distribution to define the classes:
+
+.. doctest::
+
+ >>> q5 = np.array([pysal.Quantiles(y).yb for y in pci]).transpose()
+ >>> q5.shape
+ (48, 81)
+ >>> q5[:, 0]
+ array([0, 2, 0, 4, 2, 4, 4, 1, 0, 1, 4, 2, 2, 1, 0, 1, 2, 3, 4, 4, 2, 0, 2,
+ 2, 2, 4, 3, 4, 0, 4, 0, 0, 3, 1, 3, 3, 4, 0, 1, 0, 1, 2, 2, 1, 3, 1,
+ 3, 3])
+
+A number of things need to be noted here. First, we are relying on the
+classification methods in PySAL for defining our quintiles. The class
+Quantiles uses quintiles as the default and will create an instance of
+this class that has multiple attributes, the one we are extracting in the
+first line is yb - the class id for each observation. The second thing to
+note is the transpose operator which gets our resulting array q5 in the
+proper structure required for use of Markov. Thus we see that the first
+spatial unit (Alabama with an income of 323) fell in the first quintile
+in 1929, while the last unit (Wyoming with an income of 675) fell in the
+fourth quintile [#]_.
+
+So now we have a time series for each state of its quintile membership.
+For example, Colorado's quintile time series is:
+
+.. doctest::
+
+ >>> q5[4, :]
+ array([2, 3, 2, 2, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 2, 3, 2, 3,
+ 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 3, 3, 3, 3, 3, 3,
+ 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4,
+ 4, 4, 4, 4, 4, 3, 3, 3, 4, 3, 3, 3])
+
+indicating that it has occupied the 3rd, 4th and 5th quintiles in the
+distribution at different points in time. To summarize the transition
+dynamics for all units, we instantiate a Markov object:
+
+.. doctest::
+
+ >>> m5 = pysal.Markov(q5)
+ >>> m5.transitions
+ array([[ 729., 71., 1., 0., 0.],
+ [ 72., 567., 80., 3., 0.],
+ [ 0., 81., 631., 86., 2.],
+ [ 0., 3., 86., 573., 56.],
+ [ 0., 0., 1., 57., 741.]])
+
+Assuming we can treat these transitions as a first order Markov chain, we can estimate
+the transition probabilities:
+
+.. doctest::
+
+ >>> m5.p
+ matrix([[ 0.91011236, 0.0886392 , 0.00124844, 0. , 0. ],
+ [ 0.09972299, 0.78531856, 0.11080332, 0.00415512, 0. ],
+ [ 0. , 0.10125 , 0.78875 , 0.1075 , 0.0025 ],
+ [ 0. , 0.00417827, 0.11977716, 0.79805014, 0.07799443],
+ [ 0. , 0. , 0.00125156, 0.07133917, 0.92740926]])
+
+as well as the long run steady state distribution:
+
+.. doctest::
+
+ >>> m5.steady_state
+ matrix([[ 0.20774716],
+ [ 0.18725774],
+ [ 0.20740537],
+ [ 0.18821787],
+ [ 0.20937187]])
+
+With the transition probability matrix in hand, we can estimate the first
+mean passage time:
+
+.. doctest::
+
+ >>> pysal.ergodic.fmpt(m5.p)
+ matrix([[ 4.81354357, 11.50292712, 29.60921231, 53.38594954,
+ 103.59816743],
+ [ 42.04774505, 5.34023324, 18.74455332, 42.50023268,
+ 92.71316899],
+ [ 69.25849753, 27.21075248, 4.82147603, 25.27184624,
+ 75.43305672],
+ [ 84.90689329, 42.85914824, 17.18082642, 5.31299186,
+ 51.60953369],
+ [ 98.41295543, 56.36521038, 30.66046735, 14.21158356,
+ 4.77619083]])
+
+Thus, for a state with income in the first quintile, it takes on average
+11.5 years for it to first enter the second quintile, 29.6 to get to the
+third quintile, 53.4 years to enter the fourth, and 103.6 years to reach
+the richest quintile.
+
+
+Spatial Markov
+--------------
+
+Thus far we have treated all the spatial units as independent to estimate
+the transition probabilities. This hides a number of implicit assumptions.
+First, the transition dynamics are assumed to hold for all units and for
+all time periods. Second, interactions between the transitions of
+individual units are ignored. In other words regional context may be
+important to understand regional income dynamics, but the classic Markov
+approach is silent on this issue.
+
+PySAL includes a number of spatially explicit extensions to the Markov
+framework. The first is the spatial Markov class that we illustrate here.
+We first are going to transform the income series to relative incomes (by
+standardizing by each period by the mean):
+
+.. doctest::
+
+ >>> import pysal
+ >>> f = pysal.open("../pysal/examples/usjoin.csv")
+ >>> pci = np.array([f.by_col[str(y)] for y in range(1929, 2010)])
+ >>> pci = pci.transpose()
+ >>> rpci = pci / (pci.mean(axis = 0))
+
+
+Next, we require a spatial weights object, and here we will create one
+from an external GAL file:
+
+.. doctest::
+
+ >>> w = pysal.open("../pysal/examples/states48.gal").read()
+ >>> w.transform = 'r'
+
+Finally, we create an instance of the Spatial Markov class using 5 states
+for the chain:
+
+.. doctest::
+
+ >>> sm = pysal.Spatial_Markov(rpci, w, fixed = True, k = 5)
+
+Here we are keeping the quintiles fixed, meaning the data are pooled over
+space and time and the quintiles calculated for the pooled data. This is
+why we first transformed the data to relative incomes. We can next
+examine the global transition probability matrix for relative incomes:
+
+.. doctest::
+
+ >>> sm.p
+ matrix([[ 0.91461837, 0.07503234, 0.00905563, 0.00129366, 0. ],
+ [ 0.06570302, 0.82654402, 0.10512484, 0.00131406, 0.00131406],
+ [ 0.00520833, 0.10286458, 0.79427083, 0.09505208, 0.00260417],
+ [ 0. , 0.00913838, 0.09399478, 0.84856397, 0.04830287],
+ [ 0. , 0. , 0. , 0.06217617, 0.93782383]])
+
+The Spatial Markov allows us to compare the global transition dynamics to
+those conditioned on regional context. More specifically, the transition
+dynamics are split across economies who have spatial lags in different
+quintiles at the beginning of the year. In our example we have 5 classes,
+so 5 different conditioned transition probability matrices are estimated:
+
+.. doctest::
+
+ >>> for p in sm.P:
+ ... print p
+ ...
+ [[ 0.96341463 0.0304878 0.00609756 0. 0. ]
+ [ 0.06040268 0.83221477 0.10738255 0. 0. ]
+ [ 0. 0.14 0.74 0.12 0. ]
+ [ 0. 0.03571429 0.32142857 0.57142857 0.07142857]
+ [ 0. 0. 0. 0.16666667 0.83333333]]
+ [[ 0.79831933 0.16806723 0.03361345 0. 0. ]
+ [ 0.0754717 0.88207547 0.04245283 0. 0. ]
+ [ 0.00537634 0.06989247 0.8655914 0.05913978 0. ]
+ [ 0. 0. 0.06372549 0.90196078 0.03431373]
+ [ 0. 0. 0. 0.19444444 0.80555556]]
+ [[ 0.84693878 0.15306122 0. 0. 0. ]
+ [ 0.08133971 0.78947368 0.1291866 0. 0. ]
+ [ 0.00518135 0.0984456 0.79274611 0.0984456 0.00518135]
+ [ 0. 0. 0.09411765 0.87058824 0.03529412]
+ [ 0. 0. 0. 0.10204082 0.89795918]]
+ [[ 0.8852459 0.09836066 0. 0.01639344 0. ]
+ [ 0.03875969 0.81395349 0.13953488 0. 0.00775194]
+ [ 0.0049505 0.09405941 0.77722772 0.11881188 0.0049505 ]
+ [ 0. 0.02339181 0.12865497 0.75438596 0.09356725]
+ [ 0. 0. 0. 0.09661836 0.90338164]]
+ [[ 0.33333333 0.66666667 0. 0. 0. ]
+ [ 0.0483871 0.77419355 0.16129032 0.01612903 0. ]
+ [ 0.01149425 0.16091954 0.74712644 0.08045977 0. ]
+ [ 0. 0.01036269 0.06217617 0.89637306 0.03108808]
+ [ 0. 0. 0. 0.02352941 0.97647059]]
+
+
+The probability of a poor state remaining poor is 0.963 if their
+neighbors are in the 1st quintile and 0.798 if their neighbors are
+in the 2nd quintile. The probability of a rich economy remaining
+rich is 0.977 if their neighbors are in the 5th quintile, but if their
+neighbors are in the 4th quintile this drops to 0.903.
+
+We can also explore the different steady state distributions implied by
+these different transition probabilities:
+
+.. doctest::
+
+ >>> sm.S
+ array([[ 0.43509425, 0.2635327 , 0.20363044, 0.06841983, 0.02932278],
+ [ 0.13391287, 0.33993305, 0.25153036, 0.23343016, 0.04119356],
+ [ 0.12124869, 0.21137444, 0.2635101 , 0.29013417, 0.1137326 ],
+ [ 0.0776413 , 0.19748806, 0.25352636, 0.22480415, 0.24654013],
+ [ 0.01776781, 0.19964349, 0.19009833, 0.25524697, 0.3372434 ]])
+
+The long run distribution for states with poor (rich) neighbors has
+0.435 (0.018) of the values in the first quintile, 0.263 (0.200) in
+the second quintile, 0.204 (0.190) in the third, 0.0684 (0.255) in the
+fourth and 0.029 (0.337) in the fifth quintile. And, finally the first mean
+passage times:
+
+.. doctest::
+
+ >>> for f in sm.F:
+ ... print f
+ ...
+ [[ 2.29835259 28.95614035 46.14285714 80.80952381 279.42857143]
+ [ 33.86549708 3.79459555 22.57142857 57.23809524 255.85714286]
+ [ 43.60233918 9.73684211 4.91085714 34.66666667 233.28571429]
+ [ 46.62865497 12.76315789 6.25714286 14.61564626 198.61904762]
+ [ 52.62865497 18.76315789 12.25714286 6. 34.1031746 ]]
+ [[ 7.46754205 9.70574606 25.76785714 74.53116883 194.23446197]
+ [ 27.76691978 2.94175577 24.97142857 73.73474026 193.4380334 ]
+ [ 53.57477715 28.48447637 3.97566318 48.76331169 168.46660482]
+ [ 72.03631562 46.94601483 18.46153846 4.28393653 119.70329314]
+ [ 77.17917276 52.08887197 23.6043956 5.14285714 24.27564033]]
+ [[ 8.24751154 6.53333333 18.38765432 40.70864198 112.76732026]
+ [ 47.35040872 4.73094099 11.85432099 34.17530864 106.23398693]
+ [ 69.42288828 24.76666667 3.794921 22.32098765 94.37966594]
+ [ 83.72288828 39.06666667 14.3 3.44668119 76.36702977]
+ [ 93.52288828 48.86666667 24.1 9.8 8.79255406]]
+ [[ 12.87974382 13.34847151 19.83446328 28.47257282 55.82395142]
+ [ 99.46114206 5.06359731 10.54545198 23.05133495 49.68944423]
+ [ 117.76777159 23.03735526 3.94436301 15.0843986 43.57927247]
+ [ 127.89752089 32.4393006 14.56853107 4.44831643 31.63099455]
+ [ 138.24752089 42.7893006 24.91853107 10.35 4.05613474]]
+ [[ 56.2815534 1.5 10.57236842 27.02173913 110.54347826]
+ [ 82.9223301 5.00892857 9.07236842 25.52173913 109.04347826]
+ [ 97.17718447 19.53125 5.26043557 21.42391304 104.94565217]
+ [ 127.1407767 48.74107143 33.29605263 3.91777427 83.52173913]
+ [ 169.6407767 91.24107143 75.79605263 42.5 2.96521739]]
+
+States with incomes in the first quintile with neighbors in the
+first quintile return to the first quintile after 2.298 years, after
+leaving the first quintile. They enter the fourth quintile
+80.810 years after leaving the first quintile, on average.
+Poor states within neighbors in the fourth quintile return to the
+first quintile, on average, after 12.88 years, and would enter the
+fourth quintile after 28.473 years.
+
+
+LISA Markov
+-----------
+
+The Spatial Markov conditions the transitions on the value of the spatial
+lag for an observation at the beginning of the transition period. An
+alternative approach to spatial dynamics is to consider the joint
+transitions of an observation and its spatial lag in the distribution.
+By exploiting the form of the static :ref:`LISA <lisa>` and embedding it
+in a dynamic context we develop the LISA Markov in which the states of the
+chain are defined as the four quadrants in the Moran scatter plot.
+Continuing on with our US example:
+
+.. doctest::
+
+ >>> import numpy as np
+ >>> f = pysal.open("../pysal/examples/usjoin.csv")
+ >>> pci = np.array([f.by_col[str(y)] for y in range(1929, 2010)]).transpose()
+ >>> w = pysal.open("../pysal/examples/states48.gal").read()
+ >>> lm = pysal.LISA_Markov(pci, w)
+ >>> lm.classes
+ array([1, 2, 3, 4])
+
+The LISA transitions are:
+
+.. doctest::
+
+ >>> lm.transitions
+ array([[ 1.08700000e+03, 4.40000000e+01, 4.00000000e+00,
+ 3.40000000e+01],
+ [ 4.10000000e+01, 4.70000000e+02, 3.60000000e+01,
+ 1.00000000e+00],
+ [ 5.00000000e+00, 3.40000000e+01, 1.42200000e+03,
+ 3.90000000e+01],
+ [ 3.00000000e+01, 1.00000000e+00, 4.00000000e+01,
+ 5.52000000e+02]])
+
+
+and the estimated transition probability matrix is:
+
+.. doctest::
+
+ >>> lm.p
+ matrix([[ 0.92985458, 0.03763901, 0.00342173, 0.02908469],
+ [ 0.07481752, 0.85766423, 0.06569343, 0.00182482],
+ [ 0.00333333, 0.02266667, 0.948 , 0.026 ],
+ [ 0.04815409, 0.00160514, 0.06420546, 0.88603531]])
+
+
+
+The diagonal elements indicate the staying probabilities and we see that
+there is greater mobility for observations in quadrants 1 and 3 than 2 and
+4.
+
+The implied long run steady state distribution of the chain is
+
+.. doctest::
+
+ >>> lm.steady_state
+ matrix([[ 0.28561505],
+ [ 0.14190226],
+ [ 0.40493672],
+ [ 0.16754598]])
+
+again reflecting the dominance of quadrants 1 and 3 (positive
+autocorrelation). [#]_ Finally the first mean passage time for the LISAs is:
+
+.. doctest::
+
+ >>> pysal.ergodic.fmpt(lm.p)
+ matrix([[ 3.50121609, 37.93025465, 40.55772829, 43.17412009],
+ [ 31.72800152, 7.04710419, 28.68182751, 49.91485137],
+ [ 52.44489385, 47.42097495, 2.46952168, 43.75609676],
+ [ 38.76794022, 51.51755827, 26.31568558, 5.96851095]])
+
+
+
+Rank Based Methods
+==================
+
+The second set of spatial dynamic methods in PySAL are based on rank
+correlations and spatial extensions of the classic rank statistics.
+
+Spatial Rank Correlation
+------------------------
+
+Kendall's :math:`\tau` is based on a comparison of the number of pairs of :math:`n`
+observations that have concordant ranks between two variables. For spatial
+dynamics in PySAL, the two variables in question are the values of an attribute
+measured at two points in time over :math:`n` spatial units. This classic
+measure of rank correlation indicates how much relative stability there
+has been in the map pattern over the two periods.
+
+The spatial :math:`\tau` decomposes these pairs into those that are
+spatial neighbors and those that are not, and examines whether the rank
+correlation is different between the two sets. [4]_ To illustrate this we
+turn to the case of regional incomes in Mexico over the 1940 to 2010
+period:
+
+.. doctest::
+
+ >>> import pysal
+ >>> f = pysal.open("../pysal/examples/mexico.csv")
+ >>> vnames = ["pcgdp%d"%dec for dec in range(1940, 2010, 10)]
+ >>> y = np.transpose(np.array([f.by_col[v] for v in vnames]))
+
+We also introduce the concept of regime weights that defines the neighbor
+set as those spatial units belonging to the same region. In this example
+the variable "esquivel99" represents a categorical classification of
+Mexican states into regions:
+
+.. doctest::
+
+ >>> regime = np.array(f.by_col['esquivel99'])
+ >>> w = pysal.weights.block_weights(regime)
+ >>> np.random.seed(12345)
+
+Now we will calculate the spatial tau for decade transitions from 1940 through
+2000 and report the observed spatial tau against that expected if the rank
+changes were randomly distributed in space by using 99 permutations:
+
+.. doctest::
+
+ >>> res=[pysal.SpatialTau(y[:,i],y[:,i+1],w,99) for i in range(6)]
+ >>> for r in res:
+ ... ev = r.taus.mean()
+ ... "%8.3f %8.3f %8.3f"%(r.tau_spatial, ev, r.tau_spatial_psim)
+ ...
+ ' 0.397 0.659 0.010'
+ ' 0.492 0.706 0.010'
+ ' 0.651 0.772 0.020'
+ ' 0.714 0.752 0.210'
+ ' 0.683 0.705 0.270'
+ ' 0.810 0.819 0.280'
+
+The observed level of spatial concordance during the 1940-50 transition was
+0.397 which is significantly lower (p=0.010) than the average level of spatial
+concordance (0.659) from randomly permuted incomes in Mexico. Similar patterns
+are found for the next two transition periods as well. In other words the
+amount of rank concordance is significantly distinct between pairs of
+observations that are geographical neighbors and those that are not in these
+first three transition periods. This reflects the greater degree of spatial
+similarity within rather than between the regimes making the
+discordant pairs dominated by neighboring pairs.
+
+Rank Decomposition
+------------------
+
+For a sequence of time periods, :math:`\theta` measures the extent to which rank
+changes for a variable measured over :math:`n` locations are in the same direction
+within mutually exclusive and exhaustive partitions (regimes) of the
+:math:`n` locations.
+
+Theta is defined as the sum of the absolute sum of rank changes within
+the regimes over the sum of all absolute rank changes. [#]_
+
+.. doctest::
+
+ >>> import pysal
+ >>> f = pysal.open("../pysal/examples/mexico.csv")
+ >>> vnames = ["pcgdp%d"%dec for dec in range(1940, 2010, 10)]
+ >>> y = np.transpose(np.array([f.by_col[v] for v in vnames]))
+ >>> regime = np.array(f.by_col['esquivel99'])
+ >>> np.random.seed(10)
+ >>> t = pysal.Theta(y, regime, 999)
+ >>> t.theta
+ array([[ 0.41538462, 0.28070175, 0.61363636, 0.62222222, 0.33333333,
+ 0.47222222]])
+ >>> t.pvalue_left
+ array([ 0.307, 0.077, 0.823, 0.552, 0.045, 0.735])
+
+
+Space-Time Interaction Tests
+============================
+
+The third set of spatial dynamic methods in PySAL are global tests of space-time interaction. The purpose of these tests is to detect clustering within space-time event patterns. These patterns are composed of unique events that are labeled with spatial and temporal coordinates. The tests are designed to detect clustering of events in both space and time beyond "any purely spatial or purely temporal clustering" [#]_, that is, to determine if the events are "interacting." Essentially, the [...]
+
+
+Knox Test
+---------
+
+The Knox test for space-time interaction employs user-defined critical thresholds in space and time to define proximity between events. All pairs of events are examined to determine if the distance between them in space and time is within the respective thresholds. The Knox statistic is calculated as the total number of event pairs where the spatial and temporal distances separating the pair are within the specified thresholds [#]_. If interaction is present, the test statistic will be l [...]
+
+Formally, the specification of the Knox test is given as:
+
+.. math::
+
+ X=\sum_{i}^{n}\sum_{j}^{n}a_{ij}^{s}a_{ij}^{t}\\
+
+.. math::
+
+ \begin{align} \nonumber
+ a_{ij}^{s} &=
+ \begin{cases}
+ 1, & \text{if $d^s_{ij}<\delta$}\\
+ 0, & \text{otherwise}
+ \end{cases}
+ \end{align}
+
+.. math::
+
+ \begin{align} \nonumber
+ a_{ij}^{t} &=
+ \begin{cases}
+ 1, & \text{if $d^t_{ij}<\tau$}\\
+ 0, & \text{otherwise}
+ \end{cases}
+ \end{align}
+
+Where :math:`n` = number of events, :math:`a^{s}` = adjacency in space, :math:`a^{t}` = adjacency in time, :math:`d^{s}` = distance in space, and :math:`d^{t}` = distance in time. Critical space and time distance thresholds are defined as :math:`\delta` and :math:`\tau`, respectively.
+
+We illustrate the use of the Knox test using data from a study of Burkitt's Lymphoma in Uganda during the period 1961-75 [#]_. We start by importing Numpy, PySAL and the interaction module:
+
+
+.. doctest::
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> import pysal.spatial_dynamics.interaction as interaction
+ >>> np.random.seed(100)
+
+The example data are then read in and used to create an instance of SpaceTimeEvents. This reformats the data so the test can be run by PySAL. This class requires the input of a point shapefile. The shapefile must contain a column that includes a timestamp for each point in the dataset. The class requires that the user input a path to an appropriate shapefile and the name of the column containing the timestamp. In this example, the appropriate column name is 'T'.
+
+.. doctest::
+
+ >>> path = "../pysal/examples/burkitt"
+ >>> events = interaction.SpaceTimeEvents(path,'T')
+
+Next, we run the Knox test with distance and time thresholds of 20 and 5,respectively. This counts the events that are closer than 20 units in space, and 5 units in time.
+
+.. doctest::
+
+ >>> result = interaction.knox(events.space, events.t ,delta=20,tau=5,permutations=99)
+
+Finally we examine the results. We call the statistic from the results dictionary. This reports that there are 13 events close in both space and time, based on our threshold definitions.
+
+.. doctest::
+
+ >>> print(result['stat'])
+ 13
+
+Then we look at the pseudo-significance of this value, calculated by permuting the timestamps and rerunning the statistics. Here, 99 permutations were used, but an alternative number can be specified by the user. In this case, the results indicate that we fail to reject the null hypothesis of no space-time interaction using an alpha value of 0.05.
+
+.. doctest::
+
+ >>> print("%2.2f"%result['pvalue'])
+ 0.17
+
+
+Modified Knox Test
+------------------
+
+A modification to the Knox test was proposed by Baker [#]_. Baker's modification measures the difference between the original observed Knox statistic and its expected value. This difference serves as the test statistic. Again, the significance of this statistic is assessed using a Monte Carlo permutation procedure.
+
+
+.. math::
+
+ T=\frac{1}{2}\bigg(\sum_{i=1}^{n}\sum_{j=1}^{n}f_{ij}g_{ij} - \frac{1}{n-1}\sum_{k=1}^{n}\sum_{l=1}^{n}\sum_{j=1}^{n}f_{kj}g_{lj}\bigg)\\
+
+
+Where :math:`n` = number of events, :math:`f` = adjacency in space, :math:`g` = adjacency in time (calculated in a manner equivalent to :math:`a^{s}` and :math:`a^{t}` above in the Knox test). The first part of this statistic is equivalent to the original Knox test, while the second part is the expected value under spatio-temporal randomness.
+
+Here we illustrate the use of the modified Knox test using the data on Burkitt's Lymphoma cases in Uganda from above. We start by importing Numpy, PySAL and the interaction module. Next the example data are then read in and used to create an instance of SpaceTimeEvents.
+
+
+.. doctest::
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> import pysal.spatial_dynamics.interaction as interaction
+ >>> np.random.seed(100)
+ >>> path = "../pysal/examples/burkitt"
+ >>> events = interaction.SpaceTimeEvents(path,'T')
+
+Next, we run the modified Knox test with distance and time thresholds of 20 and 5,respectively. This counts the events that are closer than 20 units in space, and 5 units in time.
+
+.. doctest::
+
+ >>> result = interaction.modified_knox(events.space, events.t,delta=20,tau=5,permutations=99)
+
+Finally we examine the results. We call the statistic from the results dictionary. This reports a statistic value of 2.810160.
+
+.. doctest::
+
+ >>> print("%2.8f"%result['stat'])
+ 2.81016043
+
+Next we look at the pseudo-significance of this value, calculated by permuting the timestamps and rerunning the statistics. Here, 99 permutations were used, but an alternative number can be specified by the user. In this case, the results indicate that we fail to reject the null hypothesis of no space-time interaction using an alpha value of 0.05.
+
+.. doctest::
+
+ >>> print("%2.2f"%result['pvalue'])
+ 0.11
+
+
+Mantel Test
+-----------
+
+Akin to the Knox test in its simplicity, the Mantel test keeps the distance information discarded by the Knox test. The unstandardized Mantel statistic is calculated by summing the product of the spatial and temporal distances between all event pairs [#]_. To prevent multiplication by 0 in instances of colocated or simultaneous events, Mantel proposed adding a constant to the distance measurements. Additionally, he suggested a reciprocal transform of the resulting distance measurement to [...]
+
+.. math::
+
+ Z=\sum_{i}^{n}\sum_{j}^{n}(d_{ij}^{s}+c)^{p}(d_{ij}^{t}+c)^{p}
+
+Where, again, :math:`d^{s}` and :math:`d^{t}` denote distance in space and time, respectively. The constant, :math:`c`, and the power, :math:`p`, are parameters set by the user. The default values are 0 and 1, respectively. A standardized version of the Mantel test is implemented here in PySAL, however. The standardized statistic (:math:`r`) is a measure of correlation between the spatial and temporal distance matrices. This is expressed formally as:
+
+.. math::
+
+ r=\frac{1}{n^2-n-1}\sum_{i}^{n}\sum_{j}^{n}\Bigg[\frac{d_{ij}^{s}-\bar{d^{s}}}{\sigma_{d^{s}}}\Bigg] \Bigg[\frac{d_{ij}^{t}-\bar{d^{t}}}{\sigma_{d^{t}}}\Bigg]
+
+Where :math:`\bar{d^{s}}` refers to the average distance in space, and :math:`\bar{d^{t}}` the average distance in time. For notational convenience :math:`\sigma_{d^{t}}` and :math:`\sigma_{d^{t}}` refer to the sample (not population) standard deviations, for distance in space and time, respectively. The same constant and power transformations may also be applied to the spatial and temporal distance matrices employed by the standardized Mantel. Significance is determined through a Monte [...]
+
+Again, we use the Burkitt's Lymphoma data to illustrate the test. We start with the usual imports and read in the example data.
+
+
+.. doctest::
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> import pysal.spatial_dynamics.interaction as interaction
+ >>> np.random.seed(100)
+ >>> path = "../pysal/examples/burkitt"
+ >>> events = interaction.SpaceTimeEvents(path,'T')
+
+The following example runs the standardized Mantel test with constants of 0 and transformations of 1, meaning the distance matrices will remain unchanged; however, as recommended by Mantel, a small constant should be added and an inverse transformation (i.e. -1) specified.
+
+.. doctest::
+
+ >>> result = interaction.mantel(events.space, events.t,99,scon=0.0,spow=1.0,tcon=0.0,tpow=1.0)
+
+Next, we examine the result of the test.
+
+.. doctest::
+
+ >>> print("%6.6f"%result['stat'])
+ 0.014154
+
+Finally, we look at the pseudo-significance of this value, calculated by permuting the timestamps and rerunning the statistic for each of the 99 permuatations. Again, note, the number of permutations can be changed by the user. According to these parameters, the results fail to reject the null hypothesis of no space-time interaction between the events.
+
+.. doctest::
+
+ >>> print("%2.2f"%result['pvalue'])
+ 0.27
+
+Jacquez Test
+------------
+
+Instead of using a set distance in space and time to determine proximity (like the Knox test) the Jacquez test employs a nearest neighbor distance approach. This allows the test to account for changes in underlying population density. The statistic is calculated as the number of event pairs that are within the set of :math:`k` nearest neighbors for each other in both space and time [#]_. Significance of this count is established using a Monte Carlo permutation method. The test is express [...]
+
+.. math::
+
+ J_{k}=\sum_{i=1}^{n} \sum_{j=1}^{n} a_{ijk}^{s}a_{ijk}^{t}\\
+
+.. math::
+
+ \begin{align} \nonumber
+ a_{ijk}^{s} =
+ \begin{cases}
+ 1, & \text{if event \emph{j} is a \emph{k} nearest neighbor of event \emph{i} in space}\\
+ 0, & \text{otherwise}
+ \end{cases}
+ \end{align}
+
+.. math::
+
+ \begin{align} \nonumber
+ a_{ijk}^{t} =
+ \begin{cases}
+ 1, & \text{if event \emph{j} is a \emph{k} nearest neighbor of event \emph{i} in time}\\
+ 0, & \text{otherwise}
+ \end{cases}
+ \end{align}
+
+Where :math:`n` = number of cases; :math:`a^{s}` = adjacency in space; :math:`a^{t}` = adjacency in time. To illustrate the test, the Burkitt's Lymphoma data are employed again. We start with the usual imports and read in the example data.
+
+.. doctest::
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> import pysal.spatial_dynamics.interaction as interaction
+ >>> np.random.seed(100)
+ >>> path = "../pysal/examples/burkitt"
+ >>> events = interaction.SpaceTimeEvents(path,'T')
+
+
+The following runs the Jacquez test on the example data for a value of :math:`k` = 3 and reports the resulting statistic. In this case, there are 13 instances where events are nearest neighbors in both space and time. The significance of this can be assessed by calling the p-value from the results dictionary. Again, there is not enough evidence to reject the null hypothesis of no space-time interaction.
+
+.. doctest::
+
+ >>> result = interaction.jacquez(events.space, events.t ,k=3,permutations=99)
+ >>> print result['stat'] # doctest: +SKIP
+ 13
+ >>> print "%3.1f"%result['pvalue'] # doctest: +SKIP
+ 0.2
+
+Spatial Dynamics API
+====================
+
+For further details see the :doc:`Spatial Dynamics API <../../library/spatial_dynamics/index>`.
+
+.. rubric:: Footnotes
+
+.. [#] Rey, S.J. 2001.
+ "`Spatial empirics for economic growth and convergence
+ <http://findarticles.com/p/articles/mi_hb4740/is_3_33/ai_n28858625/>`_",
+ 34 Geographical Analysis, 33, 195-214.
+.. [#] The states are ordered alphabetically.
+.. [#] The complex values of the steady state distribution arise from
+ complex eigenvalues in the transition probability matrix which may indicate
+ cyclicality in the chain.
+.. [#] Rey, S.J. (2004) "`Spatial dependence in the evolution of regional
+ income distributions
+ <http://econpapers.repec.org/paper/wpawuwpur/0105001.htm>`_,"
+ in A. Getis, J. Mur and H.Zoeller (eds). Spatial Econometrics and Spatial
+ Statistics. Palgrave, London, pp. 194-213.
+.. [#] Kulldorff, M. (1998). Statistical methods for spatial epidemiology: tests
+ for randomness. In Gatrell, A. and Loytonen, M., editors, GIS and
+ Health, pages 49–62. Taylor & Francis, London.
+.. [#] Tango, T. (2010). Statistical Methods for Disease Clustering. Springer,
+ New York.
+.. [#] Knox, E. (1964). The detection of space-time interactions. Journal of the
+ Royal Statistical Society. Series C (Applied Statistics), 13(1):25–30.
+.. [#] R.D. Baker. (2004). Identifying space-time disease clusters. Acta Tropica,
+ 91(3):291-299.
+.. [#] Kulldorff, M. and Hjalmars, U. (1999). The Knox method and other tests
+ for space- time interaction. Biometrics, 55(2):544–552.
+.. [#] Williams, E., Smith, P., Day, N., Geser, A., Ellice, J., and Tukei, P.
+ (1978). Space-time clustering of Burkitt’s lymphoma in the West Nile
+ district of Uganda: 1961-1975. British Journal of Cancer, 37(1):109.
+.. [#] Mantel, N. (1967). The detection of disease clustering and a generalized
+ regression approach. Cancer Research, 27(2):209–220.
+.. [#] Jacquez, G. (1996). A k nearest neighbour test for space-time
+ interaction. Statistics in Medicine, 15(18):1935–1949.
+
diff --git a/doc/source/users/tutorials/econometrics.rst b/doc/source/users/tutorials/econometrics.rst
new file mode 100644
index 0000000..878c696
--- /dev/null
+++ b/doc/source/users/tutorials/econometrics.rst
@@ -0,0 +1,23 @@
+
+
+####################
+Spatial Econometrics
+####################
+
+Comprehensive user documentation on spreg can be found in
+Anselin, L. and S.J. Rey (2014) `Modern Spatial Econometrics in Practice:
+A Guide to GeoDa, GeoDaSpace and PySAL.
+<http://www.amazon.com/Modern-Spatial-Econometrics-Practice-GeoDaSpace-ebook/dp/B00RI9I44K>`_
+GeoDa Press, Chicago.
+
+
+spreg API
+=========
+
+
+For further details see the :doc:`spreg API <../../library/spreg/index>`.
+
+
+
+
+
diff --git a/doc/source/users/tutorials/examples.rst b/doc/source/users/tutorials/examples.rst
new file mode 100644
index 0000000..1c79fd3
--- /dev/null
+++ b/doc/source/users/tutorials/examples.rst
@@ -0,0 +1,142 @@
+******************************************************************
+PySAL: Example Data Sets
+******************************************************************
+
+PySAL comes with a number of example data sets that are used in some of the
+documentation strings in the source code. All the example data sets can be
+found in the **examples** directory.
+
+10740
+=====
+Polygon shapefile for Albuquerque New Mexico.
+
+* 10740.dbf: attribute database file
+* 10740.shp: shapefile
+* 10740.shx: spatial index
+* 10740_queen.gal: queen contiguity GAL format
+* 10740_rook.gal: rook contiguity GAL format
+
+
+book
+====
+Synthetic data to illustrate spatial weights. Source: Anselin, L. and S.J. Rey (in
+progress) Spatial Econometrics: Foundations.
+
+* book.gal: rook contiguity for regular lattice
+* book.txt: attribute data for regular lattice
+
+calempdensity
+=============
+Employment density for California counties. Source: Anselin, L. and S.J. Rey (in
+progress) Spatial Econometrics: Foundations.
+
+* calempdensity.csv: data on employment and employment density in California
+ counties.
+
+chicago77
+=========
+Chicago Community Areas (n=77). Source: Anselin, L. and S.J. Rey (in
+progress) Spatial Econometrics: Foundations.
+
+* Chicago77.dbf: attribute data
+* Chicago77.shp: shapefile
+* Chicago77.shx: spatial index
+
+
+desmith
+=======
+Example data for autocorrelation analysis. Source: de Smith et al (2009)
+`Geospatial Analysis
+<http://www.spatialanalysisonline.com/output/html/MoranIandGearyC.html>`_ (Used
+with permission)
+
+* desmith.txt: attribute data for 10 spatial units
+* desmith.gal: spatial weights in GAL format
+
+juvenile
+========
+Cardiff juvenile delinquent residences.
+
+* juvenile.dbf: attribute data
+* juvenile.html: documentation
+* juvenile.shp: shapefile
+* juvenile.shx: spatial index
+* juvenile.gwt: spatial weights in GWT format
+
+mexico
+======
+State regional income Mexican states 1940-2000. Source: Rey, S.J. and M.L.
+Sastre Gutierrez. "Interregional inequality dynamics in Mexico." Spatial
+Economic Analysis. Forthcoming.
+
+* mexico.csv: attribute data
+* mexico.gal: spatial weights in GAL format
+
+rook31
+======
+Small test shapefile
+
+* rook31.dbf: attribute data
+* rook31.gal: spatia weights in GAL format
+* rook31.shp: shapefile
+* rook31.shx: spatial index
+
+
+sacramento2
+===========
+1998 and 2001 Zip Code Business Patterns (Census Bureau) for Sacramento MSA
+
+ * sacramento2.dbf
+ * sacramento2.sbn
+ * sacramento2.sbx
+ * sacramento2.shp
+ * sacramento2.shx
+
+shp_test
+========
+Sample Shapefiles used only for testing purposes. Each example include a ".shp" Shapefile, ".shx" Shapefile Index, ".dbf" DBase file, and a ".prj" ESRI Projection file.
+
+Examples include:
+
+ * Point: Example of an ESRI Shapefile of Type 1 (Point).
+ * Line: Example of an ESRI Shapefile of Type 3 (Line).
+ * Polygon: Example of an ESRI Shapefile of Type 5 (Polygon).
+
+sids2
+=====
+North Carolina county SIDS death counts and rates
+
+ * sids2.dbf: attribute data
+ * sids2.html: documentation
+ * sids2.shp: shapefile
+ * sids2.shx: spatial index
+ * sids2.gal: GAL file for spatial weights
+
+stl_hom
+=======
+Homicides and selected socio-economic characteristics for counties surrounding St Louis, MO. Data aggregated for three time periods: 1979-84 (steady decline in homicides), 1984-88 (stable period), and 1988-93 (steady increase in homicides). Source: S. Messner, L. Anselin, D. Hawkins, G. Deane, S. Tolnay, R. Baller (2000). An Atlas of the Spatial Patterning of County-Level Homicide, 1960-1990. Pittsburgh, PA, National Consortium on Violence Research (NCOVR).
+
+ * stl_hom.html: Metadata
+ * stl_hom.txt: txt file with attribute data
+ * stl_hom.wkt: A Well-Known-Text representation of the geometry.
+ * stl_hom.csv: attribute data and WKT geometry.
+ * stl.hom.gal: GAL file for spatial weights
+
+
+US Regional Incomes
+===================
+Per capita income for the lower 48 US states, 1929-2010
+
+ * us48.shp: shapefile
+ * us48.dbf: dbf for shapefile
+ * us48.shx: index for shapefile
+ * usjoin.csv: attribute data (comma delimited file)
+
+Virginia
+========
+Virginia Counties Shapefile.
+
+ * virginia.shp: Shapefile
+ * virginia.shx: shapefile index
+ * virginia.dbf: attributes
+ * virginia.prj: shapefile projection
diff --git a/doc/source/users/tutorials/fileio.rst b/doc/source/users/tutorials/fileio.rst
new file mode 100644
index 0000000..a341799
--- /dev/null
+++ b/doc/source/users/tutorials/fileio.rst
@@ -0,0 +1,429 @@
+.. _fileio:
+
+.. testsetup:: *
+
+ import pysal
+ import numpy as np
+
+******************************************
+An Overview of the FileIO system in PySAL.
+******************************************
+
+.. contents::
+
+Introduction
+============
+
+PySAL contains a new file input-output API that should be used for all file IO
+operations. The goal is to abstract file handling and return native PySAL data
+types when reading from known file types. A list of known extensions can be
+found by issuing the following command::
+
+ pysal.open.check()
+
+Note that in some cases the FileIO module will peek inside your file to
+determine its type. For example "geoda_txt" is just a unique scheme for ".txt"
+files, so when opening a ".txt" pysal will peek inside the file to determine it
+if has the necessary header information and dispatch accordingly. In the event
+that pysal does not understand your file IO operations will be dispatched to
+python's internal open.
+
+Examples: Reading files
+=======================
+
+Shapefiles
+----------
+
+.. doctest::
+
+ >>> import pysal
+ >>> shp = pysal.open('../pysal/examples/10740.shp')
+ >>> poly = shp.next()
+ >>> type(poly)
+ <class 'pysal.cg.shapes.Polygon'>
+ >>> len(shp)
+ 195
+ >>> shp.get(len(shp)-1).id
+ 195
+ >>> polys = list(shp)
+ >>> len(polys)
+ 195
+
+DBF Files
+---------
+
+.. doctest::
+
+ >>> import pysal
+ >>> db = pysal.open('../pysal/examples/10740.dbf','r')
+ >>> db.header
+ ['GIST_ID', 'FIPSSTCO', 'TRT2000', 'STFID', 'TRACTID']
+ >>> db.field_spec
+ [('N', 8, 0), ('C', 5, 0), ('C', 6, 0), ('C', 11, 0), ('C', 10, 0)]
+ >>> db.next()
+ [1, '35001', '000107', '35001000107', '1.07']
+ >>> db[0]
+ [[1, '35001', '000107', '35001000107', '1.07']]
+ >>> db[0:3]
+ [[1, '35001', '000107', '35001000107', '1.07'], [2, '35001', '000108', '35001000108', '1.08'], [3, '35001', '000109', '35001000109', '1.09']]
+ >>> db[0:5,1]
+ ['35001', '35001', '35001', '35001', '35001']
+ >>> db[0:5,0:2]
+ [[1, '35001'], [2, '35001'], [3, '35001'], [4, '35001'], [5, '35001']]
+ >>> db[-1,-1]
+ ['9712']
+
+CSV Files
+---------
+
+.. doctest::
+
+ >>> import pysal
+ >>> db = pysal.open('../pysal/examples/stl_hom.csv')
+ >>> db.header
+ ['WKT', 'NAME', 'STATE_NAME', 'STATE_FIPS', 'CNTY_FIPS', 'FIPS', 'FIPSNO', 'HR7984', 'HR8488', 'HR8893', 'HC7984', 'HC8488', 'HC8893', 'PO7984', 'PO8488', 'PO8893', 'PE77', 'PE82', 'PE87', 'RDAC80', 'RDAC85', 'RDAC90']
+ >>> db[0]
+ [['POLYGON ((-89.585220336914062 39.978794097900391,-89.581146240234375 40.094867706298828,-89.603988647460938 40.095306396484375,-89.60589599609375 40.136119842529297,-89.6103515625 40.3251953125,-89.269027709960938 40.329566955566406,-89.268562316894531 40.285579681396484,-89.154655456542969 40.285774230957031,-89.152763366699219 40.054969787597656,-89.151618957519531 39.919403076171875,-89.224777221679688 39.918678283691406,-89.411857604980469 39.918041229248047,-89.41243743896484 [...]
+ >>> fromWKT = pysal.core.util.wkt.WKTParser()
+ >>> db.cast('WKT',fromWKT)
+ >>> type(db[0][0][0])
+ <class 'pysal.cg.shapes.Polygon'>
+ >>> db[0][0][1:]
+ ['Logan', 'Illinois', 17, 107, 17107, 17107, 2.115428, 1.290722, 1.624458, 4, 2, 3, 189087, 154952, 184677, 5.10432, 6.59578, 5.832951, -0.991256, -0.940265, -0.845005]
+ >>> polys = db.by_col('WKT')
+ >>> from pysal.cg import standalone
+ >>> standalone.get_bounding_box(polys)[:]
+ [-92.70067596435547, 36.88180923461914, -87.91657257080078, 40.329566955566406]
+
+
+WKT Files
+---------
+
+.. doctest::
+
+ >>> import pysal
+ >>> wkt = pysal.open('../pysal/examples/stl_hom.wkt', 'r')
+ >>> polys = wkt.read()
+ >>> wkt.close()
+ >>> print len(polys)
+ 78
+ >>> print polys[1].centroid
+ (-91.19578469430738, 39.990883050220845)
+
+
+GeoDa Text Files
+----------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> geoda_txt = pysal.open('../pysal/examples/stl_hom.txt', 'r')
+ >>> geoda_txt.header
+ ['FIPSNO', 'HR8488', 'HR8893', 'HC8488']
+ >>> print len(geoda_txt)
+ 78
+ >>> geoda_txt.dat[0]
+ ['17107', '1.290722', '1.624458', '2']
+ >>> geoda_txt._spec
+ [<type 'int'>, <type 'float'>, <type 'float'>, <type 'int'>]
+ >>> geoda_txt.close()
+
+GAL Binary Weights Files
+------------------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> gal = pysal.open('../pysal/examples/sids2.gal','r')
+ >>> w = gal.read()
+ >>> gal.close()
+ >>> w.n
+ 100
+
+GWT Weights Files
+-----------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> gwt = pysal.open('../pysal/examples/juvenile.gwt', 'r')
+ >>> w = gwt.read()
+ >>> gwt.close()
+ >>> w.n
+ 168
+
+ArcGIS Text Weights Files
+-------------------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> arcgis_txt = pysal.open('../pysal/examples/arcgis_txt.txt','r','arcgis_text')
+ >>> w = arcgis_txt.read()
+ >>> arcgis_txt.close()
+ >>> w.n
+ 3
+
+ArcGIS DBF Weights Files
+-------------------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> arcgis_dbf = pysal.open('../pysal/examples/arcgis_ohio.dbf','r','arcgis_dbf')
+ >>> w = arcgis_dbf.read()
+ >>> arcgis_dbf.close()
+ >>> w.n
+ 88
+
+ArcGIS SWM Weights Files
+-------------------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> arcgis_swm = pysal.open('../pysal/examples/ohio.swm','r')
+ >>> w = arcgis_swm.read()
+ >>> arcgis_swm.close()
+ >>> w.n
+ 88
+
+DAT Weights Files
+-----------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> dat = pysal.open('../pysal/examples/wmat.dat','r')
+ >>> w = dat.read()
+ >>> dat.close()
+ >>> w.n
+ 49
+
+MATLAB MAT Weights Files
+-------------------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> mat = pysal.open('../pysal/examples/spat-sym-us.mat','r')
+ >>> w = mat.read()
+ >>> mat.close()
+ >>> w.n
+ 46
+
+LOTUS WK1 Weights Files
+-----------------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> wk1 = pysal.open('../pysal/examples/spat-sym-us.wk1','r')
+ >>> w = wk1.read()
+ >>> wk1.close()
+ >>> w.n
+ 46
+
+GeoBUGS Text Weights Files
+--------------------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> geobugs_txt = pysal.open('../pysal/examples/geobugs_scot','r','geobugs_text')
+ >>> w = geobugs_txt.read()
+ WARNING: there are 3 disconnected observations
+ Island ids: [6, 8, 11]
+ >>> geobugs_txt.close()
+ >>> w.n
+ 56
+
+STATA Text Weights Files
+-------------------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> stata_txt = pysal.open('../pysal/examples/stata_sparse.txt','r','stata_text')
+ >>> w = stata_txt.read()
+ WARNING: there are 7 disconnected observations
+ Island ids: [5, 9, 10, 11, 12, 14, 15]
+ >>> stata_txt.close()
+ >>> w.n
+ 56
+
+.. _mtx:
+
+MatrixMarket MTX Weights Files
+------------------------------
+
+This file format or its variant is currently under consideration of the PySAL team
+to store general spatial weights in a sparse matrix form.
+
+.. doctest::
+
+ >>> import pysal
+ >>> mtx = pysal.open('../pysal/examples/wmat.mtx','r')
+ >>> w = mtx.read()
+ >>> mtx.close()
+ >>> w.n
+ 49
+
+Examples: Writing files
+=======================
+
+GAL Binary Weights Files
+------------------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> w = pysal.queen_from_shapefile('../pysal/examples/virginia.shp',idVariable='FIPS')
+ >>> w.n
+ 136
+ >>> gal = pysal.open('../pysal/examples/virginia_queen.gal','w')
+ >>> gal.write(w)
+ >>> gal.close()
+
+GWT Weights Files
+-----------------
+
+Currently, it is not allowed to write a GWT file.
+
+ArcGIS Text Weights Files
+-------------------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> w = pysal.queen_from_shapefile('../pysal/examples/virginia.shp',idVariable='FIPS')
+ >>> w.n
+ 136
+ >>> arcgis_txt = pysal.open('../pysal/examples/virginia_queen.txt','w','arcgis_text')
+ >>> arcgis_txt.write(w, useIdIndex=True)
+ >>> arcgis_txt.close()
+
+ArcGIS DBF Weights Files
+-------------------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> w = pysal.queen_from_shapefile('../pysal/examples/virginia.shp',idVariable='FIPS')
+ >>> w.n
+ 136
+ >>> arcgis_dbf = pysal.open('../pysal/examples/virginia_queen.dbf','w','arcgis_dbf')
+ >>> arcgis_dbf.write(w, useIdIndex=True)
+ >>> arcgis_dbf.close()
+
+ArcGIS SWM Weights Files
+-------------------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> w = pysal.queen_from_shapefile('../pysal/examples/virginia.shp',idVariable='FIPS')
+ >>> w.n
+ 136
+ >>> arcgis_swm = pysal.open('../pysal/examples/virginia_queen.swm','w')
+ >>> arcgis_swm.write(w, useIdIndex=True)
+ >>> arcgis_swm.close()
+
+DAT Weights Files
+-----------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> w = pysal.queen_from_shapefile('../pysal/examples/virginia.shp',idVariable='FIPS')
+ >>> w.n
+ 136
+ >>> dat = pysal.open('../pysal/examples/virginia_queen.dat','w')
+ >>> dat.write(w)
+ >>> dat.close()
+
+MATLAB MAT Weights Files
+-------------------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> w = pysal.queen_from_shapefile('../pysal/examples/virginia.shp',idVariable='FIPS')
+ >>> w.n
+ 136
+ >>> mat = pysal.open('../pysal/examples/virginia_queen.mat','w')
+ >>> mat.write(w)
+ >>> mat.close()
+
+LOTUS WK1 Weights Files
+-----------------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> w = pysal.queen_from_shapefile('../pysal/examples/virginia.shp',idVariable='FIPS')
+ >>> w.n
+ 136
+ >>> wk1 = pysal.open('../pysal/examples/virginia_queen.wk1','w')
+ >>> wk1.write(w)
+ >>> wk1.close()
+
+GeoBUGS Text Weights Files
+--------------------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> w = pysal.queen_from_shapefile('../pysal/examples/virginia.shp',idVariable='FIPS')
+ >>> w.n
+ 136
+ >>> geobugs_txt = pysal.open('../pysal/examples/virginia_queen','w','geobugs_text')
+ >>> geobugs_txt.write(w)
+ >>> geobugs_txt.close()
+
+STATA Text Weights Files
+-------------------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> w = pysal.queen_from_shapefile('../pysal/examples/virginia.shp',idVariable='FIPS')
+ >>> w.n
+ 136
+ >>> stata_txt = pysal.open('../pysal/examples/virginia_queen.txt','w','stata_text')
+ >>> stata_txt.write(w,matrix_form=True)
+ >>> stata_txt.close()
+
+MatrixMarket MTX Weights Files
+------------------------------
+
+.. doctest::
+
+ >>> import pysal
+ >>> w = pysal.queen_from_shapefile('../pysal/examples/virginia.shp',idVariable='FIPS')
+ >>> w.n
+ 136
+ >>> mtx = pysal.open('../pysal/examples/virginia_queen.mtx','w')
+ >>> mtx.write(w)
+ >>> mtx.close()
+
+Examples: Converting the format of spatial weights files
+========================================================
+
+PySAL provides a utility tool to convert a weights file from one format to another.
+
+From GAL to ArcGIS SWM format
+
+.. doctest::
+
+ >>> from pysal.core.util.weight_converter import weight_convert
+ >>> gal_file = '../pysal/examples/sids2.gal'
+ >>> swm_file = '../pysal/examples/sids2.swm'
+ >>> weight_convert(gal_file, swm_file, useIdIndex=True)
+ >>> wold = pysal.open(gal_file, 'r').read()
+ >>> wnew = pysal.open(swm_file, 'r').read()
+ >>> wold.n == wnew.n
+ True
+
+
+For further details see the :doc:`FileIO API <../../library/core/FileIO>`.
diff --git a/doc/source/users/tutorials/index.rst b/doc/source/users/tutorials/index.rst
new file mode 100644
index 0000000..9f59d7b
--- /dev/null
+++ b/doc/source/users/tutorials/index.rst
@@ -0,0 +1,20 @@
+.. _users-tutorials:
+
+Getting Started with PySAL
+==========================
+
+
+.. toctree::
+ :maxdepth: 1
+
+ Introduction to the Tutorials <intro>
+ File Input and Output <fileio>
+ Spatial Weights <weights>
+ Spatial Autocorrelation <autocorrelation>
+ Spatial Econometrics <econometrics>
+ Spatial Smoothing <smoothing>
+ Regionalization <region>
+ Spatial Dynamics <dynamics>
+ Shapely Extension <shapely>
+ Sample Datasets <examples>
+ Next Steps <next>
diff --git a/doc/source/users/tutorials/intro.rst b/doc/source/users/tutorials/intro.rst
new file mode 100644
index 0000000..0fe12ba
--- /dev/null
+++ b/doc/source/users/tutorials/intro.rst
@@ -0,0 +1,108 @@
+#############################
+Introduction to the Tutorials
+#############################
+
+
+Assumptions
+===========
+
+The tutorials presented here are designed to illustrate a selection of the
+functionality in PySAL. Further details on PySAL functionality not covered in
+these tutorials can be found in the :doc:`API <../../library/index>`. The reader is
+**assumed to have working knowledge of the particular spatial analytical
+methods** illustrated. Background on spatial analysis can be found in the
+references cited in the tutorials.
+
+It is also assumed that the reader has already :doc:`installed PySAL <../installation>`.
+
+Examples
+============
+
+The examples use several sample data sets that are included in the pysal/examples
+directory. In the examples that follow, we refer to those using the path::
+
+ ../pysal/examples/filename_of_example
+
+You may need to adjust this path to match the location of the sample files on
+your system.
+
+
+Getting Help
+============
+
+Help for PySAL is available from a number of sources.
+
+email lists
+-----------
+The main channel for user support is the `openspace mailing list <http://groups.google.com/group/openspace-list>`_.
+
+
+Questions regarding the development of PySAL should be directed to
+`pysal-dev <http://groups.google.com/group/pysal-dev>`_.
+
+Documentation
+-------------
+Documentation is available on-line at `pysal.org <http://pysal.org>`_.
+
+You can also obtain help at the interpreter:
+
+ >>> import pysal
+ >>> help(pysal)
+
+which would bring up help on PySAL::
+
+ Help on package pysal:
+
+ NAME
+ pysal
+
+ FILE
+ /Users/serge/Dropbox/pysal/src/trunk/pysal/__init__.py
+
+ DESCRIPTION
+ Python Spatial Analysis Library
+ ===============================
+
+
+ Documentation
+ -------------
+ PySAL documentation is available in two forms: python docstrings and a html webpage at http://pysal.org/
+
+ Available sub-packages
+ ----------------------
+
+ cg
+ :
+
+Note that you can use this on any option within PySAL:
+
+ >>> w=pysal.lat2W()
+ >>> help(w)
+
+which brings up::
+
+ Help on W in module pysal.weights object:
+
+ class W(__builtin__.object)
+ | Spatial weights
+ |
+ | Parameters
+ | ----------
+ | neighbors : dictionary
+ | key is region ID, value is a list of neighbor IDS
+ | Example: {'a':['b'],'b':['a','c'],'c':['b']}
+ | weights = None : dictionary
+ | key is region ID, value is a list of edge weights
+ | If not supplied all edge wegiths are assumed to have a weight of 1.
+ | Example: {'a':[0.5],'b':[0.5,1.5],'c':[1.5]}
+ | id_order = None : list
+ | An ordered list of ids, defines the order of
+ | observations when iterating over W if not set,
+ | lexicographical ordering is used to iterate and the
+ | id_order_set property will return False. This can be
+ | set after creation by setting the 'id_order' property.
+ |
+
+
+Note that the help is truncated at the bottom of the terminal window and more of the contents can be seen by scrolling (hit any key).
+
diff --git a/doc/source/users/tutorials/next.rst b/doc/source/users/tutorials/next.rst
new file mode 100644
index 0000000..18057ea
--- /dev/null
+++ b/doc/source/users/tutorials/next.rst
@@ -0,0 +1,17 @@
+
+Next Steps with PySAL
+---------------------
+
+The tutorials you have (hopefully) just gone through should be enough to
+get you going with PySAL. They covered some, but not all, of the
+modules in PySAL, and at that, only a selection of the functionality of
+particular classes that were included in the tutorials. To learn more about
+PySAL you should consult the `documentation <../../library/index.html>`_.
+
+
+PySAL is an open source, community-based project and we highly value contributions
+from individuals to the project. There are many ways to contribute, from filing
+bug reports, suggesting feature requests, helping with documentation, to
+becoming a developer. Individuals interested in joining the team should send an
+email to pysal-dev at googlegroups.com or contact one of the `developers <https://github.com/pysal/pysal/graphs/contributors>`_
+directly.
diff --git a/doc/source/users/tutorials/region.rst b/doc/source/users/tutorials/region.rst
new file mode 100644
index 0000000..f31dd28
--- /dev/null
+++ b/doc/source/users/tutorials/region.rst
@@ -0,0 +1,211 @@
+..
+
+.. testsetup:: *
+
+ import pysal
+ import numpy as np
+
+***************
+Regionalization
+***************
+
+Introduction
+============
+
+PySAL offers a number of tools for the construction of regions. For the
+purposes of this section, a "region" is a group of "areas," and there are
+generally multiple regions in a particular dataset. At this time, PySAL
+offers the max-p regionalization algorithm and tools for constructing random
+regions.
+
+max-p
+=====
+
+Most regionalization algorithms require the user to define a priori the number
+of regions to be built (e.g. k-means clustering). The max-p algorithm [#]_
+determines the number of regions (p) endogenously based on a set of areas, a
+matrix of attributes on each area and a floor constraint. The floor
+constraint defines the minimum bound that a variable must reach for each
+region; for example, a constraint might be the minimum population each region
+must have. max-p further enforces a contiguity constraint on the areas within
+regions.
+
+To illustrate this we will use data on per capita income from the lower 48 US
+states over the period 1929-2010. The goal is to form contiguous regions of
+states displaying similar levels of income throughout this period:
+
+.. doctest::
+
+ >>> import pysal
+ >>> import numpy as np
+ >>> import random
+ >>> f = pysal.open("../pysal/examples/usjoin.csv")
+ >>> pci = np.array([f.by_col[str(y)] for y in range(1929, 2010)])
+ >>> pci = pci.transpose()
+ >>> pci.shape
+ (48, 81)
+
+We also require set of binary contiguity :ref:`weights<weights>` for the Maxp class:
+
+.. doctest::
+
+ >>> w = pysal.open("../pysal/examples/states48.gal").read()
+
+Once we have the attribute data and our weights object we can create an instance of Maxp:
+
+.. doctest::
+
+ >>> np.random.seed(100)
+ >>> random.seed(10)
+ >>> r = pysal.Maxp(w, pci, floor = 5, floor_variable = np.ones((48, 1)), initial = 99)
+
+Here we are forming regions with a minimum of 5 states in each region, so we set the floor_variable to a simple unit vector to ensure this floor constraint is satisfied. We also specify the initial number of feasible solutions to 99 - which are then searched over to pick the optimal feasible solution to then commence with the more expensive swapping component of the algorithm. [#]_
+
+The Maxp instance s has a number of attributes regarding the solution. First is the definition of the regions:
+
+.. doctest::
+
+ >>> r.regions
+ [['44', '34', '3', '25', '1', '4', '47'], ['12', '46', '20', '24', '13'], ['14', '45', '35', '30', '39'], ['6', '27', '17', '29', '5', '43'], ['33', '40', '28', '15', '41', '9', '23', '31', '38'], ['37', '8', '0', '7', '21', '2'], ['32', '19', '11', '10', '22'], ['16', '26', '42', '18', '36']]
+
+which is a list of eight lists of region ids. For example, the first nested list indicates there are seven states in the first region, while the last region has five states. To determine which states these are we can read in the names from the original csv file:
+
+.. doctest::
+
+ >>> f.header
+ ['Name', 'STATE_FIPS', '1929', '1930', '1931', '1932', '1933', '1934', '1935', '1936', '1937', '1938', '1939', '1940', '1941', '1942', '1943', '1944', '1945', '1946', '1947', '1948', '1949', '1950', '1951', '1952', '1953', '1954', '1955', '1956', '1957', '1958', '1959', '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', ' [...]
+ >>> names = f.by_col('Name')
+ >>> names = np.array(names)
+ >>> print names
+ ['Alabama' 'Arizona' 'Arkansas' 'California' 'Colorado' 'Connecticut'
+ 'Delaware' 'Florida' 'Georgia' 'Idaho' 'Illinois' 'Indiana' 'Iowa'
+ 'Kansas' 'Kentucky' 'Louisiana' 'Maine' 'Maryland' 'Massachusetts'
+ 'Michigan' 'Minnesota' 'Mississippi' 'Missouri' 'Montana' 'Nebraska'
+ 'Nevada' 'New Hampshire' 'New Jersey' 'New Mexico' 'New York'
+ 'North Carolina' 'North Dakota' 'Ohio' 'Oklahoma' 'Oregon' 'Pennsylvania'
+ 'Rhode Island' 'South Carolina' 'South Dakota' 'Tennessee' 'Texas' 'Utah'
+ 'Vermont' 'Virginia' 'Washington' 'West Virginia' 'Wisconsin' 'Wyoming']
+
+
+and then loop over the region definitions to identify the specific states comprising each of the regions:
+
+.. doctest::
+
+ >>> for region in r.regions:
+ ... ids = map(int,region)
+ ... print names[ids]
+ ...
+ ['Washington' 'Oregon' 'California' 'Nevada' 'Arizona' 'Colorado' 'Wyoming']
+ ['Iowa' 'Wisconsin' 'Minnesota' 'Nebraska' 'Kansas']
+ ['Kentucky' 'West Virginia' 'Pennsylvania' 'North Carolina' 'Tennessee']
+ ['Delaware' 'New Jersey' 'Maryland' 'New York' 'Connecticut' 'Virginia']
+ ['Oklahoma' 'Texas' 'New Mexico' 'Louisiana' 'Utah' 'Idaho' 'Montana'
+ 'North Dakota' 'South Dakota']
+ ['South Carolina' 'Georgia' 'Alabama' 'Florida' 'Mississippi' 'Arkansas']
+ ['Ohio' 'Michigan' 'Indiana' 'Illinois' 'Missouri']
+ ['Maine' 'New Hampshire' 'Vermont' 'Massachusetts' 'Rhode Island']
+
+
+We can evaluate our solution by developing a pseudo pvalue for the regionalization.
+This is done by comparing the within region sum of squares for the solution against
+simulated solutions where areas are randomly assigned to regions that maintain
+the cardinality of the original solution. This method must be explicitly called once the
+Maxp instance has been created:
+
+.. doctest::
+
+ >>> r.inference()
+ >>> r.pvalue
+ 0.01
+
+so we see we have a regionalization that is significantly different than a chance partitioning.
+
+
+Random Regions
+==============
+
+PySAL offers functionality to generate random regions based on user-defined
+constraints. There are three optional parameters to constrain the
+regionalization: number of regions, cardinality and contiguity. The default
+case simply takes a list of area IDs and randomly selects the number of
+regions and then allocates areas to each region. The user can also pass a
+vector of integers to the cardinality parameter to designate the number of
+areas to randomly assign to each region. The contiguity parameter takes a
+:ref:`spatial weights object <weights>` and uses that to ensure that each
+region is made up of spatially contiguous areas. When the contiguity
+constraint is enforced, it is possible to arrive at infeasible solutions; the
+maxiter parameter can be set to make multiple attempts to find a feasible
+solution. The following examples show some of the possible combinations of
+constraints.
+
+.. doctest::
+
+ >>> import random
+ >>> import numpy as np
+ >>> import pysal
+ >>> from pysal.region import Random_Region
+ >>> nregs = 13
+ >>> cards = range(2,14) + [10]
+ >>> w = pysal.lat2W(10,10,rook = False)
+ >>> ids = w.id_order
+ >>>
+ >>> # unconstrained
+ >>> random.seed(10)
+ >>> np.random.seed(10)
+ >>> t0 = Random_Region(ids)
+ >>> t0.regions[0]
+ [19, 14, 43, 37, 66, 3, 79, 41, 38, 68, 2, 1, 60]
+ >>> # cardinality and contiguity constrained (num_regions implied)
+ >>> random.seed(60)
+ >>> np.random.seed(60)
+ >>> t1 = pysal.region.Random_Region(ids, num_regions = nregs, cardinality = cards, contiguity = w)
+ >>> t1.regions[0]
+ [88, 97, 98, 89, 99, 86, 78, 59, 49, 69, 68, 79, 77]
+ >>> # cardinality constrained (num_regions implied)
+ >>> random.seed(100)
+ >>> np.random.seed(100)
+ >>> t2 = Random_Region(ids, num_regions = nregs, cardinality = cards)
+ >>> t2.regions[0]
+ [37, 62]
+ >>> # number of regions and contiguity constrained
+ >>> random.seed(100)
+ >>> np.random.seed(100)
+ >>> t3 = Random_Region(ids, num_regions = nregs, contiguity = w)
+ >>> t3.regions[1]
+ [71, 72, 70, 93, 51, 91, 85, 74, 63, 73, 61, 62, 82]
+ >>> # cardinality and contiguity constrained
+ >>> random.seed(60)
+ >>> np.random.seed(60)
+ >>> t4 = Random_Region(ids, cardinality = cards, contiguity = w)
+ >>> t4.regions[0]
+ [88, 97, 98, 89, 99, 86, 78, 59, 49, 69, 68, 79, 77]
+ >>> # number of regions constrained
+ >>> random.seed(100)
+ >>> np.random.seed(100)
+ >>> t5 = Random_Region(ids, num_regions = nregs)
+ >>> t5.regions[0]
+ [37, 62, 26, 41, 35, 25, 36]
+ >>> # cardinality constrained
+ >>> random.seed(100)
+ >>> np.random.seed(100)
+ >>> t6 = Random_Region(ids, cardinality = cards)
+ >>> t6.regions[0]
+ [37, 62]
+ >>> # contiguity constrained
+ >>> random.seed(100)
+ >>> np.random.seed(100)
+ >>> t7 = Random_Region(ids, contiguity = w)
+ >>> t7.regions[0]
+ [37, 27, 36, 17]
+ >>>
+
+Further Information
+====================
+
+For further details see the :doc:`Regionalization API <../../library/region/index>`.
+
+
+.. rubric:: Footnotes
+
+.. [#] Duque, J. C., L. Anselin and S. J. Rey. 2011. "The max-p-regions problem." *Journal of Regional Science* `DOI: 10.1111/j.1467-9787.2011.00743.x <http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9787.2011.00743.x/abstract>`_
+.. [#] Because this is a randomized algorithm, results may vary when replicating this example. To reproduce a regionalization solution, you should first set the random seed generator. See http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.seed.html for more information.
diff --git a/doc/source/users/tutorials/shapely.rst b/doc/source/users/tutorials/shapely.rst
new file mode 100644
index 0000000..85aeaaf
--- /dev/null
+++ b/doc/source/users/tutorials/shapely.rst
@@ -0,0 +1,94 @@
+.. testsetup:: *
+
+ import pysal
+ import numpy as np
+
+###########################################
+Using PySAL with Shapely for GIS Operations
+###########################################
+
+
+.. versionadded:: 1.3
+
+Introduction
+============
+
+The `Shapely <http://pypi.python.org/pypi/Shapely>`_ project is a BSD-licensed
+Python package for manipulation and analysis of planar geometric objects, and
+depends on the widely used GEOS library.
+
+PySAL supports interoperation with the Shapely library through Shapely's Python
+Geo Interface. All PySAL geometries provide a __geo_interface__ property which
+models the geometries as a GeoJSON object. Shapely geometry objects also export
+the __geo_interface__ property and can be adapted to PySAL geometries using
+the :py:func:`pysal.cg.asShape` function.
+
+Additionally, PySAL provides an optional contrib module that handles the
+conversion between pysal and shapely data strucutures for you. The module can
+be found in at, :py:mod:`pysal.contrib.shapely_ext`.
+
+Installation
+============
+
+Please refer to the `Shapely <http://pypi.python.org/pypi/Shapely>`_
+website for instructions on installing Shapely and its
+dependencies, *without which PySAL's Shapely extension will not work.*
+
+Usage
+=====
+
+Using the Python Geo Interface...
+
+.. doctest::
+
+ >>> import pysal
+ >>> import shapely.geometry
+ >>> # The get_path function returns the absolute system path to pysal's
+ >>> # included example files no matter where they are installed on the system.
+ >>> fpath = pysal.examples.get_path('stl_hom.shp')
+ >>> # Now, open the shapefile using pysal's FileIO
+ >>> shps = pysal.open(fpath , 'r')
+ >>> # We can read a polygon...
+ >>> polygon = shps.next()
+ >>> # To use this polygon with shapely we simply convert it with
+ >>> # Shapely's asShape method.
+ >>> polygon = shapely.geometry.asShape(polygon)
+ >>> # now we can operate on our polygons like normal shapely objects...
+ >>> print "%.4f"%polygon.area
+ 0.1701
+ >>> # We can do things like buffering...
+ >>> eroded_polygon = polygon.buffer(-0.01)
+ >>> print "%.4f"%eroded_polygon.area
+ 0.1533
+ >>> # and containment testing...
+ >>> polygon.contains(eroded_polygon)
+ True
+ >>> eroded_polygon.contains(polygon)
+ False
+ >>> # To go back to pysal shapes we call pysal.cg.asShape...
+ >>> eroded_polygon = pysal.cg.asShape(eroded_polygon)
+ >>> type(eroded_polygon)
+ <class 'pysal.cg.shapes.Polygon'>
+
+Using The PySAL shapely_ext module...
+
+.. doctest::
+
+ >>> import pysal
+ >>> from pysal.contrib import shapely_ext
+ >>> fpath = pysal.examples.get_path('stl_hom.shp')
+ >>> shps = pysal.open(fpath , 'r')
+ >>> polygon = shps.next()
+ >>> eroded_polygon = shapely_ext.buffer(polygon, -0.01)
+ >>> print "%0.4f"%eroded_polygon.area
+ 0.1533
+ >>> shapely_ext.contains(polygon,eroded_polygon)
+ True
+ >>> shapely_ext.contains(eroded_polygon,polygon)
+ False
+ >>> type(eroded_polygon)
+ <class 'pysal.cg.shapes.Polygon'>
+
+
+
+
diff --git a/doc/source/users/tutorials/smoothing.rst b/doc/source/users/tutorials/smoothing.rst
new file mode 100644
index 0000000..1a52e45
--- /dev/null
+++ b/doc/source/users/tutorials/smoothing.rst
@@ -0,0 +1,405 @@
+.. testsetup:: *
+
+ import pysal
+ import numpy as np
+
+******************
+Spatial Smoothing
+******************
+
+.. contents::
+
+Introduction
+============
+
+In the spatial analysis of attributes measured for areal units, it is often
+necessary to transform an extensive variable, such as number of disease cases
+per census tract, into an intensive variable that takes into account the
+underlying population at risk. Raw rates, counts divided by population values,
+are a common standardization in the literature, yet these tend to have unequal
+reliability due to different population sizes across the spatial units. This problem becomes
+severe for areas with small population values, since the raw rates for those
+areas tend to have higher variance.
+
+A variety of spatial smoothing methods have been suggested to address this problem by aggregating
+the counts and population values for the areas neighboring an observation and
+using these new measurements for its rate computation. PySAL provides a range
+of smoothing techniques that exploit different types of moving windows and
+non-parametric weighting schemes as well as the Empirical Bayesian principle.
+In addition, PySAL offers several methods for calculating age-standardized
+rates, since age standardization is critical in estimating rates of some events
+where the probability of an event occurrence is different across different age
+groups.
+
+In what follows, we overview the methods for age standardization and spatial smoothing
+and describe their implementations in PySAL. [#]_
+
+Age Standardization in PySAL
+============================
+
+Raw rates, counts divided by populations values, are based on an implicit assumption
+that the risk of an event is constant over all age/sex categories in the population.
+For many phenomena, however, the risk is not uniform and often highly correlated with age.
+To take this into account explicitly, the risks for individual age categories can be estimated
+separately and averaged to produce a representative value for an area.
+
+PySAL supports three approaches to this age standardization: crude, direct, and indirect
+standardization.
+
+Crude Age Standardization
+-------------------------
+
+In this approach, the rate for an area is simply the sum of age-specific rates weighted by
+the ratios of each age group in the total population.
+
+To obtain the rates based on this approach, we first need to create two variables
+that correspond to event counts and population values, respectively.
+
+.. doctest::
+
+ >>> import numpy as np
+ >>> e = np.array([30, 25, 25, 15, 33, 21, 30, 20])
+ >>> b = np.array([100, 100, 110, 90, 100, 90, 110, 90])
+
+Each set of numbers should include n by h elements where n and h are the number of areal units
+and the number of age groups. In the above example there are two regions with 4 age groups.
+Age groups are identical across regions. The first four elements in b represent the populations of 4 age
+groups in the first region, and the last four elements the populations of the same age groups in the second
+region.
+
+To apply the crude age standardization, we need to make the following function call:
+
+.. doctest::
+
+ >>> from pysal.esda import smoothing as sm
+ >>> sm.crude_age_standardization(e, b, 2)
+ array([ 0.2375 , 0.26666667])
+
+In the function call above, the last argument indicates the number of area units.
+The outcome in the second line shows that the age-standardized rates for two areas
+are about 0.24 and 0.27, respectively.
+
+Direct Age Standardization
+--------------------------
+
+Direct age standardization is a variation of the crude age standardization.
+While crude age standardization uses the ratios of each age group in the observed population,
+direct age standardization weights age-specific rates by the ratios of each age group in a reference
+population. This reference population, the so-called standard million, is another required
+argument in the PySAL implementation of direct age standardization:
+
+.. doctest::
+
+ >>> s = np.array([100, 90, 100, 90, 100, 90, 100, 90])
+ >>> rate = sm.direct_age_standardization(e, b, s, 2, alpha=0.05)
+ >>> np.array(rate).round(6)
+ array([[ 0.23744 , 0.192049, 0.290485],
+ [ 0.266507, 0.217714, 0.323051]])
+
+The outcome of direct age standardization includes a set of standardized rates and their confidence
+intervals. The confidence intervals can vary according to the value for the last argument, alpha.
+
+Indirect Age Standardization
+----------------------------
+
+While direct age standardization effectively addresses the variety in the risks across
+age groups, its indirect counterpart is better suited to handle the potential
+imprecision of age-specific rates due to the small population size. This method
+uses age-specific rates from the standard million instead of the observed
+population. It then weights the rates by the ratios of each age group in the
+observed population. To compute the age-specific rates from the standard
+million, the PySAL implementation of indirect age standardization requires
+another argument that contains the counts of the events occurred in the
+standard million.
+
+.. doctest::
+
+ >>> s_e = np.array([10, 15, 12, 10, 5, 3, 20, 8])
+ >>> rate = sm.indirect_age_standardization(e, b, s_e, s, 2, alpha=0.05)
+ >>> np.array(rate).round(6)
+ array([[ 0.208055, 0.170156, 0.254395],
+ [ 0.298892, 0.246631, 0.362228]])
+
+The outcome of indirect age standardization is the same as that of its direct counterpart.
+
+Spatial Smoothing in PySAL
+==========================
+
+Mean and Median Based Smoothing
+-------------------------------
+
+A simple approach to rate smoothing is to find a local average or median from the rates of each
+observation and its neighbors. The first method adopting this approach is the so-called locally
+weighted averages or disk smoother. In this method a rate for each observation is replaced
+by an average of rates for its neighbors. A :ref:`spatial weights object
+<weights>` is used to specify the neighborhood relationships among
+observations. To obtain locally weighted averages of the homicide rates in the
+counties surrounding St. Louis during 1979-84, we first read the corresponding
+data table and extract data values for the homicide counts (the 11th column)
+and total population (the 13th column):
+
+.. doctest::
+
+ >>> import pysal
+ >>> stl = pysal.open('../pysal/examples/stl_hom.csv', 'r')
+ >>> e, b = np.array(stl[:,10]), np.array(stl[:,13])
+
+We then read the spatial weights file defining neighborhood relationships among the counties
+and ensure that the :ref:`order <id_order>` of observations in the weights object is the same as that in the data table.
+
+.. doctest::
+
+ >>> w = pysal.open('../pysal/examples/stl.gal', 'r').read()
+ >>> if not w.id_order_set: w.id_order = range(1,len(stl) + 1)
+
+Now we calculate locally weighted averages of the homicide rates.
+
+.. doctest::
+
+ >>> rate = sm.Disk_Smoother(e, b, w)
+ >>> rate.r
+ array([ 4.56502262e-05, 3.44027685e-05, 3.38280487e-05,
+ 4.78530468e-05, 3.12278573e-05, 2.22596997e-05,
+ ...
+ 5.29577710e-05, 5.51034691e-05, 4.65160450e-05,
+ 5.32513363e-05, 3.86199097e-05, 1.92952422e-05])
+
+A variation of locally weighted averages is to use median instead of mean.
+In other words, the rate for an observation can be replaced by the median of the rates of its neighbors.
+This method is called locally weighted median and can be applied in the following way:
+
+.. doctest::
+
+ >>> rate = sm.Spatial_Median_Rate(e, b, w)
+ >>> rate.r
+ array([ 3.96047383e-05, 3.55386859e-05, 3.28308921e-05,
+ 4.30731238e-05, 3.12453969e-05, 1.97300409e-05,
+ ...
+ 6.10668237e-05, 5.86355507e-05, 3.67396656e-05,
+ 4.82535850e-05, 5.51831429e-05, 2.99877050e-05])
+
+In this method the procedure to find local medians can be iterated until no further change occurs.
+The resulting local medians are called iteratively resmoothed medians.
+
+.. doctest::
+
+ >>> rate = sm.Spatial_Median_Rate(e, b, w, iteration=10)
+ >>> rate.r
+ array([ 3.10194715e-05, 2.98419439e-05, 3.10194715e-05,
+ 3.10159267e-05, 2.99214885e-05, 2.80530524e-05,
+ ...
+ 3.81364519e-05, 4.72176972e-05, 3.75320135e-05,
+ 3.76863269e-05, 4.72176972e-05, 3.75320135e-05])
+
+The pure local medians can also be replaced by a weighted median. To obtain weighted medians,
+we need to create an array of weights. For example, we can use the total population of the counties
+as auxiliary weights:
+
+.. doctest::
+
+ >>> rate = sm.Spatial_Median_Rate(e, b, w, aw=b)
+ >>> rate.r
+ array([ 5.77412020e-05, 4.46449551e-05, 5.77412020e-05,
+ 5.77412020e-05, 4.46449551e-05, 3.61363528e-05,
+ ...
+ 5.49703305e-05, 5.86355507e-05, 3.67396656e-05,
+ 3.67396656e-05, 4.72176972e-05, 2.99877050e-05])
+
+When obtaining locally weighted medians, we can consider only a specific subset of neighbors
+rather than all of them. A representative method following this approach is the headbanging smoother.
+In this method all areal units are represented by their geometric centroids.
+Among the neighbors of each observation, only near collinear points are considered for median search.
+Then, triples of points are selected from the near collinear points, and local medians are computed
+from the triples' rates. [#]_
+We apply this headbanging smoother to the rates of the deaths from Sudden Infant Death Syndrome (SIDS)
+for North Carolina counties during 1974-78. We first need to read the source data and extract the event
+counts (the 9th column) and population values (the 9th column).
+In this example the population values correspond to the numbers of live births during 1974-78.
+
+.. doctest::
+
+ >>> sids_db = pysal.open('../pysal/examples/sids2.dbf', 'r')
+ >>> e, b = np.array(sids_db[:,9]), np.array(sids_db[:,8])
+
+Now we need to find triples for each observation. To support the search of triples, PySAL
+provides a class called Headbanging_Triples. This class requires an array of point observations,
+a spatial weights object, and the number of triples as its arguments:
+
+.. doctest::
+
+ >>> from pysal import knnW
+ >>> sids = pysal.open('../pysal/examples/sids2.shp', 'r')
+ >>> sids_d = np.array([i.centroid for i in sids])
+ >>> sids_w = knnW(sids_d,k=5)
+ >>> if not sids_w.id_order_set: sids_w.id_order = sids_w.id_order
+ >>> triples = sm.Headbanging_Triples(sids_d,sids_w,k=5)
+
+The second line in the above example shows how to extract centroids of polygons.
+In this example we define 5 neighbors for each observation by using nearest neighbors criteria.
+In the last line we define the maximum number of triples to be found as 5.
+
+Now we use the triples to compute the headbanging median rates:
+
+.. doctest::
+
+ >>> rate = sm.Headbanging_Median_Rate(e,b,triples)
+ >>> rate.r
+ array([ 0.00075586, 0. , 0.0008285 , 0.0018315 , 0.00498891,
+ 0.00482094, 0.00133156, 0.0018315 , 0.00413223, 0.00142116,
+ ...
+ 0.00221541, 0.00354767, 0.00259903, 0.00392952, 0.00207125,
+ 0.00392952, 0.00229253, 0.00392952, 0.00229253, 0.00229253])
+
+As in the locally weighted medians, we can use a set of auxiliary weights and resmooth the medians
+iteratively.
+
+Non-parametric Smoothing
+------------------------
+
+Non-parametric smoothing methods compute rates without making any assumptions of distributional
+properties of rate estimates. A representative method in this approach is spatial filtering.
+PySAL provides the most simplistic form of spatial filtering where a user-specified grid is imposed
+on the data set and a moving window withi a fixed or adaptive radius visits each vertex of the grid to
+compute the rate at the vertex. Using the previous SIDS example, we can use Spatial_Filtering class:
+
+.. doctest::
+
+ >>> bbox = [sids.bbox[:2], sids.bbox[2:]]
+ >>> rate = sm.Spatial_Filtering(bbox, sids_d, e, b, 10, 10, r=1.5)
+ >>> rate.r
+ array([ 0.00152555, 0.00079271, 0.00161253, 0.00161253, 0.00139513,
+ 0.00139513, 0.00139513, 0.00139513, 0.00139513, 0.00156348,
+ ...
+ 0.00240216, 0.00237389, 0.00240641, 0.00242211, 0.0024854 ,
+ 0.00255477, 0.00266573, 0.00288918, 0.0028991 , 0.00293492])
+
+The first and second arguments of the Spatial_Filtering class are a minimum bounding box containing the
+observations and a set of centroids representing the observations.
+Be careful that the bounding box is NOT the bounding box of the centroids.
+The fifth and sixth arguments are to specify the numbers of grid cells along x and y axes.
+The last argument, r, is to define the radius of the moving window. When this parameter is set,
+a fixed radius is applied to all grid vertices. To make the size of moving window variable,
+we can specify the minimum number of population in the moving window without specifying r:
+
+.. doctest::
+
+ >>> rate = sm.Spatial_Filtering(bbox, sids_d, e, b, 10, 10, pop=10000)
+ >>> rate.r
+ array([ 0.00157398, 0.00157398, 0.00157398, 0.00157398, 0.00166885,
+ 0.00166885, 0.00166885, 0.00166885, 0.00166885, 0.00166885,
+ ...
+ 0.00202977, 0.00215322, 0.00207378, 0.00207378, 0.00217173,
+ 0.00232408, 0.00222717, 0.00245399, 0.00267857, 0.00267857])
+
+The spatial rate smoother is another non-parametric smoothing method that PySAL supports.
+This smoother is very similar to the locally weighted averages. In this method, however,
+the weighted sum is applied to event counts and population values separately.
+The resulting weighted sum of event counts is then divided by the counterpart of population
+values. To obtain neighbor information, we need to use a spatial weights matrix as before.
+
+.. doctest::
+
+ >>> rate = sm.Spatial_Rate(e, b, sids_w)
+ >>> rate.r
+ array([ 0.00114976, 0.00104622, 0.00110001, 0.00153257, 0.00399662,
+ 0.00361428, 0.00146807, 0.00238521, 0.00288871, 0.00145228,
+ ...
+ 0.00240839, 0.00376101, 0.00244941, 0.0028813 , 0.00240839,
+ 0.00261705, 0.00226554, 0.0031575 , 0.00254536, 0.0029003 ])
+
+Another variation of spatial rate smoother is kernel smoother. PySAL supports kernel smoothing
+by using a kernel spatial weights instance in place of a general spatial weights object.
+
+.. doctest::
+
+ >>> from pysal import Kernel
+ >>> kw = Kernel(sids_d)
+ >>> if not kw.id_order_set: kw.id_order = range(0,len(sids_d))
+ >>> rate = sm.Kernel_Smoother(e, b, kw)
+ >>> rate.r
+ array([ 0.0009831 , 0.00104298, 0.00137113, 0.00166406, 0.00556741,
+ 0.00442273, 0.00158202, 0.00243354, 0.00282158, 0.00099243,
+ ...
+ 0.00221017, 0.00328485, 0.00257988, 0.00370461, 0.0020566 ,
+ 0.00378135, 0.00240358, 0.00432019, 0.00227857, 0.00251648])
+
+Age-adjusted rate smoother is another non-parametric smoother that PySAL provides.
+This smoother applies direct age standardization while computing spatial rates.
+To illustrate the age-adjusted rate smoother, we create a new set of event counts and population values
+as well as a new kernel weights object.
+
+.. doctest::
+
+ >>> e = np.array([10, 8, 1, 4, 3, 5, 4, 3, 2, 1, 5, 3])
+ >>> b = np.array([100, 90, 15, 30, 25, 20, 30, 20, 80, 80, 90, 60])
+ >>> s = np.array([98, 88, 15, 29, 20, 23, 33, 25, 76, 80, 89, 66])
+ >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+ >>> kw=Kernel(points)
+ >>> if not kw.id_order_set: kw.id_order = range(0,len(points))
+
+In the above example we created 6 observations each of which has two age groups. To apply age-adjusted
+rate smoothing, we use the Age_Adjusted_Smoother class as follows:
+
+.. doctest::
+
+ >>> rate = sm.Age_Adjusted_Smoother(e, b, kw, s)
+ >>> rate.r
+ array([ 0.10519625, 0.08494318, 0.06440072, 0.06898604, 0.06952076,
+ 0.05020968])
+
+Empirical Bayes Smoothers
+-------------------------
+
+The last group of smoothing methods that PySAL supports is based upon the Bayesian principle. These methods adjust
+a raw rate by taking into account information in the other raw rates.
+As a reference PySAL provides a method for a-spatial Empirical Bayes smoothing:
+
+.. doctest::
+
+ >>> e, b = sm.sum_by_n(e, np.ones(12), 6), sm.sum_by_n(b, np.ones(12), 6)
+ >>> rate = sm.Empirical_Bayes(e, b)
+ >>> rate.r
+ array([ 0.09080775, 0.09252352, 0.12332267, 0.10753624, 0.03301368,
+ 0.05934766])
+
+In the first line of the above example we aggregate the event counts and population values by observation.
+Next we applied the Empirical_Bayes class to the aggregated counts and population values.
+
+A spatial Empirical Bayes smoother is also implemented in PySAL. This method requires an additional
+argument, i.e., a spatial weights object. We continue to reuse the kernel spatial weights object we built before.
+
+.. doctest::
+
+ >>> rate = sm.Spatial_Empirical_Bayes(e, b, kw)
+ >>> rate.r
+ array([ 0.10105263, 0.10165261, 0.16104362, 0.11642038, 0.0226908 ,
+ 0.05270639])
+
+Excess Risk
+-----------
+
+Besides a variety of spatial smoothing methods, PySAL provides a class for estimating excess risk from event counts
+and population values. Excess risks are the ratios of observed event counts over expected event counts.
+An example for the class usage is as follows:
+
+.. doctest::
+
+ >>> risk = sm.Excess_Risk(e, b)
+ >>> risk.r
+ array([ 1.23737916, 1.45124717, 2.32199546, 1.82857143, 0.24489796,
+ 0.69659864])
+
+Further Information
+====================
+
+For further details see the :doc:`Smoothing API <../../library/esda/smoothing>`.
+
+.. rubric:: Footnotes
+
+.. [#] Although this tutorial provides an introduction to the PySAL implementations for spatial smoothing, it is not exhaustive. Complete documentation for the implementations can be found by accessing the help from within a Python interpreter.
+.. [#] For the details of triple selection and headbanging smoothing please
+ refer to Anselin, L., Lozano, N., and Koschinsky, J. (2006). "`Rate
+ Transformations and Smoothing
+ <http://geodacenter.asu.edu/pdf/smoothing_06.pdf>`_". GeoDa Center
+ Research Report.
+
diff --git a/doc/source/users/tutorials/weights.rst b/doc/source/users/tutorials/weights.rst
new file mode 100644
index 0000000..d33af9d
--- /dev/null
+++ b/doc/source/users/tutorials/weights.rst
@@ -0,0 +1,944 @@
+.. _weights:
+
+.. testsetup:: *
+
+ import pysal
+ import numpy as np
+
+***************
+Spatial Weights
+***************
+
+.. contents::
+
+Introduction
+============
+
+Spatial weights are central components of many areas of spatial analysis. In
+general terms, for a spatial data set composed of n locations (points, areal
+units, network edges, etc.), the spatial weights matrix expresses the potential
+for interaction between observations at each pair i,j of locations. There is a rich
+variety of ways to specify the structure of these weights, and
+PySAL supports the creation, manipulation and analysis of spatial weights
+matrices across three different general types:
+
+ * Contiguity Based Weights
+ * Distance Based Weights
+ * Kernel Weights
+
+These different types of weights are implemented as instances of the PySAL weights class
+:class:`~pysal.weights.W`.
+
+In what follows, we provide a high level overview of spatial weights in PySAL, starting with the three different types of weights, followed by
+a closer look at the properties of the W class and some related functions. [#]_
+
+PySAL Spatial Weight Types
+==========================
+PySAL weights are handled in objects of the :class:`pysal.weights.W`. The
+conceptual idea of spatial weights is that of a nxn matrix in which the
+diagonal elements (:math:`w_{ii}`) are set to zero by definition and the rest of
+the cells (:math:`w_{ij}`) capture the potential of interaction. However, these
+matrices tend to be fairly sparse (i.e. many cells contain zeros) and hence a
+full nxn array would not be an efficient representation. PySAL employs a
+different way of storing that is structured in two main dictionaries [#]_ :
+neighbors which, for each observation (key) contains a list of the other ones
+(value) with potential for interaction (:math:`w_{ij} \neq 0`); and weights,
+which contains the weight values for each of those observations (in the same
+order). This way, large datasets can be stored when keeping the full matrix
+would not be possible because of memory constraints. In addition to the sparse
+representation via the weights and neighbors dictionaries, a PySAL W object
+also has an attribute called sparse, which is a `scipy.sparse
+<http://docs.scipy.org/doc/scipy/reference/sparse.html>`_ CSR
+representation of the spatial weights. (See :ref:`wsp` for an alternative
+PySAL weights object.)
+
+.. _contiguity:
+
+Contiguity Based Weights
+------------------------
+
+To illustrate the general weights object, we start with a simple contiguity
+matrix constructed for a 5 by 5 lattice (composed of 25 spatial units):
+
+.. doctest::
+
+ >>> import pysal
+ >>> w = pysal.lat2W(5, 5)
+
+The w object has a number of attributes:
+
+.. doctest::
+
+ >>> w.n
+ 25
+ >>> w.pct_nonzero
+ 0.128
+ >>> w.weights[0]
+ [1.0, 1.0]
+ >>> w.neighbors[0]
+ [5, 1]
+ >>> w.neighbors[5]
+ [0, 10, 6]
+ >>> w.histogram
+ [(2, 4), (3, 12), (4, 9)]
+
+n is the number of spatial units, so conceptually we could be thinking that the
+weights are stored in a 25x25 matrix. The second attribute
+(pct_nonzero) shows the sparseness of the matrix. The key
+attributes used to store contiguity relations in W are the neighbors and
+weights attributes. In the example above we see that the observation
+with id 0 (Python is zero-offset) has two neighbors with ids [5, 1] each of
+which have equal weights of 1.0.
+
+The histogram attribute is a set of tuples indicating the cardinality of the
+neighbor relations. In this case we have a regular lattice, so there are 4 units that have 2
+neighbors (corner cells), 12 units with 3 neighbors (edge cells), and 9 units
+with 4 neighbors (internal cells).
+
+In the above example, the default criterion for contiguity on the lattice was
+that of the rook which takes as neighbors any pair of cells that share an edge.
+Alternatively, we could have used the queen criterion to include the vertices
+of the lattice to define contiguities:
+
+.. doctest::
+
+ >>> wq = pysal.lat2W(rook = False)
+ >>> wq.neighbors[0]
+ [5, 1, 6]
+ >>>
+
+The bishop criterion, which designates pairs of cells as neighbors if they share
+only a vertex, is yet a third alternative for contiguity weights. A bishop matrix
+can be computed as the :ref:`difference` between the rook and queen cases.
+
+The lat2W function is particularly useful in setting up simulation experiments
+requiring a regular grid. For empirical research, a common use case is to have
+a shapefile, which is a nontopological vector data structure, and a need
+to carry out some form of spatial analysis that requires spatial weights. Since
+topology is not stored in the underlying file there is a need to construct
+the spatial weights prior to carrying out the analysis. In PySAL spatial
+weights can be obtained directly from shapefiles:
+
+.. doctest::
+
+ >>> w = pysal.rook_from_shapefile("../pysal/examples/columbus.shp")
+ >>> w.n
+ 49
+ >>> print "%.4f"%w.pct_nonzero
+ 0.0833
+ >>> w.histogram
+ [(2, 7), (3, 10), (4, 17), (5, 8), (6, 3), (7, 3), (8, 0), (9, 1)]
+
+If queen, rather than rook, contiguity is required then the following would work:
+
+.. doctest::
+
+ >>> w = pysal.queen_from_shapefile("../pysal/examples/columbus.shp")
+ >>> print "%.4f"%w.pct_nonzero
+ 0.0983
+ >>> w.histogram
+ [(2, 5), (3, 9), (4, 12), (5, 5), (6, 9), (7, 3), (8, 4), (9, 1), (10, 1)]
+
+
+
+Distance Based Weights
+----------------------
+
+In addition to using contiguity to define neighbor relations, more general
+functions of the distance separating observations can be used to specify the
+weights.
+
+Please note that distance calculations are coded for a flat surface, so you
+will need to have your shapefile projected in advance for the output to be
+correct.
+
+k-nearest neighbor weights
+--------------------------
+
+The neighbors for a given observations can be defined using a k-nearest neighbor criterion.
+For example we could use the the centroids of our
+5x5 lattice as point locations to measure the distances. First, we import numpy to
+create the coordinates as a 25x2 numpy array named data (numpy arrays are the only
+form of input supported at this point):
+
+.. doctest::
+
+ >>> import numpy as np
+ >>> x,y=np.indices((5,5))
+ >>> x.shape=(25,1)
+ >>> y.shape=(25,1)
+ >>> data=np.hstack([x,y])
+
+
+then define the knn set as:
+
+.. doctest::
+
+ >>> wknn3 = pysal.knnW(data, k = 3)
+ >>> wknn3.neighbors[0]
+ [1, 5, 6]
+ >>> wknn3.s0
+ 75.0
+ >>> w4 = pysal.knnW(data, k = 4)
+ >>> set(w4.neighbors[0]) == set([1, 5, 6, 2])
+ True
+ >>> w4.s0
+ 100.0
+ >>> w4.weights[0]
+ [1.0, 1.0, 1.0, 1.0]
+
+Alternatively, we can use a utility function to build a knn W straight from a
+shapefile:
+
+.. doctest::
+
+ >>> wknn5 = pysal.knnW_from_shapefile(pysal.examples.get_path('columbus.shp'), k=5)
+ >>> wknn5.neighbors[0]
+ [2, 1, 3, 7, 4]
+
+Distance band weights
+---------------------
+
+Knn weights ensure that all observations have the same number of neighbors. [#]_
+An alternative distance based set of weights relies on distance bands or
+thresholds to define the neighbor set for each spatial unit as those other units
+falling within a threshold distance of the focal unit:
+
+.. doctest::
+
+ >>> wthresh = pysal.threshold_binaryW_from_array(data, 2)
+ >>> set(wthresh.neighbors[0]) == set([1, 2, 5, 6, 10])
+ True
+ >>> set(wthresh.neighbors[1]) == set( [0, 2, 5, 6, 7, 11, 3])
+ True
+ >>> wthresh.weights[0]
+ [1, 1, 1, 1, 1]
+ >>> wthresh.weights[1]
+ [1, 1, 1, 1, 1, 1, 1]
+ >>>
+
+As can be seen in the above example, the number of neighbors is likely to vary
+across observations with distance band weights in contrast to what holds for
+knn weights.
+
+Distance band weights can be generated for shapefiles as well as arrays of points. [#]_ First, the
+minimum nearest neighbor distance should be determined so that each unit is assured of at least one
+neighbor:
+
+.. doctest::
+
+ >>> thresh = pysal.min_threshold_dist_from_shapefile("../pysal/examples/columbus.shp")
+ >>> thresh
+ 0.61886415807685413
+
+with this threshold in hand, the distance band weights are obtained as:
+
+.. doctest::
+
+ >>> wt = pysal.threshold_binaryW_from_shapefile("../pysal/examples/columbus.shp", thresh)
+ >>> wt.min_neighbors
+ 1
+ >>> wt.histogram
+ [(1, 4), (2, 8), (3, 6), (4, 2), (5, 5), (6, 8), (7, 6), (8, 2), (9, 6), (10, 1), (11, 1)]
+ >>> set(wt.neighbors[0]) == set([1,2])
+ True
+ >>> set(wt.neighbors[1]) == set([3,0])
+ True
+
+Distance band weights can also be specified to take on continuous values rather
+than binary, with the values set to the inverse distance separating each pair
+within a given threshold distance. We illustrate this with a small set of 6
+points:
+
+.. doctest::
+
+ >>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+ >>> wid = pysal.threshold_continuousW_from_array(points,14.2)
+ >>> wid.weights[0]
+ [0.10000000000000001, 0.089442719099991588]
+
+If we change the distance decay exponent to -2.0 the result is so called gravity weights:
+
+.. doctest::
+
+ >>> wid2 = pysal.threshold_continuousW_from_array(points,14.2,alpha = -2.0)
+ >>> wid2.weights[0]
+ [0.01, 0.0079999999999999984]
+
+
+Kernel Weights
+--------------
+
+A combination of distance based thresholds together with continuously valued
+weights is supported through kernel weights:
+
+.. doctest::
+
+ >>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+ >>> kw = pysal.Kernel(points)
+ >>> kw.weights[0]
+ [1.0, 0.500000049999995, 0.4409830615267465]
+ >>> kw.neighbors[0]
+ [0, 1, 3]
+ >>> kw.bandwidth
+ array([[ 20.000002],
+ [ 20.000002],
+ [ 20.000002],
+ [ 20.000002],
+ [ 20.000002],
+ [ 20.000002]])
+
+
+The bandwidth attribute plays the role of the distance threshold with kernel
+weights, while the form of the kernel function determines the distance decay
+in the derived continuous weights (the following are available:
+'triangular','uniform','quadratic','epanechnikov','quartic','bisquare','gaussian').
+In the above example, the bandwidth is set to the default value and fixed
+across the observations. The user could specify a different value for a fixed
+bandwidth:
+
+.. doctest::
+
+ >>> kw15 = pysal.Kernel(points,bandwidth = 15.0)
+ >>> kw15[0]
+ {0: 1.0, 1: 0.33333333333333337, 3: 0.2546440075000701}
+ >>> kw15.neighbors[0]
+ [0, 1, 3]
+ >>> kw15.bandwidth
+ array([[ 15.],
+ [ 15.],
+ [ 15.],
+ [ 15.],
+ [ 15.],
+ [ 15.]])
+
+which results in fewer neighbors for the first unit. Adaptive bandwidths (i.e., different bandwidths
+for each unit) can also be user specified:
+
+.. doctest::
+
+ >>> bw = [25.0,15.0,25.0,16.0,14.5,25.0]
+ >>> kwa = pysal.Kernel(points,bandwidth = bw)
+ >>> kwa.weights[0]
+ [1.0, 0.6, 0.552786404500042, 0.10557280900008403]
+ >>> kwa.neighbors[0]
+ [0, 1, 3, 4]
+ >>> kwa.bandwidth
+ array([[ 25. ],
+ [ 15. ],
+ [ 25. ],
+ [ 16. ],
+ [ 14.5],
+ [ 25. ]])
+
+Alternatively the adaptive bandwidths could be defined endogenously:
+
+.. doctest::
+
+ >>> kwea = pysal.Kernel(points,fixed = False)
+ >>> kwea.weights[0]
+ [1.0, 0.10557289844279438, 9.99999900663795e-08]
+ >>> kwea.neighbors[0]
+ [0, 1, 3]
+ >>> kwea.bandwidth
+ array([[ 11.18034101],
+ [ 11.18034101],
+ [ 20.000002 ],
+ [ 11.18034101],
+ [ 14.14213704],
+ [ 18.02775818]])
+
+Finally, the kernel function could be changed (with endogenous adaptive bandwidths):
+
+.. doctest::
+
+ >>> kweag = pysal.Kernel(points,fixed = False,function = 'gaussian')
+ >>> kweag.weights[0]
+ [0.3989422804014327, 0.2674190291577696, 0.2419707487162134]
+ >>> kweag.bandwidth
+ array([[ 11.18034101],
+ [ 11.18034101],
+ [ 20.000002 ],
+ [ 11.18034101],
+ [ 14.14213704],
+ [ 18.02775818]])
+
+
+More details on kernel weights can be found in
+:class:`~pysal.weights.Distance.Kernel`.
+
+
+A Closer look at W
+==================
+
+Although the three different types of spatial weights illustrated above cover a wide array of approaches
+towards specifying spatial relations, they all share common attributes from the base W class in PySAL. Here
+we take a closer look at some of the more useful properties of this class.
+
+Attributes of W
+-----------------------------
+W objects come with a whole bunch of useful attributes that may help you when
+dealing with spatial weights matrices. To see a list of all of them, same as
+with any other Python object, type:
+
+ >>> import pysal
+ >>> help(pysal.W)
+
+If you want to be more specific and learn, for example, about the attribute
+`s0`, then type:
+
+ >>> help(pysal.W.s0)
+ Help on property:
+
+ float
+
+ .. math::
+
+ s0 = \sum_i \sum_j w_{i,j}
+
+Weight Transformations
+----------------------
+
+Often there is a need to apply a transformation to the spatial weights, such as in the case of row standardization.
+Here each value in the row of the spatial weights matrix is rescaled to sum to one:
+
+.. math::
+
+ ws_{i,j} = w_{i,j}/ \sum_j w_{i,j}
+
+This and other weights transformations in PySAL are supported by the transform property of the W class. To see this
+let's build a simple contiguity object for the Columbus data set:
+
+.. doctest::
+
+ >>> w = pysal.rook_from_shapefile("../pysal/examples/columbus.shp")
+ >>> w.weights[0]
+ [1.0, 1.0]
+
+We can row standardize this by setting the transform property:
+
+.. doctest::
+
+ >>> w.transform = 'r'
+ >>> w.weights[0]
+ [0.5, 0.5]
+
+Supported transformations are the following:
+
+ * '`b`': binary.
+ * '`r`': row standardization.
+ * '`v`': variance stabilizing.
+
+If the original weights (unstandardized) are required, the transform property can be reset:
+
+.. doctest::
+
+ >>> w.transform = 'o'
+ >>> w.weights[0]
+ [1.0, 1.0]
+
+Behind the scenes the transform property is updating all other characteristics of the spatial weights that are a function of the
+values and these standardization operations, freeing the user from having to keep these other attributes updated. To determine the current
+value of the transformation, simply query this attribute:
+
+.. doctest::
+
+ >>> w.transform
+ 'O'
+
+More details on other transformations that are supported in W can be found in
+:class:`pysal.weights.W`.
+
+
+
+W related functions
+===================
+
+Generating a full array
+-----------------------
+As the underlying data structure of the weights in W is based on a sparse representation, there may be a need to work with the full numpy array.
+This is supported through the full method of W:
+
+.. doctest::
+
+ >>> wf = w.full()
+ >>> len(wf)
+ 2
+
+The first element of the return from w.full is the numpy array:
+
+.. doctest::
+
+ >>> wf[0].shape
+ (49, 49)
+
+while the second element contains the ids for the row (column) ordering of the array:
+
+.. doctest::
+
+ >>> wf[1][0:5]
+ [0, 1, 2, 3, 4]
+
+If only the array is required, a simple Python slice can be used:
+
+.. doctest::
+
+ >>> wf = w.full()[0]
+
+
+Shimbel Matrices
+----------------
+The Shimbel matrix for a set of n objects contains the shortest path distance
+separating each pair of units. This has wide use in spatial analysis for
+solving different types of clustering and optimization problems. Using the
+function `shimbel` with a `W` instance as an argument generates this
+information:
+
+.. doctest::
+
+ >>> w = pysal.lat2W(3,3)
+ >>> ws = pysal.shimbel(w)
+ >>> ws[0]
+ [-1, 1, 2, 1, 2, 3, 2, 3, 4]
+
+Thus we see that observation 0 (the northwest cell of our 3x3 lattice) is a first order neighbor to observations 1 and 3, second order
+neighbor to observations 2, 4, and 6, a third order neighbor to 5, and 7, and a fourth order neighbor to observation 8 (the extreme southeast
+cell in the lattice). The position of the -1 simply denotes the focal unit.
+
+Higher Order Contiguity Weights
+-------------------------------
+
+Closely related to the shortest path distances is the concept of a spatial weight based on a particular order of contiguity. For example, we could
+define the second order contiguity relations using:
+
+.. doctest::
+
+ >>> w2 = pysal.higher_order(w, 2)
+ >>> w2.neighbors[0]
+ [4, 6, 2]
+
+or a fourth order set of weights:
+
+.. doctest::
+
+ >>> w4 = pysal.higher_order(w, 4)
+ WARNING: there are 5 disconnected observations
+ Island ids: [1, 3, 4, 5, 7]
+ >>> w4.neighbors[0]
+ [8]
+
+In both cases a new instance of the W class is returned with the weights and neighbors defined using the particular order of contiguity.
+
+Spatial Lag
+-----------
+
+The final function related to spatial weights that we illustrate here is used to construct a new variable called the spatial lag. The spatial
+lag is a function of the attribute values observed at neighboring locations. For example, if we continue with our regular 3x3 lattice and
+create an attribute variable y:
+
+.. doctest::
+
+ >>> import numpy as np
+ >>> y = np.arange(w.n)
+ >>> y
+ array([0, 1, 2, 3, 4, 5, 6, 7, 8])
+
+then the spatial lag can be constructed with:
+
+.. doctest::
+
+ >>> yl = pysal.lag_spatial(w,y)
+ >>> yl
+ array([ 4., 6., 6., 10., 16., 14., 10., 18., 12.])
+
+Mathematically, the spatial lag is a weighted sum of neighboring attribute values
+
+.. math::
+
+ yl_i = \sum_j w_{i,j} y_j
+
+In the example above, the weights were binary, based on the rook criterion. If we row standardize our W object first
+and then recalculate the lag, it takes the form of a weighted average of the neighboring attribute values:
+
+.. doctest::
+
+ >>> w.transform = 'r'
+ >>> ylr = pysal.lag_spatial(w,y)
+ >>> ylr
+ array([ 2. , 2. , 3. , 3.33333333, 4. ,
+ 4.66666667, 5. , 6. , 6. ])
+
+.. _id_order:
+
+One important consideration in calculating the spatial lag is that the ordering
+of the values in y aligns with the underlying order in W. In cases where the
+source for your attribute data is different from the one to construct your
+weights you may need to reorder your y values accordingly. To check if this is
+the case you can find the order in W as follows:
+
+.. doctest::
+
+ >>> w.id_order
+ [0, 1, 2, 3, 4, 5, 6, 7, 8]
+
+In this case the lag_spatial function assumes that the first value in the y
+attribute corresponds to unit 0 in the lattice (northwest cell), while the last
+value in y would correspond to unit 8 (southeast cell). In other words, for the
+value of the spatial lag to be valid the number of elements in y must match w.n
+and the orderings must be aligned.
+
+Fortunately, for the common use case where both the attribute and weights information come from a
+shapefile (and its dbf), PySAL handles the alignment automatically: [#]_
+
+.. doctest::
+
+ >>> w = pysal.rook_from_shapefile("../pysal/examples/columbus.shp")
+ >>> f = pysal.open("../pysal/examples/columbus.dbf")
+ >>> f.header
+ ['AREA', 'PERIMETER', 'COLUMBUS_', 'COLUMBUS_I', 'POLYID', 'NEIG', 'HOVAL', 'INC', 'CRIME', 'OPEN', 'PLUMB', 'DISCBD', 'X', 'Y', 'NSA', 'NSB', 'EW', 'CP', 'THOUS', 'NEIGNO']
+ >>> y = np.array(f.by_col['INC'])
+ >>> w.transform = 'r'
+ >>> y
+ array([ 19.531 , 21.232 , 15.956 , 4.477 , 11.252 ,
+ 16.028999, 8.438 , 11.337 , 17.586 , 13.598 ,
+ 7.467 , 10.048 , 9.549 , 9.963 , 9.873 ,
+ 7.625 , 9.798 , 13.185 , 11.618 , 31.07 ,
+ 10.655 , 11.709 , 21.155001, 14.236 , 8.461 ,
+ 8.085 , 10.822 , 7.856 , 8.681 , 13.906 ,
+ 16.940001, 18.941999, 9.918 , 14.948 , 12.814 ,
+ 18.739 , 17.017 , 11.107 , 18.476999, 29.833 ,
+ 22.207001, 25.872999, 13.38 , 16.961 , 14.135 ,
+ 18.323999, 18.950001, 11.813 , 18.796 ])
+ >>> yl = pysal.lag_spatial(w,y)
+ >>> yl
+ array([ 18.594 , 13.32133333, 14.123 , 14.94425 ,
+ 11.817857 , 14.419 , 10.283 , 8.3364 ,
+ 11.7576665 , 19.48466667, 10.0655 , 9.1882 ,
+ 9.483 , 10.07716667, 11.231 , 10.46185714,
+ 21.94100033, 10.8605 , 12.46133333, 15.39877778,
+ 14.36333333, 15.0838 , 19.93666633, 10.90833333,
+ 9.7 , 11.403 , 15.13825 , 10.448 ,
+ 11.81 , 12.64725 , 16.8435 , 26.0662505 ,
+ 15.6405 , 18.05175 , 15.3824 , 18.9123996 ,
+ 12.2418 , 12.76675 , 18.5314995 , 22.79225025,
+ 22.575 , 16.8435 , 14.2066 , 14.20075 ,
+ 15.2515 , 18.6079995 , 26.0200005 , 15.818 , 14.303 ])
+
+ >>> w.id_order
+ [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48]
+
+Non-Zero Diagonal
+-----------------
+The typical weights matrix has zeros along the main diagonal. This has the
+practical result of excluding the self from any computation. However, this is
+not always the desired situation, and so PySAL offers a function that adds
+values to the main diagonal of a W object.
+
+As an example, we can build a basic rook weights matrix, which has zeros on
+the diagonal, then insert ones along the diagonal:
+
+.. doctest::
+
+ >>> w = pysal.lat2W(5, 5, id_type='string')
+ >>> w['id0']
+ {'id5': 1.0, 'id1': 1.0}
+ >>> w_const = pysal.weights.insert_diagonal(w)
+ >>> w_const['id0']
+ {'id5': 1.0, 'id0': 1.0, 'id1': 1.0}
+
+The default is to add ones to the diagonal, but the function allows any values to
+be added.
+
+
+WSets
+=====
+
+PySAL offers set-like manipulation of spatial weights matrices. While a W is
+more complex than a set, the two objects have a number of commonalities
+allowing for traditional set operations to have similar functionality on a W.
+Conceptually, we treat each neighbor pair as an element of a set, and then
+return the appropriate pairs based on the operation invoked (e.g. union,
+intersection, etc.). A key distinction between a set and a W is that a W
+must keep track of the universe of possible pairs, even those that do not
+result in a neighbor relationship.
+
+PySAL follows the naming conventions for Python sets, but adds optional flags
+allowing the user to control the shape of the weights object returned. At
+this time, all the functions discussed in this section return a binary W no
+matter the weights objects passed in.
+
+Union
+-----
+
+The union of two weights objects returns a binary weights object, W, that
+includes all neighbor pairs that exist in either weights object. This
+function can be used to simply join together two weights objects, say one for
+Arizona counties and another for California counties. It can also be used
+to join two weights objects that overlap as in the example below.
+
+.. doctest::
+
+ >>> w1 = pysal.lat2W(4,4)
+ >>> w2 = pysal.lat2W(6,4)
+ >>> w = pysal.w_union(w1, w2)
+ >>> w1[0] == w[0]
+ True
+ >>> w1.neighbors[15]
+ [11, 14]
+ >>> w2.neighbors[15]
+ [11, 14, 19]
+ >>> w.neighbors[15]
+ [19, 11, 14]
+
+Intersection
+------------
+
+The intersection of two weights objects returns a binary weights object, W,
+that includes only those neighbor pairs that exist in both weights objects.
+Unlike the union case, where all pairs in either matrix are returned, the
+intersection only returns a subset of the pairs. This leaves open the
+question of the shape of the weights matrix to return. For example, you have
+one weights matrix of census tracts for City A and second matrix of tracts for
+Utility Company B's service area, and want to find the W for the tracts that
+overlap. Depending on the research question, you may want the returned W to
+have the same dimensions as City A's weights matrix, the same as the utility
+company's weights matrix, a new dimensionality based on all the census tracts
+in either matrix or with the dimensionality of just those tracts in the
+overlapping area. All of these options are available via the w_shape parameter
+and the order that the matrices are passed to the function. The following
+example uses the all case:
+
+.. doctest::
+
+ >>> w1 = pysal.lat2W(4,4)
+ >>> w2 = pysal.lat2W(6,4)
+ >>> w = pysal.w_intersection(w1, w2, 'all')
+ WARNING: there are 8 disconnected observations
+ Island ids: [16, 17, 18, 19, 20, 21, 22, 23]
+ >>> w1[0] == w[0]
+ True
+ >>> w1.neighbors[15]
+ [11, 14]
+ >>> w2.neighbors[15]
+ [11, 14, 19]
+ >>> w.neighbors[15]
+ [11, 14]
+ >>> w2.neighbors[16]
+ [12, 20, 17]
+ >>> w.neighbors[16]
+ []
+
+.. _difference:
+
+Difference
+----------
+
+The difference of two weights objects returns a binary weights object, W, that
+includes only neighbor pairs from the first object that are not in the second.
+Similar to the intersection function, the user must select the shape of the
+weights object returned using the w_shape parameter. The user must also
+consider the constrained parameter which controls whether the observations and
+the neighbor pairs are differenced or just the neighbor pairs are differenced.
+If you were to apply the difference function to our city and utility company
+example from the intersection section above, you must decide whether or not
+pairs that exist along the border of the regions should be considered
+different or not. It boils down to whether the tracts should be differenced
+first and then the differenced pairs identified (constrained=True), or if the
+differenced pairs should be identified based on the sets of pairs in the
+original weights matrices (constrained=False). In the example below we
+difference weights matrices from regions with partial overlap.
+
+.. doctest::
+
+ >>> w1 = pysal.lat2W(6,4)
+ >>> w2 = pysal.lat2W(4,4)
+ >>> w1.neighbors[15]
+ [11, 14, 19]
+ >>> w2.neighbors[15]
+ [11, 14]
+ >>> w = pysal.w_difference(w1, w2, w_shape = 'w1', constrained = False)
+ WARNING: there are 12 disconnected observations
+ Island ids: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
+ >>> w.neighbors[15]
+ [19]
+ >>> w.neighbors[19]
+ [15, 18, 23]
+ >>> w = pysal.w_difference(w1, w2, w_shape = 'min', constrained = False)
+ >>> 15 in w.neighbors
+ False
+ >>> w.neighbors[19]
+ [18, 23]
+ >>> w = pysal.w_difference(w1, w2, w_shape = 'w1', constrained = True)
+ WARNING: there are 16 disconnected observations
+ Island ids: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
+ >>> w.neighbors[15]
+ []
+ >>> w.neighbors[19]
+ [18, 23]
+ >>> w = pysal.w_difference(w1, w2, w_shape = 'min', constrained = True)
+ >>> 15 in w.neighbors
+ False
+ >>> w.neighbors[19]
+ [18, 23]
+
+The difference function can be used to construct a bishop
+:ref:`contiguity weights matrix <contiguity>`
+by differencing a queen and rook matrix.
+
+.. doctest::
+
+ >>> wr = pysal.lat2W(5,5)
+ >>> wq = pysal.lat2W(5,5,rook = False)
+ >>> wb = pysal.w_difference(wq, wr,constrained = False)
+ >>> wb.neighbors[0]
+ [6]
+
+
+Symmetric Difference
+--------------------
+
+Symmetric difference of two weights objects returns a binary weights object,
+W, that includes only neighbor pairs that are not shared by either matrix.
+This function offers options similar to those in the difference function
+described above.
+
+.. doctest::
+
+ >>> w1 = pysal.lat2W(6, 4)
+ >>> w2 = pysal.lat2W(2, 4)
+ >>> w_lower = pysal.w_difference(w1, w2, w_shape = 'min', constrained = True)
+ >>> w_upper = pysal.lat2W(4, 4)
+ >>> w = pysal.w_symmetric_difference(w_lower, w_upper, 'all', False)
+ >>> w_lower.id_order
+ [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]
+ >>> w_upper.id_order
+ [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
+ >>> w.id_order
+ [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]
+ >>> w.neighbors[11]
+ [7]
+ >>> w = pysal.w_symmetric_difference(w_lower, w_upper, 'min', False)
+ WARNING: there are 8 disconnected observations
+ Island ids: [0, 1, 2, 3, 4, 5, 6, 7]
+ >>> 11 in w.neighbors
+ False
+ >>> w.id_order
+ [0, 1, 2, 3, 4, 5, 6, 7, 16, 17, 18, 19, 20, 21, 22, 23]
+ >>> w = pysal.w_symmetric_difference(w_lower, w_upper, 'all', True)
+ WARNING: there are 16 disconnected observations
+ Island ids: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
+ >>> w.neighbors[11]
+ []
+ >>> w = pysal.w_symmetric_difference(w_lower, w_upper, 'min', True)
+ WARNING: there are 8 disconnected observations
+ Island ids: [0, 1, 2, 3, 4, 5, 6, 7]
+ >>> 11 in w.neighbors
+ False
+
+Subset
+------
+
+Subset of a weights object returns a binary weights object, W, that includes
+only those observations provided by the user. It also can be used to add
+islands to a previously existing weights object.
+
+.. doctest::
+
+ >>> w1 = pysal.lat2W(6, 4)
+ >>> w1.id_order
+ [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]
+ >>> ids = range(16)
+ >>> w = pysal.w_subset(w1, ids)
+ >>> w.id_order
+ [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
+ >>> w1[0] == w[0]
+ True
+ >>> w1.neighbors[15]
+ [11, 14, 19]
+ >>> w.neighbors[15]
+ [11, 14]
+
+
+.. _wsp:
+
+WSP
+===
+A thin PySAL weights object is available to users with extremely large weights
+matrices, on the order of 2 million or more observations, or users interested
+in holding many large weights matrices in RAM simultaneously. The
+:class:`pysal.weights.WSP` is a thin weights object that does not include the
+neighbors and weights dictionaries, but does contain the scipy.sparse form of
+the weights. For many PySAL functions the W and WSP objects can be used
+interchangeably.
+
+A WSP object can be constructed from a `Matrix Market
+<http://math.nist.gov/MatrixMarket/>`_ file (see :ref:`mtx` for more info on
+reading and writing mtx files in PySAL):
+
+.. doctest::
+
+ >>> mtx = pysal.open("../pysal/examples/wmat.mtx", 'r')
+ >>> wsp = mtx.read(sparse=True)
+
+or built directly from a scipy.sparse object:
+
+.. doctest::
+
+ >>> import scipy.sparse
+ >>> rows = [0, 1, 1, 2, 2, 3]
+ >>> cols = [1, 0, 2, 1, 3, 3]
+ >>> weights = [1, 0.75, 0.25, 0.9, 0.1, 1]
+ >>> sparse = scipy.sparse.csr_matrix((weights, (rows, cols)), shape=(4,4))
+ >>> w = pysal.weights.WSP(sparse)
+
+The WSP object has subset of the attributes of a W object; for example:
+
+.. doctest::
+
+ >>> w.n
+ 4
+ >>> w.s0
+ 4.0
+ >>> w.trcWtW_WW
+ 6.3949999999999996
+
+The following functionality is available to convert from a W to a WSP:
+
+.. doctest::
+
+ >>> w = pysal.weights.lat2W(5,5)
+ >>> w.s0
+ 80.0
+ >>> wsp = pysal.weights.WSP(w.sparse)
+ >>> wsp.s0
+ 80.0
+
+and from a WSP to W:
+
+.. doctest::
+
+ >>> sp = pysal.weights.lat2SW(5, 5)
+ >>> wsp = pysal.weights.WSP(sp)
+ >>> wsp.s0
+ 80
+ >>> w = pysal.weights.WSP2W(wsp)
+ >>> w.s0
+ 80
+
+
+Further Information
+====================
+
+For further details see the :doc:`Weights API <../../library/weights/index>`.
+
+
+
+.. rubric:: Footnotes
+
+.. [#] Although this tutorial provides an introduction to the functionality of the PySAL weights class, it is not exhaustive. Complete documentation for the class and associated functions can be found by accessing the help from within a Python interpreter.
+.. [#] The dictionaries for the weights and value attributes in W are read-only.
+.. [#] Ties at the k-nn distance band are randomly broken to ensure each observation has exactly k neighbors.
+.. [#] If the shapefile contains geographical coordinates these distance calculations will be misleading and the user should first project their coordinates using a GIS.
+.. [#] The ordering exploits the one-to-one relation between a record in the DBF file and the shape in the shapefile.
+
+
diff --git a/pysal/COPYING b/pysal/COPYING
new file mode 100644
index 0000000..9125da2
--- /dev/null
+++ b/pysal/COPYING
@@ -0,0 +1,25 @@
+Copyright (c) 2009-13 Sergio J. Rey <sjsrey at gmail.com>
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice,
+ this list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright
+ notice, this list of conditions and the following disclaimer in the
+ documentation and/or other materials provided with the distribution.
+ * Neither the name of the PySAL project team nor the names of its
+ contributors may be used to endorse or promote products derived from
+ this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/pysal/__init__.py b/pysal/__init__.py
new file mode 100644
index 0000000..16fcaf4
--- /dev/null
+++ b/pysal/__init__.py
@@ -0,0 +1,94 @@
+"""
+Python Spatial Analysis Library
+===============================
+
+
+Documentation
+-------------
+PySAL documentation is available in two forms: python docstrings and an html \
+ webpage at http://pysal.org/
+
+Available sub-packages
+----------------------
+
+cg
+ Basic data structures and tools for Computational Geometry
+core
+ Basic functions used by several sub-packages
+esda
+ Tools for Exploratory Spatial Data Analysis
+examples
+ Example data sets used by several sub-packages for examples and testing
+network
+ Spatial analysis on networks
+region
+ Regionalization algorithms and spatially constrained clustering
+spatial_dynamics
+ Space-time exploratory methods and clustering
+spreg
+ Spatial regression and econometrics
+weights
+ Tools for creating and manipulating weights
+contrib
+ Package for interfacing with third-party libraries
+
+Utilities
+---------
+`fileio`_
+ Tool for file input and output, supports many well known file formats
+"""
+import pysal.cg
+import pysal.core
+
+from pysal.version import version
+
+# toplevel imports to be explicit
+from pysal.esda.moran import Moran, Moran_BV, Moran_BV_matrix, Moran_Local
+from pysal.esda.geary import Geary
+from pysal.esda.join_counts import Join_Counts
+from pysal.esda.gamma import Gamma
+from pysal.esda.getisord import G, G_Local
+from pysal.esda.mapclassify import quantile, binC, bin, bin1d, Equal_Interval, \
+ Percentiles
+from pysal.esda.mapclassify import Box_Plot, Quantiles, Std_Mean, Maximum_Breaks
+from pysal.esda.mapclassify import Natural_Breaks, Fisher_Jenks, Jenks_Caspall
+from pysal.esda.mapclassify import Jenks_Caspall_Sampled, Jenks_Caspall_Forced
+from pysal.esda.mapclassify import User_Defined, Max_P_Classifier, gadf
+from pysal.esda.mapclassify import K_classifiers
+from pysal.inequality.theil import Theil, TheilD, TheilDSim
+from pysal.region.maxp import Maxp, Maxp_LISA
+from pysal.spatial_dynamics import Markov, Spatial_Markov, LISA_Markov, \
+ SpatialTau, Theta, Tau
+from pysal.spatial_dynamics import ergodic
+from pysal.spatial_dynamics import directional
+from pysal.weights import W, lat2W, block_weights, comb, full, shimbel, \
+ order, higher_order, higher_order_sp, remap_ids, hexLat2W, WSP, regime_weights
+from pysal.weights.Distance import knnW, Kernel, DistanceBand
+from pysal.weights.Contiguity import buildContiguity
+from pysal.weights.spatial_lag import lag_spatial
+from pysal.weights.Wsets import w_union, w_intersection, w_difference
+from pysal.weights.Wsets import w_symmetric_difference, w_subset
+from pysal.weights.user import queen_from_shapefile, rook_from_shapefile, \
+ knnW_from_array, knnW_from_shapefile, threshold_binaryW_from_array,\
+ threshold_binaryW_from_shapefile, threshold_continuousW_from_array,\
+ threshold_continuousW_from_shapefile, kernelW, kernelW_from_shapefile,\
+ adaptive_kernelW, adaptive_kernelW_from_shapefile,\
+ min_threshold_dist_from_shapefile, build_lattice_shapefile
+from pysal.core.util.weight_converter import weight_convert
+import pysal.spreg
+import pysal.examples
+from pysal.network.network import Network, NetworkG, NetworkK, NetworkF
+
+
+# Load the IOHandlers
+from pysal.core import IOHandlers
+# Assign pysal.open to dispatcher
+open = pysal.core.FileIO.FileIO
+
+#__all__=[]
+#import esda,weights
+#__all__+=esda.__all__
+#__all__+=weights.__all__
+
+# Constants
+MISSINGVALUE = None # used by fileIO to flag missing values.
diff --git a/pysal/cg/__init__.py b/pysal/cg/__init__.py
new file mode 100644
index 0000000..691068a
--- /dev/null
+++ b/pysal/cg/__init__.py
@@ -0,0 +1,10 @@
+"""
+A module for computational geometry.
+"""
+from shapes import *
+from standalone import *
+from locators import *
+from rtree import *
+from kdtree import *
+from sphere import *
+#why don't we import collection?
diff --git a/pysal/cg/kdtree.py b/pysal/cg/kdtree.py
new file mode 100644
index 0000000..f8ebd49
--- /dev/null
+++ b/pysal/cg/kdtree.py
@@ -0,0 +1,246 @@
+"""
+KDTree for PySAL: Python Spatial Analysis Library.
+
+Adds support for Arc Distance to scipy.spatial.KDTree.
+"""
+import sys
+import math
+import scipy.spatial
+import numpy
+from scipy import inf
+import sphere
+
+__author__ = "Charles R Schmidt <schmidtc at gmail.com>"
+DISTANCE_METRICS = ['Euclidean', 'Arc']
+FLOAT_EPS = numpy.finfo(float).eps
+
+
+class Arc_KDTree(scipy.spatial.KDTree):
+ def __init__(self, data, leafsize=10, radius=1.0):
+ """
+ KDTree using Arc Distance instead of Euclidean Distance.
+
+ Returned distances are based on radius.
+ For Example, pass in the the radius of earth in miles to get back miles.
+ Assumes data are Lng/Lat, does not account for geoids.
+
+ For more information see docs for scipy.spatial.KDTree
+
+ Examples
+ --------
+ >>> pts = [(0,90), (0,0), (180,0), (0,-90)]
+ >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
+ >>> d,i = kd.query((90,0), k=4)
+ >>> d
+ array([ 10007.54339801, 10007.54339801, 10007.54339801, 10007.54339801])
+ >>> circumference = 2*math.pi*sphere.RADIUS_EARTH_KM
+ >>> round(d[0],5) == round(circumference/4.0,5)
+ True
+ """
+ self.radius = radius
+ self.circumference = 2 * math.pi * radius
+ scipy.spatial.KDTree.__init__(self, map(sphere.toXYZ, data), leafsize)
+
+ def _toXYZ(self, x):
+ if not issubclass(type(x), numpy.ndarray):
+ x = numpy.array(x)
+ if len(x.shape) == 2 and x.shape[1] == 3: # assume point is already in XYZ
+ return x
+ if len(x.shape) == 1 and x.shape[0] == 3: # assume point is already in XYZ
+ return x
+ elif len(x.shape) == 1:
+ x = numpy.array(sphere.toXYZ(x))
+ else:
+ x = map(sphere.toXYZ, x)
+ return x
+
+ def count_neighbors(self, other, r, p=2):
+ """
+ See scipy.spatial.KDTree.count_neighbors
+
+ Parameters
+ ----------
+ p: ignored, kept to maintain compatibility with scipy.spatial.KDTree
+
+ Examples
+ --------
+ >>> pts = [(0,90), (0,0), (180,0), (0,-90)]
+ >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
+ >>> kd.count_neighbors(kd,0)
+ 4
+ >>> circumference = 2.0*math.pi*sphere.RADIUS_EARTH_KM
+ >>> kd.count_neighbors(kd,circumference/2.0)
+ 16
+ """
+ if r > 0.5 * self.circumference:
+ raise ValueError("r, must not exceed 1/2 circumference of the sphere (%f)." % self.circumference * 0.5)
+ r = sphere.arcdist2linear(r, self.radius)
+ return scipy.spatial.KDTree.count_neighbors(self, other, r)
+
+ def query(self, x, k=1, eps=0, p=2, distance_upper_bound=inf):
+ """
+ See scipy.spatial.KDTree.query
+
+ Parameters
+ ----------
+ x : array-like, last dimension self.m
+ query points are lng/lat.
+ p: ignored, kept to maintain compatibility with scipy.spatial.KDTree
+
+ Examples
+ --------
+ >>> pts = [(0,90), (0,0), (180,0), (0,-90)]
+ >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
+ >>> d,i = kd.query((90,0), k=4)
+ >>> d
+ array([ 10007.54339801, 10007.54339801, 10007.54339801, 10007.54339801])
+ >>> circumference = 2*math.pi*sphere.RADIUS_EARTH_KM
+ >>> round(d[0],5) == round(circumference/4.0,5)
+ True
+ >>> d,i = kd.query(kd.data, k=3)
+ >>> d2,i2 = kd.query(pts, k=3)
+ >>> (d == d2).all()
+ True
+ >>> (i == i2).all()
+ True
+ """
+ eps = sphere.arcdist2linear(eps, self.radius)
+ if distance_upper_bound != inf:
+ distance_upper_bound = sphere.arcdist2linear(
+ distance_upper_bound, self.radius)
+ d, i = scipy.spatial.KDTree.query(self, self._toXYZ(x), k,
+ eps=eps, distance_upper_bound=distance_upper_bound)
+ dims = len(d.shape)
+ r = self.radius
+ if dims == 0:
+ return sphere.linear2arcdist(d, r), i
+ if dims == 1:
+ #TODO: implement linear2arcdist on numpy arrays
+ d = [sphere.linear2arcdist(x, r) for x in d]
+ elif dims == 2:
+ d = [[sphere.linear2arcdist(x, r) for x in row] for row in d]
+ return numpy.array(d), i
+
+ def query_ball_point(self, x, r, p=2, eps=0):
+ """
+ See scipy.spatial.KDTree.query_ball_point
+
+ Parameters
+ ----------
+ p: ignored, kept to maintain compatibility with scipy.spatial.KDTree
+
+ Examples
+ --------
+ >>> pts = [(0,90), (0,0), (180,0), (0,-90)]
+ >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
+ >>> circumference = 2*math.pi*sphere.RADIUS_EARTH_KM
+ >>> kd.query_ball_point(pts, circumference/4.)
+ array([[0, 1, 2], [0, 1, 3], [0, 2, 3], [1, 2, 3]], dtype=object)
+ >>> kd.query_ball_point(pts, circumference/2.)
+ array([[0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3]], dtype=object)
+ """
+ eps = sphere.arcdist2linear(eps, self.radius)
+ #scipy.sphere.KDTree.query_ball_point appears to ignore the eps argument.
+ # we have some floating point errors moving back and forth between cordinate systems,
+ # so we'll account for that be adding some to our radius, 3*float's eps value.
+ if r > 0.5 * self.circumference:
+ raise ValueError("r, must not exceed 1/2 circumference of the sphere (%f)." % self.circumference * 0.5)
+ r = sphere.arcdist2linear(r, self.radius) + FLOAT_EPS * 3
+ return scipy.spatial.KDTree.query_ball_point(self, self._toXYZ(x), r, eps=eps)
+
+ def query_ball_tree(self, other, r, p=2, eps=0):
+ """
+ See scipy.spatial.KDTree.query_ball_tree
+
+ Parameters
+ ----------
+ p: ignored, kept to maintain compatibility with scipy.spatial.KDTree
+
+ Examples
+ --------
+ >>> pts = [(0,90), (0,0), (180,0), (0,-90)]
+ >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
+ >>> kd.query_ball_tree(kd, kd.circumference/4.)
+ [[0, 1, 2], [0, 1, 3], [0, 2, 3], [1, 2, 3]]
+ >>> kd.query_ball_tree(kd, kd.circumference/2.)
+ [[0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3]]
+ """
+ eps = sphere.arcdist2linear(eps, self.radius)
+ #scipy.sphere.KDTree.query_ball_point appears to ignore the eps argument.
+ # we have some floating point errors moving back and forth between cordinate systems,
+ # so we'll account for that be adding some to our radius, 3*float's eps value.
+ if self.radius != other.radius:
+ raise ValueError("Both trees must have the same radius.")
+ if r > 0.5 * self.circumference:
+ raise ValueError("r, must not exceed 1/2 circumference of the sphere (%f)." % self.circumference * 0.5)
+ r = sphere.arcdist2linear(r, self.radius) + FLOAT_EPS * 3
+ return scipy.spatial.KDTree.query_ball_tree(self, other, r, eps=eps)
+
+ def query_pairs(self, r, p=2, eps=0):
+ """
+ See scipy.spatial.KDTree.query_pairs
+
+ Parameters
+ ----------
+ p: ignored, kept to maintain compatibility with scipy.spatial.KDTree
+
+ Examples
+ --------
+ >>> pts = [(0,90), (0,0), (180,0), (0,-90)]
+ >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
+ >>> kd.query_pairs(kd.circumference/4.)
+ set([(0, 1), (1, 3), (2, 3), (0, 2)])
+ >>> kd.query_pairs(kd.circumference/2.)
+ set([(0, 1), (1, 2), (1, 3), (2, 3), (0, 3), (0, 2)])
+ """
+ if r > 0.5 * self.circumference:
+ raise ValueError("r, must not exceed 1/2 circumference of the sphere (%f)." % self.circumference * 0.5)
+ r = sphere.arcdist2linear(r, self.radius) + FLOAT_EPS * 3
+ return scipy.spatial.KDTree.query_pairs(self, r, eps=eps)
+
+ def sparse_distance_matrix(self, other, max_distance, p=2):
+ """
+ See scipy.spatial.KDTree.sparse_distance_matrix
+
+ Parameters
+ ----------
+ p: ignored, kept to maintain compatibility with scipy.spatial.KDTree
+
+ Examples
+ --------
+ >>> pts = [(0,90), (0,0), (180,0), (0,-90)]
+ >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
+ >>> kd.sparse_distance_matrix(kd, kd.circumference/4.).todense()
+ matrix([[ 0. , 10007.54339801, 10007.54339801, 0. ],
+ [ 10007.54339801, 0. , 0. , 10007.54339801],
+ [ 10007.54339801, 0. , 0. , 10007.54339801],
+ [ 0. , 10007.54339801, 10007.54339801, 0. ]])
+ >>> kd.sparse_distance_matrix(kd, kd.circumference/2.).todense()
+ matrix([[ 0. , 10007.54339801, 10007.54339801, 20015.08679602],
+ [ 10007.54339801, 0. , 20015.08679602, 10007.54339801],
+ [ 10007.54339801, 20015.08679602, 0. , 10007.54339801],
+ [ 20015.08679602, 10007.54339801, 10007.54339801, 0. ]])
+ """
+ if self.radius != other.radius:
+ raise ValueError("Both trees must have the same radius.")
+ if max_distance > 0.5 * self.circumference:
+ raise ValueError("max_distance, must not exceed 1/2 circumference of the sphere (%f)." % self.circumference * 0.5)
+ max_distance = sphere.arcdist2linear(
+ max_distance, self.radius) + FLOAT_EPS * 3
+ D = scipy.spatial.KDTree.sparse_distance_matrix(
+ self, other, max_distance)
+ D = D.tocoo()
+ #print D.data
+ a2l = lambda x: sphere.linear2arcdist(x, self.radius)
+ #print map(a2l,D.data)
+ return scipy.sparse.coo_matrix((map(a2l, D.data), (D.row, D.col))).todok()
+
+
+def KDTree(data, leafsize=10, distance_metric='Euclidean', radius=1.0):
+ if distance_metric == 'Euclidean':
+ if int(scipy.version.version.split(".")[1]) < 12:
+ return scipy.spatial.KDTree(data, leafsize)
+ else:
+ return scipy.spatial.cKDTree(data, leafsize)
+ elif distance_metric == 'Arc':
+ return Arc_KDTree(data, leafsize, radius)
diff --git a/pysal/cg/locators.py b/pysal/cg/locators.py
new file mode 100644
index 0000000..ba35828
--- /dev/null
+++ b/pysal/cg/locators.py
@@ -0,0 +1,990 @@
+"""
+Computational geometry code for PySAL: Python Spatial Analysis Library.
+"""
+
+__author__ = "Sergio J. Rey, Xinyue Ye, Charles Schmidt, Andrew Winslow"
+__credits__ = "Copyright (c) 2005-2011 Sergio J. Rey"
+
+import math
+import copy
+import doctest
+from rtree import *
+from standalone import *
+from shapes import *
+
+__all__ = ["IntervalTree", "Grid", "BruteForcePointLocator",
+ "PointLocator", "PolygonLocator"]
+
+
+class IntervalTree:
+ """
+ Representation of an interval tree. An interval tree is a data structure which is used to
+ quickly determine which intervals in a set contain a value or overlap with a query interval.
+
+ References
+ ----------
+
+ de Berg, van Kreveld, Overmars, Schwarzkopf. Computational Geometry: Algorithms and Application.
+ 212-217. Springer-Verlag, Berlin, 2000.
+ """
+
+ class _Node:
+ """
+ Private class representing a node in an interval tree.
+ """
+
+ def __init__(self, val, left_list, right_list, left_node, right_node):
+ self.val = val
+ self.left_list = left_list
+ self.right_list = right_list
+ self.left_node = left_node
+ self.right_node = right_node
+
+ def query(self, q):
+ i = 0
+ if q < self.val:
+ while i < len(self.left_list) and self.left_list[i][0] <= q:
+ i += 1
+ return [rec[2] for rec in self.left_list[0:i]]
+ else:
+ while i < len(self.right_list) and self.right_list[i][1] >= q:
+ i += 1
+ return [rec[2] for rec in self.right_list[0:i]]
+
+ def add(self, i):
+ """
+ Adds an interval to the IntervalTree node.
+ """
+ if not i[0] <= self.val <= i[1]:
+ raise Exception('Attempt to add an interval to an inappropriate IntervalTree node')
+ index = 0
+ while index < len(self.left_list) and self.left_list[index] < i[0]:
+ index = index + 1
+ self.left_list.insert(index, i)
+ index = 0
+ while index < len(self.right_list) and self.right_list[index] > i[1]:
+ index = index + 1
+ self.right_list.insert(index, i)
+
+ def remove(self, i):
+ """
+ Removes an interval from the IntervalTree node.
+ """
+ l = 0
+ r = len(self.left_list)
+ while l < r:
+ m = (l + r) / 2
+ if self.left_list[m] < i[0]:
+ l = m + 1
+ elif self.left_list[m] > i[0]:
+ r = m
+ else:
+ if self.left_list[m] == i:
+ self.left_list.pop(m)
+ else:
+ raise Exception('Attempt to remove an unknown interval')
+ l = 0
+ r = len(self.right_list)
+ while l < r:
+ m = (l + r) / 2
+ if self.right_list[m] > i[1]:
+ l = m + 1
+ elif self.right_left[m] < i[1]:
+ r = m
+ else:
+ if self.right_list[m] == i:
+ self.right_list.pop(m)
+ else:
+ raise Exception('Attempt to remove an unknown interval')
+
+ def __init__(self, intervals):
+ """
+ __init__((number, number, x) list) -> IntervalTree
+ Returns an interval tree containing specified intervals.
+
+ Parameters
+ ----------
+ intervals : a list of (lower, upper, item) elements to build the interval tree
+
+ Examples
+ --------
+
+ >>> intervals = [(-1, 2, 'A'), (5, 9, 'B'), (3, 6, 'C')]
+ >>> it = IntervalTree(intervals)
+ >>> isinstance(it, IntervalTree)
+ True
+ """
+ self._build(intervals)
+
+ def _build(self, intervals):
+ """
+ Build an interval tree containing _intervals_.
+ Each interval should be of the form (start, end, object).
+
+ build((number, number, x) list) -> None
+
+ Test tag: <tc>#is#IntervalTree.build</tc>
+ """
+ bad_is = filter(lambda i: i[0] > i[1], intervals)
+ if bad_is != []:
+ raise Exception('Attempt to build IntervalTree with invalid intervals: ' + str(bad_is))
+ eps = list(set([i[0] for i in intervals] + [i[1] for i in intervals]))
+ eps.sort()
+ self.root = self._recursive_build(copy.copy(intervals), eps)
+
+ def query(self, q):
+ """
+ Returns the intervals intersected by a value or interval.
+
+ query((number, number) or number) -> x list
+
+ Parameters
+ ----------
+
+ q : a value or interval to find intervals intersecting
+
+ Examples
+ --------
+
+ >>> intervals = [(-1, 2, 'A'), (5, 9, 'B'), (3, 6, 'C')]
+ >>> it = IntervalTree(intervals)
+ >>> it.query((7, 14))
+ ['B']
+ >>> it.query(1)
+ ['A']
+ """
+ if isinstance(q, tuple):
+ return self._query_range(q, self.root)
+ else:
+ return self._query_points(q)
+
+ def _query_range(self, q, root):
+ if root is None:
+ return []
+ if root.val < q[0]:
+ return self._query_range(q, root.right_node) + root.query(q[0])
+ elif root.val > q[1]:
+ return self._query_range(q, root.left_node) + root.query(q[1])
+ else:
+ return root.query(root.val) + self._query_range(q, root.left_node) + self._query_range(q, root.right_node)
+
+ def _query_points(self, q):
+ found = []
+ cur = self.root
+ while cur is not None:
+ found.extend(cur.query(q))
+ if q < cur.val:
+ cur = cur.left_node
+ else:
+ cur = cur.right_node
+ return found
+
+ def _recursive_build(self, intervals, eps):
+ def sign(x):
+ if x < 0:
+ return -1
+ elif x > 0:
+ return 1
+ else:
+ return 0
+
+ def binary_search(list, q):
+ l = 0
+ r = len(list)
+ while l < r:
+ m = (l + r) / 2
+ if list[m] < q:
+ l = m + 1
+ else:
+ r = m
+ return l
+
+ if eps == []:
+ return None
+ median = eps[len(eps) / 2]
+ hit_is = []
+ rem_is = []
+ for i in intervals:
+ if i[0] <= median <= i[1]:
+ hit_is.append(i)
+ else:
+ rem_is.append(i)
+ left_list = copy.copy(hit_is)
+ left_list.sort(lambda a, b: sign(a[0] - b[0]))
+ right_list = copy.copy(hit_is)
+ right_list.sort(lambda a, b: sign(b[1] - a[1]))
+ eps = list(set([i[0] for i in intervals] + [i[1] for i in intervals]))
+ eps.sort()
+ bp = binary_search(eps, median)
+ left_eps = eps[:bp]
+ right_eps = eps[bp:]
+ node = (IntervalTree._Node(median, left_list, right_list,
+ self._recursive_build(rem_is, left_eps),
+ self._recursive_build(rem_is, right_eps)))
+ return node
+
+
+class Grid:
+ """
+ Representation of a binning data structure.
+ """
+
+ def __init__(self, bounds, resolution):
+ """
+ Returns a grid with specified properties.
+
+ __init__(Rectangle, number) -> Grid
+
+ Parameters
+ ----------
+ bounds : the area for the grid to encompass
+ resolution : the diameter of each bin
+
+ Examples
+ --------
+ TODO: complete this doctest
+ >>> g = Grid(Rectangle(0, 0, 10, 10), 1)
+ """
+ if resolution == 0:
+ raise Exception('Cannot create grid with resolution 0')
+ self.res = resolution
+ self.hash = {}
+ self.x_range = (bounds.left, bounds.right)
+ self.y_range = (bounds.lower, bounds.upper)
+ try:
+ self.i_range = int(math.ceil(
+ (self.x_range[1] - self.x_range[0]) / self.res))
+ self.j_range = int(math.ceil(
+ (self.y_range[1] - self.y_range[0]) / self.res))
+ except Exception:
+ raise Exception('Invalid arguments for Grid(): (' +
+ str(x_range) + ', ' + str(y_range) + ', ' + str(res) + ')')
+
+ def in_grid(self, loc):
+ """
+ Returns whether a 2-tuple location _loc_ lies inside the grid bounds.
+
+ Test tag: <tc>#is#Grid.in_grid</tc>
+ """
+ return (self.x_range[0] <= loc[0] <= self.x_range[1] and
+ self.y_range[0] <= loc[1] <= self.y_range[1])
+
+ def __grid_loc(self, loc):
+ i = min(self.i_range, max(int((loc[0] - self.x_range[0]) /
+ self.res), 0))
+ j = min(self.j_range, max(int((loc[1] - self.y_range[0]) /
+ self.res), 0))
+ return (i, j)
+
+ def add(self, item, pt):
+ """
+ Adds an item to the grid at a specified location.
+
+ add(x, Point) -> x
+
+ Parameters
+ ----------
+ item : the item to insert into the grid
+ pt : the location to insert the item at
+
+ Examples
+ --------
+
+ >>> g = Grid(Rectangle(0, 0, 10, 10), 1)
+ >>> g.add('A', Point((4.2, 8.7)))
+ 'A'
+ """
+ if not self.in_grid(pt):
+ raise Exception('Attempt to insert item at location outside grid bounds: ' + str(pt))
+ grid_loc = self.__grid_loc(pt)
+ if grid_loc in self.hash:
+ self.hash[grid_loc].append((pt, item))
+ else:
+ self.hash[grid_loc] = [(pt, item)]
+ return item
+
+ def remove(self, item, pt):
+ """
+ Removes an item from the grid at a specified location.
+
+ remove(x, Point) -> x
+
+ Parameters
+ ----------
+ item : the item to remove from the grid
+ pt : the location the item was added at
+
+ Examples
+ --------
+
+ >>> g = Grid(Rectangle(0, 0, 10, 10), 1)
+ >>> g.add('A', Point((4.2, 8.7)))
+ 'A'
+ >>> g.remove('A', Point((4.2, 8.7)))
+ 'A'
+ """
+ if not self.in_grid(pt):
+ raise Exception('Attempt to remove item at location outside grid bounds: ' + str(pt))
+ grid_loc = self.__grid_loc(pt)
+ self.hash[grid_loc].remove((pt, item))
+ if self.hash[grid_loc] == []:
+ del self.hash[grid_loc]
+ return item
+
+ def bounds(self, bounds):
+ """
+ Returns a list of items found in the grid within the bounds specified.
+
+ bounds(Rectangle) -> x list
+
+ Parameters
+ ----------
+ item : the item to remove from the grid
+ pt : the location the item was added at
+
+ Examples
+ --------
+
+ >>> g = Grid(Rectangle(0, 0, 10, 10), 1)
+ >>> g.add('A', Point((1.0, 1.0)))
+ 'A'
+ >>> g.add('B', Point((4.0, 4.0)))
+ 'B'
+ >>> g.bounds(Rectangle(0, 0, 3, 3))
+ ['A']
+ >>> g.bounds(Rectangle(2, 2, 5, 5))
+ ['B']
+ >>> sorted(g.bounds(Rectangle(0, 0, 5, 5)))
+ ['A', 'B']
+ """
+ x_range = (bounds.left, bounds.right)
+ y_range = (bounds.lower, bounds.upper)
+ items = []
+ lower_left = self.__grid_loc((x_range[0], y_range[0]))
+ upper_right = self.__grid_loc((x_range[1], y_range[1]))
+ for i in xrange(lower_left[0], upper_right[0] + 1):
+ for j in xrange(lower_left[1], upper_right[1] + 1):
+ if (i, j) in self.hash:
+ items.extend(map(lambda item: item[1], filter(lambda item: x_range[0] <= item[0][0] <= x_range[1] and y_range[0] <= item[0][1] <= y_range[1], self.hash[(i, j)])))
+ return items
+
+ def proximity(self, pt, r):
+ """
+ Returns a list of items found in the grid within a specified distance of a point.
+
+ proximity(Point, number) -> x list
+
+ Parameters
+ ----------
+ pt : the location to search around
+ r : the distance to search around the point
+
+ Examples
+ --------
+ >>> g = Grid(Rectangle(0, 0, 10, 10), 1)
+ >>> g.add('A', Point((1.0, 1.0)))
+ 'A'
+ >>> g.add('B', Point((4.0, 4.0)))
+ 'B'
+ >>> g.proximity(Point((2.0, 1.0)), 2)
+ ['A']
+ >>> g.proximity(Point((6.0, 5.0)), 3.0)
+ ['B']
+ >>> sorted(g.proximity(Point((4.0, 1.0)), 4.0))
+ ['A', 'B']
+ """
+ items = []
+ lower_left = self.__grid_loc((pt[0] - r, pt[1] - r))
+ upper_right = self.__grid_loc((pt[0] + r, pt[1] + r))
+ for i in xrange(lower_left[0], upper_right[0] + 1):
+ for j in xrange(lower_left[1], upper_right[1] + 1):
+ if (i, j) in self.hash:
+ items.extend(map(lambda item: item[1], filter(lambda item: get_points_dist(pt, item[0]) <= r, self.hash[(i, j)])))
+ return items
+
+ def nearest(self, pt):
+ """
+ Returns the nearest item to a point.
+
+ nearest(Point) -> x
+
+ Parameters
+ ----------
+ pt : the location to search near
+
+ Examples
+ --------
+ >>> g = Grid(Rectangle(0, 0, 10, 10), 1)
+ >>> g.add('A', Point((1.0, 1.0)))
+ 'A'
+ >>> g.add('B', Point((4.0, 4.0)))
+ 'B'
+ >>> g.nearest(Point((2.0, 1.0)))
+ 'A'
+ >>> g.nearest(Point((7.0, 5.0)))
+ 'B'
+ """
+ search_size = self.res
+ while (self.proximity(pt, search_size) == [] and
+ (get_points_dist((self.x_range[0], self.y_range[0]), pt) > search_size or
+ get_points_dist((self.x_range[1], self.y_range[0]), pt) > search_size or
+ get_points_dist((self.x_range[0], self.y_range[1]), pt) > search_size or
+ get_points_dist((self.x_range[1], self.y_range[1]), pt) > search_size)):
+ search_size = 2 * search_size
+ items = []
+ lower_left = self.__grid_loc(
+ (pt[0] - search_size, pt[1] - search_size))
+ upper_right = self.__grid_loc(
+ (pt[0] + search_size, pt[1] + search_size))
+ for i in xrange(lower_left[0], upper_right[0] + 1):
+ for j in xrange(lower_left[1], upper_right[1] + 1):
+ if (i, j) in self.hash:
+ items.extend(map(lambda item: (get_points_dist(pt, item[
+ 0]), item[1]), self.hash[(i, j)]))
+ if items == []:
+ return None
+ return min(items)[1]
+
+
+class BruteForcePointLocator:
+ """
+ A class which does naive linear search on a set of Point objects.
+ """
+ def __init__(self, points):
+ """
+ Creates a naive index of the points specified.
+
+ __init__(Point list) -> BruteForcePointLocator
+
+ Parameters
+ ----------
+ points : a list of points to index (Point list)
+
+ Examples
+ --------
+ >>> pl = BruteForcePointLocator([Point((0, 0)), Point((5, 0)), Point((0, 10))])
+ """
+ self._points = points
+
+ def nearest(self, query_point):
+ """
+ Returns the nearest point indexed to a query point.
+
+ nearest(Point) -> Point
+
+ Parameters
+ ----------
+ query_point : a point to find the nearest indexed point to
+
+ Examples
+ --------
+ >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]
+ >>> pl = BruteForcePointLocator(points)
+ >>> n = pl.nearest(Point((1, 1)))
+ >>> str(n)
+ '(0.0, 0.0)'
+ """
+ return min(self._points, key=lambda p: get_points_dist(p, query_point))
+
+ def region(self, region_rect):
+ """
+ Returns the indexed points located inside a rectangular query region.
+
+ region(Rectangle) -> Point list
+
+ Parameters
+ ----------
+ region_rect : the rectangular range to find indexed points in
+
+ Examples
+ --------
+ >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]
+ >>> pl = BruteForcePointLocator(points)
+ >>> pts = pl.region(Rectangle(-1, -1, 10, 10))
+ >>> len(pts)
+ 3
+ """
+ return filter(lambda p: get_rectangle_point_intersect(region_rect, p) is not None, self._points)
+
+ def proximity(self, origin, r):
+ """
+ Returns the indexed points located within some distance of an origin point.
+
+ proximity(Point, number) -> Point list
+
+ Parameters
+ ----------
+ origin : the point to find indexed points near
+ r : the maximum distance to find indexed point from the origin point
+
+ Examples
+ --------
+ >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]
+ >>> pl = BruteForcePointLocator(points)
+ >>> neighs = pl.proximity(Point((1, 0)), 2)
+ >>> len(neighs)
+ 1
+ >>> p = neighs[0]
+ >>> isinstance(p, Point)
+ True
+ >>> str(p)
+ '(0.0, 0.0)'
+ """
+ return filter(lambda p: get_points_dist(p, origin) <= r, self._points)
+
+
+class PointLocator:
+ """
+ An abstract representation of a point indexing data structure.
+ """
+
+ def __init__(self, points):
+ """
+ Returns a point locator object.
+
+ __init__(Point list) -> PointLocator
+
+ Parameters
+ ----------
+ points : a list of points to index
+
+ Examples
+ --------
+ >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]
+ >>> pl = PointLocator(points)
+ """
+ self._locator = BruteForcePointLocator(points)
+
+ def nearest(self, query_point):
+ """
+ Returns the nearest point indexed to a query point.
+
+ nearest(Point) -> Point
+
+ Parameters
+ ----------
+ query_point : a point to find the nearest indexed point to
+
+ Examples
+ --------
+ >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]
+ >>> pl = PointLocator(points)
+ >>> n = pl.nearest(Point((1, 1)))
+ >>> str(n)
+ '(0.0, 0.0)'
+ """
+ return self._locator.nearest(query_point)
+
+ def region(self, region_rect):
+ """
+ Returns the indexed points located inside a rectangular query region.
+
+ region(Rectangle) -> Point list
+
+ Parameters
+ ----------
+ region_rect : the rectangular range to find indexed points in
+
+ Examples
+ --------
+ >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]
+ >>> pl = PointLocator(points)
+ >>> pts = pl.region(Rectangle(-1, -1, 10, 10))
+ >>> len(pts)
+ 3
+ """
+ return self._locator.region(region_rect)
+ overlapping = region
+
+ def polygon(self, polygon):
+ """
+ Returns the indexed points located inside a polygon
+ """
+
+ # get points in polygon bounding box
+
+ # for points in bounding box, check for inclusion in polygon
+
+ def proximity(self, origin, r):
+ """
+ Returns the indexed points located within some distance of an origin point.
+
+ proximity(Point, number) -> Point list
+
+ Parameters
+ ----------
+ origin : the point to find indexed points near
+ r : the maximum distance to find indexed point from the origin point
+
+ Examples
+ --------
+ >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]
+ >>> pl = PointLocator(points)
+ >>> len(pl.proximity(Point((1, 0)), 2))
+ 1
+ """
+ return self._locator.proximity(origin, r)
+
+
+class PolygonLocator:
+ """
+ An abstract representation of a polygon indexing data structure.
+ """
+
+ def __init__(self, polygons):
+ """
+ Returns a polygon locator object.
+
+ __init__(Polygon list) -> PolygonLocator
+
+ Parameters
+ ----------
+ polygons : a list of polygons to index
+
+ Examples
+ --------
+ >>> p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])
+ >>> p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])
+ >>> pl = PolygonLocator([p1, p2])
+ >>> isinstance(pl, PolygonLocator)
+ True
+ """
+
+ self._locator = polygons
+ # create and rtree
+ self._rtree = RTree()
+ for polygon in polygons:
+ x = polygon.bounding_box.left
+ y = polygon.bounding_box.lower
+ X = polygon.bounding_box.right
+ Y = polygon.bounding_box.upper
+ self._rtree.insert(polygon, Rect(x, y, X, Y))
+
+ def inside(self, query_rectangle):
+ """
+ Returns polygons that are inside query_rectangle
+
+ Examples
+ --------
+
+ >>> p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])
+ >>> p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])
+ >>> p3 = Polygon([Point((7, 1)), Point((8, 7)), Point((9, 1))])
+ >>> pl = PolygonLocator([p1, p2, p3])
+ >>> qr = Rectangle(0, 0, 5, 5)
+ >>> res = pl.inside( qr )
+ >>> len(res)
+ 1
+ >>> qr = Rectangle(3, 7, 5, 8)
+ >>> res = pl.inside( qr )
+ >>> len(res)
+ 0
+ >>> qr = Rectangle(10, 10, 12, 12)
+ >>> res = pl.inside( qr )
+ >>> len(res)
+ 0
+ >>> qr = Rectangle(0, 0, 12, 12)
+ >>> res = pl.inside( qr )
+ >>> len(res)
+ 3
+
+ Notes
+ -----
+
+ inside means the intersection of the query rectangle and a
+ polygon is not empty and is equal to the area of the polygon
+ """
+ left = query_rectangle.left
+ right = query_rectangle.right
+ upper = query_rectangle.upper
+ lower = query_rectangle.lower
+
+ # rtree rect
+ qr = Rect(left, lower, right, upper)
+ # bb overlaps
+ res = [r.leaf_obj() for r in self._rtree.query_rect(qr)
+ if r.is_leaf()]
+
+ qp = Polygon([Point((left, lower)), Point((right, lower)),
+ Point((right, upper)), Point((left, upper))])
+ ip = []
+ GPPI = get_polygon_point_intersect
+ for poly in res:
+ flag = True
+ lower = poly.bounding_box.lower
+ right = poly.bounding_box.right
+ upper = poly.bounding_box.upper
+ left = poly.bounding_box.left
+ p1 = Point((left, lower))
+ p2 = Point((right, upper))
+ if GPPI(qp, p1) and GPPI(qp, p2):
+ ip.append(poly)
+ return ip
+
+ def overlapping(self, query_rectangle):
+ """
+ Returns list of polygons that overlap query_rectangle
+
+ Examples
+ --------
+
+ >>> p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])
+ >>> p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])
+ >>> p3 = Polygon([Point((7, 1)), Point((8, 7)), Point((9, 1))])
+ >>> pl = PolygonLocator([p1, p2, p3])
+ >>> qr = Rectangle(0, 0, 5, 5)
+ >>> res = pl.overlapping( qr )
+ >>> len(res)
+ 2
+ >>> qr = Rectangle(3, 7, 5, 8)
+ >>> res = pl.overlapping( qr )
+ >>> len(res)
+ 1
+ >>> qr = Rectangle(10, 10, 12, 12)
+ >>> res = pl.overlapping( qr )
+ >>> len(res)
+ 0
+ >>> qr = Rectangle(0, 0, 12, 12)
+ >>> res = pl.overlapping( qr )
+ >>> len(res)
+ 3
+ >>> qr = Rectangle(8, 3, 9, 4)
+ >>> p1 = Polygon([Point((2, 1)), Point((2, 3)), Point((4, 3)), Point((4,1))])
+ >>> p2 = Polygon([Point((7, 1)), Point((7, 5)), Point((10, 5)), Point((10, 1))])
+ >>> pl = PolygonLocator([p1, p2])
+ >>> res = pl.overlapping(qr)
+ >>> len(res)
+ 1
+
+ Notes
+ -----
+ overlapping means the intersection of the query rectangle and a
+ polygon is not empty and is no larger than the area of the polygon
+ """
+ left = query_rectangle.left
+ right = query_rectangle.right
+ upper = query_rectangle.upper
+ lower = query_rectangle.lower
+
+ # rtree rect
+ qr = Rect(left, lower, right, upper)
+
+ # bb overlaps
+ res = [r.leaf_obj() for r in self._rtree.query_rect(qr)
+ if r.is_leaf()]
+ # have to check for polygon overlap using segment intersection
+
+ # add polys whose bb contains at least one of the corners of the query
+ # rectangle
+
+ sw = (left, lower)
+ se = (right, lower)
+ ne = (right, upper)
+ nw = (left, upper)
+ pnts = [sw, se, ne, nw]
+ cs = []
+ for pnt in pnts:
+ c = [r.leaf_obj() for r in self._rtree.query_point(
+ pnt) if r.is_leaf()]
+ cs.extend(c)
+
+ cs = list(set(cs))
+
+ overlapping = []
+
+ # first find polygons with at least one vertex inside query rectangle
+ remaining = copy.copy(res)
+ for polygon in res:
+ vertices = polygon.vertices
+ for vertex in vertices:
+ xb = vertex[0] >= left
+ xb *= vertex[0] < right
+ yb = vertex[1] >= lower
+ yb *= vertex[1] < upper
+ if xb * yb:
+ overlapping.append(polygon)
+ remaining.remove(polygon)
+ break
+
+ # for remaining polys in bb overlap check if vertex chains intersect
+ # segments of the query rectangle
+ left_edge = LineSegment(Point((left, lower)), Point((left,
+ upper)))
+ right_edge = LineSegment(Point((right, lower)), Point((right,
+ upper)))
+ lower_edge = LineSegment(Point((left, lower)), Point((right,
+ lower)))
+ upper_edge = LineSegment(Point((left, upper)), Point((right,
+ upper)))
+ for polygon in remaining:
+ vertices = copy.copy(polygon.vertices)
+ if vertices[-1] != vertices[0]:
+ vertices.append(vertices[0]) # put on closed cartographic form
+ nv = len(vertices)
+ for i in range(nv - 1):
+ head = vertices[i]
+ tail = vertices[i + 1]
+ edge = LineSegment(head, tail)
+ li = get_segments_intersect(edge, left_edge)
+ if li:
+ overlapping.append(polygon)
+ break
+ elif get_segments_intersect(edge, right_edge):
+ overlapping.append(polygon)
+ break
+ elif get_segments_intersect(edge, lower_edge):
+ overlapping.append(polygon)
+ break
+ elif get_segments_intersect(edge, upper_edge):
+ overlapping.append(polygon)
+ break
+ # check remaining for explicit containment of the bounding rectangle
+ # cs has candidates for this check
+ sw = Point(sw)
+ se = Point(se)
+ ne = Point(ne)
+ nw = Point(nw)
+ for polygon in cs:
+ if get_polygon_point_intersect(polygon, sw):
+ overlapping.append(polygon)
+ break
+ elif get_polygon_point_intersect(polygon, se):
+ overlapping.append(polygon)
+ break
+ elif get_polygon_point_intersect(polygon, ne):
+ overlapping.append(polygon)
+ break
+ elif get_polygon_point_intersect(polygon, nw):
+ overlapping.append(polygon)
+ break
+ return list(set(overlapping))
+
+ def nearest(self, query_point, rule='vertex'):
+ """
+ Returns the nearest polygon indexed to a query point based on
+ various rules.
+
+ nearest(Polygon) -> Polygon
+
+ Parameters
+ ----------
+ query_point : a point to find the nearest indexed polygon to
+
+ rule : representative point for polygon in nearest query.
+ vertex -- measures distance between vertices and query_point
+ centroid -- measures distance between centroid and
+ query_point
+ edge -- measures the distance between edges and query_point
+
+ Examples
+ --------
+ >>> p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])
+ >>> p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])
+ >>> pl = PolygonLocator([p1, p2])
+ >>> try: n = pl.nearest(Point((-1, 1)))
+ ... except NotImplementedError: print "future test: str(min(n.vertices())) == (0.0, 1.0)"
+ future test: str(min(n.vertices())) == (0.0, 1.0)
+ """
+ raise NotImplementedError
+
+ def region(self, region_rect):
+ """
+ Returns the indexed polygons located inside a rectangular query region.
+
+ region(Rectangle) -> Polygon list
+
+ Parameters
+ ----------
+ region_rect : the rectangular range to find indexed polygons in
+
+ Examples
+ --------
+ >>> p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])
+ >>> p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])
+ >>> pl = PolygonLocator([p1, p2])
+ >>> n = pl.region(Rectangle(0, 0, 4, 10))
+ >>> len(n)
+ 2
+ """
+ n = self._locator
+ for polygon in n:
+ points = polygon.vertices
+ pl = BruteForcePointLocator(points)
+ pts = pl.region(region_rect)
+ if len(pts) == 0:
+ n.remove(polygon)
+ return n
+
+ def contains_point(self, point):
+ """
+ Returns polygons that contain point
+
+
+ Parameters
+ ----------
+ point: point (x,y)
+
+ Returns
+ -------
+ list of polygons containing point
+
+ Examples
+ --------
+ >>> p1 = Polygon([Point((0,0)), Point((6,0)), Point((4,4))])
+ >>> p2 = Polygon([Point((1,2)), Point((4,0)), Point((4,4))])
+ >>> p1.contains_point((2,2))
+ 1
+ >>> p2.contains_point((2,2))
+ 1
+ >>> pl = PolygonLocator([p1, p2])
+ >>> len(pl.contains_point((2,2)))
+ 2
+ >>> p2.contains_point((1,1))
+ 0
+ >>> p1.contains_point((1,1))
+ 1
+ >>> len(pl.contains_point((1,1)))
+ 1
+ >>> p1.centroid
+ (3.3333333333333335, 1.3333333333333333)
+ >>> pl.contains_point((1,1))[0].centroid
+ (3.3333333333333335, 1.3333333333333333)
+
+ """
+ # bbounding box containment
+ res = [r.leaf_obj() for r in self._rtree.query_point(point)
+ if r.is_leaf()]
+ # explicit containment check for candidate polygons needed
+ return [poly for poly in res if poly.contains_point(point)]
+
+ def proximity(self, origin, r, rule='vertex'):
+ """
+ Returns the indexed polygons located within some distance of an
+ origin point based on various rules.
+
+ proximity(Polygon, number) -> Polygon list
+
+ Parameters
+ ----------
+ origin : the point to find indexed polygons near
+ r : the maximum distance to find indexed polygon from the origin point
+
+ rule : representative point for polygon in nearest query.
+ vertex -- measures distance between vertices and query_point
+ centroid -- measures distance between centroid and
+ query_point
+ edge -- measures the distance between edges and query_point
+
+ Examples
+ --------
+ >>> p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])
+ >>> p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])
+ >>> pl = PolygonLocator([p1, p2])
+ >>> try:
+ ... len(pl.proximity(Point((0, 0)), 2))
+ ... except NotImplementedError:
+ ... print "future test: len(pl.proximity(Point((0, 0)), 2)) == 2"
+ future test: len(pl.proximity(Point((0, 0)), 2)) == 2
+ """
+ raise NotImplementedError
+
diff --git a/pysal/cg/rtree.py b/pysal/cg/rtree.py
new file mode 100644
index 0000000..4e8e9a2
--- /dev/null
+++ b/pysal/cg/rtree.py
@@ -0,0 +1,639 @@
+#pylint: disable-msg=C0103, C0301
+"""
+Pure Python implementation of RTree spatial index
+
+
+
+Adaptation of
+http://code.google.com/p/pyrtree/
+
+R-tree.
+see doc/ref/r-tree-clustering-split-algo.pdf
+"""
+
+__author__ = "Sergio J. Rey"
+
+__all__ = ['RTree', 'Rect', 'Rtree']
+
+MAXCHILDREN = 10
+MAX_KMEANS = 5
+BUFFER = 0.0000001
+import math
+import random
+import time
+import array
+
+
+class Rect(object):
+ """
+ A rectangle class that stores: an axis aligned rectangle, and: two
+ flags (swapped_x and swapped_y). (The flags are stored
+ implicitly via swaps in the order of minx/y and maxx/y.)
+ """
+
+ __slots__ = ("x", "y", "xx", "yy", "swapped_x", "swapped_y")
+
+ def __getstate__(self):
+ return (self.x, self.y, self.xx, self.yy, self.swapped_x, self.swapped_y)
+
+ def __setstate__(self, state):
+ self.x, self.y, self.xx, self.yy, self.swapped_x, self.swapped_y = state
+
+ def __init__(self, minx, miny, maxx, maxy):
+ self.swapped_x = (maxx < minx)
+ self.swapped_y = (maxy < miny)
+ self.x = minx
+ self.y = miny
+ self.xx = maxx
+ self.yy = maxy
+
+ if self.swapped_x:
+ self.x, self.xx = maxx, minx
+ if self.swapped_y:
+ self.y, self.yy = maxy, miny
+
+ def coords(self):
+ return self.x, self.y, self.xx, self.yy
+
+ def overlap(self, orect):
+ return self.intersect(orect).area()
+
+ def write_raw_coords(self, toarray, idx):
+ toarray[idx] = self.x
+ toarray[idx + 1] = self.y
+ toarray[idx + 2] = self.xx
+ toarray[idx + 3] = self.yy
+ if (self.swapped_x):
+ toarray[idx] = self.xx
+ toarray[idx + 2] = self.x
+ if (self.swapped_y):
+ toarray[idx + 1] = self.yy
+ toarray[idx + 3] = self.y
+
+ def area(self):
+ w = self.xx - self.x
+ h = self.yy - self.y
+ return w * h
+
+ def extent(self):
+ x = self.x
+ y = self.y
+ return (x, y, self.xx - x, self.yy - y)
+
+ def grow(self, amt):
+ a = amt * 0.5
+ return Rect(self.x - a, self.y - a, self.xx + a, self.yy + a)
+
+ def intersect(self, o):
+ if self is NullRect:
+ return NullRect
+ if o is NullRect:
+ return NullRect
+
+ nx, ny = max(self.x, o.x), max(self.y, o.y)
+ nx2, ny2 = min(self.xx, o.xx), min(self.yy, o.yy)
+ w, h = nx2 - nx, ny2 - ny
+
+ if w <= 0 or h <= 0:
+ return NullRect
+
+ return Rect(nx, ny, nx2, ny2)
+
+ def does_contain(self, o):
+ return self.does_containpoint((o.x, o.y)) and self.does_containpoint((o.xx, o.yy))
+
+ def does_intersect(self, o):
+ return (self.intersect(o).area() > 0)
+
+ def does_containpoint(self, p):
+ x, y = p
+ return (x >= self.x and x <= self.xx and y >= self.y and y <= self.yy)
+
+ def union(self, o):
+ if o is NullRect:
+ return Rect(self.x, self.y, self.xx, self.yy)
+ if self is NullRect:
+ return Rect(o.x, o.y, o.xx, o.yy)
+
+ x = self.x
+ y = self.y
+ xx = self.xx
+ yy = self.yy
+ ox = o.x
+ oy = o.y
+ oxx = o.xx
+ oyy = o.yy
+
+ nx = x if x < ox else ox
+ ny = y if y < oy else oy
+ nx2 = xx if xx > oxx else oxx
+ ny2 = yy if yy > oyy else oyy
+
+ res = Rect(nx, ny, nx2, ny2)
+
+ return res
+
+ def union_point(self, o):
+ x, y = o
+ return self.union(Rect(x, y, x, y))
+
+ def diagonal_sq(self):
+ if self is NullRect:
+ return 0
+ w = self.xx - self.x
+ h = self.yy - self.y
+ return w * w + h * h
+
+ def diagonal(self):
+ return math.sqrt(self.diagonal_sq())
+
+NullRect = Rect(0.0, 0.0, 0.0, 0.0)
+NullRect.swapped_x = False
+NullRect.swapped_y = False
+
+
+def union_all(kids):
+ cur = NullRect
+ for k in kids:
+ cur = cur.union(k.rect)
+ assert(False == cur.swapped_x)
+ return cur
+
+
+def Rtree():
+ return RTree()
+
+
+class RTree(object):
+ def __init__(self):
+ self.count = 0
+ self.stats = {
+ "overflow_f": 0,
+ "avg_overflow_t_f": 0.0,
+ "longest_overflow": 0.0,
+ "longest_kmeans": 0.0,
+ "sum_kmeans_iter_f": 0,
+ "count_kmeans_iter_f": 0,
+ "avg_kmeans_iter_f": 0.0
+ }
+
+ # This round: not using objects directly -- they
+ # take up too much memory, and efficiency goes down the toilet
+ # (obviously) if things start to page.
+ # Less obviously: using object graph directly leads to really long GC
+ # pause times, too.
+ # Instead, it uses pools of arrays:
+ self.count = 0
+ self.leaf_count = 0
+ self.rect_pool = array.array('d')
+ self.node_pool = array.array('L')
+ self.leaf_pool = [] # leaf objects.
+
+ self.cursor = _NodeCursor.create(self, NullRect)
+
+ def _ensure_pool(self, idx):
+ if len(self.rect_pool) < (4 * idx):
+ self.rect_pool.extend([0, 0, 0, 0] * idx)
+ self.node_pool.extend([0, 0] * idx)
+
+ def insert(self, o, orect):
+ self.cursor.insert(o, orect)
+ assert(self.cursor.index == 0)
+
+ def query_rect(self, r):
+ for x in self.cursor.query_rect(r):
+ yield x
+
+ def query_point(self, p):
+ for x in self.cursor.query_point(p):
+ yield x
+
+ def walk(self, pred):
+ return self.cursor.walk(pred)
+
+ def intersection(self, boundingbox):
+ """
+ replicate c rtree method
+
+ Returns
+ -------
+
+ ids : list
+ list of object ids whose bounding boxes intersect with query
+ bounding box
+
+ """
+ # grow the bounding box slightly to handle coincident edges
+
+ bb = boundingbox[:]
+ bb[0] = bb[0] - BUFFER
+ bb[1] = bb[1] - BUFFER
+ bb[2] = bb[2] + BUFFER
+ bb[3] = bb[3] + BUFFER
+
+ qr = Rect(bb[0], bb[1], bb[2], bb[3])
+ return [r.leaf_obj() for r in self.query_rect(qr) if r.is_leaf()]
+
+ def add(self, id, boundingbox):
+ """
+ replicate c rtree method
+
+ Arguments
+ ---------
+
+ id: object id
+
+ boundingbox: list
+ bounding box [minx, miny, maxx, maxy]
+ """
+ bb = boundingbox
+ self.cursor.insert(id, Rect(bb[0], bb[1], bb[2], bb[3]))
+
+
+class _NodeCursor(object):
+ @classmethod
+ def create(cls, rooto, rect):
+ idx = rooto.count
+ rooto.count += 1
+
+ rooto._ensure_pool(idx + 1)
+ #rooto.node_pool.extend([0,0])
+ #rooto.rect_pool.extend([0,0,0,0])
+
+ retv = _NodeCursor(rooto, idx, rect, 0, 0)
+
+ retv._save_back()
+ return retv
+
+ @classmethod
+ def create_with_children(cls, children, rooto):
+ rect = union_all([c for c in children])
+ nr = Rect(rect.x, rect.y, rect.xx, rect.yy)
+ assert(not rect.swapped_x)
+ nc = _NodeCursor.create(rooto, rect)
+ nc._set_children(children)
+ assert(not nc.is_leaf())
+ return nc
+
+ @classmethod
+ def create_leaf(cls, rooto, leaf_obj, leaf_rect):
+ rect = Rect(leaf_rect.x, leaf_rect.y, leaf_rect.xx, leaf_rect.yy)
+ rect.swapped_x = True # Mark as leaf by setting the xswap flag.
+ res = _NodeCursor.create(rooto, rect)
+ idx = res.index
+ res.first_child = rooto.leaf_count
+ rooto.leaf_count += 1
+ res.next_sibling = 0
+ rooto.leaf_pool.append(leaf_obj)
+ res._save_back()
+ res._become(idx)
+ assert(res.is_leaf())
+ return res
+
+ __slots__ = ("root", "npool", "rpool", "index", "rect",
+ "next_sibling", "first_child")
+
+ def __getstate__(self):
+ return (self.root, self.npool, self.rpool, self.index, self.rect, self.next_sibling, self.first_child)
+
+ def __setstate__(self, state):
+ self.root, self.npool, self.rpool, self.index, self.rect, self.next_sibling, self.first_child = state
+
+ def __init__(self, rooto, index, rect, first_child, next_sibling):
+ self.root = rooto
+ self.rpool = rooto.rect_pool
+ self.npool = rooto.node_pool
+
+ self.index = index
+ self.rect = rect
+ self.next_sibling = next_sibling
+ self.first_child = first_child
+
+ def walk(self, predicate):
+ if (predicate(self, self.leaf_obj())):
+ yield self
+ if not self.is_leaf():
+ for c in self.children():
+ for cr in c.walk(predicate):
+ yield cr
+
+ def query_rect(self, r):
+ """ Return things that intersect with 'r'. """
+ def p(o, x):
+ return r.does_intersect(o.rect)
+ for rr in self.walk(p):
+ yield rr
+
+ def query_point(self, point):
+ """ Query by a point """
+ def p(o, x):
+ return o.rect.does_containpoint(point)
+
+ for rr in self.walk(p):
+ yield rr
+
+ def lift(self):
+ return _NodeCursor(self.root,
+ self.index,
+ self.rect,
+ self.first_child,
+ self.next_sibling)
+
+ def _become(self, index):
+ recti = index * 4
+ nodei = index * 2
+ rp = self.rpool
+ x = rp[recti]
+ y = rp[recti + 1]
+ xx = rp[recti + 2]
+ yy = rp[recti + 3]
+
+ if (x == 0.0 and y == 0.0 and xx == 0.0 and yy == 0.0):
+ self.rect = NullRect
+ else:
+ self.rect = Rect(x, y, xx, yy)
+
+ self.next_sibling = self.npool[nodei]
+ self.first_child = self.npool[nodei + 1]
+ self.index = index
+
+ def is_leaf(self):
+ return self.rect.swapped_x
+
+ def has_children(self):
+ return not self.is_leaf() and 0 != self.first_child
+
+ def holds_leaves(self):
+ if 0 == self.first_child:
+ return True
+ else:
+ return self.has_children() and self.get_first_child().is_leaf()
+
+ def get_first_child(self):
+ fc = self.first_child
+ c = _NodeCursor(self.root, 0, NullRect, 0, 0)
+ c._become(self.first_child)
+ return c
+
+ def leaf_obj(self):
+ if self.is_leaf():
+ return self.root.leaf_pool[self.first_child]
+ else:
+ return None
+
+ def _save_back(self):
+ rp = self.rpool
+ recti = self.index * 4
+ nodei = self.index * 2
+
+ if self.rect is not NullRect:
+ self.rect.write_raw_coords(rp, recti)
+ else:
+ rp[recti] = 0
+ rp[recti + 1] = 0
+ rp[recti + 2] = 0
+ rp[recti + 3] = 0
+
+ self.npool[nodei] = self.next_sibling
+ self.npool[nodei + 1] = self.first_child
+
+ def nchildren(self):
+ i = self.index
+ c = 0
+ for x in self.children():
+ c += 1
+ return c
+
+ def insert(self, leafo, leafrect):
+ index = self.index
+
+ # tail recursion, made into loop:
+ while True:
+ if self.holds_leaves():
+ self.rect = self.rect.union(leafrect)
+ self._insert_child(_NodeCursor.create_leaf(
+ self.root, leafo, leafrect))
+
+ self._balance()
+
+ # done: become the original again
+ self._become(index)
+ return
+ else:
+ # Not holding leaves, move down a level in the tree:
+
+ # Micro-optimization:
+ # inlining union() calls -- logic is:
+ # ignored,child = min([ ((c.rect.union(leafrect)).area() - c.rect.area(),c.index) for c in self.children() ])
+ child = None
+ minarea = -1.0
+ for c in self.children():
+ x, y, xx, yy = c.rect.coords()
+ lx, ly, lxx, lyy = leafrect.coords()
+ nx = x if x < lx else lx
+ nxx = xx if xx > lxx else lxx
+ ny = y if y < ly else ly
+ nyy = yy if yy > lyy else lyy
+ a = (nxx - nx) * (nyy - ny)
+ if minarea < 0 or a < minarea:
+ minarea = a
+ child = c.index
+ # End micro-optimization
+
+ self.rect = self.rect.union(leafrect)
+ self._save_back()
+ self._become(child) # recurse.
+
+ def _balance(self):
+ if (self.nchildren() <= MAXCHILDREN):
+ return
+
+ t = time.clock()
+
+ cur_score = -10
+
+ s_children = [c.lift() for c in self.children()]
+
+ memo = {}
+
+ clusterings = [k_means_cluster(
+ self.root, k, s_children) for k in range(2, MAX_KMEANS)]
+ score, bestcluster = max(
+ [(silhouette_coeff(c, memo), c) for c in clusterings])
+
+ nodes = [_NodeCursor.create_with_children(
+ c, self.root) for c in bestcluster if len(c) > 0]
+
+ self._set_children(nodes)
+
+ dur = (time.clock() - t)
+ c = float(self.root.stats["overflow_f"])
+ oa = self.root.stats["avg_overflow_t_f"]
+ self.root.stats["avg_overflow_t_f"] = (
+ dur / (c + 1.0)) + (c * oa / (c + 1.0))
+ self.root.stats["overflow_f"] += 1
+ self.root.stats["longest_overflow"] = max(
+ self.root.stats["longest_overflow"], dur)
+
+ def _set_children(self, cs):
+ self.first_child = 0
+
+ if 0 == len(cs):
+ return
+
+ pred = None
+ for c in cs:
+ if pred is not None:
+ pred.next_sibling = c.index
+ pred._save_back()
+ if 0 == self.first_child:
+ self.first_child = c.index
+ pred = c
+ pred.next_sibling = 0
+ pred._save_back()
+ self._save_back()
+
+ def _insert_child(self, c):
+ c.next_sibling = self.first_child
+ self.first_child = c.index
+ c._save_back()
+ self._save_back()
+
+ def children(self):
+ if (0 == self.first_child):
+ return
+
+ idx = self.index
+ fc = self.first_child
+ ns = self.next_sibling
+ r = self.rect
+
+ self._become(self.first_child)
+ while True:
+ yield self
+ if 0 == self.next_sibling:
+ break
+ else:
+ self._become(self.next_sibling)
+
+ # Go back to becoming the same node we were.
+ #self._become(idx)
+ self.index = idx
+ self.first_child = fc
+ self.next_sibling = ns
+ self.rect = r
+
+
+def avg_diagonals(node, onodes, memo_tab):
+ nidx = node.index
+ sv = 0.0
+ diag = 0.0
+ for onode in onodes:
+ k1 = (nidx, onode.index)
+ k2 = (onode.index, nidx)
+ if k1 in memo_tab:
+ diag = memo_tab[k1]
+ elif k2 in memo_tab:
+ diag = memo_tab[k2]
+ else:
+ diag = node.rect.union(onode.rect).diagonal()
+ memo_tab[k1] = diag
+
+ sv += diag
+
+ return sv / len(onodes)
+
+
+def silhouette_w(node, cluster, next_closest_cluster, memo):
+ ndist = avg_diagonals(node, cluster, memo)
+ sdist = avg_diagonals(node, next_closest_cluster, memo)
+ return (sdist - ndist) / max(sdist, ndist)
+
+
+def silhouette_coeff(clustering, memo_tab):
+ # special case for a clustering of 1.0
+ if (len(clustering) == 1):
+ return 1.0
+
+ coeffs = []
+ for cluster in clustering:
+ others = [c for c in clustering if c is not cluster]
+ others_cntr = [center_of_gravity(c) for c in others]
+ ws = [silhouette_w(node, cluster, others[closest(
+ others_cntr, node)], memo_tab) for node in cluster]
+ cluster_coeff = sum(ws) / len(ws)
+ coeffs.append(cluster_coeff)
+ return sum(coeffs) / len(coeffs)
+
+
+def center_of_gravity(nodes):
+ totarea = 0.0
+ xs, ys = 0, 0
+ for n in nodes:
+ if n.rect is not NullRect:
+ x, y, w, h = n.rect.extent()
+ a = w * h
+ xs = xs + (a * (x + (0.5 * w)))
+ ys = ys + (a * (y + (0.5 * h)))
+ totarea = totarea + a
+ return (xs / totarea), (ys / totarea)
+
+
+def closest(centroids, node):
+ x, y = center_of_gravity([node])
+ dist = -1
+ ridx = -1
+
+ for (i, (xx, yy)) in enumerate(centroids):
+ dsq = ((xx - x) ** 2) + ((yy - y) ** 2)
+ if -1 == dist or dsq < dist:
+ dist = dsq
+ ridx = i
+ return ridx
+
+
+def k_means_cluster(root, k, nodes):
+ t = time.clock()
+ if len(nodes) <= k:
+ return [[n] for n in nodes]
+
+ ns = list(nodes)
+ root.stats["count_kmeans_iter_f"] += 1
+
+ # Initialize: take n random nodes.
+ #random.shuffle(ns)
+
+ cluster_starts = ns[:k]
+ cluster_centers = [center_of_gravity([n]) for n in ns[:k]]
+
+ # Loop until stable:
+ while True:
+ root.stats["sum_kmeans_iter_f"] += 1
+ clusters = [[] for c in cluster_centers]
+
+ for n in ns:
+ idx = closest(cluster_centers, n)
+ clusters[idx].append(n)
+
+ #FIXME HACK TODO: is it okay for there to be empty clusters?
+ clusters = [c for c in clusters if len(c) > 0]
+
+ for c in clusters:
+ if (len(c) == 0):
+ print("Errorrr....")
+ print("Nodes: %d, centers: %s" % (len(ns),
+ repr(cluster_centers)))
+
+ assert(len(c) > 0)
+
+ rest = ns
+ first = False
+
+ new_cluster_centers = [center_of_gravity(c) for c in clusters]
+ if new_cluster_centers == cluster_centers:
+ root.stats["avg_kmeans_iter_f"] = float(root.stats["sum_kmeans_iter_f"] / root.stats["count_kmeans_iter_f"])
+ root.stats["longest_kmeans"] = max(
+ root.stats["longest_kmeans"], (time.clock() - t))
+ return clusters
+ else:
+ cluster_centers = new_cluster_centers
diff --git a/pysal/cg/segmentLocator.py b/pysal/cg/segmentLocator.py
new file mode 100644
index 0000000..07c4c64
--- /dev/null
+++ b/pysal/cg/segmentLocator.py
@@ -0,0 +1,403 @@
+import math
+import scipy
+import numpy
+from pysal.cg.shapes import Rectangle, Point, LineSegment
+from pysal.cg.standalone import get_segment_point_dist, get_bounding_box
+import random
+import time
+
+__all__ = ["SegmentGrid", "SegmentLocator",
+ "Polyline_Shapefile_SegmentLocator"]
+DEBUG = False
+
+
+class BruteSegmentLocator(object):
+ def __init__(self, segments):
+ self.data = segments
+ self.n = len(segments)
+
+ def nearest(self, pt):
+ d = self.data
+ distances = [get_segment_point_dist(
+ d[i], pt)[0] for i in xrange(self.n)]
+ return numpy.argmin(distances)
+
+
+class SegmentLocator(object):
+ def __init__(self, segments, nbins=500):
+ self.data = segments
+ if hasattr(segments, 'bounding_box'):
+ bbox = segment.bounding_box
+ else:
+ bbox = get_bounding_box(segments)
+ self.bbox = bbox
+ res = max((bbox.right - bbox.left), (bbox.upper -
+ bbox.lower)) / float(nbins)
+ self.grid = SegmentGrid(bbox, res)
+ for i, seg in enumerate(segments):
+ self.grid.add(seg, i)
+
+ def nearest(self, pt):
+ d = self.data
+ possibles = self.grid.nearest(pt)
+ distances = [get_segment_point_dist(d[i], pt)[0] for i in possibles]
+ #print "possibles",possibles
+ #print "distances",distances
+ #print "argmin", numpy.argmin(distances)
+ return possibles[numpy.argmin(distances)]
+
+
+class Polyline_Shapefile_SegmentLocator(object):
+ def __init__(self, shpfile, nbins=500):
+ self.data = shpfile
+ bbox = Rectangle(*shpfile.bbox)
+ res = max((bbox.right - bbox.left), (bbox.upper -
+ bbox.lower)) / float(nbins)
+ self.grid = SegmentGrid(bbox, res)
+ for i, polyline in enumerate(shpfile):
+ for p, part in enumerate(polyline.segments):
+ for j, seg in enumerate(part):
+ self.grid.add(seg, (i, p, j))
+
+ def nearest(self, pt):
+ d = self.data
+ possibles = self.grid.nearest(pt)
+ distances = [get_segment_point_dist(
+ d[i].segments[p][j], pt)[0] for (i, p, j) in possibles]
+ #print "possibles",possibles
+ #print "distances",distances
+ #print "argmin", numpy.argmin(distances)
+ return possibles[numpy.argmin(distances)]
+
+
+class SegmentGrid(object):
+ """
+ Notes:
+ SegmentGrid is a low level Grid class.
+ This class does not maintain a copy of the geometry in the grid.
+ It returns only approx. Solutions.
+ This Grid should be wrapped by a locator.
+ """
+ def __init__(self, bounds, resolution):
+ """
+ Returns a grid with specified properties.
+
+ __init__(Rectangle, number) -> SegmentGrid
+
+ Parameters
+ ----------
+ bounds : the area for the grid to encompass
+ resolution : the diameter of each bin
+
+ Examples
+ --------
+ TODO: complete this doctest
+ >>> g = SegmentGrid(Rectangle(0, 0, 10, 10), 1)
+ """
+ if resolution == 0:
+ raise Exception('Cannot create grid with resolution 0')
+ self.res = resolution
+ self.hash = {}
+ self._kd = None
+ self._kd2 = None
+ self._hashKeys = None
+ self.x_range = (bounds.left, bounds.right)
+ self.y_range = (bounds.lower, bounds.upper)
+ try:
+ self.i_range = int(math.ceil((self.x_range[1] -
+ self.x_range[0]) / self.res)) + 1
+ self.j_range = int(math.ceil((self.y_range[1] -
+ self.y_range[0]) / self.res)) + 1
+ self.mask = numpy.zeros((self.i_range, self.j_range), bool)
+ self.endMask = numpy.zeros((self.i_range, self.j_range), bool)
+ except Exception:
+ raise Exception('Invalid arguments for SegmentGrid(): (' + str(self.x_range) + ', ' + str(self.y_range) + ', ' + str(self.res) + ')')
+ @property
+ def hashKeys(self):
+ if self._hashKeys == None:
+ self._hashKeys = numpy.array(self.hash.keys(),dtype=float)
+ return self._hashKeys
+
+ @property
+ def kd(self):
+ if self._kd == None:
+ self._kd = scipy.spatial.cKDTree(self.hashKeys)
+ return self._kd
+
+ @property
+ def kd2(self):
+ if self._kd2 == None:
+ self._kd2 = scipy.spatial.KDTree(self.hashKeys)
+ return self._kd2
+
+ def in_grid(self, loc):
+ """
+ Returns whether a 2-tuple location _loc_ lies inside the grid bounds.
+ """
+ return (self.x_range[0] <= loc[0] <= self.x_range[1] and
+ self.y_range[0] <= loc[1] <= self.y_range[1])
+
+ def _grid_loc(self, loc):
+ i = int((loc[0] - self.x_range[0]) / self.res) # floored
+ j = int((loc[1] - self.y_range[0]) / self.res) # floored
+ #i = min(self.i_range-1, max(int((loc[0] - self.x_range[0])/self.res), 0))
+ #j = min(self.j_range-1, max(int((loc[1] - self.y_range[0])/self.res), 0))
+ #print "bin:", loc, " -> ", (i,j)
+ return (i, j)
+
+ def _real_loc(self, grid_loc):
+ x = (grid_loc[0] * self.res) + self.x_range[0]
+ y = (grid_loc[1] * self.res) + self.y_range[0]
+ return x, y
+
+ def bin_loc(self, loc, id):
+ grid_loc = self._grid_loc(loc)
+ if grid_loc not in self.hash:
+ self.hash[grid_loc] = set()
+ self.mask[grid_loc] = True
+ self.hash[grid_loc].add(id)
+ return grid_loc
+
+ def add(self, segment, id):
+ """
+ Adds segment to the grid.
+
+ add(segment, id) -> bool
+
+ Parameters
+ ----------
+ id -- id to be stored int he grid.
+ segment -- the segment which identifies where to store 'id' in the grid.
+
+ Examples
+ --------
+ >>> g = SegmentGrid(Rectangle(0, 0, 10, 10), 1)
+ >>> g.add(LineSegment(Point((0.2, 0.7)), Point((4.2, 8.7))), 0)
+ True
+ """
+ if not (self.in_grid(segment.p1) and self.in_grid(segment.p2)):
+ raise Exception('Attempt to insert item at location outside grid bounds: ' + str(segment))
+ i, j = self.bin_loc(segment.p1, id)
+ I, J = self.bin_loc(segment.p2, id)
+ self.endMask[i, j] = True
+ self.endMask[I, J] = True
+
+ bbox = segment.bounding_box
+ left = bbox.left
+ lower = bbox.lower
+ res = self.res
+ line = segment.line
+ tiny = res / 1000.
+ for i in xrange(1 + min(i, I), max(i, I)):
+ #print 'i',i
+ x = self.x_range[0] + (i * res)
+ y = line.y(x)
+ self.bin_loc((x - tiny, y), id)
+ self.bin_loc((x + tiny, y), id)
+ for j in xrange(1 + min(j, J), max(j, J)):
+ #print 'j',j
+ y = self.y_range[0] + (j * res)
+ x = line.x(y)
+ self.bin_loc((x, y - tiny), id)
+ self.bin_loc((x, y + tiny), id)
+ self._kd = None
+ self._kd2 = None
+ return True
+
+ def remove(self, segment):
+ self._kd = None
+ self._kd2 = None
+ pass
+
+ def nearest(self, pt):
+ """
+ Return a set of ids.
+
+ The ids identify line segments within a radius of the query point.
+ The true nearest segment is guaranteed to be within the set.
+
+ Filtering possibles is the responsibility of the locator not the grid.
+ This means the Grid doesn't need to keep a reference to the underlying segments,
+ which in turn means the Locator can keep the segments on disk.
+
+ Locators can be customized to different data stores (shape files, SQL, etc.)
+ """
+ grid_loc = numpy.array(self._grid_loc(pt))
+ possibles = set()
+
+ if DEBUG:
+ print "in_grid:", self.in_grid(pt)
+ i = pylab.matshow(self.mask, origin='lower',
+ extent=self.x_range + self.y_range, fignum=1)
+ # Use KD tree to search out the nearest filled bin.
+ # it may be faster to not use kdtree, or at least check grid_loc first
+ # The KD tree is build on the keys of self.hash, a dictionary of stored bins.
+ dist, i = self.kd.query(grid_loc, 1)
+
+ ### Find non-empty bins within a radius of the query point.
+ # Location of Q point
+ row, col = grid_loc
+ # distance to nearest filled cell +2.
+ # +1 returns inconsistent results (compared to BruteSegmentLocator)
+ # +2 seems to do the trick.
+ radius = int(math.ceil(dist)) + 2
+ if radius < 30:
+ a, b = numpy.ogrid[-radius:radius + 1, -radius:radius +
+ 1] # build square index arrays centered at 0,0
+ index = a ** 2 + b ** 2 <= radius ** 2 # create a boolean mask to filter indicies outside radius
+ a, b = index.nonzero()
+ # grad the (i,j)'s of the elements within radius.
+ rows, cols = row + a - radius, col + b - radius # recenter the (i,j)'s over the Q point
+ #### Filter indicies by bounds of the grid.
+ ### filters must be applied one at a time
+ ### I havn't figure out a way to group these
+ filter = rows >= 0
+ rows = rows[filter]
+ cols = cols[filter] # i >= 0
+ filter = rows < self.i_range
+ rows = rows[filter]
+ cols = cols[filter] # i < i_range
+ filter = cols >= 0
+ rows = rows[
+ filter]
+ cols = cols[filter] # j >= 0
+ filter = cols < self.j_range
+ rows = rows[
+ filter]
+ cols = cols[filter] # j < j_range
+ if DEBUG:
+ maskCopy = self.mask.copy().astype(float)
+ maskCopy += self.endMask.astype(float)
+ maskCopy[rows, cols] += 1
+ maskCopy[row, col] += 3
+ i = pylab.matshow(maskCopy, origin='lower', extent=self.x_range + self.y_range, fignum=1)
+ #raw_input('pause')
+ ### All that was just setup for this one line...
+ idx = self.mask[rows, cols].nonzero()[0] # Filter out empty bins.
+ rows, cols = rows[idx], cols[idx] # (i,j)'s of the filled grid cells within radius.
+
+ for t in zip(rows, cols):
+ possibles.update(self.hash[t])
+
+ if DEBUG:
+ print "possibles", possibles
+ else:
+ ### The old way...
+ ### previously I was using kd.query_ball_point on, but the performance was terrible.
+ I = self.kd2.query_ball_point(grid_loc, radius)
+ for i in I:
+ t = tuple(self.kd.data[i])
+ possibles.update(self.hash[t])
+ return list(possibles)
+
+
+def random_segments(n):
+ segs = []
+ for i in xrange(n):
+ a, b, c, d = [random.random() for x in [1, 2, 3, 4]]
+ seg = LineSegment(Point((a, b)), Point((c, d)))
+ segs.append(seg)
+ return segs
+
+
+def random_points(n):
+ return [Point((random.random(), random.random())) for x in xrange(n)]
+
+
+def combo_check(bins, segments, qpoints):
+ G = SegmentLocator(segments, bins)
+ G2 = BruteSegmentLocator(segs)
+ for pt in qpoints:
+ a = G.nearest(pt)
+ b = G2.nearest(pt)
+ if a != b:
+ print a, b, a == b
+ global DEBUG
+ DEBUG = True
+ a = G.nearest(pt)
+ print a
+ a = segments[a]
+ b = segments[b]
+ print "pt to a (grid)", get_segment_point_dist(a, pt)
+ print "pt to b (brut)", get_segment_point_dist(b, pt)
+ raw_input()
+ pylab.clf()
+ DEBUG = False
+
+
+def brute_check(segments, qpoints):
+ t0 = time.time()
+ G2 = BruteSegmentLocator(segs)
+ t1 = time.time()
+ print "Created Brute in %0.4f seconds" % (t1 - t0)
+ t2 = time.time()
+ q = map(G2.nearest, qpoints)
+ t3 = time.time()
+ print "Brute Found %d matches in %0.4f seconds" % (len(qpoints), t3 - t2)
+ print "Total Brute Time:", t3 - t0
+ print
+ return q
+
+
+def grid_check(bins, segments, qpoints, visualize=False):
+ t0 = time.time()
+ G = SegmentLocator(segments, bins)
+ t1 = time.time()
+ G.grid.kd
+ t2 = time.time()
+ print "Created Grid in %0.4f seconds" % (t1 - t0)
+ print "Created KDTree in %0.4f seconds" % (t2 - t1)
+ if visualize:
+ i = pylab.matshow(G.grid.mask, origin='lower',
+ extent=G.grid.x_range + G.grid.y_range)
+
+ t2 = time.time()
+ q = map(G.nearest, qpoints)
+ t3 = time.time()
+ print "Grid Found %d matches in %0.4f seconds" % (len(qpoints), t3 - t2)
+ print "Total Grid Time:", t3 - t0
+ qps = len(qpoints) / (t3 - t2)
+ print "q/s:", qps
+ #print
+ return qps
+
+
+def binSizeTest():
+ q = 100
+ minN = 1000
+ maxN = 10000
+ stepN = 1000
+ minB = 250
+ maxB = 2000
+ stepB = 250
+ sizes = range(minN, maxN, stepN)
+ binSizes = range(minB, maxB, stepB)
+ results = numpy.zeros((len(sizes), len(binSizes)))
+ for row, n in enumerate(sizes):
+ segs = random_segments(n)
+ qpts = random_points(q)
+ for col, bins in enumerate(binSizes):
+ print "N, Bins:", n, bins
+ qps = test_grid(bins, segs, qpts)
+ results[row, col] = qps
+ return results
+
+if __name__ == '__main__':
+ import pylab
+ pylab.ion()
+
+ n = 100
+ q = 1000
+
+ t0 = time.time()
+ segs = random_segments(n)
+ t1 = time.time()
+ qpts = random_points(q)
+ t2 = time.time()
+ print "segments:", t1 - t0
+ print "points:", t2 - t1
+ #test_brute(segs,qpts)
+ #test_grid(50, segs, qpts)
+
+ SG = SegmentLocator(segs)
+ grid = SG.grid
diff --git a/pysal/cg/shapes.py b/pysal/cg/shapes.py
new file mode 100644
index 0000000..2864005
--- /dev/null
+++ b/pysal/cg/shapes.py
@@ -0,0 +1,1913 @@
+"""
+Computational geometry code for PySAL: Python Spatial Analysis Library.
+
+"""
+
+__author__ = "Sergio J. Rey, Xinyue Ye, Charles Schmidt, Andrew Winslow"
+__credits__ = "Copyright (c) 2005-2009 Sergio J. Rey"
+
+import doctest
+import math
+from warnings import warn
+from sphere import arcdist
+
+__all__ = ['Point', 'LineSegment', 'Line', 'Ray', 'Chain', 'Polygon',
+ 'Rectangle', 'asShape']
+
+
+def asShape(obj):
+ """
+ Returns a pysal shape object from obj.
+ obj must support the __geo_interface__.
+ """
+ if hasattr(obj, '__geo_interface__'):
+ geo = obj.__geo_interface__
+ else:
+ geo = obj
+ if hasattr(geo, 'type'):
+ raise TypeError('%r does not appear to be a shape object' % (obj))
+ geo_type = geo['type'].lower()
+ #if geo_type.startswith('multi'):
+ # raise NotImplementedError, "%s are not supported at this time."%geo_type
+ if geo_type in _geoJSON_type_to_Pysal_type:
+ return _geoJSON_type_to_Pysal_type[geo_type].__from_geo_interface__(geo)
+ else:
+ raise NotImplementedError(
+ "%s is not supported at this time." % geo_type)
+
+
+class Point(object):
+ """
+ Geometric class for point objects.
+
+ Attributes
+ ----------
+ None
+ """
+ def __init__(self, loc):
+ """
+ Returns an instance of a Point object.
+
+ __init__((number, number)) -> Point
+
+ Test tag: <tc>#is#Point.__init__</tc>
+ Test tag: <tc>#tests#Point.__init__</tc>
+
+ Parameters
+ ----------
+ loc : tuple location (number x-tuple, x > 1)
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> p = Point((1, 3))
+ """
+ self.__loc = tuple(map(float, loc))
+
+ @classmethod
+ def __from_geo_interface__(cls, geo):
+ return cls(geo['coordinates'])
+
+ @property
+ def __geo_interface__(self):
+ return {'type': 'Point', 'coordinates': self.__loc}
+
+ def __lt__(self, other):
+ """
+ Tests if the Point is < another object.
+
+ __ne__(x) -> bool
+
+ Parameters
+ ----------
+ other : an object to test equality against
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> Point((0,1)) < Point((0,1))
+ False
+ >>> Point((0,1)) < Point((1,1))
+ True
+ """
+ return (self.__loc) < (other.__loc)
+
+ def __le__(self, other):
+ """
+ Tests if the Point is <= another object.
+
+ __ne__(x) -> bool
+
+ Parameters
+ ----------
+ other : an object to test equality against
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> Point((0,1)) <= Point((0,1))
+ True
+ >>> Point((0,1)) <= Point((1,1))
+ True
+ """
+ return (self.__loc) <= (other.__loc)
+
+ def __eq__(self, other):
+ """
+ Tests if the Point is equal to another object.
+
+ __eq__(x) -> bool
+
+ Parameters
+ ----------
+ other : an object to test equality against
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> Point((0,1)) == Point((0,1))
+ True
+ >>> Point((0,1)) == Point((1,1))
+ False
+ """
+ try:
+ return (self.__loc) == (other.__loc)
+ except AttributeError:
+ return False
+
+ def __ne__(self, other):
+ """
+ Tests if the Point is not equal to another object.
+
+ __ne__(x) -> bool
+
+ Parameters
+ ----------
+ other : an object to test equality against
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> Point((0,1)) != Point((0,1))
+ False
+ >>> Point((0,1)) != Point((1,1))
+ True
+ """
+ try:
+ return (self.__loc) != (other.__loc)
+ except AttributeError:
+ return True
+
+ def __gt__(self, other):
+ """
+ Tests if the Point is > another object.
+
+ __ne__(x) -> bool
+
+ Parameters
+ ----------
+ other : an object to test equality against
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> Point((0,1)) > Point((0,1))
+ False
+ >>> Point((0,1)) > Point((1,1))
+ False
+ """
+ return (self.__loc) > (other.__loc)
+
+ def __ge__(self, other):
+ """
+ Tests if the Point is >= another object.
+
+ __ne__(x) -> bool
+
+ Parameters
+ ----------
+ other : an object to test equality against
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> Point((0,1)) >= Point((0,1))
+ True
+ >>> Point((0,1)) >= Point((1,1))
+ False
+ """
+ return (self.__loc) >= (other.__loc)
+
+ def __hash__(self):
+ """
+ Returns the hash of the Point's location.
+
+ x.__hash__() -> hash(x)
+
+ Parameters
+ ----------
+ None
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> hash(Point((0,1))) == hash(Point((0,1)))
+ True
+ >>> hash(Point((0,1))) == hash(Point((1,1)))
+ False
+ """
+ return hash(self.__loc)
+
+ def __getitem__(self, *args):
+ """
+ Return the coordinate for the given dimension.
+
+ x.__getitem__(i) -> x[i]
+
+ Parameters
+ ----------
+ i : index of the desired dimension.
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> p = Point((5.5,4.3))
+ >>> p[0] == 5.5
+ True
+ >>> p[1] == 4.3
+ True
+ """
+ return self.__loc.__getitem__(*args)
+
+ def __getslice__(self, *args):
+ """
+ Return the coordinate for the given dimensions.
+
+ x.__getitem__(i,j) -> x[i:j]
+
+ Parameters
+ ----------
+ i : index to start slice
+ j : index to end slice (excluded).
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> p = Point((3,6,2))
+ >>> p[:2] == (3,6)
+ True
+ >>> p[1:2] == (6,)
+ True
+ """
+ return self.__loc.__getslice__(*args)
+
+ def __len__(self):
+ """
+ Returns the number of dimension in the point.
+
+ __len__() -> int
+
+ Parameters
+ ----------
+ None
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> len(Point((1,2)))
+ 2
+ """
+ return len(self.__loc)
+
+ def __repr__(self):
+ """
+ Returns the string representation of the Point
+
+ __repr__() -> string
+
+ Parameters
+ ----------
+ None
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> Point((0,1))
+ (0.0, 1.0)
+ """
+ return self.__loc.__repr__()
+
+ def __str__(self):
+ """
+ Returns a string representation of a Point object.
+
+ __str__() -> string
+
+ Test tag: <tc>#is#Point.__str__</tc>
+ Test tag: <tc>#tests#Point.__str__</tc>
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> p = Point((1, 3))
+ >>> str(p)
+ '(1.0, 3.0)'
+ """
+ return str(self.__loc)
+
+
+class LineSegment(object):
+ """
+ Geometric representation of line segment objects.
+
+ Parameters
+ ----------
+
+ start_pt : Point
+ Point where segment begins
+ end_pt : Point
+ Point where segment ends
+
+ Attributes
+ ----------
+
+ p1 : Point
+ Starting point
+ p2 : Point
+ Ending point
+ bounding_box : tuple
+ The bounding box of the segment (number 4-tuple)
+ len : float
+ The length of the segment
+ line : Line
+ The line on which the segment lies
+
+ """
+
+ def __init__(self, start_pt, end_pt):
+ """
+ Creates a LineSegment object.
+
+ __init__(Point, Point) -> LineSegment
+
+ Test tag: <tc>#is#LineSegment.__init__</tc>
+ Test tag: <tc>#tests#LineSegment.__init__</tc>
+
+
+ Attributes
+ ----------
+ None
+
+ Examples
+ --------
+ >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
+ """
+ self._p1 = start_pt
+ self._p2 = end_pt
+ self._reset_props()
+
+ def __str__(self):
+ return "LineSegment(" + str(self._p1) + ", " + str(self._p2) + ")"
+
+ def __eq__(self, other):
+ """
+ Returns true if self and other are the same line segment
+
+ Examples
+ --------
+ >>> l1 = LineSegment(Point((1, 2)), Point((5, 6)))
+ >>> l2 = LineSegment(Point((5, 6)), Point((1, 2)))
+ >>> l1 == l2
+ True
+ >>> l2 == l1
+ True
+ """
+ if not isinstance(other, self.__class__):
+ return False
+ if (other.p1 == self._p1 and other.p2 == self._p2):
+ return True
+ elif (other.p2 == self._p1 and other.p1 == self._p2):
+ return True
+ return False
+
+ def intersect(self, other):
+ """
+ Test whether segment intersects with other segment
+
+ Handles endpoints of segments being on other segment
+
+ Examples
+ --------
+
+ >>> ls = LineSegment(Point((5,0)), Point((10,0)))
+ >>> ls1 = LineSegment(Point((5,0)), Point((10,1)))
+ >>> ls.intersect(ls1)
+ True
+ >>> ls2 = LineSegment(Point((5,1)), Point((10,1)))
+ >>> ls.intersect(ls2)
+ False
+ >>> ls2 = LineSegment(Point((7,-1)), Point((7,2)))
+ >>> ls.intersect(ls2)
+ True
+ >>>
+ """
+ ccw1 = self.sw_ccw(other.p2)
+ ccw2 = self.sw_ccw(other.p1)
+ ccw3 = other.sw_ccw(self.p1)
+ ccw4 = other.sw_ccw(self.p2)
+
+ return ccw1*ccw2 <= 0 and ccw3*ccw4 <=0
+
+
+
+ def _reset_props(self):
+ """
+ HELPER METHOD. DO NOT CALL.
+
+ Resets attributes which are functions of other attributes. The getters for these attributes (implemented as
+ properties) then recompute their values if they have been reset since the last call to the getter.
+
+ _reset_props() -> None
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
+ >>> ls._reset_props()
+ """
+ self._bounding_box = None
+ self._len = None
+ self._line = False
+
+ def _get_p1(self):
+ """
+ HELPER METHOD. DO NOT CALL.
+
+ Returns the p1 attribute of the line segment.
+
+ _get_p1() -> Point
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
+ >>> r = ls._get_p1()
+ >>> r == Point((1, 2))
+ True
+ """
+ return self._p1
+
+ def _set_p1(self, p1):
+ """
+ HELPER METHOD. DO NOT CALL.
+
+ Sets the p1 attribute of the line segment.
+
+ _set_p1(Point) -> Point
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
+ >>> r = ls._set_p1(Point((3, -1)))
+ >>> r == Point((3.0, -1.0))
+ True
+ """
+ self._p1 = p1
+ self._reset_props()
+ return self._p1
+
+ p1 = property(_get_p1, _set_p1)
+
+ def _get_p2(self):
+ """
+ HELPER METHOD. DO NOT CALL.
+
+ Returns the p2 attribute of the line segment.
+
+ _get_p2() -> Point
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
+ >>> r = ls._get_p2()
+ >>> r == Point((5, 6))
+ True
+ """
+ return self._p2
+
+ def _set_p2(self, p2):
+ """
+ HELPER METHOD. DO NOT CALL.
+
+ Sets the p2 attribute of the line segment.
+
+ _set_p2(Point) -> Point
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
+ >>> r = ls._set_p2(Point((3, -1)))
+ >>> r == Point((3.0, -1.0))
+ True
+ """
+ self._p2 = p2
+ self._reset_props()
+ return self._p2
+
+ p2 = property(_get_p2, _set_p2)
+
+ def is_ccw(self, pt):
+ """
+ Returns whether a point is counterclockwise of the segment. Exclusive.
+
+ is_ccw(Point) -> bool
+
+ Test tag: <tc>#is#LineSegment.is_ccw</tc>
+ Test tag: <tc>#tests#LineSegment.is_ccw</tc>
+
+ Parameters
+ ----------
+ pt : point lying ccw or cw of a segment
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> ls = LineSegment(Point((0, 0)), Point((5, 0)))
+ >>> ls.is_ccw(Point((2, 2)))
+ True
+ >>> ls.is_ccw(Point((2, -2)))
+ False
+ """
+ v1 = (self._p2[0] - self._p1[0], self._p2[1] - self._p1[1])
+ v2 = (pt[0] - self._p1[0], pt[1] - self._p1[1])
+
+ return v1[0] * v2[1] - v1[1] * v2[0] > 0
+
+ def is_cw(self, pt):
+ """
+ Returns whether a point is clockwise of the segment. Exclusive.
+
+ is_cw(Point) -> bool
+
+ Test tag: <tc>#is#LineSegment.is_cw</tc>
+ Test tag: <tc>#tests#LineSegment.is_cw</tc>
+
+ Parameters
+ ----------
+ pt : point lying ccw or cw of a segment
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> ls = LineSegment(Point((0, 0)), Point((5, 0)))
+ >>> ls.is_cw(Point((2, 2)))
+ False
+ >>> ls.is_cw(Point((2, -2)))
+ True
+ """
+ v1 = (self._p2[0] - self._p1[0], self._p2[1] - self._p1[1])
+ v2 = (pt[0] - self._p1[0], pt[1] - self._p1[1])
+ return v1[0] * v2[1] - v1[1] * v2[0] < 0
+
+ def sw_ccw(self, pt):
+ """
+ Sedgewick test for pt being ccw of segment
+
+ Returns
+ -------
+
+ 1 if turn from self.p1 to self.p2 to pt is ccw
+ -1 if turn from self.p1 to self.p2 to pt is cw
+ -1 if the points are collinear and self.p1 is in the middle
+ 1 if the points are collinear and self.p2 is in the middle
+ 0 if the points are collinear and pt is in the middle
+
+ """
+
+ p0 = self.p1
+ p1 = self.p2
+ p2 = pt
+
+ dx1 = p1[0] - p0[0]
+ dy1 = p1[1] - p0[1]
+ dx2 = p2[0] - p0[0]
+ dy2 = p2[1] - p0[1]
+
+ if dy1*dx2 < dy2*dx1:
+ return 1
+ if dy1*dx2 > dy2*dx1:
+ return -1
+ if (dx1*dx2 < 0 or dy1*dy2 <0):
+ return -1
+ if dx1*dx1 + dy1*dy1 >= dx2*dx2 + dy2*dy2:
+ return 0
+ else:
+ return 1
+
+
+
+
+ def get_swap(self):
+ """
+ Returns a LineSegment object which has its endpoints swapped.
+
+ get_swap() -> LineSegment
+
+ Test tag: <tc>#is#LineSegment.get_swap</tc>
+ Test tag: <tc>#tests#LineSegment.get_swap</tc>
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
+ >>> swap = ls.get_swap()
+ >>> swap.p1[0]
+ 5.0
+ >>> swap.p1[1]
+ 6.0
+ >>> swap.p2[0]
+ 1.0
+ >>> swap.p2[1]
+ 2.0
+ """
+ return LineSegment(self._p2, self._p1)
+
+ @property
+ def bounding_box(self):
+ """
+ Returns the minimum bounding box of a LineSegment object.
+
+ Test tag: <tc>#is#LineSegment.bounding_box</tc>
+ Test tag: <tc>#tests#LineSegment.bounding_box</tc>
+
+ bounding_box -> Rectangle
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
+ >>> ls.bounding_box.left
+ 1.0
+ >>> ls.bounding_box.lower
+ 2.0
+ >>> ls.bounding_box.right
+ 5.0
+ >>> ls.bounding_box.upper
+ 6.0
+ """
+ if self._bounding_box is None: # If LineSegment attributes p1, p2 changed, recompute
+ self._bounding_box = Rectangle(
+ min([self._p1[0], self._p2[0]]), min([
+ self._p1[1], self._p2[1]]),
+ max([self._p1[0], self._p2[0]]), max([self._p1[1], self._p2[1]]))
+ return Rectangle(
+ self._bounding_box.left, self._bounding_box.lower, self._bounding_box.right,
+ self._bounding_box.upper)
+
+ @property
+ def len(self):
+ """
+ Returns the length of a LineSegment object.
+
+ Test tag: <tc>#is#LineSegment.len</tc>
+ Test tag: <tc>#tests#LineSegment.len</tc>
+
+ len() -> number
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> ls = LineSegment(Point((2, 2)), Point((5, 2)))
+ >>> ls.len
+ 3.0
+ """
+ if self._len is None: # If LineSegment attributes p1, p2 changed, recompute
+ self._len = math.hypot(self._p1[0] - self._p2[0],
+ self._p1[1] - self._p2[1])
+ return self._len
+
+ @property
+ def line(self):
+ """
+ Returns a Line object of the line which the segment lies on.
+
+ Test tag: <tc>#is#LineSegment.line</tc>
+ Test tag: <tc>#tests#LineSegment.line</tc>
+
+ line() -> Line
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> ls = LineSegment(Point((2, 2)), Point((3, 3)))
+ >>> l = ls.line
+ >>> l.m
+ 1.0
+ >>> l.b
+ 0.0
+ """
+ if self._line == False:
+ dx = self._p1[0] - self._p2[0]
+ dy = self._p1[1] - self._p2[1]
+ if dx == 0 and dy == 0:
+ self._line = None
+ elif dx == 0:
+ self._line = VerticalLine(self._p1[0])
+ else:
+ m = dy / float(dx)
+ b = self._p1[1] - m * self._p1[0] # y - mx
+ self._line = Line(m, b)
+ return self._line
+
+
+class VerticalLine:
+ """
+ Geometric representation of verticle line objects.
+
+ Attributes
+ ----------
+ x : float
+ x-intercept
+ """
+ def __init__(self, x):
+ """
+ Returns a VerticalLine object.
+
+ __init__(number) -> VerticalLine
+
+ Parameters
+ ----------
+ x : the x-intercept of the line
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> ls = VerticalLine(0)
+ >>> ls.m
+ inf
+ >>> ls.b
+ nan
+ """
+ self._x = float(x)
+ self.m = float('inf')
+ self.b = float('nan')
+
+ def x(self, y):
+ """
+ Returns the x-value of the line at a particular y-value.
+
+ x(number) -> number
+
+ Parameters
+ ----------
+ y : the y-value to compute x at
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> l = VerticalLine(0)
+ >>> l.x(0.25)
+ 0.0
+ """
+ return self._x
+
+ def y(self, x):
+ """
+ Returns the y-value of the line at a particular x-value.
+
+ y(number) -> number
+
+ Parameters
+ ----------
+ x : the x-value to compute y at
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> l = VerticalLine(1)
+ >>> l.y(1)
+ nan
+ """
+ return float('nan')
+
+
+class Line:
+ """
+ Geometric representation of line objects.
+
+ Attributes
+ ----------
+ m : float
+ slope
+ b : float
+ y-intercept
+
+ """
+
+ def __init__(self, m, b):
+ """
+ Returns a Line object.
+
+ __init__(number, number) -> Line
+
+ Test tag: <tc>#is#Line.__init__</tc>
+ Test tag: <tc>#tests#Line.__init__</tc>
+
+ Parameters
+ ----------
+ m : the slope of the line
+ b : the y-intercept of the line
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> ls = Line(1, 0)
+ >>> ls.m
+ 1.0
+ >>> ls.b
+ 0.0
+ """
+ if m == float('inf') or m == float('inf'):
+ raise ArithmeticError('Slope cannot be infinite.')
+ self.m = float(m)
+ self.b = float(b)
+
+ def x(self, y):
+ """
+ Returns the x-value of the line at a particular y-value.
+
+ x(number) -> number
+
+ Parameters
+ ----------
+ y : the y-value to compute x at
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> l = Line(0.5, 0)
+ >>> l.x(0.25)
+ 0.5
+ """
+ if self.m == 0:
+ raise ArithmeticError('Cannot solve for X when slope is zero.')
+ return (y - self.b) / self.m
+
+ def y(self, x):
+ """
+ Returns the y-value of the line at a particular x-value.
+
+ y(number) -> number
+
+ Parameters
+ ----------
+ x : the x-value to compute y at
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> l = Line(1, 0)
+ >>> l.y(1)
+ 1.0
+ """
+ if self.m == 0:
+ return self.b
+ return self.m * x + self.b
+
+
+class Ray:
+ """
+ Geometric representation of ray objects.
+
+ Attributes
+ ----------
+
+ o : Point
+ Origin (point where ray originates)
+ p : Point
+ Second point on the ray (not point where ray originates)
+ """
+
+ def __init__(self, origin, second_p):
+ """
+ Returns a ray with the values specified.
+
+ __init__(Point, Point) -> Ray
+
+ Parameters
+ ----------
+ origin : the point where the ray originates
+ second_p : the second point specifying the ray (not the origin)
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> l = Ray(Point((0, 0)), Point((1, 0)))
+ >>> str(l.o)
+ '(0.0, 0.0)'
+ >>> str(l.p)
+ '(1.0, 0.0)'
+ """
+ self.o = origin
+ self.p = second_p
+
+
+class Chain(object):
+ """
+ Geometric representation of a chain, also known as a polyline.
+
+ Attributes
+ ----------
+
+ vertices : list
+ List of Points of the vertices of the chain in order.
+ len : float
+ The geometric length of the chain.
+
+ """
+
+ def __init__(self, vertices):
+ """
+ Returns a chain created from the points specified.
+
+ __init__(Point list or list of Point lists) -> Chain
+
+ Parameters
+ ----------
+ vertices : list -- Point list or list of Point lists.
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> c = Chain([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((2, 1))])
+ """
+ if isinstance(vertices[0], list):
+ self._vertices = [part for part in vertices]
+ else:
+ self._vertices = [vertices]
+ self._reset_props()
+
+ @classmethod
+ def __from_geo_interface__(cls, geo):
+ verts = [Point(pt) for pt in geo['coordinates']]
+ return cls(verts)
+
+ @property
+ def __geo_interface__(self):
+ return {'type': 'LineString', 'coordinates': self.vertices}
+
+ def _reset_props(self):
+ """
+ HELPER METHOD. DO NOT CALL.
+
+ Resets attributes which are functions of other attributes. The getters for these attributes (implemented as
+ properties) then recompute their values if they have been reset since the last call to the getter.
+
+ _reset_props() -> None
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> ls = Chain([Point((1, 2)), Point((5, 6))])
+ >>> ls._reset_props()
+ """
+ self._len = None
+ self._arclen = None
+ self._bounding_box = None
+
+ @property
+ def vertices(self):
+ """
+ Returns the vertices of the chain in clockwise order.
+
+ vertices -> Point list
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> c = Chain([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((2, 1))])
+ >>> verts = c.vertices
+ >>> len(verts)
+ 4
+ """
+ return sum([part for part in self._vertices], [])
+
+ @property
+ def parts(self):
+ """
+ Returns the parts of the chain.
+
+ parts -> Point list
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> c = Chain([[Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))],[Point((2,1)),Point((2,2)),Point((1,2)),Point((1,1))]])
+ >>> len(c.parts)
+ 2
+ """
+ return [[v for v in part] for part in self._vertices]
+
+ @property
+ def bounding_box(self):
+ """
+ Returns the bounding box of the chain.
+
+ bounding_box -> Rectangle
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> c = Chain([Point((0, 0)), Point((2, 0)), Point((2, 1)), Point((0, 1))])
+ >>> c.bounding_box.left
+ 0.0
+ >>> c.bounding_box.lower
+ 0.0
+ >>> c.bounding_box.right
+ 2.0
+ >>> c.bounding_box.upper
+ 1.0
+ """
+ if self._bounding_box is None:
+ vertices = self.vertices
+ self._bounding_box = Rectangle(
+ min([v[0] for v in vertices]), min([v[1] for v in vertices]),
+ max([v[0] for v in vertices]), max([v[1] for v in vertices]))
+ return self._bounding_box
+
+ @property
+ def len(self):
+ """
+ Returns the geometric length of the chain.
+
+ len -> number
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> c = Chain([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((2, 1))])
+ >>> c.len
+ 3.0
+ >>> c = Chain([[Point((0, 0)), Point((1, 0)), Point((1, 1))],[Point((10,10)),Point((11,10)),Point((11,11))]])
+ >>> c.len
+ 4.0
+ """
+ def dist(v1, v2):
+ return math.hypot(v1[0] - v2[0], v1[1] - v2[1])
+
+ def part_perimeter(part):
+ return sum([dist(part[i], part[i + 1]) for i in xrange(len(part) - 1)])
+
+ if self._len is None:
+ self._len = sum([part_perimeter(part) for part in self._vertices])
+ return self._len
+
+ @property
+ def arclen(self):
+ """
+ Returns the geometric length of the chain computed using arcdistance (meters).
+
+ len -> number
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ """
+ def part_perimeter(part):
+ return sum([arcdist(part[i], part[i + 1]) * 1000. for i in xrange(len(part) - 1)])
+ if self._arclen is None:
+ self._arclen = sum(
+ [part_perimeter(part) for part in self._vertices])
+ return self._arclen
+
+ @property
+ def segments(self):
+ """
+ Returns the segments that compose the Chain
+ """
+ return [[LineSegment(a, b) for (a, b) in zip(part[:-1], part[1:])] for part in self._vertices]
+
+
+class Ring(object):
+ """
+ Geometric representation of a Linear Ring
+
+ Linear Rings must be closed, the first and last point must be the same. Open rings will be closed.
+
+ This class exists primarily as a geometric primitive to form complex polygons with multiple rings and holes.
+
+ The ordering of the vertices is ignored and will not be altered.
+
+ Parameters
+ ----------
+ vertices : list -- a list of vertices
+
+ Attributes
+ __________
+ vertices : list
+ List of Points with the vertices of the ring
+ len : int
+ Number of vertices
+ perimeter : float
+ Geometric length of the perimeter of the ring
+ bounding_box : Rectangle
+ Bounding box of the ring
+ area : float
+ area enclosed by the ring
+ centroid : tuple
+ The centroid of the ring defined by the 'center of gravity' or 'center or mass'
+ """
+ def __init__(self, vertices):
+ if vertices[0] != vertices[-1]:
+ vertices = vertices[:] + vertices[0:1]
+ #raise ValueError, "Supplied vertices do not form a closed ring, the first and last vertices are not the same"
+ self.vertices = tuple(vertices)
+ self._perimeter = None
+ self._bounding_box = None
+ self._area = None
+ self._centroid = None
+
+ def __len__(self):
+ return len(self.vertices)
+
+ @property
+ def len(self):
+ return len(self)
+
+ @staticmethod
+ def dist(v1, v2):
+ return math.hypot(v1[0] - v2[0], v1[1] - v2[1])
+
+ @property
+ def perimeter(self):
+ if self._perimeter is None:
+ dist = self.dist
+ v = self.vertices
+ self._perimeter = sum([dist(v[i], v[i + 1])
+ for i in xrange(-1, len(self) - 1)])
+ return self._perimeter
+
+ @property
+ def bounding_box(self):
+ """
+ Returns the bounding box of the ring
+
+ bounding_box -> Rectangle
+
+ Examples
+ --------
+ >>> r = Ring([Point((0, 0)), Point((2, 0)), Point((2, 1)), Point((0, 1)), Point((0,0))])
+ >>> r.bounding_box.left
+ 0.0
+ >>> r.bounding_box.lower
+ 0.0
+ >>> r.bounding_box.right
+ 2.0
+ >>> r.bounding_box.upper
+ 1.0
+ """
+ if self._bounding_box is None:
+ vertices = self.vertices
+ x = [v[0] for v in vertices]
+ y = [v[1] for v in vertices]
+ self._bounding_box = Rectangle(min(x), min(y), max(x), max(y))
+ return self._bounding_box
+
+ @property
+ def area(self):
+ """
+ Returns the area of the ring.
+
+ area -> number
+
+ Examples
+ --------
+ >>> r = Ring([Point((0, 0)), Point((2, 0)), Point((2, 1)), Point((0, 1)), Point((0,0))])
+ >>> r.area
+ 2.0
+ """
+ return abs(self.signed_area)
+
+ @property
+ def signed_area(self):
+ if self._area is None:
+ vertices = self.vertices
+ x = [v[0] for v in vertices]
+ y = [v[1] for v in vertices]
+ N = len(self)
+
+ A = 0.0
+ for i in xrange(N - 1):
+ A += (x[i] * y[i + 1] - x[i + 1] * y[i])
+ A = A / 2.0
+ self._area = A
+ return self._area
+
+ @property
+ def centroid(self):
+ """
+ Returns the centroid of the ring.
+
+ centroid -> Point
+
+ Notes
+ -----
+ The centroid returned by this method is the geometric centroid.
+ Also known as the 'center of gravity' or 'center of mass'.
+
+
+ Examples
+ --------
+ >>> r = Ring([Point((0, 0)), Point((2, 0)), Point((2, 1)), Point((0, 1)), Point((0,0))])
+ >>> str(r.centroid)
+ '(1.0, 0.5)'
+ """
+ if self._centroid is None:
+ vertices = self.vertices
+ x = [v[0] for v in vertices]
+ y = [v[1] for v in vertices]
+ A = self.signed_area
+ N = len(self)
+ cx = 0
+ cy = 0
+ for i in xrange(N - 1):
+ f = (x[i] * y[i + 1] - x[i + 1] * y[i])
+ cx += (x[i] + x[i + 1]) * f
+ cy += (y[i] + y[i + 1]) * f
+ cx = 1.0 / (6 * A) * cx
+ cy = 1.0 / (6 * A) * cy
+ self._centroid = Point((cx, cy))
+ return self._centroid
+
+
+class Polygon(object):
+ """
+ Geometric representation of polygon objects.
+
+ Attributes
+ ----------
+ vertices : list
+ List of Points with the vertices of the Polygon in
+ clockwise order
+ len : int
+ Number of vertices including holes
+ perimeter : float
+ Geometric length of the perimeter of the Polygon
+ bounding_box : Rectangle
+ Bounding box of the polygon
+ bbox : List
+ [left, lower, right, upper]
+ area : float
+ Area enclosed by the polygon
+ centroid : tuple
+ The 'center of gravity', i.e. the mean point of the polygon.
+ """
+
+ def __init__(self, vertices, holes=None):
+ """
+ Returns a polygon created from the objects specified.
+
+ __init__(Point list or list of Point lists, holes list ) -> Polygon
+
+ Parameters
+ ----------
+ vertices : list -- a list of vertices or a list of lists of vertices.
+ holes : list -- a list of sub-polygons to be considered as holes.
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> p1 = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
+ """
+ self._part_rings = []
+ self._hole_rings = []
+
+ def clockwise(part):
+ if standalone.is_clockwise(part):
+ return part[:]
+ else:
+ return part[::-1]
+
+ if isinstance(vertices[0], list):
+ self._part_rings = map(Ring, vertices)
+ self._vertices = [clockwise(part) for part in vertices]
+ else:
+ self._part_rings = [Ring(vertices)]
+ self._vertices = [clockwise(vertices)]
+ if holes is not None and holes != []:
+ if isinstance(holes[0], list):
+ self._hole_rings = map(Ring, holes)
+ self._holes = [clockwise(hole) for hole in holes]
+ else:
+ self._hole_rings = [Ring(holes)]
+ self._holes = [clockwise(holes)]
+ else:
+ self._holes = [[]]
+ self._reset_props()
+
+ @classmethod
+ def __from_geo_interface__(cls, geo):
+ """
+ While pysal does not differentiate polygons and multipolygons GEOS,Shapely and geoJSON do.
+ In GEOS, etc, polygons may only have a single exterior ring, all other parts are holes.
+ MultiPolygons are simply a list of polygons.
+ """
+ geo_type = geo['type'].lower()
+ if geo_type == 'multipolygon':
+ parts = []
+ holes = []
+ for polygon in geo['coordinates']:
+ verts = [[Point(pt) for pt in part] for part in polygon]
+ parts += verts[0:1]
+ holes += verts[1:]
+ if not holes:
+ holes = None
+ return cls(parts, holes)
+ else:
+ verts = [[Point(pt) for pt in part] for part in geo['coordinates']]
+ return cls(verts[0:1], verts[1:])
+
+ @property
+ def __geo_interface__(self):
+ if len(self.parts) > 1:
+ geo = {'type': 'MultiPolygon', 'coordinates': [[
+ part] for part in self.parts]}
+ if self._holes[0]:
+ geo['coordinates'][0] += self._holes
+ return geo
+ if self._holes[0]:
+ return {'type': 'Polygon', 'coordinates': self._vertices + self._holes}
+ else:
+ return {'type': 'Polygon', 'coordinates': self._vertices}
+
+ def _reset_props(self):
+ self._perimeter = None
+ self._bounding_box = None
+ self._bbox = None
+ self._area = None
+ self._centroid = None
+ self._len = None
+
+ def __len__(self):
+ return self.len
+
+ @property
+ def len(self):
+ """
+ Returns the number of vertices in the polygon.
+
+ len -> int
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> p1 = Polygon([Point((0, 0)), Point((0, 1)), Point((1, 1)), Point((1, 0))])
+ >>> p1.len
+ 4
+ >>> len(p1)
+ 4
+ """
+ if self._len is None:
+ self._len = len(self.vertices)
+ return self._len
+
+ @property
+ def vertices(self):
+ """
+ Returns the vertices of the polygon in clockwise order.
+
+ vertices -> Point list
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> p1 = Polygon([Point((0, 0)), Point((0, 1)), Point((1, 1)), Point((1, 0))])
+ >>> len(p1.vertices)
+ 4
+ """
+ return sum([part for part in self._vertices], []) + sum([part for part in self._holes], [])
+
+ @property
+ def holes(self):
+ """
+ Returns the holes of the polygon in clockwise order.
+
+ holes -> Point list
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> p = Polygon([Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], [Point((1, 2)), Point((2, 2)), Point((2, 1)), Point((1, 1))])
+ >>> len(p.holes)
+ 1
+ """
+ return [[v for v in part] for part in self._holes]
+
+ @property
+ def parts(self):
+ """
+ Returns the parts of the polygon in clockwise order.
+
+ parts -> Point list
+
+ Attributes
+ ----------
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> p = Polygon([[Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))], [Point((2,1)),Point((2,2)),Point((1,2)),Point((1,1))]])
+ >>> len(p.parts)
+ 2
+ """
+ return [[v for v in part] for part in self._vertices]
+
+ @property
+ def perimeter(self):
+ """
+ Returns the perimeter of the polygon.
+
+ perimeter() -> number
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> p = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
+ >>> p.perimeter
+ 4.0
+ """
+ def dist(v1, v2):
+ return math.hypot(v1[0] - v2[0], v1[1] - v2[1])
+
+ def part_perimeter(part):
+ return sum([dist(part[i], part[i + 1]) for i in xrange(-1, len(part) - 1)])
+
+ if self._perimeter is None:
+ self._perimeter = (sum([part_perimeter(part) for part in self._vertices]) +
+ sum([part_perimeter(hole) for hole in self._holes]))
+ return self._perimeter
+
+ @property
+ def bbox(self):
+ """
+ Returns the bounding box of the polygon as a list
+
+ See also bounding_box
+ """
+ if self._bbox is None:
+ self._bbox = [ self.bounding_box.left,
+ self.bounding_box.lower,
+ self.bounding_box.right,
+ self.bounding_box.upper]
+ return self._bbox
+
+
+ @property
+ def bounding_box(self):
+ """
+ Returns the bounding box of the polygon.
+
+ bounding_box -> Rectangle
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> p = Polygon([Point((0, 0)), Point((2, 0)), Point((2, 1)), Point((0, 1))])
+ >>> p.bounding_box.left
+ 0.0
+ >>> p.bounding_box.lower
+ 0.0
+ >>> p.bounding_box.right
+ 2.0
+ >>> p.bounding_box.upper
+ 1.0
+ """
+ if self._bounding_box is None:
+ vertices = self.vertices
+ self._bounding_box = Rectangle(
+ min([v[0] for v in vertices]), min([v[1] for v in vertices]),
+ max([v[0] for v in vertices]), max([v[1] for v in vertices]))
+ return self._bounding_box
+
+ @property
+ def area(self):
+ """
+ Returns the area of the polygon.
+
+ area -> number
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> p = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
+ >>> p.area
+ 1.0
+ >>> p = Polygon([Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],[Point((2,1)),Point((2,2)),Point((1,2)),Point((1,1))])
+ >>> p.area
+ 99.0
+ """
+ def part_area(part_verts):
+ area = 0
+ for i in xrange(-1, len(part_verts) - 1):
+ area += (part_verts[i][0] + part_verts[i + 1][0]) * \
+ (part_verts[i][1] - part_verts[i + 1][1])
+ area = area * 0.5
+ if area < 0:
+ area = -area
+ return area
+
+ return (sum([part_area(part) for part in self._vertices]) -
+ sum([part_area(hole) for hole in self._holes]))
+
+ @property
+ def centroid(self):
+ """
+ Returns the centroid of the polygon
+
+ centroid -> Point
+
+ Notes
+ -----
+ The centroid returned by this method is the geometric centroid and respects multipart polygons with holes.
+ Also known as the 'center of gravity' or 'center of mass'.
+
+
+ Examples
+ --------
+ >>> p = Polygon([Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], [Point((1, 1)), Point((1, 2)), Point((2, 2)), Point((2, 1))])
+ >>> p.centroid
+ (5.0353535353535355, 5.0353535353535355)
+ """
+ CP = [ring.centroid for ring in self._part_rings]
+ AP = [ring.area for ring in self._part_rings]
+ CH = [ring.centroid for ring in self._hole_rings]
+ AH = [-ring.area for ring in self._hole_rings]
+
+ A = AP + AH
+ cx = sum([pt[0] * area for pt, area in zip(CP + CH, A)]) / sum(A)
+ cy = sum([pt[1] * area for pt, area in zip(CP + CH, A)]) / sum(A)
+ return cx, cy
+
+ def contains_point(self, point):
+ """
+ Test if polygon contains point
+
+ Examples
+ --------
+ >>> p = Polygon([Point((0,0)), Point((4,0)), Point((4,5)), Point((2,3)), Point((0,5))])
+ >>> p.contains_point((3,3))
+ 1
+ >>> p.contains_point((0,5))
+ 0
+ >>> p.contains_point((2,3))
+ 0
+ >>> p.contains_point((4,5))
+ 0
+ >>> p.contains_point((4,0))
+ 1
+ >>>
+
+ Handles holes
+
+ >>> p = Polygon([Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], [Point((1, 2)), Point((2, 2)), Point((2, 1)), Point((1, 1))])
+ >>> p.contains_point((1.0,1.0))
+ 0
+ >>> p.contains_point((2.0,2.0))
+ 1
+ >>> p.contains_point((10,10))
+ 0
+ >>>
+
+
+ Notes
+ -----
+ Points falling exactly on polygon edges may yield unpredictable
+ results
+ """
+
+ # ray from point to just outside left edge of bb
+ left = self.bounding_box.left - 0.000001
+ y = point[1]
+ right = point[0]
+ cn = 0
+ verts = self.vertices
+ c = Point((left, y))
+ d = Point((right, y))
+ ray = LineSegment(c, d)
+ for i in xrange(-1, len(self.vertices) - 1):
+ a = verts[i]
+ b = verts[i + 1]
+ ab = LineSegment(a, b)
+ ac = LineSegment(a, c)
+ bc = LineSegment(b, c)
+ if ac.is_ccw(d) == bc.is_ccw(d):
+ pass
+ elif ab.is_ccw(c) == ab.is_ccw(d):
+ pass
+ else:
+ cn += 1
+ return cn % 2
+
+
+class Rectangle:
+ """
+ Geometric representation of rectangle objects.
+
+ Attributes
+ ----------
+
+ left : float
+ Minimum x-value of the rectangle
+ lower : float
+ Minimum y-value of the rectangle
+ right : float
+ Maximum x-value of the rectangle
+ upper : float
+ Maximum y-value of the rectangle
+ """
+
+ def __init__(self, left, lower, right, upper):
+ """
+ Returns a Rectangle object.
+
+ __init__(number, number, number, number) -> Rectangle
+
+ Parameters
+ ----------
+ left : the minimum x-value of the rectangle
+ lower : the minimum y-value of the rectangle
+ right : the maximum x-value of the rectangle
+ upper : the maximum y-value of the rectangle
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> r = Rectangle(-4, 3, 10, 17)
+ >>> r.left #minx
+ -4.0
+ >>> r.lower #miny
+ 3.0
+ >>> r.right #maxx
+ 10.0
+ >>> r.upper #maxy
+ 17.0
+ """
+ if right < left or upper < lower:
+ raise ArithmeticError('Rectangle must have positive area.')
+ self.left = float(left)
+ self.lower = float(lower)
+ self.right = float(right)
+ self.upper = float(upper)
+
+ def __nonzero__(self):
+ """
+ ___nonzero__ is used "to implement truth value testing and the built-in operation bool()" -- http://docs.python.org/reference/datamodel.html
+
+ Rectangles will evaluate to Flase if they have Zero Area.
+ >>> r = Rectangle(0,0,0,0)
+ >>> bool(r)
+ False
+ >>> r = Rectangle(0,0,1,1)
+ >>> bool(r)
+ True
+ """
+ return bool(self.area)
+
+ def __eq__(self, other):
+ if other:
+ return self[:] == other[:]
+ return False
+
+ def __add__(self, other):
+ x, y, X, Y = self[:]
+ x1, y2, X1, Y1 = other[:]
+ return Rectangle(min(self.left, other.left), min(self.lower, other.lower), max(self.right, other.right), max(self.upper, other.upper))
+
+ def __getitem__(self, key):
+ """
+ >>> r = Rectangle(-4, 3, 10, 17)
+ >>> r[:]
+ [-4.0, 3.0, 10.0, 17.0]
+ """
+ l = [self.left, self.lower, self.right, self.upper]
+ return l.__getitem__(key)
+
+ def set_centroid(self, new_center):
+ """
+ Moves the rectangle center to a new specified point.
+
+ set_centroid(Point) -> Point
+
+ Parameters
+ ----------
+ new_center : the new location of the centroid of the polygon
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> r = Rectangle(0, 0, 4, 4)
+ >>> r.set_centroid(Point((4, 4)))
+ >>> r.left
+ 2.0
+ >>> r.right
+ 6.0
+ >>> r.lower
+ 2.0
+ >>> r.upper
+ 6.0
+ """
+ shift = (new_center[0] - (self.left + self.right) / 2,
+ new_center[1] - (self.lower + self.upper) / 2)
+ self.left = self.left + shift[0]
+ self.right = self.right + shift[0]
+ self.lower = self.lower + shift[1]
+ self.upper = self.upper + shift[1]
+
+ def set_scale(self, scale):
+ """
+ Rescales the rectangle around its center.
+
+ set_scale(number) -> number
+
+ Parameters
+ ----------
+ scale : the ratio of the new scale to the old scale (e.g. 1.0 is current size)
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> r = Rectangle(0, 0, 4, 4)
+ >>> r.set_scale(2)
+ >>> r.left
+ -2.0
+ >>> r.right
+ 6.0
+ >>> r.lower
+ -2.0
+ >>> r.upper
+ 6.0
+ """
+ center = ((self.left + self.right) / 2, (self.lower + self.upper) / 2)
+ self.left = center[0] + scale * (self.left - center[0])
+ self.right = center[0] + scale * (self.right - center[0])
+ self.lower = center[1] + scale * (self.lower - center[1])
+ self.upper = center[1] + scale * (self.upper - center[1])
+
+ @property
+ def area(self):
+ """
+ Returns the area of the Rectangle.
+
+ area -> number
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> r = Rectangle(0, 0, 4, 4)
+ >>> r.area
+ 16.0
+ """
+ return (self.right - self.left) * (self.upper - self.lower)
+
+ @property
+ def width(self):
+ """
+ Returns the width of the Rectangle.
+
+ width -> number
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> r = Rectangle(0, 0, 4, 4)
+ >>> r.width
+ 4.0
+ """
+ return self.right - self.left
+
+ @property
+ def height(self):
+ """
+ Returns the height of the Rectangle.
+
+ height -> number
+
+ Examples
+ --------
+ >>> r = Rectangle(0, 0, 4, 4)
+ >>> r.height
+ 4.0
+ """
+ return self.upper - self.lower
+
+
+_geoJSON_type_to_Pysal_type = {'point': Point, 'linestring': Chain,
+ 'polygon': Polygon, 'multipolygon': Polygon}
+import standalone # moving this to top breaks unit tests !
+
+
diff --git a/pysal/cg/sphere.py b/pysal/cg/sphere.py
new file mode 100644
index 0000000..df670c9
--- /dev/null
+++ b/pysal/cg/sphere.py
@@ -0,0 +1,502 @@
+"""
+sphere: Tools for working with spherical geometry.
+
+Author(s):
+ Charles R Schmidt schmidtc at gmail.com
+ Luc Anselin luc.anselin at asu.edu
+ Xun Li xun.li at asu.edu
+
+"""
+
+__author__ = "Charles R Schmidt <schmidtc at gmail.com>, Luc Anselin <luc.anselin at asu.edu, Xun Li <xun.li at asu.edu"
+
+import math
+import random
+import numpy
+import scipy.spatial
+import scipy.constants
+from scipy.spatial.distance import euclidean
+from math import pi, cos, sin, asin
+
+__all__ = ['RADIUS_EARTH_KM', 'RADIUS_EARTH_MILES', 'arcdist', 'arcdist2linear', 'brute_knn', 'fast_knn', 'fast_threshold', 'linear2arcdist', 'toLngLat', 'toXYZ', 'lonlat','harcdist','geointerpolate','geogrid']
+
+
+RADIUS_EARTH_KM = 6371.0
+RADIUS_EARTH_MILES = (
+ RADIUS_EARTH_KM * scipy.constants.kilo) / scipy.constants.mile
+
+
+def arcdist(pt0, pt1, radius=RADIUS_EARTH_KM):
+ """
+ Parameters
+ ----------
+ pt0 : point
+ assumed to be in form (lng,lat)
+ pt1 : point
+ assumed to be in form (lng,lat)
+ radius : radius of the sphere
+ defaults to Earth's radius
+
+ Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html
+
+ Returns
+ -------
+ The arc distance between pt0 and pt1 using supplied radius
+
+ Examples
+ --------
+ >>> pt0 = (0,0)
+ >>> pt1 = (180,0)
+ >>> d = arcdist(pt0,pt1,RADIUS_EARTH_MILES)
+ >>> d == math.pi*RADIUS_EARTH_MILES
+ True
+ """
+ return linear2arcdist(euclidean(toXYZ(pt0), toXYZ(pt1)), radius)
+
+
+def arcdist2linear(arc_dist, radius=RADIUS_EARTH_KM):
+ """
+ Convert an arc distance (spherical earth) to a linear distance (R3) in the unit sphere.
+
+ Examples
+ --------
+ >>> pt0 = (0,0)
+ >>> pt1 = (180,0)
+ >>> d = arcdist(pt0,pt1,RADIUS_EARTH_MILES)
+ >>> d == math.pi*RADIUS_EARTH_MILES
+ True
+ >>> arcdist2linear(d,RADIUS_EARTH_MILES)
+ 2.0
+ """
+ c = 2 * math.pi * radius
+ d = (2 - (2 * math.cos(math.radians((arc_dist * 360.0) / c)))) ** (0.5)
+ return d
+
+
+def linear2arcdist(linear_dist, radius=RADIUS_EARTH_KM):
+ """
+ Convert a linear distance in the unit sphere (R3) to an arc distance based on supplied radius
+
+ Examples
+ --------
+ >>> pt0 = (0,0)
+ >>> pt1 = (180,0)
+ >>> d = arcdist(pt0,pt1,RADIUS_EARTH_MILES)
+ >>> d == linear2arcdist(2.0, radius = RADIUS_EARTH_MILES)
+ True
+ """
+ if linear_dist == float('inf'):
+ return float('inf')
+ elif linear_dist > 2.0:
+ raise ValueError("linear_dist, must not exceed the diameter of the unit sphere, 2.0")
+ c = 2 * math.pi * radius
+ a2 = linear_dist ** 2
+ theta = math.degrees(math.acos((2 - a2) / (2.)))
+ d = (theta * c) / 360.0
+ return d
+
+
+def toXYZ(pt):
+ """
+ Parameters
+ ----------
+ pt0 : point
+ assumed to be in form (lng,lat)
+ pt1 : point
+ assumed to be in form (lng,lat)
+
+ Returns
+ -------
+ x, y, z
+ """
+ phi, theta = map(math.radians, pt)
+ phi, theta = phi + pi, theta + (pi / 2)
+ x = 1 * sin(theta) * cos(phi)
+ y = 1 * sin(theta) * sin(phi)
+ z = 1 * cos(theta)
+ return x, y, z
+
+
+def toLngLat(xyz):
+ x, y, z = xyz
+ if z == -1 or z == 1:
+ phi = 0
+ else:
+ phi = math.atan2(y, x)
+ if phi > 0:
+ phi = phi - math.pi
+ elif phi < 0:
+ phi = phi + math.pi
+ theta = math.acos(z) - (math.pi / 2)
+ return phi, theta
+
+
+def brute_knn(pts, k, mode='arc'):
+ """
+ valid modes are ['arc','xrz']
+ """
+ n = len(pts)
+ full = numpy.zeros((n, n))
+ for i in xrange(n):
+ for j in xrange(i + 1, n):
+ if mode == 'arc':
+ lng0, lat0 = pts[i]
+ lng1, lat1 = pts[j]
+ dist = arcdist(pts[i], pts[j], radius=RADIUS_EARTH_KM)
+ elif mode == 'xyz':
+ dist = euclidean(pts[i], pts[j])
+ full[i, j] = dist
+ full[j, i] = dist
+ w = {}
+ for i in xrange(n):
+ w[i] = full[i].argsort()[1:k + 1].tolist()
+ return w
+
+
+def fast_knn(pts, k, return_dist=False):
+ """
+ Computes k nearest neighbors on a sphere.
+
+ Parameters
+ ----------
+ pts : list of x,y pairs
+ k : int
+ Number of points to query
+ return_dist : bool
+ Return distances in the 'wd' container object
+
+ Returns
+ -------
+ wn : list
+ list of neighbors
+ wd : list
+ list of neighbor distances (optional)
+
+ """
+ pts = numpy.array(pts)
+ kd = scipy.spatial.KDTree(pts)
+ d, w = kd.query(pts, k + 1)
+ w = w[:, 1:]
+ wn = {}
+ for i in xrange(len(pts)):
+ wn[i] = w[i].tolist()
+ if return_dist:
+ d = d[:, 1:]
+ wd = {}
+ for i in xrange(len(pts)):
+ wd[i] = [linear2arcdist(x,
+ radius=RADIUS_EARTH_MILES) for x in d[i].tolist()]
+ return wn, wd
+ return wn
+
+
+def fast_threshold(pts, dist, radius=RADIUS_EARTH_KM):
+ d = arcdist2linear(dist, radius)
+ kd = scipy.spatial.KDTree(pts)
+ r = kd.query_ball_tree(kd, d)
+ wd = {}
+ for i in xrange(len(pts)):
+ l = r[i]
+ l.remove(i)
+ wd[i] = l
+ return wd
+
+
+########### new functions
+
+def lonlat(pointslist):
+ """
+ Converts point order from lat-lon tuples to lon-lat (x,y) tuples
+
+ Parameters
+ ----------
+
+ pointslist : list of lat-lon tuples (Note, has to be a list, even for one point)
+
+ Returns
+ -------
+
+ newpts : list with tuples of points in lon-lat order
+
+ Example
+ -------
+ >>> points = [(41.981417, -87.893517), (41.980396, -87.776787), (41.980906, -87.696450)]
+ >>> newpoints = lonlat(points)
+ >>> newpoints
+ [(-87.893517, 41.981417), (-87.776787, 41.980396), (-87.69645, 41.980906)]
+
+ """
+ newpts = [(i[1],i[0]) for i in pointslist]
+ return newpts
+
+def haversine(x):
+ """
+ Computes the haversine formula
+
+ Parameters
+ ----------
+ x : angle in radians
+
+ Returns
+ -------
+ : square of sine of half the radian (the haversine formula)
+
+ Example
+ -------
+ >>> haversine(math.pi) # is 180 in radians, hence sin of 90 = 1
+ 1.0
+
+ """
+
+ x = math.sin(x/2)
+ return x*x
+
+# Lambda functions
+
+# degree to radian conversion
+d2r = lambda x: x * math.pi / 180.0
+
+# radian to degree conversion
+r2d = lambda x: x * 180.0 / math.pi
+
+def radangle(p0,p1):
+ """
+ Radian angle between two points on a sphere in lon-lat (x,y)
+
+ Parameters
+ ----------
+ p0 : first point as a lon,lat tuple
+ p1 : second point as a lon,lat tuple
+
+ Returns
+ -------
+ d : radian angle in radians
+
+ Example
+ -------
+ >>> p0 = (-87.893517, 41.981417)
+ >>> p1 = (-87.519295, 41.657498)
+ >>> radangle(p0,p1)
+ 0.007460167953189258
+
+ Note
+ ----
+ Uses haversine formula, function haversine and degree to radian
+ conversion lambda function d2r
+
+ """
+ x0, y0 = d2r(p0[0]),d2r(p0[1])
+ x1, y1 = d2r(p1[0]),d2r(p1[1])
+ d = 2.0 * math.asin(math.sqrt(haversine(y1 - y0) +
+ math.cos(y0) * math.cos(y1)*haversine(x1 - x0)))
+ return d
+
+def harcdist(p0,p1,lonx=True,radius=RADIUS_EARTH_KM):
+ """
+ Alternative arc distance function, uses haversine formula
+
+ Parameters
+ ----------
+ p0 : first point as a tuple in decimal degrees
+ p1 : second point as a tuple in decimal degrees
+ lonx : boolean to assess the order of the coordinates,
+ for lon,lat (default) = True, for lat,lon = False
+ radius : radius of the earth at the equator as a sphere
+ default: RADIUS_EARTH_KM (6371.0 km)
+ options: RADIUS_EARTH_MILES (3959.0 miles)
+ None (for result in radians)
+
+ Returns
+ -------
+ d : distance in units specified, km, miles or radians (for None)
+
+ Example
+ -------
+ >>> p0 = (-87.893517, 41.981417)
+ >>> p1 = (-87.519295, 41.657498)
+ >>> harcdist(p0,p1)
+ 47.52873002976876
+ >>> harcdist(p0,p1,radius=None)
+ 0.007460167953189258
+
+ Note
+ ----
+ Uses radangle function to compute radian angle
+
+ """
+ if not(lonx):
+ p = lonlat([p0,p1])
+ p0 = p[0]
+ p1 = p[1]
+
+ d = radangle(p0,p1)
+ if radius is not None:
+ d = d*radius
+ return d
+
+def geointerpolate(p0,p1,t,lonx=True):
+ """
+ Finds a point on a sphere along the great circle distance between two points
+ on a sphere
+ also known as a way point in great circle navigation
+
+ Parameters
+ ----------
+ p0 : first point as a tuple in decimal degrees
+ p1 : second point as a tuple in decimal degrees
+ t : proportion along great circle distance between p0 and p1
+ e.g., t=0.5 would find the mid-point
+ lonx : boolean to assess the order of the coordinates,
+ for lon,lat (default) = True, for lat,lon = False
+
+ Returns
+ -------
+ x,y : tuple in decimal degrees of lon-lat (default) or lat-lon,
+ depending on setting of lonx; in other words, the same
+ order is used as for the input
+
+ Example
+ -------
+ >>> p0 = (-87.893517, 41.981417)
+ >>> p1 = (-87.519295, 41.657498)
+ >>> geointerpolate(p0,p1,0.1) # using lon-lat
+ (-87.85592403438788, 41.949079912574796)
+ >>> p3 = (41.981417, -87.893517)
+ >>> p4 = (41.657498, -87.519295)
+ >>> geointerpolate(p3,p4,0.1,lonx=False) # using lat-lon
+ (41.949079912574796, -87.85592403438788)
+
+ """
+
+ if not(lonx):
+ p = lonlat([p0,p1])
+ p0 = p[0]
+ p1 = p[1]
+
+ d = radangle(p0,p1)
+ k = 1.0 / math.sin(d)
+ t = t*d
+ A = math.sin(d-t) * k
+ B = math.sin(t) * k
+
+ x0, y0 = d2r(p0[0]),d2r(p0[1])
+ x1, y1 = d2r(p1[0]),d2r(p1[1])
+
+ x = A * math.cos(y0) * math.cos(x0) + B * math.cos(y1) * math.cos(x1)
+ y = A * math.cos(y0) * math.sin(x0) + B * math.cos(y1) * math.sin(x1)
+ z = A * math.sin(y0) + B * math.sin(y1)
+
+ newpx = r2d(math.atan2(y, x))
+ newpy = r2d(math.atan2(z, math.sqrt(x*x + y*y)))
+ if not(lonx):
+ return newpy,newpx
+ return newpx,newpy
+
+def geogrid(pup,pdown,k,lonx=True):
+ """
+ Computes a k+1 by k+1 set of grid points for a bounding box in lat-lon
+ uses geointerpolate
+
+ Parameters
+ ----------
+ pup : tuple with lat-lon or lon-lat for upper left corner of bounding box
+ pdown : tuple with lat-lon or lon-lat for lower right corner of bounding box
+ k : number of grid cells (grid points will be one more)
+ lonx : boolean to assess the order of the coordinates,
+ for lon,lat (default) = True, for lat,lon = False
+
+ Returns
+ -------
+ grid : list of tuples with lat-lon or lon-lat for grid points, row by row,
+ starting with the top row and moving to the bottom; coordinate tuples
+ are returned in same order as input
+
+ Example
+ -------
+ >>> pup = (42.023768,-87.946389) # Arlington Heights IL
+ >>> pdown = (41.644415,-87.524102) # Hammond, IN
+ >>> geogrid(pup,pdown,3,lonx=False)
+ [(42.023768, -87.946389), (42.02393997819538, -87.80562679358316), (42.02393997819538, -87.66486420641684), (42.023768, -87.524102), (41.897317, -87.94638900000001), (41.8974888973743, -87.80562679296166), (41.8974888973743, -87.66486420703835), (41.897317, -87.524102), (41.770866000000005, -87.94638900000001), (41.77103781320412, -87.80562679234043), (41.77103781320412, -87.66486420765956), (41.770866000000005, -87.524102), (41.644415, -87.946389), (41.64458672568646, -87.8056267917 [...]
+
+ """
+ if lonx:
+ corners = [pup,pdown]
+ else:
+ corners = lonlat([pup,pdown])
+ tpoints = [float(i)/k for i in range(k)[1:]]
+ leftcorners = [corners[0],(corners[0][0],corners[1][1])]
+ rightcorners = [(corners[1][0],corners[0][1]),corners[1]]
+ leftside = [leftcorners[0]]
+ rightside = [rightcorners[0]]
+ for t in tpoints:
+ newpl = geointerpolate(leftcorners[0],leftcorners[1],t)
+ leftside.append(newpl)
+ newpr = geointerpolate(rightcorners[0],rightcorners[1],t)
+ rightside.append(newpr)
+ leftside.append(leftcorners[1])
+ rightside.append(rightcorners[1])
+
+ grid = []
+ for i in range(len(leftside)):
+ grid.append(leftside[i])
+ for t in tpoints:
+ newp = geointerpolate(leftside[i],rightside[i],t)
+ grid.append(newp)
+ grid.append(rightside[i])
+ if not(lonx):
+ grid = lonlat(grid)
+ return grid
+
+
+
+if __name__ == '__main__':
+ def random_ll():
+ long = (random.random() * 360) - 180
+ lat = (random.random() * 180) - 90
+ return long, lat
+
+ for i in range(1):
+ n = 99
+ # generate random surface points.
+ pts = [random_ll() for i in xrange(n)]
+ # convert to unit sphere points.
+ pts2 = map(toXYZ, pts)
+
+ w = brute_knn(pts, 4, 'arc')
+ w2 = brute_knn(pts2, 4, 'xyz')
+ w3 = fast_knn(pts2, 4)
+ assert w == w2 == w3
+
+ ### Make knn1
+ import pysal
+# f = pysal.open('/Users/charlie/Documents/data/stl_hom/stl_hom.shp', 'r')
+ f = pysal.open(pysal.examples.get_path('stl_hom.shp'),'r')
+ shapes = f.read()
+ pts = [shape.centroid for shape in shapes]
+ w0 = brute_knn(pts, 4, 'xyz')
+ w1 = brute_knn(pts, 4, 'arc')
+ pts = map(toXYZ, pts)
+ w2 = brute_knn(pts, 4, 'xyz')
+ w3 = fast_knn(pts, 4)
+
+ wn, wd = fast_knn(pts, 4, True)
+ ids = range(1, len(pts) + 1)
+
+ ### new sphere examples
+ p0 = (-87.893517, 41.981417)
+ p1 = (-87.519295, 41.657498)
+ p3 = (41.981417, -87.893517)
+ p4 = (41.657498, -87.519295)
+ d1 =harcdist(p0,p1,radius=RADIUS_EARTH_MILES)
+ print "d1",d1
+ d2 = harcdist(p3,p4,lonx=False,radius=None)
+ print "d2",d2
+ pn1 = geointerpolate(p0,p1,0.1)
+ print "pn1",pn1
+ pn2 = geointerpolate(p3,p4,0.1,lonx=False)
+ print "pn2",pn2
+ pup = (42.023768,-87.946389) # Arlington Heights IL
+ pdown = (41.644415,-87.524102) # Hammond, IN
+ grid=geogrid(pup,pdown,3,lonx=False)
+ print "grid",grid
+
diff --git a/pysal/cg/standalone.py b/pysal/cg/standalone.py
new file mode 100644
index 0000000..709edea
--- /dev/null
+++ b/pysal/cg/standalone.py
@@ -0,0 +1,913 @@
+"""
+Helper functions for computational geometry in PySAL
+
+"""
+
+__author__ = "Sergio J. Rey, Xinyue Ye, Charles Schmidt, Andrew Winslow"
+__credits__ = "Copyright (c) 2005-2009 Sergio J. Rey"
+
+import doctest
+import math
+import copy
+from shapes import *
+from itertools import islice
+import scipy.spatial
+from pysal.common import *
+
+EPSILON_SCALER = 3
+
+
+__all__ = ['bbcommon', 'get_bounding_box', 'get_angle_between', 'is_collinear', 'get_segments_intersect', 'get_segment_point_intersect', 'get_polygon_point_intersect', 'get_rectangle_point_intersect', 'get_ray_segment_intersect', 'get_rectangle_rectangle_intersection', 'get_polygon_point_dist', 'get_points_dist', 'get_segment_point_dist', 'get_point_at_angle_and_dist', 'convex_hull', 'is_clockwise', 'point_touches_rectangle', 'get_shared_segments', 'distance_matrix']
+
+
+def bbcommon(bb, bbother):
+ """
+ Old Stars method for bounding box overlap testing
+ Also defined in pysal.weights._cont_binning
+
+ Examples
+ --------
+
+ >>> b0 = [0,0,10,10]
+ >>> b1 = [10,0,20,10]
+ >>> bbcommon(b0,b1)
+ 1
+ """
+ chflag = 0
+ if not ((bbother[2] < bb[0]) or (bbother[0] > bb[2])):
+ if not ((bbother[3] < bb[1]) or (bbother[1] > bb[3])):
+ chflag = 1
+ return chflag
+
+
+def get_bounding_box(items):
+ """
+
+ Examples
+ --------
+ >>> bb = get_bounding_box([Point((-1, 5)), Rectangle(0, 6, 11, 12)])
+ >>> bb.left
+ -1.0
+ >>> bb.lower
+ 5.0
+ >>> bb.right
+ 11.0
+ >>> bb.upper
+ 12.0
+ """
+
+ def left(o):
+ if hasattr(o, 'bounding_box'): # Polygon, Ellipse
+ return o.bounding_box.left
+ elif hasattr(o, 'left'): # Rectangle
+ return o.left
+ else: # Point
+ return o[0]
+
+ def right(o):
+ if hasattr(o, 'bounding_box'): # Polygon, Ellipse
+ return o.bounding_box.right
+ elif hasattr(o, 'right'): # Rectangle
+ return o.right
+ else: # Point
+ return o[0]
+
+ def lower(o):
+ if hasattr(o, 'bounding_box'): # Polygon, Ellipse
+ return o.bounding_box.lower
+ elif hasattr(o, 'lower'): # Rectangle
+ return o.lower
+ else: # Point
+ return o[1]
+
+ def upper(o):
+ if hasattr(o, 'bounding_box'): # Polygon, Ellipse
+ return o.bounding_box.upper
+ elif hasattr(o, 'upper'): # Rectangle
+ return o.upper
+ else: # Point
+ return o[1]
+
+ return Rectangle(min(map(left, items)), min(map(lower, items)), max(map(right, items)), max(map(upper, items)))
+
+
+def get_angle_between(ray1, ray2):
+ """
+ Returns the angle formed between a pair of rays which share an origin
+ get_angle_between(Ray, Ray) -> number
+
+ Parameters
+ ----------
+ ray1 : a ray forming the beginning of the angle measured
+ ray2 : a ray forming the end of the angle measured
+
+ Examples
+ --------
+ >>> get_angle_between(Ray(Point((0, 0)), Point((1, 0))), Ray(Point((0, 0)), Point((1, 0))))
+ 0.0
+ """
+
+ if ray1.o != ray2.o:
+ raise ValueError('Rays must have the same origin.')
+ vec1 = (ray1.p[0] - ray1.o[0], ray1.p[1] - ray1.o[1])
+ vec2 = (ray2.p[0] - ray2.o[0], ray2.p[1] - ray2.o[1])
+ rot_theta = -math.atan2(vec1[1], vec1[0])
+ rot_matrix = [[math.cos(rot_theta), -math.sin(rot_theta)], [
+ math.sin(rot_theta), math.cos(rot_theta)]]
+ rot_vec2 = (rot_matrix[0][0] * vec2[0] + rot_matrix[0][1] * vec2[1],
+ rot_matrix[1][0] * vec2[0] + rot_matrix[1][1] * vec2[1])
+ return math.atan2(rot_vec2[1], rot_vec2[0])
+
+
+def is_collinear(p1, p2, p3):
+ """
+ Returns whether a triplet of points is collinear.
+
+ is_collinear(Point, Point, Point) -> bool
+
+ Parameters
+ ----------
+ p1 : a point (Point)
+ p2 : another point (Point)
+ p3 : yet another point (Point)
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> is_collinear(Point((0, 0)), Point((1, 1)), Point((5, 5)))
+ True
+ >>> is_collinear(Point((0, 0)), Point((1, 1)), Point((5, 0)))
+ False
+ """
+ eps = np.finfo(type(p1[0])).eps
+
+ return (abs((p2[0] - p1[0]) * (p3[1] - p1[1]) - (p2[1] - p1[1]) * (p3[0] - p1[0])) < EPSILON_SCALER * eps)
+
+
+def get_segments_intersect(seg1, seg2):
+ """
+ Returns the intersection of two segments.
+
+ get_segments_intersect(LineSegment, LineSegment) -> Point or LineSegment
+
+ Parameters
+ ----------
+ seg1 : a segment to check intersection for
+ seg2 : a segment to check intersection for
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> seg1 = LineSegment(Point((0, 0)), Point((0, 10)))
+ >>> seg2 = LineSegment(Point((-5, 5)), Point((5, 5)))
+ >>> i = get_segments_intersect(seg1, seg2)
+ >>> isinstance(i, Point)
+ True
+ >>> str(i)
+ '(0.0, 5.0)'
+ >>> seg3 = LineSegment(Point((100, 100)), Point((100, 101)))
+ >>> i = get_segments_intersect(seg2, seg3)
+ """
+
+ p1 = seg1.p1
+ p2 = seg1.p2
+ p3 = seg2.p1
+ p4 = seg2.p2
+ a = p2[0] - p1[0]
+ b = p3[0] - p4[0]
+ c = p2[1] - p1[1]
+ d = p3[1] - p4[1]
+ det = float(a * d - b * c)
+ if det == 0:
+ if seg1 == seg2:
+ return LineSegment(seg1.p1, seg1.p2)
+ else:
+ a = get_segment_point_intersect(seg2, seg1.p1)
+ b = get_segment_point_intersect(seg2, seg1.p2)
+ c = get_segment_point_intersect(seg1, seg2.p1)
+ d = get_segment_point_intersect(seg1, seg2.p2)
+
+ if a and b: # seg1 in seg2
+ return LineSegment(seg1.p1, seg1.p2)
+ if c and d: # seg2 in seg1
+ return LineSegment(seg2.p1, seg2.p2)
+ if (a or b) and (c or d):
+ p1 = a if a else b
+ p2 = c if c else d
+ return LineSegment(p1, p2)
+
+ return None
+ a_inv = d / det
+ b_inv = -b / det
+ c_inv = -c / det
+ d_inv = a / det
+ m = p3[0] - p1[0]
+ n = p3[1] - p1[1]
+ x = a_inv * m + b_inv * n
+ y = c_inv * m + d_inv * n
+ intersect_exists = 0 <= x <= 1 and 0 <= y <= 1
+ if not intersect_exists:
+ return None
+ return Point((p1[0] + x * (p2[0] - p1[0]), p1[1] + x * (p2[1] - p1[1])))
+
+
+def get_segment_point_intersect(seg, pt):
+ """
+ Returns the intersection of a segment and point.
+
+ get_segment_point_intersect(LineSegment, Point) -> Point
+
+ Parameters
+ ----------
+ seg : a segment to check intersection for
+ pt : a point to check intersection for
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> seg = LineSegment(Point((0, 0)), Point((0, 10)))
+ >>> pt = Point((0, 5))
+ >>> i = get_segment_point_intersect(seg, pt)
+ >>> str(i)
+ '(0.0, 5.0)'
+ >>> pt2 = Point((5, 5))
+ >>> get_segment_point_intersect(seg, pt2)
+ """
+ eps = np.finfo(type(pt[0])).eps
+
+ if is_collinear(pt, seg.p1, seg.p2):
+ if get_segment_point_dist(seg, pt)[0] < EPSILON_SCALER * eps:
+ return pt
+ else:
+ return None
+
+ vec1 = (pt[0] - seg.p1[0], pt[1] - seg.p1[1])
+ vec2 = (seg.p2[0] - seg.p1[0], seg.p2[1] - seg.p1[1])
+ if abs(vec1[0] * vec2[1] - vec1[1] * vec2[0]) < eps:
+ return pt
+ return None
+
+
+def get_polygon_point_intersect(poly, pt):
+ """
+ Returns the intersection of a polygon and point.
+
+ get_polygon_point_intersect(Polygon, Point) -> Point
+
+ Parameters
+ ----------
+ poly : a polygon to check intersection for
+ pt : a point to check intersection for
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> poly = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
+ >>> pt = Point((0.5, 0.5))
+ >>> i = get_polygon_point_intersect(poly, pt)
+ >>> str(i)
+ '(0.5, 0.5)'
+ >>> pt2 = Point((2, 2))
+ >>> get_polygon_point_intersect(poly, pt2)
+ """
+ def pt_lies_on_part_boundary(pt, vertices):
+ return filter(
+ lambda i: get_segment_point_dist(LineSegment(
+ vertices[i], vertices[i + 1]), pt)[0] == 0,
+ xrange(-1, len(vertices) - 1)) != []
+
+ ret = None
+ if get_rectangle_point_intersect(poly.bounding_box, pt) is None: # Weed out points that aren't even close
+ return None
+ elif filter(lambda verts: pt_lies_on_part_boundary(pt, verts), poly._vertices) != []:
+ ret = pt
+ elif filter(lambda verts: _point_in_vertices(pt, verts), poly._vertices) != []:
+ ret = pt
+ if poly._holes != [[]]:
+ if filter(lambda verts: pt_lies_on_part_boundary(pt, verts), poly.holes) != []:
+ # pt lies on boundary of hole.
+ pass
+ if filter(lambda verts: _point_in_vertices(pt, verts), poly.holes) != []:
+ # pt lines inside a hole.
+ ret = None
+ #raise NotImplementedError, 'Cannot compute containment for polygon with holes'
+ return ret
+
+
+def get_rectangle_point_intersect(rect, pt):
+ """
+ Returns the intersection of a rectangle and point.
+
+ get_rectangle_point_intersect(Rectangle, Point) -> Point
+
+ Parameters
+ ----------
+ rect : a rectangle to check intersection for
+ pt : a point to check intersection for
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> rect = Rectangle(0, 0, 5, 5)
+ >>> pt = Point((1, 1))
+ >>> i = get_rectangle_point_intersect(rect, pt)
+ >>> str(i)
+ '(1.0, 1.0)'
+ >>> pt2 = Point((10, 10))
+ >>> get_rectangle_point_intersect(rect, pt2)
+ """
+ if rect.left <= pt[0] <= rect.right and rect.lower <= pt[1] <= rect.upper:
+ return pt
+ return None
+
+
+def get_ray_segment_intersect(ray, seg):
+ """
+ Returns the intersection of a ray and line segment.
+
+ get_ray_segment_intersect(Ray, Point) -> Point or LineSegment
+
+ Parameters
+ ----------
+
+ ray : a ray to check intersection for
+ seg : a line segment to check intersection for
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> ray = Ray(Point((0, 0)), Point((0, 1)))
+ >>> seg = LineSegment(Point((-1, 10)), Point((1, 10)))
+ >>> i = get_ray_segment_intersect(ray, seg)
+ >>> isinstance(i, Point)
+ True
+ >>> str(i)
+ '(0.0, 10.0)'
+ >>> seg2 = LineSegment(Point((10, 10)), Point((10, 11)))
+ >>> get_ray_segment_intersect(ray, seg2)
+ """
+ d = max(math.hypot(seg.p1[0] - ray.o[0], seg.p1[1] - ray.o[1]),
+ math.hypot(seg.p2[0] - ray.o[0], seg.p2[1] - ray.o[1])) + 1 # Upper bound on origin to segment dist (+1)
+ ratio = d / math.hypot(ray.o[0] - ray.p[0], ray.o[1] - ray.p[1])
+ ray_seg = LineSegment(
+ ray.o, Point((ray.o[0] + ratio * (ray.p[0] - ray.o[0]),
+ ray.o[1] + ratio * (ray.p[1] - ray.o[1]))))
+ return get_segments_intersect(seg, ray_seg)
+
+
+def get_rectangle_rectangle_intersection(r0, r1, checkOverlap=True):
+ """
+ Returns the intersection between two rectangles.
+
+ Note: Algorithm assumes the rectangles overlap.
+ checkOverlap=False should be used with extreme caution.
+
+ get_rectangle_rectangle_intersection(r0, r1) -> Rectangle, Segment, Point or None
+
+ Parameters
+ ----------
+ r0 : a Rectangle
+ r1 : a Rectangle
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> r0 = Rectangle(0,4,6,9)
+ >>> r1 = Rectangle(4,0,9,7)
+ >>> ri = get_rectangle_rectangle_intersection(r0,r1)
+ >>> ri[:]
+ [4.0, 4.0, 6.0, 7.0]
+ >>> r0 = Rectangle(0,0,4,4)
+ >>> r1 = Rectangle(2,1,6,3)
+ >>> ri = get_rectangle_rectangle_intersection(r0,r1)
+ >>> ri[:]
+ [2.0, 1.0, 4.0, 3.0]
+ >>> r0 = Rectangle(0,0,4,4)
+ >>> r1 = Rectangle(2,1,3,2)
+ >>> ri = get_rectangle_rectangle_intersection(r0,r1)
+ >>> ri[:] == r1[:]
+ True
+ """
+ if checkOverlap:
+ if not bbcommon(r0, r1):
+ #raise ValueError, "Rectangles do not intersect"
+ return None
+ left = max(r0.left, r1.left)
+ lower = max(r0.lower, r1.lower)
+ right = min(r0.right, r1.right)
+ upper = min(r0.upper, r1.upper)
+
+ if upper == lower and left == right:
+ return Point((left, lower))
+ elif upper == lower:
+ return LineSegment(Point((left, lower)), Point((right, lower)))
+ elif left == right:
+ return LineSegment(Point((left, lower)), Point((left, upper)))
+
+ return Rectangle(left, lower, right, upper)
+
+
+def get_polygon_point_dist(poly, pt):
+ """
+ Returns the distance between a polygon and point.
+
+ get_polygon_point_dist(Polygon, Point) -> number
+
+ Parameters
+ ----------
+ poly : a polygon to compute distance from
+ pt : a point to compute distance from
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> poly = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
+ >>> pt = Point((2, 0.5))
+ >>> get_polygon_point_dist(poly, pt)
+ 1.0
+ >>> pt2 = Point((0.5, 0.5))
+ >>> get_polygon_point_dist(poly, pt2)
+ 0.0
+ """
+ if get_polygon_point_intersect(poly, pt) is not None:
+ return 0.0
+ part_prox = []
+ for vertices in poly._vertices:
+ part_prox.append(min([get_segment_point_dist(LineSegment(vertices[i], vertices[i + 1]), pt)[0]
+ for i in xrange(-1, len(vertices) - 1)]))
+ return min(part_prox)
+
+
+def get_points_dist(pt1, pt2):
+ """
+ Returns the distance between a pair of points.
+
+ get_points_dist(Point, Point) -> number
+
+ Parameters
+ ----------
+ pt1 : a point
+ pt2 : the other point
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> get_points_dist(Point((4, 4)), Point((4, 8)))
+ 4.0
+ >>> get_points_dist(Point((0, 0)), Point((0, 0)))
+ 0.0
+ """
+ return math.hypot(pt1[0] - pt2[0], pt1[1] - pt2[1])
+
+
+def get_segment_point_dist(seg, pt):
+ """
+ Returns the distance between a line segment and point and distance along the segment of the closest
+ point on the segment to the point as a ratio of the length of the segment.
+
+ get_segment_point_dist(LineSegment, Point) -> (number, number)
+
+ Parameters
+ ----------
+ seg : a line segment to compute distance from
+ pt : a point to compute distance from
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> seg = LineSegment(Point((0, 0)), Point((10, 0)))
+ >>> pt = Point((5, 5))
+ >>> get_segment_point_dist(seg, pt)
+ (5.0, 0.5)
+ >>> pt2 = Point((0, 0))
+ >>> get_segment_point_dist(seg, pt2)
+ (0.0, 0.0)
+ """
+ src_p = seg.p1
+ dest_p = seg.p2
+
+ # Shift line to go through origin
+ points_0 = pt[0] - src_p[0]
+ points_1 = pt[1] - src_p[1]
+ points_2 = 0
+ points_3 = 0
+ points_4 = dest_p[0] - src_p[0]
+ points_5 = dest_p[1] - src_p[1]
+
+ segment_length = get_points_dist(src_p, dest_p)
+
+ # Meh, robustness...maybe should incorporate this into a more general
+ # approach later
+ if segment_length == 0:
+ return (get_points_dist(pt, src_p), 0)
+
+ u_x = points_4 / segment_length
+ u_y = points_5 / segment_length
+
+ inter_x = u_x * u_x * points_0 + u_x * u_y * points_1
+ inter_y = u_x * u_y * points_0 + u_y * u_y * points_1
+
+ src_proj_dist = get_points_dist((0, 0), (inter_x, inter_y))
+ dest_proj_dist = get_points_dist((inter_x, inter_y), (points_4, points_5))
+
+ if src_proj_dist > segment_length or dest_proj_dist > segment_length:
+ src_pt_dist = get_points_dist(
+ (points_2, points_3), (points_0, points_1))
+ dest_pt_dist = get_points_dist(
+ (points_4, points_5), (points_0, points_1))
+ if src_pt_dist < dest_pt_dist:
+ return (src_pt_dist, 0)
+ else:
+ return (dest_pt_dist, 1)
+ else:
+ return (get_points_dist((inter_x, inter_y), (points_0, points_1)), src_proj_dist / segment_length)
+
+
+def get_point_at_angle_and_dist(ray, angle, dist):
+ """
+ Returns the point at a distance and angle relative to the origin of a ray.
+
+ get_point_at_angle_and_dist(Ray, number, number) -> Point
+
+ Parameters
+ ----------
+ ray : the ray which the angle and distance are relative to
+ angle : the angle relative to the ray at which the point is located
+ dist : the distance from the ray origin at which the point is located
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> ray = Ray(Point((0, 0)), Point((1, 0)))
+ >>> pt = get_point_at_angle_and_dist(ray, math.pi, 1.0)
+ >>> isinstance(pt, Point)
+ True
+ >>> round(pt[0], 8)
+ -1.0
+ >>> round(pt[1], 8)
+ 0.0
+ """
+ v = (ray.p[0] - ray.o[0], ray.p[1] - ray.o[1])
+ cur_angle = math.atan2(v[1], v[0])
+ dest_angle = cur_angle + angle
+ return Point((ray.o[0] + dist * math.cos(dest_angle), ray.o[1] + dist * math.sin(dest_angle)))
+
+
+def convex_hull(points):
+ """
+ Returns the convex hull of a set of points.
+
+ convex_hull(Point list) -> Polygon
+
+ Parameters
+ ----------
+ points : a list of points to compute the convex hull for
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> points = [Point((0, 0)), Point((4, 4)), Point((4, 0)), Point((3, 1))]
+ >>> convex_hull(points)
+ [(0.0, 0.0), (4.0, 0.0), (4.0, 4.0)]
+ """
+ points = copy.copy(points)
+ lowest = min(points, key=lambda p: (p[1], p[0]))
+
+ points.remove(lowest)
+ points.sort(key=lambda p: math.atan2(p[1] - lowest[1], p[0] - lowest[0]))
+
+ stack = [lowest]
+
+ def right_turn(p1, p2, p3):
+ # Returns if p1 -> p2 -> p3 forms a 'right turn'
+ vec1 = (p2[0] - p1[0], p2[1] - p1[1])
+ vec2 = (p3[0] - p2[0], p3[1] - p2[1])
+ return vec2[0] * vec1[1] - vec2[1] * vec1[0] >= 0
+
+ for p in points:
+ stack.append(p)
+ while len(stack) > 3 and right_turn(stack[-3], stack[-2], stack[-1]):
+ stack.pop(-2)
+
+ return stack
+
+
+def is_clockwise(vertices):
+ """
+ Returns whether a list of points describing a polygon are clockwise or counterclockwise.
+
+ is_clockwise(Point list) -> bool
+
+ Parameters
+ ----------
+ vertices : a list of points that form a single ring
+
+ Examples
+ --------
+ >>> is_clockwise([Point((0, 0)), Point((10, 0)), Point((0, 10))])
+ False
+ >>> is_clockwise([Point((0, 0)), Point((0, 10)), Point((10, 0))])
+ True
+ >>> v = [(-106.57798, 35.174143999999998), (-106.583412, 35.174141999999996), (-106.58417999999999, 35.174143000000001), (-106.58377999999999, 35.175542999999998), (-106.58287999999999, 35.180543), (-106.58263099999999, 35.181455), (-106.58257999999999, 35.181643000000001), (-106.58198299999999, 35.184615000000001), (-106.58148, 35.187242999999995), (-106.58127999999999, 35.188243), (-106.58138, 35.188243), (-106.58108, 35.189442999999997), (-106.58104, 35.189644000000001), (-106.580 [...]
+ >>> is_clockwise(v)
+ True
+ """
+ if len(vertices) < 3:
+ return True
+ area = 0.0
+ ax, ay = vertices[0]
+ for bx, by in vertices[1:]:
+ area += ax * by - ay * bx
+ ax, ay = bx, by
+ bx, by = vertices[0]
+ area += ax * by - ay * bx
+ return area < 0.0
+
+
+def ccw(vertices):
+ """
+ Returns whether a list of points is counterclockwise
+
+ >>> ccw([Point((0, 0)), Point((10, 0)), Point((0, 10))])
+ True
+ >>> ccw([Point((0, 0)), Point((0, 10)), Point((10, 0))])
+ False
+ """
+
+ if is_clockwise(vertices):
+ return False
+ else:
+ return True
+
+
+def seg_intersect(a, b, c, d):
+ """
+ Tests if two segments (a,b) (c,d) intersect
+
+ >>> a = Point((0,1))
+ >>> b = Point((0,10))
+ >>> c = Point((-2,5))
+ >>> d = Point((2,5))
+ >>> e = Point((-3,5))
+ >>> seg_intersect(a, b, c, d)
+ True
+ >>> seg_intersect(a, b, c, e)
+ False
+ """
+ if ccw([a, c, d]) == ccw([b, c, d]):
+ return False
+ elif ccw([a, b, c]) == ccw([a, b, d]):
+ return False
+ else:
+ return True
+
+
+def _point_in_vertices(pt, vertices):
+ """
+ HELPER METHOD. DO NOT CALL.
+
+ Returns whether a point is contained in a polygon specified by a sequence of vertices.
+
+ _point_in_vertices(Point, Point list) -> bool
+
+ Parameters
+ ----------
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> _point_in_vertices(Point((1, 1)), [Point((0, 0)), Point((10, 0)), Point((0, 10))])
+ True
+ """
+
+ def neg_ray_intersect(p1, p2, p3):
+ # Returns whether a ray in the negative-x direction from p3 intersects the segment between
+ if not min(p1[1], p2[1]) <= p3[1] <= max(p1[1], p2[1]):
+ return False
+ if p1[1] > p2[1]:
+ vec1 = (p2[0] - p1[0], p2[1] - p1[1])
+ else:
+ vec1 = (p1[0] - p2[0], p1[1] - p2[1])
+ vec2 = (p3[0] - p1[0], p3[1] - p1[1])
+ return vec1[0] * vec2[1] - vec2[0] * vec1[1] >= 0
+
+ vert_y_set = set([v[1] for v in vertices])
+ while pt[1] in vert_y_set:
+ pt = (pt[0], pt[1] + -1e-14 + random.random(
+ ) * 2e-14) # Perturb the location very slightly
+ inters = 0
+ for i in xrange(-1, len(vertices) - 1):
+ v1 = vertices[i]
+ v2 = vertices[i + 1]
+ if neg_ray_intersect(v1, v2, pt):
+ inters += 1
+
+ return inters % 2 == 1
+
+
+def point_touches_rectangle(point, rect):
+ """
+ Returns True if the point is in the rectangle or touches it's boundary.
+
+ point_touches_rectangle(point, rect) -> bool
+
+ Parameters
+ ----------
+ point : Point or Tuple
+ rect : Rectangle
+
+ Examples
+ --------
+ >>> rect = Rectangle(0,0,10,10)
+ >>> a = Point((5,5))
+ >>> b = Point((10,5))
+ >>> c = Point((11,11))
+ >>> point_touches_rectangle(a,rect)
+ 1
+ >>> point_touches_rectangle(b,rect)
+ 1
+ >>> point_touches_rectangle(c,rect)
+ 0
+ """
+ chflag = 0
+ if point[0] >= rect.left and point[0] <= rect.right:
+ if point[1] >= rect.lower and point[1] <= rect.upper:
+ chflag = 1
+ return chflag
+
+
+def get_shared_segments(poly1, poly2, bool_ret=False):
+ """
+ Returns the line segments in common to both polygons.
+
+ get_shared_segments(poly1, poly2) -> list
+
+ Parameters
+ ----------
+ poly1 : a Polygon
+ poly2 : a Polygon
+
+ Attributes
+ ----------
+
+ Examples
+ --------
+ >>> x = [0, 0, 1, 1]
+ >>> y = [0, 1, 1, 0]
+ >>> poly1 = Polygon( map(Point,zip(x,y)) )
+ >>> x = [a+1 for a in x]
+ >>> poly2 = Polygon( map(Point,zip(x,y)) )
+ >>> get_shared_segments(poly1, poly2, bool_ret=True)
+ True
+
+ """
+ #get_rectangle_rectangle_intersection inlined for speed.
+ r0 = poly1.bounding_box
+ r1 = poly2.bounding_box
+ wLeft = max(r0.left, r1.left)
+ wLower = max(r0.lower, r1.lower)
+ wRight = min(r0.right, r1.right)
+ wUpper = min(r0.upper, r1.upper)
+
+ segmentsA = set()
+ common = list()
+ partsA = poly1.parts
+ for part in poly1.parts + [p for p in poly1.holes if p]:
+ if part[0] != part[-1]: # not closed
+ part = part[:] + part[0:1]
+ a = part[0]
+ for b in islice(part, 1, None):
+ # inlining point_touches_rectangle for speed
+ x, y = a
+ # check if point a is in the bounding box intersection
+ if x >= wLeft and x <= wRight and y >= wLower and y <= wUpper:
+ x, y = b
+ # check if point b is in the bounding box intersection
+ if x >= wLeft and x <= wRight and y >= wLower and y <= wUpper:
+ if a > b:
+ segmentsA.add((b, a))
+ else:
+ segmentsA.add((a, b))
+ a = b
+ for part in poly2.parts + [p for p in poly2.holes if p]:
+ if part[0] != part[-1]: # not closed
+ part = part[:] + part[0:1]
+ a = part[0]
+ for b in islice(part, 1, None):
+ # inlining point_touches_rectangle for speed
+ x, y = a
+ if x >= wLeft and x <= wRight and y >= wLower and y <= wUpper:
+ x, y = b
+ if x >= wLeft and x <= wRight and y >= wLower and y <= wUpper:
+ if a > b:
+ seg = (b, a)
+ else:
+ seg = (a, b)
+ if seg in segmentsA:
+ common.append(LineSegment(*seg))
+ if bool_ret:
+ return True
+ a = b
+ if bool_ret:
+ if len(common) > 0:
+ return True
+ else:
+ return False
+ return common
+
+
+def distance_matrix(X, p=2.0, threshold=5e7):
+ """
+ Distance Matrices
+
+ XXX Needs optimization/integration with other weights in pysal
+
+ Parameters
+ ----------
+ X : An, n by k numpy.ndarray
+ Where n is number of observations
+ k is number of dimmensions (2 for x,y)
+ p : float
+ Minkowski p-norm distance metric parameter:
+ 1<=p<=infinity
+ 2: Euclidean distance
+ 1: Manhattan distance
+ threshold : positive integer
+ If (n**2)*32 > threshold use scipy.spatial.distance_matrix instead
+ of working in ram, this is roughly the ammount of ram (in bytes) that will be used.
+
+ Examples
+ --------
+ >>> x,y=[r.flatten() for r in np.indices((3,3))]
+ >>> data = np.array([x,y]).T
+ >>> d=distance_matrix(data)
+ >>> np.array(d)
+ array([[ 0. , 1. , 2. , 1. , 1.41421356,
+ 2.23606798, 2. , 2.23606798, 2.82842712],
+ [ 1. , 0. , 1. , 1.41421356, 1. ,
+ 1.41421356, 2.23606798, 2. , 2.23606798],
+ [ 2. , 1. , 0. , 2.23606798, 1.41421356,
+ 1. , 2.82842712, 2.23606798, 2. ],
+ [ 1. , 1.41421356, 2.23606798, 0. , 1. ,
+ 2. , 1. , 1.41421356, 2.23606798],
+ [ 1.41421356, 1. , 1.41421356, 1. , 0. ,
+ 1. , 1.41421356, 1. , 1.41421356],
+ [ 2.23606798, 1.41421356, 1. , 2. , 1. ,
+ 0. , 2.23606798, 1.41421356, 1. ],
+ [ 2. , 2.23606798, 2.82842712, 1. , 1.41421356,
+ 2.23606798, 0. , 1. , 2. ],
+ [ 2.23606798, 2. , 2.23606798, 1.41421356, 1. ,
+ 1.41421356, 1. , 0. , 1. ],
+ [ 2.82842712, 2.23606798, 2. , 2.23606798, 1.41421356,
+ 1. , 2. , 1. , 0. ]])
+ >>>
+ """
+ if X.ndim == 1:
+ X.shape = (X.shape[0], 1)
+ if X.ndim > 2:
+ raise TypeError("wtf?")
+ n, k = X.shape
+
+ if (n ** 2) * 32 > threshold:
+ return scipy.spatial.distance_matrix(X, X, p)
+ else:
+ M = np.ones((n, n))
+ D = np.zeros((n, n))
+ for col in range(k):
+ x = X[:, col]
+ xM = x * M
+ dx = xM - xM.T
+ if p % 2 != 0:
+ dx = np.abs(dx)
+ dx2 = dx ** p
+ D += dx2
+ D = D ** (1.0 / p)
+ return D
+
+
diff --git a/pysal/cg/tests/__init__.py b/pysal/cg/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pysal/cg/tests/test_geoJSON.py b/pysal/cg/tests/test_geoJSON.py
new file mode 100644
index 0000000..cea05ad
--- /dev/null
+++ b/pysal/cg/tests/test_geoJSON.py
@@ -0,0 +1,26 @@
+import pysal
+import doctest
+import unittest
+
+
+class test_MultiPloygon(unittest.TestCase):
+
+ def test___init__1(self):
+ """
+ Tests conversion of polygons with multiple shells to
+ geoJSON multipolygons. and back.
+ """
+ shp = pysal.open(pysal.examples.get_path("NAT.SHP"),'r')
+ multipolygons = [p for p in shp if len(p.parts) > 1]
+ geoJSON = [p.__geo_interface__ for p in multipolygons]
+ for poly in multipolygons:
+ json = poly.__geo_interface__
+ shape = pysal.cg.asShape(json)
+ self.assertEquals(json['type'],'MultiPolygon')
+ self.assertEquals(str(shape.holes), str(poly.holes))
+ self.assertEquals(str(shape.parts), str(poly.parts))
+
+if __name__ == '__main__':
+ unittest.main()
+ #runner = unittest.TextTestRunner()
+ #runner.run(suite)
diff --git a/pysal/cg/tests/test_locators.py b/pysal/cg/tests/test_locators.py
new file mode 100644
index 0000000..b36b5a6
--- /dev/null
+++ b/pysal/cg/tests/test_locators.py
@@ -0,0 +1,65 @@
+
+"""locators Unittest."""
+from pysal.cg import *
+import unittest
+
+
+class PolygonLocator_Tester(unittest.TestCase):
+ """setup class for unit tests."""
+ def setUp(self):
+ p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])
+ p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])
+ p3 = Polygon([Point((7, 1)), Point((8, 7)), Point((9, 1))])
+ self.polygons = [p1, p2, p3]
+ self.pl = PolygonLocator(self.polygons)
+
+ pt = Point
+ pg = Polygon
+ polys = []
+ for i in range(5):
+ l = i * 10
+ r = l + 10
+ b = 10
+ t = 20
+ sw = pt((l, b))
+ se = pt((r, b))
+ ne = pt((r, t))
+ nw = pt((l, t))
+ polys.append(pg([sw, se, ne, nw]))
+ self.pl2 = PolygonLocator(polys)
+
+ def test_PolygonLocator(self):
+ qr = Rectangle(3, 7, 5, 8)
+ res = self.pl.inside(qr)
+ self.assertEqual(len(res), 0)
+
+ def test_inside(self):
+ qr = Rectangle(3, 3, 5, 5)
+ res = self.pl.inside(qr)
+ self.assertEqual(len(res), 0)
+ qr = Rectangle(0, 0, 5, 5)
+ res = self.pl.inside(qr)
+ self.assertEqual(len(res), 1)
+
+ def test_overlapping(self):
+
+ qr = Rectangle(3, 3, 5, 5)
+ res = self.pl.overlapping(qr)
+ self.assertEqual(len(res), 2)
+ qr = Rectangle(8, 3, 10, 10)
+ res = self.pl.overlapping(qr)
+ self.assertEqual(len(res), 1)
+
+ qr = Rectangle(2, 12, 35, 15)
+ res = self.pl2.overlapping(qr)
+ self.assertEqual(len(res), 4)
+
+suite = unittest.TestSuite()
+test_classes = [PolygonLocator_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/cg/tests/test_rtree.py b/pysal/cg/tests/test_rtree.py
new file mode 100644
index 0000000..bfa5ca1
--- /dev/null
+++ b/pysal/cg/tests/test_rtree.py
@@ -0,0 +1,68 @@
+
+"""pyrtree Unittest."""
+from pysal.cg import RTree, Rect
+import unittest
+
+
+class Pyrtree_Tester(unittest.TestCase):
+ """setup class for unit tests."""
+ def setUp(self):
+ k = 10
+ w = 20
+ objects = {}
+ id = 0
+ for i in range(k):
+ mn_y = i * w
+ mx_y = mn_y + w
+ for j in range(k):
+ mn_x = j * w
+ mx_x = mn_x + w
+ objects[id] = Rect(mn_x, mn_y, mx_x, mx_y)
+ id += 1
+ self.objects = objects
+
+ def test_rtree(self):
+ t = RTree()
+ for object in self.objects:
+ t.insert(object, self.objects[object])
+ self.assertEqual(len(self.objects), 100)
+
+ qr = Rect(5, 5, 25, 25)
+
+ # find objects with mbrs intersecting with qr
+ res = [r.leaf_obj() for r in t.query_rect(qr) if r.is_leaf()]
+ self.assertEqual(len(res), 4)
+ res.sort()
+ self.assertEqual(res, [0, 1, 10, 11])
+
+ # vertices are shared by all coincident rectangles
+ res = [r.leaf_obj(
+ ) for r in t.query_point((20.0, 20.0)) if r.is_leaf()]
+ self.assertEqual(len(res), 4)
+
+ res = [r.leaf_obj() for r in t.query_point((21, 20)) if r.is_leaf()]
+ self.assertEqual(len(res), 2)
+
+ # single internal point
+ res = [r.leaf_obj() for r in t.query_point((21, 21)) if r.is_leaf()]
+ self.assertEqual(len(res), 1)
+
+ # single external point
+ res = [r.leaf_obj() for r in t.query_point((-12, 21)) if r.is_leaf()]
+ self.assertEqual(len(res), 0)
+
+ qr = Rect(5, 6, 65, 7)
+
+ res = [r.leaf_obj() for r in t.query_rect((qr)) if r.is_leaf()]
+ self.assertEqual(len(res), 4)
+
+
+suite = unittest.TestSuite()
+test_classes = [Pyrtree_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/cg/tests/test_segmentLocator.py b/pysal/cg/tests/test_segmentLocator.py
new file mode 100644
index 0000000..612419d
--- /dev/null
+++ b/pysal/cg/tests/test_segmentLocator.py
@@ -0,0 +1,48 @@
+"""Segment Locator Unittest."""
+from pysal.cg import *
+from pysal.cg.segmentLocator import *
+import unittest
+
+
+class SegmentGrid_Tester(unittest.TestCase):
+ """setup class for unit tests."""
+ def setUp(self):
+ # 10x10 grid with four line segments, one for each edge of the grid.
+ self.grid = SegmentGrid(Rectangle(0, 0, 10, 10), 1)
+ self.grid.add(LineSegment(Point((0.0, 0.0)), Point((0.0, 10.0))), 0)
+ self.grid.add(LineSegment(Point((0.0, 10.0)), Point((10.0, 10.0))), 1)
+ self.grid.add(LineSegment(Point((10.0, 10.0)), Point((10.0, 0.0))), 2)
+ self.grid.add(LineSegment(Point((10.0, 0.0)), Point((0.0, 0.0))), 3)
+
+ def test_nearest_1(self):
+ self.assertEquals([0, 1, 2, 3], self.grid.nearest(Point((
+ 5.0, 5.0)))) # Center
+ self.assertEquals(
+ [0], self.grid.nearest(Point((0.0, 5.0)))) # Left Edge
+ self.assertEquals(
+ [1], self.grid.nearest(Point((5.0, 10.0)))) # Top Edge
+ self.assertEquals(
+ [2], self.grid.nearest(Point((10.0, 5.0)))) # Right Edge
+ self.assertEquals(
+ [3], self.grid.nearest(Point((5.0, 0.0)))) # Bottom Edge
+
+ def test_nearest_2(self):
+ self.assertEquals([0, 1, 3], self.grid.nearest(Point((-
+ 100000.0, 5.0)))) # Left Edge
+ self.assertEquals([1, 2, 3], self.grid.nearest(Point((
+ 100000.0, 5.0)))) # Right Edge
+ self.assertEquals([0, 2, 3], self.grid.nearest(Point((5.0,
+ -100000.0)))) # Bottom Edge
+ self.assertEquals([0, 1, 2], self.grid.nearest(Point((5.0,
+ 100000.0)))) # Top Edge
+
+
+suite = unittest.TestSuite()
+test_classes = [SegmentGrid_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/cg/tests/test_shapes.py b/pysal/cg/tests/test_shapes.py
new file mode 100644
index 0000000..7dc5733
--- /dev/null
+++ b/pysal/cg/tests/test_shapes.py
@@ -0,0 +1,758 @@
+from pysal.cg import Point, LineSegment, Line, Ray, Chain, Rectangle, Polygon
+import doctest
+import unittest
+
+
+class test_Point(unittest.TestCase):
+
+ def test___init__1(self):
+ """
+ Tests whether points are created without issue.
+
+ Test tag: <tc>#tests#Point.__init__</tc>
+ """
+ for l in [(-5.0, 10.0), (0.0, -6.0), (float(1e300), float(-1e300))]:
+ p = Point(l)
+
+ def test___str__1(self):
+ """
+ Tests whether the string produced is valid for corner cases.
+
+ Test tag: <tc>#tests#Point__str__</tc>
+ """
+ for l in [(-5, 10), (0, -6.0), (float(1e300), -1e300)]:
+ p = Point(l)
+ self.assertEquals(str(p), str((float(l[0]), float(
+ l[1])))) # Recast to floats like point does
+
+
+class test_LineSegment(unittest.TestCase):
+
+ def test_is_ccw1(self):
+ """
+ Test corner cases for horizontal segment starting at origin.
+
+ Test tag: <tc>#tests#LineSegment.is_ccw</tc>
+ """
+ ls = LineSegment(Point((0, 0)), Point((5, 0)))
+ self.assertFalse(ls.is_ccw(
+ Point((10, 0)))) # At positive boundary beyond segment
+ self.assertFalse(ls.is_ccw(Point((3, 0)))) # On segment
+ self.assertFalse(ls.is_ccw(
+ Point((-10, 0)))) # At negative boundary beyond segment
+ self.assertFalse(ls.is_ccw(Point((0, 0)))) # Endpoint of segment
+ self.assertFalse(ls.is_ccw(Point((5, 0)))) # Endpoint of segment
+
+ def test_is_ccw2(self):
+ """
+ Test corner cases for vertical segment ending at origin.
+
+ Test tag: <tc>#tests#LineSegment.is_ccw</tc>
+ """
+ ls = LineSegment(Point((0, -5)), Point((0, 0)))
+ self.assertFalse(ls.is_ccw(
+ Point((0, 10)))) # At positive boundary beyond segment
+ self.assertFalse(ls.is_ccw(Point((0, -3)))) # On segment
+ self.assertFalse(ls.is_ccw(
+ Point((0, -10)))) # At negative boundary beyond segment
+ self.assertFalse(ls.is_ccw(Point((0, -5)))) # Endpoint of segment
+ self.assertFalse(ls.is_ccw(Point((0, 0)))) # Endpoint of segment
+
+ def test_is_ccw3(self):
+ """
+ Test corner cases for non-axis-aligned segment not through origin.
+
+ Test tag: <tc>#tests#LineSegment.is_ccw</tc>
+ """
+ ls = LineSegment(Point((0, 1)), Point((5, 6)))
+ self.assertFalse(ls.is_ccw(
+ Point((10, 11)))) # At positive boundary beyond segment
+ self.assertFalse(ls.is_ccw(Point((3, 4)))) # On segment
+ self.assertFalse(ls.is_ccw(
+ Point((-10, -9)))) # At negative boundary beyond segment
+ self.assertFalse(ls.is_ccw(Point((0, 1)))) # Endpoint of segment
+ self.assertFalse(ls.is_ccw(Point((5, 6)))) # Endpoint of segment
+
+ def test_is_cw1(self):
+ """
+ Test corner cases for horizontal segment starting at origin.
+
+ Test tag: <tc>#tests#LineSegment.is_cw</tc>
+ """
+ ls = LineSegment(Point((0, 0)), Point((5, 0)))
+ self.assertFalse(ls.is_cw(
+ Point((10, 0)))) # At positive boundary beyond segment
+ self.assertFalse(ls.is_cw(Point((3, 0)))) # On segment
+ self.assertFalse(ls.is_cw(
+ Point((-10, 0)))) # At negative boundary beyond segment
+ self.assertFalse(ls.is_cw(Point((0, 0)))) # Endpoint of segment
+ self.assertFalse(ls.is_cw(Point((5, 0)))) # Endpoint of segment
+
+ def test_is_cw2(self):
+ """
+ Test corner cases for vertical segment ending at origin.
+
+ Test tag: <tc>#tests#LineSegment.is_cw</tc>
+ """
+ ls = LineSegment(Point((0, -5)), Point((0, 0)))
+ self.assertFalse(ls.is_cw(
+ Point((0, 10)))) # At positive boundary beyond segment
+ self.assertFalse(ls.is_cw(Point((0, -3)))) # On segment
+ self.assertFalse(ls.is_cw(
+ Point((0, -10)))) # At negative boundary beyond segment
+ self.assertFalse(ls.is_cw(Point((0, -5)))) # Endpoint of segment
+ self.assertFalse(ls.is_cw(Point((0, 0)))) # Endpoint of segment
+
+ def test_is_cw3(self):
+ """
+ Test corner cases for non-axis-aligned segment not through origin.
+
+ Test tag: <tc>#tests#LineSegment.is_cw</tc>
+ """
+ ls = LineSegment(Point((0, 1)), Point((5, 6)))
+ self.assertFalse(ls.is_cw(
+ Point((10, 11)))) # At positive boundary beyond segment
+ self.assertFalse(ls.is_cw(Point((3, 4)))) # On segment
+ self.assertFalse(ls.is_cw(
+ Point((-10, -9)))) # At negative boundary beyond segment
+ self.assertFalse(ls.is_cw(Point((0, 1)))) # Endpoint of segment
+ self.assertFalse(ls.is_cw(Point((5, 6)))) # Endpoint of segment
+
+ def test_get_swap1(self):
+ """
+ Tests corner cases.
+
+ Test tag: <tc>#tests#LineSegment.get_swap</tc>
+ """
+ ls = LineSegment(Point((0, 0)), Point((10, 0)))
+ swap = ls.get_swap()
+ self.assertEquals(ls.p1, swap.p2)
+ self.assertEquals(ls.p2, swap.p1)
+
+ ls = LineSegment(Point((-5, 0)), Point((5, 0)))
+ swap = ls.get_swap()
+ self.assertEquals(ls.p1, swap.p2)
+ self.assertEquals(ls.p2, swap.p1)
+
+ ls = LineSegment(Point((0, 0)), Point((0, 0)))
+ swap = ls.get_swap()
+ self.assertEquals(ls.p1, swap.p2)
+ self.assertEquals(ls.p2, swap.p1)
+
+ ls = LineSegment(Point((5, 5)), Point((5, 5)))
+ swap = ls.get_swap()
+ self.assertEquals(ls.p1, swap.p2)
+ self.assertEquals(ls.p2, swap.p1)
+
+ def test_bounding_box(self):
+ """
+ Tests corner cases.
+
+ Test tag: <tc>#tests#LineSegment.bounding_box</tc>
+ """
+ ls = LineSegment(Point((0, 0)), Point((0, 10)))
+ self.assertEquals(ls.bounding_box.left, 0)
+ self.assertEquals(ls.bounding_box.lower, 0)
+ self.assertEquals(ls.bounding_box.right, 0)
+ self.assertEquals(ls.bounding_box.upper, 10)
+
+ ls = LineSegment(Point((0, 0)), Point((-3, -4)))
+ self.assertEquals(ls.bounding_box.left, -3)
+ self.assertEquals(ls.bounding_box.lower, -4)
+ self.assertEquals(ls.bounding_box.right, 0)
+ self.assertEquals(ls.bounding_box.upper, 0)
+
+ ls = LineSegment(Point((-5, 0)), Point((3, 0)))
+ self.assertEquals(ls.bounding_box.left, -5)
+ self.assertEquals(ls.bounding_box.lower, 0)
+ self.assertEquals(ls.bounding_box.right, 3)
+ self.assertEquals(ls.bounding_box.upper, 0)
+
+ def test_len1(self):
+ """
+ Tests corner cases.
+
+ Test tag: <tc>#tests#LineSegment.len</tc>
+ """
+ ls = LineSegment(Point((0, 0)), Point((0, 0)))
+ self.assertEquals(ls.len, 0)
+
+ ls = LineSegment(Point((0, 0)), Point((-3, 0)))
+ self.assertEquals(ls.len, 3)
+
+ def test_line1(self):
+ """
+ Tests corner cases.
+
+ Test tag: <tc>#tests#LineSegment.line</tc>
+ """
+ import math
+ ls = LineSegment(Point((0, 0)), Point((1, 0)))
+ self.assertEquals(ls.line.m, 0)
+ self.assertEquals(ls.line.b, 0)
+
+ ls = LineSegment(Point((0, 0)), Point((0, 1)))
+ self.assertEquals(ls.line.m, float('inf'))
+ self.assertTrue(math.isnan(ls.line.b))
+
+ ls = LineSegment(Point((0, 0)), Point((0, -1)))
+ self.assertEquals(ls.line.m, float('inf'))
+ self.assertTrue(math.isnan(ls.line.b))
+
+ ls = LineSegment(Point((0, 0)), Point((0, 0)))
+ self.assertEquals(ls.line, None)
+
+ ls = LineSegment(Point((5,0)), Point((10,0)))
+ ls1 = LineSegment(Point((5,0)), Point((10,1)))
+ self.assertTrue(ls.intersect(ls1))
+ ls2 = LineSegment(Point((5,1)), Point((10,1)))
+ self.assertFalse(ls.intersect(ls2))
+ ls2 = LineSegment(Point((7,-1)), Point((7,2)))
+ self.assertTrue(ls.intersect(ls2))
+
+
+
+
+
+class test_Line(unittest.TestCase):
+
+ def test___init__1(self):
+ """
+ Tests a variety of generic cases.
+
+ Test tag: <tc>#tests#Line.__init__</tc>
+ """
+ for m, b in [(4, 0.0), (-140, 5), (0, 0)]:
+ l = Line(m, b)
+
+ def test_y1(self):
+ """
+ Tests a variety of generic and special cases (+-infinity).
+
+ Test tag: <tc>#tests#Line.y</tc>
+ """
+ l = Line(0, 0)
+ self.assertEquals(l.y(0), 0)
+ self.assertEquals(l.y(-1e600), 0)
+ self.assertEquals(l.y(1e600), 0)
+
+ l = Line(1, 1)
+ self.assertEquals(l.y(2), 3)
+ self.assertEquals(l.y(-1e600), -1e600)
+ self.assertEquals(l.y(1e600), 1e600)
+
+ l = Line(-1, 1)
+ self.assertEquals(l.y(2), -1)
+ self.assertEquals(l.y(-1e600), 1e600)
+ self.assertEquals(l.y(1e600), -1e600)
+
+ def test_x1(self):
+ """
+ Tests a variety of generic and special cases (+-infinity).
+
+ Test tag: <tc>#tests#Line.x</tc>
+ """
+ l = Line(0, 0)
+ #self.assertEquals(l.x(0), 0)
+ with self.assertRaises(ArithmeticError):
+ l.x(0)
+ with self.assertRaises(ArithmeticError):
+ l.x(-1e600)
+ with self.assertRaises(ArithmeticError):
+ l.x(1e600)
+
+ l = Line(1, 1)
+ self.assertEquals(l.x(3), 2)
+ self.assertEquals(l.x(-1e600), -1e600)
+ self.assertEquals(l.x(1e600), 1e600)
+
+ l = Line(-1, 1)
+ self.assertEquals(l.x(2), -1)
+ self.assertEquals(l.x(-1e600), 1e600)
+ self.assertEquals(l.x(1e600), -1e600)
+
+
+class test_Ray(unittest.TestCase):
+
+ def test___init__1(self):
+ """
+ Tests generic cases.
+
+ <tc>#tests#Ray.__init__</tc>
+ """
+ r = Ray(Point((0, 0)), Point((1, 1)))
+ r = Ray(Point((8, -3)), Point((-5, 9)))
+
+
+class test_Chain(unittest.TestCase):
+
+ def test___init__1(self):
+ """
+ Generic testing that no exception is thrown.
+
+ Test tag: <tc>#tests#Chain.__init__</tc>
+ """
+ c = Chain([Point((0, 0))])
+ c = Chain([[Point((0, 0)), Point((1, 1))], [Point((2, 5))]])
+
+ def test_vertices1(self):
+ """
+ Testing for repeated vertices and multiple parts.
+
+ Test tag: <tc>#tests#Chain.vertices</tc>
+ """
+ vertices = [Point((0, 0)), Point((1, 1)), Point((2, 5)),
+ Point((0, 0)), Point((1, 1)), Point((2, 5))]
+ self.assertEquals(Chain(vertices).vertices, vertices)
+
+ vertices = [[Point((0, 0)), Point((1, 1)), Point((2, 5))],
+ [Point((0, 0)), Point((1, 1)), Point((2, 5))]]
+ self.assertEquals(Chain(vertices).vertices, vertices[0] + vertices[1])
+
+ def test_parts1(self):
+ """
+ Generic testing of parts functionality.
+
+ Test tag: <tc>#tests#Chain.parts</tc>
+ """
+ vertices = [Point((0, 0)), Point((1, 1)), Point((2, 5)),
+ Point((0, 0)), Point((1, 1)), Point((2, 5))]
+ self.assertEquals(Chain(vertices).parts, [vertices])
+
+ vertices = [[Point((0, 0)), Point((1, 1)), Point((2, 5))],
+ [Point((0, 0)), Point((1, 1)), Point((2, 5))]]
+ self.assertEquals(Chain(vertices).parts, vertices)
+
+ def test_bounding_box1(self):
+ """
+ Test correctness with multiple parts.
+
+ Test tag: <tc>#tests#Chain.bounding_box</tc>
+ """
+ vertices = [[Point((0, 0)), Point((1, 1)), Point((2, 6))],
+ [Point((-5, -5)), Point((0, 0)), Point((2, 5))]]
+ bb = Chain(vertices).bounding_box
+ self.assertEquals(bb.left, -5)
+ self.assertEquals(bb.lower, -5)
+ self.assertEquals(bb.right, 2)
+ self.assertEquals(bb.upper, 6)
+
+ def test_len1(self):
+ """
+ Test correctness with multiple parts and zero-length point-to-point distances.
+
+ Test tag: <tc>#tests#Chain.len</tc>
+ """
+ vertices = [[Point((0, 0)), Point((1, 0)), Point((1, 5))],
+ [Point((-5, -5)), Point((-5, 0)), Point((0, 0)), Point((0, 0))]]
+ self.assertEquals(Chain(vertices).len, 6 + 10)
+
+
+class test_Polygon(unittest.TestCase):
+
+ def test___init__1(self):
+ """
+ Test various input configurations (list vs. lists of lists, holes)
+
+ <tc>#tests#Polygon.__init__</tc>
+ """
+ # Input configurations tested (in order of test):
+ # one part, no holes
+ # multi parts, no holes
+ # one part, one hole
+ # multi part, one hole
+ # one part, multi holes
+ # multi part, multi holes
+ p = Polygon([Point(
+ (0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))])
+ p = Polygon(
+ [[Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
+ [Point((30, 30)), Point((40, 30)), Point((40, 40)), Point((30, 40))]])
+ p = Polygon(
+ [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
+ holes=[Point((2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))])
+ p = Polygon(
+ [[Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
+ [Point(
+ (
+ 30, 30)), Point(
+ (40, 30)), Point((40, 40)), Point((30, 40))]],
+ holes=[Point((2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))])
+ p = Polygon(
+ [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
+ holes=[[Point(
+ (2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))],
+ [Point((6, 6)), Point((6, 8)), Point((8, 8)), Point((8, 6))]])
+ p = Polygon(
+ [[Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
+ [Point(
+ (
+ 30, 30)), Point(
+ (40, 30)), Point((40, 40)), Point((30, 40))]],
+ holes=[[Point(
+ (2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))],
+ [Point((6, 6)), Point((6, 8)), Point((8, 8)), Point((8, 6))]])
+
+ def test_area1(self):
+ """
+ Test multiple parts.
+
+ Test tag: <tc>#tests#Polygon.area</tc>
+ """
+ p = Polygon(
+ [[Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
+ [Point((30, 30)), Point((40, 30)), Point((40, 40)), Point((30, 40))]])
+ self.assertEquals(p.area, 200)
+
+ def test_area2(self):
+ """
+ Test holes.
+
+ Test tag: <tc>#tests#Polygon.area</tc>
+ """
+ p = Polygon(
+ [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
+ holes=[Point((2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))])
+ self.assertEquals(p.area, 100 - 4)
+
+ p = Polygon(
+ [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
+ holes=[[Point(
+ (2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))],
+ [Point((6, 6)), Point((6, 8)), Point((8, 8)), Point((8, 6))]])
+ self.assertEquals(p.area, 100 - (4 + 4))
+
+ p = Polygon(
+ [[Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
+ [Point(
+ (
+ 30, 30)), Point(
+ (40, 30)), Point((40, 40)), Point((30, 40))]],
+ holes=[[Point(
+ (2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))],
+ [Point((36, 36)), Point((36, 38)), Point((38, 38)), Point((38, 36))]])
+ self.assertEquals(p.area, 200 - (4 + 4))
+
+ def test_area4(self):
+ """
+ Test polygons with vertices in both orders (cw, ccw).
+
+ Test tag: <tc>#tests#Polygon.area</tc>
+ """
+ p = Polygon([Point(
+ (0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))])
+ self.assertEquals(p.area, 100)
+
+ p = Polygon([Point(
+ (0, 0)), Point((0, 10)), Point((10, 10)), Point((10, 0))])
+ self.assertEquals(p.area, 100)
+
+ def test_bounding_box1(self):
+ """
+ Test polygons with multiple parts.
+
+ Test tag: <tc>#tests#Polygon.bounding_box</tc>
+ """
+ p = Polygon(
+ [[Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
+ [Point((30, 30)), Point((40, 30)), Point((40, 40)), Point((30, 40))]])
+ bb = p.bounding_box
+ self.assertEquals(bb.left, 0)
+ self.assertEquals(bb.lower, 0)
+ self.assertEquals(bb.right, 40)
+ self.assertEquals(bb.upper, 40)
+
+ def test_centroid1(self):
+ """
+ Test polygons with multiple parts of the same size.
+
+ Test tag: <tc>#tests#Polygon.centroid</tc>
+ """
+ p = Polygon(
+ [[Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
+ [Point((30, 30)), Point((40, 30)), Point((40, 40)), Point((30, 40))]])
+ c = p.centroid
+ self.assertEquals(c[0], 20)
+ self.assertEquals(c[1], 20)
+
+ def test_centroid2(self):
+ """
+ Test polygons with multiple parts of different size.
+
+ Test tag: <tc>#tests#Polygon.centroid</tc>
+ """
+ p = Polygon(
+ [[Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
+ [Point((30, 30)), Point((35, 30)), Point((35, 35)), Point((30, 35))]])
+ c = p.centroid
+ self.assertEquals(c[0], 10.5)
+ self.assertEquals(c[1], 10.5)
+
+ def test_holes1(self):
+ """
+ Test for correct vertex values/order.
+
+ Test tag: <tc>#tests#Polygon.holes</tc>
+ """
+ p = Polygon(
+ [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
+ holes=[Point((2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))])
+ self.assertEquals(len(p.holes), 1)
+ e_holes = [Point((2, 2)), Point((2, 4)), Point((4, 4)), Point((4, 2))]
+ self.assertTrue(p.holes[0] in [e_holes, [e_holes[-1]] + e_holes[:3],
+ e_holes[-2:] + e_holes[:2], e_holes[-3:] + [e_holes[0]]])
+
+ def test_holes2(self):
+ """
+ Test for multiple holes.
+
+ Test tag: <tc>#tests#Polygon.holes</tc>
+ """
+ p = Polygon(
+ [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
+ holes=[[Point(
+ (2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))],
+ [Point((6, 6)), Point((6, 8)), Point((8, 8)), Point((8, 6))]])
+ holes = p.holes
+ self.assertEquals(len(holes), 2)
+
+ def test_parts1(self):
+ """
+ Test for correct vertex values/order.
+
+ Test tag: <tc>#tests#Polygon.parts</tc>
+ """
+ p = Polygon(
+ [[Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
+ [Point((30, 30)), Point((40, 30)), Point((30, 40))]])
+ self.assertEquals(len(p.parts), 2)
+
+ part1 = [Point(
+ (0, 0)), Point((0, 10)), Point((10, 10)), Point((10, 0))]
+ part2 = [Point((30, 30)), Point((30, 40)), Point((40, 30))]
+ if len(p.parts[0]) == 4:
+ self.assertTrue(p.parts[0] in [part1, part1[-1:] + part1[:3],
+ part1[-2:] + part1[:2], part1[-3:] + part1[:1]])
+ self.assertTrue(p.parts[1] in [part2, part2[-1:] +
+ part2[:2], part2[-2:] + part2[:1]])
+ elif len(p.parts[0]) == 3:
+ self.assertTrue(p.parts[0] in [part2, part2[-1:] +
+ part2[:2], part2[-2:] + part2[:1]])
+ self.assertTrue(p.parts[1] in [part1, part1[-1:] + part1[:3],
+ part1[-2:] + part1[:2], part1[-3:] + part1[:1]])
+ else:
+ self.fail()
+
+ def test_perimeter1(self):
+ """
+ Test with multiple parts.
+
+ Test tag: <tc>#tests#Polygon.perimeter</tc>
+ """
+ p = Polygon(
+ [[Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
+ [Point((30, 30)), Point((40, 30)), Point((40, 40)), Point((30, 40))]])
+ self.assertEquals(p.perimeter, 80)
+
+ def test_perimeter2(self):
+ """
+ Test with holes.
+
+ Test tag: <tc>#tests#Polygon.perimeter</tc>
+ """
+ p = Polygon(
+ [[Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
+ [Point(
+ (
+ 30, 30)), Point(
+ (40, 30)), Point((40, 40)), Point((30, 40))]],
+ holes=[[Point(
+ (2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))],
+ [Point((6, 6)), Point((6, 8)), Point((8, 8)), Point((8, 6))]])
+ self.assertEquals(p.perimeter, 80 + 16)
+
+ def test_vertices1(self):
+ """
+ Test for correct values/order of vertices.
+
+ Test tag: <tc>#tests#Polygon.vertices</tc>
+ """
+ p = Polygon([Point(
+ (0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))])
+ self.assertEquals(len(p.vertices), 4)
+ e_verts = [Point(
+ (0, 0)), Point((0, 10)), Point((10, 10)), Point((10, 0))]
+ self.assertTrue(p.vertices in [e_verts, e_verts[-1:] + e_verts[:3],
+ e_verts[-2:] + e_verts[:2], e_verts[-3:] + e_verts[:1]])
+
+ def test_vertices2(self):
+ """
+ Test for multiple parts.
+
+ Test tag: <tc>#tests#Polygon.vertices</tc>
+ """
+ p = Polygon(
+ [[Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
+ [Point((30, 30)), Point((40, 30)), Point((40, 40)), Point((30, 40))]])
+ self.assertEquals(len(p.vertices), 8)
+
+ def test_contains_point(self):
+ p = Polygon([Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], [Point((1, 2)), Point((2, 2)), Point((2, 1)), Point((1, 1))])
+ self.assertEquals(p.contains_point((0, 0)), 1)
+ self.assertEquals(p.contains_point((1, 1)), 0)
+ self.assertEquals(p.contains_point((2, 2)), 1)
+ self.assertEquals(p.contains_point((5, 5)), 1)
+ self.assertEquals(p.contains_point((10, 10)), 0)
+
+
+class test_Rectangle(unittest.TestCase):
+
+ def test___init__1(self):
+ """
+ Test exceptions are thrown correctly.
+
+ Test tag: <tc>#tests#Rectangle.__init__</tc>
+ """
+ try:
+ r = Rectangle(1, 1, -1, 5) # right < left
+ except ArithmeticError:
+ pass
+ else:
+ self.fail()
+
+ try:
+ r = Rectangle(1, 1, 5, -1) # upper < lower
+ except ArithmeticError:
+ pass
+ else:
+ self.fail()
+
+ def test_set_centroid1(self):
+ """
+ Test with rectangles of zero width or height.
+
+ Test tag: <tc>#tests#Rectangle.set_centroid</tc>
+ """
+ r = Rectangle(5, 5, 5, 10) # Zero width
+ r.set_centroid(Point((0, 0)))
+ self.assertEquals(r.left, 0)
+ self.assertEquals(r.lower, -2.5)
+ self.assertEquals(r.right, 0)
+ self.assertEquals(r.upper, 2.5)
+
+ r = Rectangle(10, 5, 20, 5) # Zero height
+ r.set_centroid(Point((40, 40)))
+ self.assertEquals(r.left, 35)
+ self.assertEquals(r.lower, 40)
+ self.assertEquals(r.right, 45)
+ self.assertEquals(r.upper, 40)
+
+ r = Rectangle(0, 0, 0, 0) # Zero width and height
+ r.set_centroid(Point((-4, -4)))
+ self.assertEquals(r.left, -4)
+ self.assertEquals(r.lower, -4)
+ self.assertEquals(r.right, -4)
+ self.assertEquals(r.upper, -4)
+
+ def test_set_scale1(self):
+ """
+ Test repeated scaling.
+
+ Test tag: <tc>#tests#Rectangle.set_scale</tc>
+ """
+ r = Rectangle(2, 2, 4, 4)
+
+ r.set_scale(0.5)
+ self.assertEquals(r.left, 2.5)
+ self.assertEquals(r.lower, 2.5)
+ self.assertEquals(r.right, 3.5)
+ self.assertEquals(r.upper, 3.5)
+
+ r.set_scale(2)
+ self.assertEquals(r.left, 2)
+ self.assertEquals(r.lower, 2)
+ self.assertEquals(r.right, 4)
+ self.assertEquals(r.upper, 4)
+
+ def test_set_scale2(self):
+ """
+ Test scaling of rectangles with zero width/height..
+
+ Test tag: <tc>#tests#Rectangle.set_scale</tc>
+ """
+ r = Rectangle(5, 5, 5, 10) # Zero width
+ r.set_scale(2)
+ self.assertEquals(r.left, 5)
+ self.assertEquals(r.lower, 2.5)
+ self.assertEquals(r.right, 5)
+ self.assertEquals(r.upper, 12.5)
+
+ r = Rectangle(10, 5, 20, 5) # Zero height
+ r.set_scale(2)
+ self.assertEquals(r.left, 5)
+ self.assertEquals(r.lower, 5)
+ self.assertEquals(r.right, 25)
+ self.assertEquals(r.upper, 5)
+
+ r = Rectangle(0, 0, 0, 0) # Zero width and height
+ r.set_scale(100)
+ self.assertEquals(r.left, 0)
+ self.assertEquals(r.lower, 0)
+ self.assertEquals(r.right, 0)
+ self.assertEquals(r.upper, 0)
+
+ r = Rectangle(0, 0, 0, 0) # Zero width and height
+ r.set_scale(0.01)
+ self.assertEquals(r.left, 0)
+ self.assertEquals(r.lower, 0)
+ self.assertEquals(r.right, 0)
+ self.assertEquals(r.upper, 0)
+
+ def test_area1(self):
+ """
+ Test rectangles with zero width/height
+
+ Test tag: <tc>#tests#Rectangle.area</tc>
+ """
+ r = Rectangle(5, 5, 5, 10) # Zero width
+ self.assertEquals(r.area, 0)
+
+ r = Rectangle(10, 5, 20, 5) # Zero height
+ self.assertEquals(r.area, 0)
+
+ r = Rectangle(0, 0, 0, 0) # Zero width and height
+ self.assertEquals(r.area, 0)
+
+ def test_height1(self):
+ """
+ Test rectangles with zero height.
+
+ Test tag: <tc>#tests#Rectangle.height</tc>
+ """
+ r = Rectangle(10, 5, 20, 5) # Zero height
+ self.assertEquals(r.height, 0)
+
+ def test_width1(self):
+ """
+ Test rectangles with zero width.
+
+ Test tag: <tc>#tests#Rectangle.width</tc>
+ """
+ r = Rectangle(5, 5, 5, 10) # Zero width
+ self.assertEquals(r.width, 0)
+
+
+#suite = unittest.TestSuite()
+#suite.addTest(doctest.DocTestSuite('pysal.cg.shapes'))
+#A = unittest.TestLoader().loadTestsFromTestCase(_TestPoint)
+#B = unittest.TestLoader().loadTestsFromTestCase(_TestLineSegment)
+#C = unittest.TestLoader().loadTestsFromTestCase(_TestLine)
+#D = unittest.TestLoader().loadTestsFromTestCase(_TestRay)
+#E = unittest.TestLoader().loadTestsFromTestCase(_TestChain)
+#F = unittest.TestLoader().loadTestsFromTestCase(_TestPolygon)
+#G = unittest.TestLoader().loadTestsFromTestCase(_TestRectangle)
+#suite.addTests([A,B,C,D,E,D,G])
+if __name__ == '__main__':
+ unittest.main()
+ #runner = unittest.TextTestRunner()
+ #runner.run(suite)
diff --git a/pysal/cg/tests/test_standalone.py b/pysal/cg/tests/test_standalone.py
new file mode 100644
index 0000000..a5ee52f
--- /dev/null
+++ b/pysal/cg/tests/test_standalone.py
@@ -0,0 +1,601 @@
+import unittest
+import numpy as np
+import math
+
+from pysal.cg.shapes import *
+from pysal.cg.standalone import *
+
+
+class TestBbcommon(unittest.TestCase):
+ def test_bbcommon(self):
+ b0 = [0, 0, 10, 10]
+ b1 = [5, 5, 15, 15]
+ self.assertEqual(1, bbcommon(b0, b1))
+
+ def test_bbcommon_same(self):
+ b0 = [0, 0, 10, 10]
+ b1 = [0, 0, 10, 10]
+ self.assertEqual(1, bbcommon(b0, b1))
+
+ def test_bbcommon_nested(self):
+ b0 = [0, 0, 10, 10]
+ b1 = [1, 1, 9, 9]
+ self.assertEqual(1, bbcommon(b0, b1))
+
+ def test_bbcommon_top(self):
+ b0 = [0, 0, 10, 10]
+ b1 = [3, 5, 6, 15]
+ self.assertEqual(1, bbcommon(b0, b1))
+
+ def test_bbcommon_shared_edge(self):
+ b0 = [0, 0, 10, 10]
+ b1 = [0, 10, 10, 20]
+ self.assertEqual(1, bbcommon(b0, b1))
+
+ def test_bbcommon_shared_corner(self):
+ b0 = [0, 0, 10, 10]
+ b1 = [10, 10, 20, 20]
+ self.assertEqual(1, bbcommon(b0, b1))
+
+ def test_bbcommon_floats(self):
+ b0 = [0.0, 0.0, 0.1, 0.1]
+ b1 = [0.05, 0.05, 0.15, 0.15]
+ self.assertEqual(1, bbcommon(b0, b1))
+
+
+class TestGetBoundingBox(unittest.TestCase):
+ def test_get_bounding_box(self):
+ items = [Point((-1, 5)), Rectangle(0, 6, 11, 12)]
+ expected = [-1, 5, 11, 12]
+ self.assertEqual(expected, get_bounding_box(items)[:])
+
+
+class TestGetAngleBetween(unittest.TestCase):
+ def test_get_angle_between(self):
+ ray1 = Ray(Point((0, 0)), Point((1, 0)))
+ ray2 = Ray(Point((0, 0)), Point((1, 0)))
+ self.assertEqual(0.0, get_angle_between(ray1, ray2))
+
+ def test_get_angle_between_expect45(self):
+ ray1 = Ray(Point((0, 0)), Point((1, 0)))
+ ray2 = Ray(Point((0, 0)), Point((1, 1)))
+ self.assertEqual(45.0, math.degrees(get_angle_between(ray1, ray2)))
+
+ def test_get_angle_between_expect90(self):
+ ray1 = Ray(Point((0, 0)), Point((1, 0)))
+ ray2 = Ray(Point((0, 0)), Point((0, 1)))
+ self.assertEqual(90.0, math.degrees(get_angle_between(ray1, ray2)))
+
+
+class TestIsCollinear(unittest.TestCase):
+ def test_is_collinear(self):
+ self.assertEqual(True, is_collinear(Point((0, 0)), Point((
+ 1, 1)), Point((5, 5))))
+
+ def test_is_collinear_expectFalse(self):
+ self.assertEqual(False, is_collinear(Point((0, 0)), Point((
+ 1, 1)), Point((5, 0))))
+
+ def test_is_collinear_AlongX(self):
+ self.assertEqual(True, is_collinear(Point((0, 0)), Point((
+ 1, 0)), Point((5, 0))))
+
+ def test_is_collinear_AlongY(self):
+ self.assertEqual(True, is_collinear(
+ Point((0, 0)), Point((0, 1)), Point((0, -1))))
+
+ def test_is_collinear_smallFloat(self):
+ """
+ Given: p1 = (0.1, 0.2), p2 = (0.2, 0.3), p3 = (0.3, 0.4)
+
+ Line(p1,p2): y = mx + b
+ m = (0.3-0.2) / (0.2-0.1) = .1/.1 = 1
+ y - mx = b
+ b = 0.3 - 1*0.2 = 0.1
+ b = 0.2 - 1*0.1 = 0.1
+
+ y = 1*x + 0.1
+
+ Line(p2,p3): y = mx + b
+ m = (0.4-0.3) / (0.3-0.2) = .1/.1 = 1
+ y - mx = b
+ b = 0.4 - 1*0.3 = 0.1
+ b = 0.4 - 1*0.2 = 0.1
+
+ y = 1*x + 0.1
+
+ Line(p1,p2) == Line(p2,p3)
+ Therefore p1,p2,p3 are collinear.
+
+ Due to floating point rounding areas the standard test,
+ ((p2[0]-p1[0])*(p3[1]-p1[1]) - (p2[1]-p1[1])*(p3[0]-p1[0])) == 0
+ will fail. To get around this we use an epsilon. numpy.finfo function
+ return an smallest epsilon for the given data types such that,
+ (numpy.finfo(float).eps + 1.0) != 1.0
+
+ Therefore if
+ abs((p2[0]-p1[0])*(p3[1]-p1[1]) - (p2[1]-p1[1])*(
+ p3[0]-p1[0])) < numpy.finfo(p1[0]).eps
+ The points are collinear.
+ """
+ self.assertEqual(True, is_collinear(
+ Point((0.1, 0.2)), Point((0.2, 0.3)), Point((0.3, 0.4))))
+
+ def test_is_collinear_random(self):
+ for i in range(10):
+ a, b, c = np.random.random(3) * 10 ** (i)
+ self.assertEqual(True, is_collinear(
+ Point((a, a)), Point((b, b)), Point((c, c))))
+
+ def test_is_collinear_random2(self):
+ for i in range(1000):
+ a, b, c = np.random.random(3)
+ self.assertEqual(True, is_collinear(
+ Point((a, a)), Point((b, b)), Point((c, c))))
+
+
+class TestGetSegmentsIntersect(unittest.TestCase):
+ def test_get_segments_intersect(self):
+ seg1 = LineSegment(Point((0, 0)), Point((0, 10)))
+ seg2 = LineSegment(Point((-5, 5)), Point((5, 5)))
+ self.assertEqual((0.0, 5.0), get_segments_intersect(seg1, seg2)[:])
+
+ def test_get_segments_intersect_shared_vert(self):
+ seg1 = LineSegment(Point((0, 0)), Point((0, 10)))
+ seg2 = LineSegment(Point((-5, 5)), Point((0, 10)))
+ self.assertEqual((0.0, 10.0), get_segments_intersect(seg1, seg2)[:])
+
+ def test_get_segments_intersect_floats(self):
+ seg1 = LineSegment(Point((0, 0)), Point((0, .10)))
+ seg2 = LineSegment(Point((-.5, .05)), Point((.5, .05)))
+ self.assertEqual((0.0, .05), get_segments_intersect(seg1, seg2)[:])
+
+ def test_get_segments_intersect_angles(self):
+ seg1 = LineSegment(Point((0, 0)), Point((1, 1)))
+ seg2 = LineSegment(Point((1, 0)), Point((0, 1)))
+ self.assertEqual((0.5, 0.5), get_segments_intersect(seg1, seg2)[:])
+
+ def test_get_segments_intersect_no_intersect(self):
+ seg1 = LineSegment(Point((-5, 5)), Point((5, 5)))
+ seg2 = LineSegment(Point((100, 100)), Point((100, 101)))
+ self.assertEqual(None, get_segments_intersect(seg1, seg2))
+
+ def test_get_segments_intersect_overlap(self):
+ seg1 = LineSegment(Point((0.1, 0.1)), Point((0.6, 0.6)))
+ seg2 = LineSegment(Point((0.3, 0.3)), Point((0.9, 0.9)))
+ expected = LineSegment(Point((0.3, 0.3)), Point((0.6, 0.6)))
+ self.assertEqual(expected, get_segments_intersect(seg1, seg2))
+
+ def test_get_segments_intersect_same(self):
+ seg1 = LineSegment(Point((-5, 5)), Point((5, 5)))
+ self.assertEqual(seg1, get_segments_intersect(seg1, seg1))
+
+ def test_get_segments_intersect_nested(self):
+ seg1 = LineSegment(Point((0.1, 0.1)), Point((0.9, 0.9)))
+ seg2 = LineSegment(Point((0.3, 0.3)), Point((0.6, 0.6)))
+ self.assertEqual(seg2, get_segments_intersect(seg1, seg2))
+
+
+class TestGetSegmentPointIntersect(unittest.TestCase):
+ def test_get_segment_point_intersect(self):
+ seg = LineSegment(Point((0, 0)), Point((0, 10)))
+ pt = Point((0, 5))
+ self.assertEqual(pt, get_segment_point_intersect(seg, pt))
+
+ def test_get_segment_point_intersect_left_end(self):
+ seg = LineSegment(Point((0, 0)), Point((0, 10)))
+ pt = seg.p1
+ self.assertEqual(pt, get_segment_point_intersect(seg, pt))
+
+ def test_get_segment_point_intersect_right_end(self):
+ seg = LineSegment(Point((0, 0)), Point((0, 10)))
+ pt = seg.p2
+ self.assertEqual(pt, get_segment_point_intersect(seg, pt))
+
+ def test_get_segment_point_intersect_angle(self):
+ seg = LineSegment(Point((0, 0)), Point((1, 1)))
+ pt = Point((.1, .1))
+ self.assertEqual(pt, get_segment_point_intersect(seg, pt))
+
+ def test_get_segment_point_intersect_no_intersect(self):
+ seg = LineSegment(Point((0, 0)), Point((0, 10)))
+ pt = Point((5, 5))
+ self.assertEqual(None, get_segment_point_intersect(seg, pt))
+
+ def test_get_segment_point_intersect_no_intersect_collinear(self):
+ seg = LineSegment(Point((0, 0)), Point((0, 10)))
+ pt = Point((0, 20))
+ self.assertEqual(None, get_segment_point_intersect(seg, pt))
+
+ def test_get_segment_point_intersect_floats(self):
+ seg = LineSegment(Point((0.3, 0.3)), Point((.9, .9)))
+ pt = Point((.5, .5))
+ self.assertEqual(pt, get_segment_point_intersect(seg, pt))
+
+ def test_get_segment_point_intersect_floats(self):
+ seg = LineSegment(Point((0.0, 0.0)), Point((
+ 2.7071067811865475, 2.7071067811865475)))
+ pt = Point((1.0, 1.0))
+ self.assertEqual(pt, get_segment_point_intersect(seg, pt))
+
+ def test_get_segment_point_intersect_floats_no_intersect(self):
+ seg = LineSegment(Point((0.3, 0.3)), Point((.9, .9)))
+ pt = Point((.1, .1))
+ self.assertEqual(None, get_segment_point_intersect(seg, pt))
+
+
+class TestGetPolygonPointIntersect(unittest.TestCase):
+ def test_get_polygon_point_intersect(self):
+ poly = Polygon([Point(
+ (0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
+ pt = Point((0.5, 0.5))
+ self.assertEqual(pt, get_polygon_point_intersect(poly, pt))
+
+ def test_get_polygon_point_intersect_on_edge(self):
+ poly = Polygon([Point(
+ (0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
+ pt = Point((1.0, 0.5))
+ self.assertEqual(pt, get_polygon_point_intersect(poly, pt))
+
+ def test_get_polygon_point_intersect_on_vertex(self):
+ poly = Polygon([Point(
+ (0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
+ pt = Point((1.0, 1.0))
+ self.assertEqual(pt, get_polygon_point_intersect(poly, pt))
+
+ def test_get_polygon_point_intersect_outside(self):
+ poly = Polygon([Point(
+ (0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
+ pt = Point((2.0, 2.0))
+ self.assertEqual(None, get_polygon_point_intersect(poly, pt))
+
+
+class TestGetRectanglePointIntersect(unittest.TestCase):
+ def test_get_rectangle_point_intersect(self):
+ rect = Rectangle(0, 0, 5, 5)
+ pt = Point((1, 1))
+ self.assertEqual(pt, get_rectangle_point_intersect(rect, pt))
+
+ def test_get_rectangle_point_intersect_on_edge(self):
+ rect = Rectangle(0, 0, 5, 5)
+ pt = Point((2.5, 5))
+ self.assertEqual(pt, get_rectangle_point_intersect(rect, pt))
+
+ def test_get_rectangle_point_intersect_on_vertex(self):
+ rect = Rectangle(0, 0, 5, 5)
+ pt = Point((5, 5))
+ self.assertEqual(pt, get_rectangle_point_intersect(rect, pt))
+
+ def test_get_rectangle_point_intersect_outside(self):
+ rect = Rectangle(0, 0, 5, 5)
+ pt = Point((10, 10))
+ self.assertEqual(None, get_rectangle_point_intersect(rect, pt))
+
+
+class TestGetRaySegmentIntersect(unittest.TestCase):
+ def test_get_ray_segment_intersect(self):
+ ray = Ray(Point((0, 0)), Point((0, 1)))
+ seg = LineSegment(Point((-1, 10)), Point((1, 10)))
+ self.assertEqual((0.0, 10.), get_ray_segment_intersect(ray, seg)[:])
+
+ def test_get_ray_segment_intersect_orgin(self):
+ ray = Ray(Point((0, 0)), Point((0, 1)))
+ seg = LineSegment(Point((-1, 0)), Point((1, 0)))
+ self.assertEqual((0.0, 0.0), get_ray_segment_intersect(ray, seg)[:])
+
+ def test_get_ray_segment_intersect_edge(self):
+ ray = Ray(Point((0, 0)), Point((0, 1)))
+ seg = LineSegment(Point((0, 2)), Point((2, 2)))
+ self.assertEqual((0.0, 2.0), get_ray_segment_intersect(ray, seg)[:])
+
+ def test_get_ray_segment_intersect_no_intersect(self):
+ ray = Ray(Point((0, 0)), Point((0, 1)))
+ seg = LineSegment(Point((10, 10)), Point((10, 11)))
+ self.assertEqual(None, get_ray_segment_intersect(ray, seg))
+
+ def test_get_ray_segment_intersect_segment(self):
+ ray = Ray(Point((0, 0)), Point((5, 5)))
+ seg = LineSegment(Point((1, 1)), Point((2, 2)))
+ self.assertEqual(seg, get_ray_segment_intersect(ray, seg))
+
+
+class TestGetRectangleRectangleIntersection(unittest.TestCase):
+ def test_get_rectangle_rectangle_intersection_leftright(self):
+ r0 = Rectangle(0, 4, 6, 9)
+ r1 = Rectangle(4, 0, 9, 7)
+ expected = [4.0, 4.0, 6.0, 7.0]
+ self.assertEqual(
+ expected, get_rectangle_rectangle_intersection(r0, r1)[:])
+
+ def test_get_rectangle_rectangle_intersection_topbottom(self):
+ r0 = Rectangle(0, 0, 4, 4)
+ r1 = Rectangle(2, 1, 6, 3)
+ expected = [2.0, 1.0, 4.0, 3.0]
+ self.assertEqual(
+ expected, get_rectangle_rectangle_intersection(r0, r1)[:])
+
+ def test_get_rectangle_rectangle_intersection_nested(self):
+ r0 = Rectangle(0, 0, 4, 4)
+ r1 = Rectangle(2, 1, 3, 2)
+ self.assertEqual(r1, get_rectangle_rectangle_intersection(r0, r1))
+
+ def test_get_rectangle_rectangle_intersection_shared_corner(self):
+ r0 = Rectangle(0, 0, 4, 4)
+ r1 = Rectangle(4, 4, 8, 8)
+ self.assertEqual(Point(
+ (4, 4)), get_rectangle_rectangle_intersection(r0, r1))
+
+ def test_get_rectangle_rectangle_intersection_shared_edge(self):
+ r0 = Rectangle(0, 0, 4, 4)
+ r1 = Rectangle(0, 4, 4, 8)
+ self.assertEqual(LineSegment(Point((0, 4)), Point(
+ (4, 4))), get_rectangle_rectangle_intersection(r0, r1))
+
+ def test_get_rectangle_rectangle_intersection_shifted_edge(self):
+ r0 = Rectangle(0, 0, 4, 4)
+ r1 = Rectangle(2, 4, 6, 8)
+ self.assertEqual(LineSegment(Point((2, 4)), Point(
+ (4, 4))), get_rectangle_rectangle_intersection(r0, r1))
+
+ def test_get_rectangle_rectangle_intersection_no_intersect(self):
+ r0 = Rectangle(0, 0, 4, 4)
+ r1 = Rectangle(5, 5, 8, 8)
+ self.assertEqual(None, get_rectangle_rectangle_intersection(r0, r1))
+
+
+class TestGetPolygonPointDist(unittest.TestCase):
+ def test_get_polygon_point_dist(self):
+ poly = Polygon([Point(
+ (0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
+ pt = Point((2, 0.5))
+ expected = 1.0
+ self.assertEqual(expected, get_polygon_point_dist(poly, pt))
+
+ def test_get_polygon_point_dist_inside(self):
+ poly = Polygon([Point(
+ (0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
+ pt = Point((0.5, 0.5))
+ expected = 0.0
+ self.assertEqual(expected, get_polygon_point_dist(poly, pt))
+
+ def test_get_polygon_point_dist_on_vertex(self):
+ poly = Polygon([Point(
+ (0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
+ pt = Point((1.0, 1.0))
+ expected = 0.0
+ self.assertEqual(expected, get_polygon_point_dist(poly, pt))
+
+ def test_get_polygon_point_dist_on_edge(self):
+ poly = Polygon([Point(
+ (0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
+ pt = Point((0.5, 1.0))
+ expected = 0.0
+ self.assertEqual(expected, get_polygon_point_dist(poly, pt))
+
+
+class TestGetPointsDist(unittest.TestCase):
+ def test_get_points_dist(self):
+ pt1 = Point((0.5, 0.5))
+ pt2 = Point((0.5, 0.5))
+ self.assertEqual(0, get_points_dist(pt1, pt2))
+
+ def test_get_points_dist_diag(self):
+ pt1 = Point((0, 0))
+ pt2 = Point((1, 1))
+ self.assertEqual(2 ** (0.5), get_points_dist(pt1, pt2))
+
+ def test_get_points_dist_alongX(self):
+ pt1 = Point((-1000, 1 / 3.0))
+ pt2 = Point((1000, 1 / 3.0))
+ self.assertEqual(2000, get_points_dist(pt1, pt2))
+
+ def test_get_points_dist_alongY(self):
+ pt1 = Point((1 / 3.0, -500))
+ pt2 = Point((1 / 3.0, 500))
+ self.assertEqual(1000, get_points_dist(pt1, pt2))
+
+
+class TestGetSegmentPointDist(unittest.TestCase):
+ def test_get_segment_point_dist(self):
+ seg = LineSegment(Point((0, 0)), Point((10, 0)))
+ pt = Point((5, 5))
+ self.assertEqual((5.0, 0.5), get_segment_point_dist(seg, pt))
+
+ def test_get_segment_point_dist_on_endPoint(self):
+ seg = LineSegment(Point((0, 0)), Point((10, 0)))
+ pt = Point((0, 0))
+ self.assertEqual((0.0, 0.0), get_segment_point_dist(seg, pt))
+
+ def test_get_segment_point_dist_on_middle(self):
+ seg = LineSegment(Point((0, 0)), Point((10, 0)))
+ pt = Point((5, 0))
+ self.assertEqual((0.0, 0.5), get_segment_point_dist(seg, pt))
+
+ def test_get_segment_point_diag(self):
+ seg = LineSegment(Point((0, 0)), Point((10, 10)))
+ pt = Point((5, 5))
+ self.assertAlmostEqual(0.0, get_segment_point_dist(seg, pt)[0])
+ self.assertAlmostEqual(0.5, get_segment_point_dist(seg, pt)[1])
+
+ def test_get_segment_point_diag_with_dist(self):
+ seg = LineSegment(Point((0, 0)), Point((10, 10)))
+ pt = Point((0, 10))
+ self.assertAlmostEqual(50 ** (0.5), get_segment_point_dist(seg, pt)[0])
+ self.assertAlmostEqual(0.5, get_segment_point_dist(seg, pt)[1])
+
+
+class TestGetPointAtAngleAndDist(unittest.TestCase):
+ def test_get_point_at_angle_and_dist(self):
+ ray = Ray(Point((0, 0)), Point((1, 0)))
+ pt = get_point_at_angle_and_dist(ray, math.pi, 1.0)
+ self.assertAlmostEqual(-1.0, pt[0])
+ self.assertAlmostEqual(0.0, pt[1])
+
+ def test_get_point_at_angle_and_dist_diag(self):
+ ray = Ray(Point((0, 0)), Point((1, 1)))
+ pt = get_point_at_angle_and_dist(ray, math.pi, 2 ** (0.5))
+ self.assertAlmostEqual(-1.0, pt[0])
+ self.assertAlmostEqual(-1.0, pt[1])
+
+ def test_get_point_at_angle_and_dist_diag_90(self):
+ ray = Ray(Point((0, 0)), Point((1, 1)))
+ pt = get_point_at_angle_and_dist(ray, -math.pi / 2.0, 2 ** (0.5))
+ self.assertAlmostEqual(1.0, pt[0])
+ self.assertAlmostEqual(-1.0, pt[1])
+
+ def test_get_point_at_angle_and_dist_diag_45(self):
+ ray = Ray(Point((0, 0)), Point((1, 1)))
+ pt = get_point_at_angle_and_dist(ray, -math.pi / 4.0, 1)
+ self.assertAlmostEqual(1.0, pt[0])
+ self.assertAlmostEqual(0.0, pt[1])
+
+
+class TestConvexHull(unittest.TestCase):
+ def test_convex_hull(self):
+ points = [Point((0, 0)), Point((4, 4)), Point((4, 0)), Point((3, 1))]
+ self.assertEqual([Point((0.0, 0.0)), Point(
+ (4.0, 0.0)), Point((4.0, 4.0))], convex_hull(points))
+
+
+class TestIsClockwise(unittest.TestCase):
+ def test_is_clockwise(self):
+ vertices = [Point((0, 0)), Point((0, 10)), Point((10, 0))]
+ self.assertEqual(True, is_clockwise(vertices))
+
+ def test_is_clockwise_expect_false(self):
+ vertices = [Point((0, 0)), Point((10, 0)), Point((0, 10))]
+ self.assertEqual(False, is_clockwise(vertices))
+
+ def test_is_clockwise_big(self):
+ vertices = [(
+ -106.57798, 35.174143999999998), (-106.583412, 35.174141999999996),
+ (-106.58417999999999, 35.174143000000001), (-106.58377999999999, 35.175542999999998),
+ (-106.58287999999999, 35.180543), (
+ -106.58263099999999, 35.181455),
+ (-106.58257999999999, 35.181643000000001), (-106.58198299999999, 35.184615000000001),
+ (-106.58148, 35.187242999999995), (
+ -106.58127999999999, 35.188243),
+ (-106.58138, 35.188243), (-106.58108, 35.189442999999997),
+ (-106.58104, 35.189644000000001), (
+ -106.58028, 35.193442999999995),
+ (-106.580029, 35.194541000000001), (-106.57974399999999,
+ 35.195785999999998),
+ (-106.579475, 35.196961999999999), (-106.57922699999999,
+ 35.198042999999998),
+ (-106.578397, 35.201665999999996), (-106.57827999999999,
+ 35.201642999999997),
+ (-106.57737999999999, 35.201642999999997), (-106.57697999999999, 35.201543000000001),
+ (-106.56436599999999, 35.200311999999997), (
+ -106.56058, 35.199942999999998),
+ (-106.56048, 35.197342999999996), (
+ -106.56048, 35.195842999999996),
+ (-106.56048, 35.194342999999996), (
+ -106.56048, 35.193142999999999),
+ (-106.56048, 35.191873999999999), (
+ -106.56048, 35.191742999999995),
+ (-106.56048, 35.190242999999995), (-106.56037999999999,
+ 35.188642999999999),
+ (-106.56037999999999, 35.187242999999995), (-106.56037999999999, 35.186842999999996),
+ (-106.56037999999999, 35.186552999999996), (-106.56037999999999, 35.185842999999998),
+ (-106.56037999999999, 35.184443000000002), (-106.56037999999999, 35.182943000000002),
+ (-106.56037999999999, 35.181342999999998), (-106.56037999999999, 35.180433000000001),
+ (-106.56037999999999, 35.179943000000002), (-106.56037999999999, 35.178542999999998),
+ (-106.56037999999999, 35.177790999999999), (-106.56037999999999, 35.177143999999998),
+ (-106.56037999999999, 35.175643999999998), (-106.56037999999999, 35.174444000000001),
+ (-106.56037999999999, 35.174043999999995), (
+ -106.560526, 35.174043999999995),
+ (-106.56478, 35.174043999999995), (-106.56627999999999,
+ 35.174143999999998),
+ (-106.566541, 35.174144999999996), (
+ -106.569023, 35.174157000000001),
+ (-106.56917199999999, 35.174157999999998), (
+ -106.56938, 35.174143999999998),
+ (-106.57061499999999, 35.174143999999998), (-106.57097999999999, 35.174143999999998),
+ (-106.57679999999999, 35.174143999999998), (-106.57798, 35.174143999999998)]
+ self.assertEqual(True, is_clockwise(vertices))
+
+
+class TestPointTouchesRectangle(unittest.TestCase):
+ def test_point_touches_rectangle_inside(self):
+ rect = Rectangle(0, 0, 10, 10)
+ point = Point((5, 5))
+ self.assertEqual(True, point_touches_rectangle(point, rect))
+
+ def test_point_touches_rectangle_on_edge(self):
+ rect = Rectangle(0, 0, 10, 10)
+ point = Point((10, 5))
+ self.assertEqual(True, point_touches_rectangle(point, rect))
+
+ def test_point_touches_rectangle_on_corner(self):
+ rect = Rectangle(0, 0, 10, 10)
+ point = Point((10, 10))
+ self.assertEqual(True, point_touches_rectangle(point, rect))
+
+ def test_point_touches_rectangle_outside(self):
+ rect = Rectangle(0, 0, 10, 10)
+ point = Point((11, 11))
+ self.assertEqual(False, point_touches_rectangle(point, rect))
+
+
+class TestGetSharedSegments(unittest.TestCase):
+ def test_get_shared_segments(self):
+ poly1 = Polygon([Point(
+ (0, 0)), Point((0, 1)), Point((1, 1)), Point((1, 0))])
+ poly2 = Polygon([Point(
+ (1, 0)), Point((1, 1)), Point((2, 1)), Point((2, 0))])
+ poly3 = Polygon([Point(
+ (0, 1)), Point((0, 2)), Point((1, 2)), Point((1, 1))])
+ poly4 = Polygon([Point(
+ (1, 1)), Point((1, 2)), Point((2, 2)), Point((2, 1))])
+ self.assertEqual(
+ True, get_shared_segments(poly1, poly2, bool_ret=True))
+ self.assertEqual(
+ True, get_shared_segments(poly1, poly3, bool_ret=True))
+ self.assertEqual(
+ True, get_shared_segments(poly3, poly4, bool_ret=True))
+ self.assertEqual(
+ True, get_shared_segments(poly4, poly2, bool_ret=True))
+
+ self.assertEqual(
+ False, get_shared_segments(poly1, poly4, bool_ret=True))
+ self.assertEqual(
+ False, get_shared_segments(poly3, poly2, bool_ret=True))
+
+ def test_get_shared_segments_non_bool(self):
+ poly1 = Polygon([Point(
+ (0, 0)), Point((0, 1)), Point((1, 1)), Point((1, 0))])
+ poly2 = Polygon([Point(
+ (1, 0)), Point((1, 1)), Point((2, 1)), Point((2, 0))])
+ poly3 = Polygon([Point(
+ (0, 1)), Point((0, 2)), Point((1, 2)), Point((1, 1))])
+ poly4 = Polygon([Point(
+ (1, 1)), Point((1, 2)), Point((2, 2)), Point((2, 1))])
+ self.assertEqual(LineSegment(Point((1, 0)), Point((1, 1))),
+ get_shared_segments(poly1, poly2)[0])
+ self.assertEqual(LineSegment(Point((0, 1)), Point((1, 1))),
+ get_shared_segments(poly1, poly3)[0])
+ self.assertEqual(LineSegment(Point((1, 2)), Point((1, 1))),
+ get_shared_segments(poly3, poly4)[0])
+ self.assertEqual(LineSegment(Point((2, 1)), Point((1, 1))),
+ get_shared_segments(poly4, poly2)[0])
+ #expected = [LineSegment(Point((1, 1)), Point((1, 0)))]
+ #assert expected == get_shared_segments(poly1, poly3)
+ #expected = [LineSegment(Point((1, 1)), Point((1, 0)))]
+ #assert expected == get_shared_segments(poly3, poly4)
+ #expected = [LineSegment(Point((1, 1)), Point((1, 0)))]
+ #assert expected == get_shared_segments(poly4, poly2)
+
+
+class TestDistanceMatrix(unittest.TestCase):
+ def test_distance_matrix(self):
+ points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+ dist = distance_matrix(np.array(points), 2)
+ for i in range(0, len(points)):
+ for j in range(i, len(points)):
+ x, y = points[i]
+ X, Y = points[j]
+ d = ((x - X) ** 2 + (y - Y) ** 2) ** (0.5)
+ self.assertEqual(dist[i, j], d)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/common.py b/pysal/common.py
new file mode 100644
index 0000000..58e167a
--- /dev/null
+++ b/pysal/common.py
@@ -0,0 +1,25 @@
+
+# external imports
+
+try:
+ import numpy as np
+ import numpy.linalg as la
+except:
+ print 'numpy 1.3 is required'
+ raise
+try:
+ import scipy as sp
+ import scipy.stats as stats
+ from cg.kdtree import KDTree
+ from scipy.spatial.distance import pdist, cdist
+except:
+ print 'scipy 0.7+ is required'
+ raise
+
+
+import copy
+import math
+import random
+import sys
+import time
+import unittest
diff --git a/pysal/contrib/README b/pysal/contrib/README
new file mode 100644
index 0000000..ad99aa4
--- /dev/null
+++ b/pysal/contrib/README
@@ -0,0 +1,57 @@
+:mod:`pysal.contrib` -- Contributed Modules
+===========================================
+
+**Intro**
+
+The PySAL Contrib library contains user contributions that enhance PySAL, but
+are not fit for inclusion in the general library. The primary reason a
+contribution would not be allowed in the general library is external
+dependencies. PySAL has a strict no dependency policy (aside from Numpy/Scipy).
+This helps ensure the library is easy to install and maintain.
+
+However, this policy often limits our ability to make use of existing code or
+exploit performance enhancements from C-extensions. This contrib module is
+designed to alleviate this problem. There are no restrictions on external
+dependencies in contrib.
+
+**Ground Rules**
+
+ 1. Contribs must not be used within the general library.
+ 2. *Explicit imports*: each contrib must be imported manually.
+ 3. *Documentation*: each contrib must be documented, dependencies especially.
+
+**Contribs**
+
+Currently the following contribs are available:
+
+ 1. World To View Transform -- A class for modeling viewing windows, used by Weights Viewer.
+
+ - .. versionadded:: 1.3
+ - Path: pysal.contrib.weights_viewer.transforms
+ - Requires: None
+
+ 2. Weights Viewer -- A Graphical tool for examining spatial weights.
+
+ - .. versionadded:: 1.3
+ - Path: pysal.contrib.weights_viewer.weights_viewer
+ - Requires: wxPython
+
+ 3. Shapely Extension -- Exposes shapely methods as standalone functions
+
+ - .. versionadded:: 1.3
+ - Path: pysal.contrib.shapely_ext
+ - Requires: shapely
+
+ 4. Shared Perimeter Weights -- calculate shared perimeters weights.
+
+ - .. versionadded:: 1.3
+ - Path: pysal.contrib.shared_perimeter_weights
+ - Requires: shapely
+
+5. Clusterpy Extension -- spatially constrained clustering algorithms.
+
+ - .. versionadded:: 1.8
+ - Path: pysal.contrib.clusterpy
+ - Requires: clusterpy
+
+
diff --git a/pysal/contrib/__init__.py b/pysal/contrib/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pysal/contrib/clusterpy/README.md b/pysal/contrib/clusterpy/README.md
new file mode 100644
index 0000000..d4ed06d
--- /dev/null
+++ b/pysal/contrib/clusterpy/README.md
@@ -0,0 +1,15 @@
+# PySAL Contrib Module for clusterPy
+
+**Author: Serge Rey <sjsrey at gmail.com>**
+
+
+[clusterPy][cp] is a library of spatially constrained clustering algorithms. This contrib module provides an interface to the family of spatially constrained clustering algorithms offered by clusterPy. It also extends a select set of clusterPy classes to leverage optimized data structures in PySAL, and it provides utility functions that support use of clusterPy functionality with other PySAL modules.
+
+
+## Notes
+
+- 2014-05-28 This is working with clusterPy 0.9.9 installed from [pip][pip].
+
+[cp]: http://www.rise-group.org/risem/clusterpy/
+[cpr]: https://github.com/clusterpy/clusterpy
+[pip]: https://pypi.python.org/pypi/clusterPy/0.9.9
diff --git a/pysal/contrib/clusterpy/__init__.py b/pysal/contrib/clusterpy/__init__.py
new file mode 100644
index 0000000..27bb912
--- /dev/null
+++ b/pysal/contrib/clusterpy/__init__.py
@@ -0,0 +1,12 @@
+try:
+ import clusterpy
+except ImportError:
+ print 'pysal.contrib.clusterpy requires clusterpy'
+ print 'clusterpy not found.'
+else:
+ from clusterpy_ext import Layer
+ from clusterpy_ext import loadArcData
+ from clusterpy_ext import importCsvData
+ from clusterpy_ext import addRook2Layer
+ from clusterpy_ext import addQueen2Layer
+ from clusterpy_ext import addArray2Layer
diff --git a/pysal/contrib/clusterpy/clusterpy.ipynb b/pysal/contrib/clusterpy/clusterpy.ipynb
new file mode 100644
index 0000000..9974d81
--- /dev/null
+++ b/pysal/contrib/clusterpy/clusterpy.ipynb
@@ -0,0 +1,3320 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:a138ff2bb82f0ff84b1c6ddbfcdd96a0d757ce0a91b7848a1d377b8e5b58d79b"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import pysal.contrib.clusterpy as cp\n",
+ "%pylab inline"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "ClusterPy: Library of spatially constrained clustering algorithms\n",
+ "Populating the interactive namespace from numpy and matplotlib"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import numpy as np\n",
+ "import pysal as ps\n",
+ "from collections import Counter"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "columbus = cp.loadArcData(ps.examples.get_path('columbus.shp'))\n",
+ "columbus.fieldNames\n",
+ "n = len(columbus.Wqueen)\n",
+ "#columbus.generateData('Uniform', 'rook', 1, 1, 10)\n",
+ "columbus.dataOperation(\"CONSTANT = 1\")\n",
+ "columbus.cluster('maxpTabu', ['CRIME', 'CONSTANT'], threshold=4, dissolve=0, std=0)\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Adding CONSTANT to fieldNames\n",
+ "Adding values from 1 to Y\n",
+ "Adding variables\n",
+ "Done\n",
+ "Getting variables\n",
+ "Variables successfully extracted\n",
+ "Running max-p-regions model (Duque, Anselin and Rey, 2010)\n",
+ "Local search method: Tabu Search\n",
+ "Number of areas: 49\n",
+ "threshold value: 4\n",
+ "FINAL SOLUTION: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " [6, 6, 6, 6, 7, 1, 6, 6, 1, 1, 3, 3, 3, 3, 7, 3, 1, 8, 8, 5, 8, 1, 5, 8, 7, 7, 0, 0, 0, 0, 4, 5, 9, 4, 9, 4, 2, 0, 4, 5, 5, 4, 2, 9, 2, 4, 5, 2, 9]\n",
+ "FINAL OF: 2615.44707346\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "Counter(columbus.region2areas)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 4,
+ "text": [
+ "Counter({4: 6, 5: 6, 6: 6, 0: 5, 1: 5, 3: 5, 2: 4, 7: 4, 8: 4, 9: 4})"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "columbus.cluster('arisel', ['CRIME'], 5, wType='rook', inits=10, dissolve=0)\n",
+ "#calif.cluster('arisel', ['PCR2002'], 9, wType='rook', inits=10, dissolve=1)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Getting variables\n",
+ "Variables successfully extracted\n",
+ "Running original Arisel algorithm\n",
+ "Number of areas: 49\n",
+ "Number of regions: 5\n",
+ "initial Solution: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " [2, 2, 2, 2, 0, 1, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 1, 4, 0, 1, 4, 1, 1, 4, 0, 0, 0, 0, 0, 0, 4, 1, 3, 4, 3, 4, 3, 0, 4, 1, 1, 4, 3, 3, 3, 4, 1, 3, 3]\n",
+ "initial O.F: 4292.34464226\n",
+ "FINAL SOLUTION: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " [2, 2, 2, 2, 0, 1, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 4, 1, 0, 4, 1, 4, 3, 0, 4, 1, 1, 4, 3, 1, 1, 4, 1, 1, 1]\n",
+ "FINAL OF: 3695.95184733\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n"
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# regionalization solutions are added as a list of region ids at the end\n",
+ "columbus.fieldNames"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 6,
+ "text": [
+ "['ID',\n",
+ " 'AREA',\n",
+ " 'PERIMETER',\n",
+ " 'COLUMBUS_',\n",
+ " 'COLUMBUS_I',\n",
+ " 'POLYID',\n",
+ " 'NEIG',\n",
+ " 'HOVAL',\n",
+ " 'INC',\n",
+ " 'CRIME',\n",
+ " 'OPEN',\n",
+ " 'PLUMB',\n",
+ " 'DISCBD',\n",
+ " 'X',\n",
+ " 'Y',\n",
+ " 'NSA',\n",
+ " 'NSB',\n",
+ " 'EW',\n",
+ " 'CP',\n",
+ " 'THOUS',\n",
+ " 'NEIGNO',\n",
+ " 'CONSTANT',\n",
+ " 'maxpTabu_20140823211458',\n",
+ " 'arisel_20140823211459']"
+ ]
+ }
+ ],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "warisel = ps.block_weights(columbus.region2areas)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "warisel.neighbors"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 8,
+ "text": [
+ "{0: [1L, 2L, 3L, 6L, 7L],\n",
+ " 1: [0L, 2L, 3L, 6L, 7L],\n",
+ " 2: [0L, 1L, 3L, 6L, 7L],\n",
+ " 3: [0L, 1L, 2L, 6L, 7L],\n",
+ " 4: [10L,\n",
+ " 11L,\n",
+ " 12L,\n",
+ " 13L,\n",
+ " 14L,\n",
+ " 15L,\n",
+ " 17L,\n",
+ " 18L,\n",
+ " 20L,\n",
+ " 23L,\n",
+ " 24L,\n",
+ " 25L,\n",
+ " 26L,\n",
+ " 27L,\n",
+ " 28L,\n",
+ " 29L,\n",
+ " 32L,\n",
+ " 37L],\n",
+ " 5: [8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],\n",
+ " 6: [0L, 1L, 2L, 3L, 7L],\n",
+ " 7: [0L, 1L, 2L, 3L, 6L],\n",
+ " 8: [5L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],\n",
+ " 9: [5L, 8L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],\n",
+ " 10: [4L,\n",
+ " 11L,\n",
+ " 12L,\n",
+ " 13L,\n",
+ " 14L,\n",
+ " 15L,\n",
+ " 17L,\n",
+ " 18L,\n",
+ " 20L,\n",
+ " 23L,\n",
+ " 24L,\n",
+ " 25L,\n",
+ " 26L,\n",
+ " 27L,\n",
+ " 28L,\n",
+ " 29L,\n",
+ " 32L,\n",
+ " 37L],\n",
+ " 11: [4L,\n",
+ " 10L,\n",
+ " 12L,\n",
+ " 13L,\n",
+ " 14L,\n",
+ " 15L,\n",
+ " 17L,\n",
+ " 18L,\n",
+ " 20L,\n",
+ " 23L,\n",
+ " 24L,\n",
+ " 25L,\n",
+ " 26L,\n",
+ " 27L,\n",
+ " 28L,\n",
+ " 29L,\n",
+ " 32L,\n",
+ " 37L],\n",
+ " 12: [4L,\n",
+ " 10L,\n",
+ " 11L,\n",
+ " 13L,\n",
+ " 14L,\n",
+ " 15L,\n",
+ " 17L,\n",
+ " 18L,\n",
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+ " 45: [30L, 33L, 35L, 38L, 41L],\n",
+ " 46: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 47L, 48L],\n",
+ " 47: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 48L],\n",
+ " 48: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 47L]}"
+ ]
+ }
+ ],
+ "prompt_number": 12
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Attrribute data from a csv file and a W from a gal file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "mexico = cp.importCsvData(ps.examples.get_path('mexico.csv'))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 13
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "mexico.fieldNames"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 14,
+ "text": [
+ "['ID',\n",
+ " 'State',\n",
+ " 'pcgdp1940',\n",
+ " 'pcgdp1950',\n",
+ " 'pcgdp1960',\n",
+ " 'pcgdp1970',\n",
+ " 'pcgdp1980',\n",
+ " 'pcgdp1990',\n",
+ " 'pcgdp2000',\n",
+ " 'hanson03',\n",
+ " 'hanson98',\n",
+ " 'esquivel99',\n",
+ " 'inegi',\n",
+ " 'inegi2']"
+ ]
+ }
+ ],
+ "prompt_number": 14
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "w = ps.open(ps.examples.get_path('mexico.gal')).read()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 15
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "w.n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 16,
+ "text": [
+ "32"
+ ]
+ }
+ ],
+ "prompt_number": 16
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "cp.addRook2Layer(ps.examples.get_path('mexico.gal'), mexico)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 17
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "mexico.Wrook"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 18,
+ "text": [
+ "{0: [31, 13],\n",
+ " 1: [2, 25],\n",
+ " 2: [1],\n",
+ " 3: [30, 22, 26],\n",
+ " 4: [19, 26, 29],\n",
+ " 5: [6, 9, 24, 25],\n",
+ " 6: [18, 23, 31, 9, 5],\n",
+ " 7: [13, 15],\n",
+ " 8: [16, 14],\n",
+ " 9: [5, 6, 31, 13, 17, 24],\n",
+ " 10: [23, 21, 31, 15, 13],\n",
+ " 11: [15, 14, 16, 20, 19],\n",
+ " 12: [21, 23, 29, 20, 28, 14],\n",
+ " 13: [17, 31, 0, 23, 10, 15, 7, 9],\n",
+ " 14: [21, 12, 28, 20, 16, 11, 15, 8],\n",
+ " 15: [7, 13, 10, 21, 14, 11],\n",
+ " 16: [14, 8, 20, 11],\n",
+ " 17: [24, 9, 31, 13],\n",
+ " 18: [6, 27, 23, 31],\n",
+ " 19: [11, 20, 29, 4],\n",
+ " 20: [29, 19, 11, 16, 14, 28, 12],\n",
+ " 21: [23, 12, 14, 15, 10],\n",
+ " 22: [30, 3],\n",
+ " 23: [18, 27, 29, 12, 21, 10, 31, 6, 13],\n",
+ " 24: [25, 5, 9, 17],\n",
+ " 25: [1, 5, 24],\n",
+ " 26: [3, 4, 29],\n",
+ " 27: [18, 29, 23],\n",
+ " 28: [12, 20, 14],\n",
+ " 29: [26, 4, 19, 20, 12, 23, 27],\n",
+ " 30: [3, 22],\n",
+ " 31: [18, 23, 10, 0, 13, 17, 9, 6]}"
+ ]
+ }
+ ],
+ "prompt_number": 18
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "mexico.cluster('arisel', ['pcgdp1940'], 5, wType='rook', inits=10, dissolve=0)\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Getting variables\n",
+ "Variables successfully extracted\n",
+ "Running original Arisel algorithm\n",
+ "Number of areas: 32\n",
+ "Number of regions: 5\n",
+ "initial Solution: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " [2, 3, 3, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 0, 2]\n",
+ "initial O.F: 287200188.741\n",
+ "FINAL SOLUTION: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " [2, 3, 3, 0, 0, 2, 2, 2, 4, 2, 0, 0, 2, 2, 0, 0, 0, 0, 2, 0, 0, 2, 1, 0, 0, 2, 0, 2, 0, 2, 0, 0]\n",
+ "FINAL OF: 186036319.667\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n"
+ ]
+ }
+ ],
+ "prompt_number": 19
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "mexico.fieldNames"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 20,
+ "text": [
+ "['ID',\n",
+ " 'State',\n",
+ " 'pcgdp1940',\n",
+ " 'pcgdp1950',\n",
+ " 'pcgdp1960',\n",
+ " 'pcgdp1970',\n",
+ " 'pcgdp1980',\n",
+ " 'pcgdp1990',\n",
+ " 'pcgdp2000',\n",
+ " 'hanson03',\n",
+ " 'hanson98',\n",
+ " 'esquivel99',\n",
+ " 'inegi',\n",
+ " 'inegi2',\n",
+ " 'arisel_20140823211523']"
+ ]
+ }
+ ],
+ "prompt_number": 20
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "mexico.getVars('pcgdp1940')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Getting variables\n",
+ "Variables successfully extracted\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 21,
+ "text": [
+ "{0: [10384.0],\n",
+ " 1: [22361.0],\n",
+ " 2: [9573.0],\n",
+ " 3: [3758.0],\n",
+ " 4: [2934.0],\n",
+ " 5: [8578.0],\n",
+ " 6: [8537.0],\n",
+ " 7: [6909.0],\n",
+ " 8: [17816.0],\n",
+ " 9: [12132.0],\n",
+ " 10: [4359.0],\n",
+ " 11: [2181.0],\n",
+ " 12: [4414.0],\n",
+ " 13: [5309.0],\n",
+ " 14: [3408.0],\n",
+ " 15: [3327.0],\n",
+ " 16: [6936.0],\n",
+ " 17: [4836.0],\n",
+ " 18: [9073.0],\n",
+ " 19: [1892.0],\n",
+ " 20: [3569.0],\n",
+ " 21: [11016.0],\n",
+ " 22: [21965.0],\n",
+ " 23: [4372.0],\n",
+ " 24: [4840.0],\n",
+ " 25: [6399.0],\n",
+ " 26: [2459.0],\n",
+ " 27: [7508.0],\n",
+ " 28: [3605.0],\n",
+ " 29: [5203.0],\n",
+ " 30: [7990.0],\n",
+ " 31: [3734.0]}"
+ ]
+ }
+ ],
+ "prompt_number": 21
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# mexico example all together\n",
+ "\n",
+ "csvfile = ps.examples.get_path('mexico.csv')\n",
+ "galfile = ps.examples.get_path('mexico.gal')\n",
+ "\n",
+ "mexico = cp.importCsvData(csvfile)\n",
+ "cp.addRook2Layer(galfile, mexico)\n",
+ "mexico.cluster('arisel', ['pcgdp1940'], 5, wType='rook', inits=10, dissolve=0)\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Getting variables\n",
+ "Variables successfully extracted\n",
+ "Running original Arisel algorithm\n",
+ "Number of areas: 32\n",
+ "Number of regions: 5\n",
+ "initial Solution: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " [2, 0, 0, 1, 1, 2, 2, 2, 4, 2, 2, 1, 2, 2, 1, 1, 1, 2, 2, 1, 1, 2, 3, 2, 2, 2, 1, 2, 1, 2, 1, 2]\n",
+ "initial O.F: 228683641.198\n",
+ "FINAL SOLUTION: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " [2, 0, 0, 1, 1, 2, 2, 2, 4, 2, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 3, 1, 1, 2, 1, 2, 1, 2, 1, 1]\n",
+ "FINAL OF: 186036319.667\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n"
+ ]
+ }
+ ],
+ "prompt_number": 22
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "mexico.region2areas.index(2)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 23,
+ "text": [
+ "0"
+ ]
+ }
+ ],
+ "prompt_number": 23
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "mexico.Wrook[0]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 24,
+ "text": [
+ "[31, 13]"
+ ]
+ }
+ ],
+ "prompt_number": 24
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "mexico.getVars('State')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Getting variables\n",
+ "Variables successfully extracted\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 25,
+ "text": [
+ "{0: ['Aguascalientes'],\n",
+ " 1: ['Baja California'],\n",
+ " 2: ['Baja California Sur'],\n",
+ " 3: ['Campeche'],\n",
+ " 4: ['Chiapas'],\n",
+ " 5: ['Chihuahua'],\n",
+ " 6: ['Coahuila'],\n",
+ " 7: ['Colima'],\n",
+ " 8: ['Distrito Federal'],\n",
+ " 9: ['Durango'],\n",
+ " 10: ['Guanajuato'],\n",
+ " 11: ['Guerrero'],\n",
+ " 12: ['Hidalgo'],\n",
+ " 13: ['Jalisco'],\n",
+ " 14: ['Mexico'],\n",
+ " 15: ['Michoacan'],\n",
+ " 16: ['Morelos'],\n",
+ " 17: ['Nayarit'],\n",
+ " 18: ['Nuevo Leon'],\n",
+ " 19: ['Oaxaca'],\n",
+ " 20: ['Puebla'],\n",
+ " 21: ['Quertaro'],\n",
+ " 22: ['Quintana Roo'],\n",
+ " 23: ['San Luis Potosi'],\n",
+ " 24: ['Sinaloa'],\n",
+ " 25: ['Sonora'],\n",
+ " 26: ['Tabasco'],\n",
+ " 27: ['Tamaulipas'],\n",
+ " 28: ['Tlaxcala'],\n",
+ " 29: ['Veracruz'],\n",
+ " 30: ['Yucatan'],\n",
+ " 31: ['Zacatecas']}"
+ ]
+ }
+ ],
+ "prompt_number": 25
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "regions = np.array(mexico.region2areas)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 26
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "regions"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 27,
+ "text": [
+ "array([2, 0, 0, 1, 1, 2, 2, 2, 4, 2, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 3,\n",
+ " 1, 1, 2, 1, 2, 1, 2, 1, 1])"
+ ]
+ }
+ ],
+ "prompt_number": 27
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "Counter(regions)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 28,
+ "text": [
+ "Counter({1: 16, 2: 12, 0: 2, 3: 1, 4: 1})"
+ ]
+ }
+ ],
+ "prompt_number": 28
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Shapefile and mapping results with PySAL Viz"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "usf = ps.examples.get_path('us48.shp')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 29
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "us = cp.loadArcData(usf.split(\".\")[0])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 30
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "us.Wqueen"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 31,
+ "text": [
+ "{0: [10, 7],\n",
+ " 1: [3, 4, 5, 7],\n",
+ " 2: [11],\n",
+ " 3: [1, 4, 9],\n",
+ " 4: [1, 3, 5, 9, 12, 14],\n",
+ " 5: [1, 4, 7, 14, 22, 29],\n",
+ " 6: [9, 12, 25, 47],\n",
+ " 7: [0, 1, 5, 10, 21, 22],\n",
+ " 8: [11, 13, 15],\n",
+ " 9: [12, 3, 4, 6],\n",
+ " 10: [0, 23, 21, 7],\n",
+ " 11: [8, 2, 13],\n",
+ " 12: [33, 4, 6, 9, 14, 25],\n",
+ " 13: [8, 17, 18, 11, 15],\n",
+ " 14: [33, 4, 5, 12, 29, 31],\n",
+ " 15: [8, 16, 19, 13, 17],\n",
+ " 16: [15, 19, 24, 26, 27, 28],\n",
+ " 17: [18, 13, 15],\n",
+ " 18: [17, 13],\n",
+ " 19: [16, 26, 15],\n",
+ " 20: [24, 25, 30, 47],\n",
+ " 21: [10, 23, 34, 22, 7],\n",
+ " 22: [34, 5, 39, 7, 21, 29],\n",
+ " 23: [10, 34, 21],\n",
+ " 24: [16, 27, 20, 30, 47],\n",
+ " 25: [20, 12, 30, 6, 33],\n",
+ " 26: [16, 19, 28],\n",
+ " 27: [16, 24, 28, 30, 32],\n",
+ " 28: [16, 32, 26, 27],\n",
+ " 29: [34, 35, 5, 39, 14, 22, 31],\n",
+ " 30: [32, 33, 37, 20, 24, 25, 27],\n",
+ " 31: [33, 35, 29, 14],\n",
+ " 32: [36, 27, 28, 37, 30],\n",
+ " 33: [35, 37, 44, 12, 14, 25, 30, 31],\n",
+ " 34: [39, 29, 21, 22, 23],\n",
+ " 35: [33, 38, 39, 44, 29, 31],\n",
+ " 36: [32, 42, 43, 37],\n",
+ " 37: [32, 33, 36, 40, 41, 42, 44, 30],\n",
+ " 38: [35, 44, 45, 39],\n",
+ " 39: [38, 34, 35, 29, 22],\n",
+ " 40: [41, 42, 37, 46],\n",
+ " 41: [40, 44, 37, 45],\n",
+ " 42: [40, 43, 36, 37, 46],\n",
+ " 43: [42, 36],\n",
+ " 44: [33, 35, 37, 38, 41, 45],\n",
+ " 45: [41, 44, 38],\n",
+ " 46: [40, 42],\n",
+ " 47: [24, 20, 6]}"
+ ]
+ }
+ ],
+ "prompt_number": 31
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "us.fieldNames"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 32,
+ "text": [
+ "['ID',\n",
+ " 'AREA',\n",
+ " 'PERIMETER',\n",
+ " 'STATE_',\n",
+ " 'STATE_ID',\n",
+ " 'STATE_NAME',\n",
+ " 'STATE_FIPS',\n",
+ " 'SUB_REGION',\n",
+ " 'STATE_ABBR']"
+ ]
+ }
+ ],
+ "prompt_number": 32
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "uscsv = ps.examples.get_path(\"usjoin.csv\")"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 33
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "f = ps.open(uscsv)\n",
+ "pci = np.array([f.by_col[str(y)] for y in range(1929, 2010)]).T"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 34
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "pci"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 35,
+ "text": [
+ "array([[ 323, 267, 224, ..., 31988, 32819, 32274],\n",
+ " [ 600, 520, 429, ..., 33470, 33445, 32077],\n",
+ " [ 310, 228, 215, ..., 31070, 31800, 31493],\n",
+ " ..., \n",
+ " [ 460, 408, 356, ..., 29769, 31265, 31843],\n",
+ " [ 673, 588, 469, ..., 35839, 36594, 35676],\n",
+ " [ 675, 585, 476, ..., 43453, 45177, 42504]])"
+ ]
+ }
+ ],
+ "prompt_number": 35
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "usy = cp.Layer()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 36
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "cp.addQueen2Layer(ps.examples.get_path('states48.gal'), usy)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 37
+ },
+ {
+ "cell_type": "code",
+ "collapsed": true,
+ "input": [
+ "names = [\"Y_%d\"%v for v in range(1929,2010)]\n",
+ "cp.addArray2Layer(pci, usy, names)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n"
+ ]
+ }
+ ],
+ "prompt_number": 38
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "names"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 39,
+ "text": [
+ "['Y_1929',\n",
+ " 'Y_1930',\n",
+ " 'Y_1931',\n",
+ " 'Y_1932',\n",
+ " 'Y_1933',\n",
+ " 'Y_1934',\n",
+ " 'Y_1935',\n",
+ " 'Y_1936',\n",
+ " 'Y_1937',\n",
+ " 'Y_1938',\n",
+ " 'Y_1939',\n",
+ " 'Y_1940',\n",
+ " 'Y_1941',\n",
+ " 'Y_1942',\n",
+ " 'Y_1943',\n",
+ " 'Y_1944',\n",
+ " 'Y_1945',\n",
+ " 'Y_1946',\n",
+ " 'Y_1947',\n",
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+ " 'Y_1950',\n",
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+ " 'Y_1952',\n",
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+ " 'Y_1955',\n",
+ " 'Y_1956',\n",
+ " 'Y_1957',\n",
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+ " 'Y_1960',\n",
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+ " 'Y_1977',\n",
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+ " 'Y_1980',\n",
+ " 'Y_1981',\n",
+ " 'Y_1982',\n",
+ " 'Y_1983',\n",
+ " 'Y_1984',\n",
+ " 'Y_1985',\n",
+ " 'Y_1986',\n",
+ " 'Y_1987',\n",
+ " 'Y_1988',\n",
+ " 'Y_1989',\n",
+ " 'Y_1990',\n",
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+ " 'Y_1992',\n",
+ " 'Y_1993',\n",
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+ " 'Y_1995',\n",
+ " 'Y_1996',\n",
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+ " 'Y_1999',\n",
+ " 'Y_2000',\n",
+ " 'Y_2001',\n",
+ " 'Y_2002',\n",
+ " 'Y_2003',\n",
+ " 'Y_2004',\n",
+ " 'Y_2005',\n",
+ " 'Y_2006',\n",
+ " 'Y_2007',\n",
+ " 'Y_2008',\n",
+ " 'Y_2009']"
+ ]
+ }
+ ],
+ "prompt_number": 39
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "usy.fieldNames"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 40,
+ "text": [
+ "['Y_1929',\n",
+ " 'Y_1930',\n",
+ " 'Y_1931',\n",
+ " 'Y_1932',\n",
+ " 'Y_1933',\n",
+ " 'Y_1934',\n",
+ " 'Y_1935',\n",
+ " 'Y_1936',\n",
+ " 'Y_1937',\n",
+ " 'Y_1938',\n",
+ " 'Y_1939',\n",
+ " 'Y_1940',\n",
+ " 'Y_1941',\n",
+ " 'Y_1942',\n",
+ " 'Y_1943',\n",
+ " 'Y_1944',\n",
+ " 'Y_1945',\n",
+ " 'Y_1946',\n",
+ " 'Y_1947',\n",
+ " 'Y_1948',\n",
+ " 'Y_1949',\n",
+ " 'Y_1950',\n",
+ " 'Y_1951',\n",
+ " 'Y_1952',\n",
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+ " 'Y_1956',\n",
+ " 'Y_1957',\n",
+ " 'Y_1958',\n",
+ " 'Y_1959',\n",
+ " 'Y_1960',\n",
+ " 'Y_1961',\n",
+ " 'Y_1962',\n",
+ " 'Y_1963',\n",
+ " 'Y_1964',\n",
+ " 'Y_1965',\n",
+ " 'Y_1966',\n",
+ " 'Y_1967',\n",
+ " 'Y_1968',\n",
+ " 'Y_1969',\n",
+ " 'Y_1970',\n",
+ " 'Y_1971',\n",
+ " 'Y_1972',\n",
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+ " 'Y_1975',\n",
+ " 'Y_1976',\n",
+ " 'Y_1977',\n",
+ " 'Y_1978',\n",
+ " 'Y_1979',\n",
+ " 'Y_1980',\n",
+ " 'Y_1981',\n",
+ " 'Y_1982',\n",
+ " 'Y_1983',\n",
+ " 'Y_1984',\n",
+ " 'Y_1985',\n",
+ " 'Y_1986',\n",
+ " 'Y_1987',\n",
+ " 'Y_1988',\n",
+ " 'Y_1989',\n",
+ " 'Y_1990',\n",
+ " 'Y_1991',\n",
+ " 'Y_1992',\n",
+ " 'Y_1993',\n",
+ " 'Y_1994',\n",
+ " 'Y_1995',\n",
+ " 'Y_1996',\n",
+ " 'Y_1997',\n",
+ " 'Y_1998',\n",
+ " 'Y_1999',\n",
+ " 'Y_2000',\n",
+ " 'Y_2001',\n",
+ " 'Y_2002',\n",
+ " 'Y_2003',\n",
+ " 'Y_2004',\n",
+ " 'Y_2005',\n",
+ " 'Y_2006',\n",
+ " 'Y_2007',\n",
+ " 'Y_2008',\n",
+ " 'Y_2009']"
+ ]
+ }
+ ],
+ "prompt_number": 40
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "usy.getVars('Y_1929')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Getting variables\n",
+ "Variables successfully extracted\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 41,
+ "text": [
+ "{0: [323],\n",
+ " 1: [600],\n",
+ " 2: [310],\n",
+ " 3: [991],\n",
+ " 4: [634],\n",
+ " 5: [1024],\n",
+ " 6: [1032],\n",
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+ " 18: [906],\n",
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+ " 36: [874],\n",
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+ " 38: [426],\n",
+ " 39: [378],\n",
+ " 40: [479],\n",
+ " 41: [551],\n",
+ " 42: [634],\n",
+ " 43: [434],\n",
+ " 44: [741],\n",
+ " 45: [460],\n",
+ " 46: [673],\n",
+ " 47: [675]}"
+ ]
+ }
+ ],
+ "prompt_number": 41
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "usy.Wrook"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 42,
+ "text": [
+ "{}"
+ ]
+ }
+ ],
+ "prompt_number": 42
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "usy.cluster('arisel', ['Y_1980'], 8, wType='queen', inits=10, dissolve=0)\n",
+ "#mexico.cluster('arisel', ['pcgdp1940'], 5, wType='rook', inits=10, dissolve=0)\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Getting variables\n",
+ "Variables successfully extracted\n",
+ "Running original Arisel algorithm\n",
+ "Number of areas: 48\n",
+ "Number of regions: 8\n",
+ "initial Solution: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " [4, 0, 4, 2, 0, 6, 0, 4, 4, 1, 0, 0, 0, 0, 4, 4, 5, 0, 7, 0, 0, 4, 0, 1, 0, 2, 5, 7, 1, 7, 4, 1, 0, 0, 1, 0, 3, 4, 1, 4, 0, 1, 5, 0, 1, 4, 0, 0]\n",
+ "initial O.F: 23338316.0\n",
+ "FINAL SOLUTION: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " [4, 1, 4, 2, 0, 6, 7, 4, 4, 1, 0, 0, 1, 0, 4, 1, 5, 0, 7, 0, 0, 4, 0, 1, 1, 2, 5, 7, 1, 7, 4, 1, 0, 1, 1, 0, 3, 4, 1, 4, 1, 1, 5, 0, 1, 4, 0, 0]\n",
+ "FINAL OF: 21804253.0\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n"
+ ]
+ }
+ ],
+ "prompt_number": 43
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 43
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "us = cp.Layer()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 44
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "cp.addQueen2Layer(ps.examples.get_path('states48.gal'), us)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 45
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "uscsv = ps.examples.get_path(\"usjoin.csv\")"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 46
+ },
+ {
+ "cell_type": "code",
+ "collapsed": true,
+ "input": [
+ "f = ps.open(uscsv)\n",
+ "pci = np.array([f.by_col[str(y)] for y in range(1929, 2010)]).T\n",
+ "names = [\"Y_%d\"%v for v in range(1929,2010)]\n",
+ "cp.addArray2Layer(pci, us, names)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n"
+ ]
+ }
+ ],
+ "prompt_number": 47
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "usy.cluster('arisel', ['Y_1980'], 8, wType='queen', inits=10, dissolve=0)\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Getting variables\n",
+ "Variables successfully extracted\n",
+ "Running original Arisel algorithm\n",
+ "Number of areas: 48\n",
+ "Number of regions: 8\n",
+ "initial Solution: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " [0, 6, 0, 2, 6, 5, 6, 0, 0, 7, 6, 6, 6, 6, 0, 0, 4, 6, 4, 6, 6, 0, 6, 7, 6, 2, 4, 1, 7, 4, 0, 7, 6, 6, 2, 6, 4, 0, 7, 0, 6, 7, 3, 6, 2, 0, 6, 6]\n",
+ "initial O.F: 21166215.0\n",
+ "FINAL SOLUTION: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " [0, 6, 0, 2, 6, 5, 1, 0, 0, 7, 6, 6, 6, 6, 0, 0, 4, 6, 4, 6, 6, 0, 6, 7, 7, 2, 4, 1, 7, 1, 0, 7, 6, 6, 2, 6, 4, 0, 7, 0, 6, 7, 3, 6, 2, 0, 6, 6]\n",
+ "FINAL OF: 19355956.0\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n"
+ ]
+ }
+ ],
+ "prompt_number": 48
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "us_alpha = cp.importCsvData(ps.examples.get_path('usjoin.csv'))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 49
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "alpha_fips = us_alpha.getVars('STATE_FIPS')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Getting variables\n",
+ "Variables successfully extracted\n"
+ ]
+ }
+ ],
+ "prompt_number": 50
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "alpha_fips"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 51,
+ "text": [
+ "{0: [1],\n",
+ " 1: [4],\n",
+ " 2: [5],\n",
+ " 3: [6],\n",
+ " 4: [8],\n",
+ " 5: [9],\n",
+ " 6: [10],\n",
+ " 7: [12],\n",
+ " 8: [13],\n",
+ " 9: [16],\n",
+ " 10: [17],\n",
+ " 11: [18],\n",
+ " 12: [19],\n",
+ " 13: [20],\n",
+ " 14: [21],\n",
+ " 15: [22],\n",
+ " 16: [23],\n",
+ " 17: [24],\n",
+ " 18: [25],\n",
+ " 19: [26],\n",
+ " 20: [27],\n",
+ " 21: [28],\n",
+ " 22: [29],\n",
+ " 23: [30],\n",
+ " 24: [31],\n",
+ " 25: [32],\n",
+ " 26: [33],\n",
+ " 27: [34],\n",
+ " 28: [35],\n",
+ " 29: [36],\n",
+ " 30: [37],\n",
+ " 31: [38],\n",
+ " 32: [39],\n",
+ " 33: [40],\n",
+ " 34: [41],\n",
+ " 35: [42],\n",
+ " 36: [44],\n",
+ " 37: [45],\n",
+ " 38: [46],\n",
+ " 39: [47],\n",
+ " 40: [48],\n",
+ " 41: [49],\n",
+ " 42: [50],\n",
+ " 43: [51],\n",
+ " 44: [53],\n",
+ " 45: [54],\n",
+ " 46: [55],\n",
+ " 47: [56]}"
+ ]
+ }
+ ],
+ "prompt_number": 51
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "dbf = ps.open(ps.examples.get_path('us48.dbf'))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 52
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "dbf.header"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 53,
+ "text": [
+ "['AREA',\n",
+ " 'PERIMETER',\n",
+ " 'STATE_',\n",
+ " 'STATE_ID',\n",
+ " 'STATE_NAME',\n",
+ " 'STATE_FIPS',\n",
+ " 'SUB_REGION',\n",
+ " 'STATE_ABBR']"
+ ]
+ }
+ ],
+ "prompt_number": 53
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "state_fips = dbf.by_col('STATE_FIPS')\n",
+ "names = dbf.by_col('STATE_NAME')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 54
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "names"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 55,
+ "text": [
+ "['Washington',\n",
+ " 'Montana',\n",
+ " 'Maine',\n",
+ " 'North Dakota',\n",
+ " 'South Dakota',\n",
+ " 'Wyoming',\n",
+ " 'Wisconsin',\n",
+ " 'Idaho',\n",
+ " 'Vermont',\n",
+ " 'Minnesota',\n",
+ " 'Oregon',\n",
+ " 'New Hampshire',\n",
+ " 'Iowa',\n",
+ " 'Massachusetts',\n",
+ " 'Nebraska',\n",
+ " 'New York',\n",
+ " 'Pennsylvania',\n",
+ " 'Connecticut',\n",
+ " 'Rhode Island',\n",
+ " 'New Jersey',\n",
+ " 'Indiana',\n",
+ " 'Nevada',\n",
+ " 'Utah',\n",
+ " 'California',\n",
+ " 'Ohio',\n",
+ " 'Illinois',\n",
+ " 'Delaware',\n",
+ " 'West Virginia',\n",
+ " 'Maryland',\n",
+ " 'Colorado',\n",
+ " 'Kentucky',\n",
+ " 'Kansas',\n",
+ " 'Virginia',\n",
+ " 'Missouri',\n",
+ " 'Arizona',\n",
+ " 'Oklahoma',\n",
+ " 'North Carolina',\n",
+ " 'Tennessee',\n",
+ " 'Texas',\n",
+ " 'New Mexico',\n",
+ " 'Alabama',\n",
+ " 'Mississippi',\n",
+ " 'Georgia',\n",
+ " 'South Carolina',\n",
+ " 'Arkansas',\n",
+ " 'Louisiana',\n",
+ " 'Florida',\n",
+ " 'Michigan']"
+ ]
+ }
+ ],
+ "prompt_number": 55
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "state_fips = map(int, state_fips)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 56
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "state_fips"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 57,
+ "text": [
+ "[53,\n",
+ " 30,\n",
+ " 23,\n",
+ " 38,\n",
+ " 46,\n",
+ " 56,\n",
+ " 55,\n",
+ " 16,\n",
+ " 50,\n",
+ " 27,\n",
+ " 41,\n",
+ " 33,\n",
+ " 19,\n",
+ " 25,\n",
+ " 31,\n",
+ " 36,\n",
+ " 42,\n",
+ " 9,\n",
+ " 44,\n",
+ " 34,\n",
+ " 18,\n",
+ " 32,\n",
+ " 49,\n",
+ " 6,\n",
+ " 39,\n",
+ " 17,\n",
+ " 10,\n",
+ " 54,\n",
+ " 24,\n",
+ " 8,\n",
+ " 21,\n",
+ " 20,\n",
+ " 51,\n",
+ " 29,\n",
+ " 4,\n",
+ " 40,\n",
+ " 37,\n",
+ " 47,\n",
+ " 48,\n",
+ " 35,\n",
+ " 1,\n",
+ " 28,\n",
+ " 13,\n",
+ " 45,\n",
+ " 5,\n",
+ " 22,\n",
+ " 12,\n",
+ " 26]"
+ ]
+ }
+ ],
+ "prompt_number": 57
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# the csv file has the states ordered alphabetically, but this isn't the case for the order in the shapefile so we have to reorder before any choropleths are drawn\n",
+ "alpha_fips = [i[0] for i in alpha_fips.values()]\n",
+ "reorder = [ alpha_fips.index(s) for s in state_fips]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 58
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "regions = usy.region2areas"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 59
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "regions"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 60,
+ "text": [
+ "[0,\n",
+ " 6,\n",
+ " 0,\n",
+ " 2,\n",
+ " 6,\n",
+ " 5,\n",
+ " 1,\n",
+ " 0,\n",
+ " 0,\n",
+ " 7,\n",
+ " 6,\n",
+ " 6,\n",
+ " 6,\n",
+ " 6,\n",
+ " 0,\n",
+ " 0,\n",
+ " 4,\n",
+ " 6,\n",
+ " 4,\n",
+ " 6,\n",
+ " 6,\n",
+ " 0,\n",
+ " 6,\n",
+ " 7,\n",
+ " 7,\n",
+ " 2,\n",
+ " 4,\n",
+ " 1,\n",
+ " 7,\n",
+ " 1,\n",
+ " 0,\n",
+ " 7,\n",
+ " 6,\n",
+ " 6,\n",
+ " 2,\n",
+ " 6,\n",
+ " 4,\n",
+ " 0,\n",
+ " 7,\n",
+ " 0,\n",
+ " 6,\n",
+ " 7,\n",
+ " 3,\n",
+ " 6,\n",
+ " 2,\n",
+ " 0,\n",
+ " 6,\n",
+ " 6]"
+ ]
+ }
+ ],
+ "prompt_number": 60
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "from pysal.contrib.viz import mapping as maps"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 61
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "shp = ps.examples.get_path('us48.shp')\n",
+ "regions = np.array(regions)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 62
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "maps.plot_choropleth(shp, regions[reorder], 'unique_values')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd8FNXax3/nzMz29ISQCiGUAKGXSJeOFKWKysUCCBaw\n36tXvXZFUHxt6LWCCqKiICgIUqT3GnpvIYUAaZttM3PO+8duQoCU3WSTbLz7/egH2J0588zsmWee\nec5TAD9+/Pjx48ePHz9+/Pjx48ePHz9+/Pjx48ePHz9+/Pjx48ePHz9+/Pjx48ePn7pDgEaT+fGQ\nITxAo7ECSKhtefz4KQta2wL48eMNOCD0TkhA59hYB4BOtS2PHz9lIXpxrMkA4gCoXhzTUxoCuAKg\n4IbPkwEcrHFpfJtI159ZtSpF2bQAcAxuziezwxEuEIIeDRoEbLlwoZtVln+qXvH8+Kkc3lK6kQA+\nG9g0Eu1jQ [...]
+ "text": [
+ "<matplotlib.figure.Figure at 0x149ca390>"
+ ]
+ }
+ ],
+ "prompt_number": 63
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "usy.cluster('arisel', ['Y_1929'], 8, wType='queen', inits=10, dissolve=0)\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Getting variables\n",
+ "Variables successfully extracted\n",
+ "Running original Arisel algorithm\n",
+ "Number of areas: 48\n",
+ "Number of regions: 8\n",
+ "initial Solution: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " [0, 4, 0, 1, 4, 6, 6, 0, 0, 4, 5, 5, 4, 4, 2, 0, 3, 5, 6, 5, 4, 0, 4, 7, 4, 1, 3, 6, 0, 6, 0, 4, 5, 0, 4, 5, 6, 0, 4, 2, 0, 4, 3, 2, 4, 2, 4, 7]\n",
+ "initial O.F: 320792.0\n",
+ "FINAL SOLUTION: "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " [0, 4, 0, 1, 7, 6, 6, 0, 0, 4, 5, 5, 4, 4, 0, 0, 3, 5, 6, 5, 4, 0, 4, 4, 4, 1, 3, 6, 0, 6, 0, 4, 5, 0, 4, 5, 6, 0, 4, 0, 0, 4, 3, 2, 4, 2, 5, 7]\n",
+ "FINAL OF: 311057.0\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n"
+ ]
+ }
+ ],
+ "prompt_number": 64
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "regions = usy.region2areas"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 65
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "regions = np.array(regions)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 66
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "maps.plot_choropleth(shp, regions[reorder], 'unique_values')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd8FMX7xz8zW66nkZCEltBLgNCrgBTp+EXFhuWriKAi\nKijqzwKKBUEFFQVBBTtWRAREEBSQ3os06TWUkH5ly8zvj01CgJS75C65+L33C15J7nZnn92dfXbm\nmacAIUKECBEiRIgQIUKECBEiRIgQIUKECBEiRIgQIUKECBEiRIgQIUKECBEiRIgQlQeLzZ4y/IXJ\n3GKzuwDUrmh5QoQoClrRAoQI4Q8450JSu86o16ylAqBtRcsTIkRRiH5sawSAmgB0P7bpK4kAUgFk\nXfV5UwB7yl2a4CY29+e5CpWiaJoAOAAv+5PbmRNNqYDGrTs6DuzY0llxu74LrHghQpQOfyndWACz\nWlzXA3WaJ [...]
+ "text": [
+ "<matplotlib.figure.Figure at 0x1495def0>"
+ ]
+ }
+ ],
+ "prompt_number": 67
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "names = [\"Y_%d\"%i for i in range(1929, 2010)]\n",
+ "#usy.cluster('arisel', ['Y_1929'], 8, wType='queen', inits=10, dissolve=0)\n",
+ "usy.cluster('arisel', names, 8, wType='queen', inits=10, dissolve=0)\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "regions = usy.region2areas\n",
+ "regions = np.array(regions)\n",
+ "maps.plot_choropleth(shp, regions[reorder], 'unique_values', title='All Years')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "ps.version"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "usy.cluster('arisel', names[:40], 8, wType='queen', inits=10, dissolve=0)\n",
+ "regions = usy.region2areas\n",
+ "regions = np.array(regions)\n",
+ "maps.plot_choropleth(shp, regions[reorder], 'unique_values', title='1929-68')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "usy.cluster('arisel', names[40:], 8, wType='queen', inits=10, dissolve=0)\n",
+ "regions = usy.region2areas\n",
+ "regions = np.array(regions)\n",
+ "maps.plot_choropleth(shp, regions[reorder], 'unique_values', title='1969-2009')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "usy.cluster('arisel', names[40:], 8, wType='queen', inits=10, dissolve=0)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "usy.dataOperation(\"CONSTANT = 1\")\n",
+ "usy.Wrook = usy.Wqueen\n",
+ "usy.cluster('maxpTabu', ['Y_1929', 'Y_1929'], threshold=1000, dissolve=0)\n",
+ "regions = usy.region2areas\n",
+ "regions = np.array(regions)\n",
+ "maps.plot_choropleth(shp, regions[reorder], 'unique_values', title='maxp 1929')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "Counter(regions)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "usy.getVars('Y_1929')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "usy.Wrook"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "usy.cluster('maxpTabu', ['Y_1929', 'CONSTANT'], threshold=8, dissolve=0)\n",
+ "regions = usy.region2areas\n",
+ "regions = np.array(regions)\n",
+ "maps.plot_choropleth(shp, regions[reorder], 'unique_values', title='maxp 1929')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "regions"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "Counter(regions)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "vars = names"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "vars.append('CONSTANT')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "vars"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "usy.cluster('maxpTabu', vars, threshold=8, dissolve=0)\n",
+ "regions = usy.region2areas\n",
+ "regions = np.array(regions)\n",
+ "maps.plot_choropleth(shp, regions[reorder], 'unique_values', title='maxp 1929-2009')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "Counter(regions)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "south = cp.loadArcData(ps.examples.get_path(\"south.shp\"))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "south.fieldNames"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# uncomment if you have some time ;->\n",
+ "#south.cluster('arisel', ['HR70'], 20, wType='queen', inits=10, dissolve=0)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "#regions = south.region2areas"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "shp = ps.examples.get_path('south.shp')\n",
+ "#maps.plot_choropleth(shp, np.array(regions), 'unique_values')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "south.dataOperation(\"CONSTANT = 1\")\n",
+ "south.cluster('maxpTabu', ['HR70', 'CONSTANT'], threshold=70, dissolve=0)\n",
+ "regions = south.region2areas\n",
+ "regions = np.array(regions)\n",
+ "maps.plot_choropleth(shp, regions, 'unique_values', title='maxp HR70 threshold=70')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "Counter(regions)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+}
\ No newline at end of file
diff --git a/pysal/contrib/clusterpy/clusterpy_ext.py b/pysal/contrib/clusterpy/clusterpy_ext.py
new file mode 100644
index 0000000..e48b202
--- /dev/null
+++ b/pysal/contrib/clusterpy/clusterpy_ext.py
@@ -0,0 +1,284 @@
+import clusterpy as _clusterpy
+import pysal as ps
+import struct
+
+__author__ = "Sergio Rey <sjsrey at gmail.com>"
+
+__ALL__= ['Layer', 'loadArcData', 'importCsvData', 'addRook2Layer', 'addQueen2Layer', 'addArray2Layer' ]
+
+
+def _importArcData(filename):
+ """Creates a new Layer from a shapefile (<file>.shp)
+
+ This function wraps and extends a core clusterPy function to utilize PySAL
+ W constructors and dbf readers.
+
+
+ Parameters
+ ==========
+
+ filename: string
+ suffix of shapefile (fileName not fileName.shp)
+
+
+ Returns
+ =======
+ layer: clusterpy layer instance
+
+
+
+ """
+ layer = _clusterpy.Layer()
+ layer.name = filename.split('/')[-1]
+ #print "Loading " + filename + ".dbf"
+ dbf = ps.open(filename+".dbf")
+ fields = dbf.header
+ #data, fields, specs = importDBF(filename + '.dbf')
+ data = {}
+ #print "Loading " + filename + ".shp"
+ if fields[0] != "ID":
+ fields = ["ID"] + fields
+ for y in range(dbf.n_records):
+ data[y] = [y] + dbf.by_row(y)
+ else:
+ for y in range(dbf.n_records):
+ data[y] = dbf.by_row_(y)
+
+ layer.fieldNames = fields
+ layer.Y = data
+ shpf = filename+".shp"
+ layer.shpType = 5
+ #print 'pysal reader'
+ layer.Wrook = ps.rook_from_shapefile(filename+".shp").neighbors
+ layer.Wqueen = ps.queen_from_shapefile(filename+".shp").neighbors
+ #print "Done"
+ return layer
+
+_clusterpy.importArcData = _importArcData
+
+################# Public functions #######################
+
+def Layer():
+ """Provide a clusterpy Layer instance
+
+ Parameters
+ ==========
+
+ none
+
+ Returns
+ =======
+
+ layer: clusterpy.Layer instance
+
+ Examples
+ ========
+ >>> import pysal.contrib.clusterpy as cp
+ >>> l = cp.Layer()
+ >>> type(l)
+ <type 'instance'>
+ >>> l.Wrook
+ {}
+
+ """
+ return _clusterpy.Layer()
+
+def loadArcData(shapeFileName):
+ """
+ Handler to use PySAL W and dbf readers in place of clusterpy's
+
+ Parameters
+ ==========
+ shapeFileName: string
+ filename including .shp extension
+
+ Returns
+ =======
+ layer: clusterpy layer instance
+
+
+
+ Examples
+ ========
+ >>> import pysal.contrib.clusterpy as cp
+ >>> import pysal as ps
+ >>> shpFile = ps.examples.get_path('columbus.shp')
+ >>> columbus = cp.loadArcData(shpFile)
+ >>> columbus.Wrook[0]
+ [1, 2]
+ >>> columbus.Wrook[1]
+ [0, 2, 3]
+ >>> columbus.fieldNames[0:10]
+ ['ID', 'AREA', 'PERIMETER', 'COLUMBUS_', 'COLUMBUS_I', 'POLYID', 'NEIG', 'HOVAL', 'INC', 'CRIME']
+ """
+ base = shapeFileName.split(".")[0]
+ return _clusterpy.importArcData(base)
+
+def importCsvData(filename, layer=None):
+ """
+ Read a csv file of attributes into a layer
+
+ Parameters
+ ==========
+
+ filename: string
+ csf file to load
+
+ layer: clusterpy layer instance (default: None)
+ if a layer is passed, new attributes, Ws are attached to the layer.
+ Otherwise a new layer is created and returned
+
+
+
+ Returns
+ =======
+ layer: clusterpy layer instance
+
+
+ Examples
+ ========
+ >>> import pysal.contrib.clusterpy as cp
+ >>> l = cp.Layer()
+ >>> mexico = cp.importCsvData(ps.examples.get_path('mexico.csv'))
+ >>> mexico.fieldNames
+ ['ID', 'State', 'pcgdp1940', 'pcgdp1950', 'pcgdp1960', 'pcgdp1970', 'pcgdp1980', 'pcgdp1990', 'pcgdp2000', 'hanson03', 'hanson98', 'esquivel99', 'inegi', 'inegi2']
+
+ Notes
+ =====
+
+ This assumes the csv file is organized with records on the rows and attributes on the columns
+
+
+ """
+
+ if not layer:
+ layer = Layer()
+ csv = ps.open(filename,'r')
+ fields = csv.header
+ data = {}
+ if fields[0] != "ID":
+ fields = ["ID"] + fields
+ for i, rec in enumerate(csv.data):
+ data[i] = [i] + csv.by_row(i)
+ else:
+ for i, rec in enumerate(csv.data):
+ data[i] = csv.by_row(i)
+ layer.Y = data
+ layer.fieldNames = fields
+ return layer
+
+def addGal2Layer(galfile, layer, contiguity='rook'):
+ """
+ Attach an adjacency object to a layer
+
+ Parameters
+ ==========
+ galfile: string
+ galfile
+
+ layer: clusterpy layer
+
+ contiguity: type of contguity ['rook'|'queen']
+
+
+ Returns
+ =======
+ None
+
+ Examples
+ ========
+ >>> import pysal as ps
+ >>> import pysal.contrib.clusterpy as cp
+ >>> csvfile = ps.examples.get_path('mexico.csv')
+ >>> galfile = ps.examples.get_path('mexico.gal')
+ >>> mexico = cp.importCsvData(csvfile)
+ >>> cp.addRook2Layer(galfile, mexico)
+ >>> mexico.Wrook[0]
+ [31, 13]
+
+
+ """
+ gal = ps.open(galfile).read().neighbors
+ w = {}
+ for key in gal:
+ w[int(key)] = map(int, gal[key])
+
+ if contiguity.upper()== "ROOK":
+ layer.Wrook = w
+ elif contiguity.upper() == "QUEEN":
+ layer.Wqueen = w
+ else:
+ print 'Unsupported contiguity type: ', contiguity
+
+def addRook2Layer(galfile, layer):
+ """
+ User function for adding rook to layer
+
+ See addGal2Layer
+ """
+ addGal2Layer(galfile, layer)
+
+def addQueen2Layer(galfile, layer):
+ """
+ User function for adding queen to layer
+
+ See addGal2Layer
+ """
+ addGal2Layer(galfile, layer, contiguity='QUEEN')
+
+def addArray2Layer(array, layer, names=None):
+ """
+ Add a numpy array to a clusterpy layer
+
+
+ Parameters
+ ==========
+ array: nd-array
+ nxk with n observations on k attributes
+
+ layer: clusterpy layer object
+
+ names: list
+ k strings for attribute names
+
+ Returns
+ =======
+ None
+
+ Examples
+ ========
+ # Note this will report as fail since clusterpy prints 'Adding variables
+ # for each variable added. But the variables will be correctly added
+ >>> #import pysal as ps
+ >>> #import pysal.contrib.clusterpy as cp
+ >>> #import numpy as np
+ >>> #uscsv = ps.examples.get_path("usjoin.csv")
+ >>> #f = ps.open(uscsv)
+ >>> #pci = np.array([f.by_col[str(y)] for y in range(1929, 2010)]).T
+ >>> #usy = cp.Layer()
+ >>> #names = ["Y_%d"%v for v in range(1929,2010)]
+ >>> #cp.addArray2Layer(pci, usy, names)
+
+ """
+ n,k = array.shape
+ if not names:
+ names = ["X_%d"% v for v in range(k)]
+
+ for j,name in enumerate(names):
+ v = {}
+ for i in xrange(n):
+ v[i] = array[i,j]
+ layer.addVariable([name], v)
+
+if __name__ == '__main__':
+
+ import numpy as np
+
+ columbus = loadArcData(ps.examples.get_path('columbus.shp'))
+ n = len(columbus.Wqueen)
+ columbus.dataOperation("CONSTANT = 1")
+ np.random.seed(12345)
+ columbus.cluster('maxpTabu', ['CRIME', 'CONSTANT'], threshold=4, dissolve=0, std=0)
+ #np.random.seed(12345)
+ #columbus.cluster('arisel', ['CRIME'], 5, wType='rook', inits=10, dissolve=0)
+
diff --git a/pysal/contrib/db/PGDump.py b/pysal/contrib/db/PGDump.py
new file mode 100644
index 0000000..7f192d0
--- /dev/null
+++ b/pysal/contrib/db/PGDump.py
@@ -0,0 +1,104 @@
+#!/usr/bin/python
+__author__ = "Philip Stephens <philip.stephens at asu.edu "
+
+__all__ = ['db2shape', 'db_table2gal']
+
+from django.contrib.gis.gdal.geomtype import OGRGeomType
+from osgeo import ogr
+import pysal
+import os
+
+def db2shape(connstring, input, output):
+ """
+ dumps a postgis database table to shapefile
+
+ Arguments
+ ---------
+ connstring = A connection string with 4 parameters. Example is
+ "PG: host='localhost' dbname='pysaldb' user='myusername'
+ password='my_password'"
+
+ input : the db table
+
+ output : a filename
+
+ Examples
+ --------
+ TBD
+ TODO: make pysal db tables read-only and available on network
+
+ Note
+ ----
+
+ If a file exists with the same name as 'output', it will be deleted
+ before being overwritten.
+ """
+ conn = ogr.Open(connstring)
+ layer = conn.GetLayerByName(input)
+ type = layer.GetGeomType() # returns an int
+ geom_type = OGRGeomType._types[type] # map int to string description
+
+ # Schema definition of SHP file
+ out_driver = ogr.GetDriverByName( 'ESRI Shapefile' )
+ if os.path.exists(output):
+ out_driver.DeleteDataSource(output)
+ out_ds = out_driver.CreateDataSource(output)
+ out_srs = None
+ out_layer = out_ds.CreateLayer(geom_type, out_srs, type)
+ fd = ogr.FieldDefn('name',ogr.OFTString)
+ out_layer.CreateField(fd)
+
+ #layer = conn.ExecuteSQL(sql)
+
+ feat = layer.GetNextFeature()
+ while feat is not None:
+ featDef = ogr.Feature(out_layer.GetLayerDefn())
+ featDef.SetGeometry(feat.GetGeometryRef())
+ #featDef.SetField('name',feat.TITLE)
+ out_layer.CreateFeature(featDef)
+ feat.Destroy()
+ feat = layer.GetNextFeature()
+ conn.Destroy()
+ out_ds.Destroy()
+
+def db_table2gal(connstring, input, weights_type=Contiguity):
+ """
+ generates a GAL file from a postgis database table
+
+ Arguments
+ ---------
+ connstring = A connection string with 4 parameters. Example is
+ "PG: host='localhost' dbname='pysaldb' user='myusername'
+ password='my_password'"
+
+ input : the db table
+
+ weights_type : type of spatial weights calculation, defaults to
+ Contiguity
+
+ Examples
+ --------
+ TBD
+
+ Note
+ ----
+ TBD
+
+ pseudo
+ ------
+ connect to db
+ simplify
+ create topo
+ query topogeom
+ return aswkt or asgeojson
+ convert returned to pysal w
+ write w to GAL
+
+
+ """
+ pass
+
+
+if __name__ == '__main__':
+ import nose
+ nose.runmodule()
diff --git a/pysal/contrib/db/README b/pysal/contrib/db/README
new file mode 100644
index 0000000..8ecc211
--- /dev/null
+++ b/pysal/contrib/db/README
@@ -0,0 +1,125 @@
+Pysal.Contrib.db
+================
+
+This module enables simplified connection and query on spatial databases.
+It enables developers to return the query results in a
+Pysal-digestible format such as geojson or Well Known Text (WKT).
+It explicitly supports the following spatial databases:
+
+ * postgis (October, 2012)
+
+Caveat: This is experimental, use with Caution!
+
+Depends
+=======
+
+ * psycopg2
+ * pysal
+ * osgeo
+ * django.contrib.gis
+ * any of the supported databases and associated CRUD privileges
+
+Suggests
+========
+
+ * PL/PYTHON
+ * Shapely
+ * sqlalchemy
+
+Usage
+=====
+
+The first function PySAL supports is dumping data from a PostGIS table into
+shapefile format. We'll access the db and dump data using
+the ogr module.
+
+The secondary utility to be developed is a cursor manager. Developers may use this cursor to write
+their own database functions for their particular spatial analysis
+applications.
+
+For instance::
+
+ >>> import psycopg2 as pg
+ >>> conn = pg.connect("dbname=pysaldb user=stephens")
+ >>> cur = conn.cursor()
+ >>> cur.execute(<your SQL>)
+
+
+Installation
+============
+
+TBD
+
+Configuration
+=============
+
+If you have PostGres environment variables set in your userspace, you can
+instruct Pysal to use them rather than passing in database credentials each
+time. Look at the __init__.py for more information.
+
+I'm leaving the details of setting up a PostGIS database to the user.
+I developed this module on Ubuntu 12.04. I used the psql console to create the
+spatial indices for the pysal example data, and used the SPIT plugin in QGIS
+to upload the pysal example data into the PostGIS db. There is a bug for only
+some of the example data using SPIT to upload. To remedy it, use the ftools
+plugin to convert multipart polygons to single parts. Vector -> Geometry
+tools -> multi...
+
+
+Notes
+-----
+
+When choosing between psycopg2 and sqlalchemy, sqlalchemy is db
+agnostic, meaning your code will work with any db. Also,
+sqlalchemy ships with Enthought Python so the dependencies are handled by
+Pysal's new (as of 1.5) dependency policy.
+
+However you connect and query the db, return results from
+postgis as geojson or wkt so python/pysal can deal with them directly.
+
+Or, as an alternative, SQLalchemy offers the ORM path, which is more
+abstract and maps database objects to python constructs.
+
+What I want to write here is basically a cursor object manager that other
+functions or classes in pysal can use to query a database object. For example,
+in the weights class, an alternative distance-based calculator function can post
+a hard-coded query into a db using a cursor enabled by this module. This does
+not necessarily have to be a complicated module: accept a database name, a user
+name, a password, and a table; receive a cursor for that table. Not even sure if
+this module is necessary? Perhaps just so we do not repeat ourselves in the
+potentially many instances of using postgis functionality. Be sure to check out
+pandas.io for tips.
+
+
+
+Dataset Notes
+-------------
+As noted in the README, I used the psql console, pgAdmin3 and QGIS to set up
+the data. I was able to create a spatial index on almost all of the example data
+sets except Chicago77, which the console insisted did not exist, though it
+plainly appeared in pgAdmin3.
+
+From IRC with strk:
+11:32 peas am I right in thinking that the ability to easily create a topo
+representation of a postgis table is not yet available?
+looks like in future opportunities
+peas: the support was improved since the Paris presentation
+strk
+the toTopoGeom (maybe presented as a funding opportunity in those slides) it
+is now available
+strk
+should make it pretty simple to build the topology
+11:37 peas
+strk: i'll check it out, thanks again
+strk
+my blog has some recipes too
+
+
+TODO
+====
+
+I did not set the SRID when I imported the pysal example data into the
+postgis database. That may have to be remedied
+before making any of that data public, if it comes to that.
+
+Update: I did figure out how to set the SRID on import.
diff --git a/pysal/contrib/db/README_PLPYTHON b/pysal/contrib/db/README_PLPYTHON
new file mode 100644
index 0000000..05e0e0e
--- /dev/null
+++ b/pysal/contrib/db/README_PLPYTHON
@@ -0,0 +1,10 @@
+Pl/Python
+---------
+
+An alternative approach.
+
+Installation
+------------
+sudo apt-get install postgresql-plpython-9.1
+createlang plpythonu <dbname> (in this case, pysaldb)
+
diff --git a/pysal/contrib/network/__init__.py b/pysal/contrib/network/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pysal/contrib/network/access.py b/pysal/contrib/network/access.py
new file mode 100644
index 0000000..9fae70f
--- /dev/null
+++ b/pysal/contrib/network/access.py
@@ -0,0 +1,56 @@
+"""
+A library of spatial network accessibility functions.
+Not to be used without permission.
+
+Contact:
+
+Andrew Winslow
+GeoDa Center for Geospatial Analysis
+Arizona State University
+Tempe, AZ
+Andrew.Winslow at asu.edu
+"""
+
+import math
+import unittest
+
+import test
+
+
+def coverage(dists, bandwidth):
+ """
+ Takes a list of numeric distances and a numeric bandwidth and returns the
+ number of distances less than or equal to the bandwidth.
+ """
+ return len(filter(lambda d: d <= bandwidth, dists))
+
+def equity(dists):
+ """
+ Takes a list of numeric distances and returns the smallest of them.
+ """
+ return min(dists)
+
+def potential_entropy(dists, power=1):
+ """
+ Takes a list of numeric distances and returns the sum of the values
+ of a function of a distances. The function is e^(-power*distance).
+ """
+ return sum([math.e**(-power*d) for d in dists])
+
+def potential_gravity(dists, power=2):
+ """
+ Takes a list of numeric distances and returns the sum of the values
+ of a function of a distances. The function is 1/(d^power).
+ """
+ return sum([1.0/(d**power) for d in filter(lambda d: d > 0, dists)])
+
+def travel_cost(dists):
+ """
+ Takes a list of distances and compute the sum.
+ """
+ return sum(dists)
+
+
+
+
+
diff --git a/pysal/contrib/network/crimes.dbf b/pysal/contrib/network/crimes.dbf
new file mode 100644
index 0000000..523c2c8
Binary files /dev/null and b/pysal/contrib/network/crimes.dbf differ
diff --git a/pysal/contrib/network/crimes.shp b/pysal/contrib/network/crimes.shp
new file mode 100644
index 0000000..efc7d25
Binary files /dev/null and b/pysal/contrib/network/crimes.shp differ
diff --git a/pysal/contrib/network/crimes.shx b/pysal/contrib/network/crimes.shx
new file mode 100644
index 0000000..8a794bc
Binary files /dev/null and b/pysal/contrib/network/crimes.shx differ
diff --git a/pysal/contrib/network/distances.csv b/pysal/contrib/network/distances.csv
new file mode 100644
index 0000000..f9223b3
--- /dev/null
+++ b/pysal/contrib/network/distances.csv
@@ -0,0 +1,65 @@
+source id,destination id,distance
+1,1,0.0
+1,2,1625.91349431
+1,3,3295.50605182
+1,4,3825.24715194
+1,5,2053.92436941
+1,6,237.789536126
+1,7,1006.385016
+1,8,930.053249685
+3,1,3295.50605182
+3,2,3302.80671651
+3,3,0.0
+3,4,4464.60749293
+3,5,1807.7941268
+3,6,3277.17175715
+3,7,2880.68312579
+3,8,2641.72564757
+5,1,2053.92436941
+5,2,1511.33307526
+5,3,1807.7941268
+5,4,2887.51768661
+5,5,0.0
+5,6,1924.72832003
+5,7,2233.68249843
+5,8,1149.4089984
+7,1,1006.385016
+7,2,2429.77883642
+7,3,2880.68312579
+7,4,4588.68703106
+7,5,2233.68249843
+7,6,1200.33087099
+7,7,0.0
+7,8,1447.20469755
+8,1,930.053249685
+8,2,1041.45388498
+8,3,2641.72564757
+8,4,3144.97039345
+8,5,1149.4089984
+8,6,777.660767315
+8,7,1447.20469755
+8,8,0.0
+2,1,1625.91349431
+2,2,0.0
+2,3,3302.80671651
+2,4,2200.03426545
+2,5,1511.33307526
+2,6,1390.52348765
+2,7,2429.77883642
+2,8,1041.45388498
+4,1,3825.24715194
+4,2,2200.03426545
+4,3,4464.60749293
+4,4,0.0
+4,5,2887.51768661
+4,6,3588.96627183
+4,7,4588.68703106
+4,8,3144.97039345
+6,1,237.789536126
+6,2,1390.52348765
+6,3,3277.17175715
+6,4,3588.96627183
+6,5,1924.72832003
+6,6,0.0
+6,7,1200.33087099
+6,8,777.660767315
diff --git a/pysal/contrib/network/kernel.py b/pysal/contrib/network/kernel.py
new file mode 100644
index 0000000..8aca468
--- /dev/null
+++ b/pysal/contrib/network/kernel.py
@@ -0,0 +1,184 @@
+"""
+A library of spatial network kernel density functions.
+Not to be used without permission.
+
+Contact:
+
+Andrew Winslow
+GeoDa Center for Geospatial Analysis
+Arizona State University
+Tempe, AZ
+Andrew.Winslow at asu.edu
+"""
+
+import operator
+import unittest
+import test
+import priordict as priordict
+import network as pynet
+from math import exp, sqrt, pi
+import time
+
+def triangular(z):
+ return 1 - abs(z)
+
+def uniform(z):
+ return abs(z)
+
+def quadratic(z):
+ return 0.75*(1 - z*z)
+
+def quartic(z):
+ return (3.0/pi)*(1-z*z)*(1-z*z)
+ #return (15*1.0/16)*(1-z*z)*(1-z*z)
+
+def gaussian(z):
+ return sqrt(2*pi)*exp(-0.5*z*z)
+
+def dijkstras_w_prev(G, start, r=1e600):
+ D = {} # dictionary of final distances
+ P = {} # dictionary of previous nodes
+ Q = priordict.PriorityDictionary() # est.dist. of non-final vert.
+ Q[start] = 0
+ P[start] = None
+ for v in Q:
+ D[v] = Q[v]
+ if v == None or D[v] > r:
+ break
+ for w in G[v]:
+ vwLength = D[v] + G[v][w]
+ if w in D:
+ pass
+ elif w not in Q or vwLength < Q[w]:
+ Q[w] = vwLength
+ P[w] = v
+ return (D, P)
+
+def kernel_density(network, events, bandwidth, orig_nodes, kernel='quadratic'):
+ """
+ This function estimates Kernel densities on a planar undirected network.
+ It implements the equal-split discontinuous Kernel function developed by Okabe et al. (2009).
+ Particularly, it computes Kernel densities by using equation 19 and 20
+ in the paper of Okabe et al. (2009).
+
+ Parameters
+ ----------
+ network: A dictionary of dictionaries like {n1:{n2:d12,...},...}
+ A planar undirected network
+ It is assumed that this network is divided by a certain cell size
+ and is restructured to incorporate the new nodes resulting from the division as well as
+ events. Therefore, nodes in the network can be classified into three groups:
+ i) original nodes, 2) event points, and 3) cell points.
+ events: a list of tuples
+ a tuple is the network-projected coordinate of an event
+ that takes the form of (x,y)
+ bandwidth: a float
+ Kernel bandwidth
+ orig_nodes: a list of tuples
+ a tuple is the coordinate of a node that is part of the original base network
+ each tuple takes the form of (x,y)
+ kernel: string
+ the type of Kernel function
+ allowed values: 'quadratic', 'gaussian', 'quartic', 'uniform', 'triangular'
+
+ Returns
+ -------
+ A dictioinary where keys are node and values are their densities
+ Example: {n1:d1,n2:d2,...}
+
+ <tc>#is#kernel_density</tc>
+ """
+
+ # beginning of step i
+ density = {}
+ for n in network:
+ density[n] = []
+ # end of step i
+
+ # beginning of step ii
+ def compute_split_multiplier(prev_D, n):
+ '''
+ computes the demoninator of the formula 19
+
+ Parameters
+ ----------
+ prev_D: a dictionary storing pathes from n to other nodes in the network
+ its form is like: {n1:prev_node_of_n1(=n2), n2:prev_node_of_n2(=n3),...}
+ n: a tuple containing the geographic coordinate of a starting point
+ its form is like: (x,y)
+
+ Returns
+ -------
+ An integer
+
+ '''
+ split_multiplier = 1
+ p = prev_D[n]
+ while p != None:
+ if len(network[p]) > 1:
+ split_multiplier *= (len(network[p]) - 1)
+ if p not in prev_D:
+ p = None
+ else:
+ p = prev_D[p]
+ return split_multiplier
+ # end of step ii
+
+ kernel_funcs = {'triangular':triangular, 'uniform': uniform,
+ 'quadratic': quadratic, 'quartic':quartic, 'gaussian':gaussian}
+ #t1 = time.time()
+ # beginning of step iii
+ kernel_func = kernel_funcs[kernel]
+ for e in events:
+ # beginning of step a
+ src_D = pynet.dijkstras(network, e, bandwidth, True)
+ # end of step a
+ # beginning of step b
+ density[e].append(kernel_func(0))
+ # end of step b
+ # beginning of step c
+ for n in src_D[0]: # src_D[0] - a dictionary of nodes whose distance from e is smaller than e
+ if src_D[0][n] == 0: continue
+ # src_D[1] - a dictionary from which a path from e to n can be traced
+ d = src_D[0][n]
+ if d <= bandwidth:
+ n_degree = 2.0
+ if n in events and n in orig_nodes and len(network[n]) > 0:
+ n_degree = len(network[n])
+ unsplit_density = kernel_func(d*1.0/bandwidth*1.0)
+ # src_D[1] - a dictionary from which a path from e to n can be traced
+ split_multiplier = compute_split_multiplier(src_D[1], n)
+ density[n].append((1.0/split_multiplier)*(2.0/n_degree)*unsplit_density)
+ #if str(n[0]) == '724900.335127' and str(n[1]) == '872127.948935':
+ # print 'event', e
+ # print 'distance', d
+ # print 'unsplit_density', unsplit_density
+ # print 'n_degree', n_degree
+ # print 'split_multiplier', split_multiplier
+ # print 'density', (1.0/split_multiplier)*(2.0/n_degree)*unsplit_density
+ # end of step c
+
+ # beginning of step iv
+ #t1 = time.time()
+ no_events = len(events)
+ for node in density:
+ if len(density[node]) > 0:
+ #if str(node[0]) == '724900.335127' and str(node[1]) == '872127.948935':
+ # print density[node]
+ density[node] = sum(density[node])/no_events
+ #density[node] = sum(density[node])*1.0/len(density[node])
+ else:
+ density[node] = 0.0
+ # end of step iv
+
+ #for node in events:
+ # del density[node]
+
+ #print 'normalizing density: %s' % (str(time.time() - t1))
+
+ return density
+
+
+
+
+
diff --git a/pysal/contrib/network/kfuncs.py b/pysal/contrib/network/kfuncs.py
new file mode 100644
index 0000000..58cc327
--- /dev/null
+++ b/pysal/contrib/network/kfuncs.py
@@ -0,0 +1,75 @@
+"""
+A library of spatial network k-function functions.
+Not to be used without permission.
+
+Contact:
+
+Andrew Winslow
+GeoDa Center for Geospatial Analysis
+Arizona State University
+Tempe, AZ
+Andrew.Winslow at asu.edu
+"""
+
+import unittest
+import test
+
+def _fxrange(start, end, incr):
+ """
+ A float version of the xrange() built-in function.
+
+ _fxrange(number, number, number) -> iterator
+
+ Arguments:
+ start -- the lower end of the range (inclusive)
+ end -- the upper end of the range (exclusive)
+ incr -- the step size. must be positive.
+ """
+ i = 0
+ while True:
+ t = start + i*incr
+ if t >= end:
+ break
+ yield t
+ i += 1
+
+def _binary_search(list, q):
+ """
+ Returns the index in a list where an item should be found.
+
+ Arguments:
+ list -- a list of items
+ q -- a value to be searched for
+ """
+ l = 0
+ r = len(list)
+ while l < r:
+ m = (l + r)/2
+ if list[m] > q:
+ r = m
+ else:
+ l = m + 1
+ return l
+
+def kt_values(t_specs, distances, scaling_const):
+ """
+ Returns a dictionary of t numerics to k(t) numerics.
+
+ kt_values(number list, number list, number) -> number to number dictionary
+
+ Arguments:
+ t_specs -- a 3-tuple of (t_min, t_max, t_delta) specifying the t-values to compute k(t) for
+ distances -- a list of distances to compute k(t) from
+ scaling_const -- a constant to multiple k(t) by for each t
+ """
+ ks = {}
+ distances.sort()
+ if type(t_specs) == tuple:
+ t_specs = [t for t in _fxrange(t_specs[0], t_specs[1], t_specs[2])]
+
+ for t in t_specs:
+ ks[t] = scaling_const*_binary_search(distances, t)
+ return ks
+
+
+
diff --git a/pysal/contrib/network/klincs.py b/pysal/contrib/network/klincs.py
new file mode 100644
index 0000000..3bbdd4b
--- /dev/null
+++ b/pysal/contrib/network/klincs.py
@@ -0,0 +1,233 @@
+#!/usr/env python
+
+"""
+A library for computing local K function for network-constrained data
+
+Author:
+Andrew Winslow Andrew.Winslow at asu.edu
+Myunghwa Hwang mhwang4 at gmail.com
+
+"""
+import unittest
+import numpy as np
+import geodanet.network as pynet
+import geodanet.kfuncs as pykfuncs
+import geodanet.simulator as pysim
+import time
+import random
+import platform
+try:
+ if platform.system() == 'Darwin':
+ import multiprocessing
+ else:
+ multiprocessing = None
+except ImportError:
+ multiprocessing = None
+
+class WeightedRandomSampleGenerator(object):
+ """
+ A generator for randomly sampling n elements from
+ a population group with consideration to a given set of weights
+ """
+
+ def __init__(self, weights, population, n):
+ """
+ weights: an iterable with m numeric elements
+ population: a numpy array with m elements
+ n: an integer representing sample size
+ """
+ self.totals = np.cumsum(weights)
+ self.population = population
+ self.n = n
+ self.norm = self.totals[-1]
+
+ def next(self):
+ sample = []
+ for i in xrange(self.n):
+ throw = np.random.rand()*self.norm
+ sample.append(self.population[np.searchsorted(self.totals, throw)])
+ return sample
+
+ def __call__(self):
+ return self.next()
+
+class RandomSampleGenerator(object):
+ """
+ A generator for randomly sampling n elements
+ from a population group
+ """
+ def __init__(self, population, n):
+ """
+ population: a numpy array with m elements
+ n: an integer representing sample size
+ """
+ self.population = population
+ self.n = n
+
+ def next(self):
+ return random.sample(self.population, self.n)
+
+ def __call__(self):
+ return self.next()
+
+def local_k(network, events, refs, scale_set, cache=None):
+ """
+ Computes local K function
+
+ network: an undirected network data to which reference points are injected
+ refs: a set of reference points on the given network
+ points unprojected into the network
+ events: a set of event points on the given network
+ points projected into the network
+ scale_set: a tuple defining spatial scales to be examined
+ (min, max, interval)
+ """
+
+ node2localK = {}
+ net_distances = {}
+ if cache: net_distances = cache
+ for node in refs:
+ node = node[1][0]
+ a_dest = network[node].keys()[0]
+ node_proj = (node, a_dest, 0, network[node][a_dest])
+ if node not in net_distances:
+ net_distances[node] = pynet.dijkstras(network, node, scale_set[1])
+ if a_dest not in net_distances:
+ net_distances[a_dest] = pynet.dijkstras(network, node, scale_set[1])
+ distances = pynet.proj_distances_undirected(network, node_proj, events, scale_set[1], cache=net_distances).values()
+ node2localK[node] = pykfuncs.kt_values(scale_set, distances, 1)
+ return node2localK, net_distances
+
+def cluster_type(obs, lower, upper):
+ if obs < lower: return -1
+ if obs > upper: return 1
+ return 0
+
+def simulate_local_k_01(args):
+ sims = args[0]
+ n = args[1]
+ net_file = args[2]
+ network = args[3]
+ events = args[4]
+ refs = args[5]
+ scale_set = args[6]
+ cache = args[7]
+
+ #print 'simulated_local_k_01'
+ simulator = pysim.Simulation(net_file)
+ sims_outcomes = []
+ for sim in xrange(sims):
+ points = simulator.getRandomPoints(n, projected=True)
+ sim_events = []
+ for edge in points:
+ for point in points[edge]:
+ sim_events.append(point)
+ res, dists = local_k(network, sim_events, refs, scale_set, cache=cache)
+ sims_outcomes.append(res)
+
+ return sims_outcomes
+
+def simulate_local_k_02(args):
+ sims = args[0]
+ n = args[1]
+ refs = args[2]
+ scale_set = args[3]
+ cache = args[4]
+
+ #print 'simulated_local_k_02'
+ sims_outcomes = []
+ sampler = RandomSampleGenerator(refs, n).next
+ for sim in xrange(sims):
+ sim_events = sampler()
+ sim_localk = {}
+ for node in refs:
+ all_distances = cache[node[1][0]]
+ distances = []
+ for event in sim_events:
+ event = event[1][0]
+ if event in all_distances:
+ distances.append(all_distances[event])
+ sim_localk[node[1][0]] = pykfuncs.kt_values(scale_set, distances, 1)
+ sims_outcomes.append(sim_localk)
+
+ return sims_outcomes
+
+def k_cluster(network, events, refs, scale_set, sims, sig=0.1, sim_network=None, cpus=1):
+
+ """
+ Parameters:
+ network: a network to which reference points are injected
+ events: a set of event points projected into the network
+ refs: a set of reference points unprojected into the network
+ scale_set: tuple same as (min, max, resolution)
+ sims: integer; the number of simulations
+ sig: float; siginificance level
+ sim_network: the source shape file containing the network data
+ this is used to simualte point patterns for inference
+ cpus: integer: the number of cpus
+ multiprocessing can be used for inference
+ """
+
+ """
+ 1. For an observed set of n events on the network, calculate local K function
+ values for all m reference points
+ """
+ node2localK, net_dists = local_k(network, events, refs, scale_set)
+ """
+ When n < m (simulator == None):
+ 2. Select n out of m reference points randomly and
+ calculate local K function values for these randomly sampled points
+ When n >= m (simulator != None):
+ 2. Randomly simulate n points on network edges and
+ calculate local K function values for these randomly simulated points
+ 3. Repeat 2 as many as the number of simulations
+ Note: on Darwin systems, simulation will be parallelized
+ """
+ n = len(events)
+ sims_outcomes = []
+ if not multiprocessing or cpus == 1:
+ if sim_network:
+ sims_outcomes = simulate_local_k_01((sims, n, sim_network, network, events, refs, scale_set, net_dists))
+ else:
+ sims_outcomes = simulate_local_k_02((sims, n, refs, scale_set, net_dists))
+ elif multiprocessing and cpus >= 2:
+ pool = multiprocessing.Pool(cpus)
+ sims_list = range(sims)
+ sims_list = map(len, [sims_list[i::cpus] for i in xrange(cpus)])
+ partial_outcomes = None
+ if sim_network:
+ partial_outcomes = pool.map(simulate_local_k_01,
+ [(sim, n, sim_network, network, events, refs, scale_set, net_dists) for sim in sims_list])
+ else:
+ partial_outcomes = pool.map(simulate_local_k_02,
+ [(sim, n, refs, scale_set, net_dists) for sim in sims_list])
+ sims_outcomes = partial_outcomes[0]
+ for partial in partial_outcomes[1:]:
+ sims_outcomes.extend(partial)
+
+ """
+ 4. Determine lower and upper envelopes for the observed K function values
+ as well as the type of cluster (dispersion or clustering)
+ """
+ # 4. P-value evaluation
+ lower_envelope = {}
+ upper_envelope = {}
+ lower_p = int(sims*sig/2)
+ upper_p = int(sims*(1-sig/2))
+ localKs = {}
+ for node in refs:
+ node = node[1][0]
+ lower_envelope[node] = {}
+ upper_envelope[node] = {}
+ localKs[node] = {}
+ for scale in node2localK[node].keys():
+ local_outcomes = [sim[node][scale] for sim in sims_outcomes]
+ local_outcomes.sort()
+ obs = node2localK[node][scale]
+ lower = local_outcomes[lower_p]
+ upper = local_outcomes[upper_p]
+ cluster = cluster_type(obs, lower, upper)
+ localKs[node][scale] = [obs, lower, upper, cluster]
+
+ return localKs
+
diff --git a/pysal/contrib/network/lincs.py b/pysal/contrib/network/lincs.py
new file mode 100644
index 0000000..d3ee5b0
--- /dev/null
+++ b/pysal/contrib/network/lincs.py
@@ -0,0 +1,349 @@
+#!/usr/bin/env python
+
+"""
+A library for computing local indicators of network-constrained clusters
+
+Author:
+Myunghwa Hwang mhwang4 at gmail.com
+
+"""
+import unittest
+import numpy as np
+import scipy.stats as stats
+import geodanet.network as pynet
+import pysal, copy
+import time
+
+def unconditional_sim(event, base, s):
+ """
+ Parameters:
+ event: n*1 numpy array with integer values
+ observed values for an event variable
+ base: n*1 numpy array with integer values
+ observed values for a population variable
+ s: integer
+ the number of simulations
+
+ Returns:
+ : n*s numpy array
+ """
+ mean_risk = event.sum()*1.0/base.sum()
+ if base.dtype != int:
+ base = np.array([int(v) for v in base])
+ base_zeros = (base == 0.0)
+ base[base_zeros] += 1.0
+ sims = np.random.binomial(base, mean_risk, (s, len(event))).transpose()
+ sims[base_zeros, :] = 0.0
+ return sims
+
+def unconditional_sim_poisson(event, base, s):
+ """
+ Parameters:
+ event: n*1 numpy array with integer values
+ observed values for an event variable
+ base: n*1 numpy array with integer values
+ observed values for a population variable
+ s: integer
+ the number of simulations
+
+ Returns:
+ : n*s numpy array
+ """
+ mean_risk = event.sum()*1.0/base.sum()
+ E = base*mean_risk
+ return np.random.poisson(E, (s, len(event))).transpose()
+
+def conditional_multinomial(event, base, s):
+ """
+ Parameters:
+ event: n*1 numpy array with integer values
+ observed values for an event variable
+ base: n*1 numpy array with integer values
+ observed values for a population variable
+ s: integer
+ the number of simulations
+
+ Returns:
+ : n*s numpy array
+ """
+ m = int(event.sum())
+ props = base*1.0/base.sum()
+ return np.random.multinomial(m, props, s).transpose()
+
+def pseudo_pvalues(obs, sims):
+ """
+ Get pseudo p-values from a set of observed indices and their simulated ones.
+
+ Parameters:
+ obs: n*1 numpy array for observed values
+ sims: n*sims numpy array; sims is the number of simulations
+
+ Returns:
+ p_sim : n*1 numpy array for pseudo p-values
+ E_sim : mean of p_sim
+ SE_sim: standard deviation of p_sim
+ V_sim: variance of p_sim
+ z_sim: standardarized observed values
+ p_z_sim: p-value of z_sim based on normal distribution
+ """
+
+ sims = np.transpose(sims)
+ permutations = sims.shape[0]
+ above = sims >= obs
+ larger = sum(above)
+ low_extreme = (permutations - larger) < larger
+ larger[low_extreme] = permutations - larger[low_extreme]
+ p_sim = (larger + 1.0)/(permutations + 1.0)
+ E_sim = sims.mean()
+ SE_sim = sims.std()
+ V_sim = SE_sim*SE_sim
+ z_sim = (obs - E_sim)/SE_sim
+ p_z_sim = 1 - stats.norm.cdf(np.abs(z_sim))
+ return p_sim, E_sim, SE_sim, V_sim, z_sim, p_z_sim
+
+def node_weights(network, attribute=False):
+ """
+ Obtains a spatial weights matrix of edges in a network
+ if two edges share a node, they are neighbors
+
+ Parameters:
+ network: a network with/without attributes
+ attribute: boolean
+ if true, attributes of edges are added to a dictionary of edges,
+ which is a return value
+
+ Returns:
+ w: a spatial weights instance
+ id2link: an associative dictionary that connects a sequential id to a unique
+ edge on the network
+ if attribute is true, each item in the dictionary includes the attributes
+
+ """
+ link2id, id2link = {}, {}
+ counter = 0
+ neighbors, weights = {},{}
+ for n1 in network:
+ for n2 in network[n1]:
+ if (n1,n2) not in link2id or link2id[(n1,n2)] not in neighbors:
+ if (n1,n2) not in link2id:
+ link2id[(n1,n2)] = counter
+ link2id[(n2,n1)] = counter
+ if not attribute:
+ id2link[counter] = (n1, n2)
+ else:
+ id2link[counter] = tuple([(n1,n2)] + list(network[n1][n2][1:]))
+ counter += 1
+ neighbors_from_n1 = [(n1, n) for n in network[n1] if n != n2]
+ neighbors_from_n2 = [(n2, n) for n in network[n2] if n != n1]
+ neighbors_all = neighbors_from_n1 + neighbors_from_n2
+ neighbor_ids = []
+ for edge in neighbors_all:
+ if edge not in link2id:
+ link2id[edge] = counter
+ link2id[(edge[-1], edge[0])] = counter
+ if not attribute:
+ id2link[counter] = edge
+ else:
+ id2link[counter] = tuple([edge] + list(network[edge[0]][edge[1]][1:]))
+ neighbor_ids.append(counter)
+ counter += 1
+ else:
+ neighbor_ids.append(link2id[edge])
+ neighbors[link2id[(n1,n2)]] = neighbor_ids
+ weights[link2id[(n1,n2)]] = [1.0]*(len(neighbors_from_n1) + len(neighbors_from_n2))
+ return pysal.weights.W(neighbors, weights), id2link
+
+def edgepoints_from_network(network, attribute=False):
+ """
+ Obtains a list of projected points which are midpoints of edges
+
+ Parameters:
+ network: a network with/without attributes
+ attribute: boolean
+ if true, one of return values includes attributes for each edge
+
+ Returns:
+ id2linkpoints: a dictionary that associates a sequential id to a projected, midpoint of each edge
+ id2attr: a dictionary that associates a sequential id to the attributes of each edge
+ link2id: a dictionary that associates each edge to its id
+ """
+ link2id, id2linkpoints, id2attr = {}, {}, {}
+ counter = 0
+ for n1 in network:
+ for n2 in network[n1]:
+ if (n1,n2) not in link2id or (n2,n1) not in link2id:
+ link2id[(n1,n2)] = counter
+ link2id[(n2,n1)] = counter
+ if type(network[n1][n2]) != list:
+ half_dist = network[n1][n2]/2
+ else:
+ half_dist = network[n1][n2][0]/2
+ if n1[0] < n2[0] or (n1[0] == n2[0] and n1[1] < n2[1]):
+ id2linkpoints[counter] = (n1,n2,half_dist,half_dist)
+ else:
+ id2linkpoints[counter] = (n2,n1,half_dist,half_dist)
+ if attribute:
+ id2attr[counter] = network[n1][n2][1:]
+ counter += 1
+ return id2linkpoints, id2attr, link2id
+
+def dist_weights(network, id2linkpoints, link2id, bandwidth):
+ """
+ Obtains a distance-based spatial weights matrix using network distance
+
+ Parameters:
+ network: an undirected network without additional attributes
+ id2linkpoints: a dictionary that includes a list of network-projected, midpoints of edges in the network
+ link2id: a dictionary that associates each edge to a unique id
+ bandwidth: a threshold distance for creating a spatial weights matrix
+
+ Returns:
+ w : a distance-based, binary spatial weights matrix
+ id2link: a dictionary that associates a unique id to each edge of the network
+ """
+ linkpoints = id2linkpoints.values()
+ neighbors, id2link = {}, {}
+ net_distances = {}
+ for linkpoint in id2linkpoints:
+ if linkpoints[linkpoint] not in net_distances:
+ net_distances[linkpoints[linkpoint][0]] = pynet.dijkstras(network, linkpoints[linkpoint][0], r=bandwidth)
+ net_distances[linkpoints[linkpoint][1]] = pynet.dijkstras(network, linkpoints[linkpoint][1], r=bandwidth)
+ ngh = pynet.proj_distances_undirected(network, linkpoints[linkpoint], linkpoints, r=bandwidth, cache=net_distances)
+ #ngh = pynet.proj_distances_undirected(network, linkpoints[linkpoint], linkpoints, r=bandwidth)
+ if linkpoints[linkpoint] in ngh:
+ del ngh[linkpoints[linkpoint]]
+ if linkpoint not in neighbors:
+ neighbors[linkpoint] = []
+ for k in ngh.keys():
+ neighbor = link2id[k[:2]]
+ if neighbor not in neighbors[linkpoint]:
+ neighbors[linkpoint].append(neighbor)
+ if neighbor not in neighbors:
+ neighbors[neighbor] = []
+ if linkpoint not in neighbors[neighbor]:
+ neighbors[neighbor].append(linkpoint)
+ id2link[linkpoint] = id2linkpoints[linkpoint][:2]
+ weights = copy.copy(neighbors)
+ for ngh in weights:
+ weights[ngh] = [1.0]*len(weights[ngh])
+ return pysal.weights.W(neighbors, weights), id2link
+
+
+def lincs(network, event, base, weight, dist=None, lisa_func='moran', sim_method="permutations", sim_num=99):
+ """
+ Compute local Moran's I for edges in the network
+
+ Parameters:
+ network: a clean network where each edge has up to three attributes:
+ Its length, an event variable, and a base variable
+ event: integer
+ an index for the event variable
+ base: integer
+ an index for the base variable
+ weight: string
+ type of binary spatial weights
+ two options are allowed: Node-based, Distance-based
+ dist: float
+ threshold distance value for the distance-based weight
+ lisa_func: string
+ type of LISA functions
+ three options allowed: moran, g, and g_star
+ sim_method: string
+ type of simulation methods
+ four options allowed: permutations, binomial (unconditional),
+ poisson (unconditional), multinomial (conditional)
+ sim_num: integer
+ the number of simulations
+
+ Returns:
+ : a dictionary of edges
+ an edge and its moran's I are the key and item
+ : a Weights object
+ PySAL spatial weights object
+
+ """
+ if lisa_func in ['g', 'g_star'] and weight == 'Node-based':
+ print 'Local G statistics can work only with distance-based weights matrix'
+ raise
+
+ if lisa_func == 'moran':
+ lisa_func = pysal.esda.moran.Moran_Local
+ else:
+ lisa_func = pysal.esda.getisord.G_Local
+
+ star = False
+ if lisa_func == 'g_star':
+ star = True
+
+ if base:
+ def getBase(edges, edge, base):
+ return edges[edge][base]
+ else:
+ def getBase(edges, edge, base):
+ return 1.0
+ w, edges, e, b, edges_geom = None, None, None, None, []
+ if weight == 'Node-based':
+ w, edges = node_weights(network, attribute=True)
+ n = len(edges)
+ e, b = np.zeros(n), np.zeros(n)
+ for edge in edges:
+ edges_geom.append(edges[edge][0])
+ e[edge] = edges[edge][event]
+ b[edge] = getBase(edges, edge, base)
+ w.id_order = edges.keys()
+ elif dist is not None:
+ id2edgepoints, id2attr, edge2id = edgepoints_from_network(network, attribute=True)
+ for n1 in network:
+ for n2 in network[n1]:
+ network[n1][n2] = network[n1][n2][0]
+ w, edges = dist_weights(network, id2edgepoints, edge2id, dist)
+ n = len(id2attr)
+ e, b = np.zeros(n), np.zeros(n)
+ if base:
+ base -= 1
+ for edge in id2attr:
+ edges_geom.append(edges[edge])
+ e[edge] = id2attr[edge][event - 1]
+ b[edge] = getBase(id2attr, edge, base)
+ w.id_order = id2attr.keys()
+
+ Is, p_sim, Zs = None,None, None
+ if sim_method == 'permutation':
+ if lisa_func == pysal.esda.moran.Moran_Local:
+ lisa_i = lisa_func(e*1.0/b,w,transformation="r",permutations=sim_num)
+ Is = lisa_i.Is
+ Zs = lisa_i.q
+ else:
+ lisa_i = lisa_func(e*1.0/b,w,transform="R",permutations=sim_num,star=star)
+ Is = lisa_i.Gs
+ Zs = lisa_i.Zs
+ p_sim = lisa_i.p_sim
+ else:
+ sims = None
+ if lisa_func == pysal.esda.moran.Moran_Local:
+ lisa_i = lisa_func(e*1.0/b,w,transformation="r",permutations=0)
+ Is = lisa_i.Is
+ Zs = lisa_i.q
+ else:
+ lisa_i = lisa_func(e*1.0/b,w,transform="R",permutations=0,star=star)
+ Is = lisa_i.Gs
+ Zs = lisa_i.Zs
+ if sim_method == 'binomial':
+ sims = unconditional_sim(e, b, sim_num)
+ elif sim_method == 'poisson':
+ sims = unconditional_sim_poisson(e, b, sim_num)
+ else:
+ sims = conditional_multinomial(e, b, sim_num)
+ if lisa_func == pysal.esda.moran.Moran_Local:
+ for i in range(sim_num):
+ sims[:,i] = lisa_func(sims[:,i]*1.0/b,w,transformation="r",permutations=0).Is
+ else:
+ for i in range(sim_num):
+ sims[:,i] = lisa_func(sims[:,i]*1.0/b,w,permutations=0,star=star).Gs
+ sim_res = pseudo_pvalues(Is, sims)
+ p_sim = sim_res[0]
+
+ w.transform = 'O'
+ return zip(edges_geom, e, b, Is, Zs, p_sim), w
+
+
diff --git a/pysal/contrib/network/network.py b/pysal/contrib/network/network.py
new file mode 100644
index 0000000..5e0e8b2
--- /dev/null
+++ b/pysal/contrib/network/network.py
@@ -0,0 +1,599 @@
+import math
+import pysal
+from pysal.cg.shapes import Point, Chain, LineSegment, Rectangle
+from pysal.cg.locators import Grid
+import random, copy
+from heapq import heappush, heappop
+import time
+
+def no_nodes(G):
+ """
+ returns the number of nodes in a undirected network
+ """
+ return len(G)
+
+def no_edges(G):
+ """
+ returns the number of edges in a undirected network
+ """
+ e = 0.0
+ for n in G:
+ e += len(G[n])
+ return e/2.0
+
+def tot_net_length(G):
+ """
+ returns the total length of a undirected network
+ """
+ l = 0.0
+ done = set()
+ for n in G:
+ for m in G[n]:
+ if m in done: continue
+ l += G[n][m]
+ done.add(n)
+ return l/2.0
+
+def walk(G, s, S=set()):
+ """
+ Returns a traversal path from s on G
+ source: Python Algorithms Mastering Basic Algorithms in the Python Language, 2010, p.104
+ """
+ P, Q, SG = dict(), set(), dict()
+ P[s] = None
+ SG[s] = G[s]
+ Q.add(s)
+ while Q:
+ u = Q.pop()
+ for v in set(G[u].keys()).difference(P, S):
+ Q.add(v)
+ P[v] = u
+ SG[v] = G[v]
+ return SG
+
+def components(G):
+ """
+ Returns connected components of G
+ source: Python Algorithms Mastering Basic Algorithms in the Python Language, 2010, p.105
+ Complexity: O(E+V) where E is the number of edges and V is the number of nodes in a graph
+ """
+ comp, seen = [], set()
+ for u in G:
+ if u in seen: continue
+ C = walk(G, u)
+ seen.update(set(C.keys()))
+ comp.append(C)
+ return comp
+
+def no_components(G):
+ return len(components(G))
+
+def net_global_stats(G, boundary=None, detour=True):
+ v = no_nodes(G)
+ e = no_edges(G)
+ L = tot_net_length(G)
+ p = no_components(G)
+ u = e - v + p # cyclomatic number
+ alpha = u*1.0/(2*v - 5)
+ beta = e*1.0/v
+ emax = 3*(v-2)
+ gamma = e*1.0/emax
+ eta = L*1.0/e
+ net_den = None
+ if boundary:
+ s = pysal.open(boundary)
+ if s.type != pysal.cg.shapes.Polygon:
+ raise ValueError, 'File is not of type POLYGON'
+ net_den = s.next().area
+ net_dist, eucl_dist = 0.0, 0.0
+ det = None
+ if detour:
+ nodes = G.keys()
+ for n in nodes:
+ net_D = dijkstras(G, n)
+ net_dist += sum(net_D.values())
+ eucl_D = [ math.sqrt((n[0] - m[0])**2 + (n[1] - m[1])**2) for m in nodes]
+ eucl_dist += sum(eucl_D)
+ net_dist /= 2.0
+ eucl_dist /= 2.0
+ if net_dist > 0.0:
+ det = eucl_dist*1.0/net_dist
+ return v, e, L, p, u, alpha, beta, emax, gamma, eta, net_den, det
+
+
+def random_projs(G, n):
+ """
+ Returns a list of random locations on the network as projections
+ with the form (src, dest, dist_from, dist_from_src, dist_from_dest)
+ """
+
+ def binary_search(list, q):
+ l = 0
+ r = len(list)
+ while l < r:
+ m = (l + r)/2
+ if list[m][0] > q:
+ r = m
+ else:
+ l = m + 1
+ return list[l][1]
+
+ total_net_len = 0
+ for src in G:
+ for dest in G[src]:
+ total_net_len += G[src][dest]
+
+ lengthogram = [(0, (None, None))]
+ for src in G:
+ for dest in G[src]:
+ lengthogram.append((lengthogram[-1][0] + G[src][dest], (src, dest)))
+
+ projs = []
+ for i in xrange(n):
+ e = binary_search(lengthogram, random.random() * total_net_len)
+ wgt = G[e[0]][e[1]]
+ along = wgt * random.random()
+ # (src, dest, dist_from_src, dist_from_dest)
+ projs.append((e[0], e[1], along, wgt - along))
+
+ return projs
+
+def proj_distances_undirected(G, src, dests, r=1e600, cache=None):
+ if cache and src[0] in cache:
+ SND = cache[src[0]]
+ else:
+ SND = dijkstras(G, src[0], r) # Distance from edge start node to other nodes
+ if cache and src[1] in cache:
+ DND = cache[src[1]]
+ else:
+ DND = dijkstras(G, src[1], r) # Distance from edge end node to other nodes
+ D = {}
+ for d in dests:
+ # If the dest lies on the same edge as the src (or its inverse)
+ if (d[0] == src[0] and d[1] == src[1]) or (d[0] == src[1] and d[1] == src[0]):
+ dist = abs(src[2] - d[2])
+ else:
+ # get the four path distances
+ # src edge start to dest edge start
+ src2src, src2dest, dest2src, dest2dest = 1e600, 1e600, 1e600, 1e600
+ if d[0] in SND:
+ src2src = src[2] + SND[d[0]] + d[2]
+ # src edge start to dest edge end
+ if d[1] in SND:
+ src2dest = src[2] + SND[d[1]] + d[3]
+ # src edge end to dest edge start
+ if d[0] in DND:
+ dest2src = src[3] + DND[d[0]] + d[2]
+ # src edge end to dest edge end
+ if d[1] in DND:
+ dest2dest = src[3] + DND[d[1]] + d[3]
+ dist = min(src2src, src2dest, dest2src, dest2dest)
+
+ if dist <= r:
+ D[d] = dist
+
+ return D
+
+def proj_distances_directed(G, src, dests, r=1e600):
+ ND = dijkstras(G, src[1], r) # Distance from edge destination node to other nodes
+ D = {}
+ for d in dests:
+ if d[0] in ND:
+ if d[0] == src[0] and d[1] == src[1]: # Same edge and dest further along
+ dist = abs(src[2] - d[2])
+ if dist <= r:
+ D[d] = dist
+ else:
+ # dist from edge proj to end of src edge +
+ # dist from src edge dest node to dest edge src node +
+ # dist from start of dest edge to edge proj
+ dist = src[3] + ND[d[0]] + d[2]
+ #print dist
+ if dist <= r:
+ D[d] = dist
+ return D
+
+def relax(G, u, v, D, P, r=1e600):
+ """
+ Update the distance to v
+ if the route to v through u is shorter than the existing route to v
+ Code from Hetland 2010
+ Python Algorithms Mastering Basic Algorithms in the Python Language, p.200
+ """
+ d = D.get(u, r) + G[u][v]
+ if d <= D.get(v, r):
+ D[v], P[v] = d, u
+ return True
+
+def dijkstras(G, start, r=1e600, p=False):
+ """
+ Find a shortest path from s to all nodes in the network G
+ Code from Hetland 2010
+ Python Algorithms Mastering Basic Algorithms in the Python Language, p.205
+ Complexity: O(M*lgN) where M is the number of edges and N is the number of nodes
+ """
+ D, P, Q, S = {start:0}, {}, [(0,start)], set() # Distance estimates, tree (path), queue, visited
+ while Q:
+ _, u = heappop(Q)
+ if u in S:
+ continue
+ S.add(u)
+ for v in G[u]:
+ relaxed = relax(G, u, v, D, P, r=r)
+ if relaxed:
+ heappush(Q, (D[v], v))
+ if p:
+ return D, P
+ return D
+
+class Snapper:
+ """
+ Snaps points to their nearest location on the network.
+
+ Uses a novel algorithm which relies on two properties of the input network:
+ 1. Most of the edges are very short relative to the total area
+ encompassed by the network.
+ 2. The edges have a relatively constant density throughout this area.
+
+ The algorithm works by creating a binning of the midpoints of all the edges.
+ When a query point is given, all the edges in a region around the query
+ point (located by their binned midpoints) are compared to the query point.
+ If none are found, the neighborhood is enlarged. When a closest edge is found,
+ the neighborhood is enlarged slightly and checked again. The enlargement is
+ such that if the closest edge found remains the closest edge, then it will
+ always be the closest edge.
+ """
+
+ def __init__(self, network):
+ """
+ Test tag <tc>#is#Snapper.__init__</tc>
+ """
+
+ """
+ Generate a list of the edge lengths and pick a maximum length
+ allowed based on the median length. This maximum length will be
+ used to use multiple midpoints to represent edges which are
+ exceptionally long, lest they ruin the efficiency of the algorithm.
+ """
+
+ # Generate list of lengths
+ self.network = network
+ edge_lens = []
+ for n in network:
+ for m in network[n]:
+ if n != m:
+ edge_lens.append(pysal.cg.get_points_dist(Point(n), Point(m))) # it can be optional
+ if edge_lens == []:
+ raise ValueError, 'Network has no positive-length edges'
+ edge_lens.sort()
+ max_allowed_edge_len = 5 * edge_lens[len(edge_lens)/2]
+
+ """
+ Create a bin structures with proper range to hold all of the edges.
+ The size of the bin is on the order of the length of the longest edge (and
+ of the neighborhoods searched around each query point.
+ """
+ endpoints = network.keys()
+ endpoints_start, endpoints_end = [ep[0] for ep in endpoints], [ep[1] for ep in endpoints]
+ bounds = Rectangle(min(endpoints_start),min(endpoints_end),max(endpoints_start),max(endpoints_end))
+ self.grid = Grid(bounds, max_allowed_edge_len)
+
+ """
+ Insert the midpoint of each edge into the grid. If an edge is too long,
+ break it into edges of length less than the maximum allowed length and
+ add the midpoint of each.
+ """
+ self.search_rad = max_allowed_edge_len*0.55
+ for n in network:
+ for m in network[n]:
+ edge_len = pysal.cg.get_points_dist(Point(n), Point(m)) # it can be a direct extraction
+ if edge_len > max_allowed_edge_len:
+ mid_edge = []
+ num_parts = int(math.ceil(edge_len/max_allowed_edge_len))
+ part_step = 1.0/num_parts
+ dx = m[0] - n[0]
+ dy = m[1] - n[1]
+ midpoint = (n[0] + dx*part_step/2, n[1] + dy*part_step/2)
+ for r in [part_step*t for t in xrange(num_parts)]:
+ mid_edge.append(((n, m), midpoint))
+ midpoint = (midpoint[0] + dx*part_step, midpoint[1] + dy*part_step)
+ for me in mid_edge:
+ self.grid.add(me[0], Point(me[1]))
+ else:
+ self.grid.add((n, m), Point(((n[0] + m[0])/2, (n[1] + m[1])/2)))
+
+ """
+ During the snapping of a query point we will initialize the closest point on the network
+ to be a dummy location known to be invalid. This must be done in case the neighborhood
+ search does not find any edge midpoints and it must be grown repeatedly. In this case
+ we want to make sure we don't give up having not found a valid closest edge.
+ """
+ self.dummy_proj = (None, None, 0, 0) # Src, dest, dist_from_src, dist_from_dest)
+
+ def snap(self, p):
+ """
+ Test tag <tc>#is#Snapper.snap</tc>
+ """
+
+ """
+ Initialize the closest location found so far to be infinitely far away.
+ Then begin with a neighborhood on the order of the maximum edge allowed and
+ repeatedly growing it. When a closest edge is found, grow once more and check again.
+ """
+
+ cur_s_rad = self.search_rad
+ found_something = False
+ # Whle neighborhood is empty, enlarge and check again
+ while not found_something:
+ if self.grid.proximity(Point(p), cur_s_rad) != []:
+ found_something = True
+ cur_s_rad *= 2
+ # Expand to include any edges whose endpoints might lie just outside
+ # the search radius
+ cur_s_rad += self.search_rad
+ # Now find closest in this neighborhood
+ best_seg_dist = 1e600
+ for e in self.grid.proximity(Point(p), cur_s_rad):
+ seg = LineSegment(Point(e[0]), Point(e[1]))
+ p2seg = pysal.cg.get_segment_point_dist(seg, Point(p))
+ dist = p2seg[0]
+ if p2seg[0] < best_seg_dist:
+ # (src, dest, dist_from_src, dist_from_dest)
+ best_proj = (e[0], e[1], dist*p2seg[1], dist*(1-p2seg[1]))
+ best_seg_dist = p2seg[0]
+ return best_proj
+
+def network_from_endnodes(s, d, wgt, undirected=True):
+ G = {}
+ for g, r in zip(s,d):
+ start = g.vertices[0]
+ end = g.vertices[-1]
+ G.setdefault(start, {})
+ G.setdefault(end, {})
+ r_w = wgt(g,r)
+ G[start][end] = r_w
+ if undirected:
+ G[end][start] = r_w
+ s.close()
+ d.close()
+ return G
+
+def network_from_allvertices(s, d):
+ G = {}
+ for g, r in zip(s, d):
+ vertices = g.vertices
+ for i, vertex in enumerate(vertices[:-1]):
+ n1, n2 = vertex, vertices[i+1]
+ dist = pysal.cg.get_points_dist(Point(n1), Point(n2))
+ G.setdefault(n1, {})
+ G.setdefault(n2, {})
+ G[n1][n2] = dist
+ G[n2][n1] = dist
+ s.close()
+ d.close()
+ return G
+
+def read_hierarchical_network(s, d):
+ G, Gj, G_to_Gj = {}, {}, {}
+ for g, r in zip(s, d):
+ vertices = g.vertices
+ Gj.setdefault(vertices[0], {})
+ Gj.setdefault(vertices[-1], {})
+ d_total = 0.0
+ for i, vertex in enumerate(vertices[:-1]):
+ n1, n2 = vertex, vertices[i+1]
+ dist = pysal.cg.get_points_dist(Point(n1), Point(n2))
+ G.setdefault(n1, {})
+ G.setdefault(n2, {})
+ G[n1][n2] = dist
+ G[n2][n1] = dist
+ G_to_Gj[(n1,n2)] = [(vertices[0], vertices[-1]), d_total] # info for the opposite direction
+ d_total += dist
+ Gj[vertices[0]][vertices[-1]] = d_total
+ Gj[vertices[-1]][vertices[0]] = d_total
+ s.close()
+ d.close()
+ return G, Gj, G_to_Gj
+
+def read_network(filename, wgt_field=None, undirected=True, endnodes=False, hierarchical=False, attrs=None):
+ s = pysal.open(filename)
+ dbf = pysal.open(filename[:-3] + 'dbf')
+ if s.type != pysal.cg.shapes.Chain:
+ raise ValueError, 'File is not of type ARC'
+ if not endnodes and not undirected:
+ raise ValueError, 'Network using all vertices should be undirected'
+ if hierarchical and not (undirected and not endnodes):
+ raise ValueError, 'Hierarchial network should be undirected and use all vertices'
+ if endnodes:
+ if wgt_field and attrs == None:
+ w = dbf.header.index(wgt_field)
+ def wgt(g, r):
+ return r[w]
+ elif wgt_field and attrs:
+ attrs = [wgt_field] + attrs
+ w_indices = [dbf.header.index(field) for field in attrs]
+ def wgt(g, r):
+ return [r[w] for w in w_indices]
+ elif wgt_field is None and attrs:
+ w_indices = [dbf.header.index(field) for field in attrs]
+ def wgt(g, r):
+ d = pysal.cg.get_points_dist(Point(g.vertices[0]), Point(g.vertices[-1]))
+ return [d] + [r[w] for w in w_indices]
+ else:
+ def wgt(g, r):
+ return pysal.cg.get_points_dist(Point(g.vertices[0]), Point(g.vertices[-1]))
+ return network_from_endnodes(s, dbf, wgt, undirected)
+ if not endnodes and not hierarchical:
+ return network_from_allvertices(s, dbf)
+ if hierarchical:
+ return read_hierarchial_netowrk(s, dbf)
+
+def proj_pnt_coor(proj_pnt):
+ n1, n2 = proj_pnt[0], proj_pnt[1]
+ dist_n12 = pysal.cg.get_points_dist(Point(n1), Point(n2))
+ len_ratio = proj_pnt[2]*1.0/dist_n12
+ xrange, yrange = n2[0] - n1[0], n2[1] - n1[1]
+ x = n1[0] + xrange*len_ratio
+ y = n1[1] + yrange*len_ratio
+ return (x,y)
+
+def inject_points(network, proj_pnts):
+
+ pnts_by_seg = {}
+ proj_pnt_coors = []
+ for pnt in proj_pnts:
+ target_edge = None
+ if (pnt[0], pnt[1]) not in pnts_by_seg and (pnt[1], pnt[0]) not in pnts_by_seg:
+ target_edge = (pnt[0], pnt[1])
+ pnts_by_seg[target_edge] = set()
+ elif (pnt[0], pnt[1]) in pnts_by_seg:
+ target_edge = (pnt[0], pnt[1])
+ elif (pnt[1], pnt[0]) in pnts_by_seg:
+ target_edge = (pnt[1], pnt[0])
+ coor = proj_pnt_coor(pnt)
+ pnts_by_seg[target_edge].add(coor)
+ proj_pnt_coors.append(coor)
+
+ new_network = copy.deepcopy(network)
+
+ for seg in pnts_by_seg:
+ proj_nodes = set(list(pnts_by_seg[seg]) + [seg[0], seg[1]])
+ proj_nodes = list(proj_nodes)
+ if seg[0][0] == seg[1][0]:
+ proj_nodes.sort(key=lambda coords: coords[1])
+ else:
+ proj_nodes.sort()
+ proj_nodes_len = len(proj_nodes)
+ prev_seg_d, next_seg_d = 0.0, 0.0
+ for i in range(proj_nodes_len - 1):
+ start, end = proj_nodes[i], proj_nodes[i+1]
+ if start not in new_network:
+ new_network[start] = {}
+ if end not in new_network:
+ new_network[end] = {}
+ d = pysal.cg.get_points_dist(Point(start), Point(end))
+ new_network[start][end] = d
+ new_network[end][start] = d
+ if new_network.has_key(seg[0]) and new_network[seg[0]].has_key(seg[1]):
+ del new_network[seg[0]][seg[1]]
+ del new_network[seg[1]][seg[0]]
+ else:
+ print seg, network.has_key(seg[0]), network[seg[0]], network.has_key(seg[1]), network[seg[1]]
+
+ return new_network, proj_pnt_coors
+
+def mesh_network(network, cellwidth, at_center=False):
+ mesh_net = {}
+ done = {}
+ #done = set()
+ for n1 in network:
+ for n2 in network[n1]:
+ #if n2 in done: continue
+ if (n1,n2) in done or (n2,n1) in done:
+ continue
+ len_ratio = cellwidth*1.0/network[n1][n2]
+ start, end = n1, n2
+ # The order for reading a network edge is slightly different from SANET.
+ # SANET does not seem to have a set of consistent rules.
+ if n1[0] < n2[0] or (n1[0] == n2[0] and n1[1] < n2[1]):
+ start, end = n2, n1
+ xrange, yrange = end[0] - start[0], end[1] - start[1]
+ dx, dy = xrange*len_ratio, yrange*len_ratio
+ no_segments = int(math.floor(1.0/len_ratio))
+ if at_center:
+ xs = [start[0], start[0] + dx/2.0]
+ ys = [start[1], start[1] + dy/2.0]
+ xs = xs + [xs[-1] + i*dx for i in range(1, no_segments + 1)]
+ ys = ys + [ys[-1] + i*dy for i in range(1, no_segments + 1)]
+ else:
+ xs = [start[0] + i*dx for i in range(no_segments + 1)]
+ ys = [start[1] + i*dy for i in range(no_segments + 1)]
+ if xs[-1] != end[0] or ys[-1] != end[1]:
+ xs.append(end[0])
+ ys.append(end[1])
+ new_nodes = zip(xs, ys)
+ for i in range(len(new_nodes) - 1):
+ n, m = new_nodes[i], new_nodes[i+1]
+ d = pysal.cg.get_points_dist(Point(n), Point(m))
+ if n not in mesh_net: mesh_net[n] = {}
+ if m not in mesh_net: mesh_net[m] = {}
+ mesh_net[n][m] = d
+ mesh_net[m][n] = d
+ done[(n1,n2)] = True
+ #done.add(n1)
+ return mesh_net
+
+def write_network_to_pysalshp(network, filename, header=None, field_spec=None):
+
+ if not filename.endswith('shp') and not filename.endswith('SHP'):
+ print 'filename would end with shp or SHP'
+ return
+
+ shp = pysal.open(filename, 'w')
+ dbf = pysal.open(filename[:-3] + 'dbf', 'w')
+ if not header:
+ dbf.header = ['ID', 'VALUE']
+ else:
+ dbf.header = ['ID'] + header
+ if not field_spec:
+ dbf.field_spec = [('N', 9, 0), ('N', 15, 8)]
+ def getValue(G, n, m):
+ return [G[n][m]]
+ else:
+ dbf.field_spec = [('N', 9, 0)] + field_spec
+ v = network[network.keys()[0]]
+ if type(v) == dict:
+ v = v.values()[0]
+ if type(v) == list:
+ wrap_func = list
+ else:
+ def wrap_func(value):
+ return [value]
+ def getValue(G, n, m):
+ return wrap_func(G[n][m])
+
+ used, counter = set(), 0
+ for n1 in network:
+ for n2 in network[n1]:
+ if n2 in used: continue
+ shp.write(Chain([Point(n1), Point(n2)]))
+ dbf.write([counter] + getValue(network,n1,n2))
+ counter += 1
+ used.add(n1)
+
+ shp.close()
+ dbf.close()
+
+def write_valued_network_to_shp(filename, fields, types, net, values, valFunc):
+ oShp = pysal.open(filename, 'w')
+ oDbf = pysal.open(filename[:-3] + 'dbf', 'w')
+ oDbf.header = fields
+ oDbf.field_spec = types
+ #for n in net:
+ # for m in net:
+ # oShp.write(Chain([Point(n), Point(m)]))
+ # oDbf.write([valFunc(values, n), valFunc(values, m)])
+ used, counter = set(), 0
+ for n in net:
+ for m in net[n]:
+ if m in used: continue
+ oShp.write(Chain([Point(n), Point(m)]))
+ oDbf.write([valFunc(values, n), valFunc(values, m)])
+ counter += 1
+ used.add(n)
+ oShp.close()
+ oDbf.close()
+
+def write_list_network_to_shp(filename, fields, types, net):
+ oShp = pysal.open(filename, 'w')
+ oDbf = pysal.open(filename[:-3] + 'dbf', 'w')
+ oDbf.header = ['ID'] + fields
+ oDbf.field_spec = [('N',9,0)] + types
+ for i, rec in enumerate(net):
+ geom = rec[0]
+ table_data = list(rec[1:])
+ oShp.write(Chain([Point(geom[0]), Point(geom[1])]))
+ oDbf.write([i] + table_data)
+ oShp.close()
+ oDbf.close()
+
diff --git a/pysal/contrib/network/priordict.py b/pysal/contrib/network/priordict.py
new file mode 100644
index 0000000..744d945
--- /dev/null
+++ b/pysal/contrib/network/priordict.py
@@ -0,0 +1,80 @@
+"""
+Code adapted from source from:
+David Eppstein, UC Irvine, 8 Mar 2002
+"""
+
+from __future__ import generators
+
+class PriorityDictionary(dict):
+
+ def __init__(self):
+ """
+ Initialize PriorityDictionary by creating binary heap
+ of pairs (value,key). Note that changing or removing a dict entry will
+ not remove the old pair from the heap until it is found by smallest() or
+ until the heap is rebuilt.
+ """
+ self.__heap = []
+ dict.__init__(self)
+
+ def smallest(self):
+ """
+ Find smallest item after removing deleted items from heap.
+ """
+ if len(self) == 0:
+ raise IndexError, "smallest of empty PriorityDictionary"
+ heap = self.__heap
+ while heap[0][1] not in self or self[heap[0][1]] != heap[0][0]:
+ lastItem = heap.pop()
+ insertionPoint = 0
+ while 1:
+ smallChild = 2*insertionPoint+1
+ if smallChild+1 < len(heap) and \
+ heap[smallChild] > heap[smallChild+1]:
+ smallChild += 1
+ if smallChild >= len(heap) or lastItem <= heap[smallChild]:
+ heap[insertionPoint] = lastItem
+ break
+ heap[insertionPoint] = heap[smallChild]
+ insertionPoint = smallChild
+ return heap[0][1]
+
+ def __iter__(self):
+ """
+ Create destructive sorted iterator of PriorityDictionary.
+ """
+ def iterfn():
+ while len(self) > 0:
+ x = self.smallest()
+ yield x
+ del self[x]
+ return iterfn()
+
+ def __setitem__(self,key,val):
+ """
+ Change value stored in dictionary and add corresponding
+ pair to heap. Rebuilds the heap if the number of deleted items grows
+ too large, to avoid memory leakage.
+ """
+ dict.__setitem__(self,key,val)
+ heap = self.__heap
+ if len(heap) > 2 * len(self):
+ self.__heap = [(v,k) for k,v in self.iteritems()]
+ self.__heap.sort() # builtin sort likely faster than O(n) heapify
+ else:
+ newPair = (val,key)
+ insertionPoint = len(heap)
+ heap.append(None)
+ while insertionPoint > 0 and \
+ newPair < heap[(insertionPoint-1)//2]:
+ heap[insertionPoint] = heap[(insertionPoint-1)//2]
+ insertionPoint = (insertionPoint-1)//2
+ heap[insertionPoint] = newPair
+
+ def setdefault(self,key,val):
+ """
+ Reimplement setdefault to call our customized __setitem__.
+ """
+ if key not in self:
+ self[key] = val
+ return self[key]
diff --git a/pysal/contrib/network/simulator.py b/pysal/contrib/network/simulator.py
new file mode 100644
index 0000000..d9cfcd4
--- /dev/null
+++ b/pysal/contrib/network/simulator.py
@@ -0,0 +1,235 @@
+#!/usr/bin/env python
+
+"""
+Author: Ran Wei, Myunghwa Hwang
+"""
+
+import pysal
+import numpy as np
+
+class Simulation(object):
+
+ def __init__(self, src_filename):
+ "create a bidirectional network with its total length and total number of links"
+ self.nw = pysal.open(src_filename, 'r')
+ self.G = {} # {edge_index:(d12,(n1,n2))}
+ self.GNet = {} # {n1:{n2:d12}}
+ self.total_length = 0.0
+ self.nwNum = 0
+ for line in self.nw:
+ vertices = line.vertices
+ for i, vertex in enumerate(vertices[:-1]):
+ n1, n2 = vertex, vertices[i+1]
+ self.G.setdefault(self.nwNum, ())
+ self.GNet.setdefault(n1, {})
+ self.GNet.setdefault(n2, {})
+ d = pysal.cg.get_points_dist(pysal.cg.Point(n1), pysal.cg.Point(n2))
+ self.G[self.nwNum] = (d, (n1, n2))
+ self.GNet[n1][n2] = self.nwNum
+ self.GNet[n2][n1] = self.nwNum
+ self.total_length += d
+ self.nwNum += 1
+ self.nw.close()
+ self.imaginaryLineGenerated = False
+
+ def generateImaginaryLine(self):
+ '''
+ Create an imaginary line that starts from 0 and ends at 1
+ and mark the locations of end points of each link
+ '''
+ self.nwCumPro = [0.0]
+ for e in self.G.keys():
+ self.nwCumPro.append(self.nwCumPro[-1] + (self.G[e][0]/self.total_length))
+
+ # self.nwCumProDict --> {edge_index:right_side_end_point_of_the_link_on_the_imaginary_line}
+ self.nwCumProDict = dict(zip(self.G.keys(), self.nwCumPro[1:]))
+ self.imaginaryLineGenerated = True
+
+ def getRandomPoints(self, n, projected=False, toShp=False):
+ '''Create a random point pattern data set on the given network'''
+
+ if not self.imaginaryLineGenerated:
+ self.generateImaginaryLine()
+
+ ## generate n unique random numbers between 0 and 1
+ #randSet = set()
+ #while len(randSet) < n:
+ # randSet = set(np.random.random_sample(n))
+ #randSet = np.array(list(randSet))
+
+ # generate n random numbers between 0 and 1
+ randSet = np.random.random_sample(n)
+
+ # Assign the random numbers to the links on the network
+ # Think nwCumPro as bins; get bin numbers for all random numbers
+ randSet_to_bins=np.digitize(randSet,self.nwCumPro)
+ randSet_to_bins=zip(randSet_to_bins,randSet)
+ randSet_to_bins.sort()
+ # Determine geographic coordinates for each random number
+ nwPtDict = {}
+ for bin_id, rand_number in randSet_to_bins:
+ bid = bin_id - 1
+ n1, n2 = self.G[bid][1] # n1 and n2 are geographic (real) coordinates for the end points of a link
+ origin = 0 if bid <= 0 else self.nwCumProDict[bid-1]
+ length = self.nwCumProDict[bid] - origin
+ # get prop to determine the geographic coordinate of a random number on the link (n1, n2)
+ # length is the length of the link (n1, n2) on the imaginary line
+ # (self.nwCumProDict[bin_id] - rand_number) is the distance between a random point and n2
+ # on the imaginary line
+ prop = (self.nwCumProDict[bid] - rand_number)*1.0/length
+ nwPtDict.setdefault(bid, [])
+ if not projected:
+ x = n2[0] - (n2[0] - n1[0])*prop # n2[0]: the geographic coordinate of n2 on the X axis
+ y = n2[1] - (n2[1] - n1[1])*prop # n2[1]: the geographic coordinate of n2 on the Y axis
+ nwPtDict[bid].append((x,y))
+ else:
+ dist = self.G[bid][0]
+ proj_pnt = (n1, n2, dist*(1-prop), dist*prop)
+ if toShp:
+ x = n2[0] - (n2[0] - n1[0])*prop
+ y = n2[1] - (n2[1] - n1[1])*prop
+ proj_pnt = tuple(list(proj_pnt) + [x, y])
+ nwPtDict[bid].append(proj_pnt)
+
+ return nwPtDict
+
+ def countPointsOnNetwork(self, points, defaultBase=True):
+ G = {}
+ for k in self.G:
+ n1, n2 = self.G[k][-1]
+ G.setdefault(n1, {})
+ G.setdefault(n2, {})
+ if n2 not in G[n1]:
+ attr = G[n1].setdefault(n2, [self.G[n1][n2], 0])
+ if n1 not in G[n2]:
+ attr = G[n2].setdefault(n1, [self.G[n2][n1], 0])
+ if k in points:
+ attr[-1] += len(points[k])
+ if defaultBase:
+ attr += [1.0]
+ G[n1][n2] = attr
+ G[n2][n1] = attr
+ return G
+
+ def createProjRandomPointsShp(self, n, out_filename):
+ points = nwPtDict = self.getRandomPoints(n, projected=True, toShp=True)
+ shp = pysal.open(out_filename, 'w')
+ dbf = pysal.open(out_filename[:-3] + 'dbf', 'w')
+ dbf.header = ['ID', 'FROM_P1', 'FROM_P2', 'TO_P1', 'TO_P2', 'D_FROM', 'D_TO']
+ dbf.field_spec = [('N',9,0)] + [('N',18,7)]*6
+ counter = 0
+ for k in points:
+ for p in points[k]:
+ p = list(p)
+ shp.write(pysal.cg.Point(tuple(p[-2:])))
+ dbf.write([counter, p[0][0], p[0][1], p[1][0], p[1][1], p[2], p[3]])
+ counter += 1
+ shp.close()
+ dbf.close()
+
+ def createRandomPointsShp(self, n, out_filename):
+ nwPtDict = self.getRandomPoints(n)
+ self.writePoints(nwPtDict, out_filename)
+
+ def writePoints(self, points, out_filename):
+ shp = pysal.open(out_filename, 'w')
+ dbf = pysal.open(out_filename[:-3] + 'dbf', 'w')
+ dbf.header = ['ID']
+ dbf.field_spec = [('N',9,0)]
+ counter = 0
+ for k in points:
+ for p in points[k]:
+ shp.write(pysal.cg.Point(tuple(p)))
+ dbf.write([counter])
+ counter += 1
+ shp.close()
+ dbf.close()
+
+ def getClusteredPoints(self, centerNum, ptNum, percent, clusterMeta=False):
+
+ # split network into center- and non-center network
+ centerIDs = np.random.randint(0, self.nwNum, centerNum)
+ centers = set(centerIDs)
+ centerG, centerG_length = {}, 0
+ counter, counter2ID = 0, {}
+ for center in centerIDs:
+ centerG[counter] = self.G[center]
+ centerG_length += self.G[center][0]
+ counter2ID[counter] = center
+ counter += 1
+ n1, n2 = self.G[center][1]
+ for neighbor in self.GNet[n1]:
+ if neighbor != n2:
+ nghLink = self.GNet[n1][neighbor]
+ centerG[counter] = self.G[nghLink]
+ centerG_length += self.G[nghLink][0]
+ counter2ID[counter] = nghLink
+ counter += 1
+ centers.add(nghLink)
+ for neighbor in self.GNet[n2]:
+ if neighbor != n1:
+ nghLink = self.GNet[n2][neighbor]
+ centerG[counter] = self.G[nghLink]
+ centerG_length += self.G[nghLink][0]
+ counter2ID[counter] = nghLink
+ counter += 1
+ centers.add(nghLink)
+ nonCenterIDs = set(self.G.keys()).difference(centers)
+ nonCenterG, nonCenterG_length = {}, 0
+ for i, nonCenter in enumerate(nonCenterIDs):
+ nonCenterG[i] = self.G[nonCenter]
+ nonCenterG_length += self.G[nonCenter][0]
+
+ self.oldG, self.old_total_length = self.G, self.total_length
+
+ self.G, self.total_length = centerG, centerG_length
+ n_centerPoints = int(percent*ptNum*1.0)
+ self.imaginaryLineGenerated = False
+ pointsInCenter = self.getRandomPoints(n_centerPoints)
+ meta = {}
+ if clusterMeta:
+ for cluster in pointsInCenter:
+ num_points = len(pointsInCenter[cluster])
+ centerLink = self.oldG[cluster][1]
+ centerID = counter2ID[cluster]
+ centerLink = self.oldG[centerID][1]
+ meta[centerLink] = [self.oldG[centerID][0], num_points, centerID in centerIDs]
+ #meta[centerID] = [num_points] + list(self.oldG[centerID])
+
+ self.G, self.total_length = nonCenterG, nonCenterG_length
+ self.imaginaryLineGenerated = False
+ pointsInNonCenter = self.getRandomPoints(ptNum - n_centerPoints)
+ centers_no = len(centers)
+ for k in pointsInNonCenter:
+ pointsInCenter[k + centers_no] = pointsInNonCenter[k]
+ pointsInCenter.update(pointsInNonCenter)
+
+ self.G, self.total_length = self.oldG, self.old_total_length
+
+ self.imaginaryLineGenerated = False
+
+ return pointsInCenter, meta
+
+ def writeMeta(self, metaData, out_file):
+ shp = pysal.open(out_file, 'w')
+ dbf = pysal.open(out_file[:-3] + 'dbf', 'w')
+ dbf.header = ['LENGTH', 'NO_PNTS', 'INITIAL_CENTER']
+ dbf.field_spec = [('N',9,0)]*2 + [('L',1,0)]
+ for link in metaData:
+ vertices = list(link)
+ vertices = [pysal.cg.Point(v) for v in vertices]
+ shp.write(pysal.cg.Chain(vertices))
+ dbf.write(metaData[link])
+ shp.close()
+ dbf.close()
+
+ def createClusteredPointsShp(self, centerNum, n, percent, out_filename, clusterMetaFile=None):
+ nwPtDict, meta = self.getClusteredPoints(centerNum, n, percent, clusterMetaFile!=None)
+ self.writePoints(nwPtDict, out_filename)
+ if clusterMetaFile:
+ self.writeMeta(meta, clusterMetaFile)
+
+if __name__ == '__main__':
+ sim=Simulation("streets.shp")
+ sim.createProjRandomPointsShp(100, "random_100.shp")
+ sim.createClusteredPointsShp(2, 100, 0.1, "clustered_100_10p.shp", "clustered_100_10p_meta.shp")
diff --git a/pysal/contrib/network/streets.dbf b/pysal/contrib/network/streets.dbf
new file mode 100644
index 0000000..ce6d200
Binary files /dev/null and b/pysal/contrib/network/streets.dbf differ
diff --git a/pysal/contrib/network/streets.shp b/pysal/contrib/network/streets.shp
new file mode 100644
index 0000000..b5809a7
Binary files /dev/null and b/pysal/contrib/network/streets.shp differ
diff --git a/pysal/contrib/network/streets.shx b/pysal/contrib/network/streets.shx
new file mode 100644
index 0000000..4733484
Binary files /dev/null and b/pysal/contrib/network/streets.shx differ
diff --git a/pysal/contrib/network/test_access.py b/pysal/contrib/network/test_access.py
new file mode 100644
index 0000000..c20e9d0
--- /dev/null
+++ b/pysal/contrib/network/test_access.py
@@ -0,0 +1,53 @@
+"""access unittest"""
+import unittest
+import access as pyacc
+
+class Access_Tester(unittest.TestCase):
+
+ def setUp(self):
+ self.distances = {1:[1,2,3,4],2:[1,1,2,3],3:[2,1,1,2],
+ 4:[3,2,1,1],5:[4,3,2,1]}
+
+ def test_coverage(self):
+ coverage = []
+ for d in self.distances.values():
+ coverage.append(pyacc.coverage(d, 2.5))
+ self.assertEqual(coverage, [2,3,4,3,2])
+
+ def test_equity(self):
+ equity = []
+ for d in self.distances.values():
+ equity.append(pyacc.equity(d))
+ self.assertEqual(equity, [1,1,1,1,1])
+
+ def test_potential_entropy(self):
+ entropy = []
+ for d in self.distances.values():
+ entropy.append(pyacc.potential_entropy(d))
+ entropy_values = [0.57131743166465321, 0.92088123394736132,
+ 1.0064294488161101, 0.92088123394736132, 0.57131743166465321]
+ self.assertEqual(entropy, entropy_values)
+
+ def test_potential_gravity(self):
+ gravity = []
+ for d in self.distances.values():
+ gravity.append(pyacc.potential_gravity(d))
+ gravity_values = [1.4236111111111112, 2.3611111111111112, 2.5,
+ 2.3611111111111112, 1.4236111111111112]
+ self.assertEqual(gravity, gravity_values)
+
+ def test_travel_cost(self):
+ cost = []
+ for d in self.distances.values():
+ cost.append(pyacc.travel_cost(d))
+ self.assertEqual(cost, [10, 7, 6, 7, 10])
+
+suite = unittest.TestSuite()
+test_classes = [Access_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/contrib/network/test_kernel.py b/pysal/contrib/network/test_kernel.py
new file mode 100644
index 0000000..d9c2b73
--- /dev/null
+++ b/pysal/contrib/network/test_kernel.py
@@ -0,0 +1,44 @@
+"""network unittest"""
+import unittest
+import network as pynet
+import kernel as pykernel
+
+class Kernel_Tester(unittest.TestCase):
+
+ def setUp(self):
+ self.G = {(1,1): {(2,2): 0.125, (3,3): 0.75}, (2,2): {(1,1): 0.125, (4,4): 1.2},
+ (3,3): {(1,1): 0.75, (4,4): 0.375},
+ (4,4): {(2,2): 1.2, (3,3): 0.375, (5,5): 0.5},
+ (5,5): {(4,4): 0.5}, (6,6):{(7,7):1.0}, (7,7):{(6,6):1.0}}
+ self.G_meshed = pynet.mesh_network(self.G, 0.1)
+ self.points = [((3.6666666666666665, 3.6666666666666665), (3.5, 3.5),
+ 1.8011569244041523e-18, 8.0119209423694433e-18),
+ ((4.0, 4.0), (3.8333333333333335, 3.8333333333333335),
+ 6.4354219496947843e-18, 1.3190733783852405e-17),
+ ((6.5999999999999996, 6.5999999999999996), (6.5, 6.5),
+ 8.6525456558003033e-19, 4.0412843678067672e-18)]
+ self.proj_points = []
+ for p in self.points:
+ self.proj_points.append(pynet.proj_pnt_coor(p))
+
+ def test_dijkstras_w_prev(self):
+ distances, previous_nodes = pykernel.dijkstras_w_prev(self.G, (1,1))
+ distances_values = {(5, 5): 1.625, (3, 3): 0.75, (4, 4): 1.125, (1, 1): 0, (2, 2): 0.125}
+ prev_nodes = {(5, 5): (4, 4), (2, 2): (1, 1), (1, 1): None, (4, 4): (3, 3), (3, 3): (1, 1)}
+ self.assertEqual(distances, distances_values)
+ self.assertEqual(previous_nodes, prev_nodes)
+
+ def test_kernel_density(self):
+ density = pykernel.kernel_density(self.G_meshed, self.proj_points, 0.3, self.G.keys())
+ self.assertEqual(density[(4.0, 4.0)], 0.25)
+
+
+suite = unittest.TestSuite()
+test_classes = [Kernel_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/contrib/network/test_kfuncs.py b/pysal/contrib/network/test_kfuncs.py
new file mode 100644
index 0000000..ec37c06
--- /dev/null
+++ b/pysal/contrib/network/test_kfuncs.py
@@ -0,0 +1,40 @@
+"""network unittest"""
+import unittest
+import network as pynet
+import kfuncs
+
+class Kfuncs_Tester(unittest.TestCase):
+
+ def setUp(self):
+ self.distances = {1:[1,2,3,4],2:[1,1,2,3],3:[2,1,1,2],
+ 4:[3,2,1,1],5:[4,3,2,1]}
+
+ def test__fxrange(self):
+ values = kfuncs._fxrange(0.0,1.0,0.2)
+ for v1, v2 in zip(values, [0.0,0.2,0.4,0.6,0.8,1.0]):
+ self.assertAlmostEqual(v1, v2)
+
+ def test__binary_search(self):
+ v = kfuncs._binary_search([0.0,0.2,0.4,0.6,0.8,1.0],0.9)
+ self.assertEqual(v, 5)
+
+ def test_kt_values(self):
+ expected_values = {1: {0.5: 0, 1.5: 10, 2.5: 20},
+ 2: {0.5: 0, 1.5: 20, 2.5: 30},
+ 3: {0.5: 0, 1.5: 20, 2.5: 40},
+ 4: {0.5: 0, 1.5: 20, 2.5: 30},
+ 5: {0.5: 0, 1.5: 10, 2.5: 20}}
+ kfunc_values = {}
+ for k, v in self.distances.items():
+ kfunc_values[k] = kfuncs.kt_values((0.5,3.5,1.0),v,10)
+ self.assertEqual(kfunc_values, expected_values)
+
+suite = unittest.TestSuite()
+test_classes = [Kfuncs_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/contrib/network/test_klincs.py b/pysal/contrib/network/test_klincs.py
new file mode 100644
index 0000000..2204716
--- /dev/null
+++ b/pysal/contrib/network/test_klincs.py
@@ -0,0 +1,136 @@
+"""network unittest"""
+import unittest
+import network as pynet
+import klincs
+import random
+random.seed(10)
+import pysal
+import numpy as np
+
+class KLINCS_Tester(unittest.TestCase):
+
+ def setUp(self):
+ self.population = range(5)
+ self.weights = [0.1, 0.25, 0.1, 0.2, 0.35]
+ np.random.seed(10)
+ self.network_file = 'streets.shp'
+ self.G = pynet.read_network(self.network_file)
+ self.references = [[i, [n]] for i, n in enumerate(self.G.keys())]
+ self.scale_set = (0, 1500, 500)
+ self.network_distances_cache = {}
+ search_radius = self.scale_set[1]
+ for i, node in self.references:
+ n = node[0]
+ self.network_distances_cache[n] = pynet.dijkstras(self.G, n, search_radius)
+ self.snapper = pynet.Snapper(self.G)
+ self.events_file = 'crimes.shp'
+ points = self.get_points_from_shapefile(self.events_file)
+ self.events = []
+ for p in points:
+ self.events.append(self.snapper.snap(p[0]))
+ self.test_node = (724587.78057580709, 877802.4281426128)
+
+ def get_points_from_shapefile(self, src_filename, src_uid=None):
+ src_file = pysal.open(src_filename)
+ dbf = pysal.open(src_filename[:-3] + 'dbf')
+ src_uid_index = dbf.header.index(src_uid) if src_uid else None
+ if src_uid_index != None:
+ def get_index(index, record):
+ return record[src_uid_index]
+ else:
+ def get_index(index, record):
+ return index
+ if src_file.type == pysal.cg.shapes.Polygon:
+ def get_geom(g):
+ return g.centroid
+ elif src_file.type == pysal.cg.shapes.Point:
+ def get_geom(g):
+ return (g[0], g[1])
+ srcs = []
+ for i, rec in enumerate(src_file):
+ srcs.append([get_geom(rec), get_index(i, dbf.next())])
+ src_file.close()
+ return srcs
+
+ def test_WeightedRandomSampleGenerator(self):
+ generator = klincs.WeightedRandomSampleGenerator(self.weights, self.population, 3)
+ sample = generator.next()
+ self.assertEqual(sample,[4, 0, 3])
+
+ def test_RandomSampleGenerator(self):
+ generator = klincs.RandomSampleGenerator(self.population, 3)
+ sample = generator.next()
+ self.assertEqual(sample,[2, 1, 3])
+
+ def test_local_k(self):
+ network = self.G
+ references = self.references
+ events = self.events
+ scale_set = self.scale_set
+ cache = self.network_distances_cache
+ node2localK, net_distances = klincs.local_k(network, events, references, scale_set, cache)
+ # node2localK
+ # for each reference node,
+ # local_k returns the number of events within a distance
+ # the distance is determined by scale_set
+ # example: (724587.78057580709, 877802.4281426128): {0: 0, 1000: 22, 500: 9}
+ #
+ # net_distances - a dictionary containing network distances
+ # between nodes in the input network
+ test_node = self.test_node
+ self.assertEqual(node2localK[test_node][500], 9)
+
+ def test_cluster_type(self):
+ cluster1 = klincs.cluster_type(0.25, 0.01, 0.33)
+ cluster2 = klincs.cluster_type(0.45, 0.01, 0.33)
+ self.assertEqual(cluster1, 0)
+ self.assertEqual(cluster2, 1)
+
+ def test_simulate_local_k_01(self):
+ sims = 1
+ n = len(self.events)
+ net_file = self.network_file
+ network = self.G
+ events = self.events
+ refs = self.references
+ scale_set = self.scale_set
+ cache = self.network_distances_cache
+ args = (sims, n, net_file, network, events, refs, scale_set, cache)
+ sim_outcomes = klincs.simulate_local_k_01(args)
+ self.assertEqual(sim_outcomes[0][self.test_node], {0: 0, 1000: 9, 500: 4})
+
+ def test_simulate_local_k_02(self):
+ sims = 1
+ n = 50
+ refs = self.references
+ scale_set = self.scale_set
+ cache = self.network_distances_cache
+ args = (sims, n, refs, scale_set, cache)
+ sim_outcomes = klincs.simulate_local_k_02(args)
+ self.assertEqual(sim_outcomes[0][self.test_node], {0: 1, 1000: 2, 500: 2})
+
+ def test_k_cluster(self):
+ network = self.G
+ events = self.events
+ refs = self.references
+ scale_set = self.scale_set
+ sims = 1
+ sim_network = self.network_file
+ localKs = klincs.k_cluster(network, events, refs, scale_set, sims, sim_network=sim_network)
+ test_node = self.test_node
+ # in [9,4,4,1]
+ # 9 - the number of events observed at the search radius of 500
+ # 4 - the lowest number of simulated points observed at the search radius of 500
+ # 4 - the highest number of simulated points observed at the search radius of 500
+ # 1 - the type of cluster
+ self.assertEqual(localKs[test_node][500],[9,4,4,1])
+
+suite = unittest.TestSuite()
+test_classes = [KLINCS_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/contrib/network/test_lincs.py b/pysal/contrib/network/test_lincs.py
new file mode 100644
index 0000000..33f39cd
--- /dev/null
+++ b/pysal/contrib/network/test_lincs.py
@@ -0,0 +1,89 @@
+"""network unittest"""
+import unittest
+import network as pynet
+import lincs
+import random
+random.seed(10)
+import pysal
+import numpy as np
+import copy
+
+class LINCS_Tester(unittest.TestCase):
+
+ def setUp(self):
+ self.base = np.array([100, 80, 50, 120, 90])
+ self.events = np.array([20, 20, 5, 10, 25])
+ np.random.seed(10)
+ self.observed = np.array([0.1,0.15,0.2])
+ self.simulated = np.array([[0.05,0.10,0.25],[0.12,0.11,0.3],[0.11,0.09,0.27]])
+ self.network_file = 'streets.shp'
+ self.G = pynet.read_network(self.network_file)
+ self.test_link = ((724432.38723173144, 877800.08747069736),
+ (724587.78057580709, 877802.4281426128))
+ self.G2 = copy.deepcopy(self.G)
+ done = set()
+ for n1 in self.G2:
+ for n2 in self.G2[n1]:
+ if (n1, n2) in done:
+ continue
+ dist = self.G2[n1][n2]
+ base = int(random.random()*1000)
+ event = int(random.random()*base)
+ self.G2[n1][n2] = [dist, base, event]
+ self.G2[n2][n1] = [dist, base, event]
+ done.add((n1,n2))
+ done.add((n2,n1))
+
+ def test_unconditional_sim(self):
+ simulated_events = lincs.unconditional_sim(self.events, self.base, 2)
+ self.assertEqual(list(simulated_events[0]),[21,15])
+
+ def test_unconditional_sim_poisson(self):
+ simulated_events = lincs.unconditional_sim_poisson(self.events, self.base, 2)
+ self.assertEqual(list(simulated_events[0]),[22,21])
+
+ def test_conditional_multinomial(self):
+ simulated_events = lincs.conditional_multinomial(self.events, self.base, 2)
+ self.assertEqual(list(simulated_events[0]),[21,18])
+
+ def test_pseudo_pvalues(self):
+ pseudo_pvalues = lincs.pseudo_pvalues(self.observed, self.simulated)
+ self.assertEqual(list(pseudo_pvalues[0]),[ 0.5, 0.5, 0.5])
+
+ def test_node_weights(self):
+ w, id2link = lincs.node_weights(self.G)
+ self.assertEqual(w.neighbors[0],[1, 2, 3, 4])
+
+ def test_edgepoints_from_network(self):
+ id2linkpoints, id2attr, link2id = lincs.edgepoints_from_network(self.G)
+ link = id2linkpoints[0]
+ self.assertEqual(link[:2], self.test_link)
+ self.assertEqual(link2id[link[:2]], 0)
+
+ def test_dist_weights(self):
+ id2linkpoints, id2attr, link2id = lincs.edgepoints_from_network(self.G)
+ w, id2link = lincs.dist_weights(self.G, id2linkpoints, link2id, 500)
+ self.assertEqual(w.neighbors[0],[1,154,153,155])
+ self.assertEqual(id2link[0], self.test_link)
+
+ def test_lincs(self):
+ network = self.G2
+ event_index = 2
+ base_index = 1
+ weight = 'Distance-based'
+ dist = 500
+ lisa_func = 'moran'
+ sim_method = 'permutations'
+ sim_num = 2
+ lisa, w = lincs.lincs(network, event_index, base_index, weight, dist, lisa_func, sim_method, sim_num)
+ self.assertEqual(lisa[0][3], -0.64342055427251854)
+
+suite = unittest.TestSuite()
+test_classes = [LINCS_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/contrib/network/test_network.py b/pysal/contrib/network/test_network.py
new file mode 100644
index 0000000..e99e1ca
--- /dev/null
+++ b/pysal/contrib/network/test_network.py
@@ -0,0 +1,186 @@
+"""network unittest"""
+import unittest
+import network as pynet
+import random
+random.seed(10)
+import pysal
+
+class Network_Tester(unittest.TestCase):
+
+ def setUp(self):
+ self.net = 'streets.shp'
+ self.G = pynet.read_network(self.net)
+ self.G2 = {(1,1): {(2,2): 0.125, (3,3): 0.75}, (2,2): {(1,1): 0.125, (4,4): 1.2},
+ (3,3): {(1,1): 0.75, (4,4): 0.375},
+ (4,4): {(2,2): 1.2, (3,3): 0.375, (5,5): 0.5},
+ (5,5): {(4,4): 0.5}, (6,6):{(7,7):1.0}, (7,7):{(6,6):1.0}}
+ self.GDirected = {(1,1): {(2,2): 0.125, (3,3): 0.75}, (2,2): {(1,1): 0.125},
+ (3,3): {(1,1): 0.75, (4,4): 0.375},
+ (4,4): {(2,2): 1.2, (3,3): 0.375, (5,5): 0.5},
+ (5,5): {(4,4): 0.5}, (6,6):{(7,7):1.0}, (7,7):{(6,6):1.0}}
+ self.points = [((4,4), (2,2), 0.51466686561013752, 0.68533313438986243),
+ ((4,4), (3,3), 0.077286837052313151, 0.29771316294768685),
+ ((6,6), (7,7), 0.82358887253344548, 0.17641112746655452)]
+
+ def test_no_nodes(self):
+ self.assertEqual(pynet.no_nodes(self.G), 230)
+
+ def test_no_edges(self):
+ self.assertEqual(pynet.no_edges(self.G), 303)
+
+ def test_tot_net_length(self):
+ self.assertAlmostEqual(pynet.tot_net_length(self.G), 52207.04600797734, places=1)
+
+ def test_walk(self):
+ correct_path = {(5, 5): {(4, 4): 0.5}, (2, 2): {(4, 4): 1.2, (1, 1): 0.125},
+ (1, 1): {(3, 3): 0.75, (2, 2): 0.125},
+ (4, 4): {(5, 5): 0.5, (3, 3): 0.375, (2, 2): 1.2},
+ (3, 3): {(4, 4): 0.375, (1, 1): 0.75}}
+ traversal_path = pynet.walk(self.G2, (1,1))
+ self.assertEqual(traversal_path, correct_path)
+
+ def test_components(self):
+ components = pynet.components(self.G)
+ self.assertEqual(pynet.no_nodes(components[0]), 230)
+ components = pynet.components(self.G2)
+ self.assertEqual(len(components), 2)
+
+ def test_no_components(self):
+ no_components = pynet.no_components(self.G2)
+ self.assertEqual(no_components, 2)
+
+ def test_net_global_stats(self):
+ stats = ['v', 'e', 'L', 'p', 'u', 'alpha', 'beta', 'emax', 'gamma', 'eta',
+ 'net_den', 'detour']
+ values = pynet.net_global_stats(self.G, detour=True)
+ values = dict(zip(stats, values))
+ self.assertEqual(values['v'], 230)
+ self.assertEqual(values['e'], 303)
+ self.assertAlmostEqual(values['L'], 52207.04600797734, places=1)
+ self.assertAlmostEqual(values['eta'], 172.30048187451268)
+ self.assertAlmostEqual(values['beta'], 1.317391304347826, places=2)
+ self.assertAlmostEqual(values['emax'], 684)
+ self.assertAlmostEqual(values['gamma'], 0.44298245614035087, places=2)
+ self.assertAlmostEqual(values['detour'], 0.78002937059822186, places=4)
+ for k in values:
+ print k, values[k]
+
+ def test_random_projs(self):
+ random.seed(10)
+ random_points = pynet.random_projs(self.G2, 3)
+ points = [((7, 7), (6, 6), 0.42888905467511462, 0.57111094532488538),
+ ((7, 7), (6, 6), 0.20609823213950174, 0.79390176786049826),
+ ((1, 1), (3, 3), 0.61769165440008411, 0.13230834559991589)]
+ self.assertEqual(random_points, points)
+
+ def test_proj_distances_undirected(self):
+ source = self.points[0]
+ destinations = self.points[1:]
+ distances = pynet.proj_distances_undirected(self.G2, source, destinations, r=1.0)
+ self.assertAlmostEqual(distances.values()[0], 0.59195370266245062)
+
+ def test_proj_distances_directed(self):
+ source = self.points[0]
+ destinations = self.points
+ distances = pynet.proj_distances_directed(self.G2, source, destinations)
+ distance_values = [0.0, 1.9626199714421757]
+ self.assertEqual(distances.values(), distance_values)
+
+ def test_dijkstras(self):
+ distances = pynet.dijkstras(self.G2, (1,1))
+ distance_values = {(5, 5): 1.625, (2, 2): 0.125, (1, 1): 0, (4, 4): 1.125, (3, 3): 0.75}
+ self.assertEqual(distances, distance_values)
+
+ def test_snap(self):
+ snapper = pynet.Snapper(self.G2)
+ projected_point = snapper.snap((2.5,2.5))
+ self.assertEqual(projected_point, ((2, 2),(4, 4),3.9252311467094367e-17,1.1775693440128314e-16))
+
+ def test_network_from_endnodes(self):
+ shape = pysal.open(self.net)
+ dbf = pysal.open(self.net[:-3] + 'dbf')
+ def weight(geo_object, record):
+ return 1
+ graph = pynet.network_from_endnodes(shape, dbf, weight)
+ neighbors = {(724432.38723173144, 877800.08747069736): 1,
+ (725247.70571468933, 877812.36851842562): 1}
+ start_point = (724587.78057580709, 877802.4281426128)
+ self.assertEqual(graph[start_point], neighbors)
+
+ def test_network_from_allvertices(self):
+ shape = pysal.open(self.net)
+ dbf = pysal.open(self.net[:-3] + 'dbf')
+ graph = pynet.network_from_allvertices(shape, dbf)
+ neighbors = {(724432.38723173144, 877800.08747069736): 155.41097171058956,
+ (725247.70571468933, 877812.36851842562): 660.00000000003809}
+ start_point = (724587.78057580709, 877802.4281426128)
+ self.assertEqual(graph[start_point], neighbors)
+
+ def test_read_hierarchical_network(self):
+ shape = pysal.open(self.net)
+ dbf = pysal.open(self.net[:-3] + 'dbf')
+ graph_detail, graph_endnode, link = pynet.read_hierarchical_network(shape, dbf)
+ neighbors = {(725220.77363919443, 880985.09708712087): 659.99999999994725,
+ (724400.64597190416, 880984.45593258401): 160.12791790928244}
+ vertex1 = (724560.77384088072, 880984.58111639845)
+ vertex2 = (724400.64597190416, 880984.45593258401)
+ self.assertEqual(graph_detail[vertex1], neighbors)
+ self.assertEqual(graph_endnode[vertex1], neighbors)
+ self.assertEqual(link[(vertex1, vertex2)][0], (vertex1, vertex2))
+
+ def test_read_network(self):
+ graph = pynet.read_network(self.net)
+ self.assertEqual(graph, self.G)
+
+ def test_proj_pnt_coor(self):
+ point = pynet.proj_pnt_coor(self.points[0])
+ point_value = (3.6360755692750462, 3.6360755692750462)
+ self.assertEqual(point, point_value)
+
+ def test_inject_points(self):
+ graph, point_coors = pynet.inject_points(self.G2, self.points)
+ self.assertEqual(len(graph), len(self.G2) + 3)
+
+ def test_mesh_network(self):
+ graph = pynet.mesh_network(self.G2, 0.1)
+ self.assertEqual(len(graph), 41)
+
+ def test_write_network_to_pysalshp(self):
+ out = 'output_network.shp'
+ pynet.write_network_to_pysalshp(self.G, out)
+ graph = pynet.read_network(out)
+ self.assertEqual(graph, self.G)
+
+ def test_write_valued_network_to_shp(self):
+ out = 'output_network.shp'
+ fields = ['WEIGHT1','WEIGHT2']
+ types = [('N',7,3),('N',7,3)]
+ values = {(1,1):1,(2,2):2,(3,3):3,(4,4):4,(5,5):5,(6,6):6,(7,7):7}
+ def doubleX(values, node):
+ return values[node]*2.0
+ pynet.write_valued_network_to_shp(out,fields,types,self.G2,values,doubleX)
+ graph = pynet.read_network(out)
+ self.assertEqual(len(graph), len(self.G2))
+
+ def test_list_network_to_shp(self):
+ out = 'output_network.shp'
+ fields = ['WEIGHT']
+ types = [('N',7,3)]
+ list_network = []
+ for node1 in self.G2:
+ for node2 in self.G2[node1]:
+ list_network.append(((node1, node2),random.random()))
+ pynet.write_list_network_to_shp(out,fields,types,list_network)
+ graph = pynet.read_network(out)
+ self.assertEqual(len(graph), len(self.G2))
+
+
+suite = unittest.TestSuite()
+test_classes = [Network_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/contrib/network/test_weights.py b/pysal/contrib/network/test_weights.py
new file mode 100644
index 0000000..4c970b2
--- /dev/null
+++ b/pysal/contrib/network/test_weights.py
@@ -0,0 +1,22 @@
+"""network unittest"""
+import unittest
+import network as pynet
+import numpy as np
+import weights
+
+class Weights_Tester(unittest.TestCase):
+
+ def test_dist_weights(self):
+ ids = np.array(map(str,range(1,9)))
+ w = weights.dist_weights('distances.csv','knn',ids,3)
+ self.assertEqual(w.neighbors['1'],['6','8','7'])
+
+suite = unittest.TestSuite()
+test_classes = [Weights_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/contrib/network/weights.py b/pysal/contrib/network/weights.py
new file mode 100644
index 0000000..b30fe42
--- /dev/null
+++ b/pysal/contrib/network/weights.py
@@ -0,0 +1,80 @@
+"""
+A library of spatial network functions.
+Not to be used without permission.
+
+Contact:
+
+Andrew Winslow
+GeoDa Center for Geospatial Analysis
+Arizona State University
+Tempe, AZ
+Andrew.Winslow at asu.edu
+"""
+
+import csv
+import numpy as np
+from pysal import W
+import unittest
+import test
+
+def dist_weights(distfile, weight_type, ids, cutoff, inverse=False):
+ """
+ Returns a distance-based weights object using user-defined options
+
+ Parameters
+ ----------
+ distfile: string, a path to distance csv file
+ weighttype: string, either 'threshold' or 'knn'
+ ids: a numpy array of id values
+ cutoff: float or integer; float for 'threshold' weight type and integer for knn type
+ inverse: boolean; true if inversed weights required
+
+ """
+ try:
+ data_csv = csv.reader(open(distfile))
+ if csv.Sniffer().has_header(distfile):
+ data_csv.next()
+ except:
+ data_csv = None
+
+ if weight_type == 'threshold':
+ def neighbor_func(dists, threshold):
+ dists = filter(lambda x: x[0] <= threshold, dists)
+ return dists
+ else:
+ def neighbor_func(dists, k):
+ dists.sort()
+ return dists[:k]
+
+ if inverse:
+ def weight_func(dists, alpha=-1.0):
+ return list((np.array(dists)**alpha).round(decimals=6))
+ else:
+ def weight_func(dists, binary=False):
+ return [1]*len(dists)
+
+ dist_src = {}
+ for row in data_csv:
+ des = dist_src.setdefault(row[0], {})
+ if row[0] != row[1]:
+ des[row[1]] = float(row[2])
+
+ neighbors, weights = {}, {}
+ for id_val in ids:
+ if id_val not in dist_src:
+ raise ValueError, 'An ID value doest not exist in distance file'
+ else:
+ dists = zip(dist_src[id_val].values(), dist_src[id_val].keys())
+ ngh, wgt = [], []
+ if len(dists) > 0:
+ nghs = neighbor_func(dists, cutoff)
+ for d, i in nghs:
+ ngh.append(i)
+ wgt.append(d)
+ neighbors[id_val] = ngh
+ weights[id_val] = weight_func(wgt)
+ w = W(neighbors, weights)
+ w.id_order = ids
+ return w
+
+
diff --git a/pysal/contrib/opendata/__init__.py b/pysal/contrib/opendata/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pysal/contrib/opendata/google.py b/pysal/contrib/opendata/google.py
new file mode 100644
index 0000000..51c24f7
--- /dev/null
+++ b/pysal/contrib/opendata/google.py
@@ -0,0 +1,287 @@
+"""
+google: Tools for extracting information through Google Places API
+
+Author(s):
+ Luc Anselin luc.anselin at asu.edu
+ Xun Li xun.li at asu.edu
+
+"""
+
+__author__ = "Luc Anselin <luc.anselin at asu.edu, Xun Li <xun.li at asu.edu"
+
+import urllib2
+import json
+from pysal.cg import lonlat,geogrid,harcdist
+
+__all__ = ['querypoints','queryradius','googlepoints','ptdict2geojson']
+
+def querypoints(p,bbox=True,lonx=False,k=5):
+ """
+ Utility function to generate a list of grid query points from a bounding box
+ essentially a wrapper around geogrid with a check for bounding box dimension
+
+ Parameters
+ ----------
+ p : list of points in alt-lon or lon-lat
+ bbox : flag for bounding box (upper left, lower right), default = True
+ lonx : flag for lat-lon order, default is False for lat-lon (True is lon-lat)
+ k : grid size (points grid will be k+1 by k+1)
+
+ Returns
+ -------
+ grid : list of lat-lon of a grid of query points, row by row, from top to bottom
+
+ """
+ if bbox:
+ if not len(p)==2:
+ raise Exception, "invalid format for bounding box"
+ # create grid points from bounding box
+ grid = geogrid(p[0],p[1],k=k,lonx=lonx)
+ # the list must be in lat-lon format
+ if lonx:
+ grid = lonlat(grid)
+ else:
+ return p
+ return grid
+
+def queryradius(grid,k,lonx=False):
+ """
+ Utility function to find a good query range distance for grid points
+
+ Parameters
+ ----------
+ grid : list of lat-lon tuplets for regular grid points
+ (created with querypoints)
+ k : dimension of the grid, the list will consist of k+1 x k+1 tuplets
+ lonx : flag for lat-lon order, default is False for lat-lon (lon-lat is True)
+
+ Returns
+ -------
+ : maximum distance from the horizontal and vertical sides of
+ the lower right grid cell in meters
+ (to be used as the sradius argument in googlepoints)
+
+ """
+ # compute distances in horizontal and vertical direction
+ # use the lower right grid cell
+ # along longitude
+ p0 = grid[-2]
+ p1 = grid[-1]
+ # along latitude
+ p3 = grid[-(k+2)]
+ # horizontal distance
+ dist1 = harcdist(p0,p1,lonx=lonx)
+ # vertical distance
+ dist2 = harcdist(p3,p1,lonx=lonx)
+ return round(max([dist1,dist2])*1000.0)
+
+def googlepoints(querypoints,apikey,sradius,stype,newid=True,verbose=True):
+ """
+ Queries the Google Places API for locations (lat-lon) of a
+ given type of facility
+
+ Parameters
+ ----------
+ querypoints : a list of tuples with point coordinates in lat-lon format
+ (not lon-lat!)
+ apikey : the user's Google Places API key
+ sradius : search radius in meters
+ stype : facility type from list supported by Google Places API
+ newid : flag for sequential integer as new key
+ default = True
+ should be set to False when result dictionary will
+ be used as input to googlevalues
+ verbose : flag for amount of detail in output
+ default = True, for each point query, the status and
+ number of points found is printed
+ False: only a warning is printed when search is truncated
+ at 200
+
+ Returns
+ -------
+ findings : a directory with the facility Google ID as the key
+ contains lat and lng
+
+ Example
+ -------
+
+ It is not possible to give an actual example, since that requires the
+ user's unique API key. However, to illustrate the type of call and
+ return consider:
+
+ liquorlist = googlepoints([(41.981417, -87.893517)],'apikeyhere',5000,'liquor_store')
+
+ which will query a radius of 5000m (5km) around the specified query point for the
+ locations of all liquor stores in the Google data base
+
+ This retuns a dictionary:
+
+ {u'ChIJ-etp1PLJD4gRLYGl7Jm1oG4': {u'lat': 41.986742, u'lng': -87.836312},
+ u'ChIJ0fyhh9awD4gR6-vaMEbGNBE': {u'lat': 42.022235, u'lng': -87.941302},
+ ...
+ u'ChIJxfyRaki2D4gRiaHLJANgJns': {u'lat': 42.010285, u'lng': -87.875896}}
+
+ The key in the dictionary is the Google ID of the facility
+ """
+ base_url = 'https://maps.googleapis.com/maps/api/place/radarsearch/json?location=%s,%s&radius=%s&types=%s&key=%s'
+ # list of urls to query
+ query_urls = [base_url%(lat,lon,sradius,stype,apikey) for lat,lon in querypoints]
+ findings = {}
+
+ for url in query_urls:
+ rsp = urllib2.urlopen(url)
+ content = rsp.read()
+ data = json.loads(content)
+ if 'results' in data: # avoid empty records
+ results = data['results']
+ if verbose:
+ print data['status'], len(results)
+ if len(results) == 200:
+ print "WARNING: query truncated at 200"
+ for item in results:
+ place_id = item['place_id']
+ loc = item['geometry']['location']
+ # use place id as key in the dictionary
+ findings[place_id] = loc
+
+ # replace key with integer sequence number
+ if newid:
+ ii = 0
+ newdict = {}
+ for placeid in findings.keys():
+ dd = findings[placeid]
+ dd['placeid']=placeid
+ newdict[ii]=dd
+ ii = ii + 1
+ else:
+ return findings
+
+ return newdict
+
+def ptdict2geojson(d,location=["lng","lat"],values=[],ofile="output.geojson"):
+ """
+ Turns the dictionary output from googlepoints into a geojson point file
+
+ Parameters
+ ----------
+ d : dict object created by googlepoints
+ location : list with long, lat coordinates in decimal degrees
+ (no check for order)
+ values : list of keys for data items other than the id
+ (do not include the id)
+ default: no values (id only)
+ ofile : file name for the output file (include geojson extension)
+ default: "output.geojson"
+
+ Returns
+ -------
+ : geojson point file
+
+ Remarks
+ -------
+ Assumes that the dictionary is complete, i.e., every key has a value associated
+ with it. No checks are made for missing keys.
+
+ """
+ # extract location info
+ lng = location[0]
+ lat = location[1]
+ # initialize geo dictionary for point features
+ geo = {"type": "FeatureCollection","features":[]}
+ # loop over dictionary
+ for id,loc in d.iteritems():
+ feature = { "type" : "Feature",
+ "geometry": { "type": "Point", "coordinates": [ loc['lng'],loc['lat']]}}
+ properties = {"id": id}
+ for info in values:
+ properties[info] = loc[info]
+ feature["properties"] = properties
+ geo["features"].append(feature)
+ # output file
+ with open(ofile,'w') as outfile:
+ json.dump(geo,outfile,sort_keys=True,indent=4,ensure_ascii=False)
+ outfile.close()
+ return
+
+def googlept(pointlist,stype,apikey,lonx=False,bbox=True,k=5,sradius=5000,verbose=True,ofile="output.geojson"):
+ """
+ User function to query google points
+
+ Parameters
+ ----------
+ pointlist : a list of tuples with lat-lon (or lon-lat) for query points
+ or a list with the upper left and lower right points of a bounding box
+ stype : string with the type of query from the list of supported queries
+ by the Google Places API
+ apikey : user's Google Places API key
+ lonx : flag for order or lat-lon points in tuple
+ default = False for lat-lon order (lon-lat is True)
+ bbox : flag for use of bounding box as input
+ default = True for bounding box; False is list of points
+ when using a single point, must be in a list [(lat,lon)]
+ k : default size for the search grid (for bbox=True)
+ sradius : default search radius, for point list only
+ with bbox=True search radius is determined internally
+ verbose : flag for details in google query
+ default is True for query status and number of points returned
+ ofile : output file name
+
+ Returns
+ -------
+ : geojson point file
+
+ """
+ # create the grid search box
+ grid = querypoints(pointlist,bbox=bbox,lonx=lonx,k=k)
+ # compute search radius (only when bounding box specified)
+ if bbox:
+ sradius = queryradius(grid,k=k,lonx=lonx)
+ # query google places
+ ptdict = googlepoints(grid,apikey,sradius,stype,newid=True,verbose=verbose)
+ # create output file
+ ptdict2geojson(ptdict,location=["lng","lat"],values=["placeid"],ofile=ofile)
+ return
+
+if __name__ == '__main__':
+ print "Welcome to PySAL Google Points Query"
+ apikey = raw_input("Enter your API key: ")
+ stype = raw_input("Enter the type of query: ")
+ print "For each query point, enter the lat,lon separated by a comma"
+ print "For a bounding box, enter the upper left corner first,\nthen the lower right"
+ plist = []
+ while True:
+ p = raw_input("Enter lat,lon: ")
+ if p:
+ pp = tuple(map(float,p.split(",")))
+ print pp
+ plist.append(pp)
+ else:
+ break
+ if len(plist) == 2:
+ bb = raw_input("Is this a bounding box (enter Yes or No): ")
+ if bb.upper() == 'NO' or bb.upper() == 'N':
+ bbox = False
+ else:
+ bbox = True
+ else:
+ bbox = False
+ k = 5
+ sr = 5000.0
+ if bbox:
+ gp = raw_input("Enter the number of grid points or return for default: ")
+ if gp:
+ k = int(gp)
+ else:
+ sr = raw_input("Enter the search radius in meters\nor return for default: ")
+ if sr:
+ sradius = float(sr)
+
+ outfile = raw_input("Enter the name for the output file : ")
+ googlept(plist,stype,apikey,lonx=False,bbox=bbox,k=k,sradius=sr,
+ verbose=True,ofile=outfile)
+ print "Output is in file %s " % outfile
+
+
+
+
+
diff --git a/pysal/contrib/shapely_ext.py b/pysal/contrib/shapely_ext.py
new file mode 100644
index 0000000..c2a6847
--- /dev/null
+++ b/pysal/contrib/shapely_ext.py
@@ -0,0 +1,301 @@
+import shapely
+import shapely.geometry
+import shapely.ops
+_basegeom = shapely.geometry.base.BaseGeometry
+import pysal
+__all__ = ["to_wkb", "to_wkt", "area", "distance", "length", "boundary", "bounds", "centroid", "representative_point", "convex_hull", "envelope", "buffer", "simplify", "difference", "intersection", "symmetric_difference", "union", "unary_union", "cascaded_union", "has_z", "is_empty", "is_ring", "is_simple", "is_valid", "relate", "contains", "crosses", "disjoint", "equals", "intersects", "overlaps", "touches", "within", "equals_exact", "almost_equals", "project", "interpolate"]
+
+
+def to_wkb(shape):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ return o.to_wkb()
+
+def to_wkt(shape):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ return o.to_wkt()
+
+# Real-valued properties and methods
+# ----------------------------------
+def area(shape):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ return o.area
+
+def distance(shape, other):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ if not hasattr(other,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ o2 = shapely.geometry.asShape(other)
+ return o.distance(o2)
+
+def length(shape):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ return o.length
+
+# Topological properties
+# ----------------------
+def boundary(shape):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ res = o.bondary
+ return pysal.cg.shapes.asShape(res)
+
+def bounds(shape):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ return o.bounds
+
+def centroid(shape):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ res = o.centroid
+ return pysal.cg.shapes.asShape(res)
+
+def representative_point(shape):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ res = o.representative_point
+ return pysal.cg.shapes.asShape(res)
+
+def convex_hull(shape):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ res = o.convex_hull
+ return pysal.cg.shapes.asShape(res)
+
+def envelope(shape):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ res = o.envelope
+ return pysal.cg.shapes.asShape(res)
+
+def buffer(shape, radius, resolution=16):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ res = o.buffer(radius, resolution)
+ return pysal.cg.shapes.asShape(res)
+
+def simplify(shape, tolerance, preserve_topology=True):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ res = o.simplify(tolerance, preserve_topology)
+ return pysal.cg.shapes.asShape(res)
+
+# Binary operations
+# -----------------
+def difference(shape, other):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ if not hasattr(other,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ o2 = shapely.geometry.asShape(other)
+ res = o.difference(o2)
+ return pysal.cg.shapes.asShape(res)
+
+def intersection(shape, other):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ if not hasattr(other,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ o2 = shapely.geometry.asShape(other)
+ res = o.intersection(o2)
+ return pysal.cg.shapes.asShape(res)
+
+def symmetric_difference(shape, other):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ if not hasattr(other,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ o2 = shapely.geometry.asShape(other)
+ res = o.symmetric_difference(o2)
+ return pysal.cg.shapes.asShape(res)
+
+def union(shape, other):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ if not hasattr(other,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ o2 = shapely.geometry.asShape(other)
+ res = o.union(o2)
+ return pysal.cg.shapes.asShape(res)
+
+def cascaded_union(shapes):
+ o = []
+ for shape in shapes:
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o.append(shapely.geometry.asShape(shape))
+ res = shapely.ops.cascaded_union(o)
+ return pysal.cg.shapes.asShape(res)
+
+def unary_union(shapes):
+ # seems to be the same as cascade_union except that it handles multipart polygons
+ if shapely.__version__ < '1.2.16':
+ raise Exception, "shapely 1.2.16 or higher needed for unary_union; upgrade shapely or try cascade_union instead"
+ o = []
+ for shape in shapes:
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o.append(shapely.geometry.asShape(shape))
+ res = shapely.ops.unary_union(o)
+ return pysal.cg.shapes.asShape(res)
+
+# Unary predicates
+# ----------------
+def has_z(shape):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ return o.has_z
+
+def is_empty(shape):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ return o.is_empty
+
+def is_ring(shape):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ return o.is_ring
+
+def is_simple(shape):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ return o.is_simple
+
+def is_valid(shape):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ return o.is_valid
+
+# Binary predicates
+# -----------------
+def relate(shape, other):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ if not hasattr(other,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ o2 = shapely.geometry.asShape(other)
+ return o.relate(o2)
+
+def contains(shape, other):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ if not hasattr(other,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ o2 = shapely.geometry.asShape(other)
+ return o.contains(o2)
+
+def crosses(shape, other):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ if not hasattr(other,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ o2 = shapely.geometry.asShape(other)
+ return o.crosses(o2)
+
+def disjoint(shape, other):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ if not hasattr(other,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ o2 = shapely.geometry.asShape(other)
+ return o.disjoint(o2)
+
+def equals(shape, other):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ if not hasattr(other,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ o2 = shapely.geometry.asShape(other)
+ return o.equals(o2)
+
+def intersects(shape, other):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ if not hasattr(other,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ o2 = shapely.geometry.asShape(other)
+ return o.intersects(o2)
+
+def overlaps(shape, other):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ if not hasattr(other,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ o2 = shapely.geometry.asShape(other)
+ return o.overlaps(o2)
+
+def touches(shape, other):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ if not hasattr(other,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ o2 = shapely.geometry.asShape(other)
+ return o.touches(o2)
+
+def within(shape, other):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ if not hasattr(other,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ o2 = shapely.geometry.asShape(other)
+ return o.within(o2)
+
+def equals_exact(shape, other, tolerance):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ if not hasattr(other,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ o2 = shapely.geometry.asShape(other)
+ return o.equals_exact(o2, tolerance)
+
+def almost_equals(shape, other, decimal=6):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ if not hasattr(other,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ o2 = shapely.geometry.asShape(other)
+ return o.almost_equals(o2, decimal)
+
+# Linear referencing
+# ------------------
+
+def project(shape, other, normalized=False):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ if not hasattr(other,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ o2 = shapely.geometry.asShape(other)
+ return o.project(o2, normalized)
+
+def interpolate(shape, distance, normalized=False):
+ if not hasattr(shape,'__geo_interface__'): raise TypeError, "%r does not appear to be a shape"%shape
+ o = shapely.geometry.asShape(shape)
+ res = o.interpolate(distance, normalized)
+ return pysal.cg.shapes.asShape(res)
+
+
+# Copy doc strings from shapely
+for method in __all__:
+ if hasattr(_basegeom, method):
+ locals()[method].__doc__ = getattr(_basegeom,method).__doc__
+
+if __name__=='__main__':
+ #step 0, create 2 points
+ pt1 = pysal.cg.shapes.Point((0,0))
+ pt2 = pysal.cg.shapes.Point((10,10))
+ o = pysal.open('step0.shp','w')
+ o.write(pt1)
+ o.write(pt2)
+ o.close()
+
+ #step 1, buffer 2 points
+ b1 = buffer(pt1,10)
+ b2 = buffer(pt2,10)
+ o = pysal.open('step1.shp','w')
+ o.write(b1)
+ o.write(b2)
+ o.close()
+
+ #step 2, intersect 2 buffers
+ i = intersection(b1,b2)
+ o = pysal.open('step2.shp','w')
+ o.write(i)
+ o.close()
+
+ #step 3, union 2 buffers
+ u = union(b1, b2)
+ o = pysal.open('step3.shp','w')
+ o.write(u)
+ o.close()
+
+ #step 4, find convex_hull of union
+ c = convex_hull(u)
+ o = pysal.open('step4.shp','w')
+ o.write(c)
+ o.close()
diff --git a/pysal/contrib/shared_perimeter_weights.py b/pysal/contrib/shared_perimeter_weights.py
new file mode 100644
index 0000000..0827e2d
--- /dev/null
+++ b/pysal/contrib/shared_perimeter_weights.py
@@ -0,0 +1,33 @@
+"""
+shared_perimeter_weights -- calculate shared perimeters weights....
+
+wij = l_ij/P_i
+wji = l_ij/P_j
+
+l_ij = length of shared border i and j
+P_j = perimeter of j
+
+"""
+__author__ = "Charles R Schmidt <schmidtc at gmail.com>"
+__all__ = ["spw_from_shapefile"]
+
+
+import pysal
+import shapely.geometry
+
+def spw_from_shapefile(shapefile, idVariable=None):
+ polygons = pysal.open(shapefile,'r').read()
+ polygons = map(shapely.geometry.asShape,polygons)
+ perimeters = [p.length for p in polygons]
+ Wsrc = pysal.rook_from_shapefile(shapefile)
+ new_weights = {}
+ for i in Wsrc.neighbors:
+ a = polygons[i]
+ p = perimeters[i]
+ new_weights[i] = [a.intersection(polygons[j]).length/p for j in Wsrc.neighbors[i]]
+ return pysal.W(Wsrc.neighbors,new_weights)
+
+if __name__=='__main__':
+ fname = pysal.examples.get_path('stl_hom.shp')
+ W = spw_from_shapefile(fname)
+
diff --git a/pysal/contrib/spatialnet/__init__.py b/pysal/contrib/spatialnet/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pysal/contrib/spatialnet/beth_roads.shp b/pysal/contrib/spatialnet/beth_roads.shp
new file mode 100644
index 0000000..7014882
Binary files /dev/null and b/pysal/contrib/spatialnet/beth_roads.shp differ
diff --git a/pysal/contrib/spatialnet/beth_roads.shx b/pysal/contrib/spatialnet/beth_roads.shx
new file mode 100644
index 0000000..53fd135
Binary files /dev/null and b/pysal/contrib/spatialnet/beth_roads.shx differ
diff --git a/pysal/contrib/spatialnet/cleanNetShp.py b/pysal/contrib/spatialnet/cleanNetShp.py
new file mode 100644
index 0000000..0606fe4
--- /dev/null
+++ b/pysal/contrib/spatialnet/cleanNetShp.py
@@ -0,0 +1,141 @@
+"""
+cleanNetShp -- Tools to clean spatial Network Shapefiles.
+"""
+import pysal
+import numpy
+
+__author__ = "Charles R. Schmidt <schmidtc at gmail.com>"
+__all__ = ['snap_verts', 'find_nodes', 'split_at_nodes']
+
+
+def snap_verts(shp,tolerance=0.001,arc=True):
+ """
+ snap_verts -- Snap verts that are within tolerance meters of each other.
+
+ Description -- Snapping should be performed with a very small tolerance.
+ The goal is not to change the network, but to ensure rounding
+ errors don't prevent edges from being split at proper intersections.
+ The default of 1mm should be adequate if the input is of decent quality.
+ Higher snapping values can be used to correct digitizing errors, but care
+ should be taken.
+
+ Arguments
+ ---------
+ tolerance -- float -- snapping tolerance in meters
+ arc -- bool -- If true, Ard Distance will be used instead of Euclidean
+
+ Returns
+ -------
+ generator -- each element is a new pysal.cg.Chain with corrected vertices.
+ """
+ kmtol = tolerance/1000.
+
+ data = numpy.concatenate([rec.vertices for rec in shp])
+
+ if arc:
+ kd = pysal.cg.KDTree(data,distance_metric="Arc",radius = pysal.cg.sphere.RADIUS_EARTH_KM)
+ else:
+ kd = pysal.cg.KDTree(data)
+ q = kd.query_ball_tree(kd,kmtol)
+ ### Next three lines assert that snappings are mutual... if 1 snaps to 8, 8 must snap to 1.
+ for r,a in enumerate(q):
+ for o in a:
+ assert a==q[o]
+ ### non-mutual snapping can happen.
+ ### consider the three points, A (-1,0), B (0,0), C (1,0) and a snapping tolerance of 1.
+ ### A-> B
+ ### B-> A,C
+ ### C-> B
+ ### For now, try lowering adjusting the tolerance to avoid this.
+
+ data2 = numpy.empty_like(data)
+ for i,r in enumerate(q):
+ data2[i] = data[r].mean(0)
+ pos=0
+ for rec in shp:
+ vrts = rec.vertices
+ n = len(vrts)
+ nrec = pysal.cg.Chain(map(tuple,data2[pos:pos+n]))
+ pos+=n
+ yield nrec
+
+def find_nodes(shp):
+ """
+ find_nodes -- Finds vertices in a line type shapefile that appear more than once and/or are end points of a line
+
+ Arguments
+ ---------
+ shp -- Shapefile Object -- Should be of type Line.
+
+ Returns
+ -------
+ set
+ """
+ node_count = {}
+ for road in shp:
+ vrts = road.vertices
+ for node in vrts:
+ if node not in node_count:
+ node_count[node] = 0
+ node_count[node] += 1
+ node_count[vrts[0]] += 1
+ node_count[vrts[-1]] += 1
+ return set([node for node,c in node_count.iteritems() if c > 1])
+
+def split_at_nodes(shp):
+ """
+ split_at_nodes -- Split line features at nodes
+
+ Arguments
+ ---------
+ shp -- list or shapefile -- Chain features to be split at common nodes.
+
+ Returns
+ -------
+ generator -- yields pysal.cg.Chain objects
+ """
+ nodes = find_nodes(shp)
+ nodeIds = list(nodes)
+ nodeIds.sort()
+ nodeIds = dict([(node,i) for i,node in enumerate(nodeIds)])
+
+ for road in shp:
+ vrts = road.vertices
+ midVrts = set(road.vertices[1:-1]) #we know end points are nodes
+ midNodes = midVrts.intersection(nodes) # find any nodes in the middle of the feature.
+ midIdx = [vrts.index(node) for node in midNodes] # Get their indices
+ midIdx.sort()
+ if midIdx:
+ #print vrts
+ starts = [0]+midIdx
+ stops = [x+1 for x in midIdx]+[None]
+ for start,stop in zip(starts,stops):
+ feat = pysal.cg.Chain(vrts[start:stop])
+ rec = (nodeIds[feat.vertices[0]],nodeIds[feat.vertices[-1]],False)
+ yield feat,rec
+ else:
+ rec = (nodeIds[road.vertices[0]],nodeIds[road.vertices[-1]],False)
+ yield road,rec
+
+
+def createSpatialNetworkShapefile(inshp,outshp):
+ assert inshp.lower().endswith('.shp')
+ assert outshp.lower().endswith('.shp')
+ shp = pysal.open(inshp,'r')
+ snapped = list(snap_verts(shp,.001))
+ o = pysal.open(outshp,'w')
+ odb = pysal.open(outshp[:-4]+'.dbf','w')
+ odb.header = ["FNODE","TNODE","ONEWAY"]
+ odb.field_spec = [('N',20,0),('N',20,0),('L',1,0)]
+
+ new = list(split_at_nodes(snapped))
+ for feat,rec in new:
+ o.write(feat)
+ odb.write(rec)
+ o.close()
+ odb.close()
+ print "Split %d roads in %d network edges"%(len(shp),len(new))
+
+if __name__=='__main__':
+ createSpatialNetworkShapefile('beth_roads.shp','beth_network.shp')
+
diff --git a/pysal/contrib/spatialnet/eberly.shp b/pysal/contrib/spatialnet/eberly.shp
new file mode 100644
index 0000000..a6e289f
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diff --git a/pysal/contrib/spatialnet/eberly.shx b/pysal/contrib/spatialnet/eberly.shx
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diff --git a/pysal/contrib/spatialnet/spatialnet.py b/pysal/contrib/spatialnet/spatialnet.py
new file mode 100644
index 0000000..55ec17f
--- /dev/null
+++ b/pysal/contrib/spatialnet/spatialnet.py
@@ -0,0 +1,107 @@
+import pysal
+from pysal.cg.segmentLocator import Polyline_Shapefile_SegmentLocator
+import networkx
+
+EUCLIDEAN_DISTANCE = "Euclidean"
+ARC_DISTANCE = "Arc"
+
+class SpatialNetwork(object):
+ """
+ SpatialNetwork -- Represents a Spatial Network.
+
+ A Spatial Network in PySAL is a graph who's nodes and edges are represented by geographic features
+ such as Points for nodes and Lines for edges.
+ An example of a spatial network is a road network.
+
+ Arguments
+ ---------
+ shapefile -- Shapefile contains the geographic represention of the network.
+ The shapefile must be a polyline type and the associated DBF MUST contain the following fields:
+ FNODE -- source node -- ID of the source node, the first vertex in the polyline feature.
+ TNODE -- destination node -- ID of the destination node, the last vertex in the polyline feature.
+ ONEWAY -- bool -- If True, the edge will be marked oneway starting at FNODE and ending at TNODE
+ distance_metric -- EUCLIDEAN_DISTANCE or ARC_DISTANCE
+
+ """
+ def __init__(self,shapefile,distance_metric=EUCLIDEAN_DISTANCE):
+ if issubclass(type(shapefile),basestring): #Path
+ self.shp = shp = pysal.open(shapefile,'r')
+ else:
+ raise TypeError,"Expecting a string, shapefile should the path to shapefile"
+ if shp.type != pysal.cg.shapes.Chain:
+ raise ValueError,"Shapefile must contain polyline features"
+ self.dbf = dbf = pysal.open(shapefile[:-4]+'.dbf','r')
+ header = dbf.header
+ if (('FNODE' not in header) or ('TNODE' not in header) or ('ONEWAY' not in header)):
+ raise ValueError,"DBF must contain: FNODE,TNODE,ONEWAY"
+
+ oneway = [{'F':False,'T':True}[x] for x in dbf.by_col('ONEWAY')]
+ fnode = dbf.by_col('FNODE')
+ tnode = dbf.by_col('TNODE')
+ if distance_metric == EUCLIDEAN_DISTANCE:
+ lengths = [x.len for x in shp]
+ elif distance_metric == ARC_DISTANCE:
+ lengths = [x.arclen for x in shp]
+ else:
+ raise ValueError,"distance_metric must be either EUCLIDEAN_DISTANCE or ARC_DISTANCE"
+ self.lengths = lengths
+ if any(oneway):
+ G = networkx.MultiDiGraph()
+ #def isoneway(x):
+ # return x[-1]
+ #def isnotoneway(x):
+ # if(x[-1]):
+ # return False
+ # return True
+ #
+ #A = filter(isoneway,zip(fnode,tnode,oneway))
+ #B = filter(isnotoneway,zip(fnode,tnode,oneway))
+ #C = filter(isnotoneway,zip(tnode,fnode,oneway))
+ #G.add_edges_from(A)
+ #G.add_edges_from(B)
+ #G.add_edges_from(C)
+ else:
+ G = networkx.MultiGraph()
+ #zip(fnode,tnode))
+ self.G = G
+ shp.seek(0)
+ self._locator = Polyline_Shapefile_SegmentLocator(shp)
+ def snap(self,pt):
+ i,p,j = self._locator.nearest(pt) #shpID,partID,segmentID
+ segment = self.shp[i].segments[p][j] #grab segment
+ d,pct = pysal.cg.get_segment_point_dist(segment,pt) #find pct along segment
+ x0,x1 = segment.p1[0],segment.p2[0]
+ x2 = x0 + (x1-x0)*pct # find x location of snap
+ y2 = segment.line.y(x2) # find y location of snap
+
+ #dbf = self.dbf
+ #rec = dict(zip(dbf.header,dbf[i][0]))
+ #edge = (rec['FNODE'],rec['TNODE'])
+ #TODO: Calculate location along edge and distance to edge"
+ #return edge
+ return x2,y2
+
+
+if __name__=='__main__':
+ import random
+ net = SpatialNetwork('beth_network.shp',ARC_DISTANCE)
+
+ n = 1000
+ minX,minY,maxX,maxY = net.shp.bbox
+ xRange = maxX-minX
+ yRange = maxY-minY
+ qpts = [(random.random(), random.random()) for i in xrange(n)]
+ qpts = [pysal.cg.Point((minX+(xRange*x),minY+(yRange*y))) for x,y in qpts]
+ o = pysal.open('random_qpts.shp','w')
+ for p in qpts:
+ o.write(p)
+ o.close()
+ o = pysal.open('random_qpts_snapped.shp','w')
+ for qpt in qpts:
+ spt = net.snap(qpt)
+ o.write(pysal.cg.Chain([qpt,spt]))
+ o.close()
+
+
+
+
diff --git a/pysal/contrib/spatialnet/util.py b/pysal/contrib/spatialnet/util.py
new file mode 100644
index 0000000..c016329
--- /dev/null
+++ b/pysal/contrib/spatialnet/util.py
@@ -0,0 +1,221 @@
+"""
+Utility module for network contrib
+"""
+
+import pysal as ps
+import networkx as nx
+import numpy as np
+
+__author__ = "Serge Rey <sjsrey at gmail.com>"
+
+def w2dg(w):
+ """
+ Return a networkx directed graph from a PySAL W object
+
+
+ Parameters
+ ----------
+
+ w: Weights
+
+
+ Returns
+ -------
+ G: A networkx directed graph
+
+
+ Example
+ ------
+
+ >>> import networkx as nx
+ >>> import pysal as ps
+ >>> w = ps.lat2W()
+ >>> guw = w2dg(w)
+ >>> guw.in_degree()
+ {0: 2, 1: 3, 2: 3, 3: 3, 4: 2, 5: 3, 6: 4, 7: 4, 8: 4, 9: 3, 10: 3, 11: 4, 12: 4, 13: 4, 14: 3, 15: 3, 16: 4, 17: 4, 18: 4, 19: 3, 20: 2, 21: 3, 22: 3, 23: 3, 24: 2}
+ >>> dict([(k,len(w.neighbors[k])) for k in w.neighbors])
+ {0: 2, 1: 3, 2: 3, 3: 3, 4: 2, 5: 3, 6: 4, 7: 4, 8: 4, 9: 3, 10: 3, 11: 4, 12: 4, 13: 4, 14: 3, 15: 3, 16: 4, 17: 4, 18: 4, 19: 3, 20: 2, 21: 3, 22: 3, 23: 3, 24: 2}
+ >>>
+ """
+
+ w_l = [(i,j) for i in w.neighbors for j in w[i]]
+ G = nx.DiGraph()
+ G.add_edges_from(w_l)
+ return G
+
+def w2dwg(w):
+ """
+ Return a directed, weighted graph from a PySAL W object
+
+
+ Parameters
+ ----------
+
+ w: Weights
+
+
+ Returns
+ -------
+ G: A networkx directed, weighted graph
+
+
+ Example
+ -------
+ >>> import networkx as nx
+ >>> import pysal as ps
+ >>> w = ps.lat2W()
+ >>> w.transform = 'r'
+ >>> gw = w2dwg(w)
+ >>> gw.get_edge_data(0,1)
+ {'weight': 0.5}
+ >>> gw.get_edge_data(1,0)
+ {'weight': 0.33333333333333331}
+ """
+
+ w_l = [(i,j,w[i][j]) for i in w.neighbors for j in w[i]]
+ G = nx.DiGraph() # allow for asymmetries in weights
+ G.add_weighted_edges_from(w_l)
+ return G
+
+
+def dwg2w(g, weight_name = 'weight'):
+ """
+ Returns a PySAL W object from a directed-weighted graph
+
+ Parameters
+ ----------
+
+ g: networkx digraph
+
+ weight_name: name of weight attribute of g
+
+ Returns
+ -------
+ w: PySAL W
+
+ Example
+ -------
+ >>> w = ps.lat2W()
+ >>> w.transform = 'r'
+ >>> g = w2dwg(w)
+ >>> w1 = dwg2w(g)
+ >>> w1.n
+ 25
+ >>> w1.neighbors[0]
+ [1, 5]
+ >>> w1.neighbors[1]
+ [0, 2, 6]
+ >>> w1.weights[0]
+ [0.5, 0.5]
+ >>> w1.weights[1]
+ [0.33333333333333331, 0.33333333333333331, 0.33333333333333331]
+ """
+
+ neighbors = {}
+ weights = {}
+ for node in g.nodes_iter():
+ neighbors[node] = []
+ weights[node] = []
+ for neighbor in g.neighbors_iter(node):
+ neighbors[node].append(neighbor)
+ weight = g.get_edge_data(node,neighbor)
+ if weight:
+ weights[node].append(weight[weight_name])
+ else:
+ weights[node].append(1)
+ return ps.W(neighbors=neighbors, weights=weights)
+
+
+
+
+def edge2w(edgelist, nodetype=str):
+ """
+ Create a PySAL W object from an edgelist
+
+ Parameters
+ ----------
+
+ edge_file: file with edgelist
+
+ nodetype: type for node (str, int, float)
+
+
+ Returns
+ -------
+ W: PySAL W
+
+
+ Example
+ -------
+ >>> lines = ["1 2", "2 3", "3 4", "4 5"]
+ >>> w = edge2w(lines)
+ >>> w.n
+ 5
+ >>> w.neighbors["2"]
+ ['1', '3']
+
+ >>> w = edge2w(lines, nodetype=int)
+ >>> w.neighbors[2]
+ [1, 3]
+ >>> lines = ["1 2 {'weight':1.0}", "2 3 {'weight':0.5}", "3 4 {'weight':3.0}"]
+ >>> w = edge2w(lines, nodetype=int)
+ >>> w.neighbors[2]
+ [1, 3]
+ >>> w.weights[2]
+ [1.0, 0.5]
+
+ """
+ G = nx.parse_edgelist(edgelist, nodetype=nodetype)
+ return dwg2w(G)
+
+
+
+def adjl2w(adjacency_list, nodetype=str):
+ """
+ Create a PySAL W object from an adjacency list file
+
+ Parameters
+ ----------
+
+ adjacency_list: list of adjacencies
+ for directed graphs list only outgoing adjacencies
+
+ nodetype: type for node (str, int, float)
+
+
+ Returns
+ -------
+ W: PySAL W
+
+
+ Example
+ -------
+ >>> al = [[1], [0,2], [1,3], [2]]
+ >>> w = adjl2w(al)
+ >>> w.n
+ 4
+ >>> w.neighbors['0']
+ ['1']
+ >>> w = adjl2w(al, nodetype=int)
+ >>> w.n
+ 4
+ >>> w.neighbors[0]
+ [1]
+
+
+ """
+
+ adjacency_list = [ map(nodetype, neighs) for neighs in adjacency_list]
+ return ps.W(dict([(nodetype(i),neighs) for i,neighs in enumerate(adjacency_list)]))
+
+
+
+if __name__ == '__main__':
+ import doctest
+ doctest.testmod()
+
+
+
+
+
+
diff --git a/pysal/contrib/viz/__init__.py b/pysal/contrib/viz/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pysal/contrib/viz/mapping.py b/pysal/contrib/viz/mapping.py
new file mode 100644
index 0000000..9503ec1
--- /dev/null
+++ b/pysal/contrib/viz/mapping.py
@@ -0,0 +1,598 @@
+"""
+Choropleth mapping using PySAL and Matplotlib
+
+ToDo:
+ * map_line_shp, map_point_shp should take a shp object not a shp_link
+ * Same for map_poly_shp(_lonlat)
+
+"""
+
+__author__ = "Sergio Rey <sjsrey at gmail.com>", "Dani Arribas-Bel <daniel.arribas.bel at gmail.com"
+
+
+import pandas as pd
+import pysal as ps
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib import colors as clrs
+import matplotlib as mpl
+from matplotlib.pyplot import fill, text
+from matplotlib import cm
+from matplotlib.patches import Polygon
+from matplotlib.path import Path
+from matplotlib.collections import LineCollection, PathCollection, PolyCollection, PathCollection, PatchCollection
+
+def map_point_shp(shp, which='all'):
+ '''
+ Create a map object from a point shape
+ ...
+
+ Arguments
+ ---------
+
+ shp : iterable
+ PySAL point iterable with the attribute `bbox` (e.g.
+ shape object from `ps.open` a poly shapefile)
+ which : str/list
+
+ Returns
+ -------
+
+ map : PatchCollection
+ Map object with the points from the shape
+
+ '''
+ pts = []
+ if which == 'all':
+ for pt in shp:
+ pts.append(pt)
+ else:
+ for inwhich, pt in zip(which, shp):
+ if inwhich:
+ pts.append(pt)
+ pts = np.array(pts)
+ sc = plt.scatter(pts[:, 0], pts[:, 1])
+ _ = _add_axes2col(sc, shp.bbox)
+ return sc
+
+def map_line_shp(shp, which='all'):
+ '''
+ Create a map object from a line shape
+ ...
+
+ Arguments
+ ---------
+
+ shp : iterable
+ PySAL line iterable with the attribute `bbox` (e.g.
+ shape object from `ps.open` a poly shapefile)
+ which : str/list
+
+ Returns
+ -------
+
+ map : PatchCollection
+ Map object with the lines from the shape
+ This includes the attribute `shp2dbf_row` with the
+ cardinality of every line to its row in the dbf
+ (zero-offset)
+
+ '''
+ patches = []
+ rows = []
+ i = 0
+ if which == 'all':
+ for shape in shp:
+ for xy in shape.parts:
+ patches.append(xy)
+ rows.append(i)
+ i += 1
+ else:
+ for inwhich, shape in zip(which, shp):
+ if inwhich:
+ for xy in shape.parts:
+ patches.append(xy)
+ rows.append(i)
+ i += 1
+ lc = LineCollection(patches)
+ _ = _add_axes2col(lc, shp.bbox)
+ lc.shp2dbf_row = rows
+ return lc
+
+def map_poly_shp(shp, which='all'):
+ '''
+ Create a map object from a polygon shape
+ ...
+
+ Arguments
+ ---------
+
+ shp : iterable
+ PySAL polygon iterable with the attribute `bbox` (e.g.
+ shape object from `ps.open` a poly shapefile)
+ which : str/list
+ List of booleans for which polygons of the shapefile to
+ be included (True) or excluded (False)
+
+ Returns
+ -------
+
+ map : PatchCollection
+ Map object with the polygons from the shape
+ This includes the attribute `shp2dbf_row` with the
+ cardinality of every polygon to its row in the dbf
+ (zero-offset)
+
+ '''
+ patches = []
+ rows = []
+ i = 0
+ if which == 'all':
+ for shape in shp:
+ for ring in shape.parts:
+ xy = np.array(ring)
+ patches.append(xy)
+ rows.append(i)
+ i += 1
+ else:
+ for inwhich, shape in zip(which, shp):
+ if inwhich:
+ for ring in shape.parts:
+ xy = np.array(ring)
+ patches.append(xy)
+ rows.append(i)
+ i += 1
+ pc = PolyCollection(patches)
+ _ = _add_axes2col(pc, shp.bbox)
+ pc.shp2dbf_row = rows
+ return pc
+
+def setup_ax(polyCos_list, ax=None):
+ '''
+ Generate an Axes object for a list of collections
+ ...
+
+ Arguments
+ ---------
+ polyCos_list: list
+ List of Matplotlib collections (e.g. an object from
+ map_poly_shp)
+ ax : AxesSubplot
+ (Optional) Pre-existing axes to which append the collections
+ and setup
+
+ Returns
+ -------
+ ax : AxesSubplot
+ Rescaled axes object with the collection and without frame
+ or X/Yaxis
+ '''
+ if not ax:
+ ax = plt.axes()
+ # Determine bboxes of new axes
+ xlim = [np.inf, -np.inf]
+ ylim = [np.inf, -np.inf]
+ for polyCo in polyCos_list:
+ axs = polyCo.get_axes()
+ xmin, xmax = axs.get_xlim()
+ ymin, ymax = axs.get_ylim()
+ if xmin < xlim[0]:
+ xlim[0] = xmin
+ if xmax > xlim[1]:
+ xlim[1] = xmax
+ if ymin < ylim[0]:
+ ylim[0] = ymin
+ if ymax > ylim[1]:
+ ylim[1] = ymax
+ ax.set_xlim(xlim)
+ ax.set_ylim(ylim)
+ # Resize bbox of each coll and add it to axes
+ for polyCo in polyCos_list:
+ polyCo.get_axes().set_xlim(ax.get_xlim())
+ polyCo.get_axes().set_ylim(ax.get_ylim())
+ ax.add_collection(polyCo)
+ ax.set_frame_on(False)
+ ax.axes.get_yaxis().set_visible(False)
+ ax.axes.get_xaxis().set_visible(False)
+ return ax
+
+def _add_axes2col(col, bbox):
+ """
+ Adds (inplace) axes with proper limits to a poly/line collection. This is
+ still pretty much a hack! Ideally, you don't have to setup a new figure
+ for this
+ ...
+
+ Arguments
+ ---------
+ col : Collection
+ bbox : list
+ Bounding box as [xmin, ymin, xmax, ymax]
+ """
+ tf = plt.figure()
+ ax = plt.axes()
+ minx, miny, maxx, maxy = bbox
+ ax.set_xlim((minx, maxx))
+ ax.set_ylim((miny, maxy))
+ col.set_axes(ax)
+ plt.close(tf)
+ return None
+
+def plot_poly_lines(shp_link, savein=None, poly_col='none'):
+ '''
+ Quick plotting of shapefiles
+ ...
+
+ Arguments
+ ---------
+ shp_link : str
+ Path to shapefile
+ savein : str
+ Path to png file where to dump the plot. Optional,
+ defaults to None
+ poly_col : str
+ Face color of polygons
+ '''
+ fig = plt.figure()
+ ax = fig.add_subplot(111)
+ shp = ps.open(shp_link)
+ patchco = map_poly_shp(shp)
+ patchco.set_facecolor('none')
+ patchco.set_edgecolor('0.8')
+ ax = setup_ax([patchco], ax)
+ if savein:
+ plt.savefig(savein)
+ else:
+ plt.show()
+ return None
+
+def plot_choropleth(shp_link, values, type, k=5, cmap=None, \
+ shp_type='poly', sample_fisher=True, title='', \
+ savein=None, figsize=None, dpi=300):
+ '''
+ Wrapper to quickly create and plot from a lat/lon shapefile
+ ...
+
+ Arguments
+ ---------
+
+ shp_link : str
+ Path to shapefile
+ values : array
+ Numpy array with values to map
+ type : str
+ Type of choropleth. Supported methods:
+ * 'classless'
+ * 'unique_values'
+ * 'quantiles' (default)
+ * 'fisher_jenks'
+ * 'equal_interval'
+ k : int
+ Number of bins to classify values in and assign a color
+ to (defaults to 5)
+ cmap : str
+ Matplotlib coloring scheme. If None (default), uses:
+ * 'classless': 'Greys'
+ * 'unique_values': 'Paired'
+ * 'quantiles': 'hot_r'
+ * 'fisher_jenks': 'hot_r'
+ * 'equal_interval': 'hot_r'
+ shp_type : str
+ 'poly' (default) or 'line', for the kind of shapefile
+ passed
+ sample_fisher : Boolean
+ Defaults to True, controls whether Fisher-Jenks
+ classification uses a sample (faster) or the entire
+ array of values. Ignored if 'classification'!='fisher_jenks'
+ title : str
+ Optional string for the title
+ savein : str
+ Path to png file where to dump the plot. Optional,
+ defaults to None
+ figsize : tuple
+ Figure dimensions
+ dpi : int
+ resolution of graphic file
+
+ Returns
+ -------
+
+ map : PatchCollection
+ Map object with the polygons from the shapefile and
+ unique value coloring
+
+ '''
+ shp = ps.open(shp_link)
+ if shp_type == 'poly':
+ map_obj = map_poly_shp(shp)
+ if shp_type == 'line':
+ map_obj = map_line_shp(shp)
+
+ if type == 'classless':
+ if not cmap:
+ cmap = 'Greys'
+ map_obj = base_choropleth_classless(map_obj, values, cmap=cmap)
+ if type == 'unique_values':
+ if not cmap:
+ cmap = 'Paired'
+ map_obj = base_choropleth_unique(map_obj, values, cmap=cmap)
+ if type == 'quantiles':
+ if not cmap:
+ cmap = 'hot_r'
+ map_obj = base_choropleth_classif(map_obj, values, k=k, \
+ classification='quantiles', cmap=cmap)
+ if type == 'fisher_jenks':
+ if not cmap:
+ cmap = 'hot_r'
+ map_obj = base_choropleth_classif(map_obj, values, k=k, \
+ classification='fisher_jenks', cmap=cmap, \
+ sample_fisher=sample_fisher)
+ if type == 'equal_interval':
+ if not cmap:
+ cmap = 'hot_r'
+ map_obj = base_choropleth_classif(map_obj, values, k=k, \
+ classification='equal_interval', cmap=cmap)
+
+ fig = plt.figure(figsize=figsize)
+ ax = fig.add_subplot(111)
+ ax = setup_ax([map_obj], ax)
+ if title:
+ ax.set_title(title)
+ if type=='quantiles' or type=='fisher_jenks' or type=='equal_interval':
+ cmap = map_obj.get_cmap()
+ norm = map_obj.norm
+ boundaries = np.round(map_obj.norm.boundaries, decimals=3)
+ cbar = plt.colorbar(map_obj, cmap=cmap, norm=norm, boundaries=boundaries, \
+ ticks=boundaries, orientation='horizontal', shrink=0.5)
+ if savein:
+ plt.savefig(savein, dpi=dpi)
+ else:
+ plt.show()
+ return None
+
+
+def base_choropleth_classless(map_obj, values, cmap='Greys' ):
+ '''
+ Set classless coloring from a map object
+ ...
+
+ Arguments
+ ---------
+
+ map_obj : Poly/Line collection
+ Output from map_X_shp
+ values : array
+ Numpy array with values to map
+ cmap : str
+ Matplotlib coloring scheme
+
+ Returns
+ -------
+
+ map : PatchCollection
+ Map object with the polygons from the shapefile and
+ classless coloring
+
+ '''
+ cmap = cm.get_cmap(cmap)
+ map_obj.set_cmap(cmap)
+ if isinstance(map_obj, mpl.collections.PolyCollection):
+ pvalues = _expand_values(values, map_obj.shp2dbf_row)
+ map_obj.set_array(pvalues)
+ map_obj.set_edgecolor('k')
+ elif isinstance(map_obj, mpl.collections.LineCollection):
+ pvalues = _expand_values(values, map_obj.shp2dbf_row)
+ map_obj.set_array(pvalues)
+ elif isinstance(map_obj, mpl.collections.PathCollection):
+ if not hasattr(map_obj, 'shp2dbf_row'):
+ map_obj.shp2dbf_row = np.arange(values.shape[0])
+ map_obj.set_array(values)
+ return map_obj
+
+def base_choropleth_unique(map_obj, values, cmap='hot_r'):
+ '''
+ Set coloring based on unique values from a map object
+ ...
+
+ Arguments
+ ---------
+
+ map_obj : Poly/Line collection
+ Output from map_X_shp
+ values : array
+ Numpy array with values to map
+ cmap : str
+ Matplotlib coloring scheme
+
+ Returns
+ -------
+
+ map : PatchCollection
+ Map object with the polygons from the shapefile and
+ unique value coloring
+
+ '''
+ uvals = np.unique(values)
+ colormap = getattr(plt.cm, cmap)
+ colors = [colormap(i) for i in np.linspace(0, 0.9, len(uvals))]
+ colors = np.random.permutation(colors)
+ colormatch = {val: col for val, col in zip(uvals, colors)}
+
+ if isinstance(map_obj, mpl.collections.PolyCollection):
+ pvalues = _expand_values(values, map_obj.shp2dbf_row)
+ map_obj.set_color([colormatch[i] for i in pvalues])
+ map_obj.set_edgecolor('k')
+ elif isinstance(map_obj, mpl.collections.LineCollection):
+ pvalues = _expand_values(values, map_obj.shp2dbf_row)
+ map_obj.set_color([colormatch[i] for i in pvalues])
+ elif isinstance(map_obj, mpl.collections.PathCollection):
+ if not hasattr(map_obj, 'shp2dbf_row'):
+ map_obj.shp2dbf_row = np.arange(values.shape[0])
+ map_obj.set_array(values)
+ return map_obj
+
+def base_choropleth_classif(map_obj, values, classification='quantiles', \
+ k=5, cmap='hot_r', sample_fisher=True):
+ '''
+ Set coloring based based on different classification
+ methods
+ ...
+
+ Arguments
+ ---------
+
+ map_obj : Poly/Line collection
+ Output from map_X_shp
+ values : array
+ Numpy array with values to map
+ classification : str
+ Classificatio method to use. Options supported:
+ * 'quantiles' (default)
+ * 'fisher_jenks'
+ * 'equal_interval'
+
+ k : int
+ Number of bins to classify values in and assign a color
+ to
+ cmap : str
+ Matplotlib coloring scheme
+ sample_fisher : Boolean
+ Defaults to True, controls whether Fisher-Jenks
+ classification uses a sample (faster) or the entire
+ array of values. Ignored if 'classification'!='fisher_jenks'
+
+ Returns
+ -------
+
+ map : PatchCollection
+ Map object with the polygons from the shapefile and
+ unique value coloring
+
+ '''
+ if classification == 'quantiles':
+ classification = ps.Quantiles(values, k)
+ boundaries = classification.bins.tolist()
+
+ if classification == 'equal_interval':
+ classification = ps.Equal_Interval(values, k)
+ boundaries = classification.bins.tolist()
+
+ if classification == 'fisher_jenks':
+ if sample_fisher:
+ classification = ps.esda.mapclassify.Fisher_Jenks_Sampled(values,k)
+ else:
+ classification = ps.Fisher_Jenks(values,k)
+ boundaries = classification.bins[:]
+
+ map_obj.set_alpha(0.4)
+
+ cmap = cm.get_cmap(cmap, k+1)
+ map_obj.set_cmap(cmap)
+
+ boundaries.insert(0, values.min())
+ norm = clrs.BoundaryNorm(boundaries, cmap.N)
+ map_obj.set_norm(norm)
+
+ if isinstance(map_obj, mpl.collections.PolyCollection):
+ pvalues = _expand_values(values, map_obj.shp2dbf_row)
+ map_obj.set_array(pvalues)
+ map_obj.set_edgecolor('k')
+ elif isinstance(map_obj, mpl.collections.LineCollection):
+ pvalues = _expand_values(values, map_obj.shp2dbf_row)
+ map_obj.set_array(pvalues)
+ elif isinstance(map_obj, mpl.collections.PathCollection):
+ if not hasattr(map_obj, 'shp2dbf_row'):
+ map_obj.shp2dbf_row = np.arange(values.shape[0])
+ map_obj.set_array(values)
+ return map_obj
+
+def _expand_values(values, shp2dbf_row):
+ '''
+ Expand series of values based on dbf order to polygons (to allow plotting
+ of multi-part polygons).
+ ...
+
+ NOTE: this is done externally so it's easy to drop dependency on Pandas
+ when neccesary/time is available.
+
+ Arguments
+ ---------
+ values : ndarray
+ Values aligned with dbf rows to be plotted (e.d.
+ choropleth)
+ shp2dbf_row : list/sequence
+ Cardinality list of polygon to dbf row as provided by
+ map_poly_shp
+
+ Returns
+ -------
+ pvalues : ndarray
+ Values repeated enough times in the right order to be
+ passed from dbf to polygons
+ '''
+ pvalues = pd.Series(values, index=np.arange(values.shape[0]))\
+ .reindex(shp2dbf_row)#Expand values to every poly
+ return pvalues.values
+
+
+
+if __name__ == '__main__':
+
+ data = 'none'
+ if data == 'poly':
+ shp_link = ps.examples.get_path("sids2.shp")
+ shp_link = ps.examples.get_path("Polygon.shp")
+ dbf = ps.open(shp_link.replace('.shp', '.dbf'))
+ '''
+ values = np.array(dbf.by_col("SIDR74"))
+ #values[: values.shape[0]/2] = 1
+ #values[values.shape[0]/2: ] = 0
+ '''
+ patchco = map_poly_shp(ps.open(shp_link))
+ #patchco = base_choropleth_classif(shp_link, np.random.random(3))
+ #patchco = plot_choropleth(shp_link, np.random.random(3), 'quantiles')
+
+ if data == 'point':
+ shp_link = ps.examples.get_path("burkitt.shp")
+ dbf = ps.open(shp_link.replace('.shp', '.dbf'))
+ patchco = map_point_shp(ps.open(shp_link))
+
+ if data == 'line':
+ shp_link = ps.examples.get_path("eberly_net.shp")
+ dbf = ps.open(shp_link.replace('.shp', '.dbf'))
+ values = np.array(dbf.by_col('TNODE'))
+ mobj = map_line_shp(ps.open(shp_link))
+ patchco = base_choropleth_unique(mobj, values)
+
+ '''
+ which = values > 1.
+
+ for shp_link in [shp_link]:
+
+ fig = plt.figure()
+ patchco = map_poly_shp(shp_link)
+ patchcoB = map_poly_shp(shp_link, which=which)
+ patchco.set_facecolor('none')
+ ax = setup_ax([patchco, patchcoB])
+ fig.add_axes(ax)
+ plt.show()
+ break
+ '''
+
+ xy = (((0, 0), (0, 0)), ((2, 1), (2, 1)), ((3, 1), (3, 1)), ((2, 5), (2, 5)))
+ xy = np.array([[10, 30], [20, 20]])
+ markerobj = mpl.markers.MarkerStyle('o')
+ path = markerobj.get_path().transformed(
+ markerobj.get_transform())
+ scales = np.array([2, 2])
+ fig = plt.figure()
+ ax = fig.add_subplot(111)
+ pc = PathCollection((path,), scales, offsets=xy, \
+ facecolors='r', transOffset=mpl.transforms.IdentityTransform())
+ #pc.set_transform(mpl.transforms.IdentityTransform())
+ #_ = _add_axes2col(pc, [0, 0, 5, 5])
+ ax.add_collection(pc)
+ fig.add_axes(ax)
+ #ax = setup_ax([pc], ax)
+ plt.show()
+
diff --git a/pysal/contrib/viz/mapping_guide.ipynb b/pysal/contrib/viz/mapping_guide.ipynb
new file mode 100644
index 0000000..07ec8cf
--- /dev/null
+++ b/pysal/contrib/viz/mapping_guide.ipynb
@@ -0,0 +1,335 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:1ec0d41be57d08ae696d2345210c6409a6c89d84f6beeea54563187008aa78d0"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import numpy as np\n",
+ "import pysal as ps\n",
+ "import random as rdm\n",
+ "from pysal.contrib.viz import mapping as maps\n",
+ "%matplotlib inline\n",
+ "from pylab import *"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Guide for the `mapping` module in `PySAL`\n",
+ "\n",
+ "Contributors:\n",
+ "\n",
+ "* Dani Arribas-Bel `<daniel.arribas.bel at gmail.com>`\n",
+ "\n",
+ "\n",
+ "This document describes the main structure, components and usage of the mapping module in `PySAL`. The is organized around three main layers:\n",
+ "\n",
+ "* A lower-level layer that reads polygon, line and point shapefiles and returns a Matplotlib collection.\n",
+ "* A medium-level layer that performs some usual transformations on a Matplotlib object (e.g. color code polygons according to a vector of values).\n",
+ "* A higher-level layer intended for end-users for particularly useful cases and style preferences pre-defined (e.g. Create a choropleth)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Lower-level component\n",
+ "\n",
+ "This includes basic functionality to read spatial data from a file (currently only shapefiles supported) and produce rudimentary Matplotlib objects. The main methods are:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* `map_poly_shape`: to read in polygon shapefiles"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* `map_line_shape`: to read in line shapefiles"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* `map_point_shape`: to read in point shapefiles"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "These methods all support an option to subset the observations to be plotted (very useful when missing values are present). They can also be overlaid and combined by using the `setup_ax` function. the resulting object is very basic but also very flexible so, for minds used to matplotlib this should be good news as it allows to modify pretty much any property and attribute."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "shp_link = ps.examples.get_path('columbus.shp')\n",
+ "shp = ps.open(shp_link)\n",
+ "some = [bool(rdm.getrandbits(1)) for i in ps.open(shp_link)]\n",
+ "\n",
+ "fig = figure()\n",
+ "\n",
+ "base = maps.map_poly_shp(shp)\n",
+ "base.set_facecolor('none')\n",
+ "base.set_linewidth(0.75)\n",
+ "base.set_edgecolor('0.8')\n",
+ "some = maps.map_poly_shp(shp, which=some)\n",
+ "some.set_alpha(0.5)\n",
+ "some.set_linewidth(0.)\n",
+ "cents = np.array([poly.centroid for poly in ps.open(shp_link)])\n",
+ "pts = scatter(cents[:, 0], cents[:, 1])\n",
+ "pts.set_color('red')\n",
+ "\n",
+ "ax = maps.setup_ax([base, some, pts])\n",
+ "fig.add_axes(ax)\n",
+ "show()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXd8ZHd57/8+dZpGfaVVWa12tdXdBhscG2IbsDHBYGN6\nMwTCvYSQcBNI7g0k/G5Iwu/+SEgIkFxKggkl9GYwxsa9G9zX9mqrVrvqZXo79ffH0Yw0qxlpRpqR\nVrvf9+s1L2mOTpvRzOd8z/N9ns8jua7rIhAIBII1QV7vExAIBIIzCSG6AoFAsIYI0RUIBII1RIiu\nQCAQrCFCdAUCgWANEaIrEAgEa4gQXYFAIFhDhOgKBALBGiJEVyAQCNYQIboCgUCwhgjRFQgEgjVE\niK5AIBCsIUJ0BQKBYA0RoisQCARriBBdgUAgWEOE6AoEAsEaIkRXIBAI1hAhugKBQLCGqOt9AgLB\nannuuedQ1 [...]
+ "text": [
+ "<matplotlib.figure.Figure at 0x10e381a90>"
+ ]
+ }
+ ],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Medium-level component\n",
+ "\n",
+ "This layer comprises functions that perform usual transformations on matplotlib objects, such as color coding objects (points, polygons, etc.) according to a series of values. This includes the following methods:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* `base_choropleth_classless`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* `base_choropleth_unique`\n",
+ "\n",
+ "### Example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "net_link = ps.examples.get_path('eberly_net.shp')\n",
+ "net = ps.open(net_link)\n",
+ "values = np.array(ps.open(net_link.replace('.shp', '.dbf')).by_col('TNODE'))\n",
+ "\n",
+ "pts_link = ps.examples.get_path('eberly_net_pts_onnetwork.shp')\n",
+ "pts = ps.open(pts_link)\n",
+ "\n",
+ "fig = figure()\n",
+ "\n",
+ "netm = maps.map_line_shp(net)\n",
+ "netc = maps.base_choropleth_unique(netm, values)\n",
+ "\n",
+ "ptsm = maps.map_point_shp(pts)\n",
+ "ptsm = maps.base_choropleth_classif(ptsm, values)\n",
+ "ptsm.set_alpha(0.5)\n",
+ "ptsm.set_linewidth(0.)\n",
+ "\n",
+ "ax = maps.setup_ax([netc, ptsm])\n",
+ "fig.add_axes(ax)\n",
+ "show()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xd4VGX2wPHvTCrpgZBQA4EAoRMgdBt2xbKi2BDL4i4q\n/iwIFhSxsHbFlVWxwLqyrqBiV3YtQUqQ3kLHBAgtISGTPkkmM78/bkJNMpmZe+femZzP8/AYMnfu\nPWA4uTnvuec1ORwOB0IIIbzCrHcAQgjRnEjSFUIIL5KkK4QQXiRJVwghvEiSrhBCeJEkXSGE8CJJ\nuj5mw7p1vPNEN46s7c3SX67mlrFj9Q5JCOGCQL0DEK4Z2DOE3pfn8uriC3noqRA6dpqhd0hCCBeY\n5OEIH1JTAVvSoP1UiL8dmAlcBQzSNy4hRJNJecGX7J8GYX2g9YTaTwwFVusZkRDCRVJe8BXHv4fC\nb6H/JjCZa [...]
+ "text": [
+ "<matplotlib.figure.Figure at 0x10e08da50>"
+ ]
+ }
+ ],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "* `base_choropleth_classif`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Higher-level component\n",
+ "\n",
+ "This currently includes the following end-user functions:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* `plot_poly_lines`: very quick shapefile plotting."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "maps.plot_poly_lines(ps.examples.get_path('columbus.shp'))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXlX4tjWxp/MIcyK4DyWljV09+33fv+P0LfqdlVXlRMq\nCorMUwhkev9wnVxQVIYEgp7fWixHIEDyZGefvZ/N2LZtg0KhUChTgZ31BlAoFMpbgoouhUKhTBEq\nuhQKhTJFqOhSKBTKFKGiS6FQKFOEii6FQqFMEX7WG0ChjEK328WPHz/w4cMHWJYF27ZH+losFrG3\nt4dQKDTrl0J5o1DRpcwVgiDAsizwPA+O40a+f71eB8MwHmwZhTIcNL1AmSsYhoEkSeh0OmPd37Zt\nKrqUmUJFlzJ3TCq6FMosoekFytwxiegCeBTpEiEmUTCNhCleQkWXMnfIsgxVVce6L8uy+PXrF4B7\nke2NfBmGg [...]
+ "text": [
+ "<matplotlib.figure.Figure at 0x10e0b7bd0>"
+ ]
+ }
+ ],
+ "prompt_number": 9
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* `plot_choropleth`: for quick plotting of several types of chocopleths."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "shp_link = ps.examples.get_path('columbus.shp')\n",
+ "values = np.array(ps.open(ps.examples.get_path('columbus.dbf')).by_col('HOVAL'))\n",
+ "\n",
+ "types = ['classless', 'unique_values', 'quantiles', 'fisher_jenks', 'equal_interval']\n",
+ "for typ in types:\n",
+ " maps.plot_choropleth(shp_link, values, typ, title=typ)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAD8CAYAAADUv3dIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd8U9X//1+52UnTNG0pbZPuRQeUloKAggwLKAooQ0AQ\nhI/AF+GD/chQhqgoKoJ+UFFQEVRwAR8Q2aCMiuxNS4ECpbSlpTu7GTe/P/rI/VE6s1s5z8fjPpLc\ncc47afrKuee8B8tisVhAIBAIBLdAedoAAoFAeJggoksgEAhuhIgugUAguBEiugQCgeBGiOgSCASC\nGyGiSyAQCG6EiC7BLaxfvx69evVyWft9+vTB2rVrXdY+geAsiOgS/hGwWCywWCxPm0EgNAsRXQKB\nQHAjRHQJTufOnTt47rnnEBAQAH9/f8ycObPeKHTWrFkIDQ2FVCpFWloa/vrrL+bYyZMnkZaWBqlU\nisDAQLz22 [...]
+ "text": [
+ "<matplotlib.figure.Figure at 0x10e06e750>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAD8CAYAAADUv3dIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd8VfX9/5/nnLtvdkL2TiCEMGUoKooobmvditIqWuuq\no61a9evo9Nc66qq21EEVcRUcVUEFFREVlSmbyF4h++5xxu+PQABJQsYdiXyeD6/n3LM+7xtuXnmf\nz3kPyTAMA4FAIBDEBDneBggEAsGRhBBdgUAgiCFCdAUCgSCGCNEVCASCGCJEVyAQCGKIEF2BQCCI\nIUJ0BRHj+uuv509/+lO8zegWsiyzcePGeJshOAKQRJyuQNAiutXV1ZSWlsbbFMGPHOHpCgQCQQwR\nois4iB/eZl955ZXce++9AHz66afk5+fz6KOPkpWVRW5uLtOmTWvzWICHHnqI3Nxc8vPzef755w+6\n9vjx43nuu [...]
+ "text": [
+ "<matplotlib.figure.Figure at 0x10e36afd0>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADxCAYAAABoIWSWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXeYXOWd5/s5qXJ1hc5BHdQttaRWRkIiiyCDAIMsjAEb\nD+MJO/bYntlnl7l7PXvHa4/veHav7dkdB2xjG3tswCQThAVIWEgCSUhq5dxKrdStzqFyOOH+Ud1F\nN0odqjqg83meo1N16pz3fbtU9a33/N5fEAzDMDAxMTExGRPE8R6AiYmJybWEKbomJiYmY4gpuiYm\nJiZjiCm6JiYmJmOIKbomJiYmY4gpuiYmJiZjiCm6Jp9I7r33Xn73u98B8Jvf/IZbbrllnEdkYpJC\nHu8BmJiMlm9961ucPHkyLbIAb7311jiOyMTk8pgzXRMTE5MxxBRdk4yyZ88eFi5cSE5ODo8++iiP\nPvoo//RP/ [...]
+ "text": [
+ "<matplotlib.figure.Figure at 0x10e0b3090>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADxCAYAAABoIWSWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXeYW/d55/s5BTjoGMwMpnMKexdFsag3qhdKsuzIthRl\nHW/WztqJb+7G1/tsudnN3exuvHHu3bjEJVEc7yqWJcXqhbZYRLEXkRTJIWdIDocz5PQZDHo7OOf+\ngaIZ1gEGmCKez/PgAQbAOecHDPDF77y/9/2+gq7rOgYGBgYGU4I43QMwMDAwuJ4wRNfAwMBgCjFE\n18DAwGAKMUTXwMDAYAoxRNfAwMBgCjFE18DAwGAKMUTXIC/a2tpYtWoVLpcLSZL4i7/4i2tu09zc\nzObNm0s+tuXLl7N9+/ZJ7+c//af/xO/+7u8WYUQGBpciT/cADGYX3/3ud9mwYQOHDx+e8DaCICAI\nQglHlebYs [...]
+ "text": [
+ "<matplotlib.figure.Figure at 0x102652150>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADxCAYAAABoIWSWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXl8W9WZ//+WdLVasmVZ8hLvSxbb2fcASYEAIYGyFiht\n6TCdab9tp+10fkNnWjqdTtvfTKelnV9nOsN0g7bfUiBACySEkISsJCELZE8cJ87meF9ka9/u8vtD\ntrATJ/EieUnu++X7utK5955zJEsfnfuc5zyPRlEUBRUVFRWVUUE71h1QUVFRuZFQRVdFRUVlFFFF\nV0VFRWUUUUVXRUVFZRRRRVdFRUVlFFFFV0VFRWUUUUVXZdxw66238txzz131nD/+8Y+sWLFilHo0\nMp588km+853vjHU3VMYZquiqjBs0Gg0ajeaq53z6059mw4YNg6rvd7/7HUuXLk1G14bFYF6Pyo2H\nKroqKldAF [...]
+ "text": [
+ "<matplotlib.figure.Figure at 0x10e60ced0>"
+ ]
+ }
+ ],
+ "prompt_number": 10
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# To-Do list\n",
+ "\n",
+ "General concepts and specific ideas to implement over time, with enough description so they can be brought to life.\n",
+ "\n",
+ "* Support for points in medium and higher layer\n",
+ "* LISA cluster maps"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Caution note on plotting points\n",
+ "\n",
+ "Support for points (dots) is still not quite polished. Ideally, one would like to create a `PathCollection` from scratch so it is analogue to the creation of a `PolyCollection` or `LineCollection`. However, for the time being, we are relying on the wrapper `plt.scatter`, which makes it harder to extract the collection and plug it in a different figure. For that reason, it is recommended that, for the time being, one creates the line and/or polygon map as shown in this notebook and [...]
+ "\n",
+ "**NOTE**: the `PathCollection` created by `plt.scatter` is detailed on line 3142 of [`_axes.py`](https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/axes/_axes.py). Maybe we can take some inspiration from there to create our own `PathCollection` for points so they live at the same level as polygons."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+}
\ No newline at end of file
diff --git a/pysal/contrib/viz/taz_example.ipynb b/pysal/contrib/viz/taz_example.ipynb
new file mode 100644
index 0000000..dca4b1f
--- /dev/null
+++ b/pysal/contrib/viz/taz_example.ipynb
@@ -0,0 +1,454 @@
+{
+ "metadata": {
+ "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import numpy as np\n",
+ "import pysal as ps\n",
+ "import random as rdm\n",
+ "from pysal.contrib.viz import mapping as maps\n",
+ "from matplotlib.collections import LineCollection"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 28
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "shp = ps.open(ps.examples.get_path(\"taz.shp\"))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 29
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "dbf = ps.open(ps.examples.get_path(\"taz.dbf\"))\n",
+ " "
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 30
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "dbf.header"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 31,
+ "text": [
+ "['AREA',\n",
+ " 'PERIMETER',\n",
+ " 'CNTY',\n",
+ " 'RSA',\n",
+ " 'AIRDB',\n",
+ " 'TAZ2K',\n",
+ " 'SQ_MILE',\n",
+ " 'ACRE',\n",
+ " 'NEWSEQ',\n",
+ " 'CSA',\n",
+ " 'CSA_NEW',\n",
+ " 'SQMI_TAZ',\n",
+ " 'TAZ_NUM',\n",
+ " 'CountyFIPS']"
+ ]
+ }
+ ],
+ "prompt_number": 31
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "fig = figure(figsize=(9,9))\n",
+ "base = maps.map_poly_shp(shp)\n",
+ "base.set_linewidth(0.75)\n",
+ "base.set_facecolor('none')\n",
+ "base.set_edgecolor('0.8')\n",
+ "ax = maps.setup_ax([base])\n",
+ "fig.add_axes(ax)\n",
+ "show()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAAIECAYAAABmAjaWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXlb4trytp8MhHkWHEARZ227e++zv/9H+J3ere0ss8zz\nDCEk7x+e5EVFBQQCYd3X5eUESQHJWrVqVT1FSZIkgUAgEAgEwkpDq20AgUAgEAgE9SEOAYFAIBAI\nBOIQEAgEAoFAIA4BgUAgEAgEEIeAQCAQCAQCiENAIBAIBAIBxCEgEAiEhUCSJNzc3KDX66HX60EQ\nBAiCgH6/D1EUIUkS6vU6Hh8fQarFCbOAVdsAAoFA0CLypC1J0puf3/tiGAY6ne7dY1qtVrRaLaTT\naWxtbc3ldRBWB+IQEDTHxcUF9Hr9u/+naRoURYGiKAAARVHKgD34t2FfNP02qDb4nGHfh63mBv82\n+PPgOUZZB [...]
+ "text": [
+ "<matplotlib.figure.Figure at 0x11951afd0>"
+ ]
+ }
+ ],
+ "prompt_number": 32
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## County as unique values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "cnty = np.array(dbf.by_col(\"CNTY\"))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 33
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 33
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "fig = figure(figsize=(9,9))\n",
+ "base = maps.map_poly_shp(shp)\n",
+ "base.set_linewidth(0.75)\n",
+ "base.set_facecolor('none')\n",
+ "base.set_edgecolor('0.8')\n",
+ "counties = maps.base_choropleth_unique(maps.map_poly_shp(shp), cnty)\n",
+ "counties.set_linewidth(0)\n",
+ "counties.set_alpha(.5)\n",
+ "ax = maps.setup_ax([base, counties])\n",
+ "fig.add_axes(ax)\n",
+ "show()\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAAIECAYAAABmAjaWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvduPa8l13/+tqn3hbZPs6zlzZiTHimNLkGEDMvCTAySA\ngziAEQR5CWAgD/FbEMBA/pnkKQHyZgSRzoxGlkaKMJYljSwJsq14rJvHks7czznT9+a+X6vW74Gb\nHHY3u5tks3nZrA/QOH3YTbLI5q761qq1vosREUGj0Wg0Gs1Gw5c9AI1Go9FoNMtHCwKNRqPRaDRa\nEGg0Go1Go9GCQKPRaDQaDbQg0Gg0Go1GAy0INBqNRqPRADCWPQCNRqPRAESEn/z3/47O8XH/Bsb6\n/5TfMwBZnkP+4R/iN//VvwIrf67RzAstCDQajeYeGFi8ENGV76/74pyDC3HtY1qWhejb38azR4/w\n0qc/vZDXo [...]
+ "text": [
+ "<matplotlib.figure.Figure at 0x119518f50>"
+ ]
+ }
+ ],
+ "prompt_number": 34
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "cents = np.array([poly.centroid for poly in shp])\n",
+ "cents[0]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 35,
+ "text": [
+ "array([ 601741.78690918, 3939798.30153461])"
+ ]
+ }
+ ],
+ "prompt_number": 35
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "wrook = ps.rook_from_shapefile(\"taz.shp\")"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 36
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "w = wrook\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 37
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "cents.min(axis=0)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 38,
+ "text": [
+ "array([ 282150.8269443 , 3615409.10372805])"
+ ]
+ }
+ ],
+ "prompt_number": 38
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "def w2line_graph(w, centroids):\n",
+ " \n",
+ " \n",
+ " \n",
+ " segments = []\n",
+ " for i in w.id2i:\n",
+ " origin = cents[i]\n",
+ " for j in w.neighbors[i]:\n",
+ " dest = cents[j]\n",
+ " ij = [i,j]\n",
+ " ij.sort()\n",
+ " segments.append([origin, dest])\n",
+ " #segs = LineCollection(segments)\n",
+ " \n",
+ " #maps._add_axes2col(segs, [minx, miny, maxx, maxy])\n",
+ " return segments \n",
+ "\n",
+ " "
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 39
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "segs = w2line_graph(wrook, cents)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 40
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "fig = figure(figsize=(9,9))\n",
+ "base = maps.map_poly_shp(shp)\n",
+ "base.set_linewidth(0.75)\n",
+ "base.set_facecolor('none')\n",
+ "base.set_edgecolor('0.8')\n",
+ "segs = LineCollection(segs)\n",
+ "maps._add_axes2col(segs, shp.bbox)\n",
+ "segs.set_linewidth(0.20)\n",
+ "ax = maps.setup_ax([base, segs])\n",
+ "fig.add_axes(ax)\n",
+ "show()\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAAIECAYAAABmAjaWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXlwY9W957+62mXt8m7Z8tput+1uaCCdpDvsSyCQDp0O\n0IRACB0I2cib1LyZmqmpysu8WWqmkspL8oZAGggEQiAQ0kAghNBhDRAC3W237XZ7k7xJlmVJlmRJ\nlq+u5o/DlTfJlmRJV5bOp8plW8u9R7J8z/f8zu/3/YlisVgMFAqFQqFQShpG6AFQKBQKhUIRHioI\nKBQKhUKhUEFAoVAoFAqFCgIKhUKhUCiggoBCoVAoFAqoIKBQKBQKhQIqCCgUCqUgiMViGBgYwNLS\nEpaWlsCyLFiWRTQaBcdxiMVi8Pv9GB4eBq0Wp+QCidADoFAolGKEn7Rjsdi6n5N9icViSKXSpMfU\naDQIBoOw2 [...]
+ "text": [
+ "<matplotlib.figure.Figure at 0x10eab9d50>"
+ ]
+ }
+ ],
+ "prompt_number": 41
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Intersection weights"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "wb = ps.regime_weights(np.array(dbf.by_col(\"CNTY\")))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 42
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "wb.n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 43,
+ "text": [
+ "4109"
+ ]
+ }
+ ],
+ "prompt_number": 43
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "wint = ps.weights.Wsets.w_intersection(wb, wrook)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 44
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "segs = w2line_graph(wint, cents)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 45
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 45
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "fig = figure(figsize=(9,9))\n",
+ "base = maps.map_poly_shp(shp)\n",
+ "base.set_linewidth(0.75)\n",
+ "base.set_facecolor('none')\n",
+ "base.set_edgecolor('0.8')\n",
+ "segs = LineCollection(segs)\n",
+ "maps._add_axes2col(segs, shp.bbox)\n",
+ "segs.set_linewidth(0.20)\n",
+ "ax = maps.setup_ax([base, segs])\n",
+ "fig.add_axes(ax)\n",
+ "show()\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAAIECAYAAABmAjaWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXl0W9XZ7//V0SxrlmfLlsc4ju0EAjRtkzIPhUJT0hQI\npVBKCqUTvbfrDuveddfq2997h3XvalfftvdSaIBCobQUSgMUSikpY4FSCLHjOI4nyZYtWZYlWZIl\nWT46+v2xOfIk2ZIs6cjS/qzlZVvDOVuyfPZ3P/t5vo8oHo/HQaFQKBQKpaxhhB4AhUKhUCgU4aGC\ngEKhUCgUChUEFAqFQqFQqCCgUCgUCoUCKggoFAqFQqGACgIKhUKhUCiggoBCoVCKgng8jsHBQSwt\nLWFpaQksy4JlWcRiMXAch3g8jkAggJGREdBqcUo+kAg9AAqFQilF+Ek7Ho+v+znVl1gshlQqTXlM\njUaDUCgEh [...]
+ "text": [
+ "<matplotlib.figure.Figure at 0x10e210910>"
+ ]
+ }
+ ],
+ "prompt_number": 46
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "segments = w2line_graph(wint, cents)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 47
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "fig = figure(figsize=(9,9))\n",
+ "base = maps.map_poly_shp(shp)\n",
+ "base.set_linewidth(0.75)\n",
+ "base.set_facecolor('none')\n",
+ "base.set_edgecolor('0.8')\n",
+ "counties = maps.base_choropleth_unique(maps.map_poly_shp(shp), cnty)\n",
+ "counties.set_linewidth(0)\n",
+ "counties.set_alpha(.5)\n",
+ "segs = LineCollection(segments)\n",
+ "maps._add_axes2col(segs, shp.bbox)\n",
+ "segs.set_linewidth(0.20)\n",
+ "segs.set_color('0.1')\n",
+ "ax = maps.setup_ax([base, counties, segs])\n",
+ "fig.add_axes(ax)\n",
+ "show()\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAAIECAYAAABmAjaWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnVl0E1e67/9VqirNoyVb8oTDPI9xDAQ6IdBJSEMg44GY\n5KZv0ufhvp6n+3JXr/N2Xs7DebjnntXd6T4JbkjoJgRCQtM0CTEEzGwMBEJwGCzLg6x5lqrqPhQy\nNrZsSZZUsrx/a3khpKpdn2S59n9/+xsoURRFEAgEAoFAmNHQchtAIBAIBAJBfoggIBAIBAKBQAQB\ngUAgEAgEIggIBAKBQCCACAICgUAgEAgggoBAIBAIBAIARm4DCAQCgQCIogin8wAcjhgAgKKo4dfS\njxOJJFyuRZg1q2XU6wRCISCCgEAgEIpAusSLKIpjHmf6oWkaCoUi45hKJYeqqlvo67PC4ZhbkvdB\nmDkQQUCoO [...]
+ "text": [
+ "<matplotlib.figure.Figure at 0x1195fe3d0>"
+ ]
+ }
+ ],
+ "prompt_number": 48
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+}
\ No newline at end of file
diff --git a/pysal/contrib/weights_viewer/__init__.py b/pysal/contrib/weights_viewer/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pysal/contrib/weights_viewer/transforms.py b/pysal/contrib/weights_viewer/transforms.py
new file mode 100644
index 0000000..a5e1b0b
--- /dev/null
+++ b/pysal/contrib/weights_viewer/transforms.py
@@ -0,0 +1,208 @@
+import pysal
+
+__author__ = "Charles R Schmidt <schmidtc at gmail.com>"
+
+class WorldToViewTransform(object):
+ """
+ An abstract class modeling a View window.
+ Supports Panning, Zooming, Resizing.
+
+ Is observable.
+
+ Parameters:
+ worldExtent -- Extent,List -- Extent of the world, left,lower,right,upper in world coords
+ pixel_width -- int -- intial width of the view in pixels
+ pixel_height -- int -- intial height of the view in pixels
+
+ Notes:
+ World coordinates are expected to increase the X and Y direction.
+ Pixel coordinates are inverted in the Y direction.
+
+ This class helps tranform world coordinates to screen coordinates.
+ To transform a GraphicsMatrix,
+ matrix.Scale(1.0/model.scale,-1.0/model.scale)
+ matrix.Translate(*model.offset)
+
+ The transforms will be applied in reverse order,
+ The coordinates will first be translated to the origin (of the current view).
+ The coordinates will then be scaled.
+
+ Eg.
+ >>> view = WorldToViewTransform([-180,-90,180,90],500,500)
+ """
+ def __init__(self,worldExtent,pixel_width,pixel_height):
+ """ Intialize the view to the extent of the world """
+ self.__pixel_width = float(pixel_width)
+ self.__pixel_height = float(pixel_height)
+ self.__world = worldExtent
+ self.extent = worldExtent
+ # In World Coords
+ def __copy__(self):
+ return WorldToViewTransform(self.extent,self.__pixel_width,self.__pixel_height)
+ copy = __copy__
+ def __get_offset(self):
+ """
+ Returns the offset of the top left corner of the current view in world coords.
+ Move the world this many units to aling it with the view.
+ """
+ return self.__offset
+ def __set_offset(self,value):
+ """
+ Set the Offset of the top left corner in world coords.
+ """
+ assert len(value) == 2
+ self.__offset = value
+ offset = property(fget=__get_offset,fset=__set_offset)
+ def __get_scale(self):
+ """ Returns the current scale in units/pixel """
+ return self.__scale
+ def __set_scale(self,value):
+ """ Sets the current scale in units/pixel """
+ self.__scale = value
+ scale = property(fget=__get_scale,fset=__set_scale)
+ def __get_extent(self):
+ """Returns the extent of the current view in World Coordinates."""
+ left,upper = self.pixel_to_world(0,0)
+ right,lower = self.pixel_to_world(self.__pixel_width,self.__pixel_height)
+ return pysal.cg.Rectangle(left,lower,right,upper)
+ def __set_extent(self,value):
+ """ Set the extent of the current view in World Coordinates.
+ Preserve fixed scale, take the max of (sx,sy).
+
+ Use this to zoom to a sepcific region when you know the region's
+ bbox in world coords.
+ """
+ left,lower,right,upper = value
+ width = abs(right-left)
+ height = abs(upper-lower)
+ sx = width/self.__pixel_width
+ sy = height/self.__pixel_height
+ self.__scale = max(sx,sy)
+
+ #The offset translate the world to the origin.
+ #The X offset + world.left == 0
+ #The Y offset + world.upper == 0
+
+ # Move the offset a little, so that the center of the extent is in the center of the view.
+ oleft = (left+(width/2.0)) - (self.__pixel_width*self.__scale/2.0)
+ oupper = (upper-height/2.0) + (self.__pixel_height*self.__scale/2.0)
+
+ #self.__offset = (-left,-upper) # in world coords
+ self.__offset = (-oleft,-oupper) # in world coords
+ extent = property(fget=__get_extent,fset=__set_extent)
+ def __get_width(self):
+ """ Returns the width of the current view in world coords """
+ return self.__pixel_width*self.scale
+ def __set_width(self, value):
+ """
+ Sets the width of the current view, value in pixels
+
+ Eg.
+ >>> view = WorldToViewTransform([0,0,100,100],500,500)
+ >>> view.extent[:]
+ [0.0, 0.0, 100.0, 100.0]
+ >>> view.width = 250
+ >>> view.extent[:]
+ [0.0, 0.0, 50.0, 100.0]
+ """
+ if self.__pixel_width != value:
+ self.__pixel_width = value
+ width = property(fget=__get_width,fset=__set_width)
+ def __get_height(self):
+ """ Returns the height of the current view in world coords """
+ return self.__pixel_height*self.scale
+ def __set_height(self, value):
+ """
+ Sets the height of the current view, value in pixels
+
+ Eg.
+ >>> view = WorldToViewTransform([0,0,100,100],500,500)
+ >>> view.extent[:]
+ [0.0, 0.0, 100.0, 100.0]
+ >>> view.height = 250
+ >>> view.extent[:]
+ [0.0, 50.0, 100.0, 100.0]
+ """
+ if self.__pixel_height != value:
+ self.__pixel_height = value
+ height = property(fget=__get_height,fset=__set_height)
+ def __get_pixel_size(self):
+ """
+ Set and Return the current size of the view in pixels.
+ """
+ return self.__pixel_width,self.__pixel_height
+ def __set_pixel_size(self,value):
+ w,h = value
+ if self.__pixel_width != w:
+ self.__pixel_width = w
+ if self.__pixel_height != h:
+ self.__pixel_height = h
+ pixel_size = property(fget=__get_pixel_size,fset=__set_pixel_size)
+
+ def pan(self,dpx,dpy):
+ """
+ Pan the view by (dpx,dpy) pixel coordinates.
+
+ Positive deltas move the world right and down.
+ Negative deltas move the world left and up.
+
+ Eg.
+ >>> view = WorldToViewTransform([0,0,100,100],500,500)
+ >>> view.pan(500,0)
+ >>> view.extent[:]
+ [-100.0, 0.0, 0.0, 100.0]
+ >>> view.pan(-500,500)
+ >>> view.extent[:]
+ [0.0, 100.0, 100.0, 200.0]
+ >>> view.pan(0,-500)
+ >>> view.extent[:]
+ [0.0, 0.0, 100.0, 100.0]
+ >>> view.pan(490,490)
+ >>> view.extent[:]
+ [-98.0, 98.0, 2.0, 198.0]
+ >>> view.pan(-490,-490)
+ >>> view.extent[:]
+ [0.0, 0.0, 100.0, 100.0]
+ """
+ ogx,ogy = self.__offset
+ s = self.scale
+ self.__offset = ogx+(dpx*s),ogy-(dpy*s)
+ def pan_to(self,extent):
+ initScale = self.scale
+ self.extent = extent
+ self.scale = initScale
+ def pixel_to_world(self,px,py):
+ """
+ Returns the world coordinates of the Pixel (px,py).
+
+ Eg.
+ >>> view = WorldToViewTransform([0,0,100,100],500,500)
+ >>> view.pixel_to_world(0,0)
+ (0.0, 100.0)
+ >>> view.pixel_to_world(500,500)
+ (100.0, 0.0)
+ """
+ sx = self.scale
+ sy = -sx
+ ogx,ogy = self.__offset
+ return px*sx - ogx, py*sy - ogy
+ def world_to_pixel(self,x,y):
+ """
+ Returns the pixel of the world coordinate (x,y).
+
+ Eg.
+ >>> view = WorldToViewTransform([0,0,100,100],500,500)
+ >>> view.world_to_pixel(0,0)
+ (0.0, 500.0)
+ >>> view.world_to_pixel(100,100)
+ (500.0, -0.0)
+ """
+ sx = self.scale
+ sy = -sx
+ ogx,ogy = self.__offset
+ return (x+ogx)/sx, (y+ogy)/sy
+
+if __name__=="__main__":
+ import doctest
+ doctest.testmod()
+ view = WorldToViewTransform([0,0,100,100],500,500)
diff --git a/pysal/contrib/weights_viewer/weights_viewer.py b/pysal/contrib/weights_viewer/weights_viewer.py
new file mode 100644
index 0000000..f9aebc4
--- /dev/null
+++ b/pysal/contrib/weights_viewer/weights_viewer.py
@@ -0,0 +1,179 @@
+import wx
+import pysal
+from transforms import WorldToViewTransform
+
+__author__ = "Charles R Schmidt <schmidtc at gmail.com>"
+
+POINT_RADIUS = 5
+BORDER_COLOR = wx.Colour(0,0,0,255)
+SELECTION_COLOR = wx.Colour(255,128,0,255)
+NEIGHBORS_COLOR = wx.Colour(128,255,0,255)
+BACKGROUND_COLOR = wx.Colour(0,0,0,0)
+
+class WeightsMapFrame(wx.Frame):
+ def __init__(self,parent=None,size=(600,600), style=wx.DEFAULT_FRAME_STYLE, geo=None, w=None):
+ wx.Frame.__init__(self,parent,size=size,style=style)
+ self.Bind
+ self.SetTitle("Weights Inspector")
+ if issubclass(type(geo),basestring):
+ geo = pysal.open(geo,'r')
+ self.geo = geo
+ if issubclass(type(w),basestring):
+ w = pysal.open(w,'r').read()
+ self.w = w
+ self.wm = WeightsMap(self,self.geo,self.w)
+
+class WeightsMap(wx.Panel):
+ """ Display a Weights Inspection Map """
+ def __init__(self, parent, geo, w_obj):
+ wx.Panel.__init__(self,parent,size=(600,600))
+ self.status = parent.CreateStatusBar(3)
+ self.status.SetStatusWidths([-1,-2,-2])
+ self.status.SetStatusText('No Selection',0)
+ self.Bind(wx.EVT_SIZE, self.onSize)
+ self.Bind(wx.EVT_PAINT, self.onPaint)
+ self.Bind(wx.EVT_MOUSE_EVENTS, self.onMouse)
+ self.trns = 0
+ self.background = (255,255,255,255)
+ w,h = self.GetSize()
+ self.buffer = wx.EmptyBitmapRGBA(w,h,alpha=self.trns)
+ self.geo = geo
+ if geo.type == pysal.cg.shapes.Polygon:
+ self.drawfunc = self.drawpoly
+ self.locator = pysal.cg.PolygonLocator(geo)
+ elif geo.type == pysal.cg.shapes.Point:
+ self.drawfunc = self.drawpt
+ self.locator = pysal.cg.PointLocator(geo)
+ else:
+ raise TypeError, "Unsupported Type: %r"%(geo.type)
+ self.w = w_obj
+ self._ids = range(self.w.n)
+ self.transform = WorldToViewTransform(geo.bbox,w,h)
+ self.selection = None
+ def onMouse(self, evt):
+ x,y = evt.X,evt.Y
+ X,Y = self.transform.pixel_to_world(x,y)
+ if self.geo.type == pysal.cg.shapes.Polygon:
+ rs = self.locator.contains_point((X,Y))
+ if rs:
+ selection = rs[0].id-1
+ self.set_selection(selection)
+ else:
+ self.set_selection(None)
+ else:
+ print self.locator.nearest((X,Y))
+ def onSize(self, evt):
+ w,h = self.GetSize()
+ self.buffer = wx.EmptyBitmapRGBA(w,h,alpha=self.trns)
+ self.transform = WorldToViewTransform(self.geo.bbox,w,h)
+ self.draw()
+ def onPaint(self, evt):
+ pdc = wx.PaintDC(self)
+ pdc.Clear()
+ self.draw()
+ def draw(self):
+ dc = wx.MemoryDC()
+ dc.SelectObject(self.buffer)
+ dc.SetBackground(wx.Brush(wx.Colour(*self.background)))
+ dc.Clear()
+ dc.SelectObject(wx.NullBitmap)
+ self.draw_shps(self.buffer)
+ cdc = wx.ClientDC(self)
+ cdc.DrawBitmap(self.buffer,0,0)
+ def drawpoly(self,gc,matrix,fill=False,ids=None):
+ geo = self.geo
+ pth = gc.CreatePath()
+ if not ids:
+ ids = xrange(len(geo))
+ for i in ids:
+ poly = geo.get(i)
+ parts = poly.parts
+ if poly.holes[0]:
+ parts = parts+poly.holes
+ for part in parts:
+ x,y = part[0]
+ pth.MoveToPoint(x,y)
+ for x,y in part[1:]:
+ pth.AddLineToPoint(x,y)
+ pth.CloseSubpath()
+ pth.Transform(matrix)
+ if fill:
+ gc.FillPath(pth)
+ gc.StrokePath(pth)
+ return gc
+ def drawpt(self,gc,matrix,fill=False,ids=None):
+ r = POINT_RADIUS
+ radius = r/2.0
+ geo = self.geo
+ for pt in geo:
+ x,y = matrix.TransformPoint(*pt)
+ gc.DrawEllipse(x-radius,y-radius,r,r)
+ return gc
+ def draw_shps(self, buff, fill_color=None, ids=None, fill_style=wx.SOLID):
+ transform = self.transform
+ dc = wx.MemoryDC()
+ dc.SelectObject(buff)
+ gc = wx.GraphicsContext.Create(dc)
+ gc.SetPen( gc.CreatePen(wx.Pen(BORDER_COLOR,1)) )
+ if fill_color != None:
+ gc.SetBrush( gc.CreateBrush(wx.Brush(fill_color,fill_style)) )
+ fill = True
+ else:
+ fill = False
+ matrix = gc.CreateMatrix()
+ matrix.Scale(1./transform.scale,1./-transform.scale)
+ matrix.Translate(*transform.offset)
+ self.drawfunc(gc,matrix,fill,ids)
+ def set_selection(self,sel):
+ if self.selection == sel:
+ return
+ self.selection = sel
+
+ cdc = wx.ClientDC(self)
+ if sel != None:
+ w,h = self.transform.pixel_size
+ buff = self.buffer.GetSubBitmap((0,0,w,h))
+ id = self.w.id_order[sel]
+ neighbors = map(self.w.id_order.index,self.w.neighbors[id])
+ self.draw_shps(buff, NEIGHBORS_COLOR, neighbors)
+ if sel in neighbors:
+ self.draw_shps(buff, SELECTION_COLOR, [sel], wx.CROSSDIAG_HATCH)
+ else:
+ self.draw_shps(buff, SELECTION_COLOR, [sel])
+ print sel,":",neighbors
+ cdc.DrawBitmap(buff,0,0)
+ stat0 = "Selection:%s"%id
+ stat1 = "Neighbors:%s"%(','.join(map(str,self.w.neighbors[id])))
+ stat2 = "Weights:%s"%(','.join(map(str,self.w.weights[id])))
+ self.status.SetStatusText(stat0,0)
+ self.status.SetStatusText(stat1,1)
+ self.status.SetStatusText(stat2,2)
+ else:
+ self.status.SetStatusText('No Selection',0)
+ self.status.SetStatusText('',1)
+ self.status.SetStatusText('',2)
+ cdc.DrawBitmap(self.buffer,0,0)
+
+class WeightsMapApp(wx.App):
+ def __init__(self, geo=None, w=None, redirect=False):
+ self.geo = geo
+ self.w = w
+ wx.App.__init__(self, redirect)
+ def OnInit(self):
+ self.SetAppName("Weights Inspector")
+ self.frame = WeightsMapFrame(None,size=(600,600),geo=self.geo,w=self.w)
+ self.SetTopWindow(self.frame)
+ self.frame.Show()
+ return True
+
+
+if __name__=='__main__':
+ #shp = pysal.examples.get_path('sids2.shp')
+ #w = pysal.examples.get_path('sids2.gal')
+ #app = WeightsMapApp(shp,w)
+ #app.MainLoop()
+
+ shp = pysal.examples.get_path('columbus.shp')
+ w = pysal.queen_from_shapefile(shp)
+ app = WeightsMapApp(shp,w)
+ app.MainLoop()
diff --git a/pysal/core/FileIO.py b/pysal/core/FileIO.py
new file mode 100644
index 0000000..2707fea
--- /dev/null
+++ b/pysal/core/FileIO.py
@@ -0,0 +1,346 @@
+"""
+FileIO: Module for reading and writing various file types in a Pythonic way.
+This module should not be used directly, instead...
+import pysal.core.FileIO as FileIO
+Readers and Writers will mimic python file objects.
+.seek(n) seeks to the n'th object
+.read(n) reads n objects, default == all
+.next() reads the next object
+"""
+
+__author__ = "Charles R Schmidt <schmidtc at gmail.com>"
+
+__all__ = ['FileIO']
+import os.path
+import struct
+from warnings import warn
+import pysal
+
+
+class FileIO_MetaCls(type):
+ """
+ This Meta Class is instantiated when the class is first defined.
+ All subclasses of FileIO also inherit this meta class, which registers their abilities with the FileIO registry.
+ Subclasses must contain FORMATS and MODES (both are type(list))
+ """
+ def __new__(mcs, name, bases, dict):
+ cls = type.__new__(mcs, name, bases, dict)
+ if name != 'FileIO' and name != 'DataTable':
+ if "FORMATS" in dict and "MODES" in dict:
+ #print "Registering %s with FileIO.\n\tFormats: %r\n\tModes: %r"%(name,dict['FORMATS'],dict['MODES'])
+ FileIO._register(cls, dict['FORMATS'], dict['MODES'])
+ else:
+ raise TypeError("FileIO subclasses must have FORMATS and MODES defined")
+ return cls
+
+
+class FileIO(object): # should be a type?
+ """
+ How this works:
+ FileIO.open(\*args) == FileIO(\*args)
+ When creating a new instance of FileIO the .__new__ method intercepts
+ .__new__ parses the filename to determine the fileType
+ next, .__registry and checked for that type.
+ Each type supports one or more modes ['r','w','a',etc]
+ If we support the type and mode, an instance of the appropriate handler
+ is created and returned.
+ All handlers must inherit from this class, and by doing so are automatically
+ added to the .__registry and are forced to conform to the prescribed API.
+ The metaclass takes cares of the registration by parsing the class definition.
+ It doesn't make much sense to treat weights in the same way as shapefiles and dbfs,
+ ....for now we'll just return an instance of W on mode='r'
+ .... on mode='w', .write will expect an instance of W
+ """
+ __metaclass__ = FileIO_MetaCls
+ __registry = {} # {'shp':{'r':[OGRshpReader,pysalShpReader]}}
+
+ def __new__(cls, dataPath='', mode='r', dataFormat=None):
+ """
+ Intercepts the instantiation of FileIO and dispatches to the correct handler
+ If no suitable handler is found a python file object is returned.
+ """
+ if cls is FileIO:
+ try:
+ newCls = object.__new__(cls.__registry[cls.getType(dataPath,
+ mode, dataFormat)][mode][0])
+ except KeyError:
+ return open(dataPath, mode)
+ return newCls
+ else:
+ return object.__new__(cls)
+
+ @staticmethod
+ def getType(dataPath, mode, dataFormat=None):
+ """Parse the dataPath and return the data type"""
+ if dataFormat:
+ ext = dataFormat
+ else:
+ ext = os.path.splitext(dataPath)[1]
+ ext = ext.replace('.', '')
+ ext = ext.lower()
+ if ext == 'txt':
+ f = open(dataPath, 'r')
+ l1 = f.readline()
+ l2 = f.readline()
+ if ext == 'txt':
+ try:
+ n, k = l1.split(',')
+ n, k = int(n), int(k)
+ fields = l2.split(',')
+ assert len(fields) == k
+ return 'geoda_txt'
+ except:
+ return ext
+ return ext
+
+ @classmethod
+ def _register(cls, parser, formats, modes):
+ """ This method is called automatically via the MetaClass of FileIO subclasses
+ This should be private, but that hides it from the MetaClass
+ """
+ assert cls is FileIO
+ for format in formats:
+ if not format in cls.__registry:
+ cls.__registry[format] = {}
+ for mode in modes:
+ if not mode in cls.__registry[format]:
+ cls.__registry[format][mode] = []
+ cls.__registry[format][mode].append(parser)
+ #cls.check()
+
+ @classmethod
+ def check(cls):
+ """ Prints the contents of the registry """
+ print "PySAL File I/O understands the following file extensions:"
+ for key, val in cls.__registry.iteritems():
+ print "Ext: '.%s', Modes: %r" % (key, val.keys())
+
+ @classmethod
+ def open(cls, *args, **kwargs):
+ """ Alias for FileIO() """
+ return cls(*args, **kwargs)
+
+ class _By_Row:
+ def __init__(self, parent):
+ self.p = parent
+
+ def __repr__(self):
+ if not self.p.ids:
+ return "keys: range(0,n)"
+ else:
+ return "keys: " + self.p.ids.keys().__repr__()
+
+ def __getitem__(self, key):
+ if type(key) == list:
+ r = []
+ if self.p.ids:
+ for k in key:
+ r.append(self.p.get(self.p.ids[k]))
+ else:
+ for k in key:
+ r.append(self.p.get(k))
+ return r
+ if self.p.ids:
+ return self.p.get(self.p.ids[key])
+ else:
+ return self.p.get(key)
+ __call__ = __getitem__
+
+ def __init__(self, dataPath='', mode='r', dataFormat=None):
+ self.dataPath = dataPath
+ self.dataObj = ''
+ self.mode = mode
+ #pos Should ALWAYS be in the range 0,...,n
+ #for custom IDs set the ids property.
+ self.pos = 0
+ self.__ids = None # {'id':n}
+ self.__rIds = None
+ self.closed = False
+ self._spec = []
+ self.header = []
+
+ def __getitem__(self, key):
+ return self.by_row.__getitem__(key)
+
+ @property
+ def by_row(self):
+ return self._By_Row(self)
+
+ def __getIds(self):
+ return self.__ids
+
+ def __setIds(self, ids):
+ """ Property Method for .ids
+ Takes a list of ids and maps then to a 0 based index
+ Need to provide a method to set ID's based on a fieldName
+ preferably without reading the whole file.
+ """
+ if isinstance(ids, list):
+ try:
+ assert len(ids) == len(set(ids))
+ except AssertionError:
+ raise KeyError("IDs must be unique")
+ # keys: ID values: i
+ self.__ids = {}
+ # keys: i values: ID
+ self.__rIds = {}
+ for i, id in enumerate(ids):
+ self.__ids[id] = i
+ self.__rIds[i] = id
+ elif isinstance(ids, dict):
+ self.__ids = ids
+ self.__rIds = {}
+ for id, n in ids.iteritems():
+ self.__rIds[n] = id
+ elif not ids:
+ self.__ids = None
+ self.__rIds = None
+ ids = property(fget=__getIds, fset=__setIds)
+
+ @property
+ def rIds(self):
+ return self.__rIds
+
+ def __iter__(self):
+ self.seek(0)
+ return self
+
+ @staticmethod
+ def _complain_ifclosed(closed):
+ """ from StringIO """
+ if closed:
+ raise ValueError("I/O operation on closed file")
+
+ def cast(self, key, typ):
+ """cast key as typ"""
+ if key in self.header:
+ if not self._spec:
+ self._spec = [lambda x:x for key in self.header]
+ if typ is None:
+ self._spec[self.header.index(key)] = lambda x: x
+ else:
+ try:
+ assert hasattr(typ, '__call__')
+ self._spec[self.header.index(key)] = typ
+ except AssertionError:
+ raise TypeError('Cast Objects must be callable')
+ else:
+ raise KeyError("%s" % key)
+
+ def _cast(self, row):
+ if self._spec and row:
+ try:
+ return [f(v) for f, v in zip(self._spec, row)]
+ except ValueError:
+ r = []
+ for f, v in zip(self._spec, row):
+ try:
+ if not v and f != str:
+ raise ValueError
+ r.append(f(v))
+ except ValueError:
+ warn("Value '%r' could not be cast to %s, value set to pysal.MISSINGVALUE" % (v, str(f)), RuntimeWarning)
+ r.append(pysal.MISSINGVALUE)
+ return r
+
+ else:
+ return row
+
+ def next(self):
+ """A FileIO object is its own iterator, see StringIO"""
+ self._complain_ifclosed(self.closed)
+ r = self.__read()
+ if r is None:
+ raise StopIteration
+ return r
+
+ def close(self):
+ """ subclasses should clean themselves up and then call this method """
+ if not self.closed:
+ self.closed = True
+ del self.dataObj, self.pos
+
+ def get(self, n):
+ """ Seeks the file to n and returns n
+ If .ids is set n should be an id,
+ else, n should be an offset
+ """
+ prevPos = self.tell()
+ self.seek(n)
+ obj = self.__read()
+ self.seek(prevPos)
+ return obj
+
+ def seek(self, n):
+ """ Seek the FileObj to the beginning of the n'th record,
+ if ids are set, seeks to the beginning of the record at id, n"""
+ self._complain_ifclosed(self.closed)
+ self.pos = n
+
+ def tell(self):
+ """ Return id (or offset) of next object """
+ self._complain_ifclosed(self.closed)
+ return self.pos
+
+ def read(self, n=-1):
+ """ Read at most n objects, less if read hits EOF
+ if size is negative or omitted read all objects until EOF
+ returns None if EOF is reached before any objects.
+ """
+ self._complain_ifclosed(self.closed)
+ if n < 0:
+ #return list(self)
+ result = []
+ while 1:
+ try:
+ result.append(self.__read())
+ except StopIteration:
+ break
+ return result
+ elif n == 0:
+ return None
+ else:
+ result = []
+ for i in range(0, n):
+ try:
+ result.append(self.__read())
+ except StopIteration:
+ break
+ return result
+
+ def __read(self):
+ """ Gets one row from the file handler, and if necessary casts it's objects """
+ row = self._read()
+ if row is None:
+ raise StopIteration
+ row = self._cast(row)
+ return row
+
+ def _read(self):
+ """ Must be implemented by subclasses that support 'r'
+ subclasses should increment .pos
+ and redefine this doc string
+ """
+ self._complain_ifclosed(self.closed)
+ raise NotImplementedError
+
+ def truncate(self, size=None):
+ """ Should be implemented by subclasses
+ and redefine this doc string
+ """
+ self._complain_ifclosed(self.closed)
+ raise NotImplementedError
+
+ def write(self, obj):
+ """ Must be implemented by subclasses that support 'w'
+ subclasses should increment .pos
+ subclasses should also check if obj is an instance of type(list)
+ and redefine this doc string
+ """
+ self._complain_ifclosed(self.closed)
+ "Write obj to dataObj"
+ raise NotImplementedError
+
+ def flush(self):
+ self._complain_ifclosed(self.closed)
+ raise NotImplementedError
diff --git a/pysal/core/IOHandlers/__init__.py b/pysal/core/IOHandlers/__init__.py
new file mode 100644
index 0000000..b9361cf
--- /dev/null
+++ b/pysal/core/IOHandlers/__init__.py
@@ -0,0 +1,20 @@
+import warnings
+warnings.filterwarnings(
+ action='ignore', message=".*__builtin__.file size changed.*")
+import gwt
+import gal
+import dat
+import pyShpIO
+import wkt
+import geoda_txt
+import csvWrapper
+import pyDbfIO
+import arcgis_dbf
+import arcgis_swm
+import arcgis_txt
+import dat
+import geobugs_txt
+import mat
+import mtx
+import stata_txt
+import wk1
diff --git a/pysal/core/IOHandlers/arcgis_dbf.py b/pysal/core/IOHandlers/arcgis_dbf.py
new file mode 100644
index 0000000..d6047ce
--- /dev/null
+++ b/pysal/core/IOHandlers/arcgis_dbf.py
@@ -0,0 +1,231 @@
+import pysal
+import os.path
+import pysal.core.FileIO as FileIO
+from pysal.weights import W
+from pysal.weights.util import remap_ids
+from warnings import warn
+
+__author__ = "Myunghwa Hwang <mhwang4 at gmail.com>"
+__all__ = ["ArcGISDbfIO"]
+
+
+class ArcGISDbfIO(FileIO.FileIO):
+ """
+ Opens, reads, and writes weights file objects in ArcGIS dbf format.
+
+ Spatial weights objects in the ArcGIS dbf format are used in
+ ArcGIS Spatial Statistics tools.
+ This format is the same as the general dbf format,
+ but the structure of the weights dbf file is fixed unlike other dbf files.
+ This dbf format can be used with the "Generate Spatial Weights Matrix" tool,
+ but not with the tools under the "Mapping Clusters" category.
+
+ The ArcGIS dbf file is assumed to have three or four data columns.
+ When the file has four columns,
+ the first column is meaningless and will be ignored in PySAL
+ during both file reading and file writing.
+ The next three columns hold origin IDs, destinations IDs, and weight values.
+ When the file has three columns,
+ it is assumed that only these data columns exist in the stated order.
+ The name for the orgin IDs column should be the name of
+ ID variable in the original source data table.
+ The names for the destination IDs and weight values columns are NID
+ and WEIGHT, respectively.
+ ArcGIS Spatial Statistics tools support only unique integer IDs.
+ Therefore, the values for origin and destination ID columns should
+ be integer.
+ For the case where the IDs of a weights object are not integers,
+ ArcGISDbfIO allows users to use internal id values corresponding to
+ record numbers, instead of original ids.
+
+ An exemplary structure of an ArcGIS dbf file is as follows:
+ [Line 1] Field1 RECORD_ID NID WEIGHT
+ [Line 2] 0 72 76 1
+ [Line 3] 0 72 79 1
+ [Line 4] 0 72 78 1
+ ...
+
+ Unlike the ArcGIS text format, this format does not seem to include self-neighbors.
+
+ References
+ ----------
+ http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?TopicName=Convert_Spatial_Weights_Matrix_to_Table_(Spatial_Statistics)
+
+ """
+
+ FORMATS = ['arcgis_dbf']
+ MODES = ['r', 'w']
+
+ def __init__(self, *args, **kwargs):
+ self._varName = 'Unknown'
+ args = args[:2]
+ FileIO.FileIO.__init__(self, *args, **kwargs)
+ self.file = pysal.open(self.dataPath, self.mode)
+
+ def _set_varName(self, val):
+ if issubclass(type(val), basestring):
+ self._varName = val
+
+ def _get_varName(self):
+ return self._varName
+ varName = property(fget=_get_varName, fset=_set_varName)
+
+ def read(self, n=-1):
+ self._complain_ifclosed(self.closed)
+ return self._read()
+
+ def seek(self, pos):
+ self.file.seek(pos)
+ self.pos = self.file.pos
+
+ def _read(self):
+ """Reads ArcGIS dbf file
+ Returns a pysal.weights.weights.W object
+
+ Examples
+ --------
+
+ Type 'dir(w)' at the interpreter to see what methods are supported.
+ Open an ArcGIS dbf file and read it into a pysal weights object
+
+ >>> w = pysal.open(pysal.examples.get_path('arcgis_ohio.dbf'),'r','arcgis_dbf').read()
+
+ Get the number of observations from the header
+
+ >>> w.n
+ 88
+
+ Get the mean number of neighbors
+
+ >>> w.mean_neighbors
+ 5.25
+
+ Get neighbor distances for a single observation
+
+ >>> w[1]
+ {2: 1.0, 11: 1.0, 6: 1.0, 7: 1.0}
+
+ """
+ if self.pos > 0:
+ raise StopIteration
+
+ id_var = self.file.header[1]
+ startPos = len(self.file.header)
+
+ if startPos == 3:
+ startPos = 0
+ elif startPos == 4:
+ startPos = 1
+ else:
+ raise ValueError("Wrong structure, a weights dbf file requires at least three data columns")
+
+ self.varName = id_var
+ id_type = int
+ id_spec = self.file.field_spec[startPos]
+ if id_spec[0] != 'N':
+ raise TypeError('The data type for ids should be integer.')
+ self.id_var = id_var
+
+ weights = {}
+ neighbors = {}
+ for row in self.file:
+ i, j, w = tuple(row)[startPos:]
+ i = id_type(i)
+ j = id_type(j)
+ w = float(w)
+ if i not in weights:
+ weights[i] = []
+ neighbors[i] = []
+ weights[i].append(w)
+ neighbors[i].append(j)
+ self.pos = self.file.pos
+
+ return W(neighbors, weights)
+
+ def write(self, obj, useIdIndex=False):
+ """
+
+ Parameters
+ ----------
+ .write(weightsObject)
+ accepts a weights object
+
+ Returns
+ ------
+
+ an ArcGIS dbf file
+ write a weights object to the opened dbf file.
+
+ Examples
+ --------
+
+ >>> import tempfile, pysal, os
+ >>> testfile = pysal.open(pysal.examples.get_path('arcgis_ohio.dbf'),'r','arcgis_dbf')
+ >>> w = testfile.read()
+
+ Create a temporary file for this example
+
+ >>> f = tempfile.NamedTemporaryFile(suffix='.dbf')
+
+ Reassign to new var
+
+ >>> fname = f.name
+
+ Close the temporary named file
+
+ >>> f.close()
+
+ Open the new file in write mode
+
+ >>> o = pysal.open(fname,'w','arcgis_dbf')
+
+ Write the Weights object into the open file
+
+ >>> o.write(w)
+ >>> o.close()
+
+ Read in the newly created text file
+
+ >>> wnew = pysal.open(fname,'r','arcgis_dbf').read()
+
+ Compare values from old to new
+
+ >>> wnew.pct_nonzero == w.pct_nonzero
+ True
+
+ Clean up temporary file created for this example
+
+ >>> os.remove(fname)
+
+ """
+ self._complain_ifclosed(self.closed)
+ if issubclass(type(obj), W):
+ self.file.header = [self.varName, 'NID', 'WEIGHT']
+
+ id_type = type(obj.id_order[0])
+ if id_type is not int and not useIdIndex:
+ raise TypeError("ArcGIS DBF weight files support only integer IDs")
+ if useIdIndex:
+ id2i = obj.id2i
+ obj = remap_ids(obj, id2i)
+
+ id_spec = ('N', len(str(max(obj.id_order))), 0)
+ self.file.field_spec = [id_spec, id_spec, ('N', 13, 6)]
+
+ for id in obj.id_order:
+ neighbors = zip(obj.neighbors[id], obj.weights[id])
+ for neighbor, weight in neighbors:
+ self.file.write([id, neighbor, weight])
+ self.pos = self.file.pos
+
+ else:
+ raise TypeError("Expected a pysal weights object, got: %s" % (
+ type(obj)))
+
+ def flush(self):
+ self._complain_ifclosed(self.closed)
+ self.file.flush()
+
+ def close(self):
+ self.file.close()
+
diff --git a/pysal/core/IOHandlers/arcgis_swm.py b/pysal/core/IOHandlers/arcgis_swm.py
new file mode 100644
index 0000000..488b2d7
--- /dev/null
+++ b/pysal/core/IOHandlers/arcgis_swm.py
@@ -0,0 +1,212 @@
+import pysal
+import os.path
+import numpy as np
+from struct import pack, unpack
+import pysal.core.FileIO as FileIO
+from pysal.weights import W
+from pysal.weights.util import remap_ids
+from warnings import warn
+
+__author__ = "Myunghwa Hwang <mhwang4 at gmail.com>"
+__all__ = ["ArcGISSwmIO"]
+
+
+class ArcGISSwmIO(FileIO.FileIO):
+ """
+ Opens, reads, and writes weights file objects in ArcGIS swm format.
+
+ Spatial weights objects in the ArcGIS swm format are used in
+ ArcGIS Spatial Statistics tools.
+ Particularly, this format can be directly used with the tools under
+ the category of Mapping Clusters.
+
+ The values for [ORG_i] and [DST_i] should be integers,
+ as ArcGIS Spatial Statistics tools support only unique integer IDs.
+ For the case where a weights object uses non-integer IDs,
+ ArcGISSwmIO allows users to use internal ids corresponding to record numbers,
+ instead of original ids.
+
+ The specifics of each part of the above structure is as follows.
+
+ .. table:: ArcGIS SWM Components
+
+ ============ ============ ==================================== ================================
+ Part Data type Description Length
+ ============ ============ ==================================== ================================
+ ID_VAR_NAME ASCII TEXT ID variable name Flexible (Up to the 1st ;)
+ ESRI_SRS ASCII TEXT ESRI spatial reference system Flexible (Btw the 1st ; and \\n)
+ NO_OBS l.e. int Number of observations 4
+ ROW_STD l.e. int Whether or not row-standardized 4
+ WGT_i
+ ORG_i l.e. int ID of observaiton i 4
+ NO_NGH_i l.e. int Number of neighbors for obs. i (m) 4
+ NGHS_i
+ DSTS_i l.e. int IDs of all neighbors of obs. i 4*m
+ WS_i l.e. float Weights for obs. i and its neighbors 8*m
+ W_SUM_i l.e. float Sum of weights for " 8
+ ============ ============ ==================================== ================================
+
+ """
+
+ FORMATS = ['swm']
+ MODES = ['r', 'w']
+
+ def __init__(self, *args, **kwargs):
+ self._varName = 'Unknown'
+ FileIO.FileIO.__init__(self, *args, **kwargs)
+ self.file = open(self.dataPath, self.mode + 'b')
+
+ def _set_varName(self, val):
+ if issubclass(type(val), basestring):
+ self._varName = val
+
+ def _get_varName(self):
+ return self._varName
+ varName = property(fget=_get_varName, fset=_set_varName)
+
+ def read(self, n=-1):
+ self._complain_ifclosed(self.closed)
+ return self._read()
+
+ def seek(self, pos):
+ if pos == 0:
+ self.file.seek(0)
+ self.pos = 0
+
+ def _read(self):
+ """
+ Reads ArcGIS swm file.
+ Returns a pysal.weights.weights.W object
+
+ Examples
+ --------
+
+ Type 'dir(w)' at the interpreter to see what methods are supported.
+ Open an ArcGIS swm file and read it into a pysal weights object
+
+ >>> w = pysal.open(pysal.examples.get_path('ohio.swm'),'r').read()
+
+ Get the number of observations from the header
+
+ >>> w.n
+ 88
+
+ Get the mean number of neighbors
+
+ >>> w.mean_neighbors
+ 5.25
+
+ Get neighbor distances for a single observation
+
+ >>> w[1]
+ {2: 1.0, 11: 1.0, 6: 1.0, 7: 1.0}
+
+ """
+
+ if self.pos > 0:
+ raise StopIteration
+
+ header01 = self.file.readline()
+ id_var, srs = header01[:-1].split(';')
+ self.varName = id_var
+ self.header_len = len(header01) + 8
+ no_obs, row_std = tuple(unpack('<2l', self.file.read(8)))
+
+ neighbors = {}
+ weights = {}
+ for i in xrange(no_obs):
+ origin, no_nghs = tuple(unpack('<2l', self.file.read(8)))
+ neighbors[origin] = []
+ weights[origin] = []
+ if no_nghs > 0:
+ neighbors[origin] = list(unpack('<%il' %
+ no_nghs, self.file.read(4 * no_nghs)))
+ weights[origin] = list(unpack('<%id' %
+ no_nghs, self.file.read(8 * no_nghs)))
+ w_sum = list(unpack('<d', self.file.read(8)))[0]
+
+ self.pos += 1
+ return W(neighbors, weights)
+
+ def write(self, obj, useIdIndex=False):
+ """
+ Writes a spatial weights matrix data file in swm format.
+
+ Parameters
+ ----------
+ .write(weightsObject)
+ accepts a weights object
+
+ Returns
+ -------
+
+ an ArcGIS swm file
+ write a weights object to the opened swm file.
+
+ Examples
+ --------
+
+ >>> import tempfile, pysal, os
+ >>> testfile = pysal.open(pysal.examples.get_path('ohio.swm'),'r')
+ >>> w = testfile.read()
+
+ Create a temporary file for this example
+
+ >>> f = tempfile.NamedTemporaryFile(suffix='.swm')
+
+ Reassign to new var
+
+ >>> fname = f.name
+
+ Close the temporary named file
+
+ >>> f.close()
+
+ Open the new file in write mode
+
+ >>> o = pysal.open(fname,'w')
+
+ Write the Weights object into the open file
+
+ >>> o.write(w)
+ >>> o.close()
+
+ Read in the newly created text file
+
+ >>> wnew = pysal.open(fname,'r').read()
+
+ Compare values from old to new
+
+ >>> wnew.pct_nonzero == w.pct_nonzero
+ True
+
+ Clean up temporary file created for this example
+
+ >>> os.remove(fname) """
+
+ self._complain_ifclosed(self.closed)
+ if issubclass(type(obj), W):
+ if not (type(obj.id_order[0]) in (np.int32, np.int64, int)) and not useIdIndex:
+ raise TypeError("ArcGIS SWM files support only integer IDs")
+ if useIdIndex:
+ id2i = obj.id2i
+ obj = remap_ids(obj, id2i)
+ self.file.write('%s;Unknown\n' % self.varName)
+ self.file.write(pack('<l', obj.n))
+ self.file.write(pack('<l', obj.transform.upper() == 'R'))
+ for obs in obj.weights:
+ self.file.write(pack('<l', obs))
+ no_nghs = len(obj.weights[obs])
+ self.file.write(pack('<l', no_nghs))
+ self.file.write(pack('<%il' % no_nghs, *obj.neighbors[obs]))
+ self.file.write(pack('<%id' % no_nghs, *obj.weights[obs]))
+ self.file.write(pack('<d', sum(obj.weights[obs])))
+ self.pos += 1
+
+ else:
+ raise TypeError("Expected a pysal weights object, got: %s" % (
+ type(obj)))
+
+ def close(self):
+ self.file.close()
+ FileIO.FileIO.close(self)
diff --git a/pysal/core/IOHandlers/arcgis_txt.py b/pysal/core/IOHandlers/arcgis_txt.py
new file mode 100644
index 0000000..77491c4
--- /dev/null
+++ b/pysal/core/IOHandlers/arcgis_txt.py
@@ -0,0 +1,208 @@
+import pysal
+import os.path
+import gwt
+from pysal.weights import W
+from pysal.weights.util import remap_ids
+from warnings import warn
+
+__author__ = "Myunghwa Hwang <mhwang4 at gmail.com>"
+__all__ = ["ArcGISTextIO"]
+
+
+class ArcGISTextIO(gwt.GwtIO):
+ """
+ Opens, reads, and writes weights file objects in ArcGIS ASCII text format.
+
+ Spatial weights objects in the ArcGIS text format are used in
+ ArcGIS Spatial Statistics tools.
+ This format is a simple text file with ASCII encoding.
+ This format can be directly used with the tools under
+ the category of "Mapping Clusters." But, it cannot be used with
+ the "Generate Spatial Weights Matrix" tool.
+
+ The first line of the ArcGIS text file is a header including the name of
+ a data column that holded the ID variable in the original source data table.
+ After this header line, it includes three data columns
+ for origin id, destination id, and weight values.
+ ArcGIS Spatial Statistics tools support only unique integer ids.
+ Thus, the values in the first two columns should be integers.
+ For the case where a weights object uses non-integer IDs,
+ ArcGISTextIO allows users to use internal ids corresponding to record numbers,
+ instead of original ids.
+
+ An exemplary structure of an ArcGIS text file is as follows:
+ [Line 1] StationID
+ [Line 2] 1 1 0.0
+ [Line 3] 1 2 0.1
+ [Line 4] 1 3 0.14286
+ [Line 5] 2 1 0.1
+ [Line 6] 2 3 0.05
+ [Line 7] 3 1 0.16667
+ [Line 8] 3 2 0.06667
+ [Line 9] 3 3 0.0
+ ...
+
+ As shown in the above example, this file format allows explicit specification
+ of weights for self-neighbors.
+ When no entry is available for self-neighbors,
+ ArcGIS spatial statistics tools consider they have zero weights.
+ PySAL ArcGISTextIO class ignores self-neighbors if their weights are zero.
+
+ References
+ ----------
+ http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?TopicName=Modeling_spatial_relationships
+
+ Notes
+ -----
+ When there are an dbf file whose name is identical to the name of the source text file,
+ ArcGISTextIO checks the data type of the ID data column and uses it for reading and
+ writing the text file. Otherwise, it considers IDs are strings.
+
+ """
+
+ FORMATS = ['arcgis_text']
+ MODES = ['r', 'w']
+
+ def __init__(self, *args, **kwargs):
+ args = args[:2]
+ gwt.GwtIO.__init__(self, *args, **kwargs)
+
+ def _read(self):
+ """Reads ArcGIS Text file
+ Returns a pysal.weights.weights.W object
+
+ Examples
+ --------
+
+ Type 'dir(w)' at the interpreter to see what methods are supported.
+ Open a text file and read it into a pysal weights object
+
+ >>> w = pysal.open(pysal.examples.get_path('arcgis_txt.txt'),'r','arcgis_text').read()
+
+ Get the number of observations from the header
+
+ >>> w.n
+ 3
+
+ Get the mean number of neighbors
+
+ >>> w.mean_neighbors
+ 2.0
+
+ Get neighbor distances for a single observation
+
+ >>> w[1]
+ {2: 0.1, 3: 0.14286}
+
+ """
+ if self.pos > 0:
+ raise StopIteration
+
+ id_var = self.file.readline().strip()
+ self.varName = id_var
+ id_order = None
+ id_type = int
+ try:
+ dbf = os.path.join(self.dataPath + '.dbf')
+ if os.path.exists(dbf):
+ db = pysal.open(dbf, 'r')
+ if id_var in db.header:
+ id_order = db.by_col(id_var)
+ id_type = type(id_order[0])
+ else:
+ warn("ID_VAR:'%s' was in in the DBF header, proceeding with unordered string ids." % (id_var), RuntimeWarning)
+ else:
+ warn("DBF relating to ArcGIS TEXT was not found, proceeding with unordered string ids.", RuntimeWarning)
+ except:
+ warn("Exception occurred will reading DBF, proceeding with unordered string ids.", RuntimeWarning)
+
+ if (id_type is not int) or (id_order and type(id_order)[0] is not int):
+ raise TypeError("The data type for ids should be integer.")
+
+ if id_order:
+ self.n = len(id_order)
+ self.shp = os.path.split(self.dataPath)[1].split('.')[0]
+ self.id_var = id_var
+
+ weights, neighbors = self._readlines(id_type)
+ for k in neighbors:
+ if k in neighbors[k]:
+ k_index = neighbors[k].index(k)
+ if weights[k][k_index] == 0.0:
+ del neighbors[k][k_index]
+ del weights[k][k_index]
+
+ self.pos += 1
+ return W(neighbors, weights)
+
+ def write(self, obj, useIdIndex=False):
+ """
+
+ Parameters
+ ----------
+ .write(weightsObject)
+ accepts a weights object
+
+ Returns
+ ------
+
+ an ArcGIS text file
+ write a weights object to the opened text file.
+
+ Examples
+ --------
+
+ >>> import tempfile, pysal, os
+ >>> testfile = pysal.open(pysal.examples.get_path('arcgis_txt.txt'),'r','arcgis_text')
+ >>> w = testfile.read()
+
+ Create a temporary file for this example
+
+ >>> f = tempfile.NamedTemporaryFile(suffix='.txt')
+
+ Reassign to new var
+
+ >>> fname = f.name
+
+ Close the temporary named file
+
+ >>> f.close()
+
+ Open the new file in write mode
+
+ >>> o = pysal.open(fname,'w','arcgis_text')
+
+ Write the Weights object into the open file
+
+ >>> o.write(w)
+ >>> o.close()
+
+ Read in the newly created text file
+
+ >>> wnew = pysal.open(fname,'r','arcgis_text').read()
+
+ Compare values from old to new
+
+ >>> wnew.pct_nonzero == w.pct_nonzero
+ True
+
+ Clean up temporary file created for this example
+
+ >>> os.remove(fname)
+ """
+ self._complain_ifclosed(self.closed)
+ if issubclass(type(obj), W):
+ id_type = type(obj.id_order[0])
+ if id_type is not int and not useIdIndex:
+ raise TypeError("ArcGIS TEXT weight files support only integer IDs")
+ if useIdIndex:
+ id2i = obj.id2i
+ obj = remap_ids(obj, id2i)
+
+ header = '%s\n' % self.varName
+ self.file.write(header)
+ self._writelines(obj)
+ else:
+ raise TypeError("Expected a pysal weights object, got: %s" % (
+ type(obj)))
+
diff --git a/pysal/core/IOHandlers/csvWrapper.py b/pysal/core/IOHandlers/csvWrapper.py
new file mode 100644
index 0000000..2ef0e0b
--- /dev/null
+++ b/pysal/core/IOHandlers/csvWrapper.py
@@ -0,0 +1,97 @@
+import pysal.core.Tables as Tables
+import csv
+
+__author__ = "Charles R Schmidt <schmidtc at gmail.com>"
+__all__ = ['csvWrapper']
+
+
+class csvWrapper(Tables.DataTable):
+
+ __doc__ = Tables.DataTable.__doc__
+
+ FORMATS = ['csv']
+ READ_MODES = ['r','Ur','rU','U']
+ MODES = READ_MODES[:]
+
+ def __init__(self, *args, **kwargs):
+ """
+
+ Examples
+ --------
+ >>> import pysal
+ >>> file_name = pysal.examples.get_path('stl_hom.csv')
+ >>> f = pysal.open(file_name,'r')
+ >>> y = f.read()
+ >>> f.header
+ ['WKT', 'NAME', 'STATE_NAME', 'STATE_FIPS', 'CNTY_FIPS', 'FIPS', 'FIPSNO', 'HR7984', 'HR8488', 'HR8893', 'HC7984', 'HC8488', 'HC8893', 'PO7984', 'PO8488', 'PO8893', 'PE77', 'PE82', 'PE87', 'RDAC80', 'RDAC85', 'RDAC90']
+ >>> f._spec
+ [<type 'str'>, <type 'str'>, <type 'str'>, <type 'int'>, <type 'int'>, <type 'int'>, <type 'int'>, <type 'float'>, <type 'float'>, <type 'float'>, <type 'int'>, <type 'int'>, <type 'int'>, <type 'int'>, <type 'int'>, <type 'int'>, <type 'float'>, <type 'float'>, <type 'float'>, <type 'float'>, <type 'float'>, <type 'float'>]
+
+
+ """
+ Tables.DataTable.__init__(self, *args, **kwargs)
+ self.__idx = {}
+ self.__len = None
+ self._open()
+
+ def __len__(self):
+ return self.__len
+
+ def _open(self):
+ self.fileObj = open(self.dataPath, self.mode)
+ if self.mode in self.READ_MODES:
+ self.dataObj = csv.reader(self.fileObj)
+ data = list(self.dataObj)
+ if self._determineHeader(data):
+ self.header = data.pop(0)
+ else:
+ self.header = ['field_%d' % i for i in range(len(data[0]))]
+ self._spec = self._determineSpec(data)
+ self.data = data
+ self.fileObj.close()
+ self.__len = len(data)
+
+ def _determineHeader(self, data):
+ #head = [val.strip().replace('-','').replace('.','').isdigit() for val in data[0]]
+ #if True in head: #no numbers in header!
+ # HEADER = False
+ # return HEADER
+ headSpec = self._determineSpec([data[0]])
+ restSpec = self._determineSpec(data[1:])
+ if headSpec == restSpec:
+ HEADER = False
+ return HEADER
+ return True
+
+ @staticmethod
+ def _determineSpec(data):
+ cols = len(data[0])
+ spec = []
+ for j in range(cols):
+ isInt = True
+ isFloat = True
+ for row in data:
+ val = row[j]
+ if not val.strip().replace('-', '').replace('.', '').isdigit():
+ isInt = False
+ isFloat = False
+ break
+ else:
+ if isInt and '.' in val:
+ isInt = False
+ if isInt:
+ spec.append(int)
+ elif isFloat:
+ spec.append(float)
+ else:
+ spec.append(str)
+ return spec
+
+ def _read(self):
+ if self.pos < len(self):
+ row = self.data[self.pos]
+ self.pos += 1
+ return row
+ else:
+ return None
+
diff --git a/pysal/core/IOHandlers/dat.py b/pysal/core/IOHandlers/dat.py
new file mode 100644
index 0000000..7f668d9
--- /dev/null
+++ b/pysal/core/IOHandlers/dat.py
@@ -0,0 +1,132 @@
+import pysal
+import os.path
+import gwt
+from pysal.weights import W
+from warnings import warn
+
+__author__ = "Myunghwa Hwang <mhwang4 at gmail.com>"
+__all__ = ["DatIO"]
+
+
+class DatIO(gwt.GwtIO):
+ """
+ Opens, reads, and writes file objects in DAT format.
+
+ Spatial weights objects in DAT format are used in
+ Dr. LeSage's MatLab Econ library.
+ This DAT format is a simple text file with DAT or dat extension.
+ Without header line, it includes three data columns
+ for origin id, destination id, and weight values as follows:
+
+ [Line 1] 2 1 0.25
+ [Line 2] 5 1 0.50
+ ...
+
+ Origin/destination IDs in this file format are simply record
+ numbers starting with 1. IDs are not necessarily integers.
+ Data values for all columns should be numeric.
+
+ """
+
+ FORMATS = ['dat']
+ MODES = ['r', 'w']
+
+ def _read(self):
+ """Reads .dat file
+ Returns a pysal.weights.weights.W object
+
+ Examples
+ --------
+
+ Type 'dir(w)' at the interpreter to see what methods are supported.
+ Open .dat file and read it into a pysal weights object
+
+ >>> w = pysal.open(pysal.examples.get_path('wmat.dat'),'r').read()
+
+ Get the number of observations from the header
+
+ >>> w.n
+ 49
+
+ Get the mean number of neighbors
+
+ >>> w.mean_neighbors
+ 4.7346938775510203
+
+ Get neighbor distances for a single observation
+
+ >>> w[1]
+ {2.0: 0.3333, 5.0: 0.3333, 6.0: 0.3333}
+
+ """
+ if self.pos > 0:
+ raise StopIteration
+
+ id_type = float
+ weights, neighbors = self._readlines(id_type)
+
+ self.pos += 1
+ return W(neighbors, weights)
+
+ def write(self, obj):
+ """
+
+ Parameters
+ ----------
+ .write(weightsObject)
+ accepts a weights object
+
+ Returns
+ ------
+
+ a DAT file
+ write a weights object to the opened DAT file.
+
+ Examples
+ --------
+
+ >>> import tempfile, pysal, os
+ >>> testfile = pysal.open(pysal.examples.get_path('wmat.dat'),'r')
+ >>> w = testfile.read()
+
+ Create a temporary file for this example
+
+ >>> f = tempfile.NamedTemporaryFile(suffix='.dat')
+
+ Reassign to new var
+
+ >>> fname = f.name
+
+ Close the temporary named file
+
+ >>> f.close()
+
+ Open the new file in write mode
+
+ >>> o = pysal.open(fname,'w')
+
+ Write the Weights object into the open file
+
+ >>> o.write(w)
+ >>> o.close()
+
+ Read in the newly created dat file
+
+ >>> wnew = pysal.open(fname,'r').read()
+
+ Compare values from old to new
+
+ >>> wnew.pct_nonzero == w.pct_nonzero
+ True
+
+ Clean up temporary file created for this example
+
+ >>> os.remove(fname)
+ """
+ self._complain_ifclosed(self.closed)
+ if issubclass(type(obj), W):
+ self._writelines(obj)
+ else:
+ raise TypeError("Expected a pysal weights object, got: %s" % (
+ type(obj)))
+
diff --git a/pysal/core/IOHandlers/gal.py b/pysal/core/IOHandlers/gal.py
new file mode 100644
index 0000000..a8fcd47
--- /dev/null
+++ b/pysal/core/IOHandlers/gal.py
@@ -0,0 +1,218 @@
+import pysal.core.FileIO as FileIO
+from pysal.weights import W, WSP
+from scipy import sparse
+import numpy as np
+
+__author__ = 'Charles R Schmidt <schmidtc at gmail.com>'
+__all__ = ['GalIO']
+
+
+class GalIO(FileIO.FileIO):
+ """
+ Opens, reads, and writes file objects in GAL format.
+
+
+ """
+ FORMATS = ['gal']
+ MODES = ['r', 'w']
+
+ def __init__(self, *args, **kwargs):
+ self._typ = str
+ FileIO.FileIO.__init__(self, *args, **kwargs)
+ self.file = open(self.dataPath, self.mode)
+
+ def read(self, n=-1, sparse=False):
+ """
+
+ sparse: boolean
+ If true return scipy sparse object
+ If false return pysal w object
+ """
+ self._sparse = sparse
+ self._complain_ifclosed(self.closed)
+ return self._read()
+
+ def seek(self, pos):
+ if pos == 0:
+ self.file.seek(0)
+ self.pos = 0
+
+ def _get_data_type(self):
+ return self._typ
+
+ def _set_data_type(self, typ):
+ if callable(typ):
+ self._typ = typ
+ else:
+ raise TypeError("Expecting a callable")
+ data_type = property(fset=_set_data_type, fget=_get_data_type)
+
+ def _read(self):
+ """
+ Parameters
+ ----------
+ reads in a GalIO object
+
+ Returns
+ -------
+ returns a W object
+
+ Examples
+ --------
+
+ >>> import tempfile, pysal, os
+
+ Read in a file GAL file
+
+ >>> testfile = pysal.open(pysal.examples.get_path('sids2.gal'),'r')
+
+ Return a W object
+
+ >>> w = testfile.read()
+ >>> w.n == 100
+ True
+ >>> w.sd == 1.5151237573214935
+ True
+ >>> testfile = pysal.open(pysal.examples.get_path('sids2.gal'),'r')
+
+ Return a sparse matrix for the w information
+
+ >>> wsp = testfile.read(sparse=True)
+ >>> wsp.sparse.nnz
+ 462
+
+ """
+ if self._sparse:
+ if self.pos > 0:
+ raise StopIteration
+
+ header = self.file.readline().strip().split()
+ header_n = len(header)
+ n = int(header[0])
+ if header_n > 1:
+ n = int(header[1])
+ ids = []
+ idsappend = ids.append
+ row = []
+ extend = row.extend # avoid dot in loops
+ col = []
+ append = col.append
+ counter = 0
+ typ = self.data_type
+ for i in xrange(n):
+ id, n_neighbors = self.file.readline().strip().split()
+ id = typ(id)
+ n_neighbors = int(n_neighbors)
+ neighbors_i = map(typ, self.file.readline().strip().split())
+ nn = len(neighbors_i)
+ extend([id] * nn)
+ counter += nn
+ for id_neigh in neighbors_i:
+ append(id_neigh)
+ idsappend(id)
+ self.pos += 1
+ row = np.array(row)
+ col = np.array(col)
+ data = np.ones(counter)
+ ids = np.unique(row)
+ row = np.array([np.where(ids == j)[0] for j in row]).flatten()
+ col = np.array([np.where(ids == j)[0] for j in col]).flatten()
+ spmat = sparse.csr_matrix((data, (row, col)), shape=(n, n))
+ return WSP(spmat)
+
+ else:
+ if self.pos > 0:
+ raise StopIteration
+ neighbors = {}
+ ids = []
+ # handle case where more than n is specified in first line
+ header = self.file.readline().strip().split()
+ header_n = len(header)
+ n = int(header[0])
+ if header_n > 1:
+ n = int(header[1])
+ w = {}
+ typ = self.data_type
+ for i in range(n):
+ id, n_neighbors = self.file.readline().strip().split()
+ id = typ(id)
+ n_neighbors = int(n_neighbors)
+ neighbors_i = map(typ, self.file.readline().strip().split())
+ neighbors[id] = neighbors_i
+ ids.append(id)
+ self.pos += 1
+ return W(neighbors, id_order=ids)
+
+ def write(self, obj):
+ """
+
+ Parameters
+ ----------
+ .write(weightsObject)
+ accepts a weights object
+
+ Returns
+ ------
+
+ a GAL file
+ write a weights object to the opened GAL file.
+
+ Examples
+ --------
+
+ >>> import tempfile, pysal, os
+ >>> testfile = pysal.open(pysal.examples.get_path('sids2.gal'),'r')
+ >>> w = testfile.read()
+
+ Create a temporary file for this example
+
+ >>> f = tempfile.NamedTemporaryFile(suffix='.gal')
+
+ Reassign to new var
+
+ >>> fname = f.name
+
+ Close the temporary named file
+
+ >>> f.close()
+
+ Open the new file in write mode
+
+ >>> o = pysal.open(fname,'w')
+
+ Write the Weights object into the open file
+
+ >>> o.write(w)
+ >>> o.close()
+
+ Read in the newly created gal file
+
+ >>> wnew = pysal.open(fname,'r').read()
+
+ Compare values from old to new
+
+ >>> wnew.pct_nonzero == w.pct_nonzero
+ True
+
+ Clean up temporary file created for this example
+
+ >>> os.remove(fname)
+ """
+ self._complain_ifclosed(self.closed)
+ if issubclass(type(obj), W):
+ IDS = obj.id_order
+ self.file.write('%d\n' % (obj.n))
+ for id in IDS:
+ neighbors = obj.neighbors[id]
+ self.file.write('%s %d\n' % (str(id), len(neighbors)))
+ self.file.write(' '.join(map(str, neighbors)) + '\n')
+ self.pos += 1
+ else:
+ raise TypeError("Expected a pysal weights object, got: %s" %
+ (type(obj)))
+
+ def close(self):
+ self.file.close()
+ FileIO.FileIO.close(self)
+
+
diff --git a/pysal/core/IOHandlers/geobugs_txt.py b/pysal/core/IOHandlers/geobugs_txt.py
new file mode 100644
index 0000000..3743fa0
--- /dev/null
+++ b/pysal/core/IOHandlers/geobugs_txt.py
@@ -0,0 +1,254 @@
+import pysal
+import os.path
+import pysal.core.FileIO as FileIO
+from pysal.weights import W
+from warnings import warn
+
+__author__ = "Myunghwa Hwang <mhwang4 at gmail.com>"
+__all__ = ["GeoBUGSTextIO"]
+
+
+class GeoBUGSTextIO(FileIO.FileIO):
+ """
+ Opens, reads, and writes weights file objects in the text format
+ used in GeoBUGS. GeoBUGS generates a spatial weights matrix
+ as an R object and writes it out as an ASCII text representation of
+ the R object.
+
+ An exemplary GeoBUGS text file is as follows.
+ list([CARD],[ADJ],[WGT],[SUMNUMNEIGH])
+ where [CARD] and [ADJ] are required but the others are optional.
+ PySAL assumes [CARD] and [ADJ] always exist in an input text file.
+ It can read a GeoBUGS text file, even when its content is not written
+ in the order of [CARD], [ADJ], [WGT], and [SUMNUMNEIGH].
+ It always writes all of [CARD], [ADJ], [WGT], and [SUMNUMNEIGH].
+ PySAL does not apply text wrapping during file writing.
+
+ In the above example,
+
+ [CARD]:
+ num=c([a list of comma-splitted neighbor cardinalities])
+
+ [ADJ]:
+ adj=c([a list of comma-splitted neighbor IDs])
+ if caridnality is zero, neighbor IDs are skipped.
+ The ordering of observations is the same in both [CARD] and
+ [ADJ].
+ Neighbor IDs are record numbers starting from one.
+
+ [WGT]:
+ weights=c([a list of comma-splitted weights])
+ The restrictions for [ADJ] also apply to [WGT].
+
+ [SUMNUMNEIGH]:
+ sumNumNeigh=[The total number of neighbor pairs]
+ the total number of neighbor pairs is an integer
+ value and the same as the sum of neighbor cardinalities.
+
+ Notes
+ -----
+ For the files generated from R spdep nb2WB and dput function,
+ it is assumed that the value for the control parameter of dput function
+ is NULL. Please refer to R spdep nb2WB function help file.
+
+ References
+ ----------
+ Thomas, A., Best, N., Lunn, D., Arnold, R., and Spiegelhalter, D.
+
+ (2004) GeoBUGS User Manual.
+
+ R spdep nb2WB function help file.
+
+ """
+
+ FORMATS = ['geobugs_text']
+ MODES = ['r', 'w']
+
+ def __init__(self, *args, **kwargs):
+ args = args[:2]
+ FileIO.FileIO.__init__(self, *args, **kwargs)
+ self.file = open(self.dataPath, self.mode)
+
+ def read(self, n=-1):
+ """
+ Reads GeoBUGS text file
+
+ Returns
+ -------
+ a pysal.weights.weights.W object
+
+ Examples
+ --------
+
+ Type 'dir(w)' at the interpreter to see what methods are supported.
+ Open a GeoBUGS text file and read it into a pysal weights object
+
+ >>> w = pysal.open(pysal.examples.get_path('geobugs_scot'),'r','geobugs_text').read()
+ WARNING: there are 3 disconnected observations
+ Island ids: [6, 8, 11]
+
+ Get the number of observations from the header
+
+ >>> w.n
+ 56
+
+ Get the mean number of neighbors
+
+ >>> w.mean_neighbors
+ 4.1785714285714288
+
+ Get neighbor distances for a single observation
+
+ >>> w[1]
+ {9: 1.0, 19: 1.0, 5: 1.0}
+
+ """
+ self._complain_ifclosed(self.closed)
+ return self._read()
+
+ def seek(self, pos):
+ if pos == 0:
+ self.file.seek(0)
+ self.pos = 0
+
+ def _read(self):
+ if self.pos > 0:
+ raise StopIteration
+
+ fbody = self.file.read()
+ body_structure = {}
+ for i in ['num', 'adj', 'weights', 'sumNumNeigh']:
+ i_loc = fbody.find(i)
+ if i_loc != -1:
+ body_structure[i] = (i_loc, i)
+ body_sequence = sorted(body_structure.values())
+ body_sequence.append((-1, 'eof'))
+
+ for i in range(len(body_sequence) - 1):
+ part, next_part = body_sequence[i], body_sequence[i + 1]
+ start, end = part[0], next_part[0]
+ part_text = fbody[start:end]
+
+ part_length, start, end = len(part_text), 0, -1
+ for c in xrange(part_length):
+ if part_text[c].isdigit():
+ start = c
+ break
+
+ for c in xrange(part_length - 1, 0, -1):
+ if part_text[c].isdigit():
+ end = c + 1
+ break
+ part_text = part_text[start: end]
+ part_text = part_text.replace('\n', '')
+ value_type = int
+ if part[1] == 'weights':
+ value_type = float
+ body_structure[part[1]] = [value_type(v)
+ for v in part_text.split(',')]
+
+ cardinalities = body_structure['num']
+ adjacency = body_structure['adj']
+ raw_weights = [1.0] * int(sum(cardinalities))
+ if 'weights' in body_structure and isinstance(body_structure['weights'], list):
+ raw_weights = body_structure['weights']
+
+ no_obs = len(cardinalities)
+ neighbors = {}
+ weights = {}
+ pos = 0
+ for i in xrange(no_obs):
+ neighbors[i + 1] = []
+ weights[i + 1] = []
+ no_nghs = cardinalities[i]
+ if no_nghs > 0:
+ neighbors[i + 1] = adjacency[pos: pos + no_nghs]
+ weights[i + 1] = raw_weights[pos: pos + no_nghs]
+ pos += no_nghs
+
+ self.pos += 1
+ return W(neighbors, weights)
+
+ def write(self, obj):
+ """
+ Writes a weights object to the opened text file.
+
+ Parameters
+ ----------
+ .write(weightsObject)
+ accepts a weights object
+
+ Returns
+ ------
+
+ a GeoBUGS text file
+
+ Examples
+ --------
+
+ >>> import tempfile, pysal, os
+ >>> testfile = pysal.open(pysal.examples.get_path('geobugs_scot'),'r','geobugs_text')
+ >>> w = testfile.read()
+ WARNING: there are 3 disconnected observations
+ Island ids: [6, 8, 11]
+
+ Create a temporary file for this example
+
+ >>> f = tempfile.NamedTemporaryFile(suffix='')
+
+ Reassign to new var
+
+ >>> fname = f.name
+
+ Close the temporary named file
+
+ >>> f.close()
+
+ Open the new file in write mode
+
+ >>> o = pysal.open(fname,'w','geobugs_text')
+
+ Write the Weights object into the open file
+
+ >>> o.write(w)
+ >>> o.close()
+
+ Read in the newly created text file
+
+ >>> wnew = pysal.open(fname,'r','geobugs_text').read()
+ WARNING: there are 3 disconnected observations
+ Island ids: [6, 8, 11]
+
+ Compare values from old to new
+
+ >>> wnew.pct_nonzero == w.pct_nonzero
+ True
+
+ Clean up temporary file created for this example
+
+ >>> os.remove(fname)
+
+ """
+ self._complain_ifclosed(self.closed)
+ if issubclass(type(obj), W):
+
+ cardinalities, neighbors, weights = [], [], []
+ for i in obj.id_order:
+ cardinalities.append(obj.cardinalities[i])
+ neighbors.extend(obj.neighbors[i])
+ weights.extend(obj.weights[i])
+
+ self.file.write('list(')
+ self.file.write('num=c(%s),' % ','.join(map(str, cardinalities)))
+ self.file.write('adj=c(%s),' % ','.join(map(str, neighbors)))
+ self.file.write('sumNumNeigh=%i)' % sum(cardinalities))
+ self.pos += 1
+
+ else:
+ raise TypeError("Expected a pysal weights object, got: %s" % (
+ type(obj)))
+
+ def close(self):
+ self.file.close()
+ FileIO.FileIO.close(self)
+
diff --git a/pysal/core/IOHandlers/geoda_txt.py b/pysal/core/IOHandlers/geoda_txt.py
new file mode 100644
index 0000000..406c8a9
--- /dev/null
+++ b/pysal/core/IOHandlers/geoda_txt.py
@@ -0,0 +1,91 @@
+import pysal.core.Tables as Tables
+
+__author__ = "Charles R Schmidt <schmidtc at gmail.com>"
+__all__ = ['GeoDaTxtReader']
+
+
+class GeoDaTxtReader(Tables.DataTable):
+ """GeoDa Text File Export Format
+ """
+ __doc__ = Tables.DataTable.__doc__
+ FORMATS = ['geoda_txt']
+ MODES = ['r']
+
+ def __init__(self, *args, **kwargs):
+ """
+ Examples
+ --------
+ >>> import pysal
+ >>> f = pysal.open(pysal.examples.get_path('stl_hom.txt'),'r')
+ >>> f.header
+ ['FIPSNO', 'HR8488', 'HR8893', 'HC8488']
+ >>> len(f)
+ 78
+ >>> f.dat[0]
+ ['17107', '1.290722', '1.624458', '2']
+ >>> f.dat[-1]
+ ['29223', '0', '8.451537', '0']
+ >>> f._spec
+ [<type 'int'>, <type 'float'>, <type 'float'>, <type 'int'>]
+
+ """
+ Tables.DataTable.__init__(self, *args, **kwargs)
+ self.__idx = {}
+ self.__len = None
+ self.pos = 0
+ self._open()
+
+ def _open(self):
+ if self.mode == 'r':
+ self.fileObj = open(self.dataPath, 'r')
+ n, k = self.fileObj.readline().strip().split(',')
+ n, k = int(n), int(k)
+ header = self.fileObj.readline().strip().split(',')
+ self.header = [f.replace('"', '') for f in header]
+ try:
+ assert len(self.header) == k
+ except AssertionError:
+ raise TypeError("This is not a valid geoda_txt file.")
+ dat = self.fileObj.readlines()
+ self.dat = [line.strip().split(',') for line in dat]
+ self._spec = self._determineSpec(self.dat)
+ self.__len = len(dat)
+
+ def __len__(self):
+ return self.__len
+
+ def _read(self):
+ if self.pos < len(self):
+ row = self.dat[self.pos]
+ self.pos += 1
+ return row
+ else:
+ raise None
+
+ def close(self):
+ self.fileObj.close()
+ Tables.DataTable.close(self)
+
+ @staticmethod
+ def _determineSpec(data):
+ cols = len(data[0])
+ spec = []
+ for j in range(cols):
+ isInt = True
+ isFloat = True
+ for row in data:
+ val = row[j]
+ if not val.strip().replace('-', '').replace('.', '').isdigit():
+ isInt = False
+ isFloat = False
+ break
+ else:
+ if isInt and '.' in val:
+ isInt = False
+ if isInt:
+ spec.append(int)
+ elif isFloat:
+ spec.append(float)
+ else:
+ spec.append(str)
+ return spec
diff --git a/pysal/core/IOHandlers/gwt.py b/pysal/core/IOHandlers/gwt.py
new file mode 100644
index 0000000..858afc2
--- /dev/null
+++ b/pysal/core/IOHandlers/gwt.py
@@ -0,0 +1,286 @@
+import pysal
+import os.path
+import pysal.core.FileIO as FileIO
+from pysal.weights import W
+from warnings import warn
+
+__author__ = "Charles R Schmidt <schmidtc at gmail.com>"
+__all__ = ["GwtIO"]
+
+
+class unique_filter(object):
+ """
+ Util function:
+ When a new instance is passed as an arugment to the builtin filter
+ it will remove duplicate entries without changing the order of the list.
+
+ Besure to ceate a new instance everytime, unless you want a global filter.
+
+ Example:
+ >>> l = ['a','a','b','a','c','v','d','a','v','d']
+ >>> filter(unique_filter(),l)
+ ['a', 'b', 'c', 'v', 'd']
+ """
+ def __init__(self):
+ self.exclude = set()
+
+ def __call__(self, x):
+ if x in self.exclude:
+ return False
+ else:
+ self.exclude.add(x)
+ return True
+
+
+class GwtIO(FileIO.FileIO):
+
+ FORMATS = ['kwt','gwt']
+ MODES = ['r', 'w']
+
+ def __init__(self, *args, **kwargs):
+ self._varName = 'Unknown'
+ self._shpName = 'Unknown'
+ FileIO.FileIO.__init__(self, *args, **kwargs)
+ self.file = open(self.dataPath, self.mode)
+
+ def _set_varName(self, val):
+ if issubclass(type(val), basestring):
+ self._varName = val
+
+ def _get_varName(self):
+ return self._varName
+ varName = property(fget=_get_varName, fset=_set_varName)
+
+ def _set_shpName(self, val):
+ if issubclass(type(val), basestring):
+ self._shpName = val
+
+ def _get_shpName(self):
+ return self._shpName
+ shpName = property(fget=_get_shpName, fset=_set_shpName)
+
+ def read(self, n=-1):
+ self._complain_ifclosed(self.closed)
+ return self._read()
+
+ def seek(self, pos):
+ if pos == 0:
+ self.file.seek(0)
+ self.pos = 0
+
+ def _readlines(self, id_type, ret_ids=False):
+ """
+ Reads the main body of gwt-like weights files
+ into two dictionaries containing weights and neighbors.
+ This code part is repeatedly used for many weight file formats.
+ Header lines, however, are different from format to format.
+ So, for code reusability, this part is separated out from
+ _read function by Myunghwa Hwang.
+ """
+ data = [row.strip().split() for row in self.file.readlines()]
+ ids = filter(unique_filter(), [x[0] for x in data])
+ ids = map(id_type, ids)
+ WN = {}
+ for id in ids: # note: fromkeys is no good here, all keys end up sharing the say dict value
+ WN[id] = {}
+ for i, j, v in data:
+ i = id_type(i)
+ j = id_type(j)
+ WN[i][j] = float(v)
+ weights = {}
+ neighbors = {}
+ for i in WN:
+ weights[i] = WN[i].values()
+ neighbors[i] = WN[i].keys()
+ if ret_ids:
+ return weights, neighbors, ids
+ else:
+ return weights, neighbors
+
+ def _read(self):
+ """Reads .gwt file
+ Returns a pysal.weights.weights.W object
+
+ Examples
+ --------
+
+ Type 'dir(f)' at the interpreter to see what methods are supported.
+ Open .gwt file and read it into a pysal weights object
+
+ >>> f = pysal.open(pysal.examples.get_path('juvenile.gwt'),'r').read()
+
+ Get the number of observations from the header
+
+ >>> f.n
+ 168
+
+ Get the mean number of neighbors
+
+ >>> f.mean_neighbors
+ 16.678571428571427
+
+ Get neighbor distances for a single observation
+
+ >>> f[1]
+ {2: 14.1421356}
+
+
+ """
+ if self.pos > 0:
+ raise StopIteration
+
+ flag, n, shp, id_var = self.file.readline().strip().split()
+ self.shpName = shp
+ self.varName = id_var
+ id_order = None
+ id_type = str
+ try:
+ base = os.path.split(self.dataPath)[0]
+ dbf = os.path.join(base, self.shpName.replace('.shp', '') + '.dbf')
+ if os.path.exists(dbf):
+ db = pysal.open(dbf, 'r')
+ if id_var in db.header:
+ id_order = db.by_col(id_var)
+ id_type = type(id_order[0])
+ else:
+ warn("ID_VAR:'%s' was not in the DBF header, proceeding with unordered string ids." % (id_var), RuntimeWarning)
+ else:
+ warn("DBF relating to GWT was not found, proceeding with unordered string ids.", RuntimeWarning)
+ except:
+ warn("Exception occurred will reading DBF, proceeding with unordered string ids.", RuntimeWarning)
+ self.flag = flag
+ self.n = n
+ self.shp = shp
+ self.id_var = id_var
+ if id_order is None:
+ weights, neighbors, id_order = self._readlines(id_type, True)
+ else:
+ weights, neighbors = self._readlines(id_type)
+
+ self.pos += 1
+ w = W(neighbors, weights, id_order)
+ #w.transform = 'b'
+ #set meta data
+ w._shpName = self.shpName
+ w._varName = self.varName
+ #warn("Weights have been converted to binary. To retrieve original values use w.transform='o'", RuntimeWarning)
+ return w
+
+ def _writelines(self, obj):
+ """
+ Writes the main body of gwt-like weights files.
+ This code part is repeatedly used for many weight file formats.
+ Header lines, however, are different from format to format.
+ So, for code reusability, this part is separated out from
+ write function by Myunghwa Hwang.
+ """
+ for id in obj.id_order:
+ neighbors = zip(obj.neighbors[id], obj.weights[id])
+ str_id = "_".join(str(id).split())
+ for neighbor, weight in neighbors:
+ neighbor = "_".join(str(neighbor).split())
+
+ self.file.write('%s %s %6G\n' % (str_id,
+ neighbor, weight))
+ self.pos += 1
+
+ def write(self, obj):
+ """
+
+ Parameters
+ ----------
+ .write(weightsObject)
+ accepts a weights object
+
+ Returns
+ ------
+
+ a GWT file
+ write a weights object to the opened GWT file.
+
+ Examples
+ --------
+
+ >>> import tempfile, pysal, os
+ >>> testfile = pysal.open(pysal.examples.get_path('juvenile.gwt'),'r')
+ >>> w = testfile.read()
+
+ Create a temporary file for this example
+
+ >>> f = tempfile.NamedTemporaryFile(suffix='.gwt')
+
+ Reassign to new var
+
+ >>> fname = f.name
+
+ Close the temporary named file
+
+ >>> f.close()
+
+ Open the new file in write mode
+
+ >>> o = pysal.open(fname,'w')
+
+ Write the Weights object into the open file
+
+ >>> o.write(w)
+ >>> o.close()
+
+ Read in the newly created gwt file
+
+ >>> wnew = pysal.open(fname,'r').read()
+
+ Compare values from old to new
+
+ >>> wnew.pct_nonzero == w.pct_nonzero
+ True
+
+ Clean up temporary file created for this example
+
+ >>> os.remove(fname)
+ """
+ self._complain_ifclosed(self.closed)
+ if issubclass(type(obj), W):
+ #transform = obj.transform
+ #obj.transform = 'o'
+ if hasattr(obj, '_shpName'):
+ self.shpName = obj._shpName
+ if hasattr(obj, '_varName'):
+ self.varName = obj._varName
+ header = '%s %i %s %s\n' % ('0', obj.n, self.shpName, self.varName)
+ self.file.write(header)
+ self._writelines(obj)
+ #obj.transform = transform
+
+ else:
+ raise TypeError("Expected a pysal weights object, got: %s" % (
+ type(obj)))
+
+ def close(self):
+ self.file.close()
+ FileIO.FileIO.close(self)
+
+ @staticmethod
+ def __zero_offset(neighbors, weights, original_ids=None):
+ if not original_ids:
+ original_ids = neighbors.keys()
+ old_weights = weights
+ new_weights = {}
+ new_ids = {}
+ old_ids = {}
+ new_neighbors = {}
+ for i in original_ids:
+ new_i = original_ids.index(i)
+ new_ids[new_i] = i
+ old_ids[i] = new_i
+ neighbors_i = neighbors[i]
+ new_neighbors_i = [original_ids.index(j) for j in neighbors_i]
+ new_neighbors[new_i] = new_neighbors_i
+ new_weights[new_i] = weights[i]
+ info = {}
+ info['new_ids'] = new_ids
+ info['old_ids'] = old_ids
+ info['new_neighbors'] = new_neighbors
+ info['new_weights'] = new_weights
+ return info
+
diff --git a/pysal/core/IOHandlers/mat.py b/pysal/core/IOHandlers/mat.py
new file mode 100644
index 0000000..e79da9d
--- /dev/null
+++ b/pysal/core/IOHandlers/mat.py
@@ -0,0 +1,175 @@
+import pysal
+import os.path
+import scipy.io as sio
+import pysal.core.FileIO as FileIO
+from pysal.weights import W
+from pysal.weights.util import full, full2W
+from warnings import warn
+
+__author__ = "Myunghwa Hwang <mhwang4 at gmail.com>"
+__all__ = ["MatIO"]
+
+
+class MatIO(FileIO.FileIO):
+ """
+ Opens, reads, and writes weights file objects in MATLAB Level 4-5 MAT format.
+
+ MAT files are used in Dr. LeSage's MATLAB Econometrics library.
+ The MAT file format can handle both full and sparse matrices,
+ and it allows for a matrix dimension greater than 256.
+ In PySAL, row and column headers of a MATLAB array are ignored.
+
+ PySAL uses matlab io tools in scipy.
+ Thus, it is subject to all limits that loadmat and savemat in scipy have.
+
+ Notes
+ -----
+ If a given weights object contains too many observations to
+ write it out as a full matrix,
+ PySAL writes out the object as a sparse matrix.
+
+ References
+ ----------
+ MathWorks (2011) "MATLAB 7 MAT-File Format" at
+ http://www.mathworks.com/help/pdf_doc/matlab/matfile_format.pdf.
+
+ scipy matlab io
+ http://docs.scipy.org/doc/scipy/reference/tutorial/io.html
+
+ """
+
+ FORMATS = ['mat']
+ MODES = ['r', 'w']
+
+ def __init__(self, *args, **kwargs):
+ self._varName = 'Unknown'
+ FileIO.FileIO.__init__(self, *args, **kwargs)
+ self.file = open(self.dataPath, self.mode + 'b')
+
+ def _set_varName(self, val):
+ if issubclass(type(val), basestring):
+ self._varName = val
+
+ def _get_varName(self):
+ return self._varName
+ varName = property(fget=_get_varName, fset=_set_varName)
+
+ def read(self, n=-1):
+ self._complain_ifclosed(self.closed)
+ return self._read()
+
+ def seek(self, pos):
+ if pos == 0:
+ self.file.seek(0)
+ self.pos = 0
+
+ def _read(self):
+ """Reads MATLAB mat file
+ Returns a pysal.weights.weights.W object
+
+ Examples
+ --------
+
+ Type 'dir(w)' at the interpreter to see what methods are supported.
+ Open a MATLAB mat file and read it into a pysal weights object
+
+ >>> w = pysal.open(pysal.examples.get_path('spat-sym-us.mat'),'r').read()
+
+ Get the number of observations from the header
+
+ >>> w.n
+ 46
+
+ Get the mean number of neighbors
+
+ >>> w.mean_neighbors
+ 4.0869565217391308
+
+ Get neighbor distances for a single observation
+
+ >>> w[1]
+ {25: 1, 3: 1, 28: 1, 39: 1}
+
+ """
+ if self.pos > 0:
+ raise StopIteration
+
+ mat = sio.loadmat(self.file)
+ mat_keys = [k for k in mat if not k.startswith("_")]
+ full_w = mat[mat_keys[0]]
+
+ self.pos += 1
+ return full2W(full_w)
+
+ def write(self, obj):
+ """
+
+ Parameters
+ ----------
+ .write(weightsObject)
+ accepts a weights object
+
+ Returns
+ ------
+
+ a MATLAB mat file
+ write a weights object to the opened mat file.
+
+ Examples
+ --------
+
+ >>> import tempfile, pysal, os
+ >>> testfile = pysal.open(pysal.examples.get_path('spat-sym-us.mat'),'r')
+ >>> w = testfile.read()
+
+ Create a temporary file for this example
+
+ >>> f = tempfile.NamedTemporaryFile(suffix='.mat')
+
+ Reassign to new var
+
+ >>> fname = f.name
+
+ Close the temporary named file
+
+ >>> f.close()
+
+ Open the new file in write mode
+
+ >>> o = pysal.open(fname,'w')
+
+ Write the Weights object into the open file
+
+ >>> o.write(w)
+ >>> o.close()
+
+ Read in the newly created mat file
+
+ >>> wnew = pysal.open(fname,'r').read()
+
+ Compare values from old to new
+
+ >>> wnew.pct_nonzero == w.pct_nonzero
+ True
+
+ Clean up temporary file created for this example
+
+ >>> os.remove(fname)
+
+ """
+ self._complain_ifclosed(self.closed)
+ if issubclass(type(obj), W):
+ try:
+ w = full(obj)[0]
+ except ValueError:
+ w = obj.sparse
+ sio.savemat(self.file, {'WEIGHT': w})
+ self.pos += 1
+ else:
+ raise TypeError("Expected a pysal weights object, got: %s" % (
+ type(obj)))
+
+ def close(self):
+ self.file.close()
+ FileIO.FileIO.close(self)
+
diff --git a/pysal/core/IOHandlers/mtx.py b/pysal/core/IOHandlers/mtx.py
new file mode 100644
index 0000000..1974f18
--- /dev/null
+++ b/pysal/core/IOHandlers/mtx.py
@@ -0,0 +1,241 @@
+import pysal
+import os.path
+import scipy.io as sio
+import pysal.core.FileIO as FileIO
+from pysal.weights import W, WSP
+from pysal.weights.util import full, full2W
+from warnings import warn
+
+__author__ = "Myunghwa Hwang <mhwang4 at gmail.com>"
+__all__ = ["MtxIO"]
+
+
+class MtxIO(FileIO.FileIO):
+ """
+ Opens, reads, and writes weights file objects in Matrix Market MTX format.
+
+ The Matrix Market MTX format is used to facilitate the exchange of matrix data.
+ In PySAL, it is being tested as a new file format for delivering
+ the weights information of a spatial weights matrix.
+ Although the MTX format supports both full and sparse matrices with different
+ data types, it is assumed that spatial weights files in the mtx format always
+ use the sparse (or coordinate) format with real data values.
+ For now, no additional assumption (e.g., symmetry) is made of the structure
+ of a weights matrix.
+
+ With the above assumptions,
+ the structure of a MTX file containing a spatial weights matrix
+ can be defined as follows:
+ %%MatrixMarket matrix coordinate real general <--- header 1 (constant)
+ % Comments starts <---
+ % .... | 0 or more comment lines
+ % Comments ends <---
+ M N L <--- header 2, rows, columns, entries
+ I1 J1 A(I1,J1) <---
+ ... | L entry lines
+ IL JL A(IL,JL) <---
+
+ In the MTX foramt, the index for rows or columns starts with 1.
+
+ PySAL uses mtx io tools in scipy.
+ Thus, it is subject to all limits that scipy currently has.
+ Reengineering might be required, since scipy currently reads in
+ the entire entry into memory.
+
+ References
+ ----------
+ MTX format specification
+ http://math.nist.gov/MatrixMarket/formats.html
+
+ scipy matlab io
+ http://docs.scipy.org/doc/scipy/reference/tutorial/io.html
+
+ """
+
+ FORMATS = ['mtx']
+ MODES = ['r', 'w']
+
+ def __init__(self, *args, **kwargs):
+ FileIO.FileIO.__init__(self, *args, **kwargs)
+ self.file = open(self.dataPath, self.mode + 'b')
+
+ def read(self, n=-1, sparse=False):
+ """
+ sparse: boolean
+ if true, return pysal WSP object
+ if false, return pysal W object
+ """
+ self._sparse = sparse
+ self._complain_ifclosed(self.closed)
+ return self._read()
+
+ def seek(self, pos):
+ if pos == 0:
+ self.file.seek(0)
+ self.pos = 0
+
+ def _read(self):
+ """Reads MatrixMarket mtx file
+ Returns a pysal.weights.weights.W or pysal.weights.weights.WSP object
+
+ Examples
+ --------
+
+ Type 'dir(w)' at the interpreter to see what methods are supported.
+ Open a MatrixMarket mtx file and read it into a pysal weights object
+
+ >>> f = pysal.open(pysal.examples.get_path('wmat.mtx'),'r')
+
+ >>> w = f.read()
+
+ Get the number of observations from the header
+
+ >>> w.n
+ 49
+
+ Get the mean number of neighbors
+
+ >>> w.mean_neighbors
+ 4.7346938775510203
+
+ Get neighbor weights for a single observation
+
+ >>> w[1]
+ {2: 0.33329999999999999, 5: 0.33329999999999999, 6: 0.33329999999999999}
+
+ >>> f.close()
+
+ >>> f = pysal.open(pysal.examples.get_path('wmat.mtx'),'r')
+
+ >>> wsp = f.read(sparse=True)
+
+ Get the number of observations from the header
+
+ >>> wsp.n
+ 49
+
+ Get row from the weights matrix. Note that the first row in the sparse
+ matrix (the 0th row) corresponds to ID 1 from the original mtx file
+ read in.
+
+ >>> print wsp.sparse[0].todense()
+ [[ 0. 0.3333 0. 0. 0.3333 0.3333 0. 0. 0. 0.
+ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
+ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
+ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
+ 0. 0. 0. 0. 0. 0. 0. 0. 0. ]]
+
+ """
+ if self.pos > 0:
+ raise StopIteration
+ mtx = sio.mmread(self.file)
+ ids = range(1, mtx.shape[0] + 1) # matrix market indexes start at one
+ wsp = WSP(mtx, ids)
+ if self._sparse:
+ w = wsp
+ else:
+ w = pysal.weights.WSP2W(wsp)
+ self.pos += 1
+ return w
+
+ def write(self, obj):
+ """
+
+ Parameters
+ ----------
+ .write(weightsObject)
+ accepts a weights object
+
+ Returns
+ ------
+
+ a MatrixMarket mtx file
+ write a weights object to the opened mtx file.
+
+ Examples
+ --------
+
+ >>> import tempfile, pysal, os
+ >>> testfile = pysal.open(pysal.examples.get_path('wmat.mtx'),'r')
+ >>> w = testfile.read()
+
+ Create a temporary file for this example
+
+ >>> f = tempfile.NamedTemporaryFile(suffix='.mtx')
+
+ Reassign to new var
+
+ >>> fname = f.name
+
+ Close the temporary named file
+
+ >>> f.close()
+
+ Open the new file in write mode
+
+ >>> o = pysal.open(fname,'w')
+
+ Write the Weights object into the open file
+
+ >>> o.write(w)
+ >>> o.close()
+
+ Read in the newly created mtx file
+
+ >>> wnew = pysal.open(fname,'r').read()
+
+ Compare values from old to new
+
+ >>> wnew.pct_nonzero == w.pct_nonzero
+ True
+
+ Clean up temporary file created for this example
+
+ >>> os.remove(fname)
+
+ Go to the beginning of the test file
+
+ >>> testfile.seek(0)
+
+ Create a sparse weights instance from the test file
+
+ >>> wsp = testfile.read(sparse=True)
+
+ Open the new file in write mode
+
+ >>> o = pysal.open(fname,'w')
+
+ Write the sparse weights object into the open file
+
+ >>> o.write(wsp)
+ >>> o.close()
+
+ Read in the newly created mtx file
+
+ >>> wsp_new = pysal.open(fname,'r').read(sparse=True)
+
+ Compare values from old to new
+
+ >>> wsp_new.s0 == wsp.s0
+ True
+
+ Clean up temporary file created for this example
+
+ >>> os.remove(fname)
+
+ """
+ self._complain_ifclosed(self.closed)
+ if issubclass(type(obj), W) or issubclass(type(obj), WSP):
+ w = obj.sparse
+ sio.mmwrite(self.file, w, comment='Generated by PySAL',
+ field='real', precision=7)
+ self.pos += 1
+ else:
+ raise TypeError("Expected a pysal weights object, got: %s" % (
+ type(obj)))
+
+ def close(self):
+ self.file.close()
+ FileIO.FileIO.close(self)
+
+
diff --git a/pysal/core/IOHandlers/pyDbfIO.py b/pysal/core/IOHandlers/pyDbfIO.py
new file mode 100644
index 0000000..0677b7d
--- /dev/null
+++ b/pysal/core/IOHandlers/pyDbfIO.py
@@ -0,0 +1,306 @@
+import pysal.core.Tables
+import datetime
+import struct
+import itertools
+from warnings import warn
+import pysal
+
+__author__ = "Charles R Schmidt <schmidtc at gmail.com>"
+__all__ = ['DBF']
+
+
+class DBF(pysal.core.Tables.DataTable):
+ """
+ PySAL DBF Reader/Writer
+
+ This DBF handler implements the PySAL DataTable interface.
+
+ Attributes
+ ----------
+
+ header : list
+ A list of field names. The header is a python list of
+ strings. Each string is a field name and field name must
+ not be longer than 10 characters.
+ field_spec : list
+ A list describing the data types of each field. It is
+ comprised of a list of tuples, each tuple describing a
+ field. The format for the tuples is ("Type",len,precision).
+ Valid Types are 'C' for characters, 'L' for bool, 'D' for
+ data, 'N' or 'F' for number.
+
+ Examples
+ --------
+
+ >>> import pysal
+ >>> dbf = pysal.open(pysal.examples.get_path('juvenile.dbf'), 'r')
+ >>> dbf.header
+ ['ID', 'X', 'Y']
+ >>> dbf.field_spec
+ [('N', 9, 0), ('N', 9, 0), ('N', 9, 0)]
+
+ """
+ FORMATS = ['dbf']
+ MODES = ['r', 'w']
+
+ def __init__(self, *args, **kwargs):
+ """
+ Initializes an instance of the pysal's DBF handler.
+
+ Arguments:
+ dataPath -- str -- Path to file, including file.
+ mode -- str -- 'r' or 'w'
+ """
+ pysal.core.Tables.DataTable.__init__(self, *args, **kwargs)
+ if self.mode == 'r':
+ self.f = f = open(self.dataPath, 'rb')
+ numrec, lenheader = struct.unpack('<xxxxLH22x', f.read(32))
+ numfields = (lenheader - 33) // 32
+ self.n_records = numrec
+ self.n_fields = numfields
+ self.field_info = [('DeletionFlag', 'C', 1, 0)]
+ record_size = 1
+ fmt = 's'
+ self._col_index = {}
+ idx = 0
+ for fieldno in xrange(numfields):
+ name, typ, size, deci = struct.unpack(
+ '<11sc4xBB14x', f.read(32))
+ name = name.replace('\0', '')
+ # eliminate NULs from string
+ self._col_index[name] = (idx, record_size)
+ idx += 1
+ fmt += '%ds' % size
+ record_size += size
+ self.field_info.append((name, typ, size, deci))
+ terminator = f.read(1)
+ assert terminator == '\r'
+ self.header_size = self.f.tell()
+ self.record_size = record_size
+ self.record_fmt = fmt
+ self.pos = 0
+ self.header = [fInfo[0] for fInfo in self.field_info[1:]]
+ field_spec = []
+ for fname, ftype, flen, fpre in self.field_info[1:]:
+ field_spec.append((ftype, flen, fpre))
+ self.field_spec = field_spec
+
+ #self.spec = [types[fInfo[0]] for fInfo in self.field_info]
+ elif self.mode == 'w':
+ self.f = open(self.dataPath, 'wb')
+ self.header = None
+ self.field_spec = None
+ self.numrec = 0
+ self.FIRST_WRITE = True
+
+ def __len__(self):
+ if self.mode != 'r':
+ raise IOError("Invalid operation, Cannot read from a file opened in 'w' mode.")
+ return self.n_records
+
+ def seek(self, i):
+ self.f.seek(self.header_size + (self.record_size * i))
+ self.pos = i
+
+ def _get_col(self, key):
+ """return the column vector"""
+ if key not in self._col_index:
+ raise AttributeError('Field: % s does not exist in header' % key)
+ prevPos = self.tell()
+ idx, offset = self._col_index[key]
+ typ, size, deci = self.field_spec[idx]
+ gap = (self.record_size - size)
+ f = self.f
+ f.seek(self.header_size + offset)
+ col = [0] * self.n_records
+ for i in xrange(self.n_records):
+ value = f.read(size)
+ f.seek(gap, 1)
+ if typ == 'N':
+ value = value.replace('\0', '').lstrip()
+ if value == '':
+ value = pysal.MISSINGVALUE
+ elif deci:
+ try:
+ value = float(value)
+ except ValueError:
+ value = pysal.MISSINGVALUE
+ else:
+ try:
+ value = int(value)
+ except ValueError:
+ value = pysal.MISSINGVALUE
+ elif typ == 'D':
+ try:
+ y, m, d = int(value[:4]), int(value[4:6]), int(value[6:8])
+ value = datetime.date(y, m, d)
+ except ValueError:
+ value = pysal.MISSINGVALUE
+ elif typ == 'L':
+ value = (value in 'YyTt' and 'T') or (
+ value in 'NnFf' and 'F') or '?'
+ elif typ == 'F':
+ value = value.replace('\0', '').lstrip()
+ if value == '':
+ value = pysal.MISSINGVALUE
+ else:
+ value = float(value)
+ if isinstance(value, str):
+ value = value.rstrip()
+ col[i] = value
+ self.seek(prevPos)
+ return col
+
+ def read_record(self, i):
+ self.seek(i)
+ rec = list(struct.unpack(
+ self.record_fmt, self.f.read(self.record_size)))
+ if rec[0] != ' ':
+ return self.read_record(i + 1)
+ result = []
+ for (name, typ, size, deci), value in itertools.izip(self.field_info, rec):
+ if name == 'DeletionFlag':
+ continue
+ if typ == 'N':
+ value = value.replace('\0', '').lstrip()
+ if value == '':
+ value = pysal.MISSINGVALUE
+ elif deci:
+ try:
+ value = float(value)
+ except ValueError:
+ value = pysal.MISSINGVALUE
+ else:
+ try:
+ value = int(value)
+ except ValueError:
+ value = pysal.MISSINGVALUE
+ elif typ == 'D':
+ try:
+ y, m, d = int(value[:4]), int(value[4:6]), int(value[6:8])
+ value = datetime.date(y, m, d)
+ except ValueError:
+ #value = datetime.date.min#NULL Date: See issue 114
+ value = pysal.MISSINGVALUE
+ elif typ == 'L':
+ value = (value in 'YyTt' and 'T') or (
+ value in 'NnFf' and 'F') or '?'
+ elif typ == 'F':
+ value = value.replace('\0', '').lstrip()
+ if value == '':
+ value = pysal.MISSINGVALUE
+ else:
+ value = float(value)
+ if isinstance(value, str):
+ value = value.rstrip()
+ result.append(value)
+ return result
+
+ def _read(self):
+ if self.mode != 'r':
+ raise IOError("Invalid operation, Cannot read from a file opened in 'w' mode.")
+ if self.pos < len(self):
+ rec = self.read_record(self.pos)
+ self.pos += 1
+ return rec
+ else:
+ return None
+
+ def write(self, obj):
+ self._complain_ifclosed(self.closed)
+ if self.mode != 'w':
+ raise IOError("Invalid operation, Cannot write to a file opened in 'r' mode.")
+ if self.FIRST_WRITE:
+ self._firstWrite(obj)
+ if len(obj) != len(self.header):
+ raise TypeError("Rows must contains %d fields" % len(self.header))
+ self.numrec += 1
+ self.f.write(' ') # deletion flag
+ for (typ, size, deci), value in itertools.izip(self.field_spec, obj):
+ if value is None:
+ if typ == 'C':
+ value = ' ' * size
+ else:
+ value = '\0' * size
+ elif typ == "N" or typ == "F":
+ v = str(value).rjust(size, ' ')
+ #if len(v) == size:
+ # value = v
+ #else:
+ value = (("%" + "%d.%d" % (size, deci) + "f") % (value))[:size]
+ elif typ == 'D':
+ value = value.strftime('%Y%m%d')
+ elif typ == 'L':
+ value = str(value)[0].upper()
+ else:
+ value = str(value)[:size].ljust(size, ' ')
+ try:
+ assert len(value) == size
+ except:
+ print value, len(value), size
+ raise
+ self.f.write(value)
+ self.pos += 1
+
+ def flush(self):
+ self._complain_ifclosed(self.closed)
+ self._writeHeader()
+ self.f.flush()
+
+ def close(self):
+ if self.mode == 'w':
+ self.flush()
+ # End of file
+ self.f.write('\x1A')
+ self.f.close()
+ pysal.core.Tables.DataTable.close(self)
+
+ def _firstWrite(self, obj):
+ if not self.header:
+ raise IOError("No header, DBF files require a header.")
+ if not self.field_spec:
+ raise IOError("No field_spec, DBF files require a specification.")
+ self._writeHeader()
+ self.FIRST_WRITE = False
+
+ def _writeHeader(self):
+ """ Modified from: http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/362715 """
+ POS = self.f.tell()
+ self.f.seek(0)
+ ver = 3
+ now = datetime.datetime.now()
+ yr, mon, day = now.year - 1900, now.month, now.day
+ numrec = self.numrec
+ numfields = len(self.header)
+ lenheader = numfields * 32 + 33
+ lenrecord = sum(field[1] for field in self.field_spec) + 1
+ hdr = struct.pack('<BBBBLHH20x', ver, yr, mon, day, numrec,
+ lenheader, lenrecord)
+ self.f.write(hdr)
+ # field specs
+ for name, (typ, size, deci) in itertools.izip(self.header, self.field_spec):
+ name = name.ljust(11, '\x00')
+ fld = struct.pack('<11sc4xBB14x', name, typ, size, deci)
+ self.f.write(fld)
+ # terminator
+ self.f.write('\r')
+ if self.f.tell() != POS and not self.FIRST_WRITE:
+ self.f.seek(POS)
+
+if __name__ == '__main__':
+ import pysal
+ file_name = pysal.examples.get_path("10740.dbf")
+ f = pysal.open(file_name, 'r')
+ newDB = pysal.open('copy.dbf', 'w')
+ newDB.header = f.header
+ newDB.field_spec = f.field_spec
+ print f.header
+ for row in f:
+ print row
+ newDB.write(row)
+ newDB.close()
+ copy = pysal.open('copy.dbf', 'r')
+ f.seek(0)
+ print "HEADER: ", copy.header == f.header
+ print "SPEC: ", copy.field_spec == f.field_spec
+ print "DATA: ", list(copy) == list(f)
diff --git a/pysal/core/IOHandlers/pyShpIO.py b/pysal/core/IOHandlers/pyShpIO.py
new file mode 100644
index 0000000..d1a2c55
--- /dev/null
+++ b/pysal/core/IOHandlers/pyShpIO.py
@@ -0,0 +1,189 @@
+"""
+PySAL ShapeFile Reader and Writer based on pure python shapefile module.
+
+"""
+
+__author__ = "Charles R Schmidt <schmidtc at gmail.com>"
+__credits__ = "Copyright (c) 2009 Charles R. Schmidt"
+__all__ = ['PurePyShpWrapper']
+
+#import pysal
+import pysal.core.FileIO # as FileIO
+from pysal.core.util import shp_file
+import pysal.cg as cg
+from warnings import warn
+import unittest
+
+STRING_TO_TYPE = {'POLYGON': cg.Polygon, 'POINT': cg.Point, 'POINTM':
+ cg.Point, 'POINTZ': cg.Point, 'ARC': cg.Chain, 'POLYGONZ': cg.Polygon}
+TYPE_TO_STRING = {cg.Polygon: 'POLYGON', cg.Point: 'POINT', cg.Chain:
+ 'ARC'} # build the reverse map
+#for key,value in STRING_TO_TYPE.iteritems():
+# TYPE_TO_STRING[value] = key
+
+
+class PurePyShpWrapper(pysal.core.FileIO.FileIO):
+ """
+ FileIO handler for ESRI ShapeFiles.
+
+ Notes
+ -----
+ This class wraps _pyShpIO's shp_file class with the PySAL FileIO API.
+ shp_file can be used without PySAL.
+
+ Attributes
+ ----------
+
+ Formats : list
+ A list of support file extensions
+ Modes : list
+ A list of support file modes
+
+ Examples
+ --------
+
+ >>> import tempfile
+ >>> f = tempfile.NamedTemporaryFile(suffix='.shp'); fname = f.name; f.close()
+ >>> import pysal
+ >>> i = pysal.open(pysal.examples.get_path('10740.shp'),'r')
+ >>> o = pysal.open(fname,'w')
+ >>> for shp in i:
+ ... o.write(shp)
+ >>> o.close()
+ >>> open(pysal.examples.get_path('10740.shp'),'rb').read() == open(fname,'rb').read()
+ True
+ >>> open(pysal.examples.get_path('10740.shx'),'rb').read() == open(fname[:-1]+'x','rb').read()
+ True
+ >>> import os
+ >>> os.remove(fname); os.remove(fname.replace('.shp','.shx'))
+
+ """
+ FORMATS = ['shp', 'shx']
+ MODES = ['w', 'r', 'wb', 'rb']
+
+ def __init__(self, *args, **kwargs):
+ pysal.core.FileIO.FileIO.__init__(self, *args, **kwargs)
+ self.dataObj = None
+ if self.mode == 'r' or self.mode == 'rb':
+ self.__open()
+ elif self.mode == 'w' or self.mode == 'wb':
+ self.__create()
+
+ def __len__(self):
+ if self.dataObj:
+ return len(self.dataObj)
+ else:
+ return 0
+
+ def __open(self):
+ self.dataObj = shp_file(self.dataPath)
+ self.header = self.dataObj.header
+ self.bbox = self.dataObj.bbox
+ try:
+ self.type = STRING_TO_TYPE[self.dataObj.type()]
+ except KeyError:
+ raise TypeError('%s does not support shapes of type: %s'
+ % (self.__class__.__name__, self.dataObj.type()))
+
+ def __create(self):
+ self.write = self.__firstWrite
+
+ def __firstWrite(self, shape):
+ self.type = TYPE_TO_STRING[type(shape)]
+ if self.type == 'POINT':
+ if len(shape) == 3:
+ self.type = 'POINTM'
+ if len(shape) == 4:
+ self.type = 'POINTZ'
+ self.dataObj = shp_file(self.dataPath, 'w', self.type)
+ self.write = self.__writer
+ self.write(shape)
+
+ def __writer(self, shape):
+ if TYPE_TO_STRING[type(shape)] != self.type:
+ raise TypeError("This file only supports %s type shapes" %
+ self.type)
+ rec = {}
+ rec['Shape Type'] = shp_file.SHAPE_TYPES[self.type]
+ if self.type == 'POINT':
+ rec['X'] = shape[0]
+ rec['Y'] = shape[1]
+ if len(shape) > 2:
+ rec['M'] = shape[2]
+ if len(shape) > 3:
+ rec['Z'] = shape[3]
+ shape = rec
+ else:
+ rec['BBOX Xmin'] = shape.bounding_box.left
+ rec['BBOX Ymin'] = shape.bounding_box.lower
+ rec['BBOX Xmax'] = shape.bounding_box.right
+ rec['BBOX Ymax'] = shape.bounding_box.upper
+ if self.type == 'POLYGON':
+ holes = [hole[::-1] for hole in shape.holes if hole]
+ #holes should be in CCW order
+ rec['NumParts'] = len(shape.parts) + len(holes)
+ all_parts = shape.parts + holes
+ else:
+ rec['NumParts'] = len(shape.parts)
+ all_parts = shape.parts
+ partsIndex = [0]
+ for l in map(len, all_parts)[:-1]:
+ partsIndex.append(partsIndex[-1] + l)
+ rec['Parts Index'] = partsIndex
+ verts = sum(all_parts, [])
+ verts = [(x, y) for x, y in verts]
+ rec['NumPoints'] = len(verts)
+ rec['Vertices'] = verts
+ self.dataObj.add_shape(rec)
+ self.pos += 1
+
+ def _read(self):
+ try:
+ rec = self.dataObj.get_shape(self.pos)
+ except IndexError:
+ return None
+ self.pos += 1
+ if self.dataObj.type() == 'POINT':
+ shp = self.type((rec['X'], rec['Y']))
+ elif self.dataObj.type() == 'POINTZ':
+ shp = self.type((rec['X'], rec['Y']))
+ shp.Z = rec['Z']
+ shp.M = rec['M']
+ else:
+ if rec['NumParts'] > 1:
+ partsIndex = list(rec['Parts Index'])
+ partsIndex.append(None)
+ parts = [rec['Vertices'][partsIndex[i]:partsIndex[
+ i + 1]] for i in xrange(rec['NumParts'])]
+ if self.dataObj.type() == 'POLYGON':
+ is_cw = map(pysal.cg.is_clockwise, parts)
+ vertices = [part for part, cw in zip(parts, is_cw) if cw]
+ holes = [part for part, cw in zip(parts, is_cw) if not cw]
+ if not holes:
+ holes = None
+ shp = self.type(vertices, holes)
+ else:
+ vertices = parts
+ shp = self.type(vertices)
+ elif rec['NumParts'] == 1:
+ vertices = rec['Vertices']
+ if self.dataObj.type() == 'POLYGON' and not pysal.cg.is_clockwise(vertices):
+ ### SHAPEFILE WARNING: Polygon %d topology has been fixed. (ccw -> cw)
+ warn("SHAPEFILE WARNING: Polygon %d topology has been fixed. (ccw -> cw)" % (self.pos), RuntimeWarning)
+ print "SHAPEFILE WARNING: Polygon %d topology has been fixed. (ccw -> cw)" % (self.pos)
+
+ shp = self.type(vertices)
+ else:
+ warn("Polygon %d has zero parts" % self.pos, RuntimeWarning)
+ shp = self.type([[]])
+ #raise ValueError, "Polygon %d has zero parts"%self.pos
+ if self.ids:
+ shp.id = self.rIds[self.pos - 1] # shp IDs start at 1.
+ else:
+ shp.id = self.pos # shp IDs start at 1.
+ return shp
+
+ def close(self):
+ self.dataObj.close()
+ pysal.core.FileIO.FileIO.close(self)
+
diff --git a/pysal/core/IOHandlers/stata_txt.py b/pysal/core/IOHandlers/stata_txt.py
new file mode 100644
index 0000000..b4d1cb4
--- /dev/null
+++ b/pysal/core/IOHandlers/stata_txt.py
@@ -0,0 +1,236 @@
+import pysal
+import os.path
+import pysal.core.FileIO as FileIO
+from pysal.weights import W
+from warnings import warn
+
+__author__ = "Myunghwa Hwang <mhwang4 at gmail.com>"
+__all__ = ["StataTextIO"]
+
+
+class StataTextIO(FileIO.FileIO):
+ """
+ Opens, reads, and writes weights file objects in STATA text format.
+
+ Spatial weights objects in the STATA text format are used in
+ STATA sppack library through the spmat command.
+ This format is a simple text file delimited by a whitespace.
+ The spmat command does not specify which file extension to use.
+ But, txt seems the default file extension, which is assumed in PySAL.
+
+ The first line of the STATA text file is
+ a header including the number of observations.
+ After this header line, it includes at least one data column that contains
+ unique ids or record numbers of observations.
+ When an id variable is not specified for the original spatial weights
+ matrix in STATA, record numbers are used to identify individual observations,
+ and the record numbers start with one.
+ The spmat command seems to allow only integer IDs,
+ which is also assumed in PySAL.
+
+ A STATA text file can have one of the following structures according to
+ its export options in STATA.
+
+ Structure 1: encoding using the list of neighbor ids
+ [Line 1] [Number_of_Observations]
+ [Line 2] [ID_of_Obs_1] [ID_of_Neighbor_1_of_Obs_1] [ID_of_Neighbor_2_of_Obs_1] .... [ID_of_Neighbor_m_of_Obs_1]
+ [Line 3] [ID_of_Obs_2]
+ [Line 4] [ID_of_Obs_3] [ID_of_Neighbor_1_of_Obs_3] [ID_of_Neighbor_2_of_Obs_3]
+ ...
+ Note that for island observations their IDs are still recorded.
+
+ Structure 2: encoding using a full matrix format
+ [Line 1] [Number_of_Observations]
+ [Line 2] [ID_of_Obs_1] [w_11] [w_12] ... [w_1n]
+ [Line 3] [ID_of_Obs_2] [w_21] [w_22] ... [w_2n]
+ [Line 4] [ID_of_Obs_3] [w_31] [w_32] ... [w_3n]
+ ...
+ [Line n+1] [ID_of_Obs_n] [w_n1] [w_n2] ... [w_nn]
+ where w_ij can be a form of general weight.
+ That is, w_ij can be both a binary value or a general numeric value.
+ If an observation is an island, all of its w columns contains 0.
+
+ References
+ ----------
+ Drukker D.M., Peng H., Prucha I.R., and Raciborski R. (2011)
+ "Creating and managing spatial-weighting matrices using the spmat command"
+
+ Notes
+ -----
+ The spmat command allows users to add any note to a spatial weights matrix object in STATA.
+ However, all those notes are lost when the matrix is exported.
+ PySAL also does not take care of those notes.
+
+ """
+
+ FORMATS = ['stata_text']
+ MODES = ['r', 'w']
+
+ def __init__(self, *args, **kwargs):
+ args = args[:2]
+ FileIO.FileIO.__init__(self, *args, **kwargs)
+ self.file = open(self.dataPath, self.mode)
+
+ def read(self, n=-1):
+ self._complain_ifclosed(self.closed)
+ return self._read()
+
+ def seek(self, pos):
+ if pos == 0:
+ self.file.seek(0)
+ self.pos = 0
+
+ def _read(self):
+ """Reads STATA Text file
+ Returns a pysal.weights.weights.W object
+
+ Examples
+ --------
+
+ Type 'dir(w)' at the interpreter to see what methods are supported.
+ Open a text file and read it into a pysal weights object
+
+ >>> w = pysal.open(pysal.examples.get_path('stata_sparse.txt'),'r','stata_text').read()
+ WARNING: there are 7 disconnected observations
+ Island ids: [5, 9, 10, 11, 12, 14, 15]
+
+ Get the number of observations from the header
+
+ >>> w.n
+ 56
+
+ Get the mean number of neighbors
+
+ >>> w.mean_neighbors
+ 4.0
+
+ Get neighbor distances for a single observation
+
+ >>> w[1]
+ {53: 1.0, 51: 1.0, 45: 1.0, 54: 1.0, 7: 1.0}
+
+ """
+ if self.pos > 0:
+ raise StopIteration
+
+ n = int(self.file.readline().strip())
+ line1 = self.file.readline().strip()
+ obs_01 = line1.split(' ')
+ matrix_form = False
+ if len(obs_01) == 1 or float(obs_01[1]) != 0.0:
+ def line2wgt(line):
+ row = [int(i) for i in line.strip().split(' ')]
+ return row[0], row[1:], [1.0] * len(row[1:])
+ else:
+ matrix_form = True
+
+ def line2wgt(line):
+ row = line.strip().split(' ')
+ obs = int(float(row[0]))
+ ngh, wgt = [], []
+ for i in range(n):
+ w = float(row[i + 1])
+ if w > 0:
+ ngh.append(i)
+ wgt.append(w)
+ return obs, ngh, wgt
+
+ id_order = []
+ weights, neighbors = {}, {}
+ l = line1
+ for i in range(n):
+ obs, ngh, wgt = line2wgt(l)
+ id_order.append(obs)
+ neighbors[obs] = ngh
+ weights[obs] = wgt
+ l = self.file.readline()
+ if matrix_form:
+ for obs in neighbors:
+ neighbors[obs] = [id_order[ngh] for ngh in neighbors[obs]]
+
+ self.pos += 1
+ return W(neighbors, weights)
+
+ def write(self, obj, matrix_form=False):
+ """
+
+ Parameters
+ ----------
+ .write(weightsObject)
+ accepts a weights object
+
+ Returns
+ ------
+
+ a STATA text file
+ write a weights object to the opened text file.
+
+ Examples
+ --------
+
+ >>> import tempfile, pysal, os
+ >>> testfile = pysal.open(pysal.examples.get_path('stata_sparse.txt'),'r','stata_text')
+ >>> w = testfile.read()
+ WARNING: there are 7 disconnected observations
+ Island ids: [5, 9, 10, 11, 12, 14, 15]
+
+ Create a temporary file for this example
+
+ >>> f = tempfile.NamedTemporaryFile(suffix='.txt')
+
+ Reassign to new var
+
+ >>> fname = f.name
+
+ Close the temporary named file
+
+ >>> f.close()
+
+ Open the new file in write mode
+
+ >>> o = pysal.open(fname,'w','stata_text')
+
+ Write the Weights object into the open file
+
+ >>> o.write(w)
+ >>> o.close()
+
+ Read in the newly created text file
+
+ >>> wnew = pysal.open(fname,'r','stata_text').read()
+ WARNING: there are 7 disconnected observations
+ Island ids: [5, 9, 10, 11, 12, 14, 15]
+
+ Compare values from old to new
+
+ >>> wnew.pct_nonzero == w.pct_nonzero
+ True
+
+ Clean up temporary file created for this example
+
+ >>> os.remove(fname)
+ """
+ self._complain_ifclosed(self.closed)
+ if issubclass(type(obj), W):
+ header = '%s\n' % obj.n
+ self.file.write(header)
+ if matrix_form:
+ def wgt2line(obs_id, neighbor, weight):
+ w = ['0.0'] * obj.n
+ for ngh, wgt in zip(neighbor, weight):
+ w[obj.id2i[ngh]] = str(wgt)
+ return [str(obs_id)] + w
+ else:
+ def wgt2line(obs_id, neighbor, weight):
+ return [str(obs_id)] + [str(ngh) for ngh in neighbor]
+ for id in obj.id_order:
+ line = wgt2line(id, obj.neighbors[id], obj.weights[id])
+ self.file.write('%s\n' % ' '.join(line))
+ else:
+ raise TypeError("Expected a pysal weights object, got: %s" % (
+ type(obj)))
+
+ def close(self):
+ self.file.close()
+ FileIO.FileIO.close(self)
+
diff --git a/pysal/core/IOHandlers/template.py b/pysal/core/IOHandlers/template.py
new file mode 100644
index 0000000..03adf57
--- /dev/null
+++ b/pysal/core/IOHandlers/template.py
@@ -0,0 +1,145 @@
+""" Example Reader and Writer
+
+ These are working readers/writers that parse '.foo' and '.bar' files
+
+"""
+
+import pysal
+from pysal.core.FileIO import FileIO
+__author__ = "Charles R Schmidt <schmidtc at gmail.com>"
+__all__ = ['TemplateWriter', 'TemplateReaderWriter']
+
+
+# Always subclass FileIO
+class TemplateWriter(FileIO):
+ #REQUIRED, List the formats this class supports.
+ FORMATS = ['foo']
+ #REQUIRED, List the modes supported by this class.
+ # One class can support both reading and writing.
+ # For simplicity this class will only support one.
+ # You could support custom modes, but these could be hard to document.
+ MODES = ['w']
+
+ # use .__init__ to open any need file handlers
+ def __init__(self, *args, **kwargs):
+ # initialize the parent class...
+ FileIO.__init__(self, *args, **kwargs)
+ # this gives you:
+ # self.dataPath == the connection string or path to file
+ # self.mode == the mode the file should be opened in
+
+ self.fileObj = open(self.dataPath, self.mode)
+
+ #writers must subclass .write
+ def write(self, obj):
+ """ .write method of the 'foobar' template, receives an obj """
+
+ # GOOD TO HAVE, this will prevent invalid operations on closed files.
+ self._complain_ifclosed(self.closed)
+
+ # it's up to the writer to understand the object, you should check that object is of the type you expect and raise a TypeError is its now.
+ # we will support writing string objects in this example, all string are derived from basestring...
+ if issubclass(type(obj), basestring):
+
+ #Non-essential ...
+ def foobar(c):
+ if c in 'foobar':
+ return True
+ else:
+ return False
+ result = filter(foobar, obj) # e.g. 'foobara' == filter(foobar,'my little foobar example')
+
+ #do the actual writing...
+ self.fileObj.write(result + '\n')
+ #REQUIRED, increment the internal pos pointer.
+ self.pos += 1
+
+ else:
+ raise TypeError("Expected a string, got: %s" % (type(obj)))
+
+ #default is to raise "NotImplementedError"
+ def flush(self):
+ self._complain_ifclosed(self.closed)
+ self.fileObj.flush()
+
+ #REQUIRED
+ def close(self):
+ self.fileObj.close()
+ #clean up the parent class too....
+ FileIO.close(self)
+
+
+class TemplateReaderWriter(FileIO):
+ FORMATS = ['bar']
+ MODES = ['r', 'w']
+
+ def __init__(self, *args, **kwargs):
+ FileIO.__init__(self, *args, **kwargs)
+ self.fileObj = open(self.dataPath, self.mode)
+ #Notice reading is a bit different
+
+ def _filter(self, st):
+ def foobar(c):
+ if c in 'foobar':
+ return True
+ else:
+ return False
+ return filter(foobar, st) # e.g. 'foobara' == filter(foobar,'my little foobar example')
+
+ def _read(self):
+ """ the _read method should return only ONE object and raise StopIteration at the EOF."""
+ line = self.fileObj.readline()
+ obj = self._filter(line)
+ self.pos += 1 # REQUIRED
+ if line:
+ return obj + '\n'
+ else:
+ raise StopIteration # REQUIRED
+
+ def write(self, obj):
+ """ .write method of the 'foobar' template, receives an obj """
+ self._complain_ifclosed(self.closed)
+ if issubclass(type(obj), basestring):
+ result = self._filter(obj)
+ self.fileObj.write(result + '\n')
+ self.pos += 1
+ else:
+ raise TypeError("Expected a string, got: %s" % (type(obj)))
+
+ def flush(self):
+ self._complain_ifclosed(self.closed)
+ self.fileObj.flush()
+
+ def close(self):
+ self.fileObj.close()
+ FileIO.close(self)
+
+
+if __name__ == '__main__':
+ "note, by running OR importing this module it's automatically added to the pysal fileIO registry."
+ pysal.open.check()
+
+ lines = ['This is an example of template FileIO classes',
+ 'Each call to write expects a string object',
+ 'that string is filtered and only letters "f,o,b,a,r" are kept',
+ 'these kept letters are written to the file and a new line char is appends to each line',
+ 'likewise the reader filters each line from a file']
+ f = pysal.open('test.foo', 'w')
+ for line in lines:
+ f.write(line)
+ f.close()
+
+ f = pysal.open('test.bar', 'w')
+ for line in lines:
+ f.write(line)
+ f.close()
+
+ f = pysal.open('test.bar', 'r')
+ s = ''.join(f.read())
+ f.close()
+ print s
+
+ f = open('test.foo', 'r')
+ s2 = f.read()
+ f.close()
+ print s == s2
diff --git a/pysal/core/IOHandlers/tests/__init__.py b/pysal/core/IOHandlers/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pysal/core/IOHandlers/tests/test_arcgis_dbf.py b/pysal/core/IOHandlers/tests/test_arcgis_dbf.py
new file mode 100644
index 0000000..849243f
--- /dev/null
+++ b/pysal/core/IOHandlers/tests/test_arcgis_dbf.py
@@ -0,0 +1,57 @@
+import unittest
+import pysal
+from pysal.core.IOHandlers.arcgis_dbf import ArcGISDbfIO
+import tempfile
+import os
+import warnings
+
+
+class test_ArcGISDbfIO(unittest.TestCase):
+ def setUp(self):
+ self.test_file = test_file = pysal.examples.get_path('arcgis_ohio.dbf')
+ self.obj = ArcGISDbfIO(test_file, 'r')
+
+ def test_close(self):
+ f = self.obj
+ f.close()
+ self.failUnlessRaises(ValueError, f.read)
+
+ def test_read(self):
+ with warnings.catch_warnings(record=True) as warn:
+ warnings.simplefilter("always")
+ w = self.obj.read()
+ if len(warn) > 0:
+ assert issubclass(warn[0].category, RuntimeWarning)
+ assert "Missing Value Found, setting value to pysal.MISSINGVALUE" in str(warn[0].message)
+ self.assertEqual(88, w.n)
+ self.assertEqual(5.25, w.mean_neighbors)
+ self.assertEqual([1.0, 1.0, 1.0, 1.0], w[1].values())
+
+ def test_seek(self):
+ self.test_read()
+ self.failUnlessRaises(StopIteration, self.obj.read)
+ self.obj.seek(0)
+ self.test_read()
+
+ def test_write(self):
+ with warnings.catch_warnings(record=True) as warn:
+ warnings.simplefilter("always")
+ w = self.obj.read()
+ if len(warn) > 0:
+ assert issubclass(warn[0].category, RuntimeWarning)
+ assert "Missing Value Found, setting value to pysal.MISSINGVALUE" in str(warn[0].message)
+ f = tempfile.NamedTemporaryFile(
+ suffix='.dbf', dir=pysal.examples.get_path(''))
+ fname = f.name
+ f.close()
+ o = pysal.open(fname, 'w', 'arcgis_dbf')
+ o.write(w)
+ o.close()
+ f = pysal.open(fname, 'r', 'arcgis_dbf')
+ wnew = f.read()
+ f.close()
+ self.assertEqual(wnew.pct_nonzero, w.pct_nonzero)
+ os.remove(fname)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/core/IOHandlers/tests/test_arcgis_swm.py b/pysal/core/IOHandlers/tests/test_arcgis_swm.py
new file mode 100644
index 0000000..4e09f0e
--- /dev/null
+++ b/pysal/core/IOHandlers/tests/test_arcgis_swm.py
@@ -0,0 +1,44 @@
+import unittest
+import pysal
+from pysal.core.IOHandlers.arcgis_swm import ArcGISSwmIO
+import tempfile
+import os
+
+
+class test_ArcGISSwmIO(unittest.TestCase):
+ def setUp(self):
+ self.test_file = test_file = pysal.examples.get_path('ohio.swm')
+ self.obj = ArcGISSwmIO(test_file, 'r')
+
+ def test_close(self):
+ f = self.obj
+ f.close()
+ self.failUnlessRaises(ValueError, f.read)
+
+ def test_read(self):
+ w = self.obj.read()
+ self.assertEqual(88, w.n)
+ self.assertEqual(5.25, w.mean_neighbors)
+ self.assertEqual([1.0, 1.0, 1.0, 1.0], w[1].values())
+
+ def test_seek(self):
+ self.test_read()
+ self.failUnlessRaises(StopIteration, self.obj.read)
+ self.obj.seek(0)
+ self.test_read()
+
+ def test_write(self):
+ w = self.obj.read()
+ f = tempfile.NamedTemporaryFile(
+ suffix='.swm', dir=pysal.examples.get_path(''))
+ fname = f.name
+ f.close()
+ o = pysal.open(fname, 'w')
+ o.write(w)
+ o.close()
+ wnew = pysal.open(fname, 'r').read()
+ self.assertEqual(wnew.pct_nonzero, w.pct_nonzero)
+ os.remove(fname)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/core/IOHandlers/tests/test_arcgis_txt.py b/pysal/core/IOHandlers/tests/test_arcgis_txt.py
new file mode 100644
index 0000000..ec93172
--- /dev/null
+++ b/pysal/core/IOHandlers/tests/test_arcgis_txt.py
@@ -0,0 +1,60 @@
+import unittest
+import pysal
+from pysal.core.IOHandlers.arcgis_txt import ArcGISTextIO
+import tempfile
+import os
+import warnings
+
+
+class test_ArcGISTextIO(unittest.TestCase):
+ def setUp(self):
+ self.test_file = test_file = pysal.examples.get_path('arcgis_txt.txt')
+ self.obj = ArcGISTextIO(test_file, 'r')
+
+ def test_close(self):
+ f = self.obj
+ f.close()
+ self.failUnlessRaises(ValueError, f.read)
+
+ def test_read(self):
+ with warnings.catch_warnings(record=True) as warn:
+ warnings.simplefilter("always")
+ w = self.obj.read()
+ if len(warn) > 0:
+ assert issubclass(warn[0].category, RuntimeWarning)
+ assert "DBF relating to ArcGIS TEXT was not found, proceeding with unordered string ids." in str(warn[0].message)
+ self.assertEqual(3, w.n)
+ self.assertEqual(2.0, w.mean_neighbors)
+ self.assertEqual([0.1, 0.05], w[2].values())
+
+ def test_seek(self):
+ self.test_read()
+ self.failUnlessRaises(StopIteration, self.obj.read)
+ self.obj.seek(0)
+ self.test_read()
+
+ def test_write(self):
+ with warnings.catch_warnings(record=True) as warn:
+ warnings.simplefilter("always")
+ w = self.obj.read()
+ if len(warn) > 0:
+ assert issubclass(warn[0].category, RuntimeWarning)
+ assert "DBF relating to ArcGIS TEXT was not found, proceeding with unordered string ids." in str(warn[0].message)
+ f = tempfile.NamedTemporaryFile(
+ suffix='.txt', dir=pysal.examples.get_path(''))
+ fname = f.name
+ f.close()
+ o = pysal.open(fname, 'w', 'arcgis_text')
+ o.write(w)
+ o.close()
+ with warnings.catch_warnings(record=True) as warn:
+ warnings.simplefilter("always")
+ wnew = pysal.open(fname, 'r', 'arcgis_text').read()
+ if len(warn) > 0:
+ assert issubclass(warn[0].category, RuntimeWarning)
+ assert "DBF relating to ArcGIS TEXT was not found, proceeding with unordered string ids." in str(warn[0].message)
+ self.assertEqual(wnew.pct_nonzero, w.pct_nonzero)
+ os.remove(fname)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/core/IOHandlers/tests/test_csvWrapper.py b/pysal/core/IOHandlers/tests/test_csvWrapper.py
new file mode 100644
index 0000000..70d0d52
--- /dev/null
+++ b/pysal/core/IOHandlers/tests/test_csvWrapper.py
@@ -0,0 +1,61 @@
+import unittest
+import pysal
+import tempfile
+import os
+
+
+class test_csvWrapper(unittest.TestCase):
+ def setUp(self):
+ self.test_file = test_file = pysal.examples.get_path('stl_hom.csv')
+ self.obj = pysal.core.IOHandlers.csvWrapper.csvWrapper(test_file, 'r')
+
+ def test_len(self):
+ self.assertEquals(len(self.obj), 78)
+
+ def test_tell(self):
+ self.assertEquals(self.obj.tell(), 0)
+ self.obj.read(1)
+ self.assertEquals(self.obj.tell(), 1)
+ self.obj.read(50)
+ self.assertEquals(self.obj.tell(), 51)
+ self.obj.read()
+ self.assertEquals(self.obj.tell(), 78)
+
+ def test_seek(self):
+ self.obj.seek(0)
+ self.assertEquals(self.obj.tell(), 0)
+ self.obj.seek(55)
+ self.assertEquals(self.obj.tell(), 55)
+ self.obj.read(1)
+ self.assertEquals(self.obj.tell(), 56)
+
+ def test_read(self):
+ self.obj.seek(0)
+ objs = self.obj.read()
+ self.assertEquals(len(objs), 78)
+ self.obj.seek(0)
+ objsB = list(self.obj)
+ self.assertEquals(len(objsB), 78)
+ for rowA, rowB in zip(objs, objsB):
+ self.assertEquals(rowA, rowB)
+
+ def test_casting(self):
+ self.obj.cast('WKT', pysal.core.util.WKTParser())
+ verts = [(-89.585220336914062, 39.978794097900391), (-89.581146240234375, 40.094867706298828), (-89.603988647460938, 40.095306396484375), (-89.60589599609375, 40.136119842529297), (-89.6103515625, 40.3251953125), (-89.269027709960938, 40.329566955566406), (-89.268562316894531, 40.285579681396484), (-89.154655456542969, 40.285774230957031), (-89.152763366699219, 40.054969787597656), (-89.151618957519531, 39.919403076171875), (-89.224777221679688, 39.918678283691406), (-89.41185760 [...]
+ for i, pt in enumerate(self.obj.next()[0].vertices):
+ self.assertEquals(pt[:], verts[i])
+
+ def test_by_col(self):
+ for field in self.obj.header:
+ self.assertEquals(len(self.obj.by_col[field]), 78)
+
+ def test_slicing(self):
+ chunk = self.obj[50:55, 1:3]
+ self.assertEquals(chunk[0], ['Jefferson', 'Missouri'])
+ self.assertEquals(chunk[1], ['Jefferson', 'Illinois'])
+ self.assertEquals(chunk[2], ['Miller', 'Missouri'])
+ self.assertEquals(chunk[3], ['Maries', 'Missouri'])
+ self.assertEquals(chunk[4], ['White', 'Illinois'])
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/core/IOHandlers/tests/test_dat.py b/pysal/core/IOHandlers/tests/test_dat.py
new file mode 100644
index 0000000..69935f6
--- /dev/null
+++ b/pysal/core/IOHandlers/tests/test_dat.py
@@ -0,0 +1,44 @@
+import unittest
+import pysal
+from pysal.core.IOHandlers.dat import DatIO
+import tempfile
+import os
+
+
+class test_DatIO(unittest.TestCase):
+ def setUp(self):
+ self.test_file = test_file = pysal.examples.get_path('wmat.dat')
+ self.obj = DatIO(test_file, 'r')
+
+ def test_close(self):
+ f = self.obj
+ f.close()
+ self.failUnlessRaises(ValueError, f.read)
+
+ def test_read(self):
+ w = self.obj.read()
+ self.assertEqual(49, w.n)
+ self.assertEqual(4.7346938775510203, w.mean_neighbors)
+ self.assertEqual([0.5, 0.5], w[5.0].values())
+
+ def test_seek(self):
+ self.test_read()
+ self.failUnlessRaises(StopIteration, self.obj.read)
+ self.obj.seek(0)
+ self.test_read()
+
+ def test_write(self):
+ w = self.obj.read()
+ f = tempfile.NamedTemporaryFile(
+ suffix='.dat', dir=pysal.examples.get_path(''))
+ fname = f.name
+ f.close()
+ o = pysal.open(fname, 'w')
+ o.write(w)
+ o.close()
+ wnew = pysal.open(fname, 'r').read()
+ self.assertEqual(wnew.pct_nonzero, w.pct_nonzero)
+ os.remove(fname)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/core/IOHandlers/tests/test_gal.py b/pysal/core/IOHandlers/tests/test_gal.py
new file mode 100644
index 0000000..83f12ff
--- /dev/null
+++ b/pysal/core/IOHandlers/tests/test_gal.py
@@ -0,0 +1,49 @@
+"""Unit tests for gal.py"""
+import unittest
+import pysal
+import tempfile
+import os
+from pysal.core.IOHandlers.gal import GalIO
+
+
+class test_GalIO(unittest.TestCase):
+ def setUp(self):
+ self.test_file = test_file = pysal.examples.get_path('sids2.gal')
+ self.obj = GalIO(test_file, 'r')
+
+ def test___init__(self):
+ self.assertEqual(self.obj._typ, str)
+
+ def test_close(self):
+ f = self.obj
+ f.close()
+ self.failUnlessRaises(ValueError, f.read)
+
+ def test_read(self):
+ # reading a GAL returns a W
+ w = self.obj.read()
+ self.assertEqual(w.n, 100)
+ self.assertAlmostEqual(w.sd, 1.5151237573214935)
+ self.assertEqual(w.s0, 462.0)
+ self.assertEqual(w.s1, 924.0)
+
+ def test_seek(self):
+ self.test_read()
+ self.failUnlessRaises(StopIteration, self.obj.read)
+ self.obj.seek(0)
+ self.test_read()
+
+ def test_write(self):
+ w = self.obj.read()
+ f = tempfile.NamedTemporaryFile(suffix='.gal')
+ fname = f.name
+ f.close()
+ o = pysal.open(fname, 'w')
+ o.write(w)
+ o.close()
+ wnew = pysal.open(fname, 'r').read()
+ self.assertEqual(wnew.pct_nonzero, w.pct_nonzero)
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/core/IOHandlers/tests/test_geobugs_txt.py b/pysal/core/IOHandlers/tests/test_geobugs_txt.py
new file mode 100644
index 0000000..f49e489
--- /dev/null
+++ b/pysal/core/IOHandlers/tests/test_geobugs_txt.py
@@ -0,0 +1,57 @@
+import unittest
+import pysal
+from pysal.core.IOHandlers.geobugs_txt import GeoBUGSTextIO
+import tempfile
+import os
+
+
+class test_GeoBUGSTextIO(unittest.TestCase):
+ def setUp(self):
+ self.test_file_scot = test_file_scot = pysal.examples.get_path(
+ 'geobugs_scot')
+ self.test_file_col = test_file_col = pysal.examples.get_path(
+ 'spdep_listw2WB_columbus')
+ self.obj_scot = GeoBUGSTextIO(test_file_scot, 'r')
+ self.obj_col = GeoBUGSTextIO(test_file_col, 'r')
+
+ def test_close(self):
+ for obj in [self.obj_scot, self.obj_col]:
+ f = obj
+ f.close()
+ self.failUnlessRaises(ValueError, f.read)
+
+ def test_read(self):
+ w_scot = self.obj_scot.read()
+ self.assertEqual(56, w_scot.n)
+ self.assertEqual(4.1785714285714288, w_scot.mean_neighbors)
+ self.assertEqual([1.0, 1.0, 1.0], w_scot[1].values())
+
+ w_col = self.obj_col.read()
+ self.assertEqual(49, w_col.n)
+ self.assertEqual(4.6938775510204085, w_col.mean_neighbors)
+ self.assertEqual([0.5, 0.5], w_col[1].values())
+
+ def test_seek(self):
+ self.test_read()
+ self.failUnlessRaises(StopIteration, self.obj_scot.read)
+ self.failUnlessRaises(StopIteration, self.obj_col.read)
+ self.obj_scot.seek(0)
+ self.obj_col.seek(0)
+ self.test_read()
+
+ def test_write(self):
+ for obj in [self.obj_scot, self.obj_col]:
+ w = obj.read()
+ f = tempfile.NamedTemporaryFile(
+ suffix='', dir=pysal.examples.get_path(''))
+ fname = f.name
+ f.close()
+ o = pysal.open(fname, 'w', 'geobugs_text')
+ o.write(w)
+ o.close()
+ wnew = pysal.open(fname, 'r', 'geobugs_text').read()
+ self.assertEqual(wnew.pct_nonzero, w.pct_nonzero)
+ os.remove(fname)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/core/IOHandlers/tests/test_geoda_txt.py b/pysal/core/IOHandlers/tests/test_geoda_txt.py
new file mode 100644
index 0000000..2402309
--- /dev/null
+++ b/pysal/core/IOHandlers/tests/test_geoda_txt.py
@@ -0,0 +1,26 @@
+'''GeoDa Text File Reader Unit Tests'''
+import unittest
+import pysal
+from pysal.core.IOHandlers.geoda_txt import GeoDaTxtReader as GTR
+
+
+class test_GeoDaTxtReader(unittest.TestCase):
+ def setUp(self):
+ test_file = pysal.examples.get_path('stl_hom.txt')
+ self.obj = GTR(test_file, 'r')
+
+ def test___init__(self):
+ self.assertEqual(
+ self.obj.header, ['FIPSNO', 'HR8488', 'HR8893', 'HC8488'])
+
+ def test___len__(self):
+ expected = 78
+ self.assertEqual(expected, len(self.obj))
+
+ def test_close(self):
+ f = self.obj
+ f.close()
+ self.failUnlessRaises(ValueError, f.read)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/core/IOHandlers/tests/test_gwt.py b/pysal/core/IOHandlers/tests/test_gwt.py
new file mode 100644
index 0000000..b6f74da
--- /dev/null
+++ b/pysal/core/IOHandlers/tests/test_gwt.py
@@ -0,0 +1,54 @@
+import unittest
+import pysal
+from pysal.core.IOHandlers.gwt import GwtIO
+import tempfile
+import os
+import warnings
+
+
+class test_GwtIO(unittest.TestCase):
+ def setUp(self):
+ self.test_file = test_file = pysal.examples.get_path('juvenile.gwt')
+ self.obj = GwtIO(test_file, 'r')
+
+ def test_close(self):
+ f = self.obj
+ f.close()
+ self.failUnlessRaises(ValueError, f.read)
+
+ def test_read(self):
+ w = self.obj.read()
+ self.assertEqual(168, w.n)
+ self.assertEqual(16.678571428571427, w.mean_neighbors)
+ w.transform = 'B'
+ self.assertEqual([1.0], w[1].values())
+
+ def test_seek(self):
+ self.test_read()
+ self.failUnlessRaises(StopIteration, self.obj.read)
+ self.obj.seek(0)
+ self.test_read()
+
+ # Commented out by CRS, GWT 'w' mode removed until we can find a good solution for retaining distances.
+ # see issue #153.
+ # Added back by CRS,
+ def test_write(self):
+ w = self.obj.read()
+ f = tempfile.NamedTemporaryFile(
+ suffix='.gwt', dir=pysal.examples.get_path(''))
+ fname = f.name
+ f.close()
+ o = pysal.open(fname, 'w')
+ #copy the shapefile and ID variable names from the old gwt.
+ # this is only available after the read() method has been called.
+ #o.shpName = self.obj.shpName
+ #o.varName = self.obj.varName
+ o.write(w)
+ o.close()
+ wnew = pysal.open(fname, 'r').read()
+ self.assertEqual(wnew.pct_nonzero, w.pct_nonzero)
+ os.remove(fname)
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/core/IOHandlers/tests/test_mat.py b/pysal/core/IOHandlers/tests/test_mat.py
new file mode 100644
index 0000000..49f97e4
--- /dev/null
+++ b/pysal/core/IOHandlers/tests/test_mat.py
@@ -0,0 +1,49 @@
+import unittest
+import pysal
+from pysal.core.IOHandlers.mat import MatIO
+import tempfile
+import os
+import warnings
+
+
+class test_MatIO(unittest.TestCase):
+ def setUp(self):
+ self.test_file = test_file = pysal.examples.get_path('spat-sym-us.mat')
+ self.obj = MatIO(test_file, 'r')
+
+ def test_close(self):
+ f = self.obj
+ f.close()
+ self.failUnlessRaises(ValueError, f.read)
+
+ def test_read(self):
+ w = self.obj.read()
+ self.assertEqual(46, w.n)
+ self.assertEqual(4.0869565217391308, w.mean_neighbors)
+ self.assertEqual([1.0, 1.0, 1.0, 1.0], w[1].values())
+
+ def test_seek(self):
+ self.test_read()
+ self.failUnlessRaises(StopIteration, self.obj.read)
+ self.obj.seek(0)
+ self.test_read()
+
+ def test_write(self):
+ w = self.obj.read()
+ f = tempfile.NamedTemporaryFile(
+ suffix='.mat', dir=pysal.examples.get_path(''))
+ fname = f.name
+ f.close()
+ o = pysal.open(fname, 'w')
+ with warnings.catch_warnings(record=True) as warn:
+ warnings.simplefilter("always")
+ o.write(w)
+ if len(warn) > 0:
+ assert issubclass(warn[0].category, FutureWarning)
+ o.close()
+ wnew = pysal.open(fname, 'r').read()
+ self.assertEqual(wnew.pct_nonzero, w.pct_nonzero)
+ os.remove(fname)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/core/IOHandlers/tests/test_mtx.py b/pysal/core/IOHandlers/tests/test_mtx.py
new file mode 100644
index 0000000..40df7f0
--- /dev/null
+++ b/pysal/core/IOHandlers/tests/test_mtx.py
@@ -0,0 +1,57 @@
+import unittest
+import pysal
+from pysal.core.IOHandlers.mtx import MtxIO
+import tempfile
+import os
+import warnings
+import scipy.sparse as SP
+
+
+class test_MtxIO(unittest.TestCase):
+ def setUp(self):
+ self.test_file = test_file = pysal.examples.get_path('wmat.mtx')
+ self.obj = MtxIO(test_file, 'r')
+
+ def test_close(self):
+ f = self.obj
+ f.close()
+ self.failUnlessRaises(ValueError, f.read)
+
+ def test_read(self):
+ w = self.obj.read()
+ self.assertEqual(49, w.n)
+ self.assertEqual(4.7346938775510203, w.mean_neighbors)
+ self.assertEqual([0.33329999999999999, 0.33329999999999999,
+ 0.33329999999999999], w[1].values())
+ s0 = w.s0
+ self.obj.seek(0)
+ wsp = self.obj.read(sparse=True)
+ self.assertEqual(49, wsp.n)
+ self.assertEqual(s0, wsp.s0)
+
+ def test_seek(self):
+ self.test_read()
+ self.failUnlessRaises(StopIteration, self.obj.read)
+ self.obj.seek(0)
+ self.test_read()
+
+ def test_write(self):
+ for i in [False, True]:
+ self.obj.seek(0)
+ w = self.obj.read(sparse=i)
+ f = tempfile.NamedTemporaryFile(
+ suffix='.mtx', dir=pysal.examples.get_path(''))
+ fname = f.name
+ f.close()
+ o = pysal.open(fname, 'w')
+ o.write(w)
+ o.close()
+ wnew = pysal.open(fname, 'r').read(sparse=i)
+ if i:
+ self.assertEqual(wnew.s0, w.s0)
+ else:
+ self.assertEqual(wnew.pct_nonzero, w.pct_nonzero)
+ os.remove(fname)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/core/IOHandlers/tests/test_pyDbfIO.py b/pysal/core/IOHandlers/tests/test_pyDbfIO.py
new file mode 100644
index 0000000..1b232f9
--- /dev/null
+++ b/pysal/core/IOHandlers/tests/test_pyDbfIO.py
@@ -0,0 +1,111 @@
+import unittest
+import pysal
+import tempfile
+import os
+
+
+class test_DBF(unittest.TestCase):
+ def setUp(self):
+ self.test_file = test_file = pysal.examples.get_path('10740.dbf')
+ self.dbObj = pysal.core.IOHandlers.pyDbfIO.DBF(test_file, 'r')
+
+ def test_len(self):
+ self.assertEquals(len(self.dbObj), 195)
+
+ def test_tell(self):
+ self.assertEquals(self.dbObj.tell(), 0)
+ self.dbObj.read(1)
+ self.assertEquals(self.dbObj.tell(), 1)
+ self.dbObj.read(50)
+ self.assertEquals(self.dbObj.tell(), 51)
+ self.dbObj.read()
+ self.assertEquals(self.dbObj.tell(), 195)
+
+ def test_seek(self):
+ self.dbObj.seek(0)
+ self.assertEquals(self.dbObj.tell(), 0)
+ self.dbObj.seek(55)
+ self.assertEquals(self.dbObj.tell(), 55)
+ self.dbObj.read(1)
+ self.assertEquals(self.dbObj.tell(), 56)
+
+ def test_read(self):
+ self.dbObj.seek(0)
+ objs = self.dbObj.read()
+ self.assertEquals(len(objs), 195)
+ self.dbObj.seek(0)
+ objsB = list(self.dbObj)
+ self.assertEquals(len(objsB), 195)
+ for rowA, rowB in zip(objs, objsB):
+ self.assertEquals(rowA, rowB)
+
+ def test_random_access(self):
+ self.dbObj.seek(0)
+ db0 = self.dbObj.read(1)[0]
+ self.assertEquals(db0, [1, '35001', '000107', '35001000107', '1.07'])
+ self.dbObj.seek(57)
+ db57 = self.dbObj.read(1)[0]
+ self.assertEquals(db57, [58, '35001', '001900', '35001001900', '19'])
+ self.dbObj.seek(32)
+ db32 = self.dbObj.read(1)[0]
+ self.assertEquals(db32, [33, '35001', '000500', '35001000500', '5'])
+ self.dbObj.seek(0)
+ self.assertEquals(self.dbObj.next(), db0)
+ self.dbObj.seek(57)
+ self.assertEquals(self.dbObj.next(), db57)
+ self.dbObj.seek(32)
+ self.assertEquals(self.dbObj.next(), db32)
+
+ def test_write(self):
+ f = tempfile.NamedTemporaryFile(suffix='.dbf')
+ fname = f.name
+ f.close()
+ self.dbfcopy = fname
+ self.out = pysal.core.IOHandlers.pyDbfIO.DBF(fname, 'w')
+ self.dbObj.seek(0)
+ self.out.header = self.dbObj.header
+ self.out.field_spec = self.dbObj.field_spec
+ for row in self.dbObj:
+ self.out.write(row)
+ self.out.close()
+
+ orig = open(self.test_file, 'rb')
+ copy = open(self.dbfcopy, 'rb')
+ orig.seek(32) # self.dbObj.header_size) #skip the header, file date has changed
+ copy.seek(32) # self.dbObj.header_size) #skip the header, file date has changed
+
+ #PySAL writes proper DBF files with a terminator at the end, not everyone does.
+ n = self.dbObj.record_size * self.dbObj.n_records # bytes to read.
+ self.assertEquals(orig.read(n), copy.read(n))
+ #self.assertEquals(orig.read(1), copy.read(1)) # last byte may fail
+ orig.close()
+ copy.close()
+ os.remove(self.dbfcopy)
+
+ def test_writeNones(self):
+ import datetime
+ import time
+ f = tempfile.NamedTemporaryFile(
+ suffix='.dbf')
+ fname = f.name
+ f.close()
+ db = pysal.core.IOHandlers.pyDbfIO.DBF(fname, 'w')
+ db.header = ["recID", "date", "strID", "aFloat"]
+ db.field_spec = [('N', 10, 0), ('D', 8, 0), ('C', 10, 0), ('N', 5, 5)]
+ records = []
+ for i in range(10):
+ d = datetime.date(*time.localtime()[:3])
+ rec = [i + 1, d, str(i + 1), (i + 1) / 2.0]
+ records.append(rec)
+ records.append([None, None, '', None])
+ records.append(rec)
+ for rec in records:
+ db.write(rec)
+ db.close()
+ db2 = pysal.core.IOHandlers.pyDbfIO.DBF(fname, 'r')
+ self.assertEquals(records, db2.read())
+
+ os.remove(fname)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/core/IOHandlers/tests/test_pyShpIO.py b/pysal/core/IOHandlers/tests/test_pyShpIO.py
new file mode 100644
index 0000000..ac48681
--- /dev/null
+++ b/pysal/core/IOHandlers/tests/test_pyShpIO.py
@@ -0,0 +1,85 @@
+import unittest
+import pysal
+import tempfile
+import os
+
+
+class test_PurePyShpWrapper(unittest.TestCase):
+ def setUp(self):
+ test_file = pysal.examples.get_path('10740.shp')
+ self.test_file = test_file
+ self.shpObj = pysal.core.IOHandlers.pyShpIO.PurePyShpWrapper(
+ test_file, 'r')
+ f = tempfile.NamedTemporaryFile(suffix='.shp')
+ shpcopy = f.name
+ f.close()
+ self.shpcopy = shpcopy
+ self.shxcopy = shpcopy.replace('.shp', '.shx')
+
+ def test_len(self):
+ self.assertEquals(len(self.shpObj), 195)
+
+ def test_tell(self):
+ self.assertEquals(self.shpObj.tell(), 0)
+ self.shpObj.read(1)
+ self.assertEquals(self.shpObj.tell(), 1)
+ self.shpObj.read(50)
+ self.assertEquals(self.shpObj.tell(), 51)
+ self.shpObj.read()
+ self.assertEquals(self.shpObj.tell(), 195)
+
+ def test_seek(self):
+ self.shpObj.seek(0)
+ self.assertEquals(self.shpObj.tell(), 0)
+ self.shpObj.seek(55)
+ self.assertEquals(self.shpObj.tell(), 55)
+ self.shpObj.read(1)
+ self.assertEquals(self.shpObj.tell(), 56)
+
+ def test_read(self):
+ self.shpObj.seek(0)
+ objs = self.shpObj.read()
+ self.assertEquals(len(objs), 195)
+
+ self.shpObj.seek(0)
+ objsB = list(self.shpObj)
+ self.assertEquals(len(objsB), 195)
+
+ for shpA, shpB in zip(objs, objsB):
+ self.assertEquals(shpA.vertices, shpB.vertices)
+
+ def test_random_access(self):
+ self.shpObj.seek(57)
+ shp57 = self.shpObj.read(1)[0]
+ self.shpObj.seek(32)
+ shp32 = self.shpObj.read(1)[0]
+
+ self.shpObj.seek(57)
+ self.assertEquals(self.shpObj.read(1)[0].vertices, shp57.vertices)
+ self.shpObj.seek(32)
+ self.assertEquals(self.shpObj.read(1)[0].vertices, shp32.vertices)
+
+ def test_write(self):
+ out = pysal.core.IOHandlers.pyShpIO.PurePyShpWrapper(self.shpcopy, 'w')
+ self.shpObj.seek(0)
+ for shp in self.shpObj:
+ out.write(shp)
+ out.close()
+
+ orig = open(self.test_file, 'rb')
+ copy = open(self.shpcopy, 'rb')
+ self.assertEquals(orig.read(), copy.read())
+ orig.close()
+ copy.close()
+
+ oshx = open(self.test_file.replace('.shp', '.shx'), 'rb')
+ cshx = open(self.shxcopy, 'rb')
+ self.assertEquals(oshx.read(), cshx.read())
+ oshx.close()
+ cshx.close()
+
+ os.remove(self.shpcopy)
+ os.remove(self.shxcopy)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/core/IOHandlers/tests/test_stata_txt.py b/pysal/core/IOHandlers/tests/test_stata_txt.py
new file mode 100644
index 0000000..81a9a70
--- /dev/null
+++ b/pysal/core/IOHandlers/tests/test_stata_txt.py
@@ -0,0 +1,61 @@
+import unittest
+import pysal
+from pysal.core.IOHandlers.stata_txt import StataTextIO
+import tempfile
+import os
+
+
+class test_StataTextIO(unittest.TestCase):
+ def setUp(self):
+ self.test_file_sparse = test_file_sparse = pysal.examples.get_path(
+ 'stata_sparse.txt')
+ self.test_file_full = test_file_full = pysal.examples.get_path(
+ 'stata_full.txt')
+ self.obj_sparse = StataTextIO(test_file_sparse, 'r')
+ self.obj_full = StataTextIO(test_file_full, 'r')
+
+ def test_close(self):
+ for obj in [self.obj_sparse, self.obj_full]:
+ f = obj
+ f.close()
+ self.failUnlessRaises(ValueError, f.read)
+
+ def test_read(self):
+ w_sparse = self.obj_sparse.read()
+ self.assertEqual(56, w_sparse.n)
+ self.assertEqual(4.0, w_sparse.mean_neighbors)
+ self.assertEqual([1.0, 1.0, 1.0, 1.0, 1.0], w_sparse[1].values())
+
+ w_full = self.obj_full.read()
+ self.assertEqual(56, w_full.n)
+ self.assertEqual(4.0, w_full.mean_neighbors)
+ self.assertEqual(
+ [0.125, 0.125, 0.125, 0.125, 0.125], w_full[1].values())
+
+ def test_seek(self):
+ self.test_read()
+ self.failUnlessRaises(StopIteration, self.obj_sparse.read)
+ self.failUnlessRaises(StopIteration, self.obj_full.read)
+ self.obj_sparse.seek(0)
+ self.obj_full.seek(0)
+ self.test_read()
+
+ def test_write(self):
+ for obj in [self.obj_sparse, self.obj_full]:
+ w = obj.read()
+ f = tempfile.NamedTemporaryFile(
+ suffix='.txt', dir=pysal.examples.get_path(''))
+ fname = f.name
+ f.close()
+ o = pysal.open(fname, 'w', 'stata_text')
+ if obj == self.obj_sparse:
+ o.write(w)
+ else:
+ o.write(w, matrix_form=True)
+ o.close()
+ wnew = pysal.open(fname, 'r', 'stata_text').read()
+ self.assertEqual(wnew.pct_nonzero, w.pct_nonzero)
+ os.remove(fname)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/core/IOHandlers/tests/test_wk1.py b/pysal/core/IOHandlers/tests/test_wk1.py
new file mode 100644
index 0000000..2a32f20
--- /dev/null
+++ b/pysal/core/IOHandlers/tests/test_wk1.py
@@ -0,0 +1,44 @@
+import unittest
+import pysal
+from pysal.core.IOHandlers.wk1 import Wk1IO
+import tempfile
+import os
+
+
+class test_Wk1IO(unittest.TestCase):
+ def setUp(self):
+ self.test_file = test_file = pysal.examples.get_path('spat-sym-us.wk1')
+ self.obj = Wk1IO(test_file, 'r')
+
+ def test_close(self):
+ f = self.obj
+ f.close()
+ self.failUnlessRaises(ValueError, f.read)
+
+ def test_read(self):
+ w = self.obj.read()
+ self.assertEqual(46, w.n)
+ self.assertEqual(4.0869565217391308, w.mean_neighbors)
+ self.assertEqual([1.0, 1.0, 1.0, 1.0], w[1].values())
+
+ def test_seek(self):
+ self.test_read()
+ self.failUnlessRaises(StopIteration, self.obj.read)
+ self.obj.seek(0)
+ self.test_read()
+
+ def test_write(self):
+ w = self.obj.read()
+ f = tempfile.NamedTemporaryFile(
+ suffix='.wk1', dir=pysal.examples.get_path(''))
+ fname = f.name
+ f.close()
+ o = pysal.open(fname, 'w')
+ o.write(w)
+ o.close()
+ wnew = pysal.open(fname, 'r').read()
+ self.assertEqual(wnew.pct_nonzero, w.pct_nonzero)
+ os.remove(fname)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/core/IOHandlers/tests/test_wkt.py b/pysal/core/IOHandlers/tests/test_wkt.py
new file mode 100644
index 0000000..caeb639
--- /dev/null
+++ b/pysal/core/IOHandlers/tests/test_wkt.py
@@ -0,0 +1,31 @@
+import unittest
+import pysal
+from pysal.core.IOHandlers.wkt import WKTReader
+
+
+class test_WKTReader(unittest.TestCase):
+ def setUp(self):
+ self.test_file = test_file = pysal.examples.get_path('stl_hom.wkt')
+ self.obj = WKTReader(test_file, 'r')
+
+ def test_close(self):
+ f = self.obj
+ f.close()
+ self.failUnlessRaises(ValueError, f.read)
+ # w_kt_reader = WKTReader(*args, **kwargs)
+ # self.assertEqual(expected, w_kt_reader.close())
+
+ def test_open(self):
+ f = self.obj
+ expected = ['wkt']
+ self.assertEqual(expected, f.FORMATS)
+
+ def test__read(self):
+ polys = self.obj.read()
+ self.assertEqual(78, len(polys))
+ self.assertEqual((-91.195784694307383, 39.990883050220845),
+ polys[1].centroid)
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/core/IOHandlers/wk1.py b/pysal/core/IOHandlers/wk1.py
new file mode 100644
index 0000000..fccca98
--- /dev/null
+++ b/pysal/core/IOHandlers/wk1.py
@@ -0,0 +1,330 @@
+import pysal
+import os.path
+import struct
+import pysal.core.FileIO as FileIO
+from pysal.weights import W
+from warnings import warn
+
+__author__ = "Myunghwa Hwang <mhwang4 at gmail.com>"
+__all__ = ["Wk1IO"]
+
+
+class Wk1IO(FileIO.FileIO):
+ """
+ MATLAB wk1read.m and wk1write.m that were written by Brian M. Bourgault in 10/22/93
+
+ Opens, reads, and writes weights file objects in Lotus Wk1 format.
+
+ Lotus Wk1 file is used in Dr. LeSage's MATLAB Econometrics library.
+
+ A Wk1 file holds a spatial weights object in a full matrix form
+ without any row and column headers.
+ The maximum number of columns supported in a Wk1 file is 256.
+ Wk1 starts the row (column) number from 0 and
+ uses little endian binary endcoding.
+ In PySAL, when the number of observations is n,
+ it is assumed that each cell of a n\*n(=m) matrix either is a blank or
+ have a number.
+
+ The internal structure of a Wk1 file written by PySAL is as follows:
+ [BOF][DIM][CPI][CAL][CMODE][CORD][SPLIT][SYNC][CURS][WIN]
+ [HCOL][MRG][LBL][CELL_1]...[CELL_m][EOF]
+ where [CELL_k] equals to [DTYPE][DLEN][DFORMAT][CINDEX][CVALUE].
+ The parts between [BOF] and [CELL_1] are variable according to the software
+ program used to write a wk1 file. While reading a wk1 file,
+ PySAL ignores them.
+ Each part of this structure is detailed below.
+
+ .. table:: Lotus WK1 fields
+
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ |Part |Description |Data Type |Length |Value |
+ +=============+=====================+=========================+=======+=============================+
+ |[BOF] |Begining of field |unsigned character |6 |0,0,2,0,6,4 |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ |[DIM] |Matrix dimension |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ | [DIMDTYPE] |Type of dim. rec |unsigned short |2 |6 |
+ | [DIMLEN] |Length of dim. rec |unsigned short |2 |8 |
+ | [DIMVAL] |Value of dim. rec |unsigned short |8 |0,0,n,n |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ |[CPI] |CPI |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ | [CPITYPE] |Type of cpi rec |unsigned short |2 |150 |
+ | [CPILEN] |Length of cpi rec |unsigned short |2 |6 |
+ | [CPIVAL] |Value of cpi rec |unsigned char |6 |0,0,0,0,0,0 |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ |[CAL] |calcount |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ | [CALTYPE] |Type of calcount rec |unsigned short |2 |47 |
+ | [CALLEN] |Length calcount rec |unsigned short |2 |1 |
+ | [CALVAL] |Value of calcount rec|unsigned char |1 |0 |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ |[CMODE] |calmode |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ | [CMODETYP] |Type of calmode rec |unsigned short |2 |2 |
+ | [CMODELEN] |Length of calmode rec|unsigned short |2 |1 |
+ | [CMODEVAL] |Value of calmode rec |signed char |1 |0 |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ |[CORD] |calorder |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ | [CORDTYPE] |Type of calorder rec |unsigned short |2 |3 |
+ | [CORDLEN] |Length calorder rec |unsigned short |2 |1 |
+ | [CORDVAL] |Value of calorder rec|signed char |1 |0 |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ |[SPLIT] |split |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ | [SPLTYPE] |Type of split rec |unsigned short |2 |4 |
+ | [SPLLEN] |Length of split rec |unsigned short |2 |1 |
+ | [SPLVAL] |Value of split rec |signed char |1 |0 |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ |[SYNC] |sync |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ | [SYNCTYP] |Type of sync rec |unsigned short |2 |5 |
+ | [SYNCLEN] |Length of sync rec |unsigned short |2 |1 |
+ | [SYNCVAL] |Value of sync rec |singed char |1 |0 |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ |[CURS] |cursor |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ | [CURSTYP] |Type of cursor rec |unsigned short |2 |49 |
+ | [CURSLEN] |Length of cursor rec |unsigned short |2 |1 |
+ | [CURSVAL] |Value of cursor rec |signed char |1 |1 |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ |[WIN] |window |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ | [WINTYPE] |Type of window rec |unsigned short |2 |7 |
+ | [WINLEN] |Length of window rec |unsigned short |2 |32 |
+ | [WINVAL1] |Value 1 of window rec|unsigned short |4 |0,0 |
+ | [WINVAL2] |Value 2 of window rec|signed char |2 |113,0 |
+ | [WINVAL3] |Value 3 of window rec|unsigned short |26 |10,n,n,0,0,0,0,0,0,0,0,72,0 |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ |[HCOL] |hidcol |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ | [HCOLTYP] |Type of hidcol rec |unsigned short |2 |100 |
+ | [HCOLLEN] |Length of hidcol rec |unsigned short |2 |32 |
+ | [HCOLVAL] |Value of hidcol rec |signed char |32 |0*32 |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ |[MRG] |margins |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ | [MRGTYPE] |Type of margins rec |unsigned short |2 |40 |
+ | [MRGLEN] |Length of margins rec|unsigned short |2 |10 |
+ | [MRGVAL] |Value of margins rec |unsigned short |10 |4,76,66,2,2 |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ |[LBL] |labels |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ | [LBLTYPE] |Type of labels rec |unsigned short |2 |41 |
+ | [LBLLEN] |Length of labels rec |unsigned short |2 |1 |
+ | [LBLVAL] |Value of labels rec |char |1 |' |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ |[CELL_k] |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+ | [DTYPE] |Type of cell data |unsigned short |2 |[DTYPE][0]==0: end of file |
+ | | | | | ==14: number |
+ | | | | | ==16: formula |
+ | | | | | ==13: integer |
+ | | | | | ==11: nrange |
+ | | | | | ==else: unknown |
+ | [DLEN] |Length of cell data |unsigned short |2 | |
+ | [DFORMAT] |Format of cell data |not sure |1 | |
+ | [CINDEX] |Row, column of cell |unsigned short |4 | |
+ | [CVALUE] |Value of cell |double, [DTYPE][0]==14 |8 | |
+ | | |formula,[DTYPE][0]==16 |8 + |[DTYPE][1] - 13 |
+ | | |integer,[DTYPE][0]==13 |2 | |
+ | | |nrange, [DTYPE][0]==11 |24 | |
+ | | |else, [DTYPE][0]==else | |[DTYPE][1] |
+ | [EOF] |End of file |unsigned short |4 |1,0,0,0 |
+ +-------------+---------------------+-------------------------+-------+-----------------------------+
+
+
+ """
+
+ FORMATS = ['wk1']
+ MODES = ['r', 'w']
+
+ def __init__(self, *args, **kwargs):
+ self._varName = 'Unknown'
+ FileIO.FileIO.__init__(self, *args, **kwargs)
+ self.file = open(self.dataPath, self.mode + 'b')
+
+ def _set_varName(self, val):
+ if issubclass(type(val), basestring):
+ self._varName = val
+
+ def _get_varName(self):
+ return self._varName
+ varName = property(fget=_get_varName, fset=_set_varName)
+
+ def read(self, n=-1):
+ self._complain_ifclosed(self.closed)
+ return self._read()
+
+ def seek(self, pos):
+ if pos == 0:
+ self.file.seek(0)
+ self.pos = 0
+
+ def _read(self):
+ """
+ Reads Lotus Wk1 file
+
+ Returns
+ -------
+ A pysal.weights.weights.W object
+
+ Examples
+ --------
+
+ Type 'dir(w)' at the interpreter to see what methods are supported.
+ Open a Lotus Wk1 file and read it into a pysal weights object
+
+ >>> w = pysal.open(pysal.examples.get_path('spat-sym-us.wk1'),'r').read()
+
+ Get the number of observations from the header
+
+ >>> w.n
+ 46
+
+ Get the mean number of neighbors
+
+ >>> w.mean_neighbors
+ 4.0869565217391308
+
+ Get neighbor distances for a single observation
+
+ >>> w[1]
+ {25: 1.0, 3: 1.0, 28: 1.0, 39: 1.0}
+
+ """
+ if self.pos > 0:
+ raise StopIteration
+
+ bof = struct.unpack('<6B', self.file.read(6))
+ if bof != (0, 0, 2, 0, 6, 4):
+ raise ValueError('The header of your file is wrong!')
+
+ neighbors = {}
+ weights = {}
+ dtype, dlen = struct.unpack('<2H', self.file.read(4))
+ while(dtype != 1):
+ if dtype in [13, 14, 16]:
+ self.file.read(1)
+ row, column = struct.unpack('2H', self.file.read(4))
+ format, length = '<d', 8
+ if dtype == 13:
+ format, length = '<h', 2
+ value = float(struct.unpack(format, self.file.read(length))[0])
+ if value > 0:
+ ngh = neighbors.setdefault(row, [])
+ ngh.append(column)
+ wgt = weights.setdefault(row, [])
+ wgt.append(value)
+ if dtype == 16:
+ self.file.read(dlen - 13)
+ elif dtype == 11:
+ self.file.read(24)
+ else:
+ self.file.read(dlen)
+ dtype, dlen = struct.unpack('<2H', self.file.read(4))
+
+ self.pos += 1
+ return W(neighbors, weights)
+
+ def write(self, obj):
+ """
+
+ Parameters
+ ----------
+ .write(weightsObject)
+ accepts a weights object
+
+ Returns
+ ------
+
+ a Lotus wk1 file
+ write a weights object to the opened wk1 file.
+
+ Examples
+ --------
+
+ >>> import tempfile, pysal, os
+ >>> testfile = pysal.open(pysal.examples.get_path('spat-sym-us.wk1'),'r')
+ >>> w = testfile.read()
+
+ Create a temporary file for this example
+
+ >>> f = tempfile.NamedTemporaryFile(suffix='.wk1')
+
+ Reassign to new var
+
+ >>> fname = f.name
+
+ Close the temporary named file
+
+ >>> f.close()
+
+ Open the new file in write mode
+
+ >>> o = pysal.open(fname,'w')
+
+ Write the Weights object into the open file
+
+ >>> o.write(w)
+ >>> o.close()
+
+ Read in the newly created text file
+
+ >>> wnew = pysal.open(fname,'r').read()
+
+ Compare values from old to new
+
+ >>> wnew.pct_nonzero == w.pct_nonzero
+ True
+
+ Clean up temporary file created for this example
+
+ >>> os.remove(fname)
+
+ """
+ self._complain_ifclosed(self.closed)
+ if issubclass(type(obj), W):
+ f = self.file
+ n = obj.n
+ if n > 256:
+ raise ValueError('WK1 file format supports only up to 256 observations.')
+ pack = struct.pack
+ f.write(pack('<6B', 0, 0, 2, 0, 6, 4))
+ f.write(pack('<6H', 6, 8, 0, 0, n, n))
+ f.write(pack('<2H6B', 150, 6, 0, 0, 0, 0, 0, 0))
+ f.write(pack('<2H1B', 47, 1, 0))
+ f.write(pack('<2H1b', 2, 1, 0))
+ f.write(pack('<2H1b', 3, 1, 0))
+ f.write(pack('<2H1b', 4, 1, 0))
+ f.write(pack('<2H1b', 5, 1, 0))
+ f.write(pack('<2H1b', 49, 1, 1))
+ f.write(pack('<4H2b13H', 7, 32, 0, 0, 113, 0, 10,
+ n, n, 0, 0, 0, 0, 0, 0, 0, 0, 72, 0))
+ hidcol = tuple(['<2H32b', 100, 32] + [0] * 32)
+ f.write(pack(*hidcol))
+ f.write(pack('<7H', 40, 10, 4, 76, 66, 2, 2))
+ f.write(pack('<2H1c', 41, 1, "'"))
+
+ id2i = obj.id2i
+ for i, w_i in enumerate(obj):
+ row = [0.0] * n
+ for k in w_i[1]:
+ row[id2i[k]] = w_i[1][k]
+ for c, v in enumerate(row):
+ cell = tuple(['<2H1b2H1d', 14, 13, 113, i, c, v])
+ f.write(pack(*cell))
+ f.write(pack('<4B', 1, 0, 0, 0))
+ self.pos += 1
+
+ else:
+ raise TypeError("Expected a pysal weights object, got: %s" % (
+ type(obj)))
+
+ def close(self):
+ self.file.close()
+ FileIO.FileIO.close(self)
+
+
diff --git a/pysal/core/IOHandlers/wkt.py b/pysal/core/IOHandlers/wkt.py
new file mode 100644
index 0000000..320afc2
--- /dev/null
+++ b/pysal/core/IOHandlers/wkt.py
@@ -0,0 +1,98 @@
+import pysal.core.FileIO as FileIO
+from pysal.core.util import WKTParser
+from pysal import cg
+import re
+
+__author__ = "Charles R Schmidt <schmidtc at gmail.com>"
+__all__ = ['WKTReader']
+#####################################################################
+## ToDo: Add Well-Known-Binary support...
+## * WKB spec:
+## http://webhelp.esri.com/arcgisserver/9.3/dotNet/index.htm#geodatabases/the_ogc_103951442.htm
+##
+##
+#####################################################################
+
+
+class WKTReader(FileIO.FileIO):
+ """
+ Parameters
+ ----------
+ Reads Well-Known Text
+ Returns a list of PySAL Polygon objects
+
+ Examples
+ --------
+ Read in WKT-formatted file
+
+ >>> import pysal
+ >>> f = pysal.open(pysal.examples.get_path('stl_hom.wkt'), 'r')
+
+ Convert wkt to pysal polygons
+
+ >>> polys = f.read()
+
+ Check length
+
+ >>> len(polys)
+ 78
+
+ Return centroid of polygon at index 1
+
+ >>> polys[1].centroid
+ (-91.19578469430738, 39.990883050220845)
+
+ Type dir(polys[1]) at the python interpreter to get a list of supported methods
+
+ """
+ MODES = ['r']
+ FORMATS = ['wkt']
+
+ def __init__(self, *args, **kwargs):
+ FileIO.FileIO.__init__(self, *args, **kwargs)
+ self.__idx = {}
+ self.__pos = 0
+ self.__open()
+
+ def open(self):
+ self.__open()
+
+ def __open(self):
+ self.dataObj = open(self.dataPath, self.mode)
+ self.wkt = WKTParser()
+
+ def _read(self):
+ FileIO.FileIO._complain_ifclosed(self.closed)
+ if self.__pos not in self.__idx:
+ self.__idx[self.__pos] = self.dataObj.tell()
+ line = self.dataObj.readline()
+ if line:
+ shape = self.wkt.fromWKT(line)
+ shape.id = self.pos
+ self.__pos += 1
+ self.pos += 1
+ return shape
+ else:
+ self.seek(0)
+ return None
+
+ def seek(self, n):
+ FileIO.FileIO.seek(self, n)
+ pos = self.pos
+ if pos in self.__idx:
+ self.dataObj.seek(self.__idx[pos])
+ self.__pos = pos
+ else:
+ while pos not in self.__idx:
+ s = self._read()
+ if not s:
+ raise IndexError("%d not in range(0,%d)" % (
+ pos, max(self.__idx.keys())))
+ self.pos = pos
+ self.__pos = pos
+ self.dataObj.seek(self.__idx[pos])
+
+ def close(self):
+ self.dataObj.close()
+ FileIO.FileIO.close(self)
+
diff --git a/pysal/core/Tables.py b/pysal/core/Tables.py
new file mode 100644
index 0000000..04c8a26
--- /dev/null
+++ b/pysal/core/Tables.py
@@ -0,0 +1,167 @@
+__all__ = ['DataTable']
+import FileIO
+import numpy as np
+
+__author__ = "Charles R Schmidt <schmidtc at gmail.com>"
+
+
+class DataTable(FileIO.FileIO):
+ """ DataTable provides additional functionality to FileIO for data table file tables
+ FileIO Handlers that provide data tables should subclass this instead of FileIO """
+ class _By_Col:
+ def __init__(self, parent):
+ self.p = parent
+
+ def __repr__(self):
+ return "keys: " + self.p.header.__repr__()
+
+ def __getitem__(self, key):
+ return self.p._get_col(key)
+
+ def __setitem__(self, key, val):
+ self.p.cast(key, val)
+
+ def __call__(self, key):
+ return self.p._get_col(key)
+
+ def __init__(self, *args, **kwargs):
+ FileIO.FileIO.__init__(self, *args, **kwargs)
+
+ def __repr__(self):
+ return 'DataTable: % s' % self.dataPath
+
+ def __len__(self):
+ """ __len__ should be implemented by DataTable Subclasses """
+ raise NotImplementedError
+
+ @property
+ def by_col(self):
+ return self._By_Col(self)
+
+ def _get_col(self, key):
+ """ returns the column vector
+ """
+ if not self.header:
+ raise AttributeError('Please set the header')
+ if key in self.header:
+ return self[:, self.header.index(key)]
+ else:
+ raise AttributeError('Field: % s does not exist in header' % key)
+
+ def by_col_array(self, variable_names):
+ """
+ Return columns of table as a numpy array
+
+ Parameters
+ ----------
+
+ variable_names: list of strings of length k
+ names of variables to extract
+
+ Returns
+ -------
+ implicit: numpy array of shape (n,k)
+
+
+ Notes
+ -----
+
+ If the variables are not all of the same data type, then numpy rules
+ for casting will result in a uniform type applied to all variables.
+
+ Examples
+ --------
+
+ >>> import pysal as ps
+ >>> dbf = ps.open(ps.examples.get_path('NAT.dbf'))
+ >>> hr = dbf.by_col_array(['HR70', 'HR80'])
+ >>> hr[0:5]
+ array([[ 0. , 8.85582713],
+ [ 0. , 17.20874204],
+ [ 1.91515848, 3.4507747 ],
+ [ 1.28864319, 3.26381409],
+ [ 0. , 7.77000777]])
+ >>> hr = dbf.by_col_array(['HR80', 'HR70'])
+ >>> hr[0:5]
+ array([[ 8.85582713, 0. ],
+ [ 17.20874204, 0. ],
+ [ 3.4507747 , 1.91515848],
+ [ 3.26381409, 1.28864319],
+ [ 7.77000777, 0. ]])
+ >>> hr = dbf.by_col_array(['HR80'])
+ >>> hr[0:5]
+ array([[ 8.85582713],
+ [ 17.20874204],
+ [ 3.4507747 ],
+ [ 3.26381409],
+ [ 7.77000777]])
+
+ Numpy only supports homogeneous arrays. See Notes above.
+
+ >>> hr = dbf.by_col_array(['STATE_NAME', 'HR80'])
+ >>> hr[0:5]
+ array([['Minnesota', '8.8558271343'],
+ ['Washington', '17.208742041'],
+ ['Washington', '3.4507746989'],
+ ['Washington', '3.2638140931'],
+ ['Washington', '7.77000777']],
+ dtype='|S20')
+
+
+ """
+ lst = [self._get_col(variable) for variable in variable_names]
+ return np.array(lst).T
+
+ def __getitem__(self, key):
+ """ DataTables fully support slicing in 2D,
+ To provide slicing, handlers must provide __len__
+ Slicing accepts up to two arguments.
+ Syntax,
+ table[row]
+ table[row, col]
+ table[row_start:row_stop]
+ table[row_start:row_stop:row_step]
+ table[:, col]
+ table[:, col_start:col_stop]
+ etc.
+
+ ALL indices are Zero-Offsets,
+ i.e.
+ #>>> assert index in range(0, len(table))
+ """
+ prevPos = self.tell()
+ if issubclass(type(key), basestring):
+ raise TypeError("index should be int or slice")
+ if issubclass(type(key), int) or isinstance(key, slice):
+ rows = key
+ cols = None
+ elif len(key) > 2:
+ raise TypeError("DataTables support two dimmensional slicing, % d slices provided" % len(key))
+ elif len(key) == 2:
+ rows, cols = key
+ else:
+ raise TypeError("Key: % r, is confusing me. I don't know what to do" % key)
+ if isinstance(rows, slice):
+ row_start, row_stop, row_step = rows.indices(len(self))
+ self.seek(row_start)
+ data = [self.next() for i in range(row_start, row_stop, row_step)]
+ else:
+ self.seek(slice(rows).indices(len(self))[1])
+ data = [self.next()]
+ if cols is not None:
+ if isinstance(cols, slice):
+ col_start, col_stop, col_step = cols.indices(len(data[0]))
+ data = [r[col_start:col_stop:col_step] for r in data]
+ else:
+ #col_start, col_stop, col_step = cols, cols+1, 1
+ data = [r[cols] for r in data]
+ self.seek(prevPos)
+ return data
+
+
+def _test():
+ import doctest
+ doctest.testmod(verbose=True)
+
+if __name__ == '__main__':
+ _test()
diff --git a/pysal/core/__init__.py b/pysal/core/__init__.py
new file mode 100644
index 0000000..d18cc4f
--- /dev/null
+++ b/pysal/core/__init__.py
@@ -0,0 +1 @@
+import FileIO
diff --git a/pysal/core/tests/__init__.py b/pysal/core/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pysal/core/tests/test_FileIO.py b/pysal/core/tests/test_FileIO.py
new file mode 100644
index 0000000..e69de29
diff --git a/pysal/core/util/__init__.py b/pysal/core/util/__init__.py
new file mode 100644
index 0000000..2a87f35
--- /dev/null
+++ b/pysal/core/util/__init__.py
@@ -0,0 +1,2 @@
+from wkt import *
+from shapefile import *
diff --git a/pysal/core/util/shapefile.py b/pysal/core/util/shapefile.py
new file mode 100644
index 0000000..973b152
--- /dev/null
+++ b/pysal/core/util/shapefile.py
@@ -0,0 +1,758 @@
+"""
+A Pure Python ShapeFile Reader and Writer
+This module is selfcontained and does not require pysal.
+This module returns and expects dictionary based data strucutres.
+This module should be wrapped into your native data strcutures.
+
+Contact:
+Charles Schmidt
+GeoDa Center
+Arizona State University
+Tempe, AZ
+http://geodacenter.asu.edu
+"""
+
+__author__ = "Charles R Schmidt <schmidtc at gmail.com>"
+
+from struct import calcsize, unpack, pack
+from cStringIO import StringIO
+from itertools import izip, islice
+import array
+import sys
+if sys.byteorder == 'little':
+ SYS_BYTE_ORDER = '<'
+else:
+ SYS_BYTE_ORDER = '>'
+STRUCT_ITEMSIZE = {}
+STRUCT_ITEMSIZE['i'] = calcsize('i')
+STRUCT_ITEMSIZE['d'] = calcsize('d')
+
+__all__ = ['shp_file', 'shx_file']
+
+#SHAPEFILE Globals
+
+
+def struct2arrayinfo(struct):
+ struct = list(struct)
+ names = [x[0] for x in struct]
+ types = [x[1] for x in struct]
+ orders = [x[2] for x in struct]
+ lname, ltype, lorder = struct.pop(0)
+ groups = {}
+ g = 0
+ groups[g] = {'names': [lname], 'size': STRUCT_ITEMSIZE[ltype],
+ 'fmt': ltype, 'order': lorder}
+ while struct:
+ name, type, order = struct.pop(0)
+ if order == lorder:
+ groups[g]['names'].append(name)
+ groups[g]['size'] += STRUCT_ITEMSIZE[type]
+ groups[g]['fmt'] += type
+ else:
+ g += 1
+ groups[g] = {'names': [name], 'size': STRUCT_ITEMSIZE[
+ type], 'fmt': type, 'order': order}
+ lname, ltype, lorder = name, type, order
+ return [groups[x] for x in range(g + 1)]
+
+HEADERSTRUCT = (
+ ('File Code', 'i', '>'),
+ ('Unused0', 'i', '>'),
+ ('Unused1', 'i', '>'),
+ ('Unused2', 'i', '>'),
+ ('Unused3', 'i', '>'),
+ ('Unused4', 'i', '>'),
+ ('File Length', 'i', '>'),
+ ('Version', 'i', '<'),
+ ('Shape Type', 'i', '<'),
+ ('BBOX Xmin', 'd', '<'),
+ ('BBOX Ymin', 'd', '<'),
+ ('BBOX Xmax', 'd', '<'),
+ ('BBOX Ymax', 'd', '<'),
+ ('BBOX Zmin', 'd', '<'),
+ ('BBOX Zmax', 'd', '<'),
+ ('BBOX Mmin', 'd', '<'),
+ ('BBOX Mmax', 'd', '<'))
+UHEADERSTRUCT = struct2arrayinfo(HEADERSTRUCT)
+RHEADERSTRUCT = (
+ ('Record Number', 'i', '>'),
+ ('Content Length', 'i', '>'))
+URHEADERSTRUCT = struct2arrayinfo(RHEADERSTRUCT)
+
+
+def noneMax(a, b):
+ if a is None:
+ return b
+ if b is None:
+ return a
+ return max(a, b)
+
+
+def noneMin(a, b):
+ if a is None:
+ return b
+ if b is None:
+ return a
+ return min(a, b)
+
+
+def _unpackDict(structure, fileObj):
+ """Utility Function, Requires a Tuple of tuples that desribes the element structure...
+
+ _unpackDict(structure tuple, fileObj file) -> dict
+
+ Arguments:
+ structure -- tuple of tuples -- (('FieldName 1','type','byteOrder'),('FieldName 2','type','byteOrder'))
+ fileObj -- file -- an open file at the correct position!
+ Returns:
+ {'FieldName 1': value, 'FieldName 2': value}
+ Side Effects:
+ #file at new position
+
+ Example:
+ >>> import pysal
+ >>> _unpackDict(UHEADERSTRUCT,open(pysal.examples.get_path('10740.shx'),'rb')) == {'BBOX Xmax': -105.29012, 'BBOX Ymax': 36.219799000000002, 'BBOX Mmax': 0.0, 'BBOX Zmin': 0.0, 'BBOX Mmin': 0.0, 'File Code': 9994, 'BBOX Ymin': 34.259672000000002, 'BBOX Xmin': -107.62651, 'Unused0': 0, 'Unused1': 0, 'Unused2': 0, 'Unused3': 0, 'Unused4': 0, 'Version': 1000, 'BBOX Zmax': 0.0, 'Shape Type': 5, 'File Length': 830}
+ True
+ """
+ d = {}
+ for struct in structure:
+ items = unpack(struct['order'] + struct['fmt'],
+ fileObj.read(struct['size']))
+ for i, name in enumerate(struct['names']):
+ d[name] = items[i]
+ return d
+
+
+def _unpackDict2(d, structure, fileObj):
+ """Utility Function, used arrays instead from struct
+
+ Arguments:
+ d -- dict -- Dictionary to be updated.
+ structure -- tuple of tuples -- (('FieldName 1',('type',n),'byteOrder'),('FieldName 2',('type',n,'byteOrder'))
+ """
+ for name, dtype, order in structure:
+ dtype, n = dtype
+ result = array.array(dtype)
+ result.fromstring(fileObj.read(result.itemsize * n))
+ if order != SYS_BYTE_ORDER:
+ result.byteswap()
+ d[name] = result.tolist()
+ return d
+
+
+def _packDict(structure, d):
+ """Utility Function
+
+ _packDict(structure tuple, d dict) -> str
+
+ Arguments:
+ structure -- tuple of tuples -- (('FieldName 1','type','byteOrder'),('FieldName 2','type','byteOrder'))
+ d -- dict -- {'FieldName 1': value, 'FieldName 2': value}
+
+ Example:
+ >>> s = _packDict( (('FieldName 1','i','<'),('FieldName 2','i','<')), {'FieldName 1': 1, 'FieldName 2': 2} )
+ >>> s==pack('<ii',1,2)
+ True
+ >>> unpack('<ii',s)
+ (1, 2)
+ """
+ string = b''
+ for name, dtype, order in structure:
+ if len(dtype) > 1:
+ string += pack(order + dtype, *d[name])
+ else:
+ string += pack(order + dtype, d[name])
+ return string
+
+
+class shp_file:
+ """
+ Reads and Writes the SHP compenent of a ShapeFile
+
+ Attributes:
+ header -- dict -- Contents of the SHP header. #For contents see: HEADERSTRUCT
+ shape -- int -- ShapeType.
+
+ Notes: The header of both the SHP and SHX files are indentical.
+
+ """
+ SHAPE_TYPES = {'POINT': 1, 'ARC': 3, 'POLYGON': 5, 'MULTIPOINT': 8, 'POINTZ': 11, 'ARCZ': 13, 'POLYGONZ': 15, 'MULTIPOINTZ': 18, 'POINTM': 21, 'ARCM': 23, 'POLYGONM': 25, 'MULTIPOINTM': 28, 'MULTIPATCH': 31}
+
+ def __iswritable(self):
+ try:
+ assert self.__mode == 'w'
+ except AssertionError:
+ raise IOError("[Errno 9] Bad file descriptor")
+ return True
+
+ def __isreadable(self):
+ try:
+ assert self.__mode == 'r'
+ except AssertionError:
+ raise IOError("[Errno 9] Bad file descriptor")
+ return True
+
+ def __init__(self, fileName, mode='r', shape_type=None):
+ self.__mode = mode
+ if fileName.lower().endswith('.shp') or fileName.lower().endswith('.shx') or fileName.lower().endswith('.dbf'):
+ fileName = fileName[:-4]
+ self.fileName = fileName
+
+ if mode == 'r':
+ self._open_shp_file()
+ elif mode == 'w':
+ if shape_type not in self.SHAPE_TYPES:
+ raise Exception('Attempt to create shp/shx file of invalid type')
+ self._create_shp_file(shape_type)
+ else:
+ raise Exception('Only "w" and "r" modes are supported')
+
+ def _open_shp_file(self):
+ """
+ Opens a shp/shx file.
+
+ shp_file(fileName string, 'r') -> Shpfile
+
+ Arguments:
+ filename -- the name of the file to create
+ mode -- string -- 'r'
+ shape_type -- None
+
+ Example:
+ >>> import pysal
+ >>> shp = shp_file(pysal.examples.get_path('10740.shp'))
+ >>> shp.header == {'BBOX Xmax': -105.29012, 'BBOX Ymax': 36.219799000000002, 'BBOX Mmax': 0.0, 'BBOX Zmin': 0.0, 'BBOX Mmin': 0.0, 'File Code': 9994, 'BBOX Ymin': 34.259672000000002, 'BBOX Xmin': -107.62651, 'Unused0': 0, 'Unused1': 0, 'Unused2': 0, 'Unused3': 0, 'Unused4': 0, 'Version': 1000, 'BBOX Zmax': 0.0, 'Shape Type': 5, 'File Length': 260534}
+ True
+ >>> len(shp)
+ 195
+ """
+ self.__isreadable()
+ fileName = self.fileName
+ self.fileObj = open(fileName + '.shp', 'rb')
+ self._shx = shx_file(fileName)
+ self.header = _unpackDict(UHEADERSTRUCT, self.fileObj)
+ self.shape = TYPE_DISPATCH[self.header['Shape Type']]
+ self.__lastShape = 0
+ # localizing for convenience
+ self.__numRecords = self._shx.numRecords
+ # constructing bounding box from header
+ h = self.header
+ self.bbox = [h['BBOX Xmin'], h['BBOX Ymin'],
+ h['BBOX Xmax'], h['BBOX Ymax']]
+ self.shapeType = self.header['Shape Type']
+
+ def _create_shp_file(self, shape_type):
+ """
+ Creates a shp/shx file.
+
+ shp_file(fileName string, 'w', shape_type string) -> Shpfile
+
+ Arguments:
+ filename -- the name of the file to create
+ mode -- string -- must be 'w'
+ shape_type -- string -- the type of shp/shx file to create. must be one of
+ the following: 'POINT', 'POINTZ', 'POINTM',
+ 'ARC', 'ARCZ', 'ARCM', 'POLYGON', 'POLYGONZ', 'POLYGONM',
+ 'MULTIPOINT', 'MULTIPOINTZ', 'MULTIPOINTM', 'MULTIPATCH'
+
+ Example:
+ >>> import pysal,os
+ >>> shp = shp_file('test','w','POINT')
+ >>> p = shp_file(pysal.examples.get_path('Point.shp'))
+ >>> for pt in p:
+ ... shp.add_shape(pt)
+ ...
+ >>> shp.close()
+ >>> open('test.shp','rb').read() == open(pysal.examples.get_path('Point.shp'),'rb').read()
+ True
+ >>> open('test.shx','rb').read() == open(pysal.examples.get_path('Point.shx'),'rb').read()
+ True
+ >>> os.remove('test.shx')
+ >>> os.remove('test.shp')
+ """
+ self.__iswritable()
+ fileName = self.fileName
+ self.fileObj = open(fileName + '.shp', 'wb')
+ self._shx = shx_file(fileName, 'w')
+ self.header = {}
+ self.header['Shape Type'] = self.SHAPE_TYPES[shape_type]
+ self.header['Version'] = 1000
+ self.header['Unused0'] = 0
+ self.header['Unused1'] = 0
+ self.header['Unused2'] = 0
+ self.header['Unused3'] = 0
+ self.header['Unused4'] = 0
+ self.header['File Code'] = 9994
+ self.__file_Length = 100
+ self.header['File Length'] = 0
+ self.header['BBOX Xmax'] = None
+ self.header['BBOX Ymax'] = None
+ self.header['BBOX Mmax'] = None
+ self.header['BBOX Zmax'] = None
+ self.header['BBOX Xmin'] = None
+ self.header['BBOX Ymin'] = None
+ self.header['BBOX Mmin'] = None
+ self.header['BBOX Zmin'] = None
+ self.shape = TYPE_DISPATCH[self.header['Shape Type']]
+ #self.__numRecords = self._shx.numRecords
+
+ def __len__(self):
+ return self.__numRecords
+
+ def __iter__(self):
+ return self
+
+ def type(self):
+ return self.shape.String_Type
+
+ def next(self):
+ """returns the next Shape in the shapeFile
+
+ Example:
+ >>> import pysal
+ >>> list(shp_file(pysal.examples.get_path('Point.shp'))) == [{'Y': -0.25904661905760773, 'X': -0.00068176617532103578, 'Shape Type': 1}, {'Y': -0.25630328607387354, 'X': 0.11697145363360706, 'Shape Type': 1}, {'Y': -0.33930131004366804, 'X': 0.05043668122270728, 'Shape Type': 1}, {'Y': -0.41266375545851519, 'X': -0.041266375545851552, 'Shape Type': 1}, {'Y': -0.44017467248908293, 'X': -0.011462882096069604, 'Shape Type': 1}, {'Y': -0.46080786026200882, 'X': 0.027510917030567628, [...]
+ True
+ """
+ self.__isreadable()
+ nextShape = self.__lastShape
+ if nextShape == self._shx.numRecords:
+ self.__lastShape = 0
+ raise StopIteration
+ else:
+ self.__lastShape = nextShape + 1
+ return self.get_shape(nextShape)
+
+ def __seek(self, pos):
+ if pos != self.fileObj.tell():
+ self.fileObj.seek(pos)
+
+ def __read(self, pos, size):
+ self.__isreadable()
+ if pos != self.fileObj.tell():
+ self.fileObj.seek(pos)
+ return self.fileObj.read(size)
+
+ def get_shape(self, shpId):
+ self.__isreadable()
+ if shpId + 1 > self.__numRecords:
+ raise IndexError
+ fPosition, bytes = self._shx.index[shpId]
+ self.__seek(fPosition)
+ #the index does not include the 2 byte record header (which contains, Record ID and Content Length)
+ rec_id, con_len = _unpackDict(URHEADERSTRUCT, self.fileObj)
+ return self.shape.unpack(StringIO(self.fileObj.read(bytes)))
+ #return self.shape.unpack(self.fileObj.read(bytes))
+
+ def __update_bbox(self, s):
+ h = self.header
+ if s.get('Shape Type') == 1:
+ h['BBOX Xmax'] = noneMax(h['BBOX Xmax'], s.get('X'))
+ h['BBOX Ymax'] = noneMax(h['BBOX Ymax'], s.get('Y'))
+ h['BBOX Mmax'] = noneMax(h['BBOX Mmax'], s.get('M'))
+ h['BBOX Zmax'] = noneMax(h['BBOX Zmax'], s.get('Z'))
+ h['BBOX Xmin'] = noneMin(h['BBOX Xmin'], s.get('X'))
+ h['BBOX Ymin'] = noneMin(h['BBOX Ymin'], s.get('Y'))
+ h['BBOX Mmin'] = noneMin(h['BBOX Mmin'], s.get('M'))
+ h['BBOX Zmin'] = noneMin(h['BBOX Zmin'], s.get('Z'))
+ else:
+ h['BBOX Xmax'] = noneMax(h['BBOX Xmax'], s.get('BBOX Xmax'))
+ h['BBOX Ymax'] = noneMax(h['BBOX Ymax'], s.get('BBOX Ymax'))
+ h['BBOX Mmax'] = noneMax(h['BBOX Mmax'], s.get('BBOX Mmax'))
+ h['BBOX Zmax'] = noneMax(h['BBOX Zmax'], s.get('BBOX Zmax'))
+ h['BBOX Xmin'] = noneMin(h['BBOX Xmin'], s.get('BBOX Xmin'))
+ h['BBOX Ymin'] = noneMin(h['BBOX Ymin'], s.get('BBOX Ymin'))
+ h['BBOX Mmin'] = noneMin(h['BBOX Mmin'], s.get('BBOX Mmin'))
+ h['BBOX Zmin'] = noneMin(h['BBOX Zmin'], s.get('BBOX Zmin'))
+ if not self.shape.HASM:
+ self.header['BBOX Mmax'] = 0.0
+ self.header['BBOX Mmin'] = 0.0
+ if not self.shape.HASZ:
+ self.header['BBOX Zmax'] = 0.0
+ self.header['BBOX Zmin'] = 0.0
+
+ def add_shape(self, s):
+ self.__iswritable()
+ self.__update_bbox(s)
+ rec = self.shape.pack(s)
+ con_len = len(rec)
+ self.__file_Length += con_len + 8
+ rec_id, pos = self._shx.add_record(con_len)
+ self.__seek(pos)
+ self.fileObj.write(pack('>ii', rec_id, con_len / 2))
+ self.fileObj.write(rec)
+
+ def close(self):
+ self._shx.close(self.header)
+ if self.__mode == 'w':
+ self.header['File Length'] = self.__file_Length / 2
+ self.__seek(0)
+ self.fileObj.write(_packDict(HEADERSTRUCT, self.header))
+ self.fileObj.close()
+
+
+class shx_file:
+ """
+ Reads and Writes the SHX compenent of a ShapeFile
+
+ Attributes:
+ index -- list -- Contains the file offset and len of each recond in the SHP component
+ numRecords -- int -- Number of records
+
+ """
+ def __iswritable(self):
+ try:
+ assert self.__mode == 'w'
+ except AssertionError:
+ raise IOError("[Errno 9] Bad file descriptor")
+ return True
+
+ def __isreadable(self):
+ try:
+ assert self.__mode == 'r'
+ except AssertionError:
+ raise IOError("[Errno 9] Bad file descriptor")
+ return True
+
+ def __init__(self, fileName=None, mode='r'):
+ self.__mode = mode
+ if fileName.endswith('.shp') or fileName.endswith('.shx') or fileName.endswith('.dbf'):
+ fileName = fileName[:-4]
+ self.fileName = fileName
+
+ if mode == 'r':
+ self._open_shx_file()
+ elif mode == 'w':
+ self._create_shx_file()
+
+ def _open_shx_file(self):
+ """ Opens the SHX file.
+
+ shx_file(filename,'r') --> shx_file
+
+ Arguments:
+ filename -- string -- extension is optional, will remove '.dbf','.shx','.shp' and append '.shx'
+ mode -- string -- Must be 'r'
+
+ Example:
+ >>> import pysal
+ >>> shx = shx_file(pysal.examples.get_path('10740'))
+ >>> shx._header == {'BBOX Xmax': -105.29012, 'BBOX Ymax': 36.219799000000002, 'BBOX Mmax': 0.0, 'BBOX Zmin': 0.0, 'BBOX Mmin': 0.0, 'File Code': 9994, 'BBOX Ymin': 34.259672000000002, 'BBOX Xmin': -107.62651, 'Unused0': 0, 'Unused1': 0, 'Unused2': 0, 'Unused3': 0, 'Unused4': 0, 'Version': 1000, 'BBOX Zmax': 0.0, 'Shape Type': 5, 'File Length': 830}
+ True
+ >>> len(shx.index)
+ 195
+ """
+ self.__isreadable()
+ self.fileObj = open(self.fileName + '.shx', 'rb')
+ self._header = _unpackDict(UHEADERSTRUCT, self.fileObj)
+ self.numRecords = numRecords = (self._header['File Length'] - 50) / 4
+ index = {}
+ fmt = '>%di' % (2 * numRecords)
+ size = calcsize(fmt)
+ dat = unpack(fmt, self.fileObj.read(size))
+ self.index = [(dat[i] * 2, dat[i + 1] * 2) for i in xrange(
+ 0, len(dat), 2)]
+
+ def _create_shx_file(self):
+ """ Creates the SHX file.
+
+ shx_file(filename,'w') --> shx_file
+
+ Arguments:
+ filename -- string -- extension is optional, will remove '.dbf','.shx','.shp' and append '.shx'
+ mode -- string -- Must be 'w'
+
+ Example:
+ >>> import pysal
+ >>> shx = shx_file(pysal.examples.get_path('Point'))
+ >>> isinstance(shx,shx_file)
+ True
+ """
+ self.__iswritable()
+ self.fileObj = open(self.fileName + '.shx', 'wb')
+ self.numRecords = 0
+ self.index = []
+ self.__offset = 100 # length of header
+ self.__next_rid = 1 # record IDs start at 1
+
+ def add_record(self, size):
+ """ Add a record to the shx index.
+
+ add_record(size int) --> RecordID int
+
+ Arguments:
+ size -- int -- the length of the record in bytes NOT including the 8byte record header
+
+ Returns:
+ rec_id -- int -- the sequential record ID, 1-based.
+
+ Note: the SHX records contain (Offset, Length) in 16-bit words.
+
+ Example:
+ >>> import pysal,os
+ >>> shx = shx_file(pysal.examples.get_path('Point'))
+ >>> shx.index
+ [(100, 20), (128, 20), (156, 20), (184, 20), (212, 20), (240, 20), (268, 20), (296, 20), (324, 20)]
+ >>> shx2 = shx_file('test','w')
+ >>> [shx2.add_record(rec[1]) for rec in shx.index]
+ [(1, 100), (2, 128), (3, 156), (4, 184), (5, 212), (6, 240), (7, 268), (8, 296), (9, 324)]
+ >>> shx2.index == shx.index
+ True
+ >>> shx2.close(shx._header)
+ >>> open('test.shx','rb').read() == open(pysal.examples.get_path('Point.shx'),'rb').read()
+ True
+ >>> os.remove('test.shx')
+ """
+ self.__iswritable()
+ pos = self.__offset
+ rec_id = self.__next_rid
+ self.index.append((self.__offset, size))
+ self.__offset += size + 8 # the 8byte record Header.
+ self.numRecords += 1
+ self.__next_rid += 1
+ return rec_id, pos
+
+ def close(self, header):
+ if self.__mode == 'w':
+ self.__iswritable()
+ header['File Length'] = (
+ self.numRecords * calcsize('>ii') + 100) / 2
+ self.fileObj.seek(0)
+ self.fileObj.write(_packDict(HEADERSTRUCT, header))
+ fmt = '>%di' % (2 * self.numRecords)
+ values = []
+ for off, size in self.index:
+ values.extend([off / 2, size / 2])
+ self.fileObj.write(pack(fmt, *values))
+ self.fileObj.close()
+
+
+class NullShape:
+ Shape_Type = 0
+ STRUCT = (('Shape Type', 'i', '<'))
+
+ def unpack(self):
+ return None
+
+ def pack(self, x=None):
+ return pack('<i', 0)
+
+
+class Point(object):
+ """ Packs and Unpacks a ShapeFile Point Type
+ Example:
+ >>> import pysal
+ >>> shp = shp_file(pysal.examples.get_path('Point.shp'))
+ >>> rec = shp.get_shape(0)
+ >>> rec == {'Y': -0.25904661905760773, 'X': -0.00068176617532103578, 'Shape Type': 1}
+ True
+ >>> pos = shp.fileObj.seek(shp._shx.index[0][0]+8) #+8 byte record header
+ >>> dat = shp.fileObj.read(shp._shx.index[0][1])
+ >>> dat == Point.pack(rec)
+ True
+ """
+ Shape_Type = 1
+ String_Type = 'POINT'
+ HASZ = False
+ HASM = False
+ STRUCT = (('Shape Type', 'i', '<'),
+ ('X', 'd', '<'),
+ ('Y', 'd', '<'))
+ USTRUCT = [{'fmt': 'idd', 'order': '<', 'names': ['Shape Type',
+ 'X', 'Y'], 'size': 20}]
+
+ @classmethod
+ def unpack(cls, dat):
+ return _unpackDict(cls.USTRUCT, dat)
+
+ @classmethod
+ def pack(cls, record):
+ rheader = _packDict(cls.STRUCT, record)
+ return rheader
+
+
+class PointZ(Point):
+ Shape_Type = 11
+ String_Type = "POINTZ"
+ HASZ = True
+ HASM = True
+ STRUCT = (('Shape Type', 'i', '<'),
+ ('X', 'd', '<'),
+ ('Y', 'd', '<'),
+ ('Z', 'd', '<'),
+ ('M', 'd', '<'))
+ USTRUCT = [{'fmt': 'idddd', 'order': '<', 'names': ['Shape Type',
+ 'X', 'Y', 'Z', 'M'], 'size': 36}]
+
+
+class PolyLine:
+ """ Packs and Unpacks a ShapeFile PolyLine Type
+ Example:
+ >>> import pysal
+ >>> shp = shp_file(pysal.examples.get_path('Line.shp'))
+ >>> rec = shp.get_shape(0)
+ >>> rec == {'BBOX Ymax': -0.25832280562918325, 'NumPoints': 3, 'BBOX Ymin': -0.25895877033237352, 'NumParts': 1, 'Vertices': [(-0.0090539248870159517, -0.25832280562918325), (0.0074811573959305822, -0.25895877033237352), (0.0074811573959305822, -0.25895877033237352)], 'BBOX Xmax': 0.0074811573959305822, 'BBOX Xmin': -0.0090539248870159517, 'Shape Type': 3, 'Parts Index': [0]}
+ True
+ >>> pos = shp.fileObj.seek(shp._shx.index[0][0]+8) #+8 byte record header
+ >>> dat = shp.fileObj.read(shp._shx.index[0][1])
+ >>> dat == PolyLine.pack(rec)
+ True
+ """
+ HASZ = False
+ HASM = False
+ String_Type = 'ARC'
+ STRUCT = (('Shape Type', 'i', '<'),
+ ('BBOX Xmin', 'd', '<'),
+ ('BBOX Ymin', 'd', '<'),
+ ('BBOX Xmax', 'd', '<'),
+ ('BBOX Ymax', 'd', '<'),
+ ('NumParts', 'i', '<'),
+ ('NumPoints', 'i', '<'))
+ USTRUCT = [{'fmt': 'iddddii', 'order': '<', 'names': ['Shape Type', 'BBOX Xmin', 'BBOX Ymin', 'BBOX Xmax', 'BBOX Ymax', 'NumParts', 'NumPoints'], 'size': 44}]
+
+ @classmethod
+ def unpack(cls, dat):
+ record = _unpackDict(cls.USTRUCT, dat)
+ contentStruct = (('Parts Index', ('i', record['NumParts']), '<'),
+ ('Vertices', ('d', 2 * record['NumPoints']), '<'))
+ _unpackDict2(record, contentStruct, dat)
+ #record['Vertices'] = [(record['Vertices'][i],record['Vertices'][i+1]) for i in xrange(0,record['NumPoints']*2,2)]
+ verts = record['Vertices']
+ #Next line is equivalent to: zip(verts[::2],verts[1::2])
+ record['Vertices'] = list(izip(
+ islice(verts, 0, None, 2), islice(verts, 1, None, 2)))
+ if not record['Parts Index']:
+ record['Parts Index'] = [0]
+ return record
+ #partsIndex = list(partsIndex)
+ #partsIndex.append(None)
+ #parts = [vertices[partsIndex[i]:partsIndex[i+1]] for i in xrange(header['NumParts'])]
+
+ @classmethod
+ def pack(cls, record):
+ rheader = _packDict(cls.STRUCT, record)
+ contentStruct = (('Parts Index', '%di' % record['NumParts'], '<'),
+ ('Vertices', '%dd' % (2 * record['NumPoints']), '<'))
+ content = {}
+ content['Parts Index'] = record['Parts Index']
+ verts = []
+ [verts.extend(vert) for vert in record['Vertices']]
+ content['Vertices'] = verts
+ content = _packDict(contentStruct, content)
+ return rheader + content
+
+
+class PolyLineZ(object):
+ HASZ = True
+ HASM = True
+ String_Type = 'ARC'
+ STRUCT = (('Shape Type', 'i', '<'),
+ ('BBOX Xmin', 'd', '<'),
+ ('BBOX Ymin', 'd', '<'),
+ ('BBOX Xmax', 'd', '<'),
+ ('BBOX Ymax', 'd', '<'),
+ ('NumParts', 'i', '<'),
+ ('NumPoints', 'i', '<'))
+ USTRUCT = [{'fmt': 'iddddii', 'order': '<', 'names': ['Shape Type', 'BBOX Xmin', 'BBOX Ymin', 'BBOX Xmax', 'BBOX Ymax', 'NumParts', 'NumPoints'], 'size': 44}]
+
+ @classmethod
+ def unpack(cls, dat):
+ record = _unpackDict(cls.USTRUCT, dat)
+ contentStruct = (('Parts Index', ('i', record['NumParts']), '<'),
+ ('Vertices', ('d', 2 * record['NumPoints']), '<'),
+ ('Zmin', ('d', 1), '<'),
+ ('Zmax', ('d', 1), '<'),
+ ('Zarray', ('d', record['NumPoints']), '<'),
+ ('Mmin', ('d', 1), '<'),
+ ('Mmax', ('d', 1), '<'),
+ ('Marray', ('d', record['NumPoints']), '<'),)
+ _unpackDict2(record, contentStruct, dat)
+ verts = record['Vertices']
+ record['Vertices'] = list(izip(
+ islice(verts, 0, None, 2), islice(verts, 1, None, 2)))
+ if not record['Parts Index']:
+ record['Parts Index'] = [0]
+ record['Zmin'] = record['Zmin'][0]
+ record['Zmax'] = record['Zmax'][0]
+ record['Mmin'] = record['Mmin'][0]
+ record['Mmax'] = record['Mmax'][0]
+ return record
+
+ @classmethod
+ def pack(cls, record):
+ rheader = _packDict(cls.STRUCT, record)
+ contentStruct = (('Parts Index', '%di' % record['NumParts'], '<'),
+ ('Vertices', '%dd' % (2 * record['NumPoints']), '<'),
+ ('Zmin', 'd', '<'),
+ ('Zmax', 'd', '<'),
+ ('Zarray', '%dd' % (record['NumPoints']), '<'),
+ ('Mmin', 'd', '<'),
+ ('Mmax', 'd', '<'),
+ ('Marray', '%dd' % (record['NumPoints']), '<'))
+ content = {}
+ content.update(record)
+ content['Parts Index'] = record['Parts Index']
+ verts = []
+ [verts.extend(vert) for vert in record['Vertices']]
+ content['Vertices'] = verts
+ content = _packDict(contentStruct, content)
+ return rheader + content
+
+
+class Polygon(PolyLine):
+ """ Packs and Unpacks a ShapeFile Polygon Type
+ Indentical to PolyLine.
+
+ Example:
+ >>> import pysal
+ >>> shp = shp_file(pysal.examples.get_path('Polygon.shp'))
+ >>> rec = shp.get_shape(1)
+ >>> rec == {'BBOX Ymax': -0.3126531125455273, 'NumPoints': 7, 'BBOX Ymin': -0.35957259110238166, 'NumParts': 1, 'Vertices': [(0.05396439570183631, -0.3126531125455273), (0.051473095955454629, -0.35251390848763364), (0.059777428443393454, -0.34254870950210703), (0.063099161438568974, -0.34462479262409174), (0.048981796209073003, -0.35957259110238166), (0.046905713087088297, -0.3126531125455273), (0.05396439570183631, -0.3126531125455273)], 'BBOX Xmax': 0.063099161438568974, 'BBOX Xmin [...]
+ True
+ >>> pos = shp.fileObj.seek(shp._shx.index[1][0]+8) #+8 byte record header
+ >>> dat = shp.fileObj.read(shp._shx.index[1][1])
+ >>> dat == Polygon.pack(rec)
+ True
+ """
+ String_Type = 'POLYGON'
+
+
+class MultiPoint:
+ def __init__(self):
+ raise NotImplementedError("No MultiPoint Support at this time.")
+
+
+class PolygonZ(PolyLineZ):
+ String_Type = 'POLYGONZ'
+
+
+class MultiPointZ:
+ def __init__(self):
+ raise NotImplementedError("No MultiPointZ Support at this time.")
+
+
+class PointM:
+ def __init__(self):
+ raise NotImplementedError("No PointM Support at this time.")
+
+
+class PolyLineM:
+ def __init__(self):
+ raise NotImplementedError("No PolyLineM Support at this time.")
+
+
+class PolygonM:
+ def __init__(self):
+ raise NotImplementedError("No PolygonM Support at this time.")
+
+
+class MultiPointM:
+ def __init__(self):
+ raise NotImplementedError("No MultiPointM Support at this time.")
+
+
+class MultiPatch:
+ def __init__(self):
+ raise NotImplementedError("No MultiPatch Support at this time.")
+
+TYPE_DISPATCH = {0: NullShape, 1: Point, 3: PolyLine, 5: Polygon, 8: MultiPoint, 11: PointZ, 13: PolyLineZ, 15: PolygonZ, 18: MultiPointZ, 21: PointM, 23: PolyLineM, 25: PolygonM, 28: MultiPointM, 31: MultiPatch, 'POINT': Point, 'POINTZ': PointZ, 'POINTM': PointM, 'ARC': PolyLine, 'ARCZ': PolyLineZ, 'ARCM': PolyLineM, 'POLYGON': Polygon, 'POLYGONZ': PolygonZ, 'POLYGONM': PolygonM, 'MULTIPOINT': MultiPoint, 'MULTIPOINTZ': MultiPointZ, 'MULTIPOINTM': MultiPointM, 'MULTIPATCH': MultiPatch}
+
diff --git a/pysal/core/util/tests/test_shapefile.py b/pysal/core/util/tests/test_shapefile.py
new file mode 100644
index 0000000..8959ba1
--- /dev/null
+++ b/pysal/core/util/tests/test_shapefile.py
@@ -0,0 +1,362 @@
+import unittest
+from cStringIO import StringIO
+from pysal.core.util.shapefile import noneMax, noneMin, shp_file, shx_file, NullShape, Point, PolyLine, MultiPoint, PointZ, PolyLineZ, PolygonZ, MultiPointZ, PointM, PolyLineM, PolygonM, MultiPointM, MultiPatch
+import os
+import pysal
+
+
+class TestNoneMax(unittest.TestCase):
+ def test_none_max(self):
+ self.assertEqual(5, noneMax(5, None))
+ self.assertEqual(1, noneMax(None, 1))
+ self.assertEqual(None, noneMax(None, None))
+
+
+class TestNoneMin(unittest.TestCase):
+ def test_none_min(self):
+ self.assertEqual(5, noneMin(5, None))
+ self.assertEqual(1, noneMin(None, 1))
+ self.assertEqual(None, noneMin(None, None))
+
+
+class test_shp_file(unittest.TestCase):
+ def test___init__(self):
+ shp = shp_file(pysal.examples.get_path('10740.shp'))
+ assert shp.header == {'BBOX Xmax': -105.29012, 'BBOX Ymax': 36.219799000000002, 'BBOX Mmax': 0.0, 'BBOX Zmin': 0.0, 'BBOX Mmin': 0.0, 'File Code': 9994, 'BBOX Ymin': 34.259672000000002, 'BBOX Xmin': -107.62651, 'Unused0': 0, 'Unused1': 0, 'Unused2': 0, 'Unused3': 0, 'Unused4': 0, 'Version': 1000, 'BBOX Zmax': 0.0, 'Shape Type': 5, 'File Length': 260534}
+
+ def test___iter__(self):
+ shp = shp_file(pysal.examples.get_path('Point.shp'))
+ points = [pt for pt in shp]
+ expected = [{'Y': -0.25904661905760773, 'X': -0.00068176617532103578, 'Shape Type': 1},
+ {'Y': -0.25630328607387354, 'X': 0.11697145363360706,
+ 'Shape Type': 1},
+ {'Y': -0.33930131004366804, 'X': 0.05043668122270728,
+ 'Shape Type': 1},
+ {'Y': -0.41266375545851519, 'X': -0.041266375545851552,
+ 'Shape Type': 1},
+ {'Y': -0.44017467248908293, 'X': -0.011462882096069604,
+ 'Shape Type': 1},
+ {'Y': -0.46080786026200882, 'X': 0.027510917030567628,
+ 'Shape Type': 1},
+ {'Y': -0.45851528384279472, 'X': 0.075655021834060809,
+ 'Shape Type': 1},
+ {'Y': -0.43558951965065495, 'X': 0.11233624454148461,
+ 'Shape Type': 1},
+ {'Y': -0.40578602620087334, 'X': 0.13984716157205224, 'Shape Type': 1}]
+ assert points == expected
+
+ def test___len__(self):
+ shp = shp_file(pysal.examples.get_path('10740.shp'))
+ assert len(shp) == 195
+
+ def test_add_shape(self):
+ shp = shp_file('test_point', 'w', 'POINT')
+ points = [{'Shape Type': 1, 'X': 0, 'Y': 0},
+ {'Shape Type': 1, 'X': 1, 'Y': 1},
+ {'Shape Type': 1, 'X': 2, 'Y': 2},
+ {'Shape Type': 1, 'X': 3, 'Y': 3},
+ {'Shape Type': 1, 'X': 4, 'Y': 4}]
+ for pt in points:
+ shp.add_shape(pt)
+ shp.close()
+
+ for a, b in zip(points, shp_file('test_point')):
+ self.assertEquals(a, b)
+ os.remove('test_point.shp')
+ os.remove('test_point.shx')
+
+ def test_close(self):
+ shp = shp_file(pysal.examples.get_path('10740.shp'))
+ shp.close()
+ self.assertEqual(shp.fileObj.closed, True)
+
+ def test_get_shape(self):
+ shp = shp_file(pysal.examples.get_path('Line.shp'))
+ rec = shp.get_shape(0)
+ expected = {'BBOX Ymax': -0.25832280562918325,
+ 'NumPoints': 3,
+ 'BBOX Ymin': -0.25895877033237352,
+ 'NumParts': 1,
+ 'Vertices': [(-0.0090539248870159517, -0.25832280562918325),
+ (0.0074811573959305822, -0.25895877033237352),
+ (
+ 0.0074811573959305822, -0.25895877033237352)],
+ 'BBOX Xmax': 0.0074811573959305822,
+ 'BBOX Xmin': -0.0090539248870159517,
+ 'Shape Type': 3,
+ 'Parts Index': [0]}
+ self.assertEqual(expected, shp.get_shape(0))
+
+ def test_next(self):
+ shp = shp_file(pysal.examples.get_path('Point.shp'))
+ points = [pt for pt in shp]
+ expected = {'Y': -0.25904661905760773, 'X': -
+ 0.00068176617532103578, 'Shape Type': 1}
+ self.assertEqual(expected, shp.next())
+ expected = {'Y': -0.25630328607387354, 'X':
+ 0.11697145363360706, 'Shape Type': 1}
+ self.assertEqual(expected, shp.next())
+
+ def test_type(self):
+ shp = shp_file(pysal.examples.get_path('Point.shp'))
+ self.assertEqual("POINT", shp.type())
+ shp = shp_file(pysal.examples.get_path('Polygon.shp'))
+ self.assertEqual("POLYGON", shp.type())
+ shp = shp_file(pysal.examples.get_path('Line.shp'))
+ self.assertEqual("ARC", shp.type())
+
+
+class test_shx_file(unittest.TestCase):
+ def test___init__(self):
+ shx = shx_file(pysal.examples.get_path('Point'))
+ assert isinstance(shx, shx_file)
+
+ def test_add_record(self):
+ shx = shx_file(pysal.examples.get_path('Point'))
+ expectedIndex = [(100, 20), (128, 20), (156, 20),
+ (184, 20), (212, 20), (240, 20),
+ (268, 20), (296, 20), (324, 20)]
+ assert shx.index == expectedIndex
+ shx2 = shx_file('test', 'w')
+ for i, rec in enumerate(shx.index):
+ id, location = shx2.add_record(rec[1])
+ assert id == (i + 1)
+ assert location == rec[0]
+ assert shx2.index == shx.index
+ shx2.close(shx._header)
+ new_shx = open('test.shx', 'rb').read()
+ expected_shx = open(pysal.examples.get_path('Point.shx'), 'rb').read()
+ assert new_shx == expected_shx
+ os.remove('test.shx')
+
+ def test_close(self):
+ shx = shx_file(pysal.examples.get_path('Point'))
+ shx.close(None)
+ self.assertEqual(shx.fileObj.closed, True)
+
+
+class TestNullShape(unittest.TestCase):
+ def test_pack(self):
+ null_shape = NullShape()
+ self.assertEqual(b'\x00' * 4, null_shape.pack())
+
+ def test_unpack(self):
+ null_shape = NullShape()
+ self.assertEqual(None, null_shape.unpack())
+
+
+class TestPoint(unittest.TestCase):
+ def test_pack(self):
+ record = {"X": 5, "Y": 5, "Shape Type": 1}
+ expected = b"\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x14\x40\x00\x00\x00\x00\x00\x00\x14\x40"
+ self.assertEqual(expected, Point.pack(record))
+
+ def test_unpack(self):
+ dat = StringIO(b"\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x14\x40\x00\x00\x00\x00\x00\x00\x14\x40")
+ expected = {"X": 5, "Y": 5, "Shape Type": 1}
+ self.assertEqual(expected, Point.unpack(dat))
+
+
+class TestPolyLine(unittest.TestCase):
+ def test_pack(self):
+ record = {'BBOX Ymax': -0.25832280562918325, 'NumPoints': 3, 'BBOX Ymin': -0.25895877033237352, 'NumParts': 1, 'Vertices': [(-0.0090539248870159517, -0.25832280562918325), (0.0074811573959305822, -0.25895877033237352), (0.0074811573959305822, -0.25895877033237352)], 'BBOX Xmax': 0.0074811573959305822, 'BBOX Xmin': -0.0090539248870159517, 'Shape Type': 3, 'Parts Index': [0]}
+ expected = b"""\x03\x00\x00\x00\xc0\x46\x52\x3a\xdd\x8a\x82\
+\xbf\x3d\xc1\x65\xce\xc7\x92\xd0\xbf\x00\xc5\
+\xa0\xe5\x8f\xa4\x7e\x3f\x6b\x40\x7f\x60\x5c\
+\x88\xd0\xbf\x01\x00\x00\x00\x03\x00\x00\x00\
+\x00\x00\x00\x00\xc0\x46\x52\x3a\xdd\x8a\x82\
+\xbf\x6b\x40\x7f\x60\x5c\x88\xd0\xbf\x00\xc5\
+\xa0\xe5\x8f\xa4\x7e\x3f\x3d\xc1\x65\xce\xc7\
+\x92\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4\x7e\x3f\
+\x3d\xc1\x65\xce\xc7\x92\xd0\xbf"""
+ self.assertEqual(expected, PolyLine.pack(record))
+
+ def test_unpack(self):
+ dat = StringIO(b"""\x03\x00\x00\x00\xc0\x46\x52\x3a\xdd\x8a\x82\
+\xbf\x3d\xc1\x65\xce\xc7\x92\xd0\xbf\x00\xc5\
+\xa0\xe5\x8f\xa4\x7e\x3f\x6b\x40\x7f\x60\x5c\
+\x88\xd0\xbf\x01\x00\x00\x00\x03\x00\x00\x00\
+\x00\x00\x00\x00\xc0\x46\x52\x3a\xdd\x8a\x82\
+\xbf\x6b\x40\x7f\x60\x5c\x88\xd0\xbf\x00\xc5\
+\xa0\xe5\x8f\xa4\x7e\x3f\x3d\xc1\x65\xce\xc7\
+\x92\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4\x7e\x3f\
+\x3d\xc1\x65\xce\xc7\x92\xd0\xbf""")
+ expected = {'BBOX Ymax': -0.25832280562918325, 'NumPoints': 3, 'BBOX Ymin': -0.25895877033237352, 'NumParts': 1, 'Vertices': [(-0.0090539248870159517, -0.25832280562918325), (0.0074811573959305822, -0.25895877033237352), (0.0074811573959305822, -0.25895877033237352)], 'BBOX Xmax': 0.0074811573959305822, 'BBOX Xmin': -0.0090539248870159517, 'Shape Type': 3, 'Parts Index': [0]}
+ self.assertEqual(expected, PolyLine.unpack(dat))
+
+
+class TestMultiPoint(unittest.TestCase):
+ def test___init__(self):
+ self.failUnlessRaises(NotImplementedError, MultiPoint)
+
+
+class TestPointZ(unittest.TestCase):
+ def test_pack(self):
+ record = {"X": 5, "Y": 5, "Z": 5, "M": 5, "Shape Type": 11}
+ expected = b"\x0b\x00\x00\x00\x00\x00\x00\x00\x00\x00\x14@\x00\x00\x00\x00\x00\x00\x14@\x00\x00\x00\x00\x00\x00\x14@\x00\x00\x00\x00\x00\x00\x14@"
+ self.assertEqual(expected, PointZ.pack(record))
+
+ def test_unpack(self):
+ dat = StringIO(b"\x0b\x00\x00\x00\x00\x00\x00\x00\x00\x00\x14@\x00\x00\x00\x00\x00\x00\x14@\x00\x00\x00\x00\x00\x00\x14@\x00\x00\x00\x00\x00\x00\x14@")
+ expected = {"X": 5, "Y": 5, "Z": 5, "M": 5, "Shape Type": 11}
+ self.assertEqual(expected, PointZ.unpack(dat))
+
+
+class TestPolyLineZ(unittest.TestCase):
+ def test___init__(self):
+ self.failUnlessRaises(NotImplementedError, PolyLineZ)
+
+
+class TestPolyLineZ(unittest.TestCase):
+ def test_pack(self):
+ record = {'BBOX Ymax': -0.25832280562918325, 'NumPoints': 3, 'BBOX Ymin': -0.25895877033237352, 'NumParts': 1, 'Vertices': [(-0.0090539248870159517, -0.25832280562918325), (0.0074811573959305822, -0.25895877033237352), (0.0074811573959305822, -0.25895877033237352)], 'BBOX Xmax': 0.0074811573959305822, 'BBOX Xmin': -0.0090539248870159517, 'Shape Type': 13, 'Parts Index': [0], 'Zmin': 0, 'Zmax': 10, 'Zarray': [0, 5, 10], 'Mmin': 2, 'Mmax': 4, 'Marray': [2, 3, 4]}
+ expected = b"""\r\x00\x00\x00\xc0FR:\xdd\x8a\x82\xbf=\xc1e\xce\xc7\x92\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4~?k@\x7f`\\\x88\xd0\xbf\x01\x00\x00\x00\x03\x00\x00\x00\x00\x00\x00\x00\xc0FR:\xdd\x8a\x82\xbfk@\x7f`\\\x88\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4~?=\xc1e\xce\xc7\x92\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4~?=\xc1e\xce\xc7\x92\xd0\xbf\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00$@\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x14@\x00\x00\x00\x00\x00\x00$@\x00\x00\x00\x00\x [...]
+ self.assertEqual(expected, PolyLineZ.pack(record))
+
+ def test_unpack(self):
+ dat = StringIO(b"""\r\x00\x00\x00\xc0FR:\xdd\x8a\x82\xbf=\xc1e\xce\xc7\x92\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4~?k@\x7f`\\\x88\xd0\xbf\x01\x00\x00\x00\x03\x00\x00\x00\x00\x00\x00\x00\xc0FR:\xdd\x8a\x82\xbfk@\x7f`\\\x88\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4~?=\xc1e\xce\xc7\x92\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4~?=\xc1e\xce\xc7\x92\xd0\xbf\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00$@\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x14@\x00\x00\x00\x00\x00\x00$@\x00\x00\x00\x [...]
+ expected = {'BBOX Ymax': -0.25832280562918325, 'NumPoints': 3, 'BBOX Ymin': -0.25895877033237352, 'NumParts': 1, 'Vertices': [(-0.0090539248870159517, -0.25832280562918325), (0.0074811573959305822, -0.25895877033237352), (0.0074811573959305822, -0.25895877033237352)], 'BBOX Xmax': 0.0074811573959305822, 'BBOX Xmin': -0.0090539248870159517, 'Shape Type': 13, 'Parts Index': [0], 'Zmin': 0, 'Zmax': 10, 'Zarray': [0, 5, 10], 'Mmin': 2, 'Mmax': 4, 'Marray': [2, 3, 4]}
+ self.assertEqual(expected, PolyLineZ.unpack(dat))
+
+
+class TestPolygonZ(unittest.TestCase):
+ def test_pack(self):
+ record = {
+ 'BBOX Xmin': 0.0,
+ 'BBOX Xmax': 10.0,
+ 'BBOX Ymin': 0.0,
+ 'BBOX Ymax': 10.0,
+ 'NumPoints': 4,
+ 'NumParts': 1,
+ 'Vertices': [(0.0, 0.0),
+ (10.0, 10.0),
+ (10.0, 0.0),
+ (0.0, 0.0)],
+ 'Shape Type': 15,
+ 'Parts Index': [0],
+ 'Zmin': 0,
+ 'Zmax': 10,
+ 'Zarray': [0, 10, 0, 0],
+ 'Mmin': 2,
+ 'Mmax': 4,
+ 'Marray': [2, 4, 2, 2]
+ }
+ dat = StringIO(PolygonZ.pack(record))
+ self.assertEqual(record, PolygonZ.unpack(dat))
+
+
+class TestMultiPointZ(unittest.TestCase):
+ def test___init__(self):
+ self.failUnlessRaises(NotImplementedError, MultiPointZ)
+ # multi_point_z = MultiPointZ()
+
+
+class TestPointM(unittest.TestCase):
+ def test___init__(self):
+ self.failUnlessRaises(NotImplementedError, PointM)
+ # point_m = PointM()
+
+
+class TestPolyLineM(unittest.TestCase):
+ def test___init__(self):
+ self.failUnlessRaises(NotImplementedError, PolyLineM)
+ # poly_line_m = PolyLineM()
+
+
+class TestPolygonM(unittest.TestCase):
+ def test___init__(self):
+ self.failUnlessRaises(NotImplementedError, PolygonM)
+ # polygon_m = PolygonM()
+
+
+class TestMultiPointM(unittest.TestCase):
+ def test___init__(self):
+ self.failUnlessRaises(NotImplementedError, MultiPointM)
+ # multi_point_m = MultiPointM()
+
+
+class TestMultiPatch(unittest.TestCase):
+ def test___init__(self):
+ self.failUnlessRaises(NotImplementedError, MultiPatch)
+ # multi_patch = MultiPatch()
+
+
+class _TestPoints(unittest.TestCase):
+ def test1(self):
+ """ Test creating and reading Point Shape Files """
+ shp = shp_file('test_point', 'w', 'POINT')
+ points = [{'Shape Type': 1, 'X': 0, 'Y': 0}, {'Shape Type': 1, 'X': 1, 'Y': 1}, {'Shape Type': 1, 'X': 2, 'Y': 2}, {'Shape Type': 1, 'X': 3, 'Y': 3}, {'Shape Type': 1, 'X': 4, 'Y': 4}]
+ for pt in points:
+ shp.add_shape(pt)
+ shp.close()
+
+ shp = list(shp_file('test_point'))
+ for a, b in zip(points, shp):
+ self.assertEquals(a, b)
+ os.remove('test_point.shp')
+ os.remove('test_point.shx')
+
+
+class _TestPolyLines(unittest.TestCase):
+ def test1(self):
+ """ Test creating and reading PolyLine Shape Files """
+ lines = [[(0, 0), (4, 4)], [(1, 0), (5, 4)], [(2, 0), (6, 4)]]
+ shapes = []
+ for line in lines:
+ x = [v[0] for v in line]
+ y = [v[1] for v in line]
+ rec = {}
+ rec['BBOX Xmin'] = min(x)
+ rec['BBOX Ymin'] = min(y)
+ rec['BBOX Xmax'] = max(x)
+ rec['BBOX Ymax'] = max(y)
+ rec['NumPoints'] = len(line)
+ rec['NumParts'] = 1
+ rec['Vertices'] = line
+ rec['Shape Type'] = 3
+ rec['Parts Index'] = [0]
+ shapes.append(rec)
+ shp = shp_file('test_line', 'w', 'ARC')
+ for line in shapes:
+ shp.add_shape(line)
+ shp.close()
+ shp = list(shp_file('test_line'))
+ for a, b in zip(shapes, shp):
+ self.assertEquals(a, b)
+ os.remove('test_line.shp')
+ os.remove('test_line.shx')
+
+
+class _TestPolygons(unittest.TestCase):
+ def test1(self):
+ """ Test creating and reading PolyLine Shape Files """
+ lines = [[(0, 0), (4, 4), (5, 4), (
+ 1, 0), (0, 0)], [(1, 0), (5, 4), (6, 4), (2, 0), (1, 0)]]
+ shapes = []
+ for line in lines:
+ x = [v[0] for v in line]
+ y = [v[1] for v in line]
+ rec = {}
+ rec['BBOX Xmin'] = min(x)
+ rec['BBOX Ymin'] = min(y)
+ rec['BBOX Xmax'] = max(x)
+ rec['BBOX Ymax'] = max(y)
+ rec['NumPoints'] = len(line)
+ rec['NumParts'] = 1
+ rec['Vertices'] = line
+ rec['Shape Type'] = 5
+ rec['Parts Index'] = [0]
+ shapes.append(rec)
+ shp = shp_file('test_poly', 'w', 'POLYGON')
+ for line in shapes:
+ shp.add_shape(line)
+ shp.close()
+ shp = list(shp_file('test_poly'))
+ for a, b in zip(shapes, shp):
+ self.assertEquals(a, b)
+ os.remove('test_poly.shp')
+ os.remove('test_poly.shx')
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/core/util/tests/test_weight_converter.py b/pysal/core/util/tests/test_weight_converter.py
new file mode 100644
index 0000000..4514ea3
--- /dev/null
+++ b/pysal/core/util/tests/test_weight_converter.py
@@ -0,0 +1,137 @@
+import unittest
+import pysal
+from pysal.core.util.weight_converter import WeightConverter
+from pysal.core.util.weight_converter import weight_convert
+import tempfile
+import os
+import warnings
+
+
+class test_WeightConverter(unittest.TestCase):
+ def setUp(self):
+ self.base_dir = pysal.examples.get_path('')
+ self.test_files = ['arcgis_ohio.dbf', 'arcgis_txt.txt', 'ohio.swm',
+ 'wmat.dat', 'wmat.mtx', 'sids2.gal', 'juvenile.gwt',
+ 'geobugs_scot', 'stata_full.txt', 'stata_sparse.txt',
+ 'spat-sym-us.mat', 'spat-sym-us.wk1']
+ dataformats = ['arcgis_dbf', 'arcgis_text', None, None, None, None, None,
+ 'geobugs_text', 'stata_text', 'stata_text', None, None]
+ ns = [88, 3, 88, 49, 49, 100, 168, 56, 56, 56, 46, 46]
+ self.dataformats = dict(zip(self.test_files, dataformats))
+ self.ns = dict(zip(self.test_files, ns))
+ self.fileformats = [('dbf', 'arcgis_dbf'), ('txt', 'arcgis_text'), ('swm', None),
+ ('dat', None), ('mtx', None), ('gal', None), ('',
+ 'geobugs_text'),
+ ('gwt', None), ('txt', 'stata_text'), ('mat', None), ('wk1', None)]
+
+ def test__setW(self):
+ for f in self.test_files:
+ with warnings.catch_warnings(record=True) as warn:
+ # note: we are just suppressing the warnings here; individual warnings
+ # are tested in their specific readers
+ warnings.simplefilter("always")
+ wc = WeightConverter(self.base_dir + f,
+ dataFormat=self.dataformats[f])
+ self.assertEqual(wc.w_set(), True)
+ self.assertEqual(wc.w.n, self.ns[f])
+
+ def test_write(self):
+ for f in self.test_files:
+ with warnings.catch_warnings(record=True) as warn:
+ # note: we are just suppressing the warnings here; individual warnings
+ # are tested in their specific readers
+ warnings.simplefilter("always")
+ wc = WeightConverter(self.base_dir + f,
+ dataFormat=self.dataformats[f])
+
+ for ext, dataformat in self.fileformats:
+ if f.lower().endswith(ext):
+ continue
+ temp_f = tempfile.NamedTemporaryFile(
+ suffix='.%s' % ext, dir=self.base_dir)
+ temp_fname = temp_f.name
+ temp_f.close()
+
+ with warnings.catch_warnings(record=True) as warn:
+ # note: we are just suppressing the warnings here; individual warnings
+ # are tested in their specific readers
+ warnings.simplefilter("always")
+ if ext == 'swm':
+ wc.write(temp_fname, useIdIndex=True)
+ elif dataformat is None:
+ wc.write(temp_fname)
+ elif dataformat in ['arcgis_dbf', 'arcgis_text']:
+ wc.write(temp_fname, dataFormat=dataformat,
+ useIdIndex=True)
+ elif dataformat == 'stata_text':
+ wc.write(temp_fname, dataFormat=dataformat,
+ matrix_form=True)
+ else:
+ wc.write(temp_fname, dataFormat=dataformat)
+
+ with warnings.catch_warnings(record=True) as warn:
+ # note: we are just suppressing the warnings here; individual warnings
+ # are tested in their specific readers
+ warnings.simplefilter("always")
+ if dataformat is None:
+ wnew = pysal.open(temp_fname, 'r').read()
+ else:
+ wnew = pysal.open(temp_fname, 'r', dataformat).read()
+
+ if (ext in ['dbf', 'swm', 'dat', 'wk1', 'gwt'] or dataformat == 'arcgis_text'):
+ self.assertEqual(wnew.n, wc.w.n - len(wc.w.islands))
+ else:
+ self.assertEqual(wnew.n, wc.w.n)
+ os.remove(temp_fname)
+
+ def test_weight_convert(self):
+ for f in self.test_files:
+ inFile = self.base_dir + f
+ inDataFormat = self.dataformats[f]
+ with warnings.catch_warnings(record=True) as warn:
+ # note: we are just suppressing the warnings here; individual warnings
+ # are tested in their specific readers
+ warnings.simplefilter("always")
+ if inDataFormat is None:
+ in_file = pysal.open(inFile, 'r')
+ else:
+ in_file = pysal.open(inFile, 'r', inDataFormat)
+ wold = in_file.read()
+ in_file.close()
+
+ for ext, dataformat in self.fileformats:
+ if f.lower().endswith(ext):
+ continue
+ temp_f = tempfile.NamedTemporaryFile(
+ suffix='.%s' % ext, dir=self.base_dir)
+ outFile = temp_f.name
+ temp_f.close()
+ outDataFormat, useIdIndex, matrix_form = dataformat, False, False
+ if ext == 'swm' or dataformat in ['arcgis_dbf', 'arcgis_text']:
+ useIdIndex = True
+ elif dataformat == 'stata_text':
+ matrix_form = True
+
+ with warnings.catch_warnings(record=True) as warn:
+ # note: we are just suppressing the warnings here; individual warnings
+ # are tested in their specific readers
+ warnings.simplefilter("always")
+ weight_convert(inFile, outFile, inDataFormat, outDataFormat, useIdIndex, matrix_form)
+
+ with warnings.catch_warnings(record=True) as warn:
+ # note: we are just suppressing the warnings here; individual warnings
+ # are tested in their specific readers
+ warnings.simplefilter("always")
+ if dataformat is None:
+ wnew = pysal.open(outFile, 'r').read()
+ else:
+ wnew = pysal.open(outFile, 'r', dataformat).read()
+
+ if (ext in ['dbf', 'swm', 'dat', 'wk1', 'gwt'] or dataformat == 'arcgis_text'):
+ self.assertEqual(wnew.n, wold.n - len(wold.islands))
+ else:
+ self.assertEqual(wnew.n, wold.n)
+ os.remove(outFile)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/core/util/tests/test_wkt.py b/pysal/core/util/tests/test_wkt.py
new file mode 100644
index 0000000..6821310
--- /dev/null
+++ b/pysal/core/util/tests/test_wkt.py
@@ -0,0 +1,52 @@
+import unittest
+import pysal
+
+
+class test_WKTParser(unittest.TestCase):
+ def setUp(self):
+ #Create some Well-Known Text objects
+ self.wktPOINT = 'POINT(6 10)'
+ self.wktLINESTRING = 'LINESTRING(3 4,10 50,20 25)'
+ self.wktPOLYGON = 'POLYGON((1 1,5 1,5 5,1 5,1 1),(2 2, 3 2, 3 3, 2 3,2 2))'
+ self.unsupported = ['MULTIPOINT(3.5 5.6,4.8 10.5)',
+ 'MULTILINESTRING((3 4,10 50,20 25),(-5 -8,-10 -8,-15 -4))',
+ 'MULTIPOLYGON(((1 1,5 1,5 5,1 5,1 1),(2 2, 3 2, 3 3, 2 3,2 2)),((3 3,6 2,6 4,3 3)))',
+ 'GEOMETRYCOLLECTION(POINT(4 6),LINESTRING(4 6,7 10))',
+ 'POINT ZM (1 1 5 60)',
+ 'POINT M (1 1 80)']
+ self.empty = ['POINT EMPTY', 'MULTIPOLYGON EMPTY']
+ self.parser = pysal.core.util.WKTParser()
+
+ def test_Point(self):
+ pt = self.parser(self.wktPOINT)
+ self.assert_(issubclass(type(pt), pysal.cg.Point))
+ self.assertEquals(pt[:], (6.0, 10.0))
+
+ def test_LineString(self):
+ line = self.parser(self.wktLINESTRING)
+ self.assert_(issubclass(type(line), pysal.cg.Chain))
+ parts = [[pt[:] for pt in part] for part in line.parts]
+ self.assertEquals(parts, [[(3.0, 4.0), (10.0, 50.0), (20.0, 25.0)]])
+ self.assertEquals(line.len, 73.455384532199886)
+
+ def test_Polygon(self):
+ poly = self.parser(self.wktPOLYGON)
+ self.assert_(issubclass(type(poly), pysal.cg.Polygon))
+ parts = [[pt[:] for pt in part] for part in poly.parts]
+ self.assertEquals(parts, [[(1.0, 1.0), (1.0, 5.0), (5.0, 5.0), (5.0,
+ 1.0), (1.0, 1.0)], [(2.0, 2.0), (2.0, 3.0), (3.0, 3.0), (3.0, 2.0),
+ (2.0, 2.0)]])
+ self.assertEquals(
+ poly.centroid, (2.9705882352941178, 2.9705882352941178))
+ self.assertEquals(poly.area, 17.0)
+
+ def test_fromWKT(self):
+ for wkt in self.unsupported:
+ self.failUnlessRaises(
+ NotImplementedError, self.parser.fromWKT, wkt)
+ for wkt in self.empty:
+ self.assertEquals(self.parser.fromWKT(wkt), None)
+ self.assertEquals(self.parser.__call__, self.parser.fromWKT)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/core/util/weight_converter.py b/pysal/core/util/weight_converter.py
new file mode 100644
index 0000000..5f8068d
--- /dev/null
+++ b/pysal/core/util/weight_converter.py
@@ -0,0 +1,243 @@
+import os
+import pysal
+
+__author__ = "Myunghwa Hwang <mhwang4 at gmail.com>"
+__all__ = ["weight_convert"]
+
+
+class WeightConverter(object):
+
+ """
+ Open and reads a weights file in a format.
+ Then, writes the file in other formats.
+
+ WeightConverter can read a weights file in the following formats:
+ GAL, GWT, ArcGIS DBF/SWM/Text, DAT, MAT, MTX, WK1, GeoBUGS Text, and STATA Text.
+ It can convert the input file into all of the formats listed above, except GWT.
+ Currently, PySAL does not support writing a weights object in the GWT format.
+
+ When an input weight file includes multiple islands and
+ the format of an output weight file is ArcGIS DBF/SWM/TEXT, DAT, or WK1,
+ the number of observations in the new weights file will be
+ the original number of observations substracted by the number of islands.
+ This is because ArcGIS DBF/SWM/TEXT, DAT, WK1 formats ignore islands.
+
+ """
+
+ def __init__(self, inputPath, dataFormat=None):
+ self.inputPath = inputPath
+ self.inputDataFormat = dataFormat
+ self._setW()
+
+ def _setW(self):
+ """
+ Reads a weights file and sets a pysal.weights.weights.W object as an attribute
+
+ Examples
+ --------
+
+ Create a WeightConvert object
+
+ >>> wc = WeightConverter(pysal.examples.get_path('arcgis_ohio.dbf'),dataFormat='arcgis_dbf')
+
+ Check whether or not the W object is set as an attribute
+
+ >>> wc.w_set()
+ True
+
+ Get the number of observations included in the W object
+
+ >>> wc.w.n
+ 88
+
+ """
+ try:
+ if self.inputDataFormat:
+ f = pysal.open(self.inputPath, 'r', self.inputDataFormat)
+ else:
+ f = pysal.open(self.inputPath, 'r')
+ except:
+ raise IOError('A problem occurred while reading the input file.')
+ else:
+ try:
+ self.w = f.read()
+ except:
+ raise RuntimeError('A problem occurred while creating a weights object.')
+ finally:
+ f.close()
+
+ def w_set(self):
+ """
+ Checks if a source w object is set
+ """
+ return hasattr(self, 'w')
+
+ def write(self, outputPath, dataFormat=None, useIdIndex=True, matrix_form=True):
+ """
+ Parameters
+ ----------
+ outputPath: string
+ path to the output weights file
+ dataFormat: string
+ 'arcgis_dbf' for ArcGIS DBF format
+ 'arcgis_text' for ArcGIS Text format
+ 'geobugs_text' for GeoBUGS Text format
+ 'stata_text' for STATA Text format
+ useIdIndex: boolean
+ True or False
+ Applies only to ArcGIS DBF/SWM/Text formats
+ matrix_form: boolean
+ True or False
+ STATA Text format
+
+ Returns
+ -------
+ A weights file is created
+
+ Examples
+ --------
+ >>> import tempfile, os, pysal
+
+ Create a WeightConverter object
+
+ >>> wc = WeightConverter(pysal.examples.get_path('sids2.gal'))
+
+ Check whether or not the W object is set as an attribute
+
+ >>> wc.w_set()
+ True
+
+ Create a temporary file for this example
+
+ >>> f = tempfile.NamedTemporaryFile(suffix='.dbf')
+
+ Reassign to new variable
+
+ >>> fname = f.name
+
+ Close the temporary named file
+
+ >>> f.close()
+
+ Write the input gal file in the ArcGIS dbf format
+
+ >>> wc.write(fname, dataFormat='arcgis_dbf', useIdIndex=True)
+
+ Create a new weights object from the converted dbf file
+
+ >>> wnew = pysal.open(fname, 'r', 'arcgis_dbf').read()
+
+ Compare the number of observations in two W objects
+
+ >>> wc.w.n == wnew.n
+ True
+
+ Clean up the temporary file
+
+ >>> os.remove(fname)
+
+ """
+ ext = os.path.splitext(outputPath)[1]
+ ext = ext.replace('.', '')
+ #if ext.lower() == 'gwt':
+ # raise TypeError, 'Currently, PySAL does not support writing a weights object into a gwt file.'
+
+ if not self.w_set():
+ raise RuntimeError('There is no weights object to write out.')
+
+ try:
+ if dataFormat:
+ o = pysal.open(outputPath, 'w', dataFormat)
+ else:
+ o = pysal.open(outputPath, 'w')
+ except:
+ raise IOError('A problem occurred while creating the output file.')
+ else:
+ try:
+ if dataFormat in ['arcgis_text', 'arcgis_dbf'] or ext == 'swm':
+ o.write(self.w, useIdIndex=useIdIndex)
+ elif dataFormat == 'stata_text':
+ o.write(self.w, matrix_form=matrix_form)
+ else:
+ o.write(self.w)
+ except:
+ raise RuntimeError('A problem occurred while writing out the weights object')
+ finally:
+ o.close()
+
+
+def weight_convert(inPath, outPath, inDataFormat=None, outDataFormat=None, useIdIndex=True, matrix_form=True):
+ """
+ A utility function for directly converting a given weight
+ file into the format specified in outPath
+
+ Parameters
+ ----------
+ inPath: string
+ path to the input weights file
+ outPath: string
+ path to the output weights file
+ indataFormat: string
+ 'arcgis_dbf' for ArcGIS DBF format
+ 'arcgis_text' for ArcGIS Text format
+ 'geobugs_text' for GeoBUGS Text format
+ 'stata_text' for STATA Text format
+ outdataFormat: string
+ 'arcgis_dbf' for ArcGIS DBF format
+ 'arcgis_text' for ArcGIS Text format
+ 'geobugs_text' for GeoBUGS Text format
+ 'stata_text' for STATA Text format
+ useIdIndex: boolean
+ True or False
+ Applies only to ArcGIS DBF/SWM/Text formats
+ matrix_form: boolean
+ True or False
+ STATA Text format
+
+ Returns
+ -------
+ A weights file is created
+
+ Examples
+ --------
+ >>> import tempfile, os, pysal
+
+ Create a temporary file for this example
+
+ >>> f = tempfile.NamedTemporaryFile(suffix='.dbf')
+
+ Reassign to new variable
+
+ >>> fname = f.name
+
+ Close the temporary named file
+
+ >>> f.close()
+
+ Create a WeightConverter object
+
+ >>> weight_convert(pysal.examples.get_path('sids2.gal'), fname, outDataFormat='arcgis_dbf', useIdIndex=True)
+
+ Create a new weights object from the gal file
+
+ >>> wold = pysal.open(pysal.examples.get_path('sids2.gal'), 'r').read()
+
+ Create a new weights object from the converted dbf file
+
+ >>> wnew = pysal.open(fname, 'r', 'arcgis_dbf').read()
+
+ Compare the number of observations in two W objects
+
+ >>> wold.n == wnew.n
+ True
+
+ Clean up the temporary file
+
+ >>> os.remove(fname)
+
+ """
+
+ converter = WeightConverter(inPath, dataFormat=inDataFormat)
+ converter.write(outPath, dataFormat=outDataFormat,
+ useIdIndex=useIdIndex, matrix_form=matrix_form)
+
diff --git a/pysal/core/util/wkt.py b/pysal/core/util/wkt.py
new file mode 100644
index 0000000..0907629
--- /dev/null
+++ b/pysal/core/util/wkt.py
@@ -0,0 +1,125 @@
+from pysal import cg
+import re
+
+__author__ = "Charles R Schmidt <schmidtc at gmail.com>"
+__all__ = ['WKTParser']
+#####################################################################
+## ToDo: Add Well-Known-Binary support...
+## * WKB spec:
+## http://webhelp.esri.com/arcgisserver/9.3/dotNet/index.htm#geodatabases/the_ogc_103951442.htm
+##
+##
+#####################################################################
+
+
+class WKTParser:
+ """ Class to represent OGC WKT, supports reading and writing
+ Modified from...
+ # URL: http://dev.openlayers.org/releases/OpenLayers-2.7/lib/OpenLayers/Format/WKT.js
+ #Reg Ex Strings copied from OpenLayers.Format.WKT
+
+ Example
+ -------
+ >>> from pysal.core.IOHandlers import wkt
+ >>> import pysal
+
+ Create some Well-Known Text objects
+
+ >>> p = 'POLYGON((1 1,5 1,5 5,1 5,1 1),(2 2, 3 2, 3 3, 2 3,2 2))'
+ >>> pt = 'POINT(6 10)'
+ >>> l = 'LINESTRING(3 4,10 50,20 25)'
+
+ Instantiate the parser
+
+ >>> wkt = WKTParser()
+
+ Inspect our WKT polygon
+
+ >>> wkt(p).parts
+ [[(1.0, 1.0), (1.0, 5.0), (5.0, 5.0), (5.0, 1.0), (1.0, 1.0)], [(2.0, 2.0), (2.0, 3.0), (3.0, 3.0), (3.0, 2.0), (2.0, 2.0)]]
+ >>> wkt(p).centroid
+ (2.9705882352941178, 2.9705882352941178)
+ >>> wkt(p).area
+ 17.0
+
+ Inspect pt, our WKT point object
+
+ >>> wkt(pt)
+ (6.0, 10.0)
+
+ Inspect our WKT linestring
+
+ >>> wkt(l).len
+ 73.45538453219989
+ >>> wkt(l).parts
+ [[(3.0, 4.0), (10.0, 50.0), (20.0, 25.0)]]
+
+ Read in WKT from a file
+
+ >>> f = pysal.open(pysal.examples.get_path('stl_hom.wkt'))
+ >>> f.mode
+ 'r'
+ >>> f.header
+ []
+
+ See local doctest output for the items not tested...
+
+ """
+ regExes = {'typeStr': re.compile('^\s*([\w\s]+)\s*\(\s*(.*)\s*\)\s*$'),
+ 'spaces': re.compile('\s+'),
+ 'parenComma': re.compile('\)\s*,\s*\('),
+ 'doubleParenComma': re.compile('\)\s*\)\s*,\s*\(\s*\('), # can't use {2} here
+ 'trimParens': re.compile('^\s*\(?(.*?)\)?\s*$')}
+
+ def __init__(self):
+ self.parsers = p = {}
+ p['point'] = self.Point
+ p['linestring'] = self.LineString
+ p['polygon'] = self.Polygon
+
+ def Point(self, geoStr):
+ coords = self.regExes['spaces'].split(geoStr.strip())
+ return cg.Point((coords[0], coords[1]))
+
+ def LineString(self, geoStr):
+ points = geoStr.strip().split(',')
+ points = map(self.Point, points)
+ return cg.Chain(points)
+
+ def Polygon(self, geoStr):
+ rings = self.regExes['parenComma'].split(geoStr.strip())
+ for i, ring in enumerate(rings):
+ ring = self.regExes['trimParens'].match(ring).groups()[0]
+ ring = self.LineString(ring).vertices
+ rings[i] = ring
+ return cg.Polygon(rings)
+
+ def fromWKT(self, wkt):
+ matches = self.regExes['typeStr'].match(wkt)
+ if matches:
+ geoType, geoStr = matches.groups()
+ geoType = geoType.lower().strip()
+ try:
+ return self.parsers[geoType](geoStr)
+ except KeyError:
+ raise NotImplementedError("Unsupported WKT Type: %s" % geoType)
+ else:
+ return None
+ __call__ = fromWKT
+if __name__ == '__main__':
+ p = 'POLYGON((1 1,5 1,5 5,1 5,1 1),(2 2, 3 2, 3 3, 2 3,2 2))'
+ pt = 'POINT(6 10)'
+ l = 'LINESTRING(3 4,10 50,20 25)'
+ wktExamples = ['POINT(6 10)',
+ 'LINESTRING(3 4,10 50,20 25)',
+ 'POLYGON((1 1,5 1,5 5,1 5,1 1),(2 2, 3 2, 3 3, 2 3,2 2))',
+ 'MULTIPOINT(3.5 5.6,4.8 10.5)',
+ 'MULTILINESTRING((3 4,10 50,20 25),(-5 -8,-10 -8,-15 -4))',
+ 'MULTIPOLYGON(((1 1,5 1,5 5,1 5,1 1),(2 2, 3 2, 3 3, 2 3,2 2)),((3 3,6 2,6 4,3 3)))',
+ 'GEOMETRYCOLLECTION(POINT(4 6),LINESTRING(4 6,7 10))',
+ 'POINT ZM (1 1 5 60)',
+ 'POINT M (1 1 80)',
+ 'POINT EMPTY',
+ 'MULTIPOLYGON EMPTY']
+ wkt = WKTParser()
+
diff --git a/pysal/esda/__init__.py b/pysal/esda/__init__.py
new file mode 100644
index 0000000..cee82b1
--- /dev/null
+++ b/pysal/esda/__init__.py
@@ -0,0 +1,12 @@
+"""
+:mod:`esda` --- Exploratory Spatial Data Analysis
+=================================================
+
+"""
+import mapclassify
+import moran
+import smoothing
+import getisord
+import geary
+import join_counts
+import gamma
diff --git a/pysal/esda/gamma.py b/pysal/esda/gamma.py
new file mode 100644
index 0000000..810f98b
--- /dev/null
+++ b/pysal/esda/gamma.py
@@ -0,0 +1,204 @@
+"""
+Gamma index for spatial autocorrelation
+
+
+"""
+__author__ = "Luc Anselin <luc.anselin at asu.edu>"
+
+import pysal
+import numpy as np
+
+__all__ = ['Gamma']
+
+PERMUTATIONS = 999
+
+
+class Gamma:
+ """Gamma index for spatial autocorrelation
+
+
+ Parameters
+ ----------
+
+ y : array
+ variable measured across n spatial units
+ w : W
+ spatial weights instance
+ can be binary or row-standardized
+ operation : attribute similarity function
+ 'c' cross product (default)
+ 's' squared difference
+ 'a' absolute difference
+ standardize : standardize variables first
+ 'no' keep as is (default)
+ 'yes' or 'y' standardize to mean zero and variance one
+ permutations : int
+ number of random permutations for calculation of pseudo-p_values
+
+ Attributes
+ ----------
+ y : array
+ original variable
+ w : W
+ original w object
+ op : attribute similarity function
+ stand : standardization
+ permutations : int
+ number of permutations
+ gamma : float
+ value of Gamma index
+ sim_g : array (if permutations>0)
+ vector of Gamma index values for permuted samples
+ p_sim_g : array (if permutations>0)
+ p-value based on permutations (one-sided)
+ null: spatial randomness
+ alternative: the observed Gamma is more extreme than under randomness
+ implemented as a two-sided test
+ mean_g : average of permuted Gamma values
+ min_g : minimum of permuted Gamma values
+ max_g : maximum of permuted Gamma values
+
+
+ Examples
+ --------
+
+ use same example as for join counts to show similarity
+
+ >>> import numpy as np
+ >>> w=pysal.lat2W(4,4)
+ >>> y=np.ones(16)
+ >>> y[0:8]=0
+ >>> np.random.seed(12345)
+ >>> g = pysal.Gamma(y,w)
+ >>> g.g
+ 20.0
+ >>> g.g_z
+ 3.1879280354548638
+ >>> g.p_sim_g
+ 0.0030000000000000001
+ >>> g.min_g
+ 0.0
+ >>> g.max_g
+ 20.0
+ >>> g.mean_g
+ 11.093093093093094
+ >>> np.random.seed(12345)
+ >>> g1 = pysal.Gamma(y,w,operation='s')
+ >>> g1.g
+ 8.0
+ >>> g1.g_z
+ -3.7057554345954791
+ >>> g1.p_sim_g
+ 0.001
+ >>> g1.min_g
+ 14.0
+ >>> g1.max_g
+ 48.0
+ >>> g1.mean_g
+ 25.623623623623622
+ >>> np.random.seed(12345)
+ >>> g2 = pysal.Gamma(y,w,operation='a')
+ >>> g2.g
+ 8.0
+ >>> g2.g_z
+ -3.7057554345954791
+ >>> g2.p_sim_g
+ 0.001
+ >>> g2.min_g
+ 14.0
+ >>> g2.max_g
+ 48.0
+ >>> g2.mean_g
+ 25.623623623623622
+ >>> np.random.seed(12345)
+ >>> g3 = pysal.Gamma(y,w,standardize='y')
+ >>> g3.g
+ 32.0
+ >>> g3.g_z
+ 3.7057554345954791
+ >>> g3.p_sim_g
+ 0.001
+ >>> g3.min_g
+ -48.0
+ >>> g3.max_g
+ 20.0
+ >>> g3.mean_g
+ -3.2472472472472473
+ >>> np.random.seed(12345)
+ >>> def func(z,i,j):
+ ... q = z[i]*z[j]
+ ... return q
+ ...
+ >>> g4 = pysal.Gamma(y,w,operation=func)
+ >>> g4.g
+ 20.0
+ >>> g4.g_z
+ 3.1879280354548638
+ >>> g4.p_sim_g
+ 0.0030000000000000001
+
+ """
+ def __init__(self, y, w, operation='c', standardize='no', permutations=PERMUTATIONS):
+ self.w = w
+ self.y = y
+ self.op = operation
+ self.stand = standardize.lower()
+ self.permutations = permutations
+ if self.stand == 'yes' or self.stand == 'y':
+ ym = np.mean(self.y)
+ ysd = np.std(self.y)
+ ys = (self.y - ym) / ysd
+ self.y = ys
+ self.g = self.__calc(self.y, self.op)
+
+ if permutations:
+ sim = [self.__calc(np.random.permutation(self.y), self.op)
+ for i in xrange(permutations)]
+ self.sim_g = np.array(sim)
+ self.min_g = np.min(self.sim_g)
+ self.mean_g = np.mean(self.sim_g)
+ self.max_g = np.max(self.sim_g)
+ p_sim_g = self.__pseudop(self.sim_g, self.g)
+ self.p_sim_g = p_sim_g
+ self.g_z = (self.g - self.mean_g) / np.std(self.sim_g)
+
+ def __calc(self, z, op):
+ if op == 'c': # cross-product
+ zl = pysal.lag_spatial(self.w, z)
+ g = (z * zl).sum()
+ elif op == 's': # squared difference
+ zs = np.zeros(z.shape)
+ z2 = z ** 2
+ for i, i0 in enumerate(self.w.id_order):
+ neighbors = self.w.neighbor_offsets[i0]
+ wijs = self.w.weights[i0]
+ zw = zip(neighbors, wijs)
+ zs[i] = sum([wij * (z2[i] - 2.0 * z[i] * z[
+ j] + z2[j]) for j, wij in zw])
+ g = zs.sum()
+ elif op == 'a': # absolute difference
+ zs = np.zeros(z.shape)
+ for i, i0 in enumerate(self.w.id_order):
+ neighbors = self.w.neighbor_offsets[i0]
+ wijs = self.w.weights[i0]
+ zw = zip(neighbors, wijs)
+ zs[i] = sum([wij * abs(z[i] - z[j]) for j, wij in zw])
+ g = zs.sum()
+ else: # any previously defined function op
+ zs = np.zeros(z.shape)
+ for i, i0 in enumerate(self.w.id_order):
+ neighbors = self.w.neighbor_offsets[i0]
+ wijs = self.w.weights[i0]
+ zw = zip(neighbors, wijs)
+ zs[i] = sum([wij * op(z, i, j) for j, wij in zw])
+ g = zs.sum()
+ return g
+
+ def __pseudop(self, sim, g):
+ above = sim >= g
+ larger = above.sum()
+ psim = (larger + 1.) / (self.permutations + 1.)
+ if psim > 0.5:
+ psim = (self.permutations - larger + 1.) / (self.permutations + 1.)
+ return psim
+
diff --git a/pysal/esda/geary.py b/pysal/esda/geary.py
new file mode 100644
index 0000000..238c58d
--- /dev/null
+++ b/pysal/esda/geary.py
@@ -0,0 +1,161 @@
+"""
+Geary's C statistic for spatial autocorrelation
+"""
+__author__ = "Sergio J. Rey <srey at asu.edu> "
+
+import numpy as np
+import scipy.stats as stats
+
+__all__ = ['Geary']
+
+
+class Geary:
+ """
+ Global Geary C Autocorrelation statistic
+
+ Parameters
+ ----------
+ y : array
+ w : W
+ spatial weights
+ transformation : string
+ weights transformation, default is binary.
+ Other options include "R": row-standardized, "D":
+ doubly-standardized, "U": untransformed (general
+ weights), "V": variance-stabilizing.
+ permutations : int
+ number of random permutations for calculation of
+ pseudo-p_values
+
+ Attributes
+ ----------
+ y : array
+ original variable
+ w : W
+ spatial weights
+ permutations : int
+ number of permutations
+ C : float
+ value of statistic
+ EC : float
+ expected value
+ VC : float
+ variance of G under normality assumption
+ z_norm : float
+ z-statistic for C under normality assumption
+ z_rand : float
+ z-statistic for C under randomization assumption
+ p_norm : float
+ p-value under normality assumption (one-tailed)
+ p_rand : float
+ p-value under randomization assumption (one-tailed)
+ sim : array (if permutations!=0)
+ vector of I values for permutated samples
+ p_sim : float (if permutations!=0)
+ p-value based on permutations (one-tailed)
+ null: sptial randomness
+ alternative: the observed C is extreme
+ it is either extremely high or extremely low
+ EC_sim : float (if permutations!=0)
+ average value of C from permutations
+ VC_sim : float (if permutations!=0)
+ variance of C from permutations
+ seC_sim : float (if permutations!=0)
+ standard deviation of C under permutations.
+ z_sim : float (if permutations!=0)
+ standardized C based on permutations
+ p_z_sim : float (if permutations!=0)
+ p-value based on standard normal approximation from
+ permutations (one-tailed)
+
+ Examples
+ --------
+ >>> import pysal
+ >>> w = pysal.open(pysal.examples.get_path("book.gal")).read()
+ >>> f = pysal.open(pysal.examples.get_path("book.txt"))
+ >>> y = np.array(f.by_col['y'])
+ >>> c = Geary(y,w,permutations=0)
+ >>> print round(c.C,7)
+ 0.3330108
+ >>> print round(c.p_norm,7)
+ 9.2e-05
+ >>>
+ """
+ def __init__(self, y, w, transformation="r", permutations=999):
+ self.n = len(y)
+ self.y = y
+ w.transform = transformation
+ self.w = w
+ self.permutations = permutations
+ self.__moments()
+ xn = xrange(len(y))
+ self.xn = xn
+ self.y2 = y * y
+ yd = y - y.mean()
+ yss = sum(yd * yd)
+ self.den = yss * self.w.s0 * 2.0
+ self.C = self.__calc(y)
+ de = self.C - 1.0
+ self.EC = 1.0
+ self.z_norm = de / self.seC_norm
+ self.z_rand = de / self.seC_rand
+ if de > 0:
+ self.p_norm = 1 - stats.norm.cdf(self.z_norm)
+ self.p_rand = 1 - stats.norm.cdf(self.z_rand)
+ else:
+ self.p_norm = stats.norm.cdf(self.z_norm)
+ self.p_rand = stats.norm.cdf(self.z_rand)
+
+
+ if permutations:
+ sim = [self.__calc(np.random.permutation(self.y))
+ for i in xrange(permutations)]
+ self.sim = sim = np.array(sim)
+ above = sim >= self.C
+ larger = sum(above)
+ if (permutations - larger) < larger:
+ larger = permutations - larger
+ self.p_sim = (larger + 1.) / (permutations + 1.)
+ self.EC_sim = sum(sim) / permutations
+ self.seC_sim = np.array(sim).std()
+ self.VC_sim = self.seC_sim ** 2
+ self.z_sim = (self.C - self.EC_sim) / self.seC_sim
+ self.p_z_sim = 1 - stats.norm.cdf(np.abs(self.z_sim))
+
+ def __moments(self):
+ y = self.y
+ n = self.n
+ w = self.w
+ s0 = w.s0
+ s1 = w.s1
+ s2 = w.s2
+ s02 = s0 * s0
+
+ yd = y - y.mean()
+ k = (1 / (sum(yd ** 4)) * ((sum(yd ** 2)) ** 2))
+ vc_rand = (1 / (n * ((n - 2) ** 2) * s02)) * \
+ ((((n - 1) * s1) * (n * n - 3 * n + 3 - (n - 1) * k))
+ - ((.25 * (n - 1) * s2) * (n * n + 3 * n - 6 -
+ (n * n - n + 2) * k))
+ + (s02 * (n * n - 3 - ((n - 1) ** 2) * k)))
+ vc_norm = ((1 / (2 * (n + 1) * s02)) *
+ ((2 * s1 + s2) * (n - 1) - 4 * s02))
+
+ self.VC_rand = vc_rand
+ self.VC_norm = vc_norm
+ self.seC_rand = vc_rand ** (0.5)
+ self.seC_norm = vc_norm ** (0.5)
+
+ def __calc(self, y):
+ ys = np.zeros(y.shape)
+ y2 = y ** 2
+ for i, i0 in enumerate(self.w.id_order):
+ neighbors = self.w.neighbor_offsets[i0]
+ wijs = self.w.weights[i0]
+ z = zip(neighbors, wijs)
+ ys[i] = sum([wij * (y2[i] - 2 * y[i] * y[j] + y2[j])
+ for j, wij in z])
+ a = (self.n - 1) * sum(ys)
+ return a / self.den
+
+
diff --git a/pysal/esda/getisord.py b/pysal/esda/getisord.py
new file mode 100644
index 0000000..30c1076
--- /dev/null
+++ b/pysal/esda/getisord.py
@@ -0,0 +1,394 @@
+"""
+Getis and Ord G statistic for spatial autocorrelation
+"""
+__author__ = "Sergio J. Rey <srey at asu.edu>, Myunghwa Hwang <mhwang4 at gmail.com> "
+__all__ = ['G', 'G_Local']
+
+from pysal.common import np, stats, math
+from pysal.weights.spatial_lag import lag_spatial as slag
+
+PERMUTATIONS = 999
+
+
+class G:
+ """
+ Global G Autocorrelation Statistic
+
+ Parameters
+ ----------
+ y : array
+ w : DistanceBand W spatial weights based on distance band
+ permutations : int
+ the number of random permutations for calculating pseudo p_values
+
+ Attributes
+ ----------
+ y : array
+ original variable
+ w : DistanceBand W spatial weights based on distance band
+ permutation : int
+ the number of permutations
+ G : float
+ the value of statistic
+ EG : float
+ the expected value of statistic
+ VG : float
+ the variance of G under normality assumption
+ z_norm : float
+ standard normal test statistic
+ p_norm : float
+ p-value under normality assumption (one-sided)
+ sim : array (if permutations > 0)
+ vector of G values for permutated samples
+ p_sim : float
+ p-value based on permutations (one-sided)
+ null: spatial randomness
+ alternative: the observed G is extreme it is either extremely high or extremely low
+ EG_sim : float
+ average value of G from permutations
+ VG_sim : float
+ variance of G from permutations
+ seG_sim : float
+ standard deviation of G under permutations.
+ z_sim : float
+ standardized G based on permutations
+ p_z_sim : float
+ p-value based on standard normal approximation from
+ permutations (one-sided)
+
+ Notes
+ -----
+ Moments are based on normality assumption.
+
+ Examples
+ --------
+ >>> from pysal.weights.Distance import DistanceBand
+ >>> import numpy
+ >>> numpy.random.seed(10)
+
+ Preparing a point data set
+ >>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+
+ Creating a weights object from points
+ >>> w = DistanceBand(points,threshold=15)
+ >>> w.transform = "B"
+
+ Preparing a variable
+ >>> y = numpy.array([2, 3, 3.2, 5, 8, 7])
+
+ Applying Getis and Ord G test
+ >>> g = G(y,w)
+
+ Examining the results
+ >>> print "%.8f" % g.G
+ 0.55709779
+
+ >>> print "%.4f" % g.p_norm
+ 0.1729
+
+ """
+
+ def __init__(self, y, w, permutations=PERMUTATIONS):
+ self.n = len(y)
+ self.y = y
+ w.transform = "B"
+ self.w = w
+ self.permutations = permutations
+ self.__moments()
+ self.y2 = y * y
+ y = y.reshape(len(y), 1) # Ensure that y is an n by 1 vector, otherwise y*y.T == y*y
+ self.den_sum = (y * y.T).sum() - (y * y).sum()
+ self.G = self.__calc(self.y)
+ self.z_norm = (self.G - self.EG) / math.sqrt(self.VG)
+ self.p_norm = 1.0 - stats.norm.cdf(np.abs(self.z_norm))
+
+ if permutations:
+ sim = [self.__calc(np.random.permutation(self.y))
+ for i in xrange(permutations)]
+ self.sim = sim = np.array(sim)
+ above = sim >= self.G
+ larger = sum(above)
+ if (self.permutations - larger) < larger:
+ larger = self.permutations - larger
+ self.p_sim = (larger + 1.0) / (permutations + 1.)
+ self.EG_sim = sum(sim) / permutations
+ self.seG_sim = sim.std()
+ self.VG_sim = self.seG_sim ** 2
+ self.z_sim = (self.G - self.EG_sim) / self.seG_sim
+ self.p_z_sim = 1. - stats.norm.cdf(np.abs(self.z_sim))
+
+ def __moments(self):
+ y = self.y
+ n = self.n
+ w = self.w
+ n2 = n * n
+ s0 = w.s0
+ self.EG = s0 / (n * (n - 1))
+ s02 = s0 * s0
+ s1 = w.s1
+ s2 = w.s2
+ b0 = (n2 - 3 * n + 3) * s1 - n * s2 + 3 * s02
+ b1 = (-1.) * ((n2 - n) * s1 - 2 * n * s2 + 6 * s02)
+ b2 = (-1.) * (2 * n * s1 - (n + 3) * s2 + 6 * s02)
+ b3 = 4 * (n - 1) * s1 - 2 * (n + 1) * s2 + 8 * s02
+ b4 = s1 - s2 + s02
+ self.b0 = b0
+ self.b1 = b1
+ self.b2 = b2
+ self.b3 = b3
+ self.b4 = b4
+ y2 = y * y
+ y3 = y * y2
+ y4 = y2 * y2
+ EG2 = (b0 * (sum(
+ y2) ** 2) + b1 * sum(y4) + b2 * (sum(y) ** 2) * sum(y2))
+ EG2 += b3 * sum(y) * sum(y3) + b4 * (sum(y) ** 4)
+ EG2NUM = EG2
+ EG2DEN = (((sum(y) ** 2 - sum(y2)) ** 2) * n * (n - 1) * (
+ n - 2) * (n - 3))
+ self.EG2 = EG2NUM / EG2DEN
+ self.VG = self.EG2 - self.EG ** 2
+
+ def __calc(self, y):
+ yl = slag(self.w, y)
+ self.num = y * yl
+ return self.num.sum() / self.den_sum
+
+
+class G_Local:
+ """
+ Generalized Local G Autocorrelation Statistic
+
+ Parameters
+ ----------
+ y: array
+ variable
+ w: DistanceBand W
+ weights instance that is based on threshold distance
+ and is assumed to be aligned with y
+ transform: string
+ the type of w, either 'B' (binary) or 'R' (row-standardized)
+ permutations: int
+ the number of random permutations for calculating
+ pseudo p values
+ star: boolean
+ whether or not to include focal observation in sums
+ default is False
+
+ Attributes
+ ----------
+ y: array
+ original variable
+ w: DistanceBand W
+ original weights object
+ permutations: int
+ the number of permutations
+ Gs: array of floats
+ the value of the orginal G statistic in Getis & Ord (1992)
+ EGs: float
+ expected value of Gs under normality assumption
+ the values is scalar, since the expectation is identical
+ across all observations
+ VGs: array of floats
+ variance values of Gs under normality assumption
+ Zs: array of floats
+ standardized Gs
+ p_norm: array of floats
+ p-value under normality assumption (one-sided)
+ for two-sided tests, this value should be multiplied by 2
+ sim: array of arrays of floats (if permutations>0)
+ vector of I values for permutated samples
+ p_sim: array of floats
+ p-value based on permutations (one-sided)
+ null: spatial randomness
+ alternative: the observed G is extreme
+ it is either extremely high or extremely low
+ EG_sim: array of floats
+ average value of G from permutations
+ VG_sim: array of floats
+ variance of G from permutations
+ seG_sim: array of floats
+ standard deviation of G under permutations.
+ z_sim: array of floats
+ standardized G based on permutations
+ p_z_sim: array of floats
+ p-value based on standard normal approximation from
+ permutations (one-sided)
+
+ Notes
+ -----
+ To compute moments of Gs under normality assumption,
+ PySAL considers w is either binary or row-standardized.
+ For binary weights object, the weight value for self is 1
+ For row-standardized weights object, the weight value for self is
+ 1/(the number of its neighbors + 1).
+
+ References
+ ----------
+ Getis, A. and Ord., J.K. (1992) The analysis of spatial association by use of
+ distance statistics. Geographical Analysis, 24(3):189-206
+ Ord, J.K. and Getis, A. (1995) Local spatial autocorrelation statistics:
+ distributional issues and an application. Geographical Analysis, 27(4):286-306
+ Getis, A. and Ord, J. K. (1996) Local spatial statistics: an overview,
+ in Spatial Analysis: Modelling in a GIS Environment, edited by Longley, P.
+ and Batty, M.
+
+ Examples
+ --------
+ >>> from pysal.weights.Distance import DistanceBand
+ >>> import numpy
+ >>> numpy.random.seed(10)
+
+ Preparing a point data set
+
+ >>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+
+ Creating a weights object from points
+
+ >>> w = DistanceBand(points,threshold=15)
+
+ Prepareing a variable
+
+ >>> y = numpy.array([2, 3, 3.2, 5, 8, 7])
+
+ Applying Getis and Ord local G test using a binary weights object
+ >>> lg = G_Local(y,w,transform='B')
+
+ Examining the results
+ >>> lg.Zs
+ array([-1.0136729 , -0.04361589, 1.31558703, -0.31412676, 1.15373986,
+ 1.77833941])
+ >>> lg.p_sim[0]
+ 0.10100000000000001
+
+ >>> numpy.random.seed(10)
+
+ Applying Getis and Ord local G* test using a binary weights object
+ >>> lg_star = G_Local(y,w,transform='B',star=True)
+
+ Examining the results
+ >>> lg_star.Zs
+ array([-1.39727626, -0.28917762, 0.65064964, -0.28917762, 1.23452088,
+ 2.02424331])
+ >>> lg_star.p_sim[0]
+ 0.10100000000000001
+
+ >>> numpy.random.seed(10)
+
+ Applying Getis and Ord local G test using a row-standardized weights object
+ >>> lg = G_Local(y,w,transform='R')
+
+ Examining the results
+ >>> lg.Zs
+ array([-0.62074534, -0.01780611, 1.31558703, -0.12824171, 0.28843496,
+ 1.77833941])
+ >>> lg.p_sim[0]
+ 0.10100000000000001
+
+ >>> numpy.random.seed(10)
+
+ Applying Getis and Ord local G* test using a row-standardized weights object
+ >>> lg_star = G_Local(y,w,transform='R',star=True)
+
+ Examining the results
+ >>> lg_star.Zs
+ array([-0.62488094, -0.09144599, 0.41150696, -0.09144599, 0.24690418,
+ 1.28024388])
+ >>> lg_star.p_sim[0]
+ 0.10100000000000001
+
+ """
+ def __init__(self, y, w, transform='R', permutations=PERMUTATIONS, star=False):
+ self.n = len(y)
+ self.y = y
+ self.w = w
+ self.w_original = w.transform
+ self.w.transform = self.w_transform = transform.lower()
+ self.permutations = permutations
+ self.star = star
+ self.calc()
+ self.p_norm = np.array(
+ [1 - stats.norm.cdf(np.abs(i)) for i in self.Zs])
+ if permutations:
+ self.__crand()
+ sim = np.transpose(self.rGs)
+ above = sim >= self.Gs
+ larger = sum(above)
+ low_extreme = (self.permutations - larger) < larger
+ larger[low_extreme] = self.permutations - larger[low_extreme]
+ self.p_sim = (larger + 1.0) / (permutations + 1)
+ self.sim = sim
+ self.EG_sim = sim.mean()
+ self.seG_sim = sim.std()
+ self.VG_sim = self.seG_sim * self.seG_sim
+ self.z_sim = (self.Gs - self.EG_sim) / self.seG_sim
+ self.p_z_sim = 1 - stats.norm.cdf(np.abs(self.z_sim))
+
+ def __crand(self):
+ y = self.y
+ rGs = np.zeros((self.n, self.permutations))
+ n_1 = self.n - 1
+ rid = range(n_1)
+ prange = range(self.permutations)
+ k = self.w.max_neighbors + 1
+ rids = np.array([np.random.permutation(rid)[0:k] for i in prange])
+ ids = np.arange(self.w.n)
+ ido = self.w.id_order
+ wc = self.__getCardinalities()
+ if self.w_transform == 'r':
+ den = np.array(wc) + self.star
+ else:
+ den = np.ones(self.w.n)
+ for i in range(self.w.n):
+ idsi = ids[ids != i]
+ np.random.shuffle(idsi)
+ yi_star = y[i] * self.star
+ wci = wc[i]
+ rGs[i] = (y[idsi[rids[:, 0:wci]]]).sum(1) + yi_star
+ rGs[i] = (np.array(rGs[i]) / den[i]) / (
+ self.y_sum - (1 - self.star) * y[i])
+ self.rGs = rGs
+
+ def __getCardinalities(self):
+ ido = self.w.id_order
+ self.wc = np.array(
+ [self.w.cardinalities[ido[i]] for i in range(self.n)])
+ return self.wc
+
+ def calc(self):
+ y = self.y
+ y2 = y * y
+ self.y_sum = y_sum = sum(y)
+ y2_sum = sum(y2)
+
+ if not self.star:
+ yl = 1.0 * slag(self.w, y)
+ ydi = y_sum - y
+ self.Gs = yl / ydi
+ N = self.n - 1
+ yl_mean = ydi / N
+ s2 = (y2_sum - y2) / N - (yl_mean) ** 2
+ else:
+ self.w.transform = 'B'
+ yl = 1.0 * slag(self.w, y)
+ yl += y
+ if self.w_transform == 'r':
+ yl = yl / (self.__getCardinalities() + 1.0)
+ self.Gs = yl / y_sum
+ N = self.n
+ yl_mean = y.mean()
+ s2 = y.var()
+
+ EGs_num, VGs_num = 1.0, 1.0
+ if self.w_transform == 'b':
+ W = self.__getCardinalities()
+ W += self.star
+ EGs_num = W * 1.0
+ VGs_num = (W * (1.0 * N - W)) / (1.0 * N - 1)
+
+ self.EGs = (EGs_num * 1.0) / N
+ self.VGs = (VGs_num) * (1.0 / (N ** 2)) * ((s2 * 1.0) / (yl_mean ** 2))
+ self.Zs = (self.Gs - self.EGs) / np.sqrt(self.VGs)
+
+ self.w.transform = self.w_original
+
diff --git a/pysal/esda/join_counts.py b/pysal/esda/join_counts.py
new file mode 100644
index 0000000..a16e291
--- /dev/null
+++ b/pysal/esda/join_counts.py
@@ -0,0 +1,144 @@
+"""
+Spatial autocorrelation for binary attributes
+
+"""
+__author__ = "Sergio J. Rey <srey at asu.edu> , Luc Anselin <luc.anselin at asu.edu>"
+
+import pysal
+import numpy as np
+
+__all__ = ['Join_Counts']
+
+PERMUTATIONS = 999
+
+
+class Join_Counts:
+ """Binary Join Counts
+
+
+ Parameters
+ ----------
+
+ y : array
+ binary variable measured across n spatial units
+ w : W
+ spatial weights instance
+ permutations : int
+ number of random permutations for calculation of pseudo-p_values
+
+ Attributes
+ ----------
+ y : array
+ original variable
+ w : W
+ original w object
+ permutations : int
+ number of permutations
+ bb : float
+ number of black-black joins
+ ww : float
+ number of white-white joins
+ bw : float
+ number of black-white joins
+ J : float
+ number of joins
+ sim_bb : array (if permutations>0)
+ vector of bb values for permuted samples
+ p_sim_bb : array (if permutations>0)
+ p-value based on permutations (one-sided)
+ null: spatial randomness
+ alternative: the observed bb is greater than under randomness
+ mean_bb : average of permuted bb values
+ min_bb : minimum of permuted bb values
+ max_bb : maximum of permuted bb values
+ sim_bw : array (if permutations>0)
+ vector of bw values for permuted samples
+ p_sim_bw : array (if permutations>0)
+ p-value based on permutations (one-sided)
+ null: spatial randomness
+ alternative: the observed bw is greater than under randomness
+ mean_bw : average of permuted bw values
+ min_bw : minimum of permuted bw values
+ max_bw : maximum of permuted bw values
+
+
+ Examples
+ --------
+
+ Replicate example from anselin and rey
+
+ >>> import numpy as np
+ >>> w = pysal.lat2W(4, 4)
+ >>> y = np.ones(16)
+ >>> y[0:8] = 0
+ >>> np.random.seed(12345)
+ >>> jc = pysal.Join_Counts(y, w)
+ >>> jc.bb
+ 10.0
+ >>> jc.bw
+ 4.0
+ >>> jc.ww
+ 10.0
+ >>> jc.J
+ 24.0
+ >>> len(jc.sim_bb)
+ 999
+ >>> jc.p_sim_bb
+ 0.0030000000000000001
+ >>> np.mean(jc.sim_bb)
+ 5.5465465465465469
+ >>> np.max(jc.sim_bb)
+ 10.0
+ >>> np.min(jc.sim_bb)
+ 0.0
+ >>> len(jc.sim_bw)
+ 999
+ >>> jc.p_sim_bw
+ 1.0
+ >>> np.mean(jc.sim_bw)
+ 12.811811811811811
+ >>> np.max(jc.sim_bw)
+ 24.0
+ >>> np.min(jc.sim_bw)
+ 7.0
+ >>>
+ """
+ def __init__(self, y, w, permutations=PERMUTATIONS):
+ w.transformation = 'b' # ensure we have binary weights
+ self.w = w
+ self.y = y
+ self.permutations = permutations
+ self.J = w.s0 / 2.
+ self.bb, self.ww, self.bw = self.__calc(self.y)
+
+ if permutations:
+ sim = [self.__calc(np.random.permutation(self.y))
+ for i in xrange(permutations)]
+ sim_jc = np.array(sim)
+ self.sim_bb = sim_jc[:, 0]
+ self.min_bb = np.min(self.sim_bb)
+ self.mean_bb = np.mean(self.sim_bb)
+ self.max_bb = np.max(self.sim_bb)
+ self.sim_bw = sim_jc[:, 2]
+ self.min_bw = np.min(self.sim_bw)
+ self.mean_bw = np.mean(self.sim_bw)
+ self.max_bw = np.max(self.sim_bw)
+ p_sim_bb = self.__pseudop(self.sim_bb, self.bb)
+ p_sim_bw = self.__pseudop(self.sim_bw, self.bw)
+ self.p_sim_bb = p_sim_bb
+ self.p_sim_bw = p_sim_bw
+
+ def __calc(self, z):
+ zl = pysal.lag_spatial(self.w, z)
+ bb = sum(z * zl) / 2.0
+ zw = 1 - z
+ zl = pysal.lag_spatial(self.w, zw)
+ ww = sum(zw * zl) / 2.0
+ bw = self.J - (bb + ww)
+ return (bb, ww, bw)
+
+ def __pseudop(self, sim, jc):
+ above = sim >= jc
+ larger = sum(above)
+ psim = (larger + 1.) / (self.permutations + 1.)
+ return psim
diff --git a/pysal/esda/mapclassify.py b/pysal/esda/mapclassify.py
new file mode 100644
index 0000000..36e37f7
--- /dev/null
+++ b/pysal/esda/mapclassify.py
@@ -0,0 +1,1867 @@
+"""
+A module of classification schemes for choropleth mapping.
+"""
+__author__ = "Sergio J. Rey"
+
+__all__ = ['Map_Classifier', 'quantile', 'Box_Plot', 'Equal_Interval',
+ 'Fisher_Jenks', 'Fisher_Jenks_Sampled', 'Jenks_Caspall',
+ 'Jenks_Caspall_Forced', 'Jenks_Caspall_Sampled',
+ 'Max_P_Classifier', 'Maximum_Breaks', 'Natural_Breaks',
+ 'Quantiles', 'Percentiles', 'Std_Mean', 'User_Defined',
+ 'gadf', 'K_classifiers']
+
+from pysal.common import *
+
+K = 5 # default number of classes in any map scheme with this as an argument
+
+
+def quantile(y, k=4):
+ """
+ Calculates the quantiles for an array
+
+ Parameters
+ ----------
+ y : array (n,1)
+ values to classify
+ k : int
+ number of quantiles
+
+ Returns
+ -------
+ implicit : array (n,1)
+ quantile values
+
+ Examples
+ --------
+ >>> x = np.arange(1000)
+ >>> quantile(x)
+ array([ 249.75, 499.5 , 749.25, 999. ])
+ >>> quantile(x, k = 3)
+ array([ 333., 666., 999.])
+ >>>
+
+ Note that if there are enough ties that the quantile values repeat, we
+ collapse to pseudo quantiles in which case the number of classes will be less than k
+
+ >>> x = [1.0] * 100
+ >>> x.extend([3.0] * 40)
+ >>> len(x)
+ 140
+ >>> y = np.array(x)
+ >>> quantile(y)
+ array([ 1., 3.])
+ """
+ w = 100. / k
+ p = np.arange(w, 100 + w, w)
+ if p[-1] > 100.0:
+ p[-1] = 100.0
+ q = np.array([stats.scoreatpercentile(y, pct) for pct in p])
+ return np.unique(q)
+
+
+def binC(y, bins):
+ """
+ Bin categorical/qualitative data
+
+ Parameters
+ ----------
+ y : array (n,q)
+ categorical values
+ bins : array (k,1)
+ unique values associated with each bin
+
+ Return
+ ------
+ b : array (n,q)
+ bin membership, values between 0 and k-1
+
+ Examples
+ --------
+ >>> np.random.seed(1)
+ >>> x = np.random.randint(2, 8, (10, 3))
+ >>> bins = range(2, 8)
+ >>> x
+ array([[7, 5, 6],
+ [2, 3, 5],
+ [7, 2, 2],
+ [3, 6, 7],
+ [6, 3, 4],
+ [6, 7, 4],
+ [6, 5, 6],
+ [4, 6, 7],
+ [4, 6, 3],
+ [3, 2, 7]])
+ >>> y = binC(x, bins)
+ >>> y
+ array([[5, 3, 4],
+ [0, 1, 3],
+ [5, 0, 0],
+ [1, 4, 5],
+ [4, 1, 2],
+ [4, 5, 2],
+ [4, 3, 4],
+ [2, 4, 5],
+ [2, 4, 1],
+ [1, 0, 5]])
+ >>>
+ """
+
+ if np.rank(y) == 1:
+ k = 1
+ n = np.shape(y)[0]
+ else:
+ n, k = np.shape(y)
+ b = np.zeros((n, k), dtype='int')
+ for i, bin in enumerate(bins):
+ b[np.nonzero(y == bin)] = i
+
+ # check for non-binned items and print a warning if needed
+ vals = set(y.flatten())
+ for val in vals:
+ if val not in bins:
+ print 'warning: value not in bin: ', val
+ print 'bins: ', bins
+
+ return b
+
+
+def bin(y, bins):
+ """
+ bin interval/ratio data
+
+ Parameters
+ ----------
+ y : array (n,q)
+ values to bin
+ bins : array (k,1)
+ upper bounds of each bin (monotonic)
+
+ Returns
+ -------
+ b : array (n,q)
+ values of values between 0 and k-1
+
+ Examples
+ --------
+ >>> np.random.seed(1)
+ >>> x = np.random.randint(2, 20, (10, 3))
+ >>> bins = [10, 15, 20]
+ >>> b = bin(x, bins)
+ >>> x
+ array([[ 7, 13, 14],
+ [10, 11, 13],
+ [ 7, 17, 2],
+ [18, 3, 14],
+ [ 9, 15, 8],
+ [ 7, 13, 12],
+ [16, 6, 11],
+ [19, 2, 15],
+ [11, 11, 9],
+ [ 3, 2, 19]])
+ >>> b
+ array([[0, 1, 1],
+ [0, 1, 1],
+ [0, 2, 0],
+ [2, 0, 1],
+ [0, 1, 0],
+ [0, 1, 1],
+ [2, 0, 1],
+ [2, 0, 1],
+ [1, 1, 0],
+ [0, 0, 2]])
+ >>>
+ """
+ if np.rank(y) == 1:
+ k = 1
+ n = np.shape(y)[0]
+ else:
+ n, k = np.shape(y)
+ b = np.zeros((n, k), dtype='int')
+ i = len(bins)
+ if type(bins) != list:
+ bins = bins.tolist()
+ binsc = copy.copy(bins)
+ while binsc:
+ i -= 1
+ c = binsc.pop(-1)
+ b[np.nonzero(y <= c)] = i
+ return b
+
+
+def bin1d(x, bins):
+ """
+ place values of a 1-d array into bins and determine counts of values in
+ each bin
+
+ Parameters
+ ----------
+ y : 1-d array
+ values to bin
+ bins : array (k,1)
+ upper bounds of each bin (monotonic)
+
+ Returns
+ -------
+ tuple(binIds,counts)
+
+ binIds: 1-d array of integer bin Ids
+
+ counts: number of elements of x falling in each bin
+
+ Examples
+ --------
+ >>> x = np.arange(100, dtype = 'float')
+ >>> bins = [25, 74, 100]
+ >>> binIds, counts = bin1d(x, bins)
+ >>> binIds
+ array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+ 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
+ 2, 2, 2, 2, 2, 2, 2, 2])
+ >>> counts
+ array([26, 49, 25])
+ """
+ left = [-sys.maxint]
+ left.extend(bins[0:-1])
+ right = bins
+ cuts = zip(left, right)
+ k = len(bins)
+ binIds = np.zeros(x.shape, dtype='int')
+ while cuts:
+ k -= 1
+ l, r = cuts.pop(-1)
+ binIds += (x > l) * (x <= r) * k
+ counts = np.bincount(binIds)
+ return (binIds, counts)
+
+
+def load_example():
+ """
+ Helper function for doc tests"""
+ import pysal
+ np.random.seed(10)
+ dat = pysal.open(pysal.examples.get_path('calempdensity.csv'))
+ cal = np.array([record[-1] for record in dat])
+ return cal
+
+
+def natural_breaks(values, k=5, itmax=100):
+ """
+ natural breaks helper function
+ """
+ values = np.array(values)
+ n = len(values)
+ uv = np.unique(values)
+ uvk = len(uv)
+ if uvk < k:
+ print 'Warning: Not enough unique values in array to form k classes'
+ print "Warning: setting k to %d" % uvk
+ k = uvk
+ sids = np.random.permutation(range(len(uv)))[0:k]
+ seeds = uv[sids]
+ seeds.sort()
+ diffs = abs(np.matrix([values - seed for seed in seeds]))
+ c0 = diffs.argmin(axis=0)
+ c0 = np.array(c0)[0]
+ solving = True
+ solved = False
+ rk = range(k)
+ it = 0
+ while solving:
+ # get centroids of clusters
+ seeds = [np.median(values[c0 == c]) for c in rk]
+ seeds.sort()
+ # for each value find closest centroid
+ diffs = abs(np.matrix([values - seed for seed in seeds]))
+ # assign value to that centroid
+ c1 = diffs.argmin(axis=0)
+ c1 = np.array(c1)[0]
+ #compare new classids to previous
+ d = abs(c1 - c0)
+ if d.sum() == 0:
+ solving = False
+ solved = True
+ else:
+ c0 = c1
+ it += 1
+ if it == itmax:
+ solving = False
+ class_ids = c1
+ cuts = [max(values[c1 == c]) for c in rk]
+ return sids, seeds, diffs, class_ids, solved, it, cuts
+
+
+
+def _fisher_jenks_means(values, classes=5, sort=True):
+ """
+ Jenks Optimal (Natural Breaks) algorithm implemented in Python.
+ The original Python code comes from here:
+ http://danieljlewis.org/2010/06/07/jenks-natural-breaks-algorithm-in-python/
+ and is based on a JAVA and Fortran code available here:
+ https://stat.ethz.ch/pipermail/r-sig-geo/2006-March/000811.html
+
+ Returns class breaks such that classes are internally homogeneous while
+ assuring heterogeneity among classes.
+
+ """
+
+ if sort:
+ values.sort()
+ mat1 = []
+ for i in range(0, len(values) + 1):
+ temp = []
+ for j in range(0, classes + 1):
+ temp.append(0)
+ mat1.append(temp)
+ mat2 = []
+ for i in range(0, len(values) + 1):
+ temp = []
+ for j in range(0, classes + 1):
+ temp.append(0)
+ mat2.append(temp)
+ for i in range(1, classes + 1):
+ mat1[1][i] = 1
+ mat2[1][i] = 0
+ for j in range(2, len(values) + 1):
+ mat2[j][i] = float('inf')
+ v = 0.0
+ for l in range(2, len(values) + 1):
+ s1 = 0.0
+ s2 = 0.0
+ w = 0.0
+ for m in range(1, l + 1):
+ i3 = l - m + 1
+ val = float(values[i3 - 1])
+ s2 += val * val
+ s1 += val
+ w += 1
+ v = s2 - (s1 * s1) / w
+ i4 = i3 - 1
+ if i4 != 0:
+ for j in range(2, classes + 1):
+ if mat2[l][j] >= (v + mat2[i4][j - 1]):
+ mat1[l][j] = i3
+ mat2[l][j] = v + mat2[i4][j - 1]
+ mat1[l][1] = 1
+ mat2[l][1] = v
+
+ k = len(values)
+
+ kclass = []
+ for i in range(0, classes + 1):
+ kclass.append(0)
+ kclass[classes] = float(values[len(values) - 1])
+ kclass[0] = float(values[0])
+ countNum = classes
+ while countNum >= 2:
+ pivot = mat1[k][countNum]
+ id = int(pivot - 2)
+ kclass[countNum - 1] = values[id]
+ k = int(pivot - 1)
+ countNum -= 1
+ return kclass
+
+
+class Map_Classifier:
+ """
+ Abstract class for all map classifications
+ For an array :math:`y` of :math:`n` values, a map classifier places each value
+ :math:`y_i` into one of :math:`k` mutually exclusive and exhaustive classes.
+ Each classifer defines the classes based on different criteria, but in all
+ cases the following hold for the classifiers in PySAL:
+
+ .. math::
+
+ C_j^l < y_i \le C_j^u \ forall i \in C_j
+
+ where :math:`C_j` denotes class :math:`j` which has lower bound :math:`C_j^l` and upper bound :math:`C_j^u`.
+
+
+
+
+ Map Classifiers Supported
+
+ * :class:`~pysal.esda.mapclassify.Box_Plot`
+ * :class:`~pysal.esda.mapclassify.Equal_Interval`
+ * :class:`~pysal.esda.mapclassify.Fisher_Jenks`
+ * :class:`~pysal.esda.mapclassify.Fisher_Jenks_Sampled`
+ * :class:`~pysal.esda.mapclassify.Jenks_Caspall`
+ * :class:`~pysal.esda.mapclassify.Jenks_Caspall_Forced`
+ * :class:`~pysal.esda.mapclassify.Jenks_Caspall_Sampled`
+ * :class:`~pysal.esda.mapclassify.Max_P_Classifier`
+ * :class:`~pysal.esda.mapclassify.Maximum_Breaks`
+ * :class:`~pysal.esda.mapclassify.Natural_Breaks`
+ * :class:`~pysal.esda.mapclassify.Quantiles`
+ * :class:`~pysal.esda.mapclassify.Percentiles`
+ * :class:`~pysal.esda.mapclassify.Std_Mean`
+ * :class:`~pysal.esda.mapclassify.User_Defined`
+
+ Utilities:
+
+ In addition to the classifiers, there are several utility functions that can be used to evaluate the properties of a specific classifier for different parameter values, or for automatic selection of a classifier and number of classes.
+
+ * :func:`~pysal.esda.mapclassify.gadf`
+ * :class:`~pysal.esda.mapclassify.K_classifiers`
+
+ References
+ ----------
+
+ Slocum, T.A., R.B. McMaster, F.C. Kessler and H.H. Howard (2009) *Thematic Cartography and Geovisualization*. Pearson Prentice Hall, Upper Saddle River.
+
+ """
+
+ def __init__(self, y):
+ self.name = 'Map Classifier'
+ if hasattr(y, 'values'):
+ y = y.values # fix for pandas
+ self.y = y
+ self._classify()
+ self._summary()
+
+ def _summary(self):
+ yb = self.yb
+ self.classes = [np.nonzero(yb == c)[0].tolist() for c in range(self.k)]
+ self.tss = self.get_tss()
+ self.adcm = self.get_adcm()
+ self.gadf = self.get_gadf()
+
+ def _classify(self):
+ self._set_bins()
+ self.yb, self.counts = bin1d(self.y, self.bins)
+
+ def __str__(self):
+ st = self._table_string()
+ return st
+
+ def __repr__(self):
+ return self._table_string()
+
+ def get_tss(self):
+ """
+ Total sum of squares around class means
+
+ Returns sum of squares over all class means
+ """
+ tss = 0
+ for class_def in self.classes:
+ if len(class_def) > 0:
+ yc = self.y[class_def]
+ css = yc - yc.mean()
+ css *= css
+ tss += sum(css)
+ return tss
+
+ def _set_bins(self):
+ pass
+
+ def get_adcm(self):
+ """
+ Absolute deviation around class median (ADCM).
+
+ Calculates the absolute deviations of each observation about its class
+ median as a measure of fit for the classification method.
+
+ Returns sum of ADCM over all classes
+ """
+ adcm = 0
+ for class_def in self.classes:
+ if len(class_def) > 0:
+ yc = self.y[class_def]
+ yc_med = np.median(yc)
+ ycd = np.abs(yc - yc_med)
+ adcm += sum(ycd)
+ return adcm
+
+ def get_gadf(self):
+ """
+ Goodness of absolute deviation of fit
+ """
+ adam = (np.abs(self.y - np.median(self.y))).sum()
+ gadf = 1 - self.adcm / adam
+ return gadf
+
+ def _table_string(self, width=12, decimal=3):
+ fmt = ".%df" % decimal
+ fmt = "%" + fmt
+ largest = max([len(fmt % i) for i in self.bins])
+ width = largest
+ fmt = "%d.%df" % (width, decimal)
+ fmt = "%" + fmt
+ k1 = self.k - 1
+ h1 = "Lower"
+ h1 = h1.center(largest)
+ h2 = " "
+ h2 = h2.center(10)
+ h3 = "Upper"
+ h3 = h3.center(largest + 1)
+
+ largest = "%d" % max(self.counts)
+ largest = len(largest) + 15
+ h4 = "Count"
+
+ h4 = h4.rjust(largest)
+ table = []
+ header = h1 + h2 + h3 + h4
+ table.append(header)
+ table.append("=" * len(header))
+
+ rows = []
+ for i, up in enumerate(self.bins):
+ if i == 0:
+ left = " " * width
+ left += " x[i] <= "
+ else:
+ left = fmt % self.bins[i - 1]
+ left += " < x[i] <= "
+ right = fmt % self.bins[i]
+ row = left + right
+ cnt = "%d" % self.counts[i]
+ cnt = cnt.rjust(largest)
+ row += cnt
+ table.append(row)
+ name = self.name
+ top = name.center(len(row))
+ table.insert(0, top)
+ table.insert(1, " ")
+ table = "\n".join(table)
+ return table
+
+
+class Equal_Interval(Map_Classifier):
+ """
+ Equal Interval Classification
+
+ Parameters
+ ----------
+ y : array (n,1)
+ values to classify
+ k : int
+ number of classes required
+
+ Attributes
+ ----------
+
+ yb : array (n,1)
+ bin ids for observations,
+ each value is the id of the class the observation belongs to
+ yb[i] = j for j>=1 if bins[j-1] < y[i] <= bins[j], yb[i] = 0 otherwise
+ bins : array (k,1)
+ the upper bounds of each class
+ k : int
+ the number of classes
+ counts : array (k,1)
+ the number of observations falling in each class
+
+ Examples
+ --------
+ >>> cal = load_example()
+ >>> ei = Equal_Interval(cal, k = 5)
+ >>> ei.k
+ 5
+ >>> ei.counts
+ array([57, 0, 0, 0, 1])
+ >>> ei.bins
+ array([ 822.394, 1644.658, 2466.922, 3289.186, 4111.45 ])
+ >>>
+
+
+ Notes
+ -----
+ Intervals defined to have equal width:
+
+ .. math::
+
+ bins_j = min(y)+w*(j+1)
+
+ with :math:`w=\\frac{max(y)-min(j)}{k}`
+ """
+
+ def __init__(self, y, k=K):
+ """
+ see class docstring
+
+ """
+
+ self.k = k
+ Map_Classifier.__init__(self, y)
+ self.name = 'Equal Interval'
+
+ def _set_bins(self):
+ y = self.y
+ k = self.k
+ max_y = max(y)
+ min_y = min(y)
+ rg = max_y - min_y
+ width = rg * 1. / k
+ cuts = np.arange(min_y + width, max_y + width, width)
+ if len(cuts) > self.k: # handle overshooting
+ cuts = cuts[0:k]
+ cuts[-1] = max_y
+ bins = cuts.copy()
+ self.bins = bins
+
+
+class Percentiles(Map_Classifier):
+ """
+ Percentiles Map Classification
+
+ Parameters
+ ----------
+
+ y : array
+ attribute to classify
+ pct : array
+ percentiles default=[1,10,50,90,99,100]
+
+ Attributes
+ ----------
+ yb : array
+ bin ids for observations (numpy array n x 1)
+
+ bins : array
+ the upper bounds of each class (numpy array k x 1)
+
+ k : int
+ the number of classes
+
+ counts : int
+ the number of observations falling in each class (numpy array k x 1)
+
+ Examples
+ --------
+ >>> cal = load_example()
+ >>> p = Percentiles(cal)
+ >>> p.bins
+ array([ 1.35700000e-01, 5.53000000e-01, 9.36500000e+00,
+ 2.13914000e+02, 2.17994800e+03, 4.11145000e+03])
+ >>> p.counts
+ array([ 1, 5, 23, 23, 5, 1])
+ >>> p2 = Percentiles(cal, pct = [50, 100])
+ >>> p2.bins
+ array([ 9.365, 4111.45 ])
+ >>> p2.counts
+ array([29, 29])
+ >>> p2.k
+ 2
+ """
+
+ def __init__(self, y, pct=[1, 10, 50, 90, 99, 100]):
+ self.pct = pct
+ Map_Classifier.__init__(self, y)
+ self.name = 'Percentiles'
+
+ def _set_bins(self):
+ y = self.y
+ pct = self.pct
+ self.bins = np.array([stats.scoreatpercentile(y, p) for p in pct])
+ self.k = len(self.bins)
+
+
+class Box_Plot(Map_Classifier):
+ """
+ Box_Plot Map Classification
+
+
+ Parameters
+ ----------
+ y : array
+ attribute to classify
+ hinge : float
+ multiplier for IQR
+
+ Attributes
+ ----------
+ yb : array (n,1)
+ bin ids for observations
+ bins : array (n,1)
+ the upper bounds of each class (monotonic)
+ k : int
+ the number of classes
+ counts : array (k,1)
+ the number of observations falling in each class
+ low_outlier_ids : array
+ indices of observations that are low outliers
+ high_outlier_ids : array
+ indices of observations that are high outliers
+
+ Notes
+ -----
+
+ The bins are set as follows::
+
+ bins[0] = q[0]-hinge*IQR
+ bins[1] = q[0]
+ bins[2] = q[1]
+ bins[3] = q[2]
+ bins[4] = q[2]+hinge*IQR
+ bins[5] = inf (see Notes)
+
+ where q is an array of the first three quartiles of y and
+ IQR=q[2]-q[0]
+
+
+ If q[2]+hinge*IQR > max(y) there will only be 5 classes and no high outliers,
+ otherwise, there will be 6 classes and at least one high outlier.
+
+ Examples
+ --------
+ >>> cal = load_example()
+ >>> bp = Box_Plot(cal)
+ >>> bp.bins
+ array([ -5.28762500e+01, 2.56750000e+00, 9.36500000e+00,
+ 3.95300000e+01, 9.49737500e+01, 4.11145000e+03])
+ >>> bp.counts
+ array([ 0, 15, 14, 14, 6, 9])
+ >>> bp.high_outlier_ids
+ array([ 0, 6, 18, 29, 33, 36, 37, 40, 42])
+ >>> cal[bp.high_outlier_ids]
+ array([ 329.92, 181.27, 370.5 , 722.85, 192.05, 110.74,
+ 4111.45, 317.11, 264.93])
+ >>> bx = Box_Plot(np.arange(100))
+ >>> bx.bins
+ array([ -49.5 , 24.75, 49.5 , 74.25, 148.5 ])
+
+ """
+
+ def __init__(self, y, hinge=1.5):
+ """
+ Parameters
+ ----------
+ y : array (n,1)
+ attribute to classify
+ hinge : float
+ multiple of inter-quartile range (default=1.5)
+ """
+ self.hinge = hinge
+ Map_Classifier.__init__(self, y)
+ self.name = 'Box Plot'
+
+ def _set_bins(self):
+ y = self.y
+ pct = [25, 50, 75, 100]
+ bins = [stats.scoreatpercentile(y, p) for p in pct]
+ iqr = bins[-2] - bins[0]
+ self.iqr = iqr
+ pivot = self.hinge * iqr
+ left_fence = bins[0] - pivot
+ right_fence = bins[-2] + pivot
+ if right_fence < bins[-1]:
+ bins.insert(-1, right_fence)
+ else:
+ bins[-1] = right_fence
+ bins.insert(0, left_fence)
+ self.bins = np.array(bins)
+ self.k = len(pct)
+
+ def _classify(self):
+ Map_Classifier._classify(self)
+ self.low_outlier_ids = np.nonzero(self.yb == 0)[0]
+ self.high_outlier_ids = np.nonzero(self.yb == 5)[0]
+
+
+class Quantiles(Map_Classifier):
+ """Quantile Map Classification
+
+ Parameters
+ ----------
+ y : array (n,1)
+ values to classify
+ k : int
+ number of classes required
+
+ Attributes
+ ----------
+
+ yb : array (n,1)
+ bin ids for observations,
+ each value is the id of the class the observation belongs to
+ yb[i] = j for j>=1 if bins[j-1] < y[i] <= bins[j], yb[i] = 0 otherwise
+ bins : array (k,1)
+ the upper bounds of each class
+ k : int
+ the number of classes
+ counts : array (k,1)
+ the number of observations falling in each class
+
+
+ Examples
+ --------
+ >>> cal = load_example()
+ >>> q = Quantiles(cal, k = 5)
+ >>> q.bins
+ array([ 1.46400000e+00, 5.79800000e+00, 1.32780000e+01,
+ 5.46160000e+01, 4.11145000e+03])
+ >>> q.counts
+ array([12, 11, 12, 11, 12])
+ >>>
+ """
+
+ def __init__(self, y, k=K):
+ self.k = k
+ Map_Classifier.__init__(self, y)
+ self.name = 'Quantiles'
+
+ def _set_bins(self):
+ y = self.y
+ k = self.k
+ self.bins = quantile(y, k=k)
+
+
+class Std_Mean(Map_Classifier):
+ """
+ Standard Deviation and Mean Map Classification
+
+ Parameters
+ ----------
+ y : array (n,1)
+ values to classify
+ multiples : array
+ the multiples of the standard deviation to add/subtract from
+ the sample mean to define the bins, default=[-2,-1,1,2]
+
+ Attributes
+ ----------
+
+ yb : array (n,1)
+ bin ids for observations,
+ bins : array (k,1)
+ the upper bounds of each class
+ k : int
+ the number of classes
+ counts : array (k,1)
+ the number of observations falling in each class
+
+
+ Examples
+ --------
+ >>> cal = load_example()
+ >>> st = Std_Mean(cal)
+ >>> st.k
+ 5
+ >>> st.bins
+ array([ -967.36235382, -420.71712519, 672.57333208, 1219.21856072,
+ 4111.45 ])
+ >>> st.counts
+ array([ 0, 0, 56, 1, 1])
+ >>>
+ >>> st3 = Std_Mean(cal, multiples = [-3, -1.5, 1.5, 3])
+ >>> st3.bins
+ array([-1514.00758246, -694.03973951, 945.8959464 , 1765.86378936,
+ 4111.45 ])
+ >>> st3.counts
+ array([ 0, 0, 57, 0, 1])
+ >>>
+
+ """
+ def __init__(self, y, multiples=[-2, -1, 1, 2]):
+ self.multiples = multiples
+ Map_Classifier.__init__(self, y)
+ self.name = 'Std_Mean'
+
+ def _set_bins(self):
+ y = self.y
+ s = y.std(ddof=1)
+ m = y.mean()
+ cuts = [m + s * w for w in self.multiples]
+ y_max = y.max()
+ if cuts[-1] < y_max:
+ cuts.append(y_max)
+ self.bins = np.array(cuts)
+ self.k = len(cuts)
+
+
+class Maximum_Breaks(Map_Classifier):
+ """
+ Maximum Breaks Map Classification
+
+ Parameters
+ ----------
+ y : array (n x 1)
+ values to classify
+
+ k : int
+ number of classes required
+
+ Attributes
+ ----------
+ yb : array (nx1)
+ bin ids for observations
+
+ bins : array (kx1)
+ the upper bounds of each class
+
+ k : int
+ the number of classes
+
+ counts : array (kx1)
+ the number of observations falling in each class (numpy array k x 1)
+
+ Examples
+ --------
+ >>> cal = load_example()
+ >>> mb = Maximum_Breaks(cal, k = 5)
+ >>> mb.k
+ 5
+ >>> mb.bins
+ array([ 146.005, 228.49 , 546.675, 2417.15 , 4111.45 ])
+ >>> mb.counts
+ array([50, 2, 4, 1, 1])
+ >>>
+
+ """
+ def __init__(self, y, k=K, mindiff=0):
+ self.k = k
+ self.mindiff = mindiff
+ Map_Classifier.__init__(self, y)
+ self.name = 'Maximum_Breaks'
+
+ def _set_bins(self):
+ xs = self.y.copy()
+ y = self.y.copy()
+ k = self.k
+ xs.sort()
+ min_diff = self.mindiff
+ d = xs[1:] - xs[:-1]
+ diffs = d[np.nonzero(d > min_diff)]
+ diffs = sp.unique(diffs)
+ k1 = k - 1
+ if len(diffs) > k1:
+ diffs = diffs[-k1:]
+ mp = []
+ self.cids = []
+ for diff in diffs:
+ ids = np.nonzero(d == diff)
+ for id in ids:
+ self.cids.append(id[0])
+ cp = ((xs[id] + xs[id + 1]) / 2.)
+ mp.append(cp[0])
+ mp.append(xs[-1])
+ mp.sort()
+ self.bins = np.array(mp)
+
+
+class Natural_Breaks(Map_Classifier):
+ """
+ Natural Breaks Map Classification
+
+ Parameters
+ ----------
+ y : array (n,1)
+ values to classify
+ k : int
+ number of classes required
+ initial : int (default=100)
+ number of initial solutions to generate
+
+ Attributes
+ ----------
+
+ yb : array (n,1)
+ bin ids for observations,
+ bins : array (k,1)
+ the upper bounds of each class
+ k : int
+ the number of classes
+ counts : array (k,1)
+ the number of observations falling in each class
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> np.random.seed(10)
+ >>> cal = load_example()
+ >>> nb = Natural_Breaks(cal, k = 5)
+ >>> nb.k
+ 5
+ >>> nb.counts
+ array([14, 13, 14, 10, 7])
+ >>> nb.bins
+ array([ 1.81000000e+00, 7.60000000e+00, 2.98200000e+01,
+ 1.81270000e+02, 4.11145000e+03])
+ >>> x = np.array([1] * 50)
+ >>> x[-1] = 20
+ >>> nb = Natural_Breaks(x, k = 5, initial = 0)
+ Warning: Not enough unique values in array to form k classes
+ Warning: setting k to 2
+ >>> nb.bins
+ array([ 1, 20])
+ >>> nb.counts
+ array([49, 1])
+
+
+ Notes
+ -----
+ There is a tradeoff here between speed and consistency of the
+ classification
+ If you want more speed, set initial to a smaller value (0
+ would result in the best speed, if you want more consistent classes in
+ multiple runs of Natural_Breaks on the same data, set initial to a
+ higher value.
+
+
+ """
+ def __init__(self, y, k=K, initial=100):
+ self.k = k
+ self.initial = initial
+ Map_Classifier.__init__(self, y)
+ self.name = 'Natural_Breaks'
+
+ def _set_bins(self):
+
+ x = self.y.copy()
+ k = self.k
+ res0 = natural_breaks(x, k)
+ fit = res0[2].sum()
+ for i in xrange(self.initial):
+ res = natural_breaks(x, k)
+ fit_i = res[2].sum()
+ if fit_i < fit:
+ res0 = res
+ self.bins = np.array(res0[-1])
+ self.k = len(self.bins)
+ self.iterations = res0[-2]
+
+
+class Fisher_Jenks(Map_Classifier):
+ """
+ Fisher Jenks optimal classifier - mean based
+
+ Parameters
+ ----------
+ y : array (n,1)
+ values to classify
+ k : int
+ number of classes required
+
+ Attributes
+ ----------
+
+ yb : array (n,1)
+ bin ids for observations
+ bins : array (k,1)
+ the upper bounds of each class
+ k : int
+ the number of classes
+ counts : array (k,1)
+ the number of observations falling in each class
+
+
+ Examples
+ --------
+
+ >>> cal = load_example()
+ >>> fj = Fisher_Jenks(cal)
+ >>> fj.adcm
+ 799.24000000000001
+ >>> fj.bins
+ array([ 75.29, 192.05, 370.5 , 722.85, 4111.45])
+ >>> fj.counts
+ array([49, 3, 4, 1, 1])
+ >>>
+ """
+
+ def __init__(self, y, k=K):
+
+ nu = len(np.unique(y))
+ if nu < k:
+ raise ValueError("Fewer unique values than specified classes.")
+ self.k = k
+ Map_Classifier.__init__(self, y)
+ self.name = "Fisher_Jenks"
+
+
+ def _set_bins(self):
+ x = self.y.copy()
+ self.bins = np.array(_fisher_jenks_means(x, classes=self.k)[1:])
+
+
+class Fisher_Jenks_Sampled(Map_Classifier):
+ """
+ Fisher Jenks optimal classifier - mean based using random sample
+
+ Parameters
+ ----------
+ y : array (n,1)
+ values to classify
+ k : int
+ number of classes required
+ pct : float
+ The percentage of n that should form the sample
+ If pct is specified such that n*pct > 1000, then
+ pct = 1000./n, unless truncate is False
+ truncate : binary (Default True)
+ truncate pct in cases where pct * n > 1000.
+
+ Attributes
+ ----------
+
+ yb : array (n,1)
+ bin ids for observations
+ bins : array (k,1)
+ the upper bounds of each class
+ k : int
+ the number of classes
+ counts : array (k,1)
+ the number of observations falling in each class
+
+ Examples
+ --------
+
+ (Turned off due to timing being different across hardware)
+
+ """
+
+ def __init__(self, y, k=K, pct=0.10, truncate=True):
+ self.k = k
+ n = y.size
+
+ if (pct * n > 1000) and truncate:
+ pct = 1000. / n
+ ids = np.random.random_integers(0, n - 1, n * pct)
+ yr = y[ids]
+ yr[-1] = max(y) # make sure we have the upper bound
+ yr[0] = min(y) # make sure we have the min
+ self.original_y = y
+ self.pct = pct
+ self.yr = yr
+ self.yr_n = yr.size
+ Map_Classifier.__init__(self, yr)
+ self.yb, self.counts = bin1d(y, self.bins)
+ self.name = "Fisher_Jenks_Sampled"
+ self.y = y
+ self._summary() # have to recalculate summary stats
+
+ def _set_bins(self):
+ fj = Fisher_Jenks(self.y, self.k)
+ self.bins = fj.bins
+
+
+class Jenks_Caspall(Map_Classifier):
+ """
+ Jenks Caspall Map Classification
+
+ Parameters
+ ----------
+ y : array (n,1)
+ values to classify
+ k : int
+ number of classes required
+
+ Attributes
+ ----------
+
+ yb : array (n,1)
+ bin ids for observations,
+ bins : array (k,1)
+ the upper bounds of each class
+ k : int
+ the number of classes
+ counts : array (k,1)
+ the number of observations falling in each class
+
+
+ Examples
+ --------
+ >>> cal = load_example()
+ >>> jc = Jenks_Caspall(cal, k = 5)
+ >>> jc.bins
+ array([ 1.81000000e+00, 7.60000000e+00, 2.98200000e+01,
+ 1.81270000e+02, 4.11145000e+03])
+ >>> jc.counts
+ array([14, 13, 14, 10, 7])
+
+ """
+ def __init__(self, y, k=K):
+ self.k = k
+ Map_Classifier.__init__(self, y)
+ self.name = "Jenks_Caspall"
+
+ def _set_bins(self):
+ x = self.y.copy()
+ k = self.k
+ # start with quantiles
+ q = quantile(x, k)
+ solving = True
+ xb, cnts = bin1d(x, q)
+ #class means
+ if x.ndim == 1:
+ x.shape = (x.size, 1)
+ n, k = x.shape
+ xm = [np.median(x[xb == i]) for i in np.unique(xb)]
+ xb0 = xb.copy()
+ q = xm
+ it = 0
+ rk = range(self.k)
+ while solving:
+ xb = np.zeros(xb0.shape, int)
+ d = abs(x - q)
+ xb = d.argmin(axis=1)
+ if (xb0 == xb).all():
+ solving = False
+ else:
+ xb0 = xb
+ it += 1
+ q = np.array([np.median(x[xb == i]) for i in rk])
+ cuts = np.array([max(x[xb == i]) for i in sp.unique(xb)])
+ cuts.shape = (len(cuts),)
+ self.bins = cuts
+ self.iterations = it
+
+
+class Jenks_Caspall_Sampled(Map_Classifier):
+ """
+ Jenks Caspall Map Classification using a random sample
+
+ Parameters
+ ----------
+
+ y : array (n,1)
+ values to classify
+ k : int
+ number of classes required
+ pct : float
+ The percentage of n that should form the sample
+ If pct is specified such that n*pct > 1000, then pct = 1000./n
+
+ Attributes
+ ----------
+
+ yb : array (n,1)
+ bin ids for observations,
+ bins : array (k,1)
+ the upper bounds of each class
+ k : int
+ the number of classes
+ counts : array (k,1)
+ the number of observations falling in each class
+
+
+ Examples
+ --------
+
+ >>> cal = load_example()
+ >>> x = np.random.random(100000)
+ >>> jc = Jenks_Caspall(x)
+ >>> jcs = Jenks_Caspall_Sampled(x)
+ >>> jc.bins
+ array([ 0.19770952, 0.39695769, 0.59588617, 0.79716865, 0.99999425])
+ >>> jcs.bins
+ array([ 0.18877882, 0.39341638, 0.6028286 , 0.80070925, 0.99999425])
+ >>> jc.counts
+ array([19804, 20005, 19925, 20178, 20088])
+ >>> jcs.counts
+ array([18922, 20521, 20980, 19826, 19751])
+ >>>
+
+ # not for testing since we get different times on different hardware
+ # just included for documentation of likely speed gains
+ #>>> t1 = time.time(); jc = Jenks_Caspall(x); t2 = time.time()
+ #>>> t1s = time.time(); jcs = Jenks_Caspall_Sampled(x); t2s = time.time()
+ #>>> t2 - t1; t2s - t1s
+ #1.8292930126190186
+ #0.061631917953491211
+
+ Notes
+ -----
+ This is intended for large n problems. The logic is to apply
+ Jenks_Caspall to a random subset of the y space and then bin the
+ complete vector y on the bins obtained from the subset. This would
+ trade off some "accuracy" for a gain in speed.
+
+ """
+
+ def __init__(self, y, k=K, pct=0.10):
+ self.k = k
+ n = y.size
+ if pct * n > 1000:
+ pct = 1000. / n
+ ids = np.random.random_integers(0, n - 1, n * pct)
+ yr = y[ids]
+ yr[0] = max(y) # make sure we have the upper bound
+ self.original_y = y
+ self.pct = pct
+ self.yr = yr
+ self.yr_n = yr.size
+ Map_Classifier.__init__(self, yr)
+ self.yb, self.counts = bin1d(y, self.bins)
+ self.name = "Jenks_Caspall_Sampled"
+ self.y = y
+ self._summary() # have to recalculate summary stats
+
+ def _set_bins(self):
+ jc = Jenks_Caspall(self.y, self.k)
+ self.bins = jc.bins
+ self.iterations = jc.iterations
+
+
+class Jenks_Caspall_Forced(Map_Classifier):
+ """
+
+ Jenks Caspall Map Classification with forced movements
+
+ Parameters
+ ----------
+ y : array (n,1)
+ values to classify
+ k : int
+ number of classes required
+
+ Attributes
+ ----------
+
+ yb : array (n,1)
+ bin ids for observations,
+ bins : array (k,1)
+ the upper bounds of each class
+ k : int
+ the number of classes
+ counts : array (k,1)
+ the number of observations falling in each class
+
+
+ Examples
+ --------
+ >>> cal = load_example()
+ >>> jcf = Jenks_Caspall_Forced(cal, k = 5)
+ >>> jcf.k
+ 5
+ >>> jcf.bins
+ array([[ 1.34000000e+00],
+ [ 5.90000000e+00],
+ [ 1.67000000e+01],
+ [ 5.06500000e+01],
+ [ 4.11145000e+03]])
+ >>> jcf.counts
+ array([12, 12, 13, 9, 12])
+ >>> jcf4 = Jenks_Caspall_Forced(cal, k = 4)
+ >>> jcf4.k
+ 4
+ >>> jcf4.bins
+ array([[ 2.51000000e+00],
+ [ 8.70000000e+00],
+ [ 3.66800000e+01],
+ [ 4.11145000e+03]])
+ >>> jcf4.counts
+ array([15, 14, 14, 15])
+ >>>
+ """
+ def __init__(self, y, k=K):
+ self.k = k
+ Map_Classifier.__init__(self, y)
+ self.name = "Jenks_Caspall_Forced"
+
+ def _set_bins(self):
+ x = self.y.copy()
+ k = self.k
+ q = quantile(x, k)
+ solving = True
+ xb, cnt = bin1d(x, q)
+ #class means
+ if x.ndim == 1:
+ x.shape = (x.size, 1)
+ n, tmp = x.shape
+ xm = [x[xb == i].mean() for i in np.unique(xb)]
+ xb0 = xb.copy()
+ q = xm
+ xbar = np.array([xm[xbi] for xbi in xb])
+ xbar.shape = (n, 1)
+ ss = x - xbar
+ ss *= ss
+ ss = sum(ss)
+ maxk = k - 1
+ down_moves = up_moves = 0
+ solving = True
+ it = 0
+ while solving:
+ # try upward moves first
+ moving_up = True
+ while moving_up:
+ class_ids = sp.unique(xb)
+ nk = [sum(xb == j) for j in class_ids]
+ candidates = nk[:-1]
+ i = 0
+ up_moves = 0
+ while candidates:
+ nki = candidates.pop(0)
+ if nki > 1:
+ ids = np.nonzero(xb == class_ids[i])
+ mover = max(ids[0])
+ tmp = xb.copy()
+ tmp[mover] = xb[mover] + 1
+ tm = [x[tmp == j].mean() for j in sp.unique(tmp)]
+ txbar = np.array([tm[xbi] for xbi in tmp])
+ txbar.shape = (n, 1)
+ tss = x - txbar
+ tss *= tss
+ tss = sum(tss)
+ if tss < ss:
+ xb = tmp
+ ss = tss
+ candidates = []
+ up_moves += 1
+ i += 1
+ if not up_moves:
+ moving_up = False
+ moving_down = True
+ while moving_down:
+ class_ids = sp.unique(xb)
+ nk = [sum(xb == j) for j in class_ids]
+ candidates = nk[1:]
+ i = 1
+ down_moves = 0
+ while candidates:
+ nki = candidates.pop(0)
+ if nki > 1:
+ ids = np.nonzero(xb == class_ids[i])
+ mover = min(ids[0])
+ mover_class = xb[mover]
+ target_class = mover_class - 1
+ tmp = xb.copy()
+ tmp[mover] = target_class
+ tm = [x[tmp == j].mean() for j in sp.unique(tmp)]
+ txbar = np.array([tm[xbi] for xbi in tmp])
+ txbar.shape = (n, 1)
+ tss = x - txbar
+ tss *= tss
+ tss = sum(tss)
+ if tss < ss:
+ xb = tmp
+ ss = tss
+ candidates = []
+ down_moves += 1
+ i += 1
+ if not down_moves:
+ moving_down = False
+ if not up_moves and not down_moves:
+ solving = False
+ it += 1
+ cuts = [max(x[xb == i]) for i in sp.unique(xb)]
+ self.bins = np.array(cuts)
+ self.iterations = it
+
+
+class User_Defined(Map_Classifier):
+ """
+ User Specified Binning
+
+
+ Parameters
+ ----------
+ y : array (n,1)
+ values to classify
+ bins : array (k,1)
+ upper bounds of classes (have to be monotically increasing)
+
+ Attributes
+ ----------
+
+ yb : array (n,1)
+ bin ids for observations,
+ bins : array (k,1)
+ the upper bounds of each class
+ k : int
+ the number of classes
+ counts : array (k,1)
+ the number of observations falling in each class
+
+
+ Examples
+ --------
+ >>> cal = load_example()
+ >>> bins = [20, max(cal)]
+ >>> bins
+ [20, 4111.4499999999998]
+ >>> ud = User_Defined(cal, bins)
+ >>> ud.bins
+ array([ 20. , 4111.45])
+ >>> ud.counts
+ array([37, 21])
+ >>> bins = [20, 30]
+ >>> ud = User_Defined(cal, bins)
+ >>> ud.bins
+ array([ 20. , 30. , 4111.45])
+ >>> ud.counts
+ array([37, 4, 17])
+ >>>
+
+
+ Notes
+ -----
+ If upper bound of user bins does not exceed max(y) we append an
+ additional bin.
+
+ """
+
+ def __init__(self, y, bins):
+ if bins[-1] < max(y):
+ bins.append(max(y))
+ self.k = len(bins)
+ self.bins = np.array(bins)
+ self.y = y
+ Map_Classifier.__init__(self, y)
+ self.name = 'User Defined'
+
+ def _set_bins(self):
+ pass
+
+
+class Max_P_Classifier(Map_Classifier):
+ """
+ Max_P Map Classification
+
+ Based on Max_p regionalization algorithm
+
+ Parameters
+ ----------
+ y : array (n,1)
+ values to classify
+ k : int
+ number of classes required
+ initial : int
+ number of initial solutions to use prior to swapping
+
+ Attributes
+ ----------
+
+ yb : array (n,1)
+ bin ids for observations,
+ bins : array (k,1)
+ the upper bounds of each class
+ k : int
+ the number of classes
+ counts : array (k,1)
+ the number of observations falling in each class
+
+ Examples
+ --------
+ >>> import pysal
+ >>> cal = pysal.esda.mapclassify.load_example()
+ >>> mp = pysal.Max_P_Classifier(cal)
+ >>> mp.bins
+ array([ 8.7 , 16.7 , 20.47, 66.26, 4111.45])
+ >>> mp.counts
+ array([29, 8, 1, 10, 10])
+
+ """
+ def __init__(self, y, k=K, initial=1000):
+ self.k = k
+ self.initial = initial
+ Map_Classifier.__init__(self, y)
+ self.name = "Max_P"
+
+ def _set_bins(self):
+ x = self.y.copy()
+ k = self.k
+ q = quantile(x, k)
+ if x.ndim == 1:
+ x.shape = (x.size, 1)
+ n, tmp = x.shape
+ x.sort(axis=0)
+ # find best of initial solutions
+ solution = 0
+ best_tss = x.var() * x.shape[0]
+ tss_all = np.zeros((self.initial, 1))
+ while solution < self.initial:
+ remaining = range(n)
+ seeds = [np.nonzero(di == min(
+ di))[0][0] for di in [np.abs(x - qi) for qi in q]]
+ rseeds = np.random.permutation(range(k)).tolist()
+ tmp = [remaining.remove(seed) for seed in seeds]
+ self.classes = classes = []
+ tmp = [classes.append([seed]) for seed in seeds]
+ while rseeds:
+ seed_id = rseeds.pop()
+ current = classes[seed_id]
+ growing = True
+ while growing:
+ current = classes[seed_id]
+ low = current[0]
+ high = current[-1]
+ left = low - 1
+ right = high + 1
+ move_made = False
+ if left in remaining:
+ current.insert(0, left)
+ remaining.remove(left)
+ move_made = True
+ if right in remaining:
+ current.append(right)
+ remaining.remove(right)
+ move_made = True
+ if move_made:
+ classes[seed_id] = current
+ else:
+ growing = False
+ tss = _fit(self.y, classes)
+ tss_all[solution] = tss
+ if tss < best_tss:
+ best_solution = classes
+ best_it = solution
+ best_tss = tss
+ solution += 1
+ classes = best_solution
+ self.best_it = best_it
+ self.tss = best_tss
+ self.a2c = a2c = {}
+ self.tss_all = tss_all
+ for r, cl in enumerate(classes):
+ for a in cl:
+ a2c[a] = r
+ swapping = True
+ it = 0
+ while swapping:
+ rseeds = np.random.permutation(range(k)).tolist()
+ total_moves = 0
+ while rseeds:
+ id = rseeds.pop()
+ growing = True
+ total_moves = 0
+ while growing:
+ target = classes[id]
+ left = target[0] - 1
+ right = target[-1] + 1
+ n_moves = 0
+ if left in a2c:
+ left_class = classes[a2c[left]]
+ if len(left_class) > 1:
+ a = left_class[-1]
+ if self._swap(left_class, target, a):
+ target.insert(0, a)
+ left_class.remove(a)
+ a2c[a] = id
+ n_moves += 1
+ if right in a2c:
+ right_class = classes[a2c[right]]
+ if len(right_class) > 1:
+ a = right_class[0]
+ if self._swap(right_class, target, a):
+ target.append(a)
+ right_class.remove(a)
+ n_moves += 1
+ a2c[a] = id
+ if not n_moves:
+ growing = False
+ total_moves += n_moves
+ if not total_moves:
+ swapping = False
+ xs = self.y.copy()
+ xs.sort()
+ self.bins = np.array([xs[cl][-1] for cl in classes])
+
+ def _ss(self, class_def):
+ """calculates sum of squares for a class"""
+ yc = self.y[class_def]
+ css = yc - yc.mean()
+ css *= css
+ return sum(css)
+
+ def _swap(self, class1, class2, a):
+ """evaluate cost of moving a from class1 to class2"""
+ ss1 = self._ss(class1)
+ ss2 = self._ss(class2)
+ tss1 = ss1 + ss2
+ class1c = copy.copy(class1)
+ class2c = copy.copy(class2)
+ class1c.remove(a)
+ class2c.append(a)
+ ss1 = self._ss(class1c)
+ ss2 = self._ss(class2c)
+ tss2 = ss1 + ss2
+ if tss1 < tss2:
+ return False
+ else:
+ return True
+
+
+def _fit(y, classes):
+ """Calculate the total sum of squares for a vector y classified into
+ classes
+
+ Parameters
+ ----------
+ y : array, variable to be classified
+
+ classes : array, integer values denoting class membership
+
+ """
+ tss = 0
+ for class_def in classes:
+ yc = y[class_def]
+ css = yc - yc.mean()
+ css *= css
+ tss += sum(css)
+ return tss
+
+kmethods = {}
+kmethods["Quantiles"] = Quantiles
+kmethods["Fisher_Jenks"] = Fisher_Jenks
+kmethods['Natural_Breaks'] = Natural_Breaks
+kmethods['Maximum_Breaks'] = Maximum_Breaks
+
+
+def gadf(y, method="Quantiles", maxk=15, pct=0.8):
+ """
+ Evaluate the Goodness of Absolute Deviation Fit of a Classifier
+ Finds the minimum value of k for which gadf>pct
+
+ Parameters
+ ----------
+
+ y : array (nx1)
+ values to be classified
+ method : string
+ Name of classifier ["Quantiles,"Fisher_Jenks","Maximum_Breaks",
+ "Natural_Breaks"]
+ maxk : int
+ maximum value of k to evaluate
+ pct : float
+ The percentage of GADF to exceed
+
+ Returns
+ -------
+
+ implicit : tuple
+ first value is k, second value is instance of classifier at k,
+ third is the pct obtained
+
+ Examples
+ --------
+ >>> cal = load_example()
+ >>> qgadf = gadf(cal)
+ >>> qgadf[0]
+ 15
+ >>> qgadf[-1]
+ 0.37402575909092828
+
+ Quantiles fail to exceed 0.80 before 15 classes. If we lower the bar to
+ 0.2 we see quintiles as a result
+
+ >>> qgadf2 = gadf(cal, pct = 0.2)
+ >>> qgadf2[0]
+ 5
+ >>> qgadf2[-1]
+ 0.21710231966462412
+ >>>
+
+ Notes
+ -----
+
+ The GADF is defined as:
+
+ .. math::
+
+ GADF = 1 - \sum_c \sum_{i \in c} |y_i - y_{c,med}| / \sum_i |y_i - y_{med}|
+
+ where :math:`y_{med}` is the global median and :math:`y_{c,med}` is
+ the median for class :math:`c`.
+
+ See Also
+ --------
+ K_classifiers
+ """
+
+ y = np.array(y)
+ adam = (np.abs(y - np.median(y))).sum()
+ for k in range(2, maxk + 1):
+ cl = kmethods[method](y, k)
+ gadf = 1 - cl.adcm / adam
+ if gadf > pct:
+ break
+ return (k, cl, gadf)
+
+
+class K_classifiers:
+ """
+ Evaluate all k-classifers and pick optimal based on k and GADF
+
+ Parameters
+ ----------
+ y : array (nx1)
+ values to be classified
+ pct : float
+ The percentage of GADF to exceed
+
+ Attributes
+ ----------
+ best : instance of Map_Classifier
+ the optimal classifer
+ results : dictionary
+ keys are classifier names, values are the Map_Classifier instances with the best pct for each classifer
+
+ Examples
+ --------
+
+ >>> cal = load_example()
+ >>> ks = K_classifiers(cal)
+ >>> ks.best.name
+ 'Fisher_Jenks'
+ >>> ks.best.k
+ 4
+ >>> ks.best.gadf
+ 0.84810327199081048
+ >>>
+
+ Notes
+ -----
+ This can be used to suggest a classification scheme.
+
+ See Also
+ --------
+ gadf
+
+ """
+ def __init__(self, y, pct=0.8):
+ results = {}
+ c = 0
+ best = gadf(y, "Fisher_Jenks", maxk=len(y) - 1, pct=pct)
+ pct0 = best[0]
+ k0 = best[-1]
+ keys = kmethods.keys()
+ keys.remove("Fisher_Jenks")
+ results["Fisher_Jenks"] = best
+ for method in keys:
+ results[method] = gadf(y, method, maxk=len(y) - 1, pct=pct)
+ k1 = results[method][0]
+ pct1 = results[method][-1]
+ if (k1 < k0) or (k1 == k0 and pct0 < pct1):
+ best = results[method]
+ k0 = k1
+ pct0 = pct1
+ self.results = results
+ self.best = best[1]
+
+
+def fj(x, k=5):
+ y = x.copy()
+ y.sort()
+ d = {}
+ initial = opt_part(y)
+ # d has key = number of groups
+ # value: list of ids, list of group tss, group size
+ split_id = [initial[0]]
+ tss = initial[1:] # left and right within tss
+ sizes = [split_id - 1, len(y) - split_id]
+ d[2] = [split_id, tss, sizes]
+ return d
+
+
+def opt_part(x):
+ """
+ Find optimal bi-partition of x values
+ """
+
+ n = len(x)
+ tss = np.inf
+ opt_i = -999
+ for i in xrange(1, n):
+ print i
+ left = x[:i].var() * i
+ right = x[i:].var() * (n - i)
+ tss_i = left + right
+ if tss_i < tss:
+ opt_i = i
+ tss = tss_i
+ left_min = left
+ right_min = right
+ return (opt_i, tss, left_min, right_min)
diff --git a/pysal/esda/mixture_smoothing.py b/pysal/esda/mixture_smoothing.py
new file mode 100644
index 0000000..6d8b70d
--- /dev/null
+++ b/pysal/esda/mixture_smoothing.py
@@ -0,0 +1,311 @@
+"""
+Emprical Bayesian smoother using non-parametric mixture models
+to specify the prior distribution of risks
+
+This module is a python translation of mixlag function
+in CAMAN R package that is originally written by Peter Schlattmann.
+"""
+
+__author__ = "Myunghwa Hwang <mhwang4 at gmail.com>, Luc Anselin <luc.anselin at asu.edu>, Serge Rey <srey at asu.edu"
+
+import numpy as np
+from scipy.stats import poisson
+import math
+__all__ = ['NP_Mixture_Smoother']
+
+
+class NP_Mixture_Smoother(object):
+ """Empirical Bayesian Rate Smoother Using Mixture Prior Distributions
+ It goes through 1) defining an initial set of subpopulations,
+ 2) VEM algorithm to determine the number of major subpopulations,
+ 3) EM algorithm, 4) combining simialr subpopulations, and 5) estimating
+ EB rates from a mixture of prior distributions from subpopulation
+ models.
+
+ Parameters
+ ----------
+ e : array (n, 1)
+ event variable measured across n spatial units
+ b : array (n, 1)
+ population at risk variable measured across n spatial units
+ k : integer
+ a seed number to specify the number of subpopulations
+ acc : float
+ convergence criterion; VEM and EM loops stop
+ when the increase of log likelihood is less than acc
+ numiter : integer
+ the maximum number of iterations for VEM and EM loops
+ limit : float
+ a parameter to cotrol the limit for combing subpopulation
+ models
+
+ Attributes
+ ----------
+ e : same as e in parameters
+ b : same as b in parameters
+ n : integer
+ the number of observations
+ w : float
+ a global weight value, 1 devided by n
+ k : integer
+ the number of subpopulations
+ acc : same as acc in parameters
+ numiter : same as numiter in parameters
+ limit : same as limit in parameters
+ p : array (k, 1)
+ the proportions of individual subpopulations
+ t : array (k, 1)
+ prior risks of individual subpopulations
+ r : array (n, 1)
+ estimated rate values
+ category : array (n, 1)
+ indices of subpopulations to which each observation belongs
+
+ Examples
+ --------
+
+ importing pysal, numpy, and NP_Mixture_Smoother
+
+ >>> import pysal
+ >>> import numpy as np
+ >>> from pysal.esda.mixture_smoothing import NP_Mixture_Smoother
+
+ creating an arrary including event values
+
+ >>> e = np.array([10, 5, 12, 20])
+
+ creating an array including population-at-risk values
+
+ >>> b = np.array([100, 150, 80, 200])
+
+ applying non-parametric mixture smoothing to e and b
+
+ >>> mixture = NP_Mixture_Smoother(e,b)
+
+ extracting the smoothed rates through the property r of the NP_Mixture_Smoother instance
+
+ >>> mixture.r
+ array([ 0.10982278, 0.03445531, 0.11018404, 0.11018604])
+
+ Checking the subpopulations to which each observation belongs
+
+ >>> mixture.category
+ array([1, 0, 1, 1])
+
+ computing an initial set of prior distributions for the subpopulations
+
+ >>> mixture.getSeed()
+ (array([ 0.5, 0.5]), array([ 0.03333333, 0.15 ]))
+
+
+ applying the mixture algorithm
+
+ >>> mixture.mixalg()
+ {'mix_den': array([ 0., 0., 0., 0.]), 'gradient': array([ 0.]), 'k': 1, 'p': array([ 1.]), 'grid': array([ 11.27659574]), 'accuracy': 1.0}
+
+ estimating empirical Bayesian smoothed rates
+
+ >>> mixture.getRateEstimates()
+ (array([ 0.0911574, 0.0911574, 0.0911574, 0.0911574]), array([1, 1, 1, 1]))
+
+ """
+
+ def __init__(self, e, b, k=50, acc=1.E-7, numiter=5000, limit=0.01):
+ self.e = e
+ self.b = b
+ self.n = len(e)
+ self.w = 1. / self.n
+ self.k = k
+ self.acc = acc
+ self.numiter = numiter
+ self.limit = limit
+ r = self.mixalg()
+ self.p = r['p']
+ self.t = r['grid']
+ self.r, self.category = self.getRateEstimates()
+
+ def getSeed(self):
+ self.raw_r = self.e * 1.0 / self.b
+ r_max, r_min = self.raw_r.max(), self.raw_r.min()
+ r_diff = r_max - r_min
+ step = r_diff / (self.k - 1)
+ grid = np.arange(r_min, r_max + step, step)
+ p = np.ones(self.k) * 1. / self.k
+ return p, grid
+
+ def getMixedProb(self, grid):
+ mix = np.zeros((self.n, self.k))
+ for i in range(self.n):
+ for j in range(self.k):
+ mix[i, j] = poisson.pmf(self.e[i], self.b[i] * grid[j])
+ return mix
+
+ def getGradient(self, mix, p):
+ mix_p = mix * p
+ mix_den = mix_p.sum(axis=1)
+ obs_id = mix_den > 1.E-13
+ for i in range(self.k):
+ mix_den_len = len(mix_den)
+ if (mix_den > 1.E-13).sum() == mix_den_len:
+ mix_p[:, i] = (1. / mix_den_len) * mix[:, i] / mix_den
+ gradient = []
+ for i in range(self.k):
+ gradient.append(mix_p[:, i][obs_id].sum())
+ return np.array(gradient), mix_den
+
+ def getMaxGradient(self, gradient):
+ grad_max = gradient.max()
+ grad_max_inx = gradient.argmax()
+ if grad_max <= 0:
+ return (0, 1)
+ return (grad_max, grad_max_inx)
+
+ def getMinGradient(self, gradient, p):
+ p_fil = p > 1.E-8
+ grad_fil = gradient[p_fil]
+ grad_min = grad_fil.min()
+ grad_min_inx = np.where(p_fil)[0][grad_fil.argmin()]
+ if grad_min >= 1.E+7:
+ return (1.E+7, 1)
+ return (grad_min, grad_min_inx)
+
+ def getStepsize(self, mix_den, ht):
+ mix_den_fil = np.fabs(mix_den) > 1.E-7
+ a = ht[mix_den_fil] / mix_den[mix_den_fil]
+ b = 1.0 + a
+ b_fil = np.fabs(b) > 1.E-7
+ w = self.w
+ sl = w * ht[b_fil] / b[b_fil]
+ s11 = sl.sum()
+ s0 = (w * ht).sum()
+
+ step, oldstep = 0., 0.
+ for i in range(50):
+ grad1, grad2 = 0., 0.
+ for j in range(self.n):
+ a = mix_den[j] + step * ht[j]
+ if math.fabs(a) > 1.E-7:
+ b = ht[j] / a
+ grad1 = grad1 + w * b
+ grad2 = grad2 - w * b * b
+ if math.fabs(grad2) > 1.E-10:
+ step = step - grad1 / grad2
+ if oldstep > 1.0 and step > oldstep:
+ step = 1.
+ break
+ if grad1 < 1.E-7:
+ break
+ oldstep = step
+ if step > 1.0:
+ return 1.0
+ return step
+
+ def vem(self, mix, p, grid):
+ res = {}
+ for it in range(self.numiter):
+ grad, mix_den = self.getGradient(mix, p)
+ grad_max, grad_max_inx = self.getMaxGradient(grad)
+ grad_min, grad_min_inx = self.getMinGradient(grad, p)
+ ht = (mix[:, grad_max_inx] - mix[:, grad_min_inx]
+ ) * p[grad_min_inx]
+ st = self.getStepsize(mix_den, ht)
+ xs = st * p[grad_min_inx]
+ p[grad_min_inx] = p[grad_min_inx] - xs
+ p[grad_max_inx] = p[grad_max_inx] + xs
+ if (grad_max - 1.0) < self.acc or it == (self.numiter - 1):
+ res = {'k': self.k, 'accuracy': grad_max - 1.0, 'p': p, 'grid': grid, 'gradient': grad, 'mix_den': mix_den}
+ break
+ return res
+
+ def update(self, p, grid):
+ p_inx = p > 1.E-3
+ new_p = p[p_inx]
+ new_grid = grid[p_inx]
+ self.k = len(new_p)
+ return new_p, new_grid
+
+ def em(self, nstep, grid, p):
+ l = self.k - 1
+ w, n, e, b = self.w, self.n, self.e, self.b
+ if self.k == 1:
+ s11 = (w * b / np.ones(n)).sum()
+ s12 = (w * e / np.ones(n)).sum()
+ grid[l] = s11 / s12
+ p[l] = 1.
+ mix = self.getMixedProb(grid)
+ grad, mix_den = self.getGradient(mix, p)
+ grad_max, grad_max_inx = self.getMaxGradient(grad)
+ return {'accuracy': math.fabs(grad_max - 1), 'k': self.k, 'p': p, 'grid': grid, 'gradient': grad, 'mix_den': mix_den}
+ else:
+ res = {}
+ for counter in range(nstep):
+ mix = self.getMixedProb(grid)
+ grad, mix_den = self.getGradient(mix, p)
+ p = p * grad
+ su = p[:-1].sum()
+ p[l] = 1. - su
+ for j in range(self.k):
+ mix_den_fil = mix_den > 1.E-10
+ f_len = len(mix_den_fil)
+ s11 = (w * e[mix_den_fil] / np.ones(f_len) * mix[mix_den_fil, j] / mix_den[mix_den_fil]).sum()
+ s12 = (w * b[mix_den_fil] * (mix[mix_den_fil, j] / np.ones(f_len)) / mix_den[mix_den_fil]).sum()
+ if s12 > 1.E-12:
+ grid[j] = s11 / s12
+ grad_max, grad_max_inx = self.getMaxGradient(grad)
+ res = {'accuracy': math.fabs(grad_max - 1.), 'step': counter + 1, 'k': self.k, 'p': p, 'grid': grid, 'gradient': grad, 'mix_den': mix_den}
+ if res['accuracy'] < self.acc and counter > 10:
+ break
+ return res
+
+ def getLikelihood(self, mix_den):
+ mix_den_fil = mix_den > 0
+ r = np.log(mix_den[mix_den_fil]).sum()
+ return r
+
+ def combine(self, res):
+ p, grid, k = res['p'], res['grid'], self.k
+ diff = np.fabs(grid[:-1] - grid[1:])
+ bp_seeds = (diff >= self.limit).nonzero()[0] + 1
+ if k - len(bp_seeds) > 1:
+ bp = [0]
+ if len(bp_seeds) == 1:
+ bp.append(bp_seeds[0])
+ bp.append(k - 1)
+ else:
+ if bp_seeds[1] - bp_seeds[0] > 1:
+ bp.append(bp_seeds[0])
+ for i in range(1, len(bp_seeds)):
+ if bp_seeds[i] - bp_seeds[i - 1] > 1:
+ bp.append(a[i])
+ new_grid, new_p = [], []
+ for i in range(len(bp) - 1):
+ new_grid.append(grid[bp[i]])
+ new_p.append(p[bp[i]:bp[i + 1]].sum())
+ self.k = new_k = len(new_p)
+ new_grid, new_p = np.array(new_grid), np.array(new_p)
+ mix = self.getMixedProb(new_grid)
+ grad, mix_den = self.getGradient(mix, new_p)
+ res = self.em(1, new_grid, new_p)
+ if res is not None:
+ res['likelihood'] = self.getLikelihood(mix_den)
+ return res
+
+ def mixalg(self):
+ e, b, k, n = self.e, self.b, self.k, self.n
+ p, grid = self.getSeed()
+ mix = self.getMixedProb(grid)
+ vem_res = self.vem(mix, p, grid)
+ p, grid, k = vem_res['p'], vem_res['grid'], vem_res['k']
+ n_p, n_g = self.update(p, grid)
+ em_res = self.em(self.numiter, n_g, n_p)
+ com_res = self.combine(em_res)
+ return com_res
+
+ def getRateEstimates(self):
+ mix = self.getMixedProb(self.t)
+ mix_p = mix * self.p
+ denom = mix_p.sum(axis=1)
+ categ = (mix_p / denom.reshape((self.n, 1))).argmax(axis=1)
+ r = (self.t * mix_p).sum(axis=1) / denom
+ return r, categ
+
diff --git a/pysal/esda/moran.py b/pysal/esda/moran.py
new file mode 100644
index 0000000..681d98f
--- /dev/null
+++ b/pysal/esda/moran.py
@@ -0,0 +1,793 @@
+"""
+Moran's I Spatial Autocorrelation Statistics
+
+"""
+__author__ = "Sergio J. Rey <srey at asu.edu>"
+from pysal.weights.spatial_lag import lag_spatial as slag
+from pysal.esda.smoothing import assuncao_rate
+import scipy.stats as stats
+import numpy as np
+
+__all__ = ["Moran", "Moran_Local", "Moran_BV", "Moran_BV_matrix",
+ "Moran_Rate", "Moran_Local_Rate"]
+
+
+PERMUTATIONS = 999
+
+
+class Moran:
+ """Moran's I Global Autocorrelation Statistic
+
+ Parameters
+ ----------
+
+ y : array
+ variable measured across n spatial units
+ w : W
+ spatial weights instance
+ transformation : string
+ weights transformation, default is row-standardized "r".
+ Other options include "B": binary, "D":
+ doubly-standardized, "U": untransformed
+ (general weights), "V": variance-stabilizing.
+ permutations : int
+ number of random permutations for calculation of
+ pseudo-p_values
+ two_tailed : boolean
+ If True (default) analytical p-values for Moran are two
+ tailed, otherwise if False, they are one-tailed.
+
+ Attributes
+ ----------
+ y : array
+ original variable
+ w : W
+ original w object
+ permutations : int
+ number of permutations
+ I : float
+ value of Moran's I
+ EI : float
+ expected value under normality assumption
+ VI_norm : float
+ variance of I under normality assumption
+ seI_norm : float
+ standard deviation of I under normality assumption
+ z_norm : float
+ z-value of I under normality assumption
+ p_norm : float
+ p-value of I under normality assumption
+ VI_rand : float
+ variance of I under randomization assumption
+ seI_rand : float
+ standard deviation of I under randomization assumption
+ z_rand : float
+ z-value of I under randomization assumption
+ p_rand : float
+ p-value of I under randomization assumption
+ two_tailed : Boolean
+ If True p_norm and p_rand are two-tailed, otherwise they
+ are one-tailed.
+ sim : array (if permutations>0)
+ vector of I values for permuted samples
+ p_sim : array (if permutations>0)
+ p-value based on permutations (one-tailed)
+ null: spatial randomness
+ alternative: the observed I is extreme if
+ it is either extremely greater or extremely lower
+ than the values obtained based on permutations
+ EI_sim : float (if permutations>0)
+ average value of I from permutations
+ VI_sim : float (if permutations>0)
+ variance of I from permutations
+ seI_sim : float (if permutations>0)
+ standard deviation of I under permutations.
+ z_sim : float (if permutations>0)
+ standardized I based on permutations
+ p_z_sim : float (if permutations>0)
+ p-value based on standard normal approximation from
+ permutations
+
+ Examples
+ --------
+ >>> import pysal
+ >>> w = pysal.open(pysal.examples.get_path("stl.gal")).read()
+ >>> f = pysal.open(pysal.examples.get_path("stl_hom.txt"))
+ >>> y = np.array(f.by_col['HR8893'])
+ >>> mi = Moran(y, w)
+ >>> "%7.5f" % mi.I
+ '0.24366'
+ >>> mi.EI
+ -0.012987012987012988
+ >>> mi.p_norm
+ 0.00027147862770937614
+
+ SIDS example replicating OpenGeoda
+
+ >>> w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
+ >>> f = pysal.open(pysal.examples.get_path("sids2.dbf"))
+ >>> SIDR = np.array(f.by_col("SIDR74"))
+ >>> mi = pysal.Moran(SIDR, w)
+ >>> "%6.4f" % mi.I
+ '0.2477'
+ >>> mi.p_norm
+ 0.0001158330781489969
+
+ One-tailed
+
+ >>> mi_1 = pysal.Moran(SIDR, w, two_tailed=False)
+ >>> "%6.4f" % mi_1.I
+ '0.2477'
+ >>> mi_1.p_norm
+ 5.7916539074498452e-05
+
+
+ 5.7916539074498452e-05
+ """
+ def __init__(self, y, w, transformation="r", permutations=PERMUTATIONS,
+ two_tailed=True):
+ self.y = y
+ w.transform = transformation
+ self.w = w
+ self.permutations = permutations
+ self.__moments()
+ self.I = self.__calc(self.z)
+ self.z_norm = (self.I - self.EI) / self.seI_norm
+ self.z_rand = (self.I - self.EI) / self.seI_rand
+
+ if self.z_norm > 0:
+ self.p_norm = 1 - stats.norm.cdf(self.z_norm)
+ self.p_rand = 1 - stats.norm.cdf(self.z_rand)
+ else:
+ self.p_norm = stats.norm.cdf(self.z_norm)
+ self.p_rand = stats.norm.cdf(self.z_rand)
+
+ if two_tailed:
+ self.p_norm *= 2.
+ self.p_rand *= 2.
+
+
+ if permutations:
+ sim = [self.__calc(np.random.permutation(self.z))
+ for i in xrange(permutations)]
+ self.sim = sim = np.array(sim)
+ above = sim >= self.I
+ larger = sum(above)
+ if (self.permutations - larger) < larger:
+ larger = self.permutations - larger
+ self.p_sim = (larger + 1.) / (permutations + 1.)
+ self.EI_sim = sum(sim) / permutations
+ self.seI_sim = np.array(sim).std()
+ self.VI_sim = self.seI_sim ** 2
+ self.z_sim = (self.I - self.EI_sim) / self.seI_sim
+ if self.z_sim > 0:
+ self.p_z_sim = 1 - stats.norm.cdf(self.z_sim)
+ else:
+ self.p_z_sim = stats.norm.cdf(self.z_sim)
+
+ def __moments(self):
+ self.n = len(self.y)
+ y = self.y
+ #z = (y-y.mean())/y.std()
+ z = y - y.mean()
+ self.z = z
+ self.z2ss = sum(z * z)
+ self.EI = -1. / (self.n - 1)
+ n = self.n
+ s1 = self.w.s1
+ s0 = self.w.s0
+ s2 = self.w.s2
+ s02 = s0 * s0
+ v_num = n * n * s1 - n * s2 + 3 * s0 * s0
+ v_den = (n - 1) * (n + 1) * s0 * s0
+ self.VI_norm = v_num / v_den - (1.0 / (n - 1)) ** 2
+ self.seI_norm = self.VI_norm ** (1 / 2.)
+
+ k = (1 / (sum(z ** 4)) * ((sum(z ** 2)) ** 2))
+ vi = (1 / (((n - 1) ** 3) * s02)) * ((n * ((n * n - 3 * n + 3)
+ * s1 - n * s2 + 3 * s02))
+ - (k * ((n * n - n) * s1 - 2 * n *
+ s2 + 6 * s02)))
+ self.VI_rand = vi
+ self.seI_rand = vi ** (1 / 2.)
+
+ def __calc(self, z):
+ zl = slag(self.w, z)
+ inum = sum(z * zl)
+ return self.n / self.w.s0 * inum / self.z2ss
+
+
+class Moran_BV:
+ """
+ Bivariate Moran's I
+
+ Parameters
+ ----------
+ x : array
+ x-axis variable
+ y : array
+ (wy will be on y axis)
+ w : W
+ weight instance assumed to be aligned with y
+ transformation : string
+ weights transformation, default is row-standardized "r".
+ Other options include
+ "B": binary,
+ "D": doubly-standardized,
+ "U": untransformed (general weights),
+ "V": variance-stabilizing.
+ permutations : int
+ number of random permutations for calculation of pseudo
+ p_values
+
+ Attributes
+ ----------
+ zx : array
+ original x variable standardized by mean and std
+ zy : array
+ original y variable standardized by mean and std
+ w : W
+ original w object
+ permutation : int
+ number of permutations
+ I : float
+ value of bivariate Moran's I
+ sim : array (if permutations>0)
+ vector of I values for permuted samples
+ p_sim : float (if permutations>0)
+ p-value based on permutations (one-sided)
+ null: spatial randomness
+ alternative: the observed I is extreme
+ it is either extremely high or extremely low
+ EI_sim : array (if permutations>0)
+ average value of I from permutations
+ VI_sim : array (if permutations>0)
+ variance of I from permutations
+ seI_sim : array (if permutations>0)
+ standard deviation of I under permutations.
+ z_sim : array (if permutations>0)
+ standardized I based on permutations
+ p_z_sim : float (if permutations>0)
+ p-value based on standard normal approximation from
+ permutations
+
+ Notes
+ -----
+
+ Inference is only based on permutations as analytical results are none too
+ reliable.
+
+ Examples
+ --------
+ >>> import pysal
+ >>> import numpy as np
+
+ Set random number generator seed so we can replicate the example
+
+ >>> np.random.seed(10)
+
+ Open the sudden infant death dbf file and read in rates for 74 and 79
+ converting each to a numpy array
+
+ >>> f = pysal.open(pysal.examples.get_path("sids2.dbf"))
+ >>> SIDR74 = np.array(f.by_col['SIDR74'])
+ >>> SIDR79 = np.array(f.by_col['SIDR79'])
+
+ Read a GAL file and construct our spatial weights object
+
+ >>> w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
+
+ Create an instance of Moran_BV
+
+ >>> mbi = Moran_BV(SIDR79, SIDR74, w)
+
+ What is the bivariate Moran's I value
+
+ >>> print mbi.I
+ 0.156131961696
+
+ Based on 999 permutations, what is the p-value of our statistic
+
+ >>> mbi.p_z_sim
+ 0.0014186617421765302
+
+
+ """
+ def __init__(self, x, y, w, transformation="r", permutations=PERMUTATIONS):
+ zy = (y - y.mean()) / y.std(ddof=1)
+ zx = (x - x.mean()) / x.std(ddof=1)
+ self.zx = zx
+ self.zy = zy
+ w.transform = transformation
+ self.w = w
+ self.I = self.__calc(zy)
+ if permutations:
+ nrp = np.random.permutation
+ sim = [self.__calc(nrp(zy)) for i in xrange(permutations)]
+ self.sim = sim = np.array(sim)
+ above = sim >= self.I
+ larger = sum(above)
+ if (permutations - larger) < larger:
+ larger = permutations - larger
+ self.p_sim = (larger + 1.) / (permutations + 1.)
+ self.EI_sim = sum(sim) / permutations
+ self.seI_sim = np.array(sim).std()
+ self.VI_sim = self.seI_sim ** 2
+ self.z_sim = (self.I - self.EI_sim) / self.seI_sim
+ if self.z_sim > 0:
+ self.p_z_sim = 1 - stats.norm.cdf(self.z_sim)
+ else:
+ self.p_z_sim = stats.norm.cdf(self.z_sim)
+
+ def __calc(self, zy):
+ wzy = slag(self.w, zy)
+ self.num = sum(self.zx * wzy)
+ self.den = sum(zy * zy)
+ return self.num / self.den
+
+
+def Moran_BV_matrix(variables, w, permutations=0, varnames=None):
+ """Bivariate Moran Matrix
+
+ Calculates bivariate Moran between all pairs of a set of variables.
+
+ Parameters
+ ----------
+ variables : list
+ sequence of variables
+ w : W
+ a spatial weights object
+ permutations : int
+ number of permutations
+ varnames : list
+ strings for variable names. If specified runtime summary is
+ printed
+
+ Returns
+ -------
+ results : dictionary
+ (i, j) is the key for the pair of variables, values are
+ the Moran_BV objects.
+
+ Examples
+ --------
+ >>> import pysal
+
+ open dbf
+
+ >>> f = pysal.open(pysal.examples.get_path("sids2.dbf"))
+
+ pull of selected variables from dbf and create numpy arrays for each
+
+ >>> varnames = ['SIDR74', 'SIDR79', 'NWR74', 'NWR79']
+ >>> vars = [np.array(f.by_col[var]) for var in varnames]
+
+ create a contiguity matrix from an external gal file
+
+ >>> w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
+
+ create an instance of Moran_BV_matrix
+
+ >>> res = Moran_BV_matrix(vars, w, varnames = varnames)
+
+ check values
+
+ >>> print round(res[(0, 1)].I,7)
+ 0.1936261
+ >>> print round(res[(3, 0)].I,7)
+ 0.3770138
+
+
+ """
+
+ k = len(variables)
+ rk = range(0, k - 1)
+ results = {}
+ for i in rk:
+ for j in range(i + 1, k):
+ y1 = variables[i]
+ y2 = variables[j]
+ results[i, j] = Moran_BV(y1, y2, w, permutations=permutations)
+ results[j, i] = Moran_BV(y2, y1, w, permutations=permutations)
+ return results
+
+
+class Moran_Rate(Moran):
+ """
+ Adjusted Moran's I Global Autocorrelation Statistic for Rate Variables
+
+ Parameters
+ ----------
+
+ e : array
+ an event variable measured across n spatial units
+ b : array
+ a population-at-risk variable measured across n spatial
+ units
+ w : W
+ spatial weights instance
+ adjusted : boolean
+ whether or not Moran's I needs to be adjusted for rate
+ variable
+ transformation : string
+ weights transformation, default is row-standardized "r".
+ Other options include
+ "B": binary,
+ "D": doubly-standardized,
+ "U": untransformed (general weights),
+ "V": variance-stabilizing.
+ two_tailed : Boolean
+ If True (default), analytical p-values for Moran's I are
+ two-tailed, otherwise they are one tailed.
+ permutations : int
+ number of random permutations for calculation of pseudo
+ p_values
+
+ Attributes
+ ----------
+ y : array
+ rate variable computed from parameters e and b
+ if adjusted is True, y is standardized rates
+ otherwise, y is raw rates
+ w : W
+ original w object
+ permutations : int
+ number of permutations
+ I : float
+ value of Moran's I
+ EI : float
+ expected value under normality assumption
+ VI_norm : float
+ variance of I under normality assumption
+ seI_norm : float
+ standard deviation of I under normality assumption
+ z_norm : float
+ z-value of I under normality assumption
+ p_norm : float
+ p-value of I under normality assumption
+ VI_rand : float
+ variance of I under randomization assumption
+ seI_rand : float
+ standard deviation of I under randomization assumption
+ z_rand : float
+ z-value of I under randomization assumption
+ p_rand : float
+ p-value of I under randomization assumption
+ two_tailed : Boolean
+ If True, p_norm and p_rand are two-tailed p-values,
+ otherwise they are one-tailed.
+ sim : array (if permutations>0)
+ vector of I values for permuted samples
+ p_sim : array (if permutations>0)
+ p-value based on permutations (one-sided)
+ null: spatial randomness
+ alternative: the observed I is extreme if it is
+ either extremely greater or extremely lower than the values
+ obtained from permutaitons
+ EI_sim : float (if permutations>0)
+ average value of I from permutations
+ VI_sim : float (if permutations>0)
+ variance of I from permutations
+ seI_sim : float (if permutations>0)
+ standard deviation of I under permutations.
+ z_sim : float (if permutations>0)
+ standardized I based on permutations
+ p_z_sim : float (if permutations>0)
+ p-value based on standard normal approximation from
+
+ References
+ ----------
+ Assuncao, R. E. and Reis, E. A. 1999. A new proposal to adjust Moran's I
+ for population density. Statistics in Medicine. 18, 2147-2162
+
+ Examples
+ --------
+ >>> import pysal
+ >>> w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
+ >>> f = pysal.open(pysal.examples.get_path("sids2.dbf"))
+ >>> e = np.array(f.by_col('SID79'))
+ >>> b = np.array(f.by_col('BIR79'))
+ >>> mi = pysal.esda.moran.Moran_Rate(e, b, w, two_tailed=False)
+ >>> "%6.4f" % mi.I
+ '0.1662'
+ >>> "%6.4f" % mi.p_norm
+ '0.0042'
+ """
+
+ def __init__(self, e, b, w, adjusted=True, transformation="r",
+ permutations=PERMUTATIONS, two_tailed=True):
+ if adjusted:
+ y = assuncao_rate(e, b)
+ else:
+ y = e * 1.0 / b
+ Moran.__init__(self, y, w, transformation=transformation,
+ permutations=permutations, two_tailed=two_tailed)
+
+
+class Moran_Local:
+ """Local Moran Statistics
+
+
+ Parameters
+ ----------
+ y : n*1 array
+
+ w : weight instance assumed to be aligned with y
+
+ transformation : string
+ weights transformation, default is row-standardized "r".
+ Other options include
+ "B": binary,
+ "D": doubly-standardized,
+ "U": untransformed (general weights),
+ "V": variance-stabilizing.
+
+ permutations : number of random permutations for calculation of pseudo
+ p_values
+ geoda_quads : boolean (default=False)
+ If True use GeoDa scheme: HH=1, LL=2, LH=3, HL=4
+ If False use PySAL Scheme: HH=1, LH=2, LL=3, HL=4
+
+ Attributes
+ ----------
+
+ y : array
+ original variable
+ w : W
+ original w object
+ permutations : int
+ number of random permutations for calculation of pseudo
+ p_values
+ Is : float
+ value of Moran's I
+ q : array (if permutations>0)
+ values indicate quadrat location 1 HH, 2 LH, 3 LL, 4 HL
+ sim : array (if permutations>0)
+ vector of I values for permuted samples
+ p_sim : array (if permutations>0)
+ p-value based on permutations (one-sided)
+ null: spatial randomness
+ alternative: the observed Ii is further away or extreme
+ from the median of simulated values. It is either extremelyi
+ high or extremely low in the distribution of simulated Is.
+ EI_sim : float (if permutations>0)
+ average value of I from permutations
+ VI_sim : float (if permutations>0)
+ variance of I from permutations
+ seI_sim : float (if permutations>0)
+ standard deviation of I under permutations.
+ z_sim : float (if permutations>0)
+ standardized I based on permutations
+ p_z_sim : float (if permutations>0)
+ p-value based on standard normal approximation from
+ permutations (one-sided)
+ for two-sided tests, these values should be multiplied by 2
+
+ Examples
+ --------
+ >>> import pysal as ps
+ >>> import numpy as np
+ >>> np.random.seed(10)
+ >>> w = ps.open(ps.examples.get_path("desmith.gal")).read()
+ >>> f = ps.open(ps.examples.get_path("desmith.txt"))
+ >>> y = np.array(f.by_col['z'])
+ >>> lm = ps.Moran_Local(y, w, transformation = "r", permutations = 99)
+ >>> lm.q
+ array([4, 4, 4, 2, 3, 3, 1, 4, 3, 3])
+ >>> lm.p_z_sim[0]
+ 0.46756830387716064
+ >>> lm = ps.Moran_Local(y, w, transformation = "r", permutations = 99, geoda_quads=True)
+ >>> lm.q
+ array([4, 4, 4, 3, 2, 2, 1, 4, 2, 2])
+
+ Note random components result is slightly different values across
+ architectures so the results have been removed from doctests and will be
+ moved into unittests that are conditional on architectures
+ """
+ def __init__(self, y, w, transformation="r", permutations=PERMUTATIONS,
+ geoda_quads=False):
+ self.y = y
+ n = len(y)
+ self.n = n
+ self.n_1 = n - 1
+ z = y - y.mean()
+ # setting for floating point noise
+ orig_settings = np.seterr()
+ np.seterr(all="ignore")
+ sy = y.std()
+ z /= sy
+ np.seterr(**orig_settings)
+ self.z = z
+ w.transform = transformation
+ self.w = w
+ self.permutations = permutations
+ self.den = sum(z * z)
+ self.Is = self.calc(self.w, self.z)
+ self.geoda_quads = geoda_quads
+ quads = [1, 2, 3, 4]
+ if geoda_quads:
+ quads = [1, 3, 2, 4]
+ self.quads = quads
+ self.__quads()
+ if permutations:
+ self.__crand()
+ sim = np.transpose(self.rlisas)
+ above = sim >= self.Is
+ larger = np.sum(above, axis=0)
+ low_extreme = (self.permutations - larger) < larger
+ larger[low_extreme] = self.permutations - larger[low_extreme]
+ self.p_sim = (larger + 1.0) / (permutations + 1.0)
+ self.sim = sim
+ self.EI_sim = sim.mean()
+ self.seI_sim = sim.std()
+ self.VI_sim = self.seI_sim * self.seI_sim
+ self.z_sim = (self.Is - self.EI_sim) / self.seI_sim
+ self.p_z_sim = 1 - stats.norm.cdf(np.abs(self.z_sim))
+
+ def calc(self, w, z):
+ zl = slag(w, z)
+ return self.n_1 * self.z * zl / self.den
+
+ def __crand(self):
+ """
+ conditional randomization
+
+ for observation i with ni neighbors, the candidate set cannot include
+ i (we don't want i being a neighbor of i). we have to sample without
+ replacement from a set of ids that doesn't include i. numpy doesn't
+ directly support sampling wo replacement and it is expensive to
+ implement this. instead we omit i from the original ids, permute the
+ ids and take the first ni elements of the permuted ids as the
+ neighbors to i in each randomization.
+
+ """
+ z = self.z
+ lisas = np.zeros((self.n, self.permutations))
+ n_1 = self.n - 1
+ prange = range(self.permutations)
+ k = self.w.max_neighbors + 1
+ nn = self.n - 1
+ rids = np.array([np.random.permutation(nn)[0:k] for i in prange])
+ ids = np.arange(self.w.n)
+ ido = self.w.id_order
+ w = [self.w.weights[ido[i]] for i in ids]
+ wc = [self.w.cardinalities[ido[i]] for i in ids]
+
+ for i in xrange(self.w.n):
+ idsi = ids[ids != i]
+ np.random.shuffle(idsi)
+ tmp = z[idsi[rids[:, 0:wc[i]]]]
+ lisas[i] = z[i] * (w[i] * tmp).sum(1)
+ self.rlisas = (n_1 / self.den) * lisas
+
+ def __quads(self):
+ zl = slag(self.w, self.z)
+ zp = self.z > 0
+ lp = zl > 0
+ pp = zp * lp
+ np = (1 - zp) * lp
+ nn = (1 - zp) * (1 - lp)
+ pn = zp * (1 - lp)
+ self.q = self.quads[0] * pp + self.quads[1] * np + self.quads[2] * nn + self.quads[3] * pn
+
+
+
+class Moran_Local_Rate(Moran_Local):
+ """
+ Adjusted Local Moran Statistics for Rate Variables
+
+ Parameters
+ ----------
+ e : n*1 array
+ an event variable across n spatial units
+ b : n*1 array
+ a population-at-risk variable across n spatial units
+ w : weight instance assumed to be aligned with y
+ adjusted: boolean
+ whether or not local Moran statistics need to be adjusted for
+ rate variable
+ transformation : string
+ weights transformation, default is row-standardized "r".
+ Other options include
+ "B": binary,
+ "D": doubly-standardized,
+ "U": untransformed (general weights),
+ "V": variance-stabilizing.
+ permutations : number of random permutations for calculation of pseudo
+ p_values
+ geoda_quads : boolean (default=False)
+ If True use GeoDa scheme: HH=1, LL=2, LH=3, HL=4
+ If False use PySAL Scheme: HH=1, LH=2, LL=3, HL=4
+ Attributes
+ ----------
+ y : array
+ rate variables computed from parameters e and b
+ if adjusted is True, y is standardized rates
+ otherwise, y is raw rates
+ w : W
+ original w object
+ permutations : int
+ number of random permutations for calculation of pseudo
+ p_values
+ I : float
+ value of Moran's I
+ q : array (if permutations>0)
+ values indicate quadrat location 1 HH, 2 LH, 3 LL, 4 HL
+ sim : array (if permutations>0)
+ vector of I values for permuted samples
+ p_sim : array (if permutations>0)
+ p-value based on permutations (one-sided)
+ null: spatial randomness
+ alternative: the observed Ii is further away or extreme
+ from the median of simulated Iis. It is either extremely
+ high or extremely low in the distribution of simulated Is
+ EI_sim : float (if permutations>0)
+ average value of I from permutations
+ VI_sim : float (if permutations>0)
+ variance of I from permutations
+ seI_sim : float (if permutations>0)
+ standard deviation of I under permutations.
+ z_sim : float (if permutations>0)
+ standardized I based on permutations
+ p_z_sim : float (if permutations>0)
+ p-value based on standard normal approximation from
+ permutations (one-sided)
+ for two-sided tests, these values should be multiplied by 2
+
+ References
+ ----------
+ Assuncao, R. E. and Reis, E. A. 1999. A new proposal to adjust Moran's I
+ for population density. Statistics in Medicine. 18, 2147-2162
+
+ Examples
+ --------
+ >>> import pysal as ps
+ >>> import numpy as np
+ >>> np.random.seed(10)
+ >>> w = ps.open(ps.examples.get_path("sids2.gal")).read()
+ >>> f = ps.open(ps.examples.get_path("sids2.dbf"))
+ >>> e = np.array(f.by_col('SID79'))
+ >>> b = np.array(f.by_col('BIR79'))
+ >>> lm = ps.esda.moran.Moran_Local_Rate(e, b, w, \
+ transformation = "r", \
+ permutations = 99)
+ >>> lm.q[:10]
+ array([2, 4, 3, 1, 2, 1, 1, 4, 2, 4])
+ >>> lm.p_z_sim[0]
+ 0.39319552026912641
+ >>> lm = ps.esda.moran.Moran_Local_Rate(e, b, w, \
+ transformation = "r", \
+ permutations = 99, \
+ geoda_quads=True)
+ >>> lm.q[:10]
+ array([3, 4, 2, 1, 3, 1, 1, 4, 3, 4])
+
+ Note random components result is slightly different values across
+ architectures so the results have been removed from doctests and will be
+ moved into unittests that are conditional on architectures
+ """
+
+ def __init__(self, e, b, w, adjusted=True, transformation="r",
+ permutations=PERMUTATIONS, geoda_quads=False):
+ if adjusted:
+ y = assuncao_rate(e, b)
+ else:
+ y = e * 1.0 / b
+ Moran_Local.__init__(self, y, w,
+ transformation=transformation,
+ permutations=permutations,
+ geoda_quads=geoda_quads)
+
+
+def _test():
+ import doctest
+ # the following line could be used to define an alternative to the
+ # '<BLANKLINE>' flag
+ # doctest.BLANKLINE_MARKER = 'something better than <BLANKLINE>'
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ doctest.testmod()
+ np.set_printoptions(suppress=start_suppress)
+
+if __name__ == '__main__':
+ _test()
diff --git a/pysal/esda/smoothing.py b/pysal/esda/smoothing.py
new file mode 100644
index 0000000..e250a96
--- /dev/null
+++ b/pysal/esda/smoothing.py
@@ -0,0 +1,1573 @@
+"""
+Apply smoothing to rate computation
+
+[Longer Description]
+
+Author(s):
+ Myunghwa Hwang mhwang4 at gmail.com
+ David Folch dfolch at asu.edu
+ Luc Anselin luc.anselin at asu.edu
+ Serge Rey srey at asu.edu
+
+"""
+
+__author__ = "Myunghwa Hwang <mhwang4 at gmail.com>, David Folch <dfolch at asu.edu>, Luc Anselin <luc.anselin at asu.edu>, Serge Rey <srey at asu.edu"
+
+import pysal
+from pysal.weights import comb, Kernel
+from pysal.cg import Point, Ray, LineSegment
+from pysal.cg import get_angle_between, get_points_dist, get_segment_point_dist
+from pysal.cg import get_point_at_angle_and_dist, convex_hull
+from pysal.common import np, KDTree
+from pysal.weights.spatial_lag import lag_spatial as slag
+from scipy.stats import gamma, norm, chi2, poisson
+
+__all__ = ['Excess_Risk', 'Empirical_Bayes', 'Spatial_Empirical_Bayes', 'Spatial_Rate', 'Kernel_Smoother', 'Age_Adjusted_Smoother', 'Disk_Smoother', 'Spatial_Median_Rate', 'Spatial_Filtering', 'Headbanging_Triples', 'Headbanging_Median_Rate', 'flatten', 'weighted_median', 'sum_by_n', 'crude_age_standardization', 'direct_age_standardization', 'indirect_age_standardization', 'standardized_mortality_ratio', 'choynowski', 'assuncao_rate']
+
+
+def flatten(l, unique=True):
+ """flatten a list of lists
+
+ Parameters
+ ----------
+ l : list of lists
+ unique : boolean
+ whether or not only unique items are wanted
+
+ Returns
+ -------
+ : list of single items
+
+ Examples
+ --------
+
+ Creating a sample list whose elements are lists of integers
+
+ >>> l = [[1, 2], [3, 4, ], [5, 6]]
+
+ Applying flatten function
+
+ >>> flatten(l)
+ [1, 2, 3, 4, 5, 6]
+
+ """
+ l = reduce(lambda x, y: x + y, l)
+ if not unique:
+ return list(l)
+ return list(set(l))
+
+
+def weighted_median(d, w):
+ """A utility function to find a median of d based on w
+
+ Parameters
+ ----------
+ d : array (n, 1)
+ variable for which median will be found
+ w : array (n, 1)
+ variable on which d's medain will be decided
+
+ Notes
+ -----
+ d and w are arranged in the same order
+
+ Returns
+ -------
+ : numeric
+ median of d
+
+ Examples
+ --------
+
+ Creating an array including five integers.
+ We will get the median of these integers.
+
+ >>> d = np.array([5,4,3,1,2])
+
+ Creating another array including weight values for the above integers.
+ The median of d will be decided with a consideration to these weight
+ values.
+
+ >>> w = np.array([10, 22, 9, 2, 5])
+
+ Applying weighted_median function
+
+ >>> weighted_median(d, w)
+ 4
+
+ """
+ dtype = [('w', '%s' % w.dtype), ('v', '%s' % d.dtype)]
+ d_w = np.array(zip(w, d), dtype=dtype)
+ d_w.sort(order='v')
+ reordered_w = d_w['w'].cumsum()
+ cumsum_threshold = reordered_w[-1] * 1.0 / 2
+ median_inx = (reordered_w >= cumsum_threshold).nonzero()[0][0]
+ if reordered_w[median_inx] == cumsum_threshold and len(d) - 1 > median_inx:
+ return np.sort(d)[median_inx:median_inx + 2].mean()
+ return np.sort(d)[median_inx]
+
+
+def sum_by_n(d, w, n):
+ """A utility function to summarize a data array into n values
+ after weighting the array with another weight array w
+
+ Parameters
+ ----------
+ d : array(t, 1)
+ numerical values
+ w : array(t, 1)
+ numerical values for weighting
+ n : integer
+ the number of groups
+ t = c*n (c is a constant)
+
+ Returns
+ -------
+ : array(n, 1)
+ an array with summarized values
+
+ Examples
+ --------
+
+ Creating an array including four integers.
+ We will compute weighted means for every two elements.
+
+ >>> d = np.array([10, 9, 20, 30])
+
+ Here is another array with the weight values for d's elements.
+
+ >>> w = np.array([0.5, 0.1, 0.3, 0.8])
+
+ We specify the number of groups for which the weighted mean is computed.
+
+ >>> n = 2
+
+ Applying sum_by_n function
+
+ >>> sum_by_n(d, w, n)
+ array([ 5.9, 30. ])
+
+ """
+ t = len(d)
+ h = t / n
+ d = d * w
+ return np.array([sum(d[i: i + h]) for i in range(0, t, h)])
+
+
+def crude_age_standardization(e, b, n):
+ """A utility function to compute rate through crude age standardization
+
+ Parameters
+ ----------
+ e : array(n*h, 1)
+ event variable measured for each age group across n spatial units
+ b : array(n*h, 1)
+ population at risk variable measured for each age group across n spatial units
+ n : integer
+ the number of spatial units
+
+ Notes
+ -----
+ e and b are arranged in the same order
+
+ Returns
+ -------
+ : array(n, 1)
+ age standardized rate
+
+ Examples
+ --------
+
+ Creating an array of an event variable (e.g., the number of cancer patients)
+ for 2 regions in each of which 4 age groups are available.
+ The first 4 values are event values for 4 age groups in the region 1,
+ and the next 4 values are for 4 age groups in the region 2.
+
+ >>> e = np.array([30, 25, 25, 15, 33, 21, 30, 20])
+
+ Creating another array of a population-at-risk variable (e.g., total population)
+ for the same two regions.
+ The order for entering values is the same as the case of e.
+
+ >>> b = np.array([100, 100, 110, 90, 100, 90, 110, 90])
+
+ Specifying the number of regions.
+
+ >>> n = 2
+
+ Applying crude_age_standardization function to e and b
+
+ >>> crude_age_standardization(e, b, n)
+ array([ 0.2375 , 0.26666667])
+
+ """
+ r = e * 1.0 / b
+ b_by_n = sum_by_n(b, 1.0, n)
+ age_weight = b * 1.0 / b_by_n.repeat(len(e) / n)
+ return sum_by_n(r, age_weight, n)
+
+
+def direct_age_standardization(e, b, s, n, alpha=0.05):
+ """A utility function to compute rate through direct age standardization
+
+ Parameters
+ ----------
+ e : array(n*h, 1)
+ event variable measured for each age group across n spatial units
+ b : array(n*h, 1)
+ population at risk variable measured for each age group across n spatial units
+ s : array(n*h, 1)
+ standard population for each age group across n spatial units
+ n : integer
+ the number of spatial units
+ alpha : float
+ significance level for confidence interval
+
+ Notes
+ -----
+ e, b, and s are arranged in the same order
+
+ Returns
+ -------
+ : a list of n tuples; a tuple has a rate and its lower and upper limits
+ age standardized rates and confidence intervals
+
+ Examples
+ --------
+
+ Creating an array of an event variable (e.g., the number of cancer patients)
+ for 2 regions in each of which 4 age groups are available.
+ The first 4 values are event values for 4 age groups in the region 1,
+ and the next 4 values are for 4 age groups in the region 2.
+
+ >>> e = np.array([30, 25, 25, 15, 33, 21, 30, 20])
+
+ Creating another array of a population-at-risk variable (e.g., total population)
+ for the same two regions.
+ The order for entering values is the same as the case of e.
+
+ >>> b = np.array([1000, 1000, 1100, 900, 1000, 900, 1100, 900])
+
+ For direct age standardization, we also need the data for standard population.
+ Standard population is a reference population-at-risk (e.g., population distribution for the U.S.)
+ whose age distribution can be used as a benchmarking point for comparing age distributions
+ across regions (e.g., popoulation distribution for Arizona and California).
+ Another array including standard population is created.
+
+ >>> s = np.array([1000, 900, 1000, 900, 1000, 900, 1000, 900])
+
+ Specifying the number of regions.
+
+ >>> n = 2
+
+ Applying direct_age_standardization function to e and b
+
+ >>> [i[0] for i in direct_age_standardization(e, b, s, n)]
+ [0.023744019138755977, 0.026650717703349279]
+
+ """
+ age_weight = (1.0 / b) * (s * 1.0 / sum_by_n(s, 1.0, n).repeat(len(s) / n))
+ adjusted_r = sum_by_n(e, age_weight, n)
+ var_estimate = sum_by_n(e, np.square(age_weight), n)
+ g_a = np.square(adjusted_r) / var_estimate
+ g_b = var_estimate / adjusted_r
+ k = [age_weight[i:i + len(b) / n].max() for i in range(0, len(b),
+ len(b) / n)]
+ g_a_k = np.square(adjusted_r + k) / (var_estimate + np.square(k))
+ g_b_k = (var_estimate + np.square(k)) / (adjusted_r + k)
+ summed_b = sum_by_n(b, 1.0, n)
+ res = []
+ for i in range(len(adjusted_r)):
+ if adjusted_r[i] == 0:
+ upper = 0.5 * chi2(1 - 0.5 * alpha)
+ lower = 0.0
+ else:
+ lower = gamma.ppf(0.5 * alpha, g_a[i], scale=g_b[i])
+ upper = gamma.ppf(1 - 0.5 * alpha, g_a_k[i], scale=g_b_k[i])
+ res.append((adjusted_r[i], lower, upper))
+ return res
+
+
+def indirect_age_standardization(e, b, s_e, s_b, n, alpha=0.05):
+ """A utility function to compute rate through indirect age standardization
+
+ Parameters
+ ----------
+ e : array(n*h, 1)
+ event variable measured for each age group across n spatial units
+ b : array(n*h, 1)
+ population at risk variable measured for each age group across n spatial units
+ s_e : array(n*h, 1)
+ event variable measured for each age group across n spatial units in a standard population
+ s_b : array(n*h, 1)
+ population variable measured for each age group across n spatial units in a standard population
+ n : integer
+ the number of spatial units
+ alpha : float
+ significance level for confidence interval
+
+ Notes
+ -----
+ e, b, s_e, and s_b are arranged in the same order
+
+ Returns
+ -------
+ : a list of n tuples; a tuple has a rate and its lower and upper limits
+ age standardized rate
+
+ Examples
+ --------
+
+ Creating an array of an event variable (e.g., the number of cancer patients)
+ for 2 regions in each of which 4 age groups are available.
+ The first 4 values are event values for 4 age groups in the region 1,
+ and the next 4 values are for 4 age groups in the region 2.
+
+ >>> e = np.array([30, 25, 25, 15, 33, 21, 30, 20])
+
+ Creating another array of a population-at-risk variable (e.g., total population)
+ for the same two regions.
+ The order for entering values is the same as the case of e.
+
+ >>> b = np.array([100, 100, 110, 90, 100, 90, 110, 90])
+
+ For indirect age standardization, we also need the data for standard population and event.
+ Standard population is a reference population-at-risk (e.g., population distribution for the U.S.)
+ whose age distribution can be used as a benchmarking point for comparing age distributions
+ across regions (e.g., popoulation distribution for Arizona and California).
+ When the same concept is applied to the event variable,
+ we call it standard event (e.g., the number of cancer patients in the U.S.).
+ Two additional arrays including standard population and event are created.
+
+ >>> s_e = np.array([100, 45, 120, 100, 50, 30, 200, 80])
+ >>> s_b = np.array([1000, 900, 1000, 900, 1000, 900, 1000, 900])
+
+ Specifying the number of regions.
+
+ >>> n = 2
+
+ Applying indirect_age_standardization function to e and b
+
+ >>> [i[0] for i in indirect_age_standardization(e, b, s_e, s_b, n)]
+ [0.23723821989528798, 0.2610803324099723]
+
+ """
+ smr = standardized_mortality_ratio(e, b, s_e, s_b, n)
+ s_r_all = sum(s_e * 1.0) / sum(s_b * 1.0)
+ adjusted_r = s_r_all * smr
+
+ e_by_n = sum_by_n(e, 1.0, n)
+ log_smr = np.log(smr)
+ log_smr_sd = 1.0 / np.sqrt(e_by_n)
+ norm_thres = norm.ppf(1 - 0.5 * alpha)
+ log_smr_lower = log_smr - norm_thres * log_smr_sd
+ log_smr_upper = log_smr + norm_thres * log_smr_sd
+ smr_lower = np.exp(log_smr_lower) * s_r_all
+ smr_upper = np.exp(log_smr_upper) * s_r_all
+ res = zip(adjusted_r, smr_lower, smr_upper)
+ return res
+
+
+def standardized_mortality_ratio(e, b, s_e, s_b, n):
+ """A utility function to compute standardized mortality ratio (SMR).
+
+ Parameters
+ ----------
+ e : array(n*h, 1)
+ event variable measured for each age group across n spatial units
+ b : array(n*h, 1)
+ population at risk variable measured for each age group across n spatial units
+ s_e : array(n*h, 1)
+ event variable measured for each age group across n spatial units in a standard population
+ s_b : array(n*h, 1)
+ population variable measured for each age group across n spatial units in a standard population
+ n : integer
+ the number of spatial units
+
+ Notes
+ -----
+ e, b, s_e, and s_b are arranged in the same order
+
+ Returns
+ -------
+ : array (nx1)
+
+ Examples
+ --------
+
+ Creating an array of an event variable (e.g., the number of cancer patients)
+ for 2 regions in each of which 4 age groups are available.
+ The first 4 values are event values for 4 age groups in the region 1,
+ and the next 4 values are for 4 age groups in the region 2.
+
+ >>> e = np.array([30, 25, 25, 15, 33, 21, 30, 20])
+
+ Creating another array of a population-at-risk variable (e.g., total population)
+ for the same two regions.
+ The order for entering values is the same as the case of e.
+
+ >>> b = np.array([100, 100, 110, 90, 100, 90, 110, 90])
+
+ To compute standardized mortality ratio (SMR),
+ we need two additional arrays for standard population and event.
+ Creating s_e and s_b for standard event and population, respectively.
+
+ >>> s_e = np.array([100, 45, 120, 100, 50, 30, 200, 80])
+ >>> s_b = np.array([1000, 900, 1000, 900, 1000, 900, 1000, 900])
+
+ Specifying the number of regions.
+
+ >>> n = 2
+
+ Applying indirect_age_standardization function to e and b
+
+ >>> standardized_mortality_ratio(e, b, s_e, s_b, n)
+ array([ 2.48691099, 2.73684211])
+
+ """
+ s_r = s_e * 1.0 / s_b
+ e_by_n = sum_by_n(e, 1.0, n)
+ expected = sum_by_n(b, s_r, n)
+ smr = e_by_n * 1.0 / expected
+ return smr
+
+
+def choynowski(e, b, n, threshold=None):
+ """Choynowski map probabilities.
+
+ Parameters
+ ----------
+ e : array(n*h, 1)
+ event variable measured for each age group across n spatial units
+ b : array(n*h, 1)
+ population at risk variable measured for each age group across n spatial units
+ n : integer
+ the number of spatial units
+ threshold : float
+ Returns zero for any p-value greater than threshold
+
+ Notes
+ -----
+ e and b are arranged in the same order
+
+ Returns
+ -------
+ : array (nx1)
+
+ References
+ ----------
+ [1] M. Choynowski. 1959. Maps based on probabilities. Journal of the
+ American Statistical Association, 54, 385-388.
+
+ Examples
+ --------
+
+ Creating an array of an event variable (e.g., the number of cancer patients)
+ for 2 regions in each of which 4 age groups are available.
+ The first 4 values are event values for 4 age groups in the region 1,
+ and the next 4 values are for 4 age groups in the region 2.
+
+ >>> e = np.array([30, 25, 25, 15, 33, 21, 30, 20])
+
+ Creating another array of a population-at-risk variable (e.g., total population)
+ for the same two regions.
+ The order for entering values is the same as the case of e.
+
+ >>> b = np.array([100, 100, 110, 90, 100, 90, 110, 90])
+
+ Specifying the number of regions.
+
+ >>> n = 2
+
+ Applying indirect_age_standardization function to e and b
+
+ >>> print choynowski(e, b, n)
+ [ 0.30437751 0.29367033]
+
+ """
+ e_by_n = sum_by_n(e, 1.0, n)
+ b_by_n = sum_by_n(b, 1.0, n)
+ r_by_n = sum(e_by_n) * 1.0 / sum(b_by_n)
+ expected = r_by_n * b_by_n
+ p = []
+ for index, i in enumerate(e_by_n):
+ if i <= expected[index]:
+ p.append(poisson.cdf(i, expected[index]))
+ else:
+ p.append(1 - poisson.cdf(i - 1, expected[index]))
+ if threshold:
+ p = [i if i < threshold else 0.0 for i in p]
+ return np.array(p)
+
+
+def assuncao_rate(e, b):
+ """The standardized rates where the mean and stadard deviation used for
+ the standardization are those of Empirical Bayes rate estimates
+ The standardized rates resulting from this function are used to compute
+ Moran's I corrected for rate variables.
+
+ Parameters
+ ----------
+ e : array(n, 1)
+ event variable measured at n spatial units
+ b : array(n, 1)
+ population at risk variable measured at n spatial units
+
+ Notes
+ -----
+ e and b are arranged in the same order
+
+ Returns
+ -------
+ : array (nx1)
+
+ References
+ ----------
+ [1] Assuncao R. M. and Reis E. A., 1999, A new proposal to adjust Moran's I
+ for population density. Statistics in Medicine, 18, 2147-2162.
+
+ Examples
+ --------
+
+ Creating an array of an event variable (e.g., the number of cancer patients)
+ for 8 regions.
+
+ >>> e = np.array([30, 25, 25, 15, 33, 21, 30, 20])
+
+ Creating another array of a population-at-risk variable (e.g., total population)
+ for the same 8 regions.
+ The order for entering values is the same as the case of e.
+
+ >>> b = np.array([100, 100, 110, 90, 100, 90, 110, 90])
+
+ Computing the rates
+
+ >>> print assuncao_rate(e, b)[:4]
+ [ 1.04319254 -0.04117865 -0.56539054 -1.73762547]
+
+ """
+
+ y = e * 1.0 / b
+ e_sum, b_sum = sum(e), sum(b)
+ ebi_b = e_sum * 1.0 / b_sum
+ s2 = sum(b * ((y - ebi_b) ** 2)) / b_sum
+ ebi_a = s2 - ebi_b / (b_sum / len(e))
+ ebi_v = ebi_a + ebi_b / b
+ return (y - ebi_b) / np.sqrt(ebi_v)
+
+
+class Excess_Risk:
+ """Excess Risk
+
+ Parameters
+ ----------
+ e : array (n, 1)
+ event variable measured across n spatial units
+ b : array (n, 1)
+ population at risk variable measured across n spatial units
+
+ Attributes
+ ----------
+ r : array (n, 1)
+ execess risk values
+
+ Examples
+ --------
+
+ Reading data in stl_hom.csv into stl to extract values
+ for event and population-at-risk variables
+
+ >>> stl = pysal.open(pysal.examples.get_path('stl_hom.csv'), 'r')
+
+ The 11th and 14th columns in stl_hom.csv includes the number of homocides and population.
+ Creating two arrays from these columns.
+
+ >>> stl_e, stl_b = np.array(stl[:,10]), np.array(stl[:,13])
+
+ Creating an instance of Excess_Risk class using stl_e and stl_b
+
+ >>> er = Excess_Risk(stl_e, stl_b)
+
+ Extracting the excess risk values through the property r of the Excess_Risk instance, er
+
+ >>> er.r[:10]
+ array([ 0.20665681, 0.43613787, 0.42078261, 0.22066928, 0.57981596,
+ 0.35301709, 0.56407549, 0.17020994, 0.3052372 , 0.25821905])
+
+ """
+ def __init__(self, e, b):
+ r_mean = e.sum() * 1.0 / b.sum()
+ self.r = e * 1.0 / (b * r_mean)
+
+
+class Empirical_Bayes:
+ """Aspatial Empirical Bayes Smoothing
+
+ Parameters
+ ----------
+ e : array (n, 1)
+ event variable measured across n spatial units
+ b : array (n, 1)
+ population at risk variable measured across n spatial units
+
+ Attributes
+ ----------
+ r : array (n, 1)
+ rate values from Empirical Bayes Smoothing
+
+ Examples
+ --------
+
+ Reading data in stl_hom.csv into stl to extract values
+ for event and population-at-risk variables
+
+ >>> stl = pysal.open(pysal.examples.get_path('stl_hom.csv'), 'r')
+
+ The 11th and 14th columns in stl_hom.csv includes the number of homocides and population.
+ Creating two arrays from these columns.
+
+ >>> stl_e, stl_b = np.array(stl[:,10]), np.array(stl[:,13])
+
+ Creating an instance of Empirical_Bayes class using stl_e and stl_b
+
+ >>> eb = Empirical_Bayes(stl_e, stl_b)
+
+ Extracting the risk values through the property r of the Empirical_Bayes instance, eb
+
+ >>> eb.r[:10]
+ array([ 2.36718950e-05, 4.54539167e-05, 4.78114019e-05,
+ 2.76907146e-05, 6.58989323e-05, 3.66494122e-05,
+ 5.79952721e-05, 2.03064590e-05, 3.31152999e-05,
+ 3.02748380e-05])
+
+ """
+ def __init__(self, e, b):
+ e_sum, b_sum = e.sum() * 1.0, b.sum() * 1.0
+ r_mean = e_sum / b_sum
+ rate = e * 1.0 / b
+ r_variat = rate - r_mean
+ r_var_left = (b * r_variat * r_variat).sum() * 1.0 / b_sum
+ r_var_right = r_mean * 1.0 / b.mean()
+ r_var = r_var_left - r_var_right
+ weight = r_var / (r_var + r_mean / b)
+ self.r = weight * rate + (1.0 - weight) * r_mean
+
+
+class Spatial_Empirical_Bayes:
+ """Spatial Empirical Bayes Smoothing
+
+ Parameters
+ ----------
+ e : array (n, 1)
+ event variable measured across n spatial units
+ b : array (n, 1)
+ population at risk variable measured across n spatial units
+ w : spatial weights instance
+
+ Attributes
+ ----------
+ r : array (n, 1)
+ rate values from Empirical Bayes Smoothing
+
+ Examples
+ --------
+
+ Reading data in stl_hom.csv into stl to extract values
+ for event and population-at-risk variables
+
+ >>> stl = pysal.open(pysal.examples.get_path('stl_hom.csv'), 'r')
+
+ The 11th and 14th columns in stl_hom.csv includes the number of homocides and population.
+ Creating two arrays from these columns.
+
+ >>> stl_e, stl_b = np.array(stl[:,10]), np.array(stl[:,13])
+
+ Creating a spatial weights instance by reading in stl.gal file.
+
+ >>> stl_w = pysal.open(pysal.examples.get_path('stl.gal'), 'r').read()
+
+ Ensuring that the elements in the spatial weights instance are ordered
+ by the given sequential numbers from 1 to the number of observations in stl_hom.csv
+
+ >>> if not stl_w.id_order_set: stl_w.id_order = range(1,len(stl) + 1)
+
+ Creating an instance of Spatial_Empirical_Bayes class using stl_e, stl_b, and stl_w
+
+ >>> s_eb = Spatial_Empirical_Bayes(stl_e, stl_b, stl_w)
+
+ Extracting the risk values through the property r of s_eb
+
+ >>> s_eb.r[:10]
+ array([ 4.01485749e-05, 3.62437513e-05, 4.93034844e-05,
+ 5.09387329e-05, 3.72735210e-05, 3.69333797e-05,
+ 5.40245456e-05, 2.99806055e-05, 3.73034109e-05,
+ 3.47270722e-05])
+ """
+ def __init__(self, e, b, w):
+ if not w.id_order_set:
+ raise ValueError("w id_order must be set to align with the order of e an b")
+ r_mean = Spatial_Rate(e, b, w).r
+ rate = e * 1.0 / b
+ r_var_left = np.ones(len(e)) * 1.
+ ngh_num = np.ones(len(e))
+ bi = slag(w, b) + b
+ for i, idv in enumerate(w.id_order):
+ ngh = w[idv].keys() + [idv]
+ nghi = [w.id2i[k] for k in ngh]
+ ngh_num[i] = len(nghi)
+ v = sum(np.square(rate[nghi] - r_mean[i]) * b[nghi])
+ r_var_left[i] = v
+ r_var_left = r_var_left / bi
+ r_var_right = r_mean / (bi / ngh_num)
+ r_var = r_var_left - r_var_right
+ r_var[r_var < 0] = 0.0
+ self.r = r_mean + (rate - r_mean) * (r_var / (r_var + (r_mean / b)))
+
+
+class Spatial_Rate:
+ """Spatial Rate Smoothing
+
+ Parameters
+ ----------
+ e : array (n, 1)
+ event variable measured across n spatial units
+ b : array (n, 1)
+ population at risk variable measured across n spatial units
+ w : spatial weights instance
+
+ Attributes
+ ----------
+ r : array (n, 1)
+ rate values from spatial rate smoothing
+
+ Examples
+ --------
+
+ Reading data in stl_hom.csv into stl to extract values
+ for event and population-at-risk variables
+
+ >>> stl = pysal.open(pysal.examples.get_path('stl_hom.csv'), 'r')
+
+ The 11th and 14th columns in stl_hom.csv includes the number of homocides and population.
+ Creating two arrays from these columns.
+
+ >>> stl_e, stl_b = np.array(stl[:,10]), np.array(stl[:,13])
+
+ Creating a spatial weights instance by reading in stl.gal file.
+
+ >>> stl_w = pysal.open(pysal.examples.get_path('stl.gal'), 'r').read()
+
+ Ensuring that the elements in the spatial weights instance are ordered
+ by the given sequential numbers from 1 to the number of observations in stl_hom.csv
+
+ >>> if not stl_w.id_order_set: stl_w.id_order = range(1,len(stl) + 1)
+
+ Creating an instance of Spatial_Rate class using stl_e, stl_b, and stl_w
+
+ >>> sr = Spatial_Rate(stl_e,stl_b,stl_w)
+
+ Extracting the risk values through the property r of sr
+
+ >>> sr.r[:10]
+ array([ 4.59326407e-05, 3.62437513e-05, 4.98677081e-05,
+ 5.09387329e-05, 3.72735210e-05, 4.01073093e-05,
+ 3.79372794e-05, 3.27019246e-05, 4.26204928e-05,
+ 3.47270722e-05])
+ """
+ def __init__(self, e, b, w):
+ if not w.id_order_set:
+ raise ValueError("w id_order must be set to align with the order of e and b")
+ else:
+ w.transform = 'b'
+ w_e, w_b = slag(w, e), slag(w, b)
+ self.r = (e + w_e) / (b + w_b)
+ w.transform = 'o'
+
+
+class Kernel_Smoother:
+ """Kernal smoothing
+
+ Parameters
+ ----------
+ e : array (n, 1)
+ event variable measured across n spatial units
+ b : array (n, 1)
+ population at risk variable measured across n spatial units
+ w : Kernel weights instance
+
+ Attributes
+ ----------
+ r : array (n, 1)
+ rate values from spatial rate smoothing
+
+ Examples
+ --------
+
+ Creating an array including event values for 6 regions
+
+ >>> e = np.array([10, 1, 3, 4, 2, 5])
+
+ Creating another array including population-at-risk values for the 6 regions
+
+ >>> b = np.array([100, 15, 20, 20, 80, 90])
+
+ Creating a list containing geographic coordinates of the 6 regions' centroids
+
+ >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+
+ Creating a kernel-based spatial weights instance by using the above points
+
+ >>> kw=Kernel(points)
+
+ Ensuring that the elements in the kernel-based weights are ordered
+ by the given sequential numbers from 0 to 5
+
+ >>> if not kw.id_order_set: kw.id_order = range(0,len(points))
+
+ Applying kernel smoothing to e and b
+
+ >>> kr = Kernel_Smoother(e, b, kw)
+
+ Extracting the smoothed rates through the property r of the Kernel_Smoother instance
+
+ >>> kr.r
+ array([ 0.10543301, 0.0858573 , 0.08256196, 0.09884584, 0.04756872,
+ 0.04845298])
+ """
+ def __init__(self, e, b, w):
+ if type(w) != Kernel:
+ raise Error('w must be an instance of Kernel weights')
+ if not w.id_order_set:
+ raise ValueError("w id_order must be set to align with the order of e and b")
+ else:
+ w_e, w_b = slag(w, e), slag(w, b)
+ self.r = w_e / w_b
+
+
+class Age_Adjusted_Smoother:
+ """Age-adjusted rate smoothing
+
+ Parameters
+ ----------
+ e : array (n*h, 1)
+ event variable measured for each age group across n spatial units
+ b : array (n*h, 1)
+ population at risk variable measured for each age group across n spatial units
+ w : spatial weights instance
+ s : array (n*h, 1)
+ standard population for each age group across n spatial units
+
+ Attributes
+ ----------
+ r : array (n, 1)
+ rate values from spatial rate smoothing
+
+ Notes
+ -----
+ Weights used to smooth age-specific events and populations are simple binary weights
+
+ Examples
+ --------
+
+ Creating an array including 12 values for the 6 regions with 2 age groups
+
+ >>> e = np.array([10, 8, 1, 4, 3, 5, 4, 3, 2, 1, 5, 3])
+
+ Creating another array including 12 population-at-risk values for the 6 regions
+
+ >>> b = np.array([100, 90, 15, 30, 25, 20, 30, 20, 80, 80, 90, 60])
+
+ For age adjustment, we need another array of values containing standard population
+ s includes standard population data for the 6 regions
+
+ >>> s = np.array([98, 88, 15, 29, 20, 23, 33, 25, 76, 80, 89, 66])
+
+ Creating a list containing geographic coordinates of the 6 regions' centroids
+
+ >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+
+ Creating a kernel-based spatial weights instance by using the above points
+
+ >>> kw=Kernel(points)
+
+ Ensuring that the elements in the kernel-based weights are ordered
+ by the given sequential numbers from 0 to 5
+
+ >>> if not kw.id_order_set: kw.id_order = range(0,len(points))
+
+ Applying age-adjusted smoothing to e and b
+
+ >>> ar = Age_Adjusted_Smoother(e, b, kw, s)
+
+ Extracting the smoothed rates through the property r of the Age_Adjusted_Smoother instance
+
+ >>> ar.r
+ array([ 0.10519625, 0.08494318, 0.06440072, 0.06898604, 0.06952076,
+ 0.05020968])
+ """
+ def __init__(self, e, b, w, s, alpha=0.05):
+ t = len(e)
+ h = t / w.n
+ w.transform = 'b'
+ e_n, b_n = [], []
+ for i in range(h):
+ e_n.append(slag(w, e[i::h]).tolist())
+ b_n.append(slag(w, b[i::h]).tolist())
+ e_n = np.array(e_n).reshape((1, t), order='F')[0]
+ b_n = np.array(b_n).reshape((1, t), order='F')[0]
+ r = direct_age_standardization(e_n, b_n, s, w.n, alpha=alpha)
+ self.r = np.array([i[0] for i in r])
+ w.transform = 'o'
+
+
+class Disk_Smoother:
+ """Locally weighted averages or disk smoothing
+
+ Parameters
+ ----------
+ e : array (n, 1)
+ event variable measured across n spatial units
+ b : array (n, 1)
+ population at risk variable measured across n spatial units
+ w : spatial weights matrix
+
+ Attributes
+ ----------
+ r : array (n, 1)
+ rate values from disk smoothing
+
+ Examples
+ --------
+
+ Reading data in stl_hom.csv into stl to extract values
+ for event and population-at-risk variables
+
+ >>> stl = pysal.open(pysal.examples.get_path('stl_hom.csv'), 'r')
+
+ The 11th and 14th columns in stl_hom.csv includes the number of homocides and population.
+ Creating two arrays from these columns.
+
+ >>> stl_e, stl_b = np.array(stl[:,10]), np.array(stl[:,13])
+
+ Creating a spatial weights instance by reading in stl.gal file.
+
+ >>> stl_w = pysal.open(pysal.examples.get_path('stl.gal'), 'r').read()
+
+ Ensuring that the elements in the spatial weights instance are ordered
+ by the given sequential numbers from 1 to the number of observations in stl_hom.csv
+
+ >>> if not stl_w.id_order_set: stl_w.id_order = range(1,len(stl) + 1)
+
+ Applying disk smoothing to stl_e and stl_b
+
+ >>> sr = Disk_Smoother(stl_e,stl_b,stl_w)
+
+ Extracting the risk values through the property r of s_eb
+
+ >>> sr.r[:10]
+ array([ 4.56502262e-05, 3.44027685e-05, 3.38280487e-05,
+ 4.78530468e-05, 3.12278573e-05, 2.22596997e-05,
+ 2.67074856e-05, 2.36924573e-05, 3.48801587e-05,
+ 3.09511832e-05])
+ """
+
+ def __init__(self, e, b, w):
+ if not w.id_order_set:
+ raise ValueError("w id_order must be set to align with the order of e and b")
+ else:
+ r = e * 1.0 / b
+ weight_sum = []
+ for i in w.id_order:
+ weight_sum.append(sum(w.weights[i]))
+ self.r = slag(w, r) / np.array(weight_sum)
+
+
+class Spatial_Median_Rate:
+ """Spatial Median Rate Smoothing
+
+ Parameters
+ ----------
+ e : array (n, 1)
+ event variable measured across n spatial units
+ b : array (n, 1)
+ population at risk variable measured across n spatial units
+ w : spatial weights instance
+ aw : array (n, 1)
+ auxiliary weight variable measured across n spatial units
+ iteration : integer
+ the number of interations
+
+ Attributes
+ ----------
+ r : array (n, 1)
+ rate values from spatial median rate smoothing
+ w : spatial weights instance
+ aw : array (n, 1)
+ auxiliary weight variable measured across n spatial units
+
+ Examples
+ --------
+
+ Reading data in stl_hom.csv into stl to extract values
+ for event and population-at-risk variables
+
+ >>> stl = pysal.open(pysal.examples.get_path('stl_hom.csv'), 'r')
+
+ The 11th and 14th columns in stl_hom.csv includes the number of homocides and population.
+ Creating two arrays from these columns.
+
+ >>> stl_e, stl_b = np.array(stl[:,10]), np.array(stl[:,13])
+
+ Creating a spatial weights instance by reading in stl.gal file.
+
+ >>> stl_w = pysal.open(pysal.examples.get_path('stl.gal'), 'r').read()
+
+ Ensuring that the elements in the spatial weights instance are ordered
+ by the given sequential numbers from 1 to the number of observations in stl_hom.csv
+
+ >>> if not stl_w.id_order_set: stl_w.id_order = range(1,len(stl) + 1)
+
+ Computing spatial median rates without iteration
+
+ >>> smr0 = Spatial_Median_Rate(stl_e,stl_b,stl_w)
+
+ Extracting the computed rates through the property r of the Spatial_Median_Rate instance
+
+ >>> smr0.r[:10]
+ array([ 3.96047383e-05, 3.55386859e-05, 3.28308921e-05,
+ 4.30731238e-05, 3.12453969e-05, 1.97300409e-05,
+ 3.10159267e-05, 2.19279204e-05, 2.93763432e-05,
+ 2.93763432e-05])
+
+ Recomputing spatial median rates with 5 iterations
+
+ >>> smr1 = Spatial_Median_Rate(stl_e,stl_b,stl_w,iteration=5)
+
+ Extracting the computed rates through the property r of the Spatial_Median_Rate instance
+
+ >>> smr1.r[:10]
+ array([ 3.11293620e-05, 2.95956330e-05, 3.11293620e-05,
+ 3.10159267e-05, 2.98436066e-05, 2.76406686e-05,
+ 3.10159267e-05, 2.94788171e-05, 2.99460806e-05,
+ 2.96981070e-05])
+
+ Computing spatial median rates by using the base variable as auxilliary weights
+ without iteration
+
+ >>> smr2 = Spatial_Median_Rate(stl_e,stl_b,stl_w,aw=stl_b)
+
+ Extracting the computed rates through the property r of the Spatial_Median_Rate instance
+
+ >>> smr2.r[:10]
+ array([ 5.77412020e-05, 4.46449551e-05, 5.77412020e-05,
+ 5.77412020e-05, 4.46449551e-05, 3.61363528e-05,
+ 3.61363528e-05, 4.46449551e-05, 5.77412020e-05,
+ 4.03987355e-05])
+
+ Recomputing spatial median rates by using the base variable as auxilliary weights
+ with 5 iterations
+
+ >>> smr3 = Spatial_Median_Rate(stl_e,stl_b,stl_w,aw=stl_b,iteration=5)
+
+ Extracting the computed rates through the property r of the Spatial_Median_Rate instance
+
+ >>> smr3.r[:10]
+ array([ 3.61363528e-05, 4.46449551e-05, 3.61363528e-05,
+ 3.61363528e-05, 4.46449551e-05, 3.61363528e-05,
+ 3.61363528e-05, 4.46449551e-05, 3.61363528e-05,
+ 4.46449551e-05])
+ >>>
+ """
+ def __init__(self, e, b, w, aw=None, iteration=1):
+ if not w.id_order_set:
+ raise ValueError("w id_order must be set to align with the order of e and b")
+ self.r = e * 1.0 / b
+ self.aw, self.w = aw, w
+ while iteration:
+ self.__search_median()
+ iteration -= 1
+
+ def __search_median(self):
+ r, aw, w = self.r, self.aw, self.w
+ new_r = []
+ if self.aw is None:
+ for i, id in enumerate(w.id_order):
+ r_disk = np.append(r[i], r[w.neighbor_offsets[id]])
+ new_r.append(np.median(r_disk))
+ else:
+ for i, id in enumerate(w.id_order):
+ id_d = [i] + list(w.neighbor_offsets[id])
+ aw_d, r_d = aw[id_d], r[id_d]
+ new_r.append(weighted_median(r_d, aw_d))
+ self.r = np.array(new_r)
+
+
+class Spatial_Filtering:
+ """Spatial Filtering
+
+ Parameters
+ ----------
+ bbox : a list of two lists where each list is a pair of coordinates
+ a bounding box for the entire n spatial units
+ data : array (n, 2)
+ x, y coordinates
+ e : array (n, 1)
+ event variable measured across n spatial units
+ b : array (n, 1)
+ population at risk variable measured across n spatial units
+ x_grid : integer
+ the number of cells on x axis
+ y_grid : integer
+ the number of cells on y axis
+ r : float
+ fixed radius of a moving window
+ pop : integer
+ population threshold to create adaptive moving windows
+
+ Attributes
+ ----------
+ grid : array (x_grid*y_grid, 2)
+ x, y coordinates for grid points
+ r : array (x_grid*y_grid, 1)
+ rate values for grid points
+
+ Notes
+ -----
+ No tool is provided to find an optimal value for r or pop.
+
+ Examples
+ --------
+
+ Reading data in stl_hom.csv into stl to extract values
+ for event and population-at-risk variables
+
+ >>> stl = pysal.open(pysal.examples.get_path('stl_hom.csv'), 'r')
+
+ Reading the stl data in the WKT format so that
+ we can easily extract polygon centroids
+
+ >>> fromWKT = pysal.core.util.WKTParser()
+ >>> stl.cast('WKT',fromWKT)
+
+ Extracting polygon centroids through iteration
+
+ >>> d = np.array([i.centroid for i in stl[:,0]])
+
+ Specifying the bounding box for the stl_hom data.
+ The bbox should includes two points for the left-bottom and the right-top corners
+
+ >>> bbox = [[-92.700676, 36.881809], [-87.916573, 40.3295669]]
+
+ The 11th and 14th columns in stl_hom.csv includes the number of homocides and population.
+ Creating two arrays from these columns.
+
+ >>> stl_e, stl_b = np.array(stl[:,10]), np.array(stl[:,13])
+
+ Applying spatial filtering by using a 10*10 mesh grid and a moving window
+ with 2 radius
+
+ >>> sf_0 = Spatial_Filtering(bbox,d,stl_e,stl_b,10,10,r=2)
+
+ Extracting the resulting rates through the property r of the Spatial_Filtering instance
+
+ >>> sf_0.r[:10]
+ array([ 4.23561763e-05, 4.45290850e-05, 4.56456221e-05,
+ 4.49133384e-05, 4.39671835e-05, 4.44903042e-05,
+ 4.19845497e-05, 4.11936548e-05, 3.93463504e-05,
+ 4.04376345e-05])
+
+ Applying another spatial filtering by allowing the moving window to grow until
+ 600000 people are found in the window
+
+ >>> sf = Spatial_Filtering(bbox,d,stl_e,stl_b,10,10,pop=600000)
+
+ Checking the size of the reulting array including the rates
+
+ >>> sf.r.shape
+ (100,)
+
+ Extracting the resulting rates through the property r of the Spatial_Filtering instance
+
+ >>> sf.r[:10]
+ array([ 3.73728738e-05, 4.04456300e-05, 4.04456300e-05,
+ 3.81035327e-05, 4.54831940e-05, 4.54831940e-05,
+ 3.75658628e-05, 3.75658628e-05, 3.75658628e-05,
+ 3.75658628e-05])
+ """
+
+ def __init__(self, bbox, data, e, b, x_grid, y_grid, r=None, pop=None):
+ data_tree = KDTree(data)
+ x_range = bbox[1][0] - bbox[0][0]
+ y_range = bbox[1][1] - bbox[0][1]
+ x, y = np.mgrid[bbox[0][0]:bbox[1][0]:x_range / x_grid,
+ bbox[0][1]:bbox[1][1]:y_range / y_grid]
+ self.grid = zip(x.ravel(), y.ravel())
+ self.r = []
+ if r is None and pop is None:
+ raise ValueError("Either r or pop should not be None")
+ if r is not None:
+ pnts_in_disk = data_tree.query_ball_point(self.grid, r=r)
+ for i in pnts_in_disk:
+ r = e[i].sum() * 1.0 / b[i].sum()
+ self.r.append(r)
+ if pop is not None:
+ half_nearest_pnts = data_tree.query(self.grid, k=len(e))[1]
+ for i in half_nearest_pnts:
+ e_n, b_n = e[i].cumsum(), b[i].cumsum()
+ b_n_filter = b_n <= pop
+ e_n_f, b_n_f = e_n[b_n_filter], b_n[b_n_filter]
+ if len(e_n_f) == 0:
+ e_n_f = e_n[[0]]
+ b_n_f = b_n[[0]]
+ self.r.append(e_n_f[-1] * 1.0 / b_n_f[-1])
+ self.r = np.array(self.r)
+
+
+class Headbanging_Triples:
+ """Generate a pseudo spatial weights instance that contains headbaning triples
+
+ Parameters
+ ----------
+ data : array (n, 2)
+ numpy array of x, y coordinates
+ w : spatial weights instance
+ k : integer number of nearest neighbors
+ t : integer
+ the number of triples
+ angle : integer between 0 and 180
+ the angle criterium for a set of triples
+ edgecorr : boolean
+ whether or not correction for edge points is made
+
+ Attributes
+ ----------
+ triples : dictionary
+ key is observation record id, value is a list of lists of triple ids
+ extra : dictionary
+ key is observation record id, value is a list of the following:
+ tuple of original triple observations
+ distance between original triple observations
+ distance between an original triple observation and its extrapolated point
+
+ Examples
+ --------
+
+ importing k-nearest neighbor weights creator
+
+ >>> from pysal import knnW
+
+ Reading data in stl_hom.csv into stl_db to extract values
+ for event and population-at-risk variables
+
+ >>> stl_db = pysal.open(pysal.examples.get_path('stl_hom.csv'),'r')
+
+ Reading the stl data in the WKT format so that
+ we can easily extract polygon centroids
+
+ >>> fromWKT = pysal.core.util.WKTParser()
+ >>> stl_db.cast('WKT',fromWKT)
+
+ Extracting polygon centroids through iteration
+
+ >>> d = np.array([i.centroid for i in stl_db[:,0]])
+
+ Using the centroids, we create a 5-nearst neighbor weights
+
+ >>> w = knnW(d,k=5)
+
+ Ensuring that the elements in the spatial weights instance are ordered
+ by the order of stl_db's IDs
+
+ >>> if not w.id_order_set: w.id_order = w.id_order
+
+ Finding headbaning triples by using 5 nearest neighbors
+
+ >>> ht = Headbanging_Triples(d,w,k=5)
+
+ Checking the members of triples
+
+ >>> for k, item in ht.triples.items()[:5]: print k, item
+ 0 [(5, 6), (10, 6)]
+ 1 [(4, 7), (4, 14), (9, 7)]
+ 2 [(0, 8), (10, 3), (0, 6)]
+ 3 [(4, 2), (2, 12), (8, 4)]
+ 4 [(8, 1), (12, 1), (8, 9)]
+
+ Opening sids2.shp file
+
+ >>> sids = pysal.open(pysal.examples.get_path('sids2.shp'),'r')
+
+ Extracting the centroids of polygons in the sids data
+
+ >>> sids_d = np.array([i.centroid for i in sids])
+
+ Creating a 5-nearest neighbors weights from the sids centroids
+
+ >>> sids_w = knnW(sids_d,k=5)
+
+ Ensuring that the members in sids_w are ordered by
+ the order of sids_d's ID
+
+ >>> if not sids_w.id_order_set: sids_w.id_order = sids_w.id_order
+
+ Finding headbaning triples by using 5 nearest neighbors
+
+ >>> s_ht = Headbanging_Triples(sids_d,sids_w,k=5)
+
+ Checking the members of the found triples
+
+ >>> for k, item in s_ht.triples.items()[:5]: print k, item
+ 0 [(1, 18), (1, 21), (1, 33)]
+ 1 [(2, 40), (2, 22), (22, 40)]
+ 2 [(39, 22), (1, 9), (39, 17)]
+ 3 [(16, 6), (19, 6), (20, 6)]
+ 4 [(5, 15), (27, 15), (35, 15)]
+
+ Finding headbanging tirpes by using 5 nearest neighbors with edge correction
+
+ >>> s_ht2 = Headbanging_Triples(sids_d,sids_w,k=5,edgecor=True)
+
+ Checking the members of the found triples
+
+ >>> for k, item in s_ht2.triples.items()[:5]: print k, item
+ 0 [(1, 18), (1, 21), (1, 33)]
+ 1 [(2, 40), (2, 22), (22, 40)]
+ 2 [(39, 22), (1, 9), (39, 17)]
+ 3 [(16, 6), (19, 6), (20, 6)]
+ 4 [(5, 15), (27, 15), (35, 15)]
+
+ Checking the extrapolated point that is introduced into the triples
+ during edge correction
+
+ >>> extrapolated = s_ht2.extra[72]
+
+ Checking the observation IDs constituting the extrapolated triple
+
+ >>> extrapolated[0]
+ (89, 77)
+
+ Checking the distances between the exploated point and the observation 89 and 77
+
+ >>> round(extrapolated[1],5), round(extrapolated[2],6)
+ (0.33753, 0.302707)
+ """
+ def __init__(self, data, w, k=5, t=3, angle=135.0, edgecor=False):
+ if k < 3:
+ raise ValueError("w should be NeareastNeighbors instance & the number of neighbors should be more than 3.")
+ if not w.id_order_set:
+ raise ValueError("w id_order must be set to align with the order of data")
+ self.triples, points = {}, {}
+ for i, pnt in enumerate(data):
+ ng = w.neighbor_offsets[i]
+ points[(i, Point(pnt))] = dict(zip(ng, [Point(d)
+ for d in data[ng]]))
+ for i, pnt in points.keys():
+ ng = points[(i, pnt)]
+ tr, tr_dis = {}, []
+ for c in comb(ng.keys(), 2):
+ p2, p3 = ng[c[0]], ng[c[-1]]
+ ang = get_angle_between(Ray(pnt, p2), Ray(pnt, p3))
+ if ang > angle or (ang < 0.0 and ang + 360 > angle):
+ tr[tuple(c)] = (p2, p3)
+ if len(tr) > t:
+ for c in tr.keys():
+ p2, p3 = tr[c]
+ tr_dis.append((get_segment_point_dist(
+ LineSegment(p2, p3), pnt), c))
+ tr_dis = sorted(tr_dis)[:t]
+ self.triples[i] = [trp for dis, trp in tr_dis]
+ else:
+ self.triples[i] = tr.keys()
+ if edgecor:
+ self.extra = {}
+ ps = dict([(p, i) for i, p in points.keys()])
+ chull = convex_hull(ps.keys())
+ chull = [p for p in chull if len(self.triples[ps[p]]) == 0]
+ for point in chull:
+ key = (ps[point], point)
+ ng = points[key]
+ ng_dist = [(get_points_dist(point, p), p) for p in ng.values()]
+ ng_dist_s = sorted(ng_dist, reverse=True)
+ extra = None
+ while extra is None and len(ng_dist_s) > 0:
+ p2 = ng_dist_s.pop()[-1]
+ p3s = ng.values()
+ p3s.remove(p2)
+ for p3 in p3s:
+ dist_p2_p3 = get_points_dist(p2, p3)
+ dist_p_p2 = get_points_dist(point, p2)
+ dist_p_p3 = get_points_dist(point, p3)
+ if dist_p_p2 <= dist_p_p3:
+ ray1, ray2, s_pnt, dist, c = Ray(p2, point), Ray(p2, p3), p2, dist_p_p2, (ps[p2], ps[p3])
+ else:
+ ray1, ray2, s_pnt, dist, c = Ray(p3, point), Ray(p3, p2), p3, dist_p_p3, (ps[p3], ps[p2])
+ ang = get_angle_between(ray1, ray2)
+ if ang >= 90 + angle / 2 or (ang < 0 and ang + 360 >= 90 + angle / 2):
+ ex_point = get_point_at_angle_and_dist(
+ ray1, angle, dist)
+ extra = [c, dist_p2_p3, get_points_dist(
+ s_pnt, ex_point)]
+ break
+ self.triples[ps[point]].append(extra[0])
+ self.extra[ps[point]] = extra
+
+
+class Headbanging_Median_Rate:
+ """Headbaning Median Rate Smoothing
+
+ Parameters
+ ----------
+ e : array (n, 1)
+ event variable measured across n spatial units
+ b : array (n, 1)
+ population at risk variable measured across n spatial units
+ t : Headbanging_Triples instance
+ aw : array (n, 1)
+ auxilliary weight variable measured across n spatial units
+ iteration : integer
+ the number of iterations
+
+ Attributes
+ ----------
+ r : array (n, 1)
+ rate values from headbaning median smoothing
+
+ Examples
+ --------
+
+ importing k-nearest neighbor weights creator
+
+ >>> from pysal import knnW
+
+ opening the sids2 shapefile
+
+ >>> sids = pysal.open(pysal.examples.get_path('sids2.shp'), 'r')
+
+ extracting the centroids of polygons in the sids2 data
+
+ >>> sids_d = np.array([i.centroid for i in sids])
+
+ creating a 5-nearest neighbors weights from the centroids
+
+ >>> sids_w = knnW(sids_d,k=5)
+
+ ensuring that the members in sids_w are ordered
+
+ >>> if not sids_w.id_order_set: sids_w.id_order = sids_w.id_order
+
+ finding headbanging triples by using 5 neighbors
+
+ >>> s_ht = Headbanging_Triples(sids_d,sids_w,k=5)
+
+ reading in the sids2 data table
+
+ >>> sids_db = pysal.open(pysal.examples.get_path('sids2.dbf'), 'r')
+
+ extracting the 10th and 9th columns in the sids2.dbf and
+ using data values as event and population-at-risk variables
+
+ >>> s_e, s_b = np.array(sids_db[:,9]), np.array(sids_db[:,8])
+
+ computing headbanging median rates from s_e, s_b, and s_ht
+
+ >>> sids_hb_r = Headbanging_Median_Rate(s_e,s_b,s_ht)
+
+ extracting the computed rates through the property r of the Headbanging_Median_Rate instance
+
+ >>> sids_hb_r.r[:5]
+ array([ 0.00075586, 0. , 0.0008285 , 0.0018315 , 0.00498891])
+
+ recomputing headbanging median rates with 5 iterations
+
+ >>> sids_hb_r2 = Headbanging_Median_Rate(s_e,s_b,s_ht,iteration=5)
+
+ extracting the computed rates through the property r of the Headbanging_Median_Rate instance
+
+ >>> sids_hb_r2.r[:5]
+ array([ 0.0008285 , 0.00084331, 0.00086896, 0.0018315 , 0.00498891])
+
+ recomputing headbanging median rates by considring a set of auxilliary weights
+
+ >>> sids_hb_r3 = Headbanging_Median_Rate(s_e,s_b,s_ht,aw=s_b)
+
+ extracting the computed rates through the property r of the Headbanging_Median_Rate instance
+
+ >>> sids_hb_r3.r[:5]
+ array([ 0.00091659, 0. , 0.00156838, 0.0018315 , 0.00498891])
+ """
+ def __init__(self, e, b, t, aw=None, iteration=1):
+ self.r = e * 1.0 / b
+ self.tr, self.aw = t.triples, aw
+ if hasattr(t, 'exta'):
+ self.extra = t.extra
+ while iteration:
+ self.__search_headbanging_median()
+ iteration -= 1
+
+ def __get_screens(self, id, triples, weighted=False):
+ r, tr = self.r, self.tr
+ if len(triples) == 0:
+ return r[id]
+ if hasattr(self, 'extra') and id in self.extra:
+ extra = self.extra
+ trp_r = r[list(triples[0])]
+ trp_r[-1] = trp_r[0] + (trp_r[0] - trp_r[-1]) * (
+ extra[id][-1] * 1.0 / extra[id][1])
+ trp_r = sorted(trp_r)
+ if not weighted:
+ return r, trp_r[0], trp_r[-1]
+ else:
+ trp_aw = self.aw[trp]
+ extra_w = trp_aw[0] + (trp_aw[0] - trp_aw[-
+ 1]) * (extra[id][-1] * 1.0 / extra[id][1])
+ return r, trp_r[0], trp_r[-1], self.aw[id], trp_aw[0] + extra_w
+ if not weighted:
+ lowest, highest = [], []
+ for trp in triples:
+ trp_r = np.sort(r[list(trp)])
+ lowest.append(trp_r[0])
+ highest.append(trp_r[-1])
+ return r[id], np.median(np.array(lowest)), np.median(np.array(highest))
+ if weighted:
+ lowest, highest = [], []
+ lowest_aw, highest_aw = [], []
+ for trp in triples:
+ trp_r = r[list(trp)]
+ dtype = [('r', '%s' % trp_r.dtype), ('w',
+ '%s' % self.aw.dtype)]
+ trp_r = np.array(zip(trp_r, list(trp)), dtype=dtype)
+ trp_r.sort(order='r')
+ lowest.append(trp_r['r'][0])
+ highest.append(trp_r['r'][-1])
+ lowest_aw.append(self.aw[trp_r['w'][0]])
+ highest_aw.append(self.aw[trp_r['w'][-1]])
+ wm_lowest = weighted_median(np.array(lowest), np.array(lowest_aw))
+ wm_highest = weighted_median(
+ np.array(highest), np.array(highest_aw))
+ triple_members = flatten(triples, unique=False)
+ return r[id], wm_lowest, wm_highest, self.aw[id] * len(triples), self.aw[triple_members].sum()
+
+ def __get_median_from_screens(self, screens):
+ if isinstance(screens, float):
+ return screens
+ elif len(screens) == 3:
+ return np.median(np.array(screens))
+ elif len(screens) == 5:
+ rk, wm_lowest, wm_highest, w1, w2 = screens
+ if rk >= wm_lowest and rk <= wm_highest:
+ return rk
+ elif rk < wm_lowest and w1 < w2:
+ return wm_lowest
+ elif rk > wm_highest and w1 < w2:
+ return wm_highest
+ else:
+ return rk
+
+ def __search_headbanging_median(self):
+ r, tr = self.r, self.tr
+ new_r = []
+ for k in tr.keys():
+ screens = self.__get_screens(
+ k, tr[k], weighted=(self.aw is not None))
+ new_r.append(self.__get_median_from_screens(screens))
+ self.r = np.array(new_r)
diff --git a/pysal/esda/tests/__init__.py b/pysal/esda/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pysal/esda/tests/test_gamma.py b/pysal/esda/tests/test_gamma.py
new file mode 100644
index 0000000..0cdae76
--- /dev/null
+++ b/pysal/esda/tests/test_gamma.py
@@ -0,0 +1,68 @@
+import unittest
+import numpy as np
+import pysal
+from pysal.esda.gamma import Gamma
+
+
+class Gamma_Tester(unittest.TestCase):
+ """Unit test for Gamma Index"""
+ def setUp(self):
+ self.w = pysal.lat2W(4, 4)
+ self.y = np.ones(16)
+ self.y[0:8] = 0
+
+ def test_Gamma(self):
+ """Test method"""
+ np.random.seed(12345)
+ g = Gamma(self.y, self.w)
+ self.assertAlmostEquals(g.g, 20.0)
+ self.assertAlmostEquals(g.g_z, 3.1879280354548638)
+ self.assertAlmostEquals(g.p_sim_g, 0.0030000000000000001)
+ self.assertAlmostEquals(g.min_g, 0.0)
+ self.assertAlmostEquals(g.max_g, 20.0)
+ self.assertAlmostEquals(g.mean_g, 11.093093093093094)
+ np.random.seed(12345)
+ g1 = Gamma(self.y, self.w, operation='s')
+ self.assertAlmostEquals(g1.g, 8.0)
+ self.assertAlmostEquals(g1.g_z, -3.7057554345954791)
+ self.assertAlmostEquals(g1.p_sim_g, 0.001)
+ self.assertAlmostEquals(g1.min_g, 14.0)
+ self.assertAlmostEquals(g1.max_g, 48.0)
+ self.assertAlmostEquals(g1.mean_g, 25.623623623623622)
+ np.random.seed(12345)
+ g2 = Gamma(self.y, self.w, operation='a')
+ self.assertAlmostEquals(g2.g, 8.0)
+ self.assertAlmostEquals(g2.g_z, -3.7057554345954791)
+ self.assertAlmostEquals(g2.p_sim_g, 0.001)
+ self.assertAlmostEquals(g2.min_g, 14.0)
+ self.assertAlmostEquals(g2.max_g, 48.0)
+ self.assertAlmostEquals(g2.mean_g, 25.623623623623622)
+ np.random.seed(12345)
+ g3 = Gamma(self.y, self.w, standardize='y')
+ self.assertAlmostEquals(g3.g, 32.0)
+ self.assertAlmostEquals(g3.g_z, 3.7057554345954791)
+ self.assertAlmostEquals(g3.p_sim_g, 0.001)
+ self.assertAlmostEquals(g3.min_g, -48.0)
+ self.assertAlmostEquals(g3.max_g, 20.0)
+ self.assertAlmostEquals(g3.mean_g, -3.2472472472472473)
+ np.random.seed(12345)
+
+ def func(z, i, j):
+ q = z[i] * z[j]
+ return q
+
+ g4 = Gamma(self.y, self.w, operation=func)
+ self.assertAlmostEquals(g4.g, 20.0)
+ self.assertAlmostEquals(g4.g_z, 3.1879280354548638)
+ self.assertAlmostEquals(g4.p_sim_g, 0.0030000000000000001)
+
+
+suite = unittest.TestSuite()
+test_classes = [Gamma_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/esda/tests/test_geary.py b/pysal/esda/tests/test_geary.py
new file mode 100644
index 0000000..1e12d2e
--- /dev/null
+++ b/pysal/esda/tests/test_geary.py
@@ -0,0 +1,61 @@
+"""Geary Unittest."""
+import unittest
+import pysal
+from pysal.esda import geary
+import numpy as np
+
+
+class Geary_Tester(unittest.TestCase):
+ """Geary class for unit tests."""
+ def setUp(self):
+ self.w = pysal.open(pysal.examples.get_path("book.gal")).read()
+ f = pysal.open(pysal.examples.get_path("book.txt"))
+ self.y = np.array(f.by_col['y'])
+
+ def test_Geary(self):
+ c = geary.Geary(self.y, self.w, permutations=0)
+ self.assertAlmostEquals(c.C, 0.33301083591331254)
+ self.assertAlmostEquals(c.EC, 1.0)
+
+ self.assertAlmostEquals(c.VC_norm, 0.031805300245097874)
+ self.assertAlmostEquals(c.p_norm, 9.2018240680169505e-05)
+ self.assertAlmostEquals(c.z_norm, -3.7399778367629564)
+ self.assertAlmostEquals(c.seC_norm, 0.17834040553138225)
+
+ self.assertAlmostEquals(c.VC_rand, 0.018437747611029367)
+ self.assertAlmostEquals(c.p_rand, 4.5059156794646782e-07)
+ self.assertAlmostEquals(c.z_rand, -4.9120733751216008)
+ self.assertAlmostEquals(c.seC_rand, 0.13578566791465646)
+
+ np.random.seed(12345)
+ c = geary.Geary(self.y, self.w, permutations=999)
+ self.assertAlmostEquals(c.C, 0.33301083591331254)
+ self.assertAlmostEquals(c.EC, 1.0)
+
+ self.assertAlmostEquals(c.VC_norm, 0.031805300245097874)
+ self.assertAlmostEquals(c.p_norm, 9.2018240680169505e-05)
+ self.assertAlmostEquals(c.z_norm, -3.7399778367629564)
+ self.assertAlmostEquals(c.seC_norm, 0.17834040553138225)
+
+ self.assertAlmostEquals(c.VC_rand, 0.018437747611029367)
+ self.assertAlmostEquals(c.p_rand, 4.5059156794646782e-07)
+ self.assertAlmostEquals(c.z_rand, -4.9120733751216008)
+ self.assertAlmostEquals(c.seC_rand, 0.13578566791465646)
+
+ self.assertAlmostEquals(c.EC_sim, 0.9980676303238214)
+ self.assertAlmostEquals(c.VC_sim, 0.034430408799858946)
+ self.assertAlmostEquals(c.p_sim, 0.001)
+ self.assertAlmostEquals(c.p_z_sim, 0.00016908100514811952)
+ self.assertAlmostEquals(c.z_sim, -3.5841621159171746)
+ self.assertAlmostEquals(c.seC_sim, 0.18555432843202269)
+
+
+suite = unittest.TestSuite()
+test_classes = [Geary_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/esda/tests/test_getisord.py b/pysal/esda/tests/test_getisord.py
new file mode 100644
index 0000000..97b0baf
--- /dev/null
+++ b/pysal/esda/tests/test_getisord.py
@@ -0,0 +1,59 @@
+import unittest
+from pysal.weights.Distance import DistanceBand
+from pysal.esda import getisord
+import numpy as np
+
+POINTS = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+W = DistanceBand(POINTS, threshold=15)
+Y = np.array([2, 3, 3.2, 5, 8, 7])
+
+
+class G_Tester(unittest.TestCase):
+
+ def setUp(self):
+ self.w = W
+ self.y = Y
+ np.random.seed(10)
+
+ def test_G(self):
+ g = getisord.G(self.y, self.w)
+ self.assertAlmostEquals(g.G, 0.55709779, places=8)
+ self.assertAlmostEquals(g.p_norm, 0.1729, places=4)
+
+
+class G_Local_Tester(unittest.TestCase):
+
+ def setUp(self):
+ self.w = W
+ self.y = Y
+ np.random.seed(10)
+
+ def test_G_Local_Binary(self):
+ lg = getisord.G_Local(self.y, self.w, transform='B')
+ self.assertAlmostEquals(lg.Zs[0], -1.0136729, places=7)
+ self.assertAlmostEquals(lg.p_sim[0], 0.10100000000000001, places=7)
+
+ def test_G_Local_Row_Standardized(self):
+ lg = getisord.G_Local(self.y, self.w, transform='R')
+ self.assertAlmostEquals(lg.Zs[0], -0.62074534, places=7)
+ self.assertAlmostEquals(lg.p_sim[0], 0.10100000000000001, places=7)
+
+ def test_G_star_Local_Binary(self):
+ lg = getisord.G_Local(self.y, self.w, transform='B', star=True)
+ self.assertAlmostEquals(lg.Zs[0], -1.39727626, places=8)
+ self.assertAlmostEquals(lg.p_sim[0], 0.10100000000000001, places=7)
+
+ def test_G_star_Row_Standardized(self):
+ lg = getisord.G_Local(self.y, self.w, transform='R', star=True)
+ self.assertAlmostEquals(lg.Zs[0], -0.62488094, places=8)
+ self.assertAlmostEquals(lg.p_sim[0], 0.10100000000000001, places=7)
+
+suite = unittest.TestSuite()
+test_classes = [G_Tester, G_Local_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/esda/tests/test_join_counts.py b/pysal/esda/tests/test_join_counts.py
new file mode 100644
index 0000000..ed51af8
--- /dev/null
+++ b/pysal/esda/tests/test_join_counts.py
@@ -0,0 +1,41 @@
+import unittest
+import numpy as np
+import pysal
+from pysal.esda.join_counts import Join_Counts
+
+
+class Join_Counts_Tester(unittest.TestCase):
+ """Unit test for Join Counts"""
+ def setUp(self):
+ self.w = pysal.lat2W(4, 4)
+ self.y = np.ones(16)
+ self.y[0:8] = 0
+
+ def test_Join_Counts(self):
+ """Test method"""
+ np.random.seed(12345)
+ jc = Join_Counts(self.y, self.w)
+ self.assertAlmostEquals(jc.bb, 10.0)
+ self.assertAlmostEquals(jc.bw, 4.0)
+ self.assertAlmostEquals(jc.ww, 10.0)
+ self.assertAlmostEquals(jc.J, 24.0)
+ self.assertAlmostEquals(len(jc.sim_bb), 999)
+ self.assertAlmostEquals(jc.p_sim_bb, 0.0030000000000000001)
+ self.assertAlmostEquals(np.mean(jc.sim_bb), 5.5465465465465469)
+ self.assertAlmostEquals(np.max(jc.sim_bb), 10.0)
+ self.assertAlmostEquals(np.min(jc.sim_bb), 0.0)
+ self.assertAlmostEquals(len(jc.sim_bw), 999)
+ self.assertAlmostEquals(jc.p_sim_bw, 1.0)
+ self.assertAlmostEquals(np.mean(jc.sim_bw), 12.811811811811811)
+ self.assertAlmostEquals(np.max(jc.sim_bw), 24.0)
+ self.assertAlmostEquals(np.min(jc.sim_bw), 7.0)
+
+suite = unittest.TestSuite()
+test_classes = [Join_Counts_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/esda/tests/test_mapclassify.py b/pysal/esda/tests/test_mapclassify.py
new file mode 100644
index 0000000..0bca060
--- /dev/null
+++ b/pysal/esda/tests/test_mapclassify.py
@@ -0,0 +1,373 @@
+import pysal
+from pysal.esda.mapclassify import *
+from pysal.esda.mapclassify import binC, bin, bin1d
+import numpy as np
+import unittest
+
+
+class TestQuantile(unittest.TestCase):
+ def test_quantile(self):
+ y = np.arange(1000)
+ expected = np.array([333., 666., 999.])
+ np.testing.assert_almost_equal(expected, quantile(y, k=3))
+
+ def test_quantile_k4(self):
+ x = np.arange(1000)
+ qx = quantile(x, k=4)
+ expected = np.array([249.75, 499.5, 749.25, 999.])
+ np.testing.assert_array_almost_equal(expected, qx)
+
+ def test_quantile_k(self):
+ y = np.random.random(1000)
+ for k in range(5, 10):
+ np.testing.assert_almost_equal(k, len(quantile(y, k)))
+ self.assertEqual(k, len(quantile(y, k)))
+
+
+class TestBinC(unittest.TestCase):
+ def test_bin_c(self):
+ bins = range(2, 8)
+ y = np.array([[7, 5, 6],
+ [2, 3, 5],
+ [7, 2, 2],
+ [3, 6, 7],
+ [6, 3, 4],
+ [6, 7, 4],
+ [6, 5, 6],
+ [4, 6, 7],
+ [4, 6, 3],
+ [3, 2, 7]])
+
+ expected = np.array([[5, 3, 4],
+ [0, 1, 3],
+ [5, 0, 0],
+ [1, 4, 5],
+ [4, 1, 2],
+ [4, 5, 2],
+ [4, 3, 4],
+ [2, 4, 5],
+ [2, 4, 1],
+ [1, 0, 5]])
+ np.testing.assert_array_equal(expected, binC(y, bins))
+
+
+class TestBin(unittest.TestCase):
+ def test_bin(self):
+ y = np.array([[7, 13, 14],
+ [10, 11, 13],
+ [7, 17, 2],
+ [18, 3, 14],
+ [9, 15, 8],
+ [7, 13, 12],
+ [16, 6, 11],
+ [19, 2, 15],
+ [11, 11, 9],
+ [3, 2, 19]])
+ bins = [10, 15, 20]
+ expected = np.array([[0, 1, 1],
+ [0, 1, 1],
+ [0, 2, 0],
+ [2, 0, 1],
+ [0, 1, 0],
+ [0, 1, 1],
+ [2, 0, 1],
+ [2, 0, 1],
+ [1, 1, 0],
+ [0, 0, 2]])
+
+ np.testing.assert_array_equal(expected, bin(y, bins))
+
+
+class TestBin1d(unittest.TestCase):
+ def test_bin1d(self):
+ y = np.arange(100, dtype='float')
+ bins = [25, 74, 100]
+ binIds = np.array(
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+ 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
+ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
+ counts = np.array([26, 49, 25])
+
+ np.testing.assert_array_equal(binIds, bin1d(y, bins)[0])
+ np.testing.assert_array_equal(counts, bin1d(y, bins)[1])
+
+
+class TestNaturalBreaks(unittest.TestCase):
+ def setUp(self):
+ dat = pysal.open(pysal.examples.get_path("calempdensity.csv"))
+ self.V = np.array([record[-1] for record in dat])
+
+ def test_natural_breaks(self):
+ # self.assertEqual(expected, natural_breaks(values, k, itmax))
+ assert True # TODO: implement your test here
+
+ def test_Natural_Breaks(self):
+ nb = Natural_Breaks(self.V, 5)
+ self.assertEquals(nb.k, 5)
+ self.assertEquals(len(nb.counts), 5)
+ np.testing.assert_array_almost_equal(
+ nb.counts, np.array([14, 13, 14, 10, 7]))
+
+ def test_Natural_Breaks_stability(self):
+ for i in range(10):
+ nb = Natural_Breaks(self.V, 5)
+ self.assertEquals(nb.k, 5)
+ self.assertEquals(len(nb.counts), 5)
+
+ def test_Natural_Breaks_randomData(self):
+ for i in range(10):
+ V = np.random.random(50) * (i + 1)
+ nb = Natural_Breaks(V, 5)
+ self.assertEquals(nb.k, 5)
+ self.assertEquals(len(nb.counts), 5)
+
+
+class TestMapClassifier(unittest.TestCase):
+ def test_Map_Classifier(self):
+ # map__classifier = Map_Classifier(y)
+ assert True # TODO: implement your test here
+
+ def test___repr__(self):
+ # map__classifier = Map_Classifier(y)
+ # self.assertEqual(expected, map__classifier.__repr__())
+ assert True # TODO: implement your test here
+
+ def test___str__(self):
+ # map__classifier = Map_Classifier(y)
+ # self.assertEqual(expected, map__classifier.__str__())
+ assert True # TODO: implement your test here
+
+ def test_get_adcm(self):
+ # map__classifier = Map_Classifier(y)
+ # self.assertEqual(expected, map__classifier.get_adcm())
+ assert True # TODO: implement your test here
+
+ def test_get_gadf(self):
+ # map__classifier = Map_Classifier(y)
+ # self.assertEqual(expected, map__classifier.get_gadf())
+ assert True # TODO: implement your test here
+
+ def test_get_tss(self):
+ # map__classifier = Map_Classifier(y)
+ # self.assertEqual(expected, map__classifier.get_tss())
+ assert True # TODO: implement your test here
+
+
+class TestEqualInterval(unittest.TestCase):
+ def setUp(self):
+ dat = pysal.open(pysal.examples.get_path("calempdensity.csv"))
+ self.V = np.array([record[-1] for record in dat])
+
+ def test_Equal_Interval(self):
+ ei = Equal_Interval(self.V)
+ np.testing.assert_array_almost_equal(ei.counts,
+ np.array([57, 0, 0, 0, 1]))
+ np.testing.assert_array_almost_equal(ei.bins,
+ np.array(
+ [822.394, 1644.658, 2466.922, 3289.186,
+ 4111.45]))
+
+
+class TestPercentiles(unittest.TestCase):
+ def setUp(self):
+ dat = pysal.open(pysal.examples.get_path("calempdensity.csv"))
+ self.V = np.array([record[-1] for record in dat])
+
+ def test_Percentiles(self):
+ pc = Percentiles(self.V)
+ np.testing.assert_array_almost_equal(pc.bins, np.array([
+ 1.35700000e-01, 5.53000000e-01, 9.36500000e+00, 2.13914000e+02,
+ 2.17994800e+03, 4.11145000e+03]))
+ np.testing.assert_array_almost_equal(pc.counts,
+ np.array([1, 5, 23, 23, 5, 1]))
+
+
+class TestBoxPlot(unittest.TestCase):
+ def setUp(self):
+ dat = pysal.open(pysal.examples.get_path("calempdensity.csv"))
+ self.V = np.array([record[-1] for record in dat])
+
+ def test_Box_Plot(self):
+ bp = Box_Plot(self.V)
+ bins = np.array([-5.28762500e+01, 2.56750000e+00, 9.36500000e+00,
+ 3.95300000e+01, 9.49737500e+01, 4.11145000e+03])
+ np.testing.assert_array_almost_equal(bp.bins, bins)
+
+
+class TestQuantiles(unittest.TestCase):
+ def setUp(self):
+ dat = pysal.open(pysal.examples.get_path("calempdensity.csv"))
+ self.V = np.array([record[-1] for record in dat])
+
+ def test_Quantiles(self):
+ q = Quantiles(self.V, k=5)
+ np.testing.assert_array_almost_equal(q.bins,
+ np.array(
+ [1.46400000e+00, 5.79800000e+00,
+ 1.32780000e+01, 5.46160000e+01, 4.11145000e+03]))
+ np.testing.assert_array_almost_equal(q.counts,
+ np.array([12, 11, 12, 11, 12]))
+
+
+class TestStdMean(unittest.TestCase):
+ def setUp(self):
+ dat = pysal.open(pysal.examples.get_path("calempdensity.csv"))
+ self.V = np.array([record[-1] for record in dat])
+
+ def test_Std_Mean(self):
+ s = Std_Mean(self.V)
+ np.testing.assert_array_almost_equal(s.bins,
+ np.array(
+ [-967.36235382, -420.71712519, 672.57333208,
+ 1219.21856072, 4111.45]))
+ np.testing.assert_array_almost_equal(s.counts,
+ np.array([0, 0, 56, 1, 1]))
+
+
+class TestMaximumBreaks(unittest.TestCase):
+ def setUp(self):
+ dat = pysal.open(pysal.examples.get_path("calempdensity.csv"))
+ self.V = np.array([record[-1] for record in dat])
+
+ def test_Maximum_Breaks(self):
+ mb = Maximum_Breaks(self.V, k=5)
+ self.assertEquals(mb.k, 5)
+ np.testing.assert_array_almost_equal(mb.bins,
+ np.array(
+ [146.005, 228.49, 546.675, 2417.15,
+ 4111.45]))
+ np.testing.assert_array_almost_equal(mb.counts,
+ np.array([50, 2, 4, 1, 1]))
+
+
+class TestFisherJenks(unittest.TestCase):
+ def setUp(self):
+ dat = pysal.open(pysal.examples.get_path("calempdensity.csv"))
+ self.V = np.array([record[-1] for record in dat])
+
+ def test_Fisher_Jenks(self):
+ fj = Fisher_Jenks(self.V)
+ self.assertEquals(fj.adcm, 799.24000000000001)
+ np.testing.assert_array_almost_equal(fj.bins, np.array([ 75.29, 192.05, 370.5 ,
+ 722.85, 4111.45]))
+ np.testing.assert_array_almost_equal(fj.counts, np.array([49, 3, 4,
+ 1, 1]))
+
+
+class TestJenksCaspall(unittest.TestCase):
+ def setUp(self):
+ dat = pysal.open(pysal.examples.get_path("calempdensity.csv"))
+ self.V = np.array([record[-1] for record in dat])
+
+ def test_Jenks_Caspall(self):
+ np.random.seed(10)
+ jc = Jenks_Caspall(self.V, k=5)
+ np.testing.assert_array_almost_equal(jc.counts,
+ np.array([14, 13, 14, 10, 7]))
+ np.testing.assert_array_almost_equal(jc.bins,
+ np.array( [1.81000000e+00, 7.60000000e+00,
+ 2.98200000e+01, 1.81270000e+02,
+ 4.11145000e+03]))
+
+
+class TestJenksCaspallSampled(unittest.TestCase):
+ def setUp(self):
+ dat = pysal.open(pysal.examples.get_path("calempdensity.csv"))
+ self.V = np.array([record[-1] for record in dat])
+
+ def test_Jenks_Caspall_Sampled(self):
+ np.random.seed(100)
+ x = np.random.random(100000)
+ jc = Jenks_Caspall(x)
+ jcs = Jenks_Caspall_Sampled(x)
+ np.testing.assert_array_almost_equal(jc.bins,
+ np.array([0.19718393,
+ 0.39655886,
+ 0.59648522,
+ 0.79780763,
+ 0.99997979]))
+ np.testing.assert_array_almost_equal(jcs.bins,
+ np.array([0.20856569,
+ 0.41513931,
+ 0.62457691,
+ 0.82561423,
+ 0.99997979]))
+
+
+class TestJenksCaspallForced(unittest.TestCase):
+ def setUp(self):
+ dat = pysal.open(pysal.examples.get_path("calempdensity.csv"))
+ self.V = np.array([record[-1] for record in dat])
+
+ def test_Jenks_Caspall_Forced(self):
+ np.random.seed(100)
+ jcf = Jenks_Caspall_Forced(self.V, k=5)
+ np.testing.assert_array_almost_equal(jcf.bins,
+ np.array([[1.34000000e+00],
+ [5.90000000e+00],
+ [1.67000000e+01],
+ [5.06500000e+01],
+ [4.11145000e+03]]))
+ np.testing.assert_array_almost_equal(jcf.counts,
+ np.array([12, 12, 13, 9, 12]))
+
+
+class TestUserDefined(unittest.TestCase):
+ def setUp(self):
+ dat = pysal.open(pysal.examples.get_path("calempdensity.csv"))
+ self.V = np.array([record[-1] for record in dat])
+
+ def test_User_Defined(self):
+ bins = [20, max(self.V)]
+ ud = User_Defined(self.V, bins)
+ np.testing.assert_array_almost_equal(ud.bins,
+ np.array([20., 4111.45]))
+ np.testing.assert_array_almost_equal(ud.counts,
+ np.array([37, 21]))
+
+
+class TestMaxPClassifier(unittest.TestCase):
+ def setUp(self):
+ dat = pysal.open(pysal.examples.get_path("calempdensity.csv"))
+ self.V = np.array([record[-1] for record in dat])
+
+ def test_Max_P_Classifier(self):
+ np.random.seed(100)
+ mp = Max_P_Classifier(self.V)
+ np.testing.assert_array_almost_equal(mp.bins,
+ np.array(
+ [8.6999999999999993, 16.699999999999999,
+ 20.469999999999999, 66.260000000000005,
+ 4111.4499999999998]))
+ np.testing.assert_array_almost_equal(mp.counts,
+ np.array([29, 8, 1, 10, 10]))
+
+
+class TestGadf(unittest.TestCase):
+ def setUp(self):
+ dat = pysal.open(pysal.examples.get_path("calempdensity.csv"))
+ self.V = np.array([record[-1] for record in dat])
+
+ def test_gadf(self):
+ qgadf = gadf(self.V)
+ self.assertEquals(qgadf[0], 15)
+ self.assertEquals(qgadf[-1], 0.37402575909092828)
+
+
+class TestKClassifiers(unittest.TestCase):
+ def setUp(self):
+ dat = pysal.open(pysal.examples.get_path("calempdensity.csv"))
+ self.V = np.array([record[-1] for record in dat])
+
+ def test_K_classifiers(self):
+ np.random.seed(100)
+ ks = K_classifiers(self.V)
+ self.assertEquals(ks.best.name, 'Fisher_Jenks')
+ self.assertEquals(ks.best.gadf, 0.84810327199081048)
+ self.assertEquals(ks.best.k, 4)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/esda/tests/test_mixture_smoothing.py b/pysal/esda/tests/test_mixture_smoothing.py
new file mode 100644
index 0000000..47085d0
--- /dev/null
+++ b/pysal/esda/tests/test_mixture_smoothing.py
@@ -0,0 +1,43 @@
+import unittest
+import numpy as np
+import pysal
+from pysal.esda import mixture_smoothing as m_s
+
+
+class MS_Tester(unittest.TestCase):
+ """Mixture_Smoothing Unit Tests"""
+ def setUp(self):
+ self.e = np.array([10, 5, 12, 20])
+ self.b = np.array([100, 150, 80, 200])
+
+ def test_NP_Mixture_Smoother(self):
+ """Test the main class"""
+ mix = m_s.NP_Mixture_Smoother(self.e, self.b)
+ np.testing.assert_array_almost_equal(mix.r, np.array(
+ [0.10982278, 0.03445531, 0.11018404, 0.11018604]))
+ np.testing.assert_array_almost_equal(
+ mix.category, np.array([1, 0, 1, 1]))
+ #self.failUnless(mix.getSeed(), (np.array([ 0.5, 0.5]), np.array([ 0.03333333,
+ # 0.15 ])))
+ left, right = mix.getSeed()
+ np.testing.assert_array_almost_equal(left, np.array([0.5, 0.5]))
+ np.testing.assert_array_almost_equal(
+ right, np.array([0.03333333, 0.15]))
+ d = mix.mixalg()
+ np.testing.assert_array_almost_equal(
+ d['mix_den'], np.array([0., 0., 0., 0.]))
+ np.testing.assert_array_almost_equal(d['gradient'], np.array([0.]))
+ np.testing.assert_array_almost_equal(d['p'], np.array([1.]))
+ np.testing.assert_array_almost_equal(
+ d['grid'], np.array([11.27659574]))
+ self.assertEqual(d['k'], 1)
+ self.assertEqual(d['accuracy'], 1.0)
+ left, right = mix.getRateEstimates()
+ np.testing.assert_array_almost_equal(
+ left, np.array([0.0911574, 0.0911574,
+ 0.0911574, 0.0911574]))
+ np.testing.assert_array_almost_equal(right, np.array([1, 1, 1, 1]))
+
+
+if __name__ == "__main__":
+ unittest.main()
diff --git a/pysal/esda/tests/test_moran.py b/pysal/esda/tests/test_moran.py
new file mode 100644
index 0000000..9701c75
--- /dev/null
+++ b/pysal/esda/tests/test_moran.py
@@ -0,0 +1,94 @@
+import unittest
+import pysal
+from pysal.esda import moran
+import numpy as np
+
+
+class Moran_Tester(unittest.TestCase):
+ def setUp(self):
+ self.w = pysal.open(pysal.examples.get_path("stl.gal")).read()
+ f = pysal.open(pysal.examples.get_path("stl_hom.txt"))
+ self.y = np.array(f.by_col['HR8893'])
+
+ def test_moran(self):
+ mi = moran.Moran(self.y, self.w, two_tailed=False)
+ self.assertAlmostEquals(mi.I, 0.24365582621771659, 7)
+ self.assertAlmostEquals(mi.p_norm,0.00013573931385468807)
+
+ def test_sids(self):
+ w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
+ f = pysal.open(pysal.examples.get_path("sids2.dbf"))
+ SIDR = np.array(f.by_col("SIDR74"))
+ mi = pysal.Moran(SIDR, w, two_tailed=False)
+ self.assertAlmostEquals(mi.I, 0.24772519320480135)
+ self.assertAlmostEquals(mi.p_norm, 5.7916539074498452e-05)
+
+
+class Moran_Rate_Tester(unittest.TestCase):
+ def setUp(self):
+ self.w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
+ f = pysal.open(pysal.examples.get_path("sids2.dbf"))
+ self.e = np.array(f.by_col['SID79'])
+ self.b = np.array(f.by_col['BIR79'])
+
+ def test_moran_rate(self):
+ mi = moran.Moran_Rate(self.e, self.b, self.w, two_tailed=False)
+ self.assertAlmostEquals(mi.I, 0.16622343552567395, 7)
+ self.assertAlmostEquals(mi.p_norm, 0.004191499504892171)
+
+
+class Moran_BV_matrix_Tester(unittest.TestCase):
+ def setUp(self):
+ f = pysal.open(pysal.examples.get_path("sids2.dbf"))
+ varnames = ['SIDR74', 'SIDR79', 'NWR74', 'NWR79']
+ self.names = varnames
+ vars = [np.array(f.by_col[var]) for var in varnames]
+ self.vars = vars
+ self.w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
+
+ def test_Moran_BV_matrix(self):
+ res = moran.Moran_BV_matrix(self.vars, self.w, varnames=self.names)
+ self.assertAlmostEquals(res[(0, 1)].I, 0.19362610652874668)
+ self.assertAlmostEquals(res[(3, 0)].I, 0.37701382542927858)
+
+
+class Moran_Local_Tester(unittest.TestCase):
+ def setUp(self):
+ np.random.seed(10)
+ self.w = pysal.open(pysal.examples.get_path("desmith.gal")).read()
+ f = pysal.open(pysal.examples.get_path("desmith.txt"))
+ self.y = np.array(f.by_col['z'])
+
+ def test_Moran_Local(self):
+ lm = moran.Moran_Local(
+ self.y, self.w, transformation="r", permutations=99)
+ self.assertAlmostEquals(lm.z_sim[0], -0.081383956359666748)
+ self.assertAlmostEquals(lm.p_z_sim[0], 0.46756830387716064)
+ self.assertAlmostEquals(lm.VI_sim, 0.2067126047680822)
+
+
+class Moran_Local_Rate_Tester(unittest.TestCase):
+ def setUp(self):
+ np.random.seed(10)
+ self.w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
+ f = pysal.open(pysal.examples.get_path("sids2.dbf"))
+ self.e = np.array(f.by_col['SID79'])
+ self.b = np.array(f.by_col['BIR79'])
+
+ def test_moran_rate(self):
+ lm = moran.Moran_Local_Rate(self.e, self.b, self.w,
+ transformation="r", permutations=99)
+ self.assertAlmostEquals(lm.z_sim[0], -0.27099998923550017)
+ self.assertAlmostEquals(lm.p_z_sim[0], 0.39319552026912641)
+ self.assertAlmostEquals(lm.VI_sim, 0.21879403675396222)
+
+
+suite = unittest.TestSuite()
+test_classes = [Moran_Tester, Moran_BV_matrix_Tester, Moran_Local_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/esda/tests/test_smoothing.py b/pysal/esda/tests/test_smoothing.py
new file mode 100644
index 0000000..cfc4637
--- /dev/null
+++ b/pysal/esda/tests/test_smoothing.py
@@ -0,0 +1,248 @@
+import unittest
+import pysal
+from pysal.esda import smoothing as sm
+from pysal import knnW
+import numpy as np
+
+
+class TestFlatten(unittest.TestCase):
+ def setUp(self):
+ self.input = [[1, 2], [3, 3, 4], [5, 6]]
+
+ def test_flatten(self):
+ out1 = sm.flatten(self.input)
+ out2 = sm.flatten(self.input, unique=False)
+ self.assertEquals(out1, [1, 2, 3, 4, 5, 6])
+ self.assertEquals(out2, [1, 2, 3, 3, 4, 5, 6])
+
+
+class TestWMean(unittest.TestCase):
+ def setUp(self):
+ self.d = np.array([5, 4, 3, 1, 2])
+ self.w1 = np.array([10, 22, 9, 2, 5])
+ self.w2 = np.array([10, 14, 17, 2, 5])
+
+ def test_weighted_median(self):
+ out1 = sm.weighted_median(self.d, self.w1)
+ out2 = sm.weighted_median(self.d, self.w2)
+ self.assertEquals(out1, 4)
+ self.assertEquals(out2, 3.5)
+
+
+class TestAgeStd(unittest.TestCase):
+ def setUp(self):
+ self.e = np.array([30, 25, 25, 15, 33, 21, 30, 20])
+ self.b = np.array([1000, 1000, 1100, 900, 1000, 900, 1100, 900])
+ self.s_e = np.array([100, 45, 120, 100, 50, 30, 200, 80])
+ self.s_b = s = np.array([1000, 900, 1000, 900, 1000, 900, 1000, 900])
+ self.n = 2
+
+ def test_crude_age_standardization(self):
+ crude = sm.crude_age_standardization(self.e, self.b, self.n).round(8)
+ crude_exp = np.array([0.02375000, 0.02666667])
+ self.assertEquals(list(crude), list(crude_exp))
+
+ def test_direct_age_standardization(self):
+ direct = np.array(sm.direct_age_standardization(
+ self.e, self.b, self.s_b, self.n)).round(8)
+ direct_exp = np.array([[0.02374402, 0.01920491,
+ 0.02904848], [0.02665072, 0.02177143, 0.03230508]])
+ self.assertEquals(list(direct.flatten()), list(direct_exp.flatten()))
+
+ def test_indirect_age_standardization(self):
+ indirect = np.array(sm.indirect_age_standardization(
+ self.e, self.b, self.s_e, self.s_b, self.n)).round(8)
+ indirect_exp = np.array([[0.02372382, 0.01940230,
+ 0.02900789], [0.02610803, .02154304, 0.03164035]])
+ self.assertEquals(
+ list(indirect.flatten()), list(indirect_exp.flatten()))
+
+
+class TestSRate(unittest.TestCase):
+ def setUp(self):
+ sids = pysal.open(pysal.examples.get_path('sids2.dbf'), 'r')
+ self.w = pysal.open(pysal.examples.get_path('sids2.gal'), 'r').read()
+ self.b, self.e = np.array(sids[:, 8]), np.array(sids[:, 9])
+ er = [0.453433, 0.000000, 0.775871, 0.973810, 3.133190]
+ eb = [0.0016973, 0.0017054, 0.0017731, 0.0020129, 0.0035349]
+ sr = [0.0009922, 0.0012639, 0.0009740, 0.0007605, 0.0050154]
+ smr = [0.00083622, 0.00109402, 0.00081567, 0.0, 0.0048209]
+ smr_w = [0.00127146, 0.00127146, 0.0008433, 0.0, 0.0049889]
+ smr2 = [0.00091659, 0.00087641, 0.00091073, 0.0, 0.00467633]
+ self.er = [round(i, 5) for i in er]
+ self.eb = [round(i, 7) for i in eb]
+ self.sr = [round(i, 7) for i in sr]
+ self.smr = [round(i, 7) for i in smr]
+ self.smr_w = [round(i, 7) for i in smr_w]
+ self.smr2 = [round(i, 7) for i in smr2]
+
+ def test_Excess_Risk(self):
+ out_er = sm.Excess_Risk(self.e, self.b).r
+ out_er = [round(i, 5) for i in out_er[:5]]
+ self.assertEquals(out_er, self.er)
+
+ def test_Empirical_Bayes(self):
+ out_eb = sm.Empirical_Bayes(self.e, self.b).r
+ out_eb = [round(i, 7) for i in out_eb[:5]]
+ self.assertEquals(out_eb, self.eb)
+
+ def test_Spatial_Empirical_Bayes(self):
+ stl = pysal.open(pysal.examples.get_path('stl_hom.csv'), 'r')
+ stl_e, stl_b = np.array(stl[:, 10]), np.array(stl[:, 13])
+ stl_w = pysal.open(pysal.examples.get_path('stl.gal'), 'r').read()
+ if not stl_w.id_order_set:
+ stl_w.id_order = range(1, len(stl) + 1)
+ s_eb = sm.Spatial_Empirical_Bayes(stl_e, stl_b, stl_w)
+ s_ebr10 = np.array([4.01485749e-05, 3.62437513e-05,
+ 4.93034844e-05, 5.09387329e-05, 3.72735210e-05,
+ 3.69333797e-05, 5.40245456e-05, 2.99806055e-05,
+ 3.73034109e-05, 3.47270722e-05])
+ np.testing.assert_array_almost_equal(s_ebr10, s_eb.r[:10])
+
+ def test_Spatial_Rate(self):
+ out_sr = sm.Spatial_Rate(self.e, self.b, self.w).r
+ out_sr = [round(i, 7) for i in out_sr[:5]]
+ self.assertEquals(out_sr, self.sr)
+
+ def test_Spatial_Median_Rate(self):
+ out_smr = sm.Spatial_Median_Rate(self.e, self.b, self.w).r
+ out_smr_w = sm.Spatial_Median_Rate(self.e, self.b, self.w, aw=self.b).r
+ out_smr2 = sm.Spatial_Median_Rate(
+ self.e, self.b, self.w, iteration=2).r
+ out_smr = [round(i, 7) for i in out_smr[:5]]
+ out_smr_w = [round(i, 7) for i in out_smr_w[:5]]
+ out_smr2 = [round(i, 7) for i in out_smr2[:5]]
+ self.assertEquals(out_smr, self.smr)
+ self.assertEquals(out_smr_w, self.smr_w)
+ self.assertEquals(out_smr2, self.smr2)
+
+
+class TestHB(unittest.TestCase):
+ def setUp(self):
+ sids = pysal.open(pysal.examples.get_path('sids2.shp'), 'r')
+ self.sids = sids
+ self.d = np.array([i.centroid for i in sids])
+ self.w = knnW(self.d, k=5)
+ if not self.w.id_order_set:
+ self.w.id_order = self.w.id_order
+ sids_db = pysal.open(pysal.examples.get_path('sids2.dbf'), 'r')
+ self.b, self.e = np.array(sids_db[:, 8]), np.array(sids_db[:, 9])
+
+ def test_Headbanging_Triples(self):
+ ht = sm.Headbanging_Triples(self.d, self.w)
+ self.assertEquals(len(ht.triples), len(self.d))
+ ht2 = sm.Headbanging_Triples(self.d, self.w, edgecor=True)
+ self.assertTrue(hasattr(ht2, 'extra'))
+ self.assertEquals(len(ht2.triples), len(self.d))
+ htr = sm.Headbanging_Median_Rate(self.e, self.b, ht2, iteration=5)
+ self.assertEquals(len(htr.r), len(self.e))
+ for i in htr.r:
+ self.assertTrue(i is not None)
+
+ def test_Headbanging_Median_Rate(self):
+ sids_d = np.array([i.centroid for i in self.sids])
+ sids_w = pysal.knnW(sids_d, k=5)
+ if not sids_w.id_order_set:
+ sids_w.id_order = sids_w.id_order
+ s_ht = sm.Headbanging_Triples(sids_d, sids_w, k=5)
+ sids_db = pysal.open(pysal.examples.get_path('sids2.dbf'), 'r')
+ s_e, s_b = np.array(sids_db[:, 9]), np.array(sids_db[:, 8])
+ sids_hb_r = sm.Headbanging_Median_Rate(s_e, s_b, s_ht)
+ sids_hb_rr5 = np.array([0.00075586, 0.,
+ 0.0008285, 0.0018315, 0.00498891])
+ np.testing.assert_array_almost_equal(sids_hb_rr5, sids_hb_r.r[:5])
+ sids_hb_r2 = sm.Headbanging_Median_Rate(s_e, s_b, s_ht, iteration=5)
+ sids_hb_r2r5 = np.array([0.0008285, 0.00084331,
+ 0.00086896, 0.0018315, 0.00498891])
+ np.testing.assert_array_almost_equal(sids_hb_r2r5, sids_hb_r2.r[:5])
+ sids_hb_r3 = sm.Headbanging_Median_Rate(s_e, s_b, s_ht, aw=s_b)
+ sids_hb_r3r5 = np.array([0.00091659, 0.,
+ 0.00156838, 0.0018315, 0.00498891])
+ np.testing.assert_array_almost_equal(sids_hb_r3r5, sids_hb_r3.r[:5])
+
+
+class TestKernel_AgeAdj_SM(unittest.TestCase):
+ def setUp(self):
+ self.e = np.array([10, 1, 3, 4, 2, 5])
+ self.b = np.array([100, 15, 20, 20, 80, 90])
+ self.e1 = np.array([10, 8, 1, 4, 3, 5, 4, 3, 2, 1, 5, 3])
+ self.b1 = np.array([100, 90, 15, 30, 25, 20, 30, 20, 80, 80, 90, 60])
+ self.s = np.array([98, 88, 15, 29, 20, 23, 33, 25, 76, 80, 89, 66])
+ self.points = [(
+ 10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+ self.kw = pysal.weights.Kernel(self.points)
+ if not self.kw.id_order_set:
+ self.kw.id_order = range(0, len(self.points))
+
+ def test_Kernel_Smoother(self):
+ kr = sm.Kernel_Smoother(self.e, self.b, self.kw)
+ exp = [0.10543301, 0.0858573, 0.08256196, 0.09884584,
+ 0.04756872, 0.04845298]
+ self.assertEquals(list(kr.r.round(8)), exp)
+
+ def test_Age_Adjusted_Smoother(self):
+ ar = sm.Age_Adjusted_Smoother(self.e1, self.b1, self.kw, self.s)
+ exp = [0.10519625, 0.08494318, 0.06440072, 0.06898604,
+ 0.06952076, 0.05020968]
+ self.assertEquals(list(ar.r.round(8)), exp)
+
+ def test_Disk_Smoother(self):
+ self.kw.transform = 'b'
+ exp = [0.12222222000000001, 0.10833333, 0.08055556,
+ 0.08944444, 0.09944444, 0.09351852]
+ disk = sm.Disk_Smoother(self.e, self.b, self.kw)
+ self.assertEqual(list(disk.r.round(8)), exp)
+
+ def test_Spatial_Filtering(self):
+ points = np.array(self.points)
+ bbox = [[0, 0], [45, 45]]
+ sf = sm.Spatial_Filtering(bbox, points, self.e, self.b, 2, 2, r=30)
+ exp = [0.11111111, 0.11111111, 0.20000000000000001, 0.085106379999999995,
+ 0.076923080000000005, 0.05789474, 0.052173909999999997, 0.066666669999999997, 0.04117647]
+ self.assertEqual(list(sf.r.round(8)), exp)
+
+
+class TestUtils(unittest.TestCase):
+ def test_sum_by_n(self):
+ d = np.array([10, 9, 20, 30])
+ w = np.array([0.5, 0.1, 0.3, 0.8])
+ n = 2
+ exp_sum = np.array([5.9, 30.])
+ np.testing.assert_array_almost_equal(exp_sum, sm.sum_by_n(d, w, n))
+
+ def test_standardized_mortality_ratio(self):
+ e = np.array([30, 25, 25, 15, 33, 21, 30, 20])
+ b = np.array([100, 100, 110, 90, 100, 90, 110, 90])
+ s_e = np.array([100, 45, 120, 100, 50, 30, 200, 80])
+ s_b = np.array([1000, 900, 1000, 900, 1000, 900, 1000, 900])
+ n = 2
+ exp_smr = np.array([2.48691099, 2.73684211])
+ np.testing.assert_array_almost_equal(exp_smr,
+ sm.standardized_mortality_ratio(e, b, s_e, s_b, n))
+
+ def test_choynowski(self):
+ e = np.array([30, 25, 25, 15, 33, 21, 30, 20])
+ b = np.array([100, 100, 110, 90, 100, 90, 110, 90])
+ n = 2
+ exp_choy = np.array([0.30437751, 0.29367033])
+ np.testing.assert_array_almost_equal(exp_choy, sm.choynowski(e, b, n))
+
+ def test_assuncao_rate(self):
+ e = np.array([30, 25, 25, 15, 33, 21, 30, 20])
+ b = np.array([100, 100, 110, 90, 100, 90, 110, 90])
+ exp_assuncao = np.array(
+ [1.04319254, -0.04117865, -0.56539054, -1.73762547])
+ np.testing.assert_array_almost_equal(
+ exp_assuncao, sm.assuncao_rate(e, b)[:4])
+
+
+suite = unittest.TestSuite()
+test_classes = [TestFlatten, TestWMean, TestAgeStd, TestSRate, TestHB,
+ TestKernel_AgeAdj_SM, TestUtils]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/examples/10740.dbf b/pysal/examples/10740.dbf
new file mode 100644
index 0000000..4dc3bd5
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diff --git a/pysal/examples/10740.shp b/pysal/examples/10740.shp
new file mode 100644
index 0000000..34ee937
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diff --git a/pysal/examples/10740.shx b/pysal/examples/10740.shx
new file mode 100644
index 0000000..9f72583
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diff --git a/pysal/examples/10740_queen.gal b/pysal/examples/10740_queen.gal
new file mode 100644
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diff --git a/pysal/examples/10740_rook.gal b/pysal/examples/10740_rook.gal
new file mode 100644
index 0000000..90f3237
--- /dev/null
+++ b/pysal/examples/10740_rook.gal
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diff --git a/pysal/examples/Chicago77.dbf b/pysal/examples/Chicago77.dbf
new file mode 100644
index 0000000..cfa0934
Binary files /dev/null and b/pysal/examples/Chicago77.dbf differ
diff --git a/pysal/examples/Chicago77.shp b/pysal/examples/Chicago77.shp
new file mode 100644
index 0000000..ee30858
Binary files /dev/null and b/pysal/examples/Chicago77.shp differ
diff --git a/pysal/examples/Chicago77.shx b/pysal/examples/Chicago77.shx
new file mode 100644
index 0000000..56f35b4
Binary files /dev/null and b/pysal/examples/Chicago77.shx differ
diff --git a/pysal/examples/Line.dbf b/pysal/examples/Line.dbf
new file mode 100644
index 0000000..b3f7f90
Binary files /dev/null and b/pysal/examples/Line.dbf differ
diff --git a/pysal/examples/Line.prj b/pysal/examples/Line.prj
new file mode 100644
index 0000000..a30c00a
--- /dev/null
+++ b/pysal/examples/Line.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
\ No newline at end of file
diff --git a/pysal/examples/Line.shp b/pysal/examples/Line.shp
new file mode 100644
index 0000000..6e27301
Binary files /dev/null and b/pysal/examples/Line.shp differ
diff --git a/pysal/examples/Line.shx b/pysal/examples/Line.shx
new file mode 100644
index 0000000..b27a051
Binary files /dev/null and b/pysal/examples/Line.shx differ
diff --git a/pysal/examples/NAT.dbf b/pysal/examples/NAT.dbf
new file mode 100644
index 0000000..dff6763
Binary files /dev/null and b/pysal/examples/NAT.dbf differ
diff --git a/pysal/examples/NAT.shp b/pysal/examples/NAT.shp
new file mode 100644
index 0000000..12e35b2
Binary files /dev/null and b/pysal/examples/NAT.shp differ
diff --git a/pysal/examples/NAT.shx b/pysal/examples/NAT.shx
new file mode 100644
index 0000000..1be8468
Binary files /dev/null and b/pysal/examples/NAT.shx differ
diff --git a/pysal/examples/Point.dbf b/pysal/examples/Point.dbf
new file mode 100644
index 0000000..13e7563
Binary files /dev/null and b/pysal/examples/Point.dbf differ
diff --git a/pysal/examples/Point.prj b/pysal/examples/Point.prj
new file mode 100644
index 0000000..a30c00a
--- /dev/null
+++ b/pysal/examples/Point.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
\ No newline at end of file
diff --git a/pysal/examples/Point.shp b/pysal/examples/Point.shp
new file mode 100644
index 0000000..846c2b3
Binary files /dev/null and b/pysal/examples/Point.shp differ
diff --git a/pysal/examples/Point.shx b/pysal/examples/Point.shx
new file mode 100644
index 0000000..8a9f027
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diff --git a/pysal/examples/Polygon.dbf b/pysal/examples/Polygon.dbf
new file mode 100644
index 0000000..d925117
Binary files /dev/null and b/pysal/examples/Polygon.dbf differ
diff --git a/pysal/examples/Polygon.prj b/pysal/examples/Polygon.prj
new file mode 100644
index 0000000..a30c00a
--- /dev/null
+++ b/pysal/examples/Polygon.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
\ No newline at end of file
diff --git a/pysal/examples/Polygon.shp b/pysal/examples/Polygon.shp
new file mode 100644
index 0000000..3ad50c2
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diff --git a/pysal/examples/Polygon.shx b/pysal/examples/Polygon.shx
new file mode 100644
index 0000000..eb12139
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diff --git a/pysal/examples/README.txt b/pysal/examples/README.txt
new file mode 100644
index 0000000..11d7482
--- /dev/null
+++ b/pysal/examples/README.txt
@@ -0,0 +1,5 @@
+Documentation for all example data sets for PySAL can be found at
+
+http://pysal.org/users/tutorials/examples.html
+
+If you obtained this example data by downloading it as a separate zip file, it should be unzipped in the pysal directory, so that the path to the data herein would be like pysal/examples/rook31.dbf, for example.
diff --git a/pysal/examples/__init__.py b/pysal/examples/__init__.py
new file mode 100644
index 0000000..492f33e
--- /dev/null
+++ b/pysal/examples/__init__.py
@@ -0,0 +1,9 @@
+import os
+import pysal
+
+__all__ = ['get_path']
+
+
+def get_path(example_name):
+ base = os.path.split(pysal.__file__)[0]
+ return os.path.join(base, 'examples', example_name)
diff --git a/pysal/examples/arcgis_ohio.dbf b/pysal/examples/arcgis_ohio.dbf
new file mode 100644
index 0000000..34cef0a
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diff --git a/pysal/examples/book.gal b/pysal/examples/book.gal
new file mode 100644
index 0000000..4d6fc1d
--- /dev/null
+++ b/pysal/examples/book.gal
@@ -0,0 +1,33 @@
+16
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diff --git a/pysal/examples/book.txt b/pysal/examples/book.txt
new file mode 100644
index 0000000..87bd9c9
--- /dev/null
+++ b/pysal/examples/book.txt
@@ -0,0 +1,18 @@
+16,6
+"id","y","z","z2","z4","wzz"
+1,39,-6.375,40.640625,1.651660e+03,68.531250
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+16,55,9.625,92.640625,8.582285e+03,98.6562509
diff --git a/pysal/examples/burkitt.dbf b/pysal/examples/burkitt.dbf
new file mode 100644
index 0000000..e7ecd09
Binary files /dev/null and b/pysal/examples/burkitt.dbf differ
diff --git a/pysal/examples/burkitt.shp b/pysal/examples/burkitt.shp
new file mode 100644
index 0000000..bcd3d72
Binary files /dev/null and b/pysal/examples/burkitt.shp differ
diff --git a/pysal/examples/burkitt.shx b/pysal/examples/burkitt.shx
new file mode 100644
index 0000000..459563f
Binary files /dev/null and b/pysal/examples/burkitt.shx differ
diff --git a/pysal/examples/calempdensity.csv b/pysal/examples/calempdensity.csv
new file mode 100644
index 0000000..bfe8734
--- /dev/null
+++ b/pysal/examples/calempdensity.csv
@@ -0,0 +1,59 @@
+"Geographic Area","Geographic Area","Geographic Name","GEONAME","GEOCOMP","STATE","Number of Employees for All Sectors","Number of employees","Class Number","sq. km","emp/sq km"
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+"05000US06003","06003","Alpine County, California","Alpine County, California","00","06",813,813,1,1913.1,0.42
+"05000US06005","06005","Amador County, California","Amador County, California","00","06",9061,9061,2,1534.7,5.9
+"05000US06007","06007","Butte County, California","Butte County, California","00","06",59578,59578,3,4246.6,14.03
+"05000US06009","06009","Calaveras County, California","Calaveras County, California","00","06",7344,7344,2,2642.3,2.78
+"05000US06011","06011","Colusa County, California","Colusa County, California","00","06",4000,4000,1,2980.5,1.34
+"05000US06013","06013","Contra Costa County, California","Contra Costa County, California","00","06",338156,338156,5,1865.5,181.27
+"05000US06015","06015","Del Norte County, California","Del Norte County, California","00","06",4303,4303,1,2610.4,1.65
+"05000US06017","06017","El Dorado County, California","El Dorado County, California","00","06",44477,44477,3,4432.8,10.03
+"05000US06019","06019","Fresno County, California","Fresno County, California","00","06",257975,257975,4,15444.7,16.7
+"05000US06021","06021","Glenn County, California","Glenn County, California","00","06",4487,4487,1,3405.5,1.32
+"05000US06023","06023","Humboldt County, California","Humboldt County, California","00","06",36962,36962,3,9253.5,3.99
+"05000US06025","06025","Imperial County, California","Imperial County, California","00","06",34156,34156,3,10813.4,3.16
+"05000US06027","06027","Inyo County, California","Inyo County, California","00","06",5820,5820,1,26397.5,0.22
+"05000US06029","06029","Kern County, California","Kern County, California","00","06",183412,183412,4,21086.8,8.7
+"05000US06031","06031","Kings County, California","Kings County, California","00","06",23610,23610,2,3598.8,6.56
+"05000US06033","06033","Lake County, California","Lake County, California","00","06",10648,10648,2,3259.4,3.27
+"05000US06035","06035","Lassen County, California","Lassen County, California","00","06",3860,3860,1,11803.9,0.33
+"05000US06037","06037","Los Angeles County, California","Los Angeles County, California","00","06",3895886,3895886,5,10515.3,370.5
+"05000US06039","06039","Madera County, California","Madera County, California","00","06",24957,24957,2,5538.5,4.51
+"05000US06041","06041","Marin County, California","Marin County, California","00","06",101358,101358,4,1346.2,75.29
+"05000US06043","06043","Mariposa County, California","Mariposa County, California","00","06",3739,3739,1,3758.6,0.99
+"05000US06045","06045","Mendocino County, California","Mendocino County, California","00","06",24898,24898,2,9089,2.74
+"05000US06047","06047","Merced County, California","Merced County, California","00","06",43369,43369,3,4995.8,8.68
+"05000US06049","06049","Modoc County, California","Modoc County, California","00","06",1467,1467,1,10215.9,0.14
+"05000US06051","06051","Mono County, California","Mono County, California","00","06",7289,7289,1,7885.2,0.92
+"05000US06053","06053","Monterey County, California","Monterey County, California","00","06",108660,108660,4,8603.8,12.63
+"05000US06055","06055","Napa County, California","Napa County, California","00","06",56029,56029,3,1952.5,28.7
+"05000US06057","06057","Nevada County, California","Nevada County, California","00","06",29805,29805,3,2480.3,12.02
+"05000US06059","06059","Orange County, California","Orange County, California","00","06",1478452,1478452,5,2045.3,722.85
+"05000US06061","06061","Placer County, California","Placer County, California","00","06",133427,133427,4,3637.4,36.68
+"05000US06063","06063","Plumas County, California","Plumas County, California","00","06",4863,4863,1,6614.8,0.74
+"05000US06065","06065","Riverside County, California","Riverside County, California","00","06",556789,556789,5,18669.1,29.82
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+"05000US06071","06071","San Bernardino County, California","San Bernardino County, California","00","06",579135,579135,5,51961.2,11.15
+"05000US06073","06073","San Diego County, California","San Diego County, California","00","06",1205862,1205862,5,10889.6,110.74
+"05000US06075","06075","San Francisco County, California","San Francisco County, California","00","06",497485,497485,5,121,4111.45
+"05000US06077","06077","San Joaquin County, California","San Joaquin County, California","00","06",179276,179276,4,3624.1,49.47
+"05000US06079","06079","San Luis Obispo County, California","San Luis Obispo County, California","00","06",88413,88413,3,8558.7,10.33
+"05000US06081","06081","San Mateo County, California","San Mateo County, California","00","06",368859,368859,5,1163.2,317.11
+"05000US06083","06083","Santa Barbara County, California","Santa Barbara County, California","00","06",145202,145202,4,7092.6,20.47
+"05000US06085","06085","Santa Clara County, California","Santa Clara County, California","00","06",886011,886011,5,3344.3,264.93
+"05000US06087","06087","Santa Cruz County, California","Santa Cruz County, California","00","06",76488,76488,3,1154.3,66.26
+"05000US06089","06089","Shasta County, California","Shasta County, California","00","06",52804,52804,3,9804.8,5.39
+"05000US06091","06091","Sierra County, California","Sierra County, California","00","06",324,324,1,2469.4,0.13
+"05000US06093","06093","Siskiyou County, California","Siskiyou County, California","00","06",9992,9992,2,16284,0.61
+"05000US06095","06095","Solano County, California","Solano County, California","00","06",108653,108653,4,2145,50.65
+"05000US06097","06097","Sonoma County, California","Sonoma County, California","00","06",165261,165261,4,4082.4,40.48
+"05000US06099","06099","Stanislaus County, California","Stanislaus County, California","00","06",141928,141928,4,3870.9,36.67
+"05000US06101","06101","Sutter County, California","Sutter County, California","00","06",20430,20430,2,1561,13.09
+"05000US06103","06103","Tehama County, California","Tehama County, California","00","06",13809,13809,2,7643.2,1.81
+"05000US06105","06105","Trinity County, California","Trinity County, California","00","06",1668,1668,1,8233.3,0.2
+"05000US06107","06107","Tulare County, California","Tulare County, California","00","06",94949,94949,4,12495,7.6
+"05000US06109","06109","Tuolumne County, California","Tuolumne County, California","00","06",14519,14519,2,5790.3,2.51
+"05000US06111","06111","Ventura County, California","Ventura County, California","00","06",273745,273745,5,4781,57.26
+"05000US06113","06113","Yolo County, California","Yolo County, California","00","06",63769,63769,3,2622.2,24.32
+"05000US06115","06115","Yuba County, California","Yuba County, California","00","06",11374,11374,2,1632.9,6.97
diff --git a/pysal/examples/columbus.dbf b/pysal/examples/columbus.dbf
new file mode 100644
index 0000000..f850bda
Binary files /dev/null and b/pysal/examples/columbus.dbf differ
diff --git a/pysal/examples/columbus.gal b/pysal/examples/columbus.gal
new file mode 100644
index 0000000..b8edabc
--- /dev/null
+++ b/pysal/examples/columbus.gal
@@ -0,0 +1,99 @@
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diff --git a/pysal/examples/columbus.html b/pysal/examples/columbus.html
new file mode 100644
index 0000000..af8b1ac
--- /dev/null
+++ b/pysal/examples/columbus.html
@@ -0,0 +1,132 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.1//EN"
+ "http://www.w3.org/TR/xhtml11/DTD/xhtml11.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+ <title>SAL Data Sets - Columbus</title>
+</head>
+
+<body>
+<h1>Columbus</h1>
+
+<h2>Data provided "as is," no warranties</h2>
+
+<h2>Description</h2>
+
+<p>Crime data for 49 neighborhoods in Columbus, OH, 1980</p>
+
+<p>Type = polygon shape file, projected, arbitrary units</p>
+
+<p>Observations = 49</p>
+
+<p>Variables = 20</p>
+
+<h2>Source</h2>
+
+<p>Anselin, Luc (1988). Spatial Econometrics. Boston, Kluwer
+ Academic, Table 12.1, p. 189.</p>
+
+<h2>Variables</h2>
+
+<table>
+ <thead>
+ <tr>
+ <th>Variable</th>
+ <th>Description</th>
+ </tr>
+ </thead>
+ <tbody>
+ <tr>
+ <td>AREA</td>
+ <td>neighborhood area (computed by ArcView)</td>
+ </tr>
+ <tr>
+ <td>PERIMETER</td>
+ <td>neighborhood perimeter (computed by ArcView)</td>
+ </tr>
+ <tr>
+ <td>COLUMBUS_</td>
+ <td>internal polygon ID (generated by ArcView)</td>
+ </tr>
+ <tr>
+ <td>COLUMBUS_I</td>
+ <td>internal polygon ID (geneated by ArcView)</td>
+ </tr>
+ <tr>
+ <td>POLYID</td>
+ <td>neighborhood ID, used in GeoDa User's Guide and tutorials</td>
+ </tr>
+ <tr>
+ <td>NEIG</td>
+ <td>neighborhood ID, used in Spatial Econometrics examples</td>
+ </tr>
+ <tr>
+ <td>HOVAL</td>
+ <td>housing value (in $1,000)</td>
+ </tr>
+ <tr>
+ <td>INC</td>
+ <td>household income (in $1,000)</td>
+ </tr>
+ <tr>
+ <td>CRIME</td>
+ <td>residential burglaries and vehicle thefts per 1000 households</td>
+ </tr>
+ <tr>
+ <td>OPEN</td>
+ <td>open space (area)</td>
+ </tr>
+ <tr>
+ <td>PLUMB</td>
+ <td>percent housing units without plumbing</td>
+ </tr>
+ <tr>
+ <td>DISCBD</td>
+ <td>distance to CBD</td>
+ </tr>
+ <tr>
+ <td>X</td>
+ <td>centroid x coordinate (in arbitrary digitizing units)</td>
+ </tr>
+ <tr>
+ <td>Y</td>
+ <td>centroid y coordinate (in arbitrary digitizing units)</td>
+ </tr>
+ <tr>
+ <td>NSA</td>
+ <td>north-south indicator variable (North = 1)</td>
+ </tr>
+ <tr>
+ <td>NSB</td>
+ <td>other north-south indicator variable (North = 1)</td>
+ </tr>
+ <tr>
+ <td>EW</td>
+ <td>east-west indicator variable (East = 1)</td>
+ </tr>
+ <tr>
+ <td>CP</td>
+ <td>core-periphery indicator variable (Core = 1)</td>
+ </tr>
+ <tr>
+ <td>THOUS</td>
+ <td>constant (= 1000)</td>
+ </tr>
+ <tr>
+ <td>NEIGNO</td>
+ <td>another neighborhood ID variable (NEIG + 1000)</td>
+ </tr>
+ </tbody>
+</table>
+
+<br />
+<hr />
+
+<p>Prepared by <a href="mailto:anselin at uiuc.edu">Luc Anselin</a></p>
+
+<p><a href="http://sal.agecon.uiuc.edu">UIUC-ACE Spatial Analysis
+Laboratory</a></p>
+
+<p>Last updated June 16, 2003</p>
+</body>
+</html>
diff --git a/pysal/examples/columbus.json b/pysal/examples/columbus.json
new file mode 100644
index 0000000..579b7c3
--- /dev/null
+++ b/pysal/examples/columbus.json
@@ -0,0 +1,104 @@
+{
+"type": "FeatureCollection",
+
+"features": [
+{ "type": "Feature", "properties": { "AREA": 0.309441, "PERIMETER": 2.440629, "COLUMBUS_": 2.0, "COLUMBUS_I": 5.0, "POLYID": 1.0, "NEIG": 5, "HOVAL": 80.467003, "INC": 19.531, "CRIME": 15.72598, "OPEN": 2.850747, "PLUMB": 0.217155, "DISCBD": 5.03, "X": 38.799999, "Y": 44.07, "NSA": 1.0, "NSB": 1.0, "EW": 1.0, "CP": 0.0, "THOUS": 1000.0, "NEIGNO": 1005.0 }, "bbox": [ 8.559700012207031, 13.995059967041016, 9.09996509552002, 14.742449760437012 ], "geometry": { "type": "Polygon", "coordinate [...]
+,
+{ "type": "Feature", "properties": { "AREA": 0.259329, "PERIMETER": 2.236939, "COLUMBUS_": 3.0, "COLUMBUS_I": 1.0, "POLYID": 2.0, "NEIG": 1, "HOVAL": 44.567001, "INC": 21.232, "CRIME": 18.801754, "OPEN": 5.29672, "PLUMB": 0.320581, "DISCBD": 4.27, "X": 35.619999, "Y": 42.380001, "NSA": 1.0, "NSB": 1.0, "EW": 0.0, "CP": 0.0, "THOUS": 1000.0, "NEIGNO": 1001.0 }, "bbox": [ 7.950088977813721, 13.727390289306641, 8.666550636291504, 14.263930320739746 ], "geometry": { "type": "Polygon", "coord [...]
+,
+{ "type": "Feature", "properties": { "AREA": 0.192468, "PERIMETER": 2.187547, "COLUMBUS_": 4.0, "COLUMBUS_I": 6.0, "POLYID": 3.0, "NEIG": 6, "HOVAL": 26.35, "INC": 15.956, "CRIME": 30.626781, "OPEN": 4.534649, "PLUMB": 0.374404, "DISCBD": 3.89, "X": 39.82, "Y": 41.18, "NSA": 1.0, "NSB": 1.0, "EW": 1.0, "CP": 0.0, "THOUS": 1000.0, "NEIGNO": 1006.0 }, "bbox": [ 8.653305053710938, 13.544429779052734, 9.351485252380371, 14.008090019226074 ], "geometry": { "type": "Polygon", "coordinates": [ [...]
+,
+{ "type": "Feature", "properties": { "AREA": 0.083841, "PERIMETER": 1.427635, "COLUMBUS_": 5.0, "COLUMBUS_I": 2.0, "POLYID": 4.0, "NEIG": 2, "HOVAL": 33.200001, "INC": 4.477, "CRIME": 32.38776, "OPEN": 0.394427, "PLUMB": 1.186944, "DISCBD": 3.7, "X": 36.5, "Y": 40.52, "NSA": 1.0, "NSB": 1.0, "EW": 0.0, "CP": 0.0, "THOUS": 1000.0, "NEIGNO": 1002.0 }, "bbox": [ 8.19859504699707, 13.586509704589844, 8.685274124145508, 13.861700057983398 ], "geometry": { "type": "Polygon", "coordinates": [ [ [...]
+,
+{ "type": "Feature", "properties": { "AREA": 0.488888, "PERIMETER": 2.997133, "COLUMBUS_": 6.0, "COLUMBUS_I": 7.0, "POLYID": 5.0, "NEIG": 7, "HOVAL": 23.225, "INC": 11.252, "CRIME": 50.73151, "OPEN": 0.405664, "PLUMB": 0.624596, "DISCBD": 2.83, "X": 40.009998, "Y": 38.0, "NSA": 1.0, "NSB": 1.0, "EW": 1.0, "CP": 0.0, "THOUS": 1000.0, "NEIGNO": 1007.0 }, "bbox": [ 8.677577018737793, 12.861089706420898, 9.401384353637695, 13.722209930419922 ], "geometry": { "type": "Polygon", "coordinates": [...]
+,
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+{ "type": "Feature", "properties": { "AREA": 0.053881, "PERIMETER": 0.934509, "COLUMBUS_": 39.0, "COLUMBUS_I": 24.0, "POLYID": 38.0, "NEIG": 24, "HOVAL": 22.700001, "INC": 11.107, "CRIME": 53.710938, "OPEN": 0.0, "PLUMB": 0.8, "DISCBD": 1.58, "X": 41.040001, "Y": 28.780001, "NSA": 0.0, "NSB": 0.0, "EW": 1.0, "CP": 1.0, "THOUS": 1000.0, "NEIGNO": 1024.0 }, "bbox": [ 9.084967613220215, 11.546170234680176, 9.343691825866699, 11.792989730834961 ], "geometry": { "type": "Polygon", "coordinate [...]
+,
+{ "type": "Feature", "properties": { "AREA": 0.174823, "PERIMETER": 2.335402, "COLUMBUS_": 40.0, "COLUMBUS_I": 47.0, "POLYID": 39.0, "NEIG": 47, "HOVAL": 39.599998, "INC": 18.476999, "CRIME": 19.100863, "OPEN": 0.0, "PLUMB": 0.314663, "DISCBD": 5.53, "X": 24.25, "Y": 26.690001, "NSA": 0.0, "NSB": 0.0, "EW": 0.0, "CP": 0.0, "THOUS": 1000.0, "NEIGNO": 1047.0 }, "bbox": [ 5.87490701675415, 11.057000160217285, 6.481366157531738, 11.781330108642578 ], "geometry": { "type": "Polygon", "coordin [...]
+,
+{ "type": "Feature", "properties": { "AREA": 0.302908, "PERIMETER": 2.285487, "COLUMBUS_": 41.0, "COLUMBUS_I": 16.0, "POLYID": 40.0, "NEIG": 16, "HOVAL": 61.950001, "INC": 29.833, "CRIME": 16.241299, "OPEN": 6.45131, "PLUMB": 0.132743, "DISCBD": 4.4, "X": 48.439999, "Y": 27.93, "NSA": 0.0, "NSB": 0.0, "EW": 1.0, "CP": 0.0, "THOUS": 1000.0, "NEIGNO": 1016.0 }, "bbox": [ 10.05659008026123, 11.213310241699219, 10.808690071105957, 11.74390983581543 ], "geometry": { "type": "Polygon", "coordi [...]
+,
+{ "type": "Feature", "properties": { "AREA": 0.137024, "PERIMETER": 1.525097, "COLUMBUS_": 42.0, "COLUMBUS_I": 14.0, "POLYID": 41.0, "NEIG": 14, "HOVAL": 42.099998, "INC": 22.207001, "CRIME": 18.905146, "OPEN": 0.293317, "PLUMB": 0.247036, "DISCBD": 5.33, "X": 51.240002, "Y": 27.799999, "NSA": 0.0, "NSB": 0.0, "EW": 1.0, "CP": 0.0, "THOUS": 1000.0, "NEIGNO": 1014.0 }, "bbox": [ 10.799189567565918, 11.232259750366211, 11.141839981079102, 11.727809906005859 ], "geometry": { "type": "Polygo [...]
+,
+{ "type": "Feature", "properties": { "AREA": 0.266541, "PERIMETER": 2.176543, "COLUMBUS_": 43.0, "COLUMBUS_I": 49.0, "POLYID": 42.0, "NEIG": 49, "HOVAL": 44.333, "INC": 25.872999, "CRIME": 16.49189, "OPEN": 1.792993, "PLUMB": 0.134439, "DISCBD": 3.87, "X": 29.02, "Y": 26.58, "NSA": 0.0, "NSB": 0.0, "EW": 0.0, "CP": 0.0, "THOUS": 1000.0, "NEIGNO": 1049.0 }, "bbox": [ 6.75403881072998, 10.997920036315918, 7.404403209686279, 11.586389541625977 ], "geometry": { "type": "Polygon", "coordinate [...]
+,
+{ "type": "Feature", "properties": { "AREA": 0.060241, "PERIMETER": 0.967793, "COLUMBUS_": 44.0, "COLUMBUS_I": 29.0, "POLYID": 43.0, "NEIG": 29, "HOVAL": 25.700001, "INC": 13.38, "CRIME": 36.663612, "OPEN": 0.0, "PLUMB": 0.589226, "DISCBD": 1.95, "X": 41.09, "Y": 27.49, "NSA": 0.0, "NSB": 0.0, "EW": 1.0, "CP": 1.0, "THOUS": 1000.0, "NEIGNO": 1029.0 }, "bbox": [ 9.082836151123047, 11.308409690856934, 9.341614723205566, 11.559189796447754 ], "geometry": { "type": "Polygon", "coordinates": [...]
+,
+{ "type": "Feature", "properties": { "AREA": 0.173337, "PERIMETER": 1.868044, "COLUMBUS_": 45.0, "COLUMBUS_I": 25.0, "POLYID": 44.0, "NEIG": 25, "HOVAL": 33.5, "INC": 16.961, "CRIME": 25.962263, "OPEN": 1.463993, "PLUMB": 0.329761, "DISCBD": 2.67, "X": 43.23, "Y": 27.309999, "NSA": 0.0, "NSB": 0.0, "EW": 1.0, "CP": 0.0, "THOUS": 1000.0, "NEIGNO": 1025.0 }, "bbox": [ 9.335508346557617, 11.211409568786621, 9.963891983032227, 11.546170234680176 ], "geometry": { "type": "Polygon", "coordinat [...]
+,
+{ "type": "Feature", "properties": { "AREA": 0.256431, "PERIMETER": 2.193039, "COLUMBUS_": 46.0, "COLUMBUS_I": 28.0, "POLYID": 45.0, "NEIG": 28, "HOVAL": 27.733, "INC": 14.135, "CRIME": 29.028488, "OPEN": 1.006118, "PLUMB": 2.3912, "DISCBD": 2.13, "X": 39.32, "Y": 25.85, "NSA": 0.0, "NSB": 0.0, "EW": 1.0, "CP": 1.0, "THOUS": 1000.0, "NEIGNO": 1028.0 }, "bbox": [ 8.622117042541504, 10.821869850158691, 9.178548812866211, 11.460300445556641 ], "geometry": { "type": "Polygon", "coordinates": [...]
+,
+{ "type": "Feature", "properties": { "AREA": 0.124728, "PERIMETER": 1.841029, "COLUMBUS_": 47.0, "COLUMBUS_I": 48.0, "POLYID": 46.0, "NEIG": 48, "HOVAL": 76.099998, "INC": 18.323999, "CRIME": 16.530533, "OPEN": 9.683953, "PLUMB": 0.424628, "DISCBD": 5.27, "X": 25.469999, "Y": 25.709999, "NSA": 0.0, "NSB": 0.0, "EW": 0.0, "CP": 0.0, "THOUS": 1000.0, "NEIGNO": 1048.0 }, "bbox": [ 6.167585849761963, 10.978030204772949, 6.678176879882812, 11.447600364685059 ], "geometry": { "type": "Polygon" [...]
+,
+{ "type": "Feature", "properties": { "AREA": 0.245249, "PERIMETER": 2.079986, "COLUMBUS_": 48.0, "COLUMBUS_I": 15.0, "POLYID": 47.0, "NEIG": 15, "HOVAL": 42.5, "INC": 18.950001, "CRIME": 27.822861, "OPEN": 0.0, "PLUMB": 0.268817, "DISCBD": 5.57, "X": 50.889999, "Y": 25.24, "NSA": 0.0, "NSB": 0.0, "EW": 1.0, "CP": 0.0, "THOUS": 1000.0, "NEIGNO": 1015.0 }, "bbox": [ 10.588159561157227, 10.788629531860352, 11.287420272827148, 11.315879821777344 ], "geometry": { "type": "Polygon", "coordinat [...]
+,
+{ "type": "Feature", "properties": { "AREA": 0.069762, "PERIMETER": 1.102032, "COLUMBUS_": 49.0, "COLUMBUS_I": 27.0, "POLYID": 48.0, "NEIG": 27, "HOVAL": 26.799999, "INC": 11.813, "CRIME": 26.645266, "OPEN": 4.884389, "PLUMB": 1.034807, "DISCBD": 2.33, "X": 41.209999, "Y": 25.9, "NSA": 0.0, "NSB": 0.0, "EW": 1.0, "CP": 1.0, "THOUS": 1000.0, "NEIGNO": 1027.0 }, "bbox": [ 9.093674659729004, 11.048029899597168, 9.3948974609375, 11.312379837036133 ], "geometry": { "type": "Polygon", "coordin [...]
+,
+{ "type": "Feature", "properties": { "AREA": 0.205964, "PERIMETER": 2.199169, "COLUMBUS_": 50.0, "COLUMBUS_I": 26.0, "POLYID": 49.0, "NEIG": 26, "HOVAL": 35.799999, "INC": 18.796, "CRIME": 22.541491, "OPEN": 0.259826, "PLUMB": 0.901442, "DISCBD": 3.03, "X": 42.669998, "Y": 24.959999, "NSA": 0.0, "NSB": 0.0, "EW": 1.0, "CP": 0.0, "THOUS": 1000.0, "NEIGNO": 1026.0 }, "bbox": [ 9.095559120178223, 10.828829765319824, 9.78189754486084, 11.285369873046875 ], "geometry": { "type": "Polygon", "c [...]
+
+]
+}
diff --git a/pysal/examples/columbus.shp b/pysal/examples/columbus.shp
new file mode 100644
index 0000000..d6dde43
Binary files /dev/null and b/pysal/examples/columbus.shp differ
diff --git a/pysal/examples/columbus.shx b/pysal/examples/columbus.shx
new file mode 100644
index 0000000..7086b6f
Binary files /dev/null and b/pysal/examples/columbus.shx differ
diff --git a/pysal/examples/desmith.gal b/pysal/examples/desmith.gal
new file mode 100644
index 0000000..43bb702
--- /dev/null
+++ b/pysal/examples/desmith.gal
@@ -0,0 +1,21 @@
+10
+1 2
+2 4
+2 2
+1 5
+3 2
+4 7
+4 4
+1 3 5 8
+5 4
+2 4 6 9
+6 2
+5 10
+7 2
+3 8
+8 3
+4 7 9
+9 3
+5 8 10
+10 2
+6 9
diff --git a/pysal/examples/desmith.txt b/pysal/examples/desmith.txt
new file mode 100644
index 0000000..2b0194a
--- /dev/null
+++ b/pysal/examples/desmith.txt
@@ -0,0 +1,12 @@
+10,2
+"id","z"
+1,2.24
+2,3.10
+3,4.55
+4,-5.15
+5,-4.39
+6,0.46
+7,5.54
+8,9.02
+9,-2.09
+10,-3.06
diff --git a/pysal/examples/eberly_net.dbf b/pysal/examples/eberly_net.dbf
new file mode 100644
index 0000000..1b2478c
Binary files /dev/null and b/pysal/examples/eberly_net.dbf differ
diff --git a/pysal/examples/eberly_net.shp b/pysal/examples/eberly_net.shp
new file mode 100644
index 0000000..a6e289f
Binary files /dev/null and b/pysal/examples/eberly_net.shp differ
diff --git a/pysal/examples/eberly_net.shx b/pysal/examples/eberly_net.shx
new file mode 100644
index 0000000..cc5763d
Binary files /dev/null and b/pysal/examples/eberly_net.shx differ
diff --git a/pysal/examples/eberly_net_pts_offnetwork.dbf b/pysal/examples/eberly_net_pts_offnetwork.dbf
new file mode 100644
index 0000000..2417238
Binary files /dev/null and b/pysal/examples/eberly_net_pts_offnetwork.dbf differ
diff --git a/pysal/examples/eberly_net_pts_offnetwork.shp b/pysal/examples/eberly_net_pts_offnetwork.shp
new file mode 100644
index 0000000..873a42c
Binary files /dev/null and b/pysal/examples/eberly_net_pts_offnetwork.shp differ
diff --git a/pysal/examples/eberly_net_pts_offnetwork.shx b/pysal/examples/eberly_net_pts_offnetwork.shx
new file mode 100644
index 0000000..23b1e7d
Binary files /dev/null and b/pysal/examples/eberly_net_pts_offnetwork.shx differ
diff --git a/pysal/examples/eberly_net_pts_onnetwork.dbf b/pysal/examples/eberly_net_pts_onnetwork.dbf
new file mode 100644
index 0000000..a09cb8f
Binary files /dev/null and b/pysal/examples/eberly_net_pts_onnetwork.dbf differ
diff --git a/pysal/examples/eberly_net_pts_onnetwork.shp b/pysal/examples/eberly_net_pts_onnetwork.shp
new file mode 100644
index 0000000..412d263
Binary files /dev/null and b/pysal/examples/eberly_net_pts_onnetwork.shp differ
diff --git a/pysal/examples/eberly_net_pts_onnetwork.shx b/pysal/examples/eberly_net_pts_onnetwork.shx
new file mode 100644
index 0000000..6a01e9c
Binary files /dev/null and b/pysal/examples/eberly_net_pts_onnetwork.shx differ
diff --git a/pysal/examples/examples.txt b/pysal/examples/examples.txt
new file mode 100644
index 0000000..68640e3
--- /dev/null
+++ b/pysal/examples/examples.txt
@@ -0,0 +1,141 @@
+******************************************************************
+PySAL: Example Data Sets
+******************************************************************
+
+PySAL comes with a number of example data sets that are used in some of the
+documentation strings in the source code. All the example data sets can be
+found in the **examples** directory.
+
+10740
+=====
+Polygon shapefile for Albuquerque New Mexico.
+
+* 10740.dbf: attribute database file
+* 10740.shp: shapefile
+* 10740.shx: spatial index
+* 10740_queen.gal: queen contiguity GAL format
+* 10740_rook.gal: rook contiguity GAL format
+
+
+book
+====
+Synthetic data to illustrate spatial weights. Source: Anselin, L. and S.J. Rey (in
+progress) Spatial Econometrics: Foundations.
+
+* book.gal: rook contiguity for regular lattice
+* book.txt: attribute data for regular lattice
+
+calempdensity
+=============
+Employment density for California counties. Source: Anselin, L. and S.J. Rey (in
+progress) Spatial Econometrics: Foundations.
+
+* calempdensity.csv: data on employment and employment density in California
+ counties.
+
+chicago77
+=========
+Chicago Community Areas (n=77). Source: Anselin, L. and S.J. Rey (in
+progress) Spatial Econometrics: Foundations.
+
+* Chicago77.dbf: attribute data
+* Chicago77.shp: shapefile
+* Chicago77.shx: spatial index
+
+
+desmith
+=======
+Example data for autocorrelation analysis. Source: de Smith et al (2009)
+`Geospatial Analysis
+<http://www.spatialanalysisonline.com/output/html/MoranIandGearyC.html>`_ (Used
+with permission)
+
+* desmith.txt: attribute data for 10 spatial units
+* desmith.gal: spatial weights in GAL format
+
+juvenile
+========
+Cardiff juvenile delinquent residences.
+
+* juvenile.dbf: attribute data
+* juvenile.html: documentation
+* juvenile.shp: shapefile
+* juvenile.shx: spatial index
+
+mexico
+======
+State regional income Mexican states 1940-2000. Source: Rey, S.J. and M.L.
+Sastre Gutierrez. "Interregional inequality dynamics in Mexico." Spatial
+Economic Analysis. Forthcoming.
+
+* mexico.csv: attribute data
+* mexico.gal: spatial weights in GAL format
+
+rook31
+======
+Small test shapefile
+
+* rook31.dbf: attribute data
+* rook31.gal: spatia weights in GAL format
+* rook31.shp: shapefile
+* rook31.shx: spatial index
+
+
+sacramento2
+===========
+1998 and 2001 Zip Code Business Patterns (Census Bureau) for Sacramento MSA
+
+ * sacramento2.dbf
+ * sacramento2.sbn
+ * sacramento2.sbx
+ * sacramento2.shp
+ * sacramento2.shx
+
+shp_test
+========
+Sample Shapefiles used only for testing purposes. Each example include a ".shp" Shapefile, ".shx" Shapefile Index, ".dbf" DBase file, and a ".prj" ESRI Projection file.
+
+Examples include:
+
+ * Point: Example of an ESRI Shapefile of Type 1 (Point).
+ * Line: Example of an ESRI Shapefile of Type 3 (Line).
+ * Polygon: Example of an ESRI Shapefile of Type 5 (Polygon).
+
+sids2
+=====
+North Carolina county SIDS death counts and rates
+
+ * sids2.dbf: attribute data
+ * sids2.html: documentation
+ * sids2.shp: shapefile
+ * sids2.shx: spatial index
+ * sids2.gal: GAL file for spatial weights
+
+stl_hom
+=======
+Homicides and selected socio-economic characteristics for counties surrounding St Louis, MO. Data aggregated for three time periods: 1979-84 (steady decline in homicides), 1984-88 (stable period), and 1988-93 (steady increase in homicides). Source: S. Messner, L. Anselin, D. Hawkins, G. Deane, S. Tolnay, R. Baller (2000). An Atlas of the Spatial Patterning of County-Level Homicide, 1960-1990. Pittsburgh, PA, National Consortium on Violence Research (NCOVR).
+
+ * stl_hom.html: Metadata
+ * stl_hom.txt: txt file with attribute data
+ * stl_hom.wkt: A Well-Known-Text representation of the geometry.
+ * stl_hom.csv: attribute data and WKT geometry.
+ * stl.hom.gal: GAL file for spatial weights
+
+
+US Regional Incomes
+===================
+Per capita income for the lower 48 US states, 1929-2010
+
+ * us48.shp: shapefile
+ * us48.dbf: dbf for shapefile
+ * us48.shx: index for shapefile
+ * usjoin.csv: attribute data (comma delimited file)
+
+Virginia
+========
+Virginia Counties Shapefile.
+
+ * virginia.shp: Shapefile
+ * virginia.shx: shapefile index
+ * virginia.dbf: attributes
+ * virginia.prj: shapefile projection
diff --git a/pysal/examples/geobugs_scot b/pysal/examples/geobugs_scot
new file mode 100644
index 0000000..d3c326e
--- /dev/null
+++ b/pysal/examples/geobugs_scot
@@ -0,0 +1,66 @@
+list( num = c(3, 2, 1, 3, 3, 0, 5, 0, 5, 4,
+0, 2, 3, 3, 2, 6, 6, 6, 5, 3,
+3, 2, 4, 8, 3, 3, 4, 4, 11, 6,
+7, 3, 4, 9, 4, 2, 4, 6, 3, 4,
+5, 5, 4, 5, 4, 6, 6, 4, 9, 2,
+4, 4, 4, 5, 6, 5
+),
+adj = c(
+19, 9, 5,
+10, 7,
+12,
+28, 20, 18,
+19, 12, 1,
+
+17, 16, 13, 10, 2,
+
+29, 23, 19, 17, 1,
+22, 16, 7, 2,
+
+5, 3,
+19, 17, 7,
+35, 32, 31,
+29, 25,
+29, 22, 21, 17, 10, 7,
+29, 19, 16, 13, 9, 7,
+56, 55, 33, 28, 20, 4,
+17, 13, 9, 5, 1,
+56, 18, 4,
+50, 29, 16,
+16, 10,
+39, 34, 29, 9,
+56, 55, 48, 47, 44, 31, 30, 27,
+29, 26, 15,
+43, 29, 25,
+56, 32, 31, 24,
+45, 33, 18, 4,
+50, 43, 34, 26, 25, 23, 21, 17, 16, 15, 9,
+55, 45, 44, 42, 38, 24,
+47, 46, 35, 32, 27, 24, 14,
+31, 27, 14,
+55, 45, 28, 18,
+54, 52, 51, 43, 42, 40, 39, 29, 23,
+46, 37, 31, 14,
+41, 37,
+46, 41, 36, 35,
+54, 51, 49, 44, 42, 30,
+40, 34, 23,
+52, 49, 39, 34,
+53, 49, 46, 37, 36,
+51, 43, 38, 34, 30,
+42, 34, 29, 26,
+49, 48, 38, 30, 24,
+55, 33, 30, 28,
+53, 47, 41, 37, 35, 31,
+53, 49, 48, 46, 31, 24,
+49, 47, 44, 24,
+54, 53, 52, 48, 47, 44, 41, 40, 38,
+29, 21,
+54, 42, 38, 34,
+54, 49, 40, 34,
+49, 47, 46, 41,
+52, 51, 49, 38, 34,
+56, 45, 33, 30, 24, 18,
+55, 27, 24, 20, 18
+),
+sumNumNeigh = 234)
\ No newline at end of file
diff --git a/pysal/examples/geodanet/crimes.dbf b/pysal/examples/geodanet/crimes.dbf
new file mode 100644
index 0000000..523c2c8
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diff --git a/pysal/examples/geodanet/crimes.prj b/pysal/examples/geodanet/crimes.prj
new file mode 100644
index 0000000..b7b18c8
--- /dev/null
+++ b/pysal/examples/geodanet/crimes.prj
@@ -0,0 +1 @@
+PROJCS["NAD_1983_StatePlane_Arizona_Central_FIPS_0202_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",699998.6],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-111.9166666666667],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",31.0],UNIT["Foot_US",0.3048006096012192]]
\ No newline at end of file
diff --git a/pysal/examples/geodanet/crimes.sbn b/pysal/examples/geodanet/crimes.sbn
new file mode 100644
index 0000000..4646933
Binary files /dev/null and b/pysal/examples/geodanet/crimes.sbn differ
diff --git a/pysal/examples/geodanet/crimes.sbx b/pysal/examples/geodanet/crimes.sbx
new file mode 100644
index 0000000..4a7fea5
Binary files /dev/null and b/pysal/examples/geodanet/crimes.sbx differ
diff --git a/pysal/examples/geodanet/crimes.shp b/pysal/examples/geodanet/crimes.shp
new file mode 100644
index 0000000..efc7d25
Binary files /dev/null and b/pysal/examples/geodanet/crimes.shp differ
diff --git a/pysal/examples/geodanet/crimes.shp.xml b/pysal/examples/geodanet/crimes.shp.xml
new file mode 100644
index 0000000..6ec0a37
--- /dev/null
+++ b/pysal/examples/geodanet/crimes.shp.xml
@@ -0,0 +1,3 @@
+<?xml version="1.0"?>
+<!--<!DOCTYPE metadata SYSTEM "http://www.esri.com/metadata/esriprof80.dtd">-->
+<metadata xml:lang="en"><Esri><CreaDate>20120210</CreaDate><CreaTime>09543000</CreaTime><SyncOnce>FALSE</SyncOnce><DataProperties><lineage><Process ToolSource="C:\Program Files\ArcGIS\ArcToolbox\Toolboxes\Data Management Tools.tbx\DefineProjection" Date="20100423" Time="143725">DefineProjection MesaCrime_SPCentral_feet PROJCS['NAD_1983_StatePlane_Arizona_Central_FIPS_0202_Feet',GEOGCS['GCS_North_American_1983',DATUM['D_North_American_1983',SPHEROID['GRS_1980',6378137.0,298.257222101]],PR [...]
diff --git a/pysal/examples/geodanet/crimes.shx b/pysal/examples/geodanet/crimes.shx
new file mode 100644
index 0000000..8a794bc
Binary files /dev/null and b/pysal/examples/geodanet/crimes.shx differ
diff --git a/pysal/examples/geodanet/schools.dbf b/pysal/examples/geodanet/schools.dbf
new file mode 100644
index 0000000..70ab8d1
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diff --git a/pysal/examples/geodanet/schools.prj b/pysal/examples/geodanet/schools.prj
new file mode 100644
index 0000000..b7b18c8
--- /dev/null
+++ b/pysal/examples/geodanet/schools.prj
@@ -0,0 +1 @@
+PROJCS["NAD_1983_StatePlane_Arizona_Central_FIPS_0202_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",699998.6],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-111.9166666666667],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",31.0],UNIT["Foot_US",0.3048006096012192]]
\ No newline at end of file
diff --git a/pysal/examples/geodanet/schools.sbn b/pysal/examples/geodanet/schools.sbn
new file mode 100644
index 0000000..0508084
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diff --git a/pysal/examples/geodanet/schools.sbx b/pysal/examples/geodanet/schools.sbx
new file mode 100644
index 0000000..abadf28
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diff --git a/pysal/examples/geodanet/schools.shp b/pysal/examples/geodanet/schools.shp
new file mode 100644
index 0000000..b71949e
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diff --git a/pysal/examples/geodanet/schools.shp.xml b/pysal/examples/geodanet/schools.shp.xml
new file mode 100644
index 0000000..c09b39c
--- /dev/null
+++ b/pysal/examples/geodanet/schools.shp.xml
@@ -0,0 +1,546 @@
+<?xml version="1.0"?>
+<metadata><idinfo><citation><citeinfo><origin>Arizona Department of Environmental Quality, Arizona Department of Health Services</origin><pubdate>August 14, 2007</pubdate><title Sync="TRUE">SCHOOLS_EVERYTHING_8_7_08</title><edition>Schools 8-14-2007</edition><geoform Sync="TRUE">vector digital data</geoform><onlink>\\adeq.lcl\gisprod\data\adeq\schools-everything-8-14-07.shp</onlink><ftname Sync="TRUE">SCHOOLS_EVERYTHING_8_7_08</ftname></citeinfo></citation><descript><abstract>This data s [...]
+
+All data are provided "as is" and may contain errors. The data are for reference and illustration purposes only and are not suitable for site-specific decision making. Information found here should not be used for making financial or any other commitments. Conclusions drawn from such information are the responsibility of the user.
+
+ADEQ assumes no responsibility for errors arising from misuse of the data or maps derived from the data. ADEQ disclaims any liability for injury, damage or loss that might result from the use of this information. In no event shall ADEQ become liable to users of these data and maps, or any other party, arising from the use or modification of the data.</useconst><ptcontac><cntinfo><cntorgp><cntorg>Arizona Department of Environmental Quality</cntorg></cntorgp><cntaddr><addrtype>mailing and [...]
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diff --git a/pysal/examples/geodanet/schools.shx b/pysal/examples/geodanet/schools.shx
new file mode 100644
index 0000000..5c20492
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diff --git a/pysal/examples/geodanet/streets.dbf b/pysal/examples/geodanet/streets.dbf
new file mode 100644
index 0000000..ce6d200
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diff --git a/pysal/examples/geodanet/streets.prj b/pysal/examples/geodanet/streets.prj
new file mode 100644
index 0000000..b7b18c8
--- /dev/null
+++ b/pysal/examples/geodanet/streets.prj
@@ -0,0 +1 @@
+PROJCS["NAD_1983_StatePlane_Arizona_Central_FIPS_0202_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",699998.6],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-111.9166666666667],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",31.0],UNIT["Foot_US",0.3048006096012192]]
\ No newline at end of file
diff --git a/pysal/examples/geodanet/streets.sbn b/pysal/examples/geodanet/streets.sbn
new file mode 100644
index 0000000..4f4e82f
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diff --git a/pysal/examples/geodanet/streets.sbx b/pysal/examples/geodanet/streets.sbx
new file mode 100644
index 0000000..5fc350e
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diff --git a/pysal/examples/geodanet/streets.shp b/pysal/examples/geodanet/streets.shp
new file mode 100644
index 0000000..b5809a7
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diff --git a/pysal/examples/geodanet/streets.shx b/pysal/examples/geodanet/streets.shx
new file mode 100644
index 0000000..4733484
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diff --git a/pysal/examples/juvenile.dbf b/pysal/examples/juvenile.dbf
new file mode 100644
index 0000000..c5e8e3f
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diff --git a/pysal/examples/juvenile.gwt b/pysal/examples/juvenile.gwt
new file mode 100644
index 0000000..6d9f409
--- /dev/null
+++ b/pysal/examples/juvenile.gwt
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diff --git a/pysal/examples/juvenile.html b/pysal/examples/juvenile.html
new file mode 100644
index 0000000..39626f3
--- /dev/null
+++ b/pysal/examples/juvenile.html
@@ -0,0 +1,63 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.1//EN"
+ "http://www.w3.org/TR/xhtml11/DTD/xhtml11.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+ <title>SAL Data Sets - Juvenile</title>
+</head>
+
+<body>
+<h1>JUVENILE</h1>
+
+<h2>Data provided "as is," no warranties</h2>
+
+<h2>Description</h2>
+
+<p>Residences of juvenile offenders in Cardiff, UK</p>
+
+<p>Type = point shape file, projected, unknown projection</p>
+
+<p>Observations = 168</p>
+
+<p>Variables = 3</p>
+
+<h2>Source</h2>
+
+<p>Bailey, T. and A. Gatrell (1995). Interactive Spatial
+ Data Analysis. New York: Wiley, p. 95.</p>
+
+<h2>Variables</h2>
+
+<table>
+ <thead>
+ <tr>
+ <th>Variable</th>
+ <th>Description</th>
+ </tr>
+ </thead>
+ <tbody>
+ <tr>
+ <td>ID</td>
+ <td>location identifier</td>
+ </tr>
+ <tr>
+ <td>X</td>
+ <td>X-coordinate</td>
+ </tr>
+ <tr>
+ <td>Y</td>
+ <td>Y-coordinate</td>
+ </tr> </tbody>
+</table>
+<br />
+
+<hr />
+
+<p>Prepared by <a href="mailto:anselin at uiuc.edu">Luc Anselin</a></p>
+
+<p><a href="http://sal.agecon.uiuc.edu">UIUC-ACE Spatial Analysis
+Laboratory</a></p>
+
+<p>Last updated June 24, 2003</p>
+</body>
+</html>
diff --git a/pysal/examples/juvenile.shp b/pysal/examples/juvenile.shp
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diff --git a/pysal/examples/lattice10x10.shp b/pysal/examples/lattice10x10.shp
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diff --git a/pysal/examples/mexico.csv b/pysal/examples/mexico.csv
new file mode 100644
index 0000000..14f87ac
--- /dev/null
+++ b/pysal/examples/mexico.csv
@@ -0,0 +1,33 @@
+State,pcgdp1940,pcgdp1950,pcgdp1960,pcgdp1970,pcgdp1980,pcgdp1990,pcgdp2000,hanson03,hanson98,esquivel99,inegi,inegi2
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+Mexico,3408.000,4972.000,9053.000,17164.000,20165.000,18547.000,16322.000,4.000,4.000,1.000,3.000,3.000
+Michoacan,3327.000,5272.000,5244.000,8109.000,11206.000,10980.000,11838.000,3.000,3.000,7.000,4.000,4.000
+Morelos,6936.000,8962.000,10499.000,13892.000,16513.000,17701.000,18170.000,3.000,3.000,2.000,3.000,3.000
+Nayarit,4836.000,7515.000,7621.000,10880.000,13354.000,12757.000,11478.000,2.000,2.000,6.000,4.000,4.000
+Nuevo Leon,9073.000,11490.000,20117.000,28206.000,34856.000,34726.000,38672.000,1.000,1.000,5.000,2.000,2.000
+Oaxaca,1892.000,4538.000,4140.000,5230.000,7730.000,8465.000,9010.000,5.000,5.000,7.000,5.000,5.000
+Puebla,3569.000,6415.000,6542.000,9775.000,13374.000,11895.000,15685.000,3.000,3.000,2.000,3.000,5.000
+Quertaro,11016.000,5560.000,7110.000,14073.000,20088.000,22441.000,26149.000,3.000,3.000,3.000,3.000,4.000
+Quintana Roo,21965.000,28747.000,9677.000,17046.000,26695.000,25049.000,33442.000,6.000,5.000,4.000,5.000,5.000
+San Luis Potosi,4372.000,7533.000,6440.000,9721.000,12691.000,15436.000,15866.000,2.000,2.000,3.000,4.000,4.000
+Sinaloa,4840.000,6663.000,9613.000,14477.000,15312.000,15823.000,15242.000,2.000,2.000,6.000,1.000,1.000
+Sonora,6399.000,10345.000,12134.000,22662.000,23181.000,24784.000,24068.000,1.000,1.000,5.000,1.000,1.000
+Tabasco,2459.000,3857.000,6494.000,9367.000,42361.000,16055.000,13360.000,6.000,5.000,4.000,5.000,5.000
+Tamaulipas,7508.000,8536.000,8383.000,17128.000,21937.000,19983.000,23546.000,1.000,1.000,5.000,2.000,2.000
+Tlaxcala,3605.000,4178.000,4357.000,6245.000,9882.000,10339.000,11701.000,3.000,3.000,2.000,3.000,3.000
+Veracruz,5203.000,10143.000,11404.000,12240.000,14252.000,13796.000,12191.000,3.000,3.000,4.000,5.000,5.000
+Yucatan,7990.000,8428.000,10067.000,11665.000,15239.000,13979.000,17509.000,6.000,5.000,4.000,5.000,5.000
+Zacatecas,3734.000,6435.000,5821.000,7426.000,8876.000,11656.000,11130.000,2.000,2.000,3.000,4.000,4.000
diff --git a/pysal/examples/mexico.gal b/pysal/examples/mexico.gal
new file mode 100644
index 0000000..a489957
--- /dev/null
+++ b/pysal/examples/mexico.gal
@@ -0,0 +1,65 @@
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diff --git a/pysal/examples/nat_queen.gal b/pysal/examples/nat_queen.gal
new file mode 100644
index 0000000..c1675e9
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diff --git a/pysal/examples/nat_queen_old.gal b/pysal/examples/nat_queen_old.gal
new file mode 100644
index 0000000..555db04
--- /dev/null
+++ b/pysal/examples/nat_queen_old.gal
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diff --git a/pysal/examples/natregimes.dbf b/pysal/examples/natregimes.dbf
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diff --git a/pysal/examples/natregimes.shp b/pysal/examples/natregimes.shp
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diff --git a/pysal/examples/natregimes.shx b/pysal/examples/natregimes.shx
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diff --git a/pysal/examples/nonplanarsegments.dbf b/pysal/examples/nonplanarsegments.dbf
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diff --git a/pysal/examples/nonplanarsegments.prj b/pysal/examples/nonplanarsegments.prj
new file mode 100644
index 0000000..a30c00a
--- /dev/null
+++ b/pysal/examples/nonplanarsegments.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
\ No newline at end of file
diff --git a/pysal/examples/nonplanarsegments.qpj b/pysal/examples/nonplanarsegments.qpj
new file mode 100644
index 0000000..5fbc831
--- /dev/null
+++ b/pysal/examples/nonplanarsegments.qpj
@@ -0,0 +1 @@
+GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]
diff --git a/pysal/examples/nonplanarsegments.shp b/pysal/examples/nonplanarsegments.shp
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diff --git a/pysal/examples/ohio.swm b/pysal/examples/ohio.swm
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diff --git a/pysal/examples/rook31.dbf b/pysal/examples/rook31.dbf
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diff --git a/pysal/examples/rook31.gal b/pysal/examples/rook31.gal
new file mode 100644
index 0000000..c9d91b8
--- /dev/null
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diff --git a/pysal/examples/sacramentot2.dbf b/pysal/examples/sacramentot2.dbf
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new file mode 100644
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new file mode 100644
index 0000000..aa280d1
--- /dev/null
+++ b/pysal/examples/sids2.gal
@@ -0,0 +1,201 @@
+0 100 sids2 FIPSNO
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diff --git a/pysal/examples/sids2.html b/pysal/examples/sids2.html
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+++ b/pysal/examples/sids2.html
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+<?xml version="1.0" encoding="iso-8859-1"?>
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.1//EN"
+ "http://www.w3.org/TR/xhtml11/DTD/xhtml11.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+ <title>SAL Data Sets - SIDS2</title>
+</head>
+
+<body>
+
+<h1>SIDS2</h1>
+
+<h2>Data provided "as is," no warranties</h2>
+
+<h2>Description</h2>
+
+<p>Sudden Infant Death Syndrome sample data for North Carolina
+ counties, two time periods (1974-78 and 1979-84). Same as
+ SIDS data set, except that the computed rates are included.</p>
+
+<p>Type = polygon shape file, unprojected, lat-lon</p>
+
+<p>Observations = 100</p>
+
+<p>Variables = 18</p>
+
+
+<h2>Source</h2>
+
+<p>Cressie, Noel (1993). Statistics for Spatial Data.
+ New York, Wiley, pp. 386-389. Rates computed.</p>
+
+<h2>Variables</h2>
+
+<table>
+<thead>
+ <tr>
+ <th>Variable</th>
+ <th>Description</th>
+ </tr>
+</thead>
+<tbody>
+ <tr>
+ <td>AREA</td>
+ <td>county area (computed by ArcView)</td>
+ </tr>
+ <tr>
+ <td>PERIMETER</td>
+ <td>county perimeter (computed by ArcView)</td>
+ </tr>
+ <tr>
+ <td>CNTY_</td>
+ <td>county internal ID</td>
+ </tr>
+ <tr>
+ <td>CNTY_ID</td>
+ <td>county internal ID</td>
+ </tr>
+ <tr>
+ <td>NAME</td>
+ <td>county name</td>
+ </tr>
+ <tr>
+ <td>FIPS</td>
+ <td>county fips code, as character (state code + county code)</td>
+ </tr>
+ <tr>
+ <td>FIPSNO</td>
+ <td>county fips code, numeric, used in GeoDa User's Guide and tutorials</td>
+ </tr>
+ <tr>
+ <td>CRESS_ID</td>
+ <td>county ID used by Cressie</td>
+ </tr>
+ <tr>
+ <td>BIR74</td>
+ <td>live births, 1974-78</td>
+ </tr>
+ <tr>
+ <td>SID74</td>
+ <td>SIDS deaths, 1974-78</td>
+ </tr>
+ <tr>
+ <td>NWBIR74</td>
+ <td>non-white births, 1974-78</td>
+ </tr>
+ <tr>
+ <td>BIR79</td>
+ <td>live births, 1979-84</td>
+ </tr>
+ <tr>
+ <td>SID79</td>
+ <td>SIDS deaths, 1979-84</td>
+ </tr>
+ <tr>
+ <td>NWBIR79</td>
+ <td>non-white births, 1979-84</td>
+ </tr>
+ <tr>
+ <td>SIDR74</td>
+ <td>SIDS death rate per 1,000 (1974-78)</td>
+ </tr>
+ <tr>
+ <td>SIDR79</td>
+ <td>SIDS death rate per 1,000 (1979-84)</td>
+ </tr>
+ <tr>
+ <td>NWR74</td>
+ <td>non-white birth rate (non-white per 1000 births), 1974-78</td>
+ </tr>
+ <tr>
+ <td>NWR79</td>
+ <td>non-white birth rate (non-white per 1000 births), 1979-84</td>
+ </tr>
+</tbody>
+</table>
+
+<br />
+<hr />
+<p>Prepared by <a href="mailto:anselin at uiuc.edu">Luc Anselin</a></p>
+<p><a href="http://sal.agecon.uiuc.edu">UIUC-ACE Spatial Analysis Laboratory</a></p>
+<p>Last updated June 16, 2003</p>
+</body>
+</html>
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+PROJCS["WGS_1984_Web_Mercator_Auxiliary_Sphere",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Mercator_Auxiliary_Sphere"],PARAMETER["False_Easting",0.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",0.0],PARAMETER["Standard_Parallel_1",0.0],PARAMETER["Auxiliary_Sphere_Type",0.0],UNIT["Meter",1.0]]
\ No newline at end of file
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new file mode 100644
index 0000000..5c6f76d
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\ No newline at end of file
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diff --git a/pysal/examples/snow_maps/Soho_Network.dbf b/pysal/examples/snow_maps/Soho_Network.dbf
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diff --git a/pysal/examples/snow_maps/Soho_Network.prj b/pysal/examples/snow_maps/Soho_Network.prj
new file mode 100644
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\ No newline at end of file
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diff --git a/pysal/examples/spat-sym-us.mat b/pysal/examples/spat-sym-us.mat
new file mode 100644
index 0000000..678f0c0
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diff --git a/pysal/examples/spat-sym-us.wk1 b/pysal/examples/spat-sym-us.wk1
new file mode 100644
index 0000000..9c9bd51
Binary files /dev/null and b/pysal/examples/spat-sym-us.wk1 differ
diff --git a/pysal/examples/spdep_listw2WB_columbus b/pysal/examples/spdep_listw2WB_columbus
new file mode 100644
index 0000000..958bada
--- /dev/null
+++ b/pysal/examples/spdep_listw2WB_columbus
@@ -0,0 +1,59 @@
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\ No newline at end of file
diff --git a/pysal/examples/spi_download.csv b/pysal/examples/spi_download.csv
new file mode 100644
index 0000000..a547490
--- /dev/null
+++ b/pysal/examples/spi_download.csv
@@ -0,0 +1,66 @@
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+"400","46","South Dakota",3030 ,3286 ,3564 ,4089 ,5175 ,5187 ,5706 ,5600 ,6352 ,7302 ,8059 ,8054 ,9410 ,9946 ,10293 ,11621 ,11898 ,12452 ,13133 ,13631 ,14653 ,16075 ,16666 ,17740 ,18248 ,19503 ,19610 ,21672 ,22085 ,23736 ,24816 ,26428 ,27870 ,28073 ,30452 ,32175 ,33150 ,33767 ,36489 ,38661
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+"400","50","Vermont",3380 ,3625 ,3848 ,4163 ,4528 ,4855 ,5203 ,5747 ,6153 ,6979 ,7756 ,8599 ,9650 ,10324 ,10930 ,11977 ,12867 ,13731 ,14755 ,15822 ,17195 ,17643 ,17869 ,18941 ,19446 ,20255 ,21057 ,22106 ,23168 ,24921 ,26268 ,28184 ,29482 ,30013 ,31013 ,32713 ,33416 ,36021 ,37717 ,38686
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+"400","54","West Virginia",2792 ,3109 ,3369 ,3673 ,4009 ,4438 ,4973 ,5468 ,6026 ,6667 ,7373 ,8066 ,8767 ,9340 ,9575 ,10355 ,10851 ,11212 ,11619 ,12532 ,13398 ,14436 ,15086 ,16081 ,16549 ,17269 ,17817 ,18567 ,19373 ,20472 ,21049 ,22174 ,23610 ,24388 ,24916 ,25784 ,26684 ,28722 ,30144 ,31641
+"400","55","Wisconsin",3747 ,3981 ,4241 ,4601 ,5127 ,5619 ,6090 ,6679 ,7403 ,8247 ,9197 ,10085 ,10973 ,11573 ,12026 ,13112 ,13719 ,14424 ,15182 ,15953 ,17192 ,17986 ,18494 ,19674 ,20398 ,21550 ,22387 ,23509 ,24777 ,26619 ,27652 ,29140 ,30102 ,30809 ,31656 ,32736 ,33689 ,35665 ,37008 ,37767
+"400","56","Wyoming",3587 ,3910 ,4257 ,4692 ,5389 ,6150 ,6721 ,7224 ,8152 ,9366 ,10515 ,11668 ,12808 ,13349 ,12703 ,13416 ,14137 ,14244 ,14287 ,14821 ,16382 ,17910 ,18589 ,19344 ,20065 ,20741 ,21358 ,22233 ,23774 ,25496 ,27192 ,29280 ,31319 ,32082 ,33929 ,36274 ,39464 ,44700 ,46741 ,48608
+"400","91","New England",4175 ,4438 ,4674 ,5025 ,5477 ,5954 ,6376 ,6954 ,7586 ,8407 ,9381 ,10598 ,11800 ,12833 ,13770 ,15342 ,16440 ,17592 ,18958 ,20612 ,21848 ,22462 ,22867 ,24077 ,24773 ,25804 ,27048 ,28521 ,30087 ,32128 ,33581 ,36603 ,37979 ,38113 ,38788 ,40842 ,42391 ,45652 ,48027 ,49146
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+"400","93","Great Lakes",4034 ,4195 ,4473 ,4867 ,5447 ,5918 ,6360 ,7026 ,7780 ,8609 ,9483 ,10263 ,11215 ,11773 ,12394 ,13661 ,14479 ,15208 ,15921 ,16955 ,18117 ,19021 ,19407 ,20585 ,21273 ,22491 ,23404 ,24476 ,25699 ,27294 ,28187 ,29819 ,30441 ,30922 ,31843 ,32824 ,33717 ,35430 ,36793 ,37566
+"400","94","Plains",3592 ,3849 ,4104 ,4514 ,5265 ,5566 ,6073 ,6492 ,7154 ,8074 ,8886 ,9540 ,10757 ,11451 ,11975 ,13279 ,13984 ,14628 ,15375 ,15986 ,17121 ,18048 ,18589 ,19674 ,20071 ,21325 ,22119 ,23661 ,24692 ,26299 ,27231 ,29018 ,29896 ,30496 ,31667 ,33154 ,34096 ,35926 ,37647 ,39115
+"400","95","Southeast",3078 ,3325 ,3580 ,3941 ,4406 ,4825 ,5185 ,5713 ,6255 ,7009 ,7778 ,8629 ,9638 ,10230 ,10918 ,12024 ,12794 ,13439 ,14183 ,15229 ,16353 ,17186 ,17757 ,18709 ,19403 ,20305 ,21233 ,22230 ,23215 ,24561 ,25481 ,27050 ,27996 ,28477 ,29255 ,30858 ,32514 ,34516 ,35800 ,36336
+"400","96","Southwest",3325 ,3599 ,3826 ,4159 ,4633 ,5119 ,5635 ,6206 ,6794 ,7676 ,8649 ,9672 ,11050 ,11684 ,12057 ,13070 ,13792 ,13971 ,14277 ,15035 ,15930 ,16902 ,17394 ,18284 ,18917 ,19693 ,20536 ,21639 ,22997 ,24467 ,25407 ,27372 ,28227 ,28133 ,28793 ,30366 ,32378 ,34499 ,35892 ,36745
+"400","97","Rocky Mountain",3441 ,3765 ,4053 ,4451 ,4964 ,5494 ,5930 ,6461 ,7049 ,7950 ,8789 ,9787 ,10909 ,11542 ,12055 ,12962 ,13497 ,13907 ,14359 ,15087 ,16278 ,17248 ,17932 ,18792 ,19729 ,20614 ,21672 ,22829 ,23993 ,25528 ,26836 ,29111 ,30419 ,30447 ,30818 ,32289 ,34061 ,36312 ,37799 ,38275
+"400","98","Far West 3/",4412 ,4671 ,4898 ,5308 ,5815 ,6435 ,7037 ,7734 ,8434 ,9412 ,10506 ,11654 ,12796 ,13395 ,14162 ,15425 ,16241 ,16974 ,17823 ,18888 ,19930 ,20913 ,21381 ,22154 ,22556 ,23307 ,24315 ,25582 ,26834 ,28805 ,30236 ,32678 ,33170 ,33368 ,34270 ,36176 ,37869 ,40481 ,42331 ,42845
+"Source: Regional Economic Information System, Bureau of Economic Analysis, U.S. Department of Commerce"
+"=HYPERLINK(""http://www.bea.gov/regional/docs/footnotes.cfm?tablename=SA1-3"",""SA1-3 Footnotes"")","http://www.bea.gov/regional/docs/footnotes.cfm?tablename=SA1-3"
+"Regional Economic Information System"
+"Bureau of Economic Analysis"
+"'October 2009'"
diff --git a/pysal/examples/stata_full.txt b/pysal/examples/stata_full.txt
new file mode 100644
index 0000000..b4bc27c
--- /dev/null
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diff --git a/pysal/examples/stata_sparse.txt b/pysal/examples/stata_sparse.txt
new file mode 100644
index 0000000..5d50281
--- /dev/null
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diff --git a/pysal/examples/states48.gal b/pysal/examples/states48.gal
new file mode 100644
index 0000000..ed8fbfa
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diff --git a/pysal/examples/stl.gal b/pysal/examples/stl.gal
new file mode 100644
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diff --git a/pysal/examples/stl_hom.csv b/pysal/examples/stl_hom.csv
new file mode 100644
index 0000000..f21c203
--- /dev/null
+++ b/pysal/examples/stl_hom.csv
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diff --git a/pysal/examples/stl_hom.dbf b/pysal/examples/stl_hom.dbf
new file mode 100644
index 0000000..b3cde10
Binary files /dev/null and b/pysal/examples/stl_hom.dbf differ
diff --git a/pysal/examples/stl_hom.html b/pysal/examples/stl_hom.html
new file mode 100644
index 0000000..e37239d
--- /dev/null
+++ b/pysal/examples/stl_hom.html
@@ -0,0 +1,143 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.1//EN"
+ "http://www.w3.org/TR/xhtml11/DTD/xhtml11.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+ <title>SAL Data Sets - St Louis Region Homicides</title>
+</head>
+
+<body>
+
+<h1>St Louis Region Homicides</h1>
+
+<h2>Data provided "as is," no warranties</h2>
+
+<h2>Description</h2>
+
+<p>Homicides and selected socio-economic characteristics
+ for counties surrounding St Louis, MO. Data aggregated
+ for three time
+ periods: 1979-84 (steady decline in homicides),
+ 1984-88 (stable period), and 1988-93 (steady increase
+ in homicides).</p>
+
+<p>Type = polygon shape file, unprojected, lat-lon</p>
+
+<p>Observations = 78</p>
+
+<p>Variables = 21</p>
+
+
+<h2>Source</h2>
+
+<p>S. Messner, L. Anselin, D. Hawkins, G. Deane, S. Tolnay,
+ R. Baller (2000). An Atlas of the Spatial Patterning of
+ County-Level Homicide, 1960-1990. Pittsburgh, PA,
+ <a href="http://www.ncovr.heinz.cmu.edu" target="_blank">National
+ Consortium on Violence Research (NCOVR)</a>.</p>
+
+<h2>Variables</h2>
+
+<table>
+<thead>
+ <tr>
+ <th>Variable</th>
+ <th>Description</th>
+ </tr>
+</thead>
+<tbody>
+ <tr>
+ <td>NAME</td>
+ <td>county name</td>
+ </tr>
+ <tr>
+ <td>STATE_NAME</td>
+ <td>state name</td>
+ </tr>
+ <tr>
+ <td>STATE_FIPS</td>
+ <td>state fips code (character)</td>
+ </tr>
+ <tr>
+ <td>CNTY_FIPS</td>
+ <td>county fips code (character)</td>
+ </tr>
+ <tr>
+ <td>FIPS</td>
+ <td>combined state and county fips code (character)</td>
+ </tr>
+ <tr>
+ <td>FIPSNO</td>
+ <td>fips code as numeric variable</td>
+ </tr>
+ <tr>
+ <td>HR7984</td>
+ <td>homicide rate per 100,000 (1979-84)</td>
+ </tr>
+ <tr>
+ <td>HR8488</td>
+ <td>homicide rate per 100,000 (1984-88)</td>
+ </tr>
+ <tr>
+ <td>HR8893</td>
+ <td>homicide rate per 100,000 (1988-93)</td>
+ </tr>
+ <tr>
+ <td>HC7984</td>
+ <td>homicide count (1979-84)</td>
+ </tr>
+ <tr>
+ <td>HC8488</td>
+ <td>homicide count (1984-88)</td>
+ </tr>
+ <tr>
+ <td>HC8893</td>
+ <td>homicide count (1988-93)</td>
+ </tr>
+ <tr>
+ <td>PO7984</td>
+ <td>population total (1979-84)</td>
+ </tr>
+ <tr>
+ <td>PO8488</td>
+ <td>population total (1984-88)</td>
+ </tr>
+ <tr>
+ <td>PO8893</td>
+ <td>population total (1988-93)</td>
+ </tr>
+ <tr>
+ <td>PE77</td>
+ <td>police expenditures per capita, 1977</td>
+ </tr>
+ <tr>
+ <td>PE82</td>
+ <td>police expenditures per capita, 1982</td>
+ </tr>
+ <tr>
+ <td>PE87</td>
+ <td>police expenditures per capita, 1987</td>
+ </tr>
+ <tr>
+ <td>RDAC80</td>
+ <td>resource deprivation/affluence composite variable, 1980</td>
+ </tr>
+ <tr>
+ <td>RDAC85</td>
+ <td>resource deprivation/affluence composite variable, 1985</td>
+ </tr>
+ <tr>
+ <td>RDAC90</td>
+ <td>resource deprivation/affluence composite variable, 1990</td>
+ </tr>
+
+</tbody>
+</table>
+
+<br />
+<hr />
+<p>Prepared by <a href="mailto:anselin at uiuc.edu">Luc Anselin</a></p>
+<p><a href="http://sal.agecon.uiuc.edu">UIUC-ACE Spatial Analysis Laboratory</a></p>
+<p>Last updated June 16, 2003</p>
+</body>
+</html>
diff --git a/pysal/examples/stl_hom.shp b/pysal/examples/stl_hom.shp
new file mode 100644
index 0000000..577420f
Binary files /dev/null and b/pysal/examples/stl_hom.shp differ
diff --git a/pysal/examples/stl_hom.shx b/pysal/examples/stl_hom.shx
new file mode 100644
index 0000000..81eda1d
Binary files /dev/null and b/pysal/examples/stl_hom.shx differ
diff --git a/pysal/examples/stl_hom.txt b/pysal/examples/stl_hom.txt
new file mode 100644
index 0000000..cf94cc8
--- /dev/null
+++ b/pysal/examples/stl_hom.txt
@@ -0,0 +1,80 @@
+78,4
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+29093,9.329751,3.110904,5
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+29031,2.651026,3.47149,8
+29203,7.79403,4.334822,3
+29223,0,8.451537,0
diff --git a/pysal/examples/stl_hom.wkt b/pysal/examples/stl_hom.wkt
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diff --git a/pysal/examples/stl_hom_rook.gal b/pysal/examples/stl_hom_rook.gal
new file mode 100644
index 0000000..7d8e0f8
--- /dev/null
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diff --git a/pysal/examples/street_net_pts.dbf b/pysal/examples/street_net_pts.dbf
new file mode 100644
index 0000000..622c92c
Binary files /dev/null and b/pysal/examples/street_net_pts.dbf differ
diff --git a/pysal/examples/street_net_pts.prj b/pysal/examples/street_net_pts.prj
new file mode 100644
index 0000000..a30c00a
--- /dev/null
+++ b/pysal/examples/street_net_pts.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
\ No newline at end of file
diff --git a/pysal/examples/street_net_pts.qpj b/pysal/examples/street_net_pts.qpj
new file mode 100644
index 0000000..5fbc831
--- /dev/null
+++ b/pysal/examples/street_net_pts.qpj
@@ -0,0 +1 @@
+GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]
diff --git a/pysal/examples/street_net_pts.shp b/pysal/examples/street_net_pts.shp
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diff --git a/pysal/examples/taz.dbf b/pysal/examples/taz.dbf
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diff --git a/pysal/examples/us48.dbf b/pysal/examples/us48.dbf
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diff --git a/pysal/examples/usjoin.csv b/pysal/examples/usjoin.csv
new file mode 100644
index 0000000..24ab198
--- /dev/null
+++ b/pysal/examples/usjoin.csv
@@ -0,0 +1,49 @@
+"Name","STATE_FIPS",1929,1930,1931,1932,1933,1934,1935,1936,1937,1938,1939,1940,1941,1942,1943,1944,1945,1946,1947,1948,1949,1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009
+"Alabama",1,323,267,224,162,166,211,217,251,267,244,252,281,375,518,658,738,784,754,805,881,833,909,1045,1106,1161,1139,1273,1356,1421,1468,1526,1558,1587,1667,1758,1890,2030,2169,2294,2516,2748,2979,3225,3544,3960,4351,4765,5323,5817,6500,7199,7892,8712,9185,9783,10800,11583,12202,12912,13842,14899,15832,16536,17462,17991,18860,19683,20329,21129,22123,22987,23471,24467,25161,26065,27665,29097,30634,31988,32819,32274
+"Arizona",4,600,520,429,321,308,362,416,462,504,478,490,505,638,917,1004,1050,1124,1117,1186,1324,1305,1367,1623,1716,1716,1696,1752,1850,1893,1885,1974,2059,2103,2167,2204,2310,2412,2587,2754,3092,3489,3843,4145,4487,4929,5329,5528,6074,6642,7586,8604,9590,10658,10945,11654,12885,13808,14463,15130,15795,16568,17211,17563,18131,18756,19774,20634,21611,22781,24133,25189,25578,26232,26469,27106,28753,30671,32552,33470,33445,32077
+"Arkansas",5,310,228,215,157,157,187,207,247,256,231,249,260,342,481,559,687,740,756,741,886,813,847,957,1027,1066,1074,1176,1230,1244,1314,1412,1405,1511,1574,1656,1777,1886,2094,2219,2409,2619,2849,3096,3415,3985,4376,4655,5155,5670,6509,7088,7586,8564,8952,9476,10560,11264,11734,12184,13016,13813,14509,15255,16425,16995,17750,18546,19442,20229,21260,22244,22257,23532,23929,25074,26465,27512,29041,31070,31800,31493
+"California",6,991,887,749,580,546,603,660,771,795,771,781,844,1013,1286,1549,1583,1583,1671,1693,1763,1744,1877,2080,2207,2249,2227,2379,2495,2579,2596,2740,2823,2880,3004,3102,3274,3417,3663,3878,4207,4540,4815,5034,5451,5947,6553,7091,7815,8570,9618,10846,12029,13205,13774,14491,15927,16909,17628,18625,19713,20765,21889,22024,22722,22927,23473,24496,25563,26759,28280,29910,32275,32750,32900,33801,35663,37463,40169,41943,42377,40902
+"Colorado",8,634,578,471,354,353,368,444,542,532,506,516,545,649,895,1039,1065,1189,1211,1359,1454,1430,1521,1796,1880,1813,1773,1869,1960,2104,2157,2261,2340,2417,2471,2539,2638,2800,2982,3142,3381,3686,4055,4413,4791,5310,5864,6321,6895,7567,8539,9596,10809,12141,12945,13570,14751,15416,15772,16417,17285,18548,19703,20487,21447,22526,23498,24865,26231,27950,29860,31546,32949,34228,33963,34092,35543,37388,39662,41165,41719,40093
+"Connecticut",9,1024,921,801,620,583,653,706,806,860,769,836,918,1145,1418,1593,1599,1565,1580,1702,1720,1663,1891,2158,2298,2393,2350,2477,2684,2810,2731,2832,2926,3042,3175,3250,3401,3583,3874,4195,4443,4847,5090,5300,5697,6241,6813,7239,7885,8712,9720,10971,12439,13865,14903,15799,17580,18763,20038,21895,24007,25797,26736,26863,28635,29602,30532,31947,33472,35596,37452,39300,40640,42279,42021,42398,45009,47022,51133,53930,54528,52736
+"Delaware",10,1032,857,775,590,564,645,701,868,949,795,899,1027,1164,1291,1464,1504,1526,1565,1664,1685,1805,2075,2155,2244,2341,2306,2507,2749,2645,2651,2724,2805,2815,2933,3049,3210,3468,3610,3785,4079,4421,4608,4892,5303,5871,6347,6729,7349,7913,8658,9549,10803,11873,12727,13529,14816,16056,16781,17933,19312,20930,21636,22342,23094,23823,24530,25391,26640,27405,29571,30778,31255,32664,33463,34123,35998,37297,39358,40251,40698,40135
+"Florida",12,518,470,398,319,288,348,376,450,487,460,495,522,609,787,1009,1114,1174,1169,1167,1201,1208,1304,1388,1474,1567,1562,1673,1791,1836,1887,2010,2023,2039,2113,2200,2348,2498,2685,2909,3249,3659,4006,4286,4703,5235,5616,5895,6376,7010,7921,8879,10049,11195,11789,12637,13764,14705,15423,16415,17593,19045,19855,20189,20661,21652,22340,23512,24616,25722,26930,27780,28145,28852,29499,30277,32462,34460,36934,37781,37808,36565
+"Georgia",13,347,307,256,200,204,244,268,302,313,290,309,337,419,567,725,831,879,845,887,988,969,1065,1204,1280,1326,1302,1423,1499,1523,1583,1660,1698,1744,1844,1961,2082,2258,2462,2655,2891,3162,3394,3666,4038,4497,4867,5152,5694,6221,6989,7722,8474,9435,10054,10849,12185,13143,13990,14820,15876,16803,17738,18289,19333,20129,21170,22230,23586,24547,26134,27340,27940,28596,28660,29060,29995,31498,32739,33895,34127,33086
+"Idaho",16,507,503,374,274,227,403,399,475,423,426,437,463,596,914,1025,1095,1135,1203,1278,1345,1277,1329,1497,1644,1550,1559,1596,1728,1783,1817,1891,1898,1975,2092,2168,2250,2554,2591,2755,2910,3269,3558,3761,4150,4709,5382,5571,6116,6510,7319,7894,8735,9405,9621,10315,11069,11647,11968,12611,13548,14803,15866,16195,17236,18258,18846,19630,20353,20830,21923,22835,24180,25124,25485,25912,27846,29003,30954,32168,32322,30987
+"Illinois",17,948,807,671,486,437,505,573,650,731,648,704,751,892,1036,1258,1386,1465,1534,1639,1816,1685,1831,2026,2091,2209,2177,2270,2452,2522,2511,2642,2700,2789,2902,2981,3131,3360,3599,3785,4045,4354,4580,4874,5266,5903,6464,6986,7623,8385,9277,10243,11077,12250,12771,13289,14682,15508,16284,17289,18461,19634,20756,21320,22764,23386,24440,25643,27005,28347,29974,31145,32259,32808,33325,34205,35599,36825,39220,41238,42049,40933
+"Indiana",18,607,514,438,310,294,359,421,481,547,472,518,551,724,913,1138,1198,1251,1204,1313,1456,1361,1524,1711,1778,1943,1805,1907,2008,2042,2015,2131,2209,2246,2399,2486,2617,2856,3041,3153,3401,3714,3810,4105,4455,5100,5440,5830,6516,7199,8006,8814,9449,10355,10698,11203,12445,13143,13821,14664,15616,16770,17625,18055,19269,20112,21153,21845,22775,23748,25182,26143,27011,27590,28059,29089,30126,30768,32305,33151,33978,33174
+"Iowa",19,581,510,400,297,253,269,425,393,523,458,475,501,609,835,1024,1003,1092,1245,1217,1642,1356,1532,1638,1725,1657,1788,1670,1758,1939,1990,2026,2061,2180,2284,2432,2549,2833,3067,3093,3312,3652,3862,4005,4473,5398,5596,6192,6580,7283,8438,9114,9671,10968,11227,11485,12798,13395,14020,14899,15315,16562,17380,17859,18939,18929,20498,21181,22713,23798,24844,25615,26723,27315,28232,28835,31027,31656,33177,35008,36726,35983
+"Kansas",20,532,467,401,266,250,287,362,387,428,383,382,425,552,851,1045,1169,1165,1136,1310,1352,1299,1463,1602,1827,1739,1791,1753,1821,1911,2098,2106,2165,2227,2305,2384,2516,2689,2894,3032,3268,3548,3816,4145,4613,5274,5717,6186,6721,7307,8082,9240,10038,11248,11989,12373,13602,14330,14904,15583,16331,17093,18182,18832,19955,20510,21352,21889,23121,24355,25687,26824,27816,28979,29067,30109,31181,32367,34934,36546,37983,37036
+"Kentucky",21,393,325,291,211,205,233,265,294,341,297,305,320,395,538,701,767,803,826,865,995,938,990,1153,1237,1305,1289,1346,1437,1491,1543,1604,1633,1731,1822,1906,1972,2134,2329,2501,2718,2971,3184,3391,3715,4145,4607,4933,5462,6095,6784,7640,8231,9110,9589,9859,11062,11558,11995,12782,13570,14602,15484,16241,17320,17815,18514,19215,20155,21215,22353,23237,24294,24816,25297,25777,26891,27881,29392,30443,31302,31250
+"Louisiana",22,414,355,318,241,227,265,290,330,353,348,360,364,449,592,786,876,889,833,885,1019,1074,1117,1210,1278,1343,1339,1397,1502,1616,1635,1685,1690,1741,1806,1909,2003,2139,2331,2527,2749,2901,3106,3328,3593,3994,4510,4956,5557,6135,6951,7813,8833,10037,10558,10865,11628,12121,12028,12266,13113,13997,15223,16076,16968,17717,18779,19541,20254,21209,22352,22847,23334,25116,25683,26434,27776,29785,33438,34986,35730,35151
+"Maine",23,601,576,491,377,371,416,430,506,510,471,495,526,631,857,1102,1102,1079,1134,1169,1240,1183,1195,1319,1438,1449,1448,1581,1669,1720,1797,1850,1919,1903,1983,2049,2212,2383,2539,2658,2872,3140,3423,3594,3864,4319,4764,5019,5708,6142,6751,7497,8408,9231,9873,10551,11665,12533,13463,14595,15813,16886,17479,17662,18350,18810,19531,20240,21293,22305,23529,24603,25623,27068,27731,28727,30201,30721,32340,33620,34906,35268
+"Maryland",24,768,712,638,512,466,523,548,618,665,633,663,710,870,1117,1287,1324,1312,1315,1355,1500,1496,1642,1815,1944,2017,1938,2047,2184,2267,2258,2324,2407,2507,2638,2722,2881,3055,3284,3516,3831,4209,4573,4894,5291,5822,6382,6878,7536,8191,9062,10035,11230,12403,13246,14228,15693,16961,17966,19216,20626,22001,23023,23571,24358,25104,26046,26896,27844,29222,30850,32465,33872,35430,36293,37309,39651,41555,43990,45827,47040,47159
+"Massachusetts",25,906,836,759,613,559,609,643,714,732,672,724,779,901,1073,1262,1299,1348,1396,1435,1512,1480,1656,1817,1895,1947,1930,2071,2194,2296,2321,2430,2511,2605,2734,2799,2932,3101,3331,3583,3903,4207,4486,4748,5106,5551,6024,6439,6994,7636,8480,9472,10673,11830,12803,13859,15549,16720,17954,19504,21334,22458,23223,23749,24876,25664,26841,28051,29618,31332,33394,35551,37992,39247,39238,39869,41792,43520,46893,49361,50607,49590
+"Michigan",26,790,657,540,394,347,453,530,619,685,572,625,680,829,1050,1354,1391,1325,1329,1466,1563,1527,1718,1896,1985,2202,2076,2238,2275,2309,2246,2358,2438,2427,2592,2728,2949,3215,3450,3554,3906,4145,4194,4501,4966,5552,5926,6279,7084,7957,8834,9701,10369,11125,11462,12243,13576,14734,15573,16130,17198,18276,19022,19318,20278,21390,22862,23975,24447,25570,26807,28113,29612,30196,30410,31446,31890,32516,33452,34441,35215,34280
+"Minnesota",27,599,552,457,363,308,358,451,472,540,494,517,524,615,797,947,1006,1110,1190,1270,1451,1328,1437,1582,1633,1711,1724,1790,1838,1930,2016,2065,2155,2236,2331,2460,2540,2781,2997,3182,3463,3779,4053,4275,4628,5431,5838,6216,6729,7559,8471,9409,10320,11320,11992,12594,14255,15093,15881,16899,17592,18966,20011,20489,21698,22068,23467,24583,26267,27548,29503,30793,32101,32835,33553,34744,36505,37400,39367,41059,42299,40920
+"Mississippi",28,286,202,175,127,131,174,177,229,224,201,205,215,307,439,533,629,629,611,670,802,705,770,851,906,940,928,1045,1051,1063,1156,1245,1237,1322,1362,1499,1557,1688,1839,2001,2197,2408,2641,2867,3208,3613,3936,4205,4757,5259,5806,6549,7076,7901,8301,8615,9463,9922,10293,10913,11695,12540,13164,13806,14711,15468,16549,17185,18044,18885,20013,20688,20993,22222,22540,23365,24501,26120,27276,28772,29591,29318
+"Missouri",29,621,561,491,365,334,367,420,466,508,475,504,519,640,806,963,1068,1130,1191,1224,1376,1327,1427,1554,1659,1737,1726,1818,1905,1949,2044,2126,2156,2215,2330,2427,2533,2738,2895,3067,3370,3561,3843,4107,4443,4937,5282,5733,6306,6991,7787,8713,9390,10457,11035,11716,12960,13868,14505,15250,16086,17083,17751,18560,19542,20295,21267,22094,23099,24252,25403,26376,27445,28156,28771,29702,30847,31644,33354,34558,35775,35106
+"Montana",30,592,501,382,339,298,364,476,475,512,517,533,569,713,901,1150,1183,1206,1308,1484,1642,1412,1654,1806,1823,1810,1771,1894,1929,1983,2068,2023,2075,2025,2363,2330,2366,2548,2730,2805,2955,3284,3625,3789,4355,5012,5380,5794,6200,6636,7721,8299,9143,10244,10672,11045,11705,11900,12465,12996,13362,14623,15524,16509,17114,18072,18129,18764,19383,20167,21324,22019,22569,24342,24699,25963,27517,28987,30942,32625,33293,32699
+"Nebraska",31,596,521,413,307,275,259,409,396,415,405,400,442,551,822,1019,1091,1186,1186,1274,1558,1346,1560,1641,1761,1676,1753,1650,1675,1936,2025,2022,2125,2134,2292,2341,2392,2656,2890,2990,3167,3572,3796,4121,4527,5269,5465,6168,6453,6993,8120,8784,9272,10685,11228,11601,12968,13743,14215,15035,15984,16878,18088,18766,19688,20167,21168,22196,24045,24590,25861,27049,27829,29098,29499,31262,32371,33395,34753,36880,38128,37057
+"Nevada",32,868,833,652,550,495,546,658,843,762,780,861,895,982,1558,1511,1483,1611,1757,1770,1758,1795,1991,2211,2397,2447,2415,2527,2488,2562,2594,2749,2890,2957,3184,3174,3209,3299,3471,3660,4114,4520,4946,5227,5557,6114,6490,7009,7719,8550,9780,10765,11780,12780,12986,13465,14435,15332,16027,16886,18180,19568,20674,21283,22694,23465,24635,25808,27142,28201,29806,31022,30529,30718,30849,32182,34757,37555,38652,40326,40332,38009
+"New Hampshire",33,686,647,558,427,416,476,498,537,565,533,561,579,708,851,976,1054,1111,1150,1217,1291,1274,1348,1508,1577,1653,1703,1829,1900,2007,2004,2124,2197,2281,2392,2427,2552,2708,2963,3162,3441,3744,3896,4102,4423,4880,5279,5592,6258,6892,7786,8781,9915,11079,11906,13041,14534,15819,16974,18371,19759,20635,20713,21326,22154,22521,23820,25008,26042,27607,29679,31114,33332,33940,34335,34892,36758,37536,39997,41720,42461,41882
+"New Jersey",34,918,847,736,587,523,573,625,709,747,697,749,820,957,1167,1429,1554,1582,1526,1571,1648,1624,1802,2000,2114,2232,2219,2300,2448,2551,2525,2660,2764,2830,2990,3064,3224,3414,3652,3892,4237,4525,4835,5127,5521,6043,6576,7037,7703,8462,9408,10469,11778,13057,13999,15036,16549,17652,18711,20230,22142,23595,24766,25153,26597,27101,27885,29277,30795,32372,34310,35551,36983,37959,38240,38768,40603,42142,45668,48172,49233,48123
+"New Mexico",35,410,334,289,208,211,247,292,343,362,338,357,378,474,636,773,881,942,929,1013,1124,1145,1204,1346,1423,1444,1462,1543,1626,1733,1825,1895,1884,1952,2006,2054,2134,2250,2386,2490,2709,2921,3197,3431,3761,4137,4568,5045,5527,6087,6847,7619,8402,9334,9894,10367,11215,11999,12226,12686,13322,14085,14960,15744,16425,17226,17946,18852,19478,20233,21178,21853,22203,24193,24446,25128,26606,28180,29778,31320,32585,32197
+"New York",36,1152,1035,881,676,626,680,722,808,838,789,825,869,996,1169,1384,1538,1644,1697,1725,1771,1729,1858,2002,2057,2144,2175,2295,2416,2522,2553,2695,2788,2866,2980,3070,3254,3422,3657,3923,4295,4603,4887,5179,5538,5980,6492,6955,7477,8153,8979,9927,11095,12364,13344,14188,15739,16734,17827,19031,20604,21966,23315,23942,25199,25589,26359,27721,29266,30480,32236,33890,34547,35371,35332,36077,38312,40592,43892,47514,48692,46844
+"North Carolina",37,332,292,248,187,208,253,271,297,324,295,315,324,422,572,693,766,823,866,900,1003,969,1077,1192,1230,1274,1293,1368,1436,1424,1505,1581,1629,1689,1797,1877,2009,2143,2363,2525,2754,3051,3285,3510,3899,4365,4743,5039,5584,6058,6780,7461,8247,9184,9690,10480,11788,12649,13444,14325,15461,16539,17367,17879,19120,20042,20931,21938,22940,24188,25454,26003,27194,27650,27726,28208,29769,31209,32692,33966,34340,33564
+"North Dakota",38,382,311,187,176,146,180,272,234,326,282,319,355,529,665,966,1031,1046,1088,1494,1483,1211,1360,1444,1318,1336,1364,1490,1556,1598,1847,1679,1821,1653,2320,2142,2112,2463,2507,2592,2719,3052,3214,3669,4377,6172,6120,6334,6184,6427,8136,8398,8095,10342,10990,11386,12307,12811,13126,13565,12745,14357,15880,16270,17692,17830,19033,19084,21166,20798,22767,23313,25068,26118,26770,29109,29676,31644,32856,35882,39009,38672
+"Ohio",39,771,661,563,400,385,455,516,593,648,561,615,658,821,1021,1253,1312,1340,1310,1401,1539,1456,1608,1835,1916,2024,1965,2087,2182,2243,2177,2314,2391,2405,2515,2604,2754,2948,3181,3309,3616,3934,4101,4328,4691,5218,5733,6087,6753,7511,8326,9251,10103,10982,11485,12167,13449,14295,14933,15675,16739,17825,18792,19217,20242,20999,22063,22887,23613,24913,26164,27152,28400,28966,29522,30345,31240,32097,33643,34814,35521,35018
+"Oklahoma",40,455,368,301,216,222,252,298,321,376,346,349,374,432,626,782,947,970,952,1028,1143,1166,1144,1290,1401,1475,1458,1516,1593,1657,1794,1857,1916,1947,1994,2055,2196,2361,2517,2702,2948,3198,3477,3711,4020,4524,4986,5475,5974,6586,7387,8485,9580,11003,11817,11725,12687,13265,13288,13464,14257,15265,16214,16721,17526,18085,18730,19394,20151,21106,22199,22953,23517,25059,25059,25719,27516,29122,31753,32781,34378,33708
+"Oregon",41,668,607,505,379,358,439,458,548,556,531,571,609,818,1118,1381,1389,1357,1379,1504,1646,1604,1657,1830,1912,1916,1867,1978,2070,2055,2106,2244,2283,2349,2457,2541,2685,2852,3033,3201,3448,3677,3940,4212,4625,5135,5726,6181,6913,7556,8476,9415,10196,10862,11128,11832,12866,13547,14162,14911,16062,17222,18253,18806,19558,20404,21421,22668,23649,24845,25958,27023,28350,28866,29387,30172,31217,32108,34212,35279,35899,35210
+"Pennsylvania",42,772,712,600,449,417,482,517,601,636,563,601,650,776,951,1146,1250,1280,1289,1364,1436,1405,1552,1713,1789,1894,1827,1915,2063,2174,2164,2240,2301,2334,2439,2511,2662,2830,3040,3246,3507,3815,4077,4294,4683,5168,5704,6170,6800,7493,8305,9225,10151,11184,11887,12455,13512,14445,15186,16142,17323,18725,19823,20505,21550,22211,22864,23738,24838,26092,27358,28605,29539,30085,30840,31709,33069,34131,36375,38003,39008,38827
+"Rhode Island",44,874,788,711,575,559,600,645,711,731,672,720,750,934,1149,1201,1274,1278,1369,1459,1433,1378,1553,1717,1765,1855,1847,1958,1998,2024,2067,2183,2234,2329,2457,2552,2691,2870,3113,3336,3595,3865,4114,4295,4625,4972,5405,5844,6411,7004,7693,8595,9742,10815,11605,12439,13717,14685,15587,16651,18271,19657,20194,20363,21257,22137,22762,24046,25123,26631,28012,29377,29685,31378,32374,33690,35318,36461,38610,40421,41542,41283
+"South Carolina",45,271,243,205,159,175,211,229,258,273,250,276,310,394,545,648,732,753,780,793,911,873,925,1115,1196,1233,1166,1229,1260,1286,1316,1392,1437,1498,1602,1669,1787,1958,2176,2340,2570,2827,3064,3274,3603,4029,4459,4720,5257,5684,6334,7044,7794,8651,9071,9775,10910,11666,12258,13056,14045,14834,16050,16409,17165,17805,18686,19473,20403,21385,22544,23545,24321,24871,25279,25875,27057,28337,29990,30958,31510,30835
+"South Dakota",46,426,366,241,189,129,184,309,244,323,320,345,361,475,757,846,979,1086,1132,1270,1526,1116,1283,1497,1327,1436,1457,1342,1419,1669,1740,1564,1870,1863,2092,2020,2000,2278,2500,2577,2773,2995,3256,3538,4065,5163,5178,5667,5591,6351,7347,8158,8142,9451,9915,10195,11619,11942,12486,13217,13807,14767,16238,16961,17966,18565,19607,19848,21736,22275,23797,25045,26115,27531,27727,30072,31765,32726,33320,35998,38188,36499
+"Tennessee",47,378,325,277,198,204,245,264,304,334,300,311,340,436,561,728,864,912,872,892,965,951,1028,1122,1178,1276,1274,1327,1422,1476,1512,1598,1618,1690,1771,1855,1965,2128,2332,2473,2741,2967,3189,3451,3808,4298,4696,5017,5574,6108,6895,7618,8319,9196,9695,10276,11453,12247,12995,13909,14910,15883,16821,17503,18840,19741,20696,21800,22450,23324,24576,25574,26239,27059,27647,28501,29734,30764,32314,33578,34243,33512
+"Texas",48,479,412,348,266,257,294,326,372,418,404,417,438,528,719,944,1045,1058,1047,1147,1214,1300,1363,1488,1564,1598,1630,1697,1784,1856,1871,1948,1955,2021,2079,2155,2284,2433,2630,2840,3105,3373,3646,3861,4192,4683,5194,5738,6362,6979,7912,8929,9957,11391,11961,12303,13396,14196,14165,14486,15324,16323,17458,18150,19146,19825,20590,21526,22557,24242,25803,26858,27871,28519,28295,28929,30392,32448,34489,36020,36969,35674
+"Utah",49,551,498,369,305,298,310,389,463,444,444,458,480,594,880,1126,1049,1121,1094,1180,1257,1262,1348,1544,1596,1604,1573,1666,1758,1862,1888,1970,2035,2091,2230,2281,2386,2494,2605,2721,2900,3103,3391,3658,3979,4326,4743,5150,5739,6328,7041,7786,8464,9290,9807,10333,11233,11846,12248,12638,13156,13977,14996,15661,16354,17031,17912,18858,19955,21156,22294,23288,23907,24899,25010,25192,26169,27905,29582,31009,31253,30107
+"Vermont",50,634,576,474,365,338,383,414,471,485,457,491,516,643,775,932,953,1035,1090,1127,1192,1125,1169,1334,1380,1432,1456,1524,1655,1720,1732,1832,1923,1994,2072,2128,2266,2456,2729,2885,3128,3388,3634,3856,4176,4548,4869,5192,5753,6179,7036,7853,8702,9717,10287,10968,12048,12994,13842,14992,16197,17517,18055,18218,19293,19785,20553,21359,22295,23362,24803,25889,26901,28140,28651,29609,31240,31920,34394,36018,36940,36752
+"Virginia",51,434,384,370,284,285,320,350,390,423,390,426,467,582,785,843,900,950,1002,1015,1148,1132,1257,1420,1510,1533,1554,1637,1712,1738,1784,1885,1936,2005,2121,2218,2403,2563,2746,2967,3252,3558,3795,4092,4486,4972,5484,5934,6534,7192,8040,8995,10176,11291,12075,12936,14298,15286,16237,17332,18556,19780,20538,21092,21965,22773,23709,24456,25495,26768,28343,29789,31162,32747,33235,34451,36285,38304,40644,42506,43409,43211
+"Washington",53,741,658,534,402,376,443,490,569,599,582,614,658,864,1196,1469,1527,1419,1401,1504,1624,1595,1721,1874,1973,2066,2077,2116,2172,2262,2281,2380,2436,2535,2680,2735,2858,3078,3385,3566,3850,4097,4205,4381,4731,5312,5919,6533,7181,7832,8887,9965,10913,11903,12431,13124,14021,14738,15522,16300,17270,18670,20026,20901,21917,22414,23119,23878,25287,26817,28632,30392,31528,32053,32206,32934,34984,35738,38477,40782,41588,40619
+"West Virginia",54,460,408,356,257,259,313,337,390,418,370,388,407,498,613,739,820,888,925,1032,1110,1023,1056,1182,1247,1276,1225,1318,1477,1594,1551,1597,1625,1671,1762,1853,1974,2126,2271,2417,2573,2798,3117,3378,3682,4026,4457,4974,5479,6053,6703,7432,8172,8866,9439,9626,10417,10936,11464,11950,12708,13529,14579,15219,16118,16724,17413,17913,18566,19388,20246,20966,21915,23333,24103,24626,25484,26374,28379,29769,31265,31843
+"Wisconsin",55,673,588,469,362,333,380,461,518,551,507,513,547,672,866,1053,1109,1182,1211,1296,1434,1387,1506,1736,1799,1839,1774,1875,1990,2060,2075,2225,2258,2304,2412,2458,2616,2789,3025,3180,3441,3751,3983,4238,4595,5130,5622,6061,6670,7417,8292,9281,10161,11006,11592,12046,13182,13845,14530,15358,16201,17299,18160,18711,19872,20639,21699,22573,23554,24790,26245,27390,28232,29161,29838,30657,31703,32625,34535,35839,36594,35676
+"Wyoming",56,675,585,476,374,371,411,496,551,607,561,587,602,779,944,1150,1224,1258,1362,1506,1610,1647,1719,1955,1912,1932,1858,1913,2011,2132,2172,2278,2312,2387,2502,2535,2588,2743,2906,3121,3315,3584,3919,4269,4709,5410,6172,6701,7212,8152,9384,10572,11753,12879,13251,12723,13493,14242,14004,14194,14968,16383,17996,18867,19550,20287,20957,21514,22098,23820,24927,26396,27230,29122,29828,31544,33721,36683,41548,43453,45177,42504
diff --git a/pysal/examples/virginia.dbf b/pysal/examples/virginia.dbf
new file mode 100644
index 0000000..7ed087e
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diff --git a/pysal/examples/virginia.gal b/pysal/examples/virginia.gal
new file mode 100644
index 0000000..c8ccf1c
--- /dev/null
+++ b/pysal/examples/virginia.gal
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diff --git a/pysal/examples/virginia.prj b/pysal/examples/virginia.prj
new file mode 100644
index 0000000..a30c00a
--- /dev/null
+++ b/pysal/examples/virginia.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
\ No newline at end of file
diff --git a/pysal/examples/virginia.shp b/pysal/examples/virginia.shp
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index 0000000..5d72561
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diff --git a/pysal/examples/virginia.shx b/pysal/examples/virginia.shx
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index 0000000..e58f4a9
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diff --git a/pysal/examples/virginia_rook.gal b/pysal/examples/virginia_rook.gal
new file mode 100644
index 0000000..08f0381
--- /dev/null
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+124 123 116 89 104
+101 1
+80
+102 2
+81 97
+103 4
+134 120 114 95
+104 7
+124 119 113 83 70 100 89
+105 6
+135 127 111 77 64 99
+106 6
+125 118 99 78 97 81
+107 5
+122 90 112 79 94
+108 5
+121 114 95 84 93
+109 2
+80 98
+110 5
+129 113 118 97 88
+111 5
+132 122 94 105 77
+112 5
+122 121 90 93 107
+113 6
+119 110 129 88 83 104
+114 6
+134 121 103 120 108 95
+115 2
+117 126
+116 1
+100
+117 2
+126 115
+118 6
+133 129 106 125 97 110
+119 5
+136 124 113 129 104
+120 4
+128 126 103 114
+121 5
+130 108 114 93 112
+122 4
+112 107 111 94
+123 2
+124 100
+124 4
+119 104 100 123
+125 4
+127 106 99 118
+126 4
+128 117 120 115
+127 4
+131 105 99 125
+128 2
+126 120
+129 5
+133 110 118 119 113
+130 1
+121
+131 1
+127
+132 1
+111
+133 2
+118 129
+134 2
+103 114
+135 1
+105
+136 1
+119
diff --git a/pysal/examples/wmat.dat b/pysal/examples/wmat.dat
new file mode 100644
index 0000000..c604f0c
--- /dev/null
+++ b/pysal/examples/wmat.dat
@@ -0,0 +1,232 @@
+ 2.0000 1.0000 0.2500
+ 5.0000 1.0000 0.5000
+ 6.0000 1.0000 0.2500
+ 1.0000 2.0000 0.3333
+ 3.0000 2.0000 0.1667
+ 6.0000 2.0000 0.2500
+ 7.0000 2.0000 0.1250
+ 2.0000 3.0000 0.2500
+ 4.0000 3.0000 0.2500
+ 7.0000 3.0000 0.1250
+ 37.0000 3.0000 0.1429
+ 38.0000 3.0000 0.2000
+ 39.0000 3.0000 0.2500
+ 3.0000 4.0000 0.1667
+ 37.0000 4.0000 0.1429
+ 39.0000 4.0000 0.2500
+ 40.0000 4.0000 0.1667
+ 1.0000 5.0000 0.3333
+ 6.0000 5.0000 0.2500
+ 1.0000 6.0000 0.3333
+ 2.0000 6.0000 0.2500
+ 5.0000 6.0000 0.5000
+ 7.0000 6.0000 0.1250
+ 2.0000 7.0000 0.2500
+ 3.0000 7.0000 0.1667
+ 6.0000 7.0000 0.2500
+ 8.0000 7.0000 0.5000
+ 9.0000 7.0000 0.1667
+ 18.0000 7.0000 0.1429
+ 36.0000 7.0000 0.1250
+ 38.0000 7.0000 0.2000
+ 7.0000 8.0000 0.1250
+ 18.0000 8.0000 0.1429
+ 7.0000 9.0000 0.1250
+ 18.0000 9.0000 0.1429
+ 20.0000 9.0000 0.2000
+ 32.0000 9.0000 0.1429
+ 36.0000 9.0000 0.1250
+ 38.0000 9.0000 0.2000
+ 11.0000 10.0000 0.3333
+ 17.0000 10.0000 0.1000
+ 18.0000 10.0000 0.1429
+ 19.0000 10.0000 0.1667
+ 10.0000 11.0000 0.2500
+ 12.0000 11.0000 0.3333
+ 17.0000 11.0000 0.1000
+ 11.0000 12.0000 0.3333
+ 13.0000 12.0000 0.2500
+ 17.0000 12.0000 0.1000
+ 12.0000 13.0000 0.3333
+ 14.0000 13.0000 0.3333
+ 16.0000 13.0000 0.2000
+ 17.0000 13.0000 0.1000
+ 13.0000 14.0000 0.2500
+ 15.0000 14.0000 0.5000
+ 16.0000 14.0000 0.2000
+ 14.0000 15.0000 0.3333
+ 16.0000 15.0000 0.2000
+ 13.0000 16.0000 0.2500
+ 14.0000 16.0000 0.3333
+ 15.0000 16.0000 0.5000
+ 17.0000 16.0000 0.1000
+ 23.0000 16.0000 0.1429
+ 10.0000 17.0000 0.2500
+ 11.0000 17.0000 0.3333
+ 12.0000 17.0000 0.3333
+ 13.0000 17.0000 0.2500
+ 16.0000 17.0000 0.2000
+ 18.0000 17.0000 0.1429
+ 19.0000 17.0000 0.1667
+ 21.0000 17.0000 0.2500
+ 22.0000 17.0000 0.2500
+ 23.0000 17.0000 0.1429
+ 7.0000 18.0000 0.1250
+ 8.0000 18.0000 0.5000
+ 9.0000 18.0000 0.1667
+ 10.0000 18.0000 0.2500
+ 17.0000 18.0000 0.1000
+ 19.0000 18.0000 0.1667
+ 20.0000 18.0000 0.2000
+ 10.0000 19.0000 0.2500
+ 17.0000 19.0000 0.1000
+ 18.0000 19.0000 0.1429
+ 20.0000 19.0000 0.2000
+ 21.0000 19.0000 0.2500
+ 31.0000 19.0000 0.1111
+ 9.0000 20.0000 0.1667
+ 18.0000 20.0000 0.1429
+ 19.0000 20.0000 0.1667
+ 31.0000 20.0000 0.1111
+ 32.0000 20.0000 0.1429
+ 17.0000 21.0000 0.1000
+ 19.0000 21.0000 0.1667
+ 22.0000 21.0000 0.2500
+ 31.0000 21.0000 0.1111
+ 17.0000 22.0000 0.1000
+ 21.0000 22.0000 0.2500
+ 23.0000 22.0000 0.1429
+ 31.0000 22.0000 0.1111
+ 16.0000 23.0000 0.2000
+ 17.0000 23.0000 0.1000
+ 22.0000 23.0000 0.2500
+ 24.0000 23.0000 0.1667
+ 25.0000 23.0000 0.2000
+ 29.0000 23.0000 0.1667
+ 31.0000 23.0000 0.1111
+ 23.0000 24.0000 0.1429
+ 25.0000 24.0000 0.2000
+ 29.0000 24.0000 0.1667
+ 30.0000 24.0000 0.1667
+ 31.0000 24.0000 0.1111
+ 33.0000 24.0000 0.1667
+ 23.0000 25.0000 0.1429
+ 24.0000 25.0000 0.1667
+ 26.0000 25.0000 0.3333
+ 27.0000 25.0000 0.2500
+ 29.0000 25.0000 0.1667
+ 25.0000 26.0000 0.2000
+ 27.0000 26.0000 0.2500
+ 28.0000 26.0000 0.2500
+ 25.0000 27.0000 0.2000
+ 26.0000 27.0000 0.3333
+ 28.0000 27.0000 0.2500
+ 29.0000 27.0000 0.1667
+ 26.0000 28.0000 0.3333
+ 27.0000 28.0000 0.2500
+ 29.0000 28.0000 0.1667
+ 30.0000 28.0000 0.1667
+ 23.0000 29.0000 0.1429
+ 24.0000 29.0000 0.1667
+ 25.0000 29.0000 0.2000
+ 27.0000 29.0000 0.2500
+ 28.0000 29.0000 0.2500
+ 30.0000 29.0000 0.1667
+ 24.0000 30.0000 0.1667
+ 28.0000 30.0000 0.2500
+ 29.0000 30.0000 0.1667
+ 31.0000 30.0000 0.1111
+ 33.0000 30.0000 0.1667
+ 34.0000 30.0000 0.2000
+ 19.0000 31.0000 0.1667
+ 20.0000 31.0000 0.2000
+ 21.0000 31.0000 0.2500
+ 22.0000 31.0000 0.2500
+ 23.0000 31.0000 0.1429
+ 24.0000 31.0000 0.1667
+ 30.0000 31.0000 0.1667
+ 32.0000 31.0000 0.1429
+ 33.0000 31.0000 0.1667
+ 9.0000 32.0000 0.1667
+ 20.0000 32.0000 0.2000
+ 31.0000 32.0000 0.1111
+ 33.0000 32.0000 0.1667
+ 34.0000 32.0000 0.2000
+ 35.0000 32.0000 0.1429
+ 36.0000 32.0000 0.1250
+ 24.0000 33.0000 0.1667
+ 30.0000 33.0000 0.1667
+ 31.0000 33.0000 0.1111
+ 32.0000 33.0000 0.1429
+ 34.0000 33.0000 0.2000
+ 35.0000 33.0000 0.1429
+ 30.0000 34.0000 0.1667
+ 32.0000 34.0000 0.1429
+ 33.0000 34.0000 0.1667
+ 35.0000 34.0000 0.1429
+ 43.0000 34.0000 0.3333
+ 32.0000 35.0000 0.1429
+ 33.0000 35.0000 0.1667
+ 34.0000 35.0000 0.2000
+ 36.0000 35.0000 0.1250
+ 41.0000 35.0000 0.3333
+ 42.0000 35.0000 0.2000
+ 43.0000 35.0000 0.3333
+ 7.0000 36.0000 0.1250
+ 9.0000 36.0000 0.1667
+ 32.0000 36.0000 0.1429
+ 35.0000 36.0000 0.1429
+ 37.0000 36.0000 0.1429
+ 38.0000 36.0000 0.2000
+ 40.0000 36.0000 0.1667
+ 42.0000 36.0000 0.2000
+ 3.0000 37.0000 0.1667
+ 4.0000 37.0000 0.2500
+ 36.0000 37.0000 0.1250
+ 38.0000 37.0000 0.2000
+ 39.0000 37.0000 0.2500
+ 40.0000 37.0000 0.1667
+ 42.0000 37.0000 0.2000
+ 3.0000 38.0000 0.1667
+ 7.0000 38.0000 0.1250
+ 9.0000 38.0000 0.1667
+ 36.0000 38.0000 0.1250
+ 37.0000 38.0000 0.1429
+ 3.0000 39.0000 0.1667
+ 4.0000 39.0000 0.2500
+ 37.0000 39.0000 0.1429
+ 40.0000 39.0000 0.1667
+ 4.0000 40.0000 0.2500
+ 36.0000 40.0000 0.1250
+ 37.0000 40.0000 0.1429
+ 39.0000 40.0000 0.2500
+ 41.0000 40.0000 0.3333
+ 42.0000 40.0000 0.2000
+ 35.0000 41.0000 0.1429
+ 40.0000 41.0000 0.1667
+ 42.0000 41.0000 0.2000
+ 35.0000 42.0000 0.1429
+ 36.0000 42.0000 0.1250
+ 37.0000 42.0000 0.1429
+ 40.0000 42.0000 0.1667
+ 41.0000 42.0000 0.3333
+ 34.0000 43.0000 0.2000
+ 35.0000 43.0000 0.1429
+ 44.0000 43.0000 0.2500
+ 43.0000 44.0000 0.3333
+ 45.0000 44.0000 0.5000
+ 46.0000 44.0000 0.2000
+ 49.0000 44.0000 0.5000
+ 44.0000 45.0000 0.2500
+ 46.0000 45.0000 0.2000
+ 44.0000 46.0000 0.2500
+ 45.0000 46.0000 0.5000
+ 47.0000 46.0000 0.5000
+ 48.0000 46.0000 0.5000
+ 49.0000 46.0000 0.5000
+ 46.0000 47.0000 0.2000
+ 48.0000 47.0000 0.5000
+ 46.0000 48.0000 0.2000
+ 47.0000 48.0000 0.5000
+ 44.0000 49.0000 0.2500
+ 46.0000 49.0000 0.2000
\ No newline at end of file
diff --git a/pysal/examples/wmat.mtx b/pysal/examples/wmat.mtx
new file mode 100644
index 0000000..b6e8eb5
--- /dev/null
+++ b/pysal/examples/wmat.mtx
@@ -0,0 +1,237 @@
+%%MatrixMarket matrix coordinate real general
+%==================================================
+% This is a test file generated from wmat.dat file.
+% ==================================================
+49 49 232
+2 1 0.2500
+5 1 0.5000
+6 1 0.2500
+1 2 0.3333
+3 2 0.1667
+6 2 0.2500
+7 2 0.1250
+2 3 0.2500
+4 3 0.2500
+7 3 0.1250
+37 3 0.1429
+38 3 0.2000
+39 3 0.2500
+3 4 0.1667
+37 4 0.1429
+39 4 0.2500
+40 4 0.1667
+1 5 0.3333
+6 5 0.2500
+1 6 0.3333
+2 6 0.2500
+5 6 0.5000
+7 6 0.1250
+2 7 0.2500
+3 7 0.1667
+6 7 0.2500
+8 7 0.5000
+9 7 0.1667
+18 7 0.1429
+36 7 0.1250
+38 7 0.2000
+7 8 0.1250
+18 8 0.1429
+7 9 0.1250
+18 9 0.1429
+20 9 0.2000
+32 9 0.1429
+36 9 0.1250
+38 9 0.2000
+11 10 0.3333
+17 10 0.1000
+18 10 0.1429
+19 10 0.1667
+10 11 0.2500
+12 11 0.3333
+17 11 0.1000
+11 12 0.3333
+13 12 0.2500
+17 12 0.1000
+12 13 0.3333
+14 13 0.3333
+16 13 0.2000
+17 13 0.1000
+13 14 0.2500
+15 14 0.5000
+16 14 0.2000
+14 15 0.3333
+16 15 0.2000
+13 16 0.2500
+14 16 0.3333
+15 16 0.5000
+17 16 0.1000
+23 16 0.1429
+10 17 0.2500
+11 17 0.3333
+12 17 0.3333
+13 17 0.2500
+16 17 0.2000
+18 17 0.1429
+19 17 0.1667
+21 17 0.2500
+22 17 0.2500
+23 17 0.1429
+7 18 0.1250
+8 18 0.5000
+9 18 0.1667
+10 18 0.2500
+17 18 0.1000
+19 18 0.1667
+20 18 0.2000
+10 19 0.2500
+17 19 0.1000
+18 19 0.1429
+20 19 0.2000
+21 19 0.2500
+31 19 0.1111
+9 20 0.1667
+18 20 0.1429
+19 20 0.1667
+31 20 0.1111
+32 20 0.1429
+17 21 0.1000
+19 21 0.1667
+22 21 0.2500
+31 21 0.1111
+17 22 0.1000
+21 22 0.2500
+23 22 0.1429
+31 22 0.1111
+16 23 0.2000
+17 23 0.1000
+22 23 0.2500
+24 23 0.1667
+25 23 0.2000
+29 23 0.1667
+31 23 0.1111
+23 24 0.1429
+25 24 0.2000
+29 24 0.1667
+30 24 0.1667
+31 24 0.1111
+33 24 0.1667
+23 25 0.1429
+24 25 0.1667
+26 25 0.3333
+27 25 0.2500
+29 25 0.1667
+25 26 0.2000
+27 26 0.2500
+28 26 0.2500
+25 27 0.2000
+26 27 0.3333
+28 27 0.2500
+29 27 0.1667
+26 28 0.3333
+27 28 0.2500
+29 28 0.1667
+30 28 0.1667
+23 29 0.1429
+24 29 0.1667
+25 29 0.2000
+27 29 0.2500
+28 29 0.2500
+30 29 0.1667
+24 30 0.1667
+28 30 0.2500
+29 30 0.1667
+31 30 0.1111
+33 30 0.1667
+34 30 0.2000
+19 31 0.1667
+20 31 0.2000
+21 31 0.2500
+22 31 0.2500
+23 31 0.1429
+24 31 0.1667
+30 31 0.1667
+32 31 0.1429
+33 31 0.1667
+9 32 0.1667
+20 32 0.2000
+31 32 0.1111
+33 32 0.1667
+34 32 0.2000
+35 32 0.1429
+36 32 0.1250
+24 33 0.1667
+30 33 0.1667
+31 33 0.1111
+32 33 0.1429
+34 33 0.2000
+35 33 0.1429
+30 34 0.1667
+32 34 0.1429
+33 34 0.1667
+35 34 0.1429
+43 34 0.3333
+32 35 0.1429
+33 35 0.1667
+34 35 0.2000
+36 35 0.1250
+41 35 0.3333
+42 35 0.2000
+43 35 0.3333
+7 36 0.1250
+9 36 0.1667
+32 36 0.1429
+35 36 0.1429
+37 36 0.1429
+38 36 0.2000
+40 36 0.1667
+42 36 0.2000
+3 37 0.1667
+4 37 0.2500
+36 37 0.1250
+38 37 0.2000
+39 37 0.2500
+40 37 0.1667
+42 37 0.2000
+3 38 0.1667
+7 38 0.1250
+9 38 0.1667
+36 38 0.1250
+37 38 0.1429
+3 39 0.1667
+4 39 0.2500
+37 39 0.1429
+40 39 0.1667
+4 40 0.2500
+36 40 0.1250
+37 40 0.1429
+39 40 0.2500
+41 40 0.3333
+42 40 0.2000
+35 41 0.1429
+40 41 0.1667
+42 41 0.2000
+35 42 0.1429
+36 42 0.1250
+37 42 0.1429
+40 42 0.1667
+41 42 0.3333
+34 43 0.2000
+35 43 0.1429
+44 43 0.2500
+43 44 0.3333
+45 44 0.5000
+46 44 0.2000
+49 44 0.5000
+44 45 0.2500
+46 45 0.2000
+44 46 0.2500
+45 46 0.5000
+47 46 0.5000
+48 46 0.5000
+49 46 0.5000
+46 47 0.2000
+48 47 0.5000
+46 48 0.2000
+47 48 0.5000
+44 49 0.2500
+46 49 0.2000
\ No newline at end of file
diff --git a/pysal/inequality/__init__.py b/pysal/inequality/__init__.py
new file mode 100644
index 0000000..5e43131
--- /dev/null
+++ b/pysal/inequality/__init__.py
@@ -0,0 +1,8 @@
+"""
+:mod:`inequality` --- Spatial Inequality Analysis
+=================================================
+
+"""
+
+import theil
+import gini
diff --git a/pysal/inequality/_indices.py b/pysal/inequality/_indices.py
new file mode 100644
index 0000000..1c47758
--- /dev/null
+++ b/pysal/inequality/_indices.py
@@ -0,0 +1,579 @@
+'''
+Diversity indices as suggested in Nijkamp & Poot (2013)
+'''
+
+import itertools
+import numpy as np
+
+def abundance(x):
+ '''
+ Abundance index
+ ...
+
+ Arguments
+ ---------
+ x : array
+ N x k array containing N rows (one per neighborhood) and k columns
+ (one per cultural group)
+ Returns
+ -------
+ a : float
+ Abundance index
+ '''
+ xs = x.sum(axis=0)
+ return np.sum([1 for i in xs if i>0])
+
+def margalev_md(x):
+ '''
+ Margalev MD index
+ ...
+
+ Arguments
+ ---------
+ x : array
+ N x k array containing N rows (one per neighborhood) and k columns
+ (one per cultural group)
+ Returns
+ -------
+ a : float
+ Margalev MD index
+ '''
+ a = abundance(x)
+ return (a - 1.) / np.log(x.sum())
+
+def menhinick_mi(x):
+ '''
+ Menhinick MI index
+ ...
+
+ Arguments
+ ---------
+ x : array
+ N x k array containing N rows (one per neighborhood) and k columns
+ (one per cultural group)
+ Returns
+ -------
+ a : float
+ Menhinick MI index
+ '''
+ a = abundance(x)
+ return (a - 1.) / np.sqrt(x.sum())
+
+def simpson_so(x):
+ '''
+ Simpson diversity index SO
+ ...
+
+ Arguments
+ ---------
+ x : array
+ N x k array containing N rows (one per neighborhood) and k columns
+ (one per cultural group)
+ Returns
+ -------
+ a : float
+ Simpson diversity index SO
+ '''
+ xs0 = x.sum(axis=0)
+ xs = x.sum()
+ num = (xs0 * (xs0 - 1.)).sum()
+ den = xs * (xs - 1.)
+ return num / den
+
+def simpson_sd(x):
+ '''
+ Simpson diversity index SD
+ ...
+
+ Arguments
+ ---------
+ x : array
+ N x k array containing N rows (one per neighborhood) and k columns
+ (one per cultural group)
+ Returns
+ -------
+ a : float
+ Simpson diversity index SD
+ '''
+ return 1. - simpson_so(x)
+
+def herfindahl_hd(x):
+ '''
+ Herfindahl index HD
+ ...
+
+ Arguments
+ ---------
+ x : array
+ N x k array containing N rows (one per neighborhood) and k columns
+ (one per cultural group)
+ Returns
+ -------
+ a : float
+ Herfindahl index HD
+ '''
+ pgs = x.sum(axis=0)
+ p = pgs.sum()
+ return ((pgs * 1. / p)**2).sum()
+
+def fractionalization_gs(x):
+ '''
+ Fractionalization Gini-Simpson index GS
+ ...
+
+ Arguments
+ ---------
+ x : array
+ N x k array containing N rows (one per neighborhood) and k columns
+ (one per cultural group)
+ Returns
+ -------
+ a : float
+ Fractionalization Gini-Simpson index GS
+ '''
+ return 1. - herfindahl_hd(x)
+
+def polarization(x):
+ return 'Not implemented'
+
+def shannon_se(x):
+ '''
+ Shannon index SE
+ ...
+
+ Arguments
+ ---------
+ x : array
+ N x k array containing N rows (one per neighborhood) and k columns
+ (one per cultural group)
+ Returns
+ -------
+ a : float
+ Shannon index SE
+ '''
+ pgs = x.sum(axis=0)
+ p = pgs.sum()
+ ratios = pgs * 1. / p
+ return - (ratios * np.log(ratios)).sum()
+
+def gini_gi(x):
+ '''
+ Gini GI index
+
+ NOTE: based on 3rd eq. of "Calculation" in:
+
+ http://en.wikipedia.org/wiki/Gini_coefficient
+
+ Returns same value as `gini` method in the R package `reldist` (see
+ http://rss.acs.unt.edu/Rdoc/library/reldist/html/gini.html) if every
+ category has at least one observation
+ ...
+
+ Arguments
+ ---------
+ x : array
+ N x k array containing N rows (one per neighborhood) and k columns
+ (one per cultural group)
+ Returns
+ -------
+ a : float
+ Gini GI index
+ '''
+ ys = x.sum(axis=0)
+ return _gini(ys)
+
+def gini_gi_m(x):
+ '''
+ Gini GI index (equivalent to `gini_gi`, not vectorized)
+
+ NOTE: based on Wolfram Mathworld formula in:
+
+ http://mathworld.wolfram.com/GiniCoefficient.html
+
+ Returns same value as `gini_gi`.
+ ...
+
+ Arguments
+ ---------
+ x : array
+ N x k array containing N rows (one per neighborhood) and k columns
+ (one per cultural group)
+ Returns
+ -------
+ a : float
+ Gini GI index
+ '''
+ xs = x.sum(axis=0)
+ num = np.sum([np.abs(xi - xj) for xi, xj in itertools.permutations(xs, 2)])
+ den = 2. * xs.shape[0]**2 * np.mean(xs)
+ return num / den
+
+def _gini(ys):
+ '''
+ Gini for a single row to be used both by `gini_gi` and `gini_gig`
+ '''
+ n = ys.flatten().shape[0]
+ ys.sort()
+ num = 2. * ((np.arange(n)+1) * ys).sum()
+ den = n * ys.sum()
+ return (num / den) - ((n + 1.) / n)
+
+def hoover_hi(x):
+ '''
+ Hoover index HI
+
+ NOTE: based on
+
+ http://en.wikipedia.org/wiki/Hoover_index
+
+ ...
+
+ Arguments
+ ---------
+ x : array
+ N x k array containing N rows (one per neighborhood) and k columns
+ (one per cultural group)
+ Returns
+ -------
+ a : float
+ Hoover HI index
+ '''
+ es = x.sum(axis=0)
+ e_total = es.sum()
+ a_total = es.shape[0]
+ s = np.abs((es*1./e_total) - (1./a_total)).sum()
+ return s / 2.
+
+def similarity_w_wd(x, tau):
+ '''
+ Similarity weighted diversity
+ ...
+
+ Arguments
+ ---------
+ x : array
+ N x k array containing N rows (one per neighborhood) and k columns
+ (one per cultural group)
+ tau : array
+ k x k array where tau_ij represents dissimilarity between group
+ i and group j. Diagonal elements are assumed to be one.
+
+ Returns
+ -------
+ a : float
+ Similarity weighted diversity index
+ '''
+ pgs = x.sum(axis=0)
+ pgs = pgs * 1. / pgs.sum()
+ s = sum([pgs[i] * pgs[j] * tau[i, j] for i,j in \
+ itertools.product(np.arange(pgs.shape[0]), repeat=2)])
+ return 1. - s
+
+def segregation_gsg(x):
+ '''
+ Segregation index GS
+
+ This is a Duncan&Duncan index of a group against the rest combined
+ ...
+
+ Arguments
+ ---------
+ x : array
+ N x k array containing N rows (one per neighborhood) and k columns
+ (one per cultural group)
+ Returns
+ -------
+ a : array
+ Array with GSg indices for the k groups
+ '''
+ pgs = x.sum(axis=0)
+ pas = x.sum(axis=1)
+ p = pgs.sum()
+ first = (x.T * 1. / pgs[:, None]).T
+ paMpga = pas[:, None] - x
+ pMpg = p - pgs
+ second = paMpga * 1. / pMpg[None, :]
+ return 0.5 * (np.abs(first - second)).sum(axis=0)
+
+def modified_segregation_msg(x):
+ '''
+ Modified segregation index GS
+
+ This is a modified version of GSg index as used by Van Mourik et al. (1989)
+ ...
+
+ Arguments
+ ---------
+ x : array
+ N x k array containing N rows (one per neighborhood) and k columns
+ (one per cultural group)
+ Returns
+ -------
+ a : array
+ Array with MSg indices for the k groups
+ '''
+ pgs = x.sum(axis=0)
+ p = pgs.sum()
+ ms_inds = segregation_gsg(x) # To be updated in loop below
+ for gi in np.arange(x.shape[1]):
+ pg = pgs[gi]
+ pgp = pg * 1. / p
+ ms_inds[gi] = 2. * pgp * (1. - pgp) * ms_inds[gi]
+ return ms_inds
+
+def isolation_isg(x):
+ '''
+ Isolation index IS
+
+ ...
+
+ Arguments
+ ---------
+ x : array
+ N x k array containing N rows (one per neighborhood) and k columns
+ (one per cultural group)
+ Returns
+ -------
+ a : array
+ Array with ISg indices for the k groups
+ '''
+ ws = x * 1. / x.sum(axis=0)
+ pgapa = (x.T * 1. / x.sum(axis=1)).T
+ pgp = x.sum(axis=0) * 1. / x.sum()
+ return (ws * pgapa / pgp).sum(axis=0)
+
+def gini_gig(x):
+ '''
+ Gini GI index
+
+ NOTE: based on Wolfram Mathworld formula in:
+
+ http://mathworld.wolfram.com/GiniCoefficient.html
+
+ Returns same value as `gini_gi`.
+ ...
+
+ Arguments
+ ---------
+ x : array
+ N x k array containing N rows (one per neighborhood) and k columns
+ (one per cultural group)
+ Returns
+ -------
+ a : array
+ Gini GI index for every group k
+ '''
+ return np.apply_along_axis(_gini, 0, x)
+
+def ellison_glaeser_egg(x, hs=None):
+ '''
+ Ellison and Glaeser (1997) [1]_ index of concentration. Implemented as in
+ equation (5) of original reference
+ ...
+
+ Arguments
+ ---------
+ x : array
+ N x k array containing N rows (one per area) and k columns
+ (one per industry). Each cell indicates employment figures for
+ area n and industry k
+ hs : array
+ [Optional] Array of dimension (k,) containing the Herfindahl
+ indices of each industry's plant sizes. If not passed, it is
+ assumed every plant contains one and only one worker and thus
+ H_k = 1 / P_k, where P_k is the total employment in k
+
+ Returns
+ -------
+ a : array
+ EG index for every group k
+
+ References
+ ----------
+
+ .. [1] Ellison, G. and Glaeser, E. L. "Geographic Concentration in U.S.
+ Manufacturing Industries: A Dartboard Approach". Journal of Political
+ Economy. 105: 889-927
+
+ '''
+ industry_totals = x.sum(axis=0)
+ if hs==None:
+ hs = 1. / industry_totals
+ xs = x.sum(axis=1) * 1. / x.sum()
+ part = 1. - (xs**2).sum()
+ eg_inds = np.zeros(x.shape[1])
+ for gi in np.arange(x.shape[1]):
+ ss = x[:, gi] * 1. / industry_totals[gi]
+ g = ((ss - xs)**2).sum()
+ h = hs[gi]
+ eg_inds[gi] = (g - part * h) / (part * (1. - h))
+ return eg_inds
+
+def ellison_glaeser_egg_pop(x):
+ '''
+ Ellison and Glaeser (1997) [1]_ index of concentration. Implemented to be
+ computed with data about people (segregation/diversity) rather than as
+ industry concentration, following Mare et al (2012) [2]_
+ ...
+
+ Arguments
+ ---------
+ x : array
+ N x k array containing N rows (one per neighborhood) and k columns
+ (one per cultural group)
+ Returns
+ -------
+ a : array
+ EG index for every group k
+
+ References
+ ----------
+
+ .. [1] Ellison, G. and Glaeser, E. L. "Geographic Concentration in U.S.
+ Manufacturing Industries: A Dartboard Approach". Journal of Political
+ Economy. 105: 889-927
+
+ .. [2] Mare, D., Pinkerton, R., Poot, J. and Coleman, A. (2012)
+ Residential Sorting Across Auckland Neighbourhoods. Mimeo. Wellington:
+ Motu Economic and Public Policy Research.
+
+ '''
+ pas = x.sum(axis=1)
+ pgs = x.sum(axis=0)
+ p = pas.sum()
+ pap = pas * 1. / p
+ opg = 1./ pgs
+ oopg = 1. - opg
+ eg_inds = np.zeros(x.shape[1])
+ for g in np.arange(x.shape[1]):
+ pgas = x[:, g]
+ pg = pgs[g]
+ num1n = (((pgas * 1. / pg) - (pas * 1. / p))**2).sum()
+ num1d = 1. - ((pas * 1. / p)**2).sum()
+ num2 = opg[g]
+ den = oopg[g]
+ eg_inds[g] = ((num1n / num1d) - num2) / den
+ return eg_inds
+
+def maurel_sedillot_msg(x, hs=None):
+ '''
+ Maurel and Sedillot (1999) [1]_ index of concentration. Implemented as in
+ equation (7) of original reference
+ ...
+
+ Arguments
+ ---------
+ x : array
+ N x k array containing N rows (one per neighborhood) and k columns
+ (one per cultural group)
+ hs : array
+ [Optional] Array of dimension (k,) containing the Herfindahl
+ indices of each industry's plant sizes. If not passed, it is
+ assumed every plant contains one and only one worker and thus
+ H_k = 1 / P_k, where P_k is the total employment in k
+
+ Returns
+ -------
+ a : array
+ MS index for every group k
+
+ References
+ ----------
+
+ .. [1] Maurel, F. and Sedillot, B. (1999). "A Measure of the Geographic
+ Concentration in French Manufacturing Industries". Regional Science
+ and Urban Economics 29: 575-604
+
+ '''
+ industry_totals = x.sum(axis=0)
+ if hs==None:
+ hs = 1. / industry_totals
+ x2s = np.sum((x.sum(axis=1) * 1. / x.sum())**2)
+ ms_inds = np.zeros(x.shape[1])
+ for gi in np.arange(x.shape[1]):
+ s2s = np.sum((x[:, gi] * 1. / industry_totals[gi])**2)
+ h = hs[gi]
+ num = ((s2s - x2s) / (1. - x2s)) - h
+ den = 1. - h
+ ms_inds[gi] = num / den
+ return ms_inds
+
+def maurel_sedillot_msg_pop(x):
+ '''
+ Maurel and Sedillot (1999) [1]_ index of concentration. Implemented to be
+ computed with data about people (segregation/diversity) rather than as
+ industry concentration, following Mare et al (2012) [2]_
+
+ ...
+
+ Arguments
+ ---------
+ x : array
+ N x k array containing N rows (one per neighborhood) and k columns
+ (one per cultural group)
+ Returns
+ -------
+ a : array
+ MS index for every group k
+
+ References
+ ----------
+
+ .. [1] Maurel, F. and Sedillot, B. (1999). "A Measure of the Geographic
+ Concentration in French Manufacturing Industries". Regional Science
+ and Urban Economics 29: 575-604
+
+ .. [2] Mare, D., Pinkerton, R., Poot, J. and Coleman, A. (2012)
+ Residential Sorting Across Auckland Neighbourhoods. Mimeo. Wellington:
+ Motu Economic and Public Policy Research.
+
+ '''
+ pas = x.sum(axis=1)
+ pgs = x.sum(axis=0)
+ p = pas.sum()
+ pap = pas * 1. / p
+ eg_inds = np.zeros(x.shape[1])
+ for g in np.arange(x.shape[1]):
+ pgas = x[:, g]
+ pg = pgs[g]
+ num1n = ((pgas * 1. / pg)**2 - (pas * 1. / p)**2).sum()
+ num1d = 1. - ((pas * 1. / p)**2).sum()
+ num2 = 1. / pg
+ den = 1. - (1. / pg)
+ eg_inds[g] = ((num1n / num1d) - num2) / den
+ return eg_inds
+
+if __name__=='__main__':
+ np.random.seed(1)
+ x = np.round(np.random.random((10, 3)) * 100).astype(int)
+ #x[:, 2] = 0
+ ids = [abundance, \
+ margalev_md, \
+ menhinick_mi, \
+ simpson_so, \
+ simpson_sd, \
+ fractionalization_gs, \
+ herfindahl_hd, \
+ shannon_se, \
+ gini_gi, \
+ gini_gi_m, \
+ hoover_hi, \
+ segregation_gsg, \
+ modified_segregation_msg, \
+ isolation_isg, \
+ gini_gig, \
+ ellison_glaeser_egg, \
+ ellison_glaeser_egg_pop, \
+ maurel_sedillot_msg, \
+ maurel_sedillot_msg_pop, \
+ ]
+ res = [(f_i.func_name, f_i(x)) for f_i in ids]
+ print '\nIndices'
+ for r in res:
+ print r[1], '\t', r[0]
+
+ tau = np.random.random((x.shape[1], x.shape[1]))
+ for i in range(tau.shape[0]):
+ tau[i, i] = 1.
+ print similarity_w_wd(x, tau)
+
diff --git a/pysal/inequality/gini.py b/pysal/inequality/gini.py
new file mode 100644
index 0000000..46d65f8
--- /dev/null
+++ b/pysal/inequality/gini.py
@@ -0,0 +1,167 @@
+"""
+Gini based Inequality Metrics
+"""
+
+__author__ = "Sergio J. Rey <srey at asu.edu> "
+
+#from pysal.common import *
+import numpy as np
+from scipy.stats import norm as NORM
+
+__all__ = ['Gini', 'Gini_Spatial']
+
+
+class Gini:
+ """
+ Classic Gini coefficient in absolute deviation form
+
+ Parameters
+ ----------
+
+ y : array (n,1)
+ attribute
+
+ Attributes
+ ----------
+
+ g : float
+ Gini coefficient
+
+ """
+
+ def __init__(self, x):
+
+ x.shape = (x.shape[0],)
+ d = np.abs(np.array([x - xi for xi in x]))
+ n = len(x)
+ xbar = x.mean()
+ den = xbar * 2 * n**2
+ dtotal = d.sum()
+ self.g = dtotal/den
+
+
+class Gini_Spatial:
+ """
+ Spatial Gini coefficient
+
+ Provides for computationally based inference regarding the contribution of
+ spatial neighbor pairs to overall inequality across a set of regions. [1]_
+
+ Parameters
+ ----------
+
+ y : array (n,1)
+ attribute
+
+ w : binary spatial weights object
+
+ permutations : int (default = 99)
+ number of permutations for inference
+
+ Attributes
+ ----------
+
+ g : float
+ Gini coefficient
+
+ wg : float
+ Neighbor inequality component (geographic inequality)
+
+ wcg : float
+ Non-neighbor inequality component (geographic complement inequality)
+
+ wcg_share : float
+ Share of inequality in non-neighbor component
+
+ If Permuations > 0
+
+ p_sim : float
+ pseudo p-value for spatial gini
+
+ e_wcg : float
+ expected value of non-neighbor inequality component (level) from permutations
+
+ s_wcg : float
+ standard deviation non-neighbor inequality component (level) from permutations
+
+ z_wcg : float
+ z-value non-neighbor inequality component (level) from permutations
+
+ p_z_sim : float
+ pseudo p-value based on standard normal approximation of permutation based values
+
+
+ Examples
+ --------
+ >>> import pysal
+ >>> import numpy as np
+
+ Use data from the 32 Mexican States, Decade frequency 1940-2010
+
+ >>> f=pysal.open(pysal.examples.get_path("mexico.csv"))
+ >>> vnames=["pcgdp%d"%dec for dec in range(1940,2010,10)]
+ >>> y=np.transpose(np.array([f.by_col[v] for v in vnames]))
+
+ Define regime neighbors
+
+ >>> regimes=np.array(f.by_col('hanson98'))
+ >>> w = pysal.block_weights(regimes)
+ >>> np.random.seed(12345)
+ >>> gs = pysal.inequality.gini.Gini_Spatial(y[:,0],w)
+ >>> gs.p_sim
+ 0.01
+ >>> gs.wcg
+ 4353856.0
+ >>> gs.e_wcg
+ 1067629.2525252525
+ >>> gs.s_wcg
+ 95869.167798782844
+ >>> gs.z_wcg
+ 34.2782442252145
+ >>> gs.p_z_sim
+ 0.0
+
+ Thus, the amount of inequality between pairs of states that are not in the
+ same regime (neighbors) is significantly higher than what is expected
+ under the null of random spatial inequality.
+
+
+ References
+ ----------
+
+ .. [1] Rey, S.J. and R. Smith (2012) "A spatial decomposition of the Gini
+ coefficient." Letters in Spatial and Resource Sciences. DOI 10.1007/s12076-012-00860z
+
+ """
+ def __init__(self, x, w, permutations=99):
+ x.shape = (x.shape[0],)
+ d = np.abs(np.array([x - xi for xi in x]))
+ n = len(x)
+ xbar = x.mean()
+ den = xbar * 2 * n**2
+ wg = w.sparse.multiply(d).sum()
+ self.wg = wg # spatial inequality component
+ dtotal = d.sum()
+ wcg = dtotal - wg # complement to spatial inequality component
+ self.wcg = wcg
+ self.g = dtotal / den
+ self.wcg_share = wcg / dtotal
+ self.dtotal = dtotal
+ self.den = den
+
+ if permutations:
+ ids = np.arange(n)
+ wcgp = np.zeros((permutations, 1))
+ for perm in xrange(permutations):
+ # permute rows/cols of d
+ np.random.shuffle(ids)
+ wcgp[perm] = w.sparse.multiply(d[ids, :][:, ids]).sum()
+ above = wcgp >= self.wcg
+ larger = above.sum()
+ if (permutations - larger) < larger:
+ larger = permutations - larger
+ self.p_sim = (larger + 1.) / (permutations + 1.)
+ self.e_wcg = wcgp.mean()
+ self.s_wcg = wcgp.std()
+ self.z_wcg = (self.wcg - self.e_wcg) / self.s_wcg
+ self.p_z_sim = 1.0 - NORM.cdf(self.z_wcg)
diff --git a/pysal/inequality/tests/__init__.py b/pysal/inequality/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pysal/inequality/tests/test_theil.py b/pysal/inequality/tests/test_theil.py
new file mode 100644
index 0000000..f6266d5
--- /dev/null
+++ b/pysal/inequality/tests/test_theil.py
@@ -0,0 +1,42 @@
+import unittest
+import pysal
+import numpy as np
+from pysal.inequality.theil import *
+
+
+class test_Theil(unittest.TestCase):
+
+ def test___init__(self):
+ # theil = Theil(y)
+ f = pysal.open(pysal.examples.get_path("mexico.csv"))
+ vnames = ["pcgdp%d" % dec for dec in range(1940, 2010, 10)]
+ y = np.transpose(np.array([f.by_col[v] for v in vnames]))
+ theil_y = Theil(y)
+ np.testing.assert_almost_equal(theil_y.T, np.array([0.20894344, 0.15222451, 0.10472941, 0.10194725, 0.09560113, 0.10511256, 0.10660832]))
+
+
+class test_TheilD(unittest.TestCase):
+ def test___init__(self):
+ # theil_d = TheilD(y, partition)
+ f = pysal.open(pysal.examples.get_path("mexico.csv"))
+ vnames = ["pcgdp%d" % dec for dec in range(1940, 2010, 10)]
+ y = np.transpose(np.array([f.by_col[v] for v in vnames]))
+ regimes = np.array(f.by_col('hanson98'))
+ theil_d = TheilD(y, regimes)
+ np.testing.assert_almost_equal(theil_d.bg, np.array([0.0345889, 0.02816853, 0.05260921, 0.05931219, 0.03205257, 0.02963731, 0.03635872]))
+
+
+class test_TheilDSim(unittest.TestCase):
+ def test___init__(self):
+ f = pysal.open(pysal.examples.get_path("mexico.csv"))
+ vnames = ["pcgdp%d" % dec for dec in range(1940, 2010, 10)]
+ y = np.transpose(np.array([f.by_col[v] for v in vnames]))
+ regimes = np.array(f.by_col('hanson98'))
+ np.random.seed(10)
+ theil_ds = TheilDSim(y, regimes, 999)
+ np.testing.assert_almost_equal(theil_ds.bg_pvalue, np.array(
+ [0.4, 0.344, 0.001, 0.001, 0.034, 0.072, 0.032]))
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/inequality/theil.py b/pysal/inequality/theil.py
new file mode 100644
index 0000000..31d7ced
--- /dev/null
+++ b/pysal/inequality/theil.py
@@ -0,0 +1,201 @@
+"""Theil Inequality metrics
+
+"""
+__author__ = "Sergio J. Rey <srey at asu.edu> "
+
+from pysal.common import *
+import numpy as np
+__all__ = ['Theil', 'TheilD', 'TheilDSim']
+
+SMALL = np.finfo('float').tiny
+
+
+class Theil:
+ """
+ Classic Theil measure of inequality
+
+ .. math::
+
+ T = \sum_{i=1}^n \left( \\frac{y_i}{\sum_{i=1}^n y_i} \ln \left[ N \\frac{y_i}{\sum_{i=1}^n y_i}\\right] \\right)
+
+ Parameters
+ ----------
+ y : array (n,t) or (n,)
+ with n taken as the observations across which inequality is
+ calculated. If y is (n,) then a scalar inequality value is
+ determined. If y is (n,t) then an array of inequality values are
+ determined, one value for each column in y.
+
+ Attributes
+ ----------
+
+ T : array (t,) or (1,)
+ Theil's T for each column of y
+
+ Notes
+ -----
+ This computation involves natural logs. To prevent ln[0] from occurring, a
+ small value is added to each element of y before beginning the computation.
+
+ Examples
+ --------
+ >>> import pysal
+ >>> f=pysal.open(pysal.examples.get_path("mexico.csv"))
+ >>> vnames=["pcgdp%d"%dec for dec in range(1940,2010,10)]
+ >>> y=np.transpose(np.array([f.by_col[v] for v in vnames]))
+ >>> theil_y=Theil(y)
+ >>> theil_y.T
+ array([ 0.20894344, 0.15222451, 0.10472941, 0.10194725, 0.09560113,
+ 0.10511256, 0.10660832])
+ """
+
+ def __init__(self, y):
+
+ n = len(y)
+ y = y + SMALL * (y == 0) # can't have 0 values
+ yt = y.sum(axis=0)
+ s = y / (yt * 1.0)
+ lns = np.log(n * s)
+ slns = s * lns
+ t = sum(slns)
+ self.T = t
+
+
+class TheilD:
+ """Decomposition of Theil's T based on partitioning of
+ observations into exhaustive and mutually exclusive groups
+
+ Parameters
+ ----------
+ y : array (n,t) or (n, )
+ with n taken as the observations across which inequality is
+ calculated If y is (n,) then a scalar inequality value is
+ determined. If y is (n,t) then an array of inequality values are
+ determined, one value for each column in y.
+ partition : array (n, )
+ elements indicating which partition each observation belongs
+ to. These are assumed to be exhaustive.
+
+ Attributes
+ ----------
+ T : array (n,t) or (n,)
+ global inequality T
+ bg : array (n,t) or (n,)
+ between group inequality
+ wg : array (n,t) or (n,)
+ within group inequality
+
+ Examples
+ --------
+ >>> import pysal
+ >>> f=pysal.open(pysal.examples.get_path("mexico.csv"))
+ >>> vnames=["pcgdp%d"%dec for dec in range(1940,2010,10)]
+ >>> y=np.transpose(np.array([f.by_col[v] for v in vnames]))
+ >>> regimes=np.array(f.by_col('hanson98'))
+ >>> theil_d=TheilD(y,regimes)
+ >>> theil_d.bg
+ array([ 0.0345889 , 0.02816853, 0.05260921, 0.05931219, 0.03205257,
+ 0.02963731, 0.03635872])
+ >>> theil_d.wg
+ array([ 0.17435454, 0.12405598, 0.0521202 , 0.04263506, 0.06354856,
+ 0.07547525, 0.0702496 ])
+ """
+ def __init__(self, y, partition):
+ groups = np.unique(partition)
+ T = Theil(y).T
+ ytot = y.sum(axis=0)
+
+ #group totals
+ gtot = np.array([y[partition == gid].sum(axis=0) for gid in groups])
+ mm = np.dot
+
+ if ytot.size == 1: # y is 1-d
+ sg = gtot / (ytot * 1.)
+ sg.shape = (sg.size, 1)
+ else:
+ sg = mm(gtot, np.diag(1. / ytot))
+ ng = np.array([sum(partition == gid) for gid in groups])
+ ng.shape = (ng.size,) # ensure ng is 1-d
+ n = y.shape[0]
+ # between group inequality
+ bg = np.multiply(sg, np.log(mm(np.diag(n * 1. / ng), sg))).sum(axis=0)
+ self.T = T
+ self.bg = bg
+ self.wg = T - bg
+
+
+class TheilDSim:
+ """Random permutation based inference on Theil's inequality decomposition.
+
+ Provides for computationally based inference regarding the inequality
+ decomposition using random spatial permutations. [1]_
+
+ Parameters
+ ----------
+ y : array (n,t) or (n, )
+ with n taken as the observations across which inequality is
+ calculated If y is (n,) then a scalar inequality value is
+ determined. If y is (n,t) then an array of inequality values are
+ determined, one value for each column in y.
+ partition : array (n, )
+ elements indicating which partition each observation belongs
+ to. These are assumed to be exhaustive.
+ permutations : int
+ Number of random spatial permutations for computationally
+ based inference on the decomposition.
+
+ Attributes
+ ----------
+
+ observed : array (n,t) or (n,)
+ TheilD instance for the observed data.
+
+ bg : array (permutations+1,t)
+ between group inequality
+
+ bg_pvalue : array (t,1)
+ p-value for the between group measure. Measures the
+ percentage of the realized values that were greater than
+ or equal to the observed bg value. Includes the observed
+ value.
+
+ wg : array (size=permutations+1)
+ within group inequality Depending on the shape of y, 1 or 2-dimensional
+
+ Examples
+ --------
+ >>> import pysal
+ >>> f=pysal.open(pysal.examples.get_path("mexico.csv"))
+ >>> vnames=["pcgdp%d"%dec for dec in range(1940,2010,10)]
+ >>> y=np.transpose(np.array([f.by_col[v] for v in vnames]))
+ >>> regimes=np.array(f.by_col('hanson98'))
+ >>> np.random.seed(10)
+ >>> theil_ds=TheilDSim(y,regimes,999)
+ >>> theil_ds.bg_pvalue
+ array([ 0.4 , 0.344, 0.001, 0.001, 0.034, 0.072, 0.032])
+
+ References
+ ----------
+ .. [1] Rey, S.J. (2004) "Spatial analysis of regional economic growth,
+ inequality and change," in M.F. Goodchild and D.G. Jannelle (eds.)
+ Spatially Integrated Social Science. Oxford University Press: Oxford.
+ Pages 280-299.
+
+ """
+ def __init__(self, y, partition, permutations=99):
+
+ observed = TheilD(y, partition)
+ bg_ct = observed.bg == observed.bg # already have one extreme value
+ bg_ct = bg_ct * 1.0
+ results = [observed]
+ for perm in range(permutations):
+ yp = np.random.permutation(y)
+ t = TheilD(yp, partition)
+ bg_ct += (1.0 * t.bg >= observed.bg)
+ results.append(t)
+ self.results = results
+ self.T = observed.T
+ self.bg_pvalue = bg_ct / (permutations * 1.0 + 1)
+ self.bg = np.array([r.bg for r in results])
+ self.wg = np.array([r.wg for r in results])
+
diff --git a/pysal/meta/akern1.wmd b/pysal/meta/akern1.wmd
new file mode 100644
index 0000000..61b6d7e
--- /dev/null
+++ b/pysal/meta/akern1.wmd
@@ -0,0 +1,15 @@
+{
+ "input1": {
+ "data1": {
+ "type": "shp",
+ "uri": "../examples/columbus.shp"
+ }
+ },
+ "weight_type": "akernel",
+ "transform": "O",
+ "parameters": {
+ "function": "triangular",
+ "bandwidths": null,
+ "k": 2
+ }
+}
diff --git a/pysal/meta/chain.wmd b/pysal/meta/chain.wmd
new file mode 100644
index 0000000..89100f1
--- /dev/null
+++ b/pysal/meta/chain.wmd
@@ -0,0 +1,14 @@
+
+{
+ "input1": {
+ "type": "prov",
+ "uri": "http://toae.org/pub/wrook1.wmd"
+ },
+ "weight_type": "higher_order",
+ "transform": "O",
+ "parameters": {
+ "order":2,
+ "lower":0
+ }
+}
+
diff --git a/pysal/meta/chain2.wmd b/pysal/meta/chain2.wmd
new file mode 100644
index 0000000..0ac918c
--- /dev/null
+++ b/pysal/meta/chain2.wmd
@@ -0,0 +1,14 @@
+
+{
+ "input1": {
+ "type": "prov",
+ "uri": "chain.wmd"
+ },
+ "weight_type": "higher_order",
+ "transform": "O",
+ "parameters": {
+ "order":2,
+ "lower":0
+ }
+}
+
diff --git a/pysal/meta/kernel.wmd b/pysal/meta/kernel.wmd
new file mode 100644
index 0000000..ef3369a
--- /dev/null
+++ b/pysal/meta/kernel.wmd
@@ -0,0 +1,17 @@
+{
+ "input1": {
+ "data1":{
+ "type": "shp",
+ "uri": "../examples/columbus.shp"}
+ },
+ "weight_type": "kernel",
+ "transform": "O",
+ "parameters": {
+ "function": "triangular",
+ "bandwidths": null,
+ "k": 2
+ }
+}
+
+
+
diff --git a/pysal/meta/knn.wmd b/pysal/meta/knn.wmd
new file mode 100644
index 0000000..f5091ec
--- /dev/null
+++ b/pysal/meta/knn.wmd
@@ -0,0 +1,14 @@
+{
+ "input1": {
+ "type": "shp",
+ "uri": "../examples/columbus.shp"
+ },
+ "weight_type": "knn",
+ "transform": "O",
+ "parameters": {
+ "p": 2,
+ "k": 2,
+ "radius": null,
+ "id_variable": null
+ }
+}
diff --git a/pysal/meta/taz_block.wmd b/pysal/meta/taz_block.wmd
new file mode 100644
index 0000000..ed45335
--- /dev/null
+++ b/pysal/meta/taz_block.wmd
@@ -0,0 +1,11 @@
+
+{
+ "input1": {
+ "data1":{
+ "type": "dbf",
+ "uri": "http://toae.org/pub/taz.dbf"}
+ },
+ "weight_type": "block",
+ "parameters": {"block_variable": "CNTY"},
+ "transform": "O"
+}
diff --git a/pysal/meta/taz_intersection.wmd b/pysal/meta/taz_intersection.wmd
new file mode 100644
index 0000000..e25704b
--- /dev/null
+++ b/pysal/meta/taz_intersection.wmd
@@ -0,0 +1,16 @@
+
+{
+ "input1":{
+ "data1": {
+ "type": "prov",
+ "uri": "taz_rook.wmd"},
+ "data2": {
+ "type": "prov",
+ "uri": "taz_block.wmd"}
+ },
+ "weight_type": "intersection",
+ "transform": "O",
+ "parameters": {
+ }
+}
+
diff --git a/pysal/meta/taz_rook.wmd b/pysal/meta/taz_rook.wmd
new file mode 100644
index 0000000..06a0e32
--- /dev/null
+++ b/pysal/meta/taz_rook.wmd
@@ -0,0 +1,10 @@
+
+{
+ "input1": {
+ "data1":{
+ "type": "shp",
+ "uri": "http://toae.org/pub/taz.shp"}
+ },
+ "weight_type": "rook",
+ "transform": "O"
+}
diff --git a/pysal/meta/wmd.py b/pysal/meta/wmd.py
new file mode 100644
index 0000000..3cef3dd
--- /dev/null
+++ b/pysal/meta/wmd.py
@@ -0,0 +1,476 @@
+"""
+Weights Meta Data
+
+Prototyping meta data functions and classes for weights provenance
+
+
+Based on Anselin, L., S.J. Rey and W. Li (2014) "Metadata and provenance for
+spatial analysis: the case of spatial weights." International Journal of
+Geographical Information Science. DOI:10.1080/13658816.2014.917313
+
+
+
+TODO
+----
+
+- Document each public function with working doctest
+- Abstract the test files as they currently assume location is source
+ directory
+- have wmd_reader take either a wmd file or a wmd dictionary/object
+"""
+__author__ = "Sergio J. Rey <srey at asu.edu>, Wenwen Li <wenwen at asu.edu>"
+import pysal as ps
+import io, json
+import httplib
+from urlparse import urlparse
+import urllib2 as urllib
+import copy
+import numpy as np
+
+def wmd_reader(fileName):
+ """
+
+ Examples
+ --------
+ >>> import wmd
+ >>> wr = wmd.wmd_reader('w1rook.wmd')
+ wmd_reader failed: w1rook.wmd
+ >>> wr = wmd.wmd_reader('wrook1.wmd')
+ >>> wr.neighbors[2]
+ [0, 1, 3, 4]
+ """
+
+ try:
+ meta_data = _uri_reader(fileName)
+ except:
+ try:
+ with open(fileName, 'r') as fp:
+ meta_data = json.load(fp)
+ global fullmeta
+ fullmeta = {}
+ fullmeta['root'] = copy.deepcopy(meta_data)
+ w = _wmd_parser(meta_data)
+ return w
+ except:
+ print 'wmd_reader failed: ', fileName
+
+class WMD(ps.W):
+ """Weights Meta Data Class"""
+ def __init__(self, neighbors=None, weights=None, id_order=None):
+ self.meta_data = {}
+ super(WMD, self).__init__(neighbors, weights, id_order)
+
+ # override transform property to record any post-instantiation
+ # transformations in meta data
+
+ @ps.W.transform.setter
+ def transform(self, value):
+ super(WMD, WMD).transform.__set__(self, value)
+ self.meta_data['transform'] = self._transform
+
+ def write(self, fileName, data=False):
+ """
+
+ Examples
+ --------
+ >>> import wmd
+ >>> wr = wmd.wmd_reader('w1rook.wmd')
+ wmd_reader failed: w1rook.wmd
+ >>> wr = wmd.wmd_reader('wrook1.wmd')
+ >>> wr.write('wr1.wmd')
+ >>> wr1 = wmd.wmd_reader('wr1.wmd')
+ >>> wr.neighbors[2]
+ [0, 1, 3, 4]
+ >>> wr1.neighbors[2]
+ [0, 1, 3, 4]
+ >>>
+
+ """
+ _wmd_writer(self, fileName, data=data)
+
+######################### Private functions #########################
+
+def _wmd_writer(wmd_object, fileName, data=False):
+ try:
+ with open(fileName, 'w') as f:
+ if data:
+ wmd_object.meta_data['data'] = {}
+ wmd_object.meta_data['data']['weights'] = wmd_object.weights
+ wmd_object.meta_data['data']['neighbors'] = wmd_object.neighbors
+ json.dump(wmd_object.meta_data,
+ f,
+ indent=4,
+ separators=(',', ': '))
+ except:
+ print 'wmd_writer failed.'
+
+def _block(arg_dict):
+ """
+ General handler for block weights
+
+
+ Examples
+ --------
+ >>> w = wmd_reader('taz_block.wmd')
+ >>> w.n
+ 4109
+ >>> w.meta_data
+ {'root': {u'input1': {u'data1': {u'type': u'dbf', u'uri': u'http://toae.org/pub/taz.dbf'}}, u'weight_type': u'block', u'transform': u'O', u'parameters': {u'block_variable': u'CNTY'}}}
+
+ """
+ input1 = arg_dict['input1']
+ for key in input1:
+ input1 = input1[key]
+ break
+ uri = input1['uri']
+ weight_type = arg_dict['weight_type'].lower()
+ file_name = uri
+
+ var_name = arg_dict['parameters']['block_variable']
+ dbf = ps.open(uri)
+ block = np.array(dbf.by_col(var_name))
+ dbf.close()
+ w = ps.weights.util.block_weights(block)
+ w = WMD(w.neighbors, w.weights)
+ w.meta_data = {}
+ w.meta_data['input1'] = {"type": 'dbf', 'uri': uri}
+ w.meta_data['transform'] = w.transform
+ w.meta_data['weight_type'] = weight_type
+ w.meta_data['parameters'] = {'block_variable':var_name}
+
+ return w
+
+def _contiguity(arg_dict):
+ """
+ General handler for building contiguity weights from shapefiles
+
+ Examples
+ --------
+
+ >>> w = wmd_reader('wrook1.wmd')
+ >>> w.n
+ 49
+ >>> w.meta_data
+ {'root': {u'input1': {u'data1': {u'type': u'shp', u'uri': u'http://toae.org/pub/columbus.shp'}}, u'weight_type': u'rook', u'transform': u'O'}}
+ """
+ input1 = arg_dict['input1']
+ for key in input1:
+ input1 = input1[key]
+ break
+ uri = input1['uri']
+ weight_type = arg_dict['weight_type']
+ weight_type = weight_type.lower()
+ if weight_type == 'rook':
+ w = ps.rook_from_shapefile(uri)
+ elif weight_type == 'queen':
+ w = ps.queen_from_shapefile(uri)
+ else:
+ print "Unsupported contiguity criterion: ",weight_type
+ return None
+ if 'parameters' in arg_dict:
+ order = arg_dict['parameters'].get('order',1) # default to 1st order
+ lower = arg_dict['parameters'].get('lower',0) # default to exclude lower orders
+ if order > 1:
+ w_orig = w
+ w = ps.higher_order(w,order)
+ if lower:
+ for o in xrange(order-1,1,-1):
+ w = ps.weights.w_union(ps.higher_order(w_orig,o), w)
+ w = ps.weights.w_union(w, w_orig)
+ parameters = arg_dict['parameters']
+ else:
+ parameters = {'lower': 0, 'order':1 }
+ w = WMD(w.neighbors, w.weights)
+ w.meta_data = {}
+ w.meta_data["input1"] = {"type": 'shp', 'uri':uri}
+ w.meta_data["transform"] = w.transform
+ w.meta_data["weight_type"] = weight_type
+ w.meta_data['parameters'] = parameters
+ return w
+
+def _kernel(arg_dict):
+ """
+ General handler for building kernel based weights from shapefiles
+
+ Examples
+ --------
+
+ >>> w = wmd_reader('kernel.wmd')
+ >>> w.n
+ 49
+ >>> w.meta_data
+ {'root': {u'input1': {u'data1': {u'type': u'shp', u'uri': u'../examples/columbus.shp'}}, u'weight_type': u'kernel', u'transform': u'O', u'parameters': {u'function': u'triangular', u'bandwidths': None, u'k': 2}}}
+
+
+
+ """
+ input1 = arg_dict['input1']['data1']
+ uri = input1['uri']
+ weight_type = arg_dict['weight_type']
+ weight_type = weight_type.lower()
+ k = 2
+ bandwidths = None
+ function = 'triangular'
+ if 'parameters' in arg_dict:
+ k = arg_dict['parameters'].get('k',k) # set default to 2
+ bandwidths = arg_dict['parameters'].get('bandwidths',bandwidths)
+ function = arg_dict['parameters'].get('function', function)
+ else:
+ parameters = {}
+ parameters['k'] = k
+ parameters['bandwidths'] = bandwidths
+ parameters['function'] = function
+ arg_dict['parameters'] = parameters
+
+
+ if weight_type == 'akernel':
+ # adaptive kernel
+ w = ps.adaptive_kernelW_from_shapefile(uri, bandwidths = bandwidths,
+ k=k, function = function)
+ elif weight_type == 'kernel':
+ w = ps.kernelW_from_shapefile(uri, k=k, function = function)
+ else:
+ print "Unsupported kernel: ",weight_type
+ return None
+ w = WMD(w.neighbors, w.weights)
+ w.meta_data = {}
+ w.meta_data["input1"] = {"type": 'shp', 'uri':uri}
+ w.meta_data["transform"] = w.transform
+ w.meta_data["weight_type"] = weight_type
+ w.meta_data['parameters'] = arg_dict['parameters']
+ return w
+
+def _distance(arg_dict):
+ """
+ General handler for distance based weights obtained from shapefiles
+ """
+ input1 = arg_dict['input1']
+ uri = input1['uri']
+ weight_type = arg_dict['weight_type']
+ weight_type = weight_type.lower()
+ k = 2
+ id_variable = None
+ p = 2
+ radius = None
+ if 'parameters' in arg_dict:
+ k = arg_dict['parameters'].get('k',k) # set default to 2
+ id_variable = arg_dict['parameters'].get('id_variable', id_variable)
+ p = arg_dict['parameters'].get('p',p)
+ radius = arg_dict['parameters'].get('radius', radius)
+
+ else:
+ parameters = {}
+ parameters['k'] = 2
+ parameters['id_variable'] = None
+ parameters['radius'] = None
+ parameters['p'] = 2
+ arg_dict['parameters'] = parameters
+
+ if weight_type == 'knn':
+ w = ps.knnW_from_shapefile(uri,k=k,p=p,idVariable=id_variable,
+ radius=radius)
+ w = WMD(w.neighbors, w.weights)
+ w.meta_data = {}
+ w.meta_data["input1"] = {"type": 'shp', 'uri':uri}
+ w.meta_data["weight_type"] = 'knn'
+ w.meta_data["transform"] = w.transform
+ w.meta_data['parameters'] = arg_dict['parameters']
+ return w
+
+def _higher_order(arg_dict):
+ wmd = arg_dict['wmd']
+ order = 2
+ if 'parameters' in arg_dict:
+ order = arg_dict['parameters'].get('order', order)
+ else:
+ parameters = {}
+ parameters['order'] = order
+ arg_dict['parameters'] = parameters
+
+
+ w = ps.higher_order(wmd, order)
+ w = WMD(w.neighbors, w.weights)
+ w.meta_data = {}
+ w.meta_data['input1'] = arg_dict['input1']
+ w.meta_data['parameters'] = arg_dict['parameters']
+
+ return w
+
+def _intersection(arg_dict):
+ #wmd = arg_dict['wmd']
+ w1 = arg_dict['input1']['data1']['uri']
+ w2 = arg_dict['input1']['data2']['uri']
+ w = ps.w_intersection(w1,w2)
+ w = WMD(w.neighbors, w.weights)
+ return w
+
+def _geojsonf(arg_dict):
+ """
+ Handler for local geojson files
+ """
+ input1 = arg_dict['input1']
+ uri = input1['uri']
+ weight_type = arg_dict['weight_type']
+ weight_type = weight_type.lower()
+ id_variable = None
+
+ if weight_type == 'queen_geojsonf':
+ w = ps.weights.user.queen_from_geojsonf(uri)
+ w.meta_data = {}
+ w.meta_data["input1"] = {"type": 'geojsonf', 'uri':uri}
+ w.meta_data["weight_type"] = 'queen'
+ w.meta_data["transform"] = w.transform
+ return w
+
+# wrapper dict that maps specific weights types to a handler function that
+# builds the specific weights instance
+WEIGHT_TYPES = {}
+WEIGHT_TYPES['rook'] = _contiguity
+WEIGHT_TYPES['queen'] = _contiguity
+WEIGHT_TYPES['akernel'] = _kernel
+WEIGHT_TYPES['kernel'] = _kernel
+WEIGHT_TYPES['knn'] = _distance
+WEIGHT_TYPES['higher_order'] = _higher_order
+WEIGHT_TYPES['block'] = _block
+WEIGHT_TYPES['intersection'] = _intersection
+#WEIGHT_TYPES['queen_geojsonf'] = geojsonf
+#WEIGHT_TYPES['geojsons'] = geojsons
+
+
+def _uri_reader(uri):
+ j = json.load(urllib.urlopen(uri))
+ return j
+
+def _wmd_read_only(fileName):
+ try:
+ meta_data = _uri_reader(fileName)
+ except:
+ try:
+ with open(fileName, 'r') as fp:
+ meta_data = json.load(fp)
+ return meta_data
+ except:
+ print '_wmd_read_only failed: ', fileName
+
+def _wmd_parser(wmd_object):
+ if 'root' in wmd_object:
+ wmd_object = wmd_object['root']
+ weight_type = wmd_object['weight_type'].lower()
+ for key in wmd_object['input1']:
+ #print key
+ if wmd_object['input1'][key]['type'] == 'prov':
+ # call wmd_reader
+ uri = wmd_object['input1'][key]['uri']
+
+ meta_data = _wmd_read_only(uri)
+ fullmeta[uri] = copy.deepcopy(meta_data) #add full metadata
+ wmd = _wmd_parser(meta_data)
+ wmd_object['input1'][key]['uri'] = wmd
+ else:
+ # handle distributed files
+ uri = wmd_object['input1'][key]['uri']
+ try:
+ tmp = open(uri)
+ #print ' tmp: ', tmp
+ wmd_object['input1'][key]['uri'] = uri
+ except:
+ _download_shapefiles(uri)
+ uri = uri.split("/")[-1]
+ wmd_object['input1'][key]['uri'] = uri # use local copy
+
+ if weight_type in WEIGHT_TYPES:
+ #print weight_type
+ wmd = WEIGHT_TYPES[weight_type](wmd_object)
+ wmd.meta_data = fullmeta
+ else:
+ print 'Unsupported weight type: ', weight_type
+
+ return wmd
+
+def _download_shapefiles(file_name):
+ file_parts = file_name.split("/")
+ file_prefix = file_parts[-1].split(".")[0]
+ exts = [ ".shp", ".dbf", ".shx" ]
+ for ext in exts:
+ # rebuild url
+ file_name = file_prefix + ext
+ file_parts[-1] = file_name
+ new_url = "/".join(file_parts)
+ #print file_name, new_url
+ u = urllib.urlopen(new_url)
+ f = open(file_name, 'wb')
+ meta = u.info()
+ file_size = int(meta.getheaders("Content-Length")[0])
+ #print "Downloading: %s Bytes: %s" % (file_name, file_size)
+ file_size_dl = 0
+ block_sz = 8192
+ while True:
+ bf = u.read(block_sz)
+ if not bf:
+ break
+ file_size_dl += len(bf)
+ f.write(bf)
+ status = r"%10d [%3.2f%%]" % (file_size_dl, file_size_dl * 100. /
+ file_size)
+ status = status + chr(8)* (len(status)+1)
+ #print status, f.close()
+
+if __name__ == '__main__':
+
+ # distributed file
+ w1 = wmd_reader("wrook1.wmd")
+
+## # order
+## w1o = wmd_reader('wrooko1.wmd')
+## w2o = wmd_reader('wrooko2.wmd')
+## w2ol = wmd_reader('wrooko2l.wmd')
+##
+## # kernels
+ ak1 = wmd_reader('akern1.wmd')
+ kern = wmd_reader('kernel.wmd')
+##
+## # knn
+## knn = wmd_reader('knn.wmd')
+##
+##
+##
+## # moran workflow
+## import pysal as ps
+
+
+ # geojson
+ #wj = wmd_reader("wgeojson.wmd")
+
+
+ # here we test chaining
+# r1 = wmd_reader('chain2inputs.wmd')
+# print "full metadata is listed below: \n", fullmeta
+ # r2 = wmd_reader('chain2.wmd')
+
+ taz_int = wmd_reader("taz_intersection.wmd")
+
+
+
+ ## intersection between queen and block weights
+ #import numpy as np
+ #w = ps.lat2W(4,4)
+ #block_variable = np.ones((w.n,1))
+ #block_variable[:8] = 0
+ #w_block = ps.weights.util.block_weights(block_variable)
+
+ #w_intersection = ps.w_intersection(w, w_block)
+
+
+ ## with Columbus example using EW as the block and queen
+ #dbf = ps.open("columbus.dbf")
+ #ew = np.array(dbf.by_col("EW"))
+ #dbf.close()
+ #w_ew = ps.weights.util.block_weights(ew)
+ #wr = ps.rook_from_shapefile("columbus.shp")
+ #w_int = ps.w_intersection(w_ew, wr)
+
+
+ #blk = wmd_reader('block2.wmd')
+
+ #taz_int = wmd_reader("http://spatial.csf.asu.edu/taz_intersection.wmd")
+
diff --git a/pysal/meta/wrook1.wmd b/pysal/meta/wrook1.wmd
new file mode 100644
index 0000000..eebfd13
--- /dev/null
+++ b/pysal/meta/wrook1.wmd
@@ -0,0 +1,9 @@
+{
+ "input1": {
+ "data1":{
+ "type": "shp",
+ "uri": "http://toae.org/pub/columbus.shp"}
+ },
+ "weight_type": "rook",
+ "transform": "O"
+}
\ No newline at end of file
diff --git a/pysal/meta/wrooko1.wmd b/pysal/meta/wrooko1.wmd
new file mode 100644
index 0000000..a2a0159
--- /dev/null
+++ b/pysal/meta/wrooko1.wmd
@@ -0,0 +1,12 @@
+{
+ "input1": {
+ "type": "shp",
+ "uri": "../examples/columbus.shp"
+ },
+ "weight_type": "rook",
+ "transform": "O",
+ "parameters": {
+ "order":1,
+ "lower":0
+ }
+}
diff --git a/pysal/meta/wrooko2.wmd b/pysal/meta/wrooko2.wmd
new file mode 100644
index 0000000..5a838ea
--- /dev/null
+++ b/pysal/meta/wrooko2.wmd
@@ -0,0 +1,12 @@
+{
+ "input1": {
+ "type": "shp",
+ "uri": "../examples/columbus.shp"
+ },
+ "weight_type": "rook",
+ "transform": "O",
+ "parameters": {
+ "order":2,
+ "lower":0
+ }
+}
diff --git a/pysal/meta/wrooko2l.wmd b/pysal/meta/wrooko2l.wmd
new file mode 100644
index 0000000..58b16a6
--- /dev/null
+++ b/pysal/meta/wrooko2l.wmd
@@ -0,0 +1,12 @@
+{
+ "input1": {
+ "type": "shp",
+ "uri": "../examples/columbus.shp"
+ },
+ "weight_type": "rook",
+ "transform": "O",
+ "parameters": {
+ "order":2,
+ "lower":1
+ }
+}
diff --git a/pysal/network/Network Usage.ipynb b/pysal/network/Network Usage.ipynb
new file mode 100644
index 0000000..95aeaec
--- /dev/null
+++ b/pysal/network/Network Usage.ipynb
@@ -0,0 +1,644 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:db645dee5bfbe11d1d339edf10b272aa72bf2d40c4f62e2ec987e6dad1f6e73d"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "%pylab inline\n",
+ "import pysal as ps\n",
+ "\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "ntw = ps.Network(ps.examples.get_path('geodanet/streets.shp'))\n",
+ "\n",
+ "#Snap a point pattern to the network\n",
+ "ntw.snapobservations(ps.examples.get_path('geodanet/crimes.shp'), 'crimes', attribute=True)\n",
+ "ntw.snapobservations(ps.examples.get_path('geodanet/schools.shp'), 'schools', attribute=False)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "####A network is composed of a single topological representation of a road and $n$ point patterns which are snapped to the network."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "ntw.pointpatterns"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 3,
+ "text": [
+ "{'crimes': <pysal.network.network.PointPattern instance at 0x10a552d88>,\n",
+ " 'schools': <pysal.network.network.PointPattern instance at 0x10a5621b8>}"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "dir(ntw.pointpatterns['crimes'])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 4,
+ "text": [
+ "['__doc__',\n",
+ " '__init__',\n",
+ " '__module__',\n",
+ " 'dist_to_node',\n",
+ " 'npoints',\n",
+ " 'obs_to_edge',\n",
+ " 'obs_to_node',\n",
+ " 'points',\n",
+ " 'snapped_coordinates']"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#####Attributes for every point pattern\n",
+ "\n",
+ "1. dist_to_node dict keyed by pointid with the value being a dict in the form {node: distance to node, node: distance to node}\n",
+ "2. obs_to_edge dict keyed by edge with the value being a dict in the form {pointID:(x-coord, y-coord), pointID:(x-coord, y-coord), ... }\n",
+ "3. obs_to_node\n",
+ "4. points geojson like representation of the point pattern. Includes properties if read with attributes=True\n",
+ "5. snapped_coordinates dict keyed by pointid with the value being (x-coord, y-coord)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "####Counts per edge are important, but should not be precomputed since we have different representations of the network (digitized and graph currently). (Relatively) Uniform segmentation still needs to be done."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "counts = ntw.count_per_edge(ntw.pointpatterns['crimes'].obs_to_edge,\n",
+ " graph=False)\n",
+ "sum(counts.values()) / float(len(counts.keys()))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 5,
+ "text": [
+ "2.6822429906542058"
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "heading",
+ "level": 3,
+ "metadata": {},
+ "source": [
+ "Segmentation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "n200 = ntw.segment_edges(200.0)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "counts = n200.count_per_edge(n200.pointpatterns['crimes'].obs_to_edge, graph=False)\n",
+ "sum(counts.values()) / float(len(counts.keys()))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 7,
+ "text": [
+ "2.0354609929078014"
+ ]
+ }
+ ],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "heading",
+ "level": 4,
+ "metadata": {},
+ "source": [
+ "Visualization of the shapefile derived, unsegmented network with nodes in a larger, semi-opaque form and the distance segmented network with small, fully opaque nodes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import networkx as nx\n",
+ "figsize(10,10)\n",
+ "\n",
+ "g = nx.Graph()\n",
+ "for e in ntw.edges:\n",
+ " g.add_edge(*e)\n",
+ "for n, p in ntw.node_coords.iteritems():\n",
+ " g.node[n] = p\n",
+ "nx.draw(g, ntw.node_coords, node_size=300, alpha=0.5)\n",
+ "\n",
+ "g = nx.Graph()\n",
+ "for e in n200.edges:\n",
+ " g.add_edge(*e)\n",
+ "for n, p in n200.node_coords.iteritems():\n",
+ " g.node[n] = p\n",
+ "nx.draw(g, n200.node_coords, node_size=25, alpha=1.0)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAxkAAALxCAYAAAAwg2ukAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xt8FOW9P/DPzu4mkw25kCsJEhRCYENIQIjc6qWtCgrq\nsT2ttlYukYtgLVRROafntD22WGgjoscqIoSLl9ZfPW0VVPBSqxYEIYRcSCCEm4FcyP22O7s7O/P7\nIzvLXmYvSWZ3B/y+X6+8lM3uM8/MPtmdz/M884xGFEURhBBCCCGEEKIQJtIVIIQQQgghhFxdKGQQ\nQgghhBBCFEUhgxBCCCGEEKIoChmEEEIIIYQQRVHIIIQQQgghhCiKQgYhhBBCCCFEURQyCCGEEEII\nIYqikEEIIYQQQghRFIUMQgghhBBCiKIoZBBCCCGEEEIURSGDEEIIIYQQoigKGYQQQgghhBBFUcgg\nhBBCCCGEK [...]
+ "text": [
+ "<matplotlib.figure.Figure at 0x10ae232d0>"
+ ]
+ }
+ ],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#####Moran's I using the digitized network"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "#Binary Adjacency\n",
+ "#ntw.contiguityweights(graph=False)\n",
+ "\n",
+ "w = ntw.contiguityweights(graph=False)\n",
+ "\n",
+ "\n",
+ "#Build the y vector\n",
+ "#edges = ntw.w.neighbors.keys()\n",
+ "edges = w.neighbors.keys()\n",
+ "y = np.zeros(len(edges))\n",
+ "for i, e in enumerate(edges):\n",
+ " if e in counts.keys():\n",
+ " y[i] = counts[e]\n",
+ "\n",
+ "#Moran's I\n",
+ "#res = ps.esda.moran.Moran(y, ntw.w, permutations=99)\n",
+ "res = ps.esda.moran.Moran(y, w, permutations=99)\n",
+ "print dir(res)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "['EI', 'EI_sim', 'I', 'VI_norm', 'VI_rand', 'VI_sim', '_Moran__calc', '_Moran__moments', '__doc__', '__init__', '__module__', 'n', 'p_norm', 'p_rand', 'p_sim', 'p_z_sim', 'permutations', 'seI_norm', 'seI_rand', 'seI_sim', 'sim', 'w', 'y', 'z', 'z2ss', 'z_norm', 'z_rand', 'z_sim']\n"
+ ]
+ }
+ ],
+ "prompt_number": 9
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "####Moran's I using the graph representation to generate the W\n",
+ "\n",
+ "Note that we have to regenerate the counts per edge, since the graph will have less edges."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "counts = ntw.count_per_edge(ntw.pointpatterns['crimes'].obs_to_edge, graph=True)\n",
+ "\n",
+ "#Binary Adjacency\n",
+ "#ntw.contiguityweights(graph=True)\n",
+ "w = ntw.contiguityweights(graph=True)\n",
+ "#Build the y vector\n",
+ "#edges = ntw.w.neighbors.keys()\n",
+ "edges = w.neighbors.keys()\n",
+ "y = np.zeros(len(edges))\n",
+ "for i, e in enumerate(edges):\n",
+ " if e in counts.keys():\n",
+ " y[i] = counts[e]\n",
+ "\n",
+ "#Moran's I\n",
+ "#res = ps.esda.moran.Moran(y, ntw.w, permutations=99)\n",
+ "res = ps.esda.moran.Moran(y, w, permutations=99)\n",
+ "\n",
+ "\n",
+ "print dir(res)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "['EI', 'EI_sim', 'I', 'VI_norm', 'VI_rand', 'VI_sim', '_Moran__calc', '_Moran__moments', '__doc__', '__init__', '__module__', 'n', 'p_norm', 'p_rand', 'p_sim', 'p_z_sim', 'permutations', 'seI_norm', 'seI_rand', 'seI_sim', 'sim', 'w', 'y', 'z', 'z2ss', 'z_norm', 'z_rand', 'z_sim']\n"
+ ]
+ }
+ ],
+ "prompt_number": 10
+ },
+ {
+ "cell_type": "heading",
+ "level": 4,
+ "metadata": {},
+ "source": [
+ "Moran's I using the segmented network and intensities instead of counts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "#Binary Adjacency\n",
+ "#n200.contiguityweights(graph=False)\n",
+ "w = n200.contiguityweights(graph=False)\n",
+ "\n",
+ "\n",
+ "#Compute the counts\n",
+ "counts = n200.count_per_edge(n200.pointpatterns['crimes'].obs_to_edge, graph=False)\n",
+ "\n",
+ "#Build the y vector and convert from raw counts to intensities\n",
+ "#edges = n200.w.neighbors.keys()\n",
+ "edges = w.neighbors.keys()\n",
+ "y = np.zeros(len(edges))\n",
+ "for i, e in enumerate(edges):\n",
+ " if e in counts.keys():\n",
+ " length = n200.edge_lengths[e]\n",
+ " y[i] = counts[e] / length\n",
+ " \n",
+ "#Moran's I\n",
+ "#res = ps.esda.moran.Moran(y, n200.w, permutations=99)\n",
+ "res = ps.esda.moran.Moran(y, w, permutations=99)\n",
+ "\n",
+ "\n",
+ "print dir(res)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "['EI', 'EI_sim', 'I', 'VI_norm', 'VI_rand', 'VI_sim', '_Moran__calc', '_Moran__moments', '__doc__', '__init__', '__module__', 'n', 'p_norm', 'p_rand', 'p_sim', 'p_z_sim', 'permutations', 'seI_norm', 'seI_rand', 'seI_sim', 'sim', 'w', 'y', 'z', 'z2ss', 'z_norm', 'z_rand', 'z_sim']\n"
+ ]
+ }
+ ],
+ "prompt_number": 11
+ },
+ {
+ "cell_type": "heading",
+ "level": 3,
+ "metadata": {},
+ "source": [
+ "Timings for distance based methods, e.g. G-function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import time\n",
+ "t1 = time.time()\n",
+ "n0 = ntw.allneighbordistances(ntw.pointpatterns['crimes'])\n",
+ "print time.time()-t1"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "1.19567084312\n"
+ ]
+ }
+ ],
+ "prompt_number": 12
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import time\n",
+ "t1 = time.time()\n",
+ "n1 = n200.allneighbordistances(n200.pointpatterns['crimes'])\n",
+ "print time.time()-t1"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "11.2402291298\n"
+ ]
+ }
+ ],
+ "prompt_number": 13
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that the first time these methods are called, the underlying node-to-node shortest path distance matrix has to be calculated. Subsequent calls will not require this, and will be much faster:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import time\n",
+ "t1 = time.time()\n",
+ "n0 = ntw.allneighbordistances(ntw.pointpatterns['crimes'])\n",
+ "print time.time()-t1"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "0.136250972748\n"
+ ]
+ }
+ ],
+ "prompt_number": 14
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import time\n",
+ "t1 = time.time()\n",
+ "n1 = n200.allneighbordistances(n200.pointpatterns['crimes'])\n",
+ "print time.time()-t1"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "0.160092830658\n"
+ ]
+ }
+ ],
+ "prompt_number": 15
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "####Simulate a point pattern on the network\n",
+ "\n",
+ "Need to supply a count of the number of points and a distirbution (default is uniform). Generally this will not be called by the user, since the simulation will be used for Monte Carlo permutation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "npts = ntw.pointpatterns['crimes'].npoints\n",
+ "sim = ntw.simulate_observations(npts)\n",
+ "sim"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 16,
+ "text": [
+ "<pysal.network.network.SimulatedPointPattern instance at 0x1208e2878>"
+ ]
+ }
+ ],
+ "prompt_number": 16
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "####Create a nearest neighbor matrix using the crimes point pattern\n",
+ "\n",
+ "Right now, both the G and K functions generate a full distance matrix. This is because, I know that the full generation is correct and I believe that the truncated generated, e.g. nearest neighbor, has a big."
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 3,
+ "metadata": {},
+ "source": [
+ "G-function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "gres = ps.NetworkG(ntw,\n",
+ " ntw.pointpatterns['crimes'],\n",
+ " permutations = 99)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 18
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "figsize(5,5)\n",
+ "plot(gres.xaxis, gres.observed, 'b-', linewidth=1.5, label='Observed')\n",
+ "plot(gres.xaxis, gres.upperenvelope, 'r--', label='Upper')\n",
+ "plot(gres.xaxis, gres.lowerenvelope, 'k--', label='Lower')\n",
+ "legend(loc='best')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 19,
+ "text": [
+ "<matplotlib.legend.Legend at 0x10bb0d690>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAUEAAAE4CAYAAADFI0E4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xd4lFX2wPHvTeghdEFIKIKIoLCAiHSC0kRlXSyAiuAq\nAuqK67MLgqtmFQR/LmV3QZdVioWiK8WGgpQg0sEAggSk915CTT2/P+6QThKSmXmnnM/zzGNm5ua9\nZ0I8ue+tRkRQSqlgFeJ0AEop5SRNgkqpoKZJUCkV1DQJKqWCmiZBpVRQ0ySolApqeSZBY8wUY8wx\nY8wvuZT5lzHmN2PMJmNME/eGqJRSnpOfluBUoOu13jTGdANuFpG6wLPA+26KTSmlPC7PJCgiy4Ez\nuRTpDnzkKrsGKGeMqeKe8JRSyrPc0ScYARzI8PwgEOmG6yqllMe5a2DEZHmua/GUUn6hiBuucQio\nnuF5pOu1T [...]
+ "text": [
+ "<matplotlib.figure.Figure at 0x10aedb510>"
+ ]
+ }
+ ],
+ "prompt_number": 19
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "###K-function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "kres = ps.NetworkK(ntw,\n",
+ " ntw.pointpatterns['crimes'],\n",
+ " permutations=99)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stderr",
+ "text": [
+ "/Users/serge/Dropbox/p/pysal/src/pysal/pysal/network/analysis.py:142: RuntimeWarning: invalid value encountered in less_equal\n",
+ " y[i] = len(nearest[nearest <= s])\n"
+ ]
+ }
+ ],
+ "prompt_number": 20
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "figsize(5,5)\n",
+ "plot(kres.xaxis, kres.observed, 'b-', linewidth=1.5, label='Observed')\n",
+ "plot(kres.xaxis, kres.upperenvelope, 'r--', label='Upper')\n",
+ "plot(kres.xaxis, kres.lowerenvelope, 'k--', label='Lower')\n",
+ "legend(loc='best')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 21,
+ "text": [
+ "<matplotlib.legend.Legend at 0x12143c7d0>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAUcAAAE+CAYAAAAeZNJiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmcjWX/wPHPNQzGTlMY+5asD/GgbFOWNIRHC4qSNknb\n01MoZRKp/EiKIokSiqylbDWIrDFkCDFkN2XJDLOd6/fHdUxnzpwZM+acc5/l+369zmvOue9r7vM9\nxnznuq9Vaa0RQgiRWYjVAQghhC+S5CiEEC5IchRCCBckOQohhAuSHIUQwgVJjkII4YLXkqNSarpS\n6pRSalcuyo5XSm23P35TSp31RoxCCHGF8tY4R6VUG+Ai8JnWumEevm8w0Fhr/ajHghNCCCdeqzlq\nrdcBmWqASqmaSqnvlFJblVJrlVJ1XHzr/cAcrwQphBB2BS1+/6nAE1rrA0qpFsBkoP2Vk0qpqkA1\n4AdrwhNCB [...]
+ "text": [
+ "<matplotlib.figure.Figure at 0x10aeb22d0>"
+ ]
+ }
+ ],
+ "prompt_number": 21
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 15
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+}
\ No newline at end of file
diff --git a/pysal/network/__init__.py b/pysal/network/__init__.py
new file mode 100644
index 0000000..610cb77
--- /dev/null
+++ b/pysal/network/__init__.py
@@ -0,0 +1,7 @@
+"""
+:mod:`network` --- Network spatial analysis
+===========================================
+
+"""
+
+import network
diff --git a/pysal/network/analysis.py b/pysal/network/analysis.py
new file mode 100644
index 0000000..2e63291
--- /dev/null
+++ b/pysal/network/analysis.py
@@ -0,0 +1,186 @@
+import numpy as np
+
+
+class NetworkBase(object):
+ def __init__(self, ntw, pointpattern,nsteps=10,
+ permutations=99, threshold=0.5,
+ distirbution='poisson',
+ lowerbound=None,upperbound=None):
+
+ self.ntw = ntw
+ self.pointpattern = pointpattern
+ self.nsteps = nsteps
+ self.permutations = permutations
+ self.threshold = threshold
+
+ self.distirbution = distirbution
+ self.validatedistribution()
+
+ self.sim = np.empty((permutations, nsteps))
+ self.npts = self.pointpattern.npoints
+
+ self.lowerbound = lowerbound
+ self.upperbound = upperbound
+
+ #Compute Statistic
+ self.computeobserved()
+ self.computepermutations()
+
+ #Compute the envelope vectors
+ self.computeenvelope()
+
+ def validatedistribution(self):
+ valid_distributions = ['uniform', 'poisson']
+ assert(self.distirbution in valid_distributions),"Disstribution not in {}".format(valid_distributions)
+
+ def computeenvelope(self):
+ upper = 1.0 - self.threshold / 2.0
+ lower = self.threshold / 2.0
+
+ self.upperenvelope = np.nanmax(self.sim, axis=0) * upper
+ self.lowerenvelope = np.nanmin(self.sim, axis=0) * lower
+
+ def setbounds(self, nearest):
+ if self.lowerbound == None:
+ self.lowerbound = 0
+ if self.upperbound == None:
+ self.upperbound = np.nanmax(nearest)
+
+class NetworkG(NetworkBase):
+ """
+ Compute a network constrained G statistic
+
+ Attributes
+ ==========
+
+ """
+
+ def computeobserved(self):
+ nearest = np.nanmin(self.ntw.allneighbordistances(self.pointpattern), axis=1)
+ self.setbounds(nearest)
+ observedx, observedy = gfunction(nearest,self.lowerbound,self.upperbound,
+ nsteps=self.nsteps)
+ self.observed = observedy
+ self.xaxis = observedx
+
+ def computepermutations(self):
+ for p in xrange(self.permutations):
+ sim = self.ntw.simulate_observations(self.npts,
+ distribution=self.distirbution)
+ nearest = np.nanmin(self.ntw.allneighbordistances(sim), axis=1)
+
+ simx, simy = gfunction(nearest,
+ self.lowerbound,
+ self.upperbound,
+ nsteps=self.nsteps)
+ self.sim[p] = simy
+
+
+class NetworkK(NetworkBase):
+ """
+ Network constrained K Function
+ """
+
+ def computeobserved(self):
+ nearest = self.ntw.allneighbordistances(self.pointpattern)
+ self.setbounds(nearest)
+
+ self.lam = self.npts / np.sum(np.array(self.ntw.edge_lengths.values()))
+ observedx, observedy = kfunction(nearest,
+ self.upperbound,
+ self.lam,
+ nsteps = self.nsteps)
+ self.observed = observedy
+ self.xaxis = observedx
+
+ def computepermutations(self):
+ for p in xrange(self.permutations):
+ sim = self.ntw.simulate_observations(self.npts,
+ distribution=self.distirbution)
+ nearest = self.ntw.allneighbordistances(sim)
+
+ simx, simy = kfunction(nearest,
+ self.upperbound,
+ self.lam,
+ nsteps=self.nsteps)
+ self.sim[p] = simy
+
+class NetworkF(NetworkBase):
+ """
+ Network constrained F Function
+
+ This requires the capability to compute a distance matrix between two
+ point patterns. In this case one will be observed and one will be simulated
+ """
+
+ def computeobserved(self):
+ self.fsim = self.ntw.simulate_observations(self.npts)
+ #Nearest neighbor distances from the simulated to the observed
+ nearest = np.nanmin(self.ntw.allneighbordistances(self.fsim, self.pointpattern), axis=1)
+ self.setbounds(nearest)
+ #Generate a random distribution of points
+ observedx, observedy = ffunction(nearest, self.lowerbound, self.upperbound,
+ nsteps=self.nsteps, npts=self.npts)
+ self.observed = observedy
+ self.xaxis = observedx
+
+ def computepermutations(self):
+ for p in xrange(self.permutations):
+ sim = self.ntw.simulate_observations(self.npts,
+ distribution=self.distribution)
+ nearest = np.nanmin(self.ntw.allneighbordistances(sim, self.fsim), axis=1)
+ simx, simy = ffunction(nearest, self.lowerbound, self.upperbound,
+ self.npts, nsteps=self.nsteps)
+ self.sim[p] = simy
+
+def kfunction(nearest, upperbound, intensity, nsteps=10):
+ nobs = len(nearest)
+ x = np.linspace(0, upperbound, nsteps)
+ y = np.empty(len(x))
+
+ for i, s in enumerate(x):
+ y[i] = len(nearest[nearest <= s])
+ y *= (intensity ** -1)
+ return x, y
+
+def ffunction(nearest, lowerbound, upperbound, npts, nsteps = 10):
+ nobs = len(nearest)
+ x = np.linspace(lowerbound, upperbound, nsteps)
+ nearest = np.sort(nearest)
+ y = np.empty(len(x))
+ for i,r in enumerate(x):
+ cnt = len(nearest[nearest <= r])
+ if cnt > 0:
+ g = cnt / float(npts)
+ else:
+ g = 0
+ y[i] = g
+ return x, y
+
+def gfunction(nearest, lowerbound, upperbound, nsteps = 10):
+ """
+ Compute a G-Function
+
+ Parameters
+ ----------
+ nearest ndarray A vector of nearest neighbor distances
+ nsteps int The number of distance bands
+ permutations int The number of permutations to perform
+ threshold int Upper and lower significance threshold
+ envelope bool Return results of all permutations
+ poisson bool Use a poisson distribution to
+ determine the number of points
+ """
+ nobs = len(nearest)
+ x = np.linspace(lowerbound, upperbound, nsteps)
+ nearest = np.sort(nearest)
+
+ y = np.empty(len(x))
+ for i,r in enumerate(x):
+ cnt = len(nearest[nearest <= r])
+ if cnt > 0:
+ g = cnt / float(nobs)
+ else:
+ g = 0
+ y[i] = g
+ return x, y
diff --git a/pysal/network/network.py b/pysal/network/network.py
new file mode 100644
index 0000000..61ea8b0
--- /dev/null
+++ b/pysal/network/network.py
@@ -0,0 +1,1162 @@
+from collections import defaultdict, OrderedDict
+import math
+import os
+import cPickle
+import copy
+
+import numpy as np
+import pysal as ps
+from pysal.weights.util import get_ids
+
+from analysis import NetworkG, NetworkK, NetworkF
+import util
+
+__all__ = ["Network", "PointPattern", "NetworkG", "NetworkK", "NetworkF" ]
+
+
+class Network:
+
+ """
+ Spatially constrained network representation and analytical functionality.
+
+ Parameters
+ -----------
+ in_shp : string
+ A topoligically correct input shapefile
+
+ Attributes
+ ----------
+ in_shp : string
+ input shapefile name
+
+ adjacencylist : list
+ of lists storing node adjacency
+
+ nodes : dict
+ key are tuple of node coords and value is the node ID
+
+ edge_lengths : dict
+ key is a tuple of sorted node IDs representing an edge
+ value is the length
+
+ pointpatterns : dict
+ key is a string name of the pattern
+ value is a point pattern class instance
+
+ node_coords : dict
+ key is th node ID and value are the (x,y) coordinates
+ inverse to nodes
+
+ edges : list
+ of edges, where each edge is a sorted tuple of node IDs
+
+ node_list : list
+ node IDs
+
+ alldistances : dict
+ key is the node ID
+ value is a list of all distances from the source to all destinations
+
+ Examples
+ --------
+
+ Instantiate an instance of a network
+
+ >>> ntw = network.Network(ps.examples.get_path('geodanet/streets.shp'))
+
+ Snap point observations to the network with attribute information
+
+ >>> ntw.snapobservations(ps.examples.get_path('geodanet/crimes.shp'), 'crimes', attribute=True)
+
+ And without attribute information
+
+ >>> ntw.snapobservations(ps.examples.get_path('geodanet/schools.shp'), 'schools', attribute=False)
+
+ """
+
+ def __init__(self, in_shp=None):
+ if in_shp:
+ self.in_shp = in_shp
+
+ self.adjacencylist = defaultdict(list)
+ self.nodes = {}
+ self.edge_lengths = {}
+ self.edges = []
+
+ self.pointpatterns = {}
+
+ self._extractnetwork()
+ self.node_coords = dict((value, key) for key, value in self.nodes.iteritems())
+
+ #This is a spatial representation of the network.
+ self.edges = sorted(self.edges)
+
+ #Extract the graph
+ self.extractgraph()
+
+ self.node_list = sorted(self.nodes.values())
+
+ def _extractnetwork(self):
+ """
+ Used internally, to extract a network from a polyline shapefile
+ """
+ nodecount = 0
+ shps = ps.open(self.in_shp)
+ for shp in shps:
+ vertices = shp.vertices
+ for i, v in enumerate(vertices[:-1]):
+ try:
+ vid = self.nodes[v]
+ except:
+ self.nodes[v] = vid = nodecount
+ nodecount += 1
+ try:
+ nvid = self.nodes[vertices[i+1]]
+ except:
+ self.nodes[vertices[i+1]] = nvid = nodecount
+ nodecount += 1
+
+ self.adjacencylist[vid].append(nvid)
+ self.adjacencylist[nvid].append(vid)
+
+ #Sort the edges so that mono-directional keys can be stored.
+ edgenodes = sorted([vid, nvid])
+ edge = tuple(edgenodes)
+ self.edges.append(edge)
+ length = util.compute_length(v, vertices[i+1])
+ self.edge_lengths[edge] = length
+
+ def extractgraph(self):
+ """
+ Using the existing network representation, create a graph based representation,
+ by removing all nodes with neighbor incidence of two. That is, we assume these
+ nodes are bridges between nodes with higher incidence.
+ """
+ self.graphedges = []
+ self.edge_to_graph = {}
+ self.graph_lengths = {}
+
+ #Find all nodes with cardinality 2
+ segment_nodes = []
+ for k, v in self.adjacencylist.iteritems():
+ #len(v) == 1 #cul-de-sac
+ #len(v) == 2 #bridge segment
+ #len(v) > 2 #intersection
+ if len(v) == 2:
+ segment_nodes.append(k)
+
+ #Start with a copy of the spatial representation and iteratively
+ # remove edges deemed to be segments
+ self.graphedges = copy.deepcopy(self.edges)
+ self.graph_lengths = copy.deepcopy(self.edge_lengths)
+ self.graph_to_edges = {} #Mapping all the edges contained within a single graph represented edge
+
+ bridges = []
+ for s in segment_nodes:
+ bridge = [s]
+ neighbors = self._yieldneighbor(s, segment_nodes, bridge)
+ while neighbors:
+ cnode = neighbors.pop()
+ segment_nodes.remove(cnode)
+ bridge.append(cnode)
+ newneighbors = self._yieldneighbor(cnode, segment_nodes, bridge)
+ neighbors += newneighbors
+ bridges.append(bridge)
+
+ for bridge in bridges:
+ if len(bridge) == 1:
+ n = self.adjacencylist[bridge[0]]
+ newedge = tuple(sorted([n[0], n[1]]))
+ #Identify the edges to be removed
+ e1 = tuple(sorted([bridge[0], n[0]]))
+ e2 = tuple(sorted([bridge[0], n[1]]))
+ #Remove from the graph
+ self.graphedges.remove(e1)
+ self.graphedges.remove(e2)
+ #Remove from the edge lengths
+ length_e1 = self.edge_lengths[e1]
+ length_e2 = self.edge_lengths[e2]
+ self.graph_lengths.pop(e1, None)
+ self.graph_lengths.pop(e2, None)
+ self.graph_lengths[newedge] = length_e1 + length_e2
+ #Update the pointers
+ self.graph_to_edges[e1] = newedge
+ self.graph_to_edges[e2] = newedge
+ else:
+ cumulative_length = 0
+ startend = {}
+ redundant = set([])
+ for b in bridge:
+ for n in self.adjacencylist[b]:
+ if n not in bridge:
+ startend[b] = n
+ else:
+ redundant.add(tuple(sorted([b,n])))
+
+ newedge = tuple(sorted(startend.values()))
+ for k, v in startend.iteritems():
+ redundant.add(tuple(sorted([k,v])))
+
+ for r in redundant:
+ self.graphedges.remove(r)
+ cumulative_length += self.edge_lengths[r]
+ self.graph_lengths.pop(r, None)
+ self.graph_to_edges[r] = newedge
+ self.graph_lengths[newedge] = cumulative_length
+
+ self.graphedges.append(newedge)
+ self.graphedges = sorted(self.graphedges)
+
+ def _yieldneighbor(self, node, segment_nodes, bridge):
+ """
+ Used internally, this method traverses a bridge segement
+ to find the source and destination nodes.
+ """
+ n = []
+ for i in self.adjacencylist[node]:
+ if i in segment_nodes and i not in bridge:
+ n.append(i)
+ return n
+
+ def contiguityweights(self, graph=True, weightings=None):
+ """
+ Create a contiguity based W object
+
+ Parameters
+ ----------
+ graph : boolean
+ {True, False } controls whether the W is generated using the spatial
+ representation or the graph representation
+
+ weightings : dict
+ of lists of weightings for each edge
+
+ Returns
+ -------
+ : W
+ A PySAL W Object representing the binary adjacency of the network
+
+ Examples
+ --------
+ >>> w = ntw.contiguityweights(graph=False)
+
+ Using the W object, access to ESDA functionality is provided. First,
+ a vector of attributes is created for all edges with observations.
+
+ >>> w = ntw.contiguityweights(graph=False)
+ >>> edges = w.neighbors.keys()
+ >>> y = np.zeros(len(edges))
+ >>> for i, e in enumerate(edges):
+ >>> if e in counts.keys():
+ >>> y[i] = counts[e]
+
+ Next, a standard call ot Moran is made and the result placed into `res`
+
+ >>> res = ps.esda.moran.Moran(y, ntw.w, permutations=99)
+
+ """
+
+ neighbors = {}
+ neighbors = OrderedDict()
+
+ if graph:
+ edges = self.graphedges
+ else:
+ edges = self.edges
+
+ if weightings:
+ weights = {}
+ else:
+ weights = None
+
+ for key in edges:
+ neighbors[key] = []
+ if weightings:
+ weights[key] = []
+
+ for neigh in edges:
+ if key == neigh:
+ continue
+ if key[0] == neigh[0] or key[0] == neigh[1] or key[1] == neigh[0] or key[1] == neigh[1]:
+ neighbors[key].append(neigh)
+ if weightings:
+ weights[key].append(weightings[neigh])
+ #TODO: Add a break condition - everything is sorted, so we know when we have stepped beyond a possible neighbor.
+ #if key[1] > neigh[1]: #NOT THIS
+ #break
+
+ return ps.weights.W(neighbors, weights=weights)
+
+ def distancebandweights(self, threshold):
+ """
+ Create distance based weights
+ """
+ try:
+ hasattr(self.alldistances)
+ except:
+ self.node_distance_matrix()
+
+ neighbor_query = np.where(self.distancematrix < threshold)
+ neighbors = defaultdict(list)
+ for i, n in enumerate(neighbor_query[0]):
+ neigh = neighbor_query[1][i]
+ if n != neigh:
+ neighbors[n].append(neighbor_query[1][i])
+
+ self.w = ps.weights.W(neighbors)
+
+ def snapobservations(self, shapefile, name, idvariable=None, attribute=None):
+ """
+ Snap a point pattern shapefile to this network object. The point pattern
+ is the stored in the network.pointpattern['key'] attribute of the network
+ object.
+
+ Parameters
+ ----------
+ shapefile : str
+ The PATH to the shapefile
+
+ name : str
+ Name to be assigned to the point dataset
+
+ idvariable : str
+ Column name to be used as ID variable
+
+ attribute : bool
+ Defines whether attributes should be extracted
+
+ Returns
+ -------
+
+ """
+
+ self.pointpatterns[name] = PointPattern(shapefile, idvariable=idvariable, attribute=attribute)
+ self._snap_to_edge(self.pointpatterns[name])
+
+ def compute_distance_to_nodes(self, x, y, edge):
+ """
+ Given an observation on a network edge, return the distance to the two
+ nodes that bound that end.
+
+ Parameters
+ ----------
+ x : float
+ x-coordinate of the snapped point
+
+ y : float
+ y-coordiante of the snapped point
+
+ edge : tuple
+ (node0, node1) representation of the network edge
+
+ Returns
+ -------
+ d1 : float
+ the distance to node0, always the node with the lesser id
+
+ d2 : float
+ the distance to node1, always the node with the greater id
+ """
+
+ d1 = util.compute_length((x,y), self.node_coords[edge[0]])
+ d2 = util.compute_length((x,y), self.node_coords[edge[1]])
+ return d1, d2
+
+ def _snap_to_edge(self, pointpattern):
+ """
+ Used internally to snap point observations to network edges.
+
+ Parameters
+ -----------
+ pointpattern : obj
+ PySAL Point Pattern Object
+
+ Returns
+ -------
+ obs_to_edge : dict
+ with edge as key and list of points as value
+
+ edge_to_obs : dict
+ with point id as key and edge tuple as value
+
+ dist_to_node : dict
+ with edge as key and tuple of distances to nodes as value
+ """
+
+ obs_to_edge = {}
+ dist_to_node = {}
+
+ pointpattern.snapped_coordinates = {}
+
+ for pt_index, point in pointpattern.points.iteritems():
+ x0 = point['coordinates'][0]
+ y0 = point['coordinates'][1]
+
+ d = {}
+ vectors = {}
+ c = 0
+
+ #Components of this for loop can be pre computed and cached, like denom to distance =
+ for edge in self.edges:
+ xi = self.node_coords[edge[0]][0]
+ yi = self.node_coords[edge[0]][1]
+ xi1 = self.node_coords[edge[1]][0]
+ yi1 = self.node_coords[edge[1]][1]
+
+ num = ((yi1 - yi)*(x0-xi)-(xi1-xi)*(y0-yi))
+ denom = ((yi1-yi)**2 + (xi1-xi)**2)
+ k = num / float(denom)
+ distance = abs(num) / math.sqrt(((yi1-yi)**2 + (xi1-xi)**2))
+ vectors[c] = (xi, xi1, yi, yi1,k,edge)
+ d[distance] = c
+ c += 1
+
+ min_dist = SortedEdges(sorted(d.items()))
+
+ for dist, vector_id in min_dist.iteritems():
+ value = vectors[vector_id]
+ xi = value[0]
+ xi1 = value[1]
+ yi = value[2]
+ yi1 = value[3]
+ k = value[4]
+ edge = value[5]
+
+ #Okabe Method
+ x = x0 - k * (yi1 - yi)
+ y = y0 + k * (xi1 - xi)
+
+ #Compute the distance from the new point to the nodes
+ d1, d2 = self.compute_distance_to_nodes(x, y, edge)
+
+ if xi <= x <= xi1 or xi1 <= x <= xi and yi <= y <= yi1 or yi1 <=y <= yi:
+ #print "{} intersections edge {} at {}".format(pt_index, edge, (x,y))
+ #We are assuming undirected - this should never be true.
+ if edge not in obs_to_edge.keys():
+ obs_to_edge[edge] = {pt_index: (x,y)}
+ else:
+ obs_to_edge[edge][pt_index] = (x,y)
+ dist_to_node[pt_index] = {edge[0]:d1, edge[1]:d2}
+ pointpattern.snapped_coordinates[pt_index] = (x,y)
+
+ break
+ else:
+ #either pi or pi+1 are the nearest point on that edge.
+ #If this point is closer than the next distance, we can break, the
+ # observation intersects the node with the shorter
+ # distance.
+ pi = (xi, yi)
+ pi1 = (xi1, yi1)
+ p0 = (x0,y0)
+ #Maybe this call to ps.cg should go as well - as per the call in the class above
+ dist_pi = ps.cg.standalone.get_points_dist(p0, pi)
+ dist_pi1 = ps.cg.standalone.get_points_dist(p0, pi1)
+
+ if dist_pi < dist_pi1:
+ node_dist = dist_pi
+ (x,y) = pi
+ else:
+ node_dist = dist_pi1
+ (x,y) = pi1
+
+ d1, d2 = self.compute_distance_to_nodes(x, y, edge)
+
+ if node_dist < min_dist.next_key(dist):
+ if edge not in obs_to_edge.keys():
+ obs_to_edge[edge] = {pt_index: (x, y)}
+ else:
+ obs_to_edge[edge][pt_index] = (x, y)
+ dist_to_node[pt_index] = {edge[0]:d1, edge[1]:d2}
+ pointpattern.snapped_coordinates[pt_index] = (x,y)
+ break
+
+ obs_to_node = defaultdict(list)
+ for k, v in obs_to_edge.iteritems():
+ keys = v.keys()
+ obs_to_node[k[0]] = keys
+ obs_to_node[k[1]] = keys
+
+ pointpattern.obs_to_edge = obs_to_edge
+ pointpattern.dist_to_node = dist_to_node
+ pointpattern.obs_to_node = obs_to_node
+
+ def count_per_edge(self, obs_on_network, graph=True):
+ """
+ Compute the counts per edge.
+
+ Parameters
+ ----------
+ obs_on_network : dict
+ of observations on the network
+ {(edge): {pt_id: (coords)}} or {edge: [(coord), (coord), (coord)]}
+ Returns
+ -------
+ counts: dict {(edge):count}
+
+ Example
+ -------
+
+ Note that this passes the obs_to_edge attribute of a point pattern
+ snapped to the network.
+
+ >>> counts = ntw.count_per_edge(ntw.pointpatterns['crimes'].obs_to_edge,
+ graph=False)
+
+ """
+ counts = {}
+ if graph:
+ for key, observations in obs_on_network.iteritems():
+ cnt = len(observations)
+ if key in self.graph_to_edges.keys():
+ key = self.graph_to_edges[key]
+ try:
+ counts[key] += cnt
+ except:
+ counts[key] = cnt
+ else:
+ for key in obs_on_network.iterkeys():
+ counts[key] = len(obs_on_network[key])
+ return counts
+
+ def _newpoint_coords(self, edge, distance):
+ """
+ Used internally to compute new point coordinates during snapping
+ """
+ x1 = self.node_coords[edge[0]][0]
+ y1 = self.node_coords[edge[0]][1]
+ x2 = self.node_coords[edge[1]][0]
+ y2 = self.node_coords[edge[1]][1]
+ m = (y2 - y1) / (x2 - x1)
+ if x1 > x2:
+ x0 = x1 - distance / math.sqrt(1 + m**2)
+ elif x1 < x2:
+ x0 = x1 + distance / math.sqrt(1 + m**2)
+ y0 = m * (x0 - x1) + y1
+ return x0, y0
+
+ def simulate_observations(self, count, distribution='uniform'):
+ """
+ Generate a simulated point pattern on the network.
+
+ Parameters
+ ----------
+ count : integer
+ number of points to create or mean of the distribution
+ if not 'uniform'
+
+ distribution : string
+ {'uniform', 'poisson'} distribution of random points
+
+ Returns
+ -------
+ random_pts : dict
+ key is the edge tuple
+ value is a list of new point coordinates
+
+ Example
+ -------
+
+ >>> npts = ntw.pointpatterns['crimes'].npoints
+ >>> sim = ntw.simulate_observations(npts)
+ >>> sim
+ <network.SimulatedPointPattern instance at 0x1133d8710>
+
+ """
+ simpts = SimulatedPointPattern()
+
+ #Cumulative Network Length
+ edges = []
+ lengths = np.zeros(len(self.edge_lengths))
+ for i, key in enumerate(self.edge_lengths.iterkeys()):
+ edges.append(key)
+ lengths[i] = self.edge_lengths[key]
+ stops = np.cumsum(lengths)
+ totallength = stops[-1]
+
+ if distribution is 'uniform':
+ nrandompts = np.random.uniform(0, totallength, size=(count,))
+ elif distribution is 'poisson':
+ nrandompts = np.random.uniform(0, totallength, size=(np.random.poisson(count),))
+
+ for i, r in enumerate(nrandompts):
+ idx = np.where(r < stops)[0][0]
+ assignment_edge = edges[idx]
+ distance_from_start = stops[idx] - r
+ #Populate the coordinates dict
+ x0, y0 = self._newpoint_coords(assignment_edge, distance_from_start)
+ simpts.snapped_coordinates[i] = (x0, y0)
+ simpts.obs_to_node[assignment_edge[0]].append(i)
+ simpts.obs_to_node[assignment_edge[1]].append(i)
+
+ #Populate the distance to node
+ simpts.dist_to_node[i] = {assignment_edge[0] : distance_from_start,
+ assignment_edge[1] : self.edge_lengths[edges[idx]] - distance_from_start}
+
+ simpts.points = simpts.snapped_coordinates
+
+ return simpts
+
+ def enum_links_node(self, v0):
+ """
+ Returns the edges (links) around node
+
+ Parameters
+ -----------
+ v0 : int
+ node id
+
+ Returns
+ -------
+ links : list
+ list of tuple edge adjacent to the node
+ """
+ links = []
+ neighbornodes = self.adjacencylist[v0]
+ for n in neighbornodes:
+ links.append(tuple(sorted([n, v0])))
+ return links
+
+ def node_distance_matrix(self):
+ self.alldistances = {}
+ nnodes = len(self.node_list)
+ self.distancematrix = np.empty((nnodes, nnodes))
+ for node in self.node_list:
+ distance, pred = util.dijkstra(self, self.edge_lengths, node, n=float('inf'))
+ pred = np.array(pred)
+ tree = util.generatetree(pred)
+ self.alldistances[node] = (distance, tree)
+ self.distancematrix[node] = distance
+
+ def allneighbordistances(self, sourcepattern, destpattern=None):
+ """
+ Compute either all distances between i and j in a single point pattern
+ or all distances between each i from a source pattern and all j
+ from a destination pattern
+
+ Parameters
+ ----------
+ sourcepattern : str
+ The key of a point pattern snapped to the network.
+
+ destpattern :str
+ (Optional) The key of a point pattern snapped to the network.
+
+ Returns
+ -------
+ nearest : array (n,n)
+ An array or shape n,n storing distances between all points
+
+ """
+
+ if not hasattr(self,'alldistances'):
+ self.node_distance_matrix()
+
+
+
+ src_indices = sourcepattern.points.keys()
+ nsource_pts = len(src_indices)
+ dist_to_node = sourcepattern.dist_to_node
+ if destpattern == None:
+ destpattern = sourcepattern
+ dest_indices = destpattern.points.keys()
+ ndest_pts = len(dest_indices)
+
+ searchpts = copy.deepcopy(dest_indices)
+ nearest = np.empty((nsource_pts, ndest_pts))
+ nearest[:] = np.inf
+
+ searchnodes = {}
+ for s in searchpts:
+ e1, e2 = dist_to_node[s].keys()
+ searchnodes[s] = (e1, e2)
+
+ for p1 in src_indices:
+ #Get the source nodes and dist to source nodes
+ source1, source2 = searchnodes[p1]
+ set1 = set(searchnodes[p1])
+ # distance from node1 to p, distance from node2 to p
+ sdist1, sdist2 = dist_to_node[p1].values()
+
+ searchpts.remove(p1)
+ for p2 in searchpts:
+ dest1, dest2 = searchnodes[p2]
+ set2 = set(searchnodes[p2])
+ if set1 == set2: #same edge
+ x1,y1 = sourcepattern.snapped_coordinates[p1]
+ x2,y2 = destpattern.snapped_coordinates[p2]
+ xd = x1-x2
+ yd = y1-y2
+ nearest[p1,p2] = np.sqrt(xd*xd + yd*yd)
+ nearest[p2,p1] = nearest[p1,p2]
+
+ else:
+ ddist1, ddist2 = dist_to_node[p2].values()
+ d11 = self.alldistances[source1][0][dest1]
+ d21 = self.alldistances[source2][0][dest1]
+ d12 = self.alldistances[source1][0][dest2]
+ d22 = self.alldistances[source2][0][dest2]
+
+ # find shortest distance from path passing through each of two origin nodes
+ # to first destination node
+ sd_1 = d11 + sdist1
+ sd_21 = d21 + sdist2
+ if sd_1 > sd_21:
+ sd_1 = sd_21
+ # now add point to node one distance on destination edge
+ len_1 = sd_1 + ddist1
+
+
+ # repeat but now for paths entering at second node of second edge
+ sd_2 = d12 + sdist1
+ sd_22 = d22 + sdist2
+ b = 0
+ if sd_2 > sd_22:
+ sd_2 = sd_22
+ b = 1
+ len_2 = sd_2 + ddist2
+
+ # now find shortest length path between the point 1 on edge 1 and
+ # point 2 on edge 2, and assign
+ sp_12 = len_1
+ if len_1 > len_2:
+ sp_12 = len_2
+ nearest[p1, p2] = sp_12
+ nearest[p2, p1] = sp_12
+ #print p1,p2, sp_12
+ np.fill_diagonal(nearest, np.nan)
+ return nearest
+
+ def nearestneighbordistances(self, sourcepattern, destpattern=None):
+ """
+ Compute the interpattern nearest neighbor distances or the intrapattern
+ nearest neight distances between a source pattern and a destination pattern.
+
+ Parameters
+ ----------
+ sourcepattern str The key of a point pattern snapped to the network.
+
+ destpattern str (Optional) The key of a point pattern snapped to the network.
+
+ Returns
+ -------
+ nearest ndarray (n,2) With column[:,0] containing the id of the nearest
+ neighbor and column [:,1] containing the distance.
+ """
+
+ if not sourcepattern in self.pointpatterns.keys():
+ print "Key Error: Available point patterns are {}".format(self.pointpatterns.key())
+ return
+
+ if not hasattr(self,'alldistances'):
+ self.node_distance_matrix()
+
+ print sourcepattern
+ pt_indices = self.pointpatterns[sourcepattern].points.keys()
+ dist_to_node = self.pointpatterns[sourcepattern].dist_to_node
+ nearest = np.zeros((len(pt_indices), 2), dtype=np.float32)
+ nearest[:,1] = np.inf
+
+ if destpattern == None:
+ destpattern = sourcepattern
+
+ searchpts = copy.deepcopy(pt_indices)
+
+
+ searchnodes = {}
+ for s in searchpts:
+ e1, e2 = dist_to_node[s].keys()
+ searchnodes[s] = (e1, e2)
+
+ for p1 in pt_indices:
+ #Get the source nodes and dist to source nodes
+ source1, source2 = searchnodes[p1]
+ sdist1, sdist2 = dist_to_node[p1].values()
+
+ searchpts.remove(p1)
+ for p2 in searchpts:
+ dest1, dest2 = searchnodes[p2]
+ ddist1, ddist2 = dist_to_node[p2].values()
+ source1_to_dest1 = sdist1 + self.alldistances[source1][0][dest1] + ddist1
+ source1_to_dest2 = sdist1 + self.alldistances[source1][0][dest2] + ddist2
+ source2_to_dest1 = sdist2 + self.alldistances[source2][0][dest1] + ddist1
+ source2_to_dest2 = sdist2 + self.alldistances[source2][0][dest2] + ddist2
+
+
+ if source1_to_dest1 < nearest[p1, 1]:
+ nearest[p1, 0] = p2
+ nearest[p1, 1] = source1_to_dest1
+ if source1_to_dest1 < nearest[p2, 1]:
+ nearest[p2, 0] = p1
+ nearest[p2, 1] = source1_to_dest1
+
+ if source1_to_dest2 < nearest[p1, 1]:
+ nearest[p1, 0] = p2
+ nearest[p1, 1] = source1_to_dest2
+ if source1_to_dest1 < nearest[p2, 1]:
+ nearest[p2, 0] = p1
+ nearest[p2, 1] = source1_to_dest2
+
+ if source2_to_dest1 < nearest[p1, 1]:
+ nearest[p1, 0] = p2
+ nearest[p1, 1] = source2_to_dest1
+ if source2_to_dest1 < nearest[p2, 1]:
+ nearest[p2, 0] = p1
+ nearest[p2, 1] = source2_to_dest1
+
+ if source2_to_dest2 < nearest[p1, 1]:
+ nearest[p1, 0] = p2
+ nearest[p1, 1] = source2_to_dest2
+ if source2_to_dest2 < nearest[p2, 1]:
+ nearest[p2, 0] = p1
+ nearest[p2, 1] = source2_to_dest2
+
+ return nearest
+
+ def NetworkF(self, pointpattern, nsteps=10, permutations=99,
+ threshold=0.2, distribution='uniform',
+ lowerbound=None, upperbound=None):
+ """
+ Computes a network constrained F-Function
+
+ Parameters
+ ----------
+ pointpattern : object
+ A PySAL point pattern object
+
+ nsteps : int
+ The number of steps at which the count of the nearest
+ neighbors is computed
+
+ permutations : int
+ The number of permutations to perform (default 99)
+
+ threshold : float
+ The level at which significance is computed. 0.5 would be 97.5% and 2.5%
+
+ distribution : str
+ The distirbution from which random points are sampled: uniform or poisson
+
+ lowerbound : float
+ The lower bound at which the G-function is computed. (default 0)
+
+ upperbound : float
+ The upper bound at which the G-function is computed.
+ Defaults to the maximum pbserved nearest neighbor distance.
+
+ Returns
+ -------
+ NetworkF : object
+ A network F class instance
+
+ """
+ return NetworkF(self, pointpattern, nsteps=nsteps,
+ permutations=permutations,threshold=threshold,
+ distribution=distribution,lowerbound=lowerbound,
+ upperbound=upperbound)
+
+ def NetworkG(self, pointpattern, nsteps=10, permutations=99,
+ threshold=0.5, distribution='uniform',
+ lowerbound=None, upperbound=None):
+ """
+ Computes a network constrained G-Function
+
+ Parameters
+ ----------
+ pointpattern : object
+ A PySAL point pattern object
+
+ nsteps : int
+ The number of steps at which the count of the nearest
+ neighbors is computed
+
+ permutations : int
+ The number of permutations to perform (default 99)
+
+ threshold : float
+ The level at which significance is computed. 0.5 would be 97.5% and 2.5%
+
+ distribution : str
+ The distirbution from which random points are sampled: uniform or poisson
+
+ lowerbound : float
+ The lower bound at which the G-function is computed. (default 0)
+
+ upperbound : float
+ The upper bound at which the G-function is computed.
+ Defaults to the maximum pbserved nearest neighbor distance.
+
+ Returns
+ -------
+ NetworkG : object
+ A network G class object
+
+ """
+
+ return NetworkG(self, pointpattern, nsteps=nsteps,
+ permutations=permutations,threshold=threshold,
+ distribution=distribution,lowerbound=lowerbound,
+ upperbound=upperbound)
+
+ def NetworkK(self, pointpattern, nsteps=10, permutations=99,
+ threshold=0.5, distribution='uniform',
+ lowerbound=None, upperbound=None):
+ """
+ Computes a network constrained G-Function
+
+ Parameters
+ ----------
+ pointpattern : object
+ A PySAL point pattern object
+
+ nsteps : int
+ The number of steps at which the count of the nearest
+ neighbors is computed
+
+ permutations : int
+ The number of permutations to perform (default 99)
+
+ threshold : float
+ The level at which significance is computed. 0.5 would be 97.5% and 2.5%
+
+ distribution : str
+ The distirbution from which random points are sampled: uniform or poisson
+
+ lowerbound : float
+ The lower bound at which the G-function is computed. (default 0)
+
+ upperbound : float
+ The upper bound at which the G-function is computed.
+ Defaults to the maximum pbserved nearest neighbor distance.
+
+ Returns
+ -------
+ NetworkK : object
+ A network K class object
+
+ """
+ return NetworkK(self, pointpattern, nsteps=nsteps,
+ permutations=permutations,threshold=threshold,
+ distribution=distribution,lowerbound=lowerbound,
+ upperbound=upperbound)
+
+ def segment_edges(self, distance):
+ """
+ Segment all of the edges in the network at either
+ a fixed distance or a fixed number of segments.
+
+ Parameters
+ -----------
+ distance : float
+ The distance at which edges are split
+
+ Returns
+ -------
+ sn : object
+ PySAL Network Object
+
+ Example
+ -------
+
+ >>> n200 = ntw.segment_edges(200.0)
+
+ """
+
+ sn = Network()
+ sn.adjacencylist = copy.deepcopy(self.adjacencylist)
+ sn.edge_lengths = copy.deepcopy(self.edge_lengths)
+ sn.edges = set(copy.deepcopy(self.edges))
+ sn.node_coords = copy.deepcopy(self.node_coords)
+ sn.node_list = copy.deepcopy(self.node_list)
+ sn.nodes = copy.deepcopy(self.nodes)
+ sn.pointpatterns = copy.deepcopy(self.pointpatterns)
+ sn.in_shp = self.in_shp
+
+ current_node_id = max(self.nodes.values())
+
+ newedges = set()
+ removeedges = set()
+ for e in sn.edges:
+ length = sn.edge_lengths[e]
+ interval = distance
+
+ totallength = 0
+ currentstart = startnode = e[0]
+ endnode = e[1]
+
+ #If the edge will be segmented, remove the
+ # current edge from the adjacency list
+ if interval < length:
+ sn.adjacencylist[e[0]].remove(e[1])
+ sn.adjacencylist[e[1]].remove(e[0])
+ sn.edge_lengths.pop(e, None)
+ removeedges.add(e)
+ else:
+ continue
+
+ while totallength < length:
+ currentstop = current_node_id
+ if totallength + interval > length:
+ currentstop = endnode
+ interval = length - totallength
+ totallength = length
+ else:
+ current_node_id += 1
+ currentstop = current_node_id
+ totallength += interval
+
+ #Compute the new node coordinate
+ newx, newy = self._newpoint_coords(e, totallength)
+
+ #Update node_list
+ if currentstop not in sn.node_list:
+ sn.node_list.append(currentstop)
+
+ #Update nodes and node_coords
+ sn.node_coords[currentstop] = newx, newy
+ sn.nodes[(newx, newy)] = currentstop
+
+ #Update the adjacencylist
+ sn.adjacencylist[currentstart].append(currentstop)
+ sn.adjacencylist[currentstop].append(currentstart)
+
+
+ #Add the new edge to the edge dict
+ #Iterating over this, so we need to add after iterating
+ newedges.add(tuple(sorted([currentstart, currentstop])))
+
+ #Modify edge_lengths
+ sn.edge_lengths[tuple(sorted([currentstart, currentstop]))] = interval
+
+ #Increment the start to the stop
+ currentstart = currentstop
+
+ sn.edges.update(newedges)
+ sn.edges.difference_update(removeedges)
+ sn.edges = list(sn.edges)
+ #Update the point pattern snapping
+ for instance in sn.pointpatterns.itervalues():
+ sn._snap_to_edge(instance)
+
+ return sn
+
+ def savenetwork(self, filename):
+ """
+ Save a network to disk as a binary file
+
+ Parameters
+ ----------
+ filename : str
+ The filename where the network should be saved.
+ This should be a full PATH or the file is saved
+ whereever this method is called from.
+
+ Example
+ --------
+
+ >>> ntw.savenetwork('mynetwork.pkl')
+
+ """
+ with open(filename, 'wb') as networkout:
+ cPickle.dump(self, networkout, protocol=2)
+
+ @staticmethod
+ def loadnetwork(filename):
+ with open(filename, 'rb') as networkin:
+ self = cPickle.load(networkin)
+
+ return self
+
+
+class PointPattern():
+ """
+ A stub point pattern class used to store a point pattern.
+ This class is monkey patched with network specific attributes
+ when the points are snapped to a network.
+
+ In the future this class may be replaced with a generic point
+ pattern class.
+
+ Parameters
+ ----------
+ shapefile : string
+ input shapefile
+
+ idvariable : string
+ field in the shapefile to use as an idvariable
+
+ attribute : boolean
+ {False, True} A flag to indicate whether all attributes
+ are tagged to this class.
+
+ Attributes
+ ----------
+ points : dict
+ key is the point id
+ value are the coordiantes
+
+ npoints : integer
+ the number of points
+
+
+ """
+ def __init__(self, shapefile, idvariable=None, attribute=False):
+ self.points = {}
+ self.npoints = 0
+
+ if idvariable:
+ ids = get_ids(shapefile, idvariable)
+ else:
+ ids = None
+
+ pts = ps.open(shapefile)
+
+ #Get attributes if requested
+ if attribute == True:
+ dbname = os.path.splitext(shapefile)[0] + '.dbf'
+ db = ps.open(dbname)
+ else:
+ db = None
+
+ for i, pt in enumerate(pts):
+ if ids and db:
+ self.points[ids[i]] = {'coordinates':pt, 'properties':db[i]}
+ elif ids and not db:
+ self.points[ids[i]] = {'coordinates':pt, 'properties':None}
+ elif not ids and db:
+ self.points[i] = {'coordinates':pt, 'properties':db[i]}
+ else:
+ self.points[i] = {'coordinates':pt, 'properties':None}
+
+ pts.close()
+ if db:
+ db.close()
+ self.npoints = len(self.points.keys())
+
+
+class SimulatedPointPattern():
+ """
+ Struct style class to mirror the Point Pattern Class.
+
+ If the PointPattern class has methods, it might make sense to
+ make this a child of that class.
+
+ This class is not intended to be used by the external user.
+
+ """
+ def __init__(self):
+ self.npoints = 0
+ self.obs_to_edge = {}
+ self.obs_to_node = defaultdict(list)
+ self.dist_to_node = {}
+ self.snapped_coordinates = {}
+
+
+class SortedEdges(OrderedDict):
+ def next_key(self, key):
+ next = self._OrderedDict__map[key][1]
+ if next is self._OrderedDict__root:
+ raise ValueError("{!r} is the last key.".format(key))
+ return next[2]
+ def first_key(self):
+ for key in self: return key
+ raise ValueError("No sorted edges remain.")
diff --git a/pysal/network/util.py b/pysal/network/util.py
new file mode 100644
index 0000000..52f77a7
--- /dev/null
+++ b/pysal/network/util.py
@@ -0,0 +1,155 @@
+from collections import OrderedDict
+import math
+import operator
+
+import numpy as np
+
+
+def nearestneighborsearch(obs_to_node, alldistances, endnode, dist):
+ """
+ Given a node on a network which is tagged to an observation, find the
+ nearest node which also has one or more observations.
+ """
+ searching = True
+ #sorted dict of nodes by distance
+ for k, v in alldistances[endnode][0].iteritems():
+ #List of the neighbors tagged to the node
+ possibleneighbors = obs_to_node[k]
+ if possibleneighbors:
+ for n in possibleneighbors:
+ if n == v:
+ continue
+ else:
+ nearest_obs = n
+ nearest_node = k
+ nearest_node_distance = v + dist
+ searching = False
+ if searching == False:
+ break
+
+ return nearest_obs, nearest_node, nearest_node_distance
+
+def compute_length(v0, v1):
+ """
+ Compute the euclidean distance between two points.
+
+ Parameters
+ ----------
+ v0 sequence in the form x, y
+ vq sequence in the form x, y
+
+ Returns
+ --------
+ Euclidean distance
+ """
+
+ return math.sqrt((v0[0] - v1[0])**2 + (v0[1] - v1[1])**2)
+
+
+def get_neighbor_distances(ntw, v0, l):
+ edges = ntw.enum_links_node(v0)
+ neighbors = {}
+ for e in edges:
+ if e[0] != v0:
+ neighbors[e[0]] = l[e]
+ else:
+ neighbors[e[1]] = l[e]
+ return neighbors
+
+
+def generatetree(pred):
+ tree = {}
+ for p in pred:
+ idx = p
+ path = [idx]
+ while idx > 0:
+ nextnode = pred[idx]
+ idx = nextnode
+ if idx > 0:
+ path.append(nextnode)
+ if p > 0:
+ tree[p] = path
+ return tree
+
+
+def cumulativedistances(distance, tree):
+ distances = {}
+ for k, v in tree.iteritems():
+ subset_distance = distance[v]
+ distances[k] = np.sum(subset_distance)
+ return OrderedDict(sorted(distances.iteritems(), key=operator.itemgetter(1)))
+
+
+def dijkstra(ntw, cost, node, n=float('inf')):
+ """
+ Compute the shortest path between a start node and
+ all other nodes in the wed.
+ Parameters
+ ----------
+ ntw: PySAL network object
+ cost: Cost per edge to travel, e.g. distance
+ node: Start node ID
+ n: integer break point to stop iteration and return n
+ neighbors
+ Returns:
+ distance: List of distances from node to all other nodes
+ pred : List of preceeding nodes for traversal route
+ """
+
+ v0 = node
+ distance = [float('inf') for x in ntw.node_list]
+ idx = ntw.node_list.index(v0)
+ distance[ntw.node_list.index(v0)] = 0
+ pred = [-1 for x in ntw.node_list]
+ a = set()
+ a.add(v0)
+ while len(a) > 0:
+ #Get node with the lowest value from distance
+ dist = float('inf')
+ for node in a:
+ if distance[node] < dist:
+ dist = distance[node]
+ v = node
+ #Remove that node from the set
+ a.remove(v)
+ last = v
+ #4. Get the neighbors to the current node
+ neighbors = get_neighbor_distances(ntw, v, cost)
+ for v1, indiv_cost in neighbors.iteritems():
+ if distance[v1] > distance[v] + indiv_cost:
+ distance[v1] = distance[v] + indiv_cost
+ pred[v1] = v
+ a.add(v1)
+ return distance, np.array(pred, dtype=np.int)
+
+
+def shortest_path(ntw, cost, start, end):
+ distance, pred = dijkstra(ntw, cost, start)
+ path = [end]
+ previous = pred[end]
+ while previous != start:
+ path.append(previous)
+ end = previous
+ previous = pred[end]
+ path.append(start)
+ return tuple(path)
+
+def nearest_neighbor_search(pt_indices, dist_to_node, obs_to_node, alldistances, snappedcoords):
+ nearest = np.empty((2,len(pt_indices)))
+
+ for i, p1 in enumerate(pt_indices):
+ dist1, dist2 = dist_to_node[p1].values()
+ endnode1, endnode2 = dist_to_node[p1].keys()
+
+ snapped_coords = snappedcoords[p1]
+ nearest_obs1, nearest_node1, nearest_node_distance1 = nearestneighborsearch(obs_to_node, alldistances, endnode1, dist1)
+ nearest_obs2, nearest_node2, nearest_node_distance2 = nearestneighborsearch(obs_to_node, alldistances, endnode2, dist2)
+
+ if nearest_node_distance2 <= nearest_node_distance1:
+ nearest[i,0] = nearest_obs2
+ nearest[i,1] = nearest_node_distance2
+ else:
+ nearest[i,0] = nearest_obs1
+ nearest[i,1] = nearest_node_distance1
+
+ return nearest
diff --git a/pysal/region/__init__.py b/pysal/region/__init__.py
new file mode 100644
index 0000000..1300f39
--- /dev/null
+++ b/pysal/region/__init__.py
@@ -0,0 +1,9 @@
+"""
+:mod:`region` -- Regionalization
+================================
+
+"""
+
+from maxp import *
+from randomregion import *
+from components import *
diff --git a/pysal/region/components.py b/pysal/region/components.py
new file mode 100644
index 0000000..7fb6398
--- /dev/null
+++ b/pysal/region/components.py
@@ -0,0 +1,160 @@
+"""
+Checking for connected components in a graph.
+"""
+__author__ = "Sergio J. Rey <srey at asu.edu>"
+
+
+__all__ = ["check_contiguity"]
+
+from operator import lt
+
+
+def is_component(w, ids):
+ """Check if the set of ids form a single connected component
+
+ Parameters
+ ----------
+
+ w : spatial weights boject
+
+ ids : list
+ identifiers of units that are tested to be a single connected
+ component
+
+
+ Returns
+ -------
+
+ True : if the list of ids represents a single connected component
+
+ False : if the list of ids forms more than a single connected component
+
+ """
+
+ components = 0
+ marks = dict([(node, 0) for node in ids])
+ q = []
+ for node in ids:
+ if marks[node] == 0:
+ components += 1
+ q.append(node)
+ if components > 1:
+ return False
+ while q:
+ node = q.pop()
+ marks[node] = components
+ others = [neighbor for neighbor in w.neighbors[node]
+ if neighbor in ids]
+ for other in others:
+ if marks[other] == 0 and other not in q:
+ q.append(other)
+ return True
+
+
+def check_contiguity(w, neighbors, leaver):
+ """Check if contiguity is maintained if leaver is removed from neighbors
+
+
+ Parameters
+ ----------
+
+ w : spatial weights object
+ simple contiguity based weights
+ neighbors : list
+ nodes that are to be checked if they form a single \
+ connected component
+ leaver : id
+ a member of neighbors to check for removal
+
+
+ Returns
+ -------
+
+ True : if removing leaver from neighbors does not break contiguity
+ of remaining set
+ in neighbors
+ False : if removing leaver from neighbors breaks contiguity
+
+ Example
+ -------
+
+ Setup imports and a 25x25 spatial weights matrix on a 5x5 square region.
+
+ >>> import pysal
+ >>> w = pysal.lat2W(5, 5)
+
+ Test removing various areas from a subset of the region's areas. In the
+ first case the subset is defined as observations 0, 1, 2, 3 and 4. The
+ test shows that observations 0, 1, 2 and 3 remain connected even if
+ observation 4 is removed.
+
+ >>> pysal.region.check_contiguity(w,[0,1,2,3,4],4)
+ True
+ >>> pysal.region.check_contiguity(w,[0,1,2,3,4],3)
+ False
+ >>> pysal.region.check_contiguity(w,[0,1,2,3,4],0)
+ True
+ >>> pysal.region.check_contiguity(w,[0,1,2,3,4],1)
+ False
+ >>>
+ """
+
+ ids = neighbors[:]
+ ids.remove(leaver)
+ return is_component(w, ids)
+
+
+class Graph(object):
+ def __init__(self, undirected=True):
+ self.nodes = set()
+ self.edges = {}
+ self.cluster_lookup = {}
+ self.no_link = {}
+ self.undirected = undirected
+
+ def add_edge(self, n1, n2, w):
+ self.nodes.add(n1)
+ self.nodes.add(n2)
+ self.edges.setdefault(n1, {}).update({n2: w})
+ if self.undirected:
+ self.edges.setdefault(n2, {}).update({n1: w})
+
+ def connected_components(self, threshold=0.9, op=lt):
+ if not self.undirected:
+ warn = "Warning, connected _components not "
+ warn += "defined for a directed graph"
+ print warn
+ return None
+ else:
+ nodes = set(self.nodes)
+ components, visited = [], set()
+ while len(nodes) > 0:
+ connected, visited = self.dfs(
+ nodes.pop(), visited, threshold, op)
+ connected = set(connected)
+ for node in connected:
+ if node in nodes:
+ nodes.remove(node)
+ subgraph = Graph()
+ subgraph.nodes = connected
+ subgraph.no_link = self.no_link
+ for s in subgraph.nodes:
+ for k, v in self.edges.get(s, {}).iteritems():
+ if k in subgraph.nodes:
+ subgraph.edges.setdefault(s, {}).update({k: v})
+ if s in self.cluster_lookup:
+ subgraph.cluster_lookup[s] = self.cluster_lookup[s]
+ components.append(subgraph)
+ return components
+
+ def dfs(self, v, visited, threshold, op=lt, first=None):
+ aux = [v]
+ visited.add(v)
+ if first is None:
+ first = v
+ for i in (n for n, w in self.edges.get(v, {}).iteritems()
+ if op(w, threshold) and n not in visited):
+ x, y = self.dfs(i, visited, threshold, op, first)
+ aux.extend(x)
+ visited = visited.union(y)
+ return aux, visited
diff --git a/pysal/region/maxp.py b/pysal/region/maxp.py
new file mode 100644
index 0000000..b097ae5
--- /dev/null
+++ b/pysal/region/maxp.py
@@ -0,0 +1,588 @@
+"""
+Max p regionalization
+
+Heuristically form the maximum number (p) of regions given a set of n
+areas and a floor constraint.
+"""
+
+__author__ = "Serge Rey <srey at asu.edu>, David Folch <david.folch at asu.edu>"
+
+
+import pysal
+from components import check_contiguity
+import copy
+import random
+import numpy as np
+#from pysal.common import *
+from pysal.region import randomregion as RR
+
+__all__ = ["Maxp", "Maxp_LISA"]
+
+LARGE = 10 ** 6
+MAX_ATTEMPTS = 100
+
+
+class Maxp:
+ """Try to find the maximum number of regions for a set of areas such that
+ each region combines contiguous areas that satisfy a given threshold
+ constraint.
+
+
+ Parameters
+ ----------
+
+ w : W
+ spatial weights object
+
+ z : array
+ n*m array of observations on m attributes across n
+ areas. This is used to calculate intra-regional
+ homogeneity
+ floor : int
+ a minimum bound for a variable that has to be
+ obtained in each region
+ floor_variable : array
+ n*1 vector of observations on variable for the floor
+ initial : int
+ number of initial solutions to generate
+ verbose : binary
+ if true debugging information is printed
+ seeds : list
+ ids of observations to form initial seeds. If
+ len(ids) is less than the number of observations, the
+ complementary ids are added to the end of seeds. Thus
+ the specified seeds get priority in the solution
+
+ Attributes
+ ----------
+
+ area2region : dict
+ mapping of areas to region. key is area id, value is
+ region id
+ regions : list
+ list of lists of regions (each list has the ids of areas
+ in that region)
+ p : int
+ number of regions
+ swap_iterations : int
+ number of swap iterations
+ total_moves : int
+ number of moves into internal regions
+
+ Examples
+ --------
+
+ Setup imports and set seeds for random number generators to insure the
+ results are identical for each run.
+
+ >>> import random
+ >>> import numpy as np
+ >>> import pysal
+ >>> random.seed(100)
+ >>> np.random.seed(100)
+
+ Setup a spatial weights matrix describing the connectivity of a square
+ community with 100 areas. Generate two random data attributes for each
+ area in the community (a 100x2 array) called z. p is the data vector used to
+ compute the floor for a region, and floor is the floor value; in this case
+ p is simply a vector of ones and the floor is set to three. This means
+ that each region will contain at least three areas. In other cases the
+ floor may be computed based on a minimum population count for example.
+
+ >>> import random
+ >>> import numpy as np
+ >>> import pysal
+ >>> random.seed(100)
+ >>> np.random.seed(100)
+ >>> w = pysal.lat2W(10,10)
+ >>> z = np.random.random_sample((w.n,2))
+ >>> p = np.ones((w.n,1), float)
+ >>> floor = 3
+ >>> solution = pysal.region.Maxp(w, z, floor, floor_variable=p, initial=100)
+ >>> solution.p
+ 29
+ >>> min([len(region) for region in solution.regions])
+ 3
+ >>> solution.regions[0]
+ [4, 14, 5, 24, 3]
+ >>>
+
+ """
+ def __init__(self, w, z, floor, floor_variable,
+ verbose=False, initial=100, seeds=[]):
+
+ self.w = w
+ self.z = z
+ self.floor = floor
+ self.floor_variable = floor_variable
+ self.verbose = verbose
+ self.seeds = seeds
+ self.initial_solution()
+ if not self.p:
+ self.feasible = False
+ else:
+ self.feasible = True
+ best_val = self.objective_function()
+ self.current_regions = copy.copy(self.regions)
+ self.current_area2region = copy.copy(self.area2region)
+ self.initial_wss = []
+ self.attempts = 0
+ for i in range(initial):
+ self.initial_solution()
+ if self.p:
+ val = self.objective_function()
+ self.initial_wss.append(val)
+ if self.verbose:
+ print 'initial solution: ', i, val, best_val
+ if val < best_val:
+ self.current_regions = copy.copy(self.regions)
+ self.current_area2region = copy.copy(self.area2region)
+ best_val = val
+ self.attempts += 1
+ self.regions = copy.copy(self.current_regions)
+ self.p = len(self.regions)
+ self.area2region = self.current_area2region
+ if verbose:
+ print "smallest region ifs: ", min([len(region) for region in self.regions])
+ raw_input='wait'
+
+ self.swap()
+
+ def initial_solution(self):
+ self.p = 0
+ solving = True
+ attempts = 0
+ while solving and attempts <= MAX_ATTEMPTS:
+ regions = []
+ enclaves = []
+ if not self.seeds:
+ candidates = copy.copy(self.w.id_order)
+ candidates = np.random.permutation(candidates)
+ candidates = candidates.tolist()
+ else:
+ seeds = copy.copy(self.seeds)
+ nonseeds = [i for i in self.w.id_order if i not in seeds]
+ candidates = seeds
+ candidates.extend(nonseeds)
+ while candidates:
+ seed = candidates.pop(0)
+ # try to grow it till threshold constraint is satisfied
+ region = [seed]
+ building_region = True
+ while building_region:
+ # check if floor is satisfied
+ if self.check_floor(region):
+ regions.append(region)
+ building_region = False
+ else:
+ potential = []
+ for area in region:
+ neighbors = self.w.neighbors[area]
+ neighbors = [neigh for neigh in neighbors if neigh in candidates]
+ neighbors = [neigh for neigh in neighbors if neigh not in region]
+ neighbors = [neigh for neigh in neighbors if neigh not in potential]
+ potential.extend(neighbors)
+ if potential:
+ # add a random neighbor
+ neigID = random.randint(0, len(potential) - 1)
+ neigAdd = potential.pop(neigID)
+ region.append(neigAdd)
+ # remove it from candidates
+ candidates.remove(neigAdd)
+ else:
+ #print 'enclave'
+ #print region
+ enclaves.extend(region)
+ building_region = False
+ # check to see if any regions were made before going to enclave stage
+ if regions:
+ feasible = True
+ else:
+ attempts += 1
+ break
+ self.enclaves = enclaves[:]
+ a2r = {}
+ for r, region in enumerate(regions):
+ for area in region:
+ a2r[area] = r
+ encCount = len(enclaves)
+ encAttempts = 0
+ while enclaves and encAttempts != encCount:
+ enclave = enclaves.pop(0)
+ neighbors = self.w.neighbors[enclave]
+ neighbors = [neighbor for neighbor in neighbors if neighbor not in enclaves]
+ candidates = []
+ for neighbor in neighbors:
+ region = a2r[neighbor]
+ if region not in candidates:
+ candidates.append(region)
+ if candidates:
+ # add enclave to random region
+ regID = random.randint(0, len(candidates) - 1)
+ rid = candidates[regID]
+ regions[rid].append(enclave)
+ a2r[enclave] = rid
+ # structure to loop over enclaves until no more joining is possible
+ encCount = len(enclaves)
+ encAttempts = 0
+ feasible = True
+ else:
+ # put back on que, no contiguous regions yet
+ enclaves.append(enclave)
+ encAttempts += 1
+ feasible = False
+ if feasible:
+ solving = False
+ self.regions = regions
+ self.area2region = a2r
+ self.p = len(regions)
+ else:
+ if attempts == MAX_ATTEMPTS:
+ print 'No initial solution found'
+ self.p = 0
+ attempts += 1
+
+ def swap(self):
+ swapping = True
+ swap_iteration = 0
+ if self.verbose:
+ print 'Initial solution, objective function: ', self.objective_function()
+ total_moves = 0
+ self.k = len(self.regions)
+ changed_regions = [1] * self.k
+ nr = range(self.k)
+ while swapping:
+ moves_made = 0
+ regionIds = [r for r in nr if changed_regions[r]]
+ np.random.permutation(regionIds)
+ changed_regions = [0] * self.k
+ swap_iteration += 1
+ for seed in regionIds:
+ local_swapping = True
+ local_attempts = 0
+ while local_swapping:
+ local_moves = 0
+ # get neighbors
+ members = self.regions[seed]
+ neighbors = []
+ for member in members:
+ candidates = self.w.neighbors[member]
+ candidates = [candidate for candidate in candidates if candidate not in members]
+ candidates = [candidate for candidate in candidates if candidate not in neighbors]
+ neighbors.extend(candidates)
+ candidates = []
+ for neighbor in neighbors:
+ block = copy.copy(self.regions[self.area2region[
+ neighbor]])
+ if check_contiguity(self.w, block, neighbor):
+ block.remove(neighbor)
+ fv = self.check_floor(block)
+ if fv:
+ candidates.append(neighbor)
+ # find the best local move
+ if not candidates:
+ local_swapping = False
+ else:
+ nc = len(candidates)
+ moves = np.zeros([nc, 1], float)
+ best = None
+ cv = 0.0
+ for area in candidates:
+ current_internal = self.regions[seed]
+ current_outter = self.regions[self.area2region[
+ area]]
+ current = self.objective_function([current_internal, current_outter])
+ new_internal = copy.copy(current_internal)
+ new_outter = copy.copy(current_outter)
+ new_internal.append(area)
+ new_outter.remove(area)
+ new = self.objective_function([new_internal,
+ new_outter])
+ change = new - current
+ if change < cv:
+ best = area
+ cv = change
+ if best:
+ # make the move
+ area = best
+ old_region = self.area2region[area]
+ self.regions[old_region].remove(area)
+ self.area2region[area] = seed
+ self.regions[seed].append(area)
+ moves_made += 1
+ changed_regions[seed] = 1
+ changed_regions[old_region] = 1
+ else:
+ # no move improves the solution
+ local_swapping = False
+ local_attempts += 1
+ if self.verbose:
+ print 'swap_iteration: ', swap_iteration, 'moves_made: ', moves_made
+ print 'number of regions: ', len(self.regions)
+ print 'number of changed regions: ', sum(
+ changed_regions)
+ print 'internal region: ', seed, 'local_attempts: ', local_attempts
+ print 'objective function: ', self.objective_function()
+ print 'smallest region size: ',min([len(region) for region in self.regions])
+ total_moves += moves_made
+ if moves_made == 0:
+ swapping = False
+ self.swap_iterations = swap_iteration
+ self.total_moves = total_moves
+ if self.verbose:
+ print 'moves_made: ', moves_made
+ print 'objective function: ', self.objective_function()
+
+ def check_floor(self, region):
+ selectionIDs = [self.w.id_order.index(i) for i in region]
+ cv = sum(self.floor_variable[selectionIDs])
+ if cv >= self.floor:
+ #print len(selectionIDs)
+ return True
+ else:
+ return False
+
+ def objective_function(self, solution=None):
+ # solution is a list of lists of region ids [[1,7,2],[0,4,3],...] such
+ # that the first region has areas 1,7,2 the second region 0,4,3 and so
+ # on. solution does not have to be exhaustive
+ if not solution:
+ solution = self.regions
+ wss = 0
+ for region in solution:
+ selectionIDs = [self.w.id_order.index(i) for i in region]
+ m = self.z[selectionIDs, :]
+ var = m.var(axis=0)
+ wss += sum(np.transpose(var)) * len(region)
+ return wss
+
+ def inference(self, nperm=99):
+ """Compare the within sum of squares for the solution against
+ simulated solutions where areas are randomly assigned to regions that
+ maintain the cardinality of the original solution.
+
+ Parameters
+ ----------
+
+ nperm : int
+ number of random permutations for calculation of
+ pseudo-p_values
+
+ Attributes
+ ----------
+
+ pvalue : float
+ pseudo p_value
+
+ Examples
+ --------
+
+ Setup is the same as shown above except using a 5x5 community.
+
+ >>> import random
+ >>> import numpy as np
+ >>> import pysal
+ >>> random.seed(100)
+ >>> np.random.seed(100)
+ >>> w=pysal.weights.lat2W(5,5)
+ >>> z=np.random.random_sample((w.n,2))
+ >>> p=np.ones((w.n,1),float)
+ >>> floor=3
+ >>> solution=pysal.region.Maxp(w,z,floor,floor_variable=p,initial=100)
+
+ Set nperm to 9 meaning that 9 random regions are computed and used for
+ the computation of a pseudo-p-value for the actual Max-p solution. In
+ empirical work this would typically be set much higher, e.g. 999 or
+ 9999.
+
+ >>> solution.inference(nperm=9)
+ >>> solution.pvalue
+ 0.2
+
+ """
+ ids = self.w.id_order
+ num_regions = len(self.regions)
+ wsss = np.zeros(nperm + 1)
+ self.wss = self.objective_function()
+ cards = [len(i) for i in self.regions]
+ sim_solutions = RR.Random_Regions(ids, num_regions,
+ cardinality=cards, permutations=nperm)
+ cv = 1
+ c = 1
+ for solution in sim_solutions.solutions_feas:
+ wss = self.objective_function(solution.regions)
+ wsss[c] = wss
+ if wss <= self.wss:
+ cv += 1
+ c += 1
+ self.pvalue = cv / (1. + len(sim_solutions.solutions_feas))
+ self.wss_perm = wsss
+ self.wss_perm[0] = self.wss
+
+ def cinference(self, nperm=99, maxiter=1000):
+ """Compare the within sum of squares for the solution against
+ conditional simulated solutions where areas are randomly assigned to
+ regions that maintain the cardinality of the original solution and
+ respect contiguity relationships.
+
+ Parameters
+ ----------
+
+ nperm : int
+ number of random permutations for calculation of
+ pseudo-p_values
+
+ maxiter : int
+ maximum number of attempts to find each permutation
+
+ Attributes
+ ----------
+
+ pvalue : float
+ pseudo p_value
+
+ feas_sols : int
+ number of feasible solutions found
+
+ Notes
+ -----
+
+ it is possible for the number of feasible solutions (feas_sols) to be
+ less than the number of permutations requested (nperm); an exception
+ is raised if this occurs.
+
+ Examples
+ --------
+
+ Setup is the same as shown above except using a 5x5 community.
+
+ >>> import random
+ >>> import numpy as np
+ >>> import pysal
+ >>> random.seed(100)
+ >>> np.random.seed(100)
+ >>> w=pysal.weights.lat2W(5,5)
+ >>> z=np.random.random_sample((w.n,2))
+ >>> p=np.ones((w.n,1),float)
+ >>> floor=3
+ >>> solution=pysal.region.Maxp(w,z,floor,floor_variable=p,initial=100)
+
+ Set nperm to 9 meaning that 9 random regions are computed and used for
+ the computation of a pseudo-p-value for the actual Max-p solution. In
+ empirical work this would typically be set much higher, e.g. 999 or
+ 9999.
+
+ >>> solution.cinference(nperm=9, maxiter=100)
+ >>> solution.cpvalue
+ 0.1
+
+ """
+ ids = self.w.id_order
+ num_regions = len(self.regions)
+ wsss = np.zeros(nperm + 1)
+ self.cwss = self.objective_function()
+ cards = [len(i) for i in self.regions]
+ sim_solutions = RR.Random_Regions(ids, num_regions,
+ cardinality=cards, contiguity=self.w,
+ maxiter=maxiter, permutations=nperm)
+ self.cfeas_sols = len(sim_solutions.solutions_feas)
+ if self.cfeas_sols < nperm:
+ raise Exception('not enough feasible solutions found')
+ cv = 1
+ c = 1
+ for solution in sim_solutions.solutions_feas:
+ wss = self.objective_function(solution.regions)
+ wsss[c] = wss
+ if wss <= self.cwss:
+ cv += 1
+ c += 1
+ self.cpvalue = cv / (1. + self.cfeas_sols)
+ self.cwss_perm = wsss
+ self.cwss_perm[0] = self.cwss
+
+
+class Maxp_LISA(Maxp):
+ """Max-p regionalization using LISA seeds
+
+ Parameters
+ ----------
+
+ w : W
+ spatial weights object
+ z : array
+ nxk array of n observations on k variables used to
+ measure similarity between areas within the regions.
+ y : array
+ nx1 array used to calculate the LISA statistics and
+ to set the intial seed order
+ floor : float
+ value that each region must obtain on floor_variable
+ floor_variable : array
+ nx1 array of values for regional floor threshold
+ initial : int
+ number of initial feasible solutions to generate
+ prior to swapping
+
+ Attributes
+ ----------
+
+ area2region : dict
+ mapping of areas to region. key is area id, value is
+ region id
+ regions : list
+ list of lists of regions (each list has the ids of areas
+ in that region)
+ swap_iterations : int
+ number of swap iterations
+ total_moves : int
+ number of moves into internal regions
+
+
+ Notes
+ -----
+
+ We sort the observations based on the value of the LISAs. This
+ ordering then gives the priority for seeds forming the p regions. The
+ initial priority seeds are not guaranteed to be separated in the final
+ solution.
+
+ Examples
+ --------
+
+ Setup imports and set seeds for random number generators to insure the
+ results are identical for each run.
+
+ >>> import random
+ >>> import numpy as np
+ >>> import pysal
+ >>> random.seed(100)
+ >>> np.random.seed(100)
+
+ Setup a spatial weights matrix describing the connectivity of a square
+ community with 100 areas. Generate two random data attributes for each area
+ in the community (a 100x2 array) called z. p is the data vector used to
+ compute the floor for a region, and floor is the floor value; in this case
+ p is simply a vector of ones and the floor is set to three. This means
+ that each region will contain at least three areas. In other cases the
+ floor may be computed based on a minimum population count for example.
+
+ >>> w=pysal.lat2W(10,10)
+ >>> z=np.random.random_sample((w.n,2))
+ >>> p=np.ones(w.n)
+ >>> mpl=pysal.region.Maxp_LISA(w,z,p,floor=3,floor_variable=p)
+ >>> mpl.p
+ 31
+ >>> mpl.regions[0]
+ [99, 89, 98]
+
+ """
+ def __init__(self, w, z, y, floor, floor_variable, initial=100):
+
+ lis = pysal.Moran_Local(y, w)
+ ids = np.argsort(lis.Is)
+ ids = ids[range(w.n - 1, -1, -1)]
+ ids = ids.tolist()
+ mp = Maxp.__init__(
+ self, w, z, floor=floor, floor_variable=floor_variable,
+ initial=initial, seeds=ids)
+
diff --git a/pysal/region/randomregion.py b/pysal/region/randomregion.py
new file mode 100644
index 0000000..7431567
--- /dev/null
+++ b/pysal/region/randomregion.py
@@ -0,0 +1,527 @@
+"""
+Generate random regions
+
+Randomly form regions given various types of constraints on cardinality and
+composition.
+"""
+
+__author__ = "David Folch dfolch at asu.edu, Serge Rey srey at asu.edu"
+
+import numpy as np
+from pysal.common import copy
+
+__all__ = ["Random_Regions", "Random_Region"]
+
+
+class Random_Regions:
+ """Generate a list of Random_Region instances.
+
+ Parameters
+ ----------
+
+ area_ids : list
+ IDs indexing the areas to be grouped into regions (must
+ be in the same order as spatial weights matrix if this
+ is provided)
+
+ num_regions : integer
+ number of regions to generate (if None then this is
+ chosen randomly from 2 to n where n is the number of
+ areas)
+
+ cardinality : list
+ list containing the number of areas to assign to regions
+ (if num_regions is also provided then len(cardinality)
+ must equal num_regions; if cardinality=None then a list
+ of length num_regions will be generated randomly)
+
+ contiguity : W
+ spatial weights object (if None then contiguity will be
+ ignored)
+
+ maxiter : int
+ maximum number attempts (for each permutation) at finding
+ a feasible solution (only affects contiguity constrained
+ regions)
+
+ compact : boolean
+ attempt to build compact regions, note (only affects
+ contiguity constrained regions)
+
+ max_swaps : int
+ maximum number of swaps to find a feasible solution
+ (only affects contiguity constrained regions)
+
+ permutations : int
+ number of Random_Region instances to generate
+
+ Attributes
+ ----------
+
+ solutions : list
+ list of length permutations containing all Random_Region instances generated
+
+ solutions_feas : list
+ list of the Random_Region instances that resulted in feasible solutions
+
+ Examples
+ --------
+
+ Setup the data
+
+ >>> import random
+ >>> import numpy as np
+ >>> import pysal
+ >>> nregs = 13
+ >>> cards = range(2,14) + [10]
+ >>> w = pysal.lat2W(10,10,rook=False)
+ >>> ids = w.id_order
+
+ Unconstrained
+
+ >>> random.seed(10)
+ >>> np.random.seed(10)
+ >>> t0 = pysal.region.Random_Regions(ids, permutations=2)
+ >>> t0.solutions[0].regions[0]
+ [19, 14, 43, 37, 66, 3, 79, 41, 38, 68, 2, 1, 60]
+
+ Cardinality and contiguity constrained (num_regions implied)
+
+ >>> random.seed(60)
+ >>> np.random.seed(60)
+ >>> t1 = pysal.region.Random_Regions(ids, num_regions=nregs, cardinality=cards, contiguity=w, permutations=2)
+ >>> t1.solutions[0].regions[0]
+ [88, 97, 98, 89, 99, 86, 78, 59, 49, 69, 68, 79, 77]
+
+ Cardinality constrained (num_regions implied)
+
+ >>> random.seed(100)
+ >>> np.random.seed(100)
+ >>> t2 = pysal.region.Random_Regions(ids, num_regions=nregs, cardinality=cards, permutations=2)
+ >>> t2.solutions[0].regions[0]
+ [37, 62]
+
+ Number of regions and contiguity constrained
+
+ >>> random.seed(100)
+ >>> np.random.seed(100)
+ >>> t3 = pysal.region.Random_Regions(ids, num_regions=nregs, contiguity=w, permutations=2)
+ >>> t3.solutions[0].regions[1]
+ [71, 72, 70, 93, 51, 91, 85, 74, 63, 73, 61, 62, 82]
+
+ Cardinality and contiguity constrained
+
+ >>> random.seed(60)
+ >>> np.random.seed(60)
+ >>> t4 = pysal.region.Random_Regions(ids, cardinality=cards, contiguity=w, permutations=2)
+ >>> t4.solutions[0].regions[0]
+ [88, 97, 98, 89, 99, 86, 78, 59, 49, 69, 68, 79, 77]
+
+ Number of regions constrained
+
+ >>> random.seed(100)
+ >>> np.random.seed(100)
+ >>> t5 = pysal.region.Random_Regions(ids, num_regions=nregs, permutations=2)
+ >>> t5.solutions[0].regions[0]
+ [37, 62, 26, 41, 35, 25, 36]
+
+ Cardinality constrained
+
+ >>> random.seed(100)
+ >>> np.random.seed(100)
+ >>> t6 = pysal.region.Random_Regions(ids, cardinality=cards, permutations=2)
+ >>> t6.solutions[0].regions[0]
+ [37, 62]
+
+ Contiguity constrained
+
+ >>> random.seed(100)
+ >>> np.random.seed(100)
+ >>> t7 = pysal.region.Random_Regions(ids, contiguity=w, permutations=2)
+ >>> t7.solutions[0].regions[1]
+ [62, 52, 51, 50]
+
+ """
+ def __init__(
+ self, area_ids, num_regions=None, cardinality=None, contiguity=None,
+ maxiter=100, compact=False, max_swaps=1000000, permutations=99):
+
+ solutions = []
+ for i in range(permutations):
+ solutions.append(Random_Region(area_ids, num_regions, cardinality,
+ contiguity, maxiter, compact, max_swaps))
+ self.solutions = solutions
+ self.solutions_feas = []
+ for i in solutions:
+ if i.feasible == True:
+ self.solutions_feas.append(i)
+
+
+class Random_Region:
+ """Randomly combine a given set of areas into two or more regions based
+ on various constraints.
+
+
+ Parameters
+ ----------
+
+ area_ids : list
+ IDs indexing the areas to be grouped into regions (must
+ be in the same order as spatial weights matrix if this
+ is provided)
+
+ num_regions : integer
+ number of regions to generate (if None then this is
+ chosen randomly from 2 to n where n is the number of
+ areas)
+
+ cardinality : list
+ list containing the number of areas to assign to regions
+ (if num_regions is also provided then len(cardinality)
+ must equal num_regions; if cardinality=None then a list
+ of length num_regions will be generated randomly)
+
+ contiguity : W
+ spatial weights object (if None then contiguity will be
+ ignored)
+
+ maxiter : int
+ maximum number attempts at finding a feasible solution
+ (only affects contiguity constrained regions)
+
+ compact : boolean
+ attempt to build compact regions (only affects
+ contiguity constrained regions)
+
+ max_swaps : int
+ maximum number of swaps to find a feasible solution
+ (only affects contiguity constrained regions)
+
+ Attributes
+ ----------
+
+ feasible : boolean
+ if True then solution was found
+
+ regions : list
+ list of lists of regions (each list has the ids of areas
+ in that region)
+
+ Examples
+ --------
+
+ Setup the data
+
+ >>> import random
+ >>> import numpy as np
+ >>> import pysal
+ >>> nregs = 13
+ >>> cards = range(2,14) + [10]
+ >>> w = pysal.weights.lat2W(10,10,rook=False)
+ >>> ids = w.id_order
+
+ Unconstrained
+
+ >>> random.seed(10)
+ >>> np.random.seed(10)
+ >>> t0 = pysal.region.Random_Region(ids)
+ >>> t0.regions[0]
+ [19, 14, 43, 37, 66, 3, 79, 41, 38, 68, 2, 1, 60]
+
+ Cardinality and contiguity constrained (num_regions implied)
+
+ >>> random.seed(60)
+ >>> np.random.seed(60)
+ >>> t1 = pysal.region.Random_Region(ids, num_regions=nregs, cardinality=cards, contiguity=w)
+ >>> t1.regions[0]
+ [88, 97, 98, 89, 99, 86, 78, 59, 49, 69, 68, 79, 77]
+
+ Cardinality constrained (num_regions implied)
+
+ >>> random.seed(100)
+ >>> np.random.seed(100)
+ >>> t2 = pysal.region.Random_Region(ids, num_regions=nregs, cardinality=cards)
+ >>> t2.regions[0]
+ [37, 62]
+
+ Number of regions and contiguity constrained
+
+ >>> random.seed(100)
+ >>> np.random.seed(100)
+ >>> t3 = pysal.region.Random_Region(ids, num_regions=nregs, contiguity=w)
+ >>> t3.regions[1]
+ [71, 72, 70, 93, 51, 91, 85, 74, 63, 73, 61, 62, 82]
+
+ Cardinality and contiguity constrained
+
+ >>> random.seed(60)
+ >>> np.random.seed(60)
+ >>> t4 = pysal.region.Random_Region(ids, cardinality=cards, contiguity=w)
+ >>> t4.regions[0]
+ [88, 97, 98, 89, 99, 86, 78, 59, 49, 69, 68, 79, 77]
+
+ Number of regions constrained
+
+ >>> random.seed(100)
+ >>> np.random.seed(100)
+ >>> t5 = pysal.region.Random_Region(ids, num_regions=nregs)
+ >>> t5.regions[0]
+ [37, 62, 26, 41, 35, 25, 36]
+
+ Cardinality constrained
+
+ >>> random.seed(100)
+ >>> np.random.seed(100)
+ >>> t6 = pysal.region.Random_Region(ids, cardinality=cards)
+ >>> t6.regions[0]
+ [37, 62]
+
+ Contiguity constrained
+
+ >>> random.seed(100)
+ >>> np.random.seed(100)
+ >>> t7 = pysal.region.Random_Region(ids, contiguity=w)
+ >>> t7.regions[0]
+ [37, 27, 36, 17]
+
+ """
+ def __init__(
+ self, area_ids, num_regions=None, cardinality=None, contiguity=None,
+ maxiter=1000, compact=False, max_swaps=1000000):
+
+ self.n = len(area_ids)
+ ids = copy.copy(area_ids)
+ self.ids = list(np.random.permutation(ids))
+ self.area_ids = area_ids
+ self.regions = []
+ self.feasible = True
+
+ # tests for input argument consistency
+ if cardinality:
+ if self.n != sum(cardinality):
+ self.feasible = False
+ raise Exception('number of areas does not match cardinality')
+ if contiguity:
+ if area_ids != contiguity.id_order:
+ self.feasible = False
+ raise Exception('order of area_ids must match order in contiguity')
+ if num_regions and cardinality:
+ if num_regions != len(cardinality):
+ self.feasible = False
+ raise Exception('number of regions does not match cardinality')
+
+ # dispatches the appropriate algorithm
+ if num_regions and cardinality and contiguity:
+ # conditioning on cardinality and contiguity (number of regions implied)
+ self.build_contig_regions(num_regions, cardinality, contiguity,
+ maxiter, compact, max_swaps)
+ elif num_regions and cardinality:
+ # conditioning on cardinality (number of regions implied)
+ region_breaks = self.cards2breaks(cardinality)
+ self.build_noncontig_regions(num_regions, region_breaks)
+ elif num_regions and contiguity:
+ # conditioning on number of regions and contiguity
+ cards = self.get_cards(num_regions)
+ self.build_contig_regions(num_regions, cards, contiguity,
+ maxiter, compact, max_swaps)
+ elif cardinality and contiguity:
+ # conditioning on cardinality and contiguity
+ num_regions = len(cardinality)
+ self.build_contig_regions(num_regions, cardinality, contiguity,
+ maxiter, compact, max_swaps)
+ elif num_regions:
+ # conditioning on number of regions only
+ region_breaks = self.get_region_breaks(num_regions)
+ self.build_noncontig_regions(num_regions, region_breaks)
+ elif cardinality:
+ # conditioning on number of cardinality only
+ num_regions = len(cardinality)
+ region_breaks = self.cards2breaks(cardinality)
+ self.build_noncontig_regions(num_regions, region_breaks)
+ elif contiguity:
+ # conditioning on number of contiguity only
+ num_regions = self.get_num_regions()
+ cards = self.get_cards(num_regions)
+ self.build_contig_regions(num_regions, cards, contiguity,
+ maxiter, compact, max_swaps)
+ else:
+ # unconditioned
+ num_regions = self.get_num_regions()
+ region_breaks = self.get_region_breaks(num_regions)
+ self.build_noncontig_regions(num_regions, region_breaks)
+
+ def get_num_regions(self):
+ return np.random.random_integers(2, self.n)
+
+ def get_region_breaks(self, num_regions):
+ region_breaks = set([])
+ while len(region_breaks) < num_regions - 1:
+ region_breaks.add(np.random.random_integers(1, self.n - 1))
+ region_breaks = list(region_breaks)
+ region_breaks.sort()
+ return region_breaks
+
+ def get_cards(self, num_regions):
+ region_breaks = self.get_region_breaks(num_regions)
+ cards = []
+ start = 0
+ for i in region_breaks:
+ cards.append(i - start)
+ start = i
+ cards.append(self.n - start)
+ return cards
+
+ def cards2breaks(self, cards):
+ region_breaks = []
+ break_point = 0
+ for i in cards:
+ break_point += i
+ region_breaks.append(break_point)
+ region_breaks.pop()
+ return region_breaks
+
+ def build_noncontig_regions(self, num_regions, region_breaks):
+ start = 0
+ for i in region_breaks:
+ self.regions.append(self.ids[start:i])
+ start = i
+ self.regions.append(self.ids[start:])
+
+ def grow_compact(self, w, test_card, region, candidates, potential):
+ # try to build a compact region by exhausting all existing
+ # potential areas before adding new potential areas
+ add_areas = []
+ while potential and len(region) < test_card:
+ pot_index = np.random.random_integers(0, len(potential) - 1)
+ add_area = potential[pot_index]
+ region.append(add_area)
+ candidates.remove(add_area)
+ potential.remove(add_area)
+ add_areas.append(add_area)
+ for i in add_areas:
+ potential.extend([j for j in w.neighbors[i]
+ if j not in region and
+ j not in potential and
+ j in candidates])
+ return region, candidates, potential
+
+ def grow_free(self, w, test_card, region, candidates, potential):
+ # increment potential areas after each new area is
+ # added to the region (faster than the grow_compact)
+ pot_index = np.random.random_integers(0, len(potential) - 1)
+ add_area = potential[pot_index]
+ region.append(add_area)
+ candidates.remove(add_area)
+ potential.remove(add_area)
+ potential.extend([i for i in w.neighbors[add_area]
+ if i not in region and
+ i not in potential and
+ i in candidates])
+ return region, candidates, potential
+
+ def build_contig_regions(self, num_regions, cardinality, w,
+ maxiter, compact, max_swaps):
+ if compact:
+ grow_region = self.grow_compact
+ else:
+ grow_region = self.grow_free
+ iter = 0
+ while iter < maxiter:
+
+ # regionalization setup
+ regions = []
+ size_pre = 0
+ counter = -1
+ area2region = {}
+ self.feasible = False
+ swap_count = 0
+ cards = copy.copy(cardinality)
+ cards.sort() # try to build largest regions first (pop from end of list)
+ candidates = copy.copy(self.ids) # these are already shuffled
+
+ # begin building regions
+ while candidates and swap_count < max_swaps:
+ # setup test to determine if swapping is needed
+ if size_pre == len(regions):
+ counter += 1
+ else:
+ counter = 0
+ size_pre = len(regions)
+ # test if swapping is needed
+ if counter == len(candidates):
+
+ # start swapping
+ # swapping simply changes the candidate list
+ swap_in = None # area to become new candidate
+ while swap_in is None: # PEP8 E711
+ swap_count += 1
+ swap_out = candidates.pop(0) # area to remove from candidates
+ swap_neighs = copy.copy(w.neighbors[swap_out])
+ swap_neighs = list(np.random.permutation(swap_neighs))
+ # select area to add to candidates (i.e. remove from an existing region)
+ for i in swap_neighs:
+ if i not in candidates:
+ join = i # area linking swap_in to swap_out
+ swap_index = area2region[join]
+ swap_region = regions[swap_index]
+ swap_region = list(np.random.permutation(swap_region))
+ for j in swap_region:
+ if j != join: # leave the join area to ensure regional connectivity
+ swap_in = j
+ break
+ if swap_in is not None: # PEP8 E711
+ break
+ else:
+ candidates.append(swap_out)
+ # swapping cleanup
+ regions[swap_index].remove(swap_in)
+ regions[swap_index].append(swap_out)
+ area2region.pop(swap_in)
+ area2region[swap_out] = swap_index
+ candidates.append(swap_in)
+ counter = 0
+
+ # setup to build a single region
+ building = True
+ seed = candidates.pop(0)
+ region = [seed]
+ potential = [i for i in w.neighbors[seed] if i in candidates]
+ test_card = cards.pop()
+
+ # begin building single region
+ while building and len(region) < test_card:
+ if potential:
+ region, candidates, potential = grow_region(
+ w, test_card,
+ region, candidates, potential)
+ else:
+ # not enough potential neighbors to reach test_card size
+ building = False
+ cards.append(test_card)
+ if len(region) in cards:
+ # constructed region matches another candidate region size
+ cards.remove(len(region))
+ else:
+ # constructed region doesn't match a candidate region size
+ candidates.extend(region)
+ region = []
+
+ # cleanup when successful region built
+ if region:
+ regions.append(region)
+ region_index = len(regions) - 1
+ for i in region:
+ area2region[i] = region_index # area2region needed for swapping
+
+ # handling of regionalization result
+ if len(regions) < num_regions:
+ # regionalization failed
+ self.ids = list(np.random.permutation(self.ids))
+ regions = []
+ iter += 1
+ else:
+ # regionalization successful
+ self.feasible = True
+ iter = maxiter
+ self.regions = regions
+
diff --git a/pysal/region/tests/__init__.py b/pysal/region/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pysal/region/tests/test_components.py b/pysal/region/tests/test_components.py
new file mode 100644
index 0000000..48b7662
--- /dev/null
+++ b/pysal/region/tests/test_components.py
@@ -0,0 +1,25 @@
+
+import unittest
+import pysal
+
+
+class Test_Components(unittest.TestCase):
+ def setUp(self):
+ self.w = pysal.lat2W(5, 5)
+
+ def test_check_contiguity(self):
+ result = pysal.region.check_contiguity(self.w, [0, 1, 2, 3, 4], 4)
+ self.assertEquals(result, True)
+ result = pysal.region.check_contiguity(self.w, [0, 1, 2, 3, 4], 3)
+ self.assertEquals(result, False)
+ result = pysal.region.check_contiguity(self.w, [0, 1, 2, 3, 4], 0)
+ self.assertEquals(result, True)
+ result = pysal.region.check_contiguity(self.w, [0, 1, 2, 3, 4], 1)
+ self.assertEquals(result, False)
+
+
+suite = unittest.TestLoader().loadTestsFromTestCase(Test_Components)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/region/tests/test_maxp.py b/pysal/region/tests/test_maxp.py
new file mode 100644
index 0000000..18a66cf
--- /dev/null
+++ b/pysal/region/tests/test_maxp.py
@@ -0,0 +1,56 @@
+
+import unittest
+import pysal
+import numpy as np
+import random
+
+
+class Test_Maxp(unittest.TestCase):
+ def setUp(self):
+ random.seed(100)
+ np.random.seed(100)
+
+ def test_Maxp(self):
+ w = pysal.lat2W(10, 10)
+ z = np.random.random_sample((w.n, 2))
+ p = np.ones((w.n, 1), float)
+ floor = 3
+ solution = pysal.region.Maxp(
+ w, z, floor, floor_variable=p, initial=100)
+ self.assertEquals(solution.p, 29)
+ self.assertEquals(solution.regions[0], [4, 14, 5, 24, 3])
+
+ def test_inference(self):
+ w = pysal.weights.lat2W(5, 5)
+ z = np.random.random_sample((w.n, 2))
+ p = np.ones((w.n, 1), float)
+ floor = 3
+ solution = pysal.region.Maxp(
+ w, z, floor, floor_variable=p, initial=100)
+ solution.inference(nperm=9)
+ self.assertAlmostEquals(solution.pvalue, 0.20000000000000001, 10)
+
+ def test_cinference(self):
+ w = pysal.weights.lat2W(5, 5)
+ z = np.random.random_sample((w.n, 2))
+ p = np.ones((w.n, 1), float)
+ floor = 3
+ solution = pysal.region.Maxp(
+ w, z, floor, floor_variable=p, initial=100)
+ solution.cinference(nperm=9, maxiter=100)
+ self.assertAlmostEquals(solution.cpvalue, 0.10000000000000001, 10)
+
+ def test_Maxp_LISA(self):
+ w = pysal.lat2W(10, 10)
+ z = np.random.random_sample((w.n, 2))
+ p = np.ones(w.n)
+ mpl = pysal.region.Maxp_LISA(w, z, p, floor=3, floor_variable=p)
+ self.assertEquals(mpl.p, 31)
+ self.assertEquals(mpl.regions[0], [99, 89, 98])
+
+
+suite = unittest.TestLoader().loadTestsFromTestCase(Test_Maxp)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/region/tests/test_randomregion.py b/pysal/region/tests/test_randomregion.py
new file mode 100644
index 0000000..8410fee
--- /dev/null
+++ b/pysal/region/tests/test_randomregion.py
@@ -0,0 +1,177 @@
+
+import unittest
+import pysal
+import numpy as np
+import random
+
+
+class Test_Random_Regions(unittest.TestCase):
+ def setUp(self):
+ self.nregs = 13
+ self.cards = range(2, 14) + [10]
+ self.w = pysal.lat2W(10, 10, rook=False)
+ self.ids = self.w.id_order
+
+ def test_Random_Regions(self):
+ random.seed(10)
+ np.random.seed(10)
+ t0 = pysal.region.Random_Regions(self.ids, permutations=2)
+ result = [19, 14, 43, 37, 66, 3, 79, 41, 38, 68, 2, 1, 60]
+ for i in range(len(result)):
+ self.assertEquals(t0.solutions[0].regions[0][i], result[i])
+ for i in range(len(t0.solutions)):
+ self.assertEquals(t0.solutions_feas[i], t0.solutions[i])
+
+ random.seed(60)
+ np.random.seed(60)
+ t0 = pysal.region.Random_Regions(self.ids, num_regions=self.nregs,
+ cardinality=self.cards, contiguity=self.w, permutations=2)
+ result = [88, 97, 98, 89, 99, 86, 78, 59, 49, 69, 68, 79, 77]
+ for i in range(len(result)):
+ self.assertEquals(t0.solutions[0].regions[0][i], result[i])
+ for i in range(len(t0.solutions)):
+ self.assertEquals(t0.solutions_feas[i], t0.solutions[i])
+
+ random.seed(100)
+ np.random.seed(100)
+ t0 = pysal.region.Random_Regions(self.ids, num_regions=self.nregs,
+ cardinality=self.cards, permutations=2)
+ result = [37, 62]
+ for i in range(len(result)):
+ self.assertEquals(t0.solutions[0].regions[0][i], result[i])
+ for i in range(len(t0.solutions)):
+ self.assertEquals(t0.solutions_feas[i], t0.solutions[i])
+
+ random.seed(100)
+ np.random.seed(100)
+ t0 = pysal.region.Random_Regions(self.ids,
+ num_regions=self.nregs, contiguity=self.w, permutations=2)
+ result = [71, 72, 70, 93, 51, 91, 85, 74, 63, 73, 61, 62, 82]
+ for i in range(len(result)):
+ self.assertEquals(t0.solutions[0].regions[1][i], result[i])
+ for i in range(len(t0.solutions)):
+ self.assertEquals(t0.solutions_feas[i], t0.solutions[i])
+
+ random.seed(60)
+ np.random.seed(60)
+ t0 = pysal.region.Random_Regions(self.ids,
+ cardinality=self.cards, contiguity=self.w, permutations=2)
+ result = [88, 97, 98, 89, 99, 86, 78, 59, 49, 69, 68, 79, 77]
+ for i in range(len(result)):
+ self.assertEquals(t0.solutions[0].regions[0][i], result[i])
+ for i in range(len(t0.solutions)):
+ self.assertEquals(t0.solutions_feas[i], t0.solutions[i])
+
+ random.seed(100)
+ np.random.seed(100)
+ t0 = pysal.region.Random_Regions(
+ self.ids, num_regions=self.nregs, permutations=2)
+ result = [37, 62, 26, 41, 35, 25, 36]
+ for i in range(len(result)):
+ self.assertEquals(t0.solutions[0].regions[0][i], result[i])
+ for i in range(len(t0.solutions)):
+ self.assertEquals(t0.solutions_feas[i], t0.solutions[i])
+
+ random.seed(100)
+ np.random.seed(100)
+ t0 = pysal.region.Random_Regions(
+ self.ids, cardinality=self.cards, permutations=2)
+ result = [37, 62]
+ for i in range(len(result)):
+ self.assertEquals(t0.solutions[0].regions[0][i], result[i])
+ for i in range(len(t0.solutions)):
+ self.assertEquals(t0.solutions_feas[i], t0.solutions[i])
+
+ random.seed(100)
+ np.random.seed(100)
+ t0 = pysal.region.Random_Regions(
+ self.ids, contiguity=self.w, permutations=2)
+ result = [62, 52, 51, 50]
+ for i in range(len(result)):
+ self.assertEquals(t0.solutions[0].regions[1][i], result[i])
+ for i in range(len(t0.solutions)):
+ self.assertEquals(t0.solutions_feas[i], t0.solutions[i])
+
+ def test_Random_Region(self):
+ random.seed(10)
+ np.random.seed(10)
+ t0 = pysal.region.Random_Region(self.ids)
+ t0.regions[0]
+ result = [19, 14, 43, 37, 66, 3, 79, 41, 38, 68, 2, 1, 60]
+ for i in range(len(result)):
+ self.assertEquals(t0.regions[0][i], result[i])
+ self.assertEquals(t0.feasible, True)
+
+ random.seed(60)
+ np.random.seed(60)
+ t0 = pysal.region.Random_Region(self.ids, num_regions=self.nregs,
+ cardinality=self.cards, contiguity=self.w)
+ t0.regions[0]
+ result = [88, 97, 98, 89, 99, 86, 78, 59, 49, 69, 68, 79, 77]
+ for i in range(len(result)):
+ self.assertEquals(t0.regions[0][i], result[i])
+ self.assertEquals(t0.feasible, True)
+
+ random.seed(100)
+ np.random.seed(100)
+ t0 = pysal.region.Random_Region(
+ self.ids, num_regions=self.nregs, cardinality=self.cards)
+ t0.regions[0]
+ result = [37, 62]
+ for i in range(len(result)):
+ self.assertEquals(t0.regions[0][i], result[i])
+ self.assertEquals(t0.feasible, True)
+
+ random.seed(100)
+ np.random.seed(100)
+ t0 = pysal.region.Random_Region(
+ self.ids, num_regions=self.nregs, contiguity=self.w)
+ t0.regions[1]
+ result = [71, 72, 70, 93, 51, 91, 85, 74, 63, 73, 61, 62, 82]
+ for i in range(len(result)):
+ self.assertEquals(t0.regions[1][i], result[i])
+ self.assertEquals(t0.feasible, True)
+
+ random.seed(60)
+ np.random.seed(60)
+ t0 = pysal.region.Random_Region(
+ self.ids, cardinality=self.cards, contiguity=self.w)
+ t0.regions[0]
+ result = [88, 97, 98, 89, 99, 86, 78, 59, 49, 69, 68, 79, 77]
+ for i in range(len(result)):
+ self.assertEquals(t0.regions[0][i], result[i])
+ self.assertEquals(t0.feasible, True)
+
+ random.seed(100)
+ np.random.seed(100)
+ t0 = pysal.region.Random_Region(self.ids, num_regions=self.nregs)
+ t0.regions[0]
+ result = [37, 62, 26, 41, 35, 25, 36]
+ for i in range(len(result)):
+ self.assertEquals(t0.regions[0][i], result[i])
+ self.assertEquals(t0.feasible, True)
+
+ random.seed(100)
+ np.random.seed(100)
+ t0 = pysal.region.Random_Region(self.ids, cardinality=self.cards)
+ t0.regions[0]
+ result = [37, 62]
+ for i in range(len(result)):
+ self.assertEquals(t0.regions[0][i], result[i])
+ self.assertEquals(t0.feasible, True)
+
+ random.seed(100)
+ np.random.seed(100)
+ t0 = pysal.region.Random_Region(self.ids, contiguity=self.w)
+ t0.regions[0]
+ result = [37, 27, 36, 17]
+ for i in range(len(result)):
+ self.assertEquals(t0.regions[0][i], result[i])
+ self.assertEquals(t0.feasible, True)
+
+
+suite = unittest.TestLoader().loadTestsFromTestCase(Test_Random_Regions)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/spatial_dynamics/__init__.py b/pysal/spatial_dynamics/__init__.py
new file mode 100644
index 0000000..23fc1f2
--- /dev/null
+++ b/pysal/spatial_dynamics/__init__.py
@@ -0,0 +1,10 @@
+"""
+:mod:`spatial_dynamics` --- Spatial Dynamics and Mobility
+=========================================================
+
+"""
+
+from markov import *
+from rank import *
+from directional import *
+from interaction import *
diff --git a/pysal/spatial_dynamics/directional.py b/pysal/spatial_dynamics/directional.py
new file mode 100644
index 0000000..43854bb
--- /dev/null
+++ b/pysal/spatial_dynamics/directional.py
@@ -0,0 +1,187 @@
+"""
+Directional Analysis of Dynamic LISAs
+
+"""
+__author__ = "Sergio J. Rey <srey at asu.edu"
+
+__all__ = ['rose']
+
+import numpy as np
+import pysal
+
+
+def rose(Y, w, k=8, permutations=0):
+ """
+ Calculation of rose diagram for local indicators of spatial association
+
+ Parameters
+ ----------
+
+ Y: array (n,2)
+ variable observed on n spatial units over 2 time periods
+
+ w: spatial weights object
+
+ k: int
+ number of circular sectors in rose diagram
+
+ permutations: int
+ number of random spatial permutations for calculation of pseudo
+ p-values
+
+ Returns
+ -------
+
+ results: dictionary (keys defined below)
+
+ counts: array (k,1)
+ number of vectors with angular movement falling in each sector
+
+ cuts: array (k,1)
+ intervals defining circular sectors (in radians)
+
+ random_counts: array (permutations,k)
+ counts from random permutations
+
+ pvalues: array (kx1)
+ one sided (upper tail) pvalues for observed counts
+
+ Notes
+ -----
+ Based on Rey, Murray, and Anselin (2011) [1]_
+
+ Examples
+ --------
+
+ Constructing data for illustration of directional LISA analytics.
+ Data is for the 48 lower US states over the period 1969-2009 and
+ includes per capita income normalized to the national average.
+
+ Load comma delimited data file in and convert to a numpy array
+
+ >>> f=open(pysal.examples.get_path("spi_download.csv"),'r')
+ >>> lines=f.readlines()
+ >>> f.close()
+ >>> lines=[line.strip().split(",") for line in lines]
+ >>> names=[line[2] for line in lines[1:-5]]
+ >>> data=np.array([map(int,line[3:]) for line in lines[1:-5]])
+
+ Bottom of the file has regional data which we don't need for this example
+ so we will subset only those records that match a state name
+
+ >>> sids=range(60)
+ >>> out=['"United States 3/"',
+ ... '"Alaska 3/"',
+ ... '"District of Columbia"',
+ ... '"Hawaii 3/"',
+ ... '"New England"',
+ ... '"Mideast"',
+ ... '"Great Lakes"',
+ ... '"Plains"',
+ ... '"Southeast"',
+ ... '"Southwest"',
+ ... '"Rocky Mountain"',
+ ... '"Far West 3/"']
+ >>> snames=[name for name in names if name not in out]
+ >>> sids=[names.index(name) for name in snames]
+ >>> states=data[sids,:]
+ >>> us=data[0]
+ >>> years=np.arange(1969,2009)
+
+ Now we convert state incomes to express them relative to the national
+ average
+
+ >>> rel=states/(us*1.)
+
+ Create our contiguity matrix from an external GAL file and row standardize
+ the resulting weights
+
+ >>> gal=pysal.open(pysal.examples.get_path('states48.gal'))
+ >>> w=gal.read()
+ >>> w.transform='r'
+
+ Take the first and last year of our income data as the interval to do the
+ directional directional analysis
+
+ >>> Y=rel[:,[0,-1]]
+
+ Set the random seed generator which is used in the permutation based
+ inference for the rose diagram so that we can replicate our example
+ results
+
+ >>> np.random.seed(100)
+
+ Call the rose function to construct the directional histogram for the
+ dynamic LISA statistics. We will use four circular sectors for our
+ histogram
+
+ >>> r4=rose(Y,w,k=4,permutations=999)
+
+ What are the cut-offs for our histogram - in radians
+
+ >>> r4['cuts']
+ array([ 0. , 1.57079633, 3.14159265, 4.71238898, 6.28318531])
+
+ How many vectors fell in each sector
+
+ >>> r4['counts']
+ array([32, 5, 9, 2])
+
+ What are the pseudo-pvalues for these counts based on 999 random spatial
+ permutations of the state income data
+
+ >>> r4['pvalues']
+ array([ 0.02 , 0.001, 0.001, 0.001])
+
+ Repeat the exercise but now for 8 rather than 4 sectors
+
+ >>> r8=rose(Y,w,permutations=999)
+ >>> r8['counts']
+ array([19, 13, 3, 2, 7, 2, 1, 1])
+ >>> r8['pvalues']
+ array([ 0.445, 0.042, 0.079, 0.003, 0.005, 0.1 , 0.269, 0.002])
+
+ References
+ ----------
+
+ .. [1] Rey, S.J., A.T. Murray and L. Anselin. 2011. "Visualizing
+ regional income distribution dynamics." Letters in Spatial and Resource Sciences, 4: 81-90.
+
+ """
+ results = {}
+ sw = 2 * np.pi / k
+ cuts = np.arange(0.0, 2 * np.pi + sw, sw)
+ wY = pysal.lag_spatial(w, Y)
+ dx = Y[:, -1] - Y[:, 0]
+ dy = wY[:, -1] - wY[:, 0]
+ theta = np.arctan2(dy, dx)
+ neg = theta < 0.0
+ utheta = theta * (1 - neg) + neg * (2 * np.pi + theta)
+ counts, bins = np.histogram(utheta, cuts)
+ results['counts'] = counts
+ results['cuts'] = cuts
+ if permutations:
+ n, k1 = Y.shape
+ ids = np.arange(n)
+ all_counts = np.zeros((permutations, k))
+ for i in range(permutations):
+ rid = np.random.permutation(ids)
+ YR = Y[rid, :]
+ wYR = pysal.lag_spatial(w, YR)
+ dx = YR[:, -1] - YR[:, 0]
+ dy = wYR[:, -1] - wYR[:, 0]
+ theta = np.arctan2(dy, dx)
+ neg = theta < 0.0
+ utheta = theta * (1 - neg) + neg * (2 * np.pi + theta)
+ c, b = np.histogram(utheta, cuts)
+ c.shape = (1, k)
+ all_counts[i, :] = c
+ larger = sum(all_counts >= counts)
+ p_l = permutations - larger
+ extreme = (p_l) < larger
+ extreme = np.where(extreme, p_l, larger)
+ p = (extreme + 1.) / (permutations + 1.)
+ results['pvalues'] = p
+ results['random_counts'] = all_counts
+
+ return results
diff --git a/pysal/spatial_dynamics/ergodic.py b/pysal/spatial_dynamics/ergodic.py
new file mode 100644
index 0000000..138a614
--- /dev/null
+++ b/pysal/spatial_dynamics/ergodic.py
@@ -0,0 +1,179 @@
+"""
+Summary measures for ergodic Markov chains
+"""
+__author__ = "Sergio J. Rey <srey at asu.edu>"
+
+__all__ = ['steady_state', 'fmpt', 'var_fmpt']
+
+import numpy as np
+import numpy.linalg as la
+
+
+def steady_state(P):
+ """
+ Calculates the steady state probability vector for a regular Markov
+ transition matrix P
+
+ Parameters
+ ----------
+
+ P : matrix (kxk)
+ an ergodic Markov transition probability matrix
+
+ Returns
+ -------
+
+ implicit : matrix (kx1)
+ steady state distribution
+
+ Examples
+ --------
+ Taken from Kemeny and Snell. Land of Oz example where the states are
+ Rain, Nice and Snow, so there is 25 percent chance that if it
+ rained in Oz today, it will snow tomorrow, while if it snowed today in
+ Oz there is a 50 percent chance of snow again tomorrow and a 25
+ percent chance of a nice day (nice, like when the witch with the monkeys
+ is melting).
+
+ >>> import numpy as np
+ >>> p=np.matrix([[.5, .25, .25],[.5,0,.5],[.25,.25,.5]])
+ >>> steady_state(p)
+ matrix([[ 0.4],
+ [ 0.2],
+ [ 0.4]])
+
+ Thus, the long run distribution for Oz is to have 40 percent of the
+ days classified as Rain, 20 percent as Nice, and 40 percent as Snow
+ (states are mutually exclusive).
+
+ """
+
+ v, d = la.eig(np.transpose(P))
+
+ # for a regular P maximum eigenvalue will be 1
+ mv = max(v)
+ # find its position
+ i = v.tolist().index(mv)
+
+ # normalize eigenvector corresponding to the eigenvalue 1
+ return d[:, i] / sum(d[:, i])
+
+
+def fmpt(P):
+ """
+ Calculates the matrix of first mean passage times for an
+ ergodic transition probability matrix.
+
+ Parameters
+ ----------
+
+ P : matrix (kxk)
+ an ergodic Markov transition probability matrix
+
+ Returns
+ -------
+
+ M : matrix (kxk)
+ elements are the expected value for the number of intervals
+ required for a chain starting in state i to first enter state j
+ If i=j then this is the recurrence time.
+
+ Examples
+ --------
+
+ >>> import numpy as np
+ >>> p=np.matrix([[.5, .25, .25],[.5,0,.5],[.25,.25,.5]])
+ >>> fm=fmpt(p)
+ >>> fm
+ matrix([[ 2.5 , 4. , 3.33333333],
+ [ 2.66666667, 5. , 2.66666667],
+ [ 3.33333333, 4. , 2.5 ]])
+
+
+ Thus, if it is raining today in Oz we can expect a nice day to come
+ along in another 4 days, on average, and snow to hit in 3.33 days. We can
+ expect another rainy day in 2.5 days. If it is nice today in Oz, we would
+ experience a change in the weather (either rain or snow) in 2.67 days from
+ today. (That wicked witch can only die once so I reckon that is the
+ ultimate absorbing state).
+
+ Notes
+ -----
+
+ Uses formulation (and examples on p. 218) in Kemeny and Snell (1976)
+
+ References
+ ----------
+
+ .. [1] Kemeny, John, G. and J. Laurie Snell (1976) Finite Markov
+ Chains. Springer-Verlag. Berlin
+
+ """
+ A = np.zeros_like(P)
+ ss = steady_state(P)
+ k = ss.shape[0]
+ for i in range(k):
+ A[:, i] = ss
+ A = A.transpose()
+ I = np.identity(k)
+ Z = la.inv(I - P + A)
+ E = np.ones_like(Z)
+ A_diag = np.diag(A)
+ A_diag = A_diag + (A_diag==0)
+ D = np.diag(1. / A_diag)
+ Zdg = np.diag(np.diag(Z))
+ M = (I - Z + E * Zdg) * D
+ return M
+
+
+def var_fmpt(P):
+ """
+ Variances of first mean passage times for an ergodic transition
+ probability matrix
+
+ Parameters
+ ----------
+
+ P : matrix (kxk)
+ an ergodic Markov transition probability matrix
+
+ Returns
+ -------
+
+ implic : matrix (kxk)
+ elements are the variances for the number of intervals
+ required for a chain starting in state i to first enter state j
+
+ Examples
+ --------
+
+ >>> import numpy as np
+ >>> p=np.matrix([[.5, .25, .25],[.5,0,.5],[.25,.25,.5]])
+ >>> vfm=var_fmpt(p)
+ >>> vfm
+ matrix([[ 5.58333333, 12. , 6.88888889],
+ [ 6.22222222, 12. , 6.22222222],
+ [ 6.88888889, 12. , 5.58333333]])
+
+
+
+ Notes
+ -----
+
+ Uses formulation (and examples on p. 83) in Kemeny and Snell (1976)
+
+
+ """
+ A = P ** 1000
+ n, k = A.shape
+ I = np.identity(k)
+ Z = la.inv(I - P + A)
+ E = np.ones_like(Z)
+ D = np.diag(1. / np.diag(A))
+ Zdg = np.diag(np.diag(Z))
+ M = (I - Z + E * Zdg) * D
+ ZM = Z * M
+ ZMdg = np.diag(np.diag(ZM))
+ W = M * (2 * Zdg * D - I) + 2 * (ZM - E * ZMdg)
+ return W - np.multiply(M, M)
+
diff --git a/pysal/spatial_dynamics/interaction.ipynb b/pysal/spatial_dynamics/interaction.ipynb
new file mode 100644
index 0000000..cbf4296
--- /dev/null
+++ b/pysal/spatial_dynamics/interaction.ipynb
@@ -0,0 +1,4151 @@
+{
+ "metadata": {
+ "name": "interaction"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import interaction"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 256
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import pysal as ps"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 257
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "events = interaction.SpaceTimeEvents(ps.examples.get_path(\"burkitt\"), 'T')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 258
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "kd_t = ps.cg.KDTree(events.time)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 259
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "kd_t"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "pyout",
+ "prompt_number": 260,
+ "text": [
+ "<scipy.spatial.kdtree.KDTree at 0x7fc57c1009d0>"
+ ]
+ }
+ ],
+ "prompt_number": 260
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "neighbors_t = kd_t.query_pairs(5)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 261
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "neighbors_t"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "pyout",
+ "prompt_number": 262,
+ "text": [
+ "set([(118, 119),\n",
+ " (175, 176),\n",
+ " (69, 70),\n",
+ " (174, 175),\n",
+ " (107, 108),\n",
+ " (128, 129),\n",
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+ " (98, 99),\n",
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+ " (155, 156)])"
+ ]
+ }
+ ],
+ "prompt_number": 262
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "kd_s = ps.cg.KDTree(events.space)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 263
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "neighbors_s = kd_s.query_pairs(20)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 264
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "neighbors_s"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "pyout",
+ "prompt_number": 265,
+ "text": [
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+ " (83, 175),\n",
+ " (120, 150),\n",
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+ " (48, 133),\n",
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+ " (90, 125),\n",
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+ " (2, 129),\n",
+ " (21, 94),\n",
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+ " (56, 180),\n",
+ " (77, 151),\n",
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+ " (9, 66),\n",
+ " (47, 53),\n",
+ " (31, 175),\n",
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+ " (106, 127),\n",
+ " (161, 181),\n",
+ " (18, 35),\n",
+ " (182, 184),\n",
+ " (93, 168),\n",
+ " (39, 172),\n",
+ " (23, 157),\n",
+ " (104, 138),\n",
+ " (33, 115),\n",
+ " (145, 172),\n",
+ " (153, 181),\n",
+ " (76, 183),\n",
+ " (21, 103),\n",
+ " (69, 80),\n",
+ " (17, 106),\n",
+ " (71, 187),\n",
+ " (56, 187),\n",
+ " (37, 103),\n",
+ " (5, 14),\n",
+ " (6, 148),\n",
+ " (14, 36),\n",
+ " (36, 185),\n",
+ " (40, 164),\n",
+ " (23, 37),\n",
+ " (20, 83),\n",
+ " (65, 172),\n",
+ " (85, 161),\n",
+ " (50, 69),\n",
+ " (18, 26),\n",
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+ " (15, 77),\n",
+ " (97, 166),\n",
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+ " (18, 110),\n",
+ " (51, 158),\n",
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+ " (54, 121),\n",
+ " (165, 170),\n",
+ " (114, 121),\n",
+ " (22, 86),\n",
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+ " (36, 150),\n",
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+ " (6, 143),\n",
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+ " (31, 59),\n",
+ " (28, 85),\n",
+ " (27, 88),\n",
+ " (16, 58),\n",
+ " (119, 139),\n",
+ " (36, 53),\n",
+ " (94, 178),\n",
+ " (39, 40),\n",
+ " (95, 159),\n",
+ " (104, 173),\n",
+ " (123, 155),\n",
+ " (106, 177),\n",
+ " (120, 158),\n",
+ " (52, 149),\n",
+ " (71, 166),\n",
+ " (88, 90),\n",
+ " (24, 57),\n",
+ " (5, 121),\n",
+ " (6, 121),\n",
+ " (137, 177),\n",
+ " (31, 131),\n",
+ " (34, 110),\n",
+ " (40, 153),\n",
+ " (21, 153),\n",
+ " (24, 168),\n",
+ " (25, 94),\n",
+ " (6, 8),\n",
+ " (120, 164),\n",
+ " (71, 121),\n",
+ " (37, 47),\n",
+ " (4, 54),\n",
+ " (116, 136),\n",
+ " (99, 112),\n",
+ " (63, 94),\n",
+ " (28, 172),\n",
+ " (32, 151),\n",
+ " (16, 49),\n",
+ " (94, 181),\n",
+ " (21, 123),\n",
+ " (57, 102),\n",
+ " (121, 172),\n",
+ " (88, 110),\n",
+ " (51, 98),\n",
+ " (71, 159),\n",
+ " (56, 87),\n",
+ " (38, 155),\n",
+ " (150, 159),\n",
+ " (100, 160),\n",
+ " (135, 157),\n",
+ " (87, 127),\n",
+ " (40, 128),\n",
+ " (114, 187),\n",
+ " (25, 39),\n",
+ " (33, 58),\n",
+ " (28, 52),\n",
+ " (138, 155),\n",
+ " (49, 153),\n",
+ " (72, 75),\n",
+ " (40, 47),\n",
+ " (103, 181),\n",
+ " (8, 56),\n",
+ " (63, 71),\n",
+ " (65, 174),\n",
+ " (16, 40),\n",
+ " (57, 154),\n",
+ " (95, 141),\n",
+ " (151, 175),\n",
+ " (7, 177),\n",
+ " (119, 172),\n",
+ " (104, 155),\n",
+ " (12, 60),\n",
+ " (71, 148),\n",
+ " (36, 138),\n",
+ " (13, 81),\n",
+ " (125, 161),\n",
+ " (35, 114),\n",
+ " (37, 149),\n",
+ " (114, 178),\n",
+ " (25, 40),\n",
+ " (45, 85),\n",
+ " (28, 51)])"
+ ]
+ }
+ ],
+ "prompt_number": 265
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "neighbors_joint = neighbors_s.intersection(neighbors_t)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 266
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "neighbors_joint"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "pyout",
+ "prompt_number": 267,
+ "text": [
+ "set([(41, 42),\n",
+ " (137, 138),\n",
+ " (122, 123),\n",
+ " (116, 118),\n",
+ " (51, 52),\n",
+ " (102, 103),\n",
+ " (162, 163),\n",
+ " (25, 26),\n",
+ " (158, 159),\n",
+ " (46, 47),\n",
+ " (30, 31),\n",
+ " (139, 140),\n",
+ " (35, 36)])"
+ ]
+ }
+ ],
+ "prompt_number": 267
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "#matches knox doc test 13 joint pairs"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 268
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Experiment with artificial data "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import numpy as np\n",
+ "n = 100"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 269
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "space = np.random.random((n,2)) # points in the unit square"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 270
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "kd_s = ps.cg.KDTree(space)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 271
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "neigh_s = kd_s.query_pairs(0.10) # pairs within 0.10 of each other"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 272
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "len(neigh_s)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "pyout",
+ "prompt_number": 273,
+ "text": [
+ "130"
+ ]
+ }
+ ],
+ "prompt_number": 273
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "times = np.random.random((n,1)) # time from 0 to 1"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 274
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "kd_t = ps.cg.KDTree(times)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 275
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "neigh_t = kd_t.query_pairs(0.01) # pairs within .01 units of time of each other"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 276
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "len(neigh_t)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "pyout",
+ "prompt_number": 277,
+ "text": [
+ "94"
+ ]
+ }
+ ],
+ "prompt_number": 277
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "joint = neigh_s.intersection(neigh_t) # neighbors in both space and time"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 278
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "npairs = len(joint) # number of pairs"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 279
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# use the number and ids of time neighbors\n",
+ "time_ids = np.array([ pair for pair in neigh_t])\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 280
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "ids = np.arange(n)\n",
+ "permutations = 99\n",
+ "larger = 0\n",
+ "for permutation in xrange(permutations):\n",
+ " np.random.shuffle(ids)\n",
+ " random_time = np.zeros((len(time_ids),2),int)\n",
+ " random_time[:,0] = ids[time_ids[:,0]]\n",
+ " random_time[:,1] = ids[time_ids[:,1]]\n",
+ " random_time.sort(axis=1)\n",
+ " random_time = set([tuple(row) for row in random_time])\n",
+ " random_joint = random_time.intersection(neigh_s)\n",
+ " if len(random_joint) >= npairs:\n",
+ " larger += 1\n",
+ " \n",
+ " "
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 281
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "print \"Number as large as observed: \",larger\n",
+ "if (permutations - larger) < larger:\n",
+ " larger = permutations - larger\n",
+ "\n",
+ "p_sim = (larger + 1.) / (permutations + 1.)\n",
+ "print \"p-value: \", p_sim"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Number as large as observed: 52\n",
+ "p-value: 0.48\n"
+ ]
+ }
+ ],
+ "prompt_number": 282
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "With Burkitt data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "events = interaction.SpaceTimeEvents(ps.examples.get_path(\"burkitt\"), 'T')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 283
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "kd_t = ps.cg.KDTree(events.time)\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 284
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "neigh_t = kd_t.query_pairs(5) # five units for time threshold"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 285
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "kd_s = ps.cg.KDTree(events.space)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 286
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "neigh_s = kd_s.query_pairs(20) # 20 unit threshold for space"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 287
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "joint = neigh_s.intersection(neigh_t)\n",
+ "npairs = len(joint)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 288
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "npairs"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "pyout",
+ "prompt_number": 289,
+ "text": [
+ "13"
+ ]
+ }
+ ],
+ "prompt_number": 289
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "np.random.seed(12345)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 290
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# use the number and ids of time neighbors\n",
+ "time_ids = np.array([ pair for pair in neigh_t])\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 291
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "ids = np.arange(len(events.time))\n",
+ "permutations = 99\n",
+ "larger = 0\n",
+ "joints = np.zeros((permutations,1), int)\n",
+ "for permutation in xrange(permutations):\n",
+ " np.random.shuffle(ids)\n",
+ " random_time = np.zeros((len(time_ids),2),int)\n",
+ " random_time[:,0] = ids[time_ids[:,0]]\n",
+ " random_time[:,1] = ids[time_ids[:,1]]\n",
+ " random_time.sort(axis=1)\n",
+ " random_time = set([tuple(row) for row in random_time])\n",
+ " random_joint = random_time.intersection(neigh_s)\n",
+ " nrj = len(random_joint)\n",
+ " joints[permutation] = nrj\n",
+ " if nrj >= npairs:\n",
+ " larger += 1\n",
+ " \n",
+ " "
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 292
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "print \"Number as large as observed: \",larger\n",
+ "if (permutations - larger) < larger:\n",
+ " larger = permutations - larger\n",
+ "\n",
+ "p_sim = (larger + 1.) / (permutations + 1.)\n",
+ "print \"p-value: \", p_sim"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Number as large as observed: 19\n",
+ "p-value: 0.2\n"
+ ]
+ }
+ ],
+ "prompt_number": 293
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 293
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "joints.mean()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "pyout",
+ "prompt_number": 294,
+ "text": [
+ "10.04040404040404"
+ ]
+ }
+ ],
+ "prompt_number": 294
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "joints.min()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "pyout",
+ "prompt_number": 295,
+ "text": [
+ "2"
+ ]
+ }
+ ],
+ "prompt_number": 295
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "joints.max()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "pyout",
+ "prompt_number": 296,
+ "text": [
+ "17"
+ ]
+ }
+ ],
+ "prompt_number": 296
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "joints.var()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "pyout",
+ "prompt_number": 297,
+ "text": [
+ "8.9074584226099311"
+ ]
+ }
+ ],
+ "prompt_number": 297
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+}
\ No newline at end of file
diff --git a/pysal/spatial_dynamics/interaction.py b/pysal/spatial_dynamics/interaction.py
new file mode 100644
index 0000000..c2a16a0
--- /dev/null
+++ b/pysal/spatial_dynamics/interaction.py
@@ -0,0 +1,641 @@
+"""
+Methods for identifying space-time interaction in spatio-temporal event
+data.
+"""
+__author__ = "Nicholas Malizia <nmalizia at asu.edu>", "Sergio J. Rey \
+<srey at asu.edu>", "Philip Stephens <philip.stephens at asu.edu"
+
+import pysal
+import numpy as np
+import scipy.stats as stats
+import pysal.weights.Distance as Distance
+from pysal import cg
+from pysal.spatial_dynamics import util
+from datetime import date
+
+__all__ = ['SpaceTimeEvents', 'knox', 'mantel', 'jacquez', 'modified_knox']
+
+
+class SpaceTimeEvents:
+ """
+ Method for reformatting event data stored in a shapefile for use in
+ calculating metrics of spatio-temporal interaction.
+
+ Parameters
+ ----------
+ path : string
+ the path to the appropriate shapefile, including the
+ file name, but excluding the extension
+ time : string
+ column header in the DBF file indicating the column
+ containing the time stamp
+ infer_timestamp : boolean
+ if the column containing the timestamp is formatted as
+ calendar dates, try to coerce them into Python datetime objects
+
+ Attributes
+ ----------
+ n : int
+ number of events
+ x : array
+ n x 1 array of the x coordinates for the events
+ y : array
+ n x 1 array of the y coordinates for the events
+ t : array
+ n x 1 array of the temporal coordinates for the events
+ space : array
+ n x 2 array of the spatial coordinates (x,y) for the
+ events
+ time : array
+ n x 2 array of the temporal coordinates (t,1) for the
+ events, the second column is a vector of ones
+
+ Examples
+ --------
+
+ Read in the example shapefile data, ensuring to omit the file
+ extension. In order to successfully create the event data the .dbf file
+ associated with the shapefile should have a column of values that are a
+ timestamp for the events. This timestamp may be a numerical value
+ or a date. Date inference was added in version 1.6.
+
+ >>> path = pysal.examples.get_path("burkitt")
+
+ Create an instance of SpaceTimeEvents from a shapefile, where the
+ temporal information is stored in a column named "T".
+
+ >>> events = SpaceTimeEvents(path,'T')
+
+ See how many events are in the instance.
+
+ >>> events.n
+ 188
+
+ Check the spatial coordinates of the first event.
+
+ >>> events.space[0]
+ array([ 300., 302.])
+
+ Check the time of the first event.
+
+ >>> events.t[0]
+ array([ 413.])
+
+ Calculate the time difference between the first two events.
+
+ >>> events.t[1] - events.t[0]
+ array([ 59.])
+
+ New, in 1.6, date support:
+
+ Now, create an instance of SpaceTimeEvents from a shapefile, where the
+ temporal information is stored in a column named "DATE".
+
+ >>> events = SpaceTimeEvents(path,'DATE')
+
+ See how many events are in the instance.
+
+ >>> events.n
+ 188
+
+ Check the spatial coordinates of the first event.
+
+ >>> events.space[0]
+ array([ 300., 302.])
+
+ Check the time of the first event. Note that this value is equivalent to
+ 413 days after January 1, 1900.
+
+ >>> events.t[0][0]
+ datetime.date(1901, 2, 16)
+
+ Calculate the time difference between the first two events.
+
+ >>> (events.t[1][0] - events.t[0][0]).days
+ 59
+
+ """
+ def __init__(self, path, time_col, infer_timestamp=False):
+ shp = pysal.open(path + '.shp')
+ dbf = pysal.open(path + '.dbf')
+
+ # extract the spatial coordinates from the shapefile
+ x = [coords[0] for coords in shp]
+ y = [coords[1] for coords in shp]
+
+ self.n = n = len(shp)
+ x = np.array(x)
+ y = np.array(y)
+ self.x = np.reshape(x, (n, 1))
+ self.y = np.reshape(y, (n, 1))
+ self.space = np.hstack((self.x, self.y))
+
+ # extract the temporal information from the database
+ if infer_timestamp:
+ col = dbf.by_col(time_col)
+ if isinstance(col[0], date):
+ day1 = min(col)
+ col = [(d - day1).days for d in col]
+ t = np.array(col)
+ else:
+ print("Unable to parse your time column as Python datetime \
+ objects, proceeding as integers.")
+ t = np.array(col)
+ else:
+ t = np.array(dbf.by_col(time_col))
+ line = np.ones((n, 1))
+ self.t = np.reshape(t, (n, 1))
+ self.time = np.hstack((self.t, line))
+
+ # close open objects
+ dbf.close()
+ shp.close()
+
+
+def knox(s_coords, t_coords, delta, tau, permutations=99, debug=False):
+ """
+ Knox test for spatio-temporal interaction. [1]_
+
+ Parameters
+ ----------
+ s_coords : array
+ nx2 spatial coordinates
+ t_coords : array
+ nx1 temporal coordinates
+ delta : float
+ threshold for proximity in space
+ tau : float
+ threshold for proximity in time
+ permutations : int
+ the number of permutations used to establish pseudo-
+ significance (default is 99)
+ debug : bool
+ if true, debugging information is printed
+
+ Returns
+ -------
+ knox_result : dictionary
+ contains the statistic (stat) for the test and the
+ associated p-value (pvalue)
+ stat : float
+ value of the knox test for the dataset
+ pvalue : float
+ pseudo p-value associated with the statistic
+ counts : int
+ count of space time neighbors
+
+ References
+ ----------
+ .. [1] E. Knox. 1964. The detection of space-time
+ interactions. Journal of the Royal Statistical Society. Series C
+ (Applied Statistics), 13(1):25-30.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+
+ Read in the example data and create an instance of SpaceTimeEvents.
+
+ >>> path = pysal.examples.get_path("burkitt")
+ >>> events = SpaceTimeEvents(path,'T')
+
+ Set the random seed generator. This is used by the permutation based
+ inference to replicate the pseudo-significance of our example results -
+ the end-user will normally omit this step.
+
+ >>> np.random.seed(100)
+
+ Run the Knox test with distance and time thresholds of 20 and 5,
+ respectively. This counts the events that are closer than 20 units in
+ space, and 5 units in time.
+
+ >>> result = knox(events.space, events.t, delta=20, tau=5, permutations=99)
+
+ Next, we examine the results. First, we call the statistic from the
+ results dictionary. This reports that there are 13 events close
+ in both space and time, according to our threshold definitions.
+
+ >>> result['stat'] == 13
+ True
+
+ Next, we look at the pseudo-significance of this value, calculated by
+ permuting the timestamps and rerunning the statistics. In this case,
+ the results indicate there is likely no space-time interaction between
+ the events.
+
+ >>> print("%2.2f"%result['pvalue'])
+ 0.17
+
+ """
+
+ # Do a kdtree on space first as the number of ties (identical points) is
+ # likely to be lower for space than time.
+
+ kd_s = pysal.cg.KDTree(s_coords)
+ neigh_s = kd_s.query_pairs(delta)
+ tau2 = tau * tau
+ ids = np.array(list(neigh_s))
+
+ # For the neighboring pairs in space, determine which are also time
+ # neighbors
+
+ d_t = (t_coords[ids[:, 0]] - t_coords[ids[:, 1]]) ** 2
+ n_st = sum(d_t <= tau2)
+
+ knox_result = {'stat': n_st[0]}
+
+ if permutations:
+ joint = np.zeros((permutations, 1), int)
+ for p in xrange(permutations):
+ np.random.shuffle(t_coords)
+ d_t = (t_coords[ids[:, 0]] - t_coords[ids[:, 1]]) ** 2
+ joint[p] = np.sum(d_t <= tau2)
+
+ larger = sum(joint >= n_st[0])
+ if (permutations - larger) < larger:
+ larger = permutations - larger
+ p_sim = (larger + 1.) / (permutations + 1.)
+ knox_result['pvalue'] = p_sim
+ return knox_result
+
+
+def mantel(s_coords, t_coords, permutations=99, scon=1.0, spow=-1.0, tcon=1.0, tpow=-1.0):
+ """
+ Standardized Mantel test for spatio-temporal interaction. [2]_
+
+ Parameters
+ ----------
+ s_coords : array
+ nx2 spatial coordinates
+
+ t_coords : array
+ nx1 temporal coordinates
+
+ permutations : int
+ the number of permutations used to establish pseudo-
+ significance (default is 99)
+
+ scon : float
+ constant added to spatial distances
+
+ spow : float
+ value for power transformation for spatial distances
+
+ tcon : float
+ constant added to temporal distances
+
+ tpow : float
+ value for power transformation for temporal distances
+
+
+ Returns
+ -------
+ mantel_result : dictionary
+ contains the statistic (stat) for the test and the
+ associated p-value (pvalue)
+ stat : float
+ value of the knox test for the dataset
+ pvalue : float
+ pseudo p-value associated with the statistic
+
+ References
+ ----------
+ .. [2] N. Mantel. 1967. The detection of disease clustering and a
+ generalized regression approach. Cancer Research, 27(2):209-220.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+
+ Read in the example data and create an instance of SpaceTimeEvents.
+
+ >>> path = pysal.examples.get_path("burkitt")
+ >>> events = SpaceTimeEvents(path,'T')
+
+ Set the random seed generator. This is used by the permutation based
+ inference to replicate the pseudo-significance of our example results -
+ the end-user will normally omit this step.
+
+ >>> np.random.seed(100)
+
+ The standardized Mantel test is a measure of matrix correlation between
+ the spatial and temporal distance matrices of the event dataset. The
+ following example runs the standardized Mantel test without a constant
+ or transformation; however, as recommended by Mantel (1967) [2]_, these
+ should be added by the user. This can be done by adjusting the constant
+ and power parameters.
+
+ >>> result = mantel(events.space, events.t, 99, scon=1.0, spow=-1.0, tcon=1.0, tpow=-1.0)
+
+ Next, we examine the result of the test.
+
+ >>> print("%6.6f"%result['stat'])
+ 0.048368
+
+ Finally, we look at the pseudo-significance of this value, calculated by
+ permuting the timestamps and rerunning the statistic for each of the 99
+ permutations. According to these parameters, the results indicate
+ space-time interaction between the events.
+
+ >>> print("%2.2f"%result['pvalue'])
+ 0.01
+
+
+ """
+
+ t = t_coords
+ s = s_coords
+ n = len(t)
+
+ # calculate the spatial and temporal distance matrices for the events
+ distmat = cg.distance_matrix(s)
+ timemat = cg.distance_matrix(t)
+
+ # calculate the transformed standardized statistic
+ timevec = (util.get_lower(timemat) + tcon) ** tpow
+ distvec = (util.get_lower(distmat) + scon) ** spow
+ stat = stats.pearsonr(timevec, distvec)[0].sum()
+
+ # return the results (if no inference)
+ if not permutations:
+ return stat
+
+ # loop for generating a random distribution to assess significance
+ dist = []
+ for i in range(permutations):
+ trand = util.shuffle_matrix(timemat, range(n))
+ timevec = (util.get_lower(trand) + tcon) ** tpow
+ m = stats.pearsonr(timevec, distvec)[0].sum()
+ dist.append(m)
+
+ ## establish the pseudo significance of the observed statistic
+ distribution = np.array(dist)
+ greater = np.ma.masked_greater_equal(distribution, stat)
+ count = np.ma.count_masked(greater)
+ pvalue = (count + 1.0) / (permutations + 1.0)
+
+ # report the results
+ mantel_result = {'stat': stat, 'pvalue': pvalue}
+ return mantel_result
+
+
+def jacquez(s_coords, t_coords, k, permutations=99):
+ """
+ Jacquez k nearest neighbors test for spatio-temporal interaction. [3]_
+
+ Parameters
+ ----------
+ s_coords : array
+ nx2 spatial coordinates
+
+ t_coords : array
+ nx1 temporal coordinates
+
+ k : int
+ the number of nearest neighbors to be searched
+
+ permutations : int
+ the number of permutations used to establish pseudo-
+ significance (default is 99)
+
+ Returns
+ -------
+ jacquez_result : dictionary
+ contains the statistic (stat) for the test and the
+ associated p-value (pvalue)
+ stat : float
+ value of the Jacquez k nearest neighbors test for the
+ dataset
+ pvalue : float
+ p-value associated with the statistic (normally
+ distributed with k-1 df)
+
+ References
+ ----------
+ .. [3] G. Jacquez. 1996. A k nearest neighbour test for space-time
+ interaction. Statistics in Medicine, 15(18):1935-1949.
+
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+
+ Read in the example data and create an instance of SpaceTimeEvents.
+
+ >>> path = pysal.examples.get_path("burkitt")
+ >>> events = SpaceTimeEvents(path,'T')
+
+ The Jacquez test counts the number of events that are k nearest
+ neighbors in both time and space. The following runs the Jacquez test
+ on the example data and reports the resulting statistic. In this case,
+ there are 13 instances where events are nearest neighbors in both space
+ and time.
+
+ # turning off as kdtree changes from scipy < 0.12 return 13
+ #>>> np.random.seed(100)
+ #>>> result = jacquez(events.space, events.t ,k=3,permutations=99)
+ #>>> print result['stat']
+ #12
+
+ The significance of this can be assessed by calling the p-
+ value from the results dictionary, as shown below. Again, no
+ space-time interaction is observed.
+
+ #>>> result['pvalue'] < 0.01
+ #False
+
+ """
+ time = t_coords
+ space = s_coords
+ n = len(time)
+
+ # calculate the nearest neighbors in space and time separately
+ knnt = Distance.knnW(time, k)
+ knns = Distance.knnW(space, k)
+
+ nnt = knnt.neighbors
+ nns = knns.neighbors
+ knn_sum = 0
+
+ # determine which events are nearest neighbors in both space and time
+ for i in range(n):
+ t_neighbors = nnt[i]
+ s_neighbors = nns[i]
+ check = set(t_neighbors)
+ inter = check.intersection(s_neighbors)
+ count = len(inter)
+ knn_sum += count
+
+ stat = knn_sum
+
+ # return the results (if no inference)
+ if not permutations:
+ return stat
+
+ # loop for generating a random distribution to assess significance
+ dist = []
+ for p in range(permutations):
+ j = 0
+ trand = np.random.permutation(time)
+ knnt = Distance.knnW(trand, k)
+ nnt = knnt.neighbors
+ for i in range(n):
+ t_neighbors = nnt[i]
+ s_neighbors = nns[i]
+ check = set(t_neighbors)
+ inter = check.intersection(s_neighbors)
+ count = len(inter)
+ j += count
+
+ dist.append(j)
+
+ # establish the pseudo significance of the observed statistic
+ distribution = np.array(dist)
+ greater = np.ma.masked_greater_equal(distribution, stat)
+ count = np.ma.count_masked(greater)
+ pvalue = (count + 1.0) / (permutations + 1.0)
+
+ # report the results
+ jacquez_result = {'stat': stat, 'pvalue': pvalue}
+ return jacquez_result
+
+
+def modified_knox(s_coords, t_coords, delta, tau, permutations=99):
+ """
+ Baker's modified Knox test for spatio-temporal interaction. [4]_
+
+ Parameters
+ ----------
+ s_coords : array
+ nx2 spatial coordinates
+ t_coords : array
+ nx1 temporal coordinates
+ delta : float
+ threshold for proximity in space
+ tau : float
+ threshold for proximity in time
+ permutations : int
+ the number of permutations used to establish pseudo-
+ significance (default is 99)
+
+ Returns
+ -------
+ modknox_result : dictionary
+ contains the statistic (stat) for the test and the
+ associated p-value (pvalue)
+ stat : float
+ value of the modified knox test for the dataset
+ pvalue : float
+ pseudo p-value associated with the statistic
+
+ References
+ ----------
+ .. [4] R.D. Baker. Identifying space-time disease clusters. Acta Tropica,
+ 91(3):291-299, 2004
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+
+ Read in the example data and create an instance of SpaceTimeEvents.
+
+ >>> path = pysal.examples.get_path("burkitt")
+ >>> events = SpaceTimeEvents(path, 'T')
+
+ Set the random seed generator. This is used by the permutation based
+ inference to replicate the pseudo-significance of our example results -
+ the end-user will normally omit this step.
+
+ >>> np.random.seed(100)
+
+ Run the modified Knox test with distance and time thresholds of 20 and 5,
+ respectively. This counts the events that are closer than 20 units in
+ space, and 5 units in time.
+
+ >>> result = modified_knox(events.space, events.t, delta=20, tau=5, permutations=99)
+
+ Next, we examine the results. First, we call the statistic from the
+ results dictionary. This reports the difference between the observed
+ and expected Knox statistic.
+
+ >>> print("%2.8f" % result['stat'])
+ 2.81016043
+
+ Next, we look at the pseudo-significance of this value, calculated by
+ permuting the timestamps and rerunning the statistics. In this case,
+ the results indicate there is likely no space-time interaction.
+
+ >>> print("%2.2f" % result['pvalue'])
+ 0.11
+
+ """
+ s = s_coords
+ t = t_coords
+ n = len(t)
+
+ # calculate the spatial and temporal distance matrices for the events
+ sdistmat = cg.distance_matrix(s)
+ tdistmat = cg.distance_matrix(t)
+
+ # identify events within thresholds
+ spacmat = np.ones((n, n))
+ spacbin = sdistmat <= delta
+ spacmat = spacmat * spacbin
+ timemat = np.ones((n, n))
+ timebin = tdistmat <= tau
+ timemat = timemat * timebin
+
+ # calculate the observed (original) statistic
+ knoxmat = timemat * spacmat
+ obsstat = (knoxmat.sum() - n)
+
+ # calculate the expectated value
+ ssumvec = np.reshape((spacbin.sum(axis=0) - 1), (n, 1))
+ tsumvec = np.reshape((timebin.sum(axis=0) - 1), (n, 1))
+ expstat = (ssumvec * tsumvec).sum()
+
+ # calculate the modified stat
+ stat = (obsstat - (expstat / (n - 1.0))) / 2.0
+
+ # return results (if no inference)
+ if not permutations:
+ return stat
+ distribution = []
+
+ # loop for generating a random distribution to assess significance
+ for p in range(permutations):
+ rtdistmat = util.shuffle_matrix(tdistmat, range(n))
+ timemat = np.ones((n, n))
+ timebin = rtdistmat <= tau
+ timemat = timemat * timebin
+
+ # calculate the observed knox again
+ knoxmat = timemat * spacmat
+ obsstat = (knoxmat.sum() - n)
+
+ # calculate the expectated value again
+ ssumvec = np.reshape((spacbin.sum(axis=0) - 1), (n, 1))
+ tsumvec = np.reshape((timebin.sum(axis=0) - 1), (n, 1))
+ expstat = (ssumvec * tsumvec).sum()
+
+ # calculate the modified stat
+ tempstat = (obsstat - (expstat / (n - 1.0))) / 2.0
+ distribution.append(tempstat)
+
+ # establish the pseudo significance of the observed statistic
+ distribution = np.array(distribution)
+ greater = np.ma.masked_greater_equal(distribution, stat)
+ count = np.ma.count_masked(greater)
+ pvalue = (count + 1.0) / (permutations + 1.0)
+
+ # return results
+ modknox_result = {'stat': stat, 'pvalue': pvalue}
+ return modknox_result
+
+if __name__ == "__main__":
+ import doctest
+ doctest.testmod()
diff --git a/pysal/spatial_dynamics/markov.py b/pysal/spatial_dynamics/markov.py
new file mode 100644
index 0000000..d5dce5e
--- /dev/null
+++ b/pysal/spatial_dynamics/markov.py
@@ -0,0 +1,1548 @@
+"""
+Markov based methods for spatial dynamics
+"""
+__author__ = "Sergio J. Rey <srey at asu.edu"
+
+import numpy as np
+import numpy.linalg as la
+from pysal.spatial_dynamics.ergodic import fmpt, steady_state
+from scipy import stats
+import pysal
+from operator import gt
+
+__all__ = ["Markov", "LISA_Markov", "Spatial_Markov", "kullback",
+ "prais", "shorrock", "homogeneity"]
+
+# TT predefine LISA transitions
+# TT[i,j] is the transition type from i to j
+# i = quadrant in period 0
+# j = quadrant in period 1
+# uses one offset so first row and col of TT are ignored
+TT = np.zeros((5, 5), int)
+c = 1
+for i in range(1, 5):
+ for j in range(1, 5):
+ TT[i, j] = c
+ c += 1
+
+# MOVE_TYPES is a dictionary that returns the move type of a LISA transition
+# filtered on the significance of the LISA end points
+# True indicates significant LISA in a particular period
+# e.g. a key of (1, 3, True, False) indicates a significant LISA located in
+# quadrant 1 in period 0 moved to quadrant 3 in period 1 but was not
+# significant in quadrant 3.
+
+MOVE_TYPES = {}
+c = 1
+cases = (True, False)
+sig_keys = [(i, j) for i in cases for j in cases]
+
+for i, sig_key in enumerate(sig_keys):
+ c = 1 + i * 16
+ for i in range(1, 5):
+ for j in range(1, 5):
+ key = (i, j, sig_key[0], sig_key[1])
+ MOVE_TYPES[key] = c
+ c += 1
+
+
+class Markov:
+ """
+ Classic Markov transition matrices
+
+ Parameters
+ ----------
+ class_ids : array (n, t)
+ One row per observation, one column recording the state of each
+ observation, with as many columns as time periods
+ classes : array (k)
+ All different classes (bins) of the matrix
+
+ Attributes
+ ----------
+ p : matrix (k, k)
+ transition probability matrix
+
+ steady_state : matrix (k, 1)
+ ergodic distribution
+
+ transitions : matrix (k, k)
+ count of transitions between each state i and j
+
+ Examples
+ --------
+ >>> c = np.array([['b','a','c'],['c','c','a'],['c','b','c'],['a','a','b'],['a','b','c']])
+ >>> m = Markov(c)
+ >>> m.classes
+ array(['a', 'b', 'c'],
+ dtype='|S1')
+ >>> m.p
+ matrix([[ 0.25 , 0.5 , 0.25 ],
+ [ 0.33333333, 0. , 0.66666667],
+ [ 0.33333333, 0.33333333, 0.33333333]])
+ >>> m.steady_state
+ matrix([[ 0.30769231],
+ [ 0.28846154],
+ [ 0.40384615]])
+
+ US nominal per capita income 48 states 81 years 1929-2009
+
+ >>> import pysal
+ >>> f = pysal.open(pysal.examples.get_path("usjoin.csv"))
+ >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)])
+
+ set classes to quintiles for each year
+
+ >>> q5 = np.array([pysal.Quantiles(y).yb for y in pci]).transpose()
+ >>> m = Markov(q5)
+ >>> m.transitions
+ array([[ 729., 71., 1., 0., 0.],
+ [ 72., 567., 80., 3., 0.],
+ [ 0., 81., 631., 86., 2.],
+ [ 0., 3., 86., 573., 56.],
+ [ 0., 0., 1., 57., 741.]])
+ >>> m.p
+ matrix([[ 0.91011236, 0.0886392 , 0.00124844, 0. , 0. ],
+ [ 0.09972299, 0.78531856, 0.11080332, 0.00415512, 0. ],
+ [ 0. , 0.10125 , 0.78875 , 0.1075 , 0.0025 ],
+ [ 0. , 0.00417827, 0.11977716, 0.79805014, 0.07799443],
+ [ 0. , 0. , 0.00125156, 0.07133917, 0.92740926]])
+ >>> m.steady_state
+ matrix([[ 0.20774716],
+ [ 0.18725774],
+ [ 0.20740537],
+ [ 0.18821787],
+ [ 0.20937187]])
+
+ Relative incomes
+
+ >>> pci = pci.transpose()
+ >>> rpci = pci/(pci.mean(axis=0))
+ >>> rq = pysal.Quantiles(rpci.flatten()).yb
+ >>> rq.shape = (48,81)
+ >>> mq = Markov(rq)
+ >>> mq.transitions
+ array([[ 707., 58., 7., 1., 0.],
+ [ 50., 629., 80., 1., 1.],
+ [ 4., 79., 610., 73., 2.],
+ [ 0., 7., 72., 650., 37.],
+ [ 0., 0., 0., 48., 724.]])
+ >>> mq.steady_state
+ matrix([[ 0.17957376],
+ [ 0.21631443],
+ [ 0.21499942],
+ [ 0.21134662],
+ [ 0.17776576]])
+
+ """
+ def __init__(self, class_ids, classes=[]):
+ #pylint; Dangerous default value [] as argument
+ if len(classes):
+ self.classes = classes
+ else:
+ self.classes = np.unique(class_ids)
+
+ n, t = class_ids.shape
+ k = len(self.classes)
+ js = range(t - 1)
+
+ classIds = self.classes.tolist()
+ transitions = np.zeros((k, k))
+ for state_0 in js:
+ state_1 = state_0 + 1
+ state_0 = class_ids[:, state_0]
+ state_1 = class_ids[:, state_1]
+ initial = np.unique(state_0)
+ for i in initial:
+ ending = state_1[state_0 == i]
+ uending = np.unique(ending)
+ row = classIds.index(i)
+ for j in uending:
+ col = classIds.index(j)
+ transitions[row, col] += sum(ending == j)
+ self.transitions = transitions
+ row_sum = transitions.sum(axis=1)
+ p = np.dot(np.diag(1 / (row_sum + (row_sum == 0))), transitions)
+ self.p = np.matrix(p)
+
+ # steady_state vector
+ v, d = la.eig(np.transpose(self.p))
+ # for a regular P maximum eigenvalue will be 1
+ mv = max(v)
+ # find its position
+ i = v.tolist().index(mv)
+ # normalize eigenvector corresponding to the eigenvalue 1
+ self.steady_state = d[:, i] / sum(d[:, i])
+
+
+class Spatial_Markov:
+ """
+ Markov transitions conditioned on the value of the spatial lag
+
+ Parameters
+ ----------
+
+ y : array (n,t)
+ One row per observation, one column per state of each
+ observation, with as many columns as time periods
+
+ w : spatial weights object
+
+ k : integer
+ number of classes (quantiles)
+
+ permutations : int
+ number of permutations (default=0) for use in randomization
+ based inference
+
+ fixed : boolean
+ If true, quantiles are taken over the entire n*t
+ pooled series. If false, quantiles are taken each
+ time period over n.
+ variable_name : string
+ name of variable
+
+ Attributes
+ ----------
+ p : matrix (k, k)
+ transition probability matrix for a-spatial Markov
+ s : matrix (k, 1)
+ ergodic distribution for a-spatial Markov
+ transitions : matrix (k, k)
+ counts of transitions between each state i and j
+ for a-spatial Markov
+ T : matrix (k, k, k)
+ counts of transitions for each conditional Markov
+ T[0] is the matrix of transitions for observations with
+ lags in the 0th quantile, T[k-1] is the transitions for
+ the observations with lags in the k-1th
+ P : matrix(k, k, k)
+ transition probability matrix for spatial Markov
+ first dimension is the conditioned on the lag
+ S : matrix(k, k)
+ steady state distributions for spatial Markov
+ each row is a conditional steady_state
+ F : matrix(k, k, k)
+ first mean passage times
+ first dimension is conditioned on the lag
+ shtest : list (k elements)
+ each element of the list is a tuple for a multinomial
+ difference test between the steady state distribution from
+ a conditional distribution versus the overall steady state
+ distribution, first element of the tuple is the chi2 value,
+ second its p-value and the third the degrees of freedom
+ chi2 : list (k elements)
+ each element of the list is a tuple for a chi-squared test
+ of the difference between the conditional transition
+ matrix against the overall transition matrix, first
+ element of the tuple is the chi2 value, second its
+ p-value and the third the degrees of freedom
+ x2 : float
+ sum of the chi2 values for each of the conditional tests
+ has an asymptotic chi2 distribution with k(k-1)(k-1)
+ degrees of freedom under the null that transition
+ probabilities are spatially homogeneous
+ (see chi2 above)
+ x2_dof : int
+ degrees of freedom for homogeneity test
+ x2_pvalue : float
+ pvalue for homogeneity test based on analytic
+ distribution
+ x2_rpvalue : float (if permutations>0)
+ pseudo p-value for x2 based on random spatial permutations
+ of the rows of the original transitions
+ x2_realizations : array (permutations,1)
+ the values of x2 for the random permutations
+ Q : float
+ Chi-square test of homogeneity across lag classes based
+ on Bickenbach and Bode (2003) [3]_
+ Q_p_value : float
+ p-value for Q
+ LR : float
+ Likelihood ratio statistic for homogeneity across lag
+ classes based on Bickenback and Bode (2003) [3]_
+ LR_p_value : float
+ p-value for LR
+ dof_hom : int
+ degrees of freedom for LR and Q, corrected for 0 cells.
+
+ Notes
+ -----
+ Based on Rey (2001) [1]_
+
+ The shtest and chi2 tests should be used with caution as they are based on
+ classic theory assuming random transitions. The x2 based test is
+ preferable since it simulates the randomness under the null. It is an
+ experimental test requiring further analysis.
+
+
+ Examples
+ --------
+ >>> import pysal as ps
+ >>> f = ps.open(ps.examples.get_path("usjoin.csv"))
+ >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)])
+ >>> pci = pci.transpose()
+ >>> rpci = pci/(pci.mean(axis=0))
+ >>> w = ps.open(ps.examples.get_path("states48.gal")).read()
+ >>> w.transform = 'r'
+ >>> sm = ps.Spatial_Markov(rpci, w, fixed=True, k=5, variable_name='rpci')
+ >>> for p in sm.P:
+ ... print p
+ ...
+ [[ 0.96341463 0.0304878 0.00609756 0. 0. ]
+ [ 0.06040268 0.83221477 0.10738255 0. 0. ]
+ [ 0. 0.14 0.74 0.12 0. ]
+ [ 0. 0.03571429 0.32142857 0.57142857 0.07142857]
+ [ 0. 0. 0. 0.16666667 0.83333333]]
+ [[ 0.79831933 0.16806723 0.03361345 0. 0. ]
+ [ 0.0754717 0.88207547 0.04245283 0. 0. ]
+ [ 0.00537634 0.06989247 0.8655914 0.05913978 0. ]
+ [ 0. 0. 0.06372549 0.90196078 0.03431373]
+ [ 0. 0. 0. 0.19444444 0.80555556]]
+ [[ 0.84693878 0.15306122 0. 0. 0. ]
+ [ 0.08133971 0.78947368 0.1291866 0. 0. ]
+ [ 0.00518135 0.0984456 0.79274611 0.0984456 0.00518135]
+ [ 0. 0. 0.09411765 0.87058824 0.03529412]
+ [ 0. 0. 0. 0.10204082 0.89795918]]
+ [[ 0.8852459 0.09836066 0. 0.01639344 0. ]
+ [ 0.03875969 0.81395349 0.13953488 0. 0.00775194]
+ [ 0.0049505 0.09405941 0.77722772 0.11881188 0.0049505 ]
+ [ 0. 0.02339181 0.12865497 0.75438596 0.09356725]
+ [ 0. 0. 0. 0.09661836 0.90338164]]
+ [[ 0.33333333 0.66666667 0. 0. 0. ]
+ [ 0.0483871 0.77419355 0.16129032 0.01612903 0. ]
+ [ 0.01149425 0.16091954 0.74712644 0.08045977 0. ]
+ [ 0. 0.01036269 0.06217617 0.89637306 0.03108808]
+ [ 0. 0. 0. 0.02352941 0.97647059]]
+
+
+ The probability of a poor state remaining poor is 0.963 if their
+ neighbors are in the 1st quintile and 0.798 if their neighbors are
+ in the 2nd quintile. The probability of a rich economy remaining
+ rich is 0.976 if their neighbors are in the 5th quintile, but if their
+ neighbors are in the 4th quintile this drops to 0.903.
+
+ The Q and likelihood ratio statistics are both significant indicating
+ the dynamics are not homogeneous across the lag classes:
+
+ >>> "%.3f"%sm.LR
+ '170.659'
+ >>> "%.3f"%sm.Q
+ '200.624'
+ >>> "%.3f"%sm.LR_p_value
+ '0.000'
+ >>> "%.3f"%sm.Q_p_value
+ '0.000'
+ >>> sm.dof_hom
+ 60
+
+ The long run distribution for states with poor (rich) neighbors has
+ 0.435 (0.018) of the values in the first quintile, 0.263 (0.200) in
+ the second quintile, 0.204 (0.190) in the third, 0.0684 (0.255) in the
+ fourth and 0.029 (0.337) in the fifth quintile.
+
+ >>> sm.S
+ array([[ 0.43509425, 0.2635327 , 0.20363044, 0.06841983, 0.02932278],
+ [ 0.13391287, 0.33993305, 0.25153036, 0.23343016, 0.04119356],
+ [ 0.12124869, 0.21137444, 0.2635101 , 0.29013417, 0.1137326 ],
+ [ 0.0776413 , 0.19748806, 0.25352636, 0.22480415, 0.24654013],
+ [ 0.01776781, 0.19964349, 0.19009833, 0.25524697, 0.3372434 ]])
+
+ States with incomes in the first quintile with neighbors in the
+ first quintile return to the first quartile after 2.298 years, after
+ leaving the first quintile. They enter the fourth quintile after
+ 80.810 years after leaving the first quintile, on average.
+ Poor states within neighbors in the fourth quintile return to the
+ first quintile, on average, after 12.88 years, and would enter the
+ fourth quintile after 28.473 years.
+
+ >>> for f in sm.F:
+ ... print f
+ ...
+ [[ 2.29835259 28.95614035 46.14285714 80.80952381 279.42857143]
+ [ 33.86549708 3.79459555 22.57142857 57.23809524 255.85714286]
+ [ 43.60233918 9.73684211 4.91085714 34.66666667 233.28571429]
+ [ 46.62865497 12.76315789 6.25714286 14.61564626 198.61904762]
+ [ 52.62865497 18.76315789 12.25714286 6. 34.1031746 ]]
+ [[ 7.46754205 9.70574606 25.76785714 74.53116883 194.23446197]
+ [ 27.76691978 2.94175577 24.97142857 73.73474026 193.4380334 ]
+ [ 53.57477715 28.48447637 3.97566318 48.76331169 168.46660482]
+ [ 72.03631562 46.94601483 18.46153846 4.28393653 119.70329314]
+ [ 77.17917276 52.08887197 23.6043956 5.14285714 24.27564033]]
+ [[ 8.24751154 6.53333333 18.38765432 40.70864198 112.76732026]
+ [ 47.35040872 4.73094099 11.85432099 34.17530864 106.23398693]
+ [ 69.42288828 24.76666667 3.794921 22.32098765 94.37966594]
+ [ 83.72288828 39.06666667 14.3 3.44668119 76.36702977]
+ [ 93.52288828 48.86666667 24.1 9.8 8.79255406]]
+ [[ 12.87974382 13.34847151 19.83446328 28.47257282 55.82395142]
+ [ 99.46114206 5.06359731 10.54545198 23.05133495 49.68944423]
+ [ 117.76777159 23.03735526 3.94436301 15.0843986 43.57927247]
+ [ 127.89752089 32.4393006 14.56853107 4.44831643 31.63099455]
+ [ 138.24752089 42.7893006 24.91853107 10.35 4.05613474]]
+ [[ 56.2815534 1.5 10.57236842 27.02173913 110.54347826]
+ [ 82.9223301 5.00892857 9.07236842 25.52173913 109.04347826]
+ [ 97.17718447 19.53125 5.26043557 21.42391304 104.94565217]
+ [ 127.1407767 48.74107143 33.29605263 3.91777427 83.52173913]
+ [ 169.6407767 91.24107143 75.79605263 42.5 2.96521739]]
+
+
+ References
+ ----------
+
+ .. [3] Bickenbach, F. and E. Bode (2003) "Evaluating the Markov property in studies of economic convergence. International Regional Science Review: 3, 363-392.
+
+ .. [1] Rey, S. (2001) "Spatial empirics for economic growth and convergence." Geographical Analysis, 33: 194-214.
+
+
+
+ """
+ def __init__(self, y, w, k=4, permutations=0, fixed=False,
+ variable_name=None):
+
+ self.y = y
+ rows, cols = y.shape
+ self.cols = cols
+ npm = np.matrix
+ npa = np.array
+ self.fixed = fixed
+ self.variable_name = variable_name
+ if fixed:
+ yf = y.flatten()
+ yb = pysal.Quantiles(yf, k=k).yb
+ yb.shape = (rows, cols)
+ classes = yb
+ else:
+ classes = npa([pysal.Quantiles(y[:, i], k=k)
+ .yb for i in np.arange(cols)]).transpose()
+ classic = Markov(classes)
+ self.classes = classes
+ self.p = classic.p
+ self.s = classic.steady_state
+ self.transitions = classic.transitions
+ T, P, ss, F = self._calc(y, w, classes, k=k)
+ self.T = T
+ self.P = P
+ self.S = ss
+ self.F = F
+ self.shtest = self._mn_test()
+ self.chi2 = self._chi2_test()
+ self.x2 = sum([c[0] for c in self.chi2])
+ dof = k * (k - 1) * (k - 1)
+ self.x2_pvalue = 1 - stats.chi2.cdf(self.x2, dof)
+ self.x2_dof = dof
+ self.k = k
+
+ # bickenbach and bode tests
+ ht = homogeneity(self.T)
+ self.Q = ht.Q
+ self.Q_p_value = ht.Q_p_value
+ self.LR = ht.LR
+ self.LR_p_value = ht.LR_p_value
+ self.dof_hom = ht.dof
+
+
+ if permutations:
+ nrp = np.random.permutation
+ rp = range(permutations)
+ counter = 0
+ x2_realizations = np.zeros((permutations, 1))
+ x2ss = []
+ for perm in range(permutations):
+ T, P, ss, F = self._calc(nrp(y), w, classes, k=k)
+ x2 = [chi2(T[i], self.transitions)[0] for i in range(k)]
+ x2s = sum(x2)
+ x2_realizations[perm] = x2s
+ if x2s >= self.x2:
+ counter += 1
+ self.x2_rpvalue = (counter + 1.0) / (permutations + 1.)
+ self.x2_realizations = x2_realizations
+
+ def _calc(self, y, w, classes, k):
+ # lag markov
+ ly = pysal.lag_spatial(w, y)
+ npm = np.matrix
+ npa = np.array
+ if self.fixed:
+ l_classes = pysal.Quantiles(ly.flatten(), k=k).yb
+ l_classes.shape = ly.shape
+ else:
+ l_classes = npa([pysal.Quantiles(
+ ly[:, i], k=k).yb for i in np.arange(self.cols)])
+ l_classes = l_classes.transpose()
+ l_classic = Markov(l_classes)
+ T = np.zeros((k, k, k))
+ n, t = y.shape
+ for t1 in range(t - 1):
+ t2 = t1 + 1
+ for i in range(n):
+ T[l_classes[i, t1], classes[i, t1], classes[i, t2]] += 1
+
+ P = np.zeros_like(T)
+ F = np.zeros_like(T) # fmpt
+ ss = np.zeros_like(T[0])
+ for i, mat in enumerate(T):
+ row_sum = mat.sum(axis=1)
+ row_sum = row_sum + (row_sum == 0)
+ p_i = np.matrix(np.diag(1. / row_sum) * np.matrix(mat))
+ #print i
+ #print mat
+ #print p_i
+ ss[i] = steady_state(p_i).transpose()
+ try:
+ F[i] = fmpt(p_i)
+ except:
+ #pylint; "No exception type(s) specified"
+ print "Singlular fmpt matrix for class ", i
+ P[i] = p_i
+ return T, P, ss, F
+
+ def _mn_test(self):
+ """
+ helper to calculate tests of differences between steady state
+ distributions from the conditional and overall distributions.
+ """
+ n, t = self.y.shape
+ nt = n * (t - 1)
+ n0, n1, n2 = self.T.shape
+ rn = range(n0)
+ mat = [self._ssmnp_test(
+ self.s, self.S[i], self.T[i].sum()) for i in rn]
+ return mat
+
+ def _ssmnp_test(self, p1, p2, nt):
+ """
+ Steady state multinomial probability difference test
+
+ Arguments
+ ---------
+
+ p1 : array (k, 1)
+ first steady state probability distribution
+
+ p1 : array (k, 1)
+ second steady state probability distribution
+
+ nt : int
+ number of transitions to base the test on
+
+
+ Returns
+ -------
+
+ implicit : tuple (3 elements)
+ (chi2 value, pvalue, degrees of freedom)
+
+ """
+ p1 = np.array(p1)
+ k, c = p1.shape
+ p1.shape = (k, )
+ o = nt * p2
+ e = nt * p1
+ d = np.multiply((o - e), (o - e))
+ d = d / e
+ chi2 = d.sum()
+ pvalue = 1 - stats.chi2.cdf(chi2, k - 1)
+ return (chi2, pvalue, k - 1)
+
+ def _chi2_test(self):
+ """
+ helper to calculate tests of differences between the conditional
+ transition matrices and the overall transitions matrix.
+ """
+ n, t = self.y.shape
+ n0, n1, n2 = self.T.shape
+ rn = range(n0)
+ mat = [chi2(self.T[i], self.transitions) for i in rn]
+ return mat
+
+ def summary(self, file_name=None):
+ class_names = ["C%d"%i for i in range(self.k)]
+ regime_names = ["LAG%d"%i for i in range(self.k)]
+ ht = homogeneity(self.T, class_names=class_names,
+ regime_names=regime_names)
+ title = "Spatial Markov Test"
+ if self.variable_name:
+ title = title + ": " + self.variable_name
+ if file_name:
+ ht.summary(file_name=file_name, title=title)
+ else:
+ ht.summary(title=title)
+
+
+
+def chi2(T1, T2):
+ """
+ chi-squared test of difference between two transition matrices.
+
+ Parameters
+ ----------
+
+ T1 : matrix (k, k)
+ matrix of transitions (counts)
+
+ T2 : matrix (k, k)
+ matrix of transitions (counts) to use to form the probabilities
+ under the null
+
+
+ Returns
+ -------
+
+ implicit : tuple (3 elements)
+ (chi2 value, pvalue, degrees of freedom)
+
+ Examples
+ --------
+
+ >>> import pysal
+ >>> f = pysal.open(pysal.examples.get_path("usjoin.csv"))
+ >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)]).transpose()
+ >>> rpci = pci/(pci.mean(axis=0))
+ >>> w = pysal.open(pysal.examples.get_path("states48.gal")).read()
+ >>> w.transform='r'
+ >>> sm = Spatial_Markov(rpci, w, fixed=True)
+ >>> T1 = sm.T[0]
+ >>> T1
+ array([[ 562., 22., 1., 0.],
+ [ 12., 201., 22., 0.],
+ [ 0., 17., 97., 4.],
+ [ 0., 0., 3., 19.]])
+ >>> T2 = sm.transitions
+ >>> T2
+ array([[ 884., 77., 4., 0.],
+ [ 68., 794., 87., 3.],
+ [ 1., 92., 815., 51.],
+ [ 1., 0., 60., 903.]])
+ >>> chi2(T1,T2)
+ (23.422628044813656, 0.0053137895983268457, 9)
+
+ Notes
+ -----
+
+ Second matrix is used to form the probabilities under the null.
+ Marginal sums from first matrix are distributed across these probabilities
+ under the null. In other words the observed transitions are taken from T1
+ while the expected transitions are formed as follows
+
+ .. math::
+
+ E_{i,j} = \sum_j T1_{i,j} * T2_{i,j}/\sum_j T2_{i,j}
+
+ Degrees of freedom corrected for any rows in either T1 or T2 that have
+ zero total transitions.
+ """
+ rs2 = T2.sum(axis=1)
+ rs1 = T1.sum(axis=1)
+ rs2nz = rs2 > 0
+ rs1nz = rs1 > 0
+ dof1 = sum(rs1nz)
+ dof2 = sum(rs2nz)
+ rs2 = rs2 + rs2nz
+ dof = (dof1 - 1) * (dof2 - 1)
+ p = np.diag(1 / rs2) * np.matrix(T2)
+ E = np.diag(rs1) * np.matrix(p)
+ num = T1 - E
+ num = np.multiply(num, num)
+ E = E + (E == 0)
+ chi2 = num / E
+ chi2 = chi2.sum()
+ pvalue = 1 - stats.chi2.cdf(chi2, dof)
+ return chi2, pvalue, dof
+
+
+class LISA_Markov(Markov):
+ """
+ Markov for Local Indicators of Spatial Association
+
+ Parameters
+ ----------
+
+ y : array (n,t)
+ n cross-sectional units observed over t time periods
+ w : weights instance
+ permutations : int
+ number of permutations used to determine LISA significance
+ default = 0
+ significance_level : float
+ significance level (two-sided) for filtering significant LISA end
+ points in a transition
+ default = 0.05
+ geoda_quads : boolean (default=False)
+ If True use GeoDa scheme: HH=1, LL=2, LH=3, HL=4
+ If False use PySAL Scheme: HH=1, LH=2, LL=3, HL=4
+
+ Attributes
+ ----------
+ chi_2 : tuple (3 elements)
+ chi square test statistic
+ p-value
+ degrees of freedom
+ for test that dynamics of y are independent of dynamics of wy
+ classes : array (4, 1)
+ 1=HH, 2=LH, 3=LL, 4=HL (own, lag)
+ 1=HH, 2=LL, 3=LH, 4=HL (own, lag) (if geoda_quads=True)
+ expected_t : array (4, 4)
+ expected number of transitions under the null that dynamics
+ of y are independent of dynamics of wy
+ move_types : matrix (n, t-1)
+ integer values indicating which type of LISA transition
+ occurred (q1 is quadrant in period 1, q2 is quadrant in
+ period 2)
+
+ .. Table:: Move Types
+
+ == == ========
+ q1 q2 move_type
+ == == ========
+ 1 1 1
+ 1 2 2
+ 1 3 3
+ 1 4 4
+ 2 1 5
+ 2 2 6
+ 2 3 7
+ 2 4 8
+ 3 1 9
+ 3 2 10
+ 3 3 11
+ 3 4 12
+ 4 1 13
+ 4 2 14
+ 4 3 15
+ 4 4 16
+ == == ========
+
+ p : matrix (k, k)
+ transition probability matrix
+ p_values : (if permutations > 0)
+ matrix (n, t)
+ LISA p-values for each end point
+ significant_moves : (if permutations > 0)
+ matrix (n, t-1)
+ integer values indicating the type and significance of a LISA
+ transition. st = 1 if significant in period t, else
+ st=0
+
+ .. Table:: Significant Moves
+
+ =============== ===================
+ (s1,s2) move_type
+ =============== ===================
+ (1,1) [1, 16]
+ (1,0) [17, 32]
+ (0,1) [33, 48]
+ (0,0) [49, 64]
+ =============== ===================
+
+
+ == == == == =========
+ q1 q2 s1 s2 move_type
+ == == == == =========
+ 1 1 1 1 1
+ 1 2 1 1 2
+ 1 3 1 1 3
+ 1 4 1 1 4
+ 2 1 1 1 5
+ 2 2 1 1 6
+ 2 3 1 1 7
+ 2 4 1 1 8
+ 3 1 1 1 9
+ 3 2 1 1 10
+ 3 3 1 1 11
+ 3 4 1 1 12
+ 4 1 1 1 13
+ 4 2 1 1 14
+ 4 3 1 1 15
+ 4 4 1 1 16
+ 1 1 1 0 17
+ 1 2 1 0 18
+ . . . . .
+ . . . . .
+ 4 3 1 0 31
+ 4 4 1 0 32
+ 1 1 0 1 33
+ 1 2 0 1 34
+ . . . . .
+ . . . . .
+ 4 3 0 1 47
+ 4 4 0 1 48
+ 1 1 0 0 49
+ 1 2 0 0 50
+ . . . . .
+ . . . . .
+ 4 3 0 0 63
+ 4 4 0 0 64
+ == == == == =========
+
+ steady_state : matrix (k, 1)
+ ergodic distribution
+ transitions : matrix (4, 4)
+ count of transitions between each state i and j
+ spillover : binary array (n, 1)
+ locations that were not part of a cluster in period 1 but
+ joined a prexisting cluster in period 2
+
+ Examples
+ --------
+
+ >>> import pysal as ps
+ >>> import numpy as np
+ >>> f = ps.open(ps.examples.get_path("usjoin.csv"))
+ >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)]).transpose()
+ >>> w = ps.open(ps.examples.get_path("states48.gal")).read()
+ >>> lm = ps.LISA_Markov(pci,w)
+ >>> lm.classes
+ array([1, 2, 3, 4])
+ >>> lm.steady_state
+ matrix([[ 0.28561505],
+ [ 0.14190226],
+ [ 0.40493672],
+ [ 0.16754598]])
+ >>> lm.transitions
+ array([[ 1.08700000e+03, 4.40000000e+01, 4.00000000e+00,
+ 3.40000000e+01],
+ [ 4.10000000e+01, 4.70000000e+02, 3.60000000e+01,
+ 1.00000000e+00],
+ [ 5.00000000e+00, 3.40000000e+01, 1.42200000e+03,
+ 3.90000000e+01],
+ [ 3.00000000e+01, 1.00000000e+00, 4.00000000e+01,
+ 5.52000000e+02]])
+ >>> lm.p
+ matrix([[ 0.92985458, 0.03763901, 0.00342173, 0.02908469],
+ [ 0.07481752, 0.85766423, 0.06569343, 0.00182482],
+ [ 0.00333333, 0.02266667, 0.948 , 0.026 ],
+ [ 0.04815409, 0.00160514, 0.06420546, 0.88603531]])
+ >>> lm.move_types
+ array([[11, 11, 11, ..., 11, 11, 11],
+ [ 6, 6, 6, ..., 6, 7, 11],
+ [11, 11, 11, ..., 11, 11, 11],
+ ...,
+ [ 6, 6, 6, ..., 6, 6, 6],
+ [ 1, 1, 1, ..., 6, 6, 6],
+ [16, 16, 16, ..., 16, 16, 16]])
+
+ Now consider only moves with one, or both, of the LISA end points being
+ significant
+
+ >>> np.random.seed(10)
+ >>> lm_random = pysal.LISA_Markov(pci, w, permutations=99)
+ >>> lm_random.significant_moves
+ array([[11, 11, 11, ..., 59, 59, 59],
+ [54, 54, 54, ..., 54, 55, 59],
+ [11, 11, 11, ..., 11, 59, 59],
+ ...,
+ [54, 54, 54, ..., 54, 54, 54],
+ [49, 49, 49, ..., 54, 54, 54],
+ [64, 64, 64, ..., 64, 64, 64]])
+
+ Any value less than 49 indicates at least one of the LISA end points was
+ significant. So for example, the first spatial unit experienced a
+ transition of type 11 (LL, LL) during the first three and last tree
+ intervals (according to lm.move_types), however, the last three of these
+ transitions involved insignificant LISAS in both the start and ending year
+ of each transition.
+
+ Test whether the moves of y are independent of the moves of wy
+
+ >>> "Chi2: %8.3f, p: %5.2f, dof: %d" % lm.chi_2
+ 'Chi2: 162.475, p: 0.00, dof: 9'
+
+ Actual transitions of LISAs
+
+ >>> lm.transitions
+ array([[ 1.08700000e+03, 4.40000000e+01, 4.00000000e+00,
+ 3.40000000e+01],
+ [ 4.10000000e+01, 4.70000000e+02, 3.60000000e+01,
+ 1.00000000e+00],
+ [ 5.00000000e+00, 3.40000000e+01, 1.42200000e+03,
+ 3.90000000e+01],
+ [ 3.00000000e+01, 1.00000000e+00, 4.00000000e+01,
+ 5.52000000e+02]])
+
+ Expected transitions of LISAs under the null y and wy are moving
+ independently of one another
+
+ >>> lm.expected_t
+ array([[ 1.12328098e+03, 1.15377356e+01, 3.47522158e-01,
+ 3.38337644e+01],
+ [ 3.50272664e+00, 5.28473882e+02, 1.59178880e+01,
+ 1.05503814e-01],
+ [ 1.53878082e-01, 2.32163556e+01, 1.46690710e+03,
+ 9.72266513e+00],
+ [ 9.60775143e+00, 9.86856346e-02, 6.23537392e+00,
+ 6.07058189e+02]])
+
+ If the LISA classes are to be defined according to GeoDa, the `geoda_quad`
+ option has to be set to true
+
+ >>> lm.q[0:5,0]
+ array([3, 2, 3, 1, 4])
+ >>> lm = ps.LISA_Markov(pci,w, geoda_quads=True)
+ >>> lm.q[0:5,0]
+ array([2, 3, 2, 1, 4])
+
+ """
+ def __init__(self, y, w, permutations=0,
+ significance_level=0.05, geoda_quads=False):
+ y = y.transpose()
+ pml = pysal.Moran_Local
+ gq = geoda_quads
+
+ #################################################################
+ # have to optimize conditional spatial permutations over a
+ # time series - this is a place holder for the foreclosure paper
+ ml = [pml(yi, w, permutations=permutations, geoda_quads=gq) for yi in y]
+ #################################################################
+
+ q = np.array([mli.q for mli in ml]).transpose()
+ classes = np.arange(1, 5) # no guarantee all 4 quadrants are visited
+ Markov.__init__(self, q, classes)
+ self.q = q
+ self.w = w
+ n, k = q.shape
+ k -= 1
+ self.significance_level = significance_level
+ move_types = np.zeros((n, k), int)
+ sm = np.zeros((n, k), int)
+ self.significance_level = significance_level
+ if permutations > 0:
+ p = np.array([mli.p_z_sim for mli in ml]).transpose()
+ self.p_values = p
+ pb = p <= significance_level
+ else:
+ pb = np.zeros_like(y.T)
+ for t in range(k):
+ origin = q[:, t]
+ dest = q[:, t + 1]
+ p_origin = pb[:, t]
+ p_dest = pb[:, t]
+ for r in range(n):
+ move_types[r, t] = TT[origin[r], dest[r]]
+ key = (origin[r], dest[r], p_origin[r], p_dest[r])
+ sm[r, t] = MOVE_TYPES[key]
+ if permutations > 0:
+ self.significant_moves = sm
+ self.move_types = move_types
+
+ # null of own and lag moves being independent
+
+ ybar = y.mean(axis=0)
+ r = y / ybar
+ ylag = np.array([pysal.lag_spatial(w, yt) for yt in y])
+ rlag = ylag / ybar
+ rc = r < 1.
+ rlagc = rlag < 1.
+ markov_y = pysal.Markov(rc)
+ markov_ylag = pysal.Markov(rlagc)
+ A = np.matrix([[1, 0, 0, 0],
+ [0, 0, 1, 0],
+ [0, 0, 0, 1],
+ [0, 1, 0, 0]])
+
+ kp = A * np.kron(markov_y.p, markov_ylag.p) * A.T
+ trans = self.transitions.sum(axis=1)
+ t1 = np.diag(trans) * kp
+ t2 = self.transitions
+ t1 = t1.getA()
+ self.chi_2 = pysal.spatial_dynamics.markov.chi2(t1, t2)
+ self.expected_t = t1
+ self.permutations = permutations
+
+ def spillover(self, quadrant=1, neighbors_on=False):
+ """
+ Detect spillover locations for diffusion in LISA Markov
+
+ Parameters
+ ----------
+ quadrant : int
+ which quadrant in the scatterplot should form the core of a
+ cluster
+
+ neighbors_on : binary
+ If false then only the 1st order neighbors of a core
+ location are included in the cluster.
+ If true, neighbors of cluster core 1st order neighbors are
+ included in the cluster
+
+ Returns
+ -------
+ dictionary : two keys - values pairs
+ 'components' - array (n, t)
+ values are integer ids (starting at 1) indicating which
+ component/cluster observation i in period t belonged to
+ 'spillover' - array (n, t-1)
+ binary values indicating if the location was a spill-over
+ location that became a new member of a previously existing
+ cluster
+
+ Examples
+ --------
+
+ >>> f = pysal.open(pysal.examples.get_path("usjoin.csv"))
+ >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)]).transpose()
+ >>> w = pysal.open(pysal.examples.get_path("states48.gal")).read()
+ >>> np.random.seed(10)
+ >>> lm_random = pysal.LISA_Markov(pci, w, permutations=99)
+ >>> r = lm_random.spillover()
+ >>> r['components'][:,12]
+ array([ 0., 1., 0., 1., 0., 2., 2., 0., 0., 0., 0., 0., 0.,
+ 0., 0., 0., 0., 2., 2., 0., 0., 0., 0., 0., 0., 1.,
+ 2., 2., 0., 2., 0., 0., 0., 0., 1., 2., 2., 0., 0.,
+ 0., 0., 0., 2., 0., 0., 0., 0., 0.])
+ >>> r['components'][:,13]
+ array([ 0., 2., 0., 2., 0., 1., 1., 0., 0., 2., 0., 0., 0.,
+ 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0., 2.,
+ 0., 1., 0., 1., 0., 0., 0., 0., 2., 1., 1., 0., 0.,
+ 0., 0., 2., 1., 0., 2., 0., 0., 0.])
+ >>> r['spill_over'][:,12]
+ array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,
+ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
+ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
+ 0., 0., 1., 0., 0., 1., 0., 0., 0.])
+
+ Including neighbors of core neighbors
+
+ >>> rn = lm_random.spillover(neighbors_on=True)
+ >>> rn['components'][:,12]
+ array([ 0., 2., 0., 2., 2., 1., 1., 0., 0., 2., 0., 0., 0.,
+ 0., 0., 0., 1., 1., 1., 0., 0., 0., 0., 0., 0., 2.,
+ 1., 1., 2., 1., 0., 0., 1., 0., 2., 1., 1., 0., 0.,
+ 0., 0., 2., 1., 1., 2., 1., 0., 0.])
+ >>> rn["components"][:,13]
+ array([ 0., 2., 0., 2., 2., 1., 1., 0., 0., 2., 0., 0., 0.,
+ 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 2., 0., 2.,
+ 1., 1., 2., 1., 0., 0., 1., 0., 2., 1., 1., 0., 0.,
+ 0., 0., 2., 1., 1., 2., 1., 0., 2.])
+ >>> rn["spill_over"][:,12]
+ array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
+ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,
+ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
+ 0., 0., 0., 0., 0., 0., 0., 0., 1.])
+
+
+ """
+ n, k = self.q.shape
+ if self.permutations:
+ spill_over = np.zeros((n, k - 1))
+ components = np.zeros((n, k))
+ i2id = {} # handle string keys
+ for key in self.w.neighbors.keys():
+ id = self.w.id2i[key] # pylint "redefining built-in 'id'
+ i2id[id] = key
+ sig_lisas = (self.q == quadrant) \
+ * (self.p_values <= self.significance_level)
+ sig_ids = [np.nonzero(
+ sig_lisas[:, i])[0].tolist() for i in range(k)]
+
+ neighbors = self.w.neighbors
+ for t in range(k - 1):
+ s1 = sig_ids[t]
+ s2 = sig_ids[t + 1]
+ g1 = pysal.region.components.Graph(undirected=True)
+ for i in s1:
+ for neighbor in neighbors[i2id[i]]:
+ g1.add_edge(i2id[i], neighbor, 1.0)
+ if neighbors_on:
+ for nn in neighbors[neighbor]:
+ g1.add_edge(neighbor, nn, 1.0)
+ components1 = g1.connected_components(op=gt)
+ components1 = [list(c.nodes) for c in components1]
+ g2 = pysal.region.components.Graph(undirected=True)
+ for i in s2:
+ for neighbor in neighbors[i2id[i]]:
+ g2.add_edge(i2id[i], neighbor, 1.0)
+ if neighbors_on:
+ for nn in neighbors[neighbor]:
+ g2.add_edge(neighbor, nn, 1.0)
+ components2 = g2.connected_components(op=gt)
+ components2 = [list(c.nodes) for c in components2]
+ c2 = []
+ c1 = []
+ for c in components2:
+ c2.extend(c)
+ for c in components1:
+ c1.extend(c)
+
+ new_ids = [j for j in c2 if j not in c1]
+ spill_ids = []
+ for j in new_ids:
+ # find j's component in period 2
+ cj = [c for c in components2 if j in c][0]
+ # for members of j's component in period 2, check if they belonged to
+ # any components in period 1
+ for i in cj:
+ if i in c1:
+ spill_ids.append(j)
+ break
+ for spill_id in spill_ids:
+ id = self.w.id2i[spill_id]
+ spill_over[id, t] = 1
+ for c, component in enumerate(components1):
+ for i in component:
+ ii = self.w.id2i[i]
+ components[ii, t] = c + 1
+ results = {}
+ results['components'] = components
+ results['spill_over'] = spill_over
+ return results
+
+ else:
+ return None
+
+
+def kullback(F):
+ """
+ Kullback information based test of Markov Homogeneity
+
+ Parameters
+ ----------
+ F : array (s, r, r)
+ Values are transitions (not probabilities) for
+ s strata
+ r initial states
+ r terminal states
+
+ Returns
+ -------
+
+ Results : Dictionary (key - value)
+
+ Conditional homogeneity - (float) test statistic for homogeneity of
+ transition probabilities across strata
+
+ Conditional homogeneity pvalue - (float) p-value for test statistic
+
+ Conditional homogeneity dof - (int) degrees of freedom = r(s-1)(r-1)
+
+ Notes
+ -----
+
+ Based on Kullback, Kupperman and Ku (1962) [2]_
+ Example below is taken from Table 9.2
+
+ Examples
+ --------
+
+ >>> s1 = np.array([
+ ... [ 22, 11, 24, 2, 2, 7],
+ ... [ 5, 23, 15, 3, 42, 6],
+ ... [ 4, 21, 190, 25, 20, 34],
+ ... [0, 2, 14, 56, 14, 28],
+ ... [32, 15, 20, 10, 56, 14],
+ ... [5, 22, 31, 18, 13, 134]
+ ... ])
+ >>> s2 = np.array([
+ ... [3, 6, 9, 3, 0, 8],
+ ... [1, 9, 3, 12, 27, 5],
+ ... [2, 9, 208, 32, 5, 18],
+ ... [0, 14, 32, 108, 40, 40],
+ ... [22, 14, 9, 26, 224, 14],
+ ... [1, 5, 13, 53, 13, 116]
+ ... ])
+ >>>
+ >>> F = np.array([s1, s2])
+ >>> res = kullback(F)
+ >>> "%8.3f"%res['Conditional homogeneity']
+ ' 160.961'
+ >>> "%d"%res['Conditional homogeneity dof']
+ '30'
+ >>> "%3.1f"%res['Conditional homogeneity pvalue']
+ '0.0'
+
+ References
+ ----------
+ .. [2] Kullback, S. Kupperman, M. and H.H. Ku. (1962) "Tests for contigency tables and Markov chains", Technometrics: 4, 573--608.
+
+ """
+
+ F1 = F == 0
+ F1 = F + F1
+ FLF = F * np.log(F1)
+ T1 = 2 * FLF.sum()
+
+ FdJK = F.sum(axis=0)
+ FdJK1 = FdJK + (FdJK == 0)
+ FdJKLFdJK = FdJK * np.log(FdJK1)
+ T2 = 2 * FdJKLFdJK.sum()
+
+ FdJd = F.sum(axis=0).sum(axis=1)
+ FdJd1 = FdJd + (FdJd == 0)
+ T3 = 2 * (FdJd * np.log(FdJd1)).sum()
+
+ FIJd = F[:, :].sum(axis=1)
+ FIJd1 = FIJd + (FIJd == 0)
+ T4 = 2 * (FIJd * np.log(FIJd1)).sum()
+
+ FIdd = F.sum(axis=1).sum(axis=1)
+ T5 = 2 * (FIdd * np.log(FIdd)).sum()
+
+ T6 = F.sum()
+ T6 = 2 * T6 * np.log(T6)
+
+ s, r, r1 = F.shape
+ chom = T1 - T4 - T2 + T3
+ cdof = r * (s - 1) * (r - 1)
+ results = {}
+ results['Conditional homogeneity'] = chom
+ results['Conditional homogeneity dof'] = cdof
+ results['Conditional homogeneity pvalue'] = 1 - stats.chi2.cdf(chom, cdof)
+ return results
+
+
+def prais(pmat):
+ """
+ Prais conditional mobility measure
+
+ Parameters
+ ----------
+
+ pmat : kxk matrix
+ Markov probability transition matrix
+
+ Returns
+ -------
+
+ pr : 1xk matrix
+ Conditional mobility measures for each of the k classes.
+
+ Notes
+ -----
+
+ Prais' conditional mobility measure for a class is defined as:
+
+ .. math::
+
+ pr_i = 1 - p_{i,i}
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> f = pysal.open(pysal.examples.get_path("usjoin.csv"))
+ >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)])
+ >>> q5 = np.array([pysal.Quantiles(y).yb for y in pci]).transpose()
+ >>> m = pysal.Markov(q5)
+ >>> m.transitions
+ array([[ 729., 71., 1., 0., 0.],
+ [ 72., 567., 80., 3., 0.],
+ [ 0., 81., 631., 86., 2.],
+ [ 0., 3., 86., 573., 56.],
+ [ 0., 0., 1., 57., 741.]])
+ >>> m.p
+ matrix([[ 0.91011236, 0.0886392 , 0.00124844, 0. , 0. ],
+ [ 0.09972299, 0.78531856, 0.11080332, 0.00415512, 0. ],
+ [ 0. , 0.10125 , 0.78875 , 0.1075 , 0.0025 ],
+ [ 0. , 0.00417827, 0.11977716, 0.79805014, 0.07799443],
+ [ 0. , 0. , 0.00125156, 0.07133917, 0.92740926]])
+ >>> pysal.spatial_dynamics.markov.prais(m.p)
+ matrix([[ 0.08988764, 0.21468144, 0.21125 , 0.20194986, 0.07259074]])
+
+ """
+ pr = (pmat.sum(axis=1) - np.diag(pmat))[0]
+ return pr
+
+
+def shorrock(pmat):
+ """
+ Shorrock's mobility measure
+
+ Parameters
+ ----------
+
+ pmat : kxk matrix
+ Markov probability transition matrix
+
+ Returns
+ -------
+
+ sh : scalar
+ Shorrock mobility measure
+
+
+
+ Notes
+ -----
+
+ Shorock's mobility measure is defined as
+
+ .. math::
+
+ sh = (k - \sum_{j=1}^{k} p_{j,j})/(k - 1)
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> f = pysal.open(pysal.examples.get_path("usjoin.csv"))
+ >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)])
+ >>> q5 = np.array([pysal.Quantiles(y).yb for y in pci]).transpose()
+ >>> m = pysal.Markov(q5)
+ >>> m.transitions
+ array([[ 729., 71., 1., 0., 0.],
+ [ 72., 567., 80., 3., 0.],
+ [ 0., 81., 631., 86., 2.],
+ [ 0., 3., 86., 573., 56.],
+ [ 0., 0., 1., 57., 741.]])
+ >>> m.p
+ matrix([[ 0.91011236, 0.0886392 , 0.00124844, 0. , 0. ],
+ [ 0.09972299, 0.78531856, 0.11080332, 0.00415512, 0. ],
+ [ 0. , 0.10125 , 0.78875 , 0.1075 , 0.0025 ],
+ [ 0. , 0.00417827, 0.11977716, 0.79805014, 0.07799443],
+ [ 0. , 0. , 0.00125156, 0.07133917, 0.92740926]])
+ >>> pysal.spatial_dynamics.markov.shorrock(m.p)
+ 0.19758992000997844
+
+ """
+ t = np.trace(pmat)
+ k = pmat.shape[1]
+ sh = (k - t) / (k - 1)
+ return sh
+
+def homogeneity(transition_matrices, regime_names=[], class_names=[], \
+ title="Markov Homogeneity Test"):
+ """
+ Test for homogeneity of Markov transition probabilities across regimes.
+
+ Parameters
+ ----------
+
+ transition_matrices: list of transition matrices for regimes
+ all matrices must have same size (r,c)
+ r is the number of rows in
+ the transition matrix and c is the number of columns
+ in the transition matrix.
+
+ regime_names: sequence
+ Labels for the regimes
+
+ class_names: sequence
+ Labels for the classes/states of the Markov chain
+
+ title: string
+ name of test
+
+ Returns
+ -------
+
+ implicit: an instance of Homogeneity_Results
+ """
+
+ return Homogeneity_Results(transition_matrices, regime_names=regime_names,
+ class_names= class_names,
+ title=title)
+
+class Homogeneity_Results:
+ """
+ Wrapper class to present homogeneity results
+
+ Parameters
+ ----------
+
+ transition_matrices: list of transition matrices for regimes
+ all matrices must have same size (r,c)
+ r is the number of rows in
+ the transition matrix and c is the number of columns
+ in the transition matrix.
+
+ regime_names: sequence
+ Labels for the regimes
+
+ class_names: sequence
+ Labels for the classes/states of the Markov chain
+ title: string
+ Title of the table
+
+ Notes
+ -----
+ Degrees of freedom adjustment follow the approach in Bickenbach and Bode (2003) [3]_
+
+ Examples
+ --------
+ See Spatial_Markov above.
+
+ """
+
+ def __init__(self, transition_matrices, regime_names=[], class_names = [],
+ title="Markov Homogeneity Test"):
+ self._homogeneity(transition_matrices)
+ self.regime_names=regime_names
+ self.class_names = class_names
+ self.title = title
+
+ def _homogeneity(self, transition_matrices):
+ # form null transition probability matrix
+ M = np.array(transition_matrices)
+ m,r,k = M.shape
+ self.k = k
+ B = np.zeros((r,m))
+ T = M.sum(axis=0)
+ self.t_total = T.sum()
+ n_i = T.sum(axis=1)
+ A_i = (T>0).sum(axis=1)
+ A_im = np.zeros((r,m))
+ p_ij = np.dot(np.diag(1./(n_i + (n_i==0)*1.)), T)
+ den = p_ij + 1. * (p_ij==0)
+ b_i = np.zeros_like(A_i)
+ p_ijm = np.zeros_like(M)
+ # get dimensions
+ m, n_rows, n_cols = M.shape
+ m = 0
+ Q = 0.0
+ LR = 0.0
+ lr_table = np.zeros_like(M)
+ q_table = np.zeros_like(M)
+
+ for nijm in M:
+ nim = nijm.sum(axis=1)
+ B[:,m] = 1.*(nim>0)
+ b_i = b_i + 1. * (nim>0)
+ p_ijm[m] = np.dot(np.diag(1./(nim + (nim==0)*1.)),nijm)
+ num = (p_ijm[m]-p_ij)**2
+ ratio = num / den
+ qijm = np.dot(np.diag(nim), ratio)
+ q_table[m] = qijm
+ Q = Q + qijm.sum()
+ # only use nonzero pijm in lr test
+ mask = (nijm > 0) * (p_ij > 0)
+ A_im[:,m] = (nijm>0).sum(axis=1)
+ unmask = 1.0 * (mask==0)
+ ratio = (mask * p_ijm[m] + unmask) / (mask * p_ij + unmask)
+ lr = nijm * np.log(ratio)
+ LR = LR + lr.sum()
+ lr_table[m] = 2 * lr
+ m += 1
+ # b_i is the number of regimes that have non-zero observations in row i
+ # A_i is the number of non-zero elements in row i of the aggregated
+ # transition matrix
+ self.dof = int(((b_i-1) * (A_i-1)).sum())
+ self.Q = Q
+ self.Q_p_value = 1 - stats.chi2.cdf(self.Q, self.dof)
+ self.LR = LR * 2.
+ self.LR_p_value = 1 - stats.chi2.cdf(self.LR, self.dof)
+ self.A = A_i
+ self.A_im = A_im
+ self.B = B
+ self.b_i = b_i
+ self.LR_table = lr_table
+ self.Q_table = q_table
+ self.m = m
+ self.p_h0 = p_ij
+ self.p_h1 = p_ijm
+
+ def summary(self, file_name=None, title="Markov Homogeneity Test"):
+ regime_names = ["%d"%i for i in range(self.m)]
+ if self.regime_names:
+ regime_names = self.regime_names
+ cols = ["P(%s)"%str(regime) for regime in regime_names]
+ if not self.class_names:
+ self.class_names = range(self.k)
+
+ max_col = max([len(col) for col in cols])
+ col_width = max([5, max_col]) #probabilities have 5 chars
+ n_tabs = self.k
+ width = n_tabs * 4 + (self.k+1)*col_width
+ lead = "-"* width
+ head = title.center(width)
+ contents = [lead,head,lead]
+ l = "Number of regimes: %d" % int(self.m)
+ k = "Number of classes: %d" % int(self.k)
+ r = "Regime names: "
+ r += ", ".join(regime_names)
+ t = "Number of transitions: %d" % int(self.t_total)
+ contents.append(k)
+ contents.append(t)
+ contents.append(l)
+ contents.append(r)
+ contents.append(lead)
+ h = "%7s %20s %20s"%('Test', 'LR', 'Chi-2')
+ contents.append(h)
+ stat = "%7s %20.3f %20.3f"%('Stat.', self.LR, self.Q)
+ contents.append(stat)
+ stat = "%7s %20d %20d"%('DOF', self.dof, self.dof)
+ contents.append(stat)
+ stat = "%7s %20.3f %20.3f"%('p-value', self.LR_p_value,
+ self.Q_p_value)
+ contents.append(stat)
+ print "\n".join(contents)
+ print lead
+
+ cols = ["P(%s)"%str(regime) for regime in self.regime_names]
+ if not self.class_names:
+ self.class_names = range(self.k)
+ cols.extend(["%s"%str(cname) for cname in self.class_names])
+
+ max_col = max([len(col) for col in cols])
+ col_width = max([5, max_col]) #probabilities have 5 chars
+ p0 = []
+ line0 = [ '{s: <{w}}'.format(s="P(H0)",w=col_width) ]
+ line0.extend([ '{s: >{w}}'.format(s=cname,w=col_width) for cname in self.class_names])
+ print " ".join(line0)
+ p0.append("&".join(line0))
+ for i,row in enumerate(self.p_h0):
+ line = ["%*s"%(col_width, str(self.class_names[i]))]
+ line.extend(["%*.3f"%(col_width,v) for v in row])
+ print " ".join(line)
+ p0.append("&".join(line))
+ pmats = [p0]
+
+ print lead
+ for r, p1 in enumerate(self.p_h1):
+ p0 = []
+ line0 = [ '{s: <{w}}'.format(s="P(%s)"%regime_names[r],w=col_width) ]
+ line0.extend([ '{s: >{w}}'.format(s=cname,w=col_width) for cname in self.class_names])
+ print " ".join(line0)
+ p0.append("&".join(line0))
+ for i,row in enumerate(p1):
+ line = ["%*s"%(col_width, str(self.class_names[i]))]
+ line.extend(["%*.3f"%(col_width,v) for v in row])
+ print " ".join(line)
+ p0.append("&".join(line))
+ pmats.append(p0)
+ print lead
+
+ if file_name:
+ k = self.k
+ ks = str(k+1)
+ with open(file_name, 'w') as f:
+ c = []
+ fmt = "r"*(k+1)
+ s="\\begin{tabular}{|%s|}\\hline\n"%fmt
+ s+= "\\multicolumn{%s}{|c|}{%s}"%(ks,title)
+ c.append(s)
+ s = "Number of classes: %d"%int(self.k)
+ c.append("\\hline\\multicolumn{%s}{|l|}{%s}"%(ks,s))
+ s = "Number of transitions: %d"%int(self.t_total)
+ c.append("\\multicolumn{%s}{|l|}{%s}"%(ks,s))
+ s = "Number of regimes: %d"%int(self.m)
+ c.append("\\multicolumn{%s}{|l|}{%s}"%(ks,s))
+ s = "Regime names: "
+ s += ", ".join(regime_names)
+ c.append("\\multicolumn{%s}{|l|}{%s}"%(ks,s))
+ s = "\\hline\\multicolumn{2}{|l}{%s}"%("Test")
+ s += "&\\multicolumn{2}{r}{LR}&\\multicolumn{2}{r|}{Q}"
+ c.append(s)
+ s = "Stat."
+ s = "\\multicolumn{2}{|l}{%s}"%(s)
+ s += "&\\multicolumn{2}{r}{%.3f}"%self.LR
+ s += "&\\multicolumn{2}{r|}{%.3f}"%self.Q
+ c.append(s)
+ s = "\\multicolumn{2}{|l}{%s}"%("DOF")
+ s += "&\\multicolumn{2}{r}{%d}"%int(self.dof)
+ s += "&\\multicolumn{2}{r|}{%d}"%int(self.dof)
+ c.append(s)
+ s = "\\multicolumn{2}{|l}{%s}"%("p-value")
+ s += "&\\multicolumn{2}{r}{%.3f}"%self.LR_p_value
+ s += "&\\multicolumn{2}{r|}{%.3f}"%self.Q_p_value
+ c.append(s)
+ s1 = "\\\\\n".join(c)
+ s1 += "\\\\\n"
+ c = []
+ for mat in pmats:
+ c.append("\\hline\n")
+ for row in mat:
+ c.append(row+"\\\\\n")
+ c.append("\\hline\n")
+ c.append("\\end{tabular}")
+ s2 = "".join(c)
+ f.write(s1+s2)
+
diff --git a/pysal/spatial_dynamics/rank.py b/pysal/spatial_dynamics/rank.py
new file mode 100644
index 0000000..2db2af0
--- /dev/null
+++ b/pysal/spatial_dynamics/rank.py
@@ -0,0 +1,443 @@
+"""
+Rank and spatial rank mobility measures
+"""
+__author__ = "Sergio J. Rey <srey at asu.edu> "
+
+#from pysal.common import *
+from scipy.stats.mstats import rankdata
+from scipy.special import erfc
+import pysal
+import numpy as np
+import scipy as sp
+from numpy.random import permutation as NRP
+
+__all__ = ['SpatialTau', 'Tau', 'Theta', ]
+
+
+class Theta:
+ """
+ Regime mobility measure
+
+ For sequence of time periods Theta measures the extent to which rank
+ changes for a variable measured over n locations are in the same direction
+ within mutually exclusive and exhaustive partitions (regimes) of the n locations.
+
+ Theta is defined as the sum of the absolute sum of rank changes within
+ the regimes over the sum of all absolute rank changes.
+
+
+ Parameters
+ ----------
+ y : array (n,k) with k>=2
+ successive columns of y are later moments in time (years,
+ months,etc)
+ regime : array (n,)
+ values corresponding to which regime each observation belongs to
+ permutations : int
+ number of random spatial permutations to generate for
+ computationally based inference
+
+ Attributes
+ ----------
+ ranks : array
+ ranks of the original y array (by columns)
+ regimes : array
+ the original regimes array
+ total : array (k-1,)
+ the total number of rank changes for each of the k periods
+ max_total : int
+ the theoretical maximum number of rank changes for n
+ observations
+ theta : array (k-1,)
+ the theta statistic for each of the k-1 intervals
+ permutations : int
+ the number of permutations
+ pvalue_left : float
+ p-value for test that observed theta is significantly lower
+ than its expectation under complete spatial randomness
+ pvalue_right : float
+ p-value for test that observed theta is significantly
+ greater than its expectation under complete spatial randomness
+
+
+ References
+ ----------
+ Rey, S.J. (2004) "Spatial dependence in the evolution of regional income
+ distributions," in A. Getis, J. Mur and H.Zoeller (eds). Spatial
+ Econometrics and Spatial Statistics. Palgrave, London, pp. 194-213.
+
+
+ Examples
+ --------
+ >>> import pysal
+ >>> f=pysal.open(pysal.examples.get_path("mexico.csv"))
+ >>> vnames=["pcgdp%d"%dec for dec in range(1940,2010,10)]
+ >>> y=np.transpose(np.array([f.by_col[v] for v in vnames]))
+ >>> regime=np.array(f.by_col['esquivel99'])
+ >>> np.random.seed(10)
+ >>> t=Theta(y,regime,999)
+ >>> t.theta
+ array([[ 0.41538462, 0.28070175, 0.61363636, 0.62222222, 0.33333333,
+ 0.47222222]])
+ >>> t.pvalue_left
+ array([ 0.307, 0.077, 0.823, 0.552, 0.045, 0.735])
+ >>> t.total
+ array([ 130., 114., 88., 90., 90., 72.])
+ >>> t.max_total
+ 512
+ >>>
+ """
+ def __init__(self, y, regime, permutations=999):
+ ranks = rankdata(y, axis=0)
+ self.ranks = ranks
+ n, k = y.shape
+ ranks_d = ranks[:, range(1, k)] - ranks[:, range(k - 1)]
+ self.ranks_d = ranks_d
+ regimes = sp.unique(regime)
+ self.regimes = regimes
+ self.total = sum(abs(ranks_d))
+ self.max_total = sum([abs(i - n + i - 1) for i in range(1, n + 1)])
+ self._calc(regime)
+ self.theta = self._calc(regime)
+ self.permutations = permutations
+ if permutations:
+ np.perm = np.random.permutation
+ sim = np.array([self._calc(
+ np.perm(regime)) for i in xrange(permutations)])
+ self.theta.shape = (1, len(self.theta))
+ sim = np.concatenate((self.theta, sim))
+ self.sim = sim
+ den = permutations + 1.
+ self.pvalue_left = (sim <= sim[0]).sum(axis=0) / den
+ self.pvalue_right = (sim > sim[0]).sum(axis=0) / den
+ self.z = (sim[0] - sim.mean(axis=0)) / sim.std(axis=0)
+
+ def _calc(self, regime):
+ within = [abs(
+ sum(self.ranks_d[regime == reg])) for reg in self.regimes]
+ return np.array(sum(within) / self.total)
+
+
+class Tau:
+ """
+ Kendall's Tau is based on a comparison of the number of pairs of n
+ observations that have concordant ranks between two variables.
+
+ Parameters
+ ----------
+ x : array (n,)
+ first variable
+ y : array (n,)
+ second variable
+
+ Attributes
+ ----------
+ tau : float
+ The classic Tau statistic
+
+ tau_p : float
+ asymptotic p-value
+
+ Notes
+ -----
+
+ Modification of algorithm suggested by Christensen (2005).
+ PySAL implementation uses a list based representation of a binary tree for
+ the accumulation of the concordance measures. Ties are handled by this
+ implementation (in other words, if there are ties in either x, or y, or
+ both, the calculation returns Tau_b, if no ties classic Tau is returned.)
+
+ References
+ ----------
+
+ Christensen, D. (2005) Fast algorithms for the calculation of
+ Kendall's tau. Computational Statistics, 20: 51-62.
+
+
+ Examples
+ --------
+
+ # from scipy example
+
+ >>> from scipy.stats import kendalltau
+ >>> x1 = [12, 2, 1, 12, 2]
+ >>> x2 = [1, 4, 7, 1, 0]
+ >>> kt = Tau(x1,x2)
+ >>> kt.tau
+ -0.47140452079103173
+ >>> kt.tau_p
+ 0.24821309157521476
+ >>> skt = kendalltau(x1,x2)
+ >>> skt
+ (-0.47140452079103173, 0.24821309157521476)
+
+ """
+
+ def __init__(self, x, y):
+ res = self._calc(x, y)
+ self.tau = res[0]
+ self.tau_p = res[1]
+ self.concordant = res[2]
+ self.discordant = res[3]
+ self.extraX = res[4]
+ self.extraY = res[5]
+
+ def _calc(self, x, y):
+ """
+ List based implementation of binary tree algorithm for concordance
+ measure after Christensen (2005).
+
+ """
+ x = np.array(x)
+ y = np.array(y)
+ n = len(y)
+ perm = range(n)
+ perm.sort(key=lambda a: (x[a], y[a]))
+ vals = y[perm]
+ ExtraY = 0
+ ExtraX = 0
+ ACount = 0
+ BCount = 0
+ CCount = 0
+ DCount = 0
+ ECount = 0
+ DCount = 0
+ Concordant = 0
+ Discordant = 0
+ # ids for left child
+ li = [None] * (n - 1)
+ # ids for right child
+ ri = [None] * (n - 1)
+ # number of left descendants for a node
+ ld = np.zeros(n)
+ # number of values equal to value i
+ nequal = np.zeros(n)
+
+ for i in range(1, n):
+ NumBefore = 0
+ NumEqual = 1
+ root = 0
+ x0 = x[perm[i - 1]]
+ y0 = y[perm[i - 1]]
+ x1 = x[perm[i]]
+ y1 = y[perm[i]]
+ if x0 != x1:
+ DCount = 0
+ ECount = 1
+ else:
+ if y0 == y1:
+ ECount += 1
+ else:
+ DCount += ECount
+ ECount = 1
+ root = 0
+ inserting = True
+ while inserting:
+ current = y[perm[i]]
+ if current > y[perm[root]]:
+ # right branch
+ NumBefore += 1 + ld[root] + nequal[root]
+ if ri[root] is None:
+ # insert as right child to root
+ ri[root] = i
+ inserting = False
+ else:
+ root = ri[root]
+ elif current < y[perm[root]]:
+ # increment number of left descendants
+ ld[root] += 1
+ if li[root] is None:
+ # insert as left child to root
+ li[root] = i
+ inserting = False
+ else:
+ root = li[root]
+ elif current == y[perm[root]]:
+ NumBefore += ld[root]
+ NumEqual += nequal[root] + 1
+ nequal[root] += 1
+ inserting = False
+
+ ACount = NumBefore - DCount
+ BCount = NumEqual - ECount
+ CCount = i - (ACount + BCount + DCount + ECount - 1)
+ ExtraY += DCount
+ ExtraX += BCount
+ Concordant += ACount
+ Discordant += CCount
+
+ cd = Concordant + Discordant
+ num = Concordant - Discordant
+ tau = num / np.sqrt((cd + ExtraX) * (cd + ExtraY))
+ v = (4. * n + 10) / (9. * n * (n - 1))
+ z = tau / np.sqrt(v)
+ pval = erfc(np.abs(z) / 1.4142136) # follow scipy
+ return tau, pval, Concordant, Discordant, ExtraX, ExtraY
+
+
+class SpatialTau:
+ """
+ Spatial version of Kendall's rank correlation statistic
+
+ Kendall's Tau is based on a comparison of the number of pairs of n
+ observations that have concordant ranks between two variables. The spatial
+ Tau decomposes these pairs into those that are spatial neighbors and those
+ that are not, and examines whether the rank correlation is different
+ between the two sets relative to what would be expected under spatial randomness.
+
+ Parameters
+ ----------
+ x : array (n,)
+ first variable
+ y : array (n,)
+ second variable
+ w : W
+ spatial weights object
+ permutations : int
+ number of random spatial permutations for computationally
+ based inference
+
+ Attributes
+ ----------
+ tau : float
+ The classic Tau statistic
+ tau_spatial : float
+ Value of Tau for pairs that are spatial neighbors
+ taus : array (permtuations x 1)
+ Values of simulated tau_spatial values under random spatial permutations in both periods. (Same permutation used for start and ending period).
+ pairs_spatial : int
+ Number of spatial pairs
+ concordant : float
+ Number of concordant pairs
+ concordant_spatial : float
+ Number of concordant pairs that are spatial neighbors
+ extraX : float
+ Number of extra X pairs
+ extraY : float
+ Number of extra Y pairs
+ discordant : float
+ Number of discordant pairs
+ discordant_spatial : float
+ Number of discordant pairs that are spatial neighbors
+ taus : float
+ spatial tau values for permuted samples (if permutations>0)
+ tau_spatial_psim:
+ : float
+ pseudo p-value for observed tau_spatial under the null of spatial randomness (if permutations>0)
+
+ Notes
+ -----
+
+ Algorithm has two stages. The first calculates classic Tau using a list
+ based implementation of the algorithm from Christensen (2005). Second
+ stage calculates concordance measures for neighboring pairs of locations
+ using a modification of the algorithm from Press et al (2007). See Rey
+ (2014) for details.
+
+ References
+ ----------
+
+ Christensen, D. (2005) "Fast algorithms for the calculation of
+ Kendall's tau." Computational Statistics, 20: 51-62.
+
+ Press, W.H, S. A Teukolsky, W.T. Vetterling and B. P. Flannery (2007).
+ Numerical Recipes: The Art of Scientific Computing. Cambridge. Pg 752.
+
+ Rey, S.J. (2004) "Spatial dependence in the evolution of regional income
+ distributions," in A. Getis, J. Mur and H.Zoeller (eds). Spatial
+ Econometrics and Spatial Statistics. Palgrave, London, pp. 194-213.
+
+ Rey, S.J. (2014) "Fast algorithms for calculation of a space-time
+ concordance measure." Computational Statistics. Forthcoming.
+
+
+ Examples
+ --------
+ >>> import pysal
+ >>> import numpy as np
+ >>> f=pysal.open(pysal.examples.get_path("mexico.csv"))
+ >>> vnames=["pcgdp%d"%dec for dec in range(1940,2010,10)]
+ >>> y=np.transpose(np.array([f.by_col[v] for v in vnames]))
+ >>> regime=np.array(f.by_col['esquivel99'])
+ >>> w=pysal.weights.block_weights(regime)
+ >>> np.random.seed(12345)
+ >>> res=[pysal.SpatialTau(y[:,i],y[:,i+1],w,99) for i in range(6)]
+ >>> for r in res:
+ ... ev = r.taus.mean()
+ ... "%8.3f %8.3f %8.3f"%(r.tau_spatial, ev, r.tau_spatial_psim)
+ ...
+ ' 0.397 0.659 0.010'
+ ' 0.492 0.706 0.010'
+ ' 0.651 0.772 0.020'
+ ' 0.714 0.752 0.210'
+ ' 0.683 0.705 0.270'
+ ' 0.810 0.819 0.280'
+ """
+
+ def __init__(self, x, y, w, permutations=0):
+
+ w.transform = 'b'
+ self.n = len(x)
+ res = Tau(x, y)
+ self.tau = res.tau
+ self.tau_p = res.tau_p
+ self.concordant = res.concordant
+ self.discordant = res.discordant
+ self.extraX = res.extraX
+ self.extraY = res.extraY
+ res = self._calc(x, y, w)
+ self.tau_spatial = res[0]
+ self.pairs_spatial = int(w.s0 / 2.)
+ self.concordant_spatial = res[1]
+ self.discordant_spatial = res[2]
+
+ if permutations > 0:
+ taus = np.zeros(permutations)
+ ids = np.arange(self.n)
+ for r in xrange(permutations):
+ rids = np.random.permutation(ids)
+ taus[r] = self._calc(x[rids], y[rids], w)[0]
+ self.taus = taus
+ self.tau_spatial_psim = pseudop(taus, self.tau_spatial,
+ permutations)
+
+ def _calc(self, x, y, w):
+ n1 = n2 = iS = gc = 0
+ ijs = {}
+ for i in w.id_order:
+ xi = x[i]
+ yi = y[i]
+ for j in w.neighbors[i]:
+ if i < j:
+ ijs[(i, j)] = (i, j)
+ xj = x[j]
+ yj = y[j]
+ dx = xi - xj
+ dy = yi - yj
+ dxdy = dx * dy
+ if dxdy != 0:
+ n1 += 1
+ n2 += 1
+ if dxdy > 0.0:
+ gc += 1
+ iS += 1
+ else:
+ iS -= 1
+ else:
+ if dx != 0.0:
+ n1 += 1
+ if dy != 0.0:
+ n2 += 1
+ tau_g = iS / (np.sqrt(n1) * np.sqrt(n2))
+ gd = gc - iS
+ return [tau_g, gc, gd]
+
+
+def pseudop(sim, observed, nperm):
+ above = sim >= observed
+ larger = above.sum()
+ psim = (larger + 1.) / (nperm + 1.)
+ if psim > 0.5:
+ psim = (nperm - larger + 1.) / (nperm + 1.)
+ return psim
+
diff --git a/pysal/spatial_dynamics/tests/__init__.py b/pysal/spatial_dynamics/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pysal/spatial_dynamics/tests/test_directional.py b/pysal/spatial_dynamics/tests/test_directional.py
new file mode 100644
index 0000000..73d9716
--- /dev/null
+++ b/pysal/spatial_dynamics/tests/test_directional.py
@@ -0,0 +1,62 @@
+import unittest
+import pysal
+from pysal.spatial_dynamics import directional
+import numpy as np
+
+
+class Rose_Tester(unittest.TestCase):
+ def setUp(self):
+ f = open(pysal.examples.get_path("spi_download.csv"), 'r')
+ lines = f.readlines()
+ f.close()
+ lines = [line.strip().split(",") for line in lines]
+ names = [line[2] for line in lines[1:-5]]
+ data = np.array([map(int, line[3:]) for line in lines[1:-5]])
+ sids = range(60)
+ out = ['"United States 3/"',
+ '"Alaska 3/"',
+ '"District of Columbia"',
+ '"Hawaii 3/"',
+ '"New England"',
+ '"Mideast"',
+ '"Great Lakes"',
+ '"Plains"',
+ '"Southeast"',
+ '"Southwest"',
+ '"Rocky Mountain"',
+ '"Far West 3/"']
+ snames = [name for name in names if name not in out]
+ sids = [names.index(name) for name in snames]
+ states = data[sids, :]
+ us = data[0]
+ years = np.arange(1969, 2009)
+ rel = states / (us * 1.)
+ gal = pysal.open(pysal.examples.get_path('states48.gal'))
+ self.w = gal.read()
+ self.w.transform = 'r'
+ self.Y = rel[:, [0, -1]]
+
+ def test_rose(self):
+ k = 4
+ np.random.seed(100)
+ r4 = directional.rose(self.Y, self.w, k, permutations=999)
+ exp = [0., 1.57079633, 3.14159265, 4.71238898, 6.28318531]
+ obs = list(r4['cuts'])
+ for i in range(k + 1):
+ self.assertAlmostEqual(exp[i], obs[i])
+ self.assertEquals(list(r4['counts']), [32, 5, 9, 2])
+ exp = [0.02, 0.001, 0.001, 0.001]
+ obs = list(r4['pvalues'])
+ for i in range(k):
+ self.assertAlmostEqual(exp[i], obs[i])
+
+
+suite = unittest.TestSuite()
+test_classes = [Rose_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/spatial_dynamics/tests/test_ergodic.py b/pysal/spatial_dynamics/tests/test_ergodic.py
new file mode 100644
index 0000000..643f16c
--- /dev/null
+++ b/pysal/spatial_dynamics/tests/test_ergodic.py
@@ -0,0 +1,55 @@
+import unittest
+import pysal
+from pysal.spatial_dynamics import ergodic
+import numpy as np
+
+
+class SteadyState_Tester(unittest.TestCase):
+ def setUp(self):
+ self.p = np.matrix([[.5, .25, .25], [.5, 0, .5], [.25, .25, .5]])
+
+ def test_steady_state(self):
+ obs = ergodic.steady_state(self.p).tolist()
+ exp = np.matrix([[0.4], [0.2], [0.4]]).tolist()
+ k = self.p.shape[0]
+ for i in range(k):
+ self.assertAlmostEqual(exp[i][0], obs[i][0])
+
+
+class Fmpt_Tester(unittest.TestCase):
+ def setUp(self):
+ self.p = np.matrix([[.5, .25, .25], [.5, 0, .5], [.25, .25, .5]])
+
+ def test_fmpt(self):
+ k = self.p.shape[0]
+ obs = ergodic.fmpt(self.p).flatten().tolist()[0]
+ exp = np.matrix([[2.5, 4., 3.33333333], [2.66666667, 5.,
+ 2.66666667], [3.33333333, 4., 2.5]])
+ exp = exp.flatten().tolist()[0]
+ for i in range(k):
+ self.assertAlmostEqual(exp[i], obs[i])
+
+
+class VarFmpt_Tester(unittest.TestCase):
+ def setUp(self):
+ self.p = np.matrix([[.5, .25, .25], [.5, 0, .5], [.25, .25, .5]])
+
+ def test_var_fmpt(self):
+ k = self.p.shape[0]
+ obs = ergodic.var_fmpt(self.p).flatten().tolist()[0]
+ exp = np.matrix([[5.58333333, 12., 6.88888889], [6.22222222,
+ 12., 6.22222222], [6.88888889, 12., 5.58333333]])
+ exp = exp.flatten().tolist()[0]
+ for i in range(k):
+ self.assertAlmostEqual(exp[i], obs[i])
+
+
+suite = unittest.TestSuite()
+test_classes = [SteadyState_Tester, Fmpt_Tester, VarFmpt_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/spatial_dynamics/tests/test_interaction.py b/pysal/spatial_dynamics/tests/test_interaction.py
new file mode 100644
index 0000000..83673c0
--- /dev/null
+++ b/pysal/spatial_dynamics/tests/test_interaction.py
@@ -0,0 +1,80 @@
+import unittest
+import pysal
+from pysal.spatial_dynamics import interaction
+import numpy as np
+import scipy
+
+scp_version = int(scipy.version.version.split(".")[1])
+
+
+class SpaceTimeEvents_Tester(unittest.TestCase):
+ def setUp(self):
+ self.path = pysal.examples.get_path("burkitt")
+
+ def test_SpaceTimeEvents(self):
+ events = interaction.SpaceTimeEvents(self.path, 'T')
+ self.assertEquals(events.n, 188)
+ self.assertEquals(list(events.space[0]), [300., 302.])
+ self.assertEquals(list(events.t[0]), [413])
+
+
+class Knox_Tester(unittest.TestCase):
+ def setUp(self):
+ path = pysal.examples.get_path("burkitt")
+ self.events = interaction.SpaceTimeEvents(path, 'T')
+
+ def test_knox(self):
+ result = interaction.knox(
+ self.events.space,
+ self.events.t, delta=20, tau=5, permutations=1)
+ self.assertEquals(result['stat'], 13.0)
+
+
+class Mantel_Tester(unittest.TestCase):
+ def setUp(self):
+ path = pysal.examples.get_path("burkitt")
+ self.events = interaction.SpaceTimeEvents(path, 'T')
+
+ def test_mantel(self):
+ result = interaction.mantel(self.events.space,
+ self.events.time, 1, scon=0.0, spow=1.0, tcon=0.0, tpow=1.0)
+ self.assertAlmostEquals(result['stat'], 0.014154, 6)
+
+
+class Jacquez_Tester(unittest.TestCase):
+ def setUp(self):
+ path = pysal.examples.get_path("burkitt")
+ self.events = interaction.SpaceTimeEvents(path, 'T')
+
+ def test_jacquez(self):
+ result = interaction.jacquez(self.events.space,
+ self.events.t, k=3, permutations=1)
+ if scp_version > 11:
+ self.assertEquals(result['stat'], 12)
+ else:
+ self.assertEquals(result['stat'], 13)
+
+
+
+class ModifiedKnox_Tester(unittest.TestCase):
+ def setUp(self):
+ path = pysal.examples.get_path("burkitt")
+ self.events = interaction.SpaceTimeEvents(path, 'T')
+
+ def test_modified_knox(self):
+ result = interaction.modified_knox(
+ self.events.space,
+ self.events.t, delta=20, tau=5, permutations=1)
+ self.assertAlmostEquals(result['stat'], 2.810160, 6)
+
+
+suite = unittest.TestSuite()
+test_classes = [SpaceTimeEvents_Tester, Knox_Tester, Mantel_Tester,
+ Jacquez_Tester, ModifiedKnox_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/spatial_dynamics/tests/test_markov.py b/pysal/spatial_dynamics/tests/test_markov.py
new file mode 100644
index 0000000..8e10daf
--- /dev/null
+++ b/pysal/spatial_dynamics/tests/test_markov.py
@@ -0,0 +1,190 @@
+import unittest
+import pysal
+from pysal.spatial_dynamics import markov
+import numpy as np
+
+
+class test_Markov(unittest.TestCase):
+ def test___init__(self):
+ # markov = Markov(class_ids, classes)
+ import pysal
+ f = pysal.open(pysal.examples.get_path('usjoin.csv'))
+ pci = np.array([f.by_col[str(y)] for y in range(1929, 2010)])
+ q5 = np.array([pysal.Quantiles(y).yb for y in pci]).transpose()
+ m = pysal.Markov(q5)
+ expected = np.array([[729., 71., 1., 0., 0.],
+ [72., 567., 80., 3., 0.],
+ [0., 81., 631., 86., 2.],
+ [0., 3., 86., 573., 56.],
+ [0., 0., 1., 57., 741.]])
+ np.testing.assert_array_equal(m.transitions, expected)
+ expected = np.matrix([[0.91011236, 0.0886392,
+ 0.00124844, 0., 0.],
+ [0.09972299, 0.78531856, 0.11080332, 0.00415512,
+ 0.],
+ [0., 0.10125, 0.78875, 0.1075, 0.0025],
+ [0., 0.00417827, 0.11977716, 0.79805014,
+ 0.07799443],
+ [0., 0., 0.00125156, 0.07133917, 0.92740926]])
+ np.testing.assert_array_almost_equal(m.p.getA(), expected.getA())
+ expected = np.matrix([[0.20774716],
+ [0.18725774],
+ [0.20740537],
+ [0.18821787],
+ [0.20937187]]).getA()
+ np.testing.assert_array_almost_equal(m.steady_state.getA(), expected)
+
+
+class test_Spatial_Markov(unittest.TestCase):
+ def test___init__(self):
+ import pysal
+ f = pysal.open(pysal.examples.get_path('usjoin.csv'))
+ pci = np.array([f.by_col[str(y)] for y in range(1929, 2010)])
+ pci = pci.transpose()
+ rpci = pci / (pci.mean(axis=0))
+ w = pysal.open(pysal.examples.get_path("states48.gal")).read()
+ w.transform = 'r'
+ sm = pysal.Spatial_Markov(rpci, w, fixed=True, k=5)
+ S = np.array(
+ [[0.43509425, 0.2635327, 0.20363044, 0.06841983, 0.02932278],
+ [0.13391287, 0.33993305, 0.25153036, 0.23343016, 0.04119356],
+ [0.12124869, 0.21137444, 0.2635101, 0.29013417, 0.1137326],
+ [0.0776413, 0.19748806, 0.25352636, 0.22480415, 0.24654013],
+ [0.01776781, 0.19964349, 0.19009833, 0.25524697, 0.3372434]])
+ np.testing.assert_array_almost_equal(S, sm.S)
+
+
+class test_chi2(unittest.TestCase):
+ def test_chi2(self):
+ import pysal
+ f = pysal.open(pysal.examples.get_path('usjoin.csv'))
+ pci = np.array([f.by_col[str(y)] for y in range(1929, 2010)])
+ pci = pci.transpose()
+ rpci = pci / (pci.mean(axis=0))
+ w = pysal.open(pysal.examples.get_path("states48.gal")).read()
+ w.transform = 'r'
+ sm = pysal.Spatial_Markov(rpci, w, fixed=True, k=5)
+ chi = np.matrix([[4.06139105e+01, 6.32961385e-04, 1.60000000e+01],
+ [5.55485793e+01, 2.88879565e-06, 1.60000000e+01],
+ [1.77772638e+01, 3.37100315e-01, 1.60000000e+01],
+ [4.00925436e+01, 7.54729084e-04, 1.60000000e+01],
+ [4.68588786e+01, 7.16364084e-05, 1.60000000e+01]]).getA()
+ obs = np.matrix(sm.chi2).getA()
+ np.testing.assert_array_almost_equal(obs, chi)
+ obs = np.matrix(
+ [[4.61209613e+02, 0.00000000e+00, 4.00000000e+00],
+ [1.48140694e+02, 0.00000000e+00, 4.00000000e+00],
+ [6.33129261e+01, 5.83089133e-13, 4.00000000e+00],
+ [7.22778509e+01, 7.54951657e-15, 4.00000000e+00],
+ [2.32659201e+02, 0.00000000e+00, 4.00000000e+00]])
+ np.testing.assert_array_almost_equal(obs.getA(),
+ np.matrix(sm.shtest).getA())
+
+
+class test_LISA_Markov(unittest.TestCase):
+ def test___init__(self):
+ import numpy as np
+ f = pysal.open(pysal.examples.get_path('usjoin.csv'))
+ pci = np.array(
+ [f.by_col[str(y)] for y in range(1929, 2010)]).transpose()
+ w = pysal.open(pysal.examples.get_path("states48.gal")).read()
+ lm = pysal.LISA_Markov(pci, w)
+ obs = np.array([1, 2, 3, 4])
+ np.testing.assert_array_almost_equal(obs, lm.classes)
+ """
+ >>> lm.steady_state
+ matrix([[ 0.28561505],
+ [ 0.14190226],
+ [ 0.40493672],
+ [ 0.16754598]])
+ >>> lm.transitions
+ array([[ 1.08700000e+03, 4.40000000e+01, 4.00000000e+00,
+ 3.40000000e+01],
+ [ 4.10000000e+01, 4.70000000e+02, 3.60000000e+01,
+ 1.00000000e+00],
+ [ 5.00000000e+00, 3.40000000e+01, 1.42200000e+03,
+ 3.90000000e+01],
+ [ 3.00000000e+01, 1.00000000e+00, 4.00000000e+01,
+ 5.52000000e+02]])
+ >>> lm.p
+ matrix([[ 0.92985458, 0.03763901, 0.00342173, 0.02908469],
+ [ 0.07481752, 0.85766423, 0.06569343, 0.00182482],
+ [ 0.00333333, 0.02266667, 0.948 , 0.026 ],
+ [ 0.04815409, 0.00160514, 0.06420546, 0.88603531]])
+ >>> lm.move_types
+ array([[ 11., 11., 11., ..., 11., 11., 11.],
+ [ 6., 6., 6., ..., 6., 7., 11.],
+ [ 11., 11., 11., ..., 11., 11., 11.],
+ ...,
+ [ 6., 6., 6., ..., 6., 6., 6.],
+ [ 1., 1., 1., ..., 6., 6., 6.],
+ [ 16., 16., 16., ..., 16., 16., 16.]])
+ >>> np.random.seed(10)
+ >>> lm_random = pysal.LISA_Markov(pci, w, permutations=99)
+ >>> lm_random.significant_moves
+ array([[11, 11, 11, ..., 59, 59, 59],
+ [54, 54, 54, ..., 54, 55, 59],
+ [11, 11, 11, ..., 11, 59, 59],
+ ...,
+ [54, 54, 54, ..., 54, 54, 54],
+ [49, 49, 49, ..., 54, 54, 54],
+ [64, 64, 64, ..., 64, 64, 64]])
+
+ """
+
+
+class test_kullback(unittest.TestCase):
+ def test___init__(self):
+ import numpy as np
+ s1 = np.array([
+ [22, 11, 24, 2, 2, 7],
+ [5, 23, 15, 3, 42, 6],
+ [4, 21, 190, 25, 20, 34],
+ [0, 2, 14, 56, 14, 28],
+ [32, 15, 20, 10, 56, 14],
+ [5, 22, 31, 18, 13, 134]
+ ])
+ s2 = np.array([
+ [3, 6, 9, 3, 0, 8],
+ [1, 9, 3, 12, 27, 5],
+ [2, 9, 208, 32, 5, 18],
+ [0, 14, 32, 108, 40, 40],
+ [22, 14, 9, 26, 224, 14],
+ [1, 5, 13, 53, 13, 116]
+ ])
+
+ F = np.array([s1, s2])
+ res = markov.kullback(F)
+ np.testing.assert_array_almost_equal(160.96060031170782,
+ res['Conditional homogeneity'])
+ np.testing.assert_array_almost_equal(30,
+ res['Conditional homogeneity dof'])
+ np.testing.assert_array_almost_equal(0.0,
+ res['Conditional homogeneity pvalue'])
+
+
+class test_prais(unittest.TestCase):
+ def test___init__(self):
+ import numpy as np
+ f = pysal.open(pysal.examples.get_path('usjoin.csv'))
+ pci = np.array([f.by_col[str(y)] for y in range(1929, 2010)])
+ q5 = np.array([pysal.Quantiles(y).yb for y in pci]).transpose()
+ m = pysal.Markov(q5)
+ res = np.matrix([[0.08988764, 0.21468144,
+ 0.21125, 0.20194986, 0.07259074]])
+ np.testing.assert_array_almost_equal(markov.prais(m.p), res)
+
+
+class test_shorrock(unittest.TestCase):
+ def test___init__(self):
+ import numpy as np
+ f = pysal.open(pysal.examples.get_path('usjoin.csv'))
+ pci = np.array([f.by_col[str(y)] for y in range(1929, 2010)])
+ q5 = np.array([pysal.Quantiles(y).yb for y in pci]).transpose()
+ m = pysal.Markov(q5)
+ np.testing.assert_array_almost_equal(markov.shorrock(m.p),
+ 0.19758992000997844)
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spatial_dynamics/tests/test_rank.py b/pysal/spatial_dynamics/tests/test_rank.py
new file mode 100644
index 0000000..05ff929
--- /dev/null
+++ b/pysal/spatial_dynamics/tests/test_rank.py
@@ -0,0 +1,73 @@
+import unittest
+import pysal
+from pysal.spatial_dynamics import rank
+import numpy as np
+
+
+class Theta_Tester(unittest.TestCase):
+ def setUp(self):
+ f = pysal.open(pysal.examples.get_path('mexico.csv'))
+ vnames = ["pcgdp%d" % dec for dec in range(1940, 2010, 10)]
+ self.y = np.transpose(np.array([f.by_col[v] for v in vnames]))
+ self.regime = np.array(f.by_col['esquivel99'])
+
+ def test_Theta(self):
+ np.random.seed(10)
+ t = rank.Theta(self.y, self.regime, 999)
+ k = self.y.shape[1]
+ obs = t.theta.tolist()
+ exp = [[0.41538462, 0.28070175, 0.61363636, 0.62222222,
+ 0.33333333, 0.47222222]]
+ for i in range(k - 1):
+ self.assertAlmostEqual(exp[0][i], obs[0][i])
+ obs = t.pvalue_left.tolist()
+ exp = [0.307, 0.077, 0.823, 0.552, 0.045, 0.735]
+ for i in range(k - 1):
+ self.assertAlmostEqual(exp[i], obs[i])
+ obs = t.total.tolist()
+ exp = [130., 114., 88., 90., 90., 72.]
+ for i in range(k - 1):
+ self.assertAlmostEqual(exp[i], obs[i])
+ self.assertEqual(t.max_total, 512)
+
+
+class SpatialTau_Tester(unittest.TestCase):
+ def setUp(self):
+ f = pysal.open(pysal.examples.get_path('mexico.csv'))
+ vnames = ["pcgdp%d" % dec for dec in range(1940, 2010, 10)]
+ self.y = np.transpose(np.array([f.by_col[v] for v in vnames]))
+ regime = np.array(f.by_col['esquivel99'])
+ self.w = pysal.weights.block_weights(regime)
+
+ def test_SpatialTau(self):
+ np.random.seed(12345)
+ k = self.y.shape[1]
+ obs = [rank.SpatialTau(self.y[:, i], self.y[:, i + 1],
+ self.w, 99) for i in range(k - 1)]
+ tau_s = [0.397, 0.492, 0.651, 0.714, 0.683, 0.810]
+ ev_tau_s = [0.659, 0.706, 0.772, 0.752, 0.705, 0.819]
+ p_vals = [0.010, 0.010, 0.020, 0.210, 0.270, 0.280]
+ for i in range(k - 1):
+ self.assertAlmostEqual(tau_s[i], obs[i].tau_spatial, 3)
+ self.assertAlmostEqual(ev_tau_s[i], obs[i].taus.mean(), 3)
+ self.assertAlmostEqual(p_vals[i], obs[i].tau_spatial_psim, 3)
+
+
+class Tau_Tester(unittest.TestCase):
+ def test_Tau(self):
+ x1 = [12, 2, 1, 12, 2]
+ x2 = [1, 4, 7, 1, 0]
+ kt = rank.Tau(x1, x2)
+ self.assertAlmostEqual(kt.tau, -0.47140452079103173, 5)
+ self.assertAlmostEqual(kt.tau_p, 0.24821309157521476, 5)
+
+
+suite = unittest.TestSuite()
+test_classes = [Theta_Tester, SpatialTau_Tester, Tau_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/spatial_dynamics/tests/test_util.py b/pysal/spatial_dynamics/tests/test_util.py
new file mode 100644
index 0000000..1284d8d
--- /dev/null
+++ b/pysal/spatial_dynamics/tests/test_util.py
@@ -0,0 +1,41 @@
+import unittest
+import pysal
+from pysal.spatial_dynamics import util
+import numpy as np
+
+
+class ShuffleMatrix_Tester(unittest.TestCase):
+ def setUp(self):
+ self.X = np.arange(16)
+ self.X.shape = (4, 4)
+
+ def test_shuffle_matrix(self):
+ np.random.seed(10)
+ obs = util.shuffle_matrix(self.X, range(4)).flatten().tolist()
+ exp = [10, 8, 11, 9, 2, 0, 3, 1, 14, 12, 15, 13, 6, 4, 7, 5]
+ for i in range(16):
+ self.assertEqual(exp[i], obs[i])
+
+
+class GetLower_Tester(unittest.TestCase):
+ def setUp(self):
+ self.X = np.arange(16)
+ self.X.shape = (4, 4)
+
+ def test_get_lower(self):
+ np.random.seed(10)
+ obs = util.get_lower(self.X).flatten().tolist()
+ exp = [4, 8, 9, 12, 13, 14]
+ for i in range(6):
+ self.assertEqual(exp[i], obs[i])
+
+
+suite = unittest.TestSuite()
+test_classes = [ShuffleMatrix_Tester, GetLower_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/spatial_dynamics/util.py b/pysal/spatial_dynamics/util.py
new file mode 100644
index 0000000..91af545
--- /dev/null
+++ b/pysal/spatial_dynamics/util.py
@@ -0,0 +1,79 @@
+"""
+Utilities for the spatial dynamics module.
+"""
+import numpy as np
+
+__all__ = ['shuffle_matrix', 'get_lower']
+
+
+def shuffle_matrix(X, ids):
+ """
+ Random permutation of rows and columns of a matrix
+
+ Parameters
+ ----------
+ X : array (k,k)
+ array to be permutated
+ ids : range (k,)
+
+ Returns
+ -------
+ X : array (k,k)
+ with rows and columns randomly shuffled
+
+ Examples
+ --------
+ >>> X=np.arange(16)
+ >>> X.shape=(4,4)
+ >>> np.random.seed(10)
+ >>> shuffle_matrix(X,range(4))
+ array([[10, 8, 11, 9],
+ [ 2, 0, 3, 1],
+ [14, 12, 15, 13],
+ [ 6, 4, 7, 5]])
+
+ """
+ np.random.shuffle(ids)
+ return X[ids, :][:, ids]
+
+
+def get_lower(matrix):
+ """
+ Flattens the lower part of an n x n matrix into an n*(n-1)/2 x 1 vector.
+
+ Parameters
+ ----------
+ matrix : numpy array
+ a distance matrix (n x n)
+
+ Returns
+ -------
+ lowvec : numpy array
+ the lower half of the distance matrix flattened into
+ a vector of length n*(n-1)/2
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> test = np.array([[0,1,2,3],[1,0,1,2],[2,1,0,1],[4,2,1,0]])
+ >>> lower = get_lower(test)
+ >>> lower
+ array([[1],
+ [2],
+ [1],
+ [4],
+ [2],
+ [1]])
+
+ """
+ n = matrix.shape[0]
+ lowerlist = []
+ for i in range(n):
+ for j in range(n):
+ if i > j:
+ lowerlist.append(matrix[i, j])
+ veclen = n * (n - 1) / 2
+ lowvec = np.reshape(lowerlist, (veclen, 1))
+ return lowvec
+
diff --git a/pysal/spreg/__init__.py b/pysal/spreg/__init__.py
new file mode 100644
index 0000000..0587fab
--- /dev/null
+++ b/pysal/spreg/__init__.py
@@ -0,0 +1,20 @@
+from ols import *
+from diagnostics import *
+from diagnostics_sp import *
+from user_output import *
+from twosls import *
+from twosls_sp import *
+from error_sp import *
+from error_sp_het import *
+from error_sp_hom import *
+from ols_regimes import *
+from twosls_regimes import *
+from twosls_sp_regimes import *
+from error_sp_regimes import *
+from error_sp_het_regimes import *
+from error_sp_hom_regimes import *
+from probit import *
+from ml_lag import *
+from ml_lag_regimes import *
+from ml_error import *
+from ml_error_regimes import *
diff --git a/pysal/spreg/diagnostics.py b/pysal/spreg/diagnostics.py
new file mode 100644
index 0000000..2a184d7
--- /dev/null
+++ b/pysal/spreg/diagnostics.py
@@ -0,0 +1,1424 @@
+"""
+Diagnostics for regression estimations.
+
+"""
+__author__ = "Luc Anselin luc.anselin at asu.edu, Nicholas Malizia nicholas.malizia at asu.edu "
+
+import pysal
+from pysal.common import *
+import scipy.sparse as SP
+from math import sqrt
+from utils import spmultiply, sphstack, spmin, spmax
+
+
+__all__ = [
+ "f_stat", "t_stat", "r2", "ar2", "se_betas", "log_likelihood", "akaike", "schwarz",
+ "condition_index", "jarque_bera", "breusch_pagan", "white", "koenker_bassett", "vif", "likratiotest"]
+
+
+def f_stat(reg):
+ """
+ Calculates the f-statistic and associated p-value of the regression.
+ (For two stage least squares see f_stat_tsls)
+
+ Parameters
+ ----------
+ reg : regression object
+ output instance from a regression model
+
+ Returns
+ ----------
+ fs_result : tuple
+ includes value of F statistic and associated p-value
+
+ References
+ ----------
+ .. [1] W. Greene. 2003. Econometric Analysis. Prentice Hall, Upper
+ Saddle River.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> import diagnostics
+ >>> from ols import OLS
+
+ Read the DBF associated with the Columbus data.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+
+ Create the dependent variable vector.
+
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+
+ Create the matrix of independent variables.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+
+ Run an OLS regression.
+
+ >>> reg = OLS(y,X)
+
+ Calculate the F-statistic for the regression.
+
+ >>> testresult = diagnostics.f_stat(reg)
+
+ Print the results tuple, including the statistic and its significance.
+
+ >>> print("%12.12f"%testresult[0],"%12.12f"%testresult[1])
+ ('28.385629224695', '0.000000009341')
+
+ """
+ k = reg.k # (scalar) number of ind. vars (includes constant)
+ n = reg.n # (scalar) number of observations
+ utu = reg.utu # (scalar) residual sum of squares
+ predy = reg.predy # (array) vector of predicted values (n x 1)
+ mean_y = reg.mean_y # (scalar) mean of dependent observations
+ Q = utu
+ U = np.sum((predy - mean_y) ** 2)
+ fStat = (U / (k - 1)) / (Q / (n - k))
+ pValue = stats.f.sf(fStat, k - 1, n - k)
+ fs_result = (fStat, pValue)
+ return fs_result
+
+
+def t_stat(reg, z_stat=False):
+ """
+ Calculates the t-statistics (or z-statistics) and associated p-values.
+
+ Parameters
+ ----------
+ reg : regression object
+ output instance from a regression model
+ z_stat : boolean
+ If True run z-stat instead of t-stat
+
+ Returns
+ -------
+ ts_result : list of tuples
+ each tuple includes value of t statistic (or z
+ statistic) and associated p-value
+
+ References
+ ----------
+
+ .. [1] W. Greene. 2003. Econometric Analysis. Prentice Hall, Upper
+ Saddle River.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> import diagnostics
+ >>> from ols import OLS
+
+ Read the DBF associated with the Columbus data.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+
+ Create the dependent variable vector.
+
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+
+ Create the matrix of independent variables.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+
+ Run an OLS regression.
+
+ >>> reg = OLS(y,X)
+
+ Calculate t-statistics for the regression coefficients.
+
+ >>> testresult = diagnostics.t_stat(reg)
+
+ Print the tuples that contain the t-statistics and their significances.
+
+ >>> print("%12.12f"%testresult[0][0], "%12.12f"%testresult[0][1], "%12.12f"%testresult[1][0], "%12.12f"%testresult[1][1], "%12.12f"%testresult[2][0], "%12.12f"%testresult[2][1])
+ ('14.490373143689', '0.000000000000', '-4.780496191297', '0.000018289595', '-2.654408642718', '0.010874504910')
+ """
+
+ k = reg.k # (scalar) number of ind. vars (includes constant)
+ n = reg.n # (scalar) number of observations
+ vm = reg.vm # (array) coefficients of variance matrix (k x k)
+ betas = reg.betas # (array) coefficients of the regressors (1 x k)
+ variance = vm.diagonal()
+ tStat = betas[range(0, len(vm))].reshape(len(vm),) / np.sqrt(variance)
+ ts_result = []
+ for t in tStat:
+ if z_stat:
+ ts_result.append((t, stats.norm.sf(abs(t)) * 2))
+ else:
+ ts_result.append((t, stats.t.sf(abs(t), n - k) * 2))
+ return ts_result
+
+
+def r2(reg):
+ """
+ Calculates the R^2 value for the regression.
+
+ Parameters
+ ----------
+ reg : regression object
+ output instance from a regression model
+
+ Returns
+ ----------
+ r2_result : float
+ value of the coefficient of determination for the
+ regression
+
+ References
+ ----------
+ .. [1] W. Greene. 2003. Econometric Analysis. Prentice Hall, Upper
+ Saddle River.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> import diagnostics
+ >>> from ols import OLS
+
+ Read the DBF associated with the Columbus data.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+
+ Create the dependent variable vector.
+
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+
+ Create the matrix of independent variables.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+
+ Run an OLS regression.
+
+ >>> reg = OLS(y,X)
+
+ Calculate the R^2 value for the regression.
+
+ >>> testresult = diagnostics.r2(reg)
+
+ Print the result.
+
+ >>> print("%1.8f"%testresult)
+ 0.55240404
+
+ """
+ y = reg.y # (array) vector of dep observations (n x 1)
+ mean_y = reg.mean_y # (scalar) mean of dep observations
+ utu = reg.utu # (scalar) residual sum of squares
+ ss_tot = ((y - mean_y) ** 2).sum(0)
+ r2 = 1 - utu / ss_tot
+ r2_result = r2[0]
+ return r2_result
+
+
+def ar2(reg):
+ """
+ Calculates the adjusted R^2 value for the regression.
+
+ Parameters
+ ----------
+ reg : regression object
+ output instance from a regression model
+
+ Returns
+ ----------
+ ar2_result : float
+ value of R^2 adjusted for the number of explanatory
+ variables.
+
+ References
+ ----------
+ .. [1] W. Greene. 2003. Econometric Analysis. Prentice Hall, Upper
+ Saddle River.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> import diagnostics
+ >>> from ols import OLS
+
+ Read the DBF associated with the Columbus data.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+
+ Create the dependent variable vector.
+
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+
+ Create the matrix of independent variables.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+
+ Run an OLS regression.
+
+ >>> reg = OLS(y,X)
+
+ Calculate the adjusted R^2 value for the regression.
+ >>> testresult = diagnostics.ar2(reg)
+
+ Print the result.
+
+ >>> print("%1.8f"%testresult)
+ 0.53294335
+
+ """
+ k = reg.k # (scalar) number of ind. variables (includes constant)
+ n = reg.n # (scalar) number of observations
+ ar2_result = 1 - (1 - r2(reg)) * (n - 1) / (n - k)
+ return ar2_result
+
+
+def se_betas(reg):
+ """
+ Calculates the standard error of the regression coefficients.
+
+ Parameters
+ ----------
+ reg : regression object
+ output instance from a regression model
+
+ Returns
+ ----------
+ se_result : array
+ includes standard errors of each coefficient (1 x k)
+
+ References
+ ----------
+ .. [1] W. Greene. 2003. Econometric Analysis. Prentice Hall, Upper
+ Saddle River.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> import diagnostics
+ >>> from ols import OLS
+
+ Read the DBF associated with the Columbus data.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+
+ Create the dependent variable vector.
+
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+
+ Create the matrix of independent variables.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+
+ Run an OLS regression.
+
+ >>> reg = OLS(y,X)
+
+ Calculate the standard errors of the regression coefficients.
+
+ >>> testresult = diagnostics.se_betas(reg)
+
+ Print the vector of standard errors.
+
+ >>> testresult
+ array([ 4.73548613, 0.33413076, 0.10319868])
+
+ """
+ vm = reg.vm # (array) coefficients of variance matrix (k x k)
+ variance = vm.diagonal()
+ se_result = np.sqrt(variance)
+ return se_result
+
+
+def log_likelihood(reg):
+ """
+ Calculates the log-likelihood value for the regression.
+
+ Parameters
+ ----------
+ reg : regression object
+ output instance from a regression model
+
+ Returns
+ -------
+ ll_result : float
+ value for the log-likelihood of the regression.
+
+ References
+ ----------
+ .. [1] W. Greene. 2003. Econometric Analysis. Prentice Hall, Upper
+ Saddle River.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> import diagnostics
+ >>> from ols import OLS
+
+ Read the DBF associated with the Columbus data.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+
+ Create the dependent variable vector.
+
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+
+ Create the matrix of independent variables.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+
+ Run an OLS regression.
+
+ >>> reg = OLS(y,X)
+
+ Calculate the log-likelihood for the regression.
+
+ >>> testresult = diagnostics.log_likelihood(reg)
+
+ Print the result.
+
+ >>> testresult
+ -187.3772388121491
+
+ """
+ n = reg.n # (scalar) number of observations
+ utu = reg.utu # (scalar) residual sum of squares
+ ll_result = -0.5 * \
+ (n * (np.log(2 * math.pi)) + n * np.log(utu / n) + (utu / (utu / n)))
+ return ll_result
+
+
+def akaike(reg):
+ """
+ Calculates the Akaike Information Criterion.
+
+ Parameters
+ ----------
+ reg : regression object
+ output instance from a regression model
+
+ Returns
+ -------
+ aic_result : scalar
+ value for Akaike Information Criterion of the
+ regression.
+
+ References
+ ----------
+ .. [1] H. Akaike. 1974. A new look at the statistical identification
+ model. IEEE Transactions on Automatic Control, 19(6):716-723.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> import diagnostics
+ >>> from ols import OLS
+
+ Read the DBF associated with the Columbus data.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+
+ Create the dependent variable vector.
+
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+
+ Create the matrix of independent variables.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+
+ Run an OLS regression.
+
+ >>> reg = OLS(y,X)
+
+ Calculate the Akaike Information Criterion (AIC).
+
+ >>> testresult = diagnostics.akaike(reg)
+
+ Print the result.
+
+ >>> testresult
+ 380.7544776242982
+
+ """
+ k = reg.k # (scalar) number of explanatory vars (including constant)
+ try: # ML estimation, logll already exists
+ # spatial coefficient included in k
+ aic_result = 2.0 * k - 2.0 * reg.logll
+ except AttributeError: # OLS case
+ n = reg.n # (scalar) number of observations
+ utu = reg.utu # (scalar) residual sum of squares
+ aic_result = 2 * k + n * (np.log((2 * np.pi * utu) / n) + 1)
+ return aic_result
+
+
+def schwarz(reg):
+ """
+ Calculates the Schwarz Information Criterion.
+
+ Parameters
+ ----------
+ reg : regression object
+ output instance from a regression model
+
+ Returns
+ -------
+ bic_result : scalar
+ value for Schwarz (Bayesian) Information Criterion of
+ the regression.
+
+ References
+ ----------
+ .. [1] G. Schwarz. 1978. Estimating the dimension of a model. The
+ Annals of Statistics, pages 461-464.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> import diagnostics
+ >>> from ols import OLS
+
+ Read the DBF associated with the Columbus data.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+
+ Create the dependent variable vector.
+
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+
+ Create the matrix of independent variables.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+
+ Run an OLS regression.
+
+ >>> reg = OLS(y,X)
+
+ Calculate the Schwarz Information Criterion.
+
+ >>> testresult = diagnostics.schwarz(reg)
+
+ Print the results.
+
+ >>> testresult
+ 386.42993851863008
+
+ """
+ n = reg.n # (scalar) number of observations
+ k = reg.k # (scalar) number of ind. variables (including constant)
+ try: # ML case logll already computed
+ # spatial coeff included in k
+ sc_result = k * np.log(n) - 2.0 * reg.logll
+ except AttributeError: # OLS case
+ utu = reg.utu # (scalar) residual sum of squares
+ sc_result = k * np.log(n) + n * (np.log((2 * np.pi * utu) / n) + 1)
+ return sc_result
+
+
+def condition_index(reg):
+ """
+ Calculates the multicollinearity condition index according to Belsey,
+ Kuh and Welsh (1980).
+
+ Parameters
+ ----------
+ reg : regression object
+ output instance from a regression model
+
+ Returns
+ -------
+ ci_result : float
+ scalar value for the multicollinearity condition
+ index.
+
+ References
+ ----------
+ .. [1] D. Belsley, E. Kuh, and R. Welsch. 1980. Regression
+ Diagnostics. New York: Wiley.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> import diagnostics
+ >>> from ols import OLS
+
+ Read the DBF associated with the Columbus data.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+
+ Create the dependent variable vector.
+
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+
+ Create the matrix of independent variables.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+
+ Run an OLS regression.
+
+ >>> reg = OLS(y,X)
+
+ Calculate the condition index to check for multicollinearity.
+
+ >>> testresult = diagnostics.condition_index(reg)
+
+ Print the result.
+
+ >>> print("%1.3f"%testresult)
+ 6.542
+
+ """
+ if hasattr(reg, 'xtx'):
+ xtx = reg.xtx # (array) k x k projection matrix (includes constant)
+ elif hasattr(reg, 'hth'):
+ xtx = reg.hth # (array) k x k projection matrix (includes constant)
+ diag = np.diagonal(xtx)
+ scale = xtx / diag
+ eigval = np.linalg.eigvals(scale)
+ max_eigval = max(eigval)
+ min_eigval = min(eigval)
+ ci_result = sqrt(max_eigval / min_eigval)
+ return ci_result
+
+
+def jarque_bera(reg):
+ """
+ Jarque-Bera test for normality in the residuals.
+
+ Parameters
+ ----------
+ reg : regression object
+ output instance from a regression model
+
+ Returns
+ -------
+ jb_result : dictionary
+ contains the statistic (jb) for the Jarque-Bera test
+ and the associated p-value (p-value)
+ df : integer
+ degrees of freedom for the test (always 2)
+ jb : float
+ value of the test statistic
+ pvalue : float
+ p-value associated with the statistic (chi^2
+ distributed with 2 df)
+
+ References
+ ----------
+ .. [1] C. Jarque and A. Bera. 1980. Efficient tests for normality,
+ homoscedasticity and serial independence of regression residuals.
+ Economics Letters, 6(3):255-259.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> import diagnostics
+ >>> from ols import OLS
+
+ Read the DBF associated with the Columbus data.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"), "r")
+
+ Create the dependent variable vector.
+
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+
+ Create the matrix of independent variables.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+
+ Run an OLS regression.
+
+ >>> reg = OLS(y,X)
+
+ Calculate the Jarque-Bera test for normality of residuals.
+
+ >>> testresult = diagnostics.jarque_bera(reg)
+
+ Print the degrees of freedom for the test.
+
+ >>> testresult['df']
+ 2
+
+ Print the test statistic.
+
+ >>> print("%1.3f"%testresult['jb'])
+ 1.836
+
+ Print the associated p-value.
+
+ >>> print("%1.4f"%testresult['pvalue'])
+ 0.3994
+
+ """
+ n = reg.n # (scalar) number of observations
+ u = reg.u # (array) residuals from regression
+ u2 = u ** 2
+ u3 = u ** 3
+ u4 = u ** 4
+ mu2 = np.mean(u2)
+ mu3 = np.mean(u3)
+ mu4 = np.mean(u4)
+ S = mu3 / (mu2 ** (1.5)) # skewness measure
+ K = (mu4 / (mu2 ** 2)) # kurtosis measure
+ jb = n * (((S ** 2) / 6) + ((K - 3) ** 2) / 24)
+ pvalue = stats.chisqprob(jb, 2)
+ jb_result = {"df": 2, "jb": jb, 'pvalue': pvalue}
+ return jb_result
+
+
+def breusch_pagan(reg, z=None):
+ """
+ Calculates the Breusch-Pagan test statistic to check for
+ heteroscedasticity.
+
+ Parameters
+ ----------
+ reg : regression object
+ output instance from a regression model
+ z : array
+ optional input for specifying an alternative set of
+ variables (Z) to explain the observed variance. By
+ default this is a matrix of the squared explanatory
+ variables (X**2) with a constant added to the first
+ column if not already present. In the default case,
+ the explanatory variables are squared to eliminate
+ negative values.
+
+ Returns
+ -------
+ bp_result : dictionary
+ contains the statistic (bp) for the test and the
+ associated p-value (p-value)
+ bp : float
+ scalar value for the Breusch-Pagan test statistic
+ df : integer
+ degrees of freedom associated with the test (k)
+ pvalue : float
+ p-value associated with the statistic (chi^2
+ distributed with k df)
+
+ Notes
+ -----
+ x attribute in the reg object must have a constant term included. This is
+ standard for spreg.OLS so no testing done to confirm constant.
+
+ References
+ ----------
+
+ .. [1] T. Breusch and A. Pagan. 1979. A simple test for
+ heteroscedasticity and random coefficient variation. Econometrica:
+ Journal of the Econometric Society, 47(5):1287-1294.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> import diagnostics
+ >>> from ols import OLS
+
+ Read the DBF associated with the Columbus data.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"), "r")
+
+ Create the dependent variable vector.
+
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+
+ Create the matrix of independent variables.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+
+ Run an OLS regression.
+
+ >>> reg = OLS(y,X)
+
+ Calculate the Breusch-Pagan test for heteroscedasticity.
+
+ >>> testresult = diagnostics.breusch_pagan(reg)
+
+ Print the degrees of freedom for the test.
+
+ >>> testresult['df']
+ 2
+
+ Print the test statistic.
+
+ >>> print("%1.3f"%testresult['bp'])
+ 7.900
+
+ Print the associated p-value.
+
+ >>> print("%1.4f"%testresult['pvalue'])
+ 0.0193
+
+ """
+ e2 = reg.u ** 2
+ e = reg.u
+ n = reg.n
+ k = reg.k
+ ete = reg.utu
+
+ den = ete / n
+ g = e2 / den - 1.0
+
+ if z == None:
+ x = reg.x
+ #constant = constant_check(x)
+ # if constant == False:
+ # z = np.hstack((np.ones((n,1)),x))**2
+ # else:
+ # z = x**2
+ z = spmultiply(x, x)
+ else:
+ #constant = constant_check(z)
+ # if constant == False:
+ # z = np.hstack((np.ones((n,1)),z))
+ pass
+
+ n, p = z.shape
+
+ # Check to identify any duplicate columns in Z
+ omitcolumn = []
+ for i in range(p):
+ current = z[:, i]
+ for j in range(p):
+ check = z[:, j]
+ if i < j:
+ test = abs(current - check).sum()
+ if test == 0:
+ omitcolumn.append(j)
+
+ uniqueomit = set(omitcolumn)
+ omitcolumn = list(uniqueomit)
+
+ # Now the identified columns must be removed (done in reverse to
+ # prevent renumbering)
+ omitcolumn.sort()
+ omitcolumn.reverse()
+ for c in omitcolumn:
+ z = np.delete(z, c, 1)
+ n, p = z.shape
+
+ df = p - 1
+
+ # Now that the variables are prepared, we calculate the statistic
+ zt = np.transpose(z)
+ gt = np.transpose(g)
+ gtz = np.dot(gt, z)
+ ztg = np.dot(zt, g)
+ ztz = np.dot(zt, z)
+ ztzi = la.inv(ztz)
+
+ part1 = np.dot(gtz, ztzi)
+ part2 = np.dot(part1, ztg)
+ bp_array = 0.5 * part2
+ bp = bp_array[0, 0]
+
+ pvalue = stats.chisqprob(bp, df)
+ bp_result = {'df': df, 'bp': bp, 'pvalue': pvalue}
+ return bp_result
+
+
+def white(reg):
+ """
+ Calculates the White test to check for heteroscedasticity.
+
+ Parameters
+ ----------
+ reg : regression object
+ output instance from a regression model
+
+ Returns
+ -------
+ white_result : dictionary
+ contains the statistic (white), degrees of freedom
+ (df) and the associated p-value (pvalue) for the
+ White test.
+ white : float
+ scalar value for the White test statistic.
+ df : integer
+ degrees of freedom associated with the test
+ pvalue : float
+ p-value associated with the statistic (chi^2
+ distributed with k df)
+
+ Notes
+ -----
+ x attribute in the reg object must have a constant term included. This is
+ standard for spreg.OLS so no testing done to confirm constant.
+
+ References
+ ----------
+ .. [1] H. White. 1980. A heteroscedasticity-consistent covariance
+ matrix estimator and a direct test for heteroskdasticity.
+ Econometrica. 48(4) 817-838.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> import diagnostics
+ >>> from ols import OLS
+
+ Read the DBF associated with the Columbus data.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+
+ Create the dependent variable vector.
+
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+
+ Create the matrix of independent variables.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+
+ Run an OLS regression.
+
+ >>> reg = OLS(y,X)
+
+ Calculate the White test for heteroscedasticity.
+
+ >>> testresult = diagnostics.white(reg)
+
+ Print the degrees of freedom for the test.
+
+ >>> print testresult['df']
+ 5
+
+ Print the test statistic.
+
+ >>> print("%1.3f"%testresult['wh'])
+ 19.946
+
+ Print the associated p-value.
+
+ >>> print("%1.4f"%testresult['pvalue'])
+ 0.0013
+
+ """
+ e = reg.u ** 2
+ k = reg.k
+ n = reg.n
+ y = reg.y
+ X = reg.x
+ #constant = constant_check(X)
+
+ # Check for constant, if none add one, see Greene 2003, pg. 222
+ # if constant == False:
+ # X = np.hstack((np.ones((n,1)),X))
+
+ # Check for multicollinearity in the X matrix
+ ci = condition_index(reg)
+ if ci > 30:
+ white_result = "Not computed due to multicollinearity."
+ return white_result
+
+ # Compute cross-products and squares of the regression variables
+ if type(X).__name__ == 'ndarray':
+ A = np.zeros((n, (k * (k + 1)) / 2.))
+ elif type(X).__name__ == 'csc_matrix' or type(X).__name__ == 'csr_matrix':
+ # this is probably inefficient
+ A = SP.lil_matrix((n, (k * (k + 1)) / 2.))
+ else:
+ raise Exception, "unknown X type, %s" % type(X).__name__
+ counter = 0
+ for i in range(k):
+ for j in range(i, k):
+ v = spmultiply(X[:, i], X[:, j], False)
+ A[:, counter] = v
+ counter += 1
+
+ # Append the original variables
+ A = sphstack(X, A) # note: this also converts a LIL to CSR
+ n, k = A.shape
+
+ # Check to identify any duplicate or constant columns in A
+ omitcolumn = []
+ for i in range(k):
+ current = A[:, i]
+ # remove all constant terms (will add a constant back later)
+ if spmax(current) == spmin(current):
+ omitcolumn.append(i)
+ pass
+ # do not allow duplicates
+ for j in range(k):
+ check = A[:, j]
+ if i < j:
+ test = abs(current - check).sum()
+ if test == 0:
+ omitcolumn.append(j)
+ uniqueomit = set(omitcolumn)
+ omitcolumn = list(uniqueomit)
+
+ # Now the identified columns must be removed
+ if type(A).__name__ == 'ndarray':
+ A = np.delete(A, omitcolumn, 1)
+ elif type(A).__name__ == 'csc_matrix' or type(A).__name__ == 'csr_matrix':
+ # this is probably inefficient
+ keepcolumn = range(k)
+ for i in omitcolumn:
+ keepcolumn.remove(i)
+ A = A[:, keepcolumn]
+ else:
+ raise Exception, "unknown A type, %s" % type(X).__name__
+ A = sphstack(np.ones((A.shape[0], 1)), A) # add a constant back in
+ n, k = A.shape
+
+ # Conduct the auxiliary regression and calculate the statistic
+ import ols as OLS
+ aux_reg = OLS.BaseOLS(e, A)
+ aux_r2 = r2(aux_reg)
+ wh = aux_r2 * n
+ df = k - 1
+ pvalue = stats.chisqprob(wh, df)
+ white_result = {'df': df, 'wh': wh, 'pvalue': pvalue}
+ return white_result
+
+
+def koenker_bassett(reg, z=None):
+ """
+ Calculates the Koenker-Bassett test statistic to check for
+ heteroscedasticity.
+
+ Parameters
+ ----------
+ reg : regression output
+ output from an instance of a regression class
+ z : array
+ optional input for specifying an alternative set of
+ variables (Z) to explain the observed variance. By
+ default this is a matrix of the squared explanatory
+ variables (X**2) with a constant added to the first
+ column if not already present. In the default case,
+ the explanatory variables are squared to eliminate
+ negative values.
+
+ Returns
+ -------
+ kb_result : dictionary
+ contains the statistic (kb), degrees of freedom (df)
+ and the associated p-value (pvalue) for the test.
+ kb : float
+ scalar value for the Koenker-Bassett test statistic.
+ df : integer
+ degrees of freedom associated with the test
+ pvalue : float
+ p-value associated with the statistic (chi^2
+ distributed)
+
+ Notes
+ -----
+ x attribute in the reg object must have a constant term included. This is
+ standard for spreg.OLS so no testing done to confirm constant.
+
+ References
+ ----------
+ .. [1] R. Koenker and G. Bassett. 1982. Robust tests for
+ heteroscedasticity based on regression quantiles. Econometrica,
+ 50(1):43-61.
+ .. [2] W. Greene. 2003. Econometric Analysis. Prentice Hall, Upper
+ Saddle River.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> import diagnostics
+ >>> from ols import OLS
+
+ Read the DBF associated with the Columbus data.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+
+ Create the dependent variable vector.
+
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+
+ Create the matrix of independent variables.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+
+ Run an OLS regression.
+
+ >>> reg = OLS(y,X)
+
+ Calculate the Koenker-Bassett test for heteroscedasticity.
+
+ >>> testresult = diagnostics.koenker_bassett(reg)
+
+ Print the degrees of freedom for the test.
+
+ >>> testresult['df']
+ 2
+
+ Print the test statistic.
+
+ >>> print("%1.3f"%testresult['kb'])
+ 5.694
+
+ Print the associated p-value.
+
+ >>> print("%1.4f"%testresult['pvalue'])
+ 0.0580
+
+ """
+ # The notation here matches that of Greene (2003).
+ u = reg.u ** 2
+ e = reg.u
+ n = reg.n
+ k = reg.k
+ x = reg.x
+ ete = reg.utu
+ #constant = constant_check(x)
+
+ ubar = ete / n
+ ubari = ubar * np.ones((n, 1))
+ g = u - ubari
+ v = (1.0 / n) * np.sum((u - ubar) ** 2)
+
+ if z == None:
+ x = reg.x
+ #constant = constant_check(x)
+ # if constant == False:
+ # z = np.hstack((np.ones((n,1)),x))**2
+ # else:
+ # z = x**2
+ z = spmultiply(x, x)
+ else:
+ #constant = constant_check(z)
+ # if constant == False:
+ # z = np.hstack((np.ones((n,1)),z))
+ pass
+
+ n, p = z.shape
+
+ # Check to identify any duplicate columns in Z
+ omitcolumn = []
+ for i in range(p):
+ current = z[:, i]
+ for j in range(p):
+ check = z[:, j]
+ if i < j:
+ test = abs(current - check).sum()
+ if test == 0:
+ omitcolumn.append(j)
+
+ uniqueomit = set(omitcolumn)
+ omitcolumn = list(uniqueomit)
+
+ # Now the identified columns must be removed (done in reverse to
+ # prevent renumbering)
+ omitcolumn.sort()
+ omitcolumn.reverse()
+ for c in omitcolumn:
+ z = np.delete(z, c, 1)
+ n, p = z.shape
+
+ df = p - 1
+
+ # Conduct the auxiliary regression.
+ zt = np.transpose(z)
+ gt = np.transpose(g)
+ gtz = np.dot(gt, z)
+ ztg = np.dot(zt, g)
+ ztz = np.dot(zt, z)
+ ztzi = la.inv(ztz)
+
+ part1 = np.dot(gtz, ztzi)
+ part2 = np.dot(part1, ztg)
+ kb_array = (1.0 / v) * part2
+ kb = kb_array[0, 0]
+
+ pvalue = stats.chisqprob(kb, df)
+ kb_result = {'kb': kb, 'df': df, 'pvalue': pvalue}
+ return kb_result
+
+
+def vif(reg):
+ """
+ Calculates the variance inflation factor for each independent variable.
+ For the ease of indexing the results, the constant is currently
+ included. This should be omitted when reporting the results to the
+ output text.
+
+ Parameters
+ ----------
+ reg : regression object
+ output instance from a regression model
+
+ Returns
+ -------
+ vif_result : list of tuples
+ each tuple includes the vif and the tolerance, the
+ order of the variables corresponds to their order in
+ the reg.x matrix
+
+ References
+ ----------
+ .. [1] W. Greene. 2003. Econometric Analysis. Prentice Hall, Upper
+ Saddle River.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> import diagnostics
+ >>> from ols import OLS
+
+ Read the DBF associated with the Columbus data.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+
+ Create the dependent variable vector.
+
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+
+ Create the matrix of independent variables.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+
+ Run an OLS regression.
+
+ >>> reg = OLS(y,X)
+
+ Calculate the variance inflation factor (VIF).
+ >>> testresult = diagnostics.vif(reg)
+
+ Select the tuple for the income variable.
+
+ >>> incvif = testresult[1]
+
+ Print the VIF for income.
+
+ >>> print("%12.12f"%incvif[0])
+ 1.333117497189
+
+ Print the tolerance for income.
+
+ >>> print("%12.12f"%incvif[1])
+ 0.750121427487
+
+ Repeat for the home value variable.
+
+ >>> hovalvif = testresult[2]
+ >>> print("%12.12f"%hovalvif[0])
+ 1.333117497189
+ >>> print("%12.12f"%hovalvif[1])
+ 0.750121427487
+
+ """
+ X = reg.x
+ n, k = X.shape
+ vif_result = []
+
+ for j in range(k):
+ Z = X.copy()
+ Z = np.delete(Z, j, 1)
+ y = X[:, j]
+ import ols as OLS
+ aux = OLS.BaseOLS(y, Z)
+ mean_y = aux.mean_y
+ utu = aux.utu
+ ss_tot = sum((y - mean_y) ** 2)
+ if ss_tot == 0:
+ resj = pysal.MISSINGVALUE
+ else:
+ r2aux = 1 - utu / ss_tot
+ tolj = 1 - r2aux
+ vifj = 1 / tolj
+ resj = (vifj, tolj)
+ vif_result.append(resj)
+ return vif_result
+
+
+def constant_check(array):
+ """
+ Checks to see numpy array includes a constant.
+
+ Parameters
+ ----------
+ array : array
+ an array of variables to be inspected
+
+ Returns
+ -------
+ constant : boolean
+ true signifies the presence of a constant
+
+ Example
+ -------
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> import diagnostics
+ >>> from ols import OLS
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+ >>> reg = OLS(y,X)
+ >>> diagnostics.constant_check(reg.x)
+ True
+
+ """
+
+ n, k = array.shape
+ constant = False
+ for j in range(k):
+ variable = array[:, j]
+ varmin = variable.min()
+ varmax = variable.max()
+ if varmin == varmax:
+ constant = True
+ break
+ return constant
+
+
+def likratiotest(reg0, reg1):
+ """
+ Likelihood ratio test statistic
+
+ Parameters
+ ----------
+
+ reg0 : regression object for constrained model (H0)
+ reg1 : regression object for unconstrained model (H1)
+
+ Returns
+ -------
+
+ likratio : dictionary
+ contains the statistic (likr), the degrees of
+ freedom (df) and the p-value (pvalue)
+ likr : float
+ likelihood ratio statistic
+ df : integer
+ degrees of freedom
+ p-value : float
+ p-value
+
+ References
+ ----------
+ .. [1] W. Greene. 2012. Econometric Analysis. Prentice Hall, Upper
+ Saddle River.
+
+ Examples
+ --------
+
+ >>> import numpy as np
+ >>> import pysal as ps
+ >>> import scipy.stats as stats
+ >>> import pysal.spreg.ml_lag as lag
+
+ Use the baltim sample data set
+
+ >>> db = ps.open(ps.examples.get_path("baltim.dbf"),'r')
+ >>> y_name = "PRICE"
+ >>> y = np.array(db.by_col(y_name)).T
+ >>> y.shape = (len(y),1)
+ >>> x_names = ["NROOM","NBATH","PATIO","FIREPL","AC","GAR","AGE","LOTSZ","SQFT"]
+ >>> x = np.array([db.by_col(var) for var in x_names]).T
+ >>> ww = ps.open(ps.examples.get_path("baltim_q.gal"))
+ >>> w = ww.read()
+ >>> ww.close()
+ >>> w.transform = 'r'
+
+ OLS regression
+
+ >>> ols1 = ps.spreg.OLS(y,x)
+
+ ML Lag regression
+
+ >>> mllag1 = lag.ML_Lag(y,x,w)
+
+ >>> lr = likratiotest(ols1,mllag1)
+
+ >>> print "Likelihood Ratio Test: {0:.4f} df: {1} p-value: {2:.4f}".format(lr["likr"],lr["df"],lr["p-value"])
+ Likelihood Ratio Test: 44.5721 df: 1 p-value: 0.0000
+
+ """
+
+ likratio = {}
+
+ try:
+ likr = 2.0 * (reg1.logll - reg0.logll)
+ except AttributeError:
+ raise Exception, "Missing or improper log-likelihoods in regression objects"
+ if likr < 0.0: # always enforces positive likelihood ratio
+ likr = -likr
+ pvalue = stats.chisqprob(likr, 1)
+ likratio = {"likr": likr, "df": 1, "p-value": pvalue}
+ return likratio
+
+
+def _test():
+ import doctest
+ doctest.testmod()
+
+if __name__ == '__main__':
+ _test()
diff --git a/pysal/spreg/diagnostics_sp.py b/pysal/spreg/diagnostics_sp.py
new file mode 100644
index 0000000..8d40bc1
--- /dev/null
+++ b/pysal/spreg/diagnostics_sp.py
@@ -0,0 +1,826 @@
+"""
+Spatial diagnostics module
+"""
+__author__ = "Luc Anselin luc.anselin at asu.edu, Daniel Arribas-Bel darribas at asu.edu"
+
+from utils import spdot
+from scipy.stats.stats import chisqprob
+from scipy.stats import norm
+import numpy as np
+import numpy.linalg as la
+
+__all__ = ['LMtests', 'MoranRes', 'AKtest']
+
+
+class LMtests:
+
+ """
+ Lagrange Multiplier tests. Implemented as presented in Anselin et al.
+ (1996) [1]_
+ ...
+
+ Attributes
+ ----------
+
+ ols : OLS
+ OLS regression object
+ w : W
+ Spatial weights instance
+ tests : list
+ Lists of strings with the tests desired to be performed.
+ Values may be:
+
+ * 'all': runs all the options (default)
+ * 'lme': LM error test
+ * 'rlme': Robust LM error test
+ * 'lml' : LM lag test
+ * 'rlml': Robust LM lag test
+
+ Parameters
+ ----------
+
+ lme : tuple
+ (Only if 'lme' or 'all' was in tests). Pair of statistic and
+ p-value for the LM error test.
+ lml : tuple
+ (Only if 'lml' or 'all' was in tests). Pair of statistic and
+ p-value for the LM lag test.
+ rlme : tuple
+ (Only if 'rlme' or 'all' was in tests). Pair of statistic
+ and p-value for the Robust LM error test.
+ rlml : tuple
+ (Only if 'rlml' or 'all' was in tests). Pair of statistic
+ and p-value for the Robust LM lag test.
+ sarma : tuple
+ (Only if 'rlml' or 'all' was in tests). Pair of statistic
+ and p-value for the SARMA test.
+
+ References
+ ----------
+ .. [1] Anselin, L., Bera, A. K., Florax, R., Yoon, M. J. (1996) "Simple
+ diagnostic tests for spatial dependence". Regional Science and Urban
+ Economics, 26, 77-104.
+
+ Examples
+ --------
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> from ols import OLS
+
+ Open the csv file to access the data for analysis
+
+ >>> csv = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+
+ Pull out from the csv the files we need ('HOVAL' as dependent as well as
+ 'INC' and 'CRIME' as independent) and directly transform them into nx1 and
+ nx2 arrays, respectively
+
+ >>> y = np.array([csv.by_col('HOVAL')]).T
+ >>> x = np.array([csv.by_col('INC'), csv.by_col('CRIME')]).T
+
+ Create the weights object from existing .gal file
+
+ >>> w = pysal.open(pysal.examples.get_path('columbus.gal'), 'r').read()
+
+ Row-standardize the weight object (not required although desirable in some
+ cases)
+
+ >>> w.transform='r'
+
+ Run an OLS regression
+
+ >>> ols = OLS(y, x)
+
+ Run all the LM tests in the residuals. These diagnostics test for the
+ presence of remaining spatial autocorrelation in the residuals of an OLS
+ model and give indication about the type of spatial model. There are five
+ types: presence of a spatial lag model (simple and robust version),
+ presence of a spatial error model (simple and robust version) and joint presence
+ of both a spatial lag as well as a spatial error model.
+
+ >>> lms = pysal.spreg.diagnostics_sp.LMtests(ols, w)
+
+ LM error test:
+
+ >>> print round(lms.lme[0],4), round(lms.lme[1],4)
+ 3.0971 0.0784
+
+ LM lag test:
+
+ >>> print round(lms.lml[0],4), round(lms.lml[1],4)
+ 0.9816 0.3218
+
+ Robust LM error test:
+
+ >>> print round(lms.rlme[0],4), round(lms.rlme[1],4)
+ 3.2092 0.0732
+
+ Robust LM lag test:
+
+ >>> print round(lms.rlml[0],4), round(lms.rlml[1],4)
+ 1.0936 0.2957
+
+ LM SARMA test:
+
+ >>> print round(lms.sarma[0],4), round(lms.sarma[1],4)
+ 4.1907 0.123
+ """
+
+ def __init__(self, ols, w, tests=['all']):
+ cache = spDcache(ols, w)
+ if tests == ['all']:
+ tests = ['lme', 'lml', 'rlme', 'rlml', 'sarma']
+ if 'lme' in tests:
+ self.lme = lmErr(ols, w, cache)
+ if 'lml' in tests:
+ self.lml = lmLag(ols, w, cache)
+ if 'rlme' in tests:
+ self.rlme = rlmErr(ols, w, cache)
+ if 'rlml' in tests:
+ self.rlml = rlmLag(ols, w, cache)
+ if 'sarma' in tests:
+ self.sarma = lmSarma(ols, w, cache)
+
+
+class MoranRes:
+
+ """
+ Moran's I for spatial autocorrelation in residuals from OLS regression
+ ...
+
+ Parameters
+ ----------
+
+ ols : OLS
+ OLS regression object
+ w : W
+ Spatial weights instance
+ z : boolean
+ If set to True computes attributes eI, vI and zI. Due to computational burden of vI, defaults to False.
+
+ Attributes
+ ----------
+ I : float
+ Moran's I statistic
+ eI : float
+ Moran's I expectation
+ vI : float
+ Moran's I variance
+ zI : float
+ Moran's I standardized value
+
+ Examples
+ --------
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> from ols import OLS
+
+ Open the csv file to access the data for analysis
+
+ >>> csv = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+
+ Pull out from the csv the files we need ('HOVAL' as dependent as well as
+ 'INC' and 'CRIME' as independent) and directly transform them into nx1 and
+ nx2 arrays, respectively
+
+ >>> y = np.array([csv.by_col('HOVAL')]).T
+ >>> x = np.array([csv.by_col('INC'), csv.by_col('CRIME')]).T
+
+ Create the weights object from existing .gal file
+
+ >>> w = pysal.open(pysal.examples.get_path('columbus.gal'), 'r').read()
+
+ Row-standardize the weight object (not required although desirable in some
+ cases)
+
+ >>> w.transform='r'
+
+ Run an OLS regression
+
+ >>> ols = OLS(y, x)
+
+ Run Moran's I test for residual spatial autocorrelation in an OLS model.
+ This computes the traditional statistic applying a correction in the
+ expectation and variance to account for the fact it comes from residuals
+ instead of an independent variable
+
+ >>> m = pysal.spreg.diagnostics_sp.MoranRes(ols, w, z=True)
+
+ Value of the Moran's I statistic:
+
+ >>> print round(m.I,4)
+ 0.1713
+
+ Value of the Moran's I expectation:
+
+ >>> print round(m.eI,4)
+ -0.0345
+
+ Value of the Moran's I variance:
+
+ >>> print round(m.vI,4)
+ 0.0081
+
+ Value of the Moran's I standardized value. This is
+ distributed as a standard Normal(0, 1)
+
+ >>> print round(m.zI,4)
+ 2.2827
+
+ P-value of the standardized Moran's I value (z):
+
+ >>> print round(m.p_norm,4)
+ 0.0224
+ """
+
+ def __init__(self, ols, w, z=False):
+ cache = spDcache(ols, w)
+ self.I = get_mI(ols, w, cache)
+ if z:
+ self.eI = get_eI(ols, w, cache)
+ self.vI = get_vI(ols, w, self.eI, cache)
+ self.zI, self.p_norm = get_zI(self.I, self.eI, self.vI)
+
+
+class AKtest:
+
+ """
+ Moran's I test of spatial autocorrelation for IV estimation.
+ Implemented following the original reference Anselin and Kelejian
+ (1997) [AK97]_
+ ...
+
+ Parameters
+ ----------
+
+ iv : TSLS
+ Regression object from TSLS class
+ w : W
+ Spatial weights instance
+ case : string
+ Flag for special cases (default to 'nosp'):
+
+ * 'nosp': Only NO spatial end. reg.
+ * 'gen': General case (spatial lag + end. reg.)
+
+ Attributes
+ ----------
+
+ mi : float
+ Moran's I statistic for IV residuals
+ ak : float
+ Square of corrected Moran's I for residuals::
+
+ .. math::
+
+ ak = \dfrac{N \times I^*}{\phi^2}
+
+ Note: if case='nosp' then it simplifies to the LMerror
+ p : float
+ P-value of the test
+
+ References
+ ----------
+
+ .. [AK97] Anselin, L., Kelejian, H. (1997) "Testing for spatial error
+ autocorrelation in the presence of endogenous regressors".
+ Interregional Regional Science Review, 20, 1.
+ .. [2] Kelejian, H.H., Prucha, I.R. and Yuzefovich, Y. (2004)
+ "Instrumental variable estimation of a spatial autorgressive model with
+ autoregressive disturbances: large and small sample results".
+ Advances in Econometrics, 18, 163-198.
+
+ Examples
+ --------
+
+ We first need to import the needed modules. Numpy is needed to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis. The TSLS is required to run the model on
+ which we will perform the tests.
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> from twosls import TSLS
+ >>> from twosls_sp import GM_Lag
+
+ Open data on Columbus neighborhood crime (49 areas) using pysal.open().
+ This is the DBF associated with the Columbus shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
+
+ Before being able to apply the diagnostics, we have to run a model and,
+ for that, we need the input variables. Extract the CRIME column (crime
+ rates) from the DBF file and make it the dependent variable for the
+ regression. Note that PySAL requires this to be an numpy array of shape
+ (n, 1) as opposed to the also common shape of (n, ) that other packages
+ accept.
+
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+
+ Extract INC (income) vector from the DBF to be used as
+ independent variables in the regression. Note that PySAL requires this to
+ be an nxj numpy array, where j is the number of independent variables (not
+ including a constant). By default this model adds a vector of ones to the
+ independent variables passed in, but this can be overridden by passing
+ constant=False.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X = np.array(X).T
+
+ In this case, we consider HOVAL (home value) as an endogenous regressor,
+ so we acknowledge that by reading it in a different category.
+
+ >>> yd = []
+ >>> yd.append(db.by_col("HOVAL"))
+ >>> yd = np.array(yd).T
+
+ In order to properly account for the endogeneity, we have to pass in the
+ instruments. Let us consider DISCBD (distance to the CBD) is a good one:
+
+ >>> q = []
+ >>> q.append(db.by_col("DISCBD"))
+ >>> q = np.array(q).T
+
+ Now we are good to run the model. It is an easy one line task.
+
+ >>> reg = TSLS(y, X, yd, q=q)
+
+ Now we are concerned with whether our non-spatial model presents spatial
+ autocorrelation in the residuals. To assess this possibility, we can run
+ the Anselin-Kelejian test, which is a version of the classical LM error
+ test adapted for the case of residuals from an instrumental variables (IV)
+ regression. First we need an extra object, the weights matrix, which
+ includes the spatial configuration of the observations
+ into the error component of the model. To do that, we can open an already
+ existing gal file or create a new one. In this case, we will create one
+ from ``columbus.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, this allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ We are good to run the test. It is a very simple task:
+
+ >>> ak = AKtest(reg, w)
+
+ And explore the information obtained:
+
+ >>> print('AK test: %f\tP-value: %f'%(ak.ak, ak.p))
+ AK test: 4.642895 P-value: 0.031182
+
+ The test also accomodates the case when the residuals come from an IV
+ regression that includes a spatial lag of the dependent variable. The only
+ requirement needed is to modify the ``case`` parameter when we call
+ ``AKtest``. First, let us run a spatial lag model:
+
+ >>> reg_lag = GM_Lag(y, X, yd, q=q, w=w)
+
+ And now we can run the AK test and obtain similar information as in the
+ non-spatial model.
+
+ >>> ak_sp = AKtest(reg, w, case='gen')
+ >>> print('AK test: %f\tP-value: %f'%(ak_sp.ak, ak_sp.p))
+ AK test: 1.157593 P-value: 0.281965
+
+ """
+
+ def __init__(self, iv, w, case='nosp'):
+ if case == 'gen':
+ cache = spDcache(iv, w)
+ self.mi, self.ak, self.p = akTest(iv, w, cache)
+ elif case == 'nosp':
+ cache = spDcache(iv, w)
+ self.mi = get_mI(iv, w, cache)
+ self.ak, self.p = lmErr(iv, w, cache)
+ else:
+ print """\n
+ Fix the optional argument 'case' to match the requirements:
+ * 'gen': General case (spatial lag + end. reg.)
+ * 'nosp': No spatial end. reg.
+ \n"""
+
+
+class spDcache:
+
+ """
+ Helper class to compute reusable pieces in the spatial diagnostics module
+ ...
+
+ Parameters
+ ----------
+
+ reg : OLS_dev, TSLS_dev, STSLS_dev
+ Instance from a regression class
+ w : W
+ Spatial weights instance
+
+ Attributes
+ ----------
+
+ j : array
+ 1x1 array with the result from:
+
+ .. math::
+
+ J = \dfrac{1}{[(WX\beta)' M (WX\beta) + T \sigma^2]}
+
+ wu : array
+ nx1 array with spatial lag of the residuals
+
+ utwuDs : array
+ 1x1 array with the result from:
+
+ .. math::
+
+ utwuDs = \dfrac{u' W u}{\tilde{\sigma^2}}
+
+ utwyDs : array
+ 1x1 array with the result from:
+
+ .. math::
+
+ utwyDs = \dfrac{u' W y}{\tilde{\sigma^2}}
+
+
+ t : array
+ 1x1 array with the result from :
+
+ .. math::
+
+ T = tr[(W' + W) W]
+
+ trA : float
+ Trace of A as in Cliff & Ord (1981)
+
+ """
+
+ def __init__(self, reg, w):
+ self.reg = reg
+ self.w = w
+ self._cache = {}
+
+ @property
+ def j(self):
+ if 'j' not in self._cache:
+ wxb = self.w.sparse * self.reg.predy
+ wxb2 = np.dot(wxb.T, wxb)
+ xwxb = spdot(self.reg.x.T, wxb)
+ num1 = wxb2 - np.dot(xwxb.T, np.dot(self.reg.xtxi, xwxb))
+ num = num1 + (self.t * self.reg.sig2n)
+ den = self.reg.n * self.reg.sig2n
+ self._cache['j'] = num / den
+ return self._cache['j']
+
+ @property
+ def wu(self):
+ if 'wu' not in self._cache:
+ self._cache['wu'] = self.w.sparse * self.reg.u
+ return self._cache['wu']
+
+ @property
+ def utwuDs(self):
+ if 'utwuDs' not in self._cache:
+ res = np.dot(self.reg.u.T, self.wu) / self.reg.sig2n
+ self._cache['utwuDs'] = res
+ return self._cache['utwuDs']
+
+ @property
+ def utwyDs(self):
+ if 'utwyDs' not in self._cache:
+ res = np.dot(self.reg.u.T, self.w.sparse * self.reg.y)
+ self._cache['utwyDs'] = res / self.reg.sig2n
+ return self._cache['utwyDs']
+
+ @property
+ def t(self):
+ if 't' not in self._cache:
+ prod = (self.w.sparse.T + self.w.sparse) * self.w.sparse
+ self._cache['t'] = np.sum(prod.diagonal())
+ return self._cache['t']
+
+ @property
+ def trA(self):
+ if 'trA' not in self._cache:
+ xtwx = spdot(self.reg.x.T, spdot(self.w.sparse, self.reg.x))
+ mw = np.dot(self.reg.xtxi, xtwx)
+ self._cache['trA'] = np.sum(mw.diagonal())
+ return self._cache['trA']
+
+ @property
+ def AB(self):
+ """
+ Computes A and B matrices as in Cliff-Ord 1981, p. 203
+ """
+ if 'AB' not in self._cache:
+ U = (self.w.sparse + self.w.sparse.T) / 2.
+ z = spdot(U, self.reg.x, array_out=False)
+ c1 = spdot(self.reg.x.T, z, array_out=False)
+ c2 = spdot(z.T, z, array_out=False)
+ G = self.reg.xtxi
+ A = spdot(G, c1)
+ B = spdot(G, c2)
+ self._cache['AB'] = [A, B]
+ return self._cache['AB']
+
+
+def lmErr(reg, w, spDcache):
+ """
+ LM error test. Implemented as presented in eq. (9) of Anselin et al.
+ (1996) [1]_
+ ...
+
+ Attributes
+ ----------
+
+ reg : OLS_dev, TSLS_dev, STSLS_dev
+ Instance from a regression class
+ w : W
+ Spatial weights instance
+ spDcache : spDcache
+ Instance of spDcache class
+
+ Returns
+ -------
+
+ lme : tuple
+ Pair of statistic and p-value for the LM error test.
+
+ References
+ ----------
+ .. _ Anselin, L., Bera, A. K., Florax, R., Yoon, M. J. (1996) "Simple
+ diagnostic tests for spatial dependence". Regional Science and Urban
+ Economics, 26, 77-104.
+ """
+ lm = spDcache.utwuDs ** 2 / spDcache.t
+ pval = chisqprob(lm, 1)
+ return (lm[0][0], pval[0][0])
+
+
+def lmLag(ols, w, spDcache):
+ """
+ LM lag test. Implemented as presented in eq. (13) of Anselin et al.
+ (1996) [1]_
+ ...
+
+ Attributes
+ ----------
+
+ ols : OLS_dev
+ Instance from an OLS_dev regression
+ w : W
+ Spatial weights instance
+ spDcache : spDcache
+ Instance of spDcache class
+
+ Returns
+ -------
+
+ lml : tuple
+ Pair of statistic and p-value for the LM lag test.
+
+ References
+ ----------
+ .. _ Anselin, L., Bera, A. K., Florax, R., Yoon, M. J. (1996) "Simple
+ diagnostic tests for spatial dependence". Regional Science and Urban
+ Economics, 26, 77-104.
+ """
+ lm = spDcache.utwyDs ** 2 / (ols.n * spDcache.j)
+ pval = chisqprob(lm, 1)
+ return (lm[0][0], pval[0][0])
+
+
+def rlmErr(ols, w, spDcache):
+ """
+ Robust LM error test. Implemented as presented in eq. (8) of Anselin et al. (1996) [1]_
+
+ NOTE: eq. (8) has an errata, the power -1 in the denominator should be inside the square bracket.
+ ...
+
+ Attributes
+ ----------
+
+ ols : OLS_dev
+ Instance from an OLS_dev regression
+ w : W
+ Spatial weights instance
+ spDcache : spDcache
+ Instance of spDcache class
+
+ Returns
+ -------
+
+ rlme : tuple
+ Pair of statistic and p-value for the Robust LM error test.
+
+ References
+ ----------
+ .. _ Anselin, L., Bera, A. K., Florax, R., Yoon, M. J. (1996) "Simple
+ diagnostic tests for spatial dependence". Regional Science and Urban
+ Economics, 26, 77-104.
+ """
+ nj = ols.n * spDcache.j
+ num = (spDcache.utwuDs - (spDcache.t * spDcache.utwyDs) / nj) ** 2
+ den = spDcache.t * (1. - (spDcache.t / nj))
+ lm = num / den
+ pval = chisqprob(lm, 1)
+ return (lm[0][0], pval[0][0])
+
+
+def rlmLag(ols, w, spDcache):
+ """
+ Robust LM lag test. Implemented as presented in eq. (12) of Anselin et al.
+ (1996) [1]_
+ ...
+
+ Attributes
+ ----------
+
+ ols : OLS_dev
+ Instance from an OLS_dev regression
+ w : W
+ Spatial weights instance
+ spDcache : spDcache
+ Instance of spDcache class
+
+ Returns
+ -------
+
+ rlml : tuple
+ Pair of statistic and p-value for the Robust LM lag test.
+
+ References
+ ----------
+ .. _ Anselin, L., Bera, A. K., Florax, R., Yoon, M. J. (1996) "Simple
+ diagnostic tests for spatial dependence". Regional Science and Urban
+ Economics, 26, 77-104.
+ """
+ lm = (spDcache.utwyDs - spDcache.utwuDs) ** 2 / \
+ ((ols.n * spDcache.j) - spDcache.t)
+ pval = chisqprob(lm, 1)
+ return (lm[0][0], pval[0][0])
+
+
+def lmSarma(ols, w, spDcache):
+ """
+ LM error test. Implemented as presented in eq. (15) of Anselin et al.
+ (1996) [1]_
+ ...
+
+ Attributes
+ ----------
+
+ ols : OLS_dev
+ Instance from an OLS_dev regression
+ w : W
+ Spatial weights instance
+ spDcache : spDcache
+ Instance of spDcache class
+
+ Returns
+ -------
+
+ sarma : tuple
+ Pair of statistic and p-value for the LM sarma test.
+
+ References
+ ----------
+ .. _ Anselin, L., Bera, A. K., Florax, R., Yoon, M. J. (1996) "Simple
+ diagnostic tests for spatial dependence". Regional Science and Urban
+ Economics, 26, 77-104.
+ """
+
+ first = (spDcache.utwyDs - spDcache.utwuDs) ** 2 / \
+ (w.n * spDcache.j - spDcache.t)
+ secnd = spDcache.utwuDs ** 2 / spDcache.t
+ lm = first + secnd
+ pval = chisqprob(lm, 2)
+ return (lm[0][0], pval[0][0])
+
+
+def get_mI(reg, w, spDcache):
+ """
+ Moran's I statistic of spatial autocorrelation as showed in Cliff & Ord
+ (1981) [CO81]_, p. 201-203
+ ...
+
+ Attributes
+ ----------
+
+ reg : OLS_dev, TSLS_dev, STSLS_dev
+ Instance from a regression class
+ w : W
+ Spatial weights instance
+ spDcache : spDcache
+ Instance of spDcache class
+
+ Returns
+ -------
+
+ moran : float
+ Statistic Moran's I test.
+
+ References
+ ----------
+ .. [CO81] Cliff, AD., Ord, JK. (1981) "Spatial processes: models & applications".
+ Pion London
+ """
+ mi = (w.n * np.dot(reg.u.T, spDcache.wu)) / (w.s0 * reg.utu)
+ return mi[0][0]
+
+
+def get_vI(ols, w, ei, spDcache):
+ """
+ Moran's I variance coded as in Cliff & Ord 1981 (p. 201-203) and R's spdep
+ """
+ A = spDcache.AB[0]
+ trA2 = np.dot(A, A)
+ trA2 = np.sum(trA2.diagonal())
+
+ B = spDcache.AB[1]
+ trB = np.sum(B.diagonal()) * 4.
+ vi = (w.n ** 2 / (w.s0 ** 2 * (w.n - ols.k) * (w.n - ols.k + 2.))) * \
+ (w.s1 + 2. * trA2 - trB -
+ ((2. * (spDcache.trA ** 2)) / (w.n - ols.k)))
+ return vi
+
+
+def get_eI(ols, w, spDcache):
+ """
+ Moran's I expectation using matrix M
+ """
+ return - (w.n * spDcache.trA) / (w.s0 * (w.n - ols.k))
+
+
+def get_zI(I, ei, vi):
+ """
+ Standardized I
+
+ Returns two-sided p-values as provided in the GeoDa family
+ """
+ z = abs((I - ei) / np.sqrt(vi))
+ pval = norm.sf(z) * 2.
+ return (z, pval)
+
+
+def akTest(iv, w, spDcache):
+ """
+ Computes AK-test for the general case (end. reg. + sp. lag)
+ ...
+
+ Parameters
+ ----------
+
+ iv : STSLS_dev
+ Instance from spatial 2SLS regression
+ w : W
+ Spatial weights instance
+ spDcache : spDcache
+ Instance of spDcache class
+
+ Attributes
+ ----------
+ mi : float
+ Moran's I statistic for IV residuals
+ ak : float
+ Square of corrected Moran's I for residuals::
+
+ .. math::
+ ak = \dfrac{N \times I^*}{\phi^2}
+
+ p : float
+ P-value of the test
+
+ ToDo:
+ * Code in as Nancy
+ * Compare both
+ """
+ mi = get_mI(iv, w, spDcache)
+ # Phi2
+ etwz = spdot(iv.u.T, spdot(w.sparse, iv.z))
+ a = np.dot(etwz, np.dot(iv.varb, etwz.T))
+ s12 = (w.s0 / w.n) ** 2
+ phi2 = (spDcache.t + (4.0 / iv.sig2n) * a) / (s12 * w.n)
+ ak = w.n * mi ** 2 / phi2
+ pval = chisqprob(ak, 1)
+ return (mi, ak[0][0], pval[0][0])
+
+
+def _test():
+ import doctest
+ doctest.testmod()
+
+if __name__ == '__main__':
+ _test()
diff --git a/pysal/spreg/diagnostics_tsls.py b/pysal/spreg/diagnostics_tsls.py
new file mode 100644
index 0000000..257887b
--- /dev/null
+++ b/pysal/spreg/diagnostics_tsls.py
@@ -0,0 +1,337 @@
+"""
+Diagnostics for two stage least squares regression estimations.
+
+"""
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, Nicholas Malizia nicholas.malizia at asu.edu "
+
+from pysal.common import *
+from scipy.stats import pearsonr
+
+__all__ = ["t_stat", "pr2_aspatial", "pr2_spatial"]
+
+
+def t_stat(reg, z_stat=False):
+ """
+ Calculates the t-statistics (or z-statistics) and associated p-values.
+
+ Parameters
+ ----------
+ reg : regression object
+ output instance from a regression model
+ z_stat : boolean
+ If True run z-stat instead of t-stat
+
+ Returns
+ -------
+ ts_result : list of tuples
+ each tuple includes value of t statistic (or z
+ statistic) and associated p-value
+
+ References
+ ----------
+ .. [1] W. Greene. 2003. Econometric Analysis. Prentice Hall, Upper
+ Saddle River.
+
+ Examples
+ --------
+
+ We first need to import the needed modules. Numpy is needed to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis. The ``diagnostics`` module is used for the tests
+ we will show here and the OLS and TSLS are required to run the models on
+ which we will perform the tests.
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> import pysal.spreg.diagnostics as diagnostics
+ >>> from pysal.spreg.ols import OLS
+ >>> from twosls import TSLS
+
+ Open data on Columbus neighborhood crime (49 areas) using pysal.open().
+ This is the DBF associated with the Columbus shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
+
+ Before being able to apply the diagnostics, we have to run a model and,
+ for that, we need the input variables. Extract the CRIME column (crime
+ rates) from the DBF file and make it the dependent variable for the
+ regression. Note that PySAL requires this to be an numpy array of shape
+ (n, 1) as opposed to the also common shape of (n, ) that other packages
+ accept.
+
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+
+ Extract INC (income) and HOVAL (home value) vector from the DBF to be used as
+ independent variables in the regression. Note that PySAL requires this to
+ be an nxj numpy array, where j is the number of independent variables (not
+ including a constant). By default this model adds a vector of ones to the
+ independent variables passed in, but this can be overridden by passing
+ constant=False.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+
+ Run an OLS regression. Since it is a non-spatial model, all we need is the
+ dependent and the independent variable.
+
+ >>> reg = OLS(y,X)
+
+ Now we can perform a t-statistic on the model:
+
+ >>> testresult = diagnostics.t_stat(reg)
+ >>> print("%12.12f"%testresult[0][0], "%12.12f"%testresult[0][1], "%12.12f"%testresult[1][0], "%12.12f"%testresult[1][1], "%12.12f"%testresult[2][0], "%12.12f"%testresult[2][1])
+ ('14.490373143689', '0.000000000000', '-4.780496191297', '0.000018289595', '-2.654408642718', '0.010874504910')
+
+ We can also use the z-stat. For that, we re-build the model so we consider
+ HOVAL as endogenous, instrument for it using DISCBD and carry out two
+ stage least squares (TSLS) estimation.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X = np.array(X).T
+ >>> yd = []
+ >>> yd.append(db.by_col("HOVAL"))
+ >>> yd = np.array(yd).T
+ >>> q = []
+ >>> q.append(db.by_col("DISCBD"))
+ >>> q = np.array(q).T
+
+ Once the variables are read as different objects, we are good to run the
+ model.
+
+ >>> reg = TSLS(y, X, yd, q)
+
+ With the output of the TSLS regression, we can perform a z-statistic:
+
+ >>> testresult = diagnostics.t_stat(reg, z_stat=True)
+ >>> print("%12.10f"%testresult[0][0], "%12.10f"%testresult[0][1], "%12.10f"%testresult[1][0], "%12.10f"%testresult[1][1], "%12.10f"%testresult[2][0], "%12.10f"%testresult[2][1])
+ ('5.8452644705', '0.0000000051', '0.3676015668', '0.7131703463', '-1.9946891308', '0.0460767956')
+ """
+
+ k = reg.k # (scalar) number of ind. vas (includes constant)
+ n = reg.n # (scalar) number of observations
+ vm = reg.vm # (array) coefficients of variance matrix (k x k)
+ betas = reg.betas # (array) coefficients of the regressors (1 x k)
+ variance = vm.diagonal()
+ tStat = betas.reshape(len(betas),) / np.sqrt(variance)
+ ts_result = []
+ for t in tStat:
+ if z_stat:
+ ts_result.append((t, stats.norm.sf(abs(t)) * 2))
+ else:
+ ts_result.append((t, stats.t.sf(abs(t), n - k) * 2))
+ return ts_result
+
+
+def pr2_aspatial(tslsreg):
+ """
+ Calculates the pseudo r^2 for the two stage least squares regression.
+
+ Parameters
+ ----------
+ tslsreg : two stage least squares regression object
+ output instance from a two stage least squares
+ regression model
+
+ Returns
+ -------
+ pr2_result : float
+ value of the squared pearson correlation between
+ the y and tsls-predicted y vectors
+
+ Examples
+ --------
+
+ We first need to import the needed modules. Numpy is needed to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis. The TSLS is required to run the model on
+ which we will perform the tests.
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> from twosls import TSLS
+
+ Open data on Columbus neighborhood crime (49 areas) using pysal.open().
+ This is the DBF associated with the Columbus shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
+
+ Before being able to apply the diagnostics, we have to run a model and,
+ for that, we need the input variables. Extract the CRIME column (crime
+ rates) from the DBF file and make it the dependent variable for the
+ regression. Note that PySAL requires this to be an numpy array of shape
+ (n, 1) as opposed to the also common shape of (n, ) that other packages
+ accept.
+
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+
+ Extract INC (income) vector from the DBF to be used as
+ independent variables in the regression. Note that PySAL requires this to
+ be an nxj numpy array, where j is the number of independent variables (not
+ including a constant). By default this model adds a vector of ones to the
+ independent variables passed in, but this can be overridden by passing
+ constant=False.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X = np.array(X).T
+
+ In this case, we consider HOVAL (home value) as an endogenous regressor,
+ so we acknowledge that by reading it in a different category.
+
+ >>> yd = []
+ >>> yd.append(db.by_col("HOVAL"))
+ >>> yd = np.array(yd).T
+
+ In order to properly account for the endogeneity, we have to pass in the
+ instruments. Let us consider DISCBD (distance to the CBD) is a good one:
+
+ >>> q = []
+ >>> q.append(db.by_col("DISCBD"))
+ >>> q = np.array(q).T
+
+ Now we are good to run the model. It is an easy one line task.
+
+ >>> reg = TSLS(y, X, yd, q=q)
+
+ In order to perform the pseudo R^2, we pass the regression object to the
+ function and we are done!
+
+ >>> result = pr2_aspatial(reg)
+ >>> print("%1.6f"%result)
+ 0.279361
+
+ """
+
+ y = tslsreg.y
+ predy = tslsreg.predy
+ pr = pearsonr(y, predy)[0]
+ pr2_result = float(pr ** 2)
+ return pr2_result
+
+
+def pr2_spatial(tslsreg):
+ """
+ Calculates the pseudo r^2 for the spatial two stage least squares
+ regression.
+
+ Parameters
+ ----------
+ stslsreg : spatial two stage least squares regression object
+ output instance from a spatial two stage least
+ squares regression model
+
+ Returns
+ -------
+ pr2_result : float
+ value of the squared pearson correlation between
+ the y and stsls-predicted y vectors
+
+ Examples
+ --------
+
+ We first need to import the needed modules. Numpy is needed to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis. The GM_Lag is required to run the model on
+ which we will perform the tests and the ``pysal.spreg.diagnostics`` module
+ contains the function with the test.
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> import pysal.spreg.diagnostics as D
+ >>> from twosls_sp import GM_Lag
+
+ Open data on Columbus neighborhood crime (49 areas) using pysal.open().
+ This is the DBF associated with the Columbus shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
+
+ Extract the HOVAL column (home value) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y = np.array(db.by_col("HOVAL"))
+ >>> y = np.reshape(y, (49,1))
+
+ Extract INC (income) vectors from the DBF to be used as
+ independent variables in the regression. Note that PySAL requires this to
+ be an nxj numpy array, where j is the number of independent variables (not
+ including a constant). By default this model adds a vector of ones to the
+ independent variables passed in, but this can be overridden by passing
+ constant=False.
+
+ >>> X = np.array(db.by_col("INC"))
+ >>> X = np.reshape(X, (49,1))
+
+ In this case, we consider CRIME (crime rates) as an endogenous regressor,
+ so we acknowledge that by reading it in a different category.
+
+ >>> yd = np.array(db.by_col("CRIME"))
+ >>> yd = np.reshape(yd, (49,1))
+
+ In order to properly account for the endogeneity, we have to pass in the
+ instruments. Let us consider DISCBD (distance to the CBD) is a good one:
+
+ >>> q = np.array(db.by_col("DISCBD"))
+ >>> q = np.reshape(q, (49,1))
+
+ Since this test has a spatial component, we need to specify the spatial
+ weights matrix that includes the spatial configuration of the observations
+ into the error component of the model. To do that, we can open an already
+ existing gal file or create a new one. In this case, we will create one
+ from ``columbus.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, this allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ Now we are good to run the spatial lag model. Make sure you pass all the
+ parameters correctly and, if desired, pass the names of the variables as
+ well so when you print the summary (reg.summary) they are included:
+
+ >>> reg = GM_Lag(y, X, w=w, yend=yd, q=q, w_lags=2, name_x=['inc'], name_y='hoval', name_yend=['crime'], name_q=['discbd'], name_ds='columbus')
+
+ Once we have a regression object, we can perform the spatial version of
+ the pesudo R^2. It is as simple as one line!
+
+ >>> result = pr2_spatial(reg)
+ >>> print("%1.6f"%result)
+ 0.299649
+
+ """
+
+ y = tslsreg.y
+ predy_e = tslsreg.predy_e
+ pr = pearsonr(y, predy_e)[0]
+ pr2_result = float(pr ** 2)
+ return pr2_result
+
+
+def _test():
+ import doctest
+ doctest.testmod()
+
+
+if __name__ == '__main__':
+ _test()
diff --git a/pysal/spreg/error_sp.py b/pysal/spreg/error_sp.py
new file mode 100644
index 0000000..408d353
--- /dev/null
+++ b/pysal/spreg/error_sp.py
@@ -0,0 +1,1133 @@
+"""
+Spatial Error Models module
+"""
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, \
+ Daniel Arribas-Bel darribas at asu.edu, \
+ Pedro V. Amaral pedro.amaral at asu.edu"
+
+import numpy as np
+from numpy import linalg as la
+import ols as OLS
+from pysal import lag_spatial
+from utils import power_expansion, set_endog, iter_msg, sp_att
+from utils import get_A1_hom, get_A2_hom, get_A1_het, optim_moments, get_spFilter, get_lags, _moments2eqs
+from utils import spdot, RegressionPropsY, set_warn
+import twosls as TSLS
+import user_output as USER
+import summary_output as SUMMARY
+
+__all__ = ["GM_Error", "GM_Endog_Error", "GM_Combo"]
+
+
+class BaseGM_Error(RegressionPropsY):
+
+ """
+ GMM method for a spatial error model (note: no consistency checks
+ diagnostics or constant added); based on Kelejian and Prucha
+ (1998, 1999)[1]_ [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ w : Sparse matrix
+ Spatial weights sparse matrix
+
+ Attributes
+ ----------
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ sig2 : float
+ Sigma squared used in computations
+
+
+ References
+ ----------
+
+ .. [1] Kelejian, H.R., Prucha, I.R. (1998) "A generalized spatial
+ two-stage least squares procedure for estimating a spatial autoregressive
+ model with autoregressive disturbances". The Journal of Real State
+ Finance and Economics, 17, 1.
+
+ .. [2] Kelejian, H.R., Prucha, I.R. (1999) "A Generalized Moments
+ Estimator for the Autoregressive Parameter in a Spatial Model".
+ International Economic Review, 40, 2.
+
+ Examples
+ --------
+
+ >>> import pysal
+ >>> import numpy as np
+ >>> dbf = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> y = np.array([dbf.by_col('HOVAL')]).T
+ >>> x = np.array([dbf.by_col('INC'), dbf.by_col('CRIME')]).T
+ >>> x = np.hstack((np.ones(y.shape),x))
+ >>> w = pysal.open(pysal.examples.get_path("columbus.gal"), 'r').read()
+ >>> w.transform='r'
+ >>> model = BaseGM_Error(y, x, w=w.sparse)
+ >>> np.around(model.betas, decimals=4)
+ array([[ 47.6946],
+ [ 0.7105],
+ [ -0.5505],
+ [ 0.3257]])
+ """
+
+ def __init__(self, y, x, w):
+
+ # 1a. OLS --> \tilde{betas}
+ ols = OLS.BaseOLS(y=y, x=x)
+ self.n, self.k = ols.x.shape
+ self.x = ols.x
+ self.y = ols.y
+
+ # 1b. GMM --> \tilde{\lambda1}
+ moments = _momentsGM_Error(w, ols.u)
+ lambda1 = optim_moments(moments)
+
+ # 2a. OLS -->\hat{betas}
+ xs = get_spFilter(w, lambda1, self.x)
+ ys = get_spFilter(w, lambda1, self.y)
+ ols2 = OLS.BaseOLS(y=ys, x=xs)
+
+ # Output
+ self.predy = spdot(self.x, ols2.betas)
+ self.u = y - self.predy
+ self.betas = np.vstack((ols2.betas, np.array([[lambda1]])))
+ self.sig2 = ols2.sig2n
+ self.e_filtered = self.u - lambda1 * w * self.u
+
+ self.vm = self.sig2 * ols2.xtxi
+ se_betas = np.sqrt(self.vm.diagonal())
+ self._cache = {}
+
+
+class GM_Error(BaseGM_Error):
+
+ """
+ GMM method for a spatial error model, with results and diagnostics; based
+ on Kelejian and Prucha (1998, 1999)[1]_ [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ w : pysal W object
+ Spatial weights object (always needed)
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ vm : array
+ Variance covariance matrix (kxk)
+ sig2 : float
+ Sigma squared used in computations
+ std_err : array
+ 1xk array of standard errors of the betas
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ title : string
+ Name of the regression method used
+
+ References
+ ----------
+
+ .. [1] Kelejian, H.R., Prucha, I.R. (1998) "A generalized spatial
+ two-stage least squares procedure for estimating a spatial autoregressive
+ model with autoregressive disturbances". The Journal of Real State
+ Finance and Economics, 17, 1.
+ .. [2] Kelejian, H.R., Prucha, I.R. (1999) "A Generalized Moments
+ Estimator for the Autoregressive Parameter in a Spatial Model".
+ International Economic Review, 40, 2.
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import pysal
+ >>> import numpy as np
+
+ Open data on Columbus neighborhood crime (49 areas) using pysal.open().
+ This is the DBF associated with the Columbus shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> dbf = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+
+ Extract the HOVAL column (home values) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y = np.array([dbf.by_col('HOVAL')]).T
+
+ Extract CRIME (crime) and INC (income) vectors from the DBF to be used as
+ independent variables in the regression. Note that PySAL requires this to
+ be an nxj numpy array, where j is the number of independent variables (not
+ including a constant). By default this class adds a vector of ones to the
+ independent variables passed in.
+
+ >>> names_to_extract = ['INC', 'CRIME']
+ >>> x = np.array([dbf.by_col(name) for name in names_to_extract]).T
+
+ Since we want to run a spatial error model, we need to specify the spatial
+ weights matrix that includes the spatial configuration of the observations
+ into the error component of the model. To do that, we can open an already
+ existing gal file or create a new one. In this case, we will use
+ ``columbus.gal``, which contains contiguity relationships between the
+ observations in the Columbus dataset we are using throughout this example.
+ Note that, in order to read the file, not only to open it, we need to
+ append '.read()' at the end of the command.
+
+ >>> w = pysal.open(pysal.examples.get_path("columbus.gal"), 'r').read()
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, his allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform='r'
+
+ We are all set with the preliminars, we are good to run the model. In this
+ case, we will need the variables and the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional.
+
+ >>> model = GM_Error(y, x, w=w, name_y='hoval', name_x=['income', 'crime'], name_ds='columbus')
+
+ Once we have run the model, we can explore a little bit the output. The
+ regression object we have created has many attributes so take your time to
+ discover them. Note that because we are running the classical GMM error
+ model from 1998/99, the spatial parameter is obtained as a point estimate, so
+ although you get a value for it (there are for coefficients under
+ model.betas), you cannot perform inference on it (there are only three
+ values in model.se_betas).
+
+ >>> print model.name_x
+ ['CONSTANT', 'income', 'crime', 'lambda']
+ >>> np.around(model.betas, decimals=4)
+ array([[ 47.6946],
+ [ 0.7105],
+ [ -0.5505],
+ [ 0.3257]])
+ >>> np.around(model.std_err, decimals=4)
+ array([ 12.412 , 0.5044, 0.1785])
+ >>> np.around(model.z_stat, decimals=6) #doctest: +SKIP
+ array([[ 3.84261100e+00, 1.22000000e-04],
+ [ 1.40839200e+00, 1.59015000e-01],
+ [ -3.08424700e+00, 2.04100000e-03]])
+ >>> round(model.sig2,4)
+ 198.5596
+
+ """
+
+ def __init__(self, y, x, w,
+ vm=False, name_y=None, name_x=None,
+ name_w=None, name_ds=None):
+
+ n = USER.check_arrays(y, x)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ x_constant = USER.check_constant(x)
+ BaseGM_Error.__init__(self, y=y, x=x_constant, w=w.sparse)
+ self.title = "SPATIALLY WEIGHTED LEAST SQUARES"
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_y = USER.set_name_y(name_y)
+ self.name_x = USER.set_name_x(name_x, x)
+ self.name_x.append('lambda')
+ self.name_w = USER.set_name_w(name_w, w)
+ SUMMARY.GM_Error(reg=self, w=w, vm=vm)
+
+
+class BaseGM_Endog_Error(RegressionPropsY):
+
+ '''
+ GMM method for a spatial error model with endogenous variables (note: no
+ consistency checks, diagnostics or constant added); based on Kelejian and
+ Prucha (1998, 1999)[1]_[2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x)
+ w : Sparse matrix
+ Spatial weights sparse matrix
+
+ Attributes
+ ----------
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ z : array
+ nxk array of variables (combination of x and yend)
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ sig2 : float
+ Sigma squared used in computations
+
+ References
+ ----------
+
+ .. [1] Kelejian, H.R., Prucha, I.R. (1998) "A generalized spatial
+ two-stage least squares procedure for estimating a spatial autoregressive
+ model with autoregressive disturbances". The Journal of Real State
+ Finance and Economics, 17, 1.
+
+ .. [2] Kelejian, H.R., Prucha, I.R. (1999) "A Generalized Moments
+ Estimator for the Autoregressive Parameter in a Spatial Model".
+ International Economic Review, 40, 2.
+
+ Examples
+ --------
+
+ >>> import pysal
+ >>> import numpy as np
+ >>> dbf = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> y = np.array([dbf.by_col('CRIME')]).T
+ >>> x = np.array([dbf.by_col('INC')]).T
+ >>> x = np.hstack((np.ones(y.shape),x))
+ >>> yend = np.array([dbf.by_col('HOVAL')]).T
+ >>> q = np.array([dbf.by_col('DISCBD')]).T
+ >>> w = pysal.open(pysal.examples.get_path("columbus.gal"), 'r').read()
+ >>> w.transform='r'
+ >>> model = BaseGM_Endog_Error(y, x, yend, q, w=w.sparse)
+ >>> np.around(model.betas, decimals=4)
+ array([[ 82.573 ],
+ [ 0.581 ],
+ [ -1.4481],
+ [ 0.3499]])
+
+ '''
+
+ def __init__(self, y, x, yend, q, w):
+
+ # 1a. TSLS --> \tilde{betas}
+ tsls = TSLS.BaseTSLS(y=y, x=x, yend=yend, q=q)
+ self.n, self.k = tsls.z.shape
+ self.x = tsls.x
+ self.y = tsls.y
+ self.yend, self.z = tsls.yend, tsls.z
+
+ # 1b. GMM --> \tilde{\lambda1}
+ moments = _momentsGM_Error(w, tsls.u)
+ lambda1 = optim_moments(moments)
+
+ # 2a. 2SLS -->\hat{betas}
+ xs = get_spFilter(w, lambda1, self.x)
+ ys = get_spFilter(w, lambda1, self.y)
+ yend_s = get_spFilter(w, lambda1, self.yend)
+ tsls2 = TSLS.BaseTSLS(ys, xs, yend_s, h=tsls.h)
+
+ # Output
+ self.betas = np.vstack((tsls2.betas, np.array([[lambda1]])))
+ self.predy = spdot(tsls.z, tsls2.betas)
+ self.u = y - self.predy
+ self.sig2 = float(np.dot(tsls2.u.T, tsls2.u)) / self.n
+ self.e_filtered = self.u - lambda1 * w * self.u
+ self.vm = self.sig2 * tsls2.varb
+ self._cache = {}
+
+
+class GM_Endog_Error(BaseGM_Endog_Error):
+
+ '''
+ GMM method for a spatial error model with endogenous variables, with
+ results and diagnostics; based on Kelejian and Prucha (1998, 1999)[1]_[2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x)
+ w : pysal W object
+ Spatial weights object (always needed)
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_q : list of strings
+ Names of instruments for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ z : array
+ nxk array of variables (combination of x and yend)
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ sig2 : float
+ Sigma squared used in computations
+ std_err : array
+ 1xk array of standard errors of the betas
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_z : list of strings
+ Names of exogenous and endogenous variables for use in
+ output
+ name_q : list of strings
+ Names of external instruments
+ name_h : list of strings
+ Names of all instruments used in ouput
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ title : string
+ Name of the regression method used
+
+ References
+ ----------
+
+ .. [1] Kelejian, H.R., Prucha, I.R. (1998) "A generalized spatial
+ two-stage least squares procedure for estimating a spatial autoregressive
+ model with autoregressive disturbances". The Journal of Real State
+ Finance and Economics, 17, 1.
+
+ .. [2] Kelejian, H.R., Prucha, I.R. (1999) "A Generalized Moments
+ Estimator for the Autoregressive Parameter in a Spatial Model".
+ International Economic Review, 40, 2.
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import pysal
+ >>> import numpy as np
+
+ Open data on Columbus neighborhood crime (49 areas) using pysal.open().
+ This is the DBF associated with the Columbus shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> dbf = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
+
+ Extract the CRIME column (crime rates) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y = np.array([dbf.by_col('CRIME')]).T
+
+ Extract INC (income) vector from the DBF to be used as
+ independent variables in the regression. Note that PySAL requires this to
+ be an nxj numpy array, where j is the number of independent variables (not
+ including a constant). By default this model adds a vector of ones to the
+ independent variables passed in.
+
+ >>> x = np.array([dbf.by_col('INC')]).T
+
+ In this case we consider HOVAL (home value) is an endogenous regressor.
+ We tell the model that this is so by passing it in a different parameter
+ from the exogenous variables (x).
+
+ >>> yend = np.array([dbf.by_col('HOVAL')]).T
+
+ Because we have endogenous variables, to obtain a correct estimate of the
+ model, we need to instrument for HOVAL. We use DISCBD (distance to the
+ CBD) for this and hence put it in the instruments parameter, 'q'.
+
+ >>> q = np.array([dbf.by_col('DISCBD')]).T
+
+ Since we want to run a spatial error model, we need to specify the spatial
+ weights matrix that includes the spatial configuration of the observations
+ into the error component of the model. To do that, we can open an already
+ existing gal file or create a new one. In this case, we will use
+ ``columbus.gal``, which contains contiguity relationships between the
+ observations in the Columbus dataset we are using throughout this example.
+ Note that, in order to read the file, not only to open it, we need to
+ append '.read()' at the end of the command.
+
+ >>> w = pysal.open(pysal.examples.get_path("columbus.gal"), 'r').read()
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, this allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform='r'
+
+ We are all set with the preliminars, we are good to run the model. In this
+ case, we will need the variables (exogenous and endogenous), the
+ instruments and the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional.
+
+ >>> model = GM_Endog_Error(y, x, yend, q, w=w, name_x=['inc'], name_y='crime', name_yend=['hoval'], name_q=['discbd'], name_ds='columbus')
+
+ Once we have run the model, we can explore a little bit the output. The
+ regression object we have created has many attributes so take your time to
+ discover them. Note that because we are running the classical GMM error
+ model from 1998/99, the spatial parameter is obtained as a point estimate, so
+ although you get a value for it (there are for coefficients under
+ model.betas), you cannot perform inference on it (there are only three
+ values in model.se_betas). Also, this regression uses a two stage least
+ squares estimation method that accounts for the endogeneity created by the
+ endogenous variables included.
+
+ >>> print model.name_z
+ ['CONSTANT', 'inc', 'hoval', 'lambda']
+ >>> np.around(model.betas, decimals=4)
+ array([[ 82.573 ],
+ [ 0.581 ],
+ [ -1.4481],
+ [ 0.3499]])
+ >>> np.around(model.std_err, decimals=4)
+ array([ 16.1381, 1.3545, 0.7862])
+
+ '''
+
+ def __init__(self, y, x, yend, q, w,
+ vm=False, name_y=None, name_x=None,
+ name_yend=None, name_q=None,
+ name_w=None, name_ds=None):
+
+ n = USER.check_arrays(y, x, yend, q)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ x_constant = USER.check_constant(x)
+ BaseGM_Endog_Error.__init__(
+ self, y=y, x=x_constant, w=w.sparse, yend=yend, q=q)
+ self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES"
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_y = USER.set_name_y(name_y)
+ self.name_x = USER.set_name_x(name_x, x)
+ self.name_yend = USER.set_name_yend(name_yend, yend)
+ self.name_z = self.name_x + self.name_yend
+ self.name_z.append('lambda')
+ self.name_q = USER.set_name_q(name_q, q)
+ self.name_h = USER.set_name_h(self.name_x, self.name_q)
+ self.name_w = USER.set_name_w(name_w, w)
+ SUMMARY.GM_Endog_Error(reg=self, w=w, vm=vm)
+
+
+class BaseGM_Combo(BaseGM_Endog_Error):
+
+ """
+ GMM method for a spatial lag and error model, with endogenous variables
+ (note: no consistency checks, diagnostics or constant added); based on
+ Kelejian and Prucha (1998, 1999)[1]_[2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x)
+ w : Sparse matrix
+ Spatial weights sparse matrix
+ w_lags : integer
+ Orders of W to include as instruments for the spatially
+ lagged dependent variable. For example, w_lags=1, then
+ instruments are WX; if w_lags=2, then WX, WWX; and so on.
+ lag_q : boolean
+ If True, then include spatial lags of the additional
+ instruments (q).
+
+ Attributes
+ ----------
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ z : array
+ nxk array of variables (combination of x and yend)
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ sig2 : float
+ Sigma squared used in computations
+
+ References
+ ----------
+
+ .. [1] Kelejian, H.R., Prucha, I.R. (1998) "A generalized spatial
+ two-stage least squares procedure for estimating a spatial autoregressive
+ model with autoregressive disturbances". The Journal of Real State
+ Finance and Economics, 17, 1.
+
+ .. [2] Kelejian, H.R., Prucha, I.R. (1999) "A Generalized Moments
+ Estimator for the Autoregressive Parameter in a Spatial Model".
+ International Economic Review, 40, 2.
+
+ Examples
+ --------
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X = np.array(X).T
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ >>> w.transform = 'r'
+ >>> w_lags = 1
+ >>> yd2, q2 = pysal.spreg.utils.set_endog(y, X, w, None, None, w_lags, True)
+ >>> X = np.hstack((np.ones(y.shape),X))
+
+ Example only with spatial lag
+
+ >>> reg = BaseGM_Combo(y, X, yend=yd2, q=q2, w=w.sparse)
+
+ Print the betas
+
+ >>> print np.around(np.hstack((reg.betas[:-1],np.sqrt(reg.vm.diagonal()).reshape(3,1))),3)
+ [[ 39.059 11.86 ]
+ [ -1.404 0.391]
+ [ 0.467 0.2 ]]
+
+ And lambda
+
+ >>> print 'Lamda: ', np.around(reg.betas[-1], 3)
+ Lamda: [-0.048]
+
+ Example with both spatial lag and other endogenous variables
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X = np.array(X).T
+ >>> yd = []
+ >>> yd.append(db.by_col("HOVAL"))
+ >>> yd = np.array(yd).T
+ >>> q = []
+ >>> q.append(db.by_col("DISCBD"))
+ >>> q = np.array(q).T
+ >>> yd2, q2 = pysal.spreg.utils.set_endog(y, X, w, yd, q, w_lags, True)
+ >>> X = np.hstack((np.ones(y.shape),X))
+ >>> reg = BaseGM_Combo(y, X, yd2, q2, w=w.sparse)
+ >>> betas = np.array([['CONSTANT'],['INC'],['HOVAL'],['W_CRIME']])
+ >>> print np.hstack((betas, np.around(np.hstack((reg.betas[:-1], np.sqrt(reg.vm.diagonal()).reshape(4,1))),4)))
+ [['CONSTANT' '50.0944' '14.3593']
+ ['INC' '-0.2552' '0.5667']
+ ['HOVAL' '-0.6885' '0.3029']
+ ['W_CRIME' '0.4375' '0.2314']]
+
+ """
+
+ def __init__(self, y, x, yend=None, q=None,
+ w=None, w_lags=1, lag_q=True):
+
+ BaseGM_Endog_Error.__init__(self, y=y, x=x, w=w, yend=yend, q=q)
+
+
+class GM_Combo(BaseGM_Combo):
+
+ """
+ GMM method for a spatial lag and error model with endogenous variables,
+ with results and diagnostics; based on Kelejian and Prucha (1998,
+ 1999)[1]_[2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x)
+ w : pysal W object
+ Spatial weights object (always needed)
+ w_lags : integer
+ Orders of W to include as instruments for the spatially
+ lagged dependent variable. For example, w_lags=1, then
+ instruments are WX; if w_lags=2, then WX, WWX; and so on.
+ lag_q : boolean
+ If True, then include spatial lags of the additional
+ instruments (q).
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_q : list of strings
+ Names of instruments for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ e_pred : array
+ nx1 array of residuals (using reduced form)
+ predy : array
+ nx1 array of predicted y values
+ predy_e : array
+ nx1 array of predicted y values (using reduced form)
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ z : array
+ nxk array of variables (combination of x and yend)
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ pr2_e : float
+ Pseudo R squared (squared correlation between y and ypred_e
+ (using reduced form))
+ sig2 : float
+ Sigma squared used in computations (based on filtered
+ residuals)
+ std_err : array
+ 1xk array of standard errors of the betas
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_z : list of strings
+ Names of exogenous and endogenous variables for use in
+ output
+ name_q : list of strings
+ Names of external instruments
+ name_h : list of strings
+ Names of all instruments used in ouput
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ title : string
+ Name of the regression method used
+
+ References
+ ----------
+
+ .. [1] Kelejian, H.R., Prucha, I.R. (1998) "A generalized spatial
+ two-stage least squares procedure for estimating a spatial autoregressive
+ model with autoregressive disturbances". The Journal of Real State
+ Finance and Economics, 17, 1.
+
+ .. [2] Kelejian, H.R., Prucha, I.R. (1999) "A Generalized Moments
+ Estimator for the Autoregressive Parameter in a Spatial Model".
+ International Economic Review, 40, 2.
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import numpy as np
+ >>> import pysal
+
+ Open data on Columbus neighborhood crime (49 areas) using pysal.open().
+ This is the DBF associated with the Columbus shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
+
+ Extract the CRIME column (crime rates) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+
+ Extract INC (income) vector from the DBF to be used as
+ independent variables in the regression. Note that PySAL requires this to
+ be an nxj numpy array, where j is the number of independent variables (not
+ including a constant). By default this model adds a vector of ones to the
+ independent variables passed in.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X = np.array(X).T
+
+ Since we want to run a spatial error model, we need to specify the spatial
+ weights matrix that includes the spatial configuration of the observations
+ into the error component of the model. To do that, we can open an already
+ existing gal file or create a new one. In this case, we will create one
+ from ``columbus.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, this allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ The Combo class runs an SARAR model, that is a spatial lag+error model.
+ In this case we will run a simple version of that, where we have the
+ spatial effects as well as exogenous variables. Since it is a spatial
+ model, we have to pass in the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional.
+
+ >>> reg = GM_Combo(y, X, w=w, name_y='crime', name_x=['income'], name_ds='columbus')
+
+ Once we have run the model, we can explore a little bit the output. The
+ regression object we have created has many attributes so take your time to
+ discover them. Note that because we are running the classical GMM error
+ model from 1998/99, the spatial parameter is obtained as a point estimate, so
+ although you get a value for it (there are for coefficients under
+ model.betas), you cannot perform inference on it (there are only three
+ values in model.se_betas). Also, this regression uses a two stage least
+ squares estimation method that accounts for the endogeneity created by the
+ spatial lag of the dependent variable. We can check the betas:
+
+ >>> print reg.name_z
+ ['CONSTANT', 'income', 'W_crime', 'lambda']
+ >>> print np.around(np.hstack((reg.betas[:-1],np.sqrt(reg.vm.diagonal()).reshape(3,1))),3)
+ [[ 39.059 11.86 ]
+ [ -1.404 0.391]
+ [ 0.467 0.2 ]]
+
+ And lambda:
+
+ >>> print 'lambda: ', np.around(reg.betas[-1], 3)
+ lambda: [-0.048]
+
+ This class also allows the user to run a spatial lag+error model with the
+ extra feature of including non-spatial endogenous regressors. This means
+ that, in addition to the spatial lag and error, we consider some of the
+ variables on the right-hand side of the equation as endogenous and we
+ instrument for this. As an example, we will include HOVAL (home value) as
+ endogenous and will instrument with DISCBD (distance to the CSB). We first
+ need to read in the variables:
+
+ >>> yd = []
+ >>> yd.append(db.by_col("HOVAL"))
+ >>> yd = np.array(yd).T
+ >>> q = []
+ >>> q.append(db.by_col("DISCBD"))
+ >>> q = np.array(q).T
+
+ And then we can run and explore the model analogously to the previous combo:
+
+ >>> reg = GM_Combo(y, X, yd, q, w=w, name_x=['inc'], name_y='crime', name_yend=['hoval'], name_q=['discbd'], name_ds='columbus')
+ >>> print reg.name_z
+ ['CONSTANT', 'inc', 'hoval', 'W_crime', 'lambda']
+ >>> names = np.array(reg.name_z).reshape(5,1)
+ >>> print np.hstack((names[0:4,:], np.around(np.hstack((reg.betas[:-1], np.sqrt(reg.vm.diagonal()).reshape(4,1))),4)))
+ [['CONSTANT' '50.0944' '14.3593']
+ ['inc' '-0.2552' '0.5667']
+ ['hoval' '-0.6885' '0.3029']
+ ['W_crime' '0.4375' '0.2314']]
+
+ >>> print 'lambda: ', np.around(reg.betas[-1], 3)
+ lambda: [ 0.254]
+
+ """
+
+ def __init__(self, y, x, yend=None, q=None,
+ w=None, w_lags=1, lag_q=True,
+ vm=False, name_y=None, name_x=None,
+ name_yend=None, name_q=None,
+ name_w=None, name_ds=None):
+
+ n = USER.check_arrays(y, x, yend, q)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ yend2, q2 = set_endog(y, x, w, yend, q, w_lags, lag_q)
+ x_constant = USER.check_constant(x)
+ BaseGM_Combo.__init__(
+ self, y=y, x=x_constant, w=w.sparse, yend=yend2, q=q2,
+ w_lags=w_lags, lag_q=lag_q)
+ self.rho = self.betas[-2]
+ self.predy_e, self.e_pred, warn = sp_att(w, self.y,
+ self.predy, yend2[:, -1].reshape(self.n, 1), self.rho)
+ set_warn(self, warn)
+ self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES"
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_y = USER.set_name_y(name_y)
+ self.name_x = USER.set_name_x(name_x, x)
+ self.name_yend = USER.set_name_yend(name_yend, yend)
+ self.name_yend.append(USER.set_name_yend_sp(self.name_y))
+ self.name_z = self.name_x + self.name_yend
+ self.name_z.append('lambda')
+ self.name_q = USER.set_name_q(name_q, q)
+ self.name_q.extend(
+ USER.set_name_q_sp(self.name_x, w_lags, self.name_q, lag_q))
+ self.name_h = USER.set_name_h(self.name_x, self.name_q)
+ self.name_w = USER.set_name_w(name_w, w)
+ SUMMARY.GM_Combo(reg=self, w=w, vm=vm)
+
+
+def _momentsGM_Error(w, u):
+ try:
+ wsparse = w.sparse
+ except:
+ wsparse = w
+ n = wsparse.shape[0]
+ u2 = np.dot(u.T, u)
+ wu = wsparse * u
+ uwu = np.dot(u.T, wu)
+ wu2 = np.dot(wu.T, wu)
+ wwu = wsparse * wu
+ uwwu = np.dot(u.T, wwu)
+ wwu2 = np.dot(wwu.T, wwu)
+ wuwwu = np.dot(wu.T, wwu)
+ wtw = wsparse.T * wsparse
+ trWtW = np.sum(wtw.diagonal())
+ g = np.array([[u2[0][0], wu2[0][0], uwu[0][0]]]).T / n
+ G = np.array(
+ [[2 * uwu[0][0], -wu2[0][0], n], [2 * wuwwu[0][0], -wwu2[0][0], trWtW],
+ [uwwu[0][0] + wu2[0][0], -wuwwu[0][0], 0.]]) / n
+ return [G, g]
+
+
+def _test():
+ import doctest
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ doctest.testmod()
+ np.set_printoptions(suppress=start_suppress)
+
+if __name__ == '__main__':
+
+ _test()
+ import pysal
+ import numpy as np
+ dbf = pysal.open(pysal.examples.get_path('columbus.dbf'), 'r')
+ y = np.array([dbf.by_col('HOVAL')]).T
+ names_to_extract = ['INC', 'CRIME']
+ x = np.array([dbf.by_col(name) for name in names_to_extract]).T
+ w = pysal.open(pysal.examples.get_path("columbus.gal"), 'r').read()
+ w.transform = 'r'
+ model = GM_Error(y, x, w, name_y='hoval',
+ name_x=['income', 'crime'], name_ds='columbus')
+ print model.summary
diff --git a/pysal/spreg/error_sp_het.py b/pysal/spreg/error_sp_het.py
new file mode 100644
index 0000000..40a7bcb
--- /dev/null
+++ b/pysal/spreg/error_sp_het.py
@@ -0,0 +1,1509 @@
+'''
+Spatial Error with Heteroskedasticity family of models
+'''
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, \
+ Pedro V. Amaral pedro.amaral at asu.edu, \
+ Daniel Arribas-Bel darribas at asu.edu, \
+ David C. Folch david.folch at asu.edu \
+ Ran Wei rwei5 at asu.edu"
+
+import numpy as np
+import numpy.linalg as la
+import ols as OLS
+import user_output as USER
+import summary_output as SUMMARY
+import twosls as TSLS
+import utils as UTILS
+from utils import RegressionPropsY, spdot, set_endog, sphstack
+from scipy import sparse as SP
+from pysal import lag_spatial
+
+__all__ = ["GM_Error_Het", "GM_Endog_Error_Het", "GM_Combo_Het"]
+
+
+class BaseGM_Error_Het(RegressionPropsY):
+
+ """
+ GMM method for a spatial error model with heteroskedasticity (note: no
+ consistency checks, diagnostics or constant added); based on Arraiz
+ et al [1]_, following Anselin [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ w : Sparse matrix
+ Spatial weights sparse matrix
+ max_iter : int
+ Maximum number of iterations of steps 2a and 2b from Arraiz
+ et al. Note: epsilon provides an additional stop condition.
+ epsilon : float
+ Minimum change in lambda required to stop iterations of
+ steps 2a and 2b from Arraiz et al. Note: max_iter provides
+ an additional stop condition.
+ step1c : boolean
+ If True, then include Step 1c from Arraiz et al.
+
+ Attributes
+ ----------
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ iter_stop : string
+ Stop criterion reached during iteration of steps 2a and 2b
+ from Arraiz et al.
+ iteration : integer
+ Number of iterations of steps 2a and 2b from Arraiz et al.
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ xtx : float
+ X'X
+
+ References
+ ----------
+
+ .. [1] Arraiz, I., Drukker, D. M., Kelejian, H., Prucha, I. R. (2010) "A
+ Spatial Cliff-Ord-Type Model with Heteroskedastic Innovations: Small and
+ Large Sample Results". Journal of Regional Science, Vol. 60, No. 2, pp.
+ 592-614.
+
+ .. [2] Anselin, L. GMM Estimation of Spatial Error Autocorrelation with Heteroskedasticity
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> y = np.array(db.by_col("HOVAL"))
+ >>> y = np.reshape(y, (49,1))
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("CRIME"))
+ >>> X = np.array(X).T
+ >>> X = np.hstack((np.ones(y.shape),X))
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ >>> w.transform = 'r'
+ >>> reg = BaseGM_Error_Het(y, X, w.sparse, step1c=True)
+ >>> print np.around(np.hstack((reg.betas,np.sqrt(reg.vm.diagonal()).reshape(4,1))),4)
+ [[ 47.9963 11.479 ]
+ [ 0.7105 0.3681]
+ [ -0.5588 0.1616]
+ [ 0.4118 0.168 ]]
+ """
+
+ def __init__(self, y, x, w,
+ max_iter=1, epsilon=0.00001, step1c=False):
+
+ self.step1c = step1c
+ # 1a. OLS --> \tilde{betas}
+ ols = OLS.BaseOLS(y=y, x=x)
+ self.x, self.y, self.n, self.k, self.xtx = ols.x, ols.y, ols.n, ols.k, ols.xtx
+ wA1 = UTILS.get_A1_het(w)
+
+ # 1b. GMM --> \tilde{\lambda1}
+ moments = UTILS._moments2eqs(wA1, w, ols.u)
+ lambda1 = UTILS.optim_moments(moments)
+
+ if step1c:
+ # 1c. GMM --> \tilde{\lambda2}
+ sigma = get_psi_sigma(w, ols.u, lambda1)
+ vc1 = get_vc_het(w, wA1, sigma)
+ lambda2 = UTILS.optim_moments(moments, vc1)
+ else:
+ lambda2 = lambda1
+ lambda_old = lambda2
+
+ self.iteration, eps = 0, 1
+ while self.iteration < max_iter and eps > epsilon:
+ # 2a. reg -->\hat{betas}
+ xs = UTILS.get_spFilter(w, lambda_old, self.x)
+ ys = UTILS.get_spFilter(w, lambda_old, self.y)
+ ols_s = OLS.BaseOLS(y=ys, x=xs)
+ self.predy = spdot(self.x, ols_s.betas)
+ self.u = self.y - self.predy
+
+ # 2b. GMM --> \hat{\lambda}
+ sigma_i = get_psi_sigma(w, self.u, lambda_old)
+ vc_i = get_vc_het(w, wA1, sigma_i)
+ moments_i = UTILS._moments2eqs(wA1, w, self.u)
+ lambda3 = UTILS.optim_moments(moments_i, vc_i)
+ eps = abs(lambda3 - lambda_old)
+ lambda_old = lambda3
+ self.iteration += 1
+
+ self.iter_stop = UTILS.iter_msg(self.iteration, max_iter)
+
+ sigma = get_psi_sigma(w, self.u, lambda3)
+ vc3 = get_vc_het(w, wA1, sigma)
+ self.vm = get_vm_het(moments_i[0], lambda3, self, w, vc3)
+ self.betas = np.vstack((ols_s.betas, lambda3))
+ self.e_filtered = self.u - lambda3 * w * self.u
+ self._cache = {}
+
+
+class GM_Error_Het(BaseGM_Error_Het):
+
+ """
+ GMM method for a spatial error model with heteroskedasticity, with results
+ and diagnostics; based on Arraiz et al [1]_, following Anselin [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ w : pysal W object
+ Spatial weights object
+ max_iter : int
+ Maximum number of iterations of steps 2a and 2b from Arraiz
+ et al. Note: epsilon provides an additional stop condition.
+ epsilon : float
+ Minimum change in lambda required to stop iterations of
+ steps 2a and 2b from Arraiz et al. Note: max_iter provides
+ an additional stop condition.
+ step1c : boolean
+ If True, then include Step 1c from Arraiz et al.
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ iter_stop : string
+ Stop criterion reached during iteration of steps 2a and 2b
+ from Arraiz et al.
+ iteration : integer
+ Number of iterations of steps 2a and 2b from Arraiz et al.
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ vm : array
+ Variance covariance matrix (kxk)
+ std_err : array
+ 1xk array of standard errors of the betas
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ xtx : float
+ X'X
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ title : string
+ Name of the regression method used
+
+ References
+ ----------
+
+ .. [1] Arraiz, I., Drukker, D. M., Kelejian, H., Prucha, I. R. (2010) "A
+ Spatial Cliff-Ord-Type Model with Heteroskedastic Innovations: Small and
+ Large Sample Results". Journal of Regional Science, Vol. 60, No. 2, pp.
+ 592-614.
+
+ .. [2] Anselin, L. GMM Estimation of Spatial Error Autocorrelation with Heteroskedasticity
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import numpy as np
+ >>> import pysal
+
+ Open data on Columbus neighborhood crime (49 areas) using pysal.open().
+ This is the DBF associated with the Columbus shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+
+ Extract the HOVAL column (home values) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y = np.array(db.by_col("HOVAL"))
+ >>> y = np.reshape(y, (49,1))
+
+ Extract INC (income) and CRIME (crime) vectors from the DBF to be used as
+ independent variables in the regression. Note that PySAL requires this to
+ be an nxj numpy array, where j is the number of independent variables (not
+ including a constant). By default this class adds a vector of ones to the
+ independent variables passed in.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("CRIME"))
+ >>> X = np.array(X).T
+
+ Since we want to run a spatial error model, we need to specify the spatial
+ weights matrix that includes the spatial configuration of the observations
+ into the error component of the model. To do that, we can open an already
+ existing gal file or create a new one. In this case, we will create one
+ from ``columbus.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, his allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ We are all set with the preliminaries, we are good to run the model. In this
+ case, we will need the variables and the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional.
+
+ >>> reg = GM_Error_Het(y, X, w=w, step1c=True, name_y='home value', name_x=['income', 'crime'], name_ds='columbus')
+
+ Once we have run the model, we can explore a little bit the output. The
+ regression object we have created has many attributes so take your time to
+ discover them. This class offers an error model that explicitly accounts
+ for heteroskedasticity and that unlike the models from
+ ``pysal.spreg.error_sp``, it allows for inference on the spatial
+ parameter.
+
+ >>> print reg.name_x
+ ['CONSTANT', 'income', 'crime', 'lambda']
+
+ Hence, we find the same number of betas as of standard errors,
+ which we calculate taking the square root of the diagonal of the
+ variance-covariance matrix:
+
+ >>> print np.around(np.hstack((reg.betas,np.sqrt(reg.vm.diagonal()).reshape(4,1))),4)
+ [[ 47.9963 11.479 ]
+ [ 0.7105 0.3681]
+ [ -0.5588 0.1616]
+ [ 0.4118 0.168 ]]
+
+ """
+
+ def __init__(self, y, x, w,
+ max_iter=1, epsilon=0.00001, step1c=False,
+ vm=False, name_y=None, name_x=None,
+ name_w=None, name_ds=None):
+
+ n = USER.check_arrays(y, x)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ x_constant = USER.check_constant(x)
+ BaseGM_Error_Het.__init__(
+ self, y, x_constant, w.sparse, max_iter=max_iter,
+ step1c=step1c, epsilon=epsilon)
+ self.title = "SPATIALLY WEIGHTED LEAST SQUARES (HET)"
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_y = USER.set_name_y(name_y)
+ self.name_x = USER.set_name_x(name_x, x)
+ self.name_x.append('lambda')
+ self.name_w = USER.set_name_w(name_w, w)
+ SUMMARY.GM_Error_Het(reg=self, w=w, vm=vm)
+
+
+class BaseGM_Endog_Error_Het(RegressionPropsY):
+
+ """
+ GMM method for a spatial error model with heteroskedasticity and
+ endogenous variables (note: no consistency checks, diagnostics or constant
+ added); based on Arraiz et al [1]_, following Anselin [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x)
+ w : Sparse matrix
+ Spatial weights sparse matrix
+ max_iter : int
+ Maximum number of iterations of steps 2a and 2b from Arraiz
+ et al. Note: epsilon provides an additional stop condition.
+ epsilon : float
+ Minimum change in lambda required to stop iterations of
+ steps 2a and 2b from Arraiz et al. Note: max_iter provides
+ an additional stop condition.
+ step1c : boolean
+ If True, then include Step 1c from Arraiz et al.
+ inv_method : string
+ If "power_exp", then compute inverse using the power
+ expansion. If "true_inv", then compute the true inverse.
+ Note that true_inv will fail for large n.
+
+
+ Attributes
+ ----------
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable used as instruments
+ z : array
+ nxk array of variables (combination of x and yend)
+ h : array
+ nxl array of instruments (combination of x and q)
+ iter_stop : string
+ Stop criterion reached during iteration of steps 2a and 2b
+ from Arraiz et al.
+ iteration : integer
+ Number of iterations of steps 2a and 2b from Arraiz et al.
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ hth : float
+ H'H
+
+
+ References
+ ----------
+
+ .. [1] Arraiz, I., Drukker, D. M., Kelejian, H., Prucha, I. R. (2010) "A
+ Spatial Cliff-Ord-Type Model with Heteroskedastic Innovations: Small and
+ Large Sample Results". Journal of Regional Science, Vol. 60, No. 2, pp.
+ 592-614.
+
+ .. [2] Anselin, L. GMM Estimation of Spatial Error Autocorrelation with Heteroskedasticity
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> y = np.array(db.by_col("HOVAL"))
+ >>> y = np.reshape(y, (49,1))
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X = np.array(X).T
+ >>> X = np.hstack((np.ones(y.shape),X))
+ >>> yd = []
+ >>> yd.append(db.by_col("CRIME"))
+ >>> yd = np.array(yd).T
+ >>> q = []
+ >>> q.append(db.by_col("DISCBD"))
+ >>> q = np.array(q).T
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ >>> w.transform = 'r'
+ >>> reg = BaseGM_Endog_Error_Het(y, X, yd, q, w=w.sparse, step1c=True)
+ >>> print np.around(np.hstack((reg.betas,np.sqrt(reg.vm.diagonal()).reshape(4,1))),4)
+ [[ 55.3971 28.8901]
+ [ 0.4656 0.7731]
+ [ -0.6704 0.468 ]
+ [ 0.4114 0.1777]]
+ """
+
+ def __init__(self, y, x, yend, q, w,
+ max_iter=1, epsilon=0.00001,
+ step1c=False, inv_method='power_exp'):
+
+ self.step1c = step1c
+ # 1a. reg --> \tilde{betas}
+ tsls = TSLS.BaseTSLS(y=y, x=x, yend=yend, q=q)
+ self.x, self.z, self.h, self.y = tsls.x, tsls.z, tsls.h, tsls.y
+ self.yend, self.q, self.n, self.k, self.hth = tsls.yend, tsls.q, tsls.n, tsls.k, tsls.hth
+ wA1 = UTILS.get_A1_het(w)
+
+ # 1b. GMM --> \tilde{\lambda1}
+ moments = UTILS._moments2eqs(wA1, w, tsls.u)
+ lambda1 = UTILS.optim_moments(moments)
+
+ if step1c:
+ # 1c. GMM --> \tilde{\lambda2}
+ self.u = tsls.u
+ zs = UTILS.get_spFilter(w, lambda1, self.z)
+ vc1 = get_vc_het_tsls(w, wA1, self, lambda1,
+ tsls.pfora1a2, zs, inv_method, filt=False)
+ lambda2 = UTILS.optim_moments(moments, vc1)
+ else:
+ lambda2 = lambda1
+ lambda_old = lambda2
+
+ self.iteration, eps = 0, 1
+ while self.iteration < max_iter and eps > epsilon:
+ # 2a. reg -->\hat{betas}
+ xs = UTILS.get_spFilter(w, lambda_old, self.x)
+ ys = UTILS.get_spFilter(w, lambda_old, self.y)
+ yend_s = UTILS.get_spFilter(w, lambda_old, self.yend)
+ tsls_s = TSLS.BaseTSLS(ys, xs, yend_s, h=self.h)
+ self.predy = spdot(self.z, tsls_s.betas)
+ self.u = self.y - self.predy
+
+ # 2b. GMM --> \hat{\lambda}
+ vc2 = get_vc_het_tsls(w, wA1, self, lambda_old,
+ tsls_s.pfora1a2, sphstack(xs, yend_s), inv_method)
+ moments_i = UTILS._moments2eqs(wA1, w, self.u)
+ lambda3 = UTILS.optim_moments(moments_i, vc2)
+ eps = abs(lambda3 - lambda_old)
+ lambda_old = lambda3
+ self.iteration += 1
+
+ self.iter_stop = UTILS.iter_msg(self.iteration, max_iter)
+
+ zs = UTILS.get_spFilter(w, lambda3, self.z)
+ P = get_P_hat(self, tsls.hthi, zs)
+ vc3 = get_vc_het_tsls(w, wA1, self, lambda3, P,
+ zs, inv_method, save_a1a2=True)
+ self.vm = get_Omega_GS2SLS(w, lambda3, self, moments_i[0], vc3, P)
+ self.betas = np.vstack((tsls_s.betas, lambda3))
+ self.e_filtered = self.u - lambda3 * w * self.u
+ self._cache = {}
+
+
+class GM_Endog_Error_Het(BaseGM_Endog_Error_Het):
+
+ """
+ GMM method for a spatial error model with heteroskedasticity and
+ endogenous variables, with results and diagnostics; based on Arraiz et al
+ [1]_, following Anselin [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x)
+ w : pysal W object
+ Spatial weights object
+ max_iter : int
+ Maximum number of iterations of steps 2a and 2b from Arraiz
+ et al. Note: epsilon provides an additional stop condition.
+ epsilon : float
+ Minimum change in lambda required to stop iterations of
+ steps 2a and 2b from Arraiz et al. Note: max_iter provides
+ an additional stop condition.
+ step1c : boolean
+ If True, then include Step 1c from Arraiz et al.
+ inv_method : string
+ If "power_exp", then compute inverse using the power
+ expansion. If "true_inv", then compute the true inverse.
+ Note that true_inv will fail for large n.
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_q : list of strings
+ Names of instruments for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable used as instruments
+ z : array
+ nxk array of variables (combination of x and yend)
+ h : array
+ nxl array of instruments (combination of x and q)
+ iter_stop : string
+ Stop criterion reached during iteration of steps 2a and 2b
+ from Arraiz et al.
+ iteration : integer
+ Number of iterations of steps 2a and 2b from Arraiz et al.
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ std_err : array
+ 1xk array of standard errors of the betas
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_z : list of strings
+ Names of exogenous and endogenous variables for use in
+ output
+ name_q : list of strings
+ Names of external instruments
+ name_h : list of strings
+ Names of all instruments used in ouput
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ title : string
+ Name of the regression method used
+ hth : float
+ H'H
+
+ References
+ ----------
+
+ .. [1] Arraiz, I., Drukker, D. M., Kelejian, H., Prucha, I. R. (2010) "A
+ Spatial Cliff-Ord-Type Model with Heteroskedastic Innovations: Small and
+ Large Sample Results". Journal of Regional Science, Vol. 60, No. 2, pp.
+ 592-614.
+
+ .. [2] Anselin, L. GMM Estimation of Spatial Error Autocorrelation with Heteroskedasticity
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import numpy as np
+ >>> import pysal
+
+ Open data on Columbus neighborhood crime (49 areas) using pysal.open().
+ This is the DBF associated with the Columbus shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+
+ Extract the HOVAL column (home values) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y = np.array(db.by_col("HOVAL"))
+ >>> y = np.reshape(y, (49,1))
+
+ Extract INC (income) vector from the DBF to be used as
+ independent variables in the regression. Note that PySAL requires this to
+ be an nxj numpy array, where j is the number of independent variables (not
+ including a constant). By default this class adds a vector of ones to the
+ independent variables passed in.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X = np.array(X).T
+
+ In this case we consider CRIME (crime rates) is an endogenous regressor.
+ We tell the model that this is so by passing it in a different parameter
+ from the exogenous variables (x).
+
+ >>> yd = []
+ >>> yd.append(db.by_col("CRIME"))
+ >>> yd = np.array(yd).T
+
+ Because we have endogenous variables, to obtain a correct estimate of the
+ model, we need to instrument for CRIME. We use DISCBD (distance to the
+ CBD) for this and hence put it in the instruments parameter, 'q'.
+
+ >>> q = []
+ >>> q.append(db.by_col("DISCBD"))
+ >>> q = np.array(q).T
+
+ Since we want to run a spatial error model, we need to specify the spatial
+ weights matrix that includes the spatial configuration of the observations
+ into the error component of the model. To do that, we can open an already
+ existing gal file or create a new one. In this case, we will create one
+ from ``columbus.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, his allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ We are all set with the preliminaries, we are good to run the model. In this
+ case, we will need the variables (exogenous and endogenous), the
+ instruments and the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional.
+
+ >>> reg = GM_Endog_Error_Het(y, X, yd, q, w=w, step1c=True, name_x=['inc'], name_y='hoval', name_yend=['crime'], name_q=['discbd'], name_ds='columbus')
+
+ Once we have run the model, we can explore a little bit the output. The
+ regression object we have created has many attributes so take your time to
+ discover them. This class offers an error model that explicitly accounts
+ for heteroskedasticity and that unlike the models from
+ ``pysal.spreg.error_sp``, it allows for inference on the spatial
+ parameter. Hence, we find the same number of betas as of standard errors,
+ which we calculate taking the square root of the diagonal of the
+ variance-covariance matrix:
+
+ >>> print reg.name_z
+ ['CONSTANT', 'inc', 'crime', 'lambda']
+ >>> print np.around(np.hstack((reg.betas,np.sqrt(reg.vm.diagonal()).reshape(4,1))),4)
+ [[ 55.3971 28.8901]
+ [ 0.4656 0.7731]
+ [ -0.6704 0.468 ]
+ [ 0.4114 0.1777]]
+
+ """
+
+ def __init__(self, y, x, yend, q, w,
+ max_iter=1, epsilon=0.00001,
+ step1c=False, inv_method='power_exp',
+ vm=False, name_y=None, name_x=None,
+ name_yend=None, name_q=None,
+ name_w=None, name_ds=None):
+
+ n = USER.check_arrays(y, x, yend, q)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ x_constant = USER.check_constant(x)
+ BaseGM_Endog_Error_Het.__init__(self, y=y, x=x_constant, yend=yend,
+ q=q, w=w.sparse, max_iter=max_iter,
+ step1c=step1c, epsilon=epsilon, inv_method=inv_method)
+ self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HET)"
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_y = USER.set_name_y(name_y)
+ self.name_x = USER.set_name_x(name_x, x)
+ self.name_yend = USER.set_name_yend(name_yend, yend)
+ self.name_z = self.name_x + self.name_yend
+ self.name_z.append('lambda') # listing lambda last
+ self.name_q = USER.set_name_q(name_q, q)
+ self.name_h = USER.set_name_h(self.name_x, self.name_q)
+ self.name_w = USER.set_name_w(name_w, w)
+ SUMMARY.GM_Endog_Error_Het(reg=self, w=w, vm=vm)
+
+
+class BaseGM_Combo_Het(BaseGM_Endog_Error_Het):
+
+ """
+ GMM method for a spatial lag and error model with heteroskedasticity and
+ endogenous variables (note: no consistency checks, diagnostics or constant
+ added); based on Arraiz et al [1]_, following Anselin [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x)
+ w : Sparse matrix
+ Spatial weights sparse matrix
+ w_lags : integer
+ Orders of W to include as instruments for the spatially
+ lagged dependent variable. For example, w_lags=1, then
+ instruments are WX; if w_lags=2, then WX, WWX; and so on.
+ lag_q : boolean
+ If True, then include spatial lags of the additional
+ instruments (q).
+ max_iter : int
+ Maximum number of iterations of steps 2a and 2b from Arraiz
+ et al. Note: epsilon provides an additional stop condition.
+ epsilon : float
+ Minimum change in lambda required to stop iterations of
+ steps 2a and 2b from Arraiz et al. Note: max_iter provides
+ an additional stop condition.
+ step1c : boolean
+ If True, then include Step 1c from Arraiz et al.
+ inv_method : string
+ If "power_exp", then compute inverse using the power
+ expansion. If "true_inv", then compute the true inverse.
+ Note that true_inv will fail for large n.
+
+
+ Attributes
+ ----------
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable used as instruments
+ z : array
+ nxk array of variables (combination of x and yend)
+ h : array
+ nxl array of instruments (combination of x and q)
+ iter_stop : string
+ Stop criterion reached during iteration of steps 2a and 2b
+ from Arraiz et al.
+ iteration : integer
+ Number of iterations of steps 2a and 2b from Arraiz et al.
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ hth : float
+ H'H
+
+ References
+ ----------
+
+ .. [1] Arraiz, I., Drukker, D. M., Kelejian, H., Prucha, I. R. (2010) "A
+ Spatial Cliff-Ord-Type Model with Heteroskedastic Innovations: Small and
+ Large Sample Results". Journal of Regional Science, Vol. 60, No. 2, pp.
+ 592-614.
+
+ .. [2] Anselin, L. GMM Estimation of Spatial Error Autocorrelation with Heteroskedasticity
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> y = np.array(db.by_col("HOVAL"))
+ >>> y = np.reshape(y, (49,1))
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X = np.array(X).T
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ >>> w.transform = 'r'
+ >>> w_lags = 1
+ >>> yd2, q2 = pysal.spreg.utils.set_endog(y, X, w, None, None, w_lags, True)
+ >>> X = np.hstack((np.ones(y.shape),X))
+
+ Example only with spatial lag
+
+ >>> reg = BaseGM_Combo_Het(y, X, yend=yd2, q=q2, w=w.sparse, step1c=True)
+ >>> print np.around(np.hstack((reg.betas,np.sqrt(reg.vm.diagonal()).reshape(4,1))),4)
+ [[ 9.9753 14.1435]
+ [ 1.5742 0.374 ]
+ [ 0.1535 0.3978]
+ [ 0.2103 0.3924]]
+
+ Example with both spatial lag and other endogenous variables
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X = np.array(X).T
+ >>> yd = []
+ >>> yd.append(db.by_col("CRIME"))
+ >>> yd = np.array(yd).T
+ >>> q = []
+ >>> q.append(db.by_col("DISCBD"))
+ >>> q = np.array(q).T
+ >>> yd2, q2 = pysal.spreg.utils.set_endog(y, X, w, yd, q, w_lags, True)
+ >>> X = np.hstack((np.ones(y.shape),X))
+ >>> reg = BaseGM_Combo_Het(y, X, yd2, q2, w=w.sparse, step1c=True)
+ >>> betas = np.array([['CONSTANT'],['inc'],['crime'],['lag_hoval'],['lambda']])
+ >>> print np.hstack((betas, np.around(np.hstack((reg.betas, np.sqrt(reg.vm.diagonal()).reshape(5,1))),5)))
+ [['CONSTANT' '113.91292' '64.38815']
+ ['inc' '-0.34822' '1.18219']
+ ['crime' '-1.35656' '0.72482']
+ ['lag_hoval' '-0.57657' '0.75856']
+ ['lambda' '0.65608' '0.15719']]
+ """
+
+ def __init__(self, y, x, yend=None, q=None,
+ w=None, w_lags=1, lag_q=True,
+ max_iter=1, epsilon=0.00001,
+ step1c=False, inv_method='power_exp'):
+
+ BaseGM_Endog_Error_Het.__init__(
+ self, y=y, x=x, w=w, yend=yend, q=q, max_iter=max_iter,
+ step1c=step1c, epsilon=epsilon, inv_method=inv_method)
+
+
+class GM_Combo_Het(BaseGM_Combo_Het):
+
+ """
+ GMM method for a spatial lag and error model with heteroskedasticity and
+ endogenous variables, with results and diagnostics; based on Arraiz et al
+ [1]_, following Anselin [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x)
+ w : pysal W object
+ Spatial weights object (always needed)
+ w_lags : integer
+ Orders of W to include as instruments for the spatially
+ lagged dependent variable. For example, w_lags=1, then
+ instruments are WX; if w_lags=2, then WX, WWX; and so on.
+ lag_q : boolean
+ If True, then include spatial lags of the additional
+ instruments (q).
+ max_iter : int
+ Maximum number of iterations of steps 2a and 2b from Arraiz
+ et al. Note: epsilon provides an additional stop condition.
+ epsilon : float
+ Minimum change in lambda required to stop iterations of
+ steps 2a and 2b from Arraiz et al. Note: max_iter provides
+ an additional stop condition.
+ step1c : boolean
+ If True, then include Step 1c from Arraiz et al.
+ inv_method : string
+ If "power_exp", then compute inverse using the power
+ expansion. If "true_inv", then compute the true inverse.
+ Note that true_inv will fail for large n.
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_q : list of strings
+ Names of instruments for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ e_pred : array
+ nx1 array of residuals (using reduced form)
+ predy : array
+ nx1 array of predicted y values
+ predy_e : array
+ nx1 array of predicted y values (using reduced form)
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable used as instruments
+ z : array
+ nxk array of variables (combination of x and yend)
+ h : array
+ nxl array of instruments (combination of x and q)
+ iter_stop : string
+ Stop criterion reached during iteration of steps 2a and 2b
+ from Arraiz et al.
+ iteration : integer
+ Number of iterations of steps 2a and 2b from Arraiz et al.
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ pr2_e : float
+ Pseudo R squared (squared correlation between y and ypred_e
+ (using reduced form))
+ std_err : array
+ 1xk array of standard errors of the betas
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_z : list of strings
+ Names of exogenous and endogenous variables for use in
+ output
+ name_q : list of strings
+ Names of external instruments
+ name_h : list of strings
+ Names of all instruments used in ouput
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ title : string
+ Name of the regression method used
+ hth : float
+ H'H
+
+ References
+ ----------
+
+ .. [1] Arraiz, I., Drukker, D. M., Kelejian, H., Prucha, I. R. (2010) "A
+ Spatial Cliff-Ord-Type Model with Heteroskedastic Innovations: Small and
+ Large Sample Results". Journal of Regional Science, Vol. 60, No. 2, pp.
+ 592-614.
+
+ .. [2] Anselin, L. GMM Estimation of Spatial Error Autocorrelation with Heteroskedasticity
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import numpy as np
+ >>> import pysal
+
+ Open data on Columbus neighborhood crime (49 areas) using pysal.open().
+ This is the DBF associated with the Columbus shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+
+ Extract the HOVAL column (home values) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y = np.array(db.by_col("HOVAL"))
+ >>> y = np.reshape(y, (49,1))
+
+ Extract INC (income) vector from the DBF to be used as
+ independent variables in the regression. Note that PySAL requires this to
+ be an nxj numpy array, where j is the number of independent variables (not
+ including a constant). By default this class adds a vector of ones to the
+ independent variables passed in.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X = np.array(X).T
+
+ Since we want to run a spatial error model, we need to specify the spatial
+ weights matrix that includes the spatial configuration of the observations
+ into the error component of the model. To do that, we can open an already
+ existing gal file or create a new one. In this case, we will create one
+ from ``columbus.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, his allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ The Combo class runs an SARAR model, that is a spatial lag+error model.
+ In this case we will run a simple version of that, where we have the
+ spatial effects as well as exogenous variables. Since it is a spatial
+ model, we have to pass in the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional.
+
+ >>> reg = GM_Combo_Het(y, X, w=w, step1c=True, name_y='hoval', name_x=['income'], name_ds='columbus')
+
+ Once we have run the model, we can explore a little bit the output. The
+ regression object we have created has many attributes so take your time to
+ discover them. This class offers an error model that explicitly accounts
+ for heteroskedasticity and that unlike the models from
+ ``pysal.spreg.error_sp``, it allows for inference on the spatial
+ parameter. Hence, we find the same number of betas as of standard errors,
+ which we calculate taking the square root of the diagonal of the
+ variance-covariance matrix:
+
+ >>> print reg.name_z
+ ['CONSTANT', 'income', 'W_hoval', 'lambda']
+ >>> print np.around(np.hstack((reg.betas,np.sqrt(reg.vm.diagonal()).reshape(4,1))),4)
+ [[ 9.9753 14.1435]
+ [ 1.5742 0.374 ]
+ [ 0.1535 0.3978]
+ [ 0.2103 0.3924]]
+
+ This class also allows the user to run a spatial lag+error model with the
+ extra feature of including non-spatial endogenous regressors. This means
+ that, in addition to the spatial lag and error, we consider some of the
+ variables on the right-hand side of the equation as endogenous and we
+ instrument for this. As an example, we will include CRIME (crime rates) as
+ endogenous and will instrument with DISCBD (distance to the CSB). We first
+ need to read in the variables:
+
+ >>> yd = []
+ >>> yd.append(db.by_col("CRIME"))
+ >>> yd = np.array(yd).T
+ >>> q = []
+ >>> q.append(db.by_col("DISCBD"))
+ >>> q = np.array(q).T
+
+ And then we can run and explore the model analogously to the previous combo:
+
+ >>> reg = GM_Combo_Het(y, X, yd, q, w=w, step1c=True, name_x=['inc'], name_y='hoval', name_yend=['crime'], name_q=['discbd'], name_ds='columbus')
+ >>> print reg.name_z
+ ['CONSTANT', 'inc', 'crime', 'W_hoval', 'lambda']
+ >>> print np.round(reg.betas,4)
+ [[ 113.9129]
+ [ -0.3482]
+ [ -1.3566]
+ [ -0.5766]
+ [ 0.6561]]
+
+ """
+
+ def __init__(self, y, x, yend=None, q=None,
+ w=None, w_lags=1, lag_q=True,
+ max_iter=1, epsilon=0.00001,
+ step1c=False, inv_method='power_exp',
+ vm=False, name_y=None, name_x=None,
+ name_yend=None, name_q=None,
+ name_w=None, name_ds=None):
+
+ n = USER.check_arrays(y, x, yend, q)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ yend2, q2 = set_endog(y, x, w, yend, q, w_lags, lag_q)
+ x_constant = USER.check_constant(x)
+ BaseGM_Combo_Het.__init__(self, y=y, x=x_constant, yend=yend2, q=q2,
+ w=w.sparse, w_lags=w_lags,
+ max_iter=max_iter, step1c=step1c, lag_q=lag_q,
+ epsilon=epsilon, inv_method=inv_method)
+ self.rho = self.betas[-2]
+ self.predy_e, self.e_pred, warn = UTILS.sp_att(w, self.y, self.predy,
+ yend2[:, -1].reshape(self.n, 1), self.rho)
+ UTILS.set_warn(self, warn)
+ self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HET)"
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_y = USER.set_name_y(name_y)
+ self.name_x = USER.set_name_x(name_x, x)
+ self.name_yend = USER.set_name_yend(name_yend, yend)
+ self.name_yend.append(USER.set_name_yend_sp(self.name_y))
+ self.name_z = self.name_x + self.name_yend
+ self.name_z.append('lambda') # listing lambda last
+ self.name_q = USER.set_name_q(name_q, q)
+ self.name_q.extend(
+ USER.set_name_q_sp(self.name_x, w_lags, self.name_q, lag_q))
+ self.name_h = USER.set_name_h(self.name_x, self.name_q)
+ self.name_w = USER.set_name_w(name_w, w)
+ SUMMARY.GM_Combo_Het(reg=self, w=w, vm=vm)
+
+
+# Functions
+
+def get_psi_sigma(w, u, lamb):
+ """
+ Computes the Sigma matrix needed to compute Psi
+
+ Parameters
+ ----------
+ w : Sparse matrix
+ Spatial weights sparse matrix
+ u : array
+ nx1 vector of residuals
+ lamb : float
+ Lambda
+
+ """
+
+ e = (u - lamb * (w * u)) ** 2
+ E = SP.dia_matrix((e.flat, 0), shape=(w.shape[0], w.shape[0]))
+ return E.tocsr()
+
+
+def get_vc_het(w, wA1, E):
+ """
+ Computes the VC matrix Psi based on lambda as in Arraiz et al [1]_:
+
+ ..math::
+
+ \tilde{Psi} = \left(\begin{array}{c c}
+ \psi_{11} & \psi_{12} \\
+ \psi_{21} & \psi_{22} \\
+ \end{array} \right)
+
+ NOTE: psi12=psi21
+
+ ...
+
+ Parameters
+ ----------
+
+ w : Sparse matrix
+ Spatial weights sparse matrix
+
+ E : sparse matrix
+ Sigma
+
+ Returns
+ -------
+
+ Psi : array
+ 2x2 array with estimator of the variance-covariance matrix
+
+ References
+ ----------
+
+ .. [1] Arraiz, I., Drukker, D. M., Kelejian, H., Prucha, I. R. (2010) "A
+ Spatial Cliff-Ord-Type Model with Heteroskedastic Innovations: Small and
+ Large Sample Results". Journal of Regional Science, Vol. 60, No. 2, pp.
+ 592-614.
+
+ """
+ aPatE = 2 * wA1 * E
+ wPwtE = (w + w.T) * E
+
+ psi11 = aPatE * aPatE
+ psi12 = aPatE * wPwtE
+ psi22 = wPwtE * wPwtE
+ psi = map(np.sum, [psi11.diagonal(), psi12.diagonal(), psi22.diagonal()])
+ return np.array([[psi[0], psi[1]], [psi[1], psi[2]]]) / (2. * w.shape[0])
+
+
+def get_vm_het(G, lamb, reg, w, psi):
+ """
+ Computes the variance-covariance matrix Omega as in Arraiz et al [1]_:
+ ...
+
+ Parameters
+ ----------
+
+ G : array
+ G from moments equations
+
+ lamb : float
+ Final lambda from spHetErr estimation
+
+ reg : regression object
+ output instance from a regression model
+
+ u : array
+ nx1 vector of residuals
+
+ w : Sparse matrix
+ Spatial weights sparse matrix
+
+ psi : array
+ 2x2 array with the variance-covariance matrix of the moment equations
+
+ Returns
+ -------
+
+ vm : array
+ (k+1)x(k+1) array with the variance-covariance matrix of the parameters
+
+ References
+ ----------
+
+ .. [1] Arraiz, I., Drukker, D. M., Kelejian, H., Prucha, I. R. (2010) "A
+ Spatial Cliff-Ord-Type Model with Heteroskedastic Innovations: Small and
+ Large Sample Results". Journal of Regional Science, Vol. 60, No. 2, pp.
+ 592-614.
+
+ """
+
+ J = np.dot(G, np.array([[1], [2 * lamb]]))
+ Zs = UTILS.get_spFilter(w, lamb, reg.x)
+ ZstEZs = spdot((Zs.T * get_psi_sigma(w, reg.u, lamb)), Zs)
+ ZsZsi = la.inv(spdot(Zs.T, Zs))
+ omega11 = w.shape[0] * np.dot(np.dot(ZsZsi, ZstEZs), ZsZsi)
+ omega22 = la.inv(np.dot(np.dot(J.T, la.inv(psi)), J))
+ zero = np.zeros((reg.k, 1), float)
+ vm = np.vstack((np.hstack((omega11, zero)), np.hstack((zero.T, omega22)))) / \
+ w.shape[0]
+ return vm
+
+
+def get_P_hat(reg, hthi, zf):
+ """
+ P_hat from Appendix B, used for a1 a2, using filtered Z
+ """
+ htzf = spdot(reg.h.T, zf)
+ P1 = spdot(hthi, htzf)
+ P2 = spdot(htzf.T, P1)
+ P2i = la.inv(P2)
+ return reg.n * np.dot(P1, P2i)
+
+
+def get_a1a2(w, wA1, reg, lambdapar, P, zs, inv_method, filt):
+ """
+ Computes the a1 in psi assuming residuals come from original regression
+ ...
+
+ Parameters
+ ----------
+
+ w : Sparse matrix
+ Spatial weights sparse matrix
+
+ reg : TSLS
+ Two stage least quare regression instance
+
+ lambdapar : float
+ Spatial autoregressive parameter
+
+ Returns
+ -------
+
+ [a1, a2] : list
+ a1 and a2 are two nx1 array in psi equation
+
+ References
+ ----------
+
+ .. [1] Anselin, L. GMM Estimation of Spatial Error Autocorrelation with Heteroskedasticity
+
+ """
+ us = UTILS.get_spFilter(w, lambdapar, reg.u)
+ alpha1 = (-2.0 / w.shape[0]) * (np.dot(spdot(zs.T, wA1), us))
+ alpha2 = (-1.0 / w.shape[0]) * (np.dot(spdot(zs.T, (w + w.T)), us))
+ a1 = np.dot(spdot(reg.h, P), alpha1)
+ a2 = np.dot(spdot(reg.h, P), alpha2)
+ if not filt:
+ a1 = UTILS.inverse_prod(
+ w, a1, lambdapar, post_multiply=True, inv_method=inv_method).T
+ a2 = UTILS.inverse_prod(
+ w, a2, lambdapar, post_multiply=True, inv_method=inv_method).T
+ return [a1, a2]
+
+
+def get_vc_het_tsls(w, wA1, reg, lambdapar, P, zs, inv_method, filt=True, save_a1a2=False):
+
+ sigma = get_psi_sigma(w, reg.u, lambdapar)
+ vc1 = get_vc_het(w, wA1, sigma)
+ a1, a2 = get_a1a2(w, wA1, reg, lambdapar, P, zs, inv_method, filt)
+ a1s = a1.T * sigma
+ a2s = a2.T * sigma
+ psi11 = float(np.dot(a1s, a1))
+ psi12 = float(np.dot(a1s, a2))
+ psi21 = float(np.dot(a2s, a1))
+ psi22 = float(np.dot(a2s, a2))
+ psi0 = np.array([[psi11, psi12], [psi21, psi22]]) / w.shape[0]
+ if save_a1a2:
+ psi = (vc1 + psi0, a1, a2)
+ else:
+ psi = vc1 + psi0
+ return psi
+
+
+def get_Omega_GS2SLS(w, lamb, reg, G, psi, P):
+ """
+ Computes the variance-covariance matrix for GS2SLS:
+ ...
+
+ Parameters
+ ----------
+
+ w : Sparse matrix
+ Spatial weights sparse matrix
+
+ lamb : float
+ Spatial autoregressive parameter
+
+ reg : GSTSLS
+ Generalized Spatial two stage least quare regression instance
+ G : array
+ Moments
+ psi : array
+ Weighting matrix
+
+ Returns
+ -------
+
+ omega : array
+ (k+1)x(k+1)
+ """
+ psi, a1, a2 = psi
+ sigma = get_psi_sigma(w, reg.u, lamb)
+ psi_dd_1 = (1.0 / w.shape[0]) * reg.h.T * sigma
+ psi_dd = spdot(psi_dd_1, reg.h)
+ psi_dl = spdot(psi_dd_1, np.hstack((a1, a2)))
+ psi_o = np.hstack(
+ (np.vstack((psi_dd, psi_dl.T)), np.vstack((psi_dl, psi))))
+ psii = la.inv(psi)
+
+ j = np.dot(G, np.array([[1.], [2 * lamb]]))
+ jtpsii = np.dot(j.T, psii)
+ jtpsiij = np.dot(jtpsii, j)
+ jtpsiiji = la.inv(jtpsiij)
+ omega_1 = np.dot(jtpsiiji, jtpsii)
+ omega_2 = np.dot(np.dot(psii, j), jtpsiiji)
+ om_1_s = omega_1.shape
+ om_2_s = omega_2.shape
+ p_s = P.shape
+ omega_left = np.hstack((np.vstack((P.T, np.zeros((om_1_s[0], p_s[0])))),
+ np.vstack((np.zeros((p_s[1], om_1_s[1])), omega_1))))
+ omega_right = np.hstack((np.vstack((P, np.zeros((om_2_s[0], p_s[1])))),
+ np.vstack((np.zeros((p_s[0], om_2_s[1])), omega_2))))
+ omega = np.dot(np.dot(omega_left, psi_o), omega_right)
+ return omega / w.shape[0]
+
+
+def _test():
+ import doctest
+ doctest.testmod()
+
+if __name__ == '__main__':
+ _test()
diff --git a/pysal/spreg/error_sp_het_regimes.py b/pysal/spreg/error_sp_het_regimes.py
new file mode 100644
index 0000000..1295adf
--- /dev/null
+++ b/pysal/spreg/error_sp_het_regimes.py
@@ -0,0 +1,1481 @@
+'''
+Spatial Error with Heteroskedasticity and Regimes family of models
+'''
+__author__ = "Luc Anselin luc.anselin at asu.edu, Pedro V. Amaral pedro.amaral at asu.edu"
+
+import numpy as np
+import multiprocessing as mp
+import user_output as USER
+import summary_output as SUMMARY
+import utils as UTILS
+import regimes as REGI
+from ols import BaseOLS
+from twosls import BaseTSLS
+from error_sp_het import BaseGM_Error_Het, BaseGM_Endog_Error_Het, get_psi_sigma, get_vc_het, get_vm_het, get_P_hat, get_a1a2, get_vc_het_tsls, get_Omega_GS2SLS
+from utils import RegressionPropsY, spdot, set_endog, sphstack, set_warn, sp_att
+from scipy import sparse as SP
+from pysal import lag_spatial
+from platform import system
+
+
+class GM_Error_Het_Regimes(RegressionPropsY, REGI.Regimes_Frame):
+
+ """
+ GMM method for a spatial error model with heteroskedasticity and regimes;
+ based on Arraiz et al [1]_, following Anselin [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ w : pysal W object
+ Spatial weights object
+ constant_regi: ['one', 'many']
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime (default)
+ cols2regi : list, 'all'
+ Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all' (default), all the variables vary by regime.
+ regime_err_sep: boolean
+ If True, a separate regression is run for each regime.
+ regime_lag_sep : boolean
+ Always False, kept for consistency, ignored.
+ max_iter : int
+ Maximum number of iterations of steps 2a and 2b from Arraiz
+ et al. Note: epsilon provides an additional stop condition.
+ epsilon : float
+ Minimum change in lambda required to stop iterations of
+ steps 2a and 2b from Arraiz et al. Note: max_iter provides
+ an additional stop condition.
+ step1c : boolean
+ If True, then include Step 1c from Arraiz et al.
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ cores : boolean
+ Specifies if multiprocessing is to be used
+ Default: no multiprocessing, cores = False
+ Note: Multiprocessing may not work on all platforms.
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regime variable for use in the output
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ iter_stop : string
+ Stop criterion reached during iteration of steps 2a and 2b
+ from Arraiz et al.
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ iteration : integer
+ Number of iterations of steps 2a and 2b from Arraiz et al.
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ vm : array
+ Variance covariance matrix (kxk)
+ sig2 : float
+ Sigma squared used in computations
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ std_err : array
+ 1xk array of standard errors of the betas
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regime variable for use in the output
+ title : string
+ Name of the regression method used
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ constant_regi: ['one', 'many']
+ Ignored if regimes=False. Constant option for regimes.
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime
+ cols2regi : list, 'all'
+ Ignored if regimes=False. Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all', all the variables vary by regime.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ kr : int
+ Number of variables/columns to be "regimized" or subject
+ to change by regime. These will result in one parameter
+ estimate by regime for each variable (i.e. nr parameters per
+ variable)
+ kf : int
+ Number of variables/columns to be considered fixed or
+ global across regimes and hence only obtain one parameter
+ estimate
+ nr : int
+ Number of different regimes in the 'regimes' list
+ multi : dictionary
+ Only available when multiple regressions are estimated,
+ i.e. when regime_err_sep=True and no variable is fixed
+ across regimes.
+ Contains all attributes of each individual regression
+
+ References
+ ----------
+
+ .. [1] Arraiz, I., Drukker, D. M., Kelejian, H., Prucha, I. R. (2010) "A
+ Spatial Cliff-Ord-Type Model with Heteroskedastic Innovations: Small and
+ Large Sample Results". Journal of Regional Science, Vol. 60, No. 2, pp.
+ 592-614.
+
+ .. [2] Anselin, L. GMM Estimation of Spatial Error Autocorrelation with Heteroskedasticity
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import numpy as np
+ >>> import pysal
+
+ Open data on NCOVR US County Homicides (3085 areas) using pysal.open().
+ This is the DBF associated with the NAT shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path("NAT.dbf"),'r')
+
+ Extract the HR90 column (homicide rates in 1990) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y_var = 'HR90'
+ >>> y = np.array([db.by_col(y_var)]).reshape(3085,1)
+
+ Extract UE90 (unemployment rate) and PS90 (population structure) vectors from
+ the DBF to be used as independent variables in the regression. Other variables
+ can be inserted by adding their names to x_var, such as x_var = ['Var1','Var2','...]
+ Note that PySAL requires this to be an nxj numpy array, where j is the
+ number of independent variables (not including a constant). By default
+ this model adds a vector of ones to the independent variables passed in.
+
+ >>> x_var = ['PS90','UE90']
+ >>> x = np.array([db.by_col(name) for name in x_var]).T
+
+ The different regimes in this data are given according to the North and
+ South dummy (SOUTH).
+
+ >>> r_var = 'SOUTH'
+ >>> regimes = db.by_col(r_var)
+
+ Since we want to run a spatial error model, we need to specify
+ the spatial weights matrix that includes the spatial configuration of the
+ observations. To do that, we can open an already existing gal file or
+ create a new one. In this case, we will create one from ``NAT.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("NAT.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, this allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ We are all set with the preliminaries, we are good to run the model. In this
+ case, we will need the variables and the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional.
+
+ >>> reg = GM_Error_Het_Regimes(y, x, regimes, w=w, step1c=True, name_y=y_var, name_x=x_var, name_regimes=r_var, name_ds='NAT.dbf')
+
+ Once we have run the model, we can explore a little bit the output. The
+ regression object we have created has many attributes so take your time to
+ discover them. This class offers an error model that explicitly accounts
+ for heteroskedasticity and that unlike the models from
+ ``pysal.spreg.error_sp``, it allows for inference on the spatial
+ parameter. Alternatively, we can have a summary of the
+ output by typing: model.summary
+
+ >>> print reg.name_x
+ ['0_CONSTANT', '0_PS90', '0_UE90', '1_CONSTANT', '1_PS90', '1_UE90', 'lambda']
+ >>> np.around(reg.betas, decimals=6)
+ array([[ 0.009121],
+ [ 0.812973],
+ [ 0.549355],
+ [ 5.00279 ],
+ [ 1.200929],
+ [ 0.614681],
+ [ 0.429277]])
+ >>> np.around(reg.std_err, decimals=6)
+ array([ 0.355844, 0.221743, 0.059276, 0.686764, 0.35843 , 0.092788,
+ 0.02524 ])
+
+ """
+
+ def __init__(self, y, x, regimes, w, max_iter=1, epsilon=0.00001, step1c=False,
+ constant_regi='many', cols2regi='all', regime_err_sep=False,
+ regime_lag_sep=False,
+ cores=False, vm=False, name_y=None, name_x=None, name_w=None,
+ name_ds=None, name_regimes=None):
+
+ n = USER.check_arrays(y, x)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ self.constant_regi = constant_regi
+ self.cols2regi = cols2regi
+ self.regime_err_sep = regime_err_sep
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_y = USER.set_name_y(name_y)
+ self.name_w = USER.set_name_w(name_w, w)
+ self.name_regimes = USER.set_name_ds(name_regimes)
+ self.n, self.step1c = n, step1c
+ self.y = y
+
+ x_constant = USER.check_constant(x)
+ name_x = USER.set_name_x(name_x, x)
+ self.name_x_r = name_x
+
+ cols2regi = REGI.check_cols2regi(constant_regi, cols2regi, x)
+ self.regimes_set = REGI._get_regimes_set(regimes)
+ self.regimes = regimes
+ USER.check_regimes(self.regimes_set, self.n, x.shape[1])
+ self.regime_err_sep = regime_err_sep
+
+ if regime_err_sep == True:
+ if set(cols2regi) == set([True]):
+ self._error_regimes_multi(y, x, regimes, w, cores,
+ max_iter, epsilon, step1c,
+ cols2regi, vm, name_x)
+ else:
+ raise Exception, "All coefficients must vary accross regimes if regime_err_sep = True."
+ else:
+ self.x, self.name_x = REGI.Regimes_Frame.__init__(self, x_constant,
+ regimes, constant_regi=None, cols2regi=cols2regi, names=name_x)
+ ols = BaseOLS(y=y, x=self.x)
+ self.k = ols.x.shape[1]
+ wA1 = UTILS.get_A1_het(w.sparse)
+
+ # 1b. GMM --> \tilde{\lambda1}
+ moments = UTILS._moments2eqs(wA1, w.sparse, ols.u)
+ lambda1 = UTILS.optim_moments(moments)
+
+ if step1c:
+ # 1c. GMM --> \tilde{\lambda2}
+ sigma = get_psi_sigma(w.sparse, ols.u, lambda1)
+ vc1 = get_vc_het(w.sparse, wA1, sigma)
+ lambda2 = UTILS.optim_moments(moments, vc1)
+ else:
+ lambda2 = lambda1
+ lambda_old = lambda2
+
+ self.iteration, eps = 0, 1
+ while self.iteration < max_iter and eps > epsilon:
+ # 2a. reg -->\hat{betas}
+ xs = UTILS.get_spFilter(w, lambda_old, x_constant)
+ ys = UTILS.get_spFilter(w, lambda_old, y)
+ xs = REGI.Regimes_Frame.__init__(self, xs,
+ regimes, constant_regi=None, cols2regi=cols2regi)[0]
+ ols_s = BaseOLS(y=ys, x=xs)
+ self.predy = spdot(self.x, ols_s.betas)
+ self.u = self.y - self.predy
+
+ # 2b. GMM --> \hat{\lambda}
+ sigma_i = get_psi_sigma(w.sparse, self.u, lambda_old)
+ vc_i = get_vc_het(w.sparse, wA1, sigma_i)
+ moments_i = UTILS._moments2eqs(wA1, w.sparse, self.u)
+ lambda3 = UTILS.optim_moments(moments_i, vc_i)
+ eps = abs(lambda3 - lambda_old)
+ lambda_old = lambda3
+ self.iteration += 1
+
+ self.iter_stop = UTILS.iter_msg(self.iteration, max_iter)
+
+ sigma = get_psi_sigma(w.sparse, self.u, lambda3)
+ vc3 = get_vc_het(w.sparse, wA1, sigma)
+ self.vm = get_vm_het(moments_i[0], lambda3, self, w.sparse, vc3)
+ self.betas = np.vstack((ols_s.betas, lambda3))
+ self.e_filtered = self.u - lambda3 * lag_spatial(w, self.u)
+ self.title = "SPATIALLY WEIGHTED LEAST SQUARES (HET) - REGIMES"
+ self.name_x.append('lambda')
+ self.kf += 1
+ self.chow = REGI.Chow(self)
+ self._cache = {}
+
+ SUMMARY.GM_Error_Het(reg=self, w=w, vm=vm, regimes=True)
+
+ def _error_regimes_multi(self, y, x, regimes, w, cores,
+ max_iter, epsilon, step1c, cols2regi, vm, name_x):
+
+ regi_ids = dict(
+ (r, list(np.where(np.array(regimes) == r)[0])) for r in self.regimes_set)
+ results_p = {}
+ """
+ for r in self.regimes_set:
+ if system() == 'Windows':
+ is_win = True
+ results_p[r] = _work_error(*(y,x,regi_ids,r,w,max_iter,epsilon,step1c,self.name_ds,self.name_y,name_x+['lambda'],self.name_w,self.name_regimes))
+ else:
+ pool = mp.Pool(cores)
+ results_p[r] = pool.apply_async(_work_error,args=(y,x,regi_ids,r,w,max_iter,epsilon,step1c,self.name_ds,self.name_y,name_x+['lambda'],self.name_w,self.name_regimes, ))
+ is_win = False
+ """
+ for r in self.regimes_set:
+ if cores:
+ pool = mp.Pool(None)
+ results_p[r] = pool.apply_async(_work_error, args=(
+ y, x, regi_ids, r, w, max_iter, epsilon, step1c, self.name_ds, self.name_y, name_x + ['lambda'], self.name_w, self.name_regimes, ))
+ else:
+ results_p[r] = _work_error(*(y, x, regi_ids, r, w, max_iter, epsilon, step1c,
+ self.name_ds, self.name_y, name_x + ['lambda'], self.name_w, self.name_regimes))
+
+ self.kryd = 0
+ self.kr = len(cols2regi) + 1
+ self.kf = 0
+ self.nr = len(self.regimes_set)
+ self.vm = np.zeros((self.nr * self.kr, self.nr * self.kr), float)
+ self.betas = np.zeros((self.nr * self.kr, 1), float)
+ self.u = np.zeros((self.n, 1), float)
+ self.predy = np.zeros((self.n, 1), float)
+ self.e_filtered = np.zeros((self.n, 1), float)
+ """
+ if not is_win:
+ pool.close()
+ pool.join()
+ """
+ if cores:
+ pool.close()
+ pool.join()
+
+ results = {}
+ self.name_y, self.name_x = [], []
+ counter = 0
+ for r in self.regimes_set:
+ """
+ if is_win:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+ """
+ if not cores:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+
+ self.vm[(counter * self.kr):((counter + 1) * self.kr),
+ (counter * self.kr):((counter + 1) * self.kr)] = results[r].vm
+ self.betas[
+ (counter * self.kr):((counter + 1) * self.kr), ] = results[r].betas
+ self.u[regi_ids[r], ] = results[r].u
+ self.predy[regi_ids[r], ] = results[r].predy
+ self.e_filtered[regi_ids[r], ] = results[r].e_filtered
+ self.name_y += results[r].name_y
+ self.name_x += results[r].name_x
+ counter += 1
+ self.chow = REGI.Chow(self)
+ self.multi = results
+ SUMMARY.GM_Error_Het_multi(
+ reg=self, multireg=self.multi, vm=vm, regimes=True)
+
+
+class GM_Endog_Error_Het_Regimes(RegressionPropsY, REGI.Regimes_Frame):
+
+ """
+ GMM method for a spatial error model with heteroskedasticity, regimes and
+ endogenous variables, with results and diagnostics; based on Arraiz et al
+ [1]_, following Anselin [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x)
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ w : pysal W object
+ Spatial weights object
+ constant_regi: ['one', 'many']
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime (default)
+ cols2regi : list, 'all'
+ Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all' (default), all the variables vary by regime.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ regime_lag_sep : boolean
+ Always False, kept for consistency, ignored.
+ max_iter : int
+ Maximum number of iterations of steps 2a and 2b from Arraiz
+ et al. Note: epsilon provides an additional stop condition.
+ epsilon : float
+ Minimum change in lambda required to stop iterations of
+ steps 2a and 2b from Arraiz et al. Note: max_iter provides
+ an additional stop condition.
+ step1c : boolean
+ If True, then include Step 1c from Arraiz et al.
+ inv_method : string
+ If "power_exp", then compute inverse using the power
+ expansion. If "true_inv", then compute the true inverse.
+ Note that true_inv will fail for large n.
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ cores : boolean
+ Specifies if multiprocessing is to be used
+ Default: no multiprocessing, cores = False
+ Note: Multiprocessing may not work on all platforms.
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_q : list of strings
+ Names of instruments for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regime variable for use in the output
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable used as instruments
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ z : array
+ nxk array of variables (combination of x and yend)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ h : array
+ nxl array of instruments (combination of x and q)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ iter_stop : string
+ Stop criterion reached during iteration of steps 2a and 2b
+ from Arraiz et al.
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ iteration : integer
+ Number of iterations of steps 2a and 2b from Arraiz et al.
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ std_err : array
+ 1xk array of standard errors of the betas
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_z : list of strings
+ Names of exogenous and endogenous variables for use in
+ output
+ name_q : list of strings
+ Names of external instruments
+ name_h : list of strings
+ Names of all instruments used in ouput
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regimes variable for use in output
+ title : string
+ Name of the regression method used
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ constant_regi : ['one', 'many']
+ Ignored if regimes=False. Constant option for regimes.
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime
+ cols2regi : list, 'all'
+ Ignored if regimes=False. Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all', all the variables vary by regime.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ kr : int
+ Number of variables/columns to be "regimized" or subject
+ to change by regime. These will result in one parameter
+ estimate by regime for each variable (i.e. nr parameters per
+ variable)
+ kf : int
+ Number of variables/columns to be considered fixed or
+ global across regimes and hence only obtain one parameter
+ estimate
+ nr : int
+ Number of different regimes in the 'regimes' list
+ multi : dictionary
+ Only available when multiple regressions are estimated,
+ i.e. when regime_err_sep=True and no variable is fixed
+ across regimes.
+ Contains all attributes of each individual regression
+
+ References
+ ----------
+
+ .. [1] Arraiz, I., Drukker, D. M., Kelejian, H., Prucha, I. R. (2010) "A
+ Spatial Cliff-Ord-Type Model with Heteroskedastic Innovations: Small and
+ Large Sample Results". Journal of Regional Science, Vol. 60, No. 2, pp.
+ 592-614.
+
+ .. [2] Anselin, L. GMM Estimation of Spatial Error Autocorrelation with Heteroskedasticity
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import numpy as np
+ >>> import pysal
+
+ Open data on NCOVR US County Homicides (3085 areas) using pysal.open().
+ This is the DBF associated with the NAT shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path("NAT.dbf"),'r')
+
+ Extract the HR90 column (homicide rates in 1990) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y_var = 'HR90'
+ >>> y = np.array([db.by_col(y_var)]).reshape(3085,1)
+
+ Extract UE90 (unemployment rate) and PS90 (population structure) vectors from
+ the DBF to be used as independent variables in the regression. Other variables
+ can be inserted by adding their names to x_var, such as x_var = ['Var1','Var2','...]
+ Note that PySAL requires this to be an nxj numpy array, where j is the
+ number of independent variables (not including a constant). By default
+ this model adds a vector of ones to the independent variables passed in.
+
+ >>> x_var = ['PS90','UE90']
+ >>> x = np.array([db.by_col(name) for name in x_var]).T
+
+ For the endogenous models, we add the endogenous variable RD90 (resource deprivation)
+ and we decide to instrument for it with FP89 (families below poverty):
+
+ >>> yd_var = ['RD90']
+ >>> yend = np.array([db.by_col(name) for name in yd_var]).T
+ >>> q_var = ['FP89']
+ >>> q = np.array([db.by_col(name) for name in q_var]).T
+
+ The different regimes in this data are given according to the North and
+ South dummy (SOUTH).
+
+ >>> r_var = 'SOUTH'
+ >>> regimes = db.by_col(r_var)
+
+ Since we want to run a spatial error model, we need to specify the spatial
+ weights matrix that includes the spatial configuration of the observations
+ into the error component of the model. To do that, we can open an already
+ existing gal file or create a new one. In this case, we will create one
+ from ``NAT.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("NAT.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, this allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ We are all set with the preliminaries, we are good to run the model. In this
+ case, we will need the variables (exogenous and endogenous), the
+ instruments and the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional.
+
+ >>> reg = GM_Endog_Error_Het_Regimes(y, x, yend, q, regimes, w=w, step1c=True, name_y=y_var, name_x=x_var, name_yend=yd_var, name_q=q_var, name_regimes=r_var, name_ds='NAT.dbf')
+
+ Once we have run the model, we can explore a little bit the output. The
+ regression object we have created has many attributes so take your time to
+ discover them. This class offers an error model that explicitly accounts
+ for heteroskedasticity and that unlike the models from
+ ``pysal.spreg.error_sp``, it allows for inference on the spatial
+ parameter. Hence, we find the same number of betas as of standard errors,
+ which we calculate taking the square root of the diagonal of the
+ variance-covariance matrix Alternatively, we can have a summary of the
+ output by typing: model.summary
+
+ >>> print reg.name_z
+ ['0_CONSTANT', '0_PS90', '0_UE90', '1_CONSTANT', '1_PS90', '1_UE90', '0_RD90', '1_RD90', 'lambda']
+
+ >>> print np.around(reg.betas,4)
+ [[ 3.5944]
+ [ 1.065 ]
+ [ 0.1587]
+ [ 9.184 ]
+ [ 1.8784]
+ [-0.2466]
+ [ 2.4617]
+ [ 3.5756]
+ [ 0.2908]]
+
+ >>> print np.around(np.sqrt(reg.vm.diagonal()),4)
+ [ 0.5043 0.2132 0.0581 0.6681 0.3504 0.0999 0.3686 0.3402 0.028 ]
+
+ """
+
+ def __init__(self, y, x, yend, q, regimes, w,
+ max_iter=1, epsilon=0.00001, step1c=False,
+ constant_regi='many', cols2regi='all', regime_err_sep=False,
+ regime_lag_sep=False,
+ inv_method='power_exp', cores=False,
+ vm=False, name_y=None, name_x=None,
+ name_yend=None, name_q=None, name_w=None, name_ds=None,
+ name_regimes=None, summ=True, add_lag=False):
+
+ n = USER.check_arrays(y, x, yend, q)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ self.constant_regi = constant_regi
+ self.cols2regi = cols2regi
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_regimes = USER.set_name_ds(name_regimes)
+ self.name_w = USER.set_name_w(name_w, w)
+ self.n, self.step1c = n, step1c
+ self.y = y
+
+ name_x = USER.set_name_x(name_x, x)
+ if summ:
+ name_yend = USER.set_name_yend(name_yend, yend)
+ self.name_y = USER.set_name_y(name_y)
+ name_q = USER.set_name_q(name_q, q)
+ self.name_x_r = name_x + name_yend
+
+ cols2regi = REGI.check_cols2regi(
+ constant_regi, cols2regi, x, yend=yend)
+ self.regimes_set = REGI._get_regimes_set(regimes)
+ self.regimes = regimes
+ USER.check_regimes(self.regimes_set, self.n, x.shape[1])
+ self.regime_err_sep = regime_err_sep
+
+ if regime_err_sep == True:
+ if set(cols2regi) == set([True]):
+ self._endog_error_regimes_multi(y, x, regimes, w, yend, q, cores,
+ max_iter, epsilon, step1c, inv_method, cols2regi, vm,
+ name_x, name_yend, name_q, add_lag)
+ else:
+ raise Exception, "All coefficients must vary accross regimes if regime_err_sep = True."
+ else:
+ x_constant = USER.check_constant(x)
+ q, name_q = REGI.Regimes_Frame.__init__(self, q,
+ regimes, constant_regi=None, cols2regi='all', names=name_q)
+ x, name_x = REGI.Regimes_Frame.__init__(self, x_constant,
+ regimes, constant_regi=None, cols2regi=cols2regi,
+ names=name_x)
+ yend2, name_yend = REGI.Regimes_Frame.__init__(self, yend,
+ regimes, constant_regi=None,
+ cols2regi=cols2regi, yend=True, names=name_yend)
+
+ # 1a. S2SLS --> \tilde{\delta}
+ tsls = BaseTSLS(y=y, x=x, yend=yend2, q=q)
+ self.k = tsls.z.shape[1]
+ self.x = tsls.x
+ self.yend, self.z, self.h = tsls.yend, tsls.z, tsls.h
+ wA1 = UTILS.get_A1_het(w.sparse)
+
+ # 1b. GMM --> \tilde{\lambda1}
+ moments = UTILS._moments2eqs(wA1, w.sparse, tsls.u)
+ lambda1 = UTILS.optim_moments(moments)
+
+ if step1c:
+ # 1c. GMM --> \tilde{\lambda2}
+ self.u = tsls.u
+ zs = UTILS.get_spFilter(w, lambda1, self.z)
+ vc1 = get_vc_het_tsls(
+ w.sparse, wA1, self, lambda1, tsls.pfora1a2, zs, inv_method, filt=False)
+ lambda2 = UTILS.optim_moments(moments, vc1)
+ else:
+ lambda2 = lambda1
+ lambda_old = lambda2
+
+ self.iteration, eps = 0, 1
+ while self.iteration < max_iter and eps > epsilon:
+ # 2a. reg -->\hat{betas}
+ xs = UTILS.get_spFilter(w, lambda1, x_constant)
+ xs = REGI.Regimes_Frame.__init__(self, xs,
+ regimes, constant_regi=None, cols2regi=cols2regi)[0]
+ ys = UTILS.get_spFilter(w, lambda1, y)
+ yend_s = UTILS.get_spFilter(w, lambda1, yend)
+ yend_s = REGI.Regimes_Frame.__init__(self, yend_s,
+ regimes, constant_regi=None, cols2regi=cols2regi,
+ yend=True)[0]
+ tsls_s = BaseTSLS(ys, xs, yend_s, h=tsls.h)
+ self.predy = spdot(self.z, tsls_s.betas)
+ self.u = self.y - self.predy
+
+ # 2b. GMM --> \hat{\lambda}
+ vc2 = get_vc_het_tsls(
+ w.sparse, wA1, self, lambda_old, tsls_s.pfora1a2, sphstack(xs, yend_s), inv_method)
+ moments_i = UTILS._moments2eqs(wA1, w.sparse, self.u)
+ lambda3 = UTILS.optim_moments(moments_i, vc2)
+ eps = abs(lambda3 - lambda_old)
+ lambda_old = lambda3
+ self.iteration += 1
+
+ self.iter_stop = UTILS.iter_msg(self.iteration, max_iter)
+
+ zs = UTILS.get_spFilter(w, lambda3, self.z)
+ P = get_P_hat(self, tsls.hthi, zs)
+ vc3 = get_vc_het_tsls(
+ w.sparse, wA1, self, lambda3, P, zs, inv_method, save_a1a2=True)
+ self.vm = get_Omega_GS2SLS(
+ w.sparse, lambda3, self, moments_i[0], vc3, P)
+ self.betas = np.vstack((tsls_s.betas, lambda3))
+ self.e_filtered = self.u - lambda3 * lag_spatial(w, self.u)
+ self.name_x = USER.set_name_x(name_x, x, constant=True)
+ self.name_yend = USER.set_name_yend(name_yend, yend)
+ self.name_z = self.name_x + self.name_yend
+ self.name_z.append('lambda') # listing lambda last
+ self.name_q = USER.set_name_q(name_q, q)
+ self.name_h = USER.set_name_h(self.name_x, self.name_q)
+ self.kf += 1
+ self.chow = REGI.Chow(self)
+ self._cache = {}
+ if summ:
+ self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HET) - REGIMES"
+ SUMMARY.GM_Endog_Error_Het(reg=self, w=w, vm=vm, regimes=True)
+
+ def _endog_error_regimes_multi(self, y, x, regimes, w, yend, q, cores,
+ max_iter, epsilon, step1c, inv_method, cols2regi, vm,
+ name_x, name_yend, name_q, add_lag):
+
+ regi_ids = dict(
+ (r, list(np.where(np.array(regimes) == r)[0])) for r in self.regimes_set)
+ if add_lag != False:
+ self.cols2regi += [True]
+ cols2regi += [True]
+ self.predy_e = np.zeros((self.n, 1), float)
+ self.e_pred = np.zeros((self.n, 1), float)
+ results_p = {}
+ """
+ for r in self.regimes_set:
+ if system() == 'Windows':
+ is_win = True
+ results_p[r] = _work_endog_error(*(y,x,yend,q,regi_ids,r,w,max_iter,epsilon,step1c,inv_method,self.name_ds,self.name_y,name_x,name_yend,name_q,self.name_w,self.name_regimes,add_lag))
+ else:
+ pool = mp.Pool(cores)
+ results_p[r] = pool.apply_async(_work_endog_error,args=(y,x,yend,q,regi_ids,r,w,max_iter,epsilon,step1c,inv_method,self.name_ds,self.name_y,name_x,name_yend,name_q,self.name_w,self.name_regimes,add_lag, ))
+ is_win = False
+ """
+ for r in self.regimes_set:
+ if cores:
+ pool = mp.Pool(None)
+ results_p[r] = pool.apply_async(_work_endog_error, args=(y, x, yend, q, regi_ids, r, w, max_iter, epsilon, step1c,
+ inv_method, self.name_ds, self.name_y, name_x, name_yend, name_q, self.name_w, self.name_regimes, add_lag, ))
+ else:
+ results_p[r] = _work_endog_error(*(y, x, yend, q, regi_ids, r, w, max_iter, epsilon, step1c, inv_method,
+ self.name_ds, self.name_y, name_x, name_yend, name_q, self.name_w, self.name_regimes, add_lag))
+
+ self.kryd, self.kf = 0, 0
+ self.kr = len(cols2regi) + 1
+ self.nr = len(self.regimes_set)
+ self.vm = np.zeros((self.nr * self.kr, self.nr * self.kr), float)
+ self.betas = np.zeros((self.nr * self.kr, 1), float)
+ self.u = np.zeros((self.n, 1), float)
+ self.predy = np.zeros((self.n, 1), float)
+ self.e_filtered = np.zeros((self.n, 1), float)
+ """
+ if not is_win:
+ pool.close()
+ pool.join()
+ """
+ if cores:
+ pool.close()
+ pool.join()
+
+ results = {}
+ self.name_y, self.name_x, self.name_yend, self.name_q, self.name_z, self.name_h = [
+ ], [], [], [], [], []
+ counter = 0
+ for r in self.regimes_set:
+ """
+ if is_win:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+ """
+ if not cores:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+
+ self.vm[(counter * self.kr):((counter + 1) * self.kr),
+ (counter * self.kr):((counter + 1) * self.kr)] = results[r].vm
+ self.betas[
+ (counter * self.kr):((counter + 1) * self.kr), ] = results[r].betas
+ self.u[regi_ids[r], ] = results[r].u
+ self.predy[regi_ids[r], ] = results[r].predy
+ self.e_filtered[regi_ids[r], ] = results[r].e_filtered
+ self.name_y += results[r].name_y
+ self.name_x += results[r].name_x
+ self.name_yend += results[r].name_yend
+ self.name_q += results[r].name_q
+ self.name_z += results[r].name_z
+ self.name_h += results[r].name_h
+ if add_lag != False:
+ self.predy_e[regi_ids[r], ] = results[r].predy_e
+ self.e_pred[regi_ids[r], ] = results[r].e_pred
+ counter += 1
+ self.chow = REGI.Chow(self)
+ self.multi = results
+ if add_lag != False:
+ SUMMARY.GM_Combo_Het_multi(
+ reg=self, multireg=self.multi, vm=vm, regimes=True)
+ else:
+ SUMMARY.GM_Endog_Error_Het_multi(
+ reg=self, multireg=self.multi, vm=vm, regimes=True)
+
+
+class GM_Combo_Het_Regimes(GM_Endog_Error_Het_Regimes):
+
+ """
+ GMM method for a spatial lag and error model with heteroskedasticity,
+ regimes and endogenous variables, with results and diagnostics;
+ based on Arraiz et al [1]_, following Anselin [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x)
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ w : pysal W object
+ Spatial weights object (always needed)
+ constant_regi: ['one', 'many']
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime (default)
+ cols2regi : list, 'all'
+ Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all' (default), all the variables vary by regime.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ regime_lag_sep : boolean
+ If True, the spatial parameter for spatial lag is also
+ computed according to different regimes. If False (default),
+ the spatial parameter is fixed accross regimes.
+ w_lags : integer
+ Orders of W to include as instruments for the spatially
+ lagged dependent variable. For example, w_lags=1, then
+ instruments are WX; if w_lags=2, then WX, WWX; and so on.
+ lag_q : boolean
+ If True, then include spatial lags of the additional
+ instruments (q).
+ max_iter : int
+ Maximum number of iterations of steps 2a and 2b from Arraiz
+ et al. Note: epsilon provides an additional stop condition.
+ epsilon : float
+ Minimum change in lambda required to stop iterations of
+ steps 2a and 2b from Arraiz et al. Note: max_iter provides
+ an additional stop condition.
+ step1c : boolean
+ If True, then include Step 1c from Arraiz et al.
+ inv_method : string
+ If "power_exp", then compute inverse using the power
+ expansion. If "true_inv", then compute the true inverse.
+ Note that true_inv will fail for large n.
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ cores : boolean
+ Specifies if multiprocessing is to be used
+ Default: no multiprocessing, cores = False
+ Note: Multiprocessing may not work on all platforms.
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_q : list of strings
+ Names of instruments for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regime variable for use in the output
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ e_pred : array
+ nx1 array of residuals (using reduced form)
+ predy : array
+ nx1 array of predicted y values
+ predy_e : array
+ nx1 array of predicted y values (using reduced form)
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable used as instruments
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ z : array
+ nxk array of variables (combination of x and yend)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ h : array
+ nxl array of instruments (combination of x and q)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ iter_stop : string
+ Stop criterion reached during iteration of steps 2a and 2b
+ from Arraiz et al.
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ iteration : integer
+ Number of iterations of steps 2a and 2b from Arraiz et al.
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ pr2_e : float
+ Pseudo R squared (squared correlation between y and ypred_e
+ (using reduced form))
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ std_err : array
+ 1xk array of standard errors of the betas
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_z : list of strings
+ Names of exogenous and endogenous variables for use in
+ output
+ name_q : list of strings
+ Names of external instruments
+ name_h : list of strings
+ Names of all instruments used in ouput
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regimes variable for use in output
+ title : string
+ Name of the regression method used
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ constant_regi : ['one', 'many']
+ Ignored if regimes=False. Constant option for regimes.
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime
+ cols2regi : list, 'all'
+ Ignored if regimes=False. Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all', all the variables vary by regime.
+ regime_err_sep: boolean
+ If True, a separate regression is run for each regime.
+ regime_lag_sep: boolean
+ If True, the spatial parameter for spatial lag is also
+ computed according to different regimes. If False (default),
+ the spatial parameter is fixed accross regimes.
+ kr : int
+ Number of variables/columns to be "regimized" or subject
+ to change by regime. These will result in one parameter
+ estimate by regime for each variable (i.e. nr parameters per
+ variable)
+ kf : int
+ Number of variables/columns to be considered fixed or
+ global across regimes and hence only obtain one parameter
+ estimate
+ nr : int
+ Number of different regimes in the 'regimes' list
+ multi : dictionary
+ Only available when multiple regressions are estimated,
+ i.e. when regime_err_sep=True and no variable is fixed
+ across regimes.
+ Contains all attributes of each individual regression
+
+ References
+ ----------
+
+ .. [1] Arraiz, I., Drukker, D. M., Kelejian, H., Prucha, I. R. (2010) "A
+ Spatial Cliff-Ord-Type Model with Heteroskedastic Innovations: Small and
+ Large Sample Results". Journal of Regional Science, Vol. 60, No. 2, pp.
+ 592-614.
+
+ .. [2] Anselin, L. GMM Estimation of Spatial Error Autocorrelation with Heteroskedasticity
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import numpy as np
+ >>> import pysal
+
+ Open data on NCOVR US County Homicides (3085 areas) using pysal.open().
+ This is the DBF associated with the NAT shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path("NAT.dbf"),'r')
+
+ Extract the HR90 column (homicide rates in 1990) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y_var = 'HR90'
+ >>> y = np.array([db.by_col(y_var)]).reshape(3085,1)
+
+ Extract UE90 (unemployment rate) and PS90 (population structure) vectors from
+ the DBF to be used as independent variables in the regression. Other variables
+ can be inserted by adding their names to x_var, such as x_var = ['Var1','Var2','...]
+ Note that PySAL requires this to be an nxj numpy array, where j is the
+ number of independent variables (not including a constant). By default
+ this model adds a vector of ones to the independent variables passed in.
+
+ >>> x_var = ['PS90','UE90']
+ >>> x = np.array([db.by_col(name) for name in x_var]).T
+
+ The different regimes in this data are given according to the North and
+ South dummy (SOUTH).
+
+ >>> r_var = 'SOUTH'
+ >>> regimes = db.by_col(r_var)
+
+ Since we want to run a spatial combo model, we need to specify
+ the spatial weights matrix that includes the spatial configuration of the
+ observations. To do that, we can open an already existing gal file or
+ create a new one. In this case, we will create one from ``NAT.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("NAT.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, this allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ We are all set with the preliminaries, we are good to run the model. In this
+ case, we will need the variables and the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional.
+
+ Example only with spatial lag
+
+ The Combo class runs an SARAR model, that is a spatial lag+error model.
+ In this case we will run a simple version of that, where we have the
+ spatial effects as well as exogenous variables. Since it is a spatial
+ model, we have to pass in the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional. We can have a
+ summary of the output by typing: model.summary
+ Alternatively, we can check the betas:
+
+ >>> reg = GM_Combo_Het_Regimes(y, x, regimes, w=w, step1c=True, name_y=y_var, name_x=x_var, name_regimes=r_var, name_ds='NAT')
+ >>> print reg.name_z
+ ['0_CONSTANT', '0_PS90', '0_UE90', '1_CONSTANT', '1_PS90', '1_UE90', '_Global_W_HR90', 'lambda']
+ >>> print np.around(reg.betas,4)
+ [[ 1.4613]
+ [ 0.9587]
+ [ 0.5658]
+ [ 9.1157]
+ [ 1.1324]
+ [ 0.6518]
+ [-0.4587]
+ [ 0.7174]]
+
+ This class also allows the user to run a spatial lag+error model with the
+ extra feature of including non-spatial endogenous regressors. This means
+ that, in addition to the spatial lag and error, we consider some of the
+ variables on the right-hand side of the equation as endogenous and we
+ instrument for this. In this case we consider RD90 (resource deprivation)
+ as an endogenous regressor. We use FP89 (families below poverty)
+ for this and hence put it in the instruments parameter, 'q'.
+
+ >>> yd_var = ['RD90']
+ >>> yd = np.array([db.by_col(name) for name in yd_var]).T
+ >>> q_var = ['FP89']
+ >>> q = np.array([db.by_col(name) for name in q_var]).T
+
+ And then we can run and explore the model analogously to the previous combo:
+
+ >>> reg = GM_Combo_Het_Regimes(y, x, regimes, yd, q, w=w, step1c=True, name_y=y_var, name_x=x_var, name_yend=yd_var, name_q=q_var, name_regimes=r_var, name_ds='NAT')
+ >>> print reg.name_z
+ ['0_CONSTANT', '0_PS90', '0_UE90', '1_CONSTANT', '1_PS90', '1_UE90', '0_RD90', '1_RD90', '_Global_W_HR90', 'lambda']
+ >>> print reg.betas
+ [[ 3.41936197]
+ [ 1.04071048]
+ [ 0.16747219]
+ [ 8.85820215]
+ [ 1.847382 ]
+ [-0.24545394]
+ [ 2.43189808]
+ [ 3.61328423]
+ [ 0.03132164]
+ [ 0.29544224]]
+ >>> print np.sqrt(reg.vm.diagonal())
+ [ 0.53103804 0.20835827 0.05755679 1.00496234 0.34332131 0.10259525
+ 0.3454436 0.37932794 0.07611667 0.07067059]
+ >>> print 'lambda: ', np.around(reg.betas[-1], 4)
+ lambda: [ 0.2954]
+
+ """
+
+ def __init__(self, y, x, regimes, yend=None, q=None,
+ w=None, w_lags=1, lag_q=True,
+ max_iter=1, epsilon=0.00001, step1c=False,
+ cores=False, inv_method='power_exp',
+ constant_regi='many', cols2regi='all',
+ regime_err_sep=False, regime_lag_sep=False,
+ vm=False, name_y=None, name_x=None,
+ name_yend=None, name_q=None,
+ name_w=None, name_ds=None, name_regimes=None):
+
+ n = USER.check_arrays(y, x)
+ self.step1c = step1c
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ name_x = USER.set_name_x(name_x, x, constant=True)
+ self.name_y = USER.set_name_y(name_y)
+ name_yend = USER.set_name_yend(name_yend, yend)
+ name_q = USER.set_name_q(name_q, q)
+ name_q.extend(
+ USER.set_name_q_sp(name_x, w_lags, name_q, lag_q, force_all=True))
+
+ cols2regi = REGI.check_cols2regi(
+ constant_regi, cols2regi, x, yend=yend, add_cons=False)
+ self.regimes_set = REGI._get_regimes_set(regimes)
+ self.regimes = regimes
+ USER.check_regimes(self.regimes_set, n, x.shape[1])
+ self.regime_err_sep = regime_err_sep
+ self.regime_lag_sep = regime_lag_sep
+
+ if regime_lag_sep == True:
+ if regime_err_sep == False:
+ raise Exception, "For spatial combo models, if spatial lag is set by regimes (regime_lag_sep=True), spatial error must also be set by regimes (regime_err_sep=True)."
+ add_lag = [w_lags, lag_q]
+ else:
+ cols2regi += [False]
+ add_lag = False
+ if regime_err_sep == True:
+ raise Exception, "For spatial combo models, if spatial error is set by regimes (regime_err_sep=True), all coefficients including lambda (regime_lag_sep=True) must be set by regimes."
+ yend, q = set_endog(y, x, w, yend, q, w_lags, lag_q)
+ name_yend.append(USER.set_name_yend_sp(self.name_y))
+
+ GM_Endog_Error_Het_Regimes.__init__(self, y=y, x=x, yend=yend,
+ q=q, regimes=regimes, w=w, constant_regi=constant_regi,
+ cols2regi=cols2regi, regime_err_sep=regime_err_sep,
+ max_iter=max_iter, epsilon=epsilon,
+ step1c=step1c, inv_method=inv_method, cores=cores,
+ vm=vm, name_y=name_y, name_x=name_x, name_yend=name_yend,
+ name_q=name_q, name_w=name_w, name_ds=name_ds,
+ name_regimes=name_regimes, summ=False, add_lag=add_lag)
+
+ if regime_err_sep != True:
+ self.rho = self.betas[-2]
+ self.predy_e, self.e_pred, warn = UTILS.sp_att(w, self.y,
+ self.predy, yend[:, -1].reshape(self.n, 1), self.rho)
+ UTILS.set_warn(self, warn)
+ self.regime_lag_sep = regime_lag_sep
+ self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HET) - REGIMES"
+ SUMMARY.GM_Combo_Het(reg=self, w=w, vm=vm, regimes=True)
+
+
+def _work_error(y, x, regi_ids, r, w, max_iter, epsilon, step1c, name_ds, name_y, name_x, name_w, name_regimes):
+ w_r, warn = REGI.w_regime(w, regi_ids[r], r, transform=True)
+ y_r = y[regi_ids[r]]
+ x_r = x[regi_ids[r]]
+ x_constant = USER.check_constant(x_r)
+ model = BaseGM_Error_Het(
+ y_r, x_constant, w_r.sparse, max_iter=max_iter, epsilon=epsilon, step1c=step1c)
+ set_warn(model, warn)
+ model.w = w_r
+ model.title = "SPATIALLY WEIGHTED LEAST SQUARES ESTIMATION (HET) - REGIME %s" % r
+ model.name_ds = name_ds
+ model.name_y = '%s_%s' % (str(r), name_y)
+ model.name_x = ['%s_%s' % (str(r), i) for i in name_x]
+ model.name_w = name_w
+ model.name_regimes = name_regimes
+ return model
+
+
+def _work_endog_error(y, x, yend, q, regi_ids, r, w, max_iter, epsilon, step1c, inv_method, name_ds, name_y, name_x, name_yend, name_q, name_w, name_regimes, add_lag):
+ w_r, warn = REGI.w_regime(w, regi_ids[r], r, transform=True)
+ y_r = y[regi_ids[r]]
+ x_r = x[regi_ids[r]]
+ if yend != None:
+ yend_r = yend[regi_ids[r]]
+ q_r = q[regi_ids[r]]
+ else:
+ yend_r, q_r = None, None
+ if add_lag != False:
+ yend_r, q_r = set_endog(
+ y_r, x_r, w_r, yend_r, q_r, add_lag[0], add_lag[1])
+ x_constant = USER.check_constant(x_r)
+ model = BaseGM_Endog_Error_Het(y_r, x_constant, yend_r, q_r, w_r.sparse,
+ max_iter=max_iter, epsilon=epsilon, step1c=step1c, inv_method=inv_method)
+ set_warn(model, warn)
+ if add_lag != False:
+ model.rho = model.betas[-2]
+ model.predy_e, model.e_pred, warn = sp_att(w_r, model.y,
+ model.predy, model.yend[:, -1].reshape(model.n, 1), model.rho)
+ set_warn(model, warn)
+ model.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HET) - REGIME %s" % r
+ model.name_ds = name_ds
+ model.name_y = '%s_%s' % (str(r), name_y)
+ model.name_x = ['%s_%s' % (str(r), i) for i in name_x]
+ model.name_yend = ['%s_%s' % (str(r), i) for i in name_yend]
+ model.name_z = model.name_x + model.name_yend + ['lambda']
+ model.name_q = ['%s_%s' % (str(r), i) for i in name_q]
+ model.name_h = model.name_x + model.name_q
+ model.name_w = name_w
+ model.name_regimes = name_regimes
+ return model
+
+
+def _test():
+ import doctest
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ doctest.testmod()
+ np.set_printoptions(suppress=start_suppress)
+
+if __name__ == '__main__':
+ _test()
diff --git a/pysal/spreg/error_sp_hom.py b/pysal/spreg/error_sp_hom.py
new file mode 100644
index 0000000..d3463d0
--- /dev/null
+++ b/pysal/spreg/error_sp_hom.py
@@ -0,0 +1,1522 @@
+'''
+Hom family of models based on:
+
+ Drukker, D. M., Egger, P., Prucha, I. R. (2010)
+ "On Two-step Estimation of a Spatial Autoregressive Model with Autoregressive
+ Disturbances and Endogenous Regressors". Working paper.
+
+Following:
+
+ Anselin, L. (2011) "GMM Estimation of Spatial Error Autocorrelation with
+ and without Heteroskedasticity".
+
+'''
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, Daniel Arribas-Bel darribas at asu.edu"
+
+from scipy import sparse as SP
+import numpy as np
+from numpy import linalg as la
+import ols as OLS
+from pysal import lag_spatial
+from utils import power_expansion, set_endog, iter_msg, sp_att
+from utils import get_A1_hom, get_A2_hom, get_A1_het, optim_moments
+from utils import get_spFilter, get_lags, _moments2eqs
+from utils import spdot, RegressionPropsY, set_warn
+import twosls as TSLS
+import user_output as USER
+import summary_output as SUMMARY
+
+__all__ = ["GM_Error_Hom", "GM_Endog_Error_Hom", "GM_Combo_Hom"]
+
+
+class BaseGM_Error_Hom(RegressionPropsY):
+
+ '''
+ GMM method for a spatial error model with homoskedasticity (note: no
+ consistency checks, diagnostics or constant added); based on
+ Drukker et al. (2010) [1]_, following Anselin (2011) [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ w : Sparse matrix
+ Spatial weights sparse matrix
+ max_iter : int
+ Maximum number of iterations of steps 2a and 2b from Arraiz
+ et al. Note: epsilon provides an additional stop condition.
+ epsilon : float
+ Minimum change in lambda required to stop iterations of
+ steps 2a and 2b from Arraiz et al. Note: max_iter provides
+ an additional stop condition.
+ A1 : string
+ If A1='het', then the matrix A1 is defined as in Arraiz et
+ al. If A1='hom', then as in Anselin (2011) (default). If
+ A1='hom_sc' (default), then as in Drukker, Egger and Prucha (2010)
+ and Drukker, Prucha and Raciborski (2010).
+
+ Attributes
+ ----------
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ iter_stop : string
+ Stop criterion reached during iteration of steps 2a and 2b
+ from Arraiz et al.
+ iteration : integer
+ Number of iterations of steps 2a and 2b from Arraiz et al.
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ sig2 : float
+ Sigma squared used in computations
+ xtx : float
+ X'X
+
+ References
+ ----------
+
+ .. [1] Drukker, D. M., Egger, P., Prucha, I. R. (2010)
+ "On Two-step Estimation of a Spatial Autoregressive Model with Autoregressive
+ Disturbances and Endogenous Regressors". Working paper.
+
+ .. [2] Anselin, L. (2011) "GMM Estimation of Spatial Error Autocorrelation
+ with and without Heteroskedasticity".
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> y = np.array(db.by_col("HOVAL"))
+ >>> y = np.reshape(y, (49,1))
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("CRIME"))
+ >>> X = np.array(X).T
+ >>> X = np.hstack((np.ones(y.shape),X))
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ >>> w.transform = 'r'
+
+ Model commands
+
+ >>> reg = BaseGM_Error_Hom(y, X, w=w.sparse, A1='hom_sc')
+ >>> print np.around(np.hstack((reg.betas,np.sqrt(reg.vm.diagonal()).reshape(4,1))),4)
+ [[ 47.9479 12.3021]
+ [ 0.7063 0.4967]
+ [ -0.556 0.179 ]
+ [ 0.4129 0.1835]]
+ >>> print np.around(reg.vm, 4) #doctest: +SKIP
+ [[ 1.51340700e+02 -5.29060000e+00 -1.85650000e+00 -2.40000000e-03]
+ [ -5.29060000e+00 2.46700000e-01 5.14000000e-02 3.00000000e-04]
+ [ -1.85650000e+00 5.14000000e-02 3.21000000e-02 -1.00000000e-04]
+ [ -2.40000000e-03 3.00000000e-04 -1.00000000e-04 3.37000000e-02]]
+ '''
+
+ def __init__(self, y, x, w,
+ max_iter=1, epsilon=0.00001, A1='hom_sc'):
+ if A1 == 'hom':
+ wA1 = get_A1_hom(w)
+ elif A1 == 'hom_sc':
+ wA1 = get_A1_hom(w, scalarKP=True)
+ elif A1 == 'het':
+ wA1 = get_A1_het(w)
+
+ wA2 = get_A2_hom(w)
+
+ # 1a. OLS --> \tilde{\delta}
+ ols = OLS.BaseOLS(y=y, x=x)
+ self.x, self.y, self.n, self.k, self.xtx = ols.x, ols.y, ols.n, ols.k, ols.xtx
+
+ # 1b. GM --> \tilde{\rho}
+ moments = moments_hom(w, wA1, wA2, ols.u)
+ lambda1 = optim_moments(moments)
+ lambda_old = lambda1
+
+ self.iteration, eps = 0, 1
+ while self.iteration < max_iter and eps > epsilon:
+ # 2a. SWLS --> \hat{\delta}
+ x_s = get_spFilter(w, lambda_old, self.x)
+ y_s = get_spFilter(w, lambda_old, self.y)
+ ols_s = OLS.BaseOLS(y=y_s, x=x_s)
+ self.predy = spdot(self.x, ols_s.betas)
+ self.u = self.y - self.predy
+
+ # 2b. GM 2nd iteration --> \hat{\rho}
+ moments = moments_hom(w, wA1, wA2, self.u)
+ psi = get_vc_hom(w, wA1, wA2, self, lambda_old)[0]
+ lambda2 = optim_moments(moments, psi)
+ eps = abs(lambda2 - lambda_old)
+ lambda_old = lambda2
+ self.iteration += 1
+
+ self.iter_stop = iter_msg(self.iteration, max_iter)
+
+ # Output
+ self.betas = np.vstack((ols_s.betas, lambda2))
+ self.vm, self.sig2 = get_omega_hom_ols(
+ w, wA1, wA2, self, lambda2, moments[0])
+ self.e_filtered = self.u - lambda2 * w * self.u
+ self._cache = {}
+
+
+class GM_Error_Hom(BaseGM_Error_Hom):
+
+ '''
+ GMM method for a spatial error model with homoskedasticity, with results
+ and diagnostics; based on Drukker et al. (2010) [1]_, following Anselin
+ (2011) [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ w : pysal W object
+ Spatial weights object
+ max_iter : int
+ Maximum number of iterations of steps 2a and 2b from Arraiz
+ et al. Note: epsilon provides an additional stop condition.
+ epsilon : float
+ Minimum change in lambda required to stop iterations of
+ steps 2a and 2b from Arraiz et al. Note: max_iter provides
+ an additional stop condition.
+ A1 : string
+ If A1='het', then the matrix A1 is defined as in Arraiz et
+ al. If A1='hom', then as in Anselin (2011). If
+ A1='hom_sc' (default), then as in Drukker, Egger and Prucha (2010)
+ and Drukker, Prucha and Raciborski (2010).
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ iter_stop : string
+ Stop criterion reached during iteration of steps 2a and 2b
+ from Arraiz et al.
+ iteration : integer
+ Number of iterations of steps 2a and 2b from Arraiz et al.
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ vm : array
+ Variance covariance matrix (kxk)
+ sig2 : float
+ Sigma squared used in computations
+ std_err : array
+ 1xk array of standard errors of the betas
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ xtx : float
+ X'X
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ title : string
+ Name of the regression method used
+
+ References
+ ----------
+
+ .. [1] Drukker, D. M., Egger, P., Prucha, I. R. (2010)
+ "On Two-step Estimation of a Spatial Autoregressive Model with Autoregressive
+ Disturbances and Endogenous Regressors". Working paper.
+
+ .. [2] Anselin, L. (2011) "GMM Estimation of Spatial Error Autocorrelation
+ with and without Heteroskedasticity".
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import numpy as np
+ >>> import pysal
+
+ Open data on Columbus neighborhood crime (49 areas) using pysal.open().
+ This is the DBF associated with the Columbus shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+
+ Extract the HOVAL column (home values) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y = np.array(db.by_col("HOVAL"))
+ >>> y = np.reshape(y, (49,1))
+
+ Extract INC (income) and CRIME (crime) vectors from the DBF to be used as
+ independent variables in the regression. Note that PySAL requires this to
+ be an nxj numpy array, where j is the number of independent variables (not
+ including a constant). By default this class adds a vector of ones to the
+ independent variables passed in.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("CRIME"))
+ >>> X = np.array(X).T
+
+ Since we want to run a spatial error model, we need to specify the spatial
+ weights matrix that includes the spatial configuration of the observations
+ into the error component of the model. To do that, we can open an already
+ existing gal file or create a new one. In this case, we will create one
+ from ``columbus.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, his allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ We are all set with the preliminars, we are good to run the model. In this
+ case, we will need the variables and the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional.
+
+ >>> reg = GM_Error_Hom(y, X, w=w, A1='hom_sc', name_y='home value', name_x=['income', 'crime'], name_ds='columbus')
+
+ Once we have run the model, we can explore a little bit the output. The
+ regression object we have created has many attributes so take your time to
+ discover them. This class offers an error model that assumes
+ homoskedasticity but that unlike the models from
+ ``pysal.spreg.error_sp``, it allows for inference on the spatial
+ parameter. This is why you obtain as many coefficient estimates as
+ standard errors, which you calculate taking the square root of the
+ diagonal of the variance-covariance matrix of the parameters:
+
+ >>> print np.around(np.hstack((reg.betas,np.sqrt(reg.vm.diagonal()).reshape(4,1))),4)
+ [[ 47.9479 12.3021]
+ [ 0.7063 0.4967]
+ [ -0.556 0.179 ]
+ [ 0.4129 0.1835]]
+
+ '''
+
+ def __init__(self, y, x, w,
+ max_iter=1, epsilon=0.00001, A1='hom_sc',
+ vm=False, name_y=None, name_x=None,
+ name_w=None, name_ds=None):
+
+ n = USER.check_arrays(y, x)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ x_constant = USER.check_constant(x)
+ BaseGM_Error_Hom.__init__(self, y=y, x=x_constant, w=w.sparse, A1=A1,
+ max_iter=max_iter, epsilon=epsilon)
+ self.title = "SPATIALLY WEIGHTED LEAST SQUARES (HOM)"
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_y = USER.set_name_y(name_y)
+ self.name_x = USER.set_name_x(name_x, x)
+ self.name_x.append('lambda')
+ self.name_w = USER.set_name_w(name_w, w)
+ SUMMARY.GM_Error_Hom(reg=self, w=w, vm=vm)
+
+
+class BaseGM_Endog_Error_Hom(RegressionPropsY):
+
+ '''
+ GMM method for a spatial error model with homoskedasticity and
+ endogenous variables (note: no consistency checks, diagnostics or constant
+ added); based on Drukker et al. (2010) [1]_, following Anselin (2011) [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x)
+ w : Sparse matrix
+ Spatial weights sparse matrix
+ max_iter : int
+ Maximum number of iterations of steps 2a and 2b from Arraiz
+ et al. Note: epsilon provides an additional stop condition.
+ epsilon : float
+ Minimum change in lambda required to stop iterations of
+ steps 2a and 2b from Arraiz et al. Note: max_iter provides
+ an additional stop condition.
+ A1 : string
+ If A1='het', then the matrix A1 is defined as in Arraiz et
+ al. If A1='hom', then as in Anselin (2011). If
+ A1='hom_sc' (default), then as in Drukker, Egger and Prucha (2010)
+ and Drukker, Prucha and Raciborski (2010).
+
+ Attributes
+ ----------
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable used as instruments
+ z : array
+ nxk array of variables (combination of x and yend)
+ h : array
+ nxl array of instruments (combination of x and q)
+ iter_stop : string
+ Stop criterion reached during iteration of steps 2a and 2b
+ from Arraiz et al.
+ iteration : integer
+ Number of iterations of steps 2a and 2b from Arraiz et al.
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ sig2 : float
+ Sigma squared used in computations
+ hth : float
+ H'H
+
+ References
+ ----------
+
+ .. [1] Drukker, D. M., Egger, P., Prucha, I. R. (2010)
+ "On Two-step Estimation of a Spatial Autoregressive Model with Autoregressive
+ Disturbances and Endogenous Regressors". Working paper.
+
+ .. [2] Anselin, L. (2011) "GMM Estimation of Spatial Error Autocorrelation
+ with and without Heteroskedasticity".
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> y = np.array(db.by_col("HOVAL"))
+ >>> y = np.reshape(y, (49,1))
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X = np.array(X).T
+ >>> X = np.hstack((np.ones(y.shape),X))
+ >>> yd = []
+ >>> yd.append(db.by_col("CRIME"))
+ >>> yd = np.array(yd).T
+ >>> q = []
+ >>> q.append(db.by_col("DISCBD"))
+ >>> q = np.array(q).T
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ >>> w.transform = 'r'
+ >>> reg = BaseGM_Endog_Error_Hom(y, X, yd, q, w=w.sparse, A1='hom_sc')
+ >>> print np.around(np.hstack((reg.betas,np.sqrt(reg.vm.diagonal()).reshape(4,1))),4)
+ [[ 55.3658 23.496 ]
+ [ 0.4643 0.7382]
+ [ -0.669 0.3943]
+ [ 0.4321 0.1927]]
+
+
+ '''
+
+ def __init__(self, y, x, yend, q, w,
+ max_iter=1, epsilon=0.00001, A1='hom_sc'):
+
+ if A1 == 'hom':
+ wA1 = get_A1_hom(w)
+ elif A1 == 'hom_sc':
+ wA1 = get_A1_hom(w, scalarKP=True)
+ elif A1 == 'het':
+ wA1 = get_A1_het(w)
+
+ wA2 = get_A2_hom(w)
+
+ # 1a. S2SLS --> \tilde{\delta}
+ tsls = TSLS.BaseTSLS(y=y, x=x, yend=yend, q=q)
+ self.x, self.z, self.h, self.y, self.hth = tsls.x, tsls.z, tsls.h, tsls.y, tsls.hth
+ self.yend, self.q, self.n, self.k = tsls.yend, tsls.q, tsls.n, tsls.k
+
+ # 1b. GM --> \tilde{\rho}
+ moments = moments_hom(w, wA1, wA2, tsls.u)
+ lambda1 = optim_moments(moments)
+ lambda_old = lambda1
+
+ self.iteration, eps = 0, 1
+ while self.iteration < max_iter and eps > epsilon:
+ # 2a. GS2SLS --> \hat{\delta}
+ x_s = get_spFilter(w, lambda_old, self.x)
+ y_s = get_spFilter(w, lambda_old, self.y)
+ yend_s = get_spFilter(w, lambda_old, self.yend)
+ tsls_s = TSLS.BaseTSLS(y=y_s, x=x_s, yend=yend_s, h=self.h)
+ self.predy = spdot(self.z, tsls_s.betas)
+ self.u = self.y - self.predy
+
+ # 2b. GM 2nd iteration --> \hat{\rho}
+ moments = moments_hom(w, wA1, wA2, self.u)
+ psi = get_vc_hom(w, wA1, wA2, self, lambda_old, tsls_s.z)[0]
+ lambda2 = optim_moments(moments, psi)
+ eps = abs(lambda2 - lambda_old)
+ lambda_old = lambda2
+ self.iteration += 1
+
+ self.iter_stop = iter_msg(self.iteration, max_iter)
+
+ # Output
+ self.betas = np.vstack((tsls_s.betas, lambda2))
+ self.vm, self.sig2 = get_omega_hom(
+ w, wA1, wA2, self, lambda2, moments[0])
+ self.e_filtered = self.u - lambda2 * w * self.u
+ self._cache = {}
+
+
+class GM_Endog_Error_Hom(BaseGM_Endog_Error_Hom):
+
+ '''
+ GMM method for a spatial error model with homoskedasticity and endogenous
+ variables, with results and diagnostics; based on Drukker et al. (2010) [1]_,
+ following Anselin (2011) [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x)
+ w : pysal W object
+ Spatial weights object
+ max_iter : int
+ Maximum number of iterations of steps 2a and 2b from Arraiz
+ et al. Note: epsilon provides an additional stop condition.
+ epsilon : float
+ Minimum change in lambda required to stop iterations of
+ steps 2a and 2b from Arraiz et al. Note: max_iter provides
+ an additional stop condition.
+ A1 : string
+ If A1='het', then the matrix A1 is defined as in Arraiz et
+ al. If A1='hom', then as in Anselin (2011). If
+ A1='hom_sc' (default), then as in Drukker, Egger and Prucha (2010)
+ and Drukker, Prucha and Raciborski (2010).
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_q : list of strings
+ Names of instruments for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable used as instruments
+ z : array
+ nxk array of variables (combination of x and yend)
+ h : array
+ nxl array of instruments (combination of x and q)
+ iter_stop : string
+ Stop criterion reached during iteration of steps 2a and 2b
+ from Arraiz et al.
+ iteration : integer
+ Number of iterations of steps 2a and 2b from Arraiz et al.
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ sig2 : float
+ Sigma squared used in computations
+ std_err : array
+ 1xk array of standard errors of the betas
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_z : list of strings
+ Names of exogenous and endogenous variables for use in
+ output
+ name_q : list of strings
+ Names of external instruments
+ name_h : list of strings
+ Names of all instruments used in ouput
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ title : string
+ Name of the regression method used
+ hth : float
+ H'H
+
+
+ References
+ ----------
+
+ .. [1] Drukker, D. M., Egger, P., Prucha, I. R. (2010)
+ "On Two-step Estimation of a Spatial Autoregressive Model with Autoregressive
+ Disturbances and Endogenous Regressors". Working paper.
+
+ .. [2] Anselin, L. (2011) "GMM Estimation of Spatial Error Autocorrelation
+ with and without Heteroskedasticity".
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import numpy as np
+ >>> import pysal
+
+ Open data on Columbus neighborhood crime (49 areas) using pysal.open().
+ This is the DBF associated with the Columbus shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+
+ Extract the HOVAL column (home values) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y = np.array(db.by_col("HOVAL"))
+ >>> y = np.reshape(y, (49,1))
+
+ Extract INC (income) vector from the DBF to be used as
+ independent variables in the regression. Note that PySAL requires this to
+ be an nxj numpy array, where j is the number of independent variables (not
+ including a constant). By default this class adds a vector of ones to the
+ independent variables passed in.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X = np.array(X).T
+
+ In this case we consider CRIME (crime rates) is an endogenous regressor.
+ We tell the model that this is so by passing it in a different parameter
+ from the exogenous variables (x).
+
+ >>> yd = []
+ >>> yd.append(db.by_col("CRIME"))
+ >>> yd = np.array(yd).T
+
+ Because we have endogenous variables, to obtain a correct estimate of the
+ model, we need to instrument for CRIME. We use DISCBD (distance to the
+ CBD) for this and hence put it in the instruments parameter, 'q'.
+
+ >>> q = []
+ >>> q.append(db.by_col("DISCBD"))
+ >>> q = np.array(q).T
+
+ Since we want to run a spatial error model, we need to specify the spatial
+ weights matrix that includes the spatial configuration of the observations
+ into the error component of the model. To do that, we can open an already
+ existing gal file or create a new one. In this case, we will create one
+ from ``columbus.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, his allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ We are all set with the preliminars, we are good to run the model. In this
+ case, we will need the variables (exogenous and endogenous), the
+ instruments and the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional.
+
+ >>> reg = GM_Endog_Error_Hom(y, X, yd, q, w=w, A1='hom_sc', name_x=['inc'], name_y='hoval', name_yend=['crime'], name_q=['discbd'], name_ds='columbus')
+
+ Once we have run the model, we can explore a little bit the output. The
+ regression object we have created has many attributes so take your time to
+ discover them. This class offers an error model that assumes
+ homoskedasticity but that unlike the models from
+ ``pysal.spreg.error_sp``, it allows for inference on the spatial
+ parameter. Hence, we find the same number of betas as of standard errors,
+ which we calculate taking the square root of the diagonal of the
+ variance-covariance matrix:
+
+ >>> print reg.name_z
+ ['CONSTANT', 'inc', 'crime', 'lambda']
+ >>> print np.around(np.hstack((reg.betas,np.sqrt(reg.vm.diagonal()).reshape(4,1))),4)
+ [[ 55.3658 23.496 ]
+ [ 0.4643 0.7382]
+ [ -0.669 0.3943]
+ [ 0.4321 0.1927]]
+
+ '''
+
+ def __init__(self, y, x, yend, q, w,
+ max_iter=1, epsilon=0.00001, A1='hom_sc',
+ vm=False, name_y=None, name_x=None,
+ name_yend=None, name_q=None,
+ name_w=None, name_ds=None):
+
+ n = USER.check_arrays(y, x, yend, q)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ x_constant = USER.check_constant(x)
+ BaseGM_Endog_Error_Hom.__init__(
+ self, y=y, x=x_constant, w=w.sparse, yend=yend, q=q,
+ A1=A1, max_iter=max_iter, epsilon=epsilon)
+ self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HOM)"
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_y = USER.set_name_y(name_y)
+ self.name_x = USER.set_name_x(name_x, x)
+ self.name_yend = USER.set_name_yend(name_yend, yend)
+ self.name_z = self.name_x + self.name_yend
+ self.name_z.append('lambda') # listing lambda last
+ self.name_q = USER.set_name_q(name_q, q)
+ self.name_h = USER.set_name_h(self.name_x, self.name_q)
+ self.name_w = USER.set_name_w(name_w, w)
+ SUMMARY.GM_Endog_Error_Hom(reg=self, w=w, vm=vm)
+
+
+class BaseGM_Combo_Hom(BaseGM_Endog_Error_Hom):
+
+ '''
+ GMM method for a spatial lag and error model with homoskedasticity and
+ endogenous variables (note: no consistency checks, diagnostics or constant
+ added); based on Drukker et al. (2010) [1]_, following Anselin (2011) [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x)
+ w : Sparse matrix
+ Spatial weights sparse matrix
+ w_lags : integer
+ Orders of W to include as instruments for the spatially
+ lagged dependent variable. For example, w_lags=1, then
+ instruments are WX; if w_lags=2, then WX, WWX; and so on.
+ lag_q : boolean
+ If True, then include spatial lags of the additional
+ instruments (q).
+ max_iter : int
+ Maximum number of iterations of steps 2a and 2b from Arraiz
+ et al. Note: epsilon provides an additional stop condition.
+ epsilon : float
+ Minimum change in lambda required to stop iterations of
+ steps 2a and 2b from Arraiz et al. Note: max_iter provides
+ an additional stop condition.
+ A1 : string
+ If A1='het', then the matrix A1 is defined as in Arraiz et
+ al. If A1='hom', then as in Anselin (2011). If
+ A1='hom_sc' (default), then as in Drukker, Egger and Prucha (2010)
+ and Drukker, Prucha and Raciborski (2010).
+
+
+ Attributes
+ ----------
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable used as instruments
+ z : array
+ nxk array of variables (combination of x and yend)
+ h : array
+ nxl array of instruments (combination of x and q)
+ iter_stop : string
+ Stop criterion reached during iteration of steps 2a and 2b
+ from Arraiz et al.
+ iteration : integer
+ Number of iterations of steps 2a and 2b from Arraiz et al.
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ sig2 : float
+ Sigma squared used in computations
+ hth : float
+ H'H
+
+
+ References
+ ----------
+
+ .. [1] Drukker, D. M., Egger, P., Prucha, I. R. (2010)
+ "On Two-step Estimation of a Spatial Autoregressive Model with Autoregressive
+ Disturbances and Endogenous Regressors". Working paper.
+
+ .. [2] Anselin, L. (2011) "GMM Estimation of Spatial Error Autocorrelation
+ with and without Heteroskedasticity".
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> y = np.array(db.by_col("HOVAL"))
+ >>> y = np.reshape(y, (49,1))
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X = np.array(X).T
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ >>> w.transform = 'r'
+ >>> w_lags = 1
+ >>> yd2, q2 = pysal.spreg.utils.set_endog(y, X, w, None, None, w_lags, True)
+ >>> X = np.hstack((np.ones(y.shape),X))
+
+ Example only with spatial lag
+
+ >>> reg = BaseGM_Combo_Hom(y, X, yend=yd2, q=q2, w=w.sparse, A1='hom_sc')
+ >>> print np.around(np.hstack((reg.betas,np.sqrt(reg.vm.diagonal()).reshape(4,1))),4)
+ [[ 10.1254 15.2871]
+ [ 1.5683 0.4407]
+ [ 0.1513 0.4048]
+ [ 0.2103 0.4226]]
+
+
+ Example with both spatial lag and other endogenous variables
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X = np.array(X).T
+ >>> yd = []
+ >>> yd.append(db.by_col("CRIME"))
+ >>> yd = np.array(yd).T
+ >>> q = []
+ >>> q.append(db.by_col("DISCBD"))
+ >>> q = np.array(q).T
+ >>> yd2, q2 = pysal.spreg.utils.set_endog(y, X, w, yd, q, w_lags, True)
+ >>> X = np.hstack((np.ones(y.shape),X))
+ >>> reg = BaseGM_Combo_Hom(y, X, yd2, q2, w=w.sparse, A1='hom_sc')
+ >>> betas = np.array([['CONSTANT'],['inc'],['crime'],['W_hoval'],['lambda']])
+ >>> print np.hstack((betas, np.around(np.hstack((reg.betas, np.sqrt(reg.vm.diagonal()).reshape(5,1))),5)))
+ [['CONSTANT' '111.7705' '67.75191']
+ ['inc' '-0.30974' '1.16656']
+ ['crime' '-1.36043' '0.6841']
+ ['W_hoval' '-0.52908' '0.84428']
+ ['lambda' '0.60116' '0.18605']]
+
+ '''
+
+ def __init__(self, y, x, yend=None, q=None,
+ w=None, w_lags=1, lag_q=True,
+ max_iter=1, epsilon=0.00001, A1='hom_sc'):
+
+ BaseGM_Endog_Error_Hom.__init__(
+ self, y=y, x=x, w=w, yend=yend, q=q, A1=A1,
+ max_iter=max_iter, epsilon=epsilon)
+
+
+class GM_Combo_Hom(BaseGM_Combo_Hom):
+
+ '''
+ GMM method for a spatial lag and error model with homoskedasticity and
+ endogenous variables, with results and diagnostics; based on Drukker et
+ al. (2010) [1]_, following Anselin (2011) [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x)
+ w : pysal W object
+ Spatial weights object (always necessary)
+ w_lags : integer
+ Orders of W to include as instruments for the spatially
+ lagged dependent variable. For example, w_lags=1, then
+ instruments are WX; if w_lags=2, then WX, WWX; and so on.
+ lag_q : boolean
+ If True, then include spatial lags of the additional
+ instruments (q).
+ max_iter : int
+ Maximum number of iterations of steps 2a and 2b from Arraiz
+ et al. Note: epsilon provides an additional stop condition.
+ epsilon : float
+ Minimum change in lambda required to stop iterations of
+ steps 2a and 2b from Arraiz et al. Note: max_iter provides
+ an additional stop condition.
+ A1 : string
+ If A1='het', then the matrix A1 is defined as in Arraiz et
+ al. If A1='hom', then as in Anselin (2011). If
+ A1='hom_sc' (default), then as in Drukker, Egger and Prucha (2010)
+ and Drukker, Prucha and Raciborski (2010).
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_q : list of strings
+ Names of instruments for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ e_pred : array
+ nx1 array of residuals (using reduced form)
+ predy : array
+ nx1 array of predicted y values
+ predy_e : array
+ nx1 array of predicted y values (using reduced form)
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable used as instruments
+ z : array
+ nxk array of variables (combination of x and yend)
+ h : array
+ nxl array of instruments (combination of x and q)
+ iter_stop : string
+ Stop criterion reached during iteration of steps 2a and 2b
+ from Arraiz et al.
+ iteration : integer
+ Number of iterations of steps 2a and 2b from Arraiz et al.
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ pr2_e : float
+ Pseudo R squared (squared correlation between y and ypred_e
+ (using reduced form))
+ sig2 : float
+ Sigma squared used in computations (based on filtered
+ residuals)
+ std_err : array
+ 1xk array of standard errors of the betas
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_z : list of strings
+ Names of exogenous and endogenous variables for use in
+ output
+ name_q : list of strings
+ Names of external instruments
+ name_h : list of strings
+ Names of all instruments used in ouput
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ title : string
+ Name of the regression method used
+ hth : float
+ H'H
+
+
+ References
+ ----------
+
+ .. [1] Drukker, D. M., Egger, P., Prucha, I. R. (2010)
+ "On Two-step Estimation of a Spatial Autoregressive Model with Autoregressive
+ Disturbances and Endogenous Regressors". Working paper.
+
+ .. [2] Anselin, L. (2011) "GMM Estimation of Spatial Error Autocorrelation
+ with and without Heteroskedasticity".
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import numpy as np
+ >>> import pysal
+
+ Open data on Columbus neighborhood crime (49 areas) using pysal.open().
+ This is the DBF associated with the Columbus shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+
+ Extract the HOVAL column (home values) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y = np.array(db.by_col("HOVAL"))
+ >>> y = np.reshape(y, (49,1))
+
+ Extract INC (income) vector from the DBF to be used as
+ independent variables in the regression. Note that PySAL requires this to
+ be an nxj numpy array, where j is the number of independent variables (not
+ including a constant). By default this class adds a vector of ones to the
+ independent variables passed in.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X = np.array(X).T
+
+ Since we want to run a spatial error model, we need to specify the spatial
+ weights matrix that includes the spatial configuration of the observations
+ into the error component of the model. To do that, we can open an already
+ existing gal file or create a new one. In this case, we will create one
+ from ``columbus.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, his allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ Example only with spatial lag
+
+ The Combo class runs an SARAR model, that is a spatial lag+error model.
+ In this case we will run a simple version of that, where we have the
+ spatial effects as well as exogenous variables. Since it is a spatial
+ model, we have to pass in the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional.
+
+ >>> reg = GM_Combo_Hom(y, X, w=w, A1='hom_sc', name_x=['inc'],\
+ name_y='hoval', name_yend=['crime'], name_q=['discbd'],\
+ name_ds='columbus')
+ >>> print np.around(np.hstack((reg.betas,np.sqrt(reg.vm.diagonal()).reshape(4,1))),4)
+ [[ 10.1254 15.2871]
+ [ 1.5683 0.4407]
+ [ 0.1513 0.4048]
+ [ 0.2103 0.4226]]
+
+ This class also allows the user to run a spatial lag+error model with the
+ extra feature of including non-spatial endogenous regressors. This means
+ that, in addition to the spatial lag and error, we consider some of the
+ variables on the right-hand side of the equation as endogenous and we
+ instrument for this. As an example, we will include CRIME (crime rates) as
+ endogenous and will instrument with DISCBD (distance to the CSB). We first
+ need to read in the variables:
+
+
+ >>> yd = []
+ >>> yd.append(db.by_col("CRIME"))
+ >>> yd = np.array(yd).T
+ >>> q = []
+ >>> q.append(db.by_col("DISCBD"))
+ >>> q = np.array(q).T
+
+ And then we can run and explore the model analogously to the previous combo:
+
+ >>> reg = GM_Combo_Hom(y, X, yd, q, w=w, A1='hom_sc', \
+ name_ds='columbus')
+ >>> betas = np.array([['CONSTANT'],['inc'],['crime'],['W_hoval'],['lambda']])
+ >>> print np.hstack((betas, np.around(np.hstack((reg.betas, np.sqrt(reg.vm.diagonal()).reshape(5,1))),5)))
+ [['CONSTANT' '111.7705' '67.75191']
+ ['inc' '-0.30974' '1.16656']
+ ['crime' '-1.36043' '0.6841']
+ ['W_hoval' '-0.52908' '0.84428']
+ ['lambda' '0.60116' '0.18605']]
+
+ '''
+
+ def __init__(self, y, x, yend=None, q=None,
+ w=None, w_lags=1, lag_q=True,
+ max_iter=1, epsilon=0.00001, A1='hom_sc',
+ vm=False, name_y=None, name_x=None,
+ name_yend=None, name_q=None,
+ name_w=None, name_ds=None):
+
+ n = USER.check_arrays(y, x, yend, q)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ yend2, q2 = set_endog(y, x, w, yend, q, w_lags, lag_q)
+ x_constant = USER.check_constant(x)
+ BaseGM_Combo_Hom.__init__(
+ self, y=y, x=x_constant, w=w.sparse, yend=yend2, q=q2,
+ w_lags=w_lags, A1=A1, lag_q=lag_q,
+ max_iter=max_iter, epsilon=epsilon)
+ self.rho = self.betas[-2]
+ self.predy_e, self.e_pred, warn = sp_att(w, self.y, self.predy,
+ yend2[:, -1].reshape(self.n, 1), self.rho)
+ set_warn(self, warn)
+ self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HOM)"
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_y = USER.set_name_y(name_y)
+ self.name_x = USER.set_name_x(name_x, x)
+ self.name_yend = USER.set_name_yend(name_yend, yend)
+ self.name_yend.append(USER.set_name_yend_sp(self.name_y))
+ self.name_z = self.name_x + self.name_yend
+ self.name_z.append('lambda') # listing lambda last
+ self.name_q = USER.set_name_q(name_q, q)
+ self.name_q.extend(
+ USER.set_name_q_sp(self.name_x, w_lags, self.name_q, lag_q))
+ self.name_h = USER.set_name_h(self.name_x, self.name_q)
+ self.name_w = USER.set_name_w(name_w, w)
+ SUMMARY.GM_Combo_Hom(reg=self, w=w, vm=vm)
+
+
+# Functions
+
+def moments_hom(w, wA1, wA2, u):
+ '''
+ Compute G and g matrices for the spatial error model with homoscedasticity
+ as in Anselin [1]_ (2011).
+ ...
+
+ Parameters
+ ----------
+
+ w : Sparse matrix
+ Spatial weights sparse matrix
+
+ u : array
+ Residuals. nx1 array assumed to be aligned with w
+
+ Attributes
+ ----------
+
+ moments : list
+ List of two arrays corresponding to the matrices 'G' and
+ 'g', respectively.
+
+
+ References
+ ----------
+
+ .. [1] Anselin, L. (2011) "GMM Estimation of Spatial Error Autocorrelation
+ with and without Heteroskedasticity".
+ '''
+ n = w.shape[0]
+ A1u = wA1 * u
+ A2u = wA2 * u
+ wu = w * u
+
+ g1 = np.dot(u.T, A1u)
+ g2 = np.dot(u.T, A2u)
+ g = np.array([[g1][0][0], [g2][0][0]]) / n
+
+ G11 = 2 * np.dot(wu.T * wA1, u)
+ G12 = -np.dot(wu.T * wA1, wu)
+ G21 = 2 * np.dot(wu.T * wA2, u)
+ G22 = -np.dot(wu.T * wA2, wu)
+ G = np.array([[G11[0][0], G12[0][0]], [G21[0][0], G22[0][0]]]) / n
+ return [G, g]
+
+
+def get_vc_hom(w, wA1, wA2, reg, lambdapar, z_s=None, for_omegaOLS=False):
+ '''
+ VC matrix \psi of Spatial error with homoscedasticity. As in
+ Anselin (2011) [1]_ (p. 20)
+ ...
+
+ Parameters
+ ----------
+ w : Sparse matrix
+ Spatial weights sparse matrix
+ reg : reg
+ Regression object
+ lambdapar : float
+ Spatial parameter estimated in previous step of the
+ procedure
+ z_s : array
+ optional argument for spatially filtered Z (to be
+ passed only if endogenous variables are present)
+ for_omegaOLS : boolean
+ If True (default=False), it also returns P, needed
+ only in the computation of Omega
+
+ Returns
+ -------
+
+ psi : array
+ 2x2 VC matrix
+ a1 : array
+ nx1 vector a1. If z_s=None, a1 = 0.
+ a2 : array
+ nx1 vector a2. If z_s=None, a2 = 0.
+ p : array
+ P matrix. If z_s=None or for_omegaOLS=False, p=0.
+
+ References
+ ----------
+
+ .. [1] Anselin, L. (2011) "GMM Estimation of Spatial Error Autocorrelation
+ with and without Heteroskedasticity".
+
+ '''
+ u_s = get_spFilter(w, lambdapar, reg.u)
+ n = float(w.shape[0])
+ sig2 = np.dot(u_s.T, u_s) / n
+ mu3 = np.sum(u_s ** 3) / n
+ mu4 = np.sum(u_s ** 4) / n
+
+ tr11 = wA1 * wA1
+ tr11 = np.sum(tr11.diagonal())
+ tr12 = wA1 * (wA2 * 2)
+ tr12 = np.sum(tr12.diagonal())
+ tr22 = wA2 * wA2 * 2
+ tr22 = np.sum(tr22.diagonal())
+ vecd1 = np.array([wA1.diagonal()]).T
+
+ psi11 = 2 * sig2 ** 2 * tr11 + \
+ (mu4 - 3 * sig2 ** 2) * np.dot(vecd1.T, vecd1)
+ psi12 = sig2 ** 2 * tr12
+ psi22 = sig2 ** 2 * tr22
+
+ a1, a2, p = 0., 0., 0.
+
+ if for_omegaOLS:
+ x_s = get_spFilter(w, lambdapar, reg.x)
+ p = la.inv(spdot(x_s.T, x_s) / n)
+
+ if issubclass(type(z_s), np.ndarray) or \
+ issubclass(type(z_s), SP.csr.csr_matrix) or \
+ issubclass(type(z_s), SP.csc.csc_matrix):
+ alpha1 = (-2 / n) * spdot(z_s.T, wA1 * u_s)
+ alpha2 = (-2 / n) * spdot(z_s.T, wA2 * u_s)
+
+ hth = spdot(reg.h.T, reg.h)
+ hthni = la.inv(hth / n)
+ htzsn = spdot(reg.h.T, z_s) / n
+ p = spdot(hthni, htzsn)
+ p = spdot(p, la.inv(spdot(htzsn.T, p)))
+ hp = spdot(reg.h, p)
+ a1 = spdot(hp, alpha1)
+ a2 = spdot(hp, alpha2)
+
+ psi11 = psi11 + \
+ sig2 * spdot(a1.T, a1) + \
+ 2 * mu3 * spdot(a1.T, vecd1)
+ psi12 = psi12 + \
+ sig2 * spdot(a1.T, a2) + \
+ mu3 * spdot(a2.T, vecd1) # 3rd term=0
+ psi22 = psi22 + \
+ sig2 * spdot(a2.T, a2) # 3rd&4th terms=0 bc vecd2=0
+
+ psi = np.array(
+ [[psi11[0][0], psi12[0][0]], [psi12[0][0], psi22[0][0]]]) / n
+ return psi, a1, a2, p
+
+
+def get_omega_hom(w, wA1, wA2, reg, lamb, G):
+ '''
+ Omega VC matrix for Hom models with endogenous variables computed as in
+ Anselin (2011) [1]_ (p. 21).
+ ...
+
+ Parameters
+ ----------
+ w : Sparse matrix
+ Spatial weights sparse matrix
+ reg : reg
+ Regression object
+ lamb : float
+ Spatial parameter estimated in previous step of the
+ procedure
+ G : array
+ Matrix 'G' of the moment equation
+
+ Returns
+ -------
+ omega : array
+ Omega matrix of VC of the model
+
+ References
+ ----------
+
+ .. [1] Anselin, L. (2011) "GMM Estimation of Spatial Error Autocorrelation
+ with and without Heteroskedasticity".
+
+ '''
+ n = float(w.shape[0])
+ z_s = get_spFilter(w, lamb, reg.z)
+ u_s = get_spFilter(w, lamb, reg.u)
+ sig2 = np.dot(u_s.T, u_s) / n
+ mu3 = np.sum(u_s ** 3) / n
+ vecdA1 = np.array([wA1.diagonal()]).T
+ psi, a1, a2, p = get_vc_hom(w, wA1, wA2, reg, lamb, z_s)
+ j = np.dot(G, np.array([[1.], [2 * lamb]]))
+ psii = la.inv(psi)
+ t2 = spdot(reg.h.T, np.hstack((a1, a2)))
+ psiDL = (mu3 * spdot(reg.h.T, np.hstack((vecdA1, np.zeros((n, 1))))) +
+ sig2 * spdot(reg.h.T, np.hstack((a1, a2)))) / n
+
+ oDD = spdot(la.inv(spdot(reg.h.T, reg.h)), spdot(reg.h.T, z_s))
+ oDD = sig2 * la.inv(spdot(z_s.T, spdot(reg.h, oDD)))
+ oLL = la.inv(spdot(j.T, spdot(psii, j))) / n
+ oDL = spdot(spdot(spdot(p.T, psiDL), spdot(psii, j)), oLL)
+
+ o_upper = np.hstack((oDD, oDL))
+ o_lower = np.hstack((oDL.T, oLL))
+ return np.vstack((o_upper, o_lower)), float(sig2)
+
+
+def get_omega_hom_ols(w, wA1, wA2, reg, lamb, G):
+ '''
+ Omega VC matrix for Hom models without endogenous variables (OLS) computed
+ as in Anselin (2011) [1]_.
+ ...
+
+ Parameters
+ ----------
+ w : Sparse matrix
+ Spatial weights sparse matrix
+ reg : reg
+ Regression object
+ lamb : float
+ Spatial parameter estimated in previous step of the
+ procedure
+ G : array
+ Matrix 'G' of the moment equation
+
+ Returns
+ -------
+ omega : array
+ Omega matrix of VC of the model
+
+ References
+ ----------
+
+ .. [1] Anselin, L. (2011) "GMM Estimation of Spatial Error Autocorrelation
+ with and without Heteroskedasticity".
+
+ '''
+ n = float(w.shape[0])
+ x_s = get_spFilter(w, lamb, reg.x)
+ u_s = get_spFilter(w, lamb, reg.u)
+ sig2 = np.dot(u_s.T, u_s) / n
+ vecdA1 = np.array([wA1.diagonal()]).T
+ psi, a1, a2, p = get_vc_hom(w, wA1, wA2, reg, lamb, for_omegaOLS=True)
+ j = np.dot(G, np.array([[1.], [2 * lamb]]))
+ psii = la.inv(psi)
+
+ oDD = sig2 * la.inv(spdot(x_s.T, x_s))
+ oLL = la.inv(spdot(j.T, spdot(psii, j))) / n
+ #oDL = np.zeros((oDD.shape[0], oLL.shape[1]))
+ mu3 = np.sum(u_s ** 3) / n
+ psiDL = (mu3 * spdot(reg.x.T, np.hstack((vecdA1, np.zeros((n, 1)))))) / n
+ oDL = spdot(spdot(spdot(p.T, psiDL), spdot(psii, j)), oLL)
+
+ o_upper = np.hstack((oDD, oDL))
+ o_lower = np.hstack((oDL.T, oLL))
+ return np.vstack((o_upper, o_lower)), float(sig2)
+
+
+def _test():
+ import doctest
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ doctest.testmod()
+ np.set_printoptions(suppress=start_suppress)
+
+if __name__ == '__main__':
+
+ _test()
diff --git a/pysal/spreg/error_sp_hom_regimes.py b/pysal/spreg/error_sp_hom_regimes.py
new file mode 100644
index 0000000..557a69b
--- /dev/null
+++ b/pysal/spreg/error_sp_hom_regimes.py
@@ -0,0 +1,1496 @@
+'''
+Hom family of models with regimes.
+'''
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, Pedro V. Amaral pedro.amaral at asu.edu, Daniel Arribas-Bel darribas at asu.edu"
+
+from scipy import sparse as SP
+import numpy as np
+import multiprocessing as mp
+from numpy import linalg as la
+from pysal import lag_spatial
+from utils import power_expansion, set_endog, iter_msg, sp_att
+from utils import get_A1_hom, get_A2_hom, get_A1_het, optim_moments
+from utils import get_spFilter, get_lags, _moments2eqs
+from utils import spdot, RegressionPropsY, set_warn
+from ols import BaseOLS
+from twosls import BaseTSLS
+from error_sp_hom import BaseGM_Error_Hom, BaseGM_Endog_Error_Hom, moments_hom, get_vc_hom, get_omega_hom, get_omega_hom_ols
+import regimes as REGI
+import user_output as USER
+import summary_output as SUMMARY
+from platform import system
+
+
+class GM_Error_Hom_Regimes(RegressionPropsY, REGI.Regimes_Frame):
+
+ '''
+ GMM method for a spatial error model with homoskedasticity, with regimes,
+ results and diagnostics; based on Drukker et al. (2010) [1]_, following
+ Anselin (2011) [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ w : pysal W object
+ Spatial weights object
+ constant_regi: ['one', 'many']
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime (default)
+ cols2regi : list, 'all'
+ Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all' (default), all the variables vary by regime.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ regime_lag_sep : boolean
+ Always False, kept for consistency, ignored.
+ max_iter : int
+ Maximum number of iterations of steps 2a and 2b from Arraiz
+ et al. Note: epsilon provides an additional stop condition.
+ epsilon : float
+ Minimum change in lambda required to stop iterations of
+ steps 2a and 2b from Arraiz et al. Note: max_iter provides
+ an additional stop condition.
+ A1 : string
+ If A1='het', then the matrix A1 is defined as in Arraiz et
+ al. If A1='hom', then as in Anselin (2011). If
+ A1='hom_sc', then as in Drukker, Egger and Prucha (2010)
+ and Drukker, Prucha and Raciborski (2010).
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ cores : boolean
+ Specifies if multiprocessing is to be used
+ Default: no multiprocessing, cores = False
+ Note: Multiprocessing may not work on all platforms.
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regime variable for use in the output
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ iter_stop : string
+ Stop criterion reached during iteration of steps 2a and 2b
+ from Arraiz et al.
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ iteration : integer
+ Number of iterations of steps 2a and 2b from Arraiz et al.
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ vm : array
+ Variance covariance matrix (kxk)
+ sig2 : float
+ Sigma squared used in computations
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ std_err : array
+ 1xk array of standard errors of the betas
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ xtx : float
+ X'X
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regime variable for use in the output
+ title : string
+ Name of the regression method used
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ constant_regi: ['one', 'many']
+ Ignored if regimes=False. Constant option for regimes.
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime
+ cols2regi : list, 'all'
+ Ignored if regimes=False. Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all', all the variables vary by regime.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ kr : int
+ Number of variables/columns to be "regimized" or subject
+ to change by regime. These will result in one parameter
+ estimate by regime for each variable (i.e. nr parameters per
+ variable)
+ kf : int
+ Number of variables/columns to be considered fixed or
+ global across regimes and hence only obtain one parameter
+ estimate
+ nr : int
+ Number of different regimes in the 'regimes' list
+ multi : dictionary
+ Only available when multiple regressions are estimated,
+ i.e. when regime_err_sep=True and no variable is fixed
+ across regimes.
+ Contains all attributes of each individual regression
+
+ References
+ ----------
+
+ .. [1] Drukker, D. M., Egger, P., Prucha, I. R. (2010)
+ "On Two-step Estimation of a Spatial Autoregressive Model with Autoregressive
+ Disturbances and Endogenous Regressors". Working paper.
+
+ .. [2] Anselin, L. (2011) "GMM Estimation of Spatial Error Autocorrelation
+ with and without Heteroskedasticity".
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import numpy as np
+ >>> import pysal
+
+ Open data on NCOVR US County Homicides (3085 areas) using pysal.open().
+ This is the DBF associated with the NAT shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path("NAT.dbf"),'r')
+
+ Extract the HR90 column (homicide rates in 1990) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y_var = 'HR90'
+ >>> y = np.array([db.by_col(y_var)]).reshape(3085,1)
+
+ Extract UE90 (unemployment rate) and PS90 (population structure) vectors from
+ the DBF to be used as independent variables in the regression. Other variables
+ can be inserted by adding their names to x_var, such as x_var = ['Var1','Var2','...]
+ Note that PySAL requires this to be an nxj numpy array, where j is the
+ number of independent variables (not including a constant). By default
+ this model adds a vector of ones to the independent variables passed in.
+
+ >>> x_var = ['PS90','UE90']
+ >>> x = np.array([db.by_col(name) for name in x_var]).T
+
+ The different regimes in this data are given according to the North and
+ South dummy (SOUTH).
+
+ >>> r_var = 'SOUTH'
+ >>> regimes = db.by_col(r_var)
+
+ Since we want to run a spatial lag model, we need to specify
+ the spatial weights matrix that includes the spatial configuration of the
+ observations. To do that, we can open an already existing gal file or
+ create a new one. In this case, we will create one from ``NAT.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("NAT.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, this allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ We are all set with the preliminaries, we are good to run the model. In this
+ case, we will need the variables and the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional.
+
+ >>> reg = GM_Error_Hom_Regimes(y, x, regimes, w=w, name_y=y_var, name_x=x_var, name_ds='NAT')
+
+ Once we have run the model, we can explore a little bit the output. The
+ regression object we have created has many attributes so take your time to
+ discover them. This class offers an error model that assumes
+ homoskedasticity but that unlike the models from
+ ``pysal.spreg.error_sp``, it allows for inference on the spatial
+ parameter. This is why you obtain as many coefficient estimates as
+ standard errors, which you calculate taking the square root of the
+ diagonal of the variance-covariance matrix of the parameters. Alternatively,
+ we can have a summary of the output by typing: model.summary
+ >>> print reg.name_x
+ ['0_CONSTANT', '0_PS90', '0_UE90', '1_CONSTANT', '1_PS90', '1_UE90', 'lambda']
+
+ >>> print np.around(reg.betas,4)
+ [[ 0.069 ]
+ [ 0.7885]
+ [ 0.5398]
+ [ 5.0948]
+ [ 1.1965]
+ [ 0.6018]
+ [ 0.4104]]
+
+ >>> print np.sqrt(reg.vm.diagonal())
+ [ 0.39105854 0.15664624 0.05254328 0.48379958 0.20018799 0.05834139
+ 0.01882401]
+
+ '''
+
+ def __init__(self, y, x, regimes, w,
+ max_iter=1, epsilon=0.00001, A1='het', cores=False,
+ constant_regi='many', cols2regi='all', regime_err_sep=False,
+ regime_lag_sep=False,
+ vm=False, name_y=None, name_x=None,
+ name_w=None, name_ds=None, name_regimes=None):
+
+ n = USER.check_arrays(y, x)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ self.constant_regi = constant_regi
+ self.cols2regi = cols2regi
+ self.regime_err_sep = regime_err_sep
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_y = USER.set_name_y(name_y)
+ self.name_w = USER.set_name_w(name_w, w)
+ self.name_regimes = USER.set_name_ds(name_regimes)
+ self.n = n
+ self.y = y
+
+ x_constant = USER.check_constant(x)
+ name_x = USER.set_name_x(name_x, x)
+ self.name_x_r = name_x
+
+ cols2regi = REGI.check_cols2regi(constant_regi, cols2regi, x)
+ self.regimes_set = REGI._get_regimes_set(regimes)
+ self.regimes = regimes
+ USER.check_regimes(self.regimes_set, self.n, x.shape[1])
+ self.regime_err_sep = regime_err_sep
+
+ if regime_err_sep == True:
+ if set(cols2regi) == set([True]):
+ self._error_regimes_multi(y, x, regimes, w, cores,
+ max_iter, epsilon, A1, cols2regi, vm, name_x)
+ else:
+ raise Exception, "All coefficients must vary accross regimes if regime_err_sep = True."
+ else:
+ if A1 == 'hom':
+ wA1 = get_A1_hom(w.sparse)
+ elif A1 == 'hom_sc':
+ wA1 = get_A1_hom(w.sparse, scalarKP=True)
+ elif A1 == 'het':
+ wA1 = get_A1_het(w.sparse)
+
+ wA2 = get_A2_hom(w.sparse)
+
+ # 1a. OLS --> \tilde{\delta}
+ self.x, self.name_x = REGI.Regimes_Frame.__init__(self, x_constant,
+ regimes, constant_regi=None, cols2regi=cols2regi, names=name_x)
+ ols = BaseOLS(y=y, x=self.x)
+ self.k = ols.x.shape[1]
+
+ # 1b. GM --> \tilde{\rho}
+ moments = moments_hom(w.sparse, wA1, wA2, ols.u)
+ lambda1 = optim_moments(moments)
+ lambda_old = lambda1
+
+ self.iteration, eps = 0, 1
+ while self.iteration < max_iter and eps > epsilon:
+ # 2a. SWLS --> \hat{\delta}
+ xs = get_spFilter(w, lambda1, x_constant)
+ ys = get_spFilter(w, lambda1, y)
+ xs = REGI.Regimes_Frame.__init__(self, xs,
+ regimes, constant_regi=None, cols2regi=cols2regi)[0]
+ ols_s = BaseOLS(y=ys, x=xs)
+ self.predy = spdot(self.x, ols_s.betas)
+ self.u = self.y - self.predy
+
+ # 2b. GM 2nd iteration --> \hat{\rho}
+ moments = moments_hom(w.sparse, wA1, wA2, self.u)
+ psi = get_vc_hom(w.sparse, wA1, wA2, self, lambda_old)[0]
+ lambda2 = optim_moments(moments, psi)
+ eps = abs(lambda2 - lambda_old)
+ lambda_old = lambda2
+ self.iteration += 1
+
+ self.iter_stop = iter_msg(self.iteration, max_iter)
+
+ # Output
+ self.betas = np.vstack((ols_s.betas, lambda2))
+ self.vm, self.sig2 = get_omega_hom_ols(
+ w.sparse, wA1, wA2, self, lambda2, moments[0])
+ self.e_filtered = self.u - lambda2 * lag_spatial(w, self.u)
+ self.title = "SPATIALLY WEIGHTED LEAST SQUARES (HOM) - REGIMES"
+ self.name_x.append('lambda')
+ self.kf += 1
+ self.chow = REGI.Chow(self)
+ self._cache = {}
+ SUMMARY.GM_Error_Hom(reg=self, w=w, vm=vm, regimes=True)
+
+ def _error_regimes_multi(self, y, x, regimes, w, cores,
+ max_iter, epsilon, A1, cols2regi, vm, name_x):
+
+ regi_ids = dict(
+ (r, list(np.where(np.array(regimes) == r)[0])) for r in self.regimes_set)
+ results_p = {}
+ """
+ for r in self.regimes_set:
+ if system() == 'Windows':
+ is_win = True
+ results_p[r] = _work_error(*(y,x,regi_ids,r,w,max_iter,epsilon,A1,self.name_ds,self.name_y,name_x+['lambda'],self.name_w,self.name_regimes))
+ else:
+ pool = mp.Pool(cores)
+ results_p[r] = pool.apply_async(_work_error,args=(y,x,regi_ids,r,w,max_iter,epsilon,A1,self.name_ds,self.name_y,name_x+['lambda'],self.name_w,self.name_regimes, ))
+ is_win = False
+ """
+ for r in self.regimes_set:
+ if cores:
+ pool = mp.Pool(None)
+ results_p[r] = pool.apply_async(_work_error, args=(
+ y, x, regi_ids, r, w, max_iter, epsilon, A1, self.name_ds, self.name_y, name_x + ['lambda'], self.name_w, self.name_regimes, ))
+ else:
+ results_p[r] = _work_error(
+ *(y, x, regi_ids, r, w, max_iter, epsilon, A1, self.name_ds, self.name_y, name_x + ['lambda'], self.name_w, self.name_regimes))
+
+ self.kryd = 0
+ self.kr = len(cols2regi) + 1
+ self.kf = 0
+ self.nr = len(self.regimes_set)
+ self.vm = np.zeros((self.nr * self.kr, self.nr * self.kr), float)
+ self.betas = np.zeros((self.nr * self.kr, 1), float)
+ self.u = np.zeros((self.n, 1), float)
+ self.predy = np.zeros((self.n, 1), float)
+ self.e_filtered = np.zeros((self.n, 1), float)
+ self.name_y, self.name_x = [], []
+ """
+ if not is_win:
+ pool.close()
+ pool.join()
+ """
+ if cores:
+ pool.close()
+ pool.join()
+
+ results = {}
+ counter = 0
+ for r in self.regimes_set:
+ """
+ if is_win:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+ """
+ if not cores:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+
+ self.vm[(counter * self.kr):((counter + 1) * self.kr),
+ (counter * self.kr):((counter + 1) * self.kr)] = results[r].vm
+ self.betas[
+ (counter * self.kr):((counter + 1) * self.kr), ] = results[r].betas
+ self.u[regi_ids[r], ] = results[r].u
+ self.predy[regi_ids[r], ] = results[r].predy
+ self.e_filtered[regi_ids[r], ] = results[r].e_filtered
+ self.name_y += results[r].name_y
+ self.name_x += results[r].name_x
+ counter += 1
+ self.chow = REGI.Chow(self)
+ self.multi = results
+ SUMMARY.GM_Error_Hom_multi(
+ reg=self, multireg=self.multi, vm=vm, regimes=True)
+
+
+class GM_Endog_Error_Hom_Regimes(RegressionPropsY, REGI.Regimes_Frame):
+
+ '''
+ GMM method for a spatial error model with homoskedasticity, regimes and
+ endogenous variables.
+ Based on Drukker et al. (2010) [1]_, following Anselin (2011) [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x)
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ w : pysal W object
+ Spatial weights object
+ constant_regi: ['one', 'many']
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime (default)
+ cols2regi : list, 'all'
+ Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all' (default), all the variables vary by regime.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ regime_lag_sep : boolean
+ Always False, kept for consistency, ignored.
+ max_iter : int
+ Maximum number of iterations of steps 2a and 2b from Arraiz
+ et al. Note: epsilon provides an additional stop condition.
+ epsilon : float
+ Minimum change in lambda required to stop iterations of
+ steps 2a and 2b from Arraiz et al. Note: max_iter provides
+ an additional stop condition.
+ A1 : string
+ If A1='het', then the matrix A1 is defined as in Arraiz et
+ al. If A1='hom', then as in Anselin (2011). If
+ A1='hom_sc', then as in Drukker, Egger and Prucha (2010)
+ and Drukker, Prucha and Raciborski (2010).
+ cores : boolean
+ Specifies if multiprocessing is to be used
+ Default: no multiprocessing, cores = False
+ Note: Multiprocessing may not work on all platforms.
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_q : list of strings
+ Names of instruments for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regime variable for use in the output
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable used as instruments
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ z : array
+ nxk array of variables (combination of x and yend)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ h : array
+ nxl array of instruments (combination of x and q)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ iter_stop : string
+ Stop criterion reached during iteration of steps 2a and 2b
+ from Arraiz et al.
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ iteration : integer
+ Number of iterations of steps 2a and 2b from Arraiz et al.
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ sig2 : float
+ Sigma squared used in computations
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ std_err : array
+ 1xk array of standard errors of the betas
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ hth : float
+ H'H
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_z : list of strings
+ Names of exogenous and endogenous variables for use in
+ output
+ name_q : list of strings
+ Names of external instruments
+ name_h : list of strings
+ Names of all instruments used in ouput
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regimes variable for use in output
+ title : string
+ Name of the regression method used
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ constant_regi : ['one', 'many']
+ Ignored if regimes=False. Constant option for regimes.
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime
+ cols2regi : list, 'all'
+ Ignored if regimes=False. Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all', all the variables vary by regime.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ kr : int
+ Number of variables/columns to be "regimized" or subject
+ to change by regime. These will result in one parameter
+ estimate by regime for each variable (i.e. nr parameters per
+ variable)
+ kf : int
+ Number of variables/columns to be considered fixed or
+ global across regimes and hence only obtain one parameter
+ estimate
+ nr : int
+ Number of different regimes in the 'regimes' list
+ multi : dictionary
+ Only available when multiple regressions are estimated,
+ i.e. when regime_err_sep=True and no variable is fixed
+ across regimes.
+ Contains all attributes of each individual regression
+
+ References
+ ----------
+
+ .. [1] Drukker, D. M., Egger, P., Prucha, I. R. (2010)
+ "On Two-step Estimation of a Spatial Autoregressive Model with Autoregressive
+ Disturbances and Endogenous Regressors". Working paper.
+
+ .. [2] Anselin, L. (2011) "GMM Estimation of Spatial Error Autocorrelation
+ with and without Heteroskedasticity".
+
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import numpy as np
+ >>> import pysal
+
+ Open data on NCOVR US County Homicides (3085 areas) using pysal.open().
+ This is the DBF associated with the NAT shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path("NAT.dbf"),'r')
+
+ Extract the HR90 column (homicide rates in 1990) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y_var = 'HR90'
+ >>> y = np.array([db.by_col(y_var)]).reshape(3085,1)
+
+ Extract UE90 (unemployment rate) and PS90 (population structure) vectors from
+ the DBF to be used as independent variables in the regression. Other variables
+ can be inserted by adding their names to x_var, such as x_var = ['Var1','Var2','...]
+ Note that PySAL requires this to be an nxj numpy array, where j is the
+ number of independent variables (not including a constant). By default
+ this model adds a vector of ones to the independent variables passed in.
+
+ >>> x_var = ['PS90','UE90']
+ >>> x = np.array([db.by_col(name) for name in x_var]).T
+
+ For the endogenous models, we add the endogenous variable RD90 (resource deprivation)
+ and we decide to instrument for it with FP89 (families below poverty):
+
+ >>> yd_var = ['RD90']
+ >>> yend = np.array([db.by_col(name) for name in yd_var]).T
+ >>> q_var = ['FP89']
+ >>> q = np.array([db.by_col(name) for name in q_var]).T
+
+ The different regimes in this data are given according to the North and
+ South dummy (SOUTH).
+
+ >>> r_var = 'SOUTH'
+ >>> regimes = db.by_col(r_var)
+
+ Since we want to run a spatial error model, we need to specify the spatial
+ weights matrix that includes the spatial configuration of the observations
+ into the error component of the model. To do that, we can open an already
+ existing gal file or create a new one. In this case, we will create one
+ from ``NAT.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("NAT.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, this allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ We are all set with the preliminaries, we are good to run the model. In this
+ case, we will need the variables (exogenous and endogenous), the
+ instruments and the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional.
+
+ >>> reg = GM_Endog_Error_Hom_Regimes(y, x, yend, q, regimes, w=w, A1='hom_sc', name_y=y_var, name_x=x_var, name_yend=yd_var, name_q=q_var, name_regimes=r_var, name_ds='NAT.dbf')
+
+ Once we have run the model, we can explore a little bit the output. The
+ regression object we have created has many attributes so take your time to
+ discover them. This class offers an error model that assumes
+ homoskedasticity but that unlike the models from
+ ``pysal.spreg.error_sp``, it allows for inference on the spatial
+ parameter. Hence, we find the same number of betas as of standard errors,
+ which we calculate taking the square root of the diagonal of the
+ variance-covariance matrix. Alternatively, we can have a summary of the
+ output by typing: model.summary
+
+ >>> print reg.name_z
+ ['0_CONSTANT', '0_PS90', '0_UE90', '1_CONSTANT', '1_PS90', '1_UE90', '0_RD90', '1_RD90', 'lambda']
+
+ >>> print np.around(reg.betas,4)
+ [[ 3.5973]
+ [ 1.0652]
+ [ 0.1582]
+ [ 9.198 ]
+ [ 1.8809]
+ [-0.2489]
+ [ 2.4616]
+ [ 3.5796]
+ [ 0.2541]]
+
+ >>> print np.around(np.sqrt(reg.vm.diagonal()),4)
+ [ 0.5204 0.1371 0.0629 0.4721 0.1824 0.0725 0.2992 0.2395 0.024 ]
+
+ '''
+
+ def __init__(self, y, x, yend, q, regimes, w,
+ constant_regi='many', cols2regi='all', regime_err_sep=False,
+ regime_lag_sep=False,
+ max_iter=1, epsilon=0.00001, A1='het', cores=False,
+ vm=False, name_y=None, name_x=None,
+ name_yend=None, name_q=None, name_w=None,
+ name_ds=None, name_regimes=None, summ=True, add_lag=False):
+
+ n = USER.check_arrays(y, x, yend, q)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ self.constant_regi = constant_regi
+ self.cols2regi = cols2regi
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_regimes = USER.set_name_ds(name_regimes)
+ self.name_w = USER.set_name_w(name_w, w)
+ self.n = n
+ self.y = y
+
+ name_x = USER.set_name_x(name_x, x)
+ if summ:
+ name_yend = USER.set_name_yend(name_yend, yend)
+ self.name_y = USER.set_name_y(name_y)
+ name_q = USER.set_name_q(name_q, q)
+ self.name_x_r = name_x + name_yend
+
+ cols2regi = REGI.check_cols2regi(
+ constant_regi, cols2regi, x, yend=yend)
+ self.regimes_set = REGI._get_regimes_set(regimes)
+ self.regimes = regimes
+ USER.check_regimes(self.regimes_set, self.n, x.shape[1])
+ self.regime_err_sep = regime_err_sep
+
+ if regime_err_sep == True:
+ if set(cols2regi) == set([True]):
+ self._endog_error_regimes_multi(y, x, regimes, w, yend, q, cores,
+ max_iter, epsilon, A1, cols2regi, vm,
+ name_x, name_yend, name_q, add_lag)
+ else:
+ raise Exception, "All coefficients must vary accross regimes if regime_err_sep = True."
+ else:
+ x_constant = USER.check_constant(x)
+ q, name_q = REGI.Regimes_Frame.__init__(self, q,
+ regimes, constant_regi=None, cols2regi='all', names=name_q)
+ x, name_x = REGI.Regimes_Frame.__init__(self, x_constant,
+ regimes, constant_regi=None, cols2regi=cols2regi,
+ names=name_x)
+ yend2, name_yend = REGI.Regimes_Frame.__init__(self, yend,
+ regimes, constant_regi=None,
+ cols2regi=cols2regi, yend=True, names=name_yend)
+
+ if A1 == 'hom':
+ wA1 = get_A1_hom(w.sparse)
+ elif A1 == 'hom_sc':
+ wA1 = get_A1_hom(w.sparse, scalarKP=True)
+ elif A1 == 'het':
+ wA1 = get_A1_het(w.sparse)
+
+ wA2 = get_A2_hom(w.sparse)
+
+ # 1a. S2SLS --> \tilde{\delta}
+ tsls = BaseTSLS(y=y, x=x, yend=yend2, q=q)
+ self.k = tsls.z.shape[1]
+ self.x = tsls.x
+ self.yend, self.z, self.h = tsls.yend, tsls.z, tsls.h
+
+ # 1b. GM --> \tilde{\rho}
+ moments = moments_hom(w.sparse, wA1, wA2, tsls.u)
+ lambda1 = optim_moments(moments)
+ lambda_old = lambda1
+
+ self.iteration, eps = 0, 1
+ while self.iteration < max_iter and eps > epsilon:
+ # 2a. GS2SLS --> \hat{\delta}
+ xs = get_spFilter(w, lambda1, x_constant)
+ xs = REGI.Regimes_Frame.__init__(self, xs,
+ regimes, constant_regi=None, cols2regi=cols2regi)[0]
+ ys = get_spFilter(w, lambda1, y)
+ yend_s = get_spFilter(w, lambda1, yend)
+ yend_s = REGI.Regimes_Frame.__init__(self, yend_s,
+ regimes, constant_regi=None, cols2regi=cols2regi,
+ yend=True)[0]
+ tsls_s = BaseTSLS(ys, xs, yend_s, h=tsls.h)
+ self.predy = spdot(self.z, tsls_s.betas)
+ self.u = self.y - self.predy
+
+ # 2b. GM 2nd iteration --> \hat{\rho}
+ moments = moments_hom(w.sparse, wA1, wA2, self.u)
+ psi = get_vc_hom(
+ w.sparse, wA1, wA2, self, lambda_old, tsls_s.z)[0]
+ lambda2 = optim_moments(moments, psi)
+ eps = abs(lambda2 - lambda_old)
+ lambda_old = lambda2
+ self.iteration += 1
+
+ self.iter_stop = iter_msg(self.iteration, max_iter)
+
+ # Output
+ self.betas = np.vstack((tsls_s.betas, lambda2))
+ self.vm, self.sig2 = get_omega_hom(
+ w.sparse, wA1, wA2, self, lambda2, moments[0])
+ self.e_filtered = self.u - lambda2 * lag_spatial(w, self.u)
+ self.name_x = USER.set_name_x(name_x, x, constant=True)
+ self.name_yend = USER.set_name_yend(name_yend, yend)
+ self.name_z = self.name_x + self.name_yend
+ self.name_z.append('lambda')
+ self.name_q = USER.set_name_q(name_q, q)
+ self.name_h = USER.set_name_h(self.name_x, self.name_q)
+ self.kf += 1
+ self.chow = REGI.Chow(self)
+ self._cache = {}
+ if summ:
+ self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HOM) - REGIMES"
+ SUMMARY.GM_Endog_Error_Hom(reg=self, w=w, vm=vm, regimes=True)
+
+ def _endog_error_regimes_multi(self, y, x, regimes, w, yend, q, cores,
+ max_iter, epsilon, A1, cols2regi, vm,
+ name_x, name_yend, name_q, add_lag):
+
+ regi_ids = dict(
+ (r, list(np.where(np.array(regimes) == r)[0])) for r in self.regimes_set)
+ if add_lag != False:
+ self.cols2regi += [True]
+ cols2regi += [True]
+ self.predy_e = np.zeros((self.n, 1), float)
+ self.e_pred = np.zeros((self.n, 1), float)
+ results_p = {}
+ """
+ for r in self.regimes_set:
+ if system() == 'Windows':
+ is_win = True
+ results_p[r] = _work_endog_error(*(y,x,yend,q,regi_ids,r,w,max_iter,epsilon,A1,self.name_ds,self.name_y,name_x,name_yend,name_q,self.name_w,self.name_regimes,add_lag))
+ else:
+ pool = mp.Pool(cores)
+ results_p[r] = pool.apply_async(_work_endog_error,args=(y,x,yend,q,regi_ids,r,w,max_iter,epsilon,A1,self.name_ds,self.name_y,name_x,name_yend,name_q,self.name_w,self.name_regimes,add_lag, ))
+ is_win = False
+ """
+ for r in self.regimes_set:
+ if cores:
+ pool = mp.Pool(None)
+ results_p[r] = pool.apply_async(_work_endog_error, args=(
+ y, x, yend, q, regi_ids, r, w, max_iter, epsilon, A1, self.name_ds, self.name_y, name_x, name_yend, name_q, self.name_w, self.name_regimes, add_lag, ))
+ else:
+ results_p[r] = _work_endog_error(*(y, x, yend, q, regi_ids, r, w, max_iter, epsilon, A1,
+ self.name_ds, self.name_y, name_x, name_yend, name_q, self.name_w, self.name_regimes, add_lag))
+
+ self.kryd, self.kf = 0, 0
+ self.kr = len(cols2regi) + 1
+ self.nr = len(self.regimes_set)
+ self.vm = np.zeros((self.nr * self.kr, self.nr * self.kr), float)
+ self.betas = np.zeros((self.nr * self.kr, 1), float)
+ self.u = np.zeros((self.n, 1), float)
+ self.predy = np.zeros((self.n, 1), float)
+ self.e_filtered = np.zeros((self.n, 1), float)
+ """
+ if not is_win:
+ pool.close()
+ pool.join()
+ """
+ if cores:
+ pool.close()
+ pool.join()
+
+ results = {}
+ self.name_y, self.name_x, self.name_yend, self.name_q, self.name_z, self.name_h = [
+ ], [], [], [], [], []
+ counter = 0
+ for r in self.regimes_set:
+ """
+ if is_win:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+ """
+ if not cores:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+
+ self.vm[(counter * self.kr):((counter + 1) * self.kr),
+ (counter * self.kr):((counter + 1) * self.kr)] = results[r].vm
+ self.betas[
+ (counter * self.kr):((counter + 1) * self.kr), ] = results[r].betas
+ self.u[regi_ids[r], ] = results[r].u
+ self.predy[regi_ids[r], ] = results[r].predy
+ self.e_filtered[regi_ids[r], ] = results[r].e_filtered
+ self.name_y += results[r].name_y
+ self.name_x += results[r].name_x
+ self.name_yend += results[r].name_yend
+ self.name_q += results[r].name_q
+ self.name_z += results[r].name_z
+ self.name_h += results[r].name_h
+ if add_lag != False:
+ self.predy_e[regi_ids[r], ] = results[r].predy_e
+ self.e_pred[regi_ids[r], ] = results[r].e_pred
+ counter += 1
+ self.chow = REGI.Chow(self)
+ self.multi = results
+ if add_lag != False:
+ SUMMARY.GM_Combo_Hom_multi(
+ reg=self, multireg=self.multi, vm=vm, regimes=True)
+ else:
+ SUMMARY.GM_Endog_Error_Hom_multi(
+ reg=self, multireg=self.multi, vm=vm, regimes=True)
+
+
+class GM_Combo_Hom_Regimes(GM_Endog_Error_Hom_Regimes):
+
+ '''
+ GMM method for a spatial lag and error model with homoskedasticity,
+ regimes and endogenous variables, with results and diagnostics;
+ based on Drukker et al. (2010) [1]_, following Anselin (2011) [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x)
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ w : pysal W object
+ Spatial weights object (always needed)
+ constant_regi: ['one', 'many']
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime (default)
+ cols2regi : list, 'all'
+ Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all' (default), all the variables vary by regime.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ regime_lag_sep : boolean
+ If True, the spatial parameter for spatial lag is also
+ computed according to different regimes. If False (default),
+ the spatial parameter is fixed accross regimes.
+ w_lags : integer
+ Orders of W to include as instruments for the spatially
+ lagged dependent variable. For example, w_lags=1, then
+ instruments are WX; if w_lags=2, then WX, WWX; and so on.
+ lag_q : boolean
+ If True, then include spatial lags of the additional
+ instruments (q).
+ max_iter : int
+ Maximum number of iterations of steps 2a and 2b from Arraiz
+ et al. Note: epsilon provides an additional stop condition.
+ epsilon : float
+ Minimum change in lambda required to stop iterations of
+ steps 2a and 2b from Arraiz et al. Note: max_iter provides
+ an additional stop condition.
+ A1 : string
+ If A1='het', then the matrix A1 is defined as in Arraiz et
+ al. If A1='hom', then as in Anselin (2011). If
+ A1='hom_sc', then as in Drukker, Egger and Prucha (2010)
+ and Drukker, Prucha and Raciborski (2010).
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ cores : boolean
+ Specifies if multiprocessing is to be used
+ Default: no multiprocessing, cores = False
+ Note: Multiprocessing may not work on all platforms.
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_q : list of strings
+ Names of instruments for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regime variable for use in the output
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ e_pred : array
+ nx1 array of residuals (using reduced form)
+ predy : array
+ nx1 array of predicted y values
+ predy_e : array
+ nx1 array of predicted y values (using reduced form)
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable used as instruments
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ z : array
+ nxk array of variables (combination of x and yend)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ h : array
+ nxl array of instruments (combination of x and q)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ iter_stop : string
+ Stop criterion reached during iteration of steps 2a and 2b
+ from Arraiz et al.
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ iteration : integer
+ Number of iterations of steps 2a and 2b from Arraiz et al.
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ pr2_e : float
+ Pseudo R squared (squared correlation between y and ypred_e
+ (using reduced form))
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ sig2 : float
+ Sigma squared used in computations (based on filtered
+ residuals)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ std_err : array
+ 1xk array of standard errors of the betas
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_z : list of strings
+ Names of exogenous and endogenous variables for use in
+ output
+ name_q : list of strings
+ Names of external instruments
+ name_h : list of strings
+ Names of all instruments used in ouput
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regimes variable for use in output
+ title : string
+ Name of the regression method used
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ constant_regi : ['one', 'many']
+ Ignored if regimes=False. Constant option for regimes.
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime
+ cols2regi : list, 'all'
+ Ignored if regimes=False. Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all', all the variables vary by regime.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ regime_lag_sep : boolean
+ If True, the spatial parameter for spatial lag is also
+ computed according to different regimes. If False (default),
+ the spatial parameter is fixed accross regimes.
+ kr : int
+ Number of variables/columns to be "regimized" or subject
+ to change by regime. These will result in one parameter
+ estimate by regime for each variable (i.e. nr parameters per
+ variable)
+ kf : int
+ Number of variables/columns to be considered fixed or
+ global across regimes and hence only obtain one parameter
+ estimate
+ nr : int
+ Number of different regimes in the 'regimes' list
+ multi : dictionary
+ Only available when multiple regressions are estimated,
+ i.e. when regime_err_sep=True and no variable is fixed
+ across regimes.
+ Contains all attributes of each individual regression
+
+
+ References
+ ----------
+
+ .. [1] Drukker, D. M., Egger, P., Prucha, I. R. (2010)
+ "On Two-step Estimation of a Spatial Autoregressive Model with Autoregressive
+ Disturbances and Endogenous Regressors". Working paper.
+
+ .. [2] Anselin, L. (2011) "GMM Estimation of Spatial Error Autocorrelation
+ with and without Heteroskedasticity".
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import numpy as np
+ >>> import pysal
+
+ Open data on NCOVR US County Homicides (3085 areas) using pysal.open().
+ This is the DBF associated with the NAT shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path("NAT.dbf"),'r')
+
+ Extract the HR90 column (homicide rates in 1990) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y_var = 'HR90'
+ >>> y = np.array([db.by_col(y_var)]).reshape(3085,1)
+
+ Extract UE90 (unemployment rate) and PS90 (population structure) vectors from
+ the DBF to be used as independent variables in the regression. Other variables
+ can be inserted by adding their names to x_var, such as x_var = ['Var1','Var2','...]
+ Note that PySAL requires this to be an nxj numpy array, where j is the
+ number of independent variables (not including a constant). By default
+ this model adds a vector of ones to the independent variables passed in.
+
+ >>> x_var = ['PS90','UE90']
+ >>> x = np.array([db.by_col(name) for name in x_var]).T
+
+ The different regimes in this data are given according to the North and
+ South dummy (SOUTH).
+
+ >>> r_var = 'SOUTH'
+ >>> regimes = db.by_col(r_var)
+
+ Since we want to run a spatial combo model, we need to specify
+ the spatial weights matrix that includes the spatial configuration of the
+ observations. To do that, we can open an already existing gal file or
+ create a new one. In this case, we will create one from ``NAT.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("NAT.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, this allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ We are all set with the preliminaries, we are good to run the model. In this
+ case, we will need the variables and the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional.
+
+ Example only with spatial lag
+
+ The Combo class runs an SARAR model, that is a spatial lag+error model.
+ In this case we will run a simple version of that, where we have the
+ spatial effects as well as exogenous variables. Since it is a spatial
+ model, we have to pass in the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional. We can have a
+ summary of the output by typing: model.summary
+ Alternatively, we can check the betas:
+
+ >>> reg = GM_Combo_Hom_Regimes(y, x, regimes, w=w, A1='hom_sc', name_y=y_var, name_x=x_var, name_regimes=r_var, name_ds='NAT')
+ >>> print reg.name_z
+ ['0_CONSTANT', '0_PS90', '0_UE90', '1_CONSTANT', '1_PS90', '1_UE90', '_Global_W_HR90', 'lambda']
+ >>> print np.around(reg.betas,4)
+ [[ 1.4607]
+ [ 0.9579]
+ [ 0.5658]
+ [ 9.1129]
+ [ 1.1339]
+ [ 0.6517]
+ [-0.4583]
+ [ 0.6634]]
+
+ This class also allows the user to run a spatial lag+error model with the
+ extra feature of including non-spatial endogenous regressors. This means
+ that, in addition to the spatial lag and error, we consider some of the
+ variables on the right-hand side of the equation as endogenous and we
+ instrument for this. In this case we consider RD90 (resource deprivation)
+ as an endogenous regressor. We use FP89 (families below poverty)
+ for this and hence put it in the instruments parameter, 'q'.
+
+ >>> yd_var = ['RD90']
+ >>> yd = np.array([db.by_col(name) for name in yd_var]).T
+ >>> q_var = ['FP89']
+ >>> q = np.array([db.by_col(name) for name in q_var]).T
+
+ And then we can run and explore the model analogously to the previous combo:
+
+ >>> reg = GM_Combo_Hom_Regimes(y, x, regimes, yd, q, w=w, A1='hom_sc', name_y=y_var, name_x=x_var, name_yend=yd_var, name_q=q_var, name_regimes=r_var, name_ds='NAT')
+ >>> print reg.name_z
+ ['0_CONSTANT', '0_PS90', '0_UE90', '1_CONSTANT', '1_PS90', '1_UE90', '0_RD90', '1_RD90', '_Global_W_HR90', 'lambda']
+ >>> print reg.betas
+ [[ 3.4196478 ]
+ [ 1.04065595]
+ [ 0.16630304]
+ [ 8.86570777]
+ [ 1.85134286]
+ [-0.24921597]
+ [ 2.43007651]
+ [ 3.61656899]
+ [ 0.03315061]
+ [ 0.22636055]]
+ >>> print np.sqrt(reg.vm.diagonal())
+ [ 0.53989913 0.13506086 0.06143434 0.77049956 0.18089997 0.07246848
+ 0.29218837 0.25378655 0.06184801 0.06323236]
+ >>> print 'lambda: ', np.around(reg.betas[-1], 4)
+ lambda: [ 0.2264]
+
+ '''
+
+ def __init__(self, y, x, regimes, yend=None, q=None,
+ w=None, w_lags=1, lag_q=True, cores=False,
+ max_iter=1, epsilon=0.00001, A1='het',
+ constant_regi='many', cols2regi='all',
+ regime_err_sep=False, regime_lag_sep=False,
+ vm=False, name_y=None, name_x=None,
+ name_yend=None, name_q=None,
+ name_w=None, name_ds=None, name_regimes=None):
+
+ n = USER.check_arrays(y, x)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ name_x = USER.set_name_x(name_x, x, constant=True)
+ self.name_y = USER.set_name_y(name_y)
+ name_yend = USER.set_name_yend(name_yend, yend)
+ name_q = USER.set_name_q(name_q, q)
+ name_q.extend(
+ USER.set_name_q_sp(name_x, w_lags, name_q, lag_q, force_all=True))
+
+ cols2regi = REGI.check_cols2regi(
+ constant_regi, cols2regi, x, yend=yend, add_cons=False)
+ self.regimes_set = REGI._get_regimes_set(regimes)
+ self.regimes = regimes
+ USER.check_regimes(self.regimes_set, n, x.shape[1])
+ self.regime_err_sep = regime_err_sep
+ self.regime_lag_sep = regime_lag_sep
+
+ if regime_lag_sep == True:
+ if regime_err_sep == False:
+ raise Exception, "For spatial combo models, if spatial lag is set by regimes (regime_lag_sep=True), spatial error must also be set by regimes (regime_err_sep=True)."
+ add_lag = [w_lags, lag_q]
+ else:
+ cols2regi += [False]
+ add_lag = False
+ if regime_err_sep == True:
+ raise Exception, "For spatial combo models, if spatial error is set by regimes (regime_err_sep=True), all coefficients including lambda (regime_lag_sep=True) must be set by regimes."
+ yend, q = set_endog(y, x, w, yend, q, w_lags, lag_q)
+ name_yend.append(USER.set_name_yend_sp(self.name_y))
+
+ GM_Endog_Error_Hom_Regimes.__init__(self, y=y, x=x, yend=yend,
+ q=q, regimes=regimes, w=w, vm=vm, constant_regi=constant_regi,
+ cols2regi=cols2regi, regime_err_sep=regime_err_sep,
+ max_iter=max_iter, epsilon=epsilon, A1=A1, cores=cores,
+ name_y=self.name_y, name_x=name_x, name_yend=name_yend,
+ name_q=name_q, name_w=name_w, name_ds=name_ds,
+ name_regimes=name_regimes, summ=False, add_lag=add_lag)
+
+ if regime_err_sep != True:
+ self.rho = self.betas[-2]
+ self.predy_e, self.e_pred, warn = sp_att(w, self.y,
+ self.predy, yend[:, -1].reshape(self.n, 1), self.rho)
+ set_warn(self, warn)
+ self.regime_lag_sep = regime_lag_sep
+ self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HOM) - REGIMES"
+ SUMMARY.GM_Combo_Hom(reg=self, w=w, vm=vm, regimes=True)
+
+
+def _work_error(y, x, regi_ids, r, w, max_iter, epsilon, A1, name_ds, name_y, name_x, name_w, name_regimes):
+ w_r, warn = REGI.w_regime(w, regi_ids[r], r, transform=True)
+ y_r = y[regi_ids[r]]
+ x_r = x[regi_ids[r]]
+ x_constant = USER.check_constant(x_r)
+ model = BaseGM_Error_Hom(
+ y_r, x_constant, w_r.sparse, max_iter=max_iter, epsilon=epsilon, A1=A1)
+ set_warn(model, warn)
+ model.w = w_r
+ model.title = "SPATIALLY WEIGHTED LEAST SQUARES ESTIMATION (HOM) - REGIME %s" % r
+ model.name_ds = name_ds
+ model.name_y = '%s_%s' % (str(r), name_y)
+ model.name_x = ['%s_%s' % (str(r), i) for i in name_x]
+ model.name_w = name_w
+ model.name_regimes = name_regimes
+ return model
+
+
+def _work_endog_error(y, x, yend, q, regi_ids, r, w, max_iter, epsilon, A1, name_ds, name_y, name_x, name_yend, name_q, name_w, name_regimes, add_lag):
+ w_r, warn = REGI.w_regime(w, regi_ids[r], r, transform=True)
+ y_r = y[regi_ids[r]]
+ x_r = x[regi_ids[r]]
+ if yend != None:
+ yend_r = yend[regi_ids[r]]
+ q_r = q[regi_ids[r]]
+ else:
+ yend_r, q_r = None, None
+ if add_lag != False:
+ yend_r, q_r = set_endog(
+ y_r, x_r, w_r, yend_r, q_r, add_lag[0], add_lag[1])
+ x_constant = USER.check_constant(x_r)
+ model = BaseGM_Endog_Error_Hom(
+ y_r, x_constant, yend_r, q_r, w_r.sparse, max_iter=max_iter, epsilon=epsilon, A1=A1)
+ set_warn(model, warn)
+ if add_lag != False:
+ model.rho = model.betas[-2]
+ model.predy_e, model.e_pred, warn = sp_att(w_r, model.y,
+ model.predy, model.yend[:, -1].reshape(model.n, 1), model.rho)
+ set_warn(model, warn)
+ model.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HOM) - REGIME %s" % r
+ model.name_ds = name_ds
+ model.name_y = '%s_%s' % (str(r), name_y)
+ model.name_x = ['%s_%s' % (str(r), i) for i in name_x]
+ model.name_yend = ['%s_%s' % (str(r), i) for i in name_yend]
+ model.name_z = model.name_x + model.name_yend + ['lambda']
+ model.name_q = ['%s_%s' % (str(r), i) for i in name_q]
+ model.name_h = model.name_x + model.name_q
+ model.name_w = name_w
+ model.name_regimes = name_regimes
+ return model
+
+
+def _test():
+ import doctest
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ doctest.testmod()
+ np.set_printoptions(suppress=start_suppress)
+
+if __name__ == '__main__':
+ _test()
diff --git a/pysal/spreg/error_sp_regimes.py b/pysal/spreg/error_sp_regimes.py
new file mode 100644
index 0000000..d5f78f2
--- /dev/null
+++ b/pysal/spreg/error_sp_regimes.py
@@ -0,0 +1,1374 @@
+"""
+Spatial Error Models with regimes module
+"""
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, Pedro V. Amaral pedro.amaral at asu.edu"
+
+import numpy as np
+import multiprocessing as mp
+import regimes as REGI
+import user_output as USER
+import summary_output as SUMMARY
+from pysal import lag_spatial
+from ols import BaseOLS
+from twosls import BaseTSLS
+from error_sp import BaseGM_Error, BaseGM_Endog_Error, _momentsGM_Error
+from utils import set_endog, iter_msg, sp_att, set_warn
+from utils import optim_moments, get_spFilter, get_lags
+from utils import spdot, RegressionPropsY
+from platform import system
+
+
+class GM_Error_Regimes(RegressionPropsY, REGI.Regimes_Frame):
+
+ """
+ GMM method for a spatial error model with regimes, with results and diagnostics;
+ based on Kelejian and Prucha (1998, 1999)[1]_ [2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ w : pysal W object
+ Spatial weights object
+ constant_regi: ['one', 'many']
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime (default)
+ cols2regi : list, 'all'
+ Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all' (default), all the variables vary by regime.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ regime_lag_sep : boolean
+ Always False, kept for consistency, ignored.
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ cores : boolean
+ Specifies if multiprocessing is to be used
+ Default: no multiprocessing, cores = False
+ Note: Multiprocessing may not work on all platforms.
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regime variable for use in the output
+
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ vm : array
+ Variance covariance matrix (kxk)
+ sig2 : float
+ Sigma squared used in computations
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ std_err : array
+ 1xk array of standard errors of the betas
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regime variable for use in the output
+ title : string
+ Name of the regression method used
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ constant_regi: ['one', 'many']
+ Ignored if regimes=False. Constant option for regimes.
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime
+ cols2regi : list, 'all'
+ Ignored if regimes=False. Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all', all the variables vary by regime.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ kr : int
+ Number of variables/columns to be "regimized" or subject
+ to change by regime. These will result in one parameter
+ estimate by regime for each variable (i.e. nr parameters per
+ variable)
+ kf : int
+ Number of variables/columns to be considered fixed or
+ global across regimes and hence only obtain one parameter
+ estimate
+ nr : int
+ Number of different regimes in the 'regimes' list
+ multi : dictionary
+ Only available when multiple regressions are estimated,
+ i.e. when regime_err_sep=True and no variable is fixed
+ across regimes.
+ Contains all attributes of each individual regression
+
+ References
+ ----------
+
+ .. [1] Kelejian, H.R., Prucha, I.R. (1998) "A generalized spatial
+ two-stage least squares procedure for estimating a spatial autoregressive
+ model with autoregressive disturbances". The Journal of Real State
+ Finance and Economics, 17, 1.
+
+ .. [2] Kelejian, H.R., Prucha, I.R. (1999) "A Generalized Moments
+ Estimator for the Autoregressive Parameter in a Spatial Model".
+ International Economic Review, 40, 2.
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import pysal
+ >>> import numpy as np
+
+ Open data on NCOVR US County Homicides (3085 areas) using pysal.open().
+ This is the DBF associated with the NAT shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path("NAT.dbf"),'r')
+
+ Extract the HR90 column (homicide rates in 1990) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y_var = 'HR90'
+ >>> y = np.array([db.by_col(y_var)]).reshape(3085,1)
+
+ Extract UE90 (unemployment rate) and PS90 (population structure) vectors from
+ the DBF to be used as independent variables in the regression. Other variables
+ can be inserted by adding their names to x_var, such as x_var = ['Var1','Var2','...]
+ Note that PySAL requires this to be an nxj numpy array, where j is the
+ number of independent variables (not including a constant). By default
+ this model adds a vector of ones to the independent variables passed in.
+
+ >>> x_var = ['PS90','UE90']
+ >>> x = np.array([db.by_col(name) for name in x_var]).T
+
+ The different regimes in this data are given according to the North and
+ South dummy (SOUTH).
+
+ >>> r_var = 'SOUTH'
+ >>> regimes = db.by_col(r_var)
+
+ Since we want to run a spatial error model, we need to specify
+ the spatial weights matrix that includes the spatial configuration of the
+ observations. To do that, we can open an already existing gal file or
+ create a new one. In this case, we will create one from ``NAT.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("NAT.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, this allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ We are all set with the preliminaries, we are good to run the model. In this
+ case, we will need the variables and the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional.
+
+ >>> model = GM_Error_Regimes(y, x, regimes, w=w, name_y=y_var, name_x=x_var, name_regimes=r_var, name_ds='NAT.dbf')
+
+ Once we have run the model, we can explore a little bit the output. The
+ regression object we have created has many attributes so take your time to
+ discover them. Note that because we are running the classical GMM error
+ model from 1998/99, the spatial parameter is obtained as a point estimate, so
+ although you get a value for it (there are for coefficients under
+ model.betas), you cannot perform inference on it (there are only three
+ values in model.se_betas). Alternatively, we can have a summary of the
+ output by typing: model.summary
+
+ >>> print model.name_x
+ ['0_CONSTANT', '0_PS90', '0_UE90', '1_CONSTANT', '1_PS90', '1_UE90', 'lambda']
+ >>> np.around(model.betas, decimals=6)
+ array([[ 0.074807],
+ [ 0.786107],
+ [ 0.538849],
+ [ 5.103756],
+ [ 1.196009],
+ [ 0.600533],
+ [ 0.364103]])
+ >>> np.around(model.std_err, decimals=6)
+ array([ 0.379864, 0.152316, 0.051942, 0.471285, 0.19867 , 0.057252])
+ >>> np.around(model.z_stat, decimals=6)
+ array([[ 0.196932, 0.843881],
+ [ 5.161042, 0. ],
+ [ 10.37397 , 0. ],
+ [ 10.829455, 0. ],
+ [ 6.02007 , 0. ],
+ [ 10.489215, 0. ]])
+ >>> np.around(model.sig2, decimals=6)
+ 28.172732
+
+ """
+
+ def __init__(self, y, x, regimes, w,
+ vm=False, name_y=None, name_x=None, name_w=None,
+ constant_regi='many', cols2regi='all', regime_err_sep=False,
+ regime_lag_sep=False,
+ cores=False, name_ds=None, name_regimes=None):
+
+ n = USER.check_arrays(y, x)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ self.constant_regi = constant_regi
+ self.cols2regi = cols2regi
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_y = USER.set_name_y(name_y)
+ self.name_w = USER.set_name_w(name_w, w)
+ self.name_regimes = USER.set_name_ds(name_regimes)
+ self.n = n
+ self.y = y
+
+ x_constant = USER.check_constant(x)
+ name_x = USER.set_name_x(name_x, x)
+ self.name_x_r = name_x
+
+ cols2regi = REGI.check_cols2regi(constant_regi, cols2regi, x)
+ self.regimes_set = REGI._get_regimes_set(regimes)
+ self.regimes = regimes
+ USER.check_regimes(self.regimes_set, self.n, x.shape[1])
+ self.regime_err_sep = regime_err_sep
+ if regime_err_sep == True:
+ if set(cols2regi) == set([True]):
+ self._error_regimes_multi(y, x, regimes, w, cores,
+ cols2regi, vm, name_x)
+ else:
+ raise Exception, "All coefficients must vary accross regimes if regime_err_sep = True."
+ else:
+ self.x, self.name_x = REGI.Regimes_Frame.__init__(self, x_constant,
+ regimes, constant_regi=None, cols2regi=cols2regi, names=name_x)
+ ols = BaseOLS(y=y, x=self.x)
+ self.k = ols.x.shape[1]
+ moments = _momentsGM_Error(w, ols.u)
+ lambda1 = optim_moments(moments)
+ xs = get_spFilter(w, lambda1, x_constant)
+ ys = get_spFilter(w, lambda1, y)
+ xs = REGI.Regimes_Frame.__init__(self, xs,
+ regimes, constant_regi=None, cols2regi=cols2regi)[0]
+ ols2 = BaseOLS(y=ys, x=xs)
+
+ # Output
+ self.predy = spdot(self.x, ols2.betas)
+ self.u = y - self.predy
+ self.betas = np.vstack((ols2.betas, np.array([[lambda1]])))
+ self.sig2 = ols2.sig2n
+ self.e_filtered = self.u - lambda1 * lag_spatial(w, self.u)
+ self.vm = self.sig2 * ols2.xtxi
+ self.title = "SPATIALLY WEIGHTED LEAST SQUARES - REGIMES"
+ self.name_x.append('lambda')
+ self.kf += 1
+ self.chow = REGI.Chow(self)
+ self._cache = {}
+ SUMMARY.GM_Error(reg=self, w=w, vm=vm, regimes=True)
+
+ def _error_regimes_multi(self, y, x, regimes, w, cores,
+ cols2regi, vm, name_x):
+ regi_ids = dict(
+ (r, list(np.where(np.array(regimes) == r)[0])) for r in self.regimes_set)
+ results_p = {}
+ """
+ for r in self.regimes_set:
+ if system() == 'Windows':
+ results_p[r] = _work_error(*(y,x,regi_ids,r,w,self.name_ds,self.name_y,name_x+['lambda'],self.name_w,self.name_regimes))
+ is_win = True
+ else:
+ pool = mp.Pool(cores)
+ results_p[r] = pool.apply_async(_work_error,args=(y,x,regi_ids,r,w,self.name_ds,self.name_y,name_x+['lambda'],self.name_w,self.name_regimes, ))
+ is_win = False
+ """
+ for r in self.regimes_set:
+ if cores:
+ pool = mp.Pool(None)
+ results_p[r] = pool.apply_async(_work_error, args=(
+ y, x, regi_ids, r, w, self.name_ds, self.name_y, name_x + ['lambda'], self.name_w, self.name_regimes, ))
+ else:
+ results_p[r] = _work_error(
+ *(y, x, regi_ids, r, w, self.name_ds, self.name_y, name_x + ['lambda'], self.name_w, self.name_regimes))
+
+ self.kryd = 0
+ self.kr = len(cols2regi)
+ self.kf = 0
+ self.nr = len(self.regimes_set)
+ self.vm = np.zeros((self.nr * self.kr, self.nr * self.kr), float)
+ self.betas = np.zeros((self.nr * (self.kr + 1), 1), float)
+ self.u = np.zeros((self.n, 1), float)
+ self.predy = np.zeros((self.n, 1), float)
+ self.e_filtered = np.zeros((self.n, 1), float)
+ """
+ if not is_win:
+ pool.close()
+ pool.join()
+ """
+ if cores:
+ pool.close()
+ pool.join()
+
+ results = {}
+ self.name_y, self.name_x = [], []
+ counter = 0
+ for r in self.regimes_set:
+ """
+ if is_win:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+ """
+ if not cores:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+
+ self.vm[(counter * self.kr):((counter + 1) * self.kr),
+ (counter * self.kr):((counter + 1) * self.kr)] = results[r].vm
+ self.betas[
+ (counter * (self.kr + 1)):((counter + 1) * (self.kr + 1)), ] = results[r].betas
+ self.u[regi_ids[r], ] = results[r].u
+ self.predy[regi_ids[r], ] = results[r].predy
+ self.e_filtered[regi_ids[r], ] = results[r].e_filtered
+ self.name_y += results[r].name_y
+ self.name_x += results[r].name_x
+ counter += 1
+ self.chow = REGI.Chow(self)
+ self.multi = results
+ SUMMARY.GM_Error_multi(
+ reg=self, multireg=self.multi, vm=vm, regimes=True)
+
+
+class GM_Endog_Error_Regimes(RegressionPropsY, REGI.Regimes_Frame):
+
+ '''
+ GMM method for a spatial error model with regimes and endogenous variables, with
+ results and diagnostics; based on Kelejian and Prucha (1998, 1999)[1]_[2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x)
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ w : pysal W object
+ Spatial weights object
+ constant_regi: ['one', 'many']
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime (default)
+ cols2regi : list, 'all'
+ Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all' (default), all the variables vary by regime.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ regime_lag_sep : boolean
+ Always False, kept for consistency, ignored.
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ cores : boolean
+ Specifies if multiprocessing is to be used
+ Default: no multiprocessing, cores = False
+ Note: Multiprocessing may not work on all platforms.
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_q : list of strings
+ Names of instruments for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regime variable for use in the output
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ z : array
+ nxk array of variables (combination of x and yend)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ sig2 : float
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ Sigma squared used in computations
+ std_err : array
+ 1xk array of standard errors of the betas
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_z : list of strings
+ Names of exogenous and endogenous variables for use in
+ output
+ name_q : list of strings
+ Names of external instruments
+ name_h : list of strings
+ Names of all instruments used in ouput
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regimes variable for use in output
+ title : string
+ Name of the regression method used
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ constant_regi : ['one', 'many']
+ Ignored if regimes=False. Constant option for regimes.
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime
+ cols2regi : list, 'all'
+ Ignored if regimes=False. Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all', all the variables vary by regime.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ kr : int
+ Number of variables/columns to be "regimized" or subject
+ to change by regime. These will result in one parameter
+ estimate by regime for each variable (i.e. nr parameters per
+ variable)
+ kf : int
+ Number of variables/columns to be considered fixed or
+ global across regimes and hence only obtain one parameter
+ estimate
+ nr : int
+ Number of different regimes in the 'regimes' list
+ multi : dictionary
+ Only available when multiple regressions are estimated,
+ i.e. when regime_err_sep=True and no variable is fixed
+ across regimes.
+ Contains all attributes of each individual regression
+
+ References
+ ----------
+
+ .. [1] Kelejian, H.R., Prucha, I.R. (1998) "A generalized spatial
+ two-stage least squares procedure for estimating a spatial autoregressive
+ model with autoregressive disturbances". The Journal of Real State
+ Finance and Economics, 17, 1.
+
+ .. [2] Kelejian, H.R., Prucha, I.R. (1999) "A Generalized Moments
+ Estimator for the Autoregressive Parameter in a Spatial Model".
+ International Economic Review, 40, 2.
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import pysal
+ >>> import numpy as np
+
+ Open data on NCOVR US County Homicides (3085 areas) using pysal.open().
+ This is the DBF associated with the NAT shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path("NAT.dbf"),'r')
+
+ Extract the HR90 column (homicide rates in 1990) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y_var = 'HR90'
+ >>> y = np.array([db.by_col(y_var)]).reshape(3085,1)
+
+ Extract UE90 (unemployment rate) and PS90 (population structure) vectors from
+ the DBF to be used as independent variables in the regression. Other variables
+ can be inserted by adding their names to x_var, such as x_var = ['Var1','Var2','...]
+ Note that PySAL requires this to be an nxj numpy array, where j is the
+ number of independent variables (not including a constant). By default
+ this model adds a vector of ones to the independent variables passed in.
+
+ >>> x_var = ['PS90','UE90']
+ >>> x = np.array([db.by_col(name) for name in x_var]).T
+
+ For the endogenous models, we add the endogenous variable RD90 (resource deprivation)
+ and we decide to instrument for it with FP89 (families below poverty):
+
+ >>> yd_var = ['RD90']
+ >>> yend = np.array([db.by_col(name) for name in yd_var]).T
+ >>> q_var = ['FP89']
+ >>> q = np.array([db.by_col(name) for name in q_var]).T
+
+ The different regimes in this data are given according to the North and
+ South dummy (SOUTH).
+
+ >>> r_var = 'SOUTH'
+ >>> regimes = db.by_col(r_var)
+
+ Since we want to run a spatial error model, we need to specify the spatial
+ weights matrix that includes the spatial configuration of the observations
+ into the error component of the model. To do that, we can open an already
+ existing gal file or create a new one. In this case, we will create one
+ from ``NAT.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("NAT.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, this allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ We are all set with the preliminaries, we are good to run the model. In this
+ case, we will need the variables (exogenous and endogenous), the
+ instruments and the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional.
+
+ >>> model = GM_Endog_Error_Regimes(y, x, yend, q, regimes, w=w, name_y=y_var, name_x=x_var, name_yend=yd_var, name_q=q_var, name_regimes=r_var, name_ds='NAT.dbf')
+
+ Once we have run the model, we can explore a little bit the output. The
+ regression object we have created has many attributes so take your time to
+ discover them. Note that because we are running the classical GMM error
+ model from 1998/99, the spatial parameter is obtained as a point estimate, so
+ although you get a value for it (there are for coefficients under
+ model.betas), you cannot perform inference on it (there are only three
+ values in model.se_betas). Also, this regression uses a two stage least
+ squares estimation method that accounts for the endogeneity created by the
+ endogenous variables included. Alternatively, we can have a summary of the
+ output by typing: model.summary
+
+ >>> print model.name_z
+ ['0_CONSTANT', '0_PS90', '0_UE90', '1_CONSTANT', '1_PS90', '1_UE90', '0_RD90', '1_RD90', 'lambda']
+ >>> np.around(model.betas, decimals=5)
+ array([[ 3.59718],
+ [ 1.0652 ],
+ [ 0.15822],
+ [ 9.19754],
+ [ 1.88082],
+ [-0.24878],
+ [ 2.46161],
+ [ 3.57943],
+ [ 0.25564]])
+ >>> np.around(model.std_err, decimals=6)
+ array([ 0.522633, 0.137555, 0.063054, 0.473654, 0.18335 , 0.072786,
+ 0.300711, 0.240413])
+
+ '''
+
+ def __init__(self, y, x, yend, q, regimes, w, cores=False,
+ vm=False, constant_regi='many', cols2regi='all',
+ regime_err_sep=False, regime_lag_sep=False, name_y=None,
+ name_x=None, name_yend=None, name_q=None, name_w=None,
+ name_ds=None, name_regimes=None, summ=True, add_lag=False):
+
+ n = USER.check_arrays(y, x, yend, q)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ self.constant_regi = constant_regi
+ self.cols2regi = cols2regi
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_regimes = USER.set_name_ds(name_regimes)
+ self.name_w = USER.set_name_w(name_w, w)
+ self.n = n
+ self.y = y
+
+ name_x = USER.set_name_x(name_x, x)
+ if summ:
+ name_yend = USER.set_name_yend(name_yend, yend)
+ self.name_y = USER.set_name_y(name_y)
+ name_q = USER.set_name_q(name_q, q)
+ self.name_x_r = name_x + name_yend
+
+ cols2regi = REGI.check_cols2regi(
+ constant_regi, cols2regi, x, yend=yend)
+ self.regimes_set = REGI._get_regimes_set(regimes)
+ self.regimes = regimes
+ USER.check_regimes(self.regimes_set, self.n, x.shape[1])
+ self.regime_err_sep = regime_err_sep
+
+ if regime_err_sep == True:
+ if set(cols2regi) == set([True]):
+ self._endog_error_regimes_multi(y, x, regimes, w, yend, q, cores,
+ cols2regi, vm, name_x, name_yend, name_q, add_lag)
+ else:
+ raise Exception, "All coefficients must vary accross regimes if regime_err_sep = True."
+ else:
+ x_constant = USER.check_constant(x)
+ q, name_q = REGI.Regimes_Frame.__init__(self, q,
+ regimes, constant_regi=None, cols2regi='all', names=name_q)
+ x, name_x = REGI.Regimes_Frame.__init__(self, x_constant,
+ regimes, constant_regi=None, cols2regi=cols2regi,
+ names=name_x)
+ yend2, name_yend = REGI.Regimes_Frame.__init__(self, yend,
+ regimes, constant_regi=None,
+ cols2regi=cols2regi, yend=True, names=name_yend)
+
+ tsls = BaseTSLS(y=y, x=x, yend=yend2, q=q)
+ self.k = tsls.z.shape[1]
+ self.x = tsls.x
+ self.yend, self.z = tsls.yend, tsls.z
+ moments = _momentsGM_Error(w, tsls.u)
+ lambda1 = optim_moments(moments)
+ xs = get_spFilter(w, lambda1, x_constant)
+ xs = REGI.Regimes_Frame.__init__(self, xs,
+ regimes, constant_regi=None, cols2regi=cols2regi)[0]
+ ys = get_spFilter(w, lambda1, y)
+ yend_s = get_spFilter(w, lambda1, yend)
+ yend_s = REGI.Regimes_Frame.__init__(self, yend_s,
+ regimes, constant_regi=None, cols2regi=cols2regi,
+ yend=True)[0]
+ tsls2 = BaseTSLS(ys, xs, yend_s, h=tsls.h)
+
+ # Output
+ self.betas = np.vstack((tsls2.betas, np.array([[lambda1]])))
+ self.predy = spdot(tsls.z, tsls2.betas)
+ self.u = y - self.predy
+ self.sig2 = float(np.dot(tsls2.u.T, tsls2.u)) / self.n
+ self.e_filtered = self.u - lambda1 * lag_spatial(w, self.u)
+ self.vm = self.sig2 * tsls2.varb
+ self.name_x = USER.set_name_x(name_x, x, constant=True)
+ self.name_yend = USER.set_name_yend(name_yend, yend)
+ self.name_z = self.name_x + self.name_yend
+ self.name_z.append('lambda')
+ self.name_q = USER.set_name_q(name_q, q)
+ self.name_h = USER.set_name_h(self.name_x, self.name_q)
+ self.kf += 1
+ self.chow = REGI.Chow(self)
+ self._cache = {}
+ if summ:
+ self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES - REGIMES"
+ SUMMARY.GM_Endog_Error(reg=self, w=w, vm=vm, regimes=True)
+
+ def _endog_error_regimes_multi(self, y, x, regimes, w, yend, q, cores,
+ cols2regi, vm, name_x, name_yend, name_q, add_lag):
+
+ regi_ids = dict(
+ (r, list(np.where(np.array(regimes) == r)[0])) for r in self.regimes_set)
+ if add_lag != False:
+ self.cols2regi += [True]
+ cols2regi += [True]
+ self.predy_e = np.zeros((self.n, 1), float)
+ self.e_pred = np.zeros((self.n, 1), float)
+ results_p = {}
+ for r in self.regimes_set:
+ """
+ if system() == 'Windows':
+ results_p[r] = _work_endog_error(*(y,x,yend,q,regi_ids,r,w,self.name_ds,self.name_y,name_x,name_yend,name_q,self.name_w,self.name_regimes,add_lag))
+ is_win = True
+ else:
+ pool = mp.Pool(cores)
+ results_p[r] = pool.apply_async(_work_endog_error,args=(y,x,yend,q,regi_ids,r,w,self.name_ds,self.name_y,name_x,name_yend,name_q,self.name_w,self.name_regimes,add_lag, ))
+ is_win = False
+ """
+ for r in self.regimes_set:
+ if cores:
+ pool = mp.Pool(None)
+ results_p[r] = pool.apply_async(_work_endog_error, args=(
+ y, x, yend, q, regi_ids, r, w, self.name_ds, self.name_y, name_x, name_yend, name_q, self.name_w, self.name_regimes, add_lag, ))
+ else:
+ results_p[r] = _work_endog_error(
+ *(y, x, yend, q, regi_ids, r, w, self.name_ds, self.name_y, name_x, name_yend, name_q, self.name_w, self.name_regimes, add_lag))
+
+ self.kryd, self.kf = 0, 0
+ self.kr = len(cols2regi)
+ self.nr = len(self.regimes_set)
+ self.vm = np.zeros((self.nr * self.kr, self.nr * self.kr), float)
+ self.betas = np.zeros((self.nr * (self.kr + 1), 1), float)
+ self.u = np.zeros((self.n, 1), float)
+ self.predy = np.zeros((self.n, 1), float)
+ self.e_filtered = np.zeros((self.n, 1), float)
+ """
+ if not is_win:
+ pool.close()
+ pool.join()
+ """
+ if cores:
+ pool.close()
+ pool.join()
+
+ results = {}
+ self.name_y, self.name_x, self.name_yend, self.name_q, self.name_z, self.name_h = [
+ ], [], [], [], [], []
+ counter = 0
+ for r in self.regimes_set:
+ """
+ if is_win:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+ """
+ if not cores:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+
+ self.vm[(counter * self.kr):((counter + 1) * self.kr),
+ (counter * self.kr):((counter + 1) * self.kr)] = results[r].vm
+ self.betas[
+ (counter * (self.kr + 1)):((counter + 1) * (self.kr + 1)), ] = results[r].betas
+ self.u[regi_ids[r], ] = results[r].u
+ self.predy[regi_ids[r], ] = results[r].predy
+ self.e_filtered[regi_ids[r], ] = results[r].e_filtered
+ self.name_y += results[r].name_y
+ self.name_x += results[r].name_x
+ self.name_yend += results[r].name_yend
+ self.name_q += results[r].name_q
+ self.name_z += results[r].name_z
+ self.name_h += results[r].name_h
+ if add_lag != False:
+ self.predy_e[regi_ids[r], ] = results[r].predy_e
+ self.e_pred[regi_ids[r], ] = results[r].e_pred
+ counter += 1
+ self.chow = REGI.Chow(self)
+ self.multi = results
+ if add_lag != False:
+ SUMMARY.GM_Combo_multi(
+ reg=self, multireg=self.multi, vm=vm, regimes=True)
+ else:
+ SUMMARY.GM_Endog_Error_multi(
+ reg=self, multireg=self.multi, vm=vm, regimes=True)
+
+
+class GM_Combo_Regimes(GM_Endog_Error_Regimes, REGI.Regimes_Frame):
+
+ """
+ GMM method for a spatial lag and error model with regimes and endogenous
+ variables, with results and diagnostics; based on Kelejian and Prucha (1998,
+ 1999)[1]_[2]_.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x)
+ w : pysal W object
+ Spatial weights object (always needed)
+ constant_regi: ['one', 'many']
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime (default)
+ cols2regi : list, 'all'
+ Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all' (default), all the variables vary by regime.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ regime_lag_sep : boolean
+ If True, the spatial parameter for spatial lag is also
+ computed according to different regimes. If False (default),
+ the spatial parameter is fixed accross regimes.
+ w_lags : integer
+ Orders of W to include as instruments for the spatially
+ lagged dependent variable. For example, w_lags=1, then
+ instruments are WX; if w_lags=2, then WX, WWX; and so on.
+ lag_q : boolean
+ If True, then include spatial lags of the additional
+ instruments (q).
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ cores : boolean
+ Specifies if multiprocessing is to be used
+ Default: no multiprocessing, cores = False
+ Note: Multiprocessing may not work on all platforms.
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_q : list of strings
+ Names of instruments for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regime variable for use in the output
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ e_pred : array
+ nx1 array of residuals (using reduced form)
+ predy : array
+ nx1 array of predicted y values
+ predy_e : array
+ nx1 array of predicted y values (using reduced form)
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ z : array
+ nxk array of variables (combination of x and yend)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ pr2_e : float
+ Pseudo R squared (squared correlation between y and ypred_e
+ (using reduced form))
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ sig2 : float
+ Sigma squared used in computations (based on filtered
+ residuals)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ std_err : array
+ 1xk array of standard errors of the betas
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_z : list of strings
+ Names of exogenous and endogenous variables for use in
+ output
+ name_q : list of strings
+ Names of external instruments
+ name_h : list of strings
+ Names of all instruments used in ouput
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regimes variable for use in output
+ title : string
+ Name of the regression method used
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ constant_regi : ['one', 'many']
+ Ignored if regimes=False. Constant option for regimes.
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime
+ cols2regi : list, 'all'
+ Ignored if regimes=False. Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all', all the variables vary by regime.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ regime_lag_sep : boolean
+ If True, the spatial parameter for spatial lag is also
+ computed according to different regimes. If False (default),
+ the spatial parameter is fixed accross regimes.
+ kr : int
+ Number of variables/columns to be "regimized" or subject
+ to change by regime. These will result in one parameter
+ estimate by regime for each variable (i.e. nr parameters per
+ variable)
+ kf : int
+ Number of variables/columns to be considered fixed or
+ global across regimes and hence only obtain one parameter
+ estimate
+ nr : int
+ Number of different regimes in the 'regimes' list
+ multi : dictionary
+ Only available when multiple regressions are estimated,
+ i.e. when regime_err_sep=True and no variable is fixed
+ across regimes.
+ Contains all attributes of each individual regression
+
+ References
+ ----------
+
+ .. [1] Kelejian, H.R., Prucha, I.R. (1998) "A generalized spatial
+ two-stage least squares procedure for estimating a spatial autoregressive
+ model with autoregressive disturbances". The Journal of Real State
+ Finance and Economics, 17, 1.
+
+ .. [2] Kelejian, H.R., Prucha, I.R. (1999) "A Generalized Moments
+ Estimator for the Autoregressive Parameter in a Spatial Model".
+ International Economic Review, 40, 2.
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import numpy as np
+ >>> import pysal
+
+ Open data on NCOVR US County Homicides (3085 areas) using pysal.open().
+ This is the DBF associated with the NAT shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path("NAT.dbf"),'r')
+
+ Extract the HR90 column (homicide rates in 1990) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y_var = 'HR90'
+ >>> y = np.array([db.by_col(y_var)]).reshape(3085,1)
+
+ Extract UE90 (unemployment rate) and PS90 (population structure) vectors from
+ the DBF to be used as independent variables in the regression. Other variables
+ can be inserted by adding their names to x_var, such as x_var = ['Var1','Var2','...]
+ Note that PySAL requires this to be an nxj numpy array, where j is the
+ number of independent variables (not including a constant). By default
+ this model adds a vector of ones to the independent variables passed in.
+
+ >>> x_var = ['PS90','UE90']
+ >>> x = np.array([db.by_col(name) for name in x_var]).T
+
+ The different regimes in this data are given according to the North and
+ South dummy (SOUTH).
+
+ >>> r_var = 'SOUTH'
+ >>> regimes = db.by_col(r_var)
+
+ Since we want to run a spatial lag model, we need to specify
+ the spatial weights matrix that includes the spatial configuration of the
+ observations. To do that, we can open an already existing gal file or
+ create a new one. In this case, we will create one from ``NAT.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("NAT.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, this allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ The Combo class runs an SARAR model, that is a spatial lag+error model.
+ In this case we will run a simple version of that, where we have the
+ spatial effects as well as exogenous variables. Since it is a spatial
+ model, we have to pass in the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional.
+
+ >>> model = GM_Combo_Regimes(y, x, regimes, w=w, name_y=y_var, name_x=x_var, name_regimes=r_var, name_ds='NAT')
+
+ Once we have run the model, we can explore a little bit the output. The
+ regression object we have created has many attributes so take your time to
+ discover them. Note that because we are running the classical GMM error
+ model from 1998/99, the spatial parameter is obtained as a point estimate, so
+ although you get a value for it (there are for coefficients under
+ model.betas), you cannot perform inference on it (there are only three
+ values in model.se_betas). Also, this regression uses a two stage least
+ squares estimation method that accounts for the endogeneity created by the
+ spatial lag of the dependent variable. We can have a summary of the
+ output by typing: model.summary
+ Alternatively, we can check the betas:
+
+ >>> print model.name_z
+ ['0_CONSTANT', '0_PS90', '0_UE90', '1_CONSTANT', '1_PS90', '1_UE90', '_Global_W_HR90', 'lambda']
+ >>> print np.around(model.betas,4)
+ [[ 1.4607]
+ [ 0.958 ]
+ [ 0.5658]
+ [ 9.113 ]
+ [ 1.1338]
+ [ 0.6517]
+ [-0.4583]
+ [ 0.6136]]
+
+ And lambda:
+
+ >>> print 'lambda: ', np.around(model.betas[-1], 4)
+ lambda: [ 0.6136]
+
+ This class also allows the user to run a spatial lag+error model with the
+ extra feature of including non-spatial endogenous regressors. This means
+ that, in addition to the spatial lag and error, we consider some of the
+ variables on the right-hand side of the equation as endogenous and we
+ instrument for this. In this case we consider RD90 (resource deprivation)
+ as an endogenous regressor. We use FP89 (families below poverty)
+ for this and hence put it in the instruments parameter, 'q'.
+
+ >>> yd_var = ['RD90']
+ >>> yd = np.array([db.by_col(name) for name in yd_var]).T
+ >>> q_var = ['FP89']
+ >>> q = np.array([db.by_col(name) for name in q_var]).T
+
+ And then we can run and explore the model analogously to the previous combo:
+
+ >>> model = GM_Combo_Regimes(y, x, regimes, yd, q, w=w, name_y=y_var, name_x=x_var, name_yend=yd_var, name_q=q_var, name_regimes=r_var, name_ds='NAT')
+ >>> print model.name_z
+ ['0_CONSTANT', '0_PS90', '0_UE90', '1_CONSTANT', '1_PS90', '1_UE90', '0_RD90', '1_RD90', '_Global_W_HR90', 'lambda']
+ >>> print model.betas
+ [[ 3.41963782]
+ [ 1.04065841]
+ [ 0.16634393]
+ [ 8.86544628]
+ [ 1.85120528]
+ [-0.24908469]
+ [ 2.43014046]
+ [ 3.61645481]
+ [ 0.03308671]
+ [ 0.18684992]]
+ >>> print np.sqrt(model.vm.diagonal())
+ [ 0.53067577 0.13271426 0.06058025 0.76406411 0.17969783 0.07167421
+ 0.28943121 0.25308326 0.06126529]
+ >>> print 'lambda: ', np.around(model.betas[-1], 4)
+ lambda: [ 0.1868]
+ """
+
+ def __init__(self, y, x, regimes, yend=None, q=None,
+ w=None, w_lags=1, lag_q=True, cores=False,
+ constant_regi='many', cols2regi='all',
+ regime_err_sep=False, regime_lag_sep=False,
+ vm=False, name_y=None, name_x=None,
+ name_yend=None, name_q=None,
+ name_w=None, name_ds=None, name_regimes=None):
+
+ n = USER.check_arrays(y, x)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ name_x = USER.set_name_x(name_x, x, constant=True)
+ self.name_y = USER.set_name_y(name_y)
+ name_yend = USER.set_name_yend(name_yend, yend)
+ name_q = USER.set_name_q(name_q, q)
+ name_q.extend(
+ USER.set_name_q_sp(name_x, w_lags, name_q, lag_q, force_all=True))
+
+ cols2regi = REGI.check_cols2regi(
+ constant_regi, cols2regi, x, yend=yend, add_cons=False)
+ self.regimes_set = REGI._get_regimes_set(regimes)
+ self.regimes = regimes
+ USER.check_regimes(self.regimes_set, n, x.shape[1])
+ self.regime_err_sep = regime_err_sep
+ self.regime_lag_sep = regime_lag_sep
+
+ if regime_lag_sep == True:
+ if regime_err_sep == False:
+ raise Exception, "For spatial combo models, if spatial lag is set by regimes (regime_lag_sep=True), spatial error must also be set by regimes (regime_err_sep=True)."
+ add_lag = [w_lags, lag_q]
+ else:
+ if regime_err_sep == True:
+ raise Exception, "For spatial combo models, if spatial error is set by regimes (regime_err_sep=True), all coefficients including lambda (regime_lag_sep=True) must be set by regimes."
+ cols2regi += [False]
+ add_lag = False
+ yend, q = set_endog(y, x, w, yend, q, w_lags, lag_q)
+ name_yend.append(USER.set_name_yend_sp(self.name_y))
+
+ GM_Endog_Error_Regimes.__init__(self, y=y, x=x, yend=yend,
+ q=q, regimes=regimes, w=w, vm=vm, constant_regi=constant_regi,
+ cols2regi=cols2regi, regime_err_sep=regime_err_sep, cores=cores,
+ name_y=self.name_y, name_x=name_x,
+ name_yend=name_yend, name_q=name_q, name_w=name_w,
+ name_ds=name_ds, name_regimes=name_regimes, summ=False, add_lag=add_lag)
+
+ if regime_err_sep != True:
+ self.rho = self.betas[-2]
+ self.predy_e, self.e_pred, warn = sp_att(w, self.y,
+ self.predy, yend[:, -1].reshape(self.n, 1), self.rho)
+ set_warn(self, warn)
+ self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES - REGIMES"
+ SUMMARY.GM_Combo(reg=self, w=w, vm=vm, regimes=True)
+
+
+def _work_error(y, x, regi_ids, r, w, name_ds, name_y, name_x, name_w, name_regimes):
+ w_r, warn = REGI.w_regime(w, regi_ids[r], r, transform=True)
+ y_r = y[regi_ids[r]]
+ x_r = x[regi_ids[r]]
+ x_constant = USER.check_constant(x_r)
+ model = BaseGM_Error(y_r, x_constant, w_r.sparse)
+ set_warn(model, warn)
+ model.w = w_r
+ model.title = "SPATIALLY WEIGHTED LEAST SQUARES ESTIMATION - REGIME %s" % r
+ model.name_ds = name_ds
+ model.name_y = '%s_%s' % (str(r), name_y)
+ model.name_x = ['%s_%s' % (str(r), i) for i in name_x]
+ model.name_w = name_w
+ model.name_regimes = name_regimes
+ return model
+
+
+def _work_endog_error(y, x, yend, q, regi_ids, r, w, name_ds, name_y, name_x, name_yend, name_q, name_w, name_regimes, add_lag):
+ w_r, warn = REGI.w_regime(w, regi_ids[r], r, transform=True)
+ y_r = y[regi_ids[r]]
+ x_r = x[regi_ids[r]]
+ if yend != None:
+ yend_r = yend[regi_ids[r]]
+ q_r = q[regi_ids[r]]
+ else:
+ yend_r, q_r = None, None
+ if add_lag != False:
+ yend_r, q_r = set_endog(
+ y_r, x_r, w_r, yend_r, q_r, add_lag[0], add_lag[1])
+ x_constant = USER.check_constant(x_r)
+ model = BaseGM_Endog_Error(y_r, x_constant, yend_r, q_r, w_r.sparse)
+ set_warn(model, warn)
+ if add_lag != False:
+ model.rho = model.betas[-2]
+ model.predy_e, model.e_pred, warn = sp_att(w_r, model.y,
+ model.predy, model.yend[:, -1].reshape(model.n, 1), model.rho)
+ set_warn(model, warn)
+ model.w = w_r
+ model.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES - REGIME %s" % r
+ model.name_ds = name_ds
+ model.name_y = '%s_%s' % (str(r), name_y)
+ model.name_x = ['%s_%s' % (str(r), i) for i in name_x]
+ model.name_yend = ['%s_%s' % (str(r), i) for i in name_yend]
+ model.name_z = model.name_x + model.name_yend + ['lambda']
+ model.name_q = ['%s_%s' % (str(r), i) for i in name_q]
+ model.name_h = model.name_x + model.name_q
+ model.name_w = name_w
+ model.name_regimes = name_regimes
+ return model
+
+
+def _test():
+ import doctest
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ doctest.testmod()
+ np.set_printoptions(suppress=start_suppress)
+
+if __name__ == '__main__':
+
+ _test()
+ import pysal
+ import numpy as np
+ dbf = pysal.open(pysal.examples.get_path('columbus.dbf'), 'r')
+ y = np.array([dbf.by_col('CRIME')]).T
+ names_to_extract = ['INC']
+ x = np.array([dbf.by_col(name) for name in names_to_extract]).T
+ yd_var = ['HOVAL']
+ yend = np.array([dbf.by_col(name) for name in yd_var]).T
+ q_var = ['DISCBD']
+ q = np.array([dbf.by_col(name) for name in q_var]).T
+ regimes = regimes = dbf.by_col('NSA')
+ w = pysal.open(pysal.examples.get_path("columbus.gal"), 'r').read()
+ w.transform = 'r'
+ model = GM_Error_Regimes(y, x, regimes=regimes, w=w, name_y='crime', name_x=[
+ 'income'], name_regimes='nsa', name_ds='columbus', regime_err_sep=True)
+ print model.summary
diff --git a/pysal/spreg/ml_error.py b/pysal/spreg/ml_error.py
new file mode 100644
index 0000000..b111cdf
--- /dev/null
+++ b/pysal/spreg/ml_error.py
@@ -0,0 +1,511 @@
+"""
+ML Estimation of Spatial Error Model
+"""
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, Serge Rey srey at asu.edu"
+
+import numpy as np
+import numpy.linalg as la
+import pysal as ps
+from pysal.spreg.utils import RegressionPropsY, RegressionPropsVM
+import diagnostics as DIAG
+import user_output as USER
+import summary_output as SUMMARY
+import regimes as REGI
+from w_utils import symmetrize
+try:
+ from scipy.optimize import minimize_scalar
+ minimize_scalar_available = True
+except ImportError:
+ minimize_scalar_available = False
+
+__all__ = ["ML_Error"]
+
+
+class BaseML_Error(RegressionPropsY, RegressionPropsVM, REGI.Regimes_Frame):
+
+ """
+ ML estimation of the spatial error model (note no consistency
+ checks, diagnostics or constants added); Anselin (1988) [1]_
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ w : Sparse matrix
+ Spatial weights sparse matrix
+ method : string
+ if 'full', brute force calculation (full matrix expressions)
+ epsilon : float
+ tolerance criterion in mimimize_scalar function and inverse_product
+ regimes_att : dictionary
+ Dictionary containing elements to be used in case of a regimes model,
+ i.e. 'x' before regimes, 'regimes' list and 'cols2regi'
+
+
+ Attributes
+ ----------
+ betas : array
+ kx1 array of estimated coefficients
+ lam : float
+ estimate of spatial autoregressive coefficient
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant, excluding the rho)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ method : string
+ log Jacobian method
+ if 'full': brute force (full matrix computations)
+ if 'ord' : Ord eigenvalue method
+ epsilon : float
+ tolerance criterion used in minimize_scalar function and inverse_product
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (k+1 x k+1) - includes lambda
+ vm1 : array
+ 2x2 array of variance covariance for lambda, sigma
+ sig2 : float
+ Sigma squared used in computations
+ logll : float
+ maximized log-likelihood (including constant terms)
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal as ps
+ >>> np.set_printoptions(suppress=True) #prevent scientific format
+ >>> db = ps.open(ps.examples.get_path("south.dbf"),'r')
+ >>> y_name = "HR90"
+ >>> y = np.array(db.by_col(y_name))
+ >>> y.shape = (len(y),1)
+ >>> x_names = ["RD90","PS90","UE90","DV90"]
+ >>> x = np.array([db.by_col(var) for var in x_names]).T
+ >>> x = np.hstack((np.ones((len(y),1)),x))
+ >>> ww = ps.open(ps.examples.get_path("south_q.gal"))
+ >>> w = ww.read()
+ >>> ww.close()
+ >>> w.transform = 'r'
+ >>> mlerr = BaseML_Error(y,x,w) #doctest: +SKIP
+ >>> "{0:.6f}".format(mlerr.lam) #doctest: +SKIP
+ '0.299078'
+ >>> np.around(mlerr.betas, decimals=4) #doctest: +SKIP
+ array([[ 6.1492],
+ [ 4.4024],
+ [ 1.7784],
+ [-0.3781],
+ [ 0.4858],
+ [ 0.2991]])
+ >>> "{0:.6f}".format(mlerr.mean_y) #doctest: +SKIP
+ '9.549293'
+ >>> "{0:.6f}".format(mlerr.std_y) #doctest: +SKIP
+ '7.038851'
+ >>> np.diag(mlerr.vm) #doctest: +SKIP
+ array([ 1.06476526, 0.05548248, 0.04544514, 0.00614425, 0.01481356,
+ 0.00143001])
+ >>> "{0:.6f}".format(mlerr.sig2[0][0]) #doctest: +SKIP
+ '32.406854'
+ >>> "{0:.6f}".format(mlerr.logll) #doctest: +SKIP
+ '-4471.407067'
+ >>> mlerr1 = BaseML_Error(y,x,w,method='ord') #doctest: +SKIP
+ >>> "{0:.6f}".format(mlerr1.lam) #doctest: +SKIP
+ '0.299078'
+ >>> np.around(mlerr1.betas, decimals=4) #doctest: +SKIP
+ array([[ 6.1492],
+ [ 4.4024],
+ [ 1.7784],
+ [-0.3781],
+ [ 0.4858],
+ [ 0.2991]])
+ >>> "{0:.6f}".format(mlerr1.mean_y) #doctest: +SKIP
+ '9.549293'
+ >>> "{0:.6f}".format(mlerr1.std_y) #doctest: +SKIP
+ '7.038851'
+ >>> np.around(np.diag(mlerr1.vm), decimals=4) #doctest: +SKIP
+ array([ 1.0648, 0.0555, 0.0454, 0.0061, 0.0148, 0.0014])
+ >>> "{0:.4f}".format(mlerr1.sig2[0][0]) #doctest: +SKIP
+ '32.4069'
+ >>> "{0:.4f}".format(mlerr1.logll) #doctest: +SKIP
+ '-4471.4071'
+
+ References
+ ----------
+
+ .. [1] Anselin, L. (1988) "Spatial Econometrics: Methods and Models".
+ Kluwer Academic Publishers. Dordrecht.
+
+ """
+
+ def __init__(self, y, x, w, method='full', epsilon=0.0000001, regimes_att=None):
+ # set up main regression variables and spatial filters
+ self.y = y
+ if regimes_att:
+ self.x = x.toarray()
+ else:
+ self.x = x
+ self.n, self.k = self.x.shape
+ self.method = method
+ self.epsilon = epsilon
+ W = w.full()[0]
+
+ ylag = ps.lag_spatial(w, self.y)
+ xlag = self.get_x_lag(w, regimes_att)
+
+ # call minimizer using concentrated log-likelihood to get lambda
+ methodML = method.upper()
+ if methodML in ['FULL', 'ORD']:
+ if methodML == 'FULL':
+ res = minimize_scalar(err_c_loglik, 0.0, bounds=(-1.0, 1.0),
+ args=(self.n, self.y, ylag, self.x,
+ xlag, W), method='bounded',
+ tol=epsilon)
+ elif methodML == 'ORD':
+ # check on symmetry structure
+ if w.asymmetry(intrinsic=False) == []:
+ ww = symmetrize(w)
+ WW = ww.todense()
+ evals = la.eigvalsh(WW)
+ else:
+ evals = la.eigvals(W)
+ res = minimize_scalar(
+ err_c_loglik_ord, 0.0, bounds=(-1.0, 1.0),
+ args=(self.n, self.y, ylag, self.x,
+ xlag, evals), method='bounded',
+ tol=epsilon)
+ else:
+ raise Exception, "{0} is an unsupported method".format(method)
+
+ self.lam = res.x
+
+ # compute full log-likelihood, including constants
+ ln2pi = np.log(2.0 * np.pi)
+ llik = -res.fun - self.n / 2.0 * ln2pi - self.n / 2.0
+
+ self.logll = llik
+
+ # b, residuals and predicted values
+
+ ys = self.y - self.lam * ylag
+ xs = self.x - self.lam * xlag
+ xsxs = np.dot(xs.T, xs)
+ xsxsi = np.linalg.inv(xsxs)
+ xsys = np.dot(xs.T, ys)
+ b = np.dot(xsxsi, xsys)
+
+ self.betas = np.vstack((b, self.lam))
+
+ self.u = y - np.dot(self.x, b)
+ self.predy = self.y - self.u
+
+ # residual variance
+
+ self.e_filtered = self.u - self.lam * ps.lag_spatial(w, self.u)
+ self.sig2 = np.dot(self.e_filtered.T, self.e_filtered) / self.n
+
+ # variance-covariance matrix betas
+
+ varb = self.sig2 * xsxsi
+
+ # variance-covariance matrix lambda, sigma
+
+ a = -self.lam * W
+ np.fill_diagonal(a, 1.0)
+ ai = la.inv(a)
+ wai = np.dot(W, ai)
+ tr1 = np.trace(wai)
+
+ wai2 = np.dot(wai, wai)
+ tr2 = np.trace(wai2)
+
+ waiTwai = np.dot(wai.T, wai)
+ tr3 = np.trace(waiTwai)
+
+ v1 = np.vstack((tr2 + tr3,
+ tr1 / self.sig2))
+ v2 = np.vstack((tr1 / self.sig2,
+ self.n / (2.0 * self.sig2 ** 2)))
+
+ v = np.hstack((v1, v2))
+
+ self.vm1 = np.linalg.inv(v)
+
+ # create variance matrix for beta, lambda
+ vv = np.hstack((varb, np.zeros((self.k, 1))))
+ vv1 = np.hstack(
+ (np.zeros((1, self.k)), self.vm1[0, 0] * np.ones((1, 1))))
+
+ self.vm = np.vstack((vv, vv1))
+
+ self._cache = {}
+
+ def get_x_lag(self, w, regimes_att):
+ if regimes_att:
+ xlag = ps.lag_spatial(w, regimes_att['x'])
+ xlag = REGI.Regimes_Frame.__init__(self, xlag,
+ regimes_att['regimes'], constant_regi=None, cols2regi=regimes_att['cols2regi'])[0]
+ xlag = xlag.toarray()
+ else:
+ xlag = ps.lag_spatial(w, self.x)
+ return xlag
+
+
+class ML_Error(BaseML_Error):
+
+ """
+ ML estimation of the spatial lag model with all results and diagnostics;
+ Anselin (1988) [1]_
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ w : Sparse matrix
+ Spatial weights sparse matrix
+ method : string
+ if 'full', brute force calculation (full matrix expressions)
+ ir 'ord', Ord eigenvalue method
+ epsilon : float
+ tolerance criterion in mimimize_scalar function and inverse_product
+ spat_diag : boolean
+ if True, include spatial diagnostics
+ vm : boolean
+ if True, include variance-covariance matrix in summary
+ results
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+
+ Attributes
+ ----------
+ betas : array
+ (k+1)x1 array of estimated coefficients (rho first)
+ lam : float
+ estimate of spatial autoregressive coefficient
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant, excluding lambda)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ method : string
+ log Jacobian method
+ if 'full': brute force (full matrix computations)
+ epsilon : float
+ tolerance criterion used in minimize_scalar function and inverse_product
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ varb : array
+ Variance covariance matrix (k+1 x k+1) - includes var(lambda)
+ vm1 : array
+ variance covariance matrix for lambda, sigma (2 x 2)
+ sig2 : float
+ Sigma squared used in computations
+ logll : float
+ maximized log-likelihood (including constant terms)
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ utu : float
+ Sum of squared residuals
+ std_err : array
+ 1xk array of standard errors of the betas
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ title : string
+ Name of the regression method used
+
+ Examples
+ --------
+
+ >>> import numpy as np
+ >>> import pysal as ps
+ >>> np.set_printoptions(suppress=True) #prevent scientific format
+ >>> db = ps.open(ps.examples.get_path("south.dbf"),'r')
+ >>> ds_name = "south.dbf"
+ >>> y_name = "HR90"
+ >>> y = np.array(db.by_col(y_name))
+ >>> y.shape = (len(y),1)
+ >>> x_names = ["RD90","PS90","UE90","DV90"]
+ >>> x = np.array([db.by_col(var) for var in x_names]).T
+ >>> ww = ps.open(ps.examples.get_path("south_q.gal"))
+ >>> w = ww.read()
+ >>> ww.close()
+ >>> w_name = "south_q.gal"
+ >>> w.transform = 'r'
+ >>> mlerr = ML_Error(y,x,w,name_y=y_name,name_x=x_names,\
+ name_w=w_name,name_ds=ds_name) #doctest: +SKIP
+ >>> np.around(mlerr.betas, decimals=4) #doctest: +SKIP
+ array([[ 6.1492],
+ [ 4.4024],
+ [ 1.7784],
+ [-0.3781],
+ [ 0.4858],
+ [ 0.2991]])
+ >>> "{0:.4f}".format(mlerr.lam) #doctest: +SKIP
+ '0.2991'
+ >>> "{0:.4f}".format(mlerr.mean_y) #doctest: +SKIP
+ '9.5493'
+ >>> "{0:.4f}".format(mlerr.std_y) #doctest: +SKIP
+ '7.0389'
+ >>> np.around(np.diag(mlerr.vm), decimals=4) #doctest: +SKIP
+ array([ 1.0648, 0.0555, 0.0454, 0.0061, 0.0148, 0.0014])
+ >>> np.around(mlerr.sig2, decimals=4) #doctest: +SKIP
+ array([[ 32.4069]])
+ >>> "{0:.4f}".format(mlerr.logll) #doctest: +SKIP
+ '-4471.4071'
+ >>> "{0:.4f}".format(mlerr.aic) #doctest: +SKIP
+ '8952.8141'
+ >>> "{0:.4f}".format(mlerr.schwarz) #doctest: +SKIP
+ '8979.0779'
+ >>> "{0:.4f}".format(mlerr.pr2) #doctest: +SKIP
+ '0.3058'
+ >>> "{0:.4f}".format(mlerr.utu) #doctest: +SKIP
+ '48534.9148'
+ >>> np.around(mlerr.std_err, decimals=4) #doctest: +SKIP
+ array([ 1.0319, 0.2355, 0.2132, 0.0784, 0.1217, 0.0378])
+ >>> np.around(mlerr.z_stat, decimals=4) #doctest: +SKIP
+ array([[ 5.9593, 0. ],
+ [ 18.6902, 0. ],
+ [ 8.3422, 0. ],
+ [ -4.8233, 0. ],
+ [ 3.9913, 0.0001],
+ [ 7.9089, 0. ]])
+ >>> mlerr.name_y #doctest: +SKIP
+ 'HR90'
+ >>> mlerr.name_x #doctest: +SKIP
+ ['CONSTANT', 'RD90', 'PS90', 'UE90', 'DV90', 'lambda']
+ >>> mlerr.name_w #doctest: +SKIP
+ 'south_q.gal'
+ >>> mlerr.name_ds #doctest: +SKIP
+ 'south.dbf'
+ >>> mlerr.title #doctest: +SKIP
+ 'MAXIMUM LIKELIHOOD SPATIAL ERROR (METHOD = FULL)'
+
+
+ References
+ ----------
+
+ .. [1] Anselin, L. (1988) "Spatial Econometrics: Methods and Models".
+ Kluwer Academic Publishers. Dordrecht.
+
+ """
+
+ def __init__(self, y, x, w, method='full', epsilon=0.0000001,
+ spat_diag=False, vm=False, name_y=None, name_x=None,
+ name_w=None, name_ds=None):
+ n = USER.check_arrays(y, x)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ x_constant = USER.check_constant(x)
+ method = method.upper()
+ if method in ['FULL', 'ORD']:
+ BaseML_Error.__init__(self, y=y, x=x_constant,
+ w=w, method=method, epsilon=epsilon)
+ self.title = "MAXIMUM LIKELIHOOD SPATIAL ERROR" + \
+ " (METHOD = " + method + ")"
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_y = USER.set_name_y(name_y)
+ self.name_x = USER.set_name_x(name_x, x)
+ self.name_x.append('lambda')
+ self.name_w = USER.set_name_w(name_w, w)
+ self.aic = DIAG.akaike(reg=self)
+ self.schwarz = DIAG.schwarz(reg=self)
+ SUMMARY.ML_Error(reg=self, w=w, vm=vm, spat_diag=spat_diag)
+ else:
+ raise Exception, "{0} is an unsupported method".format(method)
+
+
+def err_c_loglik(lam, n, y, ylag, x, xlag, W):
+ # concentrated log-lik for error model, no constants, brute force
+ ys = y - lam * ylag
+ xs = x - lam * xlag
+ ysys = np.dot(ys.T, ys)
+ xsxs = np.dot(xs.T, xs)
+ xsxsi = np.linalg.inv(xsxs)
+ xsys = np.dot(xs.T, ys)
+ x1 = np.dot(xsxsi, xsys)
+ x2 = np.dot(xsys.T, x1)
+ ee = ysys - x2
+ sig2 = ee[0][0] / n
+ nlsig2 = (n / 2.0) * np.log(sig2)
+ a = -lam * W
+ np.fill_diagonal(a, 1.0)
+ jacob = np.log(np.linalg.det(a))
+ # this is the negative of the concentrated log lik for minimization
+ clik = nlsig2 - jacob
+ return clik
+
+
+def err_c_loglik_ord(lam, n, y, ylag, x, xlag, evals):
+ # concentrated log-lik for error model, no constants, brute force
+ ys = y - lam * ylag
+ xs = x - lam * xlag
+ ysys = np.dot(ys.T, ys)
+ xsxs = np.dot(xs.T, xs)
+ xsxsi = np.linalg.inv(xsxs)
+ xsys = np.dot(xs.T, ys)
+ x1 = np.dot(xsxsi, xsys)
+ x2 = np.dot(xsys.T, x1)
+ ee = ysys - x2
+ sig2 = ee[0][0] / n
+ nlsig2 = (n / 2.0) * np.log(sig2)
+ revals = lam * evals
+ jacob = np.log(1 - revals).sum()
+ if isinstance(jacob, complex):
+ jacob = jacob.real
+ # this is the negative of the concentrated log lik for minimization
+ clik = nlsig2 - jacob
+ return clik
+
+
+def _test():
+ import doctest
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ doctest.testmod()
+ np.set_printoptions(suppress=start_suppress)
diff --git a/pysal/spreg/ml_error_regimes.py b/pysal/spreg/ml_error_regimes.py
new file mode 100644
index 0000000..fb7f4da
--- /dev/null
+++ b/pysal/spreg/ml_error_regimes.py
@@ -0,0 +1,462 @@
+"""
+ML Estimation of Spatial Error Model
+"""
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, Pedro V. Amaral pedro.amaral at asu.edu"
+
+import pysal
+import numpy as np
+import multiprocessing as mp
+import regimes as REGI
+import user_output as USER
+import summary_output as SUMMARY
+import diagnostics as DIAG
+from utils import set_warn
+from ml_error import BaseML_Error
+from platform import system
+
+__all__ = ["ML_Error_Regimes"]
+
+
+class ML_Error_Regimes(BaseML_Error, REGI.Regimes_Frame):
+
+ """
+ ML estimation of the spatial error model with regimes (note no consistency
+ checks, diagnostics or constants added); Anselin (1988) [1]_
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ constant_regi: ['one', 'many']
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime (default)
+ cols2regi : list, 'all'
+ Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all' (default), all the variables vary by regime.
+ w : Sparse matrix
+ Spatial weights sparse matrix
+ method : string
+ if 'full', brute force calculation (full matrix expressions)
+ epsilon : float
+ tolerance criterion in mimimize_scalar function and inverse_product
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ regime_lag_sep : boolean
+ Always False, kept for consistency in function call, ignored.
+ cores : boolean
+ Specifies if multiprocessing is to be used
+ Default: no multiprocessing, cores = False
+ Note: Multiprocessing may not work on all platforms.
+ spat_diag : boolean
+ if True, include spatial diagnostics
+ vm : boolean
+ if True, include variance-covariance matrix in summary
+ results
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regimes variable for use in output
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ (k+1)x1 array of estimated coefficients (lambda last)
+ lam : float
+ estimate of spatial autoregressive coefficient
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ u : array
+ nx1 array of residuals
+ e_filtered : array
+ nx1 array of spatially filtered residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant, excluding the rho)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ method : string
+ log Jacobian method
+ if 'full': brute force (full matrix computations)
+ epsilon : float
+ tolerance criterion used in minimize_scalar function and inverse_product
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (k+1 x k+1), all coefficients
+ vm1 : array
+ variance covariance matrix for lambda, sigma (2 x 2)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ sig2 : float
+ Sigma squared used in computations
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ logll : float
+ maximized log-likelihood (including constant terms)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ std_err : array
+ 1xk array of standard errors of the betas
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regimes variable for use in output
+ title : string
+ Name of the regression method used
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ constant_regi: ['one', 'many']
+ Ignored if regimes=False. Constant option for regimes.
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime
+ cols2regi : list, 'all'
+ Ignored if regimes=False. Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all', all the variables vary by regime.
+ regime_lag_sep : boolean
+ If True, the spatial parameter for spatial lag is also
+ computed according to different regimes. If False (default),
+ the spatial parameter is fixed accross regimes.
+ kr : int
+ Number of variables/columns to be "regimized" or subject
+ to change by regime. These will result in one parameter
+ estimate by regime for each variable (i.e. nr parameters per
+ variable)
+ kf : int
+ Number of variables/columns to be considered fixed or
+ global across regimes and hence only obtain one parameter
+ estimate
+ nr : int
+ Number of different regimes in the 'regimes' list
+ multi : dictionary
+ Only available when multiple regressions are estimated,
+ i.e. when regime_err_sep=True and no variable is fixed
+ across regimes.
+ Contains all attributes of each individual regression
+
+ References
+ ----------
+
+ .. [1] Anselin, L. (1988) "Spatial Econometrics: Methods and Models".
+ Kluwer Academic Publishers. Dordrecht.
+
+ Example
+ ________
+
+ Open data baltim.dbf using pysal and create the variables matrices and weights matrix.
+
+ >>> import numpy as np
+ >>> import pysal as ps
+ >>> db = ps.open(ps.examples.get_path("baltim.dbf"),'r')
+ >>> ds_name = "baltim.dbf"
+ >>> y_name = "PRICE"
+ >>> y = np.array(db.by_col(y_name)).T
+ >>> y.shape = (len(y),1)
+ >>> x_names = ["NROOM","AGE","SQFT"]
+ >>> x = np.array([db.by_col(var) for var in x_names]).T
+ >>> ww = ps.open(ps.examples.get_path("baltim_q.gal"))
+ >>> w = ww.read()
+ >>> ww.close()
+ >>> w_name = "baltim_q.gal"
+ >>> w.transform = 'r'
+
+ Since in this example we are interested in checking whether the results vary
+ by regimes, we use CITCOU to define whether the location is in the city or
+ outside the city (in the county):
+
+ >>> regimes = db.by_col("CITCOU")
+
+ Now we can run the regression with all parameters:
+
+ >>> mlerr = ML_Error_Regimes(y,x,regimes,w=w,name_y=y_name,name_x=x_names,\
+ name_w=w_name,name_ds=ds_name,name_regimes="CITCOU")
+ >>> np.around(mlerr.betas, decimals=4)
+ array([[ -2.3949],
+ [ 4.8738],
+ [ -0.0291],
+ [ 0.3328],
+ [ 31.7962],
+ [ 2.981 ],
+ [ -0.2371],
+ [ 0.8058],
+ [ 0.6177]])
+ >>> "{0:.6f}".format(mlerr.lam)
+ '0.617707'
+ >>> "{0:.6f}".format(mlerr.mean_y)
+ '44.307180'
+ >>> "{0:.6f}".format(mlerr.std_y)
+ '23.606077'
+ >>> np.around(mlerr.vm1, decimals=4)
+ array([[ 0.005 , -0.3535],
+ [ -0.3535, 441.3039]])
+ >>> np.around(np.diag(mlerr.vm), decimals=4)
+ array([ 58.5055, 2.4295, 0.0072, 0.0639, 80.5925, 3.161 ,
+ 0.012 , 0.0499, 0.005 ])
+ >>> np.around(mlerr.sig2, decimals=4)
+ array([[ 209.6064]])
+ >>> "{0:.6f}".format(mlerr.logll)
+ '-870.333106'
+ >>> "{0:.6f}".format(mlerr.aic)
+ '1756.666212'
+ >>> "{0:.6f}".format(mlerr.schwarz)
+ '1783.481077'
+ >>> mlerr.title
+ 'MAXIMUM LIKELIHOOD SPATIAL ERROR - REGIMES (METHOD = full)'
+ """
+
+ def __init__(self, y, x, regimes, w=None, constant_regi='many',
+ cols2regi='all', method='full', epsilon=0.0000001,
+ regime_err_sep=False, regime_lag_sep=False, cores=False, spat_diag=False,
+ vm=False, name_y=None, name_x=None,
+ name_w=None, name_ds=None, name_regimes=None):
+
+ n = USER.check_arrays(y, x)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ self.constant_regi = constant_regi
+ self.cols2regi = cols2regi
+ self.regime_err_sep = regime_err_sep
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_y = USER.set_name_y(name_y)
+ self.name_w = USER.set_name_w(name_w, w)
+ self.name_regimes = USER.set_name_ds(name_regimes)
+ self.n = n
+ self.y = y
+
+ x_constant = USER.check_constant(x)
+ name_x = USER.set_name_x(name_x, x)
+ self.name_x_r = name_x
+
+ cols2regi = REGI.check_cols2regi(constant_regi, cols2regi, x)
+ self.regimes_set = REGI._get_regimes_set(regimes)
+ self.regimes = regimes
+ USER.check_regimes(self.regimes_set, self.n, x.shape[1])
+ self.regime_err_sep = regime_err_sep
+
+ if regime_err_sep == True:
+ if set(cols2regi) == set([True]):
+ self._error_regimes_multi(y, x, regimes, w, cores,
+ method, epsilon, cols2regi, vm, name_x, spat_diag)
+ else:
+ raise Exception, "All coefficients must vary accross regimes if regime_err_sep = True."
+ else:
+ regimes_att = {}
+ regimes_att['x'] = x_constant
+ regimes_att['regimes'] = regimes
+ regimes_att['cols2regi'] = cols2regi
+ x, name_x = REGI.Regimes_Frame.__init__(self, x_constant,
+ regimes, constant_regi=None, cols2regi=cols2regi,
+ names=name_x)
+
+ BaseML_Error.__init__(
+ self, y=y, x=x, w=w, method=method, epsilon=epsilon, regimes_att=regimes_att)
+
+ self.title = "MAXIMUM LIKELIHOOD SPATIAL ERROR - REGIMES" + \
+ " (METHOD = " + method + ")"
+ self.name_x = USER.set_name_x(name_x, x, constant=True)
+ self.name_x.append('lambda')
+ self.kf += 1 # Adding a fixed k to account for lambda.
+ self.chow = REGI.Chow(self)
+ self.aic = DIAG.akaike(reg=self)
+ self.schwarz = DIAG.schwarz(reg=self)
+ self._cache = {}
+ SUMMARY.ML_Error(
+ reg=self, w=w, vm=vm, spat_diag=spat_diag, regimes=True)
+
+ def _error_regimes_multi(self, y, x, regimes, w, cores,
+ method, epsilon, cols2regi, vm, name_x, spat_diag):
+
+ regi_ids = dict(
+ (r, list(np.where(np.array(regimes) == r)[0])) for r in self.regimes_set)
+ results_p = {}
+ """
+ for r in self.regimes_set:
+ if system() == 'Windows':
+ is_win = True
+ results_p[r] = _work_error(*(y,x,regi_ids,r,w,method,epsilon,self.name_ds,self.name_y,name_x+['lambda'],self.name_w,self.name_regimes))
+ else:
+ pool = mp.Pool(cores)
+ results_p[r] = pool.apply_async(_work_error,args=(y,x,regi_ids,r,w,method,epsilon,self.name_ds,self.name_y,name_x+['lambda'],self.name_w,self.name_regimes, ))
+ is_win = False
+ """
+ for r in self.regimes_set:
+ if cores:
+ pool = mp.Pool(None)
+ results_p[r] = pool.apply_async(_work_error, args=(
+ y, x, regi_ids, r, w, method, epsilon, self.name_ds, self.name_y, name_x + ['lambda'], self.name_w, self.name_regimes, ))
+ else:
+ results_p[r] = _work_error(
+ *(y, x, regi_ids, r, w, method, epsilon, self.name_ds, self.name_y, name_x + ['lambda'], self.name_w, self.name_regimes))
+
+ self.kryd = 0
+ self.kr = len(cols2regi) + 1
+ self.kf = 0
+ self.nr = len(self.regimes_set)
+ self.vm = np.zeros((self.nr * self.kr, self.nr * self.kr), float)
+ self.betas = np.zeros((self.nr * self.kr, 1), float)
+ self.u = np.zeros((self.n, 1), float)
+ self.predy = np.zeros((self.n, 1), float)
+ self.e_filtered = np.zeros((self.n, 1), float)
+ self.name_y, self.name_x = [], []
+ """
+ if not is_win:
+ pool.close()
+ pool.join()
+ """
+ if cores:
+ pool.close()
+ pool.join()
+
+ results = {}
+ counter = 0
+ for r in self.regimes_set:
+ """
+ if is_win:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+ """
+ if not cores:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+
+ self.vm[(counter * self.kr):((counter + 1) * self.kr),
+ (counter * self.kr):((counter + 1) * self.kr)] = results[r].vm
+ self.betas[
+ (counter * self.kr):((counter + 1) * self.kr), ] = results[r].betas
+ self.u[regi_ids[r], ] = results[r].u
+ self.predy[regi_ids[r], ] = results[r].predy
+ self.e_filtered[regi_ids[r], ] = results[r].e_filtered
+ self.name_y += results[r].name_y
+ self.name_x += results[r].name_x
+ counter += 1
+ self.chow = REGI.Chow(self)
+ self.multi = results
+ SUMMARY.ML_Error_multi(
+ reg=self, multireg=self.multi, vm=vm, spat_diag=spat_diag, regimes=True, w=w)
+
+
+def _work_error(y, x, regi_ids, r, w, method, epsilon, name_ds, name_y, name_x, name_w, name_regimes):
+ w_r, warn = REGI.w_regime(w, regi_ids[r], r, transform=True)
+ y_r = y[regi_ids[r]]
+ x_r = x[regi_ids[r]]
+ x_constant = USER.check_constant(x_r)
+ model = BaseML_Error(
+ y=y_r, x=x_constant, w=w_r, method=method, epsilon=epsilon)
+ set_warn(model, warn)
+ model.w = w_r
+ model.title = "MAXIMUM LIKELIHOOD SPATIAL ERROR - REGIME " + \
+ str(r) + " (METHOD = " + method + ")"
+ model.name_ds = name_ds
+ model.name_y = '%s_%s' % (str(r), name_y)
+ model.name_x = ['%s_%s' % (str(r), i) for i in name_x]
+ model.name_w = name_w
+ model.name_regimes = name_regimes
+ model.aic = DIAG.akaike(reg=model)
+ model.schwarz = DIAG.schwarz(reg=model)
+ return model
+
+
+def _test():
+ import doctest
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ doctest.testmod()
+ np.set_printoptions(suppress=start_suppress)
+
+if __name__ == "__main__":
+ _test()
+ import numpy as np
+ import pysal as ps
+
+ db = ps.open(ps.examples.get_path("baltim.dbf"), 'r')
+ ds_name = "baltim.dbf"
+ y_name = "PRICE"
+ y = np.array(db.by_col(y_name)).T
+ y.shape = (len(y), 1)
+ x_names = ["NROOM", "NBATH", "PATIO", "FIREPL",
+ "AC", "GAR", "AGE", "LOTSZ", "SQFT"]
+ x = np.array([db.by_col(var) for var in x_names]).T
+ ww = ps.open(ps.examples.get_path("baltim_q.gal"))
+ w = ww.read()
+ ww.close()
+ w_name = "baltim_q.gal"
+ w.transform = 'r'
+
+ regimes = []
+ y_coord = np.array(db.by_col("Y"))
+ for i in y_coord:
+ if i > 544.5:
+ regimes.append("North")
+ else:
+ regimes.append("South")
+
+ mlerror = ML_Error_Regimes(y, x, regimes, w=w, method='full', name_y=y_name,
+ name_x=x_names, name_w=w_name, name_ds=ds_name, regime_err_sep=False,
+ name_regimes="North")
+ print mlerror.summary
diff --git a/pysal/spreg/ml_lag.py b/pysal/spreg/ml_lag.py
new file mode 100644
index 0000000..7aac794
--- /dev/null
+++ b/pysal/spreg/ml_lag.py
@@ -0,0 +1,593 @@
+"""
+ML Estimation of Spatial Lag Model
+"""
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, Serge Rey srey at asu.edu"
+
+import numpy as np
+import numpy.linalg as la
+import pysal as ps
+from pysal.spreg.utils import RegressionPropsY, RegressionPropsVM, inverse_prod
+from utils import spdot
+import diagnostics as DIAG
+import user_output as USER
+import summary_output as SUMMARY
+from w_utils import symmetrize
+try:
+ from scipy.optimize import minimize_scalar
+ minimize_scalar_available = True
+except ImportError:
+ minimize_scalar_available = False
+
+__all__ = ["ML_Lag"]
+
+
+class BaseML_Lag(RegressionPropsY, RegressionPropsVM):
+
+ """
+ ML estimation of the spatial lag model (note no consistency
+ checks, diagnostics or constants added); Anselin (1988) [1]_
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ w : pysal W object
+ Spatial weights object
+ method : string
+ if 'full', brute force calculation (full matrix expressions)
+ if 'ord', Ord eigenvalue method
+ epsilon : float
+ tolerance criterion in mimimize_scalar function and inverse_product
+
+ Attributes
+ ----------
+ betas : array
+ (k+1)x1 array of estimated coefficients (rho first)
+ rho : float
+ estimate of spatial autoregressive coefficient
+ u : array
+ nx1 array of residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant, excluding the rho)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ method : string
+ log Jacobian method
+ if 'full': brute force (full matrix computations)
+ if 'ord' : Ord eigenvalue method
+ epsilon : float
+ tolerance criterion used in minimize_scalar function and inverse_product
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (k+1 x k+1)
+ vm1 : array
+ Variance covariance matrix (k+2 x k+2) includes sigma2
+ sig2 : float
+ Sigma squared used in computations
+ logll : float
+ maximized log-likelihood (including constant terms)
+ predy_e : array
+ predicted values from reduced form
+ e_pred : array
+ prediction errors using reduced form predicted values
+
+
+ Examples
+ --------
+
+ >>> import numpy as np
+ >>> import pysal as ps
+ >>> db = ps.open(ps.examples.get_path("baltim.dbf"),'r')
+ >>> ds_name = "baltim.dbf"
+ >>> y_name = "PRICE"
+ >>> y = np.array(db.by_col(y_name)).T
+ >>> y.shape = (len(y),1)
+ >>> x_names = ["NROOM","NBATH","PATIO","FIREPL","AC","GAR","AGE","LOTSZ","SQFT"]
+ >>> x = np.array([db.by_col(var) for var in x_names]).T
+ >>> x = np.hstack((np.ones((len(y),1)),x))
+ >>> ww = ps.open(ps.examples.get_path("baltim_q.gal"))
+ >>> w = ww.read()
+ >>> ww.close()
+ >>> w.transform = 'r'
+ >>> w_name = "baltim_q.gal"
+ >>> mllag = BaseML_Lag(y,x,w,method='ord') #doctest: +SKIP
+ >>> "{0:.6f}".format(mllag.rho) #doctest: +SKIP
+ '0.425885'
+ >>> np.around(mllag.betas, decimals=4) #doctest: +SKIP
+ array([[ 4.3675],
+ [ 0.7502],
+ [ 5.6116],
+ [ 7.0497],
+ [ 7.7246],
+ [ 6.1231],
+ [ 4.6375],
+ [-0.1107],
+ [ 0.0679],
+ [ 0.0794],
+ [ 0.4259]])
+ >>> "{0:.6f}".format(mllag.mean_y) #doctest: +SKIP
+ '44.307180'
+ >>> "{0:.6f}".format(mllag.std_y) #doctest: +SKIP
+ '23.606077'
+ >>> np.around(np.diag(mllag.vm1), decimals=4) #doctest: +SKIP
+ array([ 23.8716, 1.1222, 3.0593, 7.3416, 5.6695, 5.4698,
+ 2.8684, 0.0026, 0.0002, 0.0266, 0.0032, 220.1292])
+ >>> np.around(np.diag(mllag.vm), decimals=4) #doctest: +SKIP
+ array([ 23.8716, 1.1222, 3.0593, 7.3416, 5.6695, 5.4698,
+ 2.8684, 0.0026, 0.0002, 0.0266, 0.0032])
+ >>> "{0:.6f}".format(mllag.sig2) #doctest: +SKIP
+ '151.458698'
+ >>> "{0:.6f}".format(mllag.logll) #doctest: +SKIP
+ '-832.937174'
+ >>> mllag = BaseML_Lag(y,x,w) #doctest: +SKIP
+ >>> "{0:.6f}".format(mllag.rho) #doctest: +SKIP
+ '0.425885'
+ >>> np.around(mllag.betas, decimals=4) #doctest: +SKIP
+ array([[ 4.3675],
+ [ 0.7502],
+ [ 5.6116],
+ [ 7.0497],
+ [ 7.7246],
+ [ 6.1231],
+ [ 4.6375],
+ [-0.1107],
+ [ 0.0679],
+ [ 0.0794],
+ [ 0.4259]])
+ >>> "{0:.6f}".format(mllag.mean_y) #doctest: +SKIP
+ '44.307180'
+ >>> "{0:.6f}".format(mllag.std_y) #doctest: +SKIP
+ '23.606077'
+ >>> np.around(np.diag(mllag.vm1), decimals=4) #doctest: +SKIP
+ array([ 23.8716, 1.1222, 3.0593, 7.3416, 5.6695, 5.4698,
+ 2.8684, 0.0026, 0.0002, 0.0266, 0.0032, 220.1292])
+ >>> np.around(np.diag(mllag.vm), decimals=4) #doctest: +SKIP
+ array([ 23.8716, 1.1222, 3.0593, 7.3416, 5.6695, 5.4698,
+ 2.8684, 0.0026, 0.0002, 0.0266, 0.0032])
+ >>> "{0:.6f}".format(mllag.sig2) #doctest: +SKIP
+ '151.458698'
+ >>> "{0:.6f}".format(mllag.logll) #doctest: +SKIP
+ '-832.937174'
+
+
+ References
+ ----------
+
+ .. [1] Anselin, L. (1988) "Spatial Econometrics: Methods and Models".
+ Kluwer Academic Publishers. Dordrecht.
+
+ """
+
+ def __init__(self, y, x, w, method='full', epsilon=0.0000001):
+ # set up main regression variables and spatial filters
+ self.y = y
+ self.x = x
+ self.n, self.k = self.x.shape
+ self.method = method
+ self.epsilon = epsilon
+ W = w.full()[0]
+ ylag = ps.lag_spatial(w, y)
+ # b0, b1, e0 and e1
+ xtx = spdot(self.x.T, self.x)
+ xtxi = la.inv(xtx)
+ xty = spdot(self.x.T, self.y)
+ xtyl = spdot(self.x.T, ylag)
+ b0 = np.dot(xtxi, xty)
+ b1 = np.dot(xtxi, xtyl)
+ e0 = self.y - spdot(x, b0)
+ e1 = ylag - spdot(x, b1)
+ methodML = method.upper()
+ # call minimizer using concentrated log-likelihood to get rho
+ if methodML in ['FULL', 'ORD']:
+ if methodML == 'FULL':
+ res = minimize_scalar(lag_c_loglik, 0.0, bounds=(-1.0, 1.0),
+ args=(
+ self.n, e0, e1, W), method='bounded',
+ tol=epsilon)
+ elif methodML == 'ORD':
+ # check on symmetry structure
+ if w.asymmetry(intrinsic=False) == []:
+ ww = symmetrize(w)
+ WW = ww.todense()
+ evals = la.eigvalsh(WW)
+ else:
+ evals = la.eigvals(W)
+ res = minimize_scalar(lag_c_loglik_ord, 0.0, bounds=(-1.0, 1.0),
+ args=(
+ self.n, e0, e1, evals), method='bounded',
+ tol=epsilon)
+ else:
+ # program will crash, need to catch
+ print "{0} is an unsupported method".format(methodML)
+ self = None
+ return
+
+ self.rho = res.x[0][0]
+
+ # compute full log-likelihood, including constants
+ ln2pi = np.log(2.0 * np.pi)
+ llik = -res.fun - self.n / 2.0 * ln2pi - self.n / 2.0
+ self.logll = llik[0][0]
+
+ # b, residuals and predicted values
+
+ b = b0 - self.rho * b1
+ self.betas = np.vstack((b, self.rho)) # rho added as last coefficient
+ self.u = e0 - self.rho * e1
+ self.predy = self.y - self.u
+
+ xb = spdot(x, b)
+
+ self.predy_e = inverse_prod(
+ w.sparse, xb, self.rho, inv_method="power_exp", threshold=epsilon)
+ self.e_pred = self.y - self.predy_e
+
+ # residual variance
+ self._cache = {}
+ self.sig2 = self.sig2n # no allowance for division by n-k
+
+ # information matrix
+ a = -self.rho * W
+ np.fill_diagonal(a, 1.0)
+ ai = la.inv(a)
+ wai = np.dot(W, ai)
+ tr1 = np.trace(wai)
+
+ wai2 = np.dot(wai, wai)
+ tr2 = np.trace(wai2)
+
+ waiTwai = np.dot(wai.T, wai)
+ tr3 = np.trace(waiTwai)
+
+ wpredy = ps.lag_spatial(w, self.predy_e)
+ wpyTwpy = np.dot(wpredy.T, wpredy)
+ xTwpy = spdot(x.T, wpredy)
+
+ # order of variables is beta, rho, sigma2
+
+ v1 = np.vstack(
+ (xtx / self.sig2, xTwpy.T / self.sig2, np.zeros((1, self.k))))
+ v2 = np.vstack(
+ (xTwpy / self.sig2, tr2 + tr3 + wpyTwpy / self.sig2, tr1 / self.sig2))
+ v3 = np.vstack(
+ (np.zeros((self.k, 1)), tr1 / self.sig2, self.n / (2.0 * self.sig2 ** 2)))
+
+ v = np.hstack((v1, v2, v3))
+
+ self.vm1 = la.inv(v) # vm1 includes variance for sigma2
+ self.vm = self.vm1[:-1, :-1] # vm is for coefficients only
+
+
+class ML_Lag(BaseML_Lag):
+
+ """
+ ML estimation of the spatial lag model with all results and diagnostics;
+ Anselin (1988) [1]_
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ w : pysal W object
+ Spatial weights object
+ method : string
+ if 'full', brute force calculation (full matrix expressions)
+ if 'ord', Ord eigenvalue method
+ epsilon : float
+ tolerance criterion in mimimize_scalar function and inverse_product
+ spat_diag : boolean
+ if True, include spatial diagnostics
+ vm : boolean
+ if True, include variance-covariance matrix in summary
+ results
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+
+ Attributes
+ ----------
+ betas : array
+ (k+1)x1 array of estimated coefficients (rho first)
+ rho : float
+ estimate of spatial autoregressive coefficient
+ u : array
+ nx1 array of residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant, excluding the rho)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ method : string
+ log Jacobian method
+ if 'full': brute force (full matrix computations)
+ epsilon : float
+ tolerance criterion used in minimize_scalar function and inverse_product
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (k+1 x k+1), all coefficients
+ vm1 : array
+ Variance covariance matrix (k+2 x k+2), includes sig2
+ sig2 : float
+ Sigma squared used in computations
+ logll : float
+ maximized log-likelihood (including constant terms)
+ aic : float
+ Akaike information criterion
+ schwarz : float
+ Schwarz criterion
+ predy_e : array
+ predicted values from reduced form
+ e_pred : array
+ prediction errors using reduced form predicted values
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ pr2_e : float
+ Pseudo R squared (squared correlation between y and ypred_e
+ (using reduced form))
+ utu : float
+ Sum of squared residuals
+ std_err : array
+ 1xk array of standard errors of the betas
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ title : string
+ Name of the regression method used
+
+ Examples
+ ________
+
+ >>> import numpy as np
+ >>> import pysal as ps
+ >>> db = ps.open(ps.examples.get_path("baltim.dbf"),'r')
+ >>> ds_name = "baltim.dbf"
+ >>> y_name = "PRICE"
+ >>> y = np.array(db.by_col(y_name)).T
+ >>> y.shape = (len(y),1)
+ >>> x_names = ["NROOM","NBATH","PATIO","FIREPL","AC","GAR","AGE","LOTSZ","SQFT"]
+ >>> x = np.array([db.by_col(var) for var in x_names]).T
+ >>> ww = ps.open(ps.examples.get_path("baltim_q.gal"))
+ >>> w = ww.read()
+ >>> ww.close()
+ >>> w_name = "baltim_q.gal"
+ >>> w.transform = 'r'
+ >>> mllag = ML_Lag(y,x,w,name_y=y_name,name_x=x_names,\
+ name_w=w_name,name_ds=ds_name) #doctest: +SKIP
+ >>> np.around(mllag.betas, decimals=4) #doctest: +SKIP
+ array([[ 4.3675],
+ [ 0.7502],
+ [ 5.6116],
+ [ 7.0497],
+ [ 7.7246],
+ [ 6.1231],
+ [ 4.6375],
+ [-0.1107],
+ [ 0.0679],
+ [ 0.0794],
+ [ 0.4259]])
+ >>> "{0:.6f}".format(mllag.rho) #doctest: +SKIP
+ '0.425885'
+ >>> "{0:.6f}".format(mllag.mean_y) #doctest: +SKIP
+ '44.307180'
+ >>> "{0:.6f}".format(mllag.std_y) #doctest: +SKIP
+ '23.606077'
+ >>> np.around(np.diag(mllag.vm1), decimals=4) #doctest: +SKIP
+ array([ 23.8716, 1.1222, 3.0593, 7.3416, 5.6695, 5.4698,
+ 2.8684, 0.0026, 0.0002, 0.0266, 0.0032, 220.1292])
+ >>> np.around(np.diag(mllag.vm), decimals=4) #doctest: +SKIP
+ array([ 23.8716, 1.1222, 3.0593, 7.3416, 5.6695, 5.4698,
+ 2.8684, 0.0026, 0.0002, 0.0266, 0.0032])
+ >>> "{0:.6f}".format(mllag.sig2) #doctest: +SKIP
+ '151.458698'
+ >>> "{0:.6f}".format(mllag.logll) #doctest: +SKIP
+ '-832.937174'
+ >>> "{0:.6f}".format(mllag.aic) #doctest: +SKIP
+ '1687.874348'
+ >>> "{0:.6f}".format(mllag.schwarz) #doctest: +SKIP
+ '1724.744787'
+ >>> "{0:.6f}".format(mllag.pr2) #doctest: +SKIP
+ '0.727081'
+ >>> "{0:.4f}".format(mllag.pr2_e) #doctest: +SKIP
+ '0.7062'
+ >>> "{0:.4f}".format(mllag.utu) #doctest: +SKIP
+ '31957.7853'
+ >>> np.around(mllag.std_err, decimals=4) #doctest: +SKIP
+ array([ 4.8859, 1.0593, 1.7491, 2.7095, 2.3811, 2.3388, 1.6936,
+ 0.0508, 0.0146, 0.1631, 0.057 ])
+ >>> np.around(mllag.z_stat, decimals=4) #doctest: +SKIP
+ array([[ 0.8939, 0.3714],
+ [ 0.7082, 0.4788],
+ [ 3.2083, 0.0013],
+ [ 2.6018, 0.0093],
+ [ 3.2442, 0.0012],
+ [ 2.6181, 0.0088],
+ [ 2.7382, 0.0062],
+ [-2.178 , 0.0294],
+ [ 4.6487, 0. ],
+ [ 0.4866, 0.6266],
+ [ 7.4775, 0. ]])
+ >>> mllag.name_y #doctest: +SKIP
+ 'PRICE'
+ >>> mllag.name_x #doctest: +SKIP
+ ['CONSTANT', 'NROOM', 'NBATH', 'PATIO', 'FIREPL', 'AC', 'GAR', 'AGE', 'LOTSZ', 'SQFT', 'W_PRICE']
+ >>> mllag.name_w #doctest: +SKIP
+ 'baltim_q.gal'
+ >>> mllag.name_ds #doctest: +SKIP
+ 'baltim.dbf'
+ >>> mllag.title #doctest: +SKIP
+ 'MAXIMUM LIKELIHOOD SPATIAL LAG (METHOD = FULL)'
+ >>> mllag = ML_Lag(y,x,w,method='ord',name_y=y_name,name_x=x_names,\
+ name_w=w_name,name_ds=ds_name) #doctest: +SKIP
+ >>> np.around(mllag.betas, decimals=4) #doctest: +SKIP
+ array([[ 4.3675],
+ [ 0.7502],
+ [ 5.6116],
+ [ 7.0497],
+ [ 7.7246],
+ [ 6.1231],
+ [ 4.6375],
+ [-0.1107],
+ [ 0.0679],
+ [ 0.0794],
+ [ 0.4259]])
+ >>> "{0:.6f}".format(mllag.rho) #doctest: +SKIP
+ '0.425885'
+ >>> "{0:.6f}".format(mllag.mean_y) #doctest: +SKIP
+ '44.307180'
+ >>> "{0:.6f}".format(mllag.std_y) #doctest: +SKIP
+ '23.606077'
+ >>> np.around(np.diag(mllag.vm1), decimals=4) #doctest: +SKIP
+ array([ 23.8716, 1.1222, 3.0593, 7.3416, 5.6695, 5.4698,
+ 2.8684, 0.0026, 0.0002, 0.0266, 0.0032, 220.1292])
+ >>> np.around(np.diag(mllag.vm), decimals=4) #doctest: +SKIP
+ array([ 23.8716, 1.1222, 3.0593, 7.3416, 5.6695, 5.4698,
+ 2.8684, 0.0026, 0.0002, 0.0266, 0.0032])
+ >>> "{0:.6f}".format(mllag.sig2) #doctest: +SKIP
+ '151.458698'
+ >>> "{0:.6f}".format(mllag.logll) #doctest: +SKIP
+ '-832.937174'
+ >>> "{0:.6f}".format(mllag.aic) #doctest: +SKIP
+ '1687.874348'
+ >>> "{0:.6f}".format(mllag.schwarz) #doctest: +SKIP
+ '1724.744787'
+ >>> "{0:.6f}".format(mllag.pr2) #doctest: +SKIP
+ '0.727081'
+ >>> "{0:.6f}".format(mllag.pr2_e) #doctest: +SKIP
+ '0.706198'
+ >>> "{0:.4f}".format(mllag.utu) #doctest: +SKIP
+ '31957.7853'
+ >>> np.around(mllag.std_err, decimals=4) #doctest: +SKIP
+ array([ 4.8859, 1.0593, 1.7491, 2.7095, 2.3811, 2.3388, 1.6936,
+ 0.0508, 0.0146, 0.1631, 0.057 ])
+ >>> np.around(mllag.z_stat, decimals=4) #doctest: +SKIP
+ array([[ 0.8939, 0.3714],
+ [ 0.7082, 0.4788],
+ [ 3.2083, 0.0013],
+ [ 2.6018, 0.0093],
+ [ 3.2442, 0.0012],
+ [ 2.6181, 0.0088],
+ [ 2.7382, 0.0062],
+ [-2.178 , 0.0294],
+ [ 4.6487, 0. ],
+ [ 0.4866, 0.6266],
+ [ 7.4775, 0. ]])
+ >>> mllag.name_y #doctest: +SKIP
+ 'PRICE'
+ >>> mllag.name_x #doctest: +SKIP
+ ['CONSTANT', 'NROOM', 'NBATH', 'PATIO', 'FIREPL', 'AC', 'GAR', 'AGE', 'LOTSZ', 'SQFT', 'W_PRICE']
+ >>> mllag.name_w #doctest: +SKIP
+ 'baltim_q.gal'
+ >>> mllag.name_ds #doctest: +SKIP
+ 'baltim.dbf'
+ >>> mllag.title #doctest: +SKIP
+ 'MAXIMUM LIKELIHOOD SPATIAL LAG (METHOD = ORD)'
+
+ References
+ ----------
+
+ .. [1] Anselin, L. (1988) "Spatial Econometrics: Methods and Models".
+ Kluwer Academic Publishers. Dordrecht.
+
+ """
+
+ def __init__(self, y, x, w, method='full', epsilon=0.0000001,
+ spat_diag=False, vm=False, name_y=None, name_x=None,
+ name_w=None, name_ds=None):
+ n = USER.check_arrays(y, x)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ x_constant = USER.check_constant(x)
+ method = method.upper()
+ if method in ['FULL', 'ORD']:
+ BaseML_Lag.__init__(
+ self, y=y, x=x_constant, w=w, method=method, epsilon=epsilon)
+ # increase by 1 to have correct aic and sc, include rho in count
+ self.k += 1
+ self.title = "MAXIMUM LIKELIHOOD SPATIAL LAG" + \
+ " (METHOD = " + method + ")"
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_y = USER.set_name_y(name_y)
+ self.name_x = USER.set_name_x(name_x, x)
+ name_ylag = USER.set_name_yend_sp(self.name_y)
+ self.name_x.append(name_ylag) # rho changed to last position
+ self.name_w = USER.set_name_w(name_w, w)
+ self.aic = DIAG.akaike(reg=self)
+ self.schwarz = DIAG.schwarz(reg=self)
+ SUMMARY.ML_Lag(reg=self, w=w, vm=vm, spat_diag=spat_diag)
+ else:
+ raise Exception, "{0} is an unsupported method".format(method)
+
+def lag_c_loglik(rho, n, e0, e1, W):
+ # concentrated log-lik for lag model, no constants, brute force
+ er = e0 - rho * e1
+ sig2 = np.dot(er.T, er) / n
+ nlsig2 = (n / 2.0) * np.log(sig2)
+ a = -rho * W
+ np.fill_diagonal(a, 1.0)
+ jacob = np.log(np.linalg.det(a))
+ # this is the negative of the concentrated log lik for minimization
+ clik = nlsig2 - jacob
+ return clik
+
+
+def lag_c_loglik_ord(rho, n, e0, e1, evals):
+ # concentrated log-lik for lag model, no constants, Ord eigenvalue method
+ er = e0 - rho * e1
+ sig2 = np.dot(er.T, er) / n
+ nlsig2 = (n / 2.0) * np.log(sig2)
+ revals = rho * evals
+ jacob = np.log(1 - revals).sum()
+ if isinstance(jacob, complex):
+ jacob = jacob.real
+ # this is the negative of the concentrated log lik for minimization
+ clik = nlsig2 - jacob
+ return clik
+
+def _test():
+ import doctest
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ doctest.testmod()
+ np.set_printoptions(suppress=start_suppress)
+
diff --git a/pysal/spreg/ml_lag_regimes.py b/pysal/spreg/ml_lag_regimes.py
new file mode 100644
index 0000000..b0b63c8
--- /dev/null
+++ b/pysal/spreg/ml_lag_regimes.py
@@ -0,0 +1,484 @@
+"""
+ML Estimation of Spatial Lag Model with Regimes
+"""
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, Pedro V. Amaral pedro.amaral at asu.edu"
+
+import pysal
+import numpy as np
+import regimes as REGI
+import user_output as USER
+import summary_output as SUMMARY
+import diagnostics as DIAG
+import multiprocessing as mp
+from ml_lag import BaseML_Lag
+from utils import set_warn
+from platform import system
+
+__all__ = ["ML_Lag_Regimes"]
+
+
+class ML_Lag_Regimes(BaseML_Lag, REGI.Regimes_Frame):
+
+ """
+ ML estimation of the spatial lag model with regimes (note no consistency
+ checks, diagnostics or constants added); Anselin (1988) [1]_
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ constant_regi: ['one', 'many']
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime (default)
+ cols2regi : list, 'all'
+ Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all' (default), all the variables vary by regime.
+ w : Sparse matrix
+ Spatial weights sparse matrix
+ method : string
+ if 'full', brute force calculation (full matrix expressions)
+ if 'ord', Ord eigenvalue method
+ epsilon : float
+ tolerance criterion in mimimize_scalar function and inverse_product
+ regime_lag_sep: boolean
+ If True, the spatial parameter for spatial lag is also
+ computed according to different regimes. If False (default),
+ the spatial parameter is fixed accross regimes.
+ cores : boolean
+ Specifies if multiprocessing is to be used
+ Default: no multiprocessing, cores = False
+ Note: Multiprocessing may not work on all platforms.
+ spat_diag : boolean
+ if True, include spatial diagnostics
+ vm : boolean
+ if True, include variance-covariance matrix in summary
+ results
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regimes variable for use in output
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ (k+1)x1 array of estimated coefficients (rho first)
+ rho : float
+ estimate of spatial autoregressive coefficient
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ u : array
+ nx1 array of residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant, excluding the rho)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ method : string
+ log Jacobian method
+ if 'full': brute force (full matrix computations)
+ epsilon : float
+ tolerance criterion used in minimize_scalar function and inverse_product
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (k+1 x k+1), all coefficients
+ vm1 : array
+ Variance covariance matrix (k+2 x k+2), includes sig2
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ sig2 : float
+ Sigma squared used in computations
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ logll : float
+ maximized log-likelihood (including constant terms)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ aic : float
+ Akaike information criterion
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ schwarz : float
+ Schwarz criterion
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ predy_e : array
+ predicted values from reduced form
+ e_pred : array
+ prediction errors using reduced form predicted values
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ pr2_e : float
+ Pseudo R squared (squared correlation between y and ypred_e
+ (using reduced form))
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ std_err : array
+ 1xk array of standard errors of the betas
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regimes variable for use in output
+ title : string
+ Name of the regression method used
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ constant_regi: ['one', 'many']
+ Ignored if regimes=False. Constant option for regimes.
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime
+ cols2regi : list, 'all'
+ Ignored if regimes=False. Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all', all the variables vary by regime.
+ regime_lag_sep : boolean
+ If True, the spatial parameter for spatial lag is also
+ computed according to different regimes. If False (default),
+ the spatial parameter is fixed accross regimes.
+ regime_err_sep : boolean
+ always set to False - kept for compatibility with other
+ regime models
+ kr : int
+ Number of variables/columns to be "regimized" or subject
+ to change by regime. These will result in one parameter
+ estimate by regime for each variable (i.e. nr parameters per
+ variable)
+ kf : int
+ Number of variables/columns to be considered fixed or
+ global across regimes and hence only obtain one parameter
+ estimate
+ nr : int
+ Number of different regimes in the 'regimes' list
+ multi : dictionary
+ Only available when multiple regressions are estimated,
+ i.e. when regime_err_sep=True and no variable is fixed
+ across regimes.
+ Contains all attributes of each individual regression
+
+ References
+ ----------
+
+ .. [1] Anselin, L. (1988) "Spatial Econometrics: Methods and Models".
+ Kluwer Academic Publishers. Dordrecht.
+
+ Example
+ ________
+
+ Open data baltim.dbf using pysal and create the variables matrices and weights matrix.
+
+ >>> import numpy as np
+ >>> import pysal as ps
+ >>> db = ps.open(ps.examples.get_path("baltim.dbf"),'r')
+ >>> ds_name = "baltim.dbf"
+ >>> y_name = "PRICE"
+ >>> y = np.array(db.by_col(y_name)).T
+ >>> y.shape = (len(y),1)
+ >>> x_names = ["NROOM","AGE","SQFT"]
+ >>> x = np.array([db.by_col(var) for var in x_names]).T
+ >>> ww = ps.open(ps.examples.get_path("baltim_q.gal"))
+ >>> w = ww.read()
+ >>> ww.close()
+ >>> w_name = "baltim_q.gal"
+ >>> w.transform = 'r'
+
+ Since in this example we are interested in checking whether the results vary
+ by regimes, we use CITCOU to define whether the location is in the city or
+ outside the city (in the county):
+
+ >>> regimes = db.by_col("CITCOU")
+
+ Now we can run the regression with all parameters:
+
+ >>> mllag = ML_Lag_Regimes(y,x,regimes,w=w,name_y=y_name,name_x=x_names,\
+ name_w=w_name,name_ds=ds_name,name_regimes="CITCOU")
+ >>> np.around(mllag.betas, decimals=4)
+ array([[-15.0059],
+ [ 4.496 ],
+ [ -0.0318],
+ [ 0.35 ],
+ [ -4.5404],
+ [ 3.9219],
+ [ -0.1702],
+ [ 0.8194],
+ [ 0.5385]])
+ >>> "{0:.6f}".format(mllag.rho)
+ '0.538503'
+ >>> "{0:.6f}".format(mllag.mean_y)
+ '44.307180'
+ >>> "{0:.6f}".format(mllag.std_y)
+ '23.606077'
+ >>> np.around(np.diag(mllag.vm1), decimals=4)
+ array([ 47.42 , 2.3953, 0.0051, 0.0648, 69.6765, 3.2066,
+ 0.0116, 0.0486, 0.004 , 390.7274])
+ >>> np.around(np.diag(mllag.vm), decimals=4)
+ array([ 47.42 , 2.3953, 0.0051, 0.0648, 69.6765, 3.2066,
+ 0.0116, 0.0486, 0.004 ])
+ >>> "{0:.6f}".format(mllag.sig2)
+ '200.044334'
+ >>> "{0:.6f}".format(mllag.logll)
+ '-864.985056'
+ >>> "{0:.6f}".format(mllag.aic)
+ '1747.970112'
+ >>> "{0:.6f}".format(mllag.schwarz)
+ '1778.136835'
+ >>> mllag.title
+ 'MAXIMUM LIKELIHOOD SPATIAL LAG - REGIMES (METHOD = full)'
+ """
+
+ def __init__(self, y, x, regimes, w=None, constant_regi='many',
+ cols2regi='all', method='full', epsilon=0.0000001,
+ regime_lag_sep=False, regime_err_sep=False, cores=False, spat_diag=False,
+ vm=False, name_y=None, name_x=None,
+ name_w=None, name_ds=None, name_regimes=None):
+
+ n = USER.check_arrays(y, x)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ USER.check_spat_diag(spat_diag, w)
+ name_y = USER.set_name_y(name_y)
+ self.name_y = name_y
+ self.name_x_r = USER.set_name_x(
+ name_x, x) + [USER.set_name_yend_sp(name_y)]
+ self.method = method
+ self.epsilon = epsilon
+ self.name_regimes = USER.set_name_ds(name_regimes)
+ self.constant_regi = constant_regi
+ self.n = n
+ cols2regi = REGI.check_cols2regi(
+ constant_regi, cols2regi, x, add_cons=False)
+ self.cols2regi = cols2regi
+ self.regimes_set = REGI._get_regimes_set(regimes)
+ self.regimes = regimes
+ self.regime_lag_sep = regime_lag_sep
+ self._cache = {}
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_w = USER.set_name_w(name_w, w)
+ USER.check_regimes(self.regimes_set, self.n, x.shape[1])
+
+# regime_err_sep is ignored, always False
+
+ if regime_lag_sep == True:
+ if not (set(cols2regi) == set([True]) and constant_regi == 'many'):
+ raise Exception, "All variables must vary by regimes if regime_lag_sep = True."
+ cols2regi += [True]
+ w_i, regi_ids, warn = REGI.w_regimes(
+ w, regimes, self.regimes_set, transform=True, get_ids=True, min_n=len(cols2regi) + 1)
+ set_warn(self, warn)
+ else:
+ cols2regi += [False]
+
+ if set(cols2regi) == set([True]) and constant_regi == 'many':
+ self.y = y
+ self.ML_Lag_Regimes_Multi(y, x, w_i, w, regi_ids,
+ cores=cores, cols2regi=cols2regi, method=method, epsilon=epsilon,
+ spat_diag=spat_diag, vm=vm, name_y=name_y, name_x=name_x,
+ name_regimes=self.name_regimes,
+ name_w=name_w, name_ds=name_ds)
+ else:
+ # if regime_lag_sep == True:
+ # w = REGI.w_regimes_union(w, w_i, self.regimes_set)
+ name_x = USER.set_name_x(name_x, x, constant=True)
+ x, self.name_x = REGI.Regimes_Frame.__init__(self, x,
+ regimes, constant_regi, cols2regi=cols2regi[:-1], names=name_x)
+ self.name_x.append("_Global_" + USER.set_name_yend_sp(name_y))
+ BaseML_Lag.__init__(
+ self, y=y, x=x, w=w, method=method, epsilon=epsilon)
+ self.kf += 1 # Adding a fixed k to account for spatial lag in Chow
+ # adding a fixed k to account for spatial lag in aic, sc
+ self.k += 1
+ self.chow = REGI.Chow(self)
+ self.aic = DIAG.akaike(reg=self)
+ self.schwarz = DIAG.schwarz(reg=self)
+ self.regime_lag_sep = regime_lag_sep
+ self.title = "MAXIMUM LIKELIHOOD SPATIAL LAG - REGIMES" + \
+ " (METHOD = " + method + ")"
+ SUMMARY.ML_Lag(
+ reg=self, w=w, vm=vm, spat_diag=spat_diag, regimes=True)
+
+ def ML_Lag_Regimes_Multi(self, y, x, w_i, w, regi_ids,
+ cores, cols2regi, method, epsilon,
+ spat_diag, vm, name_y, name_x,
+ name_regimes, name_w, name_ds):
+ # pool = mp.Pool(cores)
+ name_x = USER.set_name_x(name_x, x) + [USER.set_name_yend_sp(name_y)]
+ results_p = {}
+ """
+ for r in self.regimes_set:
+ if system() == 'Windows':
+ is_win = True
+ results_p[r] = _work(*(y,x,regi_ids,r,w_i[r],method,epsilon,name_ds,name_y,name_x,name_w,name_regimes))
+ else:
+ results_p[r] = pool.apply_async(_work,args=(y,x,regi_ids,r,w_i[r],method,epsilon,name_ds,name_y,name_x,name_w,name_regimes, ))
+ is_win = False
+ """
+ for r in self.regimes_set:
+ if cores:
+ pool = mp.Pool(None)
+ results_p[r] = pool.apply_async(_work, args=(y, x, regi_ids, r, w_i[
+ r], method, epsilon, name_ds, name_y, name_x, name_w, name_regimes, ))
+ else:
+ results_p[r] = _work(
+ *(y, x, regi_ids, r, w_i[r], method, epsilon, name_ds, name_y, name_x, name_w, name_regimes))
+
+ self.kryd = 0
+ self.kr = len(cols2regi) + 1
+ self.kf = 0
+ self.nr = len(self.regimes_set)
+ self.name_x_r = name_x
+ self.name_regimes = name_regimes
+ self.vm = np.zeros((self.nr * self.kr, self.nr * self.kr), float)
+ self.betas = np.zeros((self.nr * self.kr, 1), float)
+ self.u = np.zeros((self.n, 1), float)
+ self.predy = np.zeros((self.n, 1), float)
+ self.predy_e = np.zeros((self.n, 1), float)
+ self.e_pred = np.zeros((self.n, 1), float)
+ """
+ if not is_win:
+ pool.close()
+ pool.join()
+ """
+ if cores:
+ pool.close()
+ pool.join()
+
+ results = {}
+ self.name_y, self.name_x = [], []
+ counter = 0
+ for r in self.regimes_set:
+ """
+ if is_win:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+ """
+ if not cores:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+ self.vm[(counter * self.kr):((counter + 1) * self.kr),
+ (counter * self.kr):((counter + 1) * self.kr)] = results[r].vm
+ self.betas[
+ (counter * self.kr):((counter + 1) * self.kr), ] = results[r].betas
+ self.u[regi_ids[r], ] = results[r].u
+ self.predy[regi_ids[r], ] = results[r].predy
+ self.predy_e[regi_ids[r], ] = results[r].predy_e
+ self.e_pred[regi_ids[r], ] = results[r].e_pred
+ self.name_y += results[r].name_y
+ self.name_x += results[r].name_x
+ counter += 1
+ self.multi = results
+ self.chow = REGI.Chow(self)
+ SUMMARY.ML_Lag_multi(
+ reg=self, multireg=self.multi, vm=vm, spat_diag=spat_diag, regimes=True, w=w)
+
+
+def _work(y, x, regi_ids, r, w_r, method, epsilon, name_ds, name_y, name_x, name_w, name_regimes):
+ y_r = y[regi_ids[r]]
+ x_r = x[regi_ids[r]]
+ x_constant = USER.check_constant(x_r)
+ model = BaseML_Lag(y_r, x_constant, w_r, method=method, epsilon=epsilon)
+ model.title = "MAXIMUM LIKELIHOOD SPATIAL LAG - REGIME " + \
+ str(r) + " (METHOD = " + method + ")"
+ model.name_ds = name_ds
+ model.name_y = '%s_%s' % (str(r), name_y)
+ model.name_x = ['%s_%s' % (str(r), i) for i in name_x]
+ model.name_w = name_w
+ model.name_regimes = name_regimes
+ model.k += 1 # add 1 for proper df and aic, sc
+ model.aic = DIAG.akaike(reg=model)
+ model.schwarz = DIAG.schwarz(reg=model)
+ return model
+
+
+def _test():
+ import doctest
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ doctest.testmod()
+ np.set_printoptions(suppress=start_suppress)
+
+if __name__ == "__main__":
+ _test()
+ import numpy as np
+ import pysal as ps
+ db = ps.open(ps.examples.get_path("baltim.dbf"), 'r')
+ ds_name = "baltim.dbf"
+ y_name = "PRICE"
+ y = np.array(db.by_col(y_name)).T
+ y.shape = (len(y), 1)
+ x_names = ["NROOM", "NBATH", "PATIO", "FIREPL",
+ "AC", "GAR", "AGE", "LOTSZ", "SQFT"]
+ x = np.array([db.by_col(var) for var in x_names]).T
+ ww = ps.open(ps.examples.get_path("baltim_q.gal"))
+ w = ww.read()
+ ww.close()
+ w_name = "baltim_q.gal"
+ w.transform = 'r'
+ regimes = db.by_col("CITCOU")
+
+ mllag = ML_Lag_Regimes(y, x, regimes, w=w, method='full', name_y=y_name, name_x=x_names,
+ name_w=w_name, name_ds=ds_name, regime_lag_sep=True, constant_regi='many',
+ name_regimes="CITCOU")
+ print mllag.summary
diff --git a/pysal/spreg/ols.py b/pysal/spreg/ols.py
new file mode 100644
index 0000000..10710d3
--- /dev/null
+++ b/pysal/spreg/ols.py
@@ -0,0 +1,471 @@
+"""Ordinary Least Squares regression classes."""
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, David C. Folch david.folch at asu.edu"
+import numpy as np
+import copy as COPY
+import numpy.linalg as la
+import user_output as USER
+import summary_output as SUMMARY
+import robust as ROBUST
+from utils import spdot, sphstack, RegressionPropsY, RegressionPropsVM
+
+__all__ = ["OLS"]
+
+
+class BaseOLS(RegressionPropsY, RegressionPropsVM):
+
+ """
+ Ordinary least squares (OLS) (note: no consistency checks, diagnostics or
+ constant added)
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ robust : string
+ If 'white', then a White consistent estimator of the
+ variance-covariance matrix is given. If 'hac', then a
+ HAC consistent estimator of the variance-covariance
+ matrix is given. Default set to None.
+ gwk : pysal W object
+ Kernel spatial weights needed for HAC estimation. Note:
+ matrix must have ones along the main diagonal.
+ sig2n_k : boolean
+ If True, then use n-k to estimate sigma^2. If False, use n.
+
+ Attributes
+ ----------
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ utu : float
+ Sum of squared residuals
+ sig2 : float
+ Sigma squared used in computations
+ sig2n : float
+ Sigma squared (computed with n in the denominator)
+ sig2n_k : float
+ Sigma squared (computed with n-k in the denominator)
+ xtx : float
+ X'X
+ xtxi : float
+ (X'X)^-1
+
+
+ Examples
+ --------
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> y = np.array(db.by_col("HOVAL"))
+ >>> y = np.reshape(y, (49,1))
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("CRIME"))
+ >>> X = np.array(X).T
+ >>> X = np.hstack((np.ones(y.shape),X))
+ >>> ols=BaseOLS(y,X)
+ >>> ols.betas
+ array([[ 46.42818268],
+ [ 0.62898397],
+ [ -0.48488854]])
+ >>> ols.vm
+ array([[ 174.02245348, -6.52060364, -2.15109867],
+ [ -6.52060364, 0.28720001, 0.06809568],
+ [ -2.15109867, 0.06809568, 0.03336939]])
+ """
+
+ def __init__(self, y, x, robust=None, gwk=None, sig2n_k=True):
+ self.x = x
+ self.xtx = spdot(self.x.T, self.x)
+ xty = spdot(self.x.T, y)
+
+ self.xtxi = la.inv(self.xtx)
+ self.betas = np.dot(self.xtxi, xty)
+ predy = spdot(self.x, self.betas)
+
+ u = y - predy
+ self.u = u
+ self.predy = predy
+ self.y = y
+ self.n, self.k = self.x.shape
+
+ if robust:
+ self.vm = ROBUST.robust_vm(reg=self, gwk=gwk, sig2n_k=sig2n_k)
+
+ self._cache = {}
+ if sig2n_k:
+ self.sig2 = self.sig2n_k
+ else:
+ self.sig2 = self.sig2n
+
+
+class OLS(BaseOLS):
+
+ """
+ Ordinary least squares with results and diagnostics.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ w : pysal W object
+ Spatial weights object (required if running spatial
+ diagnostics)
+ robust : string
+ If 'white', then a White consistent estimator of the
+ variance-covariance matrix is given. If 'hac', then a
+ HAC consistent estimator of the variance-covariance
+ matrix is given. Default set to None.
+ gwk : pysal W object
+ Kernel spatial weights needed for HAC estimation. Note:
+ matrix must have ones along the main diagonal.
+ sig2n_k : boolean
+ If True, then use n-k to estimate sigma^2. If False, use n.
+ nonspat_diag : boolean
+ If True, then compute non-spatial diagnostics on
+ the regression.
+ spat_diag : boolean
+ If True, then compute Lagrange multiplier tests (requires
+ w). Note: see moran for further tests.
+ moran : boolean
+ If True, compute Moran's I on the residuals. Note:
+ requires spat_diag=True.
+ white_test : boolean
+ If True, compute White's specification robust test.
+ (requires nonspat_diag=True)
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_gwk : string
+ Name of kernel weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ robust : string
+ Adjustment for robust standard errors
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ r2 : float
+ R squared
+ ar2 : float
+ Adjusted R squared
+ utu : float
+ Sum of squared residuals
+ sig2 : float
+ Sigma squared used in computations
+ sig2ML : float
+ Sigma squared (maximum likelihood)
+ f_stat : tuple
+ Statistic (float), p-value (float)
+ logll : float
+ Log likelihood
+ aic : float
+ Akaike information criterion
+ schwarz : float
+ Schwarz information criterion
+ std_err : array
+ 1xk array of standard errors of the betas
+ t_stat : list of tuples
+ t statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ mulColli : float
+ Multicollinearity condition number
+ jarque_bera : dictionary
+ 'jb': Jarque-Bera statistic (float); 'pvalue': p-value
+ (float); 'df': degrees of freedom (int)
+ breusch_pagan : dictionary
+ 'bp': Breusch-Pagan statistic (float); 'pvalue': p-value
+ (float); 'df': degrees of freedom (int)
+ koenker_bassett : dictionary
+ 'kb': Koenker-Bassett statistic (float); 'pvalue':
+ p-value (float); 'df': degrees of freedom (int)
+ white : dictionary
+ 'wh': White statistic (float); 'pvalue': p-value (float);
+ 'df': degrees of freedom (int)
+ lm_error : tuple
+ Lagrange multiplier test for spatial error model; tuple
+ contains the pair (statistic, p-value), where each is a
+ float
+ lm_lag : tuple
+ Lagrange multiplier test for spatial lag model; tuple
+ contains the pair (statistic, p-value), where each is a
+ float
+ rlm_error : tuple
+ Robust lagrange multiplier test for spatial error model;
+ tuple contains the pair (statistic, p-value), where each
+ is a float
+ rlm_lag : tuple
+ Robust lagrange multiplier test for spatial lag model;
+ tuple contains the pair (statistic, p-value), where each
+ is a float
+ lm_sarma : tuple
+ Lagrange multiplier test for spatial SARMA model; tuple
+ contains the pair (statistic, p-value), where each is a
+ float
+ moran_res : tuple
+ Moran's I for the residuals; tuple containing the triple
+ (Moran's I, standardized Moran's I, p-value)
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_gwk : string
+ Name of kernel weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ title : string
+ Name of the regression method used
+ sig2n : float
+ Sigma squared (computed with n in the denominator)
+ sig2n_k : float
+ Sigma squared (computed with n-k in the denominator)
+ xtx : float
+ X'X
+ xtxi : float
+ (X'X)^-1
+
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+
+ Open data on Columbus neighborhood crime (49 areas) using pysal.open().
+ This is the DBF associated with the Columbus shapefile. Note that
+ pysal.open() also reads data in CSV format; also, the actual OLS class
+ requires data to be passed in as numpy arrays so the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+
+ Extract the HOVAL column (home values) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an nx1 numpy array.
+
+ >>> hoval = db.by_col("HOVAL")
+ >>> y = np.array(hoval)
+ >>> y.shape = (len(hoval), 1)
+
+ Extract CRIME (crime) and INC (income) vectors from the DBF to be used as
+ independent variables in the regression. Note that PySAL requires this to
+ be an nxj numpy array, where j is the number of independent variables (not
+ including a constant). pysal.spreg.OLS adds a vector of ones to the
+ independent variables passed in.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("CRIME"))
+ >>> X = np.array(X).T
+
+ The minimum parameters needed to run an ordinary least squares regression
+ are the two numpy arrays containing the independent variable and dependent
+ variables respectively. To make the printed results more meaningful, the
+ user can pass in explicit names for the variables used; this is optional.
+
+ >>> ols = OLS(y, X, name_y='home value', name_x=['income','crime'], name_ds='columbus', white_test=True)
+
+ pysal.spreg.OLS computes the regression coefficients and their standard
+ errors, t-stats and p-values. It also computes a large battery of
+ diagnostics on the regression. In this example we compute the white test
+ which by default isn't ('white_test=True'). All of these results can be independently
+ accessed as attributes of the regression object created by running
+ pysal.spreg.OLS. They can also be accessed at one time by printing the
+ summary attribute of the regression object. In the example below, the
+ parameter on crime is -0.4849, with a t-statistic of -2.6544 and p-value
+ of 0.01087.
+
+ >>> ols.betas
+ array([[ 46.42818268],
+ [ 0.62898397],
+ [ -0.48488854]])
+ >>> print round(ols.t_stat[2][0],3)
+ -2.654
+ >>> print round(ols.t_stat[2][1],3)
+ 0.011
+ >>> print round(ols.r2,3)
+ 0.35
+
+ Or we can easily obtain a full summary of all the results nicely formatted and
+ ready to be printed:
+
+ >>> print ols.summary
+ REGRESSION
+ ----------
+ SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES
+ -----------------------------------------
+ Data set : columbus
+ Dependent Variable : home value Number of Observations: 49
+ Mean dependent var : 38.4362 Number of Variables : 3
+ S.D. dependent var : 18.4661 Degrees of Freedom : 46
+ R-squared : 0.3495
+ Adjusted R-squared : 0.3212
+ Sum squared residual: 10647.015 F-statistic : 12.3582
+ Sigma-square : 231.457 Prob(F-statistic) : 5.064e-05
+ S.E. of regression : 15.214 Log likelihood : -201.368
+ Sigma-square ML : 217.286 Akaike info criterion : 408.735
+ S.E of regression ML: 14.7406 Schwarz criterion : 414.411
+ <BLANKLINE>
+ ------------------------------------------------------------------------------------
+ Variable Coefficient Std.Error t-Statistic Probability
+ ------------------------------------------------------------------------------------
+ CONSTANT 46.4281827 13.1917570 3.5194844 0.0009867
+ crime -0.4848885 0.1826729 -2.6544086 0.0108745
+ income 0.6289840 0.5359104 1.1736736 0.2465669
+ ------------------------------------------------------------------------------------
+ <BLANKLINE>
+ REGRESSION DIAGNOSTICS
+ MULTICOLLINEARITY CONDITION NUMBER 12.538
+ <BLANKLINE>
+ TEST ON NORMALITY OF ERRORS
+ TEST DF VALUE PROB
+ Jarque-Bera 2 39.706 0.0000
+ <BLANKLINE>
+ DIAGNOSTICS FOR HETEROSKEDASTICITY
+ RANDOM COEFFICIENTS
+ TEST DF VALUE PROB
+ Breusch-Pagan test 2 5.767 0.0559
+ Koenker-Bassett test 2 2.270 0.3214
+ <BLANKLINE>
+ SPECIFICATION ROBUST TEST
+ TEST DF VALUE PROB
+ White 5 2.906 0.7145
+ ================================ END OF REPORT =====================================
+
+ If the optional parameters w and spat_diag are passed to pysal.spreg.OLS,
+ spatial diagnostics will also be computed for the regression. These
+ include Lagrange multiplier tests and Moran's I of the residuals. The w
+ parameter is a PySAL spatial weights matrix. In this example, w is built
+ directly from the shapefile columbus.shp, but w can also be read in from a
+ GAL or GWT file. In this case a rook contiguity weights matrix is built,
+ but PySAL also offers queen contiguity, distance weights and k nearest
+ neighbor weights among others. In the example, the Moran's I of the
+ residuals is 0.204 with a standardized value of 2.592 and a p-value of
+ 0.0095.
+
+ >>> w = pysal.weights.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ >>> ols = OLS(y, X, w, spat_diag=True, moran=True, name_y='home value', name_x=['income','crime'], name_ds='columbus')
+ >>> ols.betas
+ array([[ 46.42818268],
+ [ 0.62898397],
+ [ -0.48488854]])
+ >>> print round(ols.moran_res[0],3)
+ 0.204
+ >>> print round(ols.moran_res[1],3)
+ 2.592
+ >>> print round(ols.moran_res[2],4)
+ 0.0095
+
+ """
+
+ def __init__(self, y, x,
+ w=None,
+ robust=None, gwk=None, sig2n_k=True,
+ nonspat_diag=True, spat_diag=False, moran=False,
+ white_test=False, vm=False, name_y=None, name_x=None,
+ name_w=None, name_gwk=None, name_ds=None):
+
+ n = USER.check_arrays(y, x)
+ USER.check_y(y, n)
+ USER.check_weights(w, y)
+ USER.check_robust(robust, gwk)
+ USER.check_spat_diag(spat_diag, w)
+ x_constant = USER.check_constant(x)
+ BaseOLS.__init__(self, y=y, x=x_constant, robust=robust,
+ gwk=gwk, sig2n_k=sig2n_k)
+ self.title = "ORDINARY LEAST SQUARES"
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_y = USER.set_name_y(name_y)
+ self.name_x = USER.set_name_x(name_x, x)
+ self.robust = USER.set_robust(robust)
+ self.name_w = USER.set_name_w(name_w, w)
+ self.name_gwk = USER.set_name_w(name_gwk, gwk)
+ SUMMARY.OLS(reg=self, vm=vm, w=w, nonspat_diag=nonspat_diag,
+ spat_diag=spat_diag, moran=moran, white_test=white_test)
+
+
+def _test():
+ import doctest
+ # the following line could be used to define an alternative to the '<BLANKLINE>' flag
+ #doctest.BLANKLINE_MARKER = 'something better than <BLANKLINE>'
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ doctest.testmod()
+ np.set_printoptions(suppress=start_suppress)
+
+if __name__ == '__main__':
+ _test()
+
+ import numpy as np
+ import pysal
+ db = pysal.open(pysal.examples.get_path("columbus.dbf"), 'r')
+ y_var = 'CRIME'
+ y = np.array([db.by_col(y_var)]).reshape(49, 1)
+ x_var = ['INC', 'HOVAL']
+ x = np.array([db.by_col(name) for name in x_var]).T
+ w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ w.transform = 'r'
+ ols = OLS(
+ y, x, w=w, nonspat_diag=True, spat_diag=True, name_y=y_var, name_x=x_var,
+ name_ds='columbus', name_w='columbus.gal', robust='white', sig2n_k=True, moran=True)
+ print ols.summary
diff --git a/pysal/spreg/ols_regimes.py b/pysal/spreg/ols_regimes.py
new file mode 100644
index 0000000..e4e3393
--- /dev/null
+++ b/pysal/spreg/ols_regimes.py
@@ -0,0 +1,541 @@
+"""
+Ordinary Least Squares regression with regimes.
+"""
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, Pedro V. Amaral pedro.amaral at asu.edu, Daniel Arribas-Bel darribas at asu.edu"
+
+import regimes as REGI
+import user_output as USER
+import multiprocessing as mp
+from ols import BaseOLS
+from utils import set_warn, spbroadcast, RegressionProps_basic, RegressionPropsY, spdot
+from robust import hac_multi
+import summary_output as SUMMARY
+import numpy as np
+from platform import system
+import scipy.sparse as SP
+
+
+class OLS_Regimes(BaseOLS, REGI.Regimes_Frame, RegressionPropsY):
+
+ """
+ Ordinary least squares with results and diagnostics.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ w : pysal W object
+ Spatial weights object (required if running spatial
+ diagnostics)
+ robust : string
+ If 'white', then a White consistent estimator of the
+ variance-covariance matrix is given. If 'hac', then a
+ HAC consistent estimator of the variance-covariance
+ matrix is given. Default set to None.
+ gwk : pysal W object
+ Kernel spatial weights needed for HAC estimation. Note:
+ matrix must have ones along the main diagonal.
+ sig2n_k : boolean
+ If True, then use n-k to estimate sigma^2. If False, use n.
+ nonspat_diag : boolean
+ If True, then compute non-spatial diagnostics on
+ the regression.
+ spat_diag : boolean
+ If True, then compute Lagrange multiplier tests (requires
+ w). Note: see moran for further tests.
+ moran : boolean
+ If True, compute Moran's I on the residuals. Note:
+ requires spat_diag=True.
+ white_test : boolean
+ If True, compute White's specification robust test.
+ (requires nonspat_diag=True)
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ constant_regi: ['one', 'many']
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime (default)
+ cols2regi : list, 'all'
+ Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all' (default), all the variables vary by regime.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ cores : boolean
+ Specifies if multiprocessing is to be used
+ Default: no multiprocessing, cores = False
+ Note: Multiprocessing may not work on all platforms.
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_gwk : string
+ Name of kernel weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regime variable for use in the output
+
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ robust : string
+ Adjustment for robust standard errors
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ r2 : float
+ R squared
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ ar2 : float
+ Adjusted R squared
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ utu : float
+ Sum of squared residuals
+ sig2 : float
+ Sigma squared used in computations
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ sig2ML : float
+ Sigma squared (maximum likelihood)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ f_stat : tuple
+ Statistic (float), p-value (float)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ logll : float
+ Log likelihood
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ aic : float
+ Akaike information criterion
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ schwarz : float
+ Schwarz information criterion
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ std_err : array
+ 1xk array of standard errors of the betas
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ t_stat : list of tuples
+ t statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ mulColli : float
+ Multicollinearity condition number
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ jarque_bera : dictionary
+ 'jb': Jarque-Bera statistic (float); 'pvalue': p-value
+ (float); 'df': degrees of freedom (int)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ breusch_pagan : dictionary
+ 'bp': Breusch-Pagan statistic (float); 'pvalue': p-value
+ (float); 'df': degrees of freedom (int)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ koenker_bassett : dictionary
+ 'kb': Koenker-Bassett statistic (float); 'pvalue':
+ p-value (float); 'df': degrees of freedom (int)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ white : dictionary
+ 'wh': White statistic (float); 'pvalue': p-value (float);
+ 'df': degrees of freedom (int)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ lm_error : tuple
+ Lagrange multiplier test for spatial error model; tuple
+ contains the pair (statistic, p-value), where each is a
+ float
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ lm_lag : tuple
+ Lagrange multiplier test for spatial lag model; tuple
+ contains the pair (statistic, p-value), where each is a
+ float
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ rlm_error : tuple
+ Robust lagrange multiplier test for spatial error model;
+ tuple contains the pair (statistic, p-value), where each
+ is a float
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ rlm_lag : tuple
+ Robust lagrange multiplier test for spatial lag model;
+ tuple contains the pair (statistic, p-value), where each
+ is a float
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ lm_sarma : tuple
+ Lagrange multiplier test for spatial SARMA model; tuple
+ contains the pair (statistic, p-value), where each is a
+ float
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ moran_res : tuple
+ Moran's I for the residuals; tuple containing the triple
+ (Moran's I, standardized Moran's I, p-value)
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_gwk : string
+ Name of kernel weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regime variable for use in the output
+ title : string
+ Name of the regression method used
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ sig2n : float
+ Sigma squared (computed with n in the denominator)
+ sig2n_k : float
+ Sigma squared (computed with n-k in the denominator)
+ xtx : float
+ X'X
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ xtxi : float
+ (X'X)^-1
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ constant_regi: ['one', 'many']
+ Ignored if regimes=False. Constant option for regimes.
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime
+ cols2regi : list, 'all'
+ Ignored if regimes=False. Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all', all the variables vary by regime.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ kr : int
+ Number of variables/columns to be "regimized" or subject
+ to change by regime. These will result in one parameter
+ estimate by regime for each variable (i.e. nr parameters per
+ variable)
+ kf : int
+ Number of variables/columns to be considered fixed or
+ global across regimes and hence only obtain one parameter
+ estimate
+ nr : int
+ Number of different regimes in the 'regimes' list
+ multi : dictionary
+ Only available when multiple regressions are estimated,
+ i.e. when regime_err_sep=True and no variable is fixed
+ across regimes.
+ Contains all attributes of each individual regression
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+
+ Open data on NCOVR US County Homicides (3085 areas) using pysal.open().
+ This is the DBF associated with the NAT shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path("NAT.dbf"),'r')
+
+ Extract the HR90 column (homicide rates in 1990) from the DBF file and make it
+ the dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y_var = 'HR90'
+ >>> y = db.by_col(y_var)
+ >>> y = np.array(y).reshape(len(y), 1)
+
+ Extract UE90 (unemployment rate) and PS90 (population structure) vectors from
+ the DBF to be used as independent variables in the regression. Other variables
+ can be inserted by adding their names to x_var, such as x_var = ['Var1','Var2','...]
+ Note that PySAL requires this to be an nxj numpy array, where j is the
+ number of independent variables (not including a constant). By default
+ this model adds a vector of ones to the independent variables passed in.
+
+ >>> x_var = ['PS90','UE90']
+ >>> x = np.array([db.by_col(name) for name in x_var]).T
+
+ The different regimes in this data are given according to the North and
+ South dummy (SOUTH).
+
+ >>> r_var = 'SOUTH'
+ >>> regimes = db.by_col(r_var)
+
+ We can now run the regression and then have a summary of the output
+ by typing: olsr.summary
+ Alternatively, we can just check the betas and standard errors of the
+ parameters:
+
+ >>> olsr = OLS_Regimes(y, x, regimes, nonspat_diag=False, name_y=y_var, name_x=['PS90','UE90'], name_regimes=r_var, name_ds='NAT')
+ >>> olsr.betas
+ array([[ 0.39642899],
+ [ 0.65583299],
+ [ 0.48703937],
+ [ 5.59835 ],
+ [ 1.16210453],
+ [ 0.53163886]])
+ >>> np.sqrt(olsr.vm.diagonal())
+ array([ 0.24816345, 0.09662678, 0.03628629, 0.46894564, 0.21667395,
+ 0.05945651])
+ >>> olsr.cols2regi
+ 'all'
+ """
+
+ def __init__(self, y, x, regimes,
+ w=None, robust=None, gwk=None, sig2n_k=True,
+ nonspat_diag=True, spat_diag=False, moran=False, white_test=False,
+ vm=False, constant_regi='many', cols2regi='all',
+ regime_err_sep=True, cores=False,
+ name_y=None, name_x=None, name_regimes=None,
+ name_w=None, name_gwk=None, name_ds=None):
+
+ n = USER.check_arrays(y, x)
+ USER.check_y(y, n)
+ USER.check_weights(w, y)
+ USER.check_robust(robust, gwk)
+ USER.check_spat_diag(spat_diag, w)
+ self.name_x_r = USER.set_name_x(name_x, x)
+ self.constant_regi = constant_regi
+ self.cols2regi = cols2regi
+ self.name_w = USER.set_name_w(name_w, w)
+ self.name_gwk = USER.set_name_w(name_gwk, gwk)
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_y = USER.set_name_y(name_y)
+ self.name_regimes = USER.set_name_ds(name_regimes)
+ self.n = n
+ cols2regi = REGI.check_cols2regi(
+ constant_regi, cols2regi, x, add_cons=False)
+ self.regimes_set = REGI._get_regimes_set(regimes)
+ self.regimes = regimes
+ USER.check_regimes(self.regimes_set, self.n, x.shape[1])
+ if regime_err_sep == True and robust == 'hac':
+ set_warn(
+ self, "Error by regimes is incompatible with HAC estimation. Hence, error by regimes has been disabled for this model.")
+ regime_err_sep = False
+ self.regime_err_sep = regime_err_sep
+ if regime_err_sep == True and set(cols2regi) == set([True]) and constant_regi == 'many':
+ self.y = y
+ name_x = USER.set_name_x(name_x, x)
+ regi_ids = dict(
+ (r, list(np.where(np.array(regimes) == r)[0])) for r in self.regimes_set)
+ self._ols_regimes_multi(x, w, regi_ids, cores,
+ gwk, sig2n_k, robust, nonspat_diag, spat_diag, vm, name_x, moran, white_test)
+ else:
+ name_x = USER.set_name_x(name_x, x, constant=True)
+ x, self.name_x = REGI.Regimes_Frame.__init__(self, x,
+ regimes, constant_regi, cols2regi, name_x)
+ BaseOLS.__init__(
+ self, y=y, x=x, robust=robust, gwk=gwk, sig2n_k=sig2n_k)
+ if regime_err_sep == True and robust == None:
+ y2, x2 = REGI._get_weighted_var(
+ regimes, self.regimes_set, sig2n_k, self.u, y, x)
+ ols2 = BaseOLS(y=y2, x=x2, sig2n_k=sig2n_k)
+ RegressionProps_basic(self, betas=ols2.betas, vm=ols2.vm)
+ self.title = "ORDINARY LEAST SQUARES - REGIMES (Group-wise heteroskedasticity)"
+ nonspat_diag = None
+ set_warn(
+ self, "Residuals treated as homoskedastic for the purpose of diagnostics.")
+ else:
+ self.title = "ORDINARY LEAST SQUARES - REGIMES"
+ self.robust = USER.set_robust(robust)
+ self.chow = REGI.Chow(self)
+ SUMMARY.OLS(reg=self, vm=vm, w=w, nonspat_diag=nonspat_diag,
+ spat_diag=spat_diag, moran=moran, white_test=white_test, regimes=True)
+
+ def _ols_regimes_multi(self, x, w, regi_ids, cores,
+ gwk, sig2n_k, robust, nonspat_diag, spat_diag, vm, name_x, moran, white_test):
+ results_p = {}
+ """
+ for r in self.regimes_set:
+ if system() == 'Windows':
+ is_win = True
+ results_p[r] = _work(*(self.y,x,w,regi_ids,r,robust,sig2n_k,self.name_ds,self.name_y,name_x,self.name_w,self.name_regimes))
+ else:
+ pool = mp.Pool(cores)
+ results_p[r] = pool.apply_async(_work,args=(self.y,x,w,regi_ids,r,robust,sig2n_k,self.name_ds,self.name_y,name_x,self.name_w,self.name_regimes))
+ is_win = False
+ """
+ for r in self.regimes_set:
+ if cores:
+ pool = mp.Pool(None)
+ results_p[r] = pool.apply_async(_work, args=(
+ self.y, x, w, regi_ids, r, robust, sig2n_k, self.name_ds, self.name_y, name_x, self.name_w, self.name_regimes))
+ else:
+ results_p[r] = _work(*(self.y, x, w, regi_ids, r, robust, sig2n_k,
+ self.name_ds, self.name_y, name_x, self.name_w, self.name_regimes))
+
+ self.kryd = 0
+ self.kr = x.shape[1] + 1
+ self.kf = 0
+ self.nr = len(self.regimes_set)
+ self.vm = np.zeros((self.nr * self.kr, self.nr * self.kr), float)
+ self.betas = np.zeros((self.nr * self.kr, 1), float)
+ self.u = np.zeros((self.n, 1), float)
+ self.predy = np.zeros((self.n, 1), float)
+ """
+ if not is_win:
+ pool.close()
+ pool.join()
+ """
+ if cores:
+ pool.close()
+ pool.join()
+
+ results = {}
+ self.name_y, self.name_x = [], []
+ counter = 0
+ for r in self.regimes_set:
+ """
+ if is_win:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+ """
+ if not cores:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+
+ self.vm[(counter * self.kr):((counter + 1) * self.kr),
+ (counter * self.kr):((counter + 1) * self.kr)] = results[r].vm
+ self.betas[
+ (counter * self.kr):((counter + 1) * self.kr), ] = results[r].betas
+ self.u[regi_ids[r], ] = results[r].u
+ self.predy[regi_ids[r], ] = results[r].predy
+ self.name_y += results[r].name_y
+ self.name_x += results[r].name_x
+ counter += 1
+ self.multi = results
+ self.hac_var = x
+ if robust == 'hac':
+ hac_multi(self, gwk)
+ self.chow = REGI.Chow(self)
+ if spat_diag:
+ self._get_spat_diag_props(x, sig2n_k)
+ SUMMARY.OLS_multi(reg=self, multireg=self.multi, vm=vm, nonspat_diag=nonspat_diag,
+ spat_diag=spat_diag, moran=moran, white_test=white_test, regimes=True, w=w)
+
+ def _get_spat_diag_props(self, x, sig2n_k):
+ self.k = self.kr
+ self._cache = {}
+ x = np.hstack((np.ones((x.shape[0], 1)), x))
+ self.x = REGI.regimeX_setup(
+ x, self.regimes, [True] * x.shape[1], self.regimes_set)
+ self.xtx = spdot(self.x.T, self.x)
+ self.xtxi = np.linalg.inv(self.xtx)
+
+
+def _work(y, x, w, regi_ids, r, robust, sig2n_k, name_ds, name_y, name_x, name_w, name_regimes):
+ y_r = y[regi_ids[r]]
+ x_r = x[regi_ids[r]]
+ x_constant = USER.check_constant(x_r)
+ if robust == 'hac':
+ robust = None
+ model = BaseOLS(y_r, x_constant, robust=robust, sig2n_k=sig2n_k)
+ model.title = "ORDINARY LEAST SQUARES ESTIMATION - REGIME %s" % r
+ model.robust = USER.set_robust(robust)
+ model.name_ds = name_ds
+ model.name_y = '%s_%s' % (str(r), name_y)
+ model.name_x = ['%s_%s' % (str(r), i) for i in name_x]
+ model.name_w = name_w
+ model.name_regimes = name_regimes
+ if w:
+ w_r, warn = REGI.w_regime(w, regi_ids[r], r, transform=True)
+ set_warn(model, warn)
+ model.w = w_r
+ return model
+
+
+def _test():
+ import doctest
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ doctest.testmod()
+ np.set_printoptions(suppress=start_suppress)
+
+if __name__ == '__main__':
+ _test()
+ import numpy as np
+ import pysal
+ db = pysal.open(pysal.examples.get_path('columbus.dbf'), 'r')
+ y_var = 'CRIME'
+ y = np.array([db.by_col(y_var)]).reshape(49, 1)
+ x_var = ['INC', 'HOVAL']
+ x = np.array([db.by_col(name) for name in x_var]).T
+ r_var = 'NSA'
+ regimes = db.by_col(r_var)
+ w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ w.transform = 'r'
+ olsr = OLS_Regimes(y, x, regimes, w=w, constant_regi='many', nonspat_diag=False, spat_diag=False, name_y=y_var, name_x=['INC', 'HOVAL'],
+ name_ds='columbus', name_regimes=r_var, name_w='columbus.gal', regime_err_sep=True, cols2regi=[True, True], sig2n_k=True, robust='white')
+ print olsr.summary
diff --git a/pysal/spreg/probit.py b/pysal/spreg/probit.py
new file mode 100644
index 0000000..4bbc0ac
--- /dev/null
+++ b/pysal/spreg/probit.py
@@ -0,0 +1,684 @@
+"""Probit regression class and diagnostics."""
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, Pedro V. Amaral pedro.amaral at asu.edu"
+
+import numpy as np
+import numpy.linalg as la
+import scipy.optimize as op
+from scipy.stats import norm, chisqprob
+import scipy.sparse as SP
+import user_output as USER
+import summary_output as SUMMARY
+
+__all__ = ["Probit"]
+
+
+class BaseProbit:
+
+ """
+ Probit class to do all the computations
+
+ Parameters
+ ----------
+
+ x : array
+ nxk array of independent variables (assumed to be aligned with y)
+ y : array
+ nx1 array of dependent binary variable
+ w : W
+ PySAL weights instance aligned with y
+ optim : string
+ Optimization method.
+ Default: 'newton' (Newton-Raphson).
+ Alternatives: 'ncg' (Newton-CG), 'bfgs' (BFGS algorithm)
+ scalem : string
+ Method to calculate the scale of the marginal effects.
+ Default: 'phimean' (Mean of individual marginal effects)
+ Alternative: 'xmean' (Marginal effects at variables mean)
+ maxiter : int
+ Maximum number of iterations until optimizer stops
+
+ Attributes
+ ----------
+
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ y : array
+ nx1 array of dependent variable
+ betas : array
+ kx1 array with estimated coefficients
+ predy : array
+ nx1 array of predicted y values
+ n : int
+ Number of observations
+ k : int
+ Number of variables
+ vm : array
+ Variance-covariance matrix (kxk)
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ xmean : array
+ Mean of the independent variables (kx1)
+ predpc : float
+ Percent of y correctly predicted
+ logl : float
+ Log-Likelihhod of the estimation
+ scalem : string
+ Method to calculate the scale of the marginal effects.
+ scale : float
+ Scale of the marginal effects.
+ slopes : array
+ Marginal effects of the independent variables (k-1x1)
+ Note: Disregards the presence of dummies.
+ slopes_vm : array
+ Variance-covariance matrix of the slopes (k-1xk-1)
+ LR : tuple
+ Likelihood Ratio test of all coefficients = 0
+ (test statistics, p-value)
+ Pinkse_error: float
+ Lagrange Multiplier test against spatial error correlation.
+ Implemented as presented in Pinkse (2004)
+ KP_error : float
+ Moran's I type test against spatial error correlation.
+ Implemented as presented in Kelejian and Prucha (2001)
+ PS_error : float
+ Lagrange Multiplier test against spatial error correlation.
+ Implemented as presented in Pinkse and Slade (1998)
+ warning : boolean
+ if True Maximum number of iterations exceeded or gradient
+ and/or function calls not changing.
+
+ References
+ ----------
+ .. [1] Pinkse, J. (2004). Moran-flavored tests with nuisance parameter. In: Anselin,
+ L., Florax, R. J., Rey, S. J. (editors) Advances in Spatial Econometrics,
+ pages 67-77. Springer-Verlag, Heidelberg.
+ .. [2] Kelejian, H., Prucha, I. (2001) "On the asymptotic distribution of the
+ Moran I test statistic with applications". Journal of Econometrics, 104(2):219-57.
+ .. [3] Pinkse, J., Slade, M. E. (1998) "Contracting in space: an application of
+ spatial statistics to discrete-choice models". Journal of Econometrics, 85(1):125-54.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> import pysal
+ >>> dbf = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> y = np.array([dbf.by_col('CRIME')]).T
+ >>> x = np.array([dbf.by_col('INC'), dbf.by_col('HOVAL')]).T
+ >>> x = np.hstack((np.ones(y.shape),x))
+ >>> w = pysal.open(pysal.examples.get_path("columbus.gal"), 'r').read()
+ >>> w.transform='r'
+ >>> model = BaseProbit((y>40).astype(float), x, w=w)
+ >>> np.around(model.betas, decimals=6)
+ array([[ 3.353811],
+ [-0.199653],
+ [-0.029514]])
+
+ >>> np.around(model.vm, decimals=6)
+ array([[ 0.852814, -0.043627, -0.008052],
+ [-0.043627, 0.004114, -0.000193],
+ [-0.008052, -0.000193, 0.00031 ]])
+
+ >>> tests = np.array([['Pinkse_error','KP_error','PS_error']])
+ >>> stats = np.array([[model.Pinkse_error[0],model.KP_error[0],model.PS_error[0]]])
+ >>> pvalue = np.array([[model.Pinkse_error[1],model.KP_error[1],model.PS_error[1]]])
+ >>> print np.hstack((tests.T,np.around(np.hstack((stats.T,pvalue.T)),6)))
+ [['Pinkse_error' '3.131719' '0.076783']
+ ['KP_error' '1.721312' '0.085194']
+ ['PS_error' '2.558166' '0.109726']]
+ """
+
+ def __init__(self, y, x, w=None, optim='newton', scalem='phimean', maxiter=100):
+ self.y = y
+ self.x = x
+ self.n, self.k = x.shape
+ self.optim = optim
+ self.scalem = scalem
+ self.w = w
+ self.maxiter = maxiter
+ par_est, self.warning = self.par_est()
+ self.betas = np.reshape(par_est[0], (self.k, 1))
+ self.logl = -float(par_est[1])
+ self._cache = {}
+
+ @property
+ def vm(self):
+ if 'vm' not in self._cache:
+ H = self.hessian(self.betas)
+ self._cache['vm'] = -la.inv(H)
+ return self._cache['vm']
+
+ @property
+ def z_stat(self):
+ if 'z_stat' not in self._cache:
+ variance = self.vm.diagonal()
+ zStat = self.betas.reshape(len(self.betas),) / np.sqrt(variance)
+ rs = {}
+ for i in range(len(self.betas)):
+ rs[i] = (zStat[i], norm.sf(abs(zStat[i])) * 2)
+ self._cache['z_stat'] = rs.values()
+ return self._cache['z_stat']
+
+ @property
+ def slopes_std_err(self):
+ if 'slopes_std_err' not in self._cache:
+ variance = self.slopes_vm.diagonal()
+ self._cache['slopes_std_err'] = np.sqrt(variance)
+ return self._cache['slopes_std_err']
+
+ @property
+ def slopes_z_stat(self):
+ if 'slopes_z_stat' not in self._cache:
+ zStat = self.slopes.reshape(
+ len(self.slopes),) / self.slopes_std_err
+ rs = {}
+ for i in range(len(self.slopes)):
+ rs[i] = (zStat[i], norm.sf(abs(zStat[i])) * 2)
+ self._cache['slopes_z_stat'] = rs.values()
+ return self._cache['slopes_z_stat']
+
+ @property
+ def xmean(self):
+ if 'xmean' not in self._cache:
+ self._cache['xmean'] = np.reshape(
+ sum(self.x) / self.n, (self.k, 1))
+ return self._cache['xmean']
+
+ @property
+ def xb(self):
+ if 'xb' not in self._cache:
+ self._cache['xb'] = np.dot(self.x, self.betas)
+ return self._cache['xb']
+
+ @property
+ def predy(self):
+ if 'predy' not in self._cache:
+ self._cache['predy'] = norm.cdf(self.xb)
+ return self._cache['predy']
+
+ @property
+ def predpc(self):
+ if 'predpc' not in self._cache:
+ predpc = abs(self.y - self.predy)
+ for i in range(len(predpc)):
+ if predpc[i] > 0.5:
+ predpc[i] = 0
+ else:
+ predpc[i] = 1
+ self._cache['predpc'] = float(100 * np.sum(predpc) / self.n)
+ return self._cache['predpc']
+
+ @property
+ def phiy(self):
+ if 'phiy' not in self._cache:
+ self._cache['phiy'] = norm.pdf(self.xb)
+ return self._cache['phiy']
+
+ @property
+ def scale(self):
+ if 'scale' not in self._cache:
+ if self.scalem == 'phimean':
+ self._cache['scale'] = float(1.0 * np.sum(self.phiy) / self.n)
+ if self.scalem == 'xmean':
+ self._cache['scale'] = float(
+ norm.pdf(np.dot(self.xmean.T, self.betas)))
+ return self._cache['scale']
+
+ @property
+ def slopes(self):
+ if 'slopes' not in self._cache:
+ # Disregard the presence of dummies.
+ self._cache['slopes'] = self.betas[1:] * self.scale
+ return self._cache['slopes']
+
+ @property
+ def slopes_vm(self):
+ if 'slopes_vm' not in self._cache:
+ x = self.xmean
+ b = self.betas
+ dfdb = np.eye(self.k) - np.dot(b.T, x) * np.dot(b, x.T)
+ slopes_vm = (self.scale ** 2) * \
+ np.dot(np.dot(dfdb, self.vm), dfdb.T)
+ self._cache['slopes_vm'] = slopes_vm[1:, 1:]
+ return self._cache['slopes_vm']
+
+ @property
+ def LR(self):
+ if 'LR' not in self._cache:
+ P = 1.0 * np.sum(self.y) / self.n
+ LR = float(
+ -2 * (self.n * (P * np.log(P) + (1 - P) * np.log(1 - P)) - self.logl))
+ self._cache['LR'] = (LR, chisqprob(LR, self.k))
+ return self._cache['LR']
+
+ @property
+ def u_naive(self):
+ if 'u_naive' not in self._cache:
+ u_naive = self.y - self.predy
+ self._cache['u_naive'] = u_naive
+ return self._cache['u_naive']
+
+ @property
+ def u_gen(self):
+ if 'u_gen' not in self._cache:
+ Phi_prod = self.predy * (1 - self.predy)
+ u_gen = self.phiy * (self.u_naive / Phi_prod)
+ self._cache['u_gen'] = u_gen
+ return self._cache['u_gen']
+
+ @property
+ def Pinkse_error(self):
+ if 'Pinkse_error' not in self._cache:
+ self._cache['Pinkse_error'], self._cache[
+ 'KP_error'], self._cache['PS_error'] = sp_tests(self)
+ return self._cache['Pinkse_error']
+
+ @property
+ def KP_error(self):
+ if 'KP_error' not in self._cache:
+ self._cache['Pinkse_error'], self._cache[
+ 'KP_error'], self._cache['PS_error'] = sp_tests(self)
+ return self._cache['KP_error']
+
+ @property
+ def PS_error(self):
+ if 'PS_error' not in self._cache:
+ self._cache['Pinkse_error'], self._cache[
+ 'KP_error'], self._cache['PS_error'] = sp_tests(self)
+ return self._cache['PS_error']
+
+ def par_est(self):
+ start = np.dot(la.inv(np.dot(self.x.T, self.x)),
+ np.dot(self.x.T, self.y))
+ flogl = lambda par: -self.ll(par)
+ if self.optim == 'newton':
+ fgrad = lambda par: self.gradient(par)
+ fhess = lambda par: self.hessian(par)
+ par_hat = newton(flogl, start, fgrad, fhess, self.maxiter)
+ warn = par_hat[2]
+ else:
+ fgrad = lambda par: -self.gradient(par)
+ if self.optim == 'bfgs':
+ par_hat = op.fmin_bfgs(
+ flogl, start, fgrad, full_output=1, disp=0)
+ warn = par_hat[6]
+ if self.optim == 'ncg':
+ fhess = lambda par: -self.hessian(par)
+ par_hat = op.fmin_ncg(
+ flogl, start, fgrad, fhess=fhess, full_output=1, disp=0)
+ warn = par_hat[5]
+ if warn > 0:
+ warn = True
+ else:
+ warn = False
+ return par_hat, warn
+
+ def ll(self, par):
+ beta = np.reshape(np.array(par), (self.k, 1))
+ q = 2 * self.y - 1
+ qxb = q * np.dot(self.x, beta)
+ ll = sum(np.log(norm.cdf(qxb)))
+ return ll
+
+ def gradient(self, par):
+ beta = np.reshape(np.array(par), (self.k, 1))
+ q = 2 * self.y - 1
+ qxb = q * np.dot(self.x, beta)
+ lamb = q * norm.pdf(qxb) / norm.cdf(qxb)
+ gradient = np.dot(lamb.T, self.x)[0]
+ return gradient
+
+ def hessian(self, par):
+ beta = np.reshape(np.array(par), (self.k, 1))
+ q = 2 * self.y - 1
+ xb = np.dot(self.x, beta)
+ qxb = q * xb
+ lamb = q * norm.pdf(qxb) / norm.cdf(qxb)
+ hessian = np.dot((self.x.T), (-lamb * (lamb + xb) * self.x))
+ return hessian
+
+
+class Probit(BaseProbit):
+
+ """
+ Classic non-spatial Probit and spatial diagnostics. The class includes a
+ printout that formats all the results and tests in a nice format.
+
+ The diagnostics for spatial dependence currently implemented are:
+
+ * Pinkse Error [1]_
+ * Kelejian and Prucha Moran's I [2]_
+ * Pinkse & Slade Error [3]_
+
+ Parameters
+ ----------
+
+ x : array
+ nxk array of independent variables (assumed to be aligned with y)
+ y : array
+ nx1 array of dependent binary variable
+ w : W
+ PySAL weights instance aligned with y
+ optim : string
+ Optimization method.
+ Default: 'newton' (Newton-Raphson).
+ Alternatives: 'ncg' (Newton-CG), 'bfgs' (BFGS algorithm)
+ scalem : string
+ Method to calculate the scale of the marginal effects.
+ Default: 'phimean' (Mean of individual marginal effects)
+ Alternative: 'xmean' (Marginal effects at variables mean)
+ maxiter : int
+ Maximum number of iterations until optimizer stops
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+
+ Attributes
+ ----------
+
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ y : array
+ nx1 array of dependent variable
+ betas : array
+ kx1 array with estimated coefficients
+ predy : array
+ nx1 array of predicted y values
+ n : int
+ Number of observations
+ k : int
+ Number of variables
+ vm : array
+ Variance-covariance matrix (kxk)
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ xmean : array
+ Mean of the independent variables (kx1)
+ predpc : float
+ Percent of y correctly predicted
+ logl : float
+ Log-Likelihhod of the estimation
+ scalem : string
+ Method to calculate the scale of the marginal effects.
+ scale : float
+ Scale of the marginal effects.
+ slopes : array
+ Marginal effects of the independent variables (k-1x1)
+ slopes_vm : array
+ Variance-covariance matrix of the slopes (k-1xk-1)
+ LR : tuple
+ Likelihood Ratio test of all coefficients = 0
+ (test statistics, p-value)
+ Pinkse_error: float
+ Lagrange Multiplier test against spatial error correlation.
+ Implemented as presented in Pinkse (2004)
+ KP_error : float
+ Moran's I type test against spatial error correlation.
+ Implemented as presented in Kelejian and Prucha (2001)
+ PS_error : float
+ Lagrange Multiplier test against spatial error correlation.
+ Implemented as presented in Pinkse and Slade (1998)
+ warning : boolean
+ if True Maximum number of iterations exceeded or gradient
+ and/or function calls not changing.
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ title : string
+ Name of the regression method used
+
+ References
+ ----------
+ .. [1] Pinkse, J. (2004). Moran-flavored tests with nuisance parameter. In: Anselin, L., Florax, R. J., Rey, S. J. (editors) Advances in Spatial Econometrics, pages 67-77. Springer-Verlag, Heidelberg.
+ .. [2] Kelejian, H., Prucha, I. (2001) "On the asymptotic distribution of the Moran I test statistic with applications". Journal of Econometrics, 104(2):219-57.
+ .. [3] Pinkse, J., Slade, M. E. (1998) "Contracting in space: an application of spatial statistics to discrete-choice models". Journal of Econometrics, 85(1):125-54.
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import numpy as np
+ >>> import pysal
+
+ Open data on Columbus neighborhood crime (49 areas) using pysal.open().
+ This is the DBF associated with the Columbus shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> dbf = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+
+ Extract the CRIME column (crime) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept. Since we want to run a probit model and for this
+ example we use the Columbus data, we also need to transform the continuous
+ CRIME variable into a binary variable. As in McMillen, D. (1992) "Probit with
+ spatial autocorrelation". Journal of Regional Science 32(3):335-48, we define
+ y = 1 if CRIME > 40.
+
+ >>> y = np.array([dbf.by_col('CRIME')]).T
+ >>> y = (y>40).astype(float)
+
+ Extract HOVAL (home values) and INC (income) vectors from the DBF to be used as
+ independent variables in the regression. Note that PySAL requires this to
+ be an nxj numpy array, where j is the number of independent variables (not
+ including a constant). By default this class adds a vector of ones to the
+ independent variables passed in.
+
+ >>> names_to_extract = ['INC', 'HOVAL']
+ >>> x = np.array([dbf.by_col(name) for name in names_to_extract]).T
+
+ Since we want to the test the probit model for spatial dependence, we need to
+ specify the spatial weights matrix that includes the spatial configuration of
+ the observations into the error component of the model. To do that, we can open
+ an already existing gal file or create a new one. In this case, we will use
+ ``columbus.gal``, which contains contiguity relationships between the
+ observations in the Columbus dataset we are using throughout this example.
+ Note that, in order to read the file, not only to open it, we need to
+ append '.read()' at the end of the command.
+
+ >>> w = pysal.open(pysal.examples.get_path("columbus.gal"), 'r').read()
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. In PySAL, this
+ can be easily performed in the following way:
+
+ >>> w.transform='r'
+
+ We are all set with the preliminaries, we are good to run the model. In this
+ case, we will need the variables and the weights matrix. If we want to
+ have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional.
+
+ >>> model = Probit(y, x, w=w, name_y='crime', name_x=['income','home value'], name_ds='columbus', name_w='columbus.gal')
+
+ Once we have run the model, we can explore a little bit the output. The
+ regression object we have created has many attributes so take your time to
+ discover them.
+
+ >>> np.around(model.betas, decimals=6)
+ array([[ 3.353811],
+ [-0.199653],
+ [-0.029514]])
+
+ >>> np.around(model.vm, decimals=6)
+ array([[ 0.852814, -0.043627, -0.008052],
+ [-0.043627, 0.004114, -0.000193],
+ [-0.008052, -0.000193, 0.00031 ]])
+
+ Since we have provided a spatial weigths matrix, the diagnostics for
+ spatial dependence have also been computed. We can access them and their
+ p-values individually:
+
+ >>> tests = np.array([['Pinkse_error','KP_error','PS_error']])
+ >>> stats = np.array([[model.Pinkse_error[0],model.KP_error[0],model.PS_error[0]]])
+ >>> pvalue = np.array([[model.Pinkse_error[1],model.KP_error[1],model.PS_error[1]]])
+ >>> print np.hstack((tests.T,np.around(np.hstack((stats.T,pvalue.T)),6)))
+ [['Pinkse_error' '3.131719' '0.076783']
+ ['KP_error' '1.721312' '0.085194']
+ ['PS_error' '2.558166' '0.109726']]
+
+ Or we can easily obtain a full summary of all the results nicely formatted and
+ ready to be printed simply by typing 'print model.summary'
+
+ """
+
+ def __init__(
+ self, y, x, w=None, optim='newton', scalem='phimean', maxiter=100,
+ vm=False, name_y=None, name_x=None, name_w=None, name_ds=None,
+ spat_diag=False):
+
+ n = USER.check_arrays(y, x)
+ USER.check_y(y, n)
+ if w:
+ USER.check_weights(w, y)
+ spat_diag = True
+ x_constant = USER.check_constant(x)
+ BaseProbit.__init__(self, y=y, x=x_constant, w=w,
+ optim=optim, scalem=scalem, maxiter=maxiter)
+ self.title = "CLASSIC PROBIT ESTIMATOR"
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_y = USER.set_name_y(name_y)
+ self.name_x = USER.set_name_x(name_x, x)
+ self.name_w = USER.set_name_w(name_w, w)
+ SUMMARY.Probit(reg=self, w=w, vm=vm, spat_diag=spat_diag)
+
+
+def newton(flogl, start, fgrad, fhess, maxiter):
+ """
+ Calculates the Newton-Raphson method
+
+ Parameters
+ ----------
+
+ flogl : lambda
+ Function to calculate the log-likelihood
+ start : array
+ kx1 array of starting values
+ fgrad : lambda
+ Function to calculate the gradient
+ fhess : lambda
+ Function to calculate the hessian
+ maxiter : int
+ Maximum number of iterations until optimizer stops
+ """
+ warn = 0
+ iteration = 0
+ par_hat0 = start
+ m = 1
+ while (iteration < maxiter and m >= 1e-04):
+ H = -la.inv(fhess(par_hat0))
+ g = fgrad(par_hat0).reshape(start.shape)
+ Hg = np.dot(H, g)
+ par_hat0 = par_hat0 + Hg
+ iteration += 1
+ m = np.dot(g.T, Hg)
+ if iteration == maxiter:
+ warn = 1
+ logl = flogl(par_hat0)
+ return (par_hat0, logl, warn)
+
+
+def sp_tests(reg):
+ """
+ Calculates tests for spatial dependence in Probit models
+
+ Parameters
+ ----------
+
+ reg : regression object
+ output instance from a probit model
+ """
+ if reg.w:
+ w = reg.w.sparse
+ Phi = reg.predy
+ phi = reg.phiy
+ # Pinkse_error:
+ Phi_prod = Phi * (1 - Phi)
+ u_naive = reg.u_naive
+ u_gen = reg.u_gen
+ sig2 = np.sum((phi * phi) / Phi_prod) / reg.n
+ LM_err_num = np.dot(u_gen.T, (w * u_gen)) ** 2
+ trWW = np.sum((w * w).diagonal())
+ trWWWWp = trWW + np.sum((w * w.T).diagonal())
+ LM_err = float(1.0 * LM_err_num / (sig2 ** 2 * trWWWWp))
+ LM_err = np.array([LM_err, chisqprob(LM_err, 1)])
+ # KP_error:
+ moran = moran_KP(reg.w, u_naive, Phi_prod)
+ # Pinkse-Slade_error:
+ u_std = u_naive / np.sqrt(Phi_prod)
+ ps_num = np.dot(u_std.T, (w * u_std)) ** 2
+ trWpW = np.sum((w.T * w).diagonal())
+ ps = float(ps_num / (trWW + trWpW))
+ # chi-square instead of bootstrap.
+ ps = np.array([ps, chisqprob(ps, 1)])
+ else:
+ raise Exception, "W matrix not provided to calculate spatial test."
+ return LM_err, moran, ps
+
+
+def moran_KP(w, u, sig2i):
+ """
+ Calculates Moran-flavoured tests
+
+ Parameters
+ ----------
+
+ w : W
+ PySAL weights instance aligned with y
+ u : array
+ nx1 array of naive residuals
+ sig2i : array
+ nx1 array of individual variance
+ """
+ w = w.sparse
+ moran_num = np.dot(u.T, (w * u))
+ E = SP.lil_matrix(w.get_shape())
+ E.setdiag(sig2i.flat)
+ E = E.asformat('csr')
+ WE = w * E
+ moran_den = np.sqrt(np.sum((WE * WE + (w.T * E) * WE).diagonal()))
+ moran = float(1.0 * moran_num / moran_den)
+ moran = np.array([moran, norm.sf(abs(moran)) * 2.])
+ return moran
+
+
+def _test():
+ import doctest
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ doctest.testmod()
+ np.set_printoptions(suppress=start_suppress)
+
+if __name__ == '__main__':
+ _test()
+ import numpy as np
+ import pysal
+ dbf = pysal.open(pysal.examples.get_path('columbus.dbf'), 'r')
+ y = np.array([dbf.by_col('CRIME')]).T
+ var_x = ['INC', 'HOVAL']
+ x = np.array([dbf.by_col(name) for name in var_x]).T
+ w = pysal.open(pysal.examples.get_path("columbus.gal"), 'r').read()
+ w.transform = 'r'
+ probit1 = Probit(
+ (y > 40).astype(float), x, w=w, name_x=var_x, name_y="CRIME",
+ name_ds="Columbus", name_w="columbus.dbf")
+ # print probit1.summary
diff --git a/pysal/spreg/regimes.py b/pysal/spreg/regimes.py
new file mode 100644
index 0000000..e633576
--- /dev/null
+++ b/pysal/spreg/regimes.py
@@ -0,0 +1,689 @@
+import numpy as np
+import pysal
+import scipy.sparse as SP
+import itertools as iter
+from scipy.stats import f, chisqprob
+import numpy.linalg as la
+from utils import spbroadcast
+
+"""
+Tools for different regimes procedure estimations
+"""
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, \
+ Daniel Arribas-Bel darribas at asu.edu, \
+ Pedro V. Amaral pedro.amaral at asu.edu"
+
+
+class Chow:
+
+ '''
+ Chow test of coefficient stability across regimes. The test is a
+ particular case of the Wald statistic in which the constraint are setup
+ according to the spatial or other type of regime structure
+ ...
+
+ Parameters
+ ==========
+ reg : regression object
+ Regression object from PySAL.spreg which is assumed to have the
+ following attributes:
+
+ * betas : coefficient estimates
+ * vm : variance covariance matrix of betas
+ * kr : Number of variables varying across regimes
+ * kryd : Number of endogenous variables varying across regimes
+ * kf : Number of variables fixed (global) across regimes
+ * nr : Number of regimes
+
+ Attributes
+ ==========
+ joint : tuple
+ Pair of Wald statistic and p-value for the setup of global
+ regime stability, that is all betas are the same across
+ regimes.
+ regi : array
+ kr x 2 array with Wald statistic (col 0) and its p-value (col 1)
+ for each beta that varies across regimes. The restrictions
+ are setup to test for the global stability (all regimes have the
+ same parameter) of the beta.
+
+ Examples
+ ========
+ >>> import numpy as np
+ >>> import pysal
+ >>> from ols_regimes import OLS_Regimes
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> y_var = 'CRIME'
+ >>> y = np.array([db.by_col(y_var)]).reshape(49,1)
+ >>> x_var = ['INC','HOVAL']
+ >>> x = np.array([db.by_col(name) for name in x_var]).T
+ >>> r_var = 'NSA'
+ >>> regimes = db.by_col(r_var)
+ >>> olsr = OLS_Regimes(y, x, regimes, constant_regi='many', nonspat_diag=False, spat_diag=False, name_y=y_var, name_x=x_var, name_ds='columbus', name_regimes=r_var, regime_err_sep=False)
+ >>> print olsr.name_x_r #x_var
+ ['CONSTANT', 'INC', 'HOVAL']
+ >>> print olsr.chow.regi
+ [[ 0.01020844 0.91952121]
+ [ 0.46024939 0.49750745]
+ [ 0.55477371 0.45637369]]
+ >>> print 'Joint test:'
+ Joint test:
+ >>> print olsr.chow.joint
+ (0.6339319928978806, 0.8886223520178802)
+ '''
+
+ def __init__(self, reg):
+ kr, kf, kryd, nr, betas, vm = reg.kr, reg.kf, reg.kryd, reg.nr, reg.betas, reg.vm
+ if betas.shape[0] != vm.shape[0]:
+ if kf > 0:
+ betas = betas[0:vm.shape[0], :]
+ kf = kf - 1
+ else:
+ brange = []
+ for i in range(nr):
+ brange.extend(range(i * (kr + 1), i * (kr + 1) + kr))
+ betas = betas[brange, :]
+ r_global = []
+ regi = np.zeros((reg.kr, 2))
+ for vari in np.arange(kr):
+ r_vari = buildR1var(vari, kr, kf, kryd, nr)
+ r_global.append(r_vari)
+ q = np.zeros((r_vari.shape[0], 1))
+ regi[vari, :] = wald_test(betas, r_vari, q, vm)
+ r_global = np.vstack(tuple(r_global))
+ q = np.zeros((r_global.shape[0], 1))
+ joint = wald_test(betas, r_global, q, vm)
+ self.joint = joint
+ self.regi = regi
+
+
+class Wald:
+
+ '''
+ Chi sq. Wald statistic to test for restriction of coefficients.
+ Implementation following Greene [1]_ eq. (17-24), p. 488
+ ...
+
+ Parameters
+ ==========
+ reg : regression object
+ Regression object from PySAL.spreg
+ r : array
+ Array of dimension Rxk (R being number of restrictions) with constrain setup.
+ q : array
+ Rx1 array with constants in the constraint setup. See Greene
+ [1]_ for reference.
+
+ Attributes
+ ==========
+ w : float
+ Wald statistic
+ pvalue : float
+ P value for Wald statistic calculated as a Chi sq. distribution
+ with R degrees of freedom
+
+ References
+ ==========
+ .. [1] W. Greene. 2003. Econometric Analysis (Fifth Edtion). Prentice Hall, Upper
+ Saddle River.
+ '''
+
+ def __init__(self, reg, r, q=None):
+ if not q:
+ q = np.zeros((r.shape[0], 1))
+ self.w, self.pvalue = wald_test(reg.betas, r, q, reg.vm)
+
+
+class Regimes_Frame:
+
+ '''
+ Setup framework to work with regimes. Basically it involves:
+ * Dealing with the constant in a regimes world
+ * Creating a sparse representation of X
+ * Generating a list of names of X taking into account regimes
+ ...
+
+ Parameters
+ ==========
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ constant_regi: [False, 'one', 'many']
+ Switcher controlling the constant term setup. It may take
+ the following values:
+
+ * False: no constant term is appended in any way
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime (default)
+ cols2regi : list, 'all'
+ Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all' (default), all the variables vary by regime.
+ names : None, list of strings
+ Names of independent variables for use in output
+
+ Returns
+ =======
+ x : csr sparse matrix
+ Sparse matrix containing X variables properly aligned for
+ regimes regression. 'xsp' is of dimension (n, k*r) where 'r'
+ is the number of different regimes
+ The structure of the alignent is X1r1 X2r1 ... X1r2 X2r2 ...
+ names : None, list of strings
+ Names of independent variables for use in output
+ conveniently arranged by regimes. The structure of the name
+ is "regimeName_-_varName"
+ kr : int
+ Number of variables/columns to be "regimized" or subject
+ to change by regime. These will result in one parameter
+ estimate by regime for each variable (i.e. nr parameters per
+ variable)
+ kf : int
+ Number of variables/columns to be considered fixed or
+ global across regimes and hence only obtain one parameter
+ estimate
+ nr : int
+ Number of different regimes in the 'regimes' list
+
+ '''
+
+ def __init__(self, x, regimes, constant_regi, cols2regi, names=None, yend=False):
+ if cols2regi == 'all':
+ cols2regi = [True] * x.shape[1]
+ else:
+ if yend:
+ cols2regi = cols2regi[-x.shape[1]:]
+ else:
+ cols2regi = cols2regi[0:x.shape[1]]
+ if constant_regi:
+ x = np.hstack((np.ones((x.shape[0], 1)), x))
+ if constant_regi == 'one':
+ cols2regi.insert(0, False)
+ elif constant_regi == 'many':
+ cols2regi.insert(0, True)
+ else:
+ raise Exception, "Invalid argument (%s) passed for 'constant_regi'. Please secify a valid term." % str(
+ constant)
+ try:
+ x = regimeX_setup(x, regimes, cols2regi,
+ self.regimes_set, constant=constant_regi)
+ except AttributeError:
+ self.regimes_set = _get_regimes_set(regimes)
+ x = regimeX_setup(x, regimes, cols2regi,
+ self.regimes_set, constant=constant_regi)
+
+ kr = len(np.where(np.array(cols2regi) == True)[0])
+ if yend:
+ self.kr += kr
+ self.kf += len(cols2regi) - kr
+ self.kryd = kr
+ else:
+ self.kr = kr
+ self.kf = len(cols2regi) - self.kr
+ self.kryd = 0
+ self.nr = len(set(regimes))
+
+ if names:
+ names = set_name_x_regimes(
+ names, regimes, constant_regi, cols2regi, self.regimes_set)
+
+ return (x, names)
+
+
+def wald_test(betas, r, q, vm):
+ '''
+ Chi sq. Wald statistic to test for restriction of coefficients.
+ Implementation following Greene [1]_ eq. (17-24), p. 488
+ ...
+
+ Parameters
+ ==========
+ betas : array
+ kx1 array with coefficient estimates
+ r : array
+ Array of dimension Rxk (R being number of restrictions) with constrain setup.
+ q : array
+ Rx1 array with constants in the constraint setup. See Greene
+ [1]_ for reference.
+ vm : array
+ kxk variance-covariance matrix of coefficient estimates
+
+ Returns
+ =======
+ w : float
+ Wald statistic
+ pvalue : float
+ P value for Wald statistic calculated as a Chi sq. distribution
+ with R degrees of freedom
+
+ References
+ ==========
+ .. [1] W. Greene. 2003. Econometric Analysis (Fifth Edtion). Prentice Hall, Upper
+ Saddle River.
+ '''
+ rbq = np.dot(r, betas) - q
+ rvri = la.inv(np.dot(r, np.dot(vm, r.T)))
+ w = np.dot(rbq.T, np.dot(rvri, rbq))[0][0]
+ df = r.shape[0]
+ pvalue = chisqprob(w, df)
+ return w, pvalue
+
+
+def buildR(kr, kf, nr):
+ '''
+ Build R matrix to globally test for spatial heterogeneity across regimes.
+ The constraint setup reflects the null every beta is the same
+ across regimes
+ ...
+
+ Parameters
+ ==========
+ kr : int
+ Number of variables that vary across regimes ("regimized")
+ kf : int
+ Number of variables that do not vary across regimes ("fixed" or
+ global)
+ nr : int
+ Number of regimes
+
+ Returns
+ =======
+ R : array
+ Array with constrain setup to test stability across regimes of
+ one variable
+ '''
+ return np.vstack(tuple(map(buildR1var, np.arange(kr), [kr] * kr, [kf] * kr, [nr] * kr)))
+
+
+def buildR1var(vari, kr, kf, kryd, nr):
+ '''
+ Build R matrix to test for spatial heterogeneity across regimes in one
+ variable. The constraint setup reflects the null betas for variable 'vari'
+ are the same across regimes
+ ...
+
+ Parameters
+ ==========
+ vari : int
+ Position of the variable to be tested (order in the sequence of
+ variables per regime)
+ kr : int
+ Number of variables that vary across regimes ("regimized")
+ kf : int
+ Number of variables that do not vary across regimes ("fixed" or
+ global)
+ nr : int
+ Number of regimes
+
+ Returns
+ =======
+ R : array
+ Array with constrain setup to test stability across regimes of
+ one variable
+ '''
+ ncols = (kr * nr)
+ nrows = nr - 1
+ r = np.zeros((nrows, ncols), dtype=int)
+ rbeg = 0
+ krexog = kr - kryd
+ if vari < krexog:
+ kr_j = krexog
+ cbeg = vari
+ else:
+ kr_j = kryd
+ cbeg = krexog * (nr - 1) + vari
+ r[rbeg: rbeg + nrows, cbeg] = 1
+ for j in np.arange(nrows):
+ r[rbeg + j, kr_j + cbeg + j * kr_j] = -1
+ return np.hstack((r, np.zeros((nrows, kf), dtype=int)))
+
+
+def regimeX_setup(x, regimes, cols2regi, regimes_set, constant=False):
+ '''
+ Flexible full setup of a regime structure
+
+ NOTE: constant term, if desired in the model, should be included in the x
+ already
+ ...
+
+ Parameters
+ ==========
+ x : np.array
+ Dense array of dimension (n, k) with values for all observations
+ IMPORTANT: constant term (if desired in the model) should be
+ included
+ regimes : list
+ list of n values with the mapping of each observation to a
+ regime. Assumed to be aligned with 'x'.
+ cols2regi : list
+ List of k booleans indicating whether each column should be
+ considered as different per regime (True) or held constant
+ across regimes (False)
+ regimes_set : list
+ List of ordered regimes tags
+ constant : [False, 'one', 'many']
+ Switcher controlling the constant term setup. It may take
+ the following values:
+
+ * False: no constant term is appended in any way
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime
+
+ Returns
+ =======
+ xsp : csr sparse matrix
+ Sparse matrix containing the full setup for a regimes model
+ as specified in the arguments passed
+ NOTE: columns are reordered so first are all the regime
+ columns then all the global columns (this makes it much more
+ efficient)
+ Structure of the output matrix (assuming X1, X2 to vary
+ across regimes and constant term, X3 and X4 to be global):
+ X1r1, X2r1, ... , X1r2, X2r2, ... , constant, X3, X4
+ '''
+ cols2regi = np.array(cols2regi)
+ if set(cols2regi) == set([True]):
+ xsp = x2xsp(x, regimes, regimes_set)
+ elif set(cols2regi) == set([False]):
+ xsp = SP.csr_matrix(x)
+ else:
+ not_regi = x[:, np.where(cols2regi == False)[0]]
+ regi_subset = x[:, np.where(cols2regi)[0]]
+ regi_subset = x2xsp(regi_subset, regimes, regimes_set)
+ xsp = SP.hstack((regi_subset, SP.csr_matrix(not_regi)), format='csr')
+ return xsp
+
+
+def set_name_x_regimes(name_x, regimes, constant_regi, cols2regi, regimes_set):
+ '''
+ Generate the set of variable names in a regimes setup, according to the
+ order of the betas
+
+ NOTE: constant term, if desired in the model, should be included in the x
+ already
+ ...
+
+ Parameters
+ ==========
+ name_x : list/None
+ If passed, list of strings with the names of the
+ variables aligned with the original dense array x
+ IMPORTANT: constant term (if desired in the model) should be
+ included
+ regimes : list
+ list of n values with the mapping of each observation to a
+ regime. Assumed to be aligned with 'x'.
+ constant_regi : [False, 'one', 'many']
+ Switcher controlling the constant term setup. It may take
+ the following values:
+
+ * False: no constant term is appended in any way
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime
+ cols2regi : list
+ List of k booleans indicating whether each column should be
+ considered as different per regime (True) or held constant
+ across regimes (False)
+ regimes_set : list
+ List of ordered regimes tags
+ Returns
+ =======
+ name_x_regi
+ '''
+ k = len(cols2regi)
+ if constant_regi:
+ k -= 1
+ if not name_x:
+ name_x = ['var_' + str(i + 1) for i in range(k)]
+ if constant_regi:
+ name_x.insert(0, 'CONSTANT')
+ nxa = np.array(name_x)
+ c2ra = np.array(cols2regi)
+ vars_regi = nxa[np.where(c2ra == True)]
+ vars_glob = nxa[np.where(c2ra == False)]
+ name_x_regi = []
+ for r in regimes_set:
+ rl = ['%s_%s' % (str(r), i) for i in vars_regi]
+ name_x_regi.extend(rl)
+ name_x_regi.extend(['_Global_%s' % i for i in vars_glob])
+ return name_x_regi
+
+
+def w_regime(w, regi_ids, regi_i, transform=True, min_n=None):
+ '''
+ Returns the subset of W matrix according to a given regime ID
+ ...
+
+ Attributes
+ ==========
+ w : pysal W object
+ Spatial weights object
+ regi_ids : list
+ Contains the location of observations in y that are assigned to regime regi_i
+ regi_i : string or float
+ The regime for which W will be subset
+
+ Returns
+ =======
+ w_regi_i : pysal W object
+ Subset of W for regime regi_i
+ '''
+ w_ids = map(w.id_order.__getitem__, regi_ids)
+ warn = None
+ w_regi_i = pysal.weights.w_subset(w, w_ids, silent_island_warning=True)
+ if min_n:
+ if w_regi_i.n < min_n:
+ raise Exception, "There are less observations than variables in regime %s." % regi_i
+ if transform:
+ w_regi_i.transform = w.get_transform()
+ if w_regi_i.islands:
+ warn = "The regimes operation resulted in islands for regime %s." % regi_i
+ return w_regi_i, warn
+
+
+def w_regimes(w, regimes, regimes_set, transform=True, get_ids=None, min_n=None):
+ '''
+ ######### DEPRECATED ##########
+ Subsets W matrix according to regimes
+ ...
+
+ Attributes
+ ==========
+ w : pysal W object
+ Spatial weights object
+ regimes : list
+ list of n values with the mapping of each observation to a
+ regime. Assumed to be aligned with 'x'.
+ regimes_set : list
+ List of ordered regimes tags
+
+ Returns
+ =======
+ w_regi : dictionary
+ Dictionary containing the subsets of W according to regimes: [r1:w1, r2:w2, ..., rR:wR]
+ '''
+ regi_ids = dict((r, list(np.where(np.array(regimes) == r)[0]))
+ for r in regimes_set)
+ w_ids = dict((r, map(w.id_order.__getitem__, regi_ids[r]))
+ for r in regimes_set)
+ w_regi_i = {}
+ warn = None
+ for r in regimes_set:
+ w_regi_i[r] = pysal.weights.w_subset(w, w_ids[r],
+ silent_island_warning=True)
+ if min_n:
+ if w_regi_i[r].n < min_n:
+ raise Exception, "There are less observations than variables in regime %s." % r
+ if transform:
+ w_regi_i[r].transform = w.get_transform()
+ if w_regi_i[r].islands:
+ warn = "The regimes operation resulted in islands for regime %s." % r
+ if get_ids:
+ get_ids = regi_ids
+ return w_regi_i, get_ids, warn
+
+
+def w_regimes_union(w, w_regi_i, regimes_set):
+ '''
+ Combines the subsets of the W matrix according to regimes
+ ...
+
+ Attributes
+ ==========
+ w : pysal W object
+ Spatial weights object
+ w_regi_i : dictionary
+ Dictionary containing the subsets of W according to regimes: [r1:w1, r2:w2, ..., rR:wR]
+ regimes_set : list
+ List of ordered regimes tags
+
+ Returns
+ =======
+ w_regi : pysal W object
+ Spatial weights object containing the union of the subsets of W
+ '''
+ w_regi = pysal.weights.w_union(w_regi_i[regimes_set[0]],
+ w_regi_i[regimes_set[1]], silent_island_warning=True)
+ if len(regimes_set) > 2:
+ for i in range(len(regimes_set))[2:]:
+ w_regi = pysal.weights.w_union(w_regi,
+ w_regi_i[regimes_set[i]], silent_island_warning=True)
+ w_regi = pysal.weights.remap_ids(w_regi, dict((i, i)
+ for i in w_regi.id_order), w.id_order)
+ w_regi.transform = w.get_transform()
+ return w_regi
+
+
+def x2xsp(x, regimes, regimes_set):
+ '''
+ Convert X matrix with regimes into a sparse X matrix that accounts for the
+ regimes
+ ...
+
+ Attributes
+ ==========
+ x : np.array
+ Dense array of dimension (n, k) with values for all observations
+ regimes : list
+ list of n values with the mapping of each observation to a
+ regime. Assumed to be aligned with 'x'.
+ regimes_set : list
+ List of ordered regimes tags
+ Returns
+ =======
+ xsp : csr sparse matrix
+ Sparse matrix containing X variables properly aligned for
+ regimes regression. 'xsp' is of dimension (n, k*r) where 'r'
+ is the number of different regimes
+ The structure of the alignent is X1r1 X2r1 ... X1r2 X2r2 ...
+ '''
+ n, k = x.shape
+ data = x.flatten()
+ R = len(regimes_set)
+ # X1r1 X2r1 ... X1r2 X2r2 ...
+ regime_by_row = np.array([[r] * k for r in list(regimes_set)]).flatten()
+ row_map = dict((r, np.where(regime_by_row == r)[0]) for r in regimes_set)
+ indices = np.array([row_map[row] for row in regimes]).flatten()
+ indptr = np.zeros((n + 1, ), dtype=int)
+ indptr[:-1] = list(np.arange(n) * k)
+ indptr[-1] = n * k
+ return SP.csr_matrix((data, indices, indptr))
+
+
+def check_cols2regi(constant_regi, cols2regi, x, yend=None, add_cons=True):
+ ''' Checks if dimensions of list cols2regi match number of variables. '''
+
+ if add_cons:
+ is_cons = 1
+ if constant_regi == 'many':
+ regi_cons = [True]
+ elif constant_regi == 'one':
+ regi_cons = [False]
+ else:
+ is_cons = 0
+ regi_cons = []
+ try:
+ tot_k = x.shape[1] + yend.shape[1]
+ except:
+ tot_k = x.shape[1]
+ if cols2regi == 'all':
+ cols2regi = regi_cons + [True] * tot_k
+ else:
+ cols2regi = regi_cons + cols2regi
+ if len(cols2regi) - is_cons != tot_k:
+ raise Exception, "The lenght of list 'cols2regi' must be equal to the amount of variables (exogenous + endogenous) when not using cols2regi=='all'."
+ return cols2regi
+
+
+def _get_regimes_set(regimes):
+ ''' Creates a list with regimes in alphabetical order. '''
+ regimes_set = list(set(regimes))
+ if isinstance(regimes_set[0], float):
+ regimes_set1 = list(set(map(int, regimes_set)))
+ if len(regimes_set1) == len(regimes_set):
+ regimes_set = regimes_set1
+ regimes_set.sort()
+ return regimes_set
+
+
+def _get_weighted_var(regimes, regimes_set, sig2n_k, u, y, x, yend=None, q=None):
+ regi_ids = dict((r, list(np.where(np.array(regimes) == r)[0]))
+ for r in regimes_set)
+ if sig2n_k:
+ sig = dict((r, np.dot(u[regi_ids[r]].T, u[regi_ids[r]]) / (len(regi_ids[r]) - x.shape[1]))
+ for r in regimes_set)
+ else:
+ sig = dict((r, np.dot(u[regi_ids[r]].T, u[regi_ids[r]]) / len(regi_ids[r]))
+ for r in regimes_set)
+ sig_vec = np.zeros(y.shape, float)
+ y2 = np.zeros(y.shape, float)
+ for r in regimes_set:
+ sig_vec[regi_ids[r]] = 1 / float(np.sqrt(sig[r]))
+ y2[regi_ids[r]] = y[regi_ids[r]] / float(np.sqrt(sig[r]))
+ x2 = spbroadcast(x, sig_vec)
+ if yend != None:
+ yend2 = spbroadcast(yend, sig_vec)
+ q2 = spbroadcast(q, sig_vec)
+ return y2, x2, yend2, q2
+ else:
+ return y2, x2
+
+
+def _test():
+ import doctest
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ doctest.testmod()
+ np.set_printoptions(suppress=start_suppress)
+
+if __name__ == '__main__':
+ _test()
+ import numpy as np
+ import pysal
+ from ols_regimes import OLS_Regimes
+ db = pysal.open(pysal.examples.get_path('columbus.dbf'), 'r')
+ y_var = 'CRIME'
+ y = np.array([db.by_col(y_var)]).reshape(49, 1)
+ x_var = ['INC', 'HOVAL']
+ x = np.array([db.by_col(name) for name in x_var]).T
+ r_var = 'NSA'
+ regimes = db.by_col(r_var)
+ w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ w.transform = 'r'
+ olsr = OLS_Regimes(
+ y, x, regimes, w=w, constant_regi='many', nonspat_diag=False, spat_diag=False,
+ name_y=y_var, name_x=x_var, name_ds='columbus', name_regimes=r_var, name_w='columbus.gal')
+ print olsr.summary
diff --git a/pysal/spreg/robust.py b/pysal/spreg/robust.py
new file mode 100644
index 0000000..0724b8d
--- /dev/null
+++ b/pysal/spreg/robust.py
@@ -0,0 +1,169 @@
+__author__ = "Luc Anselin luc.anselin at asu.edu, \
+ Pedro V. Amaral pedro.amaral at asu.edu, \
+ David C. Folch david.folch at asu.edu"
+
+import numpy as np
+import numpy.linalg as la
+from pysal import lag_spatial
+from utils import spdot, spbroadcast
+from user_output import check_constant
+
+
+def robust_vm(reg, gwk=None, sig2n_k=False):
+ """
+ Robust estimation of the variance-covariance matrix. Estimated by White (default) or HAC (if wk is provided).
+
+ Parameters
+ ----------
+
+ reg : Regression object (OLS or TSLS)
+ output instance from a regression model
+
+ gwk : PySAL weights object
+ Optional. Spatial weights based on kernel functions
+ If provided, returns the HAC variance estimation
+ sig2n_k : boolean
+ If True, then use n-k to rescale the vc matrix.
+ If False, use n. (White only)
+
+ Returns
+ --------
+
+ psi : kxk array
+ Robust estimation of the variance-covariance
+
+ Examples
+ --------
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> from ols import OLS
+ >>> from twosls import TSLS
+ >>> db=pysal.open(pysal.examples.get_path("NAT.dbf"),"r")
+ >>> y = np.array(db.by_col("HR90"))
+ >>> y = np.reshape(y, (y.shape[0],1))
+ >>> X = []
+ >>> X.append(db.by_col("RD90"))
+ >>> X.append(db.by_col("DV90"))
+ >>> X = np.array(X).T
+
+ Example with OLS with unadjusted standard errors
+
+ >>> ols = OLS(y,X)
+ >>> ols.vm
+ array([[ 0.17004545, 0.00226532, -0.02243898],
+ [ 0.00226532, 0.00941319, -0.00031638],
+ [-0.02243898, -0.00031638, 0.00313386]])
+
+ Example with OLS and White
+
+ >>> ols = OLS(y,X, robust='white')
+ >>> ols.vm
+ array([[ 0.24515481, 0.01093322, -0.03441966],
+ [ 0.01093322, 0.01798616, -0.00071414],
+ [-0.03441966, -0.00071414, 0.0050153 ]])
+
+ Example with OLS and HAC
+
+ >>> wk = pysal.kernelW_from_shapefile(pysal.examples.get_path('NAT.shp'),k=15,function='triangular', fixed=False)
+ >>> wk.transform = 'o'
+ >>> ols = OLS(y,X, robust='hac', gwk=wk)
+ >>> ols.vm
+ array([[ 0.29213532, 0.01670361, -0.03948199],
+ [ 0.01655557, 0.02295829, -0.00116874],
+ [-0.03941483, -0.00119077, 0.00568314]])
+
+ Example with 2SLS and White
+
+ >>> yd = []
+ >>> yd.append(db.by_col("UE90"))
+ >>> yd = np.array(yd).T
+ >>> q = []
+ >>> q.append(db.by_col("UE80"))
+ >>> q = np.array(q).T
+ >>> tsls = TSLS(y, X, yd, q=q, robust='white')
+ >>> tsls.vm
+ array([[ 0.29569954, 0.04119843, -0.02496858, -0.01640185],
+ [ 0.04119843, 0.03647762, 0.004702 , -0.00987345],
+ [-0.02496858, 0.004702 , 0.00648262, -0.00292891],
+ [-0.01640185, -0.00987345, -0.00292891, 0.0053322 ]])
+
+ Example with 2SLS and HAC
+
+ >>> tsls = TSLS(y, X, yd, q=q, robust='hac', gwk=wk)
+ >>> tsls.vm
+ array([[ 0.41985329, 0.06823119, -0.02883889, -0.02788116],
+ [ 0.06867042, 0.04887508, 0.00497443, -0.01367746],
+ [-0.02856454, 0.00501402, 0.0072195 , -0.00321604],
+ [-0.02810131, -0.01364908, -0.00318197, 0.00713251]])
+
+ """
+ if hasattr(reg, 'h'): # If reg has H, do 2SLS estimator. OLS otherwise.
+ tsls = True
+ xu = spbroadcast(reg.h, reg.u)
+ else:
+ tsls = False
+ xu = spbroadcast(reg.x, reg.u)
+
+ if gwk: # If gwk do HAC. White otherwise.
+ gwkxu = lag_spatial(gwk, xu)
+ psi0 = spdot(xu.T, gwkxu)
+ else:
+ psi0 = spdot(xu.T, xu)
+ if sig2n_k:
+ psi0 = psi0 * (1. * reg.n / (reg.n - reg.k))
+ if tsls:
+ psi1 = spdot(reg.varb, reg.zthhthi)
+ psi = spdot(psi1, np.dot(psi0, psi1.T))
+ else:
+ psi = spdot(reg.xtxi, np.dot(psi0, reg.xtxi))
+
+ return psi
+
+
+def hac_multi(reg, gwk, constant=False):
+ """
+ HAC robust estimation of the variance-covariance matrix for multi-regression object
+
+ Parameters
+ ----------
+
+ reg : Regression object (OLS or TSLS)
+ output instance from a regression model
+
+ gwk : PySAL weights object
+ Spatial weights based on kernel functions
+
+ Returns
+ --------
+
+ psi : kxk array
+ Robust estimation of the variance-covariance
+
+ """
+ if not constant:
+ reg.hac_var = check_constant(reg.hac_var)
+ xu = spbroadcast(reg.hac_var, reg.u)
+ gwkxu = lag_spatial(gwk, xu)
+ psi0 = spdot(xu.T, gwkxu)
+ counter = 0
+ for m in reg.multi:
+ reg.multi[m].robust = 'hac'
+ reg.multi[m].name_gwk = reg.name_gwk
+ try:
+ psi1 = spdot(reg.multi[m].varb, reg.multi[m].zthhthi)
+ reg.multi[m].vm = spdot(psi1, np.dot(psi0, psi1.T))
+ except:
+ reg.multi[m].vm = spdot(
+ reg.multi[m].xtxi, np.dot(psi0, reg.multi[m].xtxi))
+ reg.vm[(counter * reg.kr):((counter + 1) * reg.kr),
+ (counter * reg.kr):((counter + 1) * reg.kr)] = reg.multi[m].vm
+ counter += 1
+
+
+def _test():
+ import doctest
+ doctest.testmod()
+
+if __name__ == '__main__':
+ _test()
diff --git a/pysal/spreg/summary_output.py b/pysal/spreg/summary_output.py
new file mode 100644
index 0000000..26f9c0e
--- /dev/null
+++ b/pysal/spreg/summary_output.py
@@ -0,0 +1,1235 @@
+"""Internal helper files for user output."""
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, David C. Folch david.folch at asu.edu, Pedro V. Amaral pedro.amaral at asu.edu, Jing Yao jingyao at asu.edu"
+
+import textwrap as TW
+import numpy as np
+import copy as COPY
+import diagnostics as diagnostics
+import diagnostics_tsls as diagnostics_tsls
+import diagnostics_sp as diagnostics_sp
+import pysal
+import scipy
+from scipy.sparse.csr import csr_matrix
+
+__all__ = []
+
+
+###############################################################################
+############### Primary functions for running summary diagnostics #############
+###############################################################################
+
+"""
+This section contains one function for each user level regression class. These
+are called directly from the user class. Each one mixes and matches smaller
+functions located later in this module.
+"""
+
+
+def OLS(reg, vm, w, nonspat_diag, spat_diag, moran, white_test, regimes=False):
+ reg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag_ols(reg, reg.robust)
+ if nonspat_diag:
+ # compute diagnostics
+ reg.sig2ML = reg.sig2n
+ reg.f_stat = diagnostics.f_stat(reg)
+ reg.logll = diagnostics.log_likelihood(reg)
+ reg.aic = diagnostics.akaike(reg)
+ reg.schwarz = diagnostics.schwarz(reg)
+ reg.mulColli = diagnostics.condition_index(reg)
+ reg.jarque_bera = diagnostics.jarque_bera(reg)
+ reg.breusch_pagan = diagnostics.breusch_pagan(reg)
+ reg.koenker_bassett = diagnostics.koenker_bassett(reg)
+ if white_test:
+ reg.white = diagnostics.white(reg)
+ # organize summary output
+ reg.__summary['summary_nonspat_diag_1'] = summary_nonspat_diag_1(reg)
+ reg.__summary['summary_nonspat_diag_2'] = summary_nonspat_diag_2(reg)
+ if spat_diag:
+ # compute diagnostics and organize summary output
+ spat_diag_ols(reg, w, moran)
+ if regimes:
+ summary_regimes(reg)
+ summary_warning(reg)
+ summary(reg=reg, vm=vm, instruments=False,
+ nonspat_diag=nonspat_diag, spat_diag=spat_diag)
+
+
+def OLS_multi(reg, multireg, vm, nonspat_diag, spat_diag, moran, white_test, regimes=False, sur=False, w=False):
+ for m in multireg:
+ mreg = multireg[m]
+ mreg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag_ols(mreg, mreg.robust)
+ if nonspat_diag:
+ # compute diagnostics
+ mreg.sig2ML = mreg.sig2n
+ mreg.f_stat = diagnostics.f_stat(mreg)
+ mreg.logll = diagnostics.log_likelihood(mreg)
+ mreg.aic = diagnostics.akaike(mreg)
+ mreg.schwarz = diagnostics.schwarz(mreg)
+ mreg.mulColli = diagnostics.condition_index(mreg)
+ mreg.jarque_bera = diagnostics.jarque_bera(mreg)
+ mreg.breusch_pagan = diagnostics.breusch_pagan(mreg)
+ mreg.koenker_bassett = diagnostics.koenker_bassett(mreg)
+ if white_test:
+ mreg.white = diagnostics.white(mreg)
+ # organize summary output
+ mreg.__summary[
+ 'summary_nonspat_diag_1'] = summary_nonspat_diag_1(mreg)
+ mreg.__summary[
+ 'summary_nonspat_diag_2'] = summary_nonspat_diag_2(mreg)
+ if spat_diag:
+ # compute diagnostics and organize summary output
+ spat_diag_ols(mreg, mreg.w, moran)
+ if regimes:
+ summary_regimes(mreg, chow=False)
+ if sur:
+ summary_sur(mreg)
+ summary_warning(mreg)
+ multireg[m].__summary = mreg.__summary
+ reg.__summary = {}
+ if regimes:
+ summary_chow(reg)
+ if sur:
+ summary_sur(reg, u_cov=True)
+ if spat_diag:
+ # compute global diagnostics and organize summary output
+ spat_diag_ols(reg, w, moran)
+ summary_warning(reg)
+ summary_multi(reg=reg, multireg=multireg, vm=vm, instruments=False,
+ nonspat_diag=nonspat_diag, spat_diag=spat_diag)
+
+
+def TSLS(reg, vm, w, spat_diag, regimes=False):
+ reg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag(reg, reg.robust)
+ if spat_diag:
+ # compute diagnostics and organize summary output
+ spat_diag_instruments(reg, w)
+ # build coefficients table body
+ build_coefs_body_instruments(reg)
+ if regimes:
+ summary_regimes(reg)
+ summary_warning(reg)
+ summary(reg=reg, vm=vm, instruments=True,
+ nonspat_diag=False, spat_diag=spat_diag)
+
+
+def TSLS_multi(reg, multireg, vm, spat_diag, regimes=False, sur=False, w=False):
+ for m in multireg:
+ mreg = multireg[m]
+ mreg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag(mreg, mreg.robust)
+ if spat_diag:
+ # compute diagnostics and organize summary output
+ spat_diag_instruments(mreg, mreg.w)
+ # build coefficients table body
+ build_coefs_body_instruments(mreg)
+ if regimes:
+ summary_regimes(mreg, chow=False)
+ if sur:
+ summary_sur(mreg)
+ summary_warning(mreg)
+ multireg[m].__summary = mreg.__summary
+ reg.__summary = {}
+ if regimes:
+ summary_chow(reg)
+ if sur:
+ summary_sur(reg, u_cov=True)
+ if spat_diag:
+ # compute global diagnostics and organize summary output
+ spat_diag_instruments(reg, w)
+ summary_warning(reg)
+ summary_multi(reg=reg, multireg=multireg, vm=vm,
+ instruments=True, nonspat_diag=False, spat_diag=spat_diag)
+
+
+def GM_Lag(reg, vm, w, spat_diag, regimes=False):
+ reg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag_lag(reg, reg.robust, error=False)
+ if spat_diag:
+ # compute diagnostics and organize summary output
+ spat_diag_instruments(reg, w)
+ # build coefficients table body
+ summary_coefs_allx(reg, reg.z_stat)
+ summary_coefs_instruments(reg)
+ if regimes:
+ summary_regimes(reg)
+ summary_warning(reg)
+ summary(reg=reg, vm=vm, instruments=True,
+ nonspat_diag=False, spat_diag=spat_diag)
+
+
+def GM_Lag_multi(reg, multireg, vm, spat_diag, regimes=False, sur=False, w=False):
+ for m in multireg:
+ mreg = multireg[m]
+ mreg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag_lag(mreg, mreg.robust, error=False)
+ if spat_diag:
+ # compute diagnostics and organize summary output
+ spat_diag_instruments(mreg, mreg.w)
+ # build coefficients table body
+ summary_coefs_allx(mreg, mreg.z_stat)
+ summary_coefs_instruments(mreg)
+ if regimes:
+ summary_regimes(mreg, chow=False)
+ if sur:
+ summary_sur(mreg)
+ summary_warning(mreg)
+ multireg[m].__summary = mreg.__summary
+ reg.__summary = {}
+ if regimes:
+ summary_chow(reg)
+ if spat_diag:
+ pass
+ # compute global diagnostics and organize summary output
+ #spat_diag_instruments(reg, w)
+ summary_warning(reg)
+ summary_multi(reg=reg, multireg=multireg, vm=vm,
+ instruments=True, nonspat_diag=False, spat_diag=spat_diag)
+
+
+def ML_Lag(reg, w, vm, spat_diag, regimes=False): # extra space d
+ reg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag_lag(reg, robust=None, error=False)
+ reg.__summary['summary_r2'] += "%-20s:%12.3f %-22s:%12.3f\n" % (
+ 'Sigma-square ML', reg.sig2, 'Log likelihood', reg.logll)
+ reg.__summary['summary_r2'] += "%-20s:%12.3f %-22s:%12.3f\n" % (
+ 'S.E of regression', np.sqrt(reg.sig2), 'Akaike info criterion', reg.aic)
+ reg.__summary['summary_r2'] += " %-22s:%12.3f\n" % (
+ 'Schwarz criterion', reg.schwarz)
+ # build coefficients table body
+ summary_coefs_allx(reg, reg.z_stat)
+ if regimes:
+ summary_regimes(reg)
+ summary_warning(reg)
+ summary(reg=reg, vm=vm, instruments=False,
+ nonspat_diag=False, spat_diag=spat_diag)
+
+
+# extra space d
+def ML_Lag_multi(reg, multireg, vm, spat_diag, regimes=False, sur=False, w=False):
+ for m in multireg:
+ mreg = multireg[m]
+ mreg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag_lag(mreg, robust=None, error=False)
+ mreg.__summary['summary_r2'] += "%-20s:%12.3f %-22s:%12.3f\n" % (
+ 'Sigma-square ML', mreg.sig2, 'Log likelihood', mreg.logll)
+ mreg.__summary['summary_r2'] += "%-20s:%12.3f %-22s:%12.3f\n" % (
+ 'S.E of regression', np.sqrt(mreg.sig2), 'Akaike info criterion', mreg.aic)
+ mreg.__summary['summary_r2'] += " %-22s:%12.3f\n" % (
+ 'Schwarz criterion', mreg.schwarz)
+ # build coefficients table body
+ summary_coefs_allx(mreg, mreg.z_stat)
+ if regimes:
+ summary_regimes(mreg, chow=False)
+ summary_warning(mreg)
+ multireg[m].__summary = mreg.__summary
+ reg.__summary = {}
+ if regimes:
+ summary_chow(reg)
+ summary_warning(reg)
+ summary_multi(reg=reg, multireg=multireg, vm=vm,
+ instruments=False, nonspat_diag=False, spat_diag=spat_diag)
+
+
+def ML_Error(reg, w, vm, spat_diag, regimes=False): # extra space d
+ reg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag(reg, robust=None)
+ reg.__summary['summary_r2'] += "%-20s:%12.3f %-22s:%12.3f\n" % (
+ 'Sigma-square ML', reg.sig2, 'Log likelihood', reg.logll)
+ reg.__summary['summary_r2'] += "%-20s:%12.3f %-22s:%12.3f\n" % (
+ 'S.E of regression', np.sqrt(reg.sig2), 'Akaike info criterion', reg.aic)
+ reg.__summary['summary_r2'] += " %-22s:%12.3f\n" % (
+ 'Schwarz criterion', reg.schwarz)
+ # build coefficients table body
+ summary_coefs_allx(reg, reg.z_stat)
+ if regimes:
+ summary_regimes(reg)
+ summary_warning(reg)
+ summary(reg=reg, vm=vm, instruments=False,
+ nonspat_diag=False, spat_diag=spat_diag)
+
+
+# extra space d
+def ML_Error_multi(reg, multireg, vm, spat_diag, regimes=False, sur=False, w=False):
+ for m in multireg:
+ mreg = multireg[m]
+ mreg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag(mreg, robust=None)
+ mreg.__summary['summary_r2'] += "%-20s:%12.3f %-22s:%12.3f\n" % (
+ 'Sigma-square ML', mreg.sig2, 'Log likelihood', mreg.logll)
+ mreg.__summary['summary_r2'] += "%-20s:%12.3f %-22s:%12.3f\n" % (
+ 'S.E of regression', np.sqrt(mreg.sig2), 'Akaike info criterion', mreg.aic)
+ mreg.__summary['summary_r2'] += " %-22s:%12.3f\n" % (
+ 'Schwarz criterion', mreg.schwarz)
+ # build coefficients table body
+ summary_coefs_allx(mreg, mreg.z_stat)
+ if regimes:
+ summary_regimes(mreg, chow=False)
+ summary_warning(mreg)
+ multireg[m].__summary = mreg.__summary
+ reg.__summary = {}
+ if regimes:
+ summary_chow(reg, lambd=True)
+ summary_warning(reg)
+ summary_multi(reg=reg, multireg=multireg, vm=vm,
+ instruments=False, nonspat_diag=False, spat_diag=spat_diag)
+
+
+def GM_Error(reg, vm, w, regimes=False):
+ reg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag(reg, None)
+ # build coefficients table body
+ beta_position = summary_coefs_somex(reg, reg.z_stat)
+ summary_coefs_lambda(reg, reg.z_stat)
+ if regimes:
+ summary_regimes(reg)
+ summary_warning(reg)
+ summary(reg=reg, vm=vm, instruments=False,
+ nonspat_diag=False, spat_diag=False)
+
+
+def GM_Error_multi(reg, multireg, vm, regimes=False):
+ for m in multireg:
+ mreg = multireg[m]
+ mreg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag(mreg, None)
+ # build coefficients table body
+ beta_position = summary_coefs_somex(mreg, mreg.z_stat)
+ summary_coefs_lambda(mreg, mreg.z_stat)
+ if regimes:
+ summary_regimes(mreg, chow=False)
+ summary_warning(mreg)
+ multireg[m].__summary = mreg.__summary
+ reg.__summary = {}
+ summary_chow(reg, lambd=False)
+ summary_warning(reg)
+ summary_multi(reg=reg, multireg=multireg, vm=vm,
+ instruments=False, nonspat_diag=False, spat_diag=False)
+
+
+def GM_Endog_Error(reg, vm, w, regimes=False):
+ reg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag(reg, None)
+ # build coefficients table body
+ summary_coefs_allx(reg, reg.z_stat, lambd=True)
+ summary_coefs_lambda(reg, reg.z_stat)
+ summary_coefs_instruments(reg)
+ if regimes:
+ summary_regimes(reg)
+ summary_warning(reg)
+ summary(reg=reg, vm=vm, instruments=True,
+ nonspat_diag=False, spat_diag=False)
+
+
+def GM_Endog_Error_multi(reg, multireg, vm, regimes=False):
+ for m in multireg:
+ mreg = multireg[m]
+ mreg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag(mreg, None)
+ # build coefficients table body
+ summary_coefs_allx(mreg, mreg.z_stat, lambd=True)
+ summary_coefs_lambda(mreg, mreg.z_stat)
+ summary_coefs_instruments(mreg)
+ if regimes:
+ summary_regimes(mreg, chow=False)
+ summary_warning(mreg)
+ multireg[m].__summary = mreg.__summary
+ reg.__summary = {}
+ summary_chow(reg, lambd=False)
+ summary_warning(reg)
+ summary_multi(reg=reg, multireg=multireg, vm=vm,
+ instruments=True, nonspat_diag=False, spat_diag=False)
+
+
+def GM_Error_Hom(reg, vm, w, regimes=False):
+ reg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag(reg, None)
+ summary_iteration(reg)
+ # build coefficients table body
+ beta_position = summary_coefs_somex(reg, reg.z_stat)
+ summary_coefs_lambda(reg, reg.z_stat)
+ if regimes:
+ summary_regimes(reg)
+ summary_warning(reg)
+ summary(reg=reg, vm=vm, instruments=False,
+ nonspat_diag=False, spat_diag=False)
+
+
+def GM_Error_Hom_multi(reg, multireg, vm, regimes=False):
+ for m in multireg:
+ mreg = multireg[m]
+ mreg.__summary = {}
+ # compute diagnostics and organize summary output
+ summary_iteration(mreg)
+ beta_diag(mreg, None)
+ # build coefficients table body
+ beta_position = summary_coefs_somex(mreg, mreg.z_stat)
+ summary_coefs_lambda(mreg, mreg.z_stat)
+ if regimes:
+ summary_regimes(mreg, chow=False)
+ summary_warning(mreg)
+ multireg[m].__summary = mreg.__summary
+ reg.__summary = {}
+ summary_chow(reg, lambd=True)
+ summary_warning(reg)
+ summary_multi(reg=reg, multireg=multireg, vm=vm,
+ instruments=False, nonspat_diag=False, spat_diag=False)
+
+
+def GM_Endog_Error_Hom(reg, vm, w, regimes=False):
+ reg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag(reg, None)
+ summary_iteration(reg)
+ # build coefficients table body
+ summary_coefs_allx(reg, reg.z_stat, lambd=True)
+ summary_coefs_lambda(reg, reg.z_stat)
+ summary_coefs_instruments(reg)
+ if regimes:
+ summary_regimes(reg)
+ summary_warning(reg)
+ summary(reg=reg, vm=vm, instruments=True,
+ nonspat_diag=False, spat_diag=False)
+
+
+def GM_Endog_Error_Hom_multi(reg, multireg, vm, regimes=False):
+ for m in multireg:
+ mreg = multireg[m]
+ mreg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag(mreg, None)
+ summary_iteration(mreg)
+ # build coefficients table body
+ summary_coefs_allx(mreg, mreg.z_stat, lambd=True)
+ summary_coefs_lambda(mreg, mreg.z_stat)
+ summary_coefs_instruments(mreg)
+ if regimes:
+ summary_regimes(mreg, chow=False)
+ summary_warning(mreg)
+ multireg[m].__summary = mreg.__summary
+ reg.__summary = {}
+ summary_chow(reg, lambd=True)
+ summary_warning(reg)
+ summary_multi(reg=reg, multireg=multireg, vm=vm,
+ instruments=True, nonspat_diag=False, spat_diag=False)
+
+
+def GM_Error_Het(reg, vm, w, regimes=False):
+ reg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag(reg, 'het')
+ summary_iteration(reg)
+ # build coefficients table body
+ beta_position = summary_coefs_somex(reg, reg.z_stat)
+ summary_coefs_lambda(reg, reg.z_stat)
+ if regimes:
+ summary_regimes(reg)
+ summary_warning(reg)
+ summary(reg=reg, vm=vm, instruments=False,
+ nonspat_diag=False, spat_diag=False)
+
+
+def GM_Error_Het_multi(reg, multireg, vm, regimes=False):
+ for m in multireg:
+ mreg = multireg[m]
+ mreg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag(mreg, 'het')
+ summary_iteration(mreg)
+ # build coefficients table body
+ beta_position = summary_coefs_somex(mreg, mreg.z_stat)
+ summary_coefs_lambda(mreg, mreg.z_stat)
+ if regimes:
+ summary_regimes(mreg, chow=False)
+ summary_warning(mreg)
+ multireg[m].__summary = mreg.__summary
+ reg.__summary = {}
+ summary_chow(reg, lambd=True)
+ summary_warning(reg)
+ summary_multi(reg=reg, multireg=multireg, vm=vm,
+ instruments=False, nonspat_diag=False, spat_diag=False)
+
+
+def GM_Endog_Error_Het(reg, vm, w, regimes=False):
+ reg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag(reg, 'het')
+ summary_iteration(reg)
+ # build coefficients table body
+ summary_coefs_allx(reg, reg.z_stat, lambd=True)
+ summary_coefs_lambda(reg, reg.z_stat)
+ summary_coefs_instruments(reg)
+ if regimes:
+ summary_regimes(reg)
+ summary_warning(reg)
+ summary(reg=reg, vm=vm, instruments=True,
+ nonspat_diag=False, spat_diag=False)
+
+
+def GM_Endog_Error_Het_multi(reg, multireg, vm, regimes=False):
+ for m in multireg:
+ mreg = multireg[m]
+ mreg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag(mreg, 'het')
+ summary_iteration(mreg)
+ # build coefficients table body
+ summary_coefs_allx(mreg, mreg.z_stat, lambd=True)
+ summary_coefs_lambda(mreg, mreg.z_stat)
+ summary_coefs_instruments(mreg)
+ if regimes:
+ summary_regimes(mreg, chow=False)
+ summary_warning(mreg)
+ multireg[m].__summary = mreg.__summary
+ reg.__summary = {}
+ summary_chow(reg, lambd=True)
+ summary_warning(reg)
+ summary_multi(reg=reg, multireg=multireg, vm=vm,
+ instruments=True, nonspat_diag=False, spat_diag=False)
+
+
+def GM_Combo(reg, vm, w, regimes=False):
+ reg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag_lag(reg, None)
+ # build coefficients table body
+ summary_coefs_allx(reg, reg.z_stat, lambd=True)
+ summary_coefs_lambda(reg, reg.z_stat)
+ summary_coefs_instruments(reg)
+ summary_warning(reg)
+ if regimes:
+ summary_regimes(reg)
+ summary_warning(reg)
+ summary(reg=reg, vm=vm, instruments=True,
+ nonspat_diag=False, spat_diag=False)
+
+
+def GM_Combo_multi(reg, multireg, vm, regimes=False):
+ for m in multireg:
+ mreg = multireg[m]
+ mreg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag_lag(mreg, None)
+ # build coefficients table body
+ summary_coefs_allx(mreg, mreg.z_stat, lambd=True)
+ summary_coefs_lambda(mreg, mreg.z_stat)
+ summary_coefs_instruments(mreg)
+ if regimes:
+ summary_regimes(mreg, chow=False)
+ summary_warning(mreg)
+ multireg[m].__summary = mreg.__summary
+ reg.__summary = {}
+ if regimes:
+ summary_chow(reg, lambd=False)
+ summary_warning(reg)
+ summary_multi(reg=reg, multireg=multireg, vm=vm,
+ instruments=True, nonspat_diag=False, spat_diag=False)
+
+
+def GM_Combo_Hom(reg, vm, w, regimes=False):
+ reg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag_lag(reg, None)
+ summary_iteration(reg)
+ # build coefficients table body
+ summary_coefs_allx(reg, reg.z_stat, lambd=True)
+ summary_coefs_lambda(reg, reg.z_stat)
+ summary_coefs_instruments(reg)
+ if regimes:
+ summary_regimes(reg)
+ summary_warning(reg)
+ summary(reg=reg, vm=vm, instruments=True,
+ nonspat_diag=False, spat_diag=False)
+
+
+def GM_Combo_Hom_multi(reg, multireg, vm, regimes=False):
+ for m in multireg:
+ mreg = multireg[m]
+ mreg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag_lag(mreg, None)
+ summary_iteration(mreg)
+ # build coefficients table body
+ summary_coefs_allx(mreg, mreg.z_stat, lambd=True)
+ summary_coefs_lambda(mreg, mreg.z_stat)
+ summary_coefs_instruments(mreg)
+ if regimes:
+ summary_regimes(mreg, chow=False)
+ summary_warning(mreg)
+ multireg[m].__summary = mreg.__summary
+ reg.__summary = {}
+ if regimes:
+ summary_chow(reg, lambd=True)
+ summary_warning(reg)
+ summary_multi(reg=reg, multireg=multireg, vm=vm,
+ instruments=True, nonspat_diag=False, spat_diag=False)
+
+
+def GM_Combo_Het(reg, vm, w, regimes=False):
+ reg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag_lag(reg, 'het')
+ summary_iteration(reg)
+ # build coefficients table body
+ summary_coefs_allx(reg, reg.z_stat, lambd=True)
+ summary_coefs_lambda(reg, reg.z_stat)
+ summary_coefs_instruments(reg)
+ if regimes:
+ summary_regimes(reg)
+ summary_warning(reg)
+ summary(reg=reg, vm=vm, instruments=True,
+ nonspat_diag=False, spat_diag=False)
+
+
+def GM_Combo_Het_multi(reg, multireg, vm, regimes=False):
+ for m in multireg:
+ mreg = multireg[m]
+ mreg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag_lag(mreg, 'het')
+ summary_iteration(mreg)
+ # build coefficients table body
+ summary_coefs_allx(mreg, mreg.z_stat, lambd=True)
+ summary_coefs_lambda(mreg, mreg.z_stat)
+ summary_coefs_instruments(mreg)
+ if regimes:
+ summary_regimes(mreg, chow=False)
+ summary_warning(mreg)
+ multireg[m].__summary = mreg.__summary
+ reg.__summary = {}
+ if regimes:
+ summary_chow(reg, lambd=True)
+ summary_warning(reg)
+ summary_multi(reg=reg, multireg=multireg, vm=vm,
+ instruments=True, nonspat_diag=False, spat_diag=False)
+
+
+def Probit(reg, vm, w, spat_diag):
+ reg.__summary = {}
+ # compute diagnostics and organize summary output
+ beta_diag(reg, None)
+ # organize summary output
+ if spat_diag:
+ reg.__summary['summary_spat_diag'] = summary_spat_diag_probit(reg)
+ reg.__summary[
+ 'summary_r2'] = "%-21s: %3.2f\n" % ('% correctly predicted', reg.predpc)
+ reg.__summary[
+ 'summary_r2'] += "%-21s: %3.4f\n" % ('Log-Likelihood', reg.logl)
+ reg.__summary['summary_r2'] += "%-21s: %3.4f\n" % ('LR test', reg.LR[0])
+ reg.__summary[
+ 'summary_r2'] += "%-21s: %3.4f\n" % ('LR test (p-value)', reg.LR[1])
+ if reg.warning:
+ reg.__summary[
+ 'summary_r2'] += "\nMaximum number of iterations exceeded or gradient and/or function calls not changing\n"
+ # build coefficients table body
+ beta_position = summary_coefs_allx(reg, reg.z_stat)
+ reg.__summary['summary_other_mid'] = summary_coefs_slopes(reg)
+ summary(reg=reg, vm=vm, instruments=False,
+ short_intro=True, spat_diag=spat_diag)
+
+##############################################################################
+
+
+##############################################################################
+############### Helper functions for running summary diagnostics #############
+##############################################################################
+
+def beta_diag_ols(reg, robust):
+ # compute diagnostics
+ reg.std_err = diagnostics.se_betas(reg)
+ reg.t_stat = diagnostics.t_stat(reg)
+ reg.r2 = diagnostics.r2(reg)
+ reg.ar2 = diagnostics.ar2(reg)
+ # organize summary output
+ reg.__summary['summary_std_err'] = robust
+ reg.__summary['summary_zt'] = 't'
+ reg.__summary['summary_r2'] = "%-20s:%12.4f\n%-20s:%12.4f\n" % (
+ 'R-squared', reg.r2, 'Adjusted R-squared', reg.ar2)
+ # build coefficients table body
+ position = summary_coefs_allx(reg, reg.t_stat)
+
+
+def beta_diag(reg, robust):
+ # compute diagnostics
+ reg.std_err = diagnostics.se_betas(reg)
+ reg.z_stat = diagnostics.t_stat(reg, z_stat=True)
+ reg.pr2 = diagnostics_tsls.pr2_aspatial(reg)
+ # organize summary output
+ reg.__summary['summary_std_err'] = robust
+ reg.__summary['summary_zt'] = 'z'
+ reg.__summary[
+ 'summary_r2'] = "%-20s:%12.4f\n" % ('Pseudo R-squared', reg.pr2)
+
+
+def beta_diag_lag(reg, robust, error=True):
+ # compute diagnostics
+ reg.std_err = diagnostics.se_betas(reg)
+ reg.z_stat = diagnostics.t_stat(reg, z_stat=True)
+ reg.pr2 = diagnostics_tsls.pr2_aspatial(reg)
+ # organize summary output
+ reg.__summary['summary_std_err'] = robust
+ reg.__summary['summary_zt'] = 'z'
+ reg.__summary[
+ 'summary_r2'] = "%-20s: %5.4f\n" % ('Pseudo R-squared', reg.pr2)
+ if np.abs(reg.rho) < 1:
+ reg.pr2_e = diagnostics_tsls.pr2_spatial(reg)
+ reg.__summary[
+ 'summary_r2'] += "%-20s: %5.4f\n" % ('Spatial Pseudo R-squared', reg.pr2_e)
+ else:
+ reg.__summary[
+ 'summary_r2'] += "Spatial Pseudo R-squared: omitted due to rho outside the boundary (-1, 1)."
+
+
+def build_coefs_body_instruments(reg):
+ beta_position = summary_coefs_allx(reg, reg.z_stat)
+ summary_coefs_allx(reg, reg.z_stat)
+ summary_coefs_instruments(reg)
+
+
+def spat_diag_ols(reg, w, moran):
+ # compute diagnostics
+ lm_tests = diagnostics_sp.LMtests(reg, w)
+ reg.lm_error = lm_tests.lme
+ reg.lm_lag = lm_tests.lml
+ reg.rlm_error = lm_tests.rlme
+ reg.rlm_lag = lm_tests.rlml
+ reg.lm_sarma = lm_tests.sarma
+ if moran:
+ moran_res = diagnostics_sp.MoranRes(reg, w, z=True)
+ reg.moran_res = moran_res.I, moran_res.zI, moran_res.p_norm
+ # organize summary output
+ reg.__summary['summary_spat_diag'] = summary_spat_diag_ols(reg, moran)
+
+
+def spat_diag_instruments(reg, w):
+ # compute diagnostics
+ cache = diagnostics_sp.spDcache(reg, w)
+ mi, ak, ak_p = diagnostics_sp.akTest(reg, w, cache)
+ reg.ak_test = ak, ak_p
+ # organize summary output
+ reg.__summary['summary_spat_diag'] = "%-27s %2d %12.3f %9.4f\n" % (
+ "Anselin-Kelejian Test", 1, reg.ak_test[0], reg.ak_test[1])
+
+
+def summary(reg, vm, instruments, short_intro=False, nonspat_diag=False, spat_diag=False, other_end=False):
+ summary = summary_open()
+ summary += summary_intro(reg, short_intro)
+ summary += reg.__summary['summary_r2']
+ if nonspat_diag:
+ summary += reg.__summary['summary_nonspat_diag_1']
+ try:
+ summary += reg.__summary['summary_other_top']
+ except:
+ pass
+ summary += summary_coefs_intro(reg)
+ summary += reg.__summary['summary_coefs']
+ summary += "------------------------------------------------------------------------------------\n"
+ if instruments:
+ summary += reg.__summary['summary_coefs_instruments']
+ try:
+ summary += reg.__summary['summary_other_mid']
+ except:
+ pass
+ if nonspat_diag:
+ summary += reg.__summary['summary_nonspat_diag_2']
+ if spat_diag:
+ summary += summary_spat_diag_intro()
+ summary += reg.__summary['summary_spat_diag']
+ if vm:
+ summary += summary_vm(reg, instruments)
+ try:
+ summary += reg.__summary['summary_chow']
+ except:
+ pass
+ if other_end:
+ summary += reg.__summary['summary_other_end']
+ summary += summary_close()
+ reg.summary = summary
+
+
+def summary_multi(reg, multireg, vm, instruments, short_intro=False, nonspat_diag=False, spat_diag=False, other_end=False):
+ summary = summary_open(multi=True)
+ for m in multireg:
+ mreg = multireg[m]
+ summary += "----------\n\n"
+ summary += summary_intro(mreg, short_intro)
+ summary += mreg.__summary['summary_r2']
+ if nonspat_diag:
+ summary += mreg.__summary['summary_nonspat_diag_1']
+ try:
+ summary += reg.__summary['summary_other_top']
+ except:
+ pass
+ summary += summary_coefs_intro(mreg)
+ summary += mreg.__summary['summary_coefs']
+ summary += "------------------------------------------------------------------------------------\n"
+ if instruments:
+ summary += mreg.__summary['summary_coefs_instruments']
+ try:
+ summary += mreg.__summary['summary_other_mid']
+ except:
+ pass
+ if m == multireg.keys()[-1]:
+ try:
+ summary += reg.__summary['summary_other_mid']
+ except:
+ pass
+ if nonspat_diag:
+ summary += mreg.__summary['summary_nonspat_diag_2']
+ if spat_diag:
+ summary += summary_spat_diag_intro()
+ summary += mreg.__summary['summary_spat_diag']
+ if vm:
+ summary += summary_vm(mreg, instruments)
+ if other_end:
+ summary += mreg.__summary['summary_other_end']
+ if m == multireg.keys()[-1]:
+ try:
+ summary += reg.__summary['summary_chow']
+ except:
+ pass
+ if spat_diag:
+ try:
+ spat_diag_str = reg.__summary['summary_spat_diag']
+ summary += summary_spat_diag_intro_global()
+ summary += spat_diag_str
+ except:
+ pass
+ try:
+ summary += reg.__summary['summary_other_end']
+ except:
+ pass
+ summary += summary_close()
+ reg.summary = summary
+
+
+def _get_var_indices(reg, lambd=False):
+ try:
+ var_names = reg.name_z
+ except:
+ var_names = reg.name_x
+ last_v = len(var_names)
+ if lambd:
+ last_v += -1
+ indices = []
+ try:
+ kf = reg.kf
+ if lambd:
+ kf += -1
+ krex = reg.kr - reg.kryd
+ try:
+ kfyd = reg.yend.shape[1] - reg.nr * reg.kryd
+ except:
+ kfyd = 0
+ j_con = 0
+ if reg.constant_regi == 'many':
+ j_con = 1
+ for i in range(reg.nr):
+ j = i * krex
+ jyd = krex * reg.nr + i * reg.kryd + kf - kfyd
+ name_reg = var_names[j + j_con:j + krex] + \
+ var_names[jyd:jyd + reg.kryd]
+ name_reg.sort()
+ if reg.constant_regi == 'many':
+ indices += [j] + [var_names.index(ind) for ind in name_reg]
+ else:
+ indices += [var_names.index(ind) for ind in name_reg]
+ if reg.constant_regi == 'one':
+ indices += [krex * reg.nr]
+ if len(indices) < last_v:
+ name_reg = var_names[krex * reg.nr + 1 - j_con:krex * reg.nr + kf -
+ kfyd] + var_names[reg.kr * reg.nr + kf - kfyd:reg.kr * reg.nr + kf]
+ name_reg.sort()
+ indices += [var_names.index(ind) for ind in name_reg]
+ except:
+ indices = [0] + (np.argsort(var_names[1:last_v]) + 1).tolist()
+ return var_names, indices
+
+##############################################################################
+
+
+##############################################################################
+############### Guts of the summary printout #################################
+##############################################################################
+
+"""
+This section contains the pieces needed to put together the summary printout.
+"""
+
+
+def summary_open(multi=False):
+ strSummary = ""
+ strSummary += "REGRESSION\n"
+ if not multi:
+ strSummary += "----------\n"
+ return strSummary
+
+
+def summary_intro(reg, short): # extra space d
+ title = "SUMMARY OF OUTPUT: " + reg.title + "\n"
+ strSummary = title
+ strSummary += "-" * (len(title) - 1) + "\n"
+ strSummary += "%-20s:%12s\n" % ('Data set', reg.name_ds)
+ if reg.name_w:
+ strSummary += "%-20s:%12s\n" % ('Weights matrix', reg.name_w)
+ strSummary += "%-20s:%12s %-22s:%12d\n" % (
+ 'Dependent Variable', reg.name_y, 'Number of Observations', reg.n)
+ if not short:
+ strSummary += "%-20s:%12.4f %-22s:%12d\n" % (
+ 'Mean dependent var', reg.mean_y, 'Number of Variables', reg.k)
+ strSummary += "%-20s:%12.4f %-22s:%12d\n" % (
+ 'S.D. dependent var', reg.std_y, 'Degrees of Freedom', reg.n - reg.k)
+ #strSummary += '\n'
+ return strSummary
+
+
+def summary_coefs_intro(reg):
+ strSummary = "\n"
+ if reg.__summary['summary_std_err']:
+ if reg.__summary['summary_std_err'].lower() == 'white':
+ strSummary += "White Standard Errors\n"
+ elif reg.__summary['summary_std_err'].lower() == 'hac':
+ strSummary += "HAC Standard Errors; Kernel Weights: " + \
+ reg.name_gwk + "\n"
+ # elif reg.__summary['summary_std_err'].lower() == 'het':
+ #strSummary += "Heteroskedastic Corrected Standard Errors\n"
+ strSummary += "------------------------------------------------------------------------------------\n"
+ strSummary += " Variable Coefficient Std.Error %1s-Statistic Probability\n" % (
+ reg.__summary['summary_zt'])
+ strSummary += "------------------------------------------------------------------------------------\n"
+ return strSummary
+
+
+def summary_coefs_allx(reg, zt_stat, lambd=False):
+ strSummary = ""
+ var_names, indices = _get_var_indices(reg, lambd)
+ for i in indices:
+ strSummary += "%20s %12.7f %12.7f %12.7f %12.7f\n" \
+ % (var_names[i], reg.betas[i][0], reg.std_err[i], zt_stat[i][0], zt_stat[i][1])
+ reg.__summary['summary_coefs'] = strSummary
+ return i
+
+
+def summary_coefs_somex(reg, zt_stat):
+ """This is a special case needed for models that do not have inference on
+ the lambda term
+ """
+ strSummary = ""
+ var_names, indices = _get_var_indices(reg, lambd=True)
+ for i in indices:
+ strSummary += "%20s %12.7f %12.7f %12.7f %12.7f\n" \
+ % (reg.name_x[i], reg.betas[i][0], reg.std_err[i], zt_stat[i][0], zt_stat[i][1])
+ reg.__summary['summary_coefs'] = strSummary
+ return i
+"""
+def summary_coefs_yend(reg, zt_stat, lambd=False):
+ strSummary = ""
+ indices = _get_var_indices(reg, lambd)
+ for i in indices:
+ strSummary += "%20s %12.7f %12.7f %12.7f %12.7f\n" \
+ % (reg.name_z[i],reg.betas[i][0],reg.std_err[i],zt_stat[i][0],zt_stat[i][1])
+ reg.__summary['summary_coefs'] = strSummary
+"""
+
+
+def summary_coefs_lambda(reg, zt_stat):
+ try:
+ name_var = reg.name_z
+ except:
+ name_var = reg.name_x
+ if len(reg.betas) == len(zt_stat):
+ reg.__summary['summary_coefs'] += "%20s %12.7f %12.7f %12.7f %12.7f\n" \
+ % (name_var[-1], reg.betas[-1][0], reg.std_err[-1], zt_stat[-1][0], zt_stat[-1][1])
+ else:
+ reg.__summary[
+ 'summary_coefs'] += "%20s %12.7f \n" % (name_var[-1], reg.betas[-1][0])
+
+
+def summary_coefs_instruments(reg):
+ """Generates a list of the instruments used.
+ """
+ insts = "Instruments: "
+ for name in sorted(reg.name_q):
+ insts += name + ", "
+ text_wrapper = TW.TextWrapper(width=76, subsequent_indent=" ")
+ insts = text_wrapper.fill(insts[:-2])
+ insts += "\n"
+ inst2 = "Instrumented: "
+ for name in sorted(reg.name_yend):
+ inst2 += name + ", "
+ text_wrapper = TW.TextWrapper(width=76, subsequent_indent=" ")
+ inst2 = text_wrapper.fill(inst2[:-2])
+ inst2 += "\n"
+ inst2 += insts
+ reg.__summary['summary_coefs_instruments'] = inst2
+
+
+def summary_iteration(reg): # extra space d
+ """Reports the number of iterations computed.
+ """
+ try:
+ if reg.step1c:
+ step1c = 'Yes'
+ else:
+ step1c = 'No'
+ txt = "%-20s:%12s %-22s:%12s\n" % (
+ 'N. of iterations', reg.iteration, 'Step1c computed', step1c)
+ except:
+ txt = "%-20s:%12s\n" % ('N. of iterations', reg.iteration)
+ try:
+ reg.__summary['summary_other_top'] += txt
+ except:
+ reg.__summary['summary_other_top'] = txt
+
+
+def summary_regimes(reg, chow=True):
+ """Lists the regimes variable used.
+ """
+ try:
+ reg.__summary[
+ 'summary_other_mid'] += "Regimes variable: %s\n" % reg.name_regimes
+ except:
+ reg.__summary[
+ 'summary_other_mid'] = "Regimes variable: %s\n" % reg.name_regimes
+ if chow:
+ summary_chow(reg)
+
+
+def summary_sur(reg, u_cov=False):
+ """Lists the equation ID variable used.
+ """
+ if u_cov:
+ str_ucv = "\nERROR COVARIANCE MATRIX\n"
+ for i in range(reg.u_cov.shape[0]):
+ for j in range(reg.u_cov.shape[1]):
+ str_ucv += "%12.6f" % (reg.u_cov[i][j])
+ str_ucv += "\n"
+ try:
+ reg.__summary['summary_other_end'] += str_ucv
+ except:
+ reg.__summary['summary_other_end'] = str_ucv
+ else:
+ try:
+ reg.__summary[
+ 'summary_other_mid'] += "Equation ID: %s\n" % reg.name_multiID
+ except:
+ reg.__summary[
+ 'summary_other_mid'] = "Equation ID: %s\n" % reg.name_multiID
+ try:
+ reg.__summary[
+ 'summary_r2'] += "%-20s: %3.4f\n" % ('Log-Likelihood', reg.logl)
+ except:
+ pass
+
+
+def summary_chow(reg, lambd=False):
+ reg.__summary['summary_chow'] = "\nREGIMES DIAGNOSTICS - CHOW TEST\n"
+ reg.__summary[
+ 'summary_chow'] += " VARIABLE DF VALUE PROB\n"
+ if reg.cols2regi == 'all':
+ names_chow = reg.name_x_r[1:]
+ else:
+ names_chow = [reg.name_x_r[1:][i] for i in np.where(reg.cols2regi)[0]]
+ if reg.constant_regi == 'many':
+ indices = [0] + (np.argsort(names_chow) + 1).tolist()
+ names_chow = ['CONSTANT'] + names_chow
+ else:
+ indices = (np.argsort(names_chow)).tolist()
+ if lambd:
+ indices += [-1]
+ names_chow += ['lambda']
+ for i in indices:
+ reg.__summary['summary_chow'] += "%25s %2d %12.3f %9.4f\n" % (
+ names_chow[i], reg.nr - 1, reg.chow.regi[i, 0], reg.chow.regi[i, 1])
+ reg.__summary['summary_chow'] += "%25s %2d %12.3f %9.4f\n" % (
+ 'Global test', reg.kr * (reg.nr - 1), reg.chow.joint[0], reg.chow.joint[1])
+
+
+def summary_warning(reg):
+ try:
+ if reg.warning:
+ try:
+ reg.__summary['summary_other_mid'] += reg.warning
+ except:
+ reg.__summary['summary_other_mid'] = reg.warning
+ except:
+ pass
+
+
+def summary_coefs_slopes(reg):
+ strSummary = "\nMARGINAL EFFECTS\n"
+ if reg.scalem == 'phimean':
+ strSummary += "Method: Mean of individual marginal effects\n"
+ elif reg.scalem == 'xmean':
+ strSummary += "Method: Marginal effects at variables mean\n"
+ strSummary += "------------------------------------------------------------------------------------\n"
+ strSummary += " Variable Slope Std.Error %1s-Statistic Probability\n" % (
+ reg.__summary['summary_zt'])
+ strSummary += "------------------------------------------------------------------------------------\n"
+ indices = np.argsort(reg.name_x[1:]).tolist()
+ for i in indices:
+ strSummary += "%20s %12.7f %12.7f %12.7f %12.7f\n" \
+ % (reg.name_x[i + 1], reg.slopes[i][0], reg.slopes_std_err[i], reg.slopes_z_stat[i][0], reg.slopes_z_stat[i][1])
+ return strSummary + "\n\n"
+"""
+def summary_r2(reg, ols, spatial_lag):
+ if ols:
+ strSummary = "%-20s:%12.4f\n%-20s:%12.4f\n" % ('R-squared',reg.r2,'Adjusted R-squared',reg.ar2)
+ else:
+ strSummary = "%-20s:%12.4f\n" % ('Pseudo R-squared',reg.pr2)
+ if spatial_lag:
+ if reg.pr2_e != None:
+ strSummary += "%-20s:%12.4f\n" % ('Spatial Pseudo R-squared',reg.pr2_e)
+ return strSummary
+"""
+
+
+def summary_nonspat_diag_1(reg): # extra space d
+ strSummary = ""
+ strSummary += "%-20s:%12.3f %-22s:%12.4f\n" % (
+ 'Sum squared residual', reg.utu, 'F-statistic', reg.f_stat[0])
+ strSummary += "%-20s:%12.3f %-22s:%12.4g\n" % (
+ 'Sigma-square', reg.sig2, 'Prob(F-statistic)', reg.f_stat[1])
+ strSummary += "%-20s:%12.3f %-22s:%12.3f\n" % (
+ 'S.E. of regression', np.sqrt(reg.sig2), 'Log likelihood', reg.logll)
+ strSummary += "%-20s:%12.3f %-22s:%12.3f\n" % (
+ 'Sigma-square ML', reg.sig2ML, 'Akaike info criterion', reg.aic)
+ strSummary += "%-20s:%12.4f %-22s:%12.3f\n" % (
+ 'S.E of regression ML', np.sqrt(reg.sig2ML), 'Schwarz criterion', reg.schwarz)
+ return strSummary
+
+
+def summary_nonspat_diag_2(reg):
+ strSummary = ""
+ strSummary += "\nREGRESSION DIAGNOSTICS\n"
+ if reg.mulColli:
+ strSummary += "MULTICOLLINEARITY CONDITION NUMBER %16.3f\n\n" % (
+ reg.mulColli)
+ strSummary += "TEST ON NORMALITY OF ERRORS\n"
+ strSummary += "TEST DF VALUE PROB\n"
+ strSummary += "%-27s %2d %14.3f %9.4f\n\n" % (
+ 'Jarque-Bera', reg.jarque_bera['df'], reg.jarque_bera['jb'], reg.jarque_bera['pvalue'])
+ strSummary += "DIAGNOSTICS FOR HETEROSKEDASTICITY\n"
+ strSummary += "RANDOM COEFFICIENTS\n"
+ strSummary += "TEST DF VALUE PROB\n"
+ strSummary += "%-27s %2d %12.3f %9.4f\n" % (
+ 'Breusch-Pagan test', reg.breusch_pagan['df'], reg.breusch_pagan['bp'], reg.breusch_pagan['pvalue'])
+ strSummary += "%-27s %2d %12.3f %9.4f\n" % (
+ 'Koenker-Bassett test', reg.koenker_bassett['df'], reg.koenker_bassett['kb'], reg.koenker_bassett['pvalue'])
+ try:
+ if reg.white:
+ strSummary += "\nSPECIFICATION ROBUST TEST\n"
+ if len(reg.white) > 3:
+ strSummary += reg.white + '\n'
+ else:
+ strSummary += "TEST DF VALUE PROB\n"
+ strSummary += "%-27s %2d %12.3f %9.4f\n" % (
+ 'White', reg.white['df'], reg.white['wh'], reg.white['pvalue'])
+ except:
+ pass
+ return strSummary
+
+
+def summary_spat_diag_intro():
+ strSummary = ""
+ strSummary += "\nDIAGNOSTICS FOR SPATIAL DEPENDENCE\n"
+ strSummary += "TEST MI/DF VALUE PROB\n"
+ return strSummary
+
+
+def summary_spat_diag_intro_global():
+ strSummary = ""
+ strSummary += "\nDIAGNOSTICS FOR GLOBAL SPATIAL DEPENDENCE\n"
+ strSummary += "Residuals are treated as homoskedastic for the purpose of these tests\n"
+ strSummary += "TEST MI/DF VALUE PROB\n"
+ return strSummary
+
+
+def summary_spat_diag_ols(reg, moran):
+ strSummary = ""
+ if moran:
+ strSummary += "%-27s %8.4f %9.3f %9.4f\n" % (
+ "Moran's I (error)", reg.moran_res[0], reg.moran_res[1], reg.moran_res[2])
+ strSummary += "%-27s %2d %12.3f %9.4f\n" % (
+ "Lagrange Multiplier (lag)", 1, reg.lm_lag[0], reg.lm_lag[1])
+ strSummary += "%-27s %2d %12.3f %9.4f\n" % (
+ "Robust LM (lag)", 1, reg.rlm_lag[0], reg.rlm_lag[1])
+ strSummary += "%-27s %2d %12.3f %9.4f\n" % (
+ "Lagrange Multiplier (error)", 1, reg.lm_error[0], reg.lm_error[1])
+ strSummary += "%-27s %2d %12.3f %9.4f\n" % (
+ "Robust LM (error)", 1, reg.rlm_error[0], reg.rlm_error[1])
+ strSummary += "%-27s %2d %12.3f %9.4f\n\n" % (
+ "Lagrange Multiplier (SARMA)", 2, reg.lm_sarma[0], reg.lm_sarma[1])
+ return strSummary
+
+
+def summary_spat_diag_probit(reg):
+ strSummary = ""
+ strSummary += "%-27s %2d %12.3f %9.4f\n" % (
+ "Kelejian-Prucha (error)", 1, reg.KP_error[0], reg.KP_error[1])
+ strSummary += "%-27s %2d %12.3f %9.4f\n" % (
+ "Pinkse (error)", 1, reg.Pinkse_error[0], reg.Pinkse_error[1])
+ strSummary += "%-27s %2d %12.3f %9.4f\n\n" % (
+ "Pinkse-Slade (error)", 1, reg.PS_error[0], reg.PS_error[1])
+ return strSummary
+
+
+def summary_vm(reg, instruments):
+ strVM = "\n"
+ strVM += "COEFFICIENTS VARIANCE MATRIX\n"
+ strVM += "----------------------------\n"
+ if instruments:
+ for name in reg.name_z:
+ strVM += "%12s" % (name)
+ else:
+ for name in reg.name_x:
+ strVM += "%12s" % (name)
+ strVM += "\n"
+ nrow = reg.vm.shape[0]
+ ncol = reg.vm.shape[1]
+ for i in range(nrow):
+ for j in range(ncol):
+ strVM += "%12.6f" % (reg.vm[i][j])
+ strVM += "\n"
+ return strVM
+
+
+def summary_pred(reg):
+ strPred = "\n\n"
+ strPred += "%16s%16s%16s%16s\n" % ('OBS',
+ reg.name_y, 'PREDICTED', 'RESIDUAL')
+ for i in range(reg.n):
+ strPred += "%16d%16.5f%16.5f%16.5f\n" % (
+ i + 1, reg.y[i][0], reg.predy[i][0], reg.u[i][0])
+ return strPred
+
+
+def summary_close():
+ return "================================ END OF REPORT ====================================="
+
+##############################################################################
+
+
+def _test():
+ import doctest
+ doctest.testmod()
+
+if __name__ == '__main__':
+ _test()
diff --git a/pysal/spreg/tests/test_diagnostics.py b/pysal/spreg/tests/test_diagnostics.py
new file mode 100644
index 0000000..1904b58
--- /dev/null
+++ b/pysal/spreg/tests/test_diagnostics.py
@@ -0,0 +1,128 @@
+import unittest
+import numpy as np
+import pysal
+from pysal.spreg import diagnostics
+from pysal.spreg.ols import OLS
+
+
+# create regression object used by all the tests below
+db = pysal.open(pysal.examples.get_path("columbus.dbf"), "r")
+y = np.array(db.by_col("CRIME"))
+y = np.reshape(y, (49,1))
+X = []
+X.append(db.by_col("INC"))
+X.append(db.by_col("HOVAL"))
+X = np.array(X).T
+reg = OLS(y,X)
+
+
+class TestFStat(unittest.TestCase):
+ def test_f_stat(self):
+ obs = diagnostics.f_stat(reg)
+ exp = (28.385629224695, 0.000000009341)
+ for i in range(2):
+ self.assertAlmostEquals(obs[i],exp[i])
+
+class TestTStat(unittest.TestCase):
+ def test_t_stat(self):
+ obs = diagnostics.t_stat(reg)
+ exp = [(14.490373143689094, 9.2108899889173982e-19),
+ (-4.7804961912965762, 1.8289595070843232e-05),
+ (-2.6544086427176916, 0.010874504909754612)]
+ for i in range(3):
+ for j in range(2):
+ self.assertAlmostEquals(obs[i][j],exp[i][j])
+
+class TestR2(unittest.TestCase):
+ def test_r2(self):
+ obs = diagnostics.r2(reg)
+ exp = 0.55240404083742334
+ self.assertAlmostEquals(obs,exp)
+
+class TestAr2(unittest.TestCase):
+ def test_ar2(self):
+ obs = diagnostics.ar2(reg)
+ exp = 0.5329433469607896
+ self.assertAlmostEquals(obs,exp)
+
+class TestSeBetas(unittest.TestCase):
+ def test_se_betas(self):
+ obs = diagnostics.se_betas(reg)
+ exp = np.array([4.73548613, 0.33413076, 0.10319868])
+ np.testing.assert_array_almost_equal(obs,exp)
+
+class TestLogLikelihood(unittest.TestCase):
+ def test_log_likelihood(self):
+ obs = diagnostics.log_likelihood(reg)
+ exp = -187.3772388121491
+ self.assertAlmostEquals(obs,exp)
+
+class TestAkaike(unittest.TestCase):
+ def test_akaike(self):
+ obs = diagnostics.akaike(reg)
+ exp = 380.7544776242982
+ self.assertAlmostEquals(obs,exp)
+
+class TestSchwarz(unittest.TestCase):
+ def test_schwarz(self):
+ obs = diagnostics.schwarz(reg)
+ exp = 386.42993851863008
+ self.assertAlmostEquals(obs,exp)
+
+class TestConditionIndex(unittest.TestCase):
+ def test_condition_index(self):
+ obs = diagnostics.condition_index(reg)
+ exp = 6.541827751444
+ self.assertAlmostEquals(obs,exp)
+
+class TestJarqueBera(unittest.TestCase):
+ def test_jarque_bera(self):
+ obs = diagnostics.jarque_bera(reg)
+ exp = {'df':2, 'jb':1.835752520076, 'pvalue':0.399366291249}
+ self.assertEquals(obs['df'],exp['df'])
+ self.assertAlmostEquals(obs['jb'],exp['jb'])
+ self.assertAlmostEquals(obs['pvalue'],exp['pvalue'])
+
+class TestBreuschPagan(unittest.TestCase):
+ def test_breusch_pagan(self):
+ obs = diagnostics.breusch_pagan(reg)
+ exp = {'df':2, 'bp':7.900441675960, 'pvalue':0.019250450075}
+ self.assertEquals(obs['df'],exp['df'])
+ self.assertAlmostEquals(obs['bp'],exp['bp'])
+ self.assertAlmostEquals(obs['pvalue'],exp['pvalue'])
+
+class TestWhite(unittest.TestCase):
+ def test_white(self):
+ obs = diagnostics.white(reg)
+ exp = {'df':5, 'wh':19.946008239903, 'pvalue':0.001279222817}
+ self.assertEquals(obs['df'],exp['df'])
+ self.assertAlmostEquals(obs['wh'],exp['wh'])
+ self.assertAlmostEquals(obs['pvalue'],exp['pvalue'])
+
+class TestKoenkerBassett(unittest.TestCase):
+ def test_koenker_bassett(self):
+ obs = diagnostics.koenker_bassett(reg)
+ exp = {'df':2, 'kb':5.694087931707, 'pvalue':0.058015563638}
+ self.assertEquals(obs['df'],exp['df'])
+ self.assertAlmostEquals(obs['kb'],exp['kb'])
+ self.assertAlmostEquals(obs['pvalue'],exp['pvalue'])
+
+class TestVif(unittest.TestCase):
+ def test_vif(self):
+ obs = diagnostics.vif(reg)
+ exp = [(0.0, 0.0), # note [0][1] should actually be infiniity...
+ (1.3331174971891975, 0.75012142748740696),
+ (1.3331174971891973, 0.75012142748740707)]
+ for i in range(1,3):
+ for j in range(2):
+ self.assertAlmostEquals(obs[i][j],exp[i][j])
+
+class TestConstantCheck(unittest.TestCase):
+ def test_constant_check(self):
+ obs = diagnostics.constant_check(reg.x)
+ exp = True
+ self.assertEquals(obs,exp)
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/tests/test_diagnostics_sp.py b/pysal/spreg/tests/test_diagnostics_sp.py
new file mode 100644
index 0000000..3d9b8ff
--- /dev/null
+++ b/pysal/spreg/tests/test_diagnostics_sp.py
@@ -0,0 +1,179 @@
+import unittest
+import numpy as np
+import pysal
+from pysal.spreg import diagnostics
+from pysal.spreg.ols import OLS as OLS
+from pysal.spreg.twosls import TSLS as TSLS
+from pysal.spreg.twosls_sp import GM_Lag
+from pysal.spreg.diagnostics_sp import LMtests, MoranRes, spDcache, AKtest
+
+
+class TestLMtests(unittest.TestCase):
+ def setUp(self):
+ db = pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ X = np.array(X).T
+ self.y = y
+ self.X = X
+ ols = OLS(self.y, self.X)
+ self.ols = ols
+ w = pysal.open(pysal.examples.get_path('columbus.gal'), 'r').read()
+ w.transform='r'
+ self.w = w
+
+ def test_lm_err(self):
+ lms = LMtests(self.ols, self.w)
+ lme = np.array([3.097094, 0.078432])
+ np.testing.assert_array_almost_equal(lms.lme, lme, decimal=6)
+
+ def test_lm_lag(self):
+ lms = LMtests(self.ols, self.w)
+ lml = np.array([ 0.981552, 0.321816])
+ np.testing.assert_array_almost_equal(lms.lml, lml, decimal=6)
+
+ def test_rlm_err(self):
+ lms = LMtests(self.ols, self.w)
+ rlme = np.array([ 3.209187, 0.073226])
+ np.testing.assert_array_almost_equal(lms.rlme, rlme, decimal=6)
+
+ def test_rlm_lag(self):
+ lms = LMtests(self.ols, self.w)
+ rlml = np.array([ 1.093645, 0.295665])
+ np.testing.assert_array_almost_equal(lms.rlml, rlml, decimal=6)
+
+ def test_lm_sarma(self):
+ lms = LMtests(self.ols, self.w)
+ sarma = np.array([ 4.190739, 0.123025])
+ np.testing.assert_array_almost_equal(lms.sarma, sarma, decimal=6)
+
+
+class TestMoranRes(unittest.TestCase):
+ def setUp(self):
+ db = pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ X = np.array(X).T
+ self.y = y
+ self.X = X
+ ols = OLS(self.y, self.X)
+ self.ols = ols
+ w = pysal.open(pysal.examples.get_path('columbus.gal'), 'r').read()
+ w.transform='r'
+ self.w = w
+
+ def test_get_m_i(self):
+ m = MoranRes(self.ols, self.w, z=True)
+ np.testing.assert_array_almost_equal(m.I, 0.17130999999999999, decimal=6)
+
+ def test_get_v_i(self):
+ m = MoranRes(self.ols, self.w, z=True)
+ np.testing.assert_array_almost_equal(m.vI, 0.0081300000000000001, decimal=6)
+
+ def test_get_e_i(self):
+ m = MoranRes(self.ols, self.w, z=True)
+ np.testing.assert_array_almost_equal(m.eI, -0.034522999999999998, decimal=6)
+
+ def test_get_z_i(self):
+ m = MoranRes(self.ols, self.w, z=True)
+ np.testing.assert_array_almost_equal(m.zI, 2.2827389999999999, decimal=6)
+
+
+class TestAKTest(unittest.TestCase):
+ def setUp(self):
+ db = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
+ y = np.array(db.by_col("CRIME"))
+ y = np.reshape(y, (49,1))
+ self.y = y
+ X = []
+ X.append(db.by_col("INC"))
+ X = np.array(X).T
+ self.X = X
+ yd = []
+ yd.append(db.by_col("HOVAL"))
+ yd = np.array(yd).T
+ self.yd = yd
+ q = []
+ q.append(db.by_col("DISCBD"))
+ q = np.array(q).T
+ self.q = q
+ reg = TSLS(y, X, yd, q=q)
+ self.reg = reg
+ w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ w.transform = 'r'
+ self.w = w
+
+ def test_gen_mi(self):
+ ak = AKtest(self.reg, self.w)
+ np.testing.assert_array_almost_equal(ak.mi, 0.2232672865437263, decimal=6)
+
+ def test_gen_ak(self):
+ ak = AKtest(self.reg, self.w)
+ np.testing.assert_array_almost_equal(ak.ak, 4.6428948758930852, decimal=6)
+
+ def test_gen_p(self):
+ ak = AKtest(self.reg, self.w)
+ np.testing.assert_array_almost_equal(ak.p, 0.031182360054340875, decimal=6)
+
+ def test_sp_mi(self):
+ ak = AKtest(self.reg, self.w, case='gen')
+ np.testing.assert_array_almost_equal(ak.mi, 0.2232672865437263, decimal=6)
+
+ def test_sp_ak(self):
+ ak = AKtest(self.reg, self.w,case='gen')
+ np.testing.assert_array_almost_equal(ak.ak, 1.1575928784397795, decimal=6)
+
+ def test_sp_p(self):
+ ak = AKtest(self.reg, self.w, case='gen')
+ np.testing.assert_array_almost_equal(ak.p, 0.28196531619791054, decimal=6)
+
+class TestSpDcache(unittest.TestCase):
+ def setUp(self):
+ db = pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ X = np.array(X).T
+ self.y = y
+ self.X = X
+ ols = OLS(self.y, self.X)
+ self.ols = ols
+ w = pysal.open(pysal.examples.get_path('columbus.gal'), 'r').read()
+ w.transform='r'
+ self.w = w
+
+ def test_j(self):
+ cache = spDcache(self.ols, self.w)
+ np.testing.assert_array_almost_equal(cache.j[0][0], 0.62330311259039439, decimal=6)
+
+ def test_t(self):
+ cache = spDcache(self.ols, self.w)
+ np.testing.assert_array_almost_equal(cache.t, 22.751186696900984, decimal=6)
+
+ def test_trA(self):
+ cache = spDcache(self.ols, self.w)
+ np.testing.assert_array_almost_equal(cache.trA, 1.5880426389276328, decimal=6)
+
+ def test_utwuDs(self):
+ cache = spDcache(self.ols, self.w)
+ np.testing.assert_array_almost_equal(cache.utwuDs[0][0], 8.3941977502916068, decimal=6)
+
+ def test_utwyDs(self):
+ cache = spDcache(self.ols, self.w)
+ np.testing.assert_array_almost_equal(cache.utwyDs[0][0], 5.475255215067957, decimal=6)
+
+ def test_wu(self):
+ cache = spDcache(self.ols, self.w)
+ np.testing.assert_array_almost_equal(cache.wu[0][0], -10.681344941514411, decimal=6)
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/tests/test_diagnostics_tsls.py b/pysal/spreg/tests/test_diagnostics_tsls.py
new file mode 100644
index 0000000..4423b33
--- /dev/null
+++ b/pysal/spreg/tests/test_diagnostics_tsls.py
@@ -0,0 +1,66 @@
+import unittest
+import numpy as np
+import pysal
+import pysal.spreg.diagnostics_tsls as diagnostics_tsls
+import pysal.spreg.diagnostics as diagnostics
+from pysal.spreg.ols import OLS as OLS
+from pysal.spreg.twosls import TSLS as TSLS
+from pysal.spreg.twosls_sp import GM_Lag
+from scipy.stats import pearsonr
+
+
+# create regression object used by the apatial tests
+db = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
+y = np.array(db.by_col("CRIME"))
+y = np.reshape(y, (49,1))
+X = []
+X.append(db.by_col("INC"))
+X = np.array(X).T
+yd = []
+yd.append(db.by_col("HOVAL"))
+yd = np.array(yd).T
+q = []
+q.append(db.by_col("DISCBD"))
+q = np.array(q).T
+reg = TSLS(y, X, yd, q)
+
+# create regression object for spatial test
+db = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
+y = np.array(db.by_col("HOVAL"))
+y = np.reshape(y, (49,1))
+X = np.array(db.by_col("INC"))
+X = np.reshape(X, (49,1))
+yd = np.array(db.by_col("CRIME"))
+yd = np.reshape(yd, (49,1))
+q = np.array(db.by_col("DISCBD"))
+q = np.reshape(q, (49,1))
+w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+w.transform = 'r'
+regsp = GM_Lag(y, X, w=w, yend=yd, q=q, w_lags=2)
+
+
+class TestTStat(unittest.TestCase):
+ def test_t_stat(self):
+ obs = diagnostics_tsls.t_stat(reg)
+ exp = [(5.8452644704588588, 4.9369075950019865e-07),
+ (0.36760156683572748, 0.71485634049075841),
+ (-1.9946891307832111, 0.052021795864651159)]
+ for i in range(3):
+ for j in range(2):
+ self.assertAlmostEquals(obs[i][j],exp[i][j])
+
+class TestPr2Aspatial(unittest.TestCase):
+ def test_pr2_aspatial(self):
+ obs = diagnostics_tsls.pr2_aspatial(reg)
+ exp = 0.2793613712817381
+ self.assertAlmostEquals(obs,exp)
+
+class TestPr2Spatial(unittest.TestCase):
+ def test_pr2_spatial(self):
+ obs = diagnostics_tsls.pr2_spatial(regsp)
+ exp = 0.29964855438065163
+ self.assertAlmostEquals(obs,exp)
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/tests/test_error_sp.py b/pysal/spreg/tests/test_error_sp.py
new file mode 100644
index 0000000..1aba6db
--- /dev/null
+++ b/pysal/spreg/tests/test_error_sp.py
@@ -0,0 +1,317 @@
+import unittest
+import scipy
+import pysal
+import numpy as np
+from pysal.spreg import error_sp as SP
+
+class TestBaseGMError(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ reg = SP.BaseGM_Error(self.y, self.X, self.w.sparse)
+ betas = np.array([[ 47.94371455], [ 0.70598088], [ -0.55571746], [ 0.37230161]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ u = np.array([ 27.4739775])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ predy = np.array([ 52.9930255])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ n = 49
+ self.assertAlmostEqual(reg.n,n,4)
+ k = 3
+ self.assertAlmostEqual(reg.k,k,4)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ x = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ e = np.array([ 31.89620319])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ predy = np.array([ 52.9930255])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ my = 38.43622446938776
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([[ 1.51884943e+02, -5.37622793e+00, -1.86970286e+00], [ -5.37622793e+00, 2.48972661e-01, 5.26564244e-02], [ -1.86970286e+00, 5.26564244e-02, 3.18930650e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,4)
+ sig2 = 191.73716465732355
+ self.assertAlmostEqual(reg.sig2,sig2,4)
+
+class TestGMError(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ reg = SP.GM_Error(self.y, self.X, self.w)
+ betas = np.array([[ 47.94371455], [ 0.70598088], [ -0.55571746], [ 0.37230161]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ u = np.array([ 27.4739775])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ predy = np.array([ 52.9930255])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ n = 49
+ self.assertAlmostEqual(reg.n,n,4)
+ k = 3
+ self.assertAlmostEqual(reg.k,k,4)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ x = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ e = np.array([ 31.89620319])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ predy = np.array([ 52.9930255])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ my = 38.43622446938776
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([[ 1.51884943e+02, -5.37622793e+00, -1.86970286e+00], [ -5.37622793e+00, 2.48972661e-01, 5.26564244e-02], [ -1.86970286e+00, 5.26564244e-02, 3.18930650e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,4)
+ sig2 = 191.73716465732355
+ self.assertAlmostEqual(reg.sig2,sig2,4)
+ pr2 = 0.3495097406012179
+ self.assertAlmostEqual(reg.pr2,pr2)
+ std_err = np.array([ 12.32416094, 0.4989716 , 0.1785863 ])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,4)
+ z_stat = np.array([[ 3.89022140e+00, 1.00152805e-04], [ 1.41487186e+00, 1.57106070e-01], [ -3.11175868e+00, 1.85976455e-03]])
+ np.testing.assert_array_almost_equal(reg.z_stat,z_stat,4)
+
+ at unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
+"Maximum Likelihood requires SciPy version 11 or newer.")
+class TestBaseGMEndogError(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ self.X = np.array(X).T
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ yd = []
+ yd.append(db.by_col("CRIME"))
+ self.yd = np.array(yd).T
+ q = []
+ q.append(db.by_col("DISCBD"))
+ self.q = np.array(q).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ reg = SP.BaseGM_Endog_Error(self.y, self.X, self.yd, self.q, self.w.sparse)
+ betas = np.array([[ 55.36095292], [ 0.46411479], [ -0.66883535], [ 0.38989939]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ u = np.array([ 26.55951566])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ e = np.array([ 31.23925425])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ predy = np.array([ 53.9074875])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 3
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ x = np.array([ 1. , 19.531])
+ np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ yend = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ z = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.z[0],z,4)
+ my = 38.43622446938776
+ self.assertAlmostEqual(reg.mean_y,my)
+ #std_y
+ sy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,sy)
+ #vm
+ vm = np.array([[ 5.29158422e+02, -1.57833675e+01, -8.38021080e+00],
+ [ -1.57833675e+01, 5.40235041e-01, 2.31120327e-01],
+ [ -8.38021080e+00, 2.31120327e-01, 1.44977385e-01]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,4)
+ sig2 = 192.50022721929574
+ self.assertAlmostEqual(reg.sig2,sig2,4)
+
+ at unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
+"Maximum Likelihood requires SciPy version 11 or newer.")
+class TestGMEndogError(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ self.X = np.array(X).T
+ yd = []
+ yd.append(db.by_col("CRIME"))
+ self.yd = np.array(yd).T
+ q = []
+ q.append(db.by_col("DISCBD"))
+ self.q = np.array(q).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ reg = SP.GM_Endog_Error(self.y, self.X, self.yd, self.q, self.w)
+ betas = np.array([[ 55.36095292], [ 0.46411479], [ -0.66883535], [ 0.38989939]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ u = np.array([ 26.55951566])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ e = np.array([ 31.23925425])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ predy = np.array([ 53.9074875])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 3
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ x = np.array([ 1. , 19.531])
+ np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ yend = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ z = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.z[0],z,4)
+ my = 38.43622446938776
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([[ 5.29158422e+02, -1.57833675e+01, -8.38021080e+00],
+ [ -1.57833675e+01, 5.40235041e-01, 2.31120327e-01],
+ [ -8.38021080e+00, 2.31120327e-01, 1.44977385e-01]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,4)
+ pr2 = 0.346472557570858
+ self.assertAlmostEqual(reg.pr2,pr2)
+ sig2 = 192.50022721929574
+ self.assertAlmostEqual(reg.sig2,sig2,4)
+ std_err = np.array([ 23.003401 , 0.73500657, 0.38075777])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,4)
+ z_stat = np.array([[ 2.40664208, 0.01609994], [ 0.63144305, 0.52775088], [-1.75659016, 0.07898769]])
+ np.testing.assert_array_almost_equal(reg.z_stat,z_stat,4)
+
+ at unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
+"Maximum Likelihood requires SciPy version 11 or newer.")
+class TestBaseGMCombo(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ # Only spatial lag
+ yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, None, None, 1, True)
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ reg = SP.BaseGM_Combo(self.y, self.X, yend=yd2, q=q2, w=self.w.sparse)
+ betas = np.array([[ 57.61123461],[ 0.73441314], [ -0.59459416], [ -0.21762921], [ 0.54732051]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ u = np.array([ 25.57932637])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ e_filtered = np.array([ 31.65374945])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e_filtered,4)
+ predy = np.array([ 54.88767663])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 4
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ x = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ yend = np.array([ 35.4585005])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ z = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
+ np.testing.assert_array_almost_equal(reg.z[0],z,4)
+ my = 38.43622446938776
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([ 5.22438365e+02, 2.38012873e-01, 3.20924172e-02,
+ 2.15753599e-01])
+ np.testing.assert_array_almost_equal(np.diag(reg.vm),vm,4)
+ sig2 = 181.78650186468832
+ self.assertAlmostEqual(reg.sig2,sig2,4)
+
+ at unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
+"Maximum Likelihood requires SciPy version 11 or newer.")
+class TestGMCombo(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ def test_model(self):
+ # Only spatial lag
+ reg = SP.GM_Combo(self.y, self.X, w=self.w)
+ e_reduced = np.array([ 28.18617481])
+ np.testing.assert_array_almost_equal(reg.e_pred[0],e_reduced,4)
+ predy_e = np.array([ 52.28082782])
+ np.testing.assert_array_almost_equal(reg.predy_e[0],predy_e,4)
+ betas = np.array([[ 57.61123515],[ 0.73441313], [ -0.59459416], [ -0.21762921], [ 0.54732051]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ u = np.array([ 25.57932637])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ e_filtered = np.array([ 31.65374945])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e_filtered,4)
+ predy = np.array([ 54.88767685])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 4
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ x = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ yend = np.array([ 35.4585005])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ z = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
+ np.testing.assert_array_almost_equal(reg.z[0],z,4)
+ my = 38.43622446938776
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([ 5.22438333e+02, 2.38012875e-01, 3.20924173e-02,
+ 2.15753579e-01])
+ np.testing.assert_array_almost_equal(np.diag(reg.vm),vm,4)
+ sig2 = 181.78650186468832
+ self.assertAlmostEqual(reg.sig2,sig2,4)
+ pr2 = 0.3018280166937799
+ self.assertAlmostEqual(reg.pr2,pr2,4)
+ pr2_e = 0.3561355586759414
+ self.assertAlmostEqual(reg.pr2_e,pr2_e,4)
+ std_err = np.array([ 22.85692222, 0.48786559, 0.17914356, 0.46449318])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,4)
+ z_stat = np.array([[ 2.52051597e+00, 1.17182922e-02], [ 1.50535954e+00, 1.32231664e-01], [ -3.31909311e+00, 9.03103123e-04], [ -4.68530506e-01, 6.39405261e-01]])
+ np.testing.assert_array_almost_equal(reg.z_stat,z_stat,4)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/tests/test_error_sp_het.py b/pysal/spreg/tests/test_error_sp_het.py
new file mode 100644
index 0000000..1c2ce6e
--- /dev/null
+++ b/pysal/spreg/tests/test_error_sp_het.py
@@ -0,0 +1,407 @@
+import unittest
+import pysal
+import numpy as np
+from pysal.spreg import error_sp_het as HET
+
+class TestBaseGMErrorHet(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ reg = HET.BaseGM_Error_Het(self.y, self.X, self.w.sparse, step1c=True)
+ betas = np.array([[ 47.99626638], [ 0.71048989], [ -0.55876126], [ 0.41178776]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,7)
+ u = np.array([ 27.38122697])
+ np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ ef = np.array([ 32.29765975])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
+ predy = np.array([ 53.08577603])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 3
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ x = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ i_s = 'Maximum number of iterations reached.'
+ np.testing.assert_string_equal(reg.iter_stop,i_s)
+ its = 1
+ self.assertAlmostEqual(reg.iteration,its,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ stdy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,stdy)
+ vm = np.array([[ 1.31767529e+02, -3.58368748e+00, -1.65090647e+00,
+ 0.00000000e+00],
+ [ -3.58368748e+00, 1.35513711e-01, 3.77539055e-02,
+ 0.00000000e+00],
+ [ -1.65090647e+00, 3.77539055e-02, 2.61042702e-02,
+ 0.00000000e+00],
+ [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
+ 2.82398517e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ xtx = np.array([[ 4.90000000e+01, 7.04371999e+02, 1.72131237e+03],
+ [ 7.04371999e+02, 1.16866734e+04, 2.15575320e+04],
+ [ 1.72131237e+03, 2.15575320e+04, 7.39058986e+04]])
+ np.testing.assert_array_almost_equal(reg.xtx,xtx,4)
+
+class TestGMErrorHet(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ reg = HET.GM_Error_Het(self.y, self.X, self.w, step1c=True)
+ betas = np.array([[ 47.99626638], [ 0.71048989], [ -0.55876126], [ 0.41178776]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,7)
+ u = np.array([ 27.38122697])
+ np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ ef = np.array([ 32.29765975])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
+ predy = np.array([ 53.08577603])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 3
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ x = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ i_s = 'Maximum number of iterations reached.'
+ np.testing.assert_string_equal(reg.iter_stop,i_s)
+ its = 1
+ self.assertAlmostEqual(reg.iteration,its,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ stdy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,stdy)
+ vm = np.array([[ 1.31767529e+02, -3.58368748e+00, -1.65090647e+00,
+ 0.00000000e+00],
+ [ -3.58368748e+00, 1.35513711e-01, 3.77539055e-02,
+ 0.00000000e+00],
+ [ -1.65090647e+00, 3.77539055e-02, 2.61042702e-02,
+ 0.00000000e+00],
+ [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
+ 2.82398517e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ pr2 = 0.34951013222581306
+ self.assertAlmostEqual(reg.pr2,pr2)
+ stde = np.array([ 11.47900385, 0.36812187, 0.16156816, 0.16804717])
+ np.testing.assert_array_almost_equal(reg.std_err,stde,4)
+ z_stat = np.array([[ 4.18122226e+00, 2.89946274e-05],
+ [ 1.93003988e+00, 5.36018970e-02],
+ [ -3.45836247e+00, 5.43469673e-04],
+ [ 2.45042960e+00, 1.42685863e-02]])
+ np.testing.assert_array_almost_equal(reg.z_stat,z_stat,4)
+ xtx = np.array([[ 4.90000000e+01, 7.04371999e+02, 1.72131237e+03],
+ [ 7.04371999e+02, 1.16866734e+04, 2.15575320e+04],
+ [ 1.72131237e+03, 2.15575320e+04, 7.39058986e+04]])
+ np.testing.assert_array_almost_equal(reg.xtx,xtx,4)
+
+class TestBaseGMEndogErrorHet(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ self.X = np.array(X).T
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ yd = []
+ yd.append(db.by_col("CRIME"))
+ self.yd = np.array(yd).T
+ q = []
+ q.append(db.by_col("DISCBD"))
+ self.q = np.array(q).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ reg = HET.BaseGM_Endog_Error_Het(self.y, self.X, self.yd, self.q, self.w.sparse, step1c=True)
+ betas = np.array([[ 55.39707924], [ 0.46563046], [ -0.67038326], [ 0.41135023]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,7)
+ u = np.array([ 26.51812895])
+ np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ ef = np.array([ 31.46604707])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
+ predy = np.array([ 53.94887405])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 3
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ x = np.array([ 1. , 19.531])
+ np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ yend = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ q = np.array([ 5.03])
+ np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ z = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.z[0],z,7)
+ h = np.array([ 1. , 19.531, 5.03 ])
+ np.testing.assert_array_almost_equal(reg.h[0],h,7)
+ i_s = 'Maximum number of iterations reached.'
+ np.testing.assert_string_equal(reg.iter_stop,i_s)
+ its = 1
+ self.assertAlmostEqual(reg.iteration,its,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ stdy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,stdy)
+ vm = np.array([[ 8.34637805e+02, -2.16932259e+01, -1.33327894e+01,
+ 1.65840848e+00],
+ [ -2.16932259e+01, 5.97683070e-01, 3.39503523e-01,
+ -3.90111107e-02],
+ [ -1.33327894e+01, 3.39503523e-01, 2.19008080e-01,
+ -2.81929695e-02],
+ [ 1.65840848e+00, -3.90111107e-02, -2.81929695e-02,
+ 3.15686105e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ hth = np.array([[ 49. , 704.371999 , 139.75 ],
+ [ 704.371999 , 11686.67338121, 2246.12800625],
+ [ 139.75 , 2246.12800625, 498.5851 ]])
+ np.testing.assert_array_almost_equal(reg.hth,hth,6)
+
+class TestGMEndogErrorHet(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ self.X = np.array(X).T
+ yd = []
+ yd.append(db.by_col("CRIME"))
+ self.yd = np.array(yd).T
+ q = []
+ q.append(db.by_col("DISCBD"))
+ self.q = np.array(q).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ reg = HET.GM_Endog_Error_Het(self.y, self.X, self.yd, self.q, self.w, step1c=True)
+ betas = np.array([[ 55.39707924], [ 0.46563046], [ -0.67038326], [ 0.41135023]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,7)
+ u = np.array([ 26.51812895])
+ np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ predy = np.array([ 53.94887405])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 3
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ x = np.array([ 1. , 19.531])
+ np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ yend = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ q = np.array([ 5.03])
+ np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ z = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.z[0],z,7)
+ h = np.array([ 1. , 19.531, 5.03 ])
+ np.testing.assert_array_almost_equal(reg.h[0],h,7)
+ i_s = 'Maximum number of iterations reached.'
+ np.testing.assert_string_equal(reg.iter_stop,i_s)
+ its = 1
+ self.assertAlmostEqual(reg.iteration,its,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ stdy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,stdy)
+ vm = np.array([[ 8.34637805e+02, -2.16932259e+01, -1.33327894e+01,
+ 1.65840848e+00],
+ [ -2.16932259e+01, 5.97683070e-01, 3.39503523e-01,
+ -3.90111107e-02],
+ [ -1.33327894e+01, 3.39503523e-01, 2.19008080e-01,
+ -2.81929695e-02],
+ [ 1.65840848e+00, -3.90111107e-02, -2.81929695e-02,
+ 3.15686105e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ pr2 = 0.34648011338954804
+ self.assertAlmostEqual(reg.pr2,pr2,7)
+ std_err = np.array([ 28.89009873, 0.77309965, 0.46798299,
+ 0.17767558])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,6)
+ z_stat = np.array([(1.9175109006819244, 0.055173057472126787), (0.60229035155742305, 0.54698088217644414), (-1.4324949211864271, 0.15200223057569454), (2.3151759776869496, 0.020603303355572443)])
+ np.testing.assert_array_almost_equal(reg.z_stat,z_stat,6)
+ hth = np.array([[ 49. , 704.371999 , 139.75 ],
+ [ 704.371999 , 11686.67338121, 2246.12800625],
+ [ 139.75 , 2246.12800625, 498.5851 ]])
+ np.testing.assert_array_almost_equal(reg.hth,hth,6)
+
+class TestBaseGMComboHet(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ # Only spatial lag
+ yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, None, None, 1, True)
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ reg = HET.BaseGM_Combo_Het(self.y, self.X, yend=yd2, q=q2, w=self.w.sparse, step1c=True)
+ betas = np.array([[ 57.7778574 ], [ 0.73034922], [ -0.59257362], [ -0.2230231 ], [ 0.56636724]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,7)
+ u = np.array([ 25.65156033])
+ np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ ef = np.array([ 31.87664403])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
+ predy = np.array([ 54.81544267])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 4
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ x = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ yend = np.array([ 35.4585005])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ q = np.array([ 18.594 , 24.7142675])
+ np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ z = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
+ np.testing.assert_array_almost_equal(reg.z[0],z,7)
+ i_s = 'Maximum number of iterations reached.'
+ np.testing.assert_string_equal(reg.iter_stop,i_s)
+ its = 1
+ self.assertAlmostEqual(reg.iteration,its,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ stdy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,stdy,7)
+ vm = np.array([[ 4.86218274e+02, -2.77268729e+00, -1.59987770e+00,
+ -1.01969471e+01, 2.74302006e+00],
+ [ -2.77268729e+00, 1.04680972e-01, 2.51172238e-02,
+ 1.95136385e-03, 3.70052723e-03],
+ [ -1.59987770e+00, 2.51172238e-02, 2.15655720e-02,
+ 7.65868344e-03, -7.30173070e-03],
+ [ -1.01969471e+01, 1.95136385e-03, 7.65868344e-03,
+ 2.78273684e-01, -6.89402590e-02],
+ [ 2.74302006e+00, 3.70052723e-03, -7.30173070e-03,
+ -6.89402590e-02, 7.12034037e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ hth = np.array([[ 4.90000000e+01, 7.04371999e+02, 1.72131237e+03,
+ 7.24743592e+02, 1.70735413e+03],
+ [ 7.04371999e+02, 1.16866734e+04, 2.15575320e+04,
+ 1.10925200e+04, 2.23848036e+04],
+ [ 1.72131237e+03, 2.15575320e+04, 7.39058986e+04,
+ 2.34796298e+04, 6.70145378e+04],
+ [ 7.24743592e+02, 1.10925200e+04, 2.34796298e+04,
+ 1.16146226e+04, 2.30304624e+04],
+ [ 1.70735413e+03, 2.23848036e+04, 6.70145378e+04,
+ 2.30304624e+04, 6.69879858e+04]])
+ np.testing.assert_array_almost_equal(reg.hth,hth,4)
+
+class TestGMComboHet(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ # Only spatial lag
+ reg = HET.GM_Combo_Het(self.y, self.X, w=self.w, step1c=True)
+ betas = np.array([[ 57.7778574 ], [ 0.73034922], [ -0.59257362], [ -0.2230231 ], [ 0.56636724]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,7)
+ u = np.array([ 25.65156033])
+ np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ ef = np.array([ 31.87664403])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
+ ep = np.array([ 28.30648145])
+ np.testing.assert_array_almost_equal(reg.e_pred[0],ep,7)
+ pe = np.array([ 52.16052155])
+ np.testing.assert_array_almost_equal(reg.predy_e[0],pe,7)
+ predy = np.array([ 54.81544267])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 4
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ x = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ yend = np.array([ 35.4585005])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ q = np.array([ 18.594 , 24.7142675])
+ np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ z = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
+ np.testing.assert_array_almost_equal(reg.z[0],z,7)
+ i_s = 'Maximum number of iterations reached.'
+ np.testing.assert_string_equal(reg.iter_stop,i_s)
+ its = 1
+ self.assertAlmostEqual(reg.iteration,its,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ stdy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,stdy)
+ vm = np.array([[ 4.86218274e+02, -2.77268729e+00, -1.59987770e+00,
+ -1.01969471e+01, 2.74302006e+00],
+ [ -2.77268729e+00, 1.04680972e-01, 2.51172238e-02,
+ 1.95136385e-03, 3.70052723e-03],
+ [ -1.59987770e+00, 2.51172238e-02, 2.15655720e-02,
+ 7.65868344e-03, -7.30173070e-03],
+ [ -1.01969471e+01, 1.95136385e-03, 7.65868344e-03,
+ 2.78273684e-01, -6.89402590e-02],
+ [ 2.74302006e+00, 3.70052723e-03, -7.30173070e-03,
+ -6.89402590e-02, 7.12034037e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ pr2 = 0.3001582877472412
+ self.assertAlmostEqual(reg.pr2,pr2,7)
+ pr2_e = 0.35613102283621967
+ self.assertAlmostEqual(reg.pr2_e,pr2_e,7)
+ std_err = np.array([ 22.05035768, 0.32354439, 0.14685221, 0.52751653, 0.26683966])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,6)
+ z_stat = np.array([(2.6202684885795335, 0.00878605635338265), (2.2573385444145524, 0.023986928627746887), (-4.0351698589183433, 5.456281036278686e-05), (-0.42277935292121521, 0.67245625315942159), (2.1225002455741895, 0.033795752094112265)])
+ np.testing.assert_array_almost_equal(reg.z_stat,z_stat,6)
+ hth = np.array([[ 4.90000000e+01, 7.04371999e+02, 1.72131237e+03,
+ 7.24743592e+02, 1.70735413e+03],
+ [ 7.04371999e+02, 1.16866734e+04, 2.15575320e+04,
+ 1.10925200e+04, 2.23848036e+04],
+ [ 1.72131237e+03, 2.15575320e+04, 7.39058986e+04,
+ 2.34796298e+04, 6.70145378e+04],
+ [ 7.24743592e+02, 1.10925200e+04, 2.34796298e+04,
+ 1.16146226e+04, 2.30304624e+04],
+ [ 1.70735413e+03, 2.23848036e+04, 6.70145378e+04,
+ 2.30304624e+04, 6.69879858e+04]])
+ np.testing.assert_array_almost_equal(reg.hth,hth,4)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/tests/test_error_sp_het_regimes.py b/pysal/spreg/tests/test_error_sp_het_regimes.py
new file mode 100644
index 0000000..4eab0f5
--- /dev/null
+++ b/pysal/spreg/tests/test_error_sp_het_regimes.py
@@ -0,0 +1,307 @@
+import unittest
+import pysal
+import numpy as np
+from pysal.spreg import error_sp_het_regimes as SP
+from pysal.spreg.error_sp_het import GM_Error_Het, GM_Endog_Error_Het, GM_Combo_Het
+
+class TestGM_Error_Het_Regimes(unittest.TestCase):
+ def setUp(self):
+ #Columbus:
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("CRIME"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("HOVAL"))
+ X.append(db.by_col("INC"))
+ self.X = np.array(X).T
+ X2 = []
+ X2.append(db.by_col("INC"))
+ self.X2 = np.array(X2).T
+ yd = []
+ yd.append(db.by_col("HOVAL"))
+ self.yd = np.array(yd).T
+ q = []
+ q.append(db.by_col("DISCBD"))
+ self.q = np.array(q).T
+ self.w = pysal.queen_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ self.r_var = 'NSA'
+ self.regimes = db.by_col(self.r_var)
+ #Artficial:
+ n = 256
+ self.n2 = n/2
+ self.x_a1 = np.random.uniform(-10,10,(n,1))
+ self.x_a2 = np.random.uniform(1,5,(n,1))
+ self.q_a = self.x_a2 + np.random.normal(0,1,(n,1))
+ self.x_a = np.hstack((self.x_a1,self.x_a2))
+ self.y_a = np.dot(np.hstack((np.ones((n,1)),self.x_a)),np.array([[1],[0.5],[2]])) + np.random.normal(0,1,(n,1))
+ latt = int(np.sqrt(n))
+ self.w_a = pysal.lat2W(latt,latt)
+ self.w_a.transform='r'
+ self.regi_a = [0]*(n/2) + [1]*(n/2)
+ self.w_a1 = pysal.lat2W(latt/2,latt)
+ self.w_a1.transform='r'
+
+ def test_model(self):
+ reg = SP.GM_Error_Het_Regimes(self.y, self.X, self.regimes, self.w)
+ betas = np.array([[ 62.95986466],
+ [ -0.15660795],
+ [ -1.49054832],
+ [ 60.98577615],
+ [ -0.3358993 ],
+ [ -0.82129289],
+ [ 0.54662719]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ u = np.array([-2.19031456])
+ np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ predy = np.array([ 17.91629456])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ n = 49
+ self.assertAlmostEqual(reg.n,n,6)
+ k = 6
+ self.assertAlmostEqual(reg.k,k,6)
+ y = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.y[0],y,6)
+ x = np.array([[ 0. , 0. , 0. , 1. , 80.467003, 19.531 ]])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray(),x,6)
+ e = np.array([ 2.77847355])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,6)
+ my = 35.128823897959187
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 16.732092091229699
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([ 3.86154100e+01, -2.51553730e-01, -8.20138673e-01,
+ 1.71714184e+00, -1.94929113e-02, 1.23118051e-01,
+ 0.00000000e+00])
+ np.testing.assert_array_almost_equal(reg.vm[0],vm,6)
+ pr2 = 0.5515791216043385
+ self.assertAlmostEqual(reg.pr2,pr2)
+ std_err = np.array([ 6.21412987, 0.15340022, 0.44060473, 7.6032169 , 0.19353719,
+ 0.73621596, 0.13968272])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,6)
+ chow_r = np.array([[ 0.04190799, 0.83779526],
+ [ 0.5736724 , 0.44880328],
+ [ 0.62498575, 0.42920056]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,6)
+ chow_j = 0.72341901308525713
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+
+ def test_model_regi_error(self):
+ #Columbus:
+ reg = SP.GM_Error_Het_Regimes(self.y, self.X, self.regimes, self.w, regime_err_sep=True)
+ betas = np.array([[ 60.74090229],
+ [ -0.17492294],
+ [ -1.33383387],
+ [ 0.68303064],
+ [ 66.30374279],
+ [ -0.31841139],
+ [ -1.27502813],
+ [ 0.11515312]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ vm = np.array([ 44.9411672 , -0.34343354, -0.39946055, 0. ,
+ 0. , 0. , 0. , 0. ])
+ np.testing.assert_array_almost_equal(reg.vm[0],vm,6)
+ u = np.array([-0.05357818])
+ np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ predy = np.array([ 15.77955818])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ e = np.array([ 0.70542044])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,6)
+ chow_r = np.array([[ 3.11061225e-01, 5.77029704e-01],
+ [ 3.39747489e-01, 5.59975012e-01],
+ [ 3.86371771e-03, 9.50436364e-01],
+ [ 4.02884201e+00, 4.47286322e-02]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,6)
+ chow_j = 4.7467070503995412
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ #Artficial:
+ model = SP.GM_Error_Het_Regimes(self.y_a, self.x_a, self.regi_a, w=self.w_a, regime_err_sep=True)
+ model1 = GM_Error_Het(self.y_a[0:(self.n2)].reshape((self.n2),1), self.x_a[0:(self.n2)], w=self.w_a1)
+ model2 = GM_Error_Het(self.y_a[(self.n2):].reshape((self.n2),1), self.x_a[(self.n2):], w=self.w_a1)
+ tbetas = np.vstack((model1.betas, model2.betas))
+ np.testing.assert_array_almost_equal(model.betas,tbetas)
+ vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
+ np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 6)
+
+ def test_model_endog(self):
+ reg = SP.GM_Endog_Error_Het_Regimes(self.y, self.X2, self.yd, self.q, self.regimes, self.w)
+ betas = np.array([[ 77.26679984],
+ [ 4.45992905],
+ [ 78.59534391],
+ [ 0.41432319],
+ [ -3.20196286],
+ [ -1.13672283],
+ [ 0.2174965 ]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ u = np.array([ 20.50716917])
+ np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ e = np.array([ 25.13517175])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,6)
+ predy = np.array([-4.78118917])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 6
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.y[0],y,6)
+ x = np.array([[ 0. , 0. , 1. , 19.531]])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray(),x,6)
+ yend = np.array([[ 0. , 80.467003]])
+ np.testing.assert_array_almost_equal(reg.yend[0].toarray(),yend,6)
+ z = np.array([[ 0. , 0. , 1. , 19.531 , 0. ,
+ 80.467003]])
+ np.testing.assert_array_almost_equal(reg.z[0].toarray(),z,6)
+ my = 35.128823897959187
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 16.732092091229699
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([ 509.66122149, 150.5845341 , 9.64413821, 5.54782831,
+ -80.95846045, -2.25308524, -3.2045214 ])
+ np.testing.assert_array_almost_equal(reg.vm[0],vm,5)
+ pr2 = 0.19776512679331681
+ self.assertAlmostEqual(reg.pr2,pr2)
+ std_err = np.array([ 22.57567765, 11.34616946, 17.43881791, 1.30953812,
+ 5.4830829 , 0.74634612, 0.29973079])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,6)
+ chow_r = np.array([[ 0.0022216 , 0.96240654],
+ [ 0.13127347, 0.7171153 ],
+ [ 0.14367307, 0.70465645]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,6)
+ chow_j = 1.2329971019087163
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+
+ def test_model_endog_regi_error(self):
+ #Columbus:
+ reg = SP.GM_Endog_Error_Het_Regimes(self.y, self.X2, self.yd, self.q, self.regimes, self.w, regime_err_sep=True)
+ betas = np.array([[ 7.92747424e+01],
+ [ 5.78086230e+00],
+ [ -3.83173581e+00],
+ [ 2.23210962e-01],
+ [ 8.26255251e+01],
+ [ 5.48294187e-01],
+ [ -1.28432891e+00],
+ [ 3.57661629e-02]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ vm = np.array([ 7.55988579e+02, 2.53659722e+02, -1.34288316e+02,
+ -2.66141766e-01, 0.00000000e+00, 0.00000000e+00,
+ 0.00000000e+00, 0.00000000e+00])
+ np.testing.assert_array_almost_equal(reg.vm[0],vm,6)
+ u = np.array([ 25.73781918])
+ np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ predy = np.array([-10.01183918])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ e = np.array([ 26.5449135])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,6)
+ chow_r = np.array([[ 0.00998573, 0.92040097],
+ [ 0.12660165, 0.72198192],
+ [ 0.12737281, 0.72117171],
+ [ 0.43507956, 0.50950696]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,6)
+ chow_j = 1.3756768204399892
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ #Artficial:
+ model = SP.GM_Endog_Error_Het_Regimes(self.y_a, self.x_a1, yend=self.x_a2, q=self.q_a, regimes=self.regi_a, w=self.w_a, regime_err_sep=True)
+ model1 = GM_Endog_Error_Het(self.y_a[0:(self.n2)].reshape((self.n2),1), self.x_a1[0:(self.n2)], yend=self.x_a2[0:(self.n2)], q=self.q_a[0:(self.n2)], w=self.w_a1)
+ model2 = GM_Endog_Error_Het(self.y_a[(self.n2):].reshape((self.n2),1), self.x_a1[(self.n2):], yend=self.x_a2[(self.n2):], q=self.q_a[(self.n2):], w=self.w_a1)
+ tbetas = np.vstack((model1.betas, model2.betas))
+ np.testing.assert_array_almost_equal(model.betas,tbetas)
+ vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
+ np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 6)
+
+ def test_model_combo(self):
+ reg = SP.GM_Combo_Het_Regimes(self.y, self.X2, self.regimes, self.yd, self.q, w=self.w)
+ betas = np.array([[ 3.69372678e+01],
+ [ -8.29474998e-01],
+ [ 3.08667517e+01],
+ [ -7.23753444e-01],
+ [ -3.01900940e-01],
+ [ -2.21328949e-01],
+ [ 6.41902155e-01],
+ [ -2.45714919e-02]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ u = np.array([ 0.94039246])
+ np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ e_filtered = np.array([ 0.8737864])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e_filtered,5)
+ predy_e = np.array([ 18.68732105])
+ np.testing.assert_array_almost_equal(reg.predy_e[0],predy_e,6)
+ predy = np.array([ 14.78558754])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 7
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.y[0],y,6)
+ x = np.array([[ 0. , 0. , 1. , 19.531]])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray(),x,6)
+ yend = np.array([[ 0. , 80.467003 , 24.7142675]])
+ np.testing.assert_array_almost_equal(reg.yend[0].toarray(),yend,6)
+ z = np.array([[ 0. , 0. , 1. , 19.531 , 0. ,
+ 80.467003 , 24.7142675]])
+ np.testing.assert_array_almost_equal(reg.z[0].toarray(),z,6)
+ my = 35.128823897959187
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 16.732092091229699
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([ 71.26851365, -0.58278032, 50.53169815, -0.74561147,
+ -0.79510274, -0.10823496, -0.98141395, 1.16575965])
+ np.testing.assert_array_almost_equal(reg.vm[0],vm,4)
+ pr2 = 0.6504148883602958
+ self.assertAlmostEqual(reg.pr2,pr2)
+ pr2_e = 0.527136896994038
+ self.assertAlmostEqual(reg.pr2_e,pr2_e)
+ std_err = np.array([ 8.44206809, 0.72363219, 9.85790968, 0.77218082, 0.34084146,
+ 0.21752916, 0.14371614, 0.39226478])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,5)
+ chow_r = np.array([[ 0.54688708, 0.45959243],
+ [ 0.01035136, 0.91896175],
+ [ 0.03981108, 0.84185042]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,6)
+ chow_j = 0.78070369988354349
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+
+ def test_model_combo_regi_error(self):
+ #Columbus:
+ reg = SP.GM_Combo_Het_Regimes(self.y, self.X2, self.regimes, self.yd, self.q, w=self.w, regime_lag_sep=True, regime_err_sep=True)
+ betas = np.array([[ 42.01151458],
+ [ -0.13917151],
+ [ -0.65300184],
+ [ 0.54737064],
+ [ 0.2629229 ],
+ [ 34.21569751],
+ [ -0.15236089],
+ [ -0.49175217],
+ [ 0.65733173],
+ [ -0.07713581]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ vm = np.array([ 77.49519689, 0.57226879, -1.18856422, -1.28088712,
+ 0.866752 , 0. , 0. , 0. ,
+ 0. , 0. ])
+ np.testing.assert_array_almost_equal(reg.vm[0],vm,6)
+ u = np.array([ 7.81039418])
+ np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ predy = np.array([ 7.91558582])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ e = np.array([ 7.22996911])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,6)
+ chow_r = np.array([[ 1.90869079e-01, 6.62194273e-01],
+ [ 4.56118982e-05, 9.94611401e-01],
+ [ 3.12104263e-02, 8.59771748e-01],
+ [ 1.56368204e-01, 6.92522476e-01],
+ [ 7.52928732e-01, 3.85550558e-01]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,6)
+ chow_j = 1.1316136604755913
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ #Artficial:
+ model = SP.GM_Combo_Het_Regimes(self.y_a, self.x_a1, yend=self.x_a2, q=self.q_a, regimes=self.regi_a, w=self.w_a, regime_err_sep=True, regime_lag_sep=True)
+ model1 = GM_Combo_Het(self.y_a[0:(self.n2)].reshape((self.n2),1), self.x_a1[0:(self.n2)], yend=self.x_a2[0:(self.n2)], q=self.q_a[0:(self.n2)], w=self.w_a1)
+ model2 = GM_Combo_Het(self.y_a[(self.n2):].reshape((self.n2),1), self.x_a1[(self.n2):], yend=self.x_a2[(self.n2):], q=self.q_a[(self.n2):], w=self.w_a1)
+ tbetas = np.vstack((model1.betas, model2.betas))
+ np.testing.assert_array_almost_equal(model.betas,tbetas)
+ vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/tests/test_error_sp_het_sparse.py b/pysal/spreg/tests/test_error_sp_het_sparse.py
new file mode 100644
index 0000000..ff8a80d
--- /dev/null
+++ b/pysal/spreg/tests/test_error_sp_het_sparse.py
@@ -0,0 +1,414 @@
+import unittest
+import pysal
+import numpy as np
+from scipy import sparse
+from pysal.spreg import error_sp_het as HET
+
+class TestBaseGMErrorHet(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.X = sparse.csr_matrix(self.X)
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ reg = HET.BaseGM_Error_Het(self.y, self.X, self.w.sparse, step1c=True)
+ betas = np.array([[ 47.99626638], [ 0.71048989], [ -0.55876126], [ 0.41178776]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,7)
+ u = np.array([ 27.38122697])
+ np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ ef = np.array([ 32.29765975])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
+ predy = np.array([ 53.08577603])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 3
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ x = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray()[0],x,7)
+ i_s = 'Maximum number of iterations reached.'
+ np.testing.assert_string_equal(reg.iter_stop,i_s)
+ its = 1
+ self.assertAlmostEqual(reg.iteration,its,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ stdy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,stdy)
+ vm = np.array([[ 1.31767529e+02, -3.58368748e+00, -1.65090647e+00,
+ 0.00000000e+00],
+ [ -3.58368748e+00, 1.35513711e-01, 3.77539055e-02,
+ 0.00000000e+00],
+ [ -1.65090647e+00, 3.77539055e-02, 2.61042702e-02,
+ 0.00000000e+00],
+ [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
+ 2.82398517e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ xtx = np.array([[ 4.90000000e+01, 7.04371999e+02, 1.72131237e+03],
+ [ 7.04371999e+02, 1.16866734e+04, 2.15575320e+04],
+ [ 1.72131237e+03, 2.15575320e+04, 7.39058986e+04]])
+ np.testing.assert_array_almost_equal(reg.xtx,xtx,4)
+
+class TestGMErrorHet(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.X = sparse.csr_matrix(self.X)
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ reg = HET.GM_Error_Het(self.y, self.X, self.w, step1c=True)
+ betas = np.array([[ 47.99626638], [ 0.71048989], [ -0.55876126], [ 0.41178776]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,7)
+ u = np.array([ 27.38122697])
+ np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ ef = np.array([ 32.29765975])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
+ predy = np.array([ 53.08577603])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 3
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ x = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray()[0],x,7)
+ i_s = 'Maximum number of iterations reached.'
+ np.testing.assert_string_equal(reg.iter_stop,i_s)
+ its = 1
+ self.assertAlmostEqual(reg.iteration,its,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ stdy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,stdy)
+ vm = np.array([[ 1.31767529e+02, -3.58368748e+00, -1.65090647e+00,
+ 0.00000000e+00],
+ [ -3.58368748e+00, 1.35513711e-01, 3.77539055e-02,
+ 0.00000000e+00],
+ [ -1.65090647e+00, 3.77539055e-02, 2.61042702e-02,
+ 0.00000000e+00],
+ [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
+ 2.82398517e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ pr2 = 0.34951013222581306
+ self.assertAlmostEqual(reg.pr2,pr2)
+ stde = np.array([ 11.47900385, 0.36812187, 0.16156816, 0.16804717])
+ np.testing.assert_array_almost_equal(reg.std_err,stde,4)
+ z_stat = np.array([[ 4.18122226e+00, 2.89946274e-05],
+ [ 1.93003988e+00, 5.36018970e-02],
+ [ -3.45836247e+00, 5.43469673e-04],
+ [ 2.45042960e+00, 1.42685863e-02]])
+ np.testing.assert_array_almost_equal(reg.z_stat,z_stat,4)
+ xtx = np.array([[ 4.90000000e+01, 7.04371999e+02, 1.72131237e+03],
+ [ 7.04371999e+02, 1.16866734e+04, 2.15575320e+04],
+ [ 1.72131237e+03, 2.15575320e+04, 7.39058986e+04]])
+ np.testing.assert_array_almost_equal(reg.xtx,xtx,4)
+
+class TestBaseGMEndogErrorHet(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ self.X = np.array(X).T
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.X = sparse.csr_matrix(self.X)
+ yd = []
+ yd.append(db.by_col("CRIME"))
+ self.yd = np.array(yd).T
+ q = []
+ q.append(db.by_col("DISCBD"))
+ self.q = np.array(q).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ reg = HET.BaseGM_Endog_Error_Het(self.y, self.X, self.yd, self.q, self.w.sparse, step1c=True)
+ betas = np.array([[ 55.39707924], [ 0.46563046], [ -0.67038326], [ 0.41135023]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,7)
+ u = np.array([ 26.51812895])
+ np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ ef = np.array([ 31.46604707])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
+ predy = np.array([ 53.94887405])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 3
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ x = np.array([ 1. , 19.531])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray()[0],x,7)
+ yend = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ q = np.array([ 5.03])
+ np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ z = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.z[0].toarray()[0],z,7)
+ h = np.array([ 1. , 19.531, 5.03 ])
+ np.testing.assert_array_almost_equal(reg.h[0].toarray()[0],h,7)
+ i_s = 'Maximum number of iterations reached.'
+ np.testing.assert_string_equal(reg.iter_stop,i_s)
+ its = 1
+ self.assertAlmostEqual(reg.iteration,its,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ stdy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,stdy)
+ vm = np.array([[ 8.34637805e+02, -2.16932259e+01, -1.33327894e+01,
+ 1.65840848e+00],
+ [ -2.16932259e+01, 5.97683070e-01, 3.39503523e-01,
+ -3.90111107e-02],
+ [ -1.33327894e+01, 3.39503523e-01, 2.19008080e-01,
+ -2.81929695e-02],
+ [ 1.65840848e+00, -3.90111107e-02, -2.81929695e-02,
+ 3.15686105e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ hth = np.array([[ 49. , 704.371999 , 139.75 ],
+ [ 704.371999 , 11686.67338121, 2246.12800625],
+ [ 139.75 , 2246.12800625, 498.5851 ]])
+ np.testing.assert_array_almost_equal(reg.hth,hth,6)
+
+class TestGMEndogErrorHet(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ self.X = np.array(X).T
+ self.X = sparse.csr_matrix(self.X)
+ yd = []
+ yd.append(db.by_col("CRIME"))
+ self.yd = np.array(yd).T
+ q = []
+ q.append(db.by_col("DISCBD"))
+ self.q = np.array(q).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ reg = HET.GM_Endog_Error_Het(self.y, self.X, self.yd, self.q, self.w, step1c=True)
+ betas = np.array([[ 55.39707924], [ 0.46563046], [ -0.67038326], [ 0.41135023]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,7)
+ u = np.array([ 26.51812895])
+ np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ predy = np.array([ 53.94887405])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 3
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ x = np.array([ 1. , 19.531])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray()[0],x,7)
+ yend = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ q = np.array([ 5.03])
+ np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ z = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.z[0].toarray()[0],z,7)
+ h = np.array([ 1. , 19.531, 5.03 ])
+ np.testing.assert_array_almost_equal(reg.h[0].toarray()[0],h,7)
+ i_s = 'Maximum number of iterations reached.'
+ np.testing.assert_string_equal(reg.iter_stop,i_s)
+ its = 1
+ self.assertAlmostEqual(reg.iteration,its,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ stdy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,stdy)
+ vm = np.array([[ 8.34637805e+02, -2.16932259e+01, -1.33327894e+01,
+ 1.65840848e+00],
+ [ -2.16932259e+01, 5.97683070e-01, 3.39503523e-01,
+ -3.90111107e-02],
+ [ -1.33327894e+01, 3.39503523e-01, 2.19008080e-01,
+ -2.81929695e-02],
+ [ 1.65840848e+00, -3.90111107e-02, -2.81929695e-02,
+ 3.15686105e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ pr2 = 0.34648011338954804
+ self.assertAlmostEqual(reg.pr2,pr2,7)
+ std_err = np.array([ 28.89009873, 0.77309965, 0.46798299,
+ 0.17767558])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,6)
+ z_stat = np.array([(1.9175109006819244, 0.055173057472126787), (0.60229035155742305, 0.54698088217644414), (-1.4324949211864271, 0.15200223057569454), (2.3151759776869496, 0.020603303355572443)])
+ np.testing.assert_array_almost_equal(reg.z_stat,z_stat,6)
+ hth = np.array([[ 49. , 704.371999 , 139.75 ],
+ [ 704.371999 , 11686.67338121, 2246.12800625],
+ [ 139.75 , 2246.12800625, 498.5851 ]])
+ np.testing.assert_array_almost_equal(reg.hth,hth,6)
+
+class TestBaseGMComboHet(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ # Only spatial lag
+ yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, None, None, 1, True)
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.X = sparse.csr_matrix(self.X)
+ reg = HET.BaseGM_Combo_Het(self.y, self.X, yend=yd2, q=q2, w=self.w.sparse, step1c=True)
+ betas = np.array([[ 57.7778574 ], [ 0.73034922], [ -0.59257362], [ -0.2230231 ], [ 0.56636724]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,7)
+ u = np.array([ 25.65156033])
+ np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ ef = np.array([ 31.87664403])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
+ predy = np.array([ 54.81544267])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 4
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ x = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray()[0],x,7)
+ yend = np.array([ 35.4585005])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ q = np.array([ 18.594 , 24.7142675])
+ np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ z = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
+ np.testing.assert_array_almost_equal(reg.z[0].toarray()[0],z,7)
+ i_s = 'Maximum number of iterations reached.'
+ np.testing.assert_string_equal(reg.iter_stop,i_s)
+ its = 1
+ self.assertAlmostEqual(reg.iteration,its,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ stdy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,stdy,7)
+ vm = np.array([[ 4.86218274e+02, -2.77268729e+00, -1.59987770e+00,
+ -1.01969471e+01, 2.74302006e+00],
+ [ -2.77268729e+00, 1.04680972e-01, 2.51172238e-02,
+ 1.95136385e-03, 3.70052723e-03],
+ [ -1.59987770e+00, 2.51172238e-02, 2.15655720e-02,
+ 7.65868344e-03, -7.30173070e-03],
+ [ -1.01969471e+01, 1.95136385e-03, 7.65868344e-03,
+ 2.78273684e-01, -6.89402590e-02],
+ [ 2.74302006e+00, 3.70052723e-03, -7.30173070e-03,
+ -6.89402590e-02, 7.12034037e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ hth = np.array([[ 4.90000000e+01, 7.04371999e+02, 1.72131237e+03,
+ 7.24743592e+02, 1.70735413e+03],
+ [ 7.04371999e+02, 1.16866734e+04, 2.15575320e+04,
+ 1.10925200e+04, 2.23848036e+04],
+ [ 1.72131237e+03, 2.15575320e+04, 7.39058986e+04,
+ 2.34796298e+04, 6.70145378e+04],
+ [ 7.24743592e+02, 1.10925200e+04, 2.34796298e+04,
+ 1.16146226e+04, 2.30304624e+04],
+ [ 1.70735413e+03, 2.23848036e+04, 6.70145378e+04,
+ 2.30304624e+04, 6.69879858e+04]])
+ np.testing.assert_array_almost_equal(reg.hth,hth,4)
+
+class TestGMComboHet(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.X = sparse.csr_matrix(self.X)
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ # Only spatial lag
+ reg = HET.GM_Combo_Het(self.y, self.X, w=self.w, step1c=True)
+ betas = np.array([[ 57.7778574 ], [ 0.73034922], [ -0.59257362], [ -0.2230231 ], [ 0.56636724]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,7)
+ u = np.array([ 25.65156033])
+ np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ ef = np.array([ 31.87664403])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
+ ep = np.array([ 28.30648145])
+ np.testing.assert_array_almost_equal(reg.e_pred[0],ep,7)
+ pe = np.array([ 52.16052155])
+ np.testing.assert_array_almost_equal(reg.predy_e[0],pe,7)
+ predy = np.array([ 54.81544267])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 4
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ x = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray()[0],x,7)
+ yend = np.array([ 35.4585005])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ q = np.array([ 18.594 , 24.7142675])
+ np.testing.assert_array_almost_equal(reg.q[0].toarray()[0],q,7)
+ z = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
+ np.testing.assert_array_almost_equal(reg.z[0].toarray()[0],z,7)
+ i_s = 'Maximum number of iterations reached.'
+ np.testing.assert_string_equal(reg.iter_stop,i_s)
+ its = 1
+ self.assertAlmostEqual(reg.iteration,its,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ stdy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,stdy)
+ vm = np.array([[ 4.86218274e+02, -2.77268729e+00, -1.59987770e+00,
+ -1.01969471e+01, 2.74302006e+00],
+ [ -2.77268729e+00, 1.04680972e-01, 2.51172238e-02,
+ 1.95136385e-03, 3.70052723e-03],
+ [ -1.59987770e+00, 2.51172238e-02, 2.15655720e-02,
+ 7.65868344e-03, -7.30173070e-03],
+ [ -1.01969471e+01, 1.95136385e-03, 7.65868344e-03,
+ 2.78273684e-01, -6.89402590e-02],
+ [ 2.74302006e+00, 3.70052723e-03, -7.30173070e-03,
+ -6.89402590e-02, 7.12034037e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ pr2 = 0.3001582877472412
+ self.assertAlmostEqual(reg.pr2,pr2,7)
+ pr2_e = 0.35613102283621967
+ self.assertAlmostEqual(reg.pr2_e,pr2_e,7)
+ std_err = np.array([ 22.05035768, 0.32354439, 0.14685221, 0.52751653, 0.26683966])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,6)
+ z_stat = np.array([(2.6202684885795335, 0.00878605635338265), (2.2573385444145524, 0.023986928627746887), (-4.0351698589183433, 5.456281036278686e-05), (-0.42277935292121521, 0.67245625315942159), (2.1225002455741895, 0.033795752094112265)])
+ np.testing.assert_array_almost_equal(reg.z_stat,z_stat,6)
+ hth = np.array([[ 4.90000000e+01, 7.04371999e+02, 1.72131237e+03,
+ 7.24743592e+02, 1.70735413e+03],
+ [ 7.04371999e+02, 1.16866734e+04, 2.15575320e+04,
+ 1.10925200e+04, 2.23848036e+04],
+ [ 1.72131237e+03, 2.15575320e+04, 7.39058986e+04,
+ 2.34796298e+04, 6.70145378e+04],
+ [ 7.24743592e+02, 1.10925200e+04, 2.34796298e+04,
+ 1.16146226e+04, 2.30304624e+04],
+ [ 1.70735413e+03, 2.23848036e+04, 6.70145378e+04,
+ 2.30304624e+04, 6.69879858e+04]])
+ np.testing.assert_array_almost_equal(reg.hth,hth,4)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/tests/test_error_sp_hom.py b/pysal/spreg/tests/test_error_sp_hom.py
new file mode 100644
index 0000000..afc730d
--- /dev/null
+++ b/pysal/spreg/tests/test_error_sp_hom.py
@@ -0,0 +1,315 @@
+'''
+Unittests for spreg.error_sp_hom module
+
+'''
+import unittest
+import pysal
+from pysal.spreg import error_sp_hom as HOM
+import numpy as np
+
+class BaseGM_Error_Hom_Tester(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ def test_model(self):
+ reg = HOM.BaseGM_Error_Hom(self.y, self.X, self.w.sparse, A1='hom_sc')
+ np.testing.assert_array_almost_equal(reg.y[0],np.array([80.467003]),7)
+ x = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ betas = np.array([[ 47.9478524 ], [ 0.70633223], [ -0.55595633], [ 0.41288558]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,7)
+ np.testing.assert_array_almost_equal(reg.u[0],np.array([27.466734]),6)
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],np.array([ 32.37298547]),7)
+ i_s = 'Maximum number of iterations reached.'
+ self.assertAlmostEqual(reg.iter_stop,i_s,7)
+ np.testing.assert_array_almost_equal(reg.predy[0],np.array([ 53.000269]),6)
+ self.assertAlmostEquals(reg.n,49,7)
+ self.assertAlmostEquals(reg.k,3,7)
+ sig2 = 189.94459439729718
+ self.assertAlmostEqual(reg.sig2,sig2)
+ vm = np.array([[ 1.51340717e+02, -5.29057506e+00, -1.85654540e+00, -2.39139054e-03], [ -5.29057506e+00, 2.46669610e-01, 5.14259101e-02, 3.19241302e-04], [ -1.85654540e+00, 5.14259101e-02, 3.20510550e-02, -5.95640240e-05], [ -2.39139054e-03, 3.19241302e-04, -5.95640240e-05, 3.36690159e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ xtx = np.array([[ 4.90000000e+01, 7.04371999e+02, 1.72131237e+03], [ 7.04371999e+02, 1.16866734e+04, 2.15575320e+04], [ 1.72131237e+03, 2.15575320e+04, 7.39058986e+04]])
+ np.testing.assert_array_almost_equal(reg.xtx,xtx,4)
+
+class GM_Error_Hom_Tester(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ def test_model(self):
+ reg = HOM.GM_Error_Hom(self.y, self.X, self.w, A1='hom_sc')
+ np.testing.assert_array_almost_equal(reg.y[0],np.array([80.467003]),7)
+ x = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ betas = np.array([[ 47.9478524 ], [ 0.70633223], [ -0.55595633], [ 0.41288558]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,7)
+ np.testing.assert_array_almost_equal(reg.u[0],np.array([27.46673388]),6)
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],np.array([ 32.37298547]),7)
+ np.testing.assert_array_almost_equal(reg.predy[0],np.array([ 53.00026912]),6)
+ self.assertAlmostEquals(reg.n,49,7)
+ self.assertAlmostEquals(reg.k,3,7)
+ vm = np.array([[ 1.51340717e+02, -5.29057506e+00, -1.85654540e+00, -2.39139054e-03], [ -5.29057506e+00, 2.46669610e-01, 5.14259101e-02, 3.19241302e-04], [ -1.85654540e+00, 5.14259101e-02, 3.20510550e-02, -5.95640240e-05], [ -2.39139054e-03, 3.19241302e-04, -5.95640240e-05, 3.36690159e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ i_s = 'Maximum number of iterations reached.'
+ self.assertAlmostEqual(reg.iter_stop,i_s,7)
+ self.assertAlmostEqual(reg.iteration,1,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ std_y = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,std_y)
+ pr2 = 0.34950977055969729
+ self.assertAlmostEqual(reg.pr2,pr2)
+ sig2 = 189.94459439729718
+ self.assertAlmostEqual(reg.sig2,sig2)
+ std_err = np.array([ 12.30206149, 0.49665844, 0.17902808, 0.18349119])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,6)
+ z_stat = np.array([[ 3.89754616e+00, 9.71723059e-05], [ 1.42216900e+00, 1.54977196e-01], [ -3.10541409e+00, 1.90012806e-03], [ 2.25016500e+00, 2.44384731e-02]])
+ np.testing.assert_array_almost_equal(reg.z_stat,z_stat,6)
+ xtx = np.array([[ 4.90000000e+01, 7.04371999e+02, 1.72131237e+03], [ 7.04371999e+02, 1.16866734e+04, 2.15575320e+04], [ 1.72131237e+03, 2.15575320e+04, 7.39058986e+04]])
+ np.testing.assert_array_almost_equal(reg.xtx,xtx,4)
+
+
+class BaseGM_Endog_Error_Hom_Tester(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ self.X = np.array(X).T
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ yd = []
+ yd.append(db.by_col("CRIME"))
+ self.yd = np.array(yd).T
+ q = []
+ q.append(db.by_col("DISCBD"))
+ self.q = np.array(q).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ def test_model(self):
+ reg = HOM.BaseGM_Endog_Error_Hom(self.y, self.X, self.yd, self.q, self.w.sparse, A1='hom_sc')
+ np.testing.assert_array_almost_equal(reg.y[0],np.array([ 80.467003]),7)
+ x = np.array([ 1. , 19.531])
+ np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ z = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.z[0],z,7)
+ h = np.array([ 1. , 19.531, 5.03 ])
+ np.testing.assert_array_almost_equal(reg.h[0],h,7)
+ yend = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ q = np.array([ 5.03])
+ np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ betas = np.array([[ 55.36575166], [ 0.46432416], [ -0.66904404], [ 0.43205526]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ u = np.array([ 26.55390939])
+ np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],np.array([ 31.74114306]),7)
+ predy = np.array([ 53.91309361])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ self.assertAlmostEquals(reg.n,49,7)
+ self.assertAlmostEquals(reg.k,3,7)
+ sig2 = 190.59435238060928
+ self.assertAlmostEqual(reg.sig2,sig2)
+ vm = np.array([[ 5.52064057e+02, -1.61264555e+01, -8.86360735e+00, 1.04251912e+00], [ -1.61264555e+01, 5.44898242e-01, 2.39518645e-01, -1.88092950e-02], [ -8.86360735e+00, 2.39518645e-01, 1.55501840e-01, -2.18638648e-02], [ 1.04251912e+00, -1.88092950e-02, -2.18638648e-02, 3.71222222e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ i_s = 'Maximum number of iterations reached.'
+ self.assertAlmostEqual(reg.iter_stop,i_s,7)
+ its = 1
+ self.assertAlmostEqual(reg.iteration,its,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ std_y = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,std_y)
+ sig2 = 0
+ #self.assertAlmostEqual(reg.sig2,sig2)
+ hth = np.array([[ 49. , 704.371999 , 139.75 ], [ 704.371999 , 11686.67338121, 2246.12800625], [ 139.75 , 2246.12800625, 498.5851]])
+ np.testing.assert_array_almost_equal(reg.hth,hth,4)
+
+class GM_Endog_Error_Hom_Tester(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ self.X = np.array(X).T
+ yd = []
+ yd.append(db.by_col("CRIME"))
+ self.yd = np.array(yd).T
+ q = []
+ q.append(db.by_col("DISCBD"))
+ self.q = np.array(q).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ def test_model(self):
+ reg = HOM.GM_Endog_Error_Hom(self.y, self.X, self.yd, self.q, self.w, A1='hom_sc')
+ np.testing.assert_array_almost_equal(reg.y[0],np.array([ 80.467003]),7)
+ x = np.array([ 1. , 19.531])
+ np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ z = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.z[0],z,7)
+ h = np.array([ 1. , 19.531, 5.03 ])
+ np.testing.assert_array_almost_equal(reg.h[0],h,7)
+ yend = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ q = np.array([ 5.03])
+ np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ betas = np.array([[ 55.36575166], [ 0.46432416], [ -0.66904404], [ 0.43205526]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ u = np.array([ 26.55390939])
+ np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],np.array([ 31.74114306]),7)
+ predy = np.array([ 53.91309361])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ self.assertAlmostEquals(reg.n,49,7)
+ self.assertAlmostEquals(reg.k,3,7)
+ vm = np.array([[ 5.52064057e+02, -1.61264555e+01, -8.86360735e+00, 1.04251912e+00], [ -1.61264555e+01, 5.44898242e-01, 2.39518645e-01, -1.88092950e-02], [ -8.86360735e+00, 2.39518645e-01, 1.55501840e-01, -2.18638648e-02], [ 1.04251912e+00, -1.88092950e-02, -2.18638648e-02, 3.71222222e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ i_s = 'Maximum number of iterations reached.'
+ self.assertAlmostEqual(reg.iter_stop,i_s,7)
+ its = 1
+ self.assertAlmostEqual(reg.iteration,its,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ std_y = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,std_y)
+ pr2 = 0.34647366525657419
+ self.assertAlmostEqual(reg.pr2,pr2)
+ sig2 = 190.59435238060928
+ self.assertAlmostEqual(reg.sig2,sig2)
+ #std_err
+ std_err = np.array([ 23.49604343, 0.73817223, 0.39433722, 0.19267128])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,6)
+ z_stat = np.array([[ 2.35638617, 0.01845372], [ 0.62901874, 0.52933679], [-1.69662923, 0.08976678], [ 2.24244556, 0.02493259]])
+ np.testing.assert_array_almost_equal(reg.z_stat,z_stat,6)
+
+
+class BaseGM_Combo_Hom_Tester(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ self.X = np.array(X).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ def test_model(self):
+ yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, None, None, 1, True)
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ reg = HOM.BaseGM_Combo_Hom(self.y, self.X, yend=yd2, q=q2, w=self.w.sparse, A1='hom_sc')
+ np.testing.assert_array_almost_equal(reg.y[0],np.array([80.467003]),7)
+ x = np.array([ 1. , 19.531])
+ np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ betas = np.array([[ 10.12541428], [ 1.56832263], [ 0.15132076], [ 0.21033397]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,7)
+ np.testing.assert_array_almost_equal(reg.u[0],np.array([34.3450723]),7)
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],np.array([ 36.6149682]),7)
+ np.testing.assert_array_almost_equal(reg.predy[0],np.array([ 46.1219307]),7)
+ self.assertAlmostEquals(reg.n,49,7)
+ self.assertAlmostEquals(reg.k,3,7)
+ vm = np.array([[ 2.33694742e+02, -6.66856869e-01, -5.58304254e+00, 4.85488380e+00], [ -6.66856869e-01, 1.94241504e-01, -5.42327138e-02, 5.37225570e-02], [ -5.58304254e+00, -5.42327138e-02, 1.63860721e-01, -1.44425498e-01], [ 4.85488380e+00, 5.37225570e-02, -1.44425498e-01, 1.78622255e-01]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ z = np.array([ 1. , 19.531 , 35.4585005])
+ np.testing.assert_array_almost_equal(reg.z[0],z,7)
+ h = np.array([ 1. , 19.531, 18.594])
+ np.testing.assert_array_almost_equal(reg.h[0],h,7)
+ yend = np.array([ 35.4585005])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ q = np.array([ 18.594])
+ np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ i_s = 'Maximum number of iterations reached.'
+ self.assertAlmostEqual(reg.iter_stop,i_s,7)
+ its = 1
+ self.assertAlmostEqual(reg.iteration,its,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ std_y = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,std_y)
+ sig2 = 232.22680651270042
+ #self.assertAlmostEqual(reg.sig2,sig2)
+ np.testing.assert_allclose(reg.sig2,sig2)
+ hth = np.array([[ 49. , 704.371999 , 724.7435916 ], [ 704.371999 , 11686.67338121, 11092.519988 ], [ 724.7435916 , 11092.519988 , 11614.62257048]])
+ np.testing.assert_array_almost_equal(reg.hth,hth,4)
+
+
+class GM_Combo_Hom_Tester(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ self.X = np.array(X).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ def test_model(self):
+ reg = HOM.GM_Combo_Hom(self.y, self.X, w=self.w, A1='hom_sc')
+ np.testing.assert_array_almost_equal(reg.y[0],np.array([80.467003]),7)
+ x = np.array([ 1. , 19.531])
+ np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ betas = np.array([[ 10.12541428], [ 1.56832263], [ 0.15132076], [ 0.21033397]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,7)
+ np.testing.assert_array_almost_equal(reg.u[0],np.array([34.3450723]),7)
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],np.array([ 36.6149682]),7)
+ np.testing.assert_array_almost_equal(reg.e_pred[0],np.array([ 32.90372983]),7)
+ np.testing.assert_array_almost_equal(reg.predy[0],np.array([ 46.1219307]),7)
+ np.testing.assert_array_almost_equal(reg.predy_e[0],np.array([47.56327317]),7)
+ self.assertAlmostEquals(reg.n,49,7)
+ self.assertAlmostEquals(reg.k,3,7)
+ z = np.array([ 1. , 19.531 , 35.4585005])
+ np.testing.assert_array_almost_equal(reg.z[0],z,7)
+ h = np.array([ 1. , 19.531, 18.594])
+ np.testing.assert_array_almost_equal(reg.h[0],h,7)
+ yend = np.array([ 35.4585005])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ q = np.array([ 18.594])
+ np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ i_s = 'Maximum number of iterations reached.'
+ self.assertAlmostEqual(reg.iter_stop,i_s,7)
+ self.assertAlmostEqual(reg.iteration,1,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ std_y = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,std_y)
+ pr2 = 0.28379825632694394
+ self.assertAlmostEqual(reg.pr2,pr2)
+ pr2_e = 0.25082892555141506
+ self.assertAlmostEqual(reg.pr2_e,pr2_e)
+ sig2 = 232.22680651270042
+ #self.assertAlmostEqual(reg.sig2, sig2)
+ np.testing.assert_allclose(reg.sig2, sig2)
+ std_err = np.array([ 15.28707761, 0.44072838, 0.40479714, 0.42263726])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,6)
+ z_stat = np.array([[ 6.62351206e-01, 5.07746167e-01], [ 3.55847888e+00, 3.73008780e-04], [ 3.73818749e-01, 7.08539170e-01], [ 4.97670189e-01, 6.18716523e-01]])
+ np.testing.assert_array_almost_equal(reg.z_stat,z_stat,6)
+ vm = np.array([[ 2.33694742e+02, -6.66856869e-01, -5.58304254e+00, 4.85488380e+00], [ -6.66856869e-01, 1.94241504e-01, -5.42327138e-02, 5.37225570e-02], [ -5.58304254e+00, -5.42327138e-02, 1.63860721e-01, -1.44425498e-01], [ 4.85488380e+00, 5.37225570e-02, -1.44425498e-01, 1.78622255e-01]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+
+suite = unittest.TestSuite()
+test_classes = [BaseGM_Error_Hom_Tester, GM_Error_Hom_Tester,\
+ BaseGM_Endog_Error_Hom_Tester, GM_Endog_Error_Hom_Tester, \
+ BaseGM_Combo_Hom_Tester, GM_Combo_Hom_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
+
diff --git a/pysal/spreg/tests/test_error_sp_hom_regimes.py b/pysal/spreg/tests/test_error_sp_hom_regimes.py
new file mode 100644
index 0000000..8c8e650
--- /dev/null
+++ b/pysal/spreg/tests/test_error_sp_hom_regimes.py
@@ -0,0 +1,307 @@
+import unittest
+import pysal
+import numpy as np
+from pysal.spreg import error_sp_hom_regimes as SP
+from pysal.spreg.error_sp_hom import GM_Error_Hom, GM_Endog_Error_Hom, GM_Combo_Hom
+
+class TestGM_Error_Hom_Regimes(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("CRIME"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("HOVAL"))
+ X.append(db.by_col("INC"))
+ self.X = np.array(X).T
+ X2 = []
+ X2.append(db.by_col("INC"))
+ self.X2 = np.array(X2).T
+ yd = []
+ yd.append(db.by_col("HOVAL"))
+ self.yd = np.array(yd).T
+ q = []
+ q.append(db.by_col("DISCBD"))
+ self.q = np.array(q).T
+ self.w = pysal.queen_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ self.r_var = 'NSA'
+ self.regimes = db.by_col(self.r_var)
+ #Artficial:
+ n = 256
+ self.n2 = n/2
+ self.x_a1 = np.random.uniform(-10,10,(n,1))
+ self.x_a2 = np.random.uniform(1,5,(n,1))
+ self.q_a = self.x_a2 + np.random.normal(0,1,(n,1))
+ self.x_a = np.hstack((self.x_a1,self.x_a2))
+ self.y_a = np.dot(np.hstack((np.ones((n,1)),self.x_a)),np.array([[1],[0.5],[2]])) + np.random.normal(0,1,(n,1))
+ latt = int(np.sqrt(n))
+ self.w_a = pysal.lat2W(latt,latt)
+ self.w_a.transform='r'
+ self.regi_a = [0]*(n/2) + [1]*(n/2)
+ self.w_a1 = pysal.lat2W(latt/2,latt)
+ self.w_a1.transform='r'
+
+ def test_model(self):
+ reg = SP.GM_Error_Hom_Regimes(self.y, self.X, self.regimes, self.w, A1='het')
+ betas = np.array([[ 62.95986466],
+ [ -0.15660795],
+ [ -1.49054832],
+ [ 60.98577615],
+ [ -0.3358993 ],
+ [ -0.82129289],
+ [ 0.54033921]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ u = np.array([-2.19031456])
+ np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ predy = np.array([ 17.91629456])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ n = 49
+ self.assertAlmostEqual(reg.n,n,6)
+ k = 6
+ self.assertAlmostEqual(reg.k,k,6)
+ y = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.y[0],y,6)
+ x = np.array([[ 0. , 0. , 0. , 1. , 80.467003, 19.531 ]])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray(),x,6)
+ e = np.array([ 2.72131648])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,6)
+ my = 35.128823897959187
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 16.732092091229699
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([ 49.16245801, -0.12493165, -1.89294614, 5.71968257,
+ -0.0571525 , 0.05745855, 0. ])
+ np.testing.assert_array_almost_equal(reg.vm[0],vm,6)
+ sig2 = 96.96108341267626
+ self.assertAlmostEqual(reg.sig2,sig2,5)
+ pr2 = 0.5515791216023577
+ self.assertAlmostEqual(reg.pr2,pr2)
+ std_err = np.array([ 7.01159454, 0.20701411, 0.56905515, 7.90537942, 0.10268949,
+ 0.56660879, 0.15659504])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,6)
+ chow_r = np.array([[ 0.03888544, 0.84367579],
+ [ 0.61613446, 0.43248738],
+ [ 0.72632441, 0.39407719]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,6)
+ chow_j = 0.92133276766189676
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+
+ def test_model_regi_error(self):
+ #Artficial:
+ model = SP.GM_Error_Hom_Regimes(self.y_a, self.x_a, self.regi_a, w=self.w_a, regime_err_sep=True, A1='het')
+ model1 = GM_Error_Hom(self.y_a[0:(self.n2)].reshape((self.n2),1), self.x_a[0:(self.n2)], w=self.w_a1, A1='het')
+ model2 = GM_Error_Hom(self.y_a[(self.n2):].reshape((self.n2),1), self.x_a[(self.n2):], w=self.w_a1, A1='het')
+ tbetas = np.vstack((model1.betas, model2.betas))
+ np.testing.assert_array_almost_equal(model.betas,tbetas)
+ vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
+ np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 6)
+ #Columbus:
+ reg = SP.GM_Error_Hom_Regimes(self.y, self.X2, self.regimes, self.w, regime_err_sep=True, A1='het')
+ betas = np.array([[ 60.66668194],
+ [ -1.72708492],
+ [ 0.62170311],
+ [ 61.4526885 ],
+ [ -1.90700858],
+ [ 0.1102755 ]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ vm = np.array([ 45.57956967, -1.65365774, 0. , 0. ,
+ 0. , 0. ])
+ np.testing.assert_array_almost_equal(reg.vm[0],vm,6)
+ u = np.array([-8.48092392])
+ np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ predy = np.array([ 24.20690392])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ e = np.array([-8.33982604])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,6)
+ chow_r = np.array([[ 0.0050892 , 0.94312823],
+ [ 0.05746619, 0.81054651],
+ [ 1.65677138, 0.19803981]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,6)
+ chow_j = 1.7914221673031792
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+
+ def test_model_endog(self):
+ reg = SP.GM_Endog_Error_Hom_Regimes(self.y, self.X2, self.yd, self.q, self.regimes, self.w, A1='het')
+ betas = np.array([[ 77.26679984],
+ [ 4.45992905],
+ [ 78.59534391],
+ [ 0.41432319],
+ [ -3.20196286],
+ [ -1.13672283],
+ [ 0.22178164]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ u = np.array([ 20.50716917])
+ np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ e = np.array([ 25.22635318])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,6)
+ predy = np.array([-4.78118917])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 6
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.y[0],y,6)
+ x = np.array([[ 0. , 0. , 1. , 19.531]])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray(),x,6)
+ yend = np.array([[ 0. , 80.467003]])
+ np.testing.assert_array_almost_equal(reg.yend[0].toarray(),yend,6)
+ z = np.array([[ 0. , 0. , 1. , 19.531 , 0. ,
+ 80.467003]])
+ np.testing.assert_array_almost_equal(reg.z[0].toarray(),z,6)
+ my = 35.128823897959187
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 16.732092091229699
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([ 403.76852704, 69.06920553, 19.8388512 , 3.62501395,
+ -40.30472224, -1.6601927 , -1.64319352])
+ np.testing.assert_array_almost_equal(reg.vm[0],vm,5)
+ pr2 = 0.19776512679498906
+ self.assertAlmostEqual(reg.pr2,pr2)
+ sig2 = 644.23810259214
+ self.assertAlmostEqual(reg.sig2,sig2,5)
+ std_err = np.array([ 20.09399231, 7.03617703, 23.64968032, 2.176846 ,
+ 3.40352278, 0.92377997, 0.24462006])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,6)
+ chow_r = np.array([[ 0.00191145, 0.96512749],
+ [ 0.31031517, 0.57748685],
+ [ 0.34994619, 0.55414359]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,6)
+ chow_j = 1.248410480025556
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+
+ def test_model_endog_regi_error(self):
+ #Columbus:
+ reg = SP.GM_Endog_Error_Hom_Regimes(self.y, self.X2, self.yd, self.q, self.regimes, self.w, regime_err_sep=True, A1='het')
+ betas = np.array([[ 7.92747424e+01],
+ [ 5.78086230e+00],
+ [ -3.83173581e+00],
+ [ 2.14725610e-01],
+ [ 8.26255251e+01],
+ [ 5.48294187e-01],
+ [ -1.28432891e+00],
+ [ 2.98658172e-02]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ vm = np.array([ 867.50930457, 161.04430783, -92.35637083, -1.13838767,
+ 0. , 0. , 0. , 0. ])
+ np.testing.assert_array_almost_equal(reg.vm[0],vm,6)
+ u = np.array([ 25.73781918])
+ np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ predy = np.array([-10.01183918])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ e = np.array([26.41176711])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,6)
+ chow_r = np.array([[ 0.00909777, 0.92401124],
+ [ 0.24034941, 0.62395386],
+ [ 0.24322564, 0.62188603],
+ [ 0.32572159, 0.5681893 ]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,6)
+ chow_j = 1.4485058522307526
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ #Artficial:
+ model = SP.GM_Endog_Error_Hom_Regimes(self.y_a, self.x_a1, yend=self.x_a2, q=self.q_a, regimes=self.regi_a, w=self.w_a, regime_err_sep=True, A1='het')
+ model1 = GM_Endog_Error_Hom(self.y_a[0:(self.n2)].reshape((self.n2),1), self.x_a1[0:(self.n2)], yend=self.x_a2[0:(self.n2)], q=self.q_a[0:(self.n2)], w=self.w_a1, A1='het')
+ model2 = GM_Endog_Error_Hom(self.y_a[(self.n2):].reshape((self.n2),1), self.x_a1[(self.n2):], yend=self.x_a2[(self.n2):], q=self.q_a[(self.n2):], w=self.w_a1, A1='het')
+ tbetas = np.vstack((model1.betas, model2.betas))
+ np.testing.assert_array_almost_equal(model.betas,tbetas)
+ vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
+ np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 6)
+
+ def test_model_combo(self):
+ reg = SP.GM_Combo_Hom_Regimes(self.y, self.X2, self.regimes, self.yd, self.q, w=self.w, A1='het')
+ betas = np.array([[ 36.93726782],
+ [ -0.829475 ],
+ [ 30.86675168],
+ [ -0.72375344],
+ [ -0.30190094],
+ [ -0.22132895],
+ [ 0.64190215],
+ [ -0.07314671]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ u = np.array([ 0.94039246])
+ np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ e_filtered = np.array([ 0.74211331])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e_filtered,5)
+ predy_e = np.array([ 18.68732105])
+ np.testing.assert_array_almost_equal(reg.predy_e[0],predy_e,6)
+ predy = np.array([ 14.78558754])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 7
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.y[0],y,6)
+ x = np.array([[ 0. , 0. , 1. , 19.531]])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray(),x,6)
+ yend = np.array([[ 0. , 80.467003 , 24.7142675]])
+ np.testing.assert_array_almost_equal(reg.yend[0].toarray(),yend,6)
+ z = np.array([[ 0. , 0. , 1. , 19.531 , 0. ,
+ 80.467003 , 24.7142675]])
+ np.testing.assert_array_almost_equal(reg.z[0].toarray(),z,6)
+ my = 35.128823897959187
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 16.732092091229699
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([ 111.54419614, -0.23476709, 83.37295278, -1.74452409,
+ -1.60256796, -0.13151396, -1.43857915, 2.19420848])
+ np.testing.assert_array_almost_equal(reg.vm[0],vm,4)
+ sig2 = 95.57694234438294
+ self.assertAlmostEqual(reg.sig2,sig2,4)
+ pr2 = 0.6504148883591536
+ self.assertAlmostEqual(reg.pr2,pr2)
+ pr2_e = 0.5271368969923579
+ self.assertAlmostEqual(reg.pr2_e,pr2_e)
+ std_err = np.array([ 10.56144858, 0.93986958, 11.52977369, 0.61000358,
+ 0.44419535, 0.16191882, 0.1630835 , 0.41107528])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,5)
+ chow_r = np.array([[ 0.47406771, 0.49112176],
+ [ 0.00879838, 0.92526827],
+ [ 0.02943577, 0.86377672]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,6)
+ chow_j = 0.59098559257602923
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+
+ def test_model_combo_regi_error(self):
+ #Columbus:
+ reg = SP.GM_Combo_Hom_Regimes(self.y, self.X2, self.regimes, self.yd, self.q, w=self.w, regime_lag_sep=True, regime_err_sep=True, A1='het')
+ betas = np.array([[ 4.20115146e+01],
+ [ -1.39171512e-01],
+ [ -6.53001838e-01],
+ [ 5.47370644e-01],
+ [ 2.61860326e-01],
+ [ 3.42156975e+01],
+ [ -1.52360889e-01],
+ [ -4.91752171e-01],
+ [ 6.57331733e-01],
+ [ -2.68716241e-02]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ vm = np.array([ 154.23356187, 2.99104716, -3.29036767, -2.473113 ,
+ 1.65247551, 0. , 0. , 0. ,
+ 0. , 0. ])
+ np.testing.assert_array_almost_equal(reg.vm[0],vm,6)
+ u = np.array([ 7.81039418])
+ np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ predy = np.array([ 7.91558582])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ e = np.array([ 7.60819283])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,6)
+ chow_r = np.array([[ 9.59590706e-02, 7.56733881e-01],
+ [ 6.53130455e-05, 9.93551847e-01],
+ [ 4.65270134e-02, 8.29220655e-01],
+ [ 7.68939379e-02, 7.81551631e-01],
+ [ 5.04560098e-01, 4.77503278e-01]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,6)
+ chow_j = 0.74134991257940286
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ #Artficial:
+ model = SP.GM_Combo_Hom_Regimes(self.y_a, self.x_a1, yend=self.x_a2, q=self.q_a, regimes=self.regi_a, w=self.w_a, regime_err_sep=True, regime_lag_sep=True, A1='het')
+ model1 = GM_Combo_Hom(self.y_a[0:(self.n2)].reshape((self.n2),1), self.x_a1[0:(self.n2)], yend=self.x_a2[0:(self.n2)], q=self.q_a[0:(self.n2)], w=self.w_a1, A1='het')
+ model2 = GM_Combo_Hom(self.y_a[(self.n2):].reshape((self.n2),1), self.x_a1[(self.n2):], yend=self.x_a2[(self.n2):], q=self.q_a[(self.n2):], w=self.w_a1, A1='het')
+ tbetas = np.vstack((model1.betas, model2.betas))
+ np.testing.assert_array_almost_equal(model.betas,tbetas)
+ vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/tests/test_error_sp_hom_sparse.py b/pysal/spreg/tests/test_error_sp_hom_sparse.py
new file mode 100644
index 0000000..23fafce
--- /dev/null
+++ b/pysal/spreg/tests/test_error_sp_hom_sparse.py
@@ -0,0 +1,320 @@
+'''
+Unittests for spreg.error_sp_hom module
+
+'''
+import unittest
+import pysal
+from pysal.spreg import error_sp_hom as HOM
+from scipy import sparse
+import numpy as np
+
+class BaseGM_Error_Hom_Tester(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.X = sparse.csr_matrix(self.X)
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ def test_model(self):
+ reg = HOM.BaseGM_Error_Hom(self.y, self.X, self.w.sparse, A1='hom_sc')
+ np.testing.assert_array_almost_equal(reg.y[0],np.array([80.467003]),7)
+ x = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray()[0],x,7)
+ betas = np.array([[ 47.9478524 ], [ 0.70633223], [ -0.55595633], [ 0.41288558]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,7)
+ np.testing.assert_array_almost_equal(reg.u[0],np.array([27.466734]),6)
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],np.array([ 32.37298547]),7)
+ i_s = 'Maximum number of iterations reached.'
+ self.assertAlmostEqual(reg.iter_stop,i_s,7)
+ np.testing.assert_array_almost_equal(reg.predy[0],np.array([ 53.000269]),6)
+ self.assertAlmostEquals(reg.n,49,7)
+ self.assertAlmostEquals(reg.k,3,7)
+ sig2 = 189.94459439729718
+ self.assertAlmostEqual(reg.sig2,sig2)
+ vm = np.array([[ 1.51340717e+02, -5.29057506e+00, -1.85654540e+00, -2.39139054e-03], [ -5.29057506e+00, 2.46669610e-01, 5.14259101e-02, 3.19241302e-04], [ -1.85654540e+00, 5.14259101e-02, 3.20510550e-02, -5.95640240e-05], [ -2.39139054e-03, 3.19241302e-04, -5.95640240e-05, 3.36690159e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ xtx = np.array([[ 4.90000000e+01, 7.04371999e+02, 1.72131237e+03], [ 7.04371999e+02, 1.16866734e+04, 2.15575320e+04], [ 1.72131237e+03, 2.15575320e+04, 7.39058986e+04]])
+ np.testing.assert_array_almost_equal(reg.xtx,xtx,4)
+
+class GM_Error_Hom_Tester(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.X = sparse.csr_matrix(self.X)
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ def test_model(self):
+ reg = HOM.GM_Error_Hom(self.y, self.X, self.w, A1='hom_sc')
+ np.testing.assert_array_almost_equal(reg.y[0],np.array([80.467003]),7)
+ x = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray()[0],x,7)
+ betas = np.array([[ 47.9478524 ], [ 0.70633223], [ -0.55595633], [ 0.41288558]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,7)
+ np.testing.assert_array_almost_equal(reg.u[0],np.array([27.46673388]),6)
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],np.array([ 32.37298547]),7)
+ np.testing.assert_array_almost_equal(reg.predy[0],np.array([ 53.00026912]),6)
+ self.assertAlmostEquals(reg.n,49,7)
+ self.assertAlmostEquals(reg.k,3,7)
+ vm = np.array([[ 1.51340717e+02, -5.29057506e+00, -1.85654540e+00, -2.39139054e-03], [ -5.29057506e+00, 2.46669610e-01, 5.14259101e-02, 3.19241302e-04], [ -1.85654540e+00, 5.14259101e-02, 3.20510550e-02, -5.95640240e-05], [ -2.39139054e-03, 3.19241302e-04, -5.95640240e-05, 3.36690159e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ i_s = 'Maximum number of iterations reached.'
+ self.assertAlmostEqual(reg.iter_stop,i_s,7)
+ self.assertAlmostEqual(reg.iteration,1,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ std_y = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,std_y)
+ pr2 = 0.34950977055969729
+ self.assertAlmostEqual(reg.pr2,pr2)
+ sig2 = 189.94459439729718
+ self.assertAlmostEqual(reg.sig2,sig2)
+ std_err = np.array([ 12.30206149, 0.49665844, 0.17902808, 0.18349119])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,6)
+ z_stat = np.array([[ 3.89754616e+00, 9.71723059e-05], [ 1.42216900e+00, 1.54977196e-01], [ -3.10541409e+00, 1.90012806e-03], [ 2.25016500e+00, 2.44384731e-02]])
+ np.testing.assert_array_almost_equal(reg.z_stat,z_stat,6)
+ xtx = np.array([[ 4.90000000e+01, 7.04371999e+02, 1.72131237e+03], [ 7.04371999e+02, 1.16866734e+04, 2.15575320e+04], [ 1.72131237e+03, 2.15575320e+04, 7.39058986e+04]])
+ np.testing.assert_array_almost_equal(reg.xtx,xtx,4)
+
+
+class BaseGM_Endog_Error_Hom_Tester(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ self.X = np.array(X).T
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.X = sparse.csr_matrix(self.X)
+ yd = []
+ yd.append(db.by_col("CRIME"))
+ self.yd = np.array(yd).T
+ q = []
+ q.append(db.by_col("DISCBD"))
+ self.q = np.array(q).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ def test_model(self):
+ reg = HOM.BaseGM_Endog_Error_Hom(self.y, self.X, self.yd, self.q, self.w.sparse, A1='hom_sc')
+ np.testing.assert_array_almost_equal(reg.y[0],np.array([ 80.467003]),7)
+ x = np.array([ 1. , 19.531])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray()[0],x,7)
+ z = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.z[0].toarray()[0],z,7)
+ h = np.array([ 1. , 19.531, 5.03 ])
+ np.testing.assert_array_almost_equal(reg.h[0].toarray()[0],h,7)
+ yend = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ q = np.array([ 5.03])
+ np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ betas = np.array([[ 55.36575166], [ 0.46432416], [ -0.66904404], [ 0.43205526]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ u = np.array([ 26.55390939])
+ np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],np.array([ 31.74114306]),7)
+ predy = np.array([ 53.91309361])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ self.assertAlmostEquals(reg.n,49,7)
+ self.assertAlmostEquals(reg.k,3,7)
+ sig2 = 190.59435238060928
+ self.assertAlmostEqual(reg.sig2,sig2)
+ vm = np.array([[ 5.52064057e+02, -1.61264555e+01, -8.86360735e+00, 1.04251912e+00], [ -1.61264555e+01, 5.44898242e-01, 2.39518645e-01, -1.88092950e-02], [ -8.86360735e+00, 2.39518645e-01, 1.55501840e-01, -2.18638648e-02], [ 1.04251912e+00, -1.88092950e-02, -2.18638648e-02, 3.71222222e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ i_s = 'Maximum number of iterations reached.'
+ self.assertAlmostEqual(reg.iter_stop,i_s,7)
+ its = 1
+ self.assertAlmostEqual(reg.iteration,its,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ std_y = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,std_y)
+ sig2 = 0
+ #self.assertAlmostEqual(reg.sig2,sig2)
+ hth = np.array([[ 49. , 704.371999 , 139.75 ], [ 704.371999 , 11686.67338121, 2246.12800625], [ 139.75 , 2246.12800625, 498.5851]])
+ np.testing.assert_array_almost_equal(reg.hth,hth,4)
+
+class GM_Endog_Error_Hom_Tester(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ self.X = np.array(X).T
+ self.X = sparse.csr_matrix(self.X)
+ yd = []
+ yd.append(db.by_col("CRIME"))
+ self.yd = np.array(yd).T
+ q = []
+ q.append(db.by_col("DISCBD"))
+ self.q = np.array(q).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ def test_model(self):
+ reg = HOM.GM_Endog_Error_Hom(self.y, self.X, self.yd, self.q, self.w, A1='hom_sc')
+ np.testing.assert_array_almost_equal(reg.y[0],np.array([ 80.467003]),7)
+ x = np.array([ 1. , 19.531])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray()[0],x,7)
+ z = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.z[0].toarray()[0],z,7)
+ h = np.array([ 1. , 19.531, 5.03 ])
+ np.testing.assert_array_almost_equal(reg.h[0].toarray()[0],h,7)
+ yend = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ q = np.array([ 5.03])
+ np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ betas = np.array([[ 55.36575166], [ 0.46432416], [ -0.66904404], [ 0.43205526]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ u = np.array([ 26.55390939])
+ np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],np.array([ 31.74114306]),7)
+ predy = np.array([ 53.91309361])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ self.assertAlmostEquals(reg.n,49,7)
+ self.assertAlmostEquals(reg.k,3,7)
+ vm = np.array([[ 5.52064057e+02, -1.61264555e+01, -8.86360735e+00, 1.04251912e+00], [ -1.61264555e+01, 5.44898242e-01, 2.39518645e-01, -1.88092950e-02], [ -8.86360735e+00, 2.39518645e-01, 1.55501840e-01, -2.18638648e-02], [ 1.04251912e+00, -1.88092950e-02, -2.18638648e-02, 3.71222222e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ i_s = 'Maximum number of iterations reached.'
+ self.assertAlmostEqual(reg.iter_stop,i_s,7)
+ its = 1
+ self.assertAlmostEqual(reg.iteration,its,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ std_y = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,std_y)
+ pr2 = 0.34647366525657419
+ self.assertAlmostEqual(reg.pr2,pr2)
+ sig2 = 190.59435238060928
+ self.assertAlmostEqual(reg.sig2,sig2)
+ #std_err
+ std_err = np.array([ 23.49604343, 0.73817223, 0.39433722, 0.19267128])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,6)
+ z_stat = np.array([[ 2.35638617, 0.01845372], [ 0.62901874, 0.52933679], [-1.69662923, 0.08976678], [ 2.24244556, 0.02493259]])
+ np.testing.assert_array_almost_equal(reg.z_stat,z_stat,6)
+
+
+class BaseGM_Combo_Hom_Tester(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ self.X = np.array(X).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ def test_model(self):
+ yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, None, None, 1, True)
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.X = sparse.csr_matrix(self.X)
+ reg = HOM.BaseGM_Combo_Hom(self.y, self.X, yend=yd2, q=q2, w=self.w.sparse, A1='hom_sc')
+ np.testing.assert_array_almost_equal(reg.y[0],np.array([80.467003]),7)
+ x = np.array([ 1. , 19.531])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray()[0],x,7)
+ betas = np.array([[ 10.12541428], [ 1.56832263], [ 0.15132076], [ 0.21033397]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,7)
+ np.testing.assert_array_almost_equal(reg.u[0],np.array([34.3450723]),7)
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],np.array([ 36.6149682]),7)
+ np.testing.assert_array_almost_equal(reg.predy[0],np.array([ 46.1219307]),7)
+ self.assertAlmostEquals(reg.n,49,7)
+ self.assertAlmostEquals(reg.k,3,7)
+ vm = np.array([[ 2.33694742e+02, -6.66856869e-01, -5.58304254e+00, 4.85488380e+00], [ -6.66856869e-01, 1.94241504e-01, -5.42327138e-02, 5.37225570e-02], [ -5.58304254e+00, -5.42327138e-02, 1.63860721e-01, -1.44425498e-01], [ 4.85488380e+00, 5.37225570e-02, -1.44425498e-01, 1.78622255e-01]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ z = np.array([ 1. , 19.531 , 35.4585005])
+ np.testing.assert_array_almost_equal(reg.z[0].toarray()[0],z,7)
+ h = np.array([ 1. , 19.531, 18.594])
+ np.testing.assert_array_almost_equal(reg.h[0].toarray()[0],h,7)
+ yend = np.array([ 35.4585005])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ q = np.array([ 18.594])
+ np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ i_s = 'Maximum number of iterations reached.'
+ self.assertAlmostEqual(reg.iter_stop,i_s,7)
+ its = 1
+ self.assertAlmostEqual(reg.iteration,its,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ std_y = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,std_y)
+ sig2 = 232.22680644168395
+ self.assertAlmostEqual(reg.sig2,sig2, places=6)
+ hth = np.array([[ 49. , 704.371999 , 724.7435916 ], [ 704.371999 , 11686.67338121, 11092.519988 ], [ 724.7435916 , 11092.519988 , 11614.62257048]])
+ np.testing.assert_array_almost_equal(reg.hth,hth,4)
+
+
+class GM_Combo_Hom_Tester(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ self.X = np.array(X).T
+ self.X = sparse.csr_matrix(self.X)
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ def test_model(self):
+ reg = HOM.GM_Combo_Hom(self.y, self.X, w=self.w, A1='hom_sc')
+ np.testing.assert_array_almost_equal(reg.y[0],np.array([80.467003]),7)
+ x = np.array([ 1. , 19.531])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray()[0],x,7)
+ betas = np.array([[ 10.12541428], [ 1.56832263], [ 0.15132076], [ 0.21033397]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,7)
+ np.testing.assert_array_almost_equal(reg.u[0],np.array([34.3450723]),7)
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],np.array([ 36.6149682]),7)
+ np.testing.assert_array_almost_equal(reg.e_pred[0],np.array([ 32.90372983]),7)
+ np.testing.assert_array_almost_equal(reg.predy[0],np.array([ 46.1219307]),7)
+ np.testing.assert_array_almost_equal(reg.predy_e[0],np.array([47.56327317]),7)
+ self.assertAlmostEquals(reg.n,49,7)
+ self.assertAlmostEquals(reg.k,3,7)
+ z = np.array([ 1. , 19.531 , 35.4585005])
+ np.testing.assert_array_almost_equal(reg.z[0].toarray()[0],z,7)
+ h = np.array([ 1. , 19.531, 18.594])
+ np.testing.assert_array_almost_equal(reg.h[0].toarray()[0],h,7)
+ yend = np.array([ 35.4585005])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ q = np.array([ 18.594])
+ np.testing.assert_array_almost_equal(reg.q[0].toarray()[0],q,7)
+ i_s = 'Maximum number of iterations reached.'
+ self.assertAlmostEqual(reg.iter_stop,i_s,7)
+ self.assertAlmostEqual(reg.iteration,1,7)
+ my = 38.436224469387746
+ self.assertAlmostEqual(reg.mean_y,my)
+ std_y = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,std_y)
+ pr2 = 0.28379825632694394
+ self.assertAlmostEqual(reg.pr2,pr2)
+ pr2_e = 0.25082892555141506
+ self.assertAlmostEqual(reg.pr2_e,pr2_e)
+ sig2 = 232.22680644168395
+ self.assertAlmostEqual(reg.sig2,sig2, places=6)
+ std_err = np.array([ 15.28707761, 0.44072838, 0.40479714, 0.42263726])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,6)
+ z_stat = np.array([[ 6.62351206e-01, 5.07746167e-01], [ 3.55847888e+00, 3.73008780e-04], [ 3.73818749e-01, 7.08539170e-01], [ 4.97670189e-01, 6.18716523e-01]])
+ np.testing.assert_array_almost_equal(reg.z_stat,z_stat,6)
+ vm = np.array([[ 2.33694742e+02, -6.66856869e-01, -5.58304254e+00, 4.85488380e+00], [ -6.66856869e-01, 1.94241504e-01, -5.42327138e-02, 5.37225570e-02], [ -5.58304254e+00, -5.42327138e-02, 1.63860721e-01, -1.44425498e-01], [ 4.85488380e+00, 5.37225570e-02, -1.44425498e-01, 1.78622255e-01]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+
+suite = unittest.TestSuite()
+test_classes = [BaseGM_Error_Hom_Tester, GM_Error_Hom_Tester,\
+ BaseGM_Endog_Error_Hom_Tester, GM_Endog_Error_Hom_Tester, \
+ BaseGM_Combo_Hom_Tester, GM_Combo_Hom_Tester]
+for i in test_classes:
+ a = unittest.TestLoader().loadTestsFromTestCase(i)
+ suite.addTest(a)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
+
diff --git a/pysal/spreg/tests/test_error_sp_regimes.py b/pysal/spreg/tests/test_error_sp_regimes.py
new file mode 100644
index 0000000..6f3c15a
--- /dev/null
+++ b/pysal/spreg/tests/test_error_sp_regimes.py
@@ -0,0 +1,305 @@
+import unittest
+import scipy
+import pysal
+import numpy as np
+from pysal.spreg import error_sp_regimes as SP
+from pysal.spreg.error_sp import GM_Error, GM_Endog_Error, GM_Combo
+
+ at unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
+"Maximum Likelihood requires SciPy version 11 or newer.")
+class TestGM_Error_Regimes(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("CRIME"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("HOVAL"))
+ X.append(db.by_col("INC"))
+ self.X = np.array(X).T
+ self.w = pysal.queen_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ self.r_var = 'NSA'
+ self.regimes = db.by_col(self.r_var)
+ X1 = []
+ X1.append(db.by_col("INC"))
+ self.X1 = np.array(X1).T
+ yd = []
+ yd.append(db.by_col("HOVAL"))
+ self.yd = np.array(yd).T
+ q = []
+ q.append(db.by_col("DISCBD"))
+ self.q = np.array(q).T
+ #Artficial:
+ n = 256
+ self.n2 = n/2
+ self.x_a1 = np.random.uniform(-10,10,(n,1))
+ self.x_a2 = np.random.uniform(1,5,(n,1))
+ self.q_a = self.x_a2 + np.random.normal(0,1,(n,1))
+ self.x_a = np.hstack((self.x_a1,self.x_a2))
+ self.y_a = np.dot(np.hstack((np.ones((n,1)),self.x_a)),np.array([[1],[0.5],[2]])) + np.random.normal(0,1,(n,1))
+ latt = int(np.sqrt(n))
+ self.w_a = pysal.lat2W(latt,latt)
+ self.w_a.transform='r'
+ self.regi_a = [0]*(n/2) + [1]*(n/2)
+ self.w_a1 = pysal.lat2W(latt/2,latt)
+ self.w_a1.transform='r'
+
+ def test_model(self):
+ reg = SP.GM_Error_Regimes(self.y, self.X, self.regimes, self.w)
+ betas = np.array([[ 63.3443073 ],
+ [ -0.15468 ],
+ [ -1.52186509],
+ [ 61.40071412],
+ [ -0.33550084],
+ [ -0.85076108],
+ [ 0.38671608]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ u = np.array([-2.06177251])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ predy = np.array([ 17.78775251])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ n = 49
+ self.assertAlmostEqual(reg.n,n,4)
+ k = 6
+ self.assertAlmostEqual(reg.k,k,4)
+ y = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ x = np.array([[ 0. , 0. , 0. , 1. , 80.467003, 19.531 ]])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray(),x,4)
+ e = np.array([ 1.40747232])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ my = 35.128823897959187
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 16.732092091229699
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([ 50.55875289, -0.14444487, -2.05735489, 0. ,
+ 0. , 0. ])
+ np.testing.assert_array_almost_equal(reg.vm[0],vm,4)
+ sig2 = 102.13050615267227
+ self.assertAlmostEqual(reg.sig2,sig2,4)
+ pr2 = 0.5525102200608539
+ self.assertAlmostEqual(reg.pr2,pr2)
+ std_err = np.array([ 7.11046784, 0.21879293, 0.58477864, 7.50596504, 0.10800686,
+ 0.57365981])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,4)
+ chow_r = np.array([[ 0.03533785, 0.85088948],
+ [ 0.54918491, 0.45865093],
+ [ 0.67115641, 0.41264872]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,4)
+ chow_j = 0.81985446000130979
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ """
+ def test_model_regi_error(self):
+ #Columbus:
+ reg = SP.GM_Error_Regimes(self.y, self.X, self.regimes, self.w, regime_err_sep=True)
+ betas = np.array([[ 60.45730439],
+ [ -0.17732134],
+ [ -1.30936328],
+ [ 0.51314713],
+ [ 66.5487126 ],
+ [ -0.31845995],
+ [ -1.29047149],
+ [ 0.08092997]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ vm = np.array([ 39.33656288, -0.08420799, -1.50350999, 0. ,
+ 0. , 0. ])
+ np.testing.assert_array_almost_equal(reg.vm[0],vm,4)
+ u = np.array([ 0.00698341])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ predy = np.array([ 15.71899659])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ e = np.array([ 0.53685671])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ chow_r = np.array([[ 3.63674458e-01, 5.46472584e-01],
+ [ 4.29607250e-01, 5.12181727e-01],
+ [ 5.44739543e-04, 9.81379339e-01]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,4)
+ chow_j = 0.70119418251625387
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j,4)
+ #Artficial:
+ model = SP.GM_Error_Regimes(self.y_a, self.x_a, self.regi_a, w=self.w_a, regime_err_sep=True)
+ model1 = GM_Error(self.y_a[0:(self.n2)].reshape((self.n2),1), self.x_a[0:(self.n2)], w=self.w_a1)
+ model2 = GM_Error(self.y_a[(self.n2):].reshape((self.n2),1), self.x_a[(self.n2):], w=self.w_a1)
+ tbetas = np.vstack((model1.betas, model2.betas))
+ np.testing.assert_array_almost_equal(model.betas,tbetas)
+ vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
+ np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 4)
+ """
+ def test_model_endog(self):
+ reg = SP.GM_Endog_Error_Regimes(self.y, self.X1, self.yd, self.q, self.regimes, self.w)
+ betas = np.array([[ 77.48385551, 4.52986622, 78.93209405, 0.42186261,
+ -3.23823854, -1.1475775 , 0.20222108]])
+ np.testing.assert_array_almost_equal(reg.betas.T,betas,4)
+ u = np.array([ 20.89660904])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ e = np.array([ 25.21818724])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ predy = np.array([-5.17062904])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 6
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ x = np.array([[ 0. , 0. , 1. , 19.531]])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray(),x,4)
+ yend = np.array([[ 0. , 80.467003]])
+ np.testing.assert_array_almost_equal(reg.yend[0].toarray(),yend,4)
+ z = np.array([[ 0. , 0. , 1. , 19.531 , 0. ,
+ 80.467003]])
+ np.testing.assert_array_almost_equal(reg.z[0].toarray(),z,4)
+ my = 35.128823897959187
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 16.732092091229699
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([ 390.88250241, 52.25924084, 0. , 0. ,
+ -32.64274729, 0. ])
+ np.testing.assert_array_almost_equal(reg.vm[0],vm,4)
+ pr2 = 0.19623994206233333
+ self.assertAlmostEqual(reg.pr2,pr2,4)
+ sig2 = 649.4011
+ self.assertAlmostEqual(round(reg.sig2,4),round(sig2,4),4)
+ std_err = np.array([ 19.77074866, 6.07667394, 24.32254786, 2.17776972,
+ 2.97078606, 0.94392418])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,4)
+ chow_r = np.array([[ 0.0021348 , 0.96314775],
+ [ 0.40499741, 0.5245196 ],
+ [ 0.4498365 , 0.50241261]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,4)
+ chow_j = 1.2885590185243503
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+
+ def test_model_endog_regi_error(self):
+ #Columbus:
+ reg = SP.GM_Endog_Error_Regimes(self.y, self.X1, self.yd, self.q, self.regimes, self.w, regime_err_sep=True)
+ betas = np.array([[ 7.91729500e+01],
+ [ 5.80693176e+00],
+ [ -3.84036576e+00],
+ [ 1.46462983e-01],
+ [ 8.24723791e+01],
+ [ 5.68908920e-01],
+ [ -1.28824699e+00],
+ [ 6.70387351e-02]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ vm = np.array([ 791.86679123, 140.12967794, -81.37581255, 0. ,
+ 0. , 0. ])
+ np.testing.assert_array_almost_equal(reg.vm[0],vm,4)
+ u = np.array([ 25.80361497])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ predy = np.array([-10.07763497])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ e = np.array([ 27.32251813])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ chow_r = np.array([[ 0.00926459, 0.92331985],
+ [ 0.26102777, 0.60941494],
+ [ 0.26664581, 0.60559072]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,4)
+ chow_j = 1.1184631131987004
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ #Artficial:
+ model = SP.GM_Endog_Error_Regimes(self.y_a, self.x_a1, yend=self.x_a2, q=self.q_a, regimes=self.regi_a, w=self.w_a, regime_err_sep=True)
+ model1 = GM_Endog_Error(self.y_a[0:(self.n2)].reshape((self.n2),1), self.x_a1[0:(self.n2)], yend=self.x_a2[0:(self.n2)], q=self.q_a[0:(self.n2)], w=self.w_a1)
+ model2 = GM_Endog_Error(self.y_a[(self.n2):].reshape((self.n2),1), self.x_a1[(self.n2):], yend=self.x_a2[(self.n2):], q=self.q_a[(self.n2):], w=self.w_a1)
+ tbetas = np.vstack((model1.betas, model2.betas))
+ np.testing.assert_array_almost_equal(model.betas,tbetas)
+ vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
+ np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 4)
+
+ def test_model_combo(self):
+ reg = SP.GM_Combo_Regimes(self.y, self.X1, self.regimes, self.yd, self.q, w=self.w)
+ predy_e = np.array([ 18.82774339])
+ np.testing.assert_array_almost_equal(reg.predy_e[0],predy_e,4)
+ betas = np.array([[ 36.44798052],
+ [ -0.7974482 ],
+ [ 30.53782661],
+ [ -0.72602806],
+ [ -0.30953121],
+ [ -0.21736652],
+ [ 0.64801059],
+ [ -0.16601265]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ u = np.array([ 0.84393304])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ e_filtered = np.array([ 0.4040027])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e_filtered,4)
+ predy = np.array([ 14.88204696])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 7
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ x = np.array([[ 0. , 0. , 1. , 19.531]])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray(),x,4)
+ yend = np.array([[ 0. , 80.467003 , 24.7142675]])
+ np.testing.assert_array_almost_equal(reg.yend[0].toarray(),yend,4)
+ z = np.array([[ 0. , 0. , 1. , 19.531 , 0. ,
+ 80.467003 , 24.7142675]])
+ np.testing.assert_array_almost_equal(reg.z[0].toarray(),z,4)
+ my = 35.128823897959187
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 16.732092091229699
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([ 109.23549239, -0.19754121, 84.29574673, -1.99317178,
+ -1.60123994, -0.1252719 , -1.3930344 ])
+ np.testing.assert_array_almost_equal(reg.vm[0],vm,4)
+ sig2 = 94.98610921110007
+ self.assertAlmostEqual(reg.sig2,sig2,4)
+ pr2 = 0.6493586702255537
+ self.assertAlmostEqual(reg.pr2,pr2)
+ pr2_e = 0.5255332447240576
+ self.assertAlmostEqual(reg.pr2_e,pr2_e)
+ std_err = np.array([ 10.45157846, 0.93942923, 11.38484969, 0.60774708,
+ 0.44461334, 0.15871227, 0.15738141])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,4)
+ chow_r = np.array([[ 0.49716076, 0.48075032],
+ [ 0.00405377, 0.94923363],
+ [ 0.03866684, 0.84411016]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,4)
+ chow_j = 0.64531386285872072
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+
+ def test_model_combo_regi_error(self):
+ #Columbus:
+ reg = SP.GM_Combo_Regimes(self.y, self.X1, self.regimes, self.yd, self.q, w=self.w, regime_lag_sep=True, regime_err_sep=True)
+ betas = np.array([[ 42.01035248],
+ [ -0.13938772],
+ [ -0.6528306 ],
+ [ 0.54737621],
+ [ 0.2684419 ],
+ [ 34.02473255],
+ [ -0.14920259],
+ [ -0.48972903],
+ [ 0.65883658],
+ [ -0.17174845]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ vm = np.array([ 153.58614432, 2.96302131, -3.26211855, -2.46914703,
+ 0. , 0. , 0. , 0. ])
+ np.testing.assert_array_almost_equal(reg.vm[0],vm,4)
+ u = np.array([ 7.73968703])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ predy = np.array([ 7.98629297])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ e = np.array([ 6.45052714])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ chow_r = np.array([[ 1.00886404e-01, 7.50768497e-01],
+ [ 3.61843271e-05, 9.95200481e-01],
+ [ 4.69585772e-02, 8.28442711e-01],
+ [ 8.13275259e-02, 7.75506385e-01]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,4)
+ chow_j = 0.28479988992843119
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ #Artficial:
+ model = SP.GM_Combo_Regimes(self.y_a, self.x_a1, yend=self.x_a2, q=self.q_a, regimes=self.regi_a, w=self.w_a, regime_err_sep=True, regime_lag_sep=True)
+ model1 = GM_Combo(self.y_a[0:(self.n2)].reshape((self.n2),1), self.x_a1[0:(self.n2)], yend=self.x_a2[0:(self.n2)], q=self.q_a[0:(self.n2)], w=self.w_a1)
+ model2 = GM_Combo(self.y_a[(self.n2):].reshape((self.n2),1), self.x_a1[(self.n2):], yend=self.x_a2[(self.n2):], q=self.q_a[(self.n2):], w=self.w_a1)
+ tbetas = np.vstack((model1.betas, model2.betas))
+ np.testing.assert_array_almost_equal(model.betas,tbetas)
+ vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
+
+if __name__ == '__main__':
+ unittest.main()
+
+
diff --git a/pysal/spreg/tests/test_error_sp_sparse.py b/pysal/spreg/tests/test_error_sp_sparse.py
new file mode 100644
index 0000000..1d38814
--- /dev/null
+++ b/pysal/spreg/tests/test_error_sp_sparse.py
@@ -0,0 +1,332 @@
+import unittest
+import pysal
+import numpy as np
+from pysal.spreg import error_sp as SP
+import scipy
+from scipy import sparse
+
+class TestBaseGMError(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.X = sparse.csr_matrix(self.X)
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ reg = SP.BaseGM_Error(self.y, self.X, self.w.sparse)
+ betas = np.array([[ 47.94371455], [ 0.70598088], [ -0.55571746], [ 0.37230161]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ u = np.array([ 27.4739775])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ predy = np.array([ 52.9930255])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ n = 49
+ self.assertAlmostEqual(reg.n,n,4)
+ k = 3
+ self.assertAlmostEqual(reg.k,k,4)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ x = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x.toarray()[0],x,4)
+ e = np.array([ 31.89620319])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ predy = np.array([ 52.9930255])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ my = 38.43622446938776
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([[ 1.51884943e+02, -5.37622793e+00, -1.86970286e+00], [ -5.37622793e+00, 2.48972661e-01, 5.26564244e-02], [ -1.86970286e+00, 5.26564244e-02, 3.18930650e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,4)
+ sig2 = 191.73716465732355
+ self.assertAlmostEqual(reg.sig2,sig2,4)
+
+class TestGMError(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.X = sparse.csr_matrix(self.X)
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ reg = SP.GM_Error(self.y, self.X, self.w)
+ betas = np.array([[ 47.94371455], [ 0.70598088], [ -0.55571746], [ 0.37230161]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ u = np.array([ 27.4739775])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ predy = np.array([ 52.9930255])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ n = 49
+ self.assertAlmostEqual(reg.n,n,4)
+ k = 3
+ self.assertAlmostEqual(reg.k,k,4)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ x = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x.toarray()[0],x,4)
+ e = np.array([ 31.89620319])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ predy = np.array([ 52.9930255])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ my = 38.43622446938776
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([[ 1.51884943e+02, -5.37622793e+00, -1.86970286e+00], [ -5.37622793e+00, 2.48972661e-01, 5.26564244e-02], [ -1.86970286e+00, 5.26564244e-02, 3.18930650e-02]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,4)
+ sig2 = 191.73716465732355
+ self.assertAlmostEqual(reg.sig2,sig2,4)
+ pr2 = 0.3495097406012179
+ self.assertAlmostEqual(reg.pr2,pr2)
+ std_err = np.array([ 12.32416094, 0.4989716 , 0.1785863 ])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,4)
+ z_stat = np.array([[ 3.89022140e+00, 1.00152805e-04], [ 1.41487186e+00, 1.57106070e-01], [ -3.11175868e+00, 1.85976455e-03]])
+ np.testing.assert_array_almost_equal(reg.z_stat,z_stat,4)
+
+ at unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
+"Maximum Likelihood requires SciPy version 11 or newer.")
+class TestBaseGMEndogError(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ self.X = np.array(X).T
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.X = sparse.csr_matrix(self.X)
+ yd = []
+ yd.append(db.by_col("CRIME"))
+ self.yd = np.array(yd).T
+ q = []
+ q.append(db.by_col("DISCBD"))
+ self.q = np.array(q).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ reg = SP.BaseGM_Endog_Error(self.y, self.X, self.yd, self.q, self.w.sparse)
+ betas = np.array([[ 55.36095292], [ 0.46411479], [ -0.66883535], [ 0.38989939]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ u = np.array([ 26.55951566])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ e = np.array([ 31.23925425])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ predy = np.array([ 53.9074875])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 3
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ x = np.array([ 1. , 19.531])
+ np.testing.assert_array_almost_equal(reg.x.toarray()[0],x,4)
+ yend = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ z = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.z.toarray()[0],z,4)
+ my = 38.43622446938776
+ self.assertAlmostEqual(reg.mean_y,my)
+ #std_y
+ sy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,sy)
+ #vm
+ vm = np.array([[ 529.15840986, -15.78336736, -8.38021053],
+ [ -15.78336736, 0.54023504, 0.23112032],
+ [ -8.38021053, 0.23112032, 0.14497738]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,4)
+ sig2 = 192.5002
+ self.assertAlmostEqual(round(reg.sig2,4),round(sig2,4),4)
+
+ at unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
+"Maximum Likelihood requires SciPy version 11 or newer.")
+class TestGMEndogError(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ self.X = np.array(X).T
+ self.X = sparse.csr_matrix(self.X)
+ yd = []
+ yd.append(db.by_col("CRIME"))
+ self.yd = np.array(yd).T
+ q = []
+ q.append(db.by_col("DISCBD"))
+ self.q = np.array(q).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ reg = SP.GM_Endog_Error(self.y, self.X, self.yd, self.q, self.w)
+ betas = np.array([[ 55.36095292], [ 0.46411479], [ -0.66883535], [ 0.38989939]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ u = np.array([ 26.55951566])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ e = np.array([ 31.23925425])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ predy = np.array([ 53.9074875])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 3
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ x = np.array([ 1. , 19.531])
+ np.testing.assert_array_almost_equal(reg.x.toarray()[0],x,4)
+ yend = np.array([ 15.72598])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ z = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.z.toarray()[0],z,4)
+ my = 38.43622446938776
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([[ 529.15840986, -15.78336736, -8.38021053],
+ [ -15.78336736, 0.54023504, 0.23112032],
+ [ -8.38021053, 0.23112032, 0.14497738]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,4)
+ pr2 = 0.346472557570858
+ self.assertAlmostEqual(reg.pr2,pr2)
+ sig2 = 192.5002
+ self.assertAlmostEqual(round(reg.sig2,4),round(sig2,4),4)
+ std_err = np.array([ 23.003401 , 0.73500657, 0.38075777])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,4)
+ z_stat = np.array([[ 2.40664208, 0.01609994], [ 0.63144305, 0.52775088], [-1.75659016, 0.07898769]])
+ np.testing.assert_array_almost_equal(reg.z_stat,z_stat,4)
+
+ at unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
+"Maximum Likelihood requires SciPy version 11 or newer.")
+class TestBaseGMCombo(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ # Only spatial lag
+ yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, None, None, 1, True)
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.X = sparse.csr_matrix(self.X)
+ reg = SP.BaseGM_Combo(self.y, self.X, yend=yd2, q=q2, w=self.w.sparse)
+ betas = np.array([[ 57.61123461],[ 0.73441314], [ -0.59459416], [ -0.21762921], [ 0.54732051]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ u = np.array([ 25.57932637])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ e_filtered = np.array([ 31.65374945])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e_filtered,4)
+ predy = np.array([ 54.88767663])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 4
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ x = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x.toarray()[0],x,4)
+ yend = np.array([ 35.4585005])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ z = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
+ np.testing.assert_array_almost_equal(reg.z.toarray()[0],z,4)
+ my = 38.43622446938776
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([[ 522.43841148, -6.07256915, -1.91429117, -8.97133162],
+ [ -6.07256915, 0.23801287, 0.0470161 , 0.02809628],
+ [ -1.91429117, 0.0470161 , 0.03209242, 0.00314973],
+ [ -8.97133162, 0.02809628, 0.00314973, 0.21575363]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,4)
+ sig2 = 181.78650186468832
+ self.assertAlmostEqual(reg.sig2,sig2,4)
+
+ at unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
+"Maximum Likelihood requires SciPy version 11 or newer.")
+class TestGMCombo(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.X = sparse.csr_matrix(self.X)
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ def test_model(self):
+ # Only spatial lag
+ reg = SP.GM_Combo(self.y, self.X, w=self.w)
+ e_reduced = np.array([ 28.18617481])
+ np.testing.assert_array_almost_equal(reg.e_pred[0],e_reduced,4)
+ predy_e = np.array([ 52.28082782])
+ np.testing.assert_array_almost_equal(reg.predy_e[0],predy_e,4)
+ betas = np.array([[ 57.61123515],[ 0.73441313], [ -0.59459416], [ -0.21762921], [ 0.54732051]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ u = np.array([ 25.57932637])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ e_filtered = np.array([ 31.65374945])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e_filtered,4)
+ predy = np.array([ 54.88767685])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ n = 49
+ self.assertAlmostEqual(reg.n,n)
+ k = 4
+ self.assertAlmostEqual(reg.k,k)
+ y = np.array([ 80.467003])
+ np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ x = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x.toarray()[0],x,4)
+ yend = np.array([ 35.4585005])
+ np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ z = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
+ np.testing.assert_array_almost_equal(reg.z.toarray()[0],z,4)
+ my = 38.43622446938776
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 18.466069465206047
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([[ 522.43841148, -6.07256915, -1.91429117, -8.97133162],
+ [ -6.07256915, 0.23801287, 0.0470161 , 0.02809628],
+ [ -1.91429117, 0.0470161 , 0.03209242, 0.00314973],
+ [ -8.97133162, 0.02809628, 0.00314973, 0.21575363]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,4)
+ sig2 = 181.78650186468832
+ self.assertAlmostEqual(reg.sig2,sig2,4)
+ pr2 = 0.3018280166937799
+ self.assertAlmostEqual(reg.pr2,pr2,4)
+ pr2_e = 0.3561355587000738
+ self.assertAlmostEqual(reg.pr2_e,pr2_e,4)
+ std_err = np.array([ 22.85692222, 0.48786559, 0.17914356, 0.46449318])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,4)
+ z_stat = np.array([[ 2.52051597e+00, 1.17182922e-02], [ 1.50535954e+00, 1.32231664e-01], [ -3.31909311e+00, 9.03103123e-04], [ -4.68530506e-01, 6.39405261e-01]])
+ np.testing.assert_array_almost_equal(reg.z_stat,z_stat,4)
+
+if __name__ == '__main__':
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ unittest.main()
+ np.set_printoptions(suppress=start_suppress)
+
diff --git a/pysal/spreg/tests/test_ml_error.py b/pysal/spreg/tests/test_ml_error.py
new file mode 100644
index 0000000..385ee8e
--- /dev/null
+++ b/pysal/spreg/tests/test_ml_error.py
@@ -0,0 +1,71 @@
+import unittest
+import pysal
+import scipy
+import numpy as np
+from pysal.spreg.ml_error import ML_Error
+from pysal.spreg import utils
+
+ at unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
+ "Max Likelihood requires SciPy version 11 or newer.")
+class TestMLError(unittest.TestCase):
+ def setUp(self):
+ db = pysal.open(pysal.examples.get_path("south.dbf"),'r')
+ self.y_name = "HR90"
+ self.y = np.array(db.by_col(self.y_name))
+ self.y.shape = (len(self.y),1)
+ self.x_names = ["RD90","PS90","UE90","DV90"]
+ self.x = np.array([db.by_col(var) for var in self.x_names]).T
+ ww = pysal.open(pysal.examples.get_path("south_q.gal"))
+ self.w = ww.read()
+ ww.close()
+ self.w.transform = 'r'
+
+ def test_model(self):
+ reg = ML_Error(self.y,self.x,w=self.w,name_y=self.y_name,name_x=self.x_names,\
+ name_w="south_q.gal")
+ betas = np.array([[ 6.1492], [ 4.4024], [ 1.7784], [-0.3781], [ 0.4858], [ 0.2991]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ u = np.array([-5.97649777])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ predy = np.array([ 6.92258051])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ n = 1412
+ self.assertAlmostEqual(reg.n,n,4)
+ k = 5
+ self.assertAlmostEqual(reg.k,k,4)
+ y = np.array([ 0.94608274])
+ np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ x = np.array([ 1. , -0.39902838, 0.89645344, 6.85780705, 7.2636377 ])
+ np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ e = np.array([-4.92843327])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ my = 9.5492931620846928
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 7.0388508798387219
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([ 1.06476526, 0.05548248, 0.04544514, 0.00614425, 0.01481356,
+ 0.00143001])
+ np.testing.assert_array_almost_equal(reg.vm.diagonal(),vm,4)
+ sig2 = [ 32.40685441]
+ self.assertAlmostEqual(reg.sig2,sig2,4)
+ pr2 = 0.3057664820364818
+ self.assertAlmostEqual(reg.pr2,pr2)
+ std_err = np.array([ 1.03187463, 0.23554719, 0.21317867, 0.07838525, 0.12171098,
+ 0.03781546])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,4)
+ z_stat = [(5.9592751097983534, 2.5335926307459251e-09),
+ (18.690182928021841, 5.9508619446611137e-78),
+ (8.3421632936950338, 7.2943630281051907e-17),
+ (-4.8232686291115678, 1.4122456582517099e-06),
+ (3.9913060809142995, 6.5710406838016854e-05),
+ (7.9088780724028922, 2.5971882547279339e-15)]
+ np.testing.assert_array_almost_equal(reg.z_stat,z_stat,4)
+ logll = -4471.407066887894
+ self.assertAlmostEqual(reg.logll,logll,4)
+ aic = 8952.8141337757879
+ self.assertAlmostEqual(reg.aic,aic,4)
+ schwarz = 8979.0779458660545
+ self.assertAlmostEqual(reg.schwarz,schwarz,4)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/tests/test_ml_error_regimes.py b/pysal/spreg/tests/test_ml_error_regimes.py
new file mode 100644
index 0000000..dd205e1
--- /dev/null
+++ b/pysal/spreg/tests/test_ml_error_regimes.py
@@ -0,0 +1,138 @@
+import unittest
+import scipy
+import pysal
+import numpy as np
+from pysal.spreg.ml_error_regimes import ML_Error_Regimes
+from pysal.spreg.ml_error import ML_Error
+from pysal.spreg import utils
+
+ at unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
+ "Max Likelihood requires SciPy version 11 or newer.")
+class TestMLError(unittest.TestCase):
+ def setUp(self):
+ db = pysal.open(pysal.examples.get_path("baltim.dbf"),'r')
+ self.ds_name = "baltim.dbf"
+ self.y_name = "PRICE"
+ self.y = np.array(db.by_col(self.y_name)).T
+ self.y.shape = (len(self.y),1)
+ self.x_names = ["NROOM","AGE","SQFT"]
+ self.x = np.array([db.by_col(var) for var in self.x_names]).T
+ ww = pysal.open(pysal.examples.get_path("baltim_q.gal"))
+ self.w = ww.read()
+ ww.close()
+ self.w_name = "baltim_q.gal"
+ self.w.transform = 'r'
+ self.regimes = db.by_col("CITCOU")
+ #Artficial:
+ n = 256
+ self.n2 = n/2
+ self.x_a1 = np.random.uniform(-10,10,(n,1))
+ self.x_a2 = np.random.uniform(1,5,(n,1))
+ self.q_a = self.x_a2 + np.random.normal(0,1,(n,1))
+ self.x_a = np.hstack((self.x_a1,self.x_a2))
+ self.y_a = np.dot(np.hstack((np.ones((n,1)),self.x_a)),np.array([[1],[0.5],[2]])) + np.random.normal(0,1,(n,1))
+ latt = int(np.sqrt(n))
+ self.w_a = pysal.lat2W(latt,latt)
+ self.w_a.transform='r'
+ self.regi_a = [0]*(n/2) + [1]*(n/2)
+ self.w_a1 = pysal.lat2W(latt/2,latt)
+ self.w_a1.transform='r'
+
+ def test_model1(self):
+ reg = ML_Error_Regimes(self.y,self.x,self.regimes,w=self.w,name_y=self.y_name,name_x=self.x_names,\
+ name_w=self.w_name,name_ds=self.ds_name,name_regimes="CITCOU", regime_err_sep=False)
+ betas = np.array([[ -2.39491278],
+ [ 4.873757 ],
+ [ -0.02911854],
+ [ 0.33275008],
+ [ 31.79618475],
+ [ 2.98102401],
+ [ -0.23710892],
+ [ 0.80581127],
+ [ 0.61770744]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ u = np.array([ 30.46599009])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ predy = np.array([ 16.53400991])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ n = 211
+ self.assertAlmostEqual(reg.n,n,4)
+ k = 8
+ self.assertAlmostEqual(reg.k,k,4)
+ y = np.array([ 47.])
+ np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ x = np.array([ 1. , 4. , 148. , 11.25, 0. , 0. , 0. , 0. ])
+ np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ e = np.array([ 34.69181334])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ my = 44.307180094786695
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 23.606076835380495
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([ 58.50551173, 2.42952002, 0.00721525, 0.06391736,
+ 80.59249161, 3.1610047 , 0.0119782 , 0.0499432 , 0.00502785])
+ np.testing.assert_array_almost_equal(reg.vm.diagonal(),vm,4)
+ sig2 = np.array([[ 209.60639741]])
+ self.assertAlmostEqual(reg.sig2,sig2,4)
+ pr2 = 0.43600837301477025
+ self.assertAlmostEqual(reg.pr2,pr2)
+ std_err = np.array([ 7.64888957, 1.55869177, 0.08494262, 0.25281882, 8.9773321 ,
+ 1.77792146, 0.10944497, 0.22347975, 0.07090735])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,4)
+ logll = -870.3331059537576
+ self.assertAlmostEqual(reg.logll,logll,4)
+ aic = 1756.6662119075154
+ self.assertAlmostEqual(reg.aic,aic,4)
+ schwarz = 1783.481076975324
+ self.assertAlmostEqual(reg.schwarz,schwarz,4)
+ chow_r = np.array([[ 8.40437046, 0.0037432 ],
+ [ 0.64080535, 0.42341932],
+ [ 2.25389396, 0.13327865],
+ [ 1.96544702, 0.16093197]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,4)
+ chow_j = 25.367913028011799
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j,4)
+
+ def test_model2(self):
+ reg = ML_Error_Regimes(self.y,self.x,self.regimes,w=self.w,name_y=self.y_name,name_x=self.x_names,\
+ name_w=self.w_name,name_ds=self.ds_name,name_regimes="CITCOU", regime_err_sep=True)
+ betas = np.array([[ 3.66158216],
+ [ 4.55700255],
+ [ -0.08045502],
+ [ 0.44800318],
+ [ 0.17774677],
+ [ 33.3086368 ],
+ [ 2.44709405],
+ [ -0.18803509],
+ [ 0.68956598],
+ [ 0.75599089]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ vm = np.array([ 40.60994599, -7.25413138, -0.16605501, 0.48961884,
+ 0. , 0. , 0. , 0. ,
+ 0. , 0. ])
+ np.testing.assert_array_almost_equal(reg.vm[0],vm,4)
+ u = np.array([ 31.97771505])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ predy = np.array([ 15.02228495])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ e = np.array([ 33.83065421])
+ np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ chow_r = np.array([[ 6.88023639, 0.0087154 ],
+ [ 0.90512612, 0.34141092],
+ [ 0.75996258, 0.38334023],
+ [ 0.56882946, 0.45072443],
+ [ 12.18358581, 0.00048212]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,4)
+ chow_j = 26.673798071789673
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j,4)
+ #Artficial:
+ model = ML_Error_Regimes(self.y_a, self.x_a, self.regi_a, w=self.w_a, regime_err_sep=True)
+ model1 = ML_Error(self.y_a[0:(self.n2)].reshape((self.n2),1), self.x_a[0:(self.n2)], w=self.w_a1)
+ model2 = ML_Error(self.y_a[(self.n2):].reshape((self.n2),1), self.x_a[(self.n2):], w=self.w_a1)
+ tbetas = np.vstack((model1.betas, model2.betas))
+ np.testing.assert_array_almost_equal(model.betas,tbetas)
+ vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
+ np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 4)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/tests/test_ml_lag.py b/pysal/spreg/tests/test_ml_lag.py
new file mode 100644
index 0000000..462db18
--- /dev/null
+++ b/pysal/spreg/tests/test_ml_lag.py
@@ -0,0 +1,68 @@
+import unittest
+import pysal
+import scipy
+import numpy as np
+from pysal.spreg.ml_lag import ML_Lag
+from pysal.spreg import utils
+
+ at unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
+ "Max Likelihood requires SciPy version 11 or newer.")
+class TestMLError(unittest.TestCase):
+ def setUp(self):
+ db = pysal.open(pysal.examples.get_path("baltim.dbf"),'r')
+ self.ds_name = "baltim.dbf"
+ self.y_name = "PRICE"
+ self.y = np.array(db.by_col(self.y_name)).T
+ self.y.shape = (len(self.y),1)
+ self.x_names = ["NROOM","AGE","SQFT"]
+ self.x = np.array([db.by_col(var) for var in self.x_names]).T
+ ww = pysal.open(pysal.examples.get_path("baltim_q.gal"))
+ self.w = ww.read()
+ ww.close()
+ self.w_name = "baltim_q.gal"
+ self.w.transform = 'r'
+
+ def test_model1(self):
+ reg = ML_Lag(self.y,self.x,w=self.w,name_y=self.y_name,name_x=self.x_names,\
+ name_w=self.w_name,name_ds=self.ds_name)
+ betas = np.array([[-6.04040164],
+ [ 3.48995114],
+ [-0.20103955],
+ [ 0.65462382],
+ [ 0.62351143]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ u = np.array([ 47.51218398])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ predy = np.array([-0.51218398])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ n = 211
+ self.assertAlmostEqual(reg.n,n,4)
+ k = 5
+ self.assertAlmostEqual(reg.k,k,4)
+ y = np.array([ 47.])
+ np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ x = np.array([ 1. , 4. , 148. , 11.25])
+ np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ e = np.array([ 41.99251608])
+ np.testing.assert_array_almost_equal(reg.e_pred[0],e,4)
+ my = 44.307180094786695
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 23.606076835380495
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([ 28.57288755, 1.42341656, 0.00288068, 0.02956392, 0.00332139])
+ np.testing.assert_array_almost_equal(reg.vm.diagonal(),vm,4)
+ sig2 = 216.27525647243797
+ self.assertAlmostEqual(reg.sig2,sig2,4)
+ pr2 = 0.6133020721559487
+ self.assertAlmostEqual(reg.pr2,pr2)
+ std_err = np.array([ 5.34536131, 1.19307022, 0.05367198, 0.17194162, 0.05763147])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,4)
+ logll = -875.92771143484833
+ self.assertAlmostEqual(reg.logll,logll,4)
+ aic = 1761.8554228696967
+ self.assertAlmostEqual(reg.aic,aic,4)
+ schwarz = 1778.614713537077
+ self.assertAlmostEqual(reg.schwarz,schwarz,4)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/tests/test_ml_lag_regimes.py b/pysal/spreg/tests/test_ml_lag_regimes.py
new file mode 100644
index 0000000..dd77ce1
--- /dev/null
+++ b/pysal/spreg/tests/test_ml_lag_regimes.py
@@ -0,0 +1,117 @@
+import unittest
+import scipy
+import pysal
+import numpy as np
+from pysal.spreg.ml_lag_regimes import ML_Lag_Regimes
+from pysal.spreg.ml_lag import ML_Lag
+from pysal.spreg import utils
+
+ at unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
+ "Max Likelihood requires SciPy version 11 or newer.")
+class TestMLError(unittest.TestCase):
+ def setUp(self):
+ db = pysal.open(pysal.examples.get_path("baltim.dbf"),'r')
+ self.ds_name = "baltim.dbf"
+ self.y_name = "PRICE"
+ self.y = np.array(db.by_col(self.y_name)).T
+ self.y.shape = (len(self.y),1)
+ self.x_names = ["NROOM","AGE","SQFT"]
+ self.x = np.array([db.by_col(var) for var in self.x_names]).T
+ ww = pysal.open(pysal.examples.get_path("baltim_q.gal"))
+ self.w = ww.read()
+ ww.close()
+ self.w_name = "baltim_q.gal"
+ self.w.transform = 'r'
+ self.regimes = db.by_col("CITCOU")
+
+ def test_model1(self):
+ reg = ML_Lag_Regimes(self.y,self.x,self.regimes,w=self.w,name_y=self.y_name,name_x=self.x_names,\
+ name_w=self.w_name,name_ds=self.ds_name,name_regimes="CITCOU", regime_lag_sep=False)
+ betas = np.array([[-15.00586577],
+ [ 4.49600801],
+ [ -0.03180518],
+ [ 0.34995882],
+ [ -4.54040395],
+ [ 3.92187578],
+ [ -0.17021393],
+ [ 0.81941371],
+ [ 0.53850323]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ u = np.array([ 32.73718478])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ predy = np.array([ 14.26281522])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ n = 211
+ self.assertAlmostEqual(reg.n,n,4)
+ k = 9
+ self.assertAlmostEqual(reg.k,k,4)
+ y = np.array([ 47.])
+ np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ x = np.array([[ 1. , 4. , 148. , 11.25, 0. , 0. , 0. ,
+ 0. ]])
+ np.testing.assert_array_almost_equal(reg.x[0].toarray(),x,4)
+ e = np.array([ 29.45407124])
+ np.testing.assert_array_almost_equal(reg.e_pred[0],e,4)
+ my = 44.307180094786695
+ self.assertAlmostEqual(reg.mean_y,my)
+ sy = 23.606076835380495
+ self.assertAlmostEqual(reg.std_y,sy)
+ vm = np.array([ 47.42000914, 2.39526578, 0.00506895, 0.06480022,
+ 69.67653371, 3.20661492, 0.01156766, 0.04862014, 0.00400775])
+ np.testing.assert_array_almost_equal(reg.vm.diagonal(),vm,4)
+ sig2 = 200.04433357145007
+ self.assertAlmostEqual(reg.sig2,sig2,4)
+ pr2 = 0.6404460298085746
+ self.assertAlmostEqual(reg.pr2,pr2)
+ std_err = np.array([ 6.88621878, 1.54766462, 0.07119654, 0.25455888, 8.34724707,
+ 1.79070235, 0.10755305, 0.22049975, 0.0633068 ])
+ np.testing.assert_array_almost_equal(reg.std_err,std_err,4)
+ logll = -864.98505596489736
+ self.assertAlmostEqual(reg.logll,logll,4)
+ aic = 1747.9701119297947
+ self.assertAlmostEqual(reg.aic,aic,4)
+ schwarz = 1778.1368351310794
+ self.assertAlmostEqual(reg.schwarz,schwarz,4)
+ chow_r = np.array([[ 1.00180776, 0.31687348],
+ [ 0.05904944, 0.8080047 ],
+ [ 1.16987812, 0.27942629],
+ [ 1.95931177, 0.16158694]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,4)
+ chow_j = 21.648337464039283
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j,4)
+
+ def test_model2(self):
+ reg = ML_Lag_Regimes(self.y,self.x,self.regimes,w=self.w,name_y=self.y_name,name_x=self.x_names,\
+ name_w=self.w_name,name_ds=self.ds_name,name_regimes="CITCOU", regime_lag_sep=True)
+ betas = np.array([[-0.71589799],
+ [ 4.40910538],
+ [-0.08652467],
+ [ 0.46266265],
+ [ 0.1627765 ],
+ [-5.00594358],
+ [ 2.91060349],
+ [-0.18207394],
+ [ 0.71129227],
+ [ 0.66753263]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ vm = np.array([ 55.3593679 , -7.22927797, -0.19487326, 0.6030953 ,
+ -0.52249569, 0. , 0. , 0. ,
+ 0. , 0. ])
+ np.testing.assert_array_almost_equal(reg.vm[0],vm,4)
+ u = np.array([ 34.03630518])
+ np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ predy = np.array([ 12.96369482])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ e = np.array([ 32.46466912])
+ np.testing.assert_array_almost_equal(reg.e_pred[0],e,4)
+ chow_r = np.array([[ 0.15654726, 0.69235548],
+ [ 0.43533847, 0.509381 ],
+ [ 0.60552514, 0.43647766],
+ [ 0.59214981, 0.441589 ],
+ [ 11.69437282, 0.00062689]])
+ np.testing.assert_array_almost_equal(reg.chow.regi,chow_r,4)
+ chow_j = 21.978012275873063
+ self.assertAlmostEqual(reg.chow.joint[0],chow_j,4)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/tests/test_ols.py b/pysal/spreg/tests/test_ols.py
new file mode 100644
index 0000000..f2495d9
--- /dev/null
+++ b/pysal/spreg/tests/test_ols.py
@@ -0,0 +1,125 @@
+import unittest
+import numpy as np
+import pysal
+from pysal.spreg import utils
+import pysal.spreg as EC
+
+PEGP = pysal.examples.get_path
+
+class TestBaseOLS(unittest.TestCase):
+ def setUp(self):
+ db = pysal.open(PEGP('columbus.dbf'),'r')
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.w = pysal.weights.rook_from_shapefile(PEGP("columbus.shp"))
+
+ def test_ols(self):
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ ols = EC.ols.BaseOLS(self.y,self.X)
+ np.testing.assert_array_almost_equal(ols.betas, np.array([[
+ 46.42818268], [ 0.62898397], [ -0.48488854]]))
+ vm = np.array([[ 1.74022453e+02, -6.52060364e+00, -2.15109867e+00],
+ [ -6.52060364e+00, 2.87200008e-01, 6.80956787e-02],
+ [ -2.15109867e+00, 6.80956787e-02, 3.33693910e-02]])
+ np.testing.assert_array_almost_equal(ols.vm, vm,6)
+
+ def test_ols_white1(self):
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ ols = EC.ols.BaseOLS(self.y,self.X,robust='white', sig2n_k=True)
+ np.testing.assert_array_almost_equal(ols.betas, np.array([[
+ 46.42818268], [ 0.62898397], [ -0.48488854]]))
+ vm = np.array([[ 2.05819450e+02, -6.83139266e+00, -2.64825846e+00],
+ [ -6.83139266e+00, 2.58480813e-01, 8.07733167e-02],
+ [ -2.64825846e+00, 8.07733167e-02, 3.75817181e-02]])
+ np.testing.assert_array_almost_equal(ols.vm, vm,6)
+
+ def test_ols_white2(self):
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ ols = EC.ols.BaseOLS(self.y,self.X,robust='white', sig2n_k=False)
+ np.testing.assert_array_almost_equal(ols.betas, np.array([[
+ 46.42818268], [ 0.62898397], [ -0.48488854]]))
+ vm = np.array([[ 1.93218259e+02, -6.41314413e+00, -2.48612018e+00],
+ [ -6.41314413e+00, 2.42655457e-01, 7.58280116e-02],
+ [ -2.48612018e+00, 7.58280116e-02, 3.52807966e-02]])
+ np.testing.assert_array_almost_equal(ols.vm, vm,6)
+
+ def test_OLS(self):
+ ols = EC.OLS(self.y, self.X, self.w, spat_diag=True, moran=True, \
+ white_test=True, name_y='home value', name_x=['income','crime'], \
+ name_ds='columbus')
+
+ np.testing.assert_array_almost_equal(ols.aic, \
+ 408.73548964604873 ,7)
+ np.testing.assert_array_almost_equal(ols.ar2, \
+ 0.32123239427957662 ,7)
+ np.testing.assert_array_almost_equal(ols.betas, \
+ np.array([[ 46.42818268], [ 0.62898397], \
+ [ -0.48488854]]), 7)
+ bp = np.array([2, 5.7667905131212587, 0.05594449410070558])
+ ols_bp = np.array([ols.breusch_pagan['df'], ols.breusch_pagan['bp'], ols.breusch_pagan['pvalue']])
+ np.testing.assert_array_almost_equal(bp, ols_bp, 7)
+ np.testing.assert_array_almost_equal(ols.f_stat, \
+ (12.358198885356581, 5.0636903313953024e-05), 7)
+ jb = np.array([2, 39.706155069114878, 2.387360356860208e-09])
+ ols_jb = np.array([ols.jarque_bera['df'], ols.jarque_bera['jb'], ols.jarque_bera['pvalue']])
+ np.testing.assert_array_almost_equal(ols_jb,jb, 7)
+ white = np.array([5, 2.90606708, 0.71446484])
+ ols_white = np.array([ols.white['df'], ols.white['wh'], ols.white['pvalue']])
+ np.testing.assert_array_almost_equal(ols_white,white, 7)
+ np.testing.assert_equal(ols.k, 3)
+ kb = {'df': 2, 'kb': 2.2700383871478675, 'pvalue': 0.32141595215434604}
+ for key in kb:
+ self.assertAlmostEqual(ols.koenker_bassett[key], kb[key], 7)
+ np.testing.assert_array_almost_equal(ols.lm_error, \
+ (4.1508117035117893, 0.041614570655392716),7)
+ np.testing.assert_array_almost_equal(ols.lm_lag, \
+ (0.98279980617162233, 0.32150855529063727), 7)
+ np.testing.assert_array_almost_equal(ols.lm_sarma, \
+ (4.3222725729143736, 0.11519415308749938), 7)
+ np.testing.assert_array_almost_equal(ols.logll, \
+ -201.3677448230244 ,7)
+ np.testing.assert_array_almost_equal(ols.mean_y, \
+ 38.436224469387746,7)
+ np.testing.assert_array_almost_equal(ols.moran_res[0], \
+ 0.20373540938,7)
+ np.testing.assert_array_almost_equal(ols.moran_res[1], \
+ 2.59180452208,7)
+ np.testing.assert_array_almost_equal(ols.moran_res[2], \
+ 0.00954740031251,7)
+ np.testing.assert_array_almost_equal(ols.mulColli, \
+ 12.537554873824675 ,7)
+ np.testing.assert_equal(ols.n, 49)
+ np.testing.assert_equal(ols.name_ds, 'columbus')
+ np.testing.assert_equal(ols.name_gwk, None)
+ np.testing.assert_equal(ols.name_w, 'unknown')
+ np.testing.assert_equal(ols.name_x, ['CONSTANT', 'income', 'crime'])
+ np.testing.assert_equal(ols.name_y, 'home value')
+ np.testing.assert_array_almost_equal(ols.predy[3], np.array([
+ 33.53969014]),7)
+ np.testing.assert_array_almost_equal(ols.r2, \
+ 0.34951437785126105 ,7)
+ np.testing.assert_array_almost_equal(ols.rlm_error, \
+ (3.3394727667427513, 0.067636278225568919),7)
+ np.testing.assert_array_almost_equal(ols.rlm_lag, \
+ (0.17146086940258459, 0.67881673703455414), 7)
+ np.testing.assert_equal(ols.robust, 'unadjusted')
+ np.testing.assert_array_almost_equal(ols.schwarz, \
+ 414.41095054038061,7 )
+ np.testing.assert_array_almost_equal(ols.sig2, \
+ 231.4568494392652,7 )
+ np.testing.assert_array_almost_equal(ols.sig2ML, \
+ 217.28602192257551,7 )
+ np.testing.assert_array_almost_equal(ols.sig2n, \
+ 217.28602192257551, 7)
+
+ np.testing.assert_array_almost_equal(ols.t_stat[2][0], \
+ -2.65440864272,7)
+ np.testing.assert_array_almost_equal(ols.t_stat[2][1], \
+ 0.0108745049098,7)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/tests/test_ols_regimes.py b/pysal/spreg/tests/test_ols_regimes.py
new file mode 100644
index 0000000..20a9977
--- /dev/null
+++ b/pysal/spreg/tests/test_ols_regimes.py
@@ -0,0 +1,144 @@
+import unittest
+import numpy as np
+import pysal
+from pysal.spreg.ols import OLS
+from pysal.spreg.ols_regimes import OLS_Regimes
+
+PEGP = pysal.examples.get_path
+
+class TestOLS_regimes(unittest.TestCase):
+ def setUp(self):
+ db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ self.y_var = 'CRIME'
+ self.y = np.array([db.by_col(self.y_var)]).reshape(49,1)
+ self.x_var = ['INC','HOVAL']
+ self.x = np.array([db.by_col(name) for name in self.x_var]).T
+ self.r_var = 'NSA'
+ self.regimes = db.by_col(self.r_var)
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_OLS(self):
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ ols = OLS_Regimes(self.y, self.x, self.regimes, w=self.w, regime_err_sep=False, constant_regi='many', nonspat_diag=False, spat_diag=True, name_y=self.y_var, name_x=self.x_var, name_ds='columbus', name_regimes=self.r_var, name_w='columbus.gal')
+ #np.testing.assert_array_almost_equal(ols.aic, 408.73548964604873 ,7)
+ np.testing.assert_array_almost_equal(ols.ar2,0.50761700679873101 ,7)
+ np.testing.assert_array_almost_equal(ols.betas,np.array([[ 68.78670869],\
+ [ -1.9864167 ],[ -0.10887962],[ 67.73579559],[ -1.36937552],[ -0.31792362]]))
+ vm = np.array([ 48.81339213, -2.14959579, -0.19968157, 0. ,
+ 0. , 0. ])
+ np.testing.assert_array_almost_equal(ols.vm[0], vm, 6)
+ np.testing.assert_array_almost_equal(ols.lm_error, \
+ (5.92970357, 0.01488775),7)
+ np.testing.assert_array_almost_equal(ols.lm_lag, \
+ (8.78315751, 0.00304024), 7)
+ np.testing.assert_array_almost_equal(ols.lm_sarma, \
+ (8.89955982, 0.01168114), 7)
+ np.testing.assert_array_almost_equal(ols.mean_y, \
+ 35.1288238979591,7)
+ np.testing.assert_equal(ols.k, 6)
+ np.testing.assert_equal(ols.kf, 0)
+ np.testing.assert_equal(ols.kr, 3)
+ np.testing.assert_equal(ols.n, 49)
+ np.testing.assert_equal(ols.nr, 2)
+ np.testing.assert_equal(ols.name_ds, 'columbus')
+ np.testing.assert_equal(ols.name_gwk, None)
+ np.testing.assert_equal(ols.name_w, 'columbus.gal')
+ np.testing.assert_equal(ols.name_x, ['0_CONSTANT', '0_INC', '0_HOVAL', '1_CONSTANT', '1_INC', '1_HOVAL'])
+ np.testing.assert_equal(ols.name_y, 'CRIME')
+ np.testing.assert_array_almost_equal(ols.predy[3], np.array([
+ 51.05003696]),7)
+ np.testing.assert_array_almost_equal(ols.r2, \
+ 0.55890690192386316 ,7)
+ np.testing.assert_array_almost_equal(ols.rlm_error, \
+ (0.11640231, 0.73296972),7)
+ np.testing.assert_array_almost_equal(ols.rlm_lag, \
+ (2.96985625, 0.08482939), 7)
+ np.testing.assert_equal(ols.robust, 'unadjusted')
+ np.testing.assert_array_almost_equal(ols.sig2, \
+ 137.84897351821013,7 )
+ np.testing.assert_array_almost_equal(ols.sig2n, \
+ 120.96950737312316, 7)
+ np.testing.assert_array_almost_equal(ols.t_stat[2][0], \
+ -0.43342216706091791,7)
+ np.testing.assert_array_almost_equal(ols.t_stat[2][1], \
+ 0.66687472578594531,7)
+ np.set_printoptions(suppress=start_suppress)
+ """
+ def test_OLS_regi(self):
+ #Artficial:
+ n = 256
+ x1 = np.random.uniform(-10,10,(n,1))
+ y = np.dot(np.hstack((np.ones((n,1)),x1)),np.array([[1],[0.5]])) + np.random.normal(0,1,(n,1))
+ latt = int(np.sqrt(n))
+ regi = [0]*(n/2) + [1]*(n/2)
+ model = OLS_Regimes(y, x1, regimes=regi, regime_err_sep=True, sig2n_k=False)
+ model1 = OLS(y[0:(n/2)].reshape((n/2),1), x1[0:(n/2)], sig2n_k=False)
+ model2 = OLS(y[(n/2):n].reshape((n/2),1), x1[(n/2):n], sig2n_k=False)
+ tbetas = np.vstack((model1.betas, model2.betas))
+ np.testing.assert_array_almost_equal(model.betas,tbetas)
+ vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
+ np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 6)
+ #Columbus:
+ reg = OLS_Regimes(self.y, self.x, self.regimes, w=self.w, constant_regi='many', nonspat_diag=True, spat_diag=True, name_y=self.y_var, name_x=self.x_var, name_ds='columbus', name_regimes=self.r_var, name_w='columbus.gal', regime_err_sep=True)
+ np.testing.assert_array_almost_equal(reg.multi[0].aic, 192.96044303402897 ,7)
+ tbetas = np.array([[ 68.78670869],
+ [ -1.9864167 ],
+ [ -0.10887962],
+ [ 67.73579559],
+ [ -1.36937552],
+ [ -0.31792362]])
+ np.testing.assert_array_almost_equal(tbetas, reg.betas)
+ vm = np.array([ 41.68828023, -1.83582717, -0.17053478, 0. ,
+ 0. , 0. ])
+ np.testing.assert_array_almost_equal(reg.vm[0], vm, 6)
+ u_3 = np.array([[ 0.31781838],
+ [-5.6905584 ],
+ [-6.8819715 ]])
+ np.testing.assert_array_almost_equal(reg.u[0:3], u_3, 7)
+ predy_3 = np.array([[ 15.40816162],
+ [ 24.4923124 ],
+ [ 37.5087525 ]])
+ np.testing.assert_array_almost_equal(reg.predy[0:3], predy_3, 7)
+ chow_regi = np.array([[ 0.01002733, 0.92023592],
+ [ 0.46017009, 0.49754449],
+ [ 0.60732697, 0.43579603]])
+ np.testing.assert_array_almost_equal(reg.chow.regi, chow_regi, 7)
+ self.assertAlmostEqual(reg.chow.joint[0], 0.67787986791767096, 7)
+ """
+ def test_OLS_fixed(self):
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ ols = OLS_Regimes(self.y, self.x, self.regimes, w=self.w, cols2regi=[False,True], regime_err_sep=True, constant_regi='one', nonspat_diag=False, spat_diag=True, name_y=self.y_var, name_x=self.x_var, name_ds='columbus', name_regimes=self.r_var, name_w='columbus.gal')
+ np.testing.assert_array_almost_equal(ols.betas,np.array([[ -0.24385565], [ -0.26335026], [ 68.89701137], [ -1.67389685]]))
+ vm = np.array([ 0.02354271, 0.01246677, 0.00424658, -0.04921356])
+ np.testing.assert_array_almost_equal(ols.vm[0], vm, 6)
+ np.testing.assert_array_almost_equal(ols.lm_error, \
+ (5.62668744, 0.01768903),7)
+ np.testing.assert_array_almost_equal(ols.lm_lag, \
+ (9.43264957, 0.00213156), 7)
+ np.testing.assert_array_almost_equal(ols.mean_y, \
+ 35.12882389795919,7)
+ np.testing.assert_equal(ols.kf, 2)
+ np.testing.assert_equal(ols.kr, 1)
+ np.testing.assert_equal(ols.n, 49)
+ np.testing.assert_equal(ols.nr, 2)
+ np.testing.assert_equal(ols.name_ds, 'columbus')
+ np.testing.assert_equal(ols.name_gwk, None)
+ np.testing.assert_equal(ols.name_w, 'columbus.gal')
+ np.testing.assert_equal(ols.name_x, ['0_HOVAL', '1_HOVAL', '_Global_CONSTANT', '_Global_INC'])
+ np.testing.assert_equal(ols.name_y, 'CRIME')
+ np.testing.assert_array_almost_equal(ols.predy[3], np.array([
+ 52.65974636]),7)
+ np.testing.assert_array_almost_equal(ols.r2, \
+ 0.5525561183786056 ,7)
+ np.testing.assert_equal(ols.robust, 'unadjusted')
+ np.testing.assert_array_almost_equal(ols.t_stat[2][0], \
+ 13.848705206463748,7)
+ np.testing.assert_array_almost_equal(ols.t_stat[2][1], \
+ 7.776650625274256e-18,7)
+ np.set_printoptions(suppress=start_suppress)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/tests/test_ols_sparse.py b/pysal/spreg/tests/test_ols_sparse.py
new file mode 100644
index 0000000..1341c96
--- /dev/null
+++ b/pysal/spreg/tests/test_ols_sparse.py
@@ -0,0 +1,107 @@
+import unittest
+import numpy as np
+import pysal
+import pysal.spreg as EC
+from scipy import sparse
+
+PEGP = pysal.examples.get_path
+
+class TestBaseOLS(unittest.TestCase):
+ def setUp(self):
+ db = pysal.open(PEGP('columbus.dbf'),'r')
+ y = np.array(db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.w = pysal.weights.rook_from_shapefile(PEGP("columbus.shp"))
+
+ def test_ols(self):
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.X = sparse.csr_matrix(self.X)
+ ols = EC.ols.BaseOLS(self.y,self.X)
+ np.testing.assert_array_almost_equal(ols.betas, np.array([[
+ 46.42818268], [ 0.62898397], [ -0.48488854]]))
+ vm = np.array([[ 1.74022453e+02, -6.52060364e+00, -2.15109867e+00],
+ [ -6.52060364e+00, 2.87200008e-01, 6.80956787e-02],
+ [ -2.15109867e+00, 6.80956787e-02, 3.33693910e-02]])
+ np.testing.assert_array_almost_equal(ols.vm, vm,6)
+
+ def test_OLS(self):
+ self.X = sparse.csr_matrix(self.X)
+ ols = EC.OLS(self.y, self.X, self.w, spat_diag=True, moran=True, \
+ name_y='home value', name_x=['income','crime'], \
+ name_ds='columbus', nonspat_diag=True, white_test=True)
+
+ np.testing.assert_array_almost_equal(ols.aic, \
+ 408.73548964604873 ,7)
+ np.testing.assert_array_almost_equal(ols.ar2, \
+ 0.32123239427957662 ,7)
+ np.testing.assert_array_almost_equal(ols.betas, \
+ np.array([[ 46.42818268], [ 0.62898397], \
+ [ -0.48488854]]), 7)
+ bp = np.array([2, 5.7667905131212587, 0.05594449410070558])
+ ols_bp = np.array([ols.breusch_pagan['df'], ols.breusch_pagan['bp'], ols.breusch_pagan['pvalue']])
+ np.testing.assert_array_almost_equal(bp, ols_bp, 7)
+ np.testing.assert_array_almost_equal(ols.f_stat, \
+ (12.358198885356581, 5.0636903313953024e-05), 7)
+ jb = np.array([2, 39.706155069114878, 2.387360356860208e-09])
+ ols_jb = np.array([ols.jarque_bera['df'], ols.jarque_bera['jb'], ols.jarque_bera['pvalue']])
+ np.testing.assert_array_almost_equal(ols_jb,jb, 7)
+ white = np.array([5, 2.90606708, 0.71446484])
+ ols_white = np.array([ols.white['df'], ols.white['wh'], ols.white['pvalue']])
+ np.testing.assert_array_almost_equal(ols_white,white, 7)
+ np.testing.assert_equal(ols.k, 3)
+ kb = {'df': 2, 'kb': 2.2700383871478675, 'pvalue': 0.32141595215434604}
+ for key in kb:
+ self.assertAlmostEqual(ols.koenker_bassett[key], kb[key], 7)
+ np.testing.assert_array_almost_equal(ols.lm_error, \
+ (4.1508117035117893, 0.041614570655392716),7)
+ np.testing.assert_array_almost_equal(ols.lm_lag, \
+ (0.98279980617162233, 0.32150855529063727), 7)
+ np.testing.assert_array_almost_equal(ols.lm_sarma, \
+ (4.3222725729143736, 0.11519415308749938), 7)
+ np.testing.assert_array_almost_equal(ols.logll, \
+ -201.3677448230244 ,7)
+ np.testing.assert_array_almost_equal(ols.mean_y, \
+ 38.436224469387746,7)
+ np.testing.assert_array_almost_equal(ols.moran_res[0], \
+ 0.20373540938,7)
+ np.testing.assert_array_almost_equal(ols.moran_res[1], \
+ 2.59180452208,7)
+ np.testing.assert_array_almost_equal(ols.moran_res[2], \
+ 0.00954740031251,7)
+ np.testing.assert_array_almost_equal(ols.mulColli, \
+ 12.537554873824675 ,7)
+ np.testing.assert_equal(ols.n, 49)
+ np.testing.assert_equal(ols.name_ds, 'columbus')
+ np.testing.assert_equal(ols.name_gwk, None)
+ np.testing.assert_equal(ols.name_w, 'unknown')
+ np.testing.assert_equal(ols.name_x, ['CONSTANT', 'income', 'crime'])
+ np.testing.assert_equal(ols.name_y, 'home value')
+ np.testing.assert_array_almost_equal(ols.predy[3], np.array([
+ 33.53969014]),7)
+ np.testing.assert_array_almost_equal(ols.r2, \
+ 0.34951437785126105 ,7)
+ np.testing.assert_array_almost_equal(ols.rlm_error, \
+ (3.3394727667427513, 0.067636278225568919),7)
+ np.testing.assert_array_almost_equal(ols.rlm_lag, \
+ (0.17146086940258459, 0.67881673703455414), 7)
+ np.testing.assert_equal(ols.robust, 'unadjusted')
+ np.testing.assert_array_almost_equal(ols.schwarz, \
+ 414.41095054038061,7 )
+ np.testing.assert_array_almost_equal(ols.sig2, \
+ 231.4568494392652,7 )
+ np.testing.assert_array_almost_equal(ols.sig2ML, \
+ 217.28602192257551,7 )
+ np.testing.assert_array_almost_equal(ols.sig2n, \
+ 217.28602192257551, 7)
+
+ np.testing.assert_array_almost_equal(ols.t_stat[2][0], \
+ -2.65440864272,7)
+ np.testing.assert_array_almost_equal(ols.t_stat[2][1], \
+ 0.0108745049098,7)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/tests/test_probit.py b/pysal/spreg/tests/test_probit.py
new file mode 100644
index 0000000..5c4e6dd
--- /dev/null
+++ b/pysal/spreg/tests/test_probit.py
@@ -0,0 +1,108 @@
+import unittest
+import pysal
+import numpy as np
+from pysal.spreg import probit as PB
+
+class TestBaseProbit(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("CRIME"))
+ y = np.reshape(y, (49,1))
+ self.y = (y>40).astype(float)
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("HOVAL"))
+ self.X = np.array(X).T
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ reg = PB.BaseProbit(self.y, self.X, w=self.w)
+ betas = np.array([[ 3.35381078], [-0.1996531 ], [-0.02951371]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ predy = np.array([ 0.00174739])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ n = 49
+ self.assertAlmostEqual(reg.n,n,6)
+ k = 3
+ self.assertAlmostEqual(reg.k,k,6)
+ y = np.array([ 0.])
+ np.testing.assert_array_almost_equal(reg.y[0],y,6)
+ x = np.array([ 1. , 19.531 , 80.467003])
+ np.testing.assert_array_almost_equal(reg.x[0],x,6)
+ vm = np.array([[ 8.52813879e-01, -4.36272459e-02, -8.05171472e-03], [ -4.36272459e-02, 4.11381444e-03, -1.92834842e-04], [ -8.05171472e-03, -1.92834842e-04, 3.09660240e-04]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ xmean = np.array([[ 1. ], [ 14.37493876], [ 38.43622447 ]])
+ np.testing.assert_array_almost_equal(reg.xmean,xmean,6)
+ predpc = 85.714285714285708
+ self.assertAlmostEqual(reg.predpc,predpc,5)
+ logl = -20.06009093055782
+ self.assertAlmostEqual(reg.logl,logl,5)
+ scale = 0.23309310130643665
+ self.assertAlmostEqual(reg.scale,scale,5)
+ slopes = np.array([[-0.04653776], [-0.00687944]])
+ np.testing.assert_array_almost_equal(reg.slopes,slopes,6)
+ slopes_vm = np.array([[ 1.77101993e-04, -1.65021168e-05], [ -1.65021168e-05, 1.60575016e-05]])
+ np.testing.assert_array_almost_equal(reg.slopes_vm,slopes_vm,6)
+ LR = 25.317683245671716
+ self.assertAlmostEqual(reg.LR[0],LR,5)
+ Pinkse_error = 2.9632385352516728
+ self.assertAlmostEqual(reg.Pinkse_error[0],Pinkse_error,5)
+ KP_error = 1.6509224700582124
+ self.assertAlmostEqual(reg.KP_error[0],KP_error,5)
+ PS_error = 2.3732463777623511
+ self.assertAlmostEqual(reg.PS_error[0],PS_error,5)
+
+class TestProbit(unittest.TestCase):
+ def setUp(self):
+ db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
+ y = np.array(db.by_col("CRIME"))
+ y = np.reshape(y, (49,1))
+ self.y = (y>40).astype(float)
+ X = []
+ X.append(db.by_col("INC"))
+ X.append(db.by_col("HOVAL"))
+ self.X = np.array(X).T
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+
+ def test_model(self):
+ reg = PB.Probit(self.y, self.X, w=self.w)
+ betas = np.array([[ 3.35381078], [-0.1996531 ], [-0.02951371]])
+ np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ predy = np.array([ 0.00174739])
+ np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ n = 49
+ self.assertAlmostEqual(reg.n,n,6)
+ k = 3
+ self.assertAlmostEqual(reg.k,k,6)
+ y = np.array([ 0.])
+ np.testing.assert_array_almost_equal(reg.y[0],y,6)
+ x = np.array([ 1. , 19.531 , 80.467003])
+ np.testing.assert_array_almost_equal(reg.x[0],x,6)
+ vm = np.array([[ 8.52813879e-01, -4.36272459e-02, -8.05171472e-03], [ -4.36272459e-02, 4.11381444e-03, -1.92834842e-04], [ -8.05171472e-03, -1.92834842e-04, 3.09660240e-04]])
+ np.testing.assert_array_almost_equal(reg.vm,vm,6)
+ xmean = np.array([[ 1. ], [ 14.37493876], [ 38.43622447 ]])
+ np.testing.assert_array_almost_equal(reg.xmean,xmean,6)
+ predpc = 85.714285714285708
+ self.assertAlmostEqual(reg.predpc,predpc,5)
+ logl = -20.06009093055782
+ self.assertAlmostEqual(reg.logl,logl,5)
+ scale = 0.23309310130643665
+ self.assertAlmostEqual(reg.scale,scale,5)
+ slopes = np.array([[-0.04653776], [-0.00687944]])
+ np.testing.assert_array_almost_equal(reg.slopes,slopes,6)
+ slopes_vm = np.array([[ 1.77101993e-04, -1.65021168e-05], [ -1.65021168e-05, 1.60575016e-05]])
+ np.testing.assert_array_almost_equal(reg.slopes_vm,slopes_vm,6)
+ LR = 25.317683245671716
+ self.assertAlmostEqual(reg.LR[0],LR,5)
+ Pinkse_error = 2.9632385352516728
+ self.assertAlmostEqual(reg.Pinkse_error[0],Pinkse_error,5)
+ KP_error = 1.6509224700582124
+ self.assertAlmostEqual(reg.KP_error[0],KP_error,5)
+ PS_error = 2.3732463777623511
+ self.assertAlmostEqual(reg.PS_error[0],PS_error,5)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/tests/test_twosls.py b/pysal/spreg/tests/test_twosls.py
new file mode 100644
index 0000000..71021e3
--- /dev/null
+++ b/pysal/spreg/tests/test_twosls.py
@@ -0,0 +1,254 @@
+import unittest
+import numpy as np
+import pysal
+from pysal.spreg.twosls import BaseTSLS, TSLS
+
+class TestBaseTSLS(unittest.TestCase):
+ def setUp(self):
+ db = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
+ self.y = np.array(db.by_col("CRIME"))
+ self.y = np.reshape(self.y, (49,1))
+ self.X = []
+ self.X.append(db.by_col("INC"))
+ self.X = np.array(self.X).T
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.yd = []
+ self.yd.append(db.by_col("HOVAL"))
+ self.yd = np.array(self.yd).T
+ self.q = []
+ self.q.append(db.by_col("DISCBD"))
+ self.q = np.array(self.q).T
+
+ def test_basic(self):
+ reg = BaseTSLS(self.y, self.X, self.yd, self.q)
+ betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ h_0 = np.array([ 1. , 19.531, 5.03 ])
+ np.testing.assert_array_almost_equal(reg.h[0], h_0)
+ hth = np.array([[ 49. , 704.371999 , 139.75 ],
+ [ 704.371999 , 11686.67338121, 2246.12800625],
+ [ 139.75 , 2246.12800625, 498.5851 ]])
+ np.testing.assert_array_almost_equal(reg.hth, hth, 7)
+ hthi = np.array([[ 0.1597275 , -0.00762011, -0.01044191],
+ [-0.00762011, 0.00100135, -0.0023752 ],
+ [-0.01044191, -0.0023752 , 0.01563276]])
+ np.testing.assert_array_almost_equal(reg.hthi, hthi, 7)
+ self.assertEqual(reg.k, 3)
+ self.assertEqual(reg.kstar, 1)
+ self.assertAlmostEqual(reg.mean_y, 35.128823897959187, 7)
+ self.assertEqual(reg.n, 49)
+ pfora1a2 = np.array([[ 9.58156106, -0.22744226, -0.13820537],
+ [ 0.02580142, 0.08226331, -0.03143731],
+ [-3.13896453, -0.33487872, 0.20690965]])
+ np.testing.assert_array_almost_equal(reg.pfora1a2, pfora1a2, 7)
+ predy_5 = np.array([[-28.68949467], [ 28.99484984], [ 55.07344824], [ 38.26609504], [ 57.57145851]])
+ np.testing.assert_array_almost_equal(reg.predy[0:5], predy_5, 7)
+ q_5 = np.array([[ 5.03], [ 4.27], [ 3.89], [ 3.7 ], [ 2.83]])
+ np.testing.assert_array_equal(reg.q[0:5], q_5)
+ self.assertAlmostEqual(reg.sig2n_k, 587.56797852699822, 7)
+ self.assertAlmostEqual(reg.sig2n, 551.5944288212637, 7)
+ self.assertAlmostEqual(reg.sig2, 551.5944288212637, 7)
+ self.assertAlmostEqual(reg.std_y, 16.732092091229699, 7)
+ u_5 = np.array([[ 44.41547467], [-10.19309584], [-24.44666724], [ -5.87833504], [ -6.83994851]])
+ np.testing.assert_array_almost_equal(reg.u[0:5], u_5, 7)
+ self.assertAlmostEqual(reg.utu, 27028.127012241919, 7)
+ varb = np.array([[ 0.41526237, 0.01879906, -0.01730372],
+ [ 0.01879906, 0.00362823, -0.00184604],
+ [-0.01730372, -0.00184604, 0.0011406 ]])
+ np.testing.assert_array_almost_equal(reg.varb, varb, 7)
+ vm = np.array([[ 229.05640809, 10.36945783, -9.54463414],
+ [ 10.36945783, 2.0013142 , -1.01826408],
+ [ -9.54463414, -1.01826408, 0.62914915]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+ x_0 = np.array([ 1. , 19.531])
+ np.testing.assert_array_almost_equal(reg.x[0], x_0, 7)
+ y_5 = np.array([[ 15.72598 ], [ 18.801754], [ 30.626781], [ 32.38776 ], [ 50.73151 ]])
+ np.testing.assert_array_almost_equal(reg.y[0:5], y_5, 7)
+ yend_5 = np.array([[ 80.467003], [ 44.567001], [ 26.35 ], [ 33.200001], [ 23.225 ]])
+ np.testing.assert_array_almost_equal(reg.yend[0:5], yend_5, 7)
+ z_0 = np.array([ 1. , 19.531 , 80.467003])
+ np.testing.assert_array_almost_equal(reg.z[0], z_0, 7)
+ zthhthi = np.array([[ 1.00000000e+00, -1.66533454e-16, 4.44089210e-16],
+ [ 0.00000000e+00, 1.00000000e+00, 0.00000000e+00],
+ [ 1.26978671e+01, 1.05598709e+00, 3.70212359e+00]])
+ np.testing.assert_array_almost_equal(reg.zthhthi, zthhthi, 7)
+
+ def test_n_k(self):
+ reg = BaseTSLS(self.y, self.X, self.yd, self.q, sig2n_k=True)
+ betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array([[ 243.99486949, 11.04572682, -10.16711028],
+ [ 11.04572682, 2.13183469, -1.08467261],
+ [ -10.16711028, -1.08467261, 0.67018062]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+
+ def test_white(self):
+ reg = BaseTSLS(self.y, self.X, self.yd, self.q, robust='white')
+ betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array([[ 208.27139316, 15.6687805 , -11.53686154],
+ [ 15.6687805 , 2.26882747, -1.30312033],
+ [ -11.53686154, -1.30312033, 0.81940656]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+
+ def test_hac(self):
+ gwk = pysal.kernelW_from_shapefile(pysal.examples.get_path('columbus.shp'),k=15,function='triangular', fixed=False)
+ reg = BaseTSLS(self.y, self.X, self.yd, self.q, robust='hac', gwk=gwk)
+ betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array([[ 231.07254978, 15.42050291, -11.3941033 ],
+ [ 15.01376346, 1.92422887, -1.11865505],
+ [ -11.34381641, -1.1279227 , 0.72053806]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+
+class TestTSLS(unittest.TestCase):
+ def setUp(self):
+ db = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
+ self.y = np.array(db.by_col("CRIME"))
+ self.y = np.reshape(self.y, (49,1))
+ self.X = []
+ self.X.append(db.by_col("INC"))
+ self.X = np.array(self.X).T
+ self.yd = []
+ self.yd.append(db.by_col("HOVAL"))
+ self.yd = np.array(self.yd).T
+ self.q = []
+ self.q.append(db.by_col("DISCBD"))
+ self.q = np.array(self.q).T
+
+ def test_basic(self):
+ reg = TSLS(self.y, self.X, self.yd, self.q)
+ betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ h_0 = np.array([ 1. , 19.531, 5.03 ])
+ np.testing.assert_array_almost_equal(reg.h[0], h_0)
+ hth = np.array([[ 49. , 704.371999 , 139.75 ],
+ [ 704.371999 , 11686.67338121, 2246.12800625],
+ [ 139.75 , 2246.12800625, 498.5851 ]])
+ np.testing.assert_array_almost_equal(reg.hth, hth, 7)
+ hthi = np.array([[ 0.1597275 , -0.00762011, -0.01044191],
+ [-0.00762011, 0.00100135, -0.0023752 ],
+ [-0.01044191, -0.0023752 , 0.01563276]])
+ np.testing.assert_array_almost_equal(reg.hthi, hthi, 7)
+ self.assertEqual(reg.k, 3)
+ self.assertEqual(reg.kstar, 1)
+ self.assertAlmostEqual(reg.mean_y, 35.128823897959187, 7)
+ self.assertEqual(reg.n, 49)
+ pfora1a2 = np.array([[ 9.58156106, -0.22744226, -0.13820537],
+ [ 0.02580142, 0.08226331, -0.03143731],
+ [-3.13896453, -0.33487872, 0.20690965]])
+ np.testing.assert_array_almost_equal(reg.pfora1a2, pfora1a2, 7)
+ predy_5 = np.array([[-28.68949467], [ 28.99484984], [ 55.07344824], [ 38.26609504], [ 57.57145851]])
+ np.testing.assert_array_almost_equal(reg.predy[0:5], predy_5, 7)
+ q_5 = np.array([[ 5.03], [ 4.27], [ 3.89], [ 3.7 ], [ 2.83]])
+ np.testing.assert_array_equal(reg.q[0:5], q_5)
+ self.assertAlmostEqual(reg.sig2n_k, 587.56797852699822, 7)
+ self.assertAlmostEqual(reg.sig2n, 551.5944288212637, 7)
+ self.assertAlmostEqual(reg.sig2, 551.5944288212637, 7)
+ self.assertAlmostEqual(reg.std_y, 16.732092091229699, 7)
+ u_5 = np.array([[ 44.41547467], [-10.19309584], [-24.44666724], [ -5.87833504], [ -6.83994851]])
+ np.testing.assert_array_almost_equal(reg.u[0:5], u_5, 7)
+ self.assertAlmostEqual(reg.utu, 27028.127012241919, 7)
+ varb = np.array([[ 0.41526237, 0.01879906, -0.01730372],
+ [ 0.01879906, 0.00362823, -0.00184604],
+ [-0.01730372, -0.00184604, 0.0011406 ]])
+ np.testing.assert_array_almost_equal(reg.varb, varb, 7)
+ vm = np.array([[ 229.05640809, 10.36945783, -9.54463414],
+ [ 10.36945783, 2.0013142 , -1.01826408],
+ [ -9.54463414, -1.01826408, 0.62914915]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+ x_0 = np.array([ 1. , 19.531])
+ np.testing.assert_array_almost_equal(reg.x[0], x_0, 7)
+ y_5 = np.array([[ 15.72598 ], [ 18.801754], [ 30.626781], [ 32.38776 ], [ 50.73151 ]])
+ np.testing.assert_array_almost_equal(reg.y[0:5], y_5, 7)
+ yend_5 = np.array([[ 80.467003], [ 44.567001], [ 26.35 ], [ 33.200001], [ 23.225 ]])
+ np.testing.assert_array_almost_equal(reg.yend[0:5], yend_5, 7)
+ z_0 = np.array([ 1. , 19.531 , 80.467003])
+ np.testing.assert_array_almost_equal(reg.z[0], z_0, 7)
+ zthhthi = np.array([[ 1.00000000e+00, -1.66533454e-16, 4.44089210e-16],
+ [ 0.00000000e+00, 1.00000000e+00, 0.00000000e+00],
+ [ 1.26978671e+01, 1.05598709e+00, 3.70212359e+00]])
+ np.testing.assert_array_almost_equal(reg.zthhthi, zthhthi, 7)
+ self.assertAlmostEqual(reg.pr2, 0.27936137128173893, 7)
+ z_stat = np.array([[ 5.84526447e+00, 5.05764078e-09],
+ [ 3.67601567e-01, 7.13170346e-01],
+ [ -1.99468913e+00, 4.60767956e-02]])
+ np.testing.assert_array_almost_equal(reg.z_stat, z_stat, 7)
+ title = 'TWO STAGE LEAST SQUARES'
+ self.assertEqual(reg.title, title)
+
+ def test_n_k(self):
+ reg = TSLS(self.y, self.X, self.yd, self.q, sig2n_k=True)
+ betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array([[ 243.99486949, 11.04572682, -10.16711028],
+ [ 11.04572682, 2.13183469, -1.08467261],
+ [ -10.16711028, -1.08467261, 0.67018062]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+
+ def test_white(self):
+ reg = TSLS(self.y, self.X, self.yd, self.q, robust='white')
+ betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array([[ 208.27139316, 15.6687805 , -11.53686154],
+ [ 15.6687805 , 2.26882747, -1.30312033],
+ [ -11.53686154, -1.30312033, 0.81940656]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+ self.assertEqual(reg.robust, 'white')
+
+ def test_hac(self):
+ gwk = pysal.kernelW_from_shapefile(pysal.examples.get_path('columbus.shp'),k=5,function='triangular', fixed=False)
+ reg = TSLS(self.y, self.X, self.yd, self.q, robust='hac', gwk=gwk)
+ betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array([[ 225.0795089 , 17.11660041, -12.22448566],
+ [ 17.67097154, 2.47483461, -1.4183641 ],
+ [ -12.45093722, -1.40495464, 0.8700441 ]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+ self.assertEqual(reg.robust, 'hac')
+
+ def test_spatial(self):
+ w = pysal.queen_from_shapefile(pysal.examples.get_path('columbus.shp'))
+ reg = TSLS(self.y, self.X, self.yd, self.q, spat_diag=True, w=w)
+ betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array([[ 229.05640809, 10.36945783, -9.54463414],
+ [ 10.36945783, 2.0013142 , -1.01826408],
+ [ -9.54463414, -1.01826408, 0.62914915]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+ ak_test = np.array([ 1.16816972, 0.27977763])
+ np.testing.assert_array_almost_equal(reg.ak_test, ak_test, 7)
+
+ def test_names(self):
+ w = pysal.queen_from_shapefile(pysal.examples.get_path('columbus.shp'))
+ gwk = pysal.kernelW_from_shapefile(pysal.examples.get_path('columbus.shp'),k=5,function='triangular', fixed=False)
+ name_x = ['inc']
+ name_y = 'crime'
+ name_yend = ['hoval']
+ name_q = ['discbd']
+ name_w = 'queen'
+ name_gwk = 'k=5'
+ name_ds = 'columbus'
+ reg = TSLS(self.y, self.X, self.yd, self.q,
+ spat_diag=True, w=w, robust='hac', gwk=gwk,
+ name_x=name_x, name_y=name_y, name_q=name_q, name_w=name_w,
+ name_yend=name_yend, name_gwk=name_gwk, name_ds=name_ds)
+ betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array([[ 225.0795089 , 17.11660041, -12.22448566],
+ [ 17.67097154, 2.47483461, -1.4183641 ],
+ [ -12.45093722, -1.40495464, 0.8700441 ]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+ self.assertListEqual(reg.name_x, ['CONSTANT']+name_x)
+ self.assertListEqual(reg.name_yend, name_yend)
+ self.assertListEqual(reg.name_q, name_q)
+ self.assertEqual(reg.name_y, name_y)
+ self.assertEqual(reg.name_w, name_w)
+ self.assertEqual(reg.name_gwk, name_gwk)
+ self.assertEqual(reg.name_ds, name_ds)
+
+
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/tests/test_twosls_regimes.py b/pysal/spreg/tests/test_twosls_regimes.py
new file mode 100644
index 0000000..155bf03
--- /dev/null
+++ b/pysal/spreg/tests/test_twosls_regimes.py
@@ -0,0 +1,276 @@
+import unittest
+import numpy as np
+import pysal
+from pysal.spreg.twosls_regimes import TSLS_Regimes
+from pysal.spreg.twosls import TSLS
+
+class TestTSLS(unittest.TestCase):
+ def setUp(self):
+ db = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
+ self.y = np.array(db.by_col("CRIME"))
+ self.y = np.reshape(self.y, (49,1))
+ self.x = []
+ self.x.append(db.by_col("INC"))
+ self.x = np.array(self.x).T
+ self.yd = []
+ self.yd.append(db.by_col("HOVAL"))
+ self.yd = np.array(self.yd).T
+ self.q = []
+ self.q.append(db.by_col("DISCBD"))
+ self.q = np.array(self.q).T
+ self.r_var = 'NSA'
+ self.regimes = db.by_col(self.r_var)
+
+ def test_basic(self):
+ reg = TSLS_Regimes(self.y, self.x, self.yd, self.q, self.regimes, regime_err_sep=False)
+ betas = np.array([[ 80.23408166],[ 5.48218125],[ 82.98396737],[ 0.49775429],[ -3.72663211],[ -1.27451485]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ h_0 = np.array([[ 0. , 0. , 1. , 19.531, 0. , 5.03 ]])
+ np.testing.assert_array_almost_equal(reg.h[0]*np.eye(6), h_0)
+ hth = np.array([[ 25. , 416.378999 , 0. , 0. ,\
+ 76.03 , 0. ],\
+ [ 416.378999 , 7831.05477839, 0. , 0. ,\
+ 1418.65422625, 0. ],\
+ [ 0. , 0. , 24. , 287.993 ,\
+ 0. , 63.72 ],\
+ [ 0. , 0. , 287.993 , 3855.61860282,\
+ 0. , 827.47378 ],\
+ [ 76.03 , 1418.65422625, 0. , 0. ,\
+ 291.9749 , 0. ],\
+ [ 0. , 0. , 63.72 , 827.47378 ,\
+ 0. , 206.6102 ]])
+ np.testing.assert_array_almost_equal(reg.hth, hth, 7)
+ hthi = np.array([[ 0.3507855 , -0.0175615 , 0. , 0. , -0.00601601,\
+ 0. ],\
+ [-0.0175615 , 0.00194521, -0. , -0. , -0.00487844,\
+ -0. ],\
+ [ 0. , 0. , 0.42327489, -0.02563036, 0. ,\
+ -0.02789128],\
+ [-0. , -0. , -0.02563036, 0.00339841, -0. ,\
+ -0.00570605],\
+ [-0.00601601, -0.00487844, 0. , 0. , 0.02869498,\
+ 0. ],\
+ [ 0. , 0. , -0.02789128, -0.00570605, 0. ,\
+ 0.03629464]])
+ np.testing.assert_array_almost_equal(reg.hthi, hthi, 7)
+ self.assertEqual(reg.k, 6)
+ self.assertEqual(reg.kstar, 2)
+ self.assertAlmostEqual(reg.mean_y, 35.128823897959187, 7)
+ np.testing.assert_equal(reg.kf, 0)
+ np.testing.assert_equal(reg.kr, 3)
+ np.testing.assert_equal(reg.n, 49)
+ np.testing.assert_equal(reg.nr, 2)
+ pfora1a2 = np.array([[ 17.80208995, -0.46997739, 0. , 0. ,\
+ -0.21344994, 0. ],\
+ [ -0.36293902, 0.41200496, 0. , 0. ,\
+ -0.17308863, 0. ],\
+ [ 0. , 0. , 23.8584271 , -0.96035493,\
+ 0. , -0.26149141],\
+ [ 0. , 0. , -0.61800983, 0.2269828 ,\
+ 0. , -0.05349643],\
+ [ -3.22151864, -2.10181214, 0. , 0. ,\
+ 1.01810757, 0. ],\
+ [ 0. , 0. , -5.42403871, -0.6641704 ,\
+ 0. , 0.34027606]])
+ np.testing.assert_array_almost_equal(reg.pfora1a2, pfora1a2, 7)
+ predy_5 = np.array([[ -9.85078372],[ 36.75098196],[ 57.34266859],[ 42.89851907],[ 58.9840913 ]])
+ np.testing.assert_array_almost_equal(reg.predy[0:5], predy_5, 7)
+ q_5 = np.array([ 5.03, 4.27, 3.89, 3.7 , 2.83])
+ np.testing.assert_array_equal((reg.q[0:5].T*np.eye(5))[1,:], q_5)
+ self.assertAlmostEqual(reg.sig2n_k, 990.00750983736714, 7)
+ self.assertAlmostEqual(reg.sig2n, 868.78210046952631, 7)
+ self.assertAlmostEqual(reg.sig2, 990.00750983736714, 7)
+ self.assertAlmostEqual(reg.std_y, 16.732092091229699, 7)
+ u_5 = np.array([[ 25.57676372],[-17.94922796],[-26.71588759],[-10.51075907],[ -8.2525813 ]])
+ np.testing.assert_array_almost_equal(reg.u[0:5], u_5, 7)
+ self.assertAlmostEqual(reg.utu, 42570.322923006788, 7)
+ varb = np.array([[ 0.50015831, 0.07969376, 0. , 0. , -0.04760541,\
+ 0. ],\
+ [ 0.07969376, 0.06523527, 0. , 0. , -0.03105915,\
+ 0. ],\
+ [ 0. , 0. , 0.73944792, 0.01132445, 0. ,\
+ -0.02117969],\
+ [ 0. , 0. , 0.01132445, 0.00756336, 0. ,\
+ -0.00259344],\
+ [-0.04760541, -0.03105915, -0. , -0. , 0.0150449 ,\
+ -0. ],\
+ [-0. , -0. , -0.02117969, -0.00259344, -0. ,\
+ 0.0013287 ]])
+ np.testing.assert_array_almost_equal(reg.varb, varb, 7)
+ vm = np.array([[ 495.16048523, 78.89742341, 0. , 0. ,\
+ -47.12971066, 0. ],\
+ [ 78.89742341, 64.58341083, 0. , 0. ,\
+ -30.74878934, 0. ],\
+ [ 0. , 0. , 732.05899155, 11.21128921,\
+ 0. , -20.96804956],\
+ [ 0. , 0. , 11.21128921, 7.48778398,\
+ 0. , -2.56752553],\
+ [ -47.12971066, -30.74878934, 0. , 0. ,\
+ 14.89456384, 0. ],\
+ [ 0. , 0. , -20.96804956, -2.56752553,\
+ 0. , 1.3154267 ]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+ x_0 = np.array([[ 0. , 0. , 1. , 19.531]])
+ np.testing.assert_array_almost_equal(reg.x[0]*np.eye(4), x_0, 7)
+ y_5 = np.array([[ 15.72598 ], [ 18.801754], [ 30.626781], [ 32.38776 ], [ 50.73151 ]])
+ np.testing.assert_array_almost_equal(reg.y[0:5], y_5, 7)
+ yend_3 = np.array([[ 0. , 80.467003],[ 0. , 44.567001],[ 0. , 26.35 ]])
+ np.testing.assert_array_almost_equal(reg.yend[0:3]*np.eye(2), yend_3, 7)
+ z_0 = np.array([[ 0. , 0. , 1. , 19.531 , 0. , 80.467003]])
+ np.testing.assert_array_almost_equal(reg.z[0]*np.eye(6), z_0, 7)
+ zthhthi = np.array([[ 1.00000000e+00, 0.00000000e+00, 0.00000000e+00,\
+ 0.00000000e+00, -4.44089210e-16, 0.00000000e+00],\
+ [ -1.24344979e-14, 1.00000000e+00, 0.00000000e+00,\
+ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\
+ [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00,\
+ 0.00000000e+00, 0.00000000e+00, -1.11022302e-16],\
+ [ 0.00000000e+00, 0.00000000e+00, -3.55271368e-15,\
+ 1.00000000e+00, 0.00000000e+00, 0.00000000e+00],\
+ [ 2.87468088e+00, 1.82963841e+00, 0.00000000e+00,\
+ 0.00000000e+00, 1.38104644e+00, 0.00000000e+00],\
+ [ 0.00000000e+00, 0.00000000e+00, 1.19237474e+01,\
+ 1.13018165e+00, 0.00000000e+00, 5.22645427e+00]])
+ np.testing.assert_array_almost_equal(reg.zthhthi, zthhthi, 7)
+ self.assertAlmostEqual(reg.pr2, 0.17729324026706564, 7)
+ z_stat = np.array([[ 3.60566933e+00, 3.11349387e-04],\
+ [ 6.82170447e-01, 4.95131179e-01],\
+ [ 3.06705211e+00, 2.16181168e-03],\
+ [ 1.81902371e-01, 8.55659343e-01],\
+ [ -9.65611937e-01, 3.34238400e-01],\
+ [ -1.11124949e+00, 2.66460976e-01]])
+ np.testing.assert_array_almost_equal(np.array(reg.z_stat), z_stat, 7)
+ chow_regi = np.array([[ 0.00616179, 0.93743265],
+ [ 0.3447218 , 0.55711631],
+ [ 0.37093662, 0.54249417]])
+ np.testing.assert_array_almost_equal(reg.chow.regi, chow_regi, 7)
+ self.assertAlmostEqual(reg.chow.joint[0], 1.1353790779820598, 7)
+ title = 'TWO STAGE LEAST SQUARES - REGIMES'
+ self.assertEqual(reg.title, title)
+
+ def test_n_k(self):
+ reg = TSLS_Regimes(self.y, self.x, self.yd, self.q, self.regimes, sig2n_k=True, regime_err_sep=False)
+ betas = np.array([[ 80.23408166],[ 5.48218125],[ 82.98396737],[ 0.49775429],[ -3.72663211],[ -1.27451485]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array([[ 495.16048523, 78.89742341, 0. , 0. ,\
+ -47.12971066, 0. ],\
+ [ 78.89742341, 64.58341083, 0. , 0. ,\
+ -30.74878934, 0. ],\
+ [ 0. , 0. , 732.05899155, 11.21128921,\
+ 0. , -20.96804956],\
+ [ 0. , 0. , 11.21128921, 7.48778398,\
+ 0. , -2.56752553],\
+ [ -47.12971066, -30.74878934, 0. , 0. ,\
+ 14.89456384, 0. ],\
+ [ 0. , 0. , -20.96804956, -2.56752553,\
+ 0. , 1.3154267 ]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+
+ def test_spatial(self):
+ w = pysal.queen_from_shapefile(pysal.examples.get_path('columbus.shp'))
+ reg = TSLS_Regimes(self.y, self.x, self.yd, self.q, self.regimes, spat_diag=True, w=w, regime_err_sep=False)
+ betas = np.array([[ 80.23408166],[ 5.48218125],[ 82.98396737],[ 0.49775429],[ -3.72663211],[ -1.27451485]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array([[ 495.16048523, 78.89742341, 0. , 0. ,\
+ -47.12971066, 0. ],\
+ [ 78.89742341, 64.58341083, 0. , 0. ,\
+ -30.74878934, 0. ],\
+ [ 0. , 0. , 732.05899155, 11.21128921,\
+ 0. , -20.96804956],\
+ [ 0. , 0. , 11.21128921, 7.48778398,\
+ 0. , -2.56752553],\
+ [ -47.12971066, -30.74878934, 0. , 0. ,\
+ 14.89456384, 0. ],\
+ [ 0. , 0. , -20.96804956, -2.56752553,\
+ 0. , 1.3154267 ]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+ ak_test = np.array([ 0.69774552, 0.40354227])
+ np.testing.assert_array_almost_equal(reg.ak_test, ak_test, 7)
+
+ def test_names(self):
+ w = pysal.queen_from_shapefile(pysal.examples.get_path('columbus.shp'))
+ gwk = pysal.kernelW_from_shapefile(pysal.examples.get_path('columbus.shp'),k=5,function='triangular', fixed=False)
+ name_x = ['inc']
+ name_y = 'crime'
+ name_yend = ['hoval']
+ name_q = ['discbd']
+ name_w = 'queen'
+ name_gwk = 'k=5'
+ name_ds = 'columbus'
+ name_regimes= 'nsa'
+ reg = TSLS_Regimes(self.y, self.x, self.yd, self.q, self.regimes, regime_err_sep=False,
+ spat_diag=True, w=w, robust='hac', gwk=gwk, name_regimes=name_regimes,
+ name_x=name_x, name_y=name_y, name_q=name_q, name_w=name_w,
+ name_yend=name_yend, name_gwk=name_gwk, name_ds=name_ds)
+ betas = np.array([[ 80.23408166],[ 5.48218125],[ 82.98396737],[ 0.49775429],[ -3.72663211],[ -1.27451485]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array([[ 522.75813101, 120.64940697, -15.60303241, -0.976389 ,\
+ -67.15556574, 0.64553579],\
+ [ 122.83491674, 122.62303068, -5.52270916, 0.05023488,\
+ -57.89404902, 0.15750428],\
+ [ 0.1983661 , -0.03539147, 335.24731378, 17.40764168,\
+ -0.26447114, -14.3375455 ],\
+ [ -0.13612426, -0.43622084, 18.46644989, 2.70320508,\
+ 0.20398876, -1.31821991],\
+ [ -68.0704928 , -58.03685405, 2.66225388, 0.00323082,\
+ 27.68512974, -0.08124602],\
+ [ -0.08001296, 0.13575504, -14.6998294 , -1.28225201,\
+ -0.05193056, 0.79845124]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+ self.assertEqual(reg.name_x, ['0_CONSTANT', '0_inc', '1_CONSTANT', '1_inc'])
+ self.assertEqual(reg.name_yend, ['0_hoval', '1_hoval'])
+ self.assertEqual(reg.name_q, ['0_discbd', '1_discbd'])
+ self.assertEqual(reg.name_y, name_y)
+ self.assertEqual(reg.name_w, name_w)
+ self.assertEqual(reg.name_gwk, name_gwk)
+ self.assertEqual(reg.name_ds, name_ds)
+ self.assertEqual(reg.name_regimes, name_regimes)
+
+ def test_regi_err(self):
+ #Artficial:
+ n = 256
+ x1 = np.random.uniform(-10,10,(n,1))
+ x2 = np.random.uniform(1,5,(n,1))
+ q = x2 + np.random.normal(0,1,(n,1))
+ x = np.hstack((x1,x2))
+ y = np.dot(np.hstack((np.ones((n,1)),x)),np.array([[1],[0.5],[2]])) + np.random.normal(0,1,(n,1))
+ latt = int(np.sqrt(n))
+ regi = [0]*(n/2) + [1]*(n/2)
+ model = TSLS_Regimes(y, x1, regimes=regi, q=q, yend=x2, regime_err_sep=True, sig2n_k=False)
+ model1 = TSLS(y[0:(n/2)].reshape((n/2),1), x1[0:(n/2)], yend=x2[0:(n/2)], q=q[0:(n/2)], sig2n_k=False)
+ model2 = TSLS(y[(n/2):n].reshape((n/2),1), x1[(n/2):n], yend=x2[(n/2):n], q=q[(n/2):n], sig2n_k=False)
+ tbetas = np.vstack((model1.betas, model2.betas))
+ np.testing.assert_array_almost_equal(model.betas,tbetas)
+ vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
+ np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 6)
+ #Columbus:
+ reg = TSLS_Regimes(self.y, self.x, regimes=self.regimes, yend=self.yd, q=self.q, regime_err_sep=False)
+ tbetas = np.array([[ 80.23408166],
+ [ 5.48218125],
+ [ 82.98396737],
+ [ 0.49775429],
+ [ -3.72663211],
+ [ -1.27451485],])
+ np.testing.assert_array_almost_equal(tbetas, reg.betas)
+ vm = np.array([ 495.16048523, 78.89742341, 0. , 0. ,
+ -47.12971066, 0. ])
+ np.testing.assert_array_almost_equal(reg.vm[0], vm, 6)
+ u_3 = np.array([[ 25.57676372],
+ [-17.94922796],
+ [-26.71588759]])
+ np.testing.assert_array_almost_equal(reg.u[0:3], u_3, 7)
+ predy_3 = np.array([[ -9.85078372],
+ [ 36.75098196],
+ [ 57.34266859]])
+ np.testing.assert_array_almost_equal(reg.predy[0:3], predy_3, 7)
+ chow_regi = np.array([[ 0.00616179, 0.93743265],
+ [ 0.3447218 , 0.55711631],
+ [ 0.37093662, 0.54249417]])
+ np.testing.assert_array_almost_equal(reg.chow.regi, chow_regi, 7)
+ self.assertAlmostEqual(reg.chow.joint[0], 1.1353790779821029, 7)
+
+if __name__ == '__main__':
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ unittest.main()
+ np.set_printoptions(suppress=start_suppress)
+
diff --git a/pysal/spreg/tests/test_twosls_sp.py b/pysal/spreg/tests/test_twosls_sp.py
new file mode 100644
index 0000000..914aef7
--- /dev/null
+++ b/pysal/spreg/tests/test_twosls_sp.py
@@ -0,0 +1,369 @@
+import unittest
+import numpy as np
+import pysal
+import pysal.spreg.diagnostics as D
+from pysal.spreg.twosls_sp import BaseGM_Lag, GM_Lag
+
+class TestBaseGMLag(unittest.TestCase):
+ def setUp(self):
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ self.db = pysal.open(pysal.examples.get_path("columbus.dbf"), 'r')
+ y = np.array(self.db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+
+ def test___init__(self):
+ w_lags = 2
+ X = []
+ X.append(self.db.by_col("INC"))
+ X.append(self.db.by_col("CRIME"))
+ self.X = np.array(X).T
+ yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, None, None, w_lags, True)
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ reg = BaseGM_Lag(self.y, self.X, yend=yd2, q=q2, w=self.w.sparse, w_lags=w_lags)
+ betas = np.array([[ 4.53017056e+01], [ 6.20888617e-01], [ -4.80723451e-01], [ 2.83622122e-02]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ h_0 = np.array([ 1. , 19.531 , 15.72598 , 18.594 ,
+ 24.7142675 , 13.72216667, 27.82929567])
+ np.testing.assert_array_almost_equal(reg.h[0], h_0)
+ hth = np. array([ 49. , 704.371999 , 1721.312371 , 724.7435916 ,
+ 1707.35412945, 711.31248483, 1729.63201243])
+ np.testing.assert_array_almost_equal(reg.hth[0], hth, 7)
+ hthi = np.array([ 7.33701328e+00, 2.27764882e-02, 2.18153588e-02,
+ -5.11035447e-02, 1.22515181e-03, -2.38079378e-01,
+ -1.20149133e-01])
+ np.testing.assert_array_almost_equal(reg.hthi[0], hthi, 7)
+ self.assertEqual(reg.k, 4)
+ self.assertEqual(reg.kstar, 1)
+ self.assertAlmostEqual(reg.mean_y, 38.436224469387746, 7)
+ self.assertEqual(reg.n, 49)
+ pfora1a2 = np.array([ 80.5588479 , -1.06625281, -0.61703759, -1.10071931])
+ np.testing.assert_array_almost_equal(reg.pfora1a2[0], pfora1a2, 7)
+ predy_5 = np.array([[ 50.87411532],[ 50.76969931],[ 41.77223722],[ 33.44262382],[ 28.77418036]])
+ np.testing.assert_array_almost_equal(reg.predy[0:5], predy_5, 7)
+ q_5 = np.array([ 18.594 , 24.7142675 , 13.72216667, 27.82929567])
+ np.testing.assert_array_almost_equal(reg.q[0], q_5)
+ self.assertAlmostEqual(reg.sig2n_k, 234.54258763039289, 7)
+ self.assertAlmostEqual(reg.sig2n, 215.39625394627919, 7)
+ self.assertAlmostEqual(reg.sig2, 215.39625394627919, 7)
+ self.assertAlmostEqual(reg.std_y, 18.466069465206047, 7)
+ u_5 = np.array( [[ 29.59288768], [ -6.20269831], [-15.42223722], [ -0.24262282], [ -5.54918036]])
+ np.testing.assert_array_almost_equal(reg.u[0:5], u_5, 7)
+ self.assertAlmostEqual(reg.utu, 10554.41644336768, 7)
+ varb = np.array( [[ 1.48966377e+00, -2.28698061e-02, -1.20217386e-02, -1.85763498e-02],
+ [ -2.28698061e-02, 1.27893998e-03, 2.74600023e-04, -1.33497705e-04],
+ [ -1.20217386e-02, 2.74600023e-04, 1.54257766e-04, 6.86851184e-05],
+ [ -1.85763498e-02, -1.33497705e-04, 6.86851184e-05, 4.67711582e-04]])
+ np.testing.assert_array_almost_equal(reg.varb, varb, 7)
+ vm = np.array([[ 3.20867996e+02, -4.92607057e+00, -2.58943746e+00, -4.00127615e+00],
+ [ -4.92607057e+00, 2.75478880e-01, 5.91478163e-02, -2.87549056e-02],
+ [ -2.58943746e+00, 5.91478163e-02, 3.32265449e-02, 1.47945172e-02],
+ [ -4.00127615e+00, -2.87549056e-02, 1.47945172e-02, 1.00743323e-01]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 6)
+ x_0 = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x[0], x_0, 7)
+ y_5 = np.array( [[ 80.467003], [ 44.567001], [ 26.35 ], [ 33.200001], [ 23.225 ]])
+ np.testing.assert_array_almost_equal(reg.y[0:5], y_5, 7)
+ yend_5 = np.array( [[ 35.4585005 ], [ 46.67233467], [ 45.36475125], [ 32.81675025], [ 30.81785714]])
+ np.testing.assert_array_almost_equal(reg.yend[0:5], yend_5, 7)
+ z_0 = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
+ np.testing.assert_array_almost_equal(reg.z[0], z_0, 7)
+ zthhthi = np.array( [[ 1.00000000e+00, -2.22044605e-16, -2.22044605e-16 , 2.22044605e-16,
+ 4.44089210e-16, 0.00000000e+00, -8.88178420e-16],
+ [ 0.00000000e+00, 1.00000000e+00, -3.55271368e-15 , 3.55271368e-15,
+ -7.10542736e-15, 7.10542736e-14, 0.00000000e+00],
+ [ 1.81898940e-12, 2.84217094e-14, 1.00000000e+00 , 0.00000000e+00,
+ -2.84217094e-14, 5.68434189e-14, 5.68434189e-14],
+ [ -8.31133940e+00, -3.76104678e-01, -2.07028208e-01 , 1.32618931e+00,
+ -8.04284562e-01, 1.30527047e+00, 1.39136816e+00]])
+ np.testing.assert_array_almost_equal(reg.zthhthi, zthhthi, 7)
+
+ def test_init_white_(self):
+ w_lags = 2
+ X = []
+ X.append(self.db.by_col("INC"))
+ X.append(self.db.by_col("CRIME"))
+ self.X = np.array(X).T
+ yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, None, None, w_lags, True)
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ base_gm_lag = BaseGM_Lag(self.y, self.X, yend=yd2, q=q2, w=self.w.sparse, w_lags=w_lags, robust='white')
+ tbetas = np.array([[ 4.53017056e+01], [ 6.20888617e-01], [ -4.80723451e-01], [ 2.83622122e-02]])
+ np.testing.assert_array_almost_equal(base_gm_lag.betas, tbetas)
+ dbetas = D.se_betas(base_gm_lag)
+ se_betas = np.array([ 20.47077481, 0.50613931, 0.20138425, 0.38028295 ])
+ np.testing.assert_array_almost_equal(dbetas, se_betas)
+
+ def test_init_hac_(self):
+ w_lags = 2
+ X = []
+ X.append(self.db.by_col("INC"))
+ X.append(self.db.by_col("CRIME"))
+ self.X = np.array(X).T
+ yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, None, None, w_lags, True)
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ gwk = pysal.kernelW_from_shapefile(pysal.examples.get_path('columbus.shp'),k=15,function='triangular', fixed=False)
+ base_gm_lag = BaseGM_Lag(self.y, self.X, yend=yd2, q=q2, w=self.w.sparse, w_lags=w_lags, robust='hac', gwk=gwk)
+ tbetas = np.array([[ 4.53017056e+01], [ 6.20888617e-01], [ -4.80723451e-01], [ 2.83622122e-02]])
+ np.testing.assert_array_almost_equal(base_gm_lag.betas, tbetas)
+ dbetas = D.se_betas(base_gm_lag)
+ se_betas = np.array([ 19.08513569, 0.51769543, 0.18244862, 0.35460553])
+ np.testing.assert_array_almost_equal(dbetas, se_betas)
+
+ def test_init_discbd(self):
+ w_lags = 2
+ X = np.array(self.db.by_col("INC"))
+ self.X = np.reshape(X, (49,1))
+ yd = np.array(self.db.by_col("CRIME"))
+ yd = np.reshape(yd, (49,1))
+ q = np.array(self.db.by_col("DISCBD"))
+ q = np.reshape(q, (49,1))
+ yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, yd, q, w_lags, True)
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ reg = BaseGM_Lag(self.y, self.X, w=self.w.sparse, yend=yd2, q=q2, w_lags=w_lags)
+ tbetas = np.array([[ 100.79359082], [ -0.50215501], [ -1.14881711], [ -0.38235022]])
+ np.testing.assert_array_almost_equal(tbetas, reg.betas)
+ dbetas = D.se_betas(reg)
+ se_betas = np.array([ 53.0829123 , 1.02511494, 0.57589064, 0.59891744 ])
+ np.testing.assert_array_almost_equal(dbetas, se_betas)
+
+ def test_n_k(self):
+ w_lags = 2
+ X = []
+ X.append(self.db.by_col("INC"))
+ X.append(self.db.by_col("CRIME"))
+ self.X = np.array(X).T
+ yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, None, None, w_lags, True)
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ reg = BaseGM_Lag(self.y, self.X, yend=yd2, q=q2, w=self.w.sparse, w_lags=w_lags, sig2n_k=True)
+ betas = np. array([[ 4.53017056e+01], [ 6.20888617e-01], [ -4.80723451e-01], [ 2.83622122e-02]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array( [[ 3.49389596e+02, -5.36394351e+00, -2.81960968e+00, -4.35694515e+00],
+ [ -5.36394351e+00, 2.99965892e-01, 6.44054000e-02, -3.13108972e-02],
+ [ -2.81960968e+00, 6.44054000e-02, 3.61800155e-02, 1.61095854e-02],
+ [ -4.35694515e+00, -3.13108972e-02, 1.61095854e-02, 1.09698285e-01]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+
+ def test_lag_q(self):
+ w_lags = 2
+ X = np.array(self.db.by_col("INC"))
+ self.X = np.reshape(X, (49,1))
+ yd = np.array(self.db.by_col("CRIME"))
+ yd = np.reshape(yd, (49,1))
+ q = np.array(self.db.by_col("DISCBD"))
+ q = np.reshape(q, (49,1))
+ yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, yd, q, w_lags, False)
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ reg = BaseGM_Lag(self.y, self.X, w=self.w.sparse, yend=yd2, q=q2, w_lags=w_lags, lag_q=False)
+ tbetas = np.array( [[ 108.83261383], [ -0.48041099], [ -1.18950006], [ -0.56140186]])
+ np.testing.assert_array_almost_equal(tbetas, reg.betas)
+ dbetas = D.se_betas(reg)
+ se_betas = np.array([ 58.33203837, 1.09100446, 0.62315167, 0.68088777])
+ np.testing.assert_array_almost_equal(dbetas, se_betas)
+
+
+
+class TestGMLag(unittest.TestCase):
+ def setUp(self):
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ self.db = pysal.open(pysal.examples.get_path("columbus.dbf"), 'r')
+ y = np.array(self.db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+
+ def test___init__(self):
+ X = []
+ X.append(self.db.by_col("INC"))
+ X.append(self.db.by_col("CRIME"))
+ self.X = np.array(X).T
+ reg = GM_Lag(self.y, self.X, w=self.w, w_lags=2)
+ betas = np.array([[ 4.53017056e+01], [ 6.20888617e-01], [ -4.80723451e-01], [ 2.83622122e-02]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ e_5 = np.array( [[ 29.28976367], [ -6.07439501], [-15.30080685], [ -0.41773375], [ -5.67197968]])
+ np.testing.assert_array_almost_equal(reg.e_pred[0:5], e_5, 7)
+ h_0 = np.array([ 1. , 19.531 , 15.72598 , 18.594 ,
+ 24.7142675 , 13.72216667, 27.82929567])
+ np.testing.assert_array_almost_equal(reg.h[0], h_0)
+ hth = np. array([ 49. , 704.371999 , 1721.312371 , 724.7435916 ,
+ 1707.35412945, 711.31248483, 1729.63201243])
+ np.testing.assert_array_almost_equal(reg.hth[0], hth, 7)
+ hthi = np.array([ 7.33701328e+00, 2.27764882e-02, 2.18153588e-02,
+ -5.11035447e-02, 1.22515181e-03, -2.38079378e-01,
+ -1.20149133e-01])
+ np.testing.assert_array_almost_equal(reg.hthi[0], hthi, 7)
+ self.assertEqual(reg.k, 4)
+ self.assertEqual(reg.kstar, 1)
+ self.assertAlmostEqual(reg.mean_y, 38.436224469387746, 7)
+ self.assertEqual(reg.n, 49)
+ pfora1a2 = np.array([ 80.5588479 , -1.06625281, -0.61703759, -1.10071931])
+ self.assertAlmostEqual(reg.pr2, 0.3551928222612527, 7)
+ self.assertAlmostEqual(reg.pr2_e, 0.34763857386174174, 7)
+ np.testing.assert_array_almost_equal(reg.pfora1a2[0], pfora1a2, 7)
+ predy_5 = np.array([[ 50.87411532],[ 50.76969931],[ 41.77223722],[ 33.44262382],[ 28.77418036]])
+ np.testing.assert_array_almost_equal(reg.predy[0:5], predy_5, 7)
+ predy_e_5 = np.array( [[ 51.17723933], [ 50.64139601], [ 41.65080685], [ 33.61773475], [ 28.89697968]])
+ np.testing.assert_array_almost_equal(reg.predy_e[0:5], predy_e_5, 7)
+ q_5 = np.array([ 18.594 , 24.7142675 , 13.72216667, 27.82929567])
+ np.testing.assert_array_almost_equal(reg.q[0], q_5)
+ self.assertEqual(reg.robust, 'unadjusted')
+ self.assertAlmostEqual(reg.sig2n_k, 234.54258763039289, 7)
+ self.assertAlmostEqual(reg.sig2n, 215.39625394627919, 7)
+ self.assertAlmostEqual(reg.sig2, 215.39625394627919, 7)
+ self.assertAlmostEqual(reg.std_y, 18.466069465206047, 7)
+ u_5 = np.array( [[ 29.59288768], [ -6.20269831], [-15.42223722], [ -0.24262282], [ -5.54918036]])
+ np.testing.assert_array_almost_equal(reg.u[0:5], u_5, 7)
+ self.assertAlmostEqual(reg.utu, 10554.41644336768, 7)
+ varb = np.array( [[ 1.48966377e+00, -2.28698061e-02, -1.20217386e-02, -1.85763498e-02],
+ [ -2.28698061e-02, 1.27893998e-03, 2.74600023e-04, -1.33497705e-04],
+ [ -1.20217386e-02, 2.74600023e-04, 1.54257766e-04, 6.86851184e-05],
+ [ -1.85763498e-02, -1.33497705e-04, 6.86851184e-05, 4.67711582e-04]])
+ np.testing.assert_array_almost_equal(reg.varb, varb, 7)
+ vm = np.array([[ 3.20867996e+02, -4.92607057e+00, -2.58943746e+00, -4.00127615e+00],
+ [ -4.92607057e+00, 2.75478880e-01, 5.91478163e-02, -2.87549056e-02],
+ [ -2.58943746e+00, 5.91478163e-02, 3.32265449e-02, 1.47945172e-02],
+ [ -4.00127615e+00, -2.87549056e-02, 1.47945172e-02, 1.00743323e-01]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 6)
+ x_0 = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x[0], x_0, 7)
+ y_5 = np.array( [[ 80.467003], [ 44.567001], [ 26.35 ], [ 33.200001], [ 23.225 ]])
+ np.testing.assert_array_almost_equal(reg.y[0:5], y_5, 7)
+ yend_5 = np.array( [[ 35.4585005 ], [ 46.67233467], [ 45.36475125], [ 32.81675025], [ 30.81785714]])
+ np.testing.assert_array_almost_equal(reg.yend[0:5], yend_5, 7)
+ z_0 = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
+ np.testing.assert_array_almost_equal(reg.z[0], z_0, 7)
+ zthhthi = np.array( [[ 1.00000000e+00, -2.22044605e-16, -2.22044605e-16 , 2.22044605e-16,
+ 4.44089210e-16, 0.00000000e+00, -8.88178420e-16],
+ [ 0.00000000e+00, 1.00000000e+00, -3.55271368e-15 , 3.55271368e-15,
+ -7.10542736e-15, 7.10542736e-14, 0.00000000e+00],
+ [ 1.81898940e-12, 2.84217094e-14, 1.00000000e+00 , 0.00000000e+00,
+ -2.84217094e-14, 5.68434189e-14, 5.68434189e-14],
+ [ -8.31133940e+00, -3.76104678e-01, -2.07028208e-01 , 1.32618931e+00,
+ -8.04284562e-01, 1.30527047e+00, 1.39136816e+00]])
+ np.testing.assert_array_almost_equal(reg.zthhthi, zthhthi, 7)
+
+ def test_init_white_(self):
+ X = []
+ X.append(self.db.by_col("INC"))
+ X.append(self.db.by_col("CRIME"))
+ self.X = np.array(X).T
+ base_gm_lag = GM_Lag(self.y, self.X, w=self.w, w_lags=2, robust='white')
+ tbetas = np.array([[ 4.53017056e+01], [ 6.20888617e-01], [ -4.80723451e-01], [ 2.83622122e-02]])
+ np.testing.assert_array_almost_equal(base_gm_lag.betas, tbetas)
+ dbetas = D.se_betas(base_gm_lag)
+ se_betas = np.array([ 20.47077481, 0.50613931, 0.20138425, 0.38028295 ])
+ np.testing.assert_array_almost_equal(dbetas, se_betas)
+
+ def test_init_hac_(self):
+ X = []
+ X.append(self.db.by_col("INC"))
+ X.append(self.db.by_col("CRIME"))
+ self.X = np.array(X).T
+ gwk = pysal.kernelW_from_shapefile(pysal.examples.get_path('columbus.shp'),k=15,function='triangular', fixed=False)
+ base_gm_lag = GM_Lag(self.y, self.X, w=self.w, w_lags=2, robust='hac', gwk=gwk)
+ tbetas = np.array([[ 4.53017056e+01], [ 6.20888617e-01], [ -4.80723451e-01], [ 2.83622122e-02]])
+ np.testing.assert_array_almost_equal(base_gm_lag.betas, tbetas)
+ dbetas = D.se_betas(base_gm_lag)
+ se_betas = np.array([ 19.08513569, 0.51769543, 0.18244862, 0.35460553])
+ np.testing.assert_array_almost_equal(dbetas, se_betas)
+
+ def test_init_discbd(self):
+ X = np.array(self.db.by_col("INC"))
+ X = np.reshape(X, (49,1))
+ yd = np.array(self.db.by_col("CRIME"))
+ yd = np.reshape(yd, (49,1))
+ q = np.array(self.db.by_col("DISCBD"))
+ q = np.reshape(q, (49,1))
+ reg = GM_Lag(self.y, X, w=self.w, yend=yd, q=q, w_lags=2)
+ tbetas = np.array([[ 100.79359082], [ -0.50215501], [ -1.14881711], [ -0.38235022]])
+ np.testing.assert_array_almost_equal(tbetas, reg.betas)
+ dbetas = D.se_betas(reg)
+ se_betas = np.array([ 53.0829123 , 1.02511494, 0.57589064, 0.59891744 ])
+ np.testing.assert_array_almost_equal(dbetas, se_betas)
+
+ def test_n_k(self):
+ X = []
+ X.append(self.db.by_col("INC"))
+ X.append(self.db.by_col("CRIME"))
+ self.X = np.array(X).T
+ reg = GM_Lag(self.y, self.X, w=self.w, w_lags=2, sig2n_k=True)
+ betas = np. array([[ 4.53017056e+01], [ 6.20888617e-01], [ -4.80723451e-01], [ 2.83622122e-02]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array( [[ 3.49389596e+02, -5.36394351e+00, -2.81960968e+00, -4.35694515e+00],
+ [ -5.36394351e+00, 2.99965892e-01, 6.44054000e-02, -3.13108972e-02],
+ [ -2.81960968e+00, 6.44054000e-02, 3.61800155e-02, 1.61095854e-02],
+ [ -4.35694515e+00, -3.13108972e-02, 1.61095854e-02, 1.09698285e-01]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+
+ def test_lag_q(self):
+ X = np.array(self.db.by_col("INC"))
+ X = np.reshape(X, (49,1))
+ yd = np.array(self.db.by_col("CRIME"))
+ yd = np.reshape(yd, (49,1))
+ q = np.array(self.db.by_col("DISCBD"))
+ q = np.reshape(q, (49,1))
+ reg = GM_Lag(self.y, X, w=self.w, yend=yd, q=q, w_lags=2, lag_q=False)
+ tbetas = np.array( [[ 108.83261383], [ -0.48041099], [ -1.18950006], [ -0.56140186]])
+ np.testing.assert_array_almost_equal(tbetas, reg.betas)
+ dbetas = D.se_betas(reg)
+ se_betas = np.array([ 58.33203837, 1.09100446, 0.62315167, 0.68088777])
+ np.testing.assert_array_almost_equal(dbetas, se_betas)
+
+ def test_spatial(self):
+ X = np.array(self.db.by_col("INC"))
+ X = np.reshape(X, (49,1))
+ yd = np.array(self.db.by_col("CRIME"))
+ yd = np.reshape(yd, (49,1))
+ q = np.array(self.db.by_col("DISCBD"))
+ q = np.reshape(q, (49,1))
+ w = pysal.queen_from_shapefile(pysal.examples.get_path('columbus.shp'))
+ reg = GM_Lag(self.y, X, yd, q, spat_diag=True, w=w)
+ betas = np.array([[ 5.46344924e+01], [ 4.13301682e-01], [ -5.92637442e-01], [ -7.40490883e-03]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array( [[ 4.45202654e+02, -1.50290275e+01, -6.36557072e+00, -5.71403440e-03],
+ [ -1.50290275e+01, 5.93124683e-01, 2.19169508e-01, -6.70675916e-03],
+ [ -6.36557072e+00, 2.19169508e-01, 1.06577542e-01, -2.96533875e-03],
+ [ -5.71403440e-03, -6.70675916e-03, -2.96533875e-03, 1.15655425e-03]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 6)
+ ak_test = np.array([ 2.52597326, 0.11198567])
+ np.testing.assert_array_almost_equal(reg.ak_test, ak_test, 7)
+
+ def test_names(self):
+ X = np.array(self.db.by_col("INC"))
+ X = np.reshape(X, (49,1))
+ yd = np.array(self.db.by_col("CRIME"))
+ yd = np.reshape(yd, (49,1))
+ q = np.array(self.db.by_col("DISCBD"))
+ q = np.reshape(q, (49,1))
+ w = pysal.queen_from_shapefile(pysal.examples.get_path('columbus.shp'))
+ gwk = pysal.kernelW_from_shapefile(pysal.examples.get_path('columbus.shp'),k=5,function='triangular', fixed=False)
+ name_x = ['inc']
+ name_y = 'crime'
+ name_yend = ['crime']
+ name_q = ['discbd']
+ name_w = 'queen'
+ name_gwk = 'k=5'
+ name_ds = 'columbus'
+ reg = GM_Lag(self.y, X, yd, q,
+ spat_diag=True, w=w, robust='hac', gwk=gwk,
+ name_x=name_x, name_y=name_y, name_q=name_q, name_w=name_w,
+ name_yend=name_yend, name_gwk=name_gwk, name_ds=name_ds)
+ betas = np.array([[ 5.46344924e+01], [ 4.13301682e-01], [ -5.92637442e-01], [ -7.40490883e-03]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array( [[ 5.70817052e+02, -1.83655385e+01, -8.36602575e+00, 2.37538877e-02],
+ [ -1.85224661e+01, 6.53311383e-01, 2.84209566e-01, -6.47694160e-03],
+ [ -8.31105622e+00, 2.78772694e-01, 1.38144928e-01, -3.98175246e-03],
+ [ 2.66662466e-02, -6.23783104e-03, -4.11092891e-03, 1.10936528e-03]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 6)
+ self.assertListEqual(reg.name_x, ['CONSTANT']+name_x)
+ name_yend.append('W_crime')
+ self.assertListEqual(reg.name_yend, name_yend)
+ name_q.extend(['W_inc', 'W_discbd'])
+ self.assertListEqual(reg.name_q, name_q)
+ self.assertEqual(reg.name_y, name_y)
+ self.assertEqual(reg.name_w, name_w)
+ self.assertEqual(reg.name_gwk, name_gwk)
+ self.assertEqual(reg.name_ds, name_ds)
+
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/tests/test_twosls_sp_regimes.py b/pysal/spreg/tests/test_twosls_sp_regimes.py
new file mode 100644
index 0000000..b311b17
--- /dev/null
+++ b/pysal/spreg/tests/test_twosls_sp_regimes.py
@@ -0,0 +1,352 @@
+import unittest
+import numpy as np
+import pysal
+from pysal.spreg.twosls_sp_regimes import GM_Lag_Regimes
+from pysal.spreg import utils
+from pysal.spreg.twosls_sp import GM_Lag
+
+class TestGMLag_Regimes(unittest.TestCase):
+ def setUp(self):
+ self.w = pysal.queen_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ self.db = pysal.open(pysal.examples.get_path("columbus.dbf"), 'r')
+ y = np.array(self.db.by_col("CRIME"))
+ self.y = np.reshape(y, (49,1))
+ self.r_var = 'NSA'
+ self.regimes = self.db.by_col(self.r_var)
+
+ def test___init__(self):
+ #Matches SpaceStat
+ X = []
+ X.append(self.db.by_col("INC"))
+ X.append(self.db.by_col("HOVAL"))
+ self.X = np.array(X).T
+ reg = GM_Lag_Regimes(self.y, self.X, self.regimes, w=self.w, sig2n_k=True, regime_lag_sep=False, regime_err_sep=False)
+ betas = np.array([[ 45.14892906],
+ [ -1.42593383],
+ [ -0.11501037],
+ [ 40.99023016],
+ [ -0.81498302],
+ [ -0.28391409],
+ [ 0.4736163 ]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ e_5 = np.array([[ -1.47960519],
+ [ -7.93748769],
+ [ -5.88561835],
+ [-13.37941105],
+ [ 5.2524303 ]])
+ np.testing.assert_array_almost_equal(reg.e_pred[0:5], e_5, 7)
+ h_0 = np.array([[ 0. , 0. , 0. , 1. , 19.531 ,
+ 80.467003 , 0. , 0. , 18.594 , 35.4585005]])
+ np.testing.assert_array_almost_equal(reg.h[0]*np.eye(10), h_0)
+ self.assertEqual(reg.k, 7)
+ self.assertEqual(reg.kstar, 1)
+ self.assertAlmostEqual(reg.mean_y, 35.128823897959187, 7)
+ self.assertEqual(reg.n, 49)
+ self.assertAlmostEqual(reg.pr2, 0.6572182131915739, 7)
+ self.assertAlmostEqual(reg.pr2_e, 0.5779687278635434, 7)
+ pfora1a2 = np.array([ -2.15017629, -0.30169328, -0.07603704, -22.06541809,
+ 0.45738058, 0.02805828, 0.39073923])
+ np.testing.assert_array_almost_equal(reg.pfora1a2[0], pfora1a2, 7)
+ predy_5 = np.array([[ 13.93216104],
+ [ 23.46424269],
+ [ 34.43510955],
+ [ 44.32473878],
+ [ 44.39117516]])
+ np.testing.assert_array_almost_equal(reg.predy[0:5], predy_5, 7)
+ predy_e_5 = np.array([[ 17.20558519],
+ [ 26.73924169],
+ [ 36.51239935],
+ [ 45.76717105],
+ [ 45.4790797 ]])
+ np.testing.assert_array_almost_equal(reg.predy_e[0:5], predy_e_5, 7)
+ q_5 = np.array([[ 0. , 0. , 18.594 , 35.4585005]])
+ np.testing.assert_array_almost_equal(reg.q[0]*np.eye(4), q_5)
+ self.assertEqual(reg.robust, 'unadjusted')
+ self.assertAlmostEqual(reg.sig2n_k, 109.76462904625834, 7)
+ self.assertAlmostEqual(reg.sig2n, 94.08396775393571, 7)
+ self.assertAlmostEqual(reg.sig2, 109.76462904625834, 7)
+ self.assertAlmostEqual(reg.std_y, 16.732092091229699, 7)
+ u_5 = np.array([[ 1.79381896],
+ [ -4.66248869],
+ [ -3.80832855],
+ [-11.93697878],
+ [ 6.34033484]])
+ np.testing.assert_array_almost_equal(reg.u[0:5], u_5, 7)
+ self.assertAlmostEqual(reg.utu, 4610.11441994285, 7)
+ varb = np.array([ 1.23841820e+00, -3.65620114e-02, -1.21919663e-03,
+ 1.00057547e+00, -2.07403182e-02, -1.27232693e-03,
+ -1.77184084e-02])
+ np.testing.assert_array_almost_equal(reg.varb[0], varb, 7)
+ vm = np.array([ 1.35934514e+02, -4.01321561e+00, -1.33824666e-01,
+ 1.09827796e+02, -2.27655334e+00, -1.39656494e-01,
+ -1.94485452e+00])
+ np.testing.assert_array_almost_equal(reg.vm[0], vm, 6)
+ x_0 = np.array([[ 0. , 0. , 0. , 1. , 19.531 ,
+ 80.467003]])
+ np.testing.assert_array_almost_equal(reg.x[0]*np.eye(6), x_0, 7)
+ y_5 = np.array([[ 15.72598 ],
+ [ 18.801754],
+ [ 30.626781],
+ [ 32.38776 ],
+ [ 50.73151 ]])
+ np.testing.assert_array_almost_equal(reg.y[0:5], y_5, 7)
+ yend_5 = np.array([[ 24.7142675 ],
+ [ 26.24684033],
+ [ 29.411751 ],
+ [ 34.64647575],
+ [ 40.4653275 ]])
+ np.testing.assert_array_almost_equal(reg.yend[0:5]*np.array([[1]]), yend_5, 7)
+ z_0 = np.array([[ 0. , 0. , 0. , 1. , 19.531 ,
+ 80.467003 , 24.7142675]])
+ np.testing.assert_array_almost_equal(reg.z[0]*np.eye(7), z_0, 7)
+ zthhthi = np.array([ 1.00000000e+00, -2.35922393e-16, 5.55111512e-17,
+ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
+ -4.44089210e-16, 2.22044605e-16, 0.00000000e+00,
+ 0.00000000e+00])
+ np.testing.assert_array_almost_equal(reg.zthhthi[0], zthhthi, 7)
+ chow_regi = np.array([[ 0.19692667, 0.65721307],
+ [ 0.5666492 , 0.45159351],
+ [ 0.45282066, 0.5009985 ]])
+ np.testing.assert_array_almost_equal(reg.chow.regi, chow_regi, 7)
+ self.assertAlmostEqual(reg.chow.joint[0], 0.82409867601863462, 7)
+
+ def test_init_discbd(self):
+ #Matches SpaceStat.
+ X = np.array(self.db.by_col("INC"))
+ X = np.reshape(X, (49,1))
+ yd = np.array(self.db.by_col("HOVAL"))
+ yd = np.reshape(yd, (49,1))
+ q = np.array(self.db.by_col("DISCBD"))
+ q = np.reshape(q, (49,1))
+ reg = GM_Lag_Regimes(self.y, X, self.regimes, yend=yd, q=q, lag_q=False, w=self.w, sig2n_k=True, regime_lag_sep=False, regime_err_sep=False)
+ tbetas = np.array([[ 42.7266306 ],
+ [ -0.15552345],
+ [ 37.70545276],
+ [ -0.5341577 ],
+ [ -0.68305796],
+ [ -0.37106077],
+ [ 0.55809516]])
+ np.testing.assert_array_almost_equal(tbetas, reg.betas)
+ vm = np.array([ 270.62979422, 3.62539081, 327.89638627, 6.24949355,
+ -5.25333106, -6.01743515, -4.19290074])
+ np.testing.assert_array_almost_equal(reg.vm[0], vm, 6)
+ e_3 = np.array([[-0.33142796],
+ [-9.51719607],
+ [-7.86272153]])
+ np.testing.assert_array_almost_equal(reg.e_pred[0:3], e_3, 7)
+ u_3 = np.array([[ 4.51839601],
+ [-5.67363147],
+ [-5.1927562 ]])
+ np.testing.assert_array_almost_equal(reg.u[0:3], u_3, 7)
+ predy_3 = np.array([[ 11.20758399],
+ [ 24.47538547],
+ [ 35.8195372 ]])
+ np.testing.assert_array_almost_equal(reg.predy[0:3], predy_3, 7)
+ predy_e_3 = np.array([[ 16.05740796],
+ [ 28.31895007],
+ [ 38.48950253]])
+ np.testing.assert_array_almost_equal(reg.predy_e[0:3], predy_e_3, 7)
+ chow_regi = np.array([[ 0.13130991, 0.71707772],
+ [ 0.04740966, 0.82763357],
+ [ 0.15474413, 0.6940423 ]])
+ np.testing.assert_array_almost_equal(reg.chow.regi, chow_regi, 7)
+ self.assertAlmostEqual(reg.chow.joint[0], 0.31248100032096549, 7)
+
+ def test_lag_q(self):
+ X = np.array(self.db.by_col("INC"))
+ X = np.reshape(X, (49,1))
+ yd = np.array(self.db.by_col("HOVAL"))
+ yd = np.reshape(yd, (49,1))
+ q = np.array(self.db.by_col("DISCBD"))
+ q = np.reshape(q, (49,1))
+ reg = GM_Lag_Regimes(self.y, X, self.regimes, yend=yd, q=q, w=self.w, sig2n_k=True, regime_lag_sep=False, regime_err_sep=False)
+ tbetas = np.array([[ 37.87698329],
+ [ -0.89426982],
+ [ 31.4714777 ],
+ [ -0.71640525],
+ [ -0.28494432],
+ [ -0.2294271 ],
+ [ 0.62996544]])
+ np.testing.assert_array_almost_equal(tbetas, reg.betas)
+ vm = np.array([ 128.25714554, -0.38975354, 95.7271044 , -1.8429218 ,
+ -1.75331978, -0.18240338, -1.67767464])
+ np.testing.assert_array_almost_equal(reg.vm[0], vm, 6)
+ chow_regi = np.array([[ 0.43494049, 0.50957463],
+ [ 0.02089281, 0.88507135],
+ [ 0.01180501, 0.91347943]])
+ np.testing.assert_array_almost_equal(reg.chow.regi, chow_regi, 7)
+ self.assertAlmostEqual(reg.chow.joint[0], 0.54288190938307757, 7)
+
+ def test_all_regi(self):
+ X = np.array(self.db.by_col("INC"))
+ X = np.reshape(X, (49,1))
+ yd = np.array(self.db.by_col("HOVAL"))
+ yd = np.reshape(yd, (49,1))
+ q = np.array(self.db.by_col("DISCBD"))
+ q = np.reshape(q, (49,1))
+ reg = GM_Lag_Regimes(self.y, X, self.regimes, yend=yd, q=q, w=self.w, regime_lag_sep=False, regime_err_sep=True)
+ tbetas = np.array([[ 37.87698329, -0.89426982, 31.4714777 , -0.71640525,
+ -0.28494432, -0.2294271 , 0.62996544]])
+ np.testing.assert_array_almost_equal(tbetas, reg.betas.T)
+ vm = np.array([ 70.38291551, -0.64868787, 49.25453215, -0.62851534,
+ -0.75413453, -0.12674433, -0.97179236])
+ np.testing.assert_array_almost_equal(reg.vm[0], vm, 6)
+ e_3 = np.array([[-2.66997799],
+ [-7.69786264],
+ [-4.39412782]])
+ np.testing.assert_array_almost_equal(reg.e_pred[0:3], e_3, 7)
+ u_3 = np.array([[ 1.13879007],
+ [-3.76873198],
+ [-1.89671717]])
+ np.testing.assert_array_almost_equal(reg.u[0:3], u_3, 7)
+ predy_3 = np.array([[ 14.58718993],
+ [ 22.57048598],
+ [ 32.52349817]])
+ np.testing.assert_array_almost_equal(reg.predy[0:3], predy_3, 7)
+ predy_e_3 = np.array([[ 18.39595799],
+ [ 26.49961664],
+ [ 35.02090882]])
+ np.testing.assert_array_almost_equal(reg.predy_e[0:3], predy_e_3, 7)
+ chow_regi = np.array([[ 0.60091096, 0.43823066],
+ [ 0.03006744, 0.8623373 ],
+ [ 0.01943727, 0.88912016]])
+ np.testing.assert_array_almost_equal(reg.chow.regi, chow_regi, 7)
+ self.assertAlmostEqual(reg.chow.joint[0], 0.88634854058300516, 7)
+
+ def test_all_regi_sig2(self):
+ #Artficial:
+ n = 256
+ x1 = np.random.uniform(-10,10,(n,1))
+ x2 = np.random.uniform(1,5,(n,1))
+ q = x2 + np.random.normal(0,1,(n,1))
+ x = np.hstack((x1,x2))
+ y = np.dot(np.hstack((np.ones((n,1)),x)),np.array([[1],[0.5],[2]])) + np.random.normal(0,1,(n,1))
+ latt = int(np.sqrt(n))
+ w = pysal.lat2W(latt,latt)
+ w.transform='r'
+ regi = [0]*(n/2) + [1]*(n/2)
+ model = GM_Lag_Regimes(y, x1, regi, q=q, yend=x2, w=w, regime_lag_sep=True, regime_err_sep=True)
+ w1 = pysal.lat2W(latt/2,latt)
+ w1.transform='r'
+ model1 = GM_Lag(y[0:(n/2)].reshape((n/2),1), x1[0:(n/2)], yend=x2[0:(n/2)], q=q[0:(n/2)], w=w1)
+ model2 = GM_Lag(y[(n/2):n].reshape((n/2),1), x1[(n/2):n], yend=x2[(n/2):n], q=q[(n/2):n], w=w1)
+ tbetas = np.vstack((model1.betas, model2.betas))
+ np.testing.assert_array_almost_equal(model.betas,tbetas)
+ vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
+ np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 6)
+ #Columbus:
+ X = np.array(self.db.by_col("INC"))
+ X = np.reshape(X, (49,1))
+ yd = np.array(self.db.by_col("HOVAL"))
+ yd = np.reshape(yd, (49,1))
+ q = np.array(self.db.by_col("DISCBD"))
+ q = np.reshape(q, (49,1))
+ reg = GM_Lag_Regimes(self.y, X, self.regimes, yend=yd, q=q, w=self.w,regime_lag_sep=True, regime_err_sep = True)
+ tbetas = np.array([[ 42.35827477],
+ [ -0.09472413],
+ [ -0.68794223],
+ [ 0.54482537],
+ [ 32.24228762],
+ [ -0.12304063],
+ [ -0.46840307],
+ [ 0.67108156]])
+ np.testing.assert_array_almost_equal(tbetas, reg.betas)
+ vm = np.array([ 200.92894859, 4.56244927, -4.85603079, -2.9755413 ,
+ 0. , 0. , 0. , 0. ])
+ np.testing.assert_array_almost_equal(reg.vm[0], vm, 6)
+ e_3 = np.array([[ -1.32209547],
+ [-13.15611199],
+ [-11.62357696]])
+ np.testing.assert_array_almost_equal(reg.e_pred[0:3], e_3, 7)
+ u_3 = np.array([[ 6.99250069],
+ [-7.5665856 ],
+ [-7.04753328]])
+ np.testing.assert_array_almost_equal(reg.u[0:3], u_3, 7)
+ predy_3 = np.array([[ 8.73347931],
+ [ 26.3683396 ],
+ [ 37.67431428]])
+ np.testing.assert_array_almost_equal(reg.predy[0:3], predy_3, 7)
+ predy_e_3 = np.array([[ 17.04807547],
+ [ 31.95786599],
+ [ 42.25035796]])
+ np.testing.assert_array_almost_equal(reg.predy_e[0:3], predy_e_3, 7)
+ chow_regi = np.array([[ 1.51825373e-01, 6.96797034e-01],
+ [ 3.20105698e-04, 9.85725412e-01],
+ [ 8.58836996e-02, 7.69476896e-01],
+ [ 1.01357290e-01, 7.50206873e-01]])
+ np.testing.assert_array_almost_equal(reg.chow.regi, chow_regi, 7)
+ self.assertAlmostEqual(reg.chow.joint[0], 0.38417230022512161, 7)
+
+ def test_fixed_const(self):
+ X = np.array(self.db.by_col("INC"))
+ X = np.reshape(X, (49,1))
+ yd = np.array(self.db.by_col("HOVAL"))
+ yd = np.reshape(yd, (49,1))
+ q = np.array(self.db.by_col("DISCBD"))
+ q = np.reshape(q, (49,1))
+ reg = GM_Lag_Regimes(self.y, X, self.regimes, yend=yd, q=q, w=self.w, constant_regi='one', regime_lag_sep=False, regime_err_sep=False)
+ tbetas = np.array([[ -0.37658823],
+ [ -0.9666079 ],
+ [ 35.5445944 ],
+ [ -0.45793559],
+ [ -0.24216904],
+ [ 0.62500602]])
+ np.testing.assert_array_almost_equal(tbetas, reg.betas)
+ vm = np.array([ 1.4183697 , -0.05975784, -0.27161863, -0.62517245, 0.02266177,
+ 0.00312976])
+ np.testing.assert_array_almost_equal(reg.vm[0], vm, 6)
+ e_3 = np.array([[ 0.17317815],
+ [-5.53766328],
+ [-3.82889307]])
+ np.testing.assert_array_almost_equal(reg.e_pred[0:3], e_3, 7)
+ u_3 = np.array([[ 3.10025518],
+ [-1.83150689],
+ [-1.49598494]])
+ np.testing.assert_array_almost_equal(reg.u[0:3], u_3, 7)
+ predy_3 = np.array([[ 12.62572482],
+ [ 20.63326089],
+ [ 32.12276594]])
+ np.testing.assert_array_almost_equal(reg.predy[0:3], predy_3, 7)
+ predy_e_3 = np.array([[ 15.55280185],
+ [ 24.33941728],
+ [ 34.45567407]])
+ np.testing.assert_array_almost_equal(reg.predy_e[0:3], predy_e_3, 7)
+ chow_regi = np.array([[ 1.85767047e-01, 6.66463269e-01],
+ [ 1.19445012e+01, 5.48089036e-04]])
+ np.testing.assert_array_almost_equal(reg.chow.regi, chow_regi, 7)
+ self.assertAlmostEqual(reg.chow.joint[0], 12.017256217621382, 7)
+
+ def test_names(self):
+ y_var = 'CRIME'
+ x_var = ['INC']
+ x = np.array([self.db.by_col(name) for name in x_var]).T
+ yd_var = ['HOVAL']
+ yd = np.array([self.db.by_col(name) for name in yd_var]).T
+ q_var = ['DISCBD']
+ q = np.array([self.db.by_col(name) for name in q_var]).T
+ r_var = 'NSA'
+ reg = GM_Lag_Regimes(self.y, x, self.regimes, yend=yd, q=q, w=self.w, name_y=y_var, name_x=x_var, name_yend=yd_var, name_q=q_var, name_regimes=r_var, name_ds='columbus', name_w='columbus.gal', regime_lag_sep=False, regime_err_sep=False)
+ betas = np.array([[ 37.87698329],
+ [ -0.89426982],
+ [ 31.4714777 ],
+ [ -0.71640525],
+ [ -0.28494432],
+ [ -0.2294271 ],
+ [ 0.62996544]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array([ 109.93469618, -0.33407447, 82.05180377, -1.57964725,
+ -1.50284553, -0.15634575, -1.43800683])
+ np.testing.assert_array_almost_equal(reg.vm[0], vm, 6)
+ chow_regi = np.array([[ 0.50743058, 0.47625326],
+ [ 0.02437494, 0.87593468],
+ [ 0.01377251, 0.9065777 ]])
+ np.testing.assert_array_almost_equal(reg.chow.regi, chow_regi, 7)
+ self.assertAlmostEqual(reg.chow.joint[0], 0.63336222761359162, 7)
+ self.assertListEqual(reg.name_x, ['0_CONSTANT', '0_INC', '1_CONSTANT', '1_INC'])
+ self.assertListEqual(reg.name_yend, ['0_HOVAL', '1_HOVAL', '_Global_W_CRIME'])
+ self.assertListEqual(reg.name_q, ['0_DISCBD', '0_W_INC', '0_W_DISCBD', '1_DISCBD', '1_W_INC', '1_W_DISCBD'])
+ self.assertEqual(reg.name_y, y_var)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/tests/test_twosls_sp_sparse.py b/pysal/spreg/tests/test_twosls_sp_sparse.py
new file mode 100644
index 0000000..e6669e0
--- /dev/null
+++ b/pysal/spreg/tests/test_twosls_sp_sparse.py
@@ -0,0 +1,379 @@
+import unittest
+import numpy as np
+import pysal
+from pysal.spreg.twosls_sp import BaseGM_Lag, GM_Lag
+import pysal.spreg.diagnostics as D
+from scipy import sparse as SP
+
+
+class TestBaseGMLag(unittest.TestCase):
+ def setUp(self):
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ self.db = pysal.open(pysal.examples.get_path("columbus.dbf"), 'r')
+ y = np.array(self.db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+
+ def test___init__(self):
+ X = []
+ X.append(self.db.by_col("INC"))
+ X.append(self.db.by_col("CRIME"))
+ self.X = np.array(X).T
+ yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, None, None, 2, True)
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.X = SP.csr_matrix(self.X)
+ reg = BaseGM_Lag(self.y, self.X, yend=yd2, q=q2, w=self.w, w_lags=2)
+ betas = np.array([[ 4.53017056e+01], [ 6.20888617e-01], [ -4.80723451e-01], [ 2.83622122e-02]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ h_0 = np.array([ 1. , 19.531 , 15.72598 , 18.594 ,
+ 24.7142675 , 13.72216667, 27.82929567])
+ np.testing.assert_array_almost_equal(reg.h.toarray()[0], h_0)
+ hth = np.array([ 49. , 704.371999 , 1721.312371 , 724.7435916 ,
+ 1707.35412945, 711.31248483, 1729.63201243])
+ np.testing.assert_array_almost_equal(reg.hth[0], hth, 7)
+ hthi = np.array([ 7.33701328e+00, 2.27764882e-02, 2.18153588e-02,
+ -5.11035447e-02, 1.22515181e-03, -2.38079378e-01,
+ -1.20149133e-01])
+ np.testing.assert_array_almost_equal(reg.hthi[0], hthi, 7)
+ self.assertEqual(reg.k, 4)
+ self.assertEqual(reg.kstar, 1)
+ self.assertAlmostEqual(reg.mean_y, 38.436224469387746, 7)
+ self.assertEqual(reg.n, 49)
+ pfora1a2 = np.array([ 80.5588479 , -1.06625281, -0.61703759, -1.10071931])
+ np.testing.assert_array_almost_equal(reg.pfora1a2[0], pfora1a2, 7)
+ predy_5 = np.array([[ 50.87411532],[ 50.76969931],[ 41.77223722],[ 33.44262382],[ 28.77418036]])
+ np.testing.assert_array_almost_equal(reg.predy[0:5], predy_5, 7)
+ q_5 = np.array([ 18.594 , 24.7142675 , 13.72216667, 27.82929567])
+ np.testing.assert_array_almost_equal(reg.q[0], q_5)
+ self.assertAlmostEqual(reg.sig2n_k, 234.54258763039289, 7)
+ self.assertAlmostEqual(reg.sig2n, 215.39625394627919, 7)
+ self.assertAlmostEqual(reg.sig2, 215.39625394627919, 7)
+ self.assertAlmostEqual(reg.std_y, 18.466069465206047, 7)
+ u_5 = np.array( [[ 29.59288768], [ -6.20269831], [-15.42223722], [ -0.24262282], [ -5.54918036]])
+ np.testing.assert_array_almost_equal(reg.u[0:5], u_5, 7)
+ self.assertAlmostEqual(reg.utu, 10554.41644336768, 7)
+ varb = np.array( [[ 1.48966377e+00, -2.28698061e-02, -1.20217386e-02, -1.85763498e-02],
+ [ -2.28698061e-02, 1.27893998e-03, 2.74600023e-04, -1.33497705e-04],
+ [ -1.20217386e-02, 2.74600023e-04, 1.54257766e-04, 6.86851184e-05],
+ [ -1.85763498e-02, -1.33497705e-04, 6.86851184e-05, 4.67711582e-04]])
+ np.testing.assert_array_almost_equal(reg.varb, varb, 7)
+ vm = np.array([[ 3.20867996e+02, -4.92607057e+00, -2.58943746e+00, -4.00127615e+00],
+ [ -4.92607057e+00, 2.75478880e-01, 5.91478163e-02, -2.87549056e-02],
+ [ -2.58943746e+00, 5.91478163e-02, 3.32265449e-02, 1.47945172e-02],
+ [ -4.00127615e+00, -2.87549056e-02, 1.47945172e-02, 1.00743323e-01]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 6)
+ x_0 = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x.toarray()[0], x_0, 7)
+ y_5 = np.array( [[ 80.467003], [ 44.567001], [ 26.35 ], [ 33.200001], [ 23.225 ]])
+ np.testing.assert_array_almost_equal(reg.y[0:5], y_5, 7)
+ yend_5 = np.array( [[ 35.4585005 ], [ 46.67233467], [ 45.36475125], [ 32.81675025], [ 30.81785714]])
+ np.testing.assert_array_almost_equal(reg.yend[0:5], yend_5, 7)
+ z_0 = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
+ np.testing.assert_array_almost_equal(reg.z.toarray()[0], z_0, 7)
+ zthhthi = np.array( [[ 1.00000000e+00, -2.22044605e-16, -2.22044605e-16 , 2.22044605e-16,
+ 4.44089210e-16, 0.00000000e+00, -8.88178420e-16],
+ [ 0.00000000e+00, 1.00000000e+00, -3.55271368e-15 , 3.55271368e-15,
+ -7.10542736e-15, 7.10542736e-14, 0.00000000e+00],
+ [ 1.81898940e-12, 2.84217094e-14, 1.00000000e+00 , 0.00000000e+00,
+ -2.84217094e-14, 5.68434189e-14, 5.68434189e-14],
+ [ -8.31133940e+00, -3.76104678e-01, -2.07028208e-01 , 1.32618931e+00,
+ -8.04284562e-01, 1.30527047e+00, 1.39136816e+00]])
+ np.testing.assert_array_almost_equal(reg.zthhthi, zthhthi, 7)
+
+ def test_init_white_(self):
+ X = []
+ X.append(self.db.by_col("INC"))
+ X.append(self.db.by_col("CRIME"))
+ self.X = np.array(X).T
+ yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, None, None, 2, True)
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.X = SP.csr_matrix(self.X)
+ base_gm_lag = BaseGM_Lag(self.y, self.X, yend=yd2, q=q2, w=self.w, w_lags=2, robust='white')
+ tbetas = np.array([[ 4.53017056e+01], [ 6.20888617e-01], [ -4.80723451e-01], [ 2.83622122e-02]])
+ np.testing.assert_array_almost_equal(base_gm_lag.betas, tbetas)
+ dbetas = D.se_betas(base_gm_lag)
+ se_betas = np.array([ 20.47077481, 0.50613931, 0.20138425, 0.38028295 ])
+ np.testing.assert_array_almost_equal(dbetas, se_betas)
+
+ def test_init_hac_(self):
+ X = []
+ X.append(self.db.by_col("INC"))
+ X.append(self.db.by_col("CRIME"))
+ self.X = np.array(X).T
+ yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, None, None, 2, True)
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.X = SP.csr_matrix(self.X)
+ gwk = pysal.kernelW_from_shapefile(pysal.examples.get_path('columbus.shp'),k=15,function='triangular', fixed=False)
+ base_gm_lag = BaseGM_Lag(self.y, self.X, yend=yd2, q=q2, w=self.w, w_lags=2, robust='hac', gwk=gwk)
+ tbetas = np.array([[ 4.53017056e+01], [ 6.20888617e-01], [ -4.80723451e-01], [ 2.83622122e-02]])
+ np.testing.assert_array_almost_equal(base_gm_lag.betas, tbetas)
+ dbetas = D.se_betas(base_gm_lag)
+ se_betas = np.array([ 19.08513569, 0.51769543, 0.18244862, 0.35460553])
+ np.testing.assert_array_almost_equal(dbetas, se_betas)
+
+ def test_init_discbd(self):
+ X = np.array(self.db.by_col("INC"))
+ self.X = np.reshape(X, (49,1))
+ yd = np.array(self.db.by_col("CRIME"))
+ yd = np.reshape(yd, (49,1))
+ q = np.array(self.db.by_col("DISCBD"))
+ q = np.reshape(q, (49,1))
+ yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, yd, q, 2, True)
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.X = SP.csr_matrix(self.X)
+ reg = BaseGM_Lag(self.y, self.X, yend=yd2, q=q2, w=self.w, w_lags=2)
+ tbetas = np.array([[ 100.79359082], [ -0.50215501], [ -1.14881711], [ -0.38235022]])
+ np.testing.assert_array_almost_equal(tbetas, reg.betas)
+ dbetas = D.se_betas(reg)
+ se_betas = np.array([ 53.0829123 , 1.02511494, 0.57589064, 0.59891744 ])
+ np.testing.assert_array_almost_equal(dbetas, se_betas)
+
+ def test_n_k(self):
+ X = []
+ X.append(self.db.by_col("INC"))
+ X.append(self.db.by_col("CRIME"))
+ self.X = np.array(X).T
+ yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, None, None, 2, True)
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.X = SP.csr_matrix(self.X)
+ reg = BaseGM_Lag(self.y, self.X, yend=yd2, q=q2, w=self.w, w_lags=2, sig2n_k=True)
+ betas = np. array([[ 4.53017056e+01], [ 6.20888617e-01], [ -4.80723451e-01], [ 2.83622122e-02]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array( [[ 3.49389596e+02, -5.36394351e+00, -2.81960968e+00, -4.35694515e+00],
+ [ -5.36394351e+00, 2.99965892e-01, 6.44054000e-02, -3.13108972e-02],
+ [ -2.81960968e+00, 6.44054000e-02, 3.61800155e-02, 1.61095854e-02],
+ [ -4.35694515e+00, -3.13108972e-02, 1.61095854e-02, 1.09698285e-01]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+
+ def test_lag_q(self):
+ X = np.array(self.db.by_col("INC"))
+ self.X = np.reshape(X, (49,1))
+ yd = np.array(self.db.by_col("CRIME"))
+ yd = np.reshape(yd, (49,1))
+ q = np.array(self.db.by_col("DISCBD"))
+ q = np.reshape(q, (49,1))
+ yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, yd, q, 2, False)
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.X = SP.csr_matrix(self.X)
+ reg = BaseGM_Lag(self.y, self.X, yend=yd2, q=q2, w=self.w, w_lags=2, lag_q=False)
+ tbetas = np.array( [[ 108.83261383], [ -0.48041099], [ -1.18950006], [ -0.56140186]])
+ np.testing.assert_array_almost_equal(tbetas, reg.betas)
+ dbetas = D.se_betas(reg)
+ se_betas = np.array([ 58.33203837, 1.09100446, 0.62315167, 0.68088777])
+ np.testing.assert_array_almost_equal(dbetas, se_betas)
+
+
+
+class TestGMLag(unittest.TestCase):
+ def setUp(self):
+ self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.w.transform = 'r'
+ self.db = pysal.open(pysal.examples.get_path("columbus.dbf"), 'r')
+ y = np.array(self.db.by_col("HOVAL"))
+ self.y = np.reshape(y, (49,1))
+
+ def test___init__(self):
+ X = []
+ X.append(self.db.by_col("INC"))
+ X.append(self.db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.X = SP.csr_matrix(self.X)
+ reg = GM_Lag(self.y, self.X, w=self.w, w_lags=2)
+ betas = np.array([[ 4.53017056e+01], [ 6.20888617e-01], [ -4.80723451e-01], [ 2.83622122e-02]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ e_5 = np.array( [[ 29.28976367], [ -6.07439501], [-15.30080685], [ -0.41773375], [ -5.67197968]])
+ np.testing.assert_array_almost_equal(reg.e_pred[0:5], e_5, 7)
+ h_0 = np.array([ 1. , 19.531 , 15.72598 , 18.594 ,
+ 24.7142675 , 13.72216667, 27.82929567])
+ np.testing.assert_array_almost_equal(reg.h.toarray()[0], h_0)
+ hth = np. array([ 49. , 704.371999 , 1721.312371 , 724.7435916 ,
+ 1707.35412945, 711.31248483, 1729.63201243])
+ np.testing.assert_array_almost_equal(reg.hth[0], hth, 7)
+ hthi = np.array([ 7.33701328e+00, 2.27764882e-02, 2.18153588e-02,
+ -5.11035447e-02, 1.22515181e-03, -2.38079378e-01,
+ -1.20149133e-01])
+ np.testing.assert_array_almost_equal(reg.hthi[0], hthi, 7)
+ self.assertEqual(reg.k, 4)
+ self.assertEqual(reg.kstar, 1)
+ self.assertAlmostEqual(reg.mean_y, 38.436224469387746, 7)
+ self.assertEqual(reg.n, 49)
+ pfora1a2 = np.array([ 80.5588479 , -1.06625281, -0.61703759, -1.10071931])
+ self.assertAlmostEqual(reg.pr2, 0.3551928222612527, 7)
+ self.assertAlmostEqual(reg.pr2_e, 0.34763857386174174, 7)
+ np.testing.assert_array_almost_equal(reg.pfora1a2[0], pfora1a2, 7)
+ predy_5 = np.array([[ 50.87411532],[ 50.76969931],[ 41.77223722],[ 33.44262382],[ 28.77418036]])
+ np.testing.assert_array_almost_equal(reg.predy[0:5], predy_5, 7)
+ predy_e_5 = np.array( [[ 51.17723933], [ 50.64139601], [ 41.65080685], [ 33.61773475], [ 28.89697968]])
+ np.testing.assert_array_almost_equal(reg.predy_e[0:5], predy_e_5, 7)
+ q_5 = np.array([ 18.594 , 24.7142675 , 13.72216667, 27.82929567])
+ np.testing.assert_array_almost_equal(reg.q.toarray()[0], q_5)
+ self.assertEqual(reg.robust, 'unadjusted')
+ self.assertAlmostEqual(reg.sig2n_k, 234.54258763039289, 7)
+ self.assertAlmostEqual(reg.sig2n, 215.39625394627919, 7)
+ self.assertAlmostEqual(reg.sig2, 215.39625394627919, 7)
+ self.assertAlmostEqual(reg.std_y, 18.466069465206047, 7)
+ u_5 = np.array( [[ 29.59288768], [ -6.20269831], [-15.42223722], [ -0.24262282], [ -5.54918036]])
+ np.testing.assert_array_almost_equal(reg.u[0:5], u_5, 7)
+ self.assertAlmostEqual(reg.utu, 10554.41644336768, 7)
+ varb = np.array( [[ 1.48966377e+00, -2.28698061e-02, -1.20217386e-02, -1.85763498e-02],
+ [ -2.28698061e-02, 1.27893998e-03, 2.74600023e-04, -1.33497705e-04],
+ [ -1.20217386e-02, 2.74600023e-04, 1.54257766e-04, 6.86851184e-05],
+ [ -1.85763498e-02, -1.33497705e-04, 6.86851184e-05, 4.67711582e-04]])
+ np.testing.assert_array_almost_equal(reg.varb, varb, 7)
+ vm = np.array([[ 3.20867996e+02, -4.92607057e+00, -2.58943746e+00, -4.00127615e+00],
+ [ -4.92607057e+00, 2.75478880e-01, 5.91478163e-02, -2.87549056e-02],
+ [ -2.58943746e+00, 5.91478163e-02, 3.32265449e-02, 1.47945172e-02],
+ [ -4.00127615e+00, -2.87549056e-02, 1.47945172e-02, 1.00743323e-01]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 6)
+ x_0 = np.array([ 1. , 19.531 , 15.72598])
+ np.testing.assert_array_almost_equal(reg.x.toarray()[0], x_0, 7)
+ y_5 = np.array( [[ 80.467003], [ 44.567001], [ 26.35 ], [ 33.200001], [ 23.225 ]])
+ np.testing.assert_array_almost_equal(reg.y[0:5], y_5, 7)
+ yend_5 = np.array( [[ 35.4585005 ], [ 46.67233467], [ 45.36475125], [ 32.81675025], [ 30.81785714]])
+ np.testing.assert_array_almost_equal(reg.yend[0:5], yend_5, 7)
+ z_0 = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
+ np.testing.assert_array_almost_equal(reg.z.toarray()[0], z_0, 7)
+ zthhthi = np.array( [[ 1.00000000e+00, -2.22044605e-16, -2.22044605e-16 , 2.22044605e-16,
+ 4.44089210e-16, 0.00000000e+00, -8.88178420e-16],
+ [ 0.00000000e+00, 1.00000000e+00, -3.55271368e-15 , 3.55271368e-15,
+ -7.10542736e-15, 7.10542736e-14, 0.00000000e+00],
+ [ 1.81898940e-12, 2.84217094e-14, 1.00000000e+00 , 0.00000000e+00,
+ -2.84217094e-14, 5.68434189e-14, 5.68434189e-14],
+ [ -8.31133940e+00, -3.76104678e-01, -2.07028208e-01 , 1.32618931e+00,
+ -8.04284562e-01, 1.30527047e+00, 1.39136816e+00]])
+ np.testing.assert_array_almost_equal(reg.zthhthi, zthhthi, 7)
+
+ def test_init_white_(self):
+ X = []
+ X.append(self.db.by_col("INC"))
+ X.append(self.db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.X = SP.csr_matrix(self.X)
+ base_gm_lag = GM_Lag(self.y, self.X, w=self.w, w_lags=2, robust='white')
+ tbetas = np.array([[ 4.53017056e+01], [ 6.20888617e-01], [ -4.80723451e-01], [ 2.83622122e-02]])
+ np.testing.assert_array_almost_equal(base_gm_lag.betas, tbetas)
+ dbetas = D.se_betas(base_gm_lag)
+ se_betas = np.array([ 20.47077481, 0.50613931, 0.20138425, 0.38028295 ])
+ np.testing.assert_array_almost_equal(dbetas, se_betas)
+
+ def test_init_hac_(self):
+ X = []
+ X.append(self.db.by_col("INC"))
+ X.append(self.db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.X = SP.csr_matrix(self.X)
+ gwk = pysal.kernelW_from_shapefile(pysal.examples.get_path('columbus.shp'),k=15,function='triangular', fixed=False)
+ base_gm_lag = GM_Lag(self.y, self.X, w=self.w, w_lags=2, robust='hac', gwk=gwk)
+ tbetas = np.array([[ 4.53017056e+01], [ 6.20888617e-01], [ -4.80723451e-01], [ 2.83622122e-02]])
+ np.testing.assert_array_almost_equal(base_gm_lag.betas, tbetas)
+ dbetas = D.se_betas(base_gm_lag)
+ se_betas = np.array([ 19.08513569, 0.51769543, 0.18244862, 0.35460553])
+ np.testing.assert_array_almost_equal(dbetas, se_betas)
+
+ def test_init_discbd(self):
+ X = np.array(self.db.by_col("INC"))
+ X = np.reshape(X, (49,1))
+ X = SP.csr_matrix(X)
+ yd = np.array(self.db.by_col("CRIME"))
+ yd = np.reshape(yd, (49,1))
+ q = np.array(self.db.by_col("DISCBD"))
+ q = np.reshape(q, (49,1))
+ reg = GM_Lag(self.y, X, w=self.w, yend=yd, q=q, w_lags=2)
+ tbetas = np.array([[ 100.79359082], [ -0.50215501], [ -1.14881711], [ -0.38235022]])
+ np.testing.assert_array_almost_equal(tbetas, reg.betas)
+ dbetas = D.se_betas(reg)
+ se_betas = np.array([ 53.0829123 , 1.02511494, 0.57589064, 0.59891744 ])
+ np.testing.assert_array_almost_equal(dbetas, se_betas)
+
+ def test_n_k(self):
+ X = []
+ X.append(self.db.by_col("INC"))
+ X.append(self.db.by_col("CRIME"))
+ self.X = np.array(X).T
+ self.X = SP.csr_matrix(self.X)
+ reg = GM_Lag(self.y, self.X, w=self.w, w_lags=2, sig2n_k=True)
+ betas = np. array([[ 4.53017056e+01], [ 6.20888617e-01], [ -4.80723451e-01], [ 2.83622122e-02]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array( [[ 3.49389596e+02, -5.36394351e+00, -2.81960968e+00, -4.35694515e+00],
+ [ -5.36394351e+00, 2.99965892e-01, 6.44054000e-02, -3.13108972e-02],
+ [ -2.81960968e+00, 6.44054000e-02, 3.61800155e-02, 1.61095854e-02],
+ [ -4.35694515e+00, -3.13108972e-02, 1.61095854e-02, 1.09698285e-01]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+
+ def test_lag_q(self):
+ X = np.array(self.db.by_col("INC"))
+ X = np.reshape(X, (49,1))
+ X = SP.csr_matrix(X)
+ yd = np.array(self.db.by_col("CRIME"))
+ yd = np.reshape(yd, (49,1))
+ q = np.array(self.db.by_col("DISCBD"))
+ q = np.reshape(q, (49,1))
+ reg = GM_Lag(self.y, X, w=self.w, yend=yd, q=q, w_lags=2, lag_q=False)
+ tbetas = np.array( [[ 108.83261383], [ -0.48041099], [ -1.18950006], [ -0.56140186]])
+ np.testing.assert_array_almost_equal(tbetas, reg.betas)
+ dbetas = D.se_betas(reg)
+ se_betas = np.array([ 58.33203837, 1.09100446, 0.62315167, 0.68088777])
+ np.testing.assert_array_almost_equal(dbetas, se_betas)
+
+ def test_spatial(self):
+ X = np.array(self.db.by_col("INC"))
+ X = np.reshape(X, (49,1))
+ X = SP.csr_matrix(X)
+ yd = np.array(self.db.by_col("CRIME"))
+ yd = np.reshape(yd, (49,1))
+ q = np.array(self.db.by_col("DISCBD"))
+ q = np.reshape(q, (49,1))
+ w = pysal.queen_from_shapefile(pysal.examples.get_path('columbus.shp'))
+ reg = GM_Lag(self.y, X, yd, q, spat_diag=True, w=w)
+ betas = np.array([[ 5.46344924e+01], [ 4.13301682e-01], [ -5.92637442e-01], [ -7.40490883e-03]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array( [[ 4.45202654e+02, -1.50290275e+01, -6.36557072e+00, -5.71403440e-03],
+ [ -1.50290275e+01, 5.93124683e-01, 2.19169508e-01, -6.70675916e-03],
+ [ -6.36557072e+00, 2.19169508e-01, 1.06577542e-01, -2.96533875e-03],
+ [ -5.71403440e-03, -6.70675916e-03, -2.96533875e-03, 1.15655425e-03]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 6)
+ ak_test = np.array([ 2.52597326, 0.11198567])
+ np.testing.assert_array_almost_equal(reg.ak_test, ak_test, 7)
+
+ def test_names(self):
+ X = np.array(self.db.by_col("INC"))
+ X = np.reshape(X, (49,1))
+ X = SP.csr_matrix(X)
+ yd = np.array(self.db.by_col("CRIME"))
+ yd = np.reshape(yd, (49,1))
+ q = np.array(self.db.by_col("DISCBD"))
+ q = np.reshape(q, (49,1))
+ w = pysal.queen_from_shapefile(pysal.examples.get_path('columbus.shp'))
+ gwk = pysal.kernelW_from_shapefile(pysal.examples.get_path('columbus.shp'),k=5,function='triangular', fixed=False)
+ name_x = ['inc']
+ name_y = 'crime'
+ name_yend = ['crime']
+ name_q = ['discbd']
+ name_w = 'queen'
+ name_gwk = 'k=5'
+ name_ds = 'columbus'
+ reg = GM_Lag(self.y, X, yd, q,
+ spat_diag=True, w=w, robust='hac', gwk=gwk,
+ name_x=name_x, name_y=name_y, name_q=name_q, name_w=name_w,
+ name_yend=name_yend, name_gwk=name_gwk, name_ds=name_ds)
+ betas = np.array([[ 5.46344924e+01], [ 4.13301682e-01], [ -5.92637442e-01], [ -7.40490883e-03]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array( [[ 5.70817052e+02, -1.83655385e+01, -8.36602575e+00, 2.37538877e-02],
+ [ -1.85224661e+01, 6.53311383e-01, 2.84209566e-01, -6.47694160e-03],
+ [ -8.31105622e+00, 2.78772694e-01, 1.38144928e-01, -3.98175246e-03],
+ [ 2.66662466e-02, -6.23783104e-03, -4.11092891e-03, 1.10936528e-03]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 6)
+ self.assertListEqual(reg.name_x, ['CONSTANT']+name_x)
+ name_yend.append('W_crime')
+ self.assertListEqual(reg.name_yend, name_yend)
+ name_q.extend(['W_inc', 'W_discbd'])
+ self.assertListEqual(reg.name_q, name_q)
+ self.assertEqual(reg.name_y, name_y)
+ self.assertEqual(reg.name_w, name_w)
+ self.assertEqual(reg.name_gwk, name_gwk)
+ self.assertEqual(reg.name_ds, name_ds)
+
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/tests/test_twosls_sparse.py b/pysal/spreg/tests/test_twosls_sparse.py
new file mode 100644
index 0000000..654dd5a
--- /dev/null
+++ b/pysal/spreg/tests/test_twosls_sparse.py
@@ -0,0 +1,258 @@
+import unittest
+import numpy as np
+import pysal
+from pysal.spreg.twosls import TSLS, BaseTSLS
+from scipy import sparse as SP
+
+
+class TestBaseTSLS(unittest.TestCase):
+ def setUp(self):
+ db = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
+ self.y = np.array(db.by_col("CRIME"))
+ self.y = np.reshape(self.y, (49,1))
+ self.X = []
+ self.X.append(db.by_col("INC"))
+ self.X = np.array(self.X).T
+ self.X = np.hstack((np.ones(self.y.shape),self.X))
+ self.X = SP.csr_matrix(self.X)
+ self.yd = []
+ self.yd.append(db.by_col("HOVAL"))
+ self.yd = np.array(self.yd).T
+ self.q = []
+ self.q.append(db.by_col("DISCBD"))
+ self.q = np.array(self.q).T
+
+ def test_basic(self):
+ reg = BaseTSLS(self.y, self.X, self.yd, self.q)
+ betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ h_0 = np.array([ 1. , 19.531, 5.03 ])
+ np.testing.assert_array_almost_equal(reg.h.toarray()[0], h_0)
+ hth = np.array([[ 49. , 704.371999 , 139.75 ],
+ [ 704.371999 , 11686.67338121, 2246.12800625],
+ [ 139.75 , 2246.12800625, 498.5851 ]])
+ np.testing.assert_array_almost_equal(reg.hth, hth, 7)
+ hthi = np.array([[ 0.1597275 , -0.00762011, -0.01044191],
+ [-0.00762011, 0.00100135, -0.0023752 ],
+ [-0.01044191, -0.0023752 , 0.01563276]])
+ np.testing.assert_array_almost_equal(reg.hthi, hthi, 7)
+ self.assertEqual(reg.k, 3)
+ self.assertEqual(reg.kstar, 1)
+ self.assertAlmostEqual(reg.mean_y, 35.128823897959187, 7)
+ self.assertEqual(reg.n, 49)
+ pfora1a2 = np.array([[ 9.58156106, -0.22744226, -0.13820537],
+ [ 0.02580142, 0.08226331, -0.03143731],
+ [-3.13896453, -0.33487872, 0.20690965]])
+ np.testing.assert_array_almost_equal(reg.pfora1a2, pfora1a2, 7)
+ predy_5 = np.array([[-28.68949467], [ 28.99484984], [ 55.07344824], [ 38.26609504], [ 57.57145851]])
+ np.testing.assert_array_almost_equal(reg.predy[0:5], predy_5, 7)
+ q_5 = np.array([[ 5.03], [ 4.27], [ 3.89], [ 3.7 ], [ 2.83]])
+ np.testing.assert_array_equal(reg.q[0:5], q_5)
+ self.assertAlmostEqual(reg.sig2n_k, 587.56797852699822, 7)
+ self.assertAlmostEqual(reg.sig2n, 551.5944288212637, 7)
+ self.assertAlmostEqual(reg.sig2, 551.5944288212637, 7)
+ self.assertAlmostEqual(reg.std_y, 16.732092091229699, 7)
+ u_5 = np.array([[ 44.41547467], [-10.19309584], [-24.44666724], [ -5.87833504], [ -6.83994851]])
+ np.testing.assert_array_almost_equal(reg.u[0:5], u_5, 7)
+ self.assertAlmostEqual(reg.utu, 27028.127012241919, 7)
+ varb = np.array([[ 0.41526237, 0.01879906, -0.01730372],
+ [ 0.01879906, 0.00362823, -0.00184604],
+ [-0.01730372, -0.00184604, 0.0011406 ]])
+ np.testing.assert_array_almost_equal(reg.varb, varb, 7)
+ vm = np.array([[ 229.05640809, 10.36945783, -9.54463414],
+ [ 10.36945783, 2.0013142 , -1.01826408],
+ [ -9.54463414, -1.01826408, 0.62914915]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+ x_0 = np.array([ 1. , 19.531])
+ np.testing.assert_array_almost_equal(reg.x.toarray()[0], x_0, 7)
+ y_5 = np.array([[ 15.72598 ], [ 18.801754], [ 30.626781], [ 32.38776 ], [ 50.73151 ]])
+ np.testing.assert_array_almost_equal(reg.y[0:5], y_5, 7)
+ yend_5 = np.array([[ 80.467003], [ 44.567001], [ 26.35 ], [ 33.200001], [ 23.225 ]])
+ np.testing.assert_array_almost_equal(reg.yend[0:5], yend_5, 7)
+ z_0 = np.array([ 1. , 19.531 , 80.467003])
+ np.testing.assert_array_almost_equal(reg.z.toarray()[0], z_0, 7)
+ zthhthi = np.array([[ 1.00000000e+00, -1.66533454e-16, 4.44089210e-16],
+ [ 0.00000000e+00, 1.00000000e+00, 0.00000000e+00],
+ [ 1.26978671e+01, 1.05598709e+00, 3.70212359e+00]])
+ np.testing.assert_array_almost_equal(reg.zthhthi, zthhthi, 7)
+
+ def test_n_k(self):
+ reg = BaseTSLS(self.y, self.X, self.yd, self.q, sig2n_k=True)
+ betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array([[ 243.99486949, 11.04572682, -10.16711028],
+ [ 11.04572682, 2.13183469, -1.08467261],
+ [ -10.16711028, -1.08467261, 0.67018062]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+
+ def test_white(self):
+ reg = BaseTSLS(self.y, self.X, self.yd, self.q, robust='white')
+ betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array([[ 208.27139316, 15.6687805 , -11.53686154],
+ [ 15.6687805 , 2.26882747, -1.30312033],
+ [ -11.53686154, -1.30312033, 0.81940656]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+
+ def test_hac(self):
+ gwk = pysal.kernelW_from_shapefile(pysal.examples.get_path('columbus.shp'),k=15,function='triangular', fixed=False)
+ reg = BaseTSLS(self.y, self.X, self.yd, self.q, robust='hac', gwk=gwk)
+ betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array([[ 231.07254978, 15.42050291, -11.3941033 ],
+ [ 15.01376346, 1.92422887, -1.11865505],
+ [ -11.34381641, -1.1279227 , 0.72053806]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+
+class TestTSLS(unittest.TestCase):
+ def setUp(self):
+ db = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
+ self.y = np.array(db.by_col("CRIME"))
+ self.y = np.reshape(self.y, (49,1))
+ self.X = []
+ self.X.append(db.by_col("INC"))
+ self.X = np.array(self.X).T
+ self.X = SP.csr_matrix(self.X)
+ self.yd = []
+ self.yd.append(db.by_col("HOVAL"))
+ self.yd = np.array(self.yd).T
+ self.q = []
+ self.q.append(db.by_col("DISCBD"))
+ self.q = np.array(self.q).T
+
+ def test_basic(self):
+ reg = TSLS(self.y, self.X, self.yd, self.q)
+ betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ h_0 = np.array([ 1. , 19.531, 5.03 ])
+ np.testing.assert_array_almost_equal(reg.h.toarray()[0], h_0)
+ hth = np.array([[ 49. , 704.371999 , 139.75 ],
+ [ 704.371999 , 11686.67338121, 2246.12800625],
+ [ 139.75 , 2246.12800625, 498.5851 ]])
+ np.testing.assert_array_almost_equal(reg.hth, hth, 7)
+ hthi = np.array([[ 0.1597275 , -0.00762011, -0.01044191],
+ [-0.00762011, 0.00100135, -0.0023752 ],
+ [-0.01044191, -0.0023752 , 0.01563276]])
+ np.testing.assert_array_almost_equal(reg.hthi, hthi, 7)
+ self.assertEqual(reg.k, 3)
+ self.assertEqual(reg.kstar, 1)
+ self.assertAlmostEqual(reg.mean_y, 35.128823897959187, 7)
+ self.assertEqual(reg.n, 49)
+ pfora1a2 = np.array([[ 9.58156106, -0.22744226, -0.13820537],
+ [ 0.02580142, 0.08226331, -0.03143731],
+ [-3.13896453, -0.33487872, 0.20690965]])
+ np.testing.assert_array_almost_equal(reg.pfora1a2, pfora1a2, 7)
+ predy_5 = np.array([[-28.68949467], [ 28.99484984], [ 55.07344824], [ 38.26609504], [ 57.57145851]])
+ np.testing.assert_array_almost_equal(reg.predy[0:5], predy_5, 7)
+ q_5 = np.array([[ 5.03], [ 4.27], [ 3.89], [ 3.7 ], [ 2.83]])
+ np.testing.assert_array_equal(reg.q[0:5], q_5)
+ self.assertAlmostEqual(reg.sig2n_k, 587.56797852699822, 7)
+ self.assertAlmostEqual(reg.sig2n, 551.5944288212637, 7)
+ self.assertAlmostEqual(reg.sig2, 551.5944288212637, 7)
+ self.assertAlmostEqual(reg.std_y, 16.732092091229699, 7)
+ u_5 = np.array([[ 44.41547467], [-10.19309584], [-24.44666724], [ -5.87833504], [ -6.83994851]])
+ np.testing.assert_array_almost_equal(reg.u[0:5], u_5, 7)
+ self.assertAlmostEqual(reg.utu, 27028.127012241919, 7)
+ varb = np.array([[ 0.41526237, 0.01879906, -0.01730372],
+ [ 0.01879906, 0.00362823, -0.00184604],
+ [-0.01730372, -0.00184604, 0.0011406 ]])
+ np.testing.assert_array_almost_equal(reg.varb, varb, 7)
+ vm = np.array([[ 229.05640809, 10.36945783, -9.54463414],
+ [ 10.36945783, 2.0013142 , -1.01826408],
+ [ -9.54463414, -1.01826408, 0.62914915]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+ x_0 = np.array([ 1. , 19.531])
+ np.testing.assert_array_almost_equal(reg.x.toarray()[0], x_0, 7)
+ y_5 = np.array([[ 15.72598 ], [ 18.801754], [ 30.626781], [ 32.38776 ], [ 50.73151 ]])
+ np.testing.assert_array_almost_equal(reg.y[0:5], y_5, 7)
+ yend_5 = np.array([[ 80.467003], [ 44.567001], [ 26.35 ], [ 33.200001], [ 23.225 ]])
+ np.testing.assert_array_almost_equal(reg.yend[0:5], yend_5, 7)
+ z_0 = np.array([ 1. , 19.531 , 80.467003])
+ np.testing.assert_array_almost_equal(reg.z.toarray()[0], z_0, 7)
+ zthhthi = np.array([[ 1.00000000e+00, -1.66533454e-16, 4.44089210e-16],
+ [ 0.00000000e+00, 1.00000000e+00, 0.00000000e+00],
+ [ 1.26978671e+01, 1.05598709e+00, 3.70212359e+00]])
+ np.testing.assert_array_almost_equal(reg.zthhthi, zthhthi, 7)
+ self.assertAlmostEqual(reg.pr2, 0.27936137128173893, 7)
+ z_stat = np.array([[ 5.84526447e+00, 5.05764078e-09],
+ [ 3.67601567e-01, 7.13170346e-01],
+ [ -1.99468913e+00, 4.60767956e-02]])
+ np.testing.assert_array_almost_equal(reg.z_stat, z_stat, 7)
+ title = 'TWO STAGE LEAST SQUARES'
+ self.assertEqual(reg.title, title)
+
+ def test_n_k(self):
+ reg = TSLS(self.y, self.X, self.yd, self.q, sig2n_k=True)
+ betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array([[ 243.99486949, 11.04572682, -10.16711028],
+ [ 11.04572682, 2.13183469, -1.08467261],
+ [ -10.16711028, -1.08467261, 0.67018062]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+
+ def test_white(self):
+ reg = TSLS(self.y, self.X, self.yd, self.q, robust='white')
+ betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array([[ 208.27139316, 15.6687805 , -11.53686154],
+ [ 15.6687805 , 2.26882747, -1.30312033],
+ [ -11.53686154, -1.30312033, 0.81940656]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+ self.assertEqual(reg.robust, 'white')
+
+ def test_hac(self):
+ gwk = pysal.kernelW_from_shapefile(pysal.examples.get_path('columbus.shp'),k=5,function='triangular', fixed=False)
+ reg = TSLS(self.y, self.X, self.yd, self.q, robust='hac', gwk=gwk)
+ betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array([[ 225.0795089 , 17.11660041, -12.22448566],
+ [ 17.67097154, 2.47483461, -1.4183641 ],
+ [ -12.45093722, -1.40495464, 0.8700441 ]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+ self.assertEqual(reg.robust, 'hac')
+
+ def test_spatial(self):
+ w = pysal.queen_from_shapefile(pysal.examples.get_path('columbus.shp'))
+ reg = TSLS(self.y, self.X, self.yd, self.q, spat_diag=True, w=w)
+ betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array([[ 229.05640809, 10.36945783, -9.54463414],
+ [ 10.36945783, 2.0013142 , -1.01826408],
+ [ -9.54463414, -1.01826408, 0.62914915]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+ ak_test = np.array([ 1.16816972, 0.27977763])
+ np.testing.assert_array_almost_equal(reg.ak_test, ak_test, 7)
+
+ def test_names(self):
+ w = pysal.queen_from_shapefile(pysal.examples.get_path('columbus.shp'))
+ gwk = pysal.kernelW_from_shapefile(pysal.examples.get_path('columbus.shp'),k=5,function='triangular', fixed=False)
+ name_x = ['inc']
+ name_y = 'crime'
+ name_yend = ['hoval']
+ name_q = ['discbd']
+ name_w = 'queen'
+ name_gwk = 'k=5'
+ name_ds = 'columbus'
+ reg = TSLS(self.y, self.X, self.yd, self.q,
+ spat_diag=True, w=w, robust='hac', gwk=gwk,
+ name_x=name_x, name_y=name_y, name_q=name_q, name_w=name_w,
+ name_yend=name_yend, name_gwk=name_gwk, name_ds=name_ds)
+ betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
+ np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ vm = np.array([[ 225.0795089 , 17.11660041, -12.22448566],
+ [ 17.67097154, 2.47483461, -1.4183641 ],
+ [ -12.45093722, -1.40495464, 0.8700441 ]])
+ np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+ self.assertListEqual(reg.name_x, ['CONSTANT']+name_x)
+ self.assertListEqual(reg.name_yend, name_yend)
+ self.assertListEqual(reg.name_q, name_q)
+ self.assertEqual(reg.name_y, name_y)
+ self.assertEqual(reg.name_w, name_w)
+ self.assertEqual(reg.name_gwk, name_gwk)
+ self.assertEqual(reg.name_ds, name_ds)
+
+
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/spreg/twosls.py b/pysal/spreg/twosls.py
new file mode 100644
index 0000000..8c85af0
--- /dev/null
+++ b/pysal/spreg/twosls.py
@@ -0,0 +1,475 @@
+import numpy as np
+import copy
+import numpy.linalg as la
+import summary_output as SUMMARY
+import robust as ROBUST
+import user_output as USER
+from utils import spdot, sphstack, RegressionPropsY, RegressionPropsVM
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, David C. Folch david.folch at asu.edu, Jing Yao jingyao at asu.edu"
+__all__ = ["TSLS"]
+
+
+class BaseTSLS(RegressionPropsY, RegressionPropsVM):
+
+ """
+ Two stage least squares (2SLS) (note: no consistency checks,
+ diagnostics or constant added)
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x); cannot be
+ used in combination with h
+ h : array
+ Two dimensional array with n rows and one column for each
+ exogenous variable to use as instruments (note: this
+ can contain variables from x); cannot be used in
+ combination with q
+ robust : string
+ If 'white', then a White consistent estimator of the
+ variance-covariance matrix is given. If 'hac', then a
+ HAC consistent estimator of the variance-covariance
+ matrix is given. Default set to None.
+ gwk : pysal W object
+ Kernel spatial weights needed for HAC estimation. Note:
+ matrix must have ones along the main diagonal.
+ sig2n_k : boolean
+ If True, then use n-k to estimate sigma^2. If False, use n.
+
+
+ Attributes
+ ----------
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ kstar : integer
+ Number of endogenous variables.
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable used as instruments
+ z : array
+ nxk array of variables (combination of x and yend)
+ h : array
+ nxl array of instruments (combination of x and q)
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ utu : float
+ Sum of squared residuals
+ sig2 : float
+ Sigma squared used in computations
+ sig2n : float
+ Sigma squared (computed with n in the denominator)
+ sig2n_k : float
+ Sigma squared (computed with n-k in the denominator)
+ hth : float
+ H'H
+ hthi : float
+ (H'H)^-1
+ varb : array
+ (Z'H (H'H)^-1 H'Z)^-1
+ zthhthi : array
+ Z'H(H'H)^-1
+ pfora1a2 : array
+ n(zthhthi)'varb
+
+
+ Examples
+ --------
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X = np.array(X).T
+ >>> X = np.hstack((np.ones(y.shape),X))
+ >>> yd = []
+ >>> yd.append(db.by_col("HOVAL"))
+ >>> yd = np.array(yd).T
+ >>> q = []
+ >>> q.append(db.by_col("DISCBD"))
+ >>> q = np.array(q).T
+ >>> reg = BaseTSLS(y, X, yd, q=q)
+ >>> print reg.betas
+ [[ 88.46579584]
+ [ 0.5200379 ]
+ [ -1.58216593]]
+ >>> reg = BaseTSLS(y, X, yd, q=q, robust="white")
+
+ """
+
+ def __init__(self, y, x, yend, q=None, h=None,
+ robust=None, gwk=None, sig2n_k=False):
+
+ if issubclass(type(q), np.ndarray) and issubclass(type(h), np.ndarray):
+ raise Exception, "Please do not provide 'q' and 'h' together"
+ if q == None and h == None:
+ raise Exception, "Please provide either 'q' or 'h'"
+
+ self.y = y
+ self.n = y.shape[0]
+ self.x = x
+
+ self.kstar = yend.shape[1]
+ # including exogenous and endogenous variables
+ z = sphstack(self.x, yend)
+ if type(h).__name__ not in ['ndarray', 'csr_matrix']:
+ # including exogenous variables and instrument
+ h = sphstack(self.x, q)
+ self.z = z
+ self.h = h
+ self.q = q
+ self.yend = yend
+ # k = number of exogenous variables and endogenous variables
+ self.k = z.shape[1]
+ hth = spdot(h.T, h)
+ hthi = la.inv(hth)
+ zth = spdot(z.T, h)
+ hty = spdot(h.T, y)
+
+ factor_1 = np.dot(zth, hthi)
+ factor_2 = np.dot(factor_1, zth.T)
+ # this one needs to be in cache to be used in AK
+ varb = la.inv(factor_2)
+ factor_3 = np.dot(varb, factor_1)
+ betas = np.dot(factor_3, hty)
+ self.betas = betas
+ self.varb = varb
+ self.zthhthi = factor_1
+
+ # predicted values
+ self.predy = spdot(z, betas)
+
+ # residuals
+ u = y - self.predy
+ self.u = u
+
+ # attributes used in property
+ self.hth = hth # Required for condition index
+ self.hthi = hthi # Used in error models
+ self.htz = zth.T
+
+ if robust:
+ self.vm = ROBUST.robust_vm(reg=self, gwk=gwk, sig2n_k=sig2n_k)
+
+ self._cache = {}
+ if sig2n_k:
+ self.sig2 = self.sig2n_k
+ else:
+ self.sig2 = self.sig2n
+
+ @property
+ def pfora1a2(self):
+ if 'pfora1a2' not in self._cache:
+ self._cache['pfora1a2'] = self.n * \
+ np.dot(self.zthhthi.T, self.varb)
+ return self._cache['pfora1a2']
+
+ @property
+ def vm(self):
+ if 'vm' not in self._cache:
+ self._cache['vm'] = np.dot(self.sig2, self.varb)
+ return self._cache['vm']
+
+
+class TSLS(BaseTSLS):
+
+ """
+ Two stage least squares with results and diagnostics.
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x)
+ w : pysal W object
+ Spatial weights object (required if running spatial
+ diagnostics)
+ robust : string
+ If 'white', then a White consistent estimator of the
+ variance-covariance matrix is given. If 'hac', then a
+ HAC consistent estimator of the variance-covariance
+ matrix is given. Default set to None.
+ gwk : pysal W object
+ Kernel spatial weights needed for HAC estimation. Note:
+ matrix must have ones along the main diagonal.
+ sig2n_k : boolean
+ If True, then use n-k to estimate sigma^2. If False, use n.
+ spat_diag : boolean
+ If True, then compute Anselin-Kelejian test (requires w)
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_q : list of strings
+ Names of instruments for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_gwk : string
+ Name of kernel weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ kstar : integer
+ Number of endogenous variables.
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable used as instruments
+ z : array
+ nxk array of variables (combination of x and yend)
+ h : array
+ nxl array of instruments (combination of x and q)
+ robust : string
+ Adjustment for robust standard errors
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ utu : float
+ Sum of squared residuals
+ sig2 : float
+ Sigma squared used in computations
+ std_err : array
+ 1xk array of standard errors of the betas
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ ak_test : tuple
+ Anselin-Kelejian test; tuple contains the pair (statistic,
+ p-value)
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_z : list of strings
+ Names of exogenous and endogenous variables for use in
+ output
+ name_q : list of strings
+ Names of external instruments
+ name_h : list of strings
+ Names of all instruments used in ouput
+ name_w : string
+ Name of weights matrix for use in output
+ name_gwk : string
+ Name of kernel weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ title : string
+ Name of the regression method used
+ sig2n : float
+ Sigma squared (computed with n in the denominator)
+ sig2n_k : float
+ Sigma squared (computed with n-k in the denominator)
+ hth : float
+ H'H
+ hthi : float
+ (H'H)^-1
+ varb : array
+ (Z'H (H'H)^-1 H'Z)^-1
+ zthhthi : array
+ Z'H(H'H)^-1
+ pfora1a2 : array
+ n(zthhthi)'varb
+
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import numpy as np
+ >>> import pysal
+
+ Open data on Columbus neighborhood crime (49 areas) using pysal.open().
+ This is the DBF associated with the Columbus shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
+
+ Extract the CRIME column (crime rates) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+
+ Extract INC (income) vector from the DBF to be used as
+ independent variables in the regression. Note that PySAL requires this to
+ be an nxj numpy array, where j is the number of independent variables (not
+ including a constant). By default this model adds a vector of ones to the
+ independent variables passed in, but this can be overridden by passing
+ constant=False.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X = np.array(X).T
+
+ In this case we consider HOVAL (home value) is an endogenous regressor.
+ We tell the model that this is so by passing it in a different parameter
+ from the exogenous variables (x).
+
+ >>> yd = []
+ >>> yd.append(db.by_col("HOVAL"))
+ >>> yd = np.array(yd).T
+
+ Because we have endogenous variables, to obtain a correct estimate of the
+ model, we need to instrument for HOVAL. We use DISCBD (distance to the
+ CBD) for this and hence put it in the instruments parameter, 'q'.
+
+ >>> q = []
+ >>> q.append(db.by_col("DISCBD"))
+ >>> q = np.array(q).T
+
+ We are all set with the preliminars, we are good to run the model. In this
+ case, we will need the variables (exogenous and endogenous) and the
+ instruments. If we want to have the names of the variables printed in the
+ output summary, we will have to pass them in as well, although this is optional.
+
+ >>> reg = TSLS(y, X, yd, q, name_x=['inc'], name_y='crime', name_yend=['hoval'], name_q=['discbd'], name_ds='columbus')
+ >>> print reg.betas
+ [[ 88.46579584]
+ [ 0.5200379 ]
+ [ -1.58216593]]
+
+ """
+
+ def __init__(self, y, x, yend, q,
+ w=None,
+ robust=None, gwk=None, sig2n_k=False,
+ spat_diag=False,
+ vm=False, name_y=None, name_x=None,
+ name_yend=None, name_q=None,
+ name_w=None, name_gwk=None, name_ds=None):
+
+ n = USER.check_arrays(y, x, yend, q)
+ USER.check_y(y, n)
+ USER.check_weights(w, y)
+ USER.check_robust(robust, gwk)
+ USER.check_spat_diag(spat_diag, w)
+ x_constant = USER.check_constant(x)
+ BaseTSLS.__init__(self, y=y, x=x_constant, yend=yend, q=q,
+ robust=robust, gwk=gwk, sig2n_k=sig2n_k)
+ self.title = "TWO STAGE LEAST SQUARES"
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_y = USER.set_name_y(name_y)
+ self.name_x = USER.set_name_x(name_x, x)
+ self.name_yend = USER.set_name_yend(name_yend, yend)
+ self.name_z = self.name_x + self.name_yend
+ self.name_q = USER.set_name_q(name_q, q)
+ self.name_h = USER.set_name_h(self.name_x, self.name_q)
+ self.robust = USER.set_robust(robust)
+ self.name_w = USER.set_name_w(name_w, w)
+ self.name_gwk = USER.set_name_w(name_gwk, gwk)
+ SUMMARY.TSLS(reg=self, vm=vm, w=w, spat_diag=spat_diag)
+
+
+def _test():
+ import doctest
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ doctest.testmod()
+ np.set_printoptions(suppress=start_suppress)
+
+
+if __name__ == '__main__':
+ _test()
+
+ import numpy as np
+ import pysal
+ db = pysal.open(pysal.examples.get_path("columbus.dbf"), 'r')
+ y_var = 'CRIME'
+ y = np.array([db.by_col(y_var)]).reshape(49, 1)
+ x_var = ['INC']
+ x = np.array([db.by_col(name) for name in x_var]).T
+ yd_var = ['HOVAL']
+ yd = np.array([db.by_col(name) for name in yd_var]).T
+ q_var = ['DISCBD']
+ q = np.array([db.by_col(name) for name in q_var]).T
+ w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ w.transform = 'r'
+ tsls = TSLS(y, x, yd, q, w=w, spat_diag=True, name_y=y_var, name_x=x_var,
+ name_yend=yd_var, name_q=q_var, name_ds='columbus', name_w='columbus.gal')
+ print tsls.summary
diff --git a/pysal/spreg/twosls_regimes.py b/pysal/spreg/twosls_regimes.py
new file mode 100644
index 0000000..7a621db
--- /dev/null
+++ b/pysal/spreg/twosls_regimes.py
@@ -0,0 +1,515 @@
+import numpy as np
+import regimes as REGI
+import user_output as USER
+import multiprocessing as mp
+import scipy.sparse as SP
+from utils import sphstack, set_warn, RegressionProps_basic, spdot, sphstack
+from twosls import BaseTSLS
+from robust import hac_multi
+import summary_output as SUMMARY
+from platform import system
+
+"""
+Two-stage Least Squares estimation with regimes.
+"""
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, Pedro V. Amaral pedro.amaral at asu.edu, David C. Folch david.folch at asu.edu"
+
+
+class TSLS_Regimes(BaseTSLS, REGI.Regimes_Frame):
+
+ """
+ Two stage least squares (2SLS) with regimes
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x)
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ constant_regi: ['one', 'many']
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime (default)
+ cols2regi : list, 'all'
+ Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all' (default), all the variables vary by regime.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ robust : string
+ If 'white', then a White consistent estimator of the
+ variance-covariance matrix is given.
+ If 'hac', then a HAC consistent estimator of the
+ variance-covariance matrix is given.
+ If 'ogmm', then Optimal GMM is used to estimate
+ betas and the variance-covariance matrix.
+ Default set to None.
+ gwk : pysal W object
+ Kernel spatial weights needed for HAC estimation. Note:
+ matrix must have ones along the main diagonal.
+ sig2n_k : boolean
+ If True, then use n-k to estimate sigma^2. If False, use n.
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ cores : boolean
+ Specifies if multiprocessing is to be used
+ Default: no multiprocessing, cores = False
+ Note: Multiprocessing may not work on all platforms.
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_q : list of strings
+ Names of instruments for use in output
+ name_regimes : string
+ Name of regimes variable for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_gwk : string
+ Name of kernel weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+
+ Attributes
+ ----------
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable used as instruments
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ vm : array
+ Variance covariance matrix (kxk)
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ constant_regi: [False, 'one', 'many']
+ Ignored if regimes=False. Constant option for regimes.
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime
+ cols2regi : list, 'all'
+ Ignored if regimes=False. Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all', all the variables vary by regime.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ kr : int
+ Number of variables/columns to be "regimized" or subject
+ to change by regime. These will result in one parameter
+ estimate by regime for each variable (i.e. nr parameters per
+ variable)
+ kf : int
+ Number of variables/columns to be considered fixed or
+ global across regimes and hence only obtain one parameter
+ estimate
+ nr : int
+ Number of different regimes in the 'regimes' list
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_q : list of strings
+ Names of instruments for use in output
+ name_regimes : string
+ Name of regimes variable for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_gwk : string
+ Name of kernel weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ multi : dictionary
+ Only available when multiple regressions are estimated,
+ i.e. when regime_err_sep=True and no variable is fixed
+ across regimes.
+ Contains all attributes of each individual regression
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import numpy as np
+ >>> import pysal
+
+ Open data on NCOVR US County Homicides (3085 areas) using pysal.open().
+ This is the DBF associated with the NAT shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path("NAT.dbf"),'r')
+
+ Extract the HR90 column (homicide rates in 1990) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y_var = 'HR90'
+ >>> y = np.array([db.by_col(y_var)]).reshape(3085,1)
+
+ Extract UE90 (unemployment rate) and PS90 (population structure) vectors from
+ the DBF to be used as independent variables in the regression. Other variables
+ can be inserted by adding their names to x_var, such as x_var = ['Var1','Var2','...]
+ Note that PySAL requires this to be an nxj numpy array, where j is the
+ number of independent variables (not including a constant). By default
+ this model adds a vector of ones to the independent variables passed in.
+
+ >>> x_var = ['PS90','UE90']
+ >>> x = np.array([db.by_col(name) for name in x_var]).T
+
+ In this case we consider RD90 (resource deprivation) as an endogenous regressor.
+ We tell the model that this is so by passing it in a different parameter
+ from the exogenous variables (x).
+
+ >>> yd_var = ['RD90']
+ >>> yd = np.array([db.by_col(name) for name in yd_var]).T
+
+ Because we have endogenous variables, to obtain a correct estimate of the
+ model, we need to instrument for RD90. We use FP89 (families below poverty)
+ for this and hence put it in the instruments parameter, 'q'.
+
+ >>> q_var = ['FP89']
+ >>> q = np.array([db.by_col(name) for name in q_var]).T
+
+ The different regimes in this data are given according to the North and
+ South dummy (SOUTH).
+
+ >>> r_var = 'SOUTH'
+ >>> regimes = db.by_col(r_var)
+
+ Since we want to perform tests for spatial dependence, we need to specify
+ the spatial weights matrix that includes the spatial configuration of the
+ observations into the error component of the model. To do that, we can open
+ an already existing gal file or create a new one. In this case, we will
+ create one from ``NAT.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("NAT.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, this allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ We can now run the regression and then have a summary of the output
+ by typing: model.summary
+ Alternatively, we can just check the betas and standard errors of the
+ parameters:
+
+ >>> tslsr = TSLS_Regimes(y, x, yd, q, regimes, w=w, constant_regi='many', spat_diag=False, name_y=y_var, name_x=x_var, name_yend=yd_var, name_q=q_var, name_regimes=r_var, name_ds='NAT', name_w='NAT.shp')
+
+ >>> tslsr.betas
+ array([[ 3.66973562],
+ [ 1.06950466],
+ [ 0.14680946],
+ [ 2.45864196],
+ [ 9.55873243],
+ [ 1.94666348],
+ [-0.30810214],
+ [ 3.68718119]])
+
+ >>> np.sqrt(tslsr.vm.diagonal())
+ array([ 0.38389901, 0.09963973, 0.04672091, 0.22725012, 0.49181223,
+ 0.19630774, 0.07784587, 0.25529011])
+
+ """
+
+ def __init__(self, y, x, yend, q, regimes,
+ w=None, robust=None, gwk=None, sig2n_k=True,
+ spat_diag=False, vm=False, constant_regi='many',
+ cols2regi='all', regime_err_sep=True, name_y=None, name_x=None,
+ cores=False, name_yend=None, name_q=None, name_regimes=None,
+ name_w=None, name_gwk=None, name_ds=None, summ=True):
+
+ n = USER.check_arrays(y, x)
+ USER.check_y(y, n)
+ USER.check_weights(w, y)
+ USER.check_robust(robust, gwk)
+ USER.check_spat_diag(spat_diag, w)
+ self.constant_regi = constant_regi
+ self.cols2regi = cols2regi
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_regimes = USER.set_name_ds(name_regimes)
+ self.name_w = USER.set_name_w(name_w, w)
+ self.name_gwk = USER.set_name_w(name_gwk, gwk)
+ self.name_y = USER.set_name_y(name_y)
+ name_yend = USER.set_name_yend(name_yend, yend)
+ name_q = USER.set_name_q(name_q, q)
+ self.name_x_r = USER.set_name_x(name_x, x) + name_yend
+ self.n = n
+ cols2regi = REGI.check_cols2regi(
+ constant_regi, cols2regi, x, yend=yend, add_cons=False)
+ self.regimes_set = REGI._get_regimes_set(regimes)
+ self.regimes = regimes
+ USER.check_regimes(self.regimes_set, self.n, x.shape[1])
+ if regime_err_sep == True and robust == 'hac':
+ set_warn(
+ self, "Error by regimes is incompatible with HAC estimation for 2SLS models. Hence, the error by regimes has been disabled for this model.")
+ regime_err_sep = False
+ self.regime_err_sep = regime_err_sep
+ if regime_err_sep == True and set(cols2regi) == set([True]) and constant_regi == 'many':
+ name_x = USER.set_name_x(name_x, x)
+ self.y = y
+ regi_ids = dict(
+ (r, list(np.where(np.array(regimes) == r)[0])) for r in self.regimes_set)
+ self._tsls_regimes_multi(x, yend, q, w, regi_ids, cores,
+ gwk, sig2n_k, robust, spat_diag, vm, name_x, name_yend, name_q)
+ else:
+ name_x = USER.set_name_x(name_x, x, constant=True)
+ q, self.name_q = REGI.Regimes_Frame.__init__(self, q,
+ regimes, constant_regi=None, cols2regi='all', names=name_q)
+ x, self.name_x = REGI.Regimes_Frame.__init__(self, x,
+ regimes, constant_regi, cols2regi=cols2regi, names=name_x)
+ yend, self.name_yend = REGI.Regimes_Frame.__init__(self, yend,
+ regimes, constant_regi=None,
+ cols2regi=cols2regi, yend=True, names=name_yend)
+ if regime_err_sep == True and robust == None:
+ robust = 'white'
+ BaseTSLS.__init__(self, y=y, x=x, yend=yend, q=q,
+ robust=robust, gwk=gwk, sig2n_k=sig2n_k)
+ self.title = "TWO STAGE LEAST SQUARES - REGIMES"
+ if robust == 'ogmm':
+ _optimal_weight(self, sig2n_k)
+ self.name_z = self.name_x + self.name_yend
+ self.name_h = USER.set_name_h(self.name_x, self.name_q)
+ self.chow = REGI.Chow(self)
+ self.robust = USER.set_robust(robust)
+ if summ:
+ SUMMARY.TSLS(
+ reg=self, vm=vm, w=w, spat_diag=spat_diag, regimes=True)
+
+ def _tsls_regimes_multi(self, x, yend, q, w, regi_ids, cores,
+ gwk, sig2n_k, robust, spat_diag, vm, name_x, name_yend, name_q):
+ results_p = {}
+ """
+ for r in self.regimes_set:
+ if system() != 'Windows':
+ is_win = True
+ results_p[r] = _work(*(self.y,x,w,regi_ids,r,yend,q,robust,sig2n_k,self.name_ds,self.name_y,name_x,name_yend,name_q,self.name_w,self.name_regimes))
+ else:
+ pool = mp.Pool(cores)
+ results_p[r] = pool.apply_async(_work,args=(self.y,x,w,regi_ids,r,yend,q,robust,sig2n_k,self.name_ds,self.name_y,name_x,name_yend,name_q,self.name_w,self.name_regimes))
+ is_win = False
+ """
+ for r in self.regimes_set:
+ if cores:
+ pool = mp.Pool(None)
+ results_p[r] = pool.apply_async(_work, args=(
+ self.y, x, w, regi_ids, r, yend, q, robust, sig2n_k, self.name_ds, self.name_y, name_x, name_yend, name_q, self.name_w, self.name_regimes))
+ else:
+ results_p[r] = _work(*(self.y, x, w, regi_ids, r, yend, q, robust, sig2n_k,
+ self.name_ds, self.name_y, name_x, name_yend, name_q, self.name_w, self.name_regimes))
+
+ self.kryd = 0
+ self.kr = x.shape[1] + yend.shape[1] + 1
+ self.kf = 0
+ self.nr = len(self.regimes_set)
+ self.vm = np.zeros((self.nr * self.kr, self.nr * self.kr), float)
+ self.betas = np.zeros((self.nr * self.kr, 1), float)
+ self.u = np.zeros((self.n, 1), float)
+ self.predy = np.zeros((self.n, 1), float)
+ """
+ if not is_win:
+ pool.close()
+ pool.join()
+ """
+ if cores:
+ pool.close()
+ pool.join()
+
+ results = {}
+ self.name_y, self.name_x, self.name_yend, self.name_q, self.name_z, self.name_h = [
+ ], [], [], [], [], []
+ counter = 0
+ for r in self.regimes_set:
+ """
+ if is_win:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+ """
+ if not cores:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+
+ self.vm[(counter * self.kr):((counter + 1) * self.kr),
+ (counter * self.kr):((counter + 1) * self.kr)] = results[r].vm
+ self.betas[
+ (counter * self.kr):((counter + 1) * self.kr), ] = results[r].betas
+ self.u[regi_ids[r], ] = results[r].u
+ self.predy[regi_ids[r], ] = results[r].predy
+ self.name_y += results[r].name_y
+ self.name_x += results[r].name_x
+ self.name_yend += results[r].name_yend
+ self.name_q += results[r].name_q
+ self.name_z += results[r].name_z
+ self.name_h += results[r].name_h
+ counter += 1
+ self.multi = results
+ self.hac_var = sphstack(x, q)
+ if robust == 'hac':
+ hac_multi(self, gwk)
+ if robust == 'ogmm':
+ set_warn(
+ self, "Residuals treated as homoskedastic for the purpose of diagnostics.")
+ self.chow = REGI.Chow(self)
+ if spat_diag:
+ self._get_spat_diag_props(results, regi_ids, x, yend, q)
+ SUMMARY.TSLS_multi(
+ reg=self, multireg=self.multi, vm=vm, spat_diag=spat_diag, regimes=True, w=w)
+
+ def _get_spat_diag_props(self, results, regi_ids, x, yend, q):
+ self._cache = {}
+ x = USER.check_constant(x)
+ x = REGI.regimeX_setup(
+ x, self.regimes, [True] * x.shape[1], self.regimes_set)
+ self.z = sphstack(x, REGI.regimeX_setup(
+ yend, self.regimes, [True] * yend.shape[1], self.regimes_set))
+ self.h = sphstack(
+ x, REGI.regimeX_setup(q, self.regimes, [True] * q.shape[1], self.regimes_set))
+ hthi = np.linalg.inv(spdot(self.h.T, self.h))
+ zth = spdot(self.z.T, self.h)
+ self.varb = np.linalg.inv(spdot(spdot(zth, hthi), zth.T))
+
+
+def _work(y, x, w, regi_ids, r, yend, q, robust, sig2n_k, name_ds, name_y, name_x, name_yend, name_q, name_w, name_regimes):
+ y_r = y[regi_ids[r]]
+ x_r = x[regi_ids[r]]
+ yend_r = yend[regi_ids[r]]
+ q_r = q[regi_ids[r]]
+ x_constant = USER.check_constant(x_r)
+ if robust == 'hac' or robust == 'ogmm':
+ robust2 = None
+ else:
+ robust2 = robust
+ model = BaseTSLS(
+ y_r, x_constant, yend_r, q_r, robust=robust2, sig2n_k=sig2n_k)
+ model.title = "TWO STAGE LEAST SQUARES ESTIMATION - REGIME %s" % r
+ if robust == 'ogmm':
+ _optimal_weight(model, sig2n_k, warn=False)
+ model.robust = USER.set_robust(robust)
+ model.name_ds = name_ds
+ model.name_y = '%s_%s' % (str(r), name_y)
+ model.name_x = ['%s_%s' % (str(r), i) for i in name_x]
+ model.name_yend = ['%s_%s' % (str(r), i) for i in name_yend]
+ model.name_z = model.name_x + model.name_yend
+ model.name_q = ['%s_%s' % (str(r), i) for i in name_q]
+ model.name_h = model.name_x + model.name_q
+ model.name_w = name_w
+ model.name_regimes = name_regimes
+ if w:
+ w_r, warn = REGI.w_regime(w, regi_ids[r], r, transform=True)
+ set_warn(model, warn)
+ model.w = w_r
+ return model
+
+
+def _optimal_weight(reg, sig2n_k, warn=True):
+ try:
+ Hu = reg.h.toarray() * reg.u ** 2
+ except:
+ Hu = reg.h * reg.u ** 2
+ if sig2n_k:
+ S = spdot(reg.h.T, Hu, array_out=True) / (reg.n - reg.k)
+ else:
+ S = spdot(reg.h.T, Hu, array_out=True) / reg.n
+ Si = np.linalg.inv(S)
+ ZtH = spdot(reg.z.T, reg.h)
+ ZtHSi = spdot(ZtH, Si)
+ fac2 = np.linalg.inv(spdot(ZtHSi, ZtH.T, array_out=True))
+ fac3 = spdot(ZtHSi, spdot(reg.h.T, reg.y), array_out=True)
+ betas = np.dot(fac2, fac3)
+ if sig2n_k:
+ vm = fac2 * (reg.n - reg.k)
+ else:
+ vm = fac2 * reg.n
+ RegressionProps_basic(reg, betas=betas, vm=vm, sig2=False)
+ reg.title += " (Optimal-Weighted GMM)"
+ if warn:
+ set_warn(
+ reg, "Residuals treated as homoskedastic for the purpose of diagnostics.")
+ return
+
+
+def _test():
+ import doctest
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ doctest.testmod()
+ np.set_printoptions(suppress=start_suppress)
+
+
+if __name__ == '__main__':
+ _test()
+ import numpy as np
+ import pysal
+ db = pysal.open(pysal.examples.get_path('NAT.dbf'), 'r')
+ y_var = 'HR60'
+ y = np.array([db.by_col(y_var)]).T
+ x_var = ['PS60', 'DV60', 'RD60']
+ x = np.array([db.by_col(name) for name in x_var]).T
+ yd_var = ['UE60']
+ yd = np.array([db.by_col(name) for name in yd_var]).T
+ q_var = ['FP59', 'MA60']
+ q = np.array([db.by_col(name) for name in q_var]).T
+ r_var = 'SOUTH'
+ regimes = db.by_col(r_var)
+ tslsr = TSLS_Regimes(y, x, yd, q, regimes, constant_regi='many', spat_diag=False, name_y=y_var, name_x=x_var,
+ name_yend=yd_var, name_q=q_var, name_regimes=r_var, cols2regi=[
+ False, True, True, True],
+ sig2n_k=False)
+ print tslsr.summary
diff --git a/pysal/spreg/twosls_sp.py b/pysal/spreg/twosls_sp.py
new file mode 100644
index 0000000..694b2c0
--- /dev/null
+++ b/pysal/spreg/twosls_sp.py
@@ -0,0 +1,540 @@
+'''
+Spatial Two Stages Least Squares
+'''
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, David C. Folch david.folch at asu.edu"
+
+import copy
+import numpy as np
+import pysal
+import numpy.linalg as la
+import twosls as TSLS
+import robust as ROBUST
+import user_output as USER
+import summary_output as SUMMARY
+from utils import get_lags, set_endog, sp_att, set_warn
+
+__all__ = ["GM_Lag"]
+
+
+class BaseGM_Lag(TSLS.BaseTSLS):
+
+ """
+ Spatial two stage least squares (S2SLS) (note: no consistency checks,
+ diagnostics or constant added); Anselin (1988) [1]_
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x); cannot be
+ used in combination with h
+ w : Sparse matrix
+ Spatial weights sparse matrix
+ w_lags : integer
+ Orders of W to include as instruments for the spatially
+ lagged dependent variable. For example, w_lags=1, then
+ instruments are WX; if w_lags=2, then WX, WWX; and so on.
+ lag_q : boolean
+ If True, then include spatial lags of the additional
+ instruments (q).
+ robust : string
+ If 'white', then a White consistent estimator of the
+ variance-covariance matrix is given. If 'hac', then a
+ HAC consistent estimator of the variance-covariance
+ matrix is given. Default set to None.
+ gwk : pysal W object
+ Kernel spatial weights needed for HAC estimation. Note:
+ matrix must have ones along the main diagonal.
+ sig2n_k : boolean
+ If True, then use n-k to estimate sigma^2. If False, use n.
+
+
+ Attributes
+ ----------
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ predy : array
+ nx1 array of predicted y values
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ kstar : integer
+ Number of endogenous variables.
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable used as instruments
+ z : array
+ nxk array of variables (combination of x and yend)
+ h : array
+ nxl array of instruments (combination of x and q)
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ utu : float
+ Sum of squared residuals
+ sig2 : float
+ Sigma squared used in computations
+ sig2n : float
+ Sigma squared (computed with n in the denominator)
+ sig2n_k : float
+ Sigma squared (computed with n-k in the denominator)
+ hth : float
+ H'H
+ hthi : float
+ (H'H)^-1
+ varb : array
+ (Z'H (H'H)^-1 H'Z)^-1
+ zthhthi : array
+ Z'H(H'H)^-1
+ pfora1a2 : array
+ n(zthhthi)'varb
+
+ References
+ ----------
+
+ .. [1] Anselin, L. (1988) "Spatial Econometrics: Methods and Models".
+ Kluwer Academic Publishers. Dordrecht.
+
+ Examples
+ --------
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> import pysal.spreg.diagnostics as D
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ >>> w.transform = 'r'
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
+ >>> y = np.array(db.by_col("HOVAL"))
+ >>> y = np.reshape(y, (49,1))
+ >>> # no non-spatial endogenous variables
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("CRIME"))
+ >>> X = np.array(X).T
+ >>> w_lags = 2
+ >>> yd2, q2 = pysal.spreg.utils.set_endog(y, X, w, None, None, w_lags, True)
+ >>> X = np.hstack((np.ones(y.shape),X))
+ >>> reg=BaseGM_Lag(y, X, yend=yd2, q=q2, w=w.sparse, w_lags=w_lags)
+ >>> reg.betas
+ array([[ 45.30170561],
+ [ 0.62088862],
+ [ -0.48072345],
+ [ 0.02836221]])
+ >>> D.se_betas(reg)
+ array([ 17.91278862, 0.52486082, 0.1822815 , 0.31740089])
+ >>> reg=BaseGM_Lag(y, X, yend=yd2, q=q2, w=w.sparse, w_lags=w_lags, robust='white')
+ >>> reg.betas
+ array([[ 45.30170561],
+ [ 0.62088862],
+ [ -0.48072345],
+ [ 0.02836221]])
+ >>> D.se_betas(reg)
+ array([ 20.47077481, 0.50613931, 0.20138425, 0.38028295])
+ >>> # instrument for HOVAL with DISCBD
+ >>> X = np.array(db.by_col("INC"))
+ >>> X = np.reshape(X, (49,1))
+ >>> yd = np.array(db.by_col("CRIME"))
+ >>> yd = np.reshape(yd, (49,1))
+ >>> q = np.array(db.by_col("DISCBD"))
+ >>> q = np.reshape(q, (49,1))
+ >>> yd2, q2 = pysal.spreg.utils.set_endog(y, X, w, yd, q, w_lags, True)
+ >>> X = np.hstack((np.ones(y.shape),X))
+ >>> reg=BaseGM_Lag(y, X, w=w.sparse, yend=yd2, q=q2, w_lags=w_lags)
+ >>> reg.betas
+ array([[ 100.79359082],
+ [ -0.50215501],
+ [ -1.14881711],
+ [ -0.38235022]])
+ >>> D.se_betas(reg)
+ array([ 53.0829123 , 1.02511494, 0.57589064, 0.59891744])
+
+ """
+
+ def __init__(self, y, x, yend=None, q=None,
+ w=None, w_lags=1, lag_q=True,
+ robust=None, gwk=None, sig2n_k=False):
+
+ TSLS.BaseTSLS.__init__(self, y=y, x=x, yend=yend, q=q,
+ robust=robust, gwk=gwk, sig2n_k=sig2n_k)
+
+
+class GM_Lag(BaseGM_Lag):
+
+ """
+ Spatial two stage least squares (S2SLS) with results and diagnostics;
+ Anselin (1988) [1]_
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x); cannot be
+ used in combination with h
+ w : pysal W object
+ Spatial weights object
+ w_lags : integer
+ Orders of W to include as instruments for the spatially
+ lagged dependent variable. For example, w_lags=1, then
+ instruments are WX; if w_lags=2, then WX, WWX; and so on.
+ lag_q : boolean
+ If True, then include spatial lags of the additional
+ instruments (q).
+ robust : string
+ If 'white', then a White consistent estimator of the
+ variance-covariance matrix is given. If 'hac', then a
+ HAC consistent estimator of the variance-covariance
+ matrix is given. Default set to None.
+ gwk : pysal W object
+ Kernel spatial weights needed for HAC estimation. Note:
+ matrix must have ones along the main diagonal.
+ sig2n_k : boolean
+ If True, then use n-k to estimate sigma^2. If False, use n.
+ spat_diag : boolean
+ If True, then compute Anselin-Kelejian test
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_q : list of strings
+ Names of instruments for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_gwk : string
+ Name of kernel weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_pred : array
+ nx1 array of residuals (using reduced form)
+ predy : array
+ nx1 array of predicted y values
+ predy_e : array
+ nx1 array of predicted y values (using reduced form)
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ kstar : integer
+ Number of endogenous variables.
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable used as instruments
+ z : array
+ nxk array of variables (combination of x and yend)
+ h : array
+ nxl array of instruments (combination of x and q)
+ robust : string
+ Adjustment for robust standard errors
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ pr2_e : float
+ Pseudo R squared (squared correlation between y and ypred_e
+ (using reduced form))
+ utu : float
+ Sum of squared residuals
+ sig2 : float
+ Sigma squared used in computations
+ std_err : array
+ 1xk array of standard errors of the betas
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ ak_test : tuple
+ Anselin-Kelejian test; tuple contains the pair (statistic,
+ p-value)
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_z : list of strings
+ Names of exogenous and endogenous variables for use in
+ output
+ name_q : list of strings
+ Names of external instruments
+ name_h : list of strings
+ Names of all instruments used in ouput
+ name_w : string
+ Name of weights matrix for use in output
+ name_gwk : string
+ Name of kernel weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ title : string
+ Name of the regression method used
+ sig2n : float
+ Sigma squared (computed with n in the denominator)
+ sig2n_k : float
+ Sigma squared (computed with n-k in the denominator)
+ hth : float
+ H'H
+ hthi : float
+ (H'H)^-1
+ varb : array
+ (Z'H (H'H)^-1 H'Z)^-1
+ zthhthi : array
+ Z'H(H'H)^-1
+ pfora1a2 : array
+ n(zthhthi)'varb
+
+
+ References
+ ----------
+
+ .. [1] Anselin, L. (1988) "Spatial Econometrics: Methods and Models".
+ Kluwer Academic Publishers. Dordrecht.
+
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis. Since we will need some tests for our
+ model, we also import the diagnostics module.
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> import pysal.spreg.diagnostics as D
+
+ Open data on Columbus neighborhood crime (49 areas) using pysal.open().
+ This is the DBF associated with the Columbus shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
+
+ Extract the HOVAL column (home value) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y = np.array(db.by_col("HOVAL"))
+ >>> y = np.reshape(y, (49,1))
+
+ Extract INC (income) and CRIME (crime rates) vectors from the DBF to be used as
+ independent variables in the regression. Note that PySAL requires this to
+ be an nxj numpy array, where j is the number of independent variables (not
+ including a constant). By default this model adds a vector of ones to the
+ independent variables passed in, but this can be overridden by passing
+ constant=False.
+
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("CRIME"))
+ >>> X = np.array(X).T
+
+ Since we want to run a spatial error model, we need to specify the spatial
+ weights matrix that includes the spatial configuration of the observations
+ into the error component of the model. To do that, we can open an already
+ existing gal file or create a new one. In this case, we will create one
+ from ``columbus.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, this allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ This class runs a lag model, which means that includes the spatial lag of
+ the dependent variable on the right-hand side of the equation. If we want
+ to have the names of the variables printed in the
+ output summary, we will have to pass them in as well, although this is
+ optional. The default most basic model to be run would be:
+
+ >>> reg=GM_Lag(y, X, w=w, w_lags=2, name_x=['inc', 'crime'], name_y='hoval', name_ds='columbus')
+ >>> reg.betas
+ array([[ 45.30170561],
+ [ 0.62088862],
+ [ -0.48072345],
+ [ 0.02836221]])
+
+ Once the model is run, we can obtain the standard error of the coefficient
+ estimates by calling the diagnostics module:
+
+ >>> D.se_betas(reg)
+ array([ 17.91278862, 0.52486082, 0.1822815 , 0.31740089])
+
+ But we can also run models that incorporates corrected standard errors
+ following the White procedure. For that, we will have to include the
+ optional parameter ``robust='white'``:
+
+ >>> reg=GM_Lag(y, X, w=w, w_lags=2, robust='white', name_x=['inc', 'crime'], name_y='hoval', name_ds='columbus')
+ >>> reg.betas
+ array([[ 45.30170561],
+ [ 0.62088862],
+ [ -0.48072345],
+ [ 0.02836221]])
+
+ And we can access the standard errors from the model object:
+
+ >>> reg.std_err
+ array([ 20.47077481, 0.50613931, 0.20138425, 0.38028295])
+
+ The class is flexible enough to accomodate a spatial lag model that,
+ besides the spatial lag of the dependent variable, includes other
+ non-spatial endogenous regressors. As an example, we will assume that
+ CRIME is actually endogenous and we decide to instrument for it with
+ DISCBD (distance to the CBD). We reload the X including INC only and
+ define CRIME as endogenous and DISCBD as instrument:
+
+ >>> X = np.array(db.by_col("INC"))
+ >>> X = np.reshape(X, (49,1))
+ >>> yd = np.array(db.by_col("CRIME"))
+ >>> yd = np.reshape(yd, (49,1))
+ >>> q = np.array(db.by_col("DISCBD"))
+ >>> q = np.reshape(q, (49,1))
+
+ And we can run the model again:
+
+ >>> reg=GM_Lag(y, X, w=w, yend=yd, q=q, w_lags=2, name_x=['inc'], name_y='hoval', name_yend=['crime'], name_q=['discbd'], name_ds='columbus')
+ >>> reg.betas
+ array([[ 100.79359082],
+ [ -0.50215501],
+ [ -1.14881711],
+ [ -0.38235022]])
+
+ Once the model is run, we can obtain the standard error of the coefficient
+ estimates by calling the diagnostics module:
+
+ >>> D.se_betas(reg)
+ array([ 53.0829123 , 1.02511494, 0.57589064, 0.59891744])
+
+ """
+
+ def __init__(self, y, x, yend=None, q=None,
+ w=None, w_lags=1, lag_q=True,
+ robust=None, gwk=None, sig2n_k=False,
+ spat_diag=False,
+ vm=False, name_y=None, name_x=None,
+ name_yend=None, name_q=None,
+ name_w=None, name_gwk=None, name_ds=None):
+
+ n = USER.check_arrays(x, yend, q)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ USER.check_robust(robust, gwk)
+ yend2, q2 = set_endog(y, x, w, yend, q, w_lags, lag_q)
+ x_constant = USER.check_constant(x)
+ BaseGM_Lag.__init__(
+ self, y=y, x=x_constant, w=w.sparse, yend=yend2, q=q2,
+ w_lags=w_lags, robust=robust, gwk=gwk,
+ lag_q=lag_q, sig2n_k=sig2n_k)
+ self.rho = self.betas[-1]
+ self.predy_e, self.e_pred, warn = sp_att(w, self.y, self.predy,
+ yend2[:, -1].reshape(self.n, 1), self.rho)
+ set_warn(self, warn)
+ self.title = "SPATIAL TWO STAGE LEAST SQUARES"
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_y = USER.set_name_y(name_y)
+ self.name_x = USER.set_name_x(name_x, x)
+ self.name_yend = USER.set_name_yend(name_yend, yend)
+ self.name_yend.append(USER.set_name_yend_sp(self.name_y))
+ self.name_z = self.name_x + self.name_yend
+ self.name_q = USER.set_name_q(name_q, q)
+ self.name_q.extend(
+ USER.set_name_q_sp(self.name_x, w_lags, self.name_q, lag_q))
+ self.name_h = USER.set_name_h(self.name_x, self.name_q)
+ self.robust = USER.set_robust(robust)
+ self.name_w = USER.set_name_w(name_w, w)
+ self.name_gwk = USER.set_name_w(name_gwk, gwk)
+ SUMMARY.GM_Lag(reg=self, w=w, vm=vm, spat_diag=spat_diag)
+
+
+def _test():
+ import doctest
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ doctest.testmod()
+ np.set_printoptions(suppress=start_suppress)
+
+
+if __name__ == '__main__':
+ _test()
+
+ import numpy as np
+ import pysal
+ db = pysal.open(pysal.examples.get_path("columbus.dbf"), 'r')
+ y_var = 'CRIME'
+ y = np.array([db.by_col(y_var)]).reshape(49, 1)
+ x_var = ['INC']
+ x = np.array([db.by_col(name) for name in x_var]).T
+ yd_var = ['HOVAL']
+ yd = np.array([db.by_col(name) for name in yd_var]).T
+ q_var = ['DISCBD']
+ q = np.array([db.by_col(name) for name in q_var]).T
+ w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ w.transform = 'r'
+ model = GM_Lag(
+ y, x, yd, q, w=w, spat_diag=True, name_y=y_var, name_x=x_var,
+ name_yend=yd_var, name_q=q_var, name_ds='columbus', name_w='columbus.gal')
+ print model.summary
diff --git a/pysal/spreg/twosls_sp_regimes.py b/pysal/spreg/twosls_sp_regimes.py
new file mode 100644
index 0000000..dd99d90
--- /dev/null
+++ b/pysal/spreg/twosls_sp_regimes.py
@@ -0,0 +1,705 @@
+'''
+Spatial Two Stages Least Squares with Regimes
+'''
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, Pedro V. Amaral pedro.amaral at asu.edu, David C. Folch david.folch at asu.edu"
+
+import numpy as np
+import pysal
+import regimes as REGI
+import user_output as USER
+import summary_output as SUMMARY
+import multiprocessing as mp
+from twosls_regimes import TSLS_Regimes, _optimal_weight
+from twosls import BaseTSLS
+from utils import set_endog, set_endog_sparse, sp_att, set_warn, sphstack, spdot
+from robust import hac_multi
+from platform import system
+
+
+class GM_Lag_Regimes(TSLS_Regimes, REGI.Regimes_Frame):
+
+ """
+ Spatial two stage least squares (S2SLS) with regimes;
+ Anselin (1988) [1]_
+
+ Parameters
+ ----------
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, excluding the constant
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable to use as instruments (note:
+ this should not contain any variables from x); cannot be
+ used in combination with h
+ constant_regi: ['one', 'many']
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime (default)
+ cols2regi : list, 'all'
+ Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all' (default), all the variables vary by regime.
+ w : pysal W object
+ Spatial weights object
+ w_lags : integer
+ Orders of W to include as instruments for the spatially
+ lagged dependent variable. For example, w_lags=1, then
+ instruments are WX; if w_lags=2, then WX, WWX; and so on.
+ lag_q : boolean
+ If True, then include spatial lags of the additional
+ instruments (q).
+ regime_lag_sep: boolean
+ If True (default), the spatial parameter for spatial lag is also
+ computed according to different regimes. If False,
+ the spatial parameter is fixed accross regimes.
+ Option valid only when regime_err_sep=True
+ regime_err_sep: boolean
+ If True, a separate regression is run for each regime.
+ robust : string
+ If 'white', then a White consistent estimator of the
+ variance-covariance matrix is given.
+ If 'hac', then a HAC consistent estimator of the
+ variance-covariance matrix is given.
+ If 'ogmm', then Optimal GMM is used to estimate
+ betas and the variance-covariance matrix.
+ Default set to None.
+ gwk : pysal W object
+ Kernel spatial weights needed for HAC estimation. Note:
+ matrix must have ones along the main diagonal.
+ sig2n_k : boolean
+ If True, then use n-k to estimate sigma^2. If False, use n.
+ spat_diag : boolean
+ If True, then compute Anselin-Kelejian test
+ vm : boolean
+ If True, include variance-covariance matrix in summary
+ results
+ cores : boolean
+ Specifies if multiprocessing is to be used
+ Default: no multiprocessing, cores = False
+ Note: Multiprocessing may not work on all platforms.
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_q : list of strings
+ Names of instruments for use in output
+ name_w : string
+ Name of weights matrix for use in output
+ name_gwk : string
+ Name of kernel weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regimes variable for use in output
+
+ Attributes
+ ----------
+ summary : string
+ Summary of regression results and diagnostics (note: use in
+ conjunction with the print command)
+ betas : array
+ kx1 array of estimated coefficients
+ u : array
+ nx1 array of residuals
+ e_pred : array
+ nx1 array of residuals (using reduced form)
+ predy : array
+ nx1 array of predicted y values
+ predy_e : array
+ nx1 array of predicted y values (using reduced form)
+ n : integer
+ Number of observations
+ k : integer
+ Number of variables for which coefficients are estimated
+ (including the constant)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ kstar : integer
+ Number of endogenous variables.
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ y : array
+ nx1 array for dependent variable
+ x : array
+ Two dimensional array with n rows and one column for each
+ independent (exogenous) variable, including the constant
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ yend : array
+ Two dimensional array with n rows and one column for each
+ endogenous variable
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ q : array
+ Two dimensional array with n rows and one column for each
+ external exogenous variable used as instruments
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ z : array
+ nxk array of variables (combination of x and yend)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ h : array
+ nxl array of instruments (combination of x and q)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ robust : string
+ Adjustment for robust standard errors
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ mean_y : float
+ Mean of dependent variable
+ std_y : float
+ Standard deviation of dependent variable
+ vm : array
+ Variance covariance matrix (kxk)
+ pr2 : float
+ Pseudo R squared (squared correlation between y and ypred)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ pr2_e : float
+ Pseudo R squared (squared correlation between y and ypred_e
+ (using reduced form))
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ utu : float
+ Sum of squared residuals
+ sig2 : float
+ Sigma squared used in computations
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ std_err : array
+ 1xk array of standard errors of the betas
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ z_stat : list of tuples
+ z statistic; each tuple contains the pair (statistic,
+ p-value), where each is a float
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ ak_test : tuple
+ Anselin-Kelejian test; tuple contains the pair (statistic,
+ p-value)
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ name_y : string
+ Name of dependent variable for use in output
+ name_x : list of strings
+ Names of independent variables for use in output
+ name_yend : list of strings
+ Names of endogenous variables for use in output
+ name_z : list of strings
+ Names of exogenous and endogenous variables for use in
+ output
+ name_q : list of strings
+ Names of external instruments
+ name_h : list of strings
+ Names of all instruments used in ouput
+ name_w : string
+ Name of weights matrix for use in output
+ name_gwk : string
+ Name of kernel weights matrix for use in output
+ name_ds : string
+ Name of dataset for use in output
+ name_regimes : string
+ Name of regimes variable for use in output
+ title : string
+ Name of the regression method used
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ sig2n : float
+ Sigma squared (computed with n in the denominator)
+ sig2n_k : float
+ Sigma squared (computed with n-k in the denominator)
+ hth : float
+ H'H
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ hthi : float
+ (H'H)^-1
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ varb : array
+ (Z'H (H'H)^-1 H'Z)^-1
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ zthhthi : array
+ Z'H(H'H)^-1
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ pfora1a2 : array
+ n(zthhthi)'varb
+ Only available in dictionary 'multi' when multiple regressions
+ (see 'multi' below for details)
+ regimes : list
+ List of n values with the mapping of each
+ observation to a regime. Assumed to be aligned with 'x'.
+ constant_regi: ['one', 'many']
+ Ignored if regimes=False. Constant option for regimes.
+ Switcher controlling the constant term setup. It may take
+ the following values:
+ * 'one': a vector of ones is appended to x and held
+ constant across regimes
+ * 'many': a vector of ones is appended to x and considered
+ different per regime
+ cols2regi : list, 'all'
+ Ignored if regimes=False. Argument indicating whether each
+ column of x should be considered as different per regime
+ or held constant across regimes (False).
+ If a list, k booleans indicating for each variable the
+ option (True if one per regime, False to be held constant).
+ If 'all', all the variables vary by regime.
+ regime_lag_sep : boolean
+ If True, the spatial parameter for spatial lag is also
+ computed according to different regimes. If False (default),
+ the spatial parameter is fixed accross regimes.
+ regime_err_sep : boolean
+ If True, a separate regression is run for each regime.
+ kr : int
+ Number of variables/columns to be "regimized" or subject
+ to change by regime. These will result in one parameter
+ estimate by regime for each variable (i.e. nr parameters per
+ variable)
+ kf : int
+ Number of variables/columns to be considered fixed or
+ global across regimes and hence only obtain one parameter
+ estimate
+ nr : int
+ Number of different regimes in the 'regimes' list
+ multi : dictionary
+ Only available when multiple regressions are estimated,
+ i.e. when regime_err_sep=True and no variable is fixed
+ across regimes.
+ Contains all attributes of each individual regression
+
+ References
+ ----------
+
+ .. [1] Anselin, L. (1988) "Spatial Econometrics: Methods and Models".
+ Kluwer Academic Publishers. Dordrecht.
+
+ Examples
+ --------
+
+ We first need to import the needed modules, namely numpy to convert the
+ data we read into arrays that ``spreg`` understands and ``pysal`` to
+ perform all the analysis.
+
+ >>> import numpy as np
+ >>> import pysal
+
+ Open data on NCOVR US County Homicides (3085 areas) using pysal.open().
+ This is the DBF associated with the NAT shapefile. Note that
+ pysal.open() also reads data in CSV format; since the actual class
+ requires data to be passed in as numpy arrays, the user can read their
+ data in using any method.
+
+ >>> db = pysal.open(pysal.examples.get_path("NAT.dbf"),'r')
+
+ Extract the HR90 column (homicide rates in 1990) from the DBF file and make it the
+ dependent variable for the regression. Note that PySAL requires this to be
+ an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+ that other packages accept.
+
+ >>> y_var = 'HR90'
+ >>> y = np.array([db.by_col(y_var)]).reshape(3085,1)
+
+ Extract UE90 (unemployment rate) and PS90 (population structure) vectors from
+ the DBF to be used as independent variables in the regression. Other variables
+ can be inserted by adding their names to x_var, such as x_var = ['Var1','Var2','...]
+ Note that PySAL requires this to be an nxj numpy array, where j is the
+ number of independent variables (not including a constant). By default
+ this model adds a vector of ones to the independent variables passed in.
+
+ >>> x_var = ['PS90','UE90']
+ >>> x = np.array([db.by_col(name) for name in x_var]).T
+
+ The different regimes in this data are given according to the North and
+ South dummy (SOUTH).
+
+ >>> r_var = 'SOUTH'
+ >>> regimes = db.by_col(r_var)
+
+ Since we want to run a spatial lag model, we need to specify
+ the spatial weights matrix that includes the spatial configuration of the
+ observations. To do that, we can open an already existing gal file or
+ create a new one. In this case, we will create one from ``NAT.shp``.
+
+ >>> w = pysal.rook_from_shapefile(pysal.examples.get_path("NAT.shp"))
+
+ Unless there is a good reason not to do it, the weights have to be
+ row-standardized so every row of the matrix sums to one. Among other
+ things, this allows to interpret the spatial lag of a variable as the
+ average value of the neighboring observations. In PySAL, this can be
+ easily performed in the following way:
+
+ >>> w.transform = 'r'
+
+ This class runs a lag model, which means that includes the spatial lag of
+ the dependent variable on the right-hand side of the equation. If we want
+ to have the names of the variables printed in the output summary, we will
+ have to pass them in as well, although this is optional.
+
+ >>> model=GM_Lag_Regimes(y, x, regimes, w=w, regime_lag_sep=False, regime_err_sep=False, name_y=y_var, name_x=x_var, name_regimes=r_var, name_ds='NAT', name_w='NAT.shp')
+ >>> model.betas
+ array([[ 1.28897623],
+ [ 0.79777722],
+ [ 0.56366891],
+ [ 8.73327838],
+ [ 1.30433406],
+ [ 0.62418643],
+ [-0.39993716]])
+
+ Once the model is run, we can have a summary of the output by typing:
+ model.summary . Alternatively, we can obtain the standard error of
+ the coefficient estimates by calling:
+
+ >>> model.std_err
+ array([ 0.44682888, 0.14358192, 0.05655124, 1.06044865, 0.20184548,
+ 0.06118262, 0.12387232])
+
+ In the example above, all coefficients but the spatial lag vary
+ according to the regime. It is also possible to have the spatial lag
+ varying according to the regime, which effective will result in an
+ independent spatial lag model estimated for each regime. To run these
+ models, the argument regime_lag_sep must be set to True:
+
+ >>> model=GM_Lag_Regimes(y, x, regimes, w=w, regime_lag_sep=True, name_y=y_var, name_x=x_var, name_regimes=r_var, name_ds='NAT', name_w='NAT.shp')
+ >>> print np.hstack((np.array(model.name_z).reshape(8,1),model.betas,np.sqrt(model.vm.diagonal().reshape(8,1))))
+ [['0_CONSTANT' '1.36584769' '0.39854720']
+ ['0_PS90' '0.80875730' '0.11324884']
+ ['0_UE90' '0.56946813' '0.04625087']
+ ['0_W_HR90' '-0.4342438' '0.13350159']
+ ['1_CONSTANT' '7.90731073' '1.63601874']
+ ['1_PS90' '1.27465703' '0.24709870']
+ ['1_UE90' '0.60167693' '0.07993322']
+ ['1_W_HR90' '-0.2960338' '0.19934459']]
+
+ Alternatively, we can type: 'model.summary' to see the organized results output.
+ The class is flexible enough to accomodate a spatial lag model that,
+ besides the spatial lag of the dependent variable, includes other
+ non-spatial endogenous regressors. As an example, we will add the endogenous
+ variable RD90 (resource deprivation) and we decide to instrument for it with
+ FP89 (families below poverty):
+
+ >>> yd_var = ['RD90']
+ >>> yd = np.array([db.by_col(name) for name in yd_var]).T
+ >>> q_var = ['FP89']
+ >>> q = np.array([db.by_col(name) for name in q_var]).T
+
+ And we can run the model again:
+
+ >>> model = GM_Lag_Regimes(y, x, regimes, yend=yd, q=q, w=w, regime_lag_sep=False, regime_err_sep=False, name_y=y_var, name_x=x_var, name_yend=yd_var, name_q=q_var, name_regimes=r_var, name_ds='NAT', name_w='NAT.shp')
+ >>> model.betas
+ array([[ 3.42195202],
+ [ 1.03311878],
+ [ 0.14308741],
+ [ 8.99740066],
+ [ 1.91877758],
+ [-0.32084816],
+ [ 2.38918212],
+ [ 3.67243761],
+ [ 0.06959139]])
+
+ Once the model is run, we can obtain the standard error of the coefficient
+ estimates. Alternatively, we can have a summary of the output by typing:
+ model.summary
+
+ >>> model.std_err
+ array([ 0.49163311, 0.12237382, 0.05633464, 0.72555909, 0.17250521,
+ 0.06749131, 0.27370369, 0.25106224, 0.05804213])
+ """
+
+ def __init__(self, y, x, regimes, yend=None, q=None,
+ w=None, w_lags=1, lag_q=True,
+ robust=None, gwk=None, sig2n_k=False,
+ spat_diag=False, constant_regi='many',
+ cols2regi='all', regime_lag_sep=False, regime_err_sep=True,
+ cores=False, vm=False, name_y=None, name_x=None,
+ name_yend=None, name_q=None, name_regimes=None,
+ name_w=None, name_gwk=None, name_ds=None):
+
+ n = USER.check_arrays(y, x)
+ USER.check_y(y, n)
+ USER.check_weights(w, y, w_required=True)
+ USER.check_robust(robust, gwk)
+ USER.check_spat_diag(spat_diag, w)
+ name_x = USER.set_name_x(name_x, x, constant=True)
+ name_y = USER.set_name_y(name_y)
+ name_yend = USER.set_name_yend(name_yend, yend)
+ name_q = USER.set_name_q(name_q, q)
+ name_q.extend(
+ USER.set_name_q_sp(name_x, w_lags, name_q, lag_q, force_all=True))
+ self.name_regimes = USER.set_name_ds(name_regimes)
+ self.constant_regi = constant_regi
+ self.n = n
+ cols2regi = REGI.check_cols2regi(
+ constant_regi, cols2regi, x, yend=yend, add_cons=False)
+ self.cols2regi = cols2regi
+ self.regimes_set = REGI._get_regimes_set(regimes)
+ self.regimes = regimes
+ USER.check_regimes(self.regimes_set, self.n, x.shape[1])
+ if regime_err_sep == True and robust == 'hac':
+ set_warn(
+ self, "Error by regimes is incompatible with HAC estimation for Spatial Lag models. Hence, error and lag by regimes have been disabled for this model.")
+ regime_err_sep = False
+ regime_lag_sep = False
+ self.regime_err_sep = regime_err_sep
+ self.regime_lag_sep = regime_lag_sep
+ if regime_lag_sep == True:
+ if not regime_err_sep:
+ raise Exception, "regime_err_sep must be True when regime_lag_sep=True."
+ cols2regi += [True]
+ w_i, regi_ids, warn = REGI.w_regimes(
+ w, regimes, self.regimes_set, transform=True, get_ids=True, min_n=len(cols2regi) + 1)
+ set_warn(self, warn)
+
+ else:
+ cols2regi += [False]
+
+ if regime_err_sep == True and set(cols2regi) == set([True]) and constant_regi == 'many':
+ self.y = y
+ self.GM_Lag_Regimes_Multi(y, x, w_i, w, regi_ids,
+ yend=yend, q=q, w_lags=w_lags, lag_q=lag_q, cores=cores,
+ robust=robust, gwk=gwk, sig2n_k=sig2n_k, cols2regi=cols2regi,
+ spat_diag=spat_diag, vm=vm, name_y=name_y, name_x=name_x,
+ name_yend=name_yend, name_q=name_q, name_regimes=self.name_regimes,
+ name_w=name_w, name_gwk=name_gwk, name_ds=name_ds)
+ else:
+ if regime_lag_sep == True:
+ w = REGI.w_regimes_union(w, w_i, self.regimes_set)
+ yend2, q2 = set_endog(y, x, w, yend, q, w_lags, lag_q)
+ name_yend.append(USER.set_name_yend_sp(name_y))
+ TSLS_Regimes.__init__(self, y=y, x=x, yend=yend2, q=q2,
+ regimes=regimes, w=w, robust=robust, gwk=gwk,
+ sig2n_k=sig2n_k, spat_diag=spat_diag, vm=vm,
+ constant_regi=constant_regi, cols2regi=cols2regi, regime_err_sep=regime_err_sep,
+ name_y=name_y, name_x=name_x, name_yend=name_yend, name_q=name_q,
+ name_regimes=name_regimes, name_w=name_w, name_gwk=name_gwk,
+ name_ds=name_ds, summ=False)
+ if regime_lag_sep:
+ self.sp_att_reg(w_i, regi_ids, yend2[:, -1].reshape(self.n, 1))
+ else:
+ self.rho = self.betas[-1]
+ self.predy_e, self.e_pred, warn = sp_att(w, self.y, self.predy,
+ yend2[:, -1].reshape(self.n, 1), self.rho)
+ set_warn(self, warn)
+ self.regime_lag_sep = regime_lag_sep
+ self.title = "SPATIAL " + self.title
+ SUMMARY.GM_Lag(
+ reg=self, w=w, vm=vm, spat_diag=spat_diag, regimes=True)
+
+ def GM_Lag_Regimes_Multi(self, y, x, w_i, w, regi_ids, cores=False,
+ yend=None, q=None, w_lags=1, lag_q=True,
+ robust=None, gwk=None, sig2n_k=False, cols2regi='all',
+ spat_diag=False, vm=False, name_y=None, name_x=None,
+ name_yend=None, name_q=None, name_regimes=None,
+ name_w=None, name_gwk=None, name_ds=None):
+ # pool = mp.Pool(cores)
+ self.name_ds = USER.set_name_ds(name_ds)
+ name_x = USER.set_name_x(name_x, x)
+ name_yend.append(USER.set_name_yend_sp(name_y))
+ self.name_w = USER.set_name_w(name_w, w_i)
+ self.name_gwk = USER.set_name_w(name_gwk, gwk)
+ results_p = {}
+ """
+ for r in self.regimes_set:
+ w_r = w_i[r].sparse
+ if system() == 'Windows':
+ is_win = True
+ results_p[r] = _work(*(y,x,regi_ids,r,yend,q,w_r,w_lags,lag_q,robust,sig2n_k,self.name_ds,name_y,name_x,name_yend,name_q,self.name_w,name_regimes))
+ else:
+ results_p[r] = pool.apply_async(_work,args=(y,x,regi_ids,r,yend,q,w_r,w_lags,lag_q,robust,sig2n_k,self.name_ds,name_y,name_x,name_yend,name_q,self.name_w,name_regimes, ))
+ is_win = False
+ """
+ for r in self.regimes_set:
+ w_r = w_i[r].sparse
+ if cores:
+ pool = mp.Pool(None)
+ results_p[r] = pool.apply_async(_work, args=(
+ y, x, regi_ids, r, yend, q, w_r, w_lags, lag_q, robust, sig2n_k, self.name_ds, name_y, name_x, name_yend, name_q, self.name_w, name_regimes, ))
+ else:
+ results_p[r] = _work(*(y, x, regi_ids, r, yend, q, w_r, w_lags, lag_q, robust,
+ sig2n_k, self.name_ds, name_y, name_x, name_yend, name_q, self.name_w, name_regimes))
+
+ self.kryd = 0
+ self.kr = len(cols2regi) + 1
+ self.kf = 0
+ self.nr = len(self.regimes_set)
+ self.name_x_r = name_x + name_yend
+ self.name_regimes = name_regimes
+ self.vm = np.zeros((self.nr * self.kr, self.nr * self.kr), float)
+ self.betas = np.zeros((self.nr * self.kr, 1), float)
+ self.u = np.zeros((self.n, 1), float)
+ self.predy = np.zeros((self.n, 1), float)
+ self.predy_e = np.zeros((self.n, 1), float)
+ self.e_pred = np.zeros((self.n, 1), float)
+ """
+ if not is_win:
+ pool.close()
+ pool.join()
+ """
+ if cores:
+ pool.close()
+ pool.join()
+ results = {}
+ self.name_y, self.name_x, self.name_yend, self.name_q, self.name_z, self.name_h = [
+ ], [], [], [], [], []
+ counter = 0
+ for r in self.regimes_set:
+ """
+ if is_win:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+ """
+ if not cores:
+ results[r] = results_p[r]
+ else:
+ results[r] = results_p[r].get()
+ results[r].predy_e, results[r].e_pred, warn = sp_att(w_i[r], results[r].y, results[
+ r].predy, results[r].yend[:, -1].reshape(results[r].n, 1), results[r].rho)
+ set_warn(results[r], warn)
+ results[r].w = w_i[r]
+ self.vm[(counter * self.kr):((counter + 1) * self.kr),
+ (counter * self.kr):((counter + 1) * self.kr)] = results[r].vm
+ self.betas[
+ (counter * self.kr):((counter + 1) * self.kr), ] = results[r].betas
+ self.u[regi_ids[r], ] = results[r].u
+ self.predy[regi_ids[r], ] = results[r].predy
+ self.predy_e[regi_ids[r], ] = results[r].predy_e
+ self.e_pred[regi_ids[r], ] = results[r].e_pred
+ self.name_y += results[r].name_y
+ self.name_x += results[r].name_x
+ self.name_yend += results[r].name_yend
+ self.name_q += results[r].name_q
+ self.name_z += results[r].name_z
+ self.name_h += results[r].name_h
+ if r == self.regimes_set[0]:
+ self.hac_var = np.zeros((self.n, results[r].h.shape[1]), float)
+ self.hac_var[regi_ids[r], ] = results[r].h
+ counter += 1
+ self.multi = results
+ if robust == 'hac':
+ hac_multi(self, gwk, constant=True)
+ if robust == 'ogmm':
+ set_warn(
+ self, "Residuals treated as homoskedastic for the purpose of diagnostics.")
+ self.chow = REGI.Chow(self)
+ if spat_diag:
+ pass
+ #self._get_spat_diag_props(y, x, w, yend, q, w_lags, lag_q)
+ SUMMARY.GM_Lag_multi(
+ reg=self, multireg=self.multi, vm=vm, spat_diag=spat_diag, regimes=True, w=w)
+
+ def sp_att_reg(self, w_i, regi_ids, wy):
+ predy_e_r, e_pred_r = {}, {}
+ self.predy_e = np.zeros((self.n, 1), float)
+ self.e_pred = np.zeros((self.n, 1), float)
+ counter = 1
+ for r in self.regimes_set:
+ self.rho = self.betas[(self.kr - self.kryd) * self.nr + self.kf - (
+ self.yend.shape[1] - self.nr * self.kryd) + self.kryd * counter - 1]
+ self.predy_e[regi_ids[r], ], self.e_pred[regi_ids[r], ], warn = sp_att(w_i[r],
+ self.y[regi_ids[r]], self.predy[
+ regi_ids[r]],
+ wy[regi_ids[r]], self.rho)
+ counter += 1
+
+ def _get_spat_diag_props(self, y, x, w, yend, q, w_lags, lag_q):
+ self._cache = {}
+ yend, q = set_endog(y, x, w, yend, q, w_lags, lag_q)
+ x = USER.check_constant(x)
+ x = REGI.regimeX_setup(
+ x, self.regimes, [True] * x.shape[1], self.regimes_set)
+ self.z = sphstack(x, REGI.regimeX_setup(
+ yend, self.regimes, [True] * (yend.shape[1] - 1) + [False], self.regimes_set))
+ self.h = sphstack(
+ x, REGI.regimeX_setup(q, self.regimes, [True] * q.shape[1], self.regimes_set))
+ hthi = np.linalg.inv(spdot(self.h.T, self.h))
+ zth = spdot(self.z.T, self.h)
+ self.varb = np.linalg.inv(spdot(spdot(zth, hthi), zth.T))
+
+
+def _work(y, x, regi_ids, r, yend, q, w_r, w_lags, lag_q, robust, sig2n_k, name_ds, name_y, name_x, name_yend, name_q, name_w, name_regimes):
+ y_r = y[regi_ids[r]]
+ x_r = x[regi_ids[r]]
+ if yend != None:
+ yend_r = yend[regi_ids[r]]
+ else:
+ yend_r = yend
+ if q != None:
+ q_r = q[regi_ids[r]]
+ else:
+ q_r = q
+ yend_r, q_r = set_endog_sparse(y_r, x_r, w_r, yend_r, q_r, w_lags, lag_q)
+ x_constant = USER.check_constant(x_r)
+ if robust == 'hac' or robust == 'ogmm':
+ robust2 = None
+ else:
+ robust2 = robust
+ model = BaseTSLS(
+ y_r, x_constant, yend_r, q_r, robust=robust2, sig2n_k=sig2n_k)
+ model.title = "SPATIAL TWO STAGE LEAST SQUARES ESTIMATION - REGIME %s" % r
+ if robust == 'ogmm':
+ _optimal_weight(model, sig2n_k, warn=False)
+ model.rho = model.betas[-1]
+ model.robust = USER.set_robust(robust)
+ model.name_ds = name_ds
+ model.name_y = '%s_%s' % (str(r), name_y)
+ model.name_x = ['%s_%s' % (str(r), i) for i in name_x]
+ model.name_yend = ['%s_%s' % (str(r), i) for i in name_yend]
+ model.name_z = model.name_x + model.name_yend
+ model.name_q = ['%s_%s' % (str(r), i) for i in name_q]
+ model.name_h = model.name_x + model.name_q
+ model.name_w = name_w
+ model.name_regimes = name_regimes
+ return model
+
+
+def _test():
+ import doctest
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ doctest.testmod()
+ np.set_printoptions(suppress=start_suppress)
+
+
+if __name__ == '__main__':
+ _test()
+ import numpy as np
+ import pysal
+ db = pysal.open(pysal.examples.get_path("columbus.dbf"), 'r')
+ y_var = 'CRIME'
+ y = np.array([db.by_col(y_var)]).reshape(49, 1)
+ x_var = ['INC']
+ x = np.array([db.by_col(name) for name in x_var]).T
+ yd_var = ['HOVAL']
+ yd = np.array([db.by_col(name) for name in yd_var]).T
+ q_var = ['DISCBD']
+ q = np.array([db.by_col(name) for name in q_var]).T
+ r_var = 'NSA'
+ regimes = db.by_col(r_var)
+ w = pysal.queen_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ w.transform = 'r'
+ model = GM_Lag_Regimes(y, x, regimes, yend=yd, q=q, w=w, constant_regi='many', spat_diag=True, sig2n_k=False, lag_q=True, name_y=y_var,
+ name_x=x_var, name_yend=yd_var, name_q=q_var, name_regimes=r_var, name_ds='columbus', name_w='columbus.gal', regime_err_sep=True, robust='white')
+ print model.summary
diff --git a/pysal/spreg/user_output.py b/pysal/spreg/user_output.py
new file mode 100644
index 0000000..a8dd35d
--- /dev/null
+++ b/pysal/spreg/user_output.py
@@ -0,0 +1,642 @@
+"""Internal helper files for user output."""
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, David C. Folch david.folch at asu.edu, Jing Yao jingyao at asu.edu"
+import textwrap as TW
+import numpy as np
+import copy as COPY
+import diagnostics as diagnostics
+import diagnostics_tsls as diagnostics_tsls
+import diagnostics_sp as diagnostics_sp
+import pysal
+import scipy
+from scipy.sparse.csr import csr_matrix
+from utils import spdot, sphstack
+
+__all__ = []
+
+
+def set_name_ds(name_ds):
+ """Set the dataset name in regression; return generic name if user
+ provides no explicit name."
+
+ Parameters
+ ----------
+
+ name_ds : string
+ User provided dataset name.
+
+ Returns
+ -------
+
+ name_ds : string
+
+ """
+ if not name_ds:
+ name_ds = 'unknown'
+ return name_ds
+
+
+def set_name_y(name_y):
+ """Set the dataset name in regression; return generic name if user
+ provides no explicit name."
+
+ Parameters
+ ----------
+
+ name_ds : string
+ User provided dataset name.
+
+ Returns
+ -------
+
+ name_ds : string
+
+ """
+ if not name_y:
+ name_y = 'dep_var'
+ return name_y
+
+
+def set_name_x(name_x, x, constant=False):
+ """Set the independent variable names in regression; return generic name if user
+ provides no explicit name."
+
+ Parameters
+ ----------
+
+ name_x : list of string
+ User provided exogenous variable names.
+
+ x : array
+ User provided exogenous variables.
+ constant : boolean
+ If False (default), constant name not included in name_x list yet
+ Append 'CONSTANT' at the front of the names
+
+ Returns
+ -------
+
+ name_x : list of strings
+
+ """
+ if not name_x:
+ name_x = ['var_' + str(i + 1) for i in range(x.shape[1])]
+ else:
+ name_x = name_x[:]
+ if not constant:
+ name_x.insert(0, 'CONSTANT')
+ return name_x
+
+
+def set_name_yend(name_yend, yend):
+ """Set the endogenous variable names in regression; return generic name if user
+ provides no explicit name."
+
+ Parameters
+ ----------
+
+ name_yend : list of strings
+ User provided exogenous variable names.
+
+ Returns
+ -------
+
+ name_yend : list of strings
+
+ """
+ if yend != None:
+ if not name_yend:
+ return ['endogenous_' + str(i + 1) for i in range(len(yend[0]))]
+ else:
+ return name_yend[:]
+ else:
+ return []
+
+
+def set_name_q(name_q, q):
+ """Set the external instrument names in regression; return generic name if user
+ provides no explicit name."
+
+ Parameters
+ ----------
+
+ name_q : string
+ User provided instrument names.
+ q : array
+ Array of instruments
+
+ Returns
+ -------
+
+ name_q : list of strings
+
+ """
+ if q != None:
+ if not name_q:
+ return ['instrument_' + str(i + 1) for i in range(len(q[0]))]
+ else:
+ return name_q[:]
+ else:
+ return []
+
+
+def set_name_yend_sp(name_y):
+ """Set the spatial lag name in regression; return generic name if user
+ provides no explicit name."
+
+ Parameters
+ ----------
+
+ name_y : string
+ User provided dependent variable name.
+
+ Returns
+ -------
+
+ name_yend_sp : string
+
+ """
+ return 'W_' + name_y
+
+
+def set_name_q_sp(name_x, w_lags, name_q, lag_q, force_all=False):
+ """Set the spatial instrument names in regression; return generic name if user
+ provides no explicit name."
+
+ Parameters
+ ----------
+
+ name_x : list of strings
+ User provided exogenous variable names.
+
+ w_lags : int
+ User provided number of spatial instruments lags
+
+ Returns
+ -------
+
+ name_q_sp : list of strings
+
+ """
+ if force_all:
+ names = name_x
+ else:
+ names = name_x[1:] # drop the constant
+ if lag_q:
+ names = names + name_q
+ sp_inst_names = []
+ for j in names:
+ sp_inst_names.append('W_' + j)
+ if w_lags > 1:
+ for i in range(2, w_lags + 1):
+ for j in names:
+ sp_inst_names.append('W' + str(i) + '_' + j)
+ return sp_inst_names
+
+
+def set_name_h(name_x, name_q):
+ """Set the full instruments names in regression; return generic name if user
+ provides no explicit name."
+
+ Parameters
+ ----------
+
+ name_x : list of strings
+ User provided exogenous variable names.
+ name_q : list of strings
+ User provided instrument variable names.
+
+ Returns
+ -------
+
+ name_h : list of strings
+
+ """
+ return name_x + name_q
+
+
+def set_robust(robust):
+ """Return generic name if user passes None to the robust parameter in a
+ regression. Note: already verified that the name is valid in
+ check_robust() if the user passed anything besides None to robust.
+
+ Parameters
+ ----------
+
+ robust : string or None
+ Object passed by the user to a regression class
+
+ Returns
+ -------
+
+ robust : string
+
+ """
+ if not robust:
+ return 'unadjusted'
+ return robust
+
+
+def set_name_w(name_w, w):
+ """Return generic name if user passes None to the robust parameter in a
+ regression. Note: already verified that the name is valid in
+ check_robust() if the user passed anything besides None to robust.
+
+ Parameters
+ ----------
+
+ name_w : string
+ Name passed in by user. Default is None.
+ w : W object
+ pysal W object passed in by user
+
+ Returns
+ -------
+
+ name_w : string
+
+ """
+ if w != None:
+ if name_w != None:
+ return name_w
+ else:
+ return 'unknown'
+ return None
+
+
+def set_name_multi(multireg, multi_set, name_multiID, y, x, name_y, name_x, name_ds, title, name_w, robust, endog=False, sp_lag=False):
+ """Returns multiple regression objects with generic names
+
+ Parameters
+ ----------
+
+ endog : tuple
+ If the regression object contains endogenous variables, endog must have the
+ following parameters in the following order: (yend, q, name_yend, name_q)
+ sp_lag : tuple
+ If the regression object contains spatial lag, sp_lag must have the
+ following parameters in the following order: (w_lags, lag_q)
+
+ """
+ name_ds = set_name_ds(name_ds)
+ name_y = set_name_y(name_y)
+ name_x = set_name_x(name_x, x)
+ name_multiID = set_name_ds(name_multiID)
+ if endog or sp_lag:
+ name_yend = set_name_yend(endog[2], endog[0])
+ name_q = set_name_q(endog[3], endog[1])
+ for r in multi_set:
+ multireg[r].title = title + "%s" % r
+ multireg[r].name_ds = name_ds
+ multireg[r].robust = set_robust(robust)
+ multireg[r].name_w = name_w
+ multireg[r].name_y = '%s_%s' % (str(r), name_y)
+ multireg[r].name_x = ['%s_%s' % (str(r), i) for i in name_x]
+ multireg[r].name_multiID = name_multiID
+ if endog or sp_lag:
+ multireg[r].name_yend = ['%s_%s' % (str(r), i) for i in name_yend]
+ multireg[r].name_q = ['%s_%s' % (str(r), i) for i in name_q]
+ if sp_lag:
+ multireg[r].name_yend.append(
+ set_name_yend_sp(multireg[r].name_y))
+ multireg[r].name_q.extend(
+ set_name_q_sp(multireg[r].name_x, sp_lag[0], multireg[r].name_q, sp_lag[1]))
+ multireg[r].name_z = multireg[r].name_x + multireg[r].name_yend
+ multireg[r].name_h = multireg[r].name_x + multireg[r].name_q
+ return multireg
+
+
+def check_arrays(*arrays):
+ """Check if the objects passed by a user to a regression class are
+ correctly structured. If the user's data is correctly formed this function
+ returns nothing, if not then an exception is raised. Note, this does not
+ check for model setup, simply the shape and types of the objects.
+
+ Parameters
+ ----------
+
+ *arrays : anything
+ Objects passed by the user to a regression class; any type
+ object can be passed and any number of objects can be passed
+
+ Returns
+ -------
+
+ Returns : int
+ number of observations
+
+ Examples
+ --------
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> # Extract CRIME column from the dbf file
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+ >>> n = check_arrays(y, X)
+ >>> print n
+ 49
+
+ """
+ allowed = ['ndarray', 'csr_matrix']
+ rows = []
+ for i in arrays:
+ if i == None:
+ continue
+ if i.__class__.__name__ not in allowed:
+ raise Exception, "all input data must be either numpy arrays or sparse csr matrices"
+ shape = i.shape
+ if len(shape) > 2:
+ raise Exception, "all input arrays must have exactly two dimensions"
+ if len(shape) == 1:
+ raise Exception, "all input arrays must have exactly two dimensions"
+ if shape[0] < shape[1]:
+ raise Exception, "one or more input arrays have more columns than rows"
+ rows.append(shape[0])
+ if len(set(rows)) > 1:
+ raise Exception, "arrays not all of same length"
+ return rows[0]
+
+
+def check_y(y, n):
+ """Check if the y object passed by a user to a regression class is
+ correctly structured. If the user's data is correctly formed this function
+ returns nothing, if not then an exception is raised. Note, this does not
+ check for model setup, simply the shape and types of the objects.
+
+ Parameters
+ ----------
+
+ y : anything
+ Object passed by the user to a regression class; any type
+ object can be passed
+
+ n : int
+ number of observations
+
+ Returns
+ -------
+
+ Returns : nothing
+ Nothing is returned
+
+ Examples
+ --------
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> # Extract CRIME column from the dbf file
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+ >>> check_y(y, 49)
+ >>> # should not raise an exception
+
+ """
+ if y.__class__.__name__ != 'ndarray':
+ print y.__class__.__name__
+ raise Exception, "y must be a numpy array"
+ shape = y.shape
+ if len(shape) > 2:
+ raise Exception, "all input arrays must have exactly two dimensions"
+ if len(shape) == 1:
+ raise Exception, "all input arrays must have exactly two dimensions"
+ if shape != (n, 1):
+ raise Exception, "y must be a single column array matching the length of other arrays"
+
+
+def check_weights(w, y, w_required=False):
+ """Check if the w parameter passed by the user is a pysal.W object and
+ check that its dimensionality matches the y parameter. Note that this
+ check is not performed if w set to None.
+
+ Parameters
+ ----------
+
+ w : any python object
+ Object passed by the user to a regression class; any type
+ object can be passed
+ y : numpy array
+ Any shape numpy array can be passed. Note: if y passed
+ check_arrays, then it will be valid for this function
+
+ Returns
+ -------
+
+ Returns : nothing
+ Nothing is returned
+
+ Examples
+ --------
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> # Extract CRIME column from the dbf file
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+ >>> w = pysal.open(pysal.examples.get_path("columbus.gal"), 'r').read()
+ >>> check_weights(w, y)
+ >>> # should not raise an exception
+
+ """
+ if w_required == True or w != None:
+ if w == None:
+ raise Exception, "A weights matrix w must be provided to run this method."
+ if not isinstance(w, pysal.W):
+ raise Exception, "w must be a pysal.W object"
+ if w.n != y.shape[0]:
+ raise Exception, "y must be nx1, and w must be an nxn PySAL W object"
+ diag = w.sparse.diagonal()
+ # check to make sure all entries equal 0
+ if diag.min() != 0:
+ raise Exception, "All entries on diagonal must equal 0."
+ if diag.max() != 0:
+ raise Exception, "All entries on diagonal must equal 0."
+
+
+def check_robust(robust, wk):
+ """Check if the combination of robust and wk parameters passed by the user
+ are valid. Note: this does not check if the W object is a valid adaptive
+ kernel weights matrix needed for the HAC.
+
+ Parameters
+ ----------
+
+ robust : string or None
+ Object passed by the user to a regression class
+ w : any python object
+ Object passed by the user to a regression class; any type
+ object can be passed
+
+ Returns
+ -------
+
+ Returns : nothing
+ Nothing is returned
+
+ Examples
+ --------
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> # Extract CRIME column from the dbf file
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+ >>> wk = None
+ >>> check_robust('White', wk)
+ >>> # should not raise an exception
+
+ """
+ if robust:
+ if robust.lower() == 'hac':
+ if type(wk).__name__ != 'W' and type(wk).__name__ != 'Kernel':
+ raise Exception, "HAC requires that wk be a pysal.W object"
+ diag = wk.sparse.diagonal()
+ # check to make sure all entries equal 1
+ if diag.min() < 1.0:
+ print diag.min()
+ raise Exception, "All entries on diagonal of kernel weights matrix must equal 1."
+ if diag.max() > 1.0:
+ print diag.max()
+ raise Exception, "All entries on diagonal of kernel weights matrix must equal 1."
+ # ensure off-diagonal entries are in the set of real numbers [0,1)
+ wegt = wk.weights
+ for i in wk.id_order:
+ vals = wegt[i]
+ vmin = min(vals)
+ vmax = max(vals)
+ if vmin < 0.0:
+ raise Exception, "Off-diagonal entries must be greater than or equal to 0."
+ if vmax > 1.0:
+ # NOTE: we are not checking for the case of exactly 1.0 ###
+ raise Exception, "Off-diagonal entries must be less than 1."
+ elif robust.lower() == 'white' or robust.lower() == 'ogmm':
+ if wk:
+ raise Exception, "White requires that wk be set to None"
+ else:
+ raise Exception, "invalid value passed to robust, see docs for valid options"
+
+
+def check_spat_diag(spat_diag, w):
+ """Check if there is a w parameter passed by the user if the user also
+ requests spatial diagnostics.
+
+ Parameters
+ ----------
+
+ spat_diag : boolean
+ Value passed by a used to a regression class
+ w : any python object
+ Object passed by the user to a regression class; any type
+ object can be passed
+
+ Returns
+ -------
+
+ Returns : nothing
+ Nothing is returned
+
+ Examples
+ --------
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> # Extract CRIME column from the dbf file
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+ >>> w = pysal.open(pysal.examples.get_path("columbus.gal"), 'r').read()
+ >>> check_spat_diag(True, w)
+ >>> # should not raise an exception
+
+ """
+ if spat_diag:
+ if type(w).__name__ != 'W':
+ raise Exception, "w must be a pysal.W object to run spatial diagnostics"
+
+
+def check_regimes(reg_set, N=None, K=None):
+ """Check if there are at least two regimes
+
+ Parameters
+ ----------
+
+ reg_set : list
+ List of the regimes IDs
+
+ Returns
+ -------
+
+ Returns : nothing
+ Nothing is returned
+
+ """
+ if len(reg_set) < 2:
+ raise Exception, "At least 2 regimes are needed to run regimes methods. Please check your regimes variable."
+ if 1.0 * N / len(reg_set) < K + 1:
+ raise Exception, "There aren't enough observations for the given number of regimes and variables. Please check your regimes variable."
+
+
+def check_constant(x):
+ """Check if the X matrix contains a constant, raise exception if it does
+ not
+
+ Parameters
+ ----------
+
+ x : array
+ Value passed by a used to a regression class
+
+ Returns
+ -------
+
+ Returns : nothing
+ Nothing is returned
+
+ Examples
+ --------
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> X = []
+ >>> X.append(db.by_col("INC"))
+ >>> X.append(db.by_col("HOVAL"))
+ >>> X = np.array(X).T
+ >>> x_constant = check_constant(X)
+ >>> x_constant.shape
+ (49, 3)
+
+ """
+ if not diagnostics.constant_check:
+ raise Exception, "x array cannot contain a constant vector; constant will be added automatically"
+ else:
+ x_constant = COPY.copy(x)
+ return sphstack(np.ones((x_constant.shape[0], 1)), x_constant)
+
+
+def _test():
+ import doctest
+ doctest.testmod()
+
+if __name__ == '__main__':
+ _test()
diff --git a/pysal/spreg/utils.py b/pysal/spreg/utils.py
new file mode 100644
index 0000000..ec87f20
--- /dev/null
+++ b/pysal/spreg/utils.py
@@ -0,0 +1,839 @@
+"""
+Tools for different procedure estimations
+"""
+
+__author__ = "Luc Anselin luc.anselin at asu.edu, \
+ Pedro V. Amaral pedro.amaral at asu.edu, \
+ David C. Folch david.folch at asu.edu, \
+ Daniel Arribas-Bel darribas at asu.edu"
+
+import numpy as np
+from scipy import sparse as SP
+import scipy.optimize as op
+import numpy.linalg as la
+from pysal import lag_spatial
+import copy
+
+
+class RegressionPropsY:
+
+ """
+ Helper class that adds common regression properties to any regression
+ class that inherits it. It takes no parameters. See BaseOLS for example
+ usage.
+
+ Parameters
+ ----------
+
+ Attributes
+ ----------
+ mean_y : float
+ Mean of the dependent variable
+ std_y : float
+ Standard deviation of the dependent variable
+
+ """
+
+ @property
+ def mean_y(self):
+ if 'mean_y' not in self._cache:
+ self._cache['mean_y'] = np.mean(self.y)
+ return self._cache['mean_y']
+
+ @property
+ def std_y(self):
+ if 'std_y' not in self._cache:
+ self._cache['std_y'] = np.std(self.y, ddof=1)
+ return self._cache['std_y']
+
+
+class RegressionPropsVM:
+
+ """
+ Helper class that adds common regression properties to any regression
+ class that inherits it. It takes no parameters. See BaseOLS for example
+ usage.
+
+ Parameters
+ ----------
+
+ Attributes
+ ----------
+ utu : float
+ Sum of the squared residuals
+ sig2n : float
+ Sigma squared with n in the denominator
+ sig2n_k : float
+ Sigma squared with n-k in the denominator
+ vm : array
+ Variance-covariance matrix (kxk)
+
+ """
+
+ @property
+ def utu(self):
+ if 'utu' not in self._cache:
+ self._cache['utu'] = np.sum(self.u ** 2)
+ return self._cache['utu']
+
+ @property
+ def sig2n(self):
+ if 'sig2n' not in self._cache:
+ self._cache['sig2n'] = self.utu / self.n
+ return self._cache['sig2n']
+
+ @property
+ def sig2n_k(self):
+ if 'sig2n_k' not in self._cache:
+ self._cache['sig2n_k'] = self.utu / (self.n - self.k)
+ return self._cache['sig2n_k']
+
+ @property
+ def vm(self):
+ if 'vm' not in self._cache:
+ self._cache['vm'] = np.dot(self.sig2, self.xtxi)
+ return self._cache['vm']
+
+
+def get_A1_het(S):
+ """
+ Builds A1 as in Arraiz et al [1]_
+
+ .. math::
+
+ A_1 = W' W - diag(w'_{.i} w_{.i})
+
+ ...
+
+ Parameters
+ ----------
+
+ S : csr_matrix
+ PySAL W object converted into Scipy sparse matrix
+
+ Returns
+ -------
+
+ Implicit : csr_matrix
+ A1 matrix in scipy sparse format
+
+ References
+ ----------
+
+ .. [1] Arraiz, I., Drukker, D. M., Kelejian, H., Prucha, I. R. (2010) "A
+ Spatial Cliff-Ord-Type Model with Heteroskedastic Innovations: Small and
+ Large Sample Results". Journal of Regional Science, Vol. 60, No. 2, pp.
+ 592-614.
+ """
+ StS = S.T * S
+ d = SP.spdiags([StS.diagonal()], [0], S.get_shape()[0], S.get_shape()[1])
+ d = d.asformat('csr')
+ return StS - d
+
+
+def get_A1_hom(s, scalarKP=False):
+ """
+ Builds A1 for the spatial error GM estimation with homoscedasticity as in Drukker et al. [1]_ (p. 9).
+
+ .. math::
+
+ A_1 = \{1 + [n^{-1} tr(W'W)]^2\}^{-1} \[W'W - n^{-1} tr(W'W) I\]
+
+ ...
+
+ Parameters
+ ----------
+
+ s : csr_matrix
+ PySAL W object converted into Scipy sparse matrix
+ scalarKP : boolean
+ Flag to include scalar corresponding to the first moment
+ condition as in Drukker et al. [1]_ (Defaults to False)
+
+ Returns
+ -------
+
+ Implicit : csr_matrix
+ A1 matrix in scipy sparse format
+ References
+ ----------
+
+ .. [1] Drukker, Prucha, I. R., Raciborski, R. (2010) "A command for
+ estimating spatial-autoregressive models with spatial-autoregressive
+ disturbances and additional endogenous variables". The Stata Journal, 1,
+ N. 1, pp. 1-13.
+ """
+ n = float(s.shape[0])
+ wpw = s.T * s
+ twpw = np.sum(wpw.diagonal())
+ e = SP.eye(n, n, format='csr')
+ e.data = np.ones(n) * (twpw / n)
+ num = wpw - e
+ if not scalarKP:
+ return num
+ else:
+ den = 1. + (twpw / n) ** 2.
+ return num / den
+
+
+def get_A2_hom(s):
+ """
+ Builds A2 for the spatial error GM estimation with homoscedasticity as in
+ Anselin (2011) [1]_
+
+ .. math::
+
+ A_2 = \dfrac{(W + W')}{2}
+
+ ...
+
+ Parameters
+ ----------
+ s : csr_matrix
+ PySAL W object converted into Scipy sparse matrix
+ Returns
+ -------
+ Implicit : csr_matrix
+ A2 matrix in scipy sparse format
+ References
+ ----------
+
+ .. [1] Anselin (2011) "GMM Estimation of Spatial Error Autocorrelation with and without Heteroskedasticity".
+ """
+ return (s + s.T) / 2.
+
+
+def _moments2eqs(A1, s, u):
+ '''
+ Helper to compute G and g in a system of two equations as in
+ the heteroskedastic error models from Drukker et al. [1]_
+ ...
+
+ Parameters
+ ----------
+
+ A1 : scipy.sparse.csr
+ A1 matrix as in the paper, different deppending on whether
+ it's homocedastic or heteroskedastic model
+
+ s : W.sparse
+ Sparse representation of spatial weights instance
+
+ u : array
+ Residuals. nx1 array assumed to be aligned with w
+
+ Attributes
+ ----------
+
+ moments : list
+ List of two arrays corresponding to the matrices 'G' and
+ 'g', respectively.
+
+
+ References
+ ----------
+
+ .. [1] Drukker, Prucha, I. R., Raciborski, R. (2010) "A command for
+ estimating spatial-autoregressive models with spatial-autoregressive
+ disturbances and additional endogenous variables". The Stata Journal, 1,
+ N. 1, pp. 1-13.
+ '''
+ n = float(s.shape[0])
+ A1u = A1 * u
+ wu = s * u
+ g1 = np.dot(u.T, A1u)
+ g2 = np.dot(u.T, wu)
+ g = np.array([[g1][0][0], [g2][0][0]]) / n
+
+ G11 = np.dot(u.T, ((A1 + A1.T) * wu))
+ G12 = -np.dot((wu.T * A1), wu)
+ G21 = np.dot(u.T, ((s + s.T) * wu))
+ G22 = -np.dot(wu.T, (s * wu))
+ G = np.array([[G11[0][0], G12[0][0]], [G21[0][0], G22[0][0]]]) / n
+ return [G, g]
+
+
+def optim_moments(moments_in, vcX=np.array([0])):
+ """
+ Optimization of moments
+ ...
+
+ Parameters
+ ----------
+
+ moments : Moments
+ Instance of gmm_utils.moments_het with G and g
+ vcX : array
+ Optional. 2x2 array with the Variance-Covariance matrix to be used as
+ weights in the optimization (applies Cholesky
+ decomposition). Set empty by default.
+
+ Returns
+ -------
+ x, f, d : tuple
+ x -- position of the minimum
+ f -- value of func at the minimum
+ d -- dictionary of information from routine
+ d['warnflag'] is
+ 0 if converged
+ 1 if too many function evaluations
+ 2 if stopped for another reason, given in d['task']
+ d['grad'] is the gradient at the minimum (should be 0 ish)
+ d['funcalls'] is the number of function calls made
+ """
+ moments = copy.deepcopy(moments_in)
+ if vcX.any():
+ Ec = np.transpose(la.cholesky(la.inv(vcX)))
+ moments[0] = np.dot(Ec, moments_in[0])
+ moments[1] = np.dot(Ec, moments_in[1])
+ scale = np.min([[np.min(moments[0]), np.min(moments[1])]])
+ moments[0], moments[1] = moments[0] / scale, moments[1] / scale
+ if moments[0].shape[0] == 2:
+ optim_par = lambda par: foptim_par(
+ np.array([[float(par[0]), float(par[0]) ** 2.]]).T, moments)
+ start = [0.0]
+ bounds = [(-1.0, 1.0)]
+ if moments[0].shape[0] == 3:
+ optim_par = lambda par: foptim_par(
+ np.array([[float(par[0]), float(par[0]) ** 2., float(par[1])]]).T, moments)
+ start = [0.0, 0.0]
+ bounds = [(-1.0, 1.0), (0.0, None)]
+ lambdaX = op.fmin_l_bfgs_b(
+ optim_par, start, approx_grad=True, bounds=bounds)
+ return lambdaX[0][0]
+
+
+def foptim_par(par, moments):
+ """
+ Preparation of the function of moments for minimization
+ ...
+
+ Parameters
+ ----------
+
+ lambdapar : float
+ Spatial autoregressive parameter
+ moments : list
+ List of Moments with G (moments[0]) and g (moments[1])
+
+ Returns
+ -------
+
+ minimum : float
+ sum of square residuals (e) of the equation system
+ moments.g - moments.G * lambdapar = e
+ """
+ vv = np.dot(moments[0], par)
+ vv2 = moments[1] - vv
+ return sum(vv2 ** 2)
+
+
+def get_spFilter(w, lamb, sf):
+ '''
+ Compute the spatially filtered variables
+
+ Parameters
+ ----------
+ w : weight
+ PySAL weights instance
+ lamb : double
+ spatial autoregressive parameter
+ sf : array
+ the variable needed to compute the filter
+ Returns
+ --------
+ rs : array
+ spatially filtered variable
+
+ Examples
+ --------
+
+ >>> import numpy as np
+ >>> import pysal
+ >>> db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+ >>> y = np.array(db.by_col("CRIME"))
+ >>> y = np.reshape(y, (49,1))
+ >>> w=pysal.open(pysal.examples.get_path("columbus.gal")).read()
+ >>> solu = get_spFilter(w,0.5,y)
+ >>> print solu[0:5]
+ [[ -8.9882875]
+ [ -20.5685065]
+ [ -28.196721 ]
+ [ -36.9051915]
+ [-111.1298 ]]
+
+ '''
+ try:
+ result = sf - lamb * (w.sparse * sf)
+ except:
+ result = sf - lamb * (w * sf)
+ return result
+
+
+def get_lags(w, x, w_lags):
+ '''
+ Calculates a given order of spatial lags and all the smaller orders
+
+ Parameters
+ ----------
+ w : weight
+ PySAL weights instance
+ x : array
+ nxk arrays with the variables to be lagged
+ w_lags : integer
+ Maximum order of spatial lag
+
+ Returns
+ --------
+ rs : array
+ nxk*(w_lags+1) array with original and spatially lagged variables
+
+ '''
+ lag = lag_spatial(w, x)
+ spat_lags = lag
+ for i in range(w_lags - 1):
+ lag = lag_spatial(w, lag)
+ spat_lags = sphstack(spat_lags, lag)
+ return spat_lags
+
+
+def inverse_prod(w, data, scalar, post_multiply=False, inv_method="power_exp", threshold=0.0000000001, max_iterations=None):
+ """
+
+ Parameters
+ ----------
+
+ w : Pysal W object
+ nxn Pysal spatial weights object
+
+ data : Numpy array
+ nx1 vector of data
+
+ scalar : float
+ Scalar value (typically rho or lambda)
+
+ post_multiply : boolean
+ If True then post-multiplies the data vector by the
+ inverse of the spatial filter, if false then
+ pre-multiplies.
+ inv_method : string
+ If "true_inv" uses the true inverse of W (slow);
+ If "power_exp" uses the power expansion method (default)
+
+ threshold : float
+ Test value to stop the iterations. Test is against
+ sqrt(increment' * increment), where increment is a
+ vector representing the contribution from each
+ iteration.
+
+ max_iterations : integer
+ Maximum number of iterations for the expansion.
+
+ Examples
+ --------
+
+ >>> import numpy, pysal
+ >>> import numpy.linalg as la
+ >>> np.random.seed(10)
+ >>> w = pysal.lat2W(5, 5)
+ >>> w.transform = 'r'
+ >>> data = np.random.randn(w.n)
+ >>> data.shape = (w.n, 1)
+ >>> rho = 0.4
+ >>> inv_pow = inverse_prod(w, data, rho, inv_method="power_exp")
+ >>> # true matrix inverse
+ >>> inv_reg = inverse_prod(w, data, rho, inv_method="true_inv")
+ >>> np.allclose(inv_pow, inv_reg, atol=0.0001)
+ True
+ >>> # test the transpose version
+ >>> inv_pow = inverse_prod(w, data, rho, inv_method="power_exp", post_multiply=True)
+ >>> inv_reg = inverse_prod(w, data, rho, inv_method="true_inv", post_multiply=True)
+ >>> np.allclose(inv_pow, inv_reg, atol=0.0001)
+ True
+
+ """
+ if inv_method == "power_exp":
+ inv_prod = power_expansion(
+ w, data, scalar, post_multiply=post_multiply,
+ threshold=threshold, max_iterations=max_iterations)
+ elif inv_method == "true_inv":
+ try:
+ matrix = la.inv(np.eye(w.n) - (scalar * w.full()[0]))
+ except:
+ matrix = la.inv(np.eye(w.shape[0]) - (scalar * w))
+ if post_multiply:
+ inv_prod = spdot(data.T, matrix)
+ else:
+ inv_prod = spdot(matrix, data)
+ else:
+ raise Exception, "Invalid method selected for inversion."
+ return inv_prod
+
+
+def power_expansion(w, data, scalar, post_multiply=False, threshold=0.0000000001, max_iterations=None):
+ """
+ Compute the inverse of a matrix using the power expansion (Leontief
+ expansion). General form is:
+
+ .. math::
+ x &= (I - \rho W)^{-1}v = [I + \rho W + \rho^2 WW + \dots]v \\
+ &= v + \rho Wv + \rho^2 WWv + \dots
+
+ Examples
+ --------
+ Tests for this function are in inverse_prod()
+
+ """
+ try:
+ ws = w.sparse
+ except:
+ ws = w
+ if post_multiply:
+ data = data.T
+ running_total = copy.copy(data)
+ increment = copy.copy(data)
+ count = 1
+ test = 10000000
+ if max_iterations == None:
+ max_iterations = 10000000
+ while test > threshold and count <= max_iterations:
+ if post_multiply:
+ increment = increment * ws * scalar
+ else:
+ increment = ws * increment * scalar
+ running_total += increment
+ test_old = test
+ test = la.norm(increment)
+ if test > test_old:
+ raise Exception, "power expansion will not converge, check model specification and that weight are less than 1"
+ count += 1
+ return running_total
+
+
+def set_endog(y, x, w, yend, q, w_lags, lag_q):
+ # Create spatial lag of y
+ yl = lag_spatial(w, y)
+ # spatial and non-spatial instruments
+ if issubclass(type(yend), np.ndarray):
+ if lag_q:
+ lag_vars = sphstack(x, q)
+ else:
+ lag_vars = x
+ spatial_inst = get_lags(w, lag_vars, w_lags)
+ q = sphstack(q, spatial_inst)
+ yend = sphstack(yend, yl)
+ elif yend == None: # spatial instruments only
+ q = get_lags(w, x, w_lags)
+ yend = yl
+ else:
+ raise Exception, "invalid value passed to yend"
+ return yend, q
+
+ lag = lag_spatial(w, x)
+ spat_lags = lag
+ for i in range(w_lags - 1):
+ lag = lag_spatial(w, lag)
+ spat_lags = sphstack(spat_lags, lag)
+ return spat_lags
+
+
+def set_endog_sparse(y, x, w, yend, q, w_lags, lag_q):
+ """
+ Same as set_endog, but with a sparse object passed as weights instead of W object.
+ """
+ yl = w * y
+ # spatial and non-spatial instruments
+ if issubclass(type(yend), np.ndarray):
+ if lag_q:
+ lag_vars = sphstack(x, q)
+ else:
+ lag_vars = x
+ spatial_inst = w * lag_vars
+ for i in range(w_lags - 1):
+ spatial_inst = sphstack(spatial_inst, w * spatial_inst)
+ q = sphstack(q, spatial_inst)
+ yend = sphstack(yend, yl)
+ elif yend == None: # spatial instruments only
+ q = w * x
+ for i in range(w_lags - 1):
+ q = sphstack(q, w * q)
+ yend = yl
+ else:
+ raise Exception, "invalid value passed to yend"
+ return yend, q
+
+
+def iter_msg(iteration, max_iter):
+ if iteration == max_iter:
+ iter_stop = "Maximum number of iterations reached."
+ else:
+ iter_stop = "Convergence threshold (epsilon) reached."
+ return iter_stop
+
+
+def sp_att(w, y, predy, w_y, rho):
+ xb = predy - rho * w_y
+ if np.abs(rho) < 1:
+ predy_sp = inverse_prod(w, xb, rho)
+ warn = None
+ # Note 1: Here if omitting pseudo-R2; If not, see Note 2.
+ resid_sp = y - predy_sp
+ else:
+ #warn = "Warning: Estimate for rho is outside the boundary (-1, 1). Computation of true inverse of W was required (slow)."
+ #predy_sp = inverse_prod(w, xb, rho, inv_method="true_inv")
+ warn = "*** WARNING: Estimate for spatial lag coefficient is outside the boundary (-1, 1). ***"
+ predy_sp = np.zeros(y.shape, float)
+ resid_sp = np.zeros(y.shape, float)
+ # resid_sp = y - predy_sp #Note 2: Here if computing true inverse; If not,
+ # see Note 1.
+ return predy_sp, resid_sp, warn
+
+
+def spdot(a, b, array_out=True):
+ """
+ Matrix multiplication function to deal with sparse and dense objects
+
+ Parameters
+ ----------
+
+ a : array
+ first multiplication factor. Can either be sparse or dense.
+ b : array
+ second multiplication factor. Can either be sparse or dense.
+ array_out : boolean
+ If True (default) the output object is always a np.array
+
+ Returns
+ -------
+
+ ab : array
+ product of a times b. Sparse if a and b are sparse. Dense otherwise.
+ """
+ if type(a).__name__ == 'ndarray' and type(b).__name__ == 'ndarray':
+ ab = np.dot(a, b)
+ elif type(a).__name__ == 'csr_matrix' or type(b).__name__ == 'csr_matrix' \
+ or type(a).__name__ == 'csc_matrix' or type(b).__name__ == 'csc_matrix':
+ ab = a * b
+ if array_out:
+ if type(ab).__name__ == 'csc_matrix' or type(ab).__name__ == 'csr_matrix':
+ ab = ab.toarray()
+ else:
+ raise Exception, "Invalid format for 'spdot' argument: %s and %s" % (
+ type(a).__name__, type(b).__name__)
+ return ab
+
+
+def spmultiply(a, b, array_out=True):
+ """
+ Element-wise multiplication function to deal with sparse and dense
+ objects. Both objects must be of the same type.
+
+ Parameters
+ ----------
+
+ a : array
+ first multiplication factor. Can either be sparse or dense.
+ b : array
+ second multiplication factor. Can either be sparse or dense.
+ integer.
+ array_out : boolean
+ If True (default) the output object is always a np.array
+
+ Returns
+ -------
+
+ ab : array
+ elementwise multiplied object. Sparse if a is sparse. Dense otherwise.
+ """
+ if type(a).__name__ == 'ndarray' and type(b).__name__ == 'ndarray':
+ ab = a * b
+ elif (type(a).__name__ == 'csr_matrix' or type(a).__name__ == 'csc_matrix') \
+ and (type(b).__name__ == 'csr_matrix' or type(b).__name__ == 'csc_matrix'):
+ ab = a.multiply(b)
+ if array_out:
+ if type(ab).__name__ == 'csc_matrix' or type(ab).__name__ == 'csr_matrix':
+ ab = ab.toarray()
+ else:
+ raise Exception, "Invalid format for 'spmultiply' argument: %s and %s" % (
+ type(a).__name__, type(b).__name__)
+ return ab
+
+
+def sphstack(a, b, array_out=False):
+ """
+ Horizontal stacking of vectors (or matrices) to deal with sparse and dense objects
+
+ Parameters
+ ----------
+
+ a : array or sparse matrix
+ First object.
+ b : array or sparse matrix
+ Object to be stacked next to a
+ array_out : boolean
+ If True the output object is a np.array; if False (default)
+ the output object is an np.array if both inputs are
+ arrays or CSR matrix if at least one input is a CSR matrix
+
+ Returns
+ -------
+
+ ab : array or sparse matrix
+ Horizontally stacked objects
+ """
+ if type(a).__name__ == 'ndarray' and type(b).__name__ == 'ndarray':
+ ab = np.hstack((a, b))
+ elif type(a).__name__ == 'csr_matrix' or type(b).__name__ == 'csr_matrix':
+ ab = SP.hstack((a, b), format='csr')
+ if array_out:
+ if type(ab).__name__ == 'csr_matrix':
+ ab = ab.toarray()
+ else:
+ raise Exception, "Invalid format for 'sphstack' argument: %s and %s" % (
+ type(a).__name__, type(b).__name__)
+ return ab
+
+
+def spbroadcast(a, b, array_out=False):
+ """
+ Element-wise multiplication of a matrix and vector to deal with sparse
+ and dense objects
+
+ Parameters
+ ----------
+
+ a : array or sparse matrix
+ Object with one or more columns.
+ b : array
+ Object with only one column
+ array_out : boolean
+ If True the output object is a np.array; if False (default)
+ the output object is an np.array if both inputs are
+ arrays or CSR matrix if at least one input is a CSR matrix
+
+ Returns
+ -------
+
+ ab : array or sparse matrix
+ Element-wise multiplication of a and b
+ """
+ if type(a).__name__ == 'ndarray' and type(b).__name__ == 'ndarray':
+ ab = a * b
+ elif type(a).__name__ == 'csr_matrix':
+ b_mod = SP.lil_matrix((b.shape[0], b.shape[0]))
+ b_mod.setdiag(b)
+ ab = (a.T * b_mod).T
+ if array_out:
+ if type(ab).__name__ == 'csr_matrix':
+ ab = ab.toarray()
+ else:
+ raise Exception, "Invalid format for 'spbroadcast' argument: %s and %s" % (
+ type(a).__name__, type(b).__name__)
+ return ab
+
+
+def spmin(a):
+ """
+ Minimum value in a matrix or vector to deal with sparse and dense objects
+
+ Parameters
+ ----------
+
+ a : array or sparse matrix
+ Object with one or more columns.
+
+ Returns
+ -------
+
+ min a : int or float
+ minimum value in a
+ """
+
+ if type(a).__name__ == 'ndarray':
+ return a.min()
+ elif type(a).__name__ == 'csr_matrix' or type(a).__name__ == 'csc_matrix':
+ try:
+ return min(a.data)
+ except:
+ if np.sum(a.data) == 0:
+ return 0
+ else:
+ raise Exception, "Error: could not evaluate the minimum value."
+ else:
+ raise Exception, "Invalid format for 'spmultiply' argument: %s and %s" % (
+ type(a).__name__, type(b).__name__)
+
+
+def spmax(a):
+ """
+ Maximum value in a matrix or vector to deal with sparse and dense objects
+
+ Parameters
+ ----------
+
+ a : array or sparse matrix
+ Object with one or more columns.
+
+ Returns
+ -------
+
+ max a : int or float
+ maximum value in a
+ """
+ if type(a).__name__ == 'ndarray':
+ return a.max()
+ elif type(a).__name__ == 'csr_matrix' or type(a).__name__ == 'csc_matrix':
+ try:
+ return max(a.data)
+ except:
+ if np.sum(a.data) == 0:
+ return 0
+ else:
+ raise Exception, "Error: could not evaluate the maximum value."
+ else:
+ raise Exception, "Invalid format for 'spmultiply' argument: %s and %s" % (
+ type(a).__name__, type(b).__name__)
+
+
+def set_warn(reg, warn):
+ ''' Groups warning messages for printout. '''
+ if warn:
+ try:
+ reg.warning += "Warning: " + warn + "\n"
+ except:
+ reg.warning = "Warning: " + warn + "\n"
+ else:
+ pass
+
+
+def RegressionProps_basic(reg, betas=None, predy=None, u=None, sig2=None, sig2n_k=None, vm=None):
+ ''' Set props based on arguments passed. '''
+ if betas != None:
+ reg.betas = betas
+ if predy != None:
+ reg.predy = predy
+ else:
+ try:
+ reg.predy = spdot(reg.z, reg.betas)
+ except:
+ reg.predy = spdot(reg.x, reg.betas)
+ if u != None:
+ reg.u = u
+ else:
+ reg.u = reg.y - reg.predy
+ if sig2 != None:
+ reg.sig2 = sig2
+ elif sig2n_k:
+ reg.sig2 = np.sum(reg.u ** 2) / (reg.n - reg.k)
+ else:
+ reg.sig2 = np.sum(reg.u ** 2) / reg.n
+ if vm != None:
+ reg.vm = vm
+
+
+def _test():
+ import doctest
+ doctest.testmod()
+
+if __name__ == '__main__':
+ _test()
diff --git a/pysal/spreg/w_utils.py b/pysal/spreg/w_utils.py
new file mode 100644
index 0000000..e0bf4fa
--- /dev/null
+++ b/pysal/spreg/w_utils.py
@@ -0,0 +1,27 @@
+import numpy as np
+import pysal as ps
+import scipy.sparse as SPARSE
+
+
+def symmetrize(w):
+ """Generate symmetric matrix that has same eigenvalues as an asymmetric row
+ standardized matrix w
+
+ Parameters
+ ----------
+ w: weights object that has been row standardized
+
+ Returns
+ -------
+ a sparse symmetric matrix with same eigenvalues as w
+
+ """
+ current = w.transform
+ w.transform = 'B'
+ d = w.sparse.sum(axis=1) # row sum
+ d.shape = (w.n,)
+ d = np.sqrt(d)
+ Di12 = SPARSE.spdiags(1. / d, [0], w.n, w.n)
+ D12 = SPARSE.spdiags(d, [0], w.n, w.n)
+ w.transform = 'r'
+ return D12 * w.sparse * Di12
diff --git a/pysal/test_NameSpace.py b/pysal/test_NameSpace.py
new file mode 100644
index 0000000..4ed7b8b
--- /dev/null
+++ b/pysal/test_NameSpace.py
@@ -0,0 +1,51 @@
+import os
+import unittest
+import pysal
+
+
+class TestNameSpace(unittest.TestCase):
+ """
+ This test makes sure we don't remove anything from the pysal NameSpace that
+ 1.0 users might expect to be there. 1.0 Namespace was taken from the 1.1
+ Code sprint wave, with special names removes (__all__, etc)
+ """
+ def test_contents(self):
+ namespace_v1_0 = ['Box_Plot', 'DistanceBand', 'Equal_Interval',
+ 'Fisher_Jenks', 'Geary', 'Jenks_Caspall',
+ 'Jenks_Caspall_Forced', 'Jenks_Caspall_Sampled',
+ 'Join_Counts', 'K_classifiers', 'Kernel',
+ 'LISA_Markov', 'Markov', 'Max_P_Classifier',
+ 'Maximum_Breaks', 'Maxp', 'Maxp_LISA', 'Moran',
+ 'Moran_BV', 'Moran_BV_matrix', 'Moran_Local',
+ 'Natural_Breaks', 'Percentiles', 'Quantiles',
+ 'SpatialTau', 'Spatial_Markov', 'Std_Mean', 'Theil',
+ 'TheilD', 'TheilDSim', 'Theta', 'User_Defined', 'W', 'adaptive_kernelW',
+ 'adaptive_kernelW_from_shapefile', 'bin', 'bin1d',
+ 'binC', 'buildContiguity', 'cg', 'comb', 'common',
+ 'core', 'directional', 'ergodic', 'esda', 'full',
+ 'gadf', 'higher_order', 'inequality', 'kernelW',
+ 'kernelW_from_shapefile', 'knnW', 'knnW_from_array',
+ 'knnW_from_shapefile', 'lag_spatial', 'lat2W',
+ 'min_threshold_dist_from_shapefile', 'open',
+ 'order', 'quantile', 'queen_from_shapefile',
+ 'block_weights', 'region', 'remap_ids',
+ 'rook_from_shapefile', 'shimbel', 'spatial_dynamics',
+ 'threshold_binaryW_from_array', 'threshold_binaryW_from_shapefile',
+ 'threshold_continuousW_from_array', 'threshold_continuousW_from_shapefile',
+ 'version', 'w_difference', 'w_intersection', 'w_subset',
+ 'w_symmetric_difference', 'w_union', 'weights']
+
+ current_namespace = dir(pysal)
+ for item in namespace_v1_0:
+ self.assertTrue(item in current_namespace)
+ for item in current_namespace:
+ if item not in namespace_v1_0 and not item.startswith('__'):
+ print item, "added to name space"
+
+
+suite = unittest.TestLoader().loadTestsFromTestCase(TestNameSpace)
+
+if __name__ == '__main__':
+ unittest.main()
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/version.py b/pysal/version.py
new file mode 100644
index 0000000..7022797
--- /dev/null
+++ b/pysal/version.py
@@ -0,0 +1 @@
+version = "1.9.1"
diff --git a/pysal/weights/Contiguity.py b/pysal/weights/Contiguity.py
new file mode 100644
index 0000000..7a3e66d
--- /dev/null
+++ b/pysal/weights/Contiguity.py
@@ -0,0 +1,97 @@
+"""
+Contiguity based spatial weights
+"""
+
+__author__ = "Sergio J. Rey <srey at asu.edu> "
+__all__ = ['buildContiguity']
+
+import pysal
+from _contW_binning import ContiguityWeights_binning as ContiguityWeights
+from _contW_binning import ContiguityWeightsPolygons
+
+
+WT_TYPE = {'rook': 2, 'queen': 1} # for _contW_Binning
+
+
+def buildContiguity(polygons, criterion="rook", ids=None):
+ """
+ Build contiguity weights from a source
+
+ Parameters
+ ----------
+
+ polygons : an instance of a pysal geo file handler
+ Any thing returned by pysal.open that is explicitly polygons
+ criterion : string
+ contiguity criterion ("rook","queen")
+ ids : list
+ identifiers for i,j
+
+ Returns
+ -------
+
+ w : W instance
+ Contiguity weights object
+
+ Examples
+ --------
+
+ >>> w = buildContiguity(pysal.open(pysal.examples.get_path('10740.shp'),'r'))
+ WARNING: there is one disconnected observation (no neighbors)
+ Island id: [163]
+ >>> w[0]
+ {1: 1.0, 4: 1.0, 101: 1.0, 85: 1.0, 5: 1.0}
+ >>> w = buildContiguity(pysal.open(pysal.examples.get_path('10740.shp'),'r'),criterion='queen')
+ WARNING: there is one disconnected observation (no neighbors)
+ Island id: [163]
+ >>> w.pct_nonzero
+ 0.031926364234056544
+ >>> w = buildContiguity(pysal.open(pysal.examples.get_path('10740.shp'),'r'),criterion='rook')
+ WARNING: there is one disconnected observation (no neighbors)
+ Island id: [163]
+ >>> w.pct_nonzero
+ 0.026351084812623275
+ >>> fips = pysal.open(pysal.examples.get_path('10740.dbf')).by_col('STFID')
+ >>> w = buildContiguity(pysal.open(pysal.examples.get_path('10740.shp'),'r'),ids=fips)
+ WARNING: there is one disconnected observation (no neighbors)
+ Island id: ['35043940300']
+ >>> w['35001000107']
+ {'35001003805': 1.0, '35001003721': 1.0, '35001000111': 1.0, '35001000112': 1.0, '35001000108': 1.0}
+
+ Notes
+ -----
+
+ The types of sources supported will expand over time.
+
+ See Also
+ --------
+ pysal.weights.W # need to fix sphinx links
+
+ """
+ if ids and len(ids) != len(set(ids)):
+ raise ValueError("The argument to the ids parameter contains duplicate entries.")
+
+ wt_type = WT_TYPE[criterion.lower()]
+ geo = polygons
+ if issubclass(type(geo), pysal.open):
+ geo.seek(0) # Make sure we read from the beginging of the file.
+ geoObj = geo
+ else:
+ raise TypeError(
+ "Argument must be a FileIO handler or connection string")
+ neighbor_data = ContiguityWeights(geoObj, wt_type).w
+ neighbors = {}
+ #weights={}
+ if ids:
+ for key in neighbor_data:
+ ida = ids[key]
+ if ida not in neighbors:
+ neighbors[ida] = set()
+ neighbors[ida].update([ids[x] for x in neighbor_data[key]])
+ for key in neighbors:
+ neighbors[key] = list(neighbors[key])
+ else:
+ for key in neighbor_data:
+ neighbors[key] = list(neighbor_data[key])
+ return pysal.weights.W(neighbors, id_order=ids)
+
diff --git a/pysal/weights/Distance.py b/pysal/weights/Distance.py
new file mode 100644
index 0000000..dc44b41
--- /dev/null
+++ b/pysal/weights/Distance.py
@@ -0,0 +1,532 @@
+"""
+Distance based spatial weights
+"""
+
+__author__ = "Sergio J. Rey <srey at asu.edu> "
+
+import pysal
+import scipy.spatial
+from pysal.common import KDTree
+from pysal.weights import W
+import scipy.stats
+import numpy as np
+
+__all__ = ["knnW", "Kernel", "DistanceBand"]
+
+
+def knnW(data, k=2, p=2, ids=None, pct_unique=0.25):
+ """
+ Creates nearest neighbor weights matrix based on k nearest
+ neighbors.
+
+ Parameters
+ ----------
+
+ data : array (n,k) or KDTree where KDtree.data is array (n,k)
+ n observations on k characteristics used to measure
+ distances between the n objects
+ k : int
+ number of nearest neighbors
+ p : float
+ Minkowski p-norm distance metric parameter:
+ 1<=p<=infinity
+ 2: Euclidean distance
+ 1: Manhattan distance
+ ids : list
+ identifiers to attach to each observation
+ pct_unique : float
+ threshold percentage of unique points in data. Below this
+ threshold tree is built on unique values only
+
+ Returns
+ -------
+
+ w : W instance
+ Weights object with binary weights
+
+ Examples
+ --------
+
+ >>> x,y=np.indices((5,5))
+ >>> x.shape=(25,1)
+ >>> y.shape=(25,1)
+ >>> data=np.hstack([x,y])
+ >>> wnn2=knnW(data,k=2)
+ >>> wnn4=knnW(data,k=4)
+ >>> set([1,5,6,2]) == set(wnn4.neighbors[0])
+ True
+ >>> set([0,6,10,1]) == set(wnn4.neighbors[5])
+ True
+ >>> set([1,5]) == set(wnn2.neighbors[0])
+ True
+ >>> set([0,6]) == set(wnn2.neighbors[5])
+ True
+ >>> "%.2f"%wnn2.pct_nonzero
+ '0.08'
+ >>> wnn4.pct_nonzero
+ 0.16
+ >>> wnn3e=knnW(data,p=2,k=3)
+ >>> set([1,5,6]) == set(wnn3e.neighbors[0])
+ True
+ >>> wnn3m=knnW(data,p=1,k=3)
+ >>> a = set([1,5,2])
+ >>> b = set([1,5,6])
+ >>> c = set([1,5,10])
+ >>> w0n = set(wnn3m.neighbors[0])
+ >>> a==w0n or b==w0n or c==w0n
+ True
+
+ ids
+
+ >>> wnn2 = knnW(data,2)
+ >>> wnn2[0]
+ {1: 1.0, 5: 1.0}
+ >>> wnn2[1]
+ {0: 1.0, 2: 1.0}
+
+ now with 1 rather than 0 offset
+
+ >>> wnn2 = knnW(data,2, ids = range(1,26))
+ >>> wnn2[1]
+ {2: 1.0, 6: 1.0}
+ >>> wnn2[2]
+ {1: 1.0, 3: 1.0}
+ >>> 0 in wnn2.neighbors
+ False
+
+ Notes
+ -----
+
+ Ties between neighbors of equal distance are arbitrarily broken.
+
+ See Also
+ --------
+ pysal.weights.W
+
+ """
+
+ if issubclass(type(data), scipy.spatial.KDTree):
+ kd = data
+ data = kd.data
+ nnq = kd.query(data, k=k+1, p=p)
+ info = nnq[1]
+ elif type(data).__name__ == 'ndarray':
+ # check if unique points are a small fraction of all points
+ ind = np.lexsort(data.T)
+ u = data[np.concatenate(([True],np.any(data[ind[1:]]!=data[ind[:-1]],axis=1)))]
+ pct_u = len(u)*1. / len(data)
+ if pct_u < pct_unique:
+ tree = KDTree(u)
+ nnq = tree.query(data, k=k+1, p=p)
+ info = nnq[1]
+ uid = [np.where((data == ui).all(axis=1))[0][0] for ui in u]
+ new_info = np.zeros((len(data), k + 1), 'int')
+ for i, row in enumerate(info):
+ new_info[i] = [uid[j] for j in row]
+ info = new_info
+ else:
+ kd = KDTree(data)
+ # calculate
+ nnq = kd.query(data, k=k + 1, p=p)
+ info = nnq[1]
+ else:
+ print 'Unsupported type'
+ return None
+
+ neighbors = {}
+ for i, row in enumerate(info):
+ row = row.tolist()
+ if i in row:
+ row.remove(i)
+ focal = i
+ if ids:
+ row = [ ids[j] for j in row]
+ focal = ids[i]
+ neighbors[focal] = row
+ return pysal.weights.W(neighbors, id_order=ids)
+
+
+class Kernel(W):
+ """Spatial weights based on kernel functions
+
+ Parameters
+ ----------
+
+ data : array (n,k) or KDTree where KDtree.data is array (n,k)
+ n observations on k characteristics used to measure
+ distances between the n objects
+ bandwidth : float or array-like (optional)
+ the bandwidth :math:`h_i` for the kernel.
+ fixed : binary
+ If true then :math:`h_i=h \\forall i`. If false then
+ bandwidth is adaptive across observations.
+ k : int
+ the number of nearest neighbors to use for determining
+ bandwidth. For fixed bandwidth, :math:`h_i=max(dknn) \\forall i`
+ where :math:`dknn` is a vector of k-nearest neighbor
+ distances (the distance to the kth nearest neighbor for each
+ observation). For adaptive bandwidths, :math:`h_i=dknn_i`
+ diagonal : boolean
+ If true, set diagonal weights = 1.0, if false (default),
+ diagonals weights are set to value according to kernel
+ function.
+ function : string {'triangular','uniform','quadratic','quartic','gaussian'}
+ kernel function defined as follows with
+
+ .. math::
+
+ z_{i,j} = d_{i,j}/h_i
+
+ triangular
+
+ .. math::
+
+ K(z) = (1 - |z|) \ if |z| \le 1
+
+ uniform
+
+ .. math::
+
+ K(z) = 1/2 \ if |z| \le 1
+
+ quadratic
+
+ .. math::
+
+ K(z) = (3/4)(1-z^2) \ if |z| \le 1
+
+ quartic
+
+ .. math::
+
+ K(z) = (15/16)(1-z^2)^2 \ if |z| \le 1
+
+ gaussian
+
+ .. math::
+
+ K(z) = (2\pi)^{(-1/2)} exp(-z^2 / 2)
+
+ eps : float
+ adjustment to ensure knn distance range is closed on the
+ knnth observations
+
+ Examples
+ --------
+
+ >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+ >>> kw=Kernel(points)
+ >>> kw.weights[0]
+ [1.0, 0.500000049999995, 0.4409830615267465]
+ >>> kw.neighbors[0]
+ [0, 1, 3]
+ >>> kw.bandwidth
+ array([[ 20.000002],
+ [ 20.000002],
+ [ 20.000002],
+ [ 20.000002],
+ [ 20.000002],
+ [ 20.000002]])
+ >>> kw15=Kernel(points,bandwidth=15.0)
+ >>> kw15[0]
+ {0: 1.0, 1: 0.33333333333333337, 3: 0.2546440075000701}
+ >>> kw15.neighbors[0]
+ [0, 1, 3]
+ >>> kw15.bandwidth
+ array([[ 15.],
+ [ 15.],
+ [ 15.],
+ [ 15.],
+ [ 15.],
+ [ 15.]])
+
+ Adaptive bandwidths user specified
+
+ >>> bw=[25.0,15.0,25.0,16.0,14.5,25.0]
+ >>> kwa=Kernel(points,bandwidth=bw)
+ >>> kwa.weights[0]
+ [1.0, 0.6, 0.552786404500042, 0.10557280900008403]
+ >>> kwa.neighbors[0]
+ [0, 1, 3, 4]
+ >>> kwa.bandwidth
+ array([[ 25. ],
+ [ 15. ],
+ [ 25. ],
+ [ 16. ],
+ [ 14.5],
+ [ 25. ]])
+
+ Endogenous adaptive bandwidths
+
+ >>> kwea=Kernel(points,fixed=False)
+ >>> kwea.weights[0]
+ [1.0, 0.10557289844279438, 9.99999900663795e-08]
+ >>> kwea.neighbors[0]
+ [0, 1, 3]
+ >>> kwea.bandwidth
+ array([[ 11.18034101],
+ [ 11.18034101],
+ [ 20.000002 ],
+ [ 11.18034101],
+ [ 14.14213704],
+ [ 18.02775818]])
+
+ Endogenous adaptive bandwidths with Gaussian kernel
+
+ >>> kweag=Kernel(points,fixed=False,function='gaussian')
+ >>> kweag.weights[0]
+ [0.3989422804014327, 0.2674190291577696, 0.2419707487162134]
+ >>> kweag.bandwidth
+ array([[ 11.18034101],
+ [ 11.18034101],
+ [ 20.000002 ],
+ [ 11.18034101],
+ [ 14.14213704],
+ [ 18.02775818]])
+
+ Diagonals to 1.0
+
+ >>> kq = Kernel(points,function='gaussian')
+ >>> kq.weights
+ {0: [0.3989422804014327, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 0.3989422804014327, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 0.3989422804014327, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 0.3989422804014327, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 0.3989422804014327, 0.35206533556593145], 5: [0.26575287272131043, 0.3520653 [...]
+ >>> kqd = Kernel(points, function='gaussian', diagonal=True)
+ >>> kqd.weights
+ {0: [1.0, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 1.0, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 1.0, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 1.0, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 1.0, 0.35206533556593145], 5: [0.26575287272131043, 0.35206533556593145, 1.0]}
+ """
+ def __init__(self, data, bandwidth=None, fixed=True, k=2,
+ function='triangular', eps=1.0000001, ids=None,
+ diagonal=False):
+ if issubclass(type(data), scipy.spatial.KDTree):
+ self.kdt = data
+ self.data = self.kdt.data
+ data = self.data
+ else:
+ self.data = data
+ self.kdt = KDTree(self.data)
+ self.k = k + 1
+ self.function = function.lower()
+ self.fixed = fixed
+ self.eps = eps
+ if bandwidth:
+ try:
+ bandwidth = np.array(bandwidth)
+ bandwidth.shape = (len(bandwidth), 1)
+ except:
+ bandwidth = np.ones((len(data), 1), 'float') * bandwidth
+ self.bandwidth = bandwidth
+ else:
+ self._set_bw()
+
+ self._eval_kernel()
+ neighbors, weights = self._k_to_W(ids)
+ if diagonal:
+ for i in neighbors:
+ weights[i][neighbors[i].index(i)] = 1.0
+ W.__init__(self, neighbors, weights, ids)
+
+ def _k_to_W(self, ids=None):
+ allneighbors = {}
+ weights = {}
+ if ids:
+ ids = np.array(ids)
+ else:
+ ids = np.arange(len(self.data))
+ for i, neighbors in enumerate(self.kernel):
+ if len(self.neigh[i]) == 0:
+ allneighbors[ids[i]] = []
+ weights[ids[i]] = []
+ else:
+ allneighbors[ids[i]] = list(ids[self.neigh[i]])
+ weights[ids[i]] = self.kernel[i].tolist()
+ return allneighbors, weights
+
+ def _set_bw(self):
+ dmat, neigh = self.kdt.query(self.data, k=self.k)
+ if self.fixed:
+ # use max knn distance as bandwidth
+ bandwidth = dmat.max() * self.eps
+ n = len(dmat)
+ self.bandwidth = np.ones((n, 1), 'float') * bandwidth
+ else:
+ # use local max knn distance
+ self.bandwidth = dmat.max(axis=1) * self.eps
+ self.bandwidth.shape = (self.bandwidth.size, 1)
+ # identify knn neighbors for each point
+ nnq = self.kdt.query(self.data, k=self.k)
+ self.neigh = nnq[1]
+
+ def _eval_kernel(self):
+ # get points within bandwidth distance of each point
+ if not hasattr(self, 'neigh'):
+ kdtq = self.kdt.query_ball_point
+ neighbors = [kdtq(self.data[i], r=bwi[0]) for i,
+ bwi in enumerate(self.bandwidth)]
+ self.neigh = neighbors
+ # get distances for neighbors
+ bw = self.bandwidth
+
+ kdtq = self.kdt.query
+ z = []
+ for i, nids in enumerate(self.neigh):
+ di, ni = kdtq(self.data[i], k=len(nids))
+ zi = np.array([dict(zip(ni, di))[nid] for nid in nids]) / bw[i]
+ z.append(zi)
+ zs = z
+ # functions follow Anselin and Rey (2010) table 5.4
+ if self.function == 'triangular':
+ self.kernel = [1 - zi for zi in zs]
+ elif self.function == 'uniform':
+ self.kernel = [np.ones(zi.shape) * 0.5 for zi in zs]
+ elif self.function == 'quadratic':
+ self.kernel = [(3. / 4) * (1 - zi ** 2) for zi in zs]
+ elif self.function == 'quartic':
+ self.kernel = [(15. / 16) * (1 - zi ** 2) ** 2 for zi in zs]
+ elif self.function == 'gaussian':
+ c = np.pi * 2
+ c = c ** (-0.5)
+ self.kernel = [c * np.exp(-(zi ** 2) / 2.) for zi in zs]
+ else:
+ print 'Unsupported kernel function', self.function
+
+
+class DistanceBand(W):
+ """Spatial weights based on distance band
+
+ Parameters
+ ----------
+
+ data : array (n,k) or KDTree where KDtree.data is array (n,k)
+ n observations on k characteristics used to measure
+ distances between the n objects
+ threshold : float
+ distance band
+ p : float
+ Minkowski p-norm distance metric parameter:
+ 1<=p<=infinity
+ 2: Euclidean distance
+ 1: Manhattan distance
+ binary : binary
+ If true w_{ij}=1 if d_{i,j}<=threshold, otherwise w_{i,j}=0
+ If false wij=dij^{alpha}
+ alpha : float
+ distance decay parameter for weight (default -1.0)
+ if alpha is positive the weights will not decline with
+ distance. If binary is True, alpha is ignored
+
+ Examples
+ --------
+
+ >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+ >>> w=DistanceBand(points,threshold=11.2)
+ WARNING: there is one disconnected observation (no neighbors)
+ Island id: [2]
+ >>> w.weights
+ {0: [1, 1], 1: [1, 1], 2: [], 3: [1, 1], 4: [1], 5: [1]}
+ >>> w.neighbors
+ {0: [1, 3], 1: [0, 3], 2: [], 3: [1, 0], 4: [5], 5: [4]}
+ >>> w=DistanceBand(points,threshold=14.2)
+ >>> w.weights
+ {0: [1, 1], 1: [1, 1, 1], 2: [1], 3: [1, 1], 4: [1, 1, 1], 5: [1]}
+ >>> w.neighbors
+ {0: [1, 3], 1: [0, 3, 4], 2: [4], 3: [1, 0], 4: [5, 1, 2], 5: [4]}
+
+ inverse distance weights
+
+ >>> w=DistanceBand(points,threshold=11.2,binary=False)
+ WARNING: there is one disconnected observation (no neighbors)
+ Island id: [2]
+ >>> w.weights[0]
+ [0.10000000000000001, 0.089442719099991588]
+ >>> w.neighbors[0]
+ [1, 3]
+ >>>
+
+ gravity weights
+
+ >>> w=DistanceBand(points,threshold=11.2,binary=False,alpha=-2.)
+ WARNING: there is one disconnected observation (no neighbors)
+ Island id: [2]
+ >>> w.weights[0]
+ [0.01, 0.0079999999999999984]
+
+ Notes
+ -----
+
+ this was initially implemented running scipy 0.8.0dev (in epd 6.1).
+ earlier versions of scipy (0.7.0) have a logic bug in scipy/sparse/dok.py
+ so serge changed line 221 of that file on sal-dev to fix the logic bug
+
+ """
+ def __init__(self, data, threshold, p=2, alpha=-1.0, binary=True, ids=None):
+ """
+ Casting to floats is a work around for a bug in scipy.spatial. See detail in pysal issue #126
+ """
+ if issubclass(type(data), scipy.spatial.KDTree):
+ self.kd = data
+ self.data = self.kd.data
+ else:
+ try:
+ data = np.asarray(data)
+ if data.dtype.kind != 'f':
+ data = data.astype(float)
+ self.data = data
+ self.kd = KDTree(self.data)
+ except:
+ raise ValueError("Could not make array from data")
+
+ self.p = p
+ self.threshold = threshold
+ self.binary = binary
+ self.alpha = alpha
+ self._band()
+ neighbors, weights = self._distance_to_W(ids)
+ W.__init__(self, neighbors, weights, ids)
+
+ def _band(self):
+ """
+ find all pairs within threshold
+ """
+ self.dmat = self.kd.sparse_distance_matrix(
+ self.kd, max_distance=self.threshold)
+
+ def _distance_to_W(self, ids=None):
+ if ids:
+ ids = np.array(ids)
+ else:
+ ids = np.arange(self.dmat.shape[0])
+ neighbors = dict([(i,[]) for i in ids])
+ weights = dict([(i,[]) for i in ids])
+ if self.binary:
+ for key,weight in self.dmat.items():
+ i,j = key
+ if j not in neighbors[i]:
+ weights[i].append(1)
+ neighbors[i].append(j)
+ if i not in neighbors[j]:
+ weights[j].append(1)
+ neighbors[j].append(i)
+
+ else:
+ for key,weight in self.dmat.items():
+ i,j = key
+ if j not in neighbors[i]:
+ weights[i].append(weight**self.alpha)
+ neighbors[i].append(j)
+ if i not in neighbors[j]:
+ weights[j].append(weight**self.alpha)
+ neighbors[j].append(i)
+
+ return neighbors, weights
+
+
+def _test():
+ import doctest
+ # the following line could be used to define an alternative to the '<BLANKLINE>' flag
+ #doctest.BLANKLINE_MARKER = 'something better than <BLANKLINE>'
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ doctest.testmod()
+ np.set_printoptions(suppress=start_suppress)
+
+if __name__ == '__main__':
+ _test()
diff --git a/pysal/weights/Wsets.py b/pysal/weights/Wsets.py
new file mode 100644
index 0000000..0e10346
--- /dev/null
+++ b/pysal/weights/Wsets.py
@@ -0,0 +1,506 @@
+"""
+Set-like manipulation of weights matrices.
+"""
+
+__author__ = "Sergio J. Rey <srey at asu.edu>, Charles Schmidt <schmidtc at gmail.com>, David Folch <david.folch at asu.edu>, Dani Arribas-Bel <darribas at asu.edu>"
+
+import pysal
+import copy
+from scipy.sparse import isspmatrix_csr
+from numpy import ones
+
+__all__ = ['w_union', 'w_intersection', 'w_difference',
+ 'w_symmetric_difference', 'w_subset', 'w_clip']
+
+
+def w_union(w1, w2, silent_island_warning=False):
+ """Returns a binary weights object, w, that includes all neighbor pairs that
+ exist in either w1 or w2.
+
+ Parameters
+ ----------
+
+ w1 : W object
+ w2 : W object
+ silent_island_warning : boolean
+ Switch to turn off (default on) print statements
+ for every observation with islands
+
+ Returns
+ -------
+
+ w : W object
+
+ Notes
+ -----
+ ID comparisons are performed using ==, therefore the integer ID 2 is
+ equivalent to the float ID 2.0. Returns a matrix with all the unique IDs
+ from w1 and w2.
+
+ Examples
+ --------
+
+ Construct rook weights matrices for two regions, one is 4x4 (16 areas)
+ and the other is 6x4 (24 areas). A union of these two weights matrices
+ results in the new weights matrix matching the larger one.
+
+ >>> import pysal
+ >>> w1 = pysal.lat2W(4,4)
+ >>> w2 = pysal.lat2W(6,4)
+ >>> w = pysal.weights.w_union(w1, w2)
+ >>> w1[0] == w[0]
+ True
+ >>> w1.neighbors[15]
+ [11, 14]
+ >>> w2.neighbors[15]
+ [11, 14, 19]
+ >>> w.neighbors[15]
+ [19, 11, 14]
+ >>>
+
+ """
+ neighbors = dict(w1.neighbors.items())
+ for i in w2.neighbors:
+ if i in neighbors:
+ add_neigh = set(neighbors[i]).union(set(w2.neighbors[i]))
+ neighbors[i] = list(add_neigh)
+ else:
+ neighbors[i] = copy.copy(w2.neighbors[i])
+ return pysal.W(neighbors, silent_island_warning=silent_island_warning)
+
+
+def w_intersection(w1, w2, w_shape='w1', silent_island_warning=False):
+ """Returns a binary weights object, w, that includes only those neighbor
+ pairs that exist in both w1 and w2.
+
+ Parameters
+ ----------
+
+ w1 : W object
+ w2 : W object
+ w_shape : string
+ Defines the shape of the returned weights matrix. 'w1' returns a
+ matrix with the same IDs as w1; 'all' returns a matrix with all
+ the unique IDs from w1 and w2; and 'min' returns a matrix with
+ only the IDs occurring in both w1 and w2.
+ silent_island_warning : boolean
+ Switch to turn off (default on) print statements
+ for every observation with islands
+
+ Returns
+ -------
+
+ w : W object
+
+ Notes
+ -----
+ ID comparisons are performed using ==, therefore the integer ID 2 is
+ equivalent to the float ID 2.0.
+
+ Examples
+ --------
+
+ Construct rook weights matrices for two regions, one is 4x4 (16 areas)
+ and the other is 6x4 (24 areas). An intersection of these two weights
+ matrices results in the new weights matrix matching the smaller one.
+
+ >>> import pysal
+ >>> w1 = pysal.lat2W(4,4)
+ >>> w2 = pysal.lat2W(6,4)
+ >>> w = pysal.weights.w_intersection(w1, w2)
+ >>> w1[0] == w[0]
+ True
+ >>> w1.neighbors[15]
+ [11, 14]
+ >>> w2.neighbors[15]
+ [11, 14, 19]
+ >>> w.neighbors[15]
+ [11, 14]
+ >>>
+
+ """
+ if w_shape == 'w1':
+ neigh_keys = w1.neighbors.keys()
+ elif w_shape == 'all':
+ neigh_keys = set(w1.neighbors.keys()).union(set(w2.neighbors.keys()))
+ elif w_shape == 'min':
+ neigh_keys = set(w1.neighbors.keys(
+ )).intersection(set(w2.neighbors.keys()))
+ else:
+ raise Exception("invalid string passed to w_shape")
+
+ neighbors = {}
+ for i in neigh_keys:
+ if i in w1.neighbors and i in w2.neighbors:
+ add_neigh = set(w1.neighbors[i]).intersection(set(w2.neighbors[i]))
+ neighbors[i] = list(add_neigh)
+ else:
+ neighbors[i] = []
+
+ return pysal.W(neighbors, silent_island_warning=silent_island_warning)
+
+
+def w_difference(w1, w2, w_shape='w1', constrained=True, silent_island_warning=False):
+ """Returns a binary weights object, w, that includes only neighbor pairs
+ in w1 that are not in w2. The w_shape and constrained parameters
+ determine which pairs in w1 that are not in w2 are returned.
+
+ Parameters
+ ----------
+
+ w1 : W object
+ w2 : W object
+ w_shape : string
+ Defines the shape of the returned weights matrix. 'w1' returns a
+ matrix with the same IDs as w1; 'all' returns a matrix with all
+ the unique IDs from w1 and w2; and 'min' returns a matrix with
+ the IDs occurring in w1 and not in w2.
+ constrained : boolean
+ If False then the full set of neighbor pairs in w1 that are
+ not in w2 are returned. If True then those pairs that would
+ not be possible if w_shape='min' are dropped. Ignored if
+ w_shape is set to 'min'.
+ silent_island_warning : boolean
+ Switch to turn off (default on) print statements
+ for every observation with islands
+
+ Returns
+ -------
+
+ w : W object
+
+ Notes
+ -----
+ ID comparisons are performed using ==, therefore the integer ID 2 is
+ equivalent to the float ID 2.0.
+
+ Examples
+ --------
+
+ Construct rook (w2) and queen (w1) weights matrices for two 4x4 regions
+ (16 areas). A queen matrix has all the joins a rook matrix does plus joins
+ between areas that share a corner. The new matrix formed by the difference
+ of rook from queen contains only join at corners (typically called a
+ bishop matrix). Note that the difference of queen from rook would result
+ in a weights matrix with no joins.
+
+ >>> import pysal
+ >>> w1 = pysal.lat2W(4,4,rook=False)
+ >>> w2 = pysal.lat2W(4,4,rook=True)
+ >>> w = pysal.weights.w_difference(w1, w2, constrained=False)
+ >>> w1[0] == w[0]
+ False
+ >>> w1.neighbors[15]
+ [10, 11, 14]
+ >>> w2.neighbors[15]
+ [11, 14]
+ >>> w.neighbors[15]
+ [10]
+ >>>
+
+ """
+ if w_shape == 'w1':
+ neigh_keys = w1.neighbors.keys()
+ elif w_shape == 'all':
+ neigh_keys = set(w1.neighbors.keys()).union(set(w2.neighbors.keys()))
+ elif w_shape == 'min':
+ neigh_keys = set(
+ w1.neighbors.keys()).difference(set(w2.neighbors.keys()))
+ if not neigh_keys:
+ raise Exception("returned an empty weights matrix")
+ else:
+ raise Exception("invalid string passed to w_shape")
+
+ neighbors = {}
+ for i in neigh_keys:
+ if i in w1.neighbors:
+ if i in w2.neighbors:
+ add_neigh = set(w1.neighbors[i]
+ ).difference(set(w2.neighbors[i]))
+ neighbors[i] = list(add_neigh)
+ else:
+ neighbors[i] = copy.copy(w1.neighbors[i])
+ else:
+ neighbors[i] = []
+
+ if constrained or w_shape == 'min':
+ constrained_keys = set(
+ w1.neighbors.keys()).difference(set(w2.neighbors.keys()))
+ island_keys = set(neighbors.keys()).difference(constrained_keys)
+ for i in island_keys:
+ neighbors[i] = []
+ for i in constrained_keys:
+ neighbors[i] = list(
+ set(neighbors[i]).intersection(constrained_keys))
+
+ return pysal.W(neighbors, silent_island_warning=silent_island_warning)
+
+
+def w_symmetric_difference(w1, w2, w_shape='all', constrained=True, silent_island_warning=False):
+ """Returns a binary weights object, w, that includes only neighbor pairs
+ that are not shared by w1 and w2. The w_shape and constrained parameters
+ determine which pairs that are not shared by w1 and w2 are returned.
+
+ Parameters
+ ----------
+
+ w1 : W object
+ w2 : W object
+ w_shape : string
+ Defines the shape of the returned weights matrix. 'all' returns a
+ matrix with all the unique IDs from w1 and w2; and 'min' returns
+ a matrix with the IDs not shared by w1 and w2.
+ constrained : boolean
+ If False then the full set of neighbor pairs that are not
+ shared by w1 and w2 are returned. If True then those pairs
+ that would not be possible if w_shape='min' are dropped.
+ Ignored if w_shape is set to 'min'.
+ silent_island_warning : boolean
+ Switch to turn off (default on) print statements
+ for every observation with islands
+
+ Returns
+ -------
+
+ w : W object
+
+ Notes
+ -----
+ ID comparisons are performed using ==, therefore the integer ID 2 is
+ equivalent to the float ID 2.0.
+
+ Examples
+ --------
+
+ Construct queen weights matrix for a 4x4 (16 areas) region (w1) and a rook
+ matrix for a 6x4 (24 areas) region (w2). The symmetric difference of these
+ two matrices (with w_shape set to 'all' and constrained set to False)
+ contains the corner joins in the overlap area, all the joins in the
+ non-overlap area.
+
+ >>> import pysal
+ >>> w1 = pysal.lat2W(4,4,rook=False)
+ >>> w2 = pysal.lat2W(6,4,rook=True)
+ >>> w = pysal.weights.w_symmetric_difference(w1, w2, constrained=False)
+ >>> w1[0] == w[0]
+ False
+ >>> w1.neighbors[15]
+ [10, 11, 14]
+ >>> w2.neighbors[15]
+ [11, 14, 19]
+ >>> w.neighbors[15]
+ [10, 19]
+ >>>
+
+ """
+ if w_shape == 'all':
+ neigh_keys = set(w1.neighbors.keys()).union(set(w2.neighbors.keys()))
+ elif w_shape == 'min':
+ neigh_keys = set(w1.neighbors.keys(
+ )).symmetric_difference(set(w2.neighbors.keys()))
+ else:
+ raise Exception("invalid string passed to w_shape")
+
+ neighbors = {}
+ for i in neigh_keys:
+ if i in w1.neighbors:
+ if i in w2.neighbors:
+ add_neigh = set(w1.neighbors[i]).symmetric_difference(
+ set(w2.neighbors[i]))
+ neighbors[i] = list(add_neigh)
+ else:
+ neighbors[i] = copy.copy(w1.neighbors[i])
+ elif i in w2.neighbors:
+ neighbors[i] = copy.copy(w2.neighbors[i])
+ else:
+ neighbors[i] = []
+
+ if constrained or w_shape == 'min':
+ constrained_keys = set(
+ w1.neighbors.keys()).difference(set(w2.neighbors.keys()))
+ island_keys = set(neighbors.keys()).difference(constrained_keys)
+ for i in island_keys:
+ neighbors[i] = []
+ for i in constrained_keys:
+ neighbors[i] = list(
+ set(neighbors[i]).intersection(constrained_keys))
+
+ return pysal.W(neighbors, silent_island_warning=silent_island_warning)
+
+
+def w_subset(w1, ids, silent_island_warning=False):
+ """Returns a binary weights object, w, that includes only those
+ observations in ids.
+
+ Parameters
+ ----------
+
+ w1 : W object
+ ids : list
+ A list containing the IDs to be include in the returned weights
+ object.
+ silent_island_warning : boolean
+ Switch to turn off (default on) print statements
+ for every observation with islands
+
+ Returns
+ -------
+
+ w : W object
+
+ Examples
+ --------
+
+ Construct a rook weights matrix for a 6x4 region (24 areas). By default
+ PySAL assigns integer IDs to the areas in a region. By passing in a list
+ of integers from 0 to 15, the first 16 areas are extracted from the
+ previous weights matrix, and only those joins relevant to the new region
+ are retained.
+
+ >>> import pysal
+ >>> w1 = pysal.lat2W(6,4)
+ >>> ids = range(16)
+ >>> w = pysal.weights.w_subset(w1, ids)
+ >>> w1[0] == w[0]
+ True
+ >>> w1.neighbors[15]
+ [11, 14, 19]
+ >>> w.neighbors[15]
+ [11, 14]
+ >>>
+
+ """
+ neighbors = {}
+ ids_set = set(ids)
+ for i in ids:
+ if i in w1.neighbors:
+ neigh_add = ids_set.intersection(set(w1.neighbors[i]))
+ neighbors[i] = list(neigh_add)
+ else:
+ neighbors[i] = []
+
+ return pysal.W(neighbors, id_order=ids, silent_island_warning=silent_island_warning)
+
+
+def w_clip(w1, w2, outSP=True, silent_island_warning=False):
+ '''
+ Clip a continuous W object (w1) with a different W object (w2) so only cells where
+ w2 has a non-zero value remain with non-zero values in w1
+
+ Checks on w1 and w2 are performed to make sure they conform to the
+ appropriate format and, if not, they are converted.
+
+ Parameters
+ ----------
+ w1 : pysal.W, scipy.sparse.csr.csr_matrix
+ Potentially continuous weights matrix to be clipped. The clipped
+ matrix wc will have at most the same elements as w1.
+ w2 : pysal.W, scipy.sparse.csr.csr_matrix
+ Weights matrix to use as shell to clip w1. Automatically
+ converted to binary format. Only non-zero elements in w2 will be
+ kept non-zero in wc. NOTE: assumed to be of the same shape as w1
+ outSP : boolean
+ If True (default) return sparse version of the clipped W, if
+ False, return pysal.W object of the clipped matrix
+ silent_island_warning : boolean
+ Switch to turn off (default on) print statements
+ for every observation with islands
+
+ Returns
+ -------
+ wc : pysal.W, scipy.sparse.csr.csr_matrix
+ Clipped W object (sparse if outSP=Ture). It inherits
+ ``id_order`` from w1.
+
+ Examples
+ --------
+ >>> import pysal as ps
+
+ First create a W object from a lattice using queen contiguity and
+ row-standardize it (note that these weights will stay when we clip the
+ object, but they will not neccesarily represent a row-standardization
+ anymore):
+
+ >>> w1 = ps.lat2W(3, 2, rook=False)
+ >>> w1.transform = 'R'
+
+ We will clip that geography assuming observations 0, 2, 3 and 4 belong to
+ one group and 1, 5 belong to another group and we don't want both groups
+ to interact with each other in our weights (i.e. w_ij = 0 if i and j in
+ different groups). For that, we use the following method:
+
+ >>> w2 = ps.block_weights(['r1', 'r2', 'r1', 'r1', 'r1', 'r2'])
+
+ To illustrate that w2 will only be considered as binary even when the
+ object passed is not, we can row-standardize it
+
+ >>> w2.transform = 'R'
+
+ The clipped object ``wc`` will contain only the spatial queen
+ relationships that occur within one group ('r1' or 'r2') but will have
+ gotten rid of those that happen across groups
+
+ >>> wcs = ps.weights.Wsets.w_clip(w1, w2, outSP=True)
+
+ This will create a sparse object (recommended when n is large).
+
+ >>> wcs.sparse.toarray()
+ array([[ 0. , 0. , 0.33333333, 0.33333333, 0. ,
+ 0. ],
+ [ 0. , 0. , 0. , 0. , 0. ,
+ 0. ],
+ [ 0.2 , 0. , 0. , 0.2 , 0.2 ,
+ 0. ],
+ [ 0.2 , 0. , 0.2 , 0. , 0.2 ,
+ 0. ],
+ [ 0. , 0. , 0.33333333, 0.33333333, 0. ,
+ 0. ],
+ [ 0. , 0. , 0. , 0. , 0. ,
+ 0. ]])
+
+ If we wanted an original W object, we can control that with the argument
+ ``outSP``:
+
+ >>> wc = ps.weights.Wsets.w_clip(w1, w2, outSP=False)
+ WARNING: there are 2 disconnected observations
+ Island ids: [1, 5]
+ >>> wc.full()[0]
+ array([[ 0. , 0. , 0.33333333, 0.33333333, 0. ,
+ 0. ],
+ [ 0. , 0. , 0. , 0. , 0. ,
+ 0. ],
+ [ 0.2 , 0. , 0. , 0.2 , 0.2 ,
+ 0. ],
+ [ 0.2 , 0. , 0.2 , 0. , 0.2 ,
+ 0. ],
+ [ 0. , 0. , 0.33333333, 0.33333333, 0. ,
+ 0. ],
+ [ 0. , 0. , 0. , 0. , 0. ,
+ 0. ]])
+
+ You can check they are actually the same:
+
+ >>> wcs.sparse.toarray() == wc.full()[0]
+ array([[ True, True, True, True, True, True],
+ [ True, True, True, True, True, True],
+ [ True, True, True, True, True, True],
+ [ True, True, True, True, True, True],
+ [ True, True, True, True, True, True],
+ [ True, True, True, True, True, True]], dtype=bool)
+
+ '''
+ if not w1.id_order:
+ w1.id_order = None
+ id_order = w1.id_order
+ if not isspmatrix_csr(w1):
+ w1 = w1.sparse
+ if not isspmatrix_csr(w2):
+ w2 = w2.sparse
+ w2.data = ones(w2.data.shape)
+ wc = w1.multiply(w2)
+ wc = pysal.weights.WSP(wc, id_order=id_order)
+ if not outSP:
+ wc = pysal.weights.WSP2W(wc, silent_island_warning=silent_island_warning)
+ return wc
+
+
diff --git a/pysal/weights/__init__.py b/pysal/weights/__init__.py
new file mode 100644
index 0000000..2eafa27
--- /dev/null
+++ b/pysal/weights/__init__.py
@@ -0,0 +1,12 @@
+"""
+:mod:`weights` --- Spatial Weights
+==================================
+
+"""
+from weights import *
+from util import *
+from Distance import *
+from Contiguity import *
+from user import *
+from spatial_lag import *
+from Wsets import *
diff --git a/pysal/weights/_contW_binning.py b/pysal/weights/_contW_binning.py
new file mode 100644
index 0000000..af81b76
--- /dev/null
+++ b/pysal/weights/_contW_binning.py
@@ -0,0 +1,389 @@
+#!/usr/bin/python
+#import math
+import pysal
+from pysal.cg.standalone import get_shared_segments
+
+__author__ = "Sergio J. Rey <srey at asu.edu> "
+__all__ = ["QUEEN", "ROOK", "ContiguityWeights_binning",
+ "ContiguityWeightsPolygons"]
+
+
+import time
+
+# delta to get buckets right
+DELTA = 0.000001
+
+QUEEN = 1
+ROOK = 2
+
+# constants for bucket sizes
+BUCK_SM = 8
+BUCK_LG = 80
+SHP_SMALL = 1000
+
+
+def bbcommon(bb, bbother):
+ """
+ Checks for overlaps of bounding boxes. First, east-west, then north-south.
+ Element 0 is west, element 2 is east, element 3 is north, element 1 is
+ south.
+ All four checks must be false for chflag to be true, meaning the two
+ bounding boxes do not overlap.
+ """
+ chflag = 0
+ if not ((bbother[2] < bb[0]) or (bbother[0] > bb[2])):
+ if not ((bbother[3] < bb[1]) or (bbother[1] > bb[3])):
+ chflag = 1
+ return chflag
+
+
+class ContiguityWeights_binning:
+
+ """
+ Contiguity using a binning algorithm
+ """
+
+ def __init__(self, shpFileObject, wttype):
+ self.shpFileObject = shpFileObject
+ self.wttype = wttype
+ self.do_weights()
+
+ def do_weights(self):
+ shpFileObject = self.shpFileObject
+
+ if shpFileObject.type != pysal.cg.Polygon:
+ return False
+
+ shapebox = shpFileObject.bbox # bounding box
+
+ numPoly = len(shpFileObject)
+ self.numPoly = numPoly
+
+ # bucket size
+ if (numPoly < SHP_SMALL):
+ bucketmin = numPoly / BUCK_SM + 2
+ else:
+ bucketmin = numPoly / BUCK_LG + 2
+ # print 'bucketmin: ', bucketmin
+ # bucket length
+ lengthx = ((shapebox[2] + DELTA) - shapebox[0]) / bucketmin
+ lengthy = ((shapebox[3] + DELTA) - shapebox[1]) / bucketmin
+
+ # print lengthx, lengthy
+
+ # initialize buckets
+ columns = [set() for i in range(bucketmin)]
+ rows = [set() for i in range(bucketmin)]
+
+ minbox = shapebox[:2] * 2 # minx,miny,minx,miny
+ binWidth = [lengthx, lengthy] * 2 # lenx,leny,lenx,leny
+ bbcache = {}
+ poly2Column = [set() for i in range(numPoly)]
+ poly2Row = [set() for i in range(numPoly)]
+ for i in range(numPoly):
+ shpObj = shpFileObject.get(i)
+ bbcache[i] = shpObj.bounding_box[:]
+ projBBox = [int((shpObj.bounding_box[:][j] -
+ minbox[j]) / binWidth[j]) for j in xrange(4)]
+ for j in range(projBBox[0], projBBox[2] + 1):
+ columns[j].add(i)
+ poly2Column[i].add(j)
+ for j in range(projBBox[1], projBBox[3] + 1):
+ rows[j].add(i)
+ poly2Row[i].add(j)
+
+ w = {}
+ if self.wttype == QUEEN:
+ # loop over polygons rather than bins
+ vertCache = {}
+ for polyId in xrange(numPoly):
+ if polyId not in vertCache:
+ vertCache[polyId] = set(shpFileObject.get(polyId).vertices)
+ idRows = poly2Row[polyId]
+ idCols = poly2Column[polyId]
+ rowPotentialNeighbors = set()
+ colPotentialNeighbors = set()
+ for row in idRows:
+ rowPotentialNeighbors = rowPotentialNeighbors.union(
+ rows[row])
+ for col in idCols:
+ colPotentialNeighbors = colPotentialNeighbors.union(
+ columns[col])
+ potentialNeighbors = rowPotentialNeighbors.intersection(
+ colPotentialNeighbors)
+ if polyId not in w:
+ w[polyId] = set()
+ for j in potentialNeighbors:
+ if polyId < j:
+ if bbcommon(bbcache[polyId], bbcache[j]):
+ if j not in vertCache:
+ vertCache[j] = set(
+ shpFileObject.get(j).vertices)
+ common = vertCache[
+ polyId].intersection(vertCache[j])
+ if len(common) > 0:
+ w[polyId].add(j)
+ if j not in w:
+ w[j] = set()
+ w[j].add(polyId)
+ elif self.wttype == ROOK:
+ # check for a shared edge
+ edgeCache = {}
+ # loop over polygons rather than bins
+ for polyId in xrange(numPoly):
+ if polyId not in edgeCache:
+ iEdges = {}
+ iVerts = shpFileObject.get(polyId).vertices
+ nv = len(iVerts)
+ ne = nv - 1
+ for i in xrange(ne):
+ l = iVerts[i]
+ r = iVerts[i + 1]
+ iEdges[(l, r)] = []
+ iEdges[(r, l)] = []
+ edgeCache[polyId] = iEdges
+ iEdgeSet = set(edgeCache[polyId].keys())
+ idRows = poly2Row[polyId]
+ idCols = poly2Column[polyId]
+ rowPotentialNeighbors = set()
+ colPotentialNeighbors = set()
+ for row in idRows:
+ rowPotentialNeighbors = rowPotentialNeighbors.union(
+ rows[row])
+ for col in idCols:
+ colPotentialNeighbors = colPotentialNeighbors.union(
+ columns[col])
+ potentialNeighbors = rowPotentialNeighbors.intersection(
+ colPotentialNeighbors)
+ if polyId not in w:
+ w[polyId] = set()
+ for j in potentialNeighbors:
+ if polyId < j:
+ if bbcommon(bbcache[polyId], bbcache[j]):
+ if j not in edgeCache:
+ jVerts = shpFileObject.get(j).vertices
+ jEdges = {}
+ nv = len(jVerts)
+ ne = nv - 1
+ for e in xrange(ne):
+ l = jVerts[e]
+ r = jVerts[e + 1]
+ jEdges[(l, r)] = []
+ jEdges[(r, l)] = []
+ edgeCache[j] = jEdges
+ # for edge in edgeCache[j]:
+ if iEdgeSet.intersection(edgeCache[j].keys()):
+ w[polyId].add(j)
+ if j not in w:
+ w[j] = set()
+ w[j].add(polyId)
+ # break
+ else:
+ print "Unsupported weight type."
+
+ self.w = w
+
+# Generalize to handle polygon collections - independent of origin file type
+
+
+class ContiguityWeightsPolygons:
+
+ """
+ Contiguity for a collection of polygons using a binning algorithm
+ """
+
+ def __init__(self, collection, wttype=1):
+ """
+
+ Parameters
+ ==========
+
+ collection: PySAL PolygonCollection
+
+ wttype: int
+ 1: Queen
+ 2: Rook
+ """
+
+ self.collection = collection
+ self.wttype = wttype
+ self.do_weights()
+
+ def do_weights(self):
+ if self.collection.type != pysal.cg.Polygon:
+ return False
+
+ shapebox = self.collection.bbox # bounding box
+
+ numPoly = self.collection.n
+ self.numPoly = numPoly
+
+ # bucket size
+ if (numPoly < SHP_SMALL):
+ bucketmin = numPoly / BUCK_SM + 2
+ else:
+ bucketmin = numPoly / BUCK_LG + 2
+ # print 'bucketmin: ', bucketmin
+ # bucket length
+ lengthx = ((shapebox[2] + DELTA) - shapebox[0]) / bucketmin
+ lengthy = ((shapebox[3] + DELTA) - shapebox[1]) / bucketmin
+
+ # print lengthx, lengthy
+
+ # initialize buckets
+ columns = [set() for i in range(bucketmin)]
+ rows = [set() for i in range(bucketmin)]
+
+ minbox = shapebox[:2] * 2 # minx,miny,minx,miny
+ binWidth = [lengthx, lengthy] * 2 # lenx,leny,lenx,leny
+ bbcache = {}
+ poly2Column = [set() for i in range(numPoly)]
+ poly2Row = [set() for i in range(numPoly)]
+ for i in range(numPoly):
+ shpObj = self.collection[i]
+ bbcache[i] = shpObj.bbox[:]
+ projBBox = [int((shpObj.bbox[:][j] -
+ minbox[j]) / binWidth[j]) for j in xrange(4)]
+ for j in range(projBBox[0], projBBox[2] + 1):
+ columns[j].add(i)
+ poly2Column[i].add(j)
+ for j in range(projBBox[1], projBBox[3] + 1):
+ rows[j].add(i)
+ poly2Row[i].add(j)
+
+ w = {}
+ if self.wttype == QUEEN:
+ # loop over polygons rather than bins
+ vertCache = {}
+ for polyId in xrange(numPoly):
+ if polyId not in vertCache:
+ vertCache[polyId] = set(self.collection[polyId].vertices)
+ idRows = poly2Row[polyId]
+ idCols = poly2Column[polyId]
+ rowPotentialNeighbors = set()
+ colPotentialNeighbors = set()
+ for row in idRows:
+ rowPotentialNeighbors = rowPotentialNeighbors.union(
+ rows[row])
+ for col in idCols:
+ colPotentialNeighbors = colPotentialNeighbors.union(
+ columns[col])
+ potentialNeighbors = rowPotentialNeighbors.intersection(
+ colPotentialNeighbors)
+ if polyId not in w:
+ w[polyId] = set()
+ for j in potentialNeighbors:
+ if polyId < j:
+ if j not in vertCache:
+ vertCache[j] = set(self.collection[j].vertices)
+ if bbcommon(bbcache[polyId], bbcache[j]):
+ vertCache[j] = set(self.collection[j].vertices)
+ common = vertCache[
+ polyId].intersection(vertCache[j])
+ if len(common) > 0:
+ w[polyId].add(j)
+ if j not in w:
+ w[j] = set()
+ w[j].add(polyId)
+ elif self.wttype == ROOK:
+ # check for a shared edge
+ edgeCache = {}
+ # loop over polygons rather than bins
+ for polyId in xrange(numPoly):
+ if polyId not in edgeCache:
+ iEdges = {}
+ iVerts = shpFileObject.get(polyId).vertices
+ nv = len(iVerts)
+ ne = nv - 1
+ for i in xrange(ne):
+ l = iVerts[i]
+ r = iVerts[i + 1]
+ iEdges[(l, r)] = []
+ iEdges[(r, l)] = []
+ edgeCache[polyId] = iEdges
+ iEdgeSet = set(edgeCache[polyId].keys())
+ idRows = poly2Row[polyId]
+ idCols = poly2Column[polyId]
+ rowPotentialNeighbors = set()
+ colPotentialNeighbors = set()
+ for row in idRows:
+ rowPotentialNeighbors = rowPotentialNeighbors.union(
+ rows[row])
+ for col in idCols:
+ colPotentialNeighbors = colPotentialNeighbors.union(
+ columns[col])
+ potentialNeighbors = rowPotentialNeighbors.intersection(
+ colPotentialNeighbors)
+ if polyId not in w:
+ w[polyId] = set()
+ for j in potentialNeighbors:
+ if polyId < j:
+ if bbcommon(bbcache[polyId], bbcache[j]):
+ if j not in edgeCache:
+ jVerts = shpFileObject.get(j).vertices
+ jEdges = {}
+ nv = len(jVerts)
+ ne = nv - 1
+ for e in xrange(ne):
+ l = jVerts[e]
+ r = jVerts[e + 1]
+ jEdges[(l, r)] = []
+ jEdges[(r, l)] = []
+ edgeCache[j] = jEdges
+ # for edge in edgeCache[j]:
+ if iEdgeSet.intersection(edgeCache[j].keys()):
+ w[polyId].add(j)
+ if j not in w:
+ w[j] = set()
+ w[j].add(polyId)
+ # break
+ else:
+ print "Unsupported weight type."
+
+ self.w = w
+
+if __name__ == "__main__":
+ import time
+ fname = pysal.examples.get_path('NAT.shp')
+ print 'QUEEN binning'
+ t0 = time.time()
+ qb = ContiguityWeights_binning(pysal.open(fname), QUEEN)
+ t1 = time.time()
+ print "using " + str(fname)
+ print "time elapsed for queen... using bins: " + str(t1 - t0)
+
+ t0 = time.time()
+ rb = ContiguityWeights_binning(pysal.open(fname), ROOK)
+ t1 = time.time()
+ print 'Rook binning'
+ print "using " + str(fname)
+ print "time elapsed for rook... using bins: " + str(t1 - t0)
+
+ from _contW_rtree import ContiguityWeights_rtree
+
+ t0 = time.time()
+ rt = ContiguityWeights_rtree(pysal.open(fname), ROOK)
+ t1 = time.time()
+
+ print "time elapsed for rook... using rtree: " + str(t1 - t0)
+ print rt.w == rb.w
+
+ print 'QUEEN'
+ t0 = time.time()
+ qt = ContiguityWeights_rtree(pysal.open(fname), QUEEN)
+ t1 = time.time()
+ print "using " + str(fname)
+ print "time elapsed for queen... using rtree: " + str(t1 - t0)
+ print qb.w == qt.w
+
+ print 'knn4'
+ t0 = time.time()
+ knn = pysal.knnW_from_shapefile(fname, k=4)
+ t1 = time.time()
+ print t1 - t0
+
+ print 'rook from shapefile'
+ t0 = time.time()
+ knn = pysal.rook_from_shapefile(fname)
+ t1 = time.time()
+ print t1 - t0
diff --git a/pysal/weights/_contW_rtree.py b/pysal/weights/_contW_rtree.py
new file mode 100644
index 0000000..de28648
--- /dev/null
+++ b/pysal/weights/_contW_rtree.py
@@ -0,0 +1,116 @@
+import pysal.cg.rtree as rtree
+from pysal.cg.standalone import get_shared_segments
+#Order by Degree of connectivity, i.e. rook is more connected then queen.
+QUEEN = 1
+ROOK = 2
+
+__author__ = "Charles R Schmidt <schmidtc at gmail.com>"
+__all__ = ["QUEEN", "ROOK", "ContiguityWeights_rtree"]
+
+Q_TARGET_MEM_SIZE = 250 * 1024 * 1024 # 250mb
+
+
+class _PolyQ(dict):
+ def __init__(self):
+ dict.__init__(self)
+ self.size = 20 # use the first 20 objects to calculate the average Size.
+ self.ids = []
+
+ def __checkSize(self):
+ """
+ Use the objects in the Q to calculate the average size of the objects
+ Adjust Q.size to hold Q_TARGET_MEM_SIZE/avgSize object
+ This is as many average size object that fit into Q_TARGET_MEM_SIZE
+ """
+ if len(self.ids) > 50:
+ return True
+ return False
+
+ def add(self, poly):
+ if poly.id not in self:
+ if len(self.ids) >= self.size:
+ if self.__checkSize():
+ del self[self.ids.pop(0)]
+ self[poly.id] = poly
+ self.ids.append(poly.id)
+
+
+class ContiguityWeights_rtree:
+ def __init__(self, geoObj, joinType=ROOK):
+ self.index = rtree.Rtree()
+ self.geoObj = geoObj
+ self.joinType = joinType
+ self.w = {}
+ self.Q = _PolyQ()
+ self.cache_hits = 0
+ self.cache_misses = 0
+ self.create()
+ #print "Misses: ",self.cache_misses
+ #print "Hits: ",self.cache_hits
+
+ def create(self):
+ for id, poly in enumerate(self.geoObj):
+ poly.id = id
+ self.append(poly)
+
+ def append(self, poly):
+ self.Q.add(poly)
+ b = poly.bounding_box
+ bbox = [b.left, b.lower, b.right, b.upper]
+ for id in self.index.intersection(bbox):
+ id = int(id)
+ if self.check(id, poly) >= self.joinType:
+ self.setW(id, poly.id)
+ if poly.id not in self.w: # add the null cases
+ self.w[poly.id] = set()
+ self.index.add(poly.id, bbox)
+
+ def setW(self, id0, id1):
+ "updates the W matrix seting two polygon's as neighbors"
+ w = self.w
+ if id0 not in w:
+ w[id0] = set()
+ if id1 not in w:
+ w[id1] = set()
+ w[id0].add(id1)
+ w[id1].add(id0)
+
+ def check(self, id0, poly1):
+ "Check's if two polygon's are neighbors"
+ if id0 in self.Q:
+ self.cache_hits += 1
+ poly0 = self.Q[id0]
+ else:
+ self.cache_misses += 1
+ poly0 = self.geoObj.get(id0)
+ poly0.id = id0
+ self.Q.add(poly0)
+ common = set(poly0.vertices).intersection(set(poly1.vertices))
+ if len(common) > 1 and self.joinType == ROOK:
+ #double check rook
+ if get_shared_segments(poly0, poly1, True):
+ return ROOK
+ return False
+ #for vert in common:
+ # idx = poly0.vertices.index(vert)
+ # IDX = poly1.vertices.index(vert)
+ # try:
+ # if poly0.vertices[idx+1] == poly1.vertices[IDX+1] or poly0.vertices[idx+1] == poly1.vertices[IDX-1]\
+ # or poly0.vertices[idx-1] == poly1.vertices[IDX+1] or poly0.vertices[idx-1] == poly1.vertices[IDX-1]:
+ # return ROOK
+ # except IndexError:
+ # pass
+ #return False
+ elif len(common) > 0:
+ return QUEEN
+ else:
+ return False
+
+if __name__ == '__main__':
+ import pysal
+ import time
+ t0 = time.time()
+ shp = pysal.open(pysal.examples.get_path('10740.shp'), 'r')
+ w = ContiguityWeights_rtree(shp, QUEEN)
+ t1 = time.time()
+ print "Completed in: ", t1 - t0, "seconds using rtree"
diff --git a/pysal/weights/spatial_lag.py b/pysal/weights/spatial_lag.py
new file mode 100644
index 0000000..d877171
--- /dev/null
+++ b/pysal/weights/spatial_lag.py
@@ -0,0 +1,84 @@
+"""
+spatial lag operations
+"""
+__authors__ = "Serge Rey <srey at asu.edu>, David C. Folch <david.folch at asu.edu>"
+__all__ = ['lag_spatial']
+
+
+def lag_spatial(w, y):
+ """
+ Spatial lag operator. If w is row standardized, returns the average of
+ each observation's neighbors; if not, returns the weighted sum of each
+ observation's neighbors.
+
+ Parameters
+ ----------
+
+ w : W
+ weights object
+ y : array
+ numpy array with dimensionality conforming to w (see examples)
+
+ Returns
+ -------
+
+ wy : array
+ array of numeric values for the spatial lag
+
+ Examples
+ --------
+
+ >>> import pysal
+ >>> import numpy as np
+
+ Setup a 9x9 binary spatial weights matrix and vector of data; compute the
+ spatial lag of the vector.
+
+ >>> w = pysal.lat2W(3, 3)
+ >>> y = np.arange(9)
+ >>> yl = pysal.lag_spatial(w, y)
+ >>> yl
+ array([ 4., 6., 6., 10., 16., 14., 10., 18., 12.])
+
+ Row standardize the weights matrix and recompute the spatial lag
+
+ >>> w.transform = 'r'
+ >>> yl = pysal.lag_spatial(w, y)
+ >>> yl
+ array([ 2. , 2. , 3. , 3.33333333, 4. ,
+ 4.66666667, 5. , 6. , 6. ])
+
+ Explicitly define data vector as 9x1 and recompute the spatial lag
+
+ >>> y.shape = (9, 1)
+ >>> yl = pysal.lag_spatial(w, y)
+ >>> yl
+ array([[ 2. ],
+ [ 2. ],
+ [ 3. ],
+ [ 3.33333333],
+ [ 4. ],
+ [ 4.66666667],
+ [ 5. ],
+ [ 6. ],
+ [ 6. ]])
+
+ Take the spatial lag of a 9x2 data matrix
+
+ >>> yr = np.arange(8, -1, -1)
+ >>> yr.shape = (9, 1)
+ >>> x = np.hstack((y, yr))
+ >>> yl = pysal.lag_spatial(w, x)
+ >>> yl
+ array([[ 2. , 6. ],
+ [ 2. , 6. ],
+ [ 3. , 5. ],
+ [ 3.33333333, 4.66666667],
+ [ 4. , 4. ],
+ [ 4.66666667, 3.33333333],
+ [ 5. , 3. ],
+ [ 6. , 2. ],
+ [ 6. , 2. ]])
+
+ """
+ return w.sparse * y
diff --git a/pysal/weights/tests/__init__.py b/pysal/weights/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pysal/weights/tests/test_Contiguity.py b/pysal/weights/tests/test_Contiguity.py
new file mode 100644
index 0000000..ca97b91
--- /dev/null
+++ b/pysal/weights/tests/test_Contiguity.py
@@ -0,0 +1,27 @@
+"""Unit test for Contiguity.py"""
+import unittest
+import pysal
+import numpy as np
+
+
+class TestContiguity(unittest.TestCase):
+ def setUp(self):
+ self.polyShp = pysal.examples.get_path('10740.shp')
+
+ def test_buildContiguity(self):
+ w = pysal.buildContiguity(pysal.open(self.polyShp, 'r'))
+ self.assertEqual(w[0], {1: 1.0, 4: 1.0, 101: 1.0, 85: 1.0, 5: 1.0})
+ w = pysal.buildContiguity(
+ pysal.open(self.polyShp, 'r'), criterion='queen')
+ self.assertEqual(w.pct_nonzero, 0.031926364234056544)
+ w = pysal.buildContiguity(
+ pysal.open(self.polyShp, 'r'), criterion='rook')
+ self.assertEqual(w.pct_nonzero, 0.026351084812623275)
+ fips = pysal.open(pysal.examples.get_path('10740.dbf')).by_col('STFID')
+ w = pysal.buildContiguity(pysal.open(self.polyShp, 'r'), ids=fips)
+ self.assertEqual(w['35001000107'], {'35001003805': 1.0, '35001003721':
+ 1.0, '35001000111': 1.0, '35001000112': 1.0, '35001000108': 1.0})
+
+
+if __name__ == "__main__":
+ unittest.main()
diff --git a/pysal/weights/tests/test_Distance.py b/pysal/weights/tests/test_Distance.py
new file mode 100644
index 0000000..873a664
--- /dev/null
+++ b/pysal/weights/tests/test_Distance.py
@@ -0,0 +1,149 @@
+import os
+import unittest
+import pysal
+import numpy as np
+
+
+class TestDistanceWeights(unittest.TestCase):
+ def setUp(self):
+ np.random.seed(1234)
+ self.polyShp = pysal.examples.get_path('columbus.shp')
+ self.arcShp = pysal.examples.get_path('stl_hom.shp')
+ self.points = [(
+ 10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+
+ def test_knnW(self):
+ x = np.indices((5, 5))
+ x, y = np.indices((5, 5))
+ x.shape = (25, 1)
+ y.shape = (25, 1)
+ data = np.hstack([x, y])
+ wnn2 = pysal.knnW(data, k=2)
+ wnn4 = pysal.knnW(data, k=4)
+ wnn4.neighbors[0]
+ self.assertEqual(set(wnn4.neighbors[0]), set([1, 5, 6, 2]))
+ self.assertEqual(set(wnn2.neighbors[5]), set([0, 6]))
+ self.assertEqual(wnn2.pct_nonzero, 0.080000000000000002)
+ wnn3e = pysal.knnW(data, p=2, k=3)
+ self.assertEqual(set(wnn3e.neighbors[0]), set([1, 5, 6]))
+ wc = pysal.knnW_from_shapefile(self.polyShp)
+ self.assertEqual(wc.pct_nonzero, 0.040816326530612242)
+ self.assertEqual(set(wc.neighbors[0]), set([2, 1]))
+ wc3 = pysal.knnW_from_shapefile(self.polyShp, k=3)
+ self.assertEqual(wc3.weights[1], [1, 1, 1])
+ self.assertEqual(set(wc3.neighbors[1]), set([0,3,7]))
+
+ def test_knnW_arc(self):
+ pts = [x.centroid for x in pysal.open(self.arcShp)]
+ dist = pysal.cg.sphere.arcdist # default radius is Earth KM
+ full = np.matrix([[dist(pts[i], pts[j]) for j in xrange(
+ len(pts))] for i in xrange(len(pts))])
+
+ kd = pysal.cg.kdtree.KDTree(pts, distance_metric='Arc',
+ radius=pysal.cg.sphere.RADIUS_EARTH_KM)
+ w = pysal.knnW(kd, 4)
+ self.assertEqual(set(w.neighbors[4]), set([1,3,9,12]))
+ self.assertEqual(set(w.neighbors[40]), set([31,38,45,49]))
+ #self.assertTrue((full.argsort()[:, 1:5] == np.array(
+ # [w.neighbors[x] for x in range(len(pts))])).all())
+
+ def test_Kernel(self):
+ kw = pysal.Kernel(self.points)
+ self.assertEqual(kw.weights[0], [1.0, 0.50000004999999503,
+ 0.44098306152674649])
+ kw15 = pysal.Kernel(self.points, bandwidth=15.0)
+ self.assertEqual(kw15[0], {0: 1.0, 1: 0.33333333333333337,
+ 3: 0.2546440075000701})
+ self.assertEqual(kw15.bandwidth[0], 15.)
+ self.assertEqual(kw15.bandwidth[-1], 15.)
+ bw = [25.0, 15.0, 25.0, 16.0, 14.5, 25.0]
+ kwa = pysal.Kernel(self.points, bandwidth=bw)
+ self.assertEqual(kwa.weights[0], [1.0, 0.59999999999999998,
+ 0.55278640450004202,
+ 0.10557280900008403])
+ self.assertEqual(kwa.neighbors[0], [0, 1, 3, 4])
+ self.assertEqual(kwa.bandwidth[0], 25.)
+ self.assertEqual(kwa.bandwidth[1], 15.)
+ self.assertEqual(kwa.bandwidth[2], 25.)
+ self.assertEqual(kwa.bandwidth[3], 16.)
+ self.assertEqual(kwa.bandwidth[4], 14.5)
+ self.assertEqual(kwa.bandwidth[5], 25.)
+ kwea = pysal.Kernel(self.points, fixed=False)
+ self.assertEqual(kwea.weights[0], [1.0, 0.10557289844279438,
+ 9.9999990066379496e-08])
+ l = kwea.bandwidth.tolist()
+ self.assertEqual(l, [[11.180341005532938], [11.180341005532938],
+ [20.000002000000002], [11.180341005532938],
+ [14.142137037944515], [18.027758180095585]])
+ kweag = pysal.Kernel(self.points, fixed=False, function='gaussian')
+ self.assertEqual(kweag.weights[0], [0.3989422804014327,
+ 0.26741902915776961,
+ 0.24197074871621341])
+ l = kweag.bandwidth.tolist()
+ self.assertEqual(l, [[11.180341005532938], [11.180341005532938],
+ [20.000002000000002], [11.180341005532938],
+ [14.142137037944515], [18.027758180095585]])
+
+ kw = pysal.kernelW_from_shapefile(self.polyShp, idVariable='POLYID')
+ self.assertEqual(set(kw.weights[1]), set([0.0070787731484506233,
+ 0.2052478782400463,
+ 0.23051223027663237,
+ 1.0
+ ]))
+ kwa = pysal.adaptive_kernelW_from_shapefile(self.polyShp)
+ self.assertEqual(kwa.weights[0], [1.0, 0.03178906767736345,
+ 9.9999990066379496e-08])
+
+ def test_threshold(self):
+ md = pysal.min_threshold_dist_from_shapefile(self.polyShp)
+ self.assertEqual(md, 0.61886415807685413)
+ wid = pysal.threshold_continuousW_from_array(self.points, 11.2)
+ self.assertEqual(wid.weights[0], [0.10000000000000001,
+ 0.089442719099991588])
+ wid2 = pysal.threshold_continuousW_from_array(
+ self.points, 11.2, alpha=-2.0)
+ self.assertEqual(wid2.weights[0], [0.01, 0.0079999999999999984])
+ w = pysal.threshold_continuousW_from_shapefile(
+ self.polyShp, 0.62, idVariable="POLYID")
+ self.assertEqual(w.weights[1], [1.6702346893743334,
+ 1.7250729841938093])
+
+ def test_DistanceBand(self):
+ """ see issue #126 """
+ w = pysal.rook_from_shapefile(
+ pysal.examples.get_path("lattice10x10.shp"))
+ polygons = pysal.open(
+ pysal.examples.get_path("lattice10x10.shp"), "r").read()
+ points1 = [poly.centroid for poly in polygons]
+ w1 = pysal.DistanceBand(points1, 1)
+ for k in range(w.n):
+ self.assertEqual(w[k], w1[k])
+
+ def test_DistanceBand_ints(self):
+ """ see issue #126 """
+ w = pysal.rook_from_shapefile(
+ pysal.examples.get_path("lattice10x10.shp"))
+ polygons = pysal.open(
+ pysal.examples.get_path("lattice10x10.shp"), "r").read()
+ points2 = [tuple(map(int, poly.vertices[0])) for poly in polygons]
+ w2 = pysal.DistanceBand(points2, 1)
+ for k in range(w.n):
+ self.assertEqual(w[k], w2[k])
+
+ def test_DistanceBand_arc(self):
+ pts = [x.centroid for x in pysal.open(self.arcShp)]
+ dist = pysal.cg.sphere.arcdist # default radius is Earth KM
+ full = np.matrix([[dist(pts[i], pts[j]) for j in xrange(
+ len(pts))] for i in xrange(len(pts))])
+
+ kd = pysal.cg.kdtree.KDTree(pts, distance_metric='Arc',
+ radius=pysal.cg.sphere.RADIUS_EARTH_KM)
+ w = pysal.DistanceBand(kd, full.max(), binary=False, alpha=1.0)
+ self.assertTrue((w.sparse.todense() == full).all())
+
+
+suite = unittest.TestLoader().loadTestsFromTestCase(TestDistanceWeights)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/weights/tests/test_Wsets.py b/pysal/weights/tests/test_Wsets.py
new file mode 100644
index 0000000..6c15030
--- /dev/null
+++ b/pysal/weights/tests/test_Wsets.py
@@ -0,0 +1,64 @@
+"""Unit test for Wsets module."""
+import unittest
+import pysal
+
+
+class TestWsets(unittest.TestCase):
+ """Unit test for Wsets module."""
+
+ def test_w_union(self):
+ """Unit test"""
+ w1 = pysal.lat2W(4, 4)
+ w2 = pysal.lat2W(6, 4)
+ w3 = pysal.weights.Wsets.w_union(w1, w2)
+ self.assertEqual(w1[0], w3[0])
+ self.assertEqual(w1.neighbors[15], [11, 14])
+ self.assertEqual(w2.neighbors[15], [11, 14, 19])
+ self.assertEqual(w3.neighbors[15], [19, 11, 14])
+
+ def test_w_intersection(self):
+ """Unit test"""
+ w1 = pysal.lat2W(4, 4)
+ w2 = pysal.lat2W(6, 4)
+ w3 = pysal.weights.Wsets.w_union(w1, w2)
+ self.assertEqual(w1[0], w3[0])
+ self.assertEqual(w1.neighbors[15], [11, 14])
+ self.assertEqual(w2.neighbors[15], [11, 14, 19])
+ self.assertEqual(w3.neighbors[15], [19, 11, 14])
+
+ def test_w_difference(self):
+ """Unit test"""
+ w1 = pysal.lat2W(4, 4, rook=False)
+ w2 = pysal.lat2W(4, 4, rook=True)
+ w3 = pysal.weights.Wsets.w_difference(w1, w2, constrained=False)
+ self.assertNotEqual(w1[0], w3[0])
+ self.assertEqual(w1.neighbors[15], [10, 11, 14])
+ self.assertEqual(w2.neighbors[15], [11, 14])
+ self.assertEqual(w3.neighbors[15], [10])
+
+ def test_w_symmetric_difference(self):
+ """Unit test"""
+ w1 = pysal.lat2W(4, 4, rook=False)
+ w2 = pysal.lat2W(6, 4, rook=True)
+ w3 = pysal.weights.Wsets.w_symmetric_difference(
+ w1, w2, constrained=False)
+ self.assertNotEqual(w1[0], w3[0])
+ self.assertEqual(w1.neighbors[15], [10, 11, 14])
+ self.assertEqual(w2.neighbors[15], [11, 14, 19])
+ self.assertEqual(w3.neighbors[15], [10, 19])
+
+ def test_w_subset(self):
+ """Unit test"""
+ w1 = pysal.lat2W(6, 4)
+ ids = range(16)
+ w2 = pysal.weights.Wsets.w_subset(w1, ids)
+ self.assertEqual(w1[0], w2[0])
+ self.assertEqual(w1.neighbors[15], [11, 14, 19])
+ self.assertEqual(w2.neighbors[15], [11, 14])
+
+
+suite = unittest.TestLoader().loadTestsFromTestCase(TestWsets)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/weights/tests/test__contW_binning.py b/pysal/weights/tests/test__contW_binning.py
new file mode 100644
index 0000000..e798bfc
--- /dev/null
+++ b/pysal/weights/tests/test__contW_binning.py
@@ -0,0 +1,132 @@
+import os
+import unittest
+import pysal
+from pysal.weights._contW_binning import ContiguityWeights_binning, QUEEN, ROOK
+
+
+class TestContiguityWeights(unittest.TestCase):
+ def setUp(self):
+ """ Setup the binning contiguity weights"""
+ shpObj = pysal.open(pysal.examples.get_path('virginia.shp'), 'r')
+ self.binningW = ContiguityWeights_binning(shpObj, QUEEN)
+ shpObj.close()
+
+ def test_w_type(self):
+ self.assert_(isinstance(self.binningW, ContiguityWeights_binning))
+
+ def test_QUEEN(self):
+ self.assertEqual(QUEEN, 1)
+
+ def test_ROOK(self):
+ self.assertEqual(ROOK, 2)
+
+ def test_ContiguityWeights_binning(self):
+ self.assert_(hasattr(self.binningW, 'w'))
+ self.assert_(issubclass(dict, type(self.binningW.w)))
+ self.assertEqual(len(self.binningW.w), 136)
+
+ def test_nested_polygons(self):
+ # load queen gal file created using Open Geoda.
+ geodaW = pysal.open(
+ pysal.examples.get_path('virginia.gal'), 'r').read()
+ # build matching W with pysal
+ pysalWb = self.build_W(
+ pysal.examples.get_path('virginia.shp'), QUEEN, 'POLY_ID')
+ # compare output.
+ for key in geodaW.neighbors:
+ geoda_neighbors = map(int, geodaW.neighbors[key])
+ pysalb_neighbors = pysalWb.neighbors[int(key)]
+ geoda_neighbors.sort()
+ pysalb_neighbors.sort()
+ self.assertEqual(geoda_neighbors, pysalb_neighbors)
+
+ def test_true_rook(self):
+ # load queen gal file created using Open Geoda.
+ geodaW = pysal.open(pysal.examples.get_path('rook31.gal'), 'r').read()
+ # build matching W with pysal
+ #pysalW = pysal.rook_from_shapefile(pysal.examples.get_path('rook31.shp'),','POLY_ID')
+ pysalWb = self.build_W(
+ pysal.examples.get_path('rook31.shp'), ROOK, 'POLY_ID')
+ # compare output.
+ for key in geodaW.neighbors:
+ geoda_neighbors = map(int, geodaW.neighbors[key])
+ pysalb_neighbors = pysalWb.neighbors[int(key)]
+ geoda_neighbors.sort()
+ pysalb_neighbors.sort()
+ self.assertEqual(geoda_neighbors, pysalb_neighbors)
+
+ def test_true_rook2(self):
+ # load queen gal file created using Open Geoda.
+ geodaW = pysal.open(
+ pysal.examples.get_path('stl_hom_rook.gal'), 'r').read()
+ # build matching W with pysal
+ pysalWb = self.build_W(pysal.examples.get_path(
+ 'stl_hom.shp'), ROOK, 'POLY_ID_OG')
+ # compare output.
+ for key in geodaW.neighbors:
+ geoda_neighbors = map(int, geodaW.neighbors[key])
+ pysalb_neighbors = pysalWb.neighbors[int(key)]
+ geoda_neighbors.sort()
+ pysalb_neighbors.sort()
+ self.assertEqual(geoda_neighbors, pysalb_neighbors)
+
+ def test_true_rook3(self):
+ # load queen gal file created using Open Geoda.
+ geodaW = pysal.open(
+ pysal.examples.get_path('sacramentot2.gal'), 'r').read()
+ # build matching W with pysal
+ pysalWb = self.build_W(pysal.examples.get_path(
+ 'sacramentot2.shp'), ROOK, 'POLYID')
+ # compare output.
+ for key in geodaW.neighbors:
+ geoda_neighbors = map(int, geodaW.neighbors[key])
+ pysalb_neighbors = pysalWb.neighbors[int(key)]
+ geoda_neighbors.sort()
+ pysalb_neighbors.sort()
+ self.assertEqual(geoda_neighbors, pysalb_neighbors)
+
+ def test_true_rook4(self):
+ # load queen gal file created using Open Geoda.
+ geodaW = pysal.open(
+ pysal.examples.get_path('virginia_rook.gal'), 'r').read()
+ # build matching W with pysal
+ pysalWb = self.build_W(
+ pysal.examples.get_path('virginia.shp'), ROOK, 'POLY_ID')
+ # compare output.
+ for key in geodaW.neighbors:
+ geoda_neighbors = map(int, geodaW.neighbors[key])
+ pysalb_neighbors = pysalWb.neighbors[int(key)]
+ geoda_neighbors.sort()
+ pysalb_neighbors.sort()
+ self.assertEqual(geoda_neighbors, pysalb_neighbors)
+
+ def build_W(self, shapefile, type, idVariable=None):
+ """ Building 2 W's the hard way. We need to do this so we can test both rtree and binning """
+ dbname = os.path.splitext(shapefile)[0] + '.dbf'
+ db = pysal.open(dbname)
+ shpObj = pysal.open(shapefile)
+ neighbor_data = ContiguityWeights_binning(shpObj, type).w
+ neighbors = {}
+ weights = {}
+ if idVariable:
+ ids = db.by_col[idVariable]
+ self.assertEqual(len(ids), len(set(ids)))
+ for key in neighbor_data:
+ id = ids[key]
+ if id not in neighbors:
+ neighbors[id] = set()
+ neighbors[id].update([ids[x] for x in neighbor_data[key]])
+ for key in neighbors:
+ neighbors[key] = list(neighbors[key])
+ binningW = pysal.W(neighbors, id_order=ids)
+ else:
+ neighbors[key] = list(neighbors[key])
+ binningW = pysal.W(neighbors)
+ return binningW
+
+#suite = unittest.TestLoader().loadTestsFromTestCase(_TestContiguityWeights)
+
+if __name__ == '__main__':
+ #runner = unittest.TextTestRunner()
+ #runner.run(suite)
+ unittest.main()
diff --git a/pysal/weights/tests/test__contW_rtree.py b/pysal/weights/tests/test__contW_rtree.py
new file mode 100644
index 0000000..d6195a0
--- /dev/null
+++ b/pysal/weights/tests/test__contW_rtree.py
@@ -0,0 +1,138 @@
+import os
+import unittest
+import pysal
+OK_TO_RUN = True
+try:
+ #import rtree
+ from pysal.weights._contW_rtree import ContiguityWeights_rtree, QUEEN, ROOK
+except ImportError:
+ OK_TO_RUN = False
+ print "Cannot test rtree contiguity weights, rtree not installed"
+
+
+class TestRtreeContiguityWeights(unittest.TestCase):
+ def setUp(self):
+ """ Setup the rtree contiguity weights"""
+ shpObj = pysal.open(pysal.examples.get_path('virginia.shp'), 'r')
+ self.rtreeW = ContiguityWeights_rtree(shpObj, QUEEN)
+ shpObj.close()
+
+ def test_w_type(self):
+ self.assert_(isinstance(self.rtreeW, ContiguityWeights_rtree))
+
+ def test_QUEEN(self):
+ self.assertEqual(QUEEN, 1)
+
+ def test_ROOK(self):
+ self.assertEqual(ROOK, 2)
+
+ def test_ContiguityWeights_rtree(self):
+ self.assert_(hasattr(self.rtreeW, 'w'))
+ self.assert_(issubclass(dict, type(self.rtreeW.w)))
+ self.assertEqual(len(self.rtreeW.w), 136)
+
+ def test_nested_polygons(self):
+ # load queen gal file created using Open Geoda.
+ geodaW = pysal.open(
+ pysal.examples.get_path('virginia.gal'), 'r').read()
+ # build matching W with pysal
+ pysalWr = self.build_W(
+ pysal.examples.get_path('virginia.shp'), QUEEN, 'POLY_ID')
+ # compare output.
+ for key in geodaW.neighbors:
+ geoda_neighbors = map(int, geodaW.neighbors[key])
+ pysalr_neighbors = pysalWr.neighbors[int(key)]
+ geoda_neighbors.sort()
+ pysalr_neighbors.sort()
+ self.assertEqual(geoda_neighbors, pysalr_neighbors)
+
+ def test_true_rook(self):
+ # load rook gal file created using Open Geoda.
+ geodaW = pysal.open(pysal.examples.get_path('rook31.gal'), 'r').read()
+ # build matching W with pysal
+ #pysalW = pysal.rook_from_shapefile(pysal.examples.get_path('rook31.shp'),'POLY_ID')
+ pysalWr = self.build_W(
+ pysal.examples.get_path('rook31.shp'), ROOK, 'POLY_ID')
+ # compare output.
+ for key in geodaW.neighbors:
+ geoda_neighbors = map(int, geodaW.neighbors[key])
+ pysalr_neighbors = pysalWr.neighbors[int(key)]
+ geoda_neighbors.sort()
+ pysalr_neighbors.sort()
+ self.assertEqual(geoda_neighbors, pysalr_neighbors)
+
+ def test_true_rook2(self):
+ # load rook gal file created using Open Geoda.
+ geodaW = pysal.open(
+ pysal.examples.get_path('stl_hom_rook.gal'), 'r').read()
+ # build matching W with pysal
+ pysalWr = self.build_W(pysal.examples.get_path(
+ 'stl_hom.shp'), ROOK, 'POLY_ID_OG')
+ # compare output.
+ for key in geodaW.neighbors:
+ geoda_neighbors = map(int, geodaW.neighbors[key])
+ pysalr_neighbors = pysalWr.neighbors[int(key)]
+ geoda_neighbors.sort()
+ pysalr_neighbors.sort()
+ self.assertEqual(geoda_neighbors, pysalr_neighbors)
+
+ def test_true_rook3(self):
+ # load rook gal file created using Open Geoda.
+ geodaW = pysal.open(
+ pysal.examples.get_path('sacramentot2.gal'), 'r').read()
+ # build matching W with pysal
+ pysalWr = self.build_W(pysal.examples.get_path(
+ 'sacramentot2.shp'), ROOK, 'POLYID')
+ # compare output.
+ for key in geodaW.neighbors:
+ geoda_neighbors = map(int, geodaW.neighbors[key])
+ pysalr_neighbors = pysalWr.neighbors[int(key)]
+ geoda_neighbors.sort()
+ pysalr_neighbors.sort()
+ self.assertEqual(geoda_neighbors, pysalr_neighbors)
+
+ def test_true_rook4(self):
+ # load rook gal file created using Open Geoda.
+ geodaW = pysal.open(
+ pysal.examples.get_path('virginia_rook.gal'), 'r').read()
+ # build matching W with pysal
+ pysalWr = self.build_W(
+ pysal.examples.get_path('virginia.shp'), ROOK, 'POLY_ID')
+ # compare output.
+ for key in geodaW.neighbors:
+ geoda_neighbors = map(int, geodaW.neighbors[key])
+ pysalr_neighbors = pysalWr.neighbors[int(key)]
+ geoda_neighbors.sort()
+ pysalr_neighbors.sort()
+ self.assertEqual(geoda_neighbors, pysalr_neighbors)
+
+ def build_W(self, shapefile, type, idVariable=None):
+ """ Building 2 W's the hard way. We need to do this so we can test both rtree and binning """
+ dbname = os.path.splitext(shapefile)[0] + '.dbf'
+ db = pysal.open(dbname)
+ shpObj = pysal.open(shapefile)
+ neighbor_data = ContiguityWeights_rtree(shpObj, type).w
+ neighbors = {}
+ weights = {}
+ if idVariable:
+ ids = db.by_col[idVariable]
+ self.assertEqual(len(ids), len(set(ids)))
+ for key in neighbor_data:
+ id = ids[key]
+ if id not in neighbors:
+ neighbors[id] = set()
+ neighbors[id].update([ids[x] for x in neighbor_data[key]])
+ for key in neighbors:
+ neighbors[key] = list(neighbors[key])
+ rtreeW = pysal.W(neighbors, id_order=ids)
+ else:
+ neighbors[key] = list(neighbors[key])
+ rtreeW = pysal.W(neighbors)
+ shpObj.seek(0)
+ return rtreeW
+
+suite = unittest.TestLoader().loadTestsFromTestCase(TestRtreeContiguityWeights)
+
+if __name__ == '__main__' and OK_TO_RUN:
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/weights/tests/test_spatial_lag.py b/pysal/weights/tests/test_spatial_lag.py
new file mode 100644
index 0000000..8883857
--- /dev/null
+++ b/pysal/weights/tests/test_spatial_lag.py
@@ -0,0 +1,41 @@
+
+import os
+import unittest
+import pysal
+import numpy as np
+
+
+class Testlag_spatial(unittest.TestCase):
+ def setUp(self):
+ self.neighbors = {'c': ['b'], 'b': ['c', 'a'], 'a': ['b']}
+ self.weights = {'c': [1.0], 'b': [1.0, 1.0], 'a': [1.0]}
+ self.id_order = ['a', 'b', 'c']
+ self.weights = {'c': [1.0], 'b': [1.0, 1.0], 'a': [1.0]}
+ self.w = pysal.W(self.neighbors, self.weights, self.id_order)
+ self.y = np.array([0, 1, 2])
+
+ def test_lag_spatial(self):
+ yl = pysal.lag_spatial(self.w, self.y)
+ np.testing.assert_array_almost_equal(yl, [1., 2., 1.])
+ self.w.id_order = ['b', 'c', 'a']
+ y = np.array([1, 2, 0])
+ yl = pysal.lag_spatial(self.w, y)
+ np.testing.assert_array_almost_equal(yl, [2., 1., 1.])
+ w = pysal.lat2W(3, 3)
+ y = np.arange(9)
+ yl = pysal.lag_spatial(w, y)
+ ylc = np.array([4., 6., 6., 10., 16., 14., 10., 18., 12.])
+ np.testing.assert_array_almost_equal(yl, ylc)
+ w.transform = 'r'
+ yl = pysal.lag_spatial(w, y)
+ ylc = np.array(
+ [2., 2., 3., 3.33333333, 4.,
+ 4.66666667, 5., 6., 6.])
+ np.testing.assert_array_almost_equal(yl, ylc)
+
+
+suite = unittest.TestLoader().loadTestsFromTestCase(Testlag_spatial)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/weights/tests/test_user.py b/pysal/weights/tests/test_user.py
new file mode 100644
index 0000000..bac1480
--- /dev/null
+++ b/pysal/weights/tests/test_user.py
@@ -0,0 +1,161 @@
+import os
+import unittest
+import pysal
+import numpy as np
+
+
+class Testuser(unittest.TestCase):
+ def setUp(self):
+ self.wq = pysal.queen_from_shapefile(
+ pysal.examples.get_path("columbus.shp"))
+ self.wr = pysal.rook_from_shapefile(
+ pysal.examples.get_path("columbus.shp"))
+
+ def test_queen_from_shapefile(self):
+ self.assertAlmostEquals(self.wq.pct_nonzero, 0.098292378175760101)
+
+ def test_rook_from_shapefile(self):
+ self.assertAlmostEquals(self.wr.pct_nonzero, 0.083298625572678045)
+
+ def test_knnW_from_array(self):
+ import numpy as np
+ x, y = np.indices((5, 5))
+ x.shape = (25, 1)
+ y.shape = (25, 1)
+ data = np.hstack([x, y])
+ wnn2 = pysal.knnW_from_array(data, k=2)
+ wnn4 = pysal.knnW_from_array(data, k=4)
+ self.assertEquals(set(wnn4.neighbors[0]), set([1, 5, 6, 2]))
+ self.assertEquals(set(wnn4.neighbors[5]), set([0, 6, 10, 1]))
+ self.assertEquals(set(wnn2.neighbors[0]), set([1, 5]))
+ self.assertEquals(set(wnn2.neighbors[5]), set([0, 6]))
+ self.assertAlmostEquals(wnn2.pct_nonzero, 0.080000000000000002)
+ self.assertAlmostEquals(wnn4.pct_nonzero, 0.16)
+ wnn4 = pysal.knnW_from_array(data, k=4)
+ self.assertEquals(set(wnn4.neighbors[0]), set([1, 5, 6, 2]))
+ wnn3e = pysal.knnW(data, p=2, k=3)
+ self.assertEquals(set(wnn3e.neighbors[0]),set([1, 5, 6]))
+ wnn3m = pysal.knnW(data, p=1, k=3)
+ self.assertEquals(set(wnn3m.neighbors[0]), set([1, 5, 2]))
+
+ def test_knnW_from_shapefile(self):
+ wc = pysal.knnW_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ self.assertAlmostEquals(wc.pct_nonzero, 0.040816326530612242)
+ wc3 = pysal.knnW_from_shapefile(pysal.examples.get_path(
+ "columbus.shp"), k=3)
+ self.assertEquals(wc3.weights[1], [1, 1, 1])
+ self.assertEquals(set(wc3.neighbors[1]), set([3, 0, 7]))
+ self.assertEquals(set(wc.neighbors[0]), set([2, 1]))
+ w = pysal.knnW_from_shapefile(pysal.examples.get_path('juvenile.shp'))
+ self.assertAlmostEquals(w.pct_nonzero, 0.011904761904761904)
+ w1 = pysal.knnW_from_shapefile(
+ pysal.examples.get_path('juvenile.shp'), k=1)
+ self.assertAlmostEquals(w1.pct_nonzero, 0.0059523809523809521)
+
+ def test_threshold_binaryW_from_array(self):
+ points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+ w = pysal.threshold_binaryW_from_array(points, threshold=11.2)
+ self.assertEquals(w.weights, {0: [1, 1], 1: [1, 1], 2: [],
+ 3: [1, 1], 4: [1], 5: [1]})
+ self.assertEquals(w.neighbors, {0: [1, 3], 1: [0, 3], 2: [
+ ], 3: [1, 0], 4: [5], 5: [4]})
+
+ def test_threshold_binaryW_from_shapefile(self):
+
+ w = pysal.threshold_binaryW_from_shapefile(pysal.examples.get_path(
+ "columbus.shp"), 0.62, idVariable="POLYID")
+ self.assertEquals(w.weights[1], [1, 1])
+
+ def test_threshold_continuousW_from_array(self):
+ points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+ wid = pysal.threshold_continuousW_from_array(points, 11.2)
+ self.assertEquals(wid.weights[0], [0.10000000000000001,
+ 0.089442719099991588])
+ wid2 = pysal.threshold_continuousW_from_array(points, 11.2, alpha=-2.0)
+ self.assertEquals(wid2.weights[0], [0.01, 0.0079999999999999984])
+
+ def test_threshold_continuousW_from_shapefile(self):
+ w = pysal.threshold_continuousW_from_shapefile(pysal.examples.get_path(
+ "columbus.shp"), 0.62, idVariable="POLYID")
+ self.assertEquals(
+ w.weights[1], [1.6702346893743334, 1.7250729841938093])
+
+ def test_kernelW(self):
+ points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+ kw = pysal.kernelW(points)
+ self.assertEquals(kw.weights[0], [1.0, 0.50000004999999503,
+ 0.44098306152674649])
+ self.assertEquals(kw.neighbors[0], [0, 1, 3])
+ np.testing.assert_array_almost_equal(
+ kw.bandwidth, np.array([[20.000002],
+ [20.000002],
+ [20.000002],
+ [20.000002],
+ [20.000002],
+ [20.000002]]))
+
+ def test_min_threshold_dist_from_shapefile(self):
+ f = pysal.examples.get_path('columbus.shp')
+ min_d = pysal.min_threshold_dist_from_shapefile(f)
+ self.assertAlmostEquals(min_d, 0.61886415807685413)
+
+ def test_kernelW_from_shapefile(self):
+ kw = pysal.kernelW_from_shapefile(pysal.examples.get_path(
+ 'columbus.shp'), idVariable='POLYID')
+ self.assertEquals(set(kw.weights[1]), set([0.0070787731484506233,
+ 0.2052478782400463,
+ 0.23051223027663237,
+ 1.0
+ ]))
+ np.testing.assert_array_almost_equal(
+ kw.bandwidth[:3], np.array([[0.75333961], [0.75333961],
+ [0.75333961]]))
+
+ def test_adaptive_kernelW(self):
+ points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+ bw = [25.0, 15.0, 25.0, 16.0, 14.5, 25.0]
+ kwa = pysal.adaptive_kernelW(points, bandwidths=bw)
+ self.assertEqual(kwa.weights[0], [1.0, 0.59999999999999998,
+ 0.55278640450004202,
+ 0.10557280900008403])
+ self.assertEqual(kwa.neighbors[0], [0, 1, 3, 4])
+ np.testing.assert_array_almost_equal(kwa.bandwidth,
+ np.array([[25.], [15.], [25.],
+ [16.], [14.5], [25.]]))
+
+ kweag = pysal.adaptive_kernelW(points, function='gaussian')
+ self.assertEqual(
+ kweag.weights[0], [0.3989422804014327, 0.26741902915776961,
+ 0.24197074871621341])
+ np.testing.assert_array_almost_equal(kweag.bandwidth,
+ np.array([[11.18034101],
+ [11.18034101],
+ [20.000002],
+ [11.18034101],
+ [14.14213704],
+ [18.02775818]]))
+
+ def test_adaptive_kernelW_from_shapefile(self):
+ kwa = pysal.adaptive_kernelW_from_shapefile(
+ pysal.examples.get_path('columbus.shp'))
+ self.assertEquals(kwa.weights[0], [1.0, 0.03178906767736345,
+ 9.9999990066379496e-08])
+ np.testing.assert_array_almost_equal(kwa.bandwidth[:3],
+ np.array([[0.59871832],
+ [0.59871832],
+ [0.56095647]]))
+
+ def test_build_lattice_shapefile(self):
+ of = "lattice.shp"
+ pysal.build_lattice_shapefile(20, 20, of)
+ w = pysal.rook_from_shapefile(of)
+ self.assertEquals(w.n, 400)
+ os.remove('lattice.shp')
+ os.remove('lattice.shx')
+
+
+suite = unittest.TestLoader().loadTestsFromTestCase(Testuser)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/weights/tests/test_util.py b/pysal/weights/tests/test_util.py
new file mode 100644
index 0000000..5ab0573
--- /dev/null
+++ b/pysal/weights/tests/test_util.py
@@ -0,0 +1,186 @@
+"""Unit test for util.py"""
+import pysal
+from pysal.common import *
+import pysal.weights
+import numpy as np
+from scipy import sparse, float32
+from scipy.spatial import KDTree
+import os
+import gc
+
+
+class Testutil(unittest.TestCase):
+ def setUp(self):
+ self.w = pysal.rook_from_shapefile(
+ pysal.examples.get_path('10740.shp'))
+
+ def test_lat2W(self):
+ w9 = pysal.lat2W(3, 3)
+ self.assertEquals(w9.pct_nonzero, 0.29629629629629628)
+ self.assertEquals(w9[0], {1: 1.0, 3: 1.0})
+ self.assertEquals(w9[3], {0: 1.0, 4: 1.0, 6: 1.0})
+
+ def test_lat2SW(self):
+ w9 = pysal.weights.lat2SW(3, 3)
+ rows, cols = w9.shape
+ n = rows * cols
+ pct_nonzero = w9.nnz / float(n)
+ self.assertEquals(pct_nonzero, 0.29629629629629628)
+ data = w9.todense().tolist()
+ self.assertEquals(data[0], [0, 1, 0, 1, 0, 0, 0, 0, 0])
+ self.assertEquals(data[1], [1, 0, 1, 0, 1, 0, 0, 0, 0])
+ self.assertEquals(data[2], [0, 1, 0, 0, 0, 1, 0, 0, 0])
+ self.assertEquals(data[3], [1, 0, 0, 0, 1, 0, 1, 0, 0])
+ self.assertEquals(data[4], [0, 1, 0, 1, 0, 1, 0, 1, 0])
+ self.assertEquals(data[5], [0, 0, 1, 0, 1, 0, 0, 0, 1])
+ self.assertEquals(data[6], [0, 0, 0, 1, 0, 0, 0, 1, 0])
+ self.assertEquals(data[7], [0, 0, 0, 0, 1, 0, 1, 0, 1])
+ self.assertEquals(data[8], [0, 0, 0, 0, 0, 1, 0, 1, 0])
+
+ def test_block_weights(self):
+ regimes = np.ones(25)
+ regimes[range(10, 20)] = 2
+ regimes[range(21, 25)] = 3
+ regimes = np.array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+ 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 1., 3., 3.,
+ 3., 3.])
+ w = pysal.block_weights(regimes)
+ ww0 = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+ self.assertEquals(w.weights[0], ww0)
+ wn0 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 20]
+ self.assertEquals(w.neighbors[0], wn0)
+ regimes = ['n', 'n', 's', 's', 'e', 'e', 'w', 'w', 'e']
+ n = len(regimes)
+ w = pysal.block_weights(regimes)
+ wn = {0: [1], 1: [0], 2: [3], 3: [2], 4: [5, 8], 5: [4, 8],
+ 6: [7], 7: [6], 8: [4, 5]}
+ self.assertEquals(w.neighbors, wn)
+
+ def test_comb(self):
+ x = range(4)
+ l = []
+ for i in pysal.comb(x, 2):
+ l.append(i)
+ lo = [[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]]
+ self.assertEquals(l, lo)
+
+ def test_order(self):
+ w3 = pysal.order(self.w, kmax=3)
+ w3105 = [1, -1, 1, 2, 1]
+ self.assertEquals(w3105, w3[1][0:5])
+
+ def test_higher_order(self):
+ w10 = pysal.lat2W(10, 10)
+ w10_2 = pysal.higher_order(w10, 2)
+ w10_20 = {2: 1.0, 11: 1.0, 20: 1.0}
+ self.assertEquals(w10_20, w10_2[0])
+ w5 = pysal.lat2W()
+ w50 = {1: 1.0, 5: 1.0}
+ self.assertEquals(w50, w5[0])
+ w51 = {0: 1.0, 2: 1.0, 6: 1.0}
+ self.assertEquals(w51, w5[1])
+ w5_2 = pysal.higher_order(w5, 2)
+ w5_20 = {2: 1.0, 10: 1.0, 6: 1.0}
+ self.assertEquals(w5_20, w5_2[0])
+
+ def test_shimbel(self):
+ w5 = pysal.lat2W()
+ w5_shimbel = pysal.shimbel(w5)
+ w5_shimbel024 = 8
+ self.assertEquals(w5_shimbel024, w5_shimbel[0][24])
+ w5_shimbel004 = [-1, 1, 2, 3]
+ self.assertEquals(w5_shimbel004, w5_shimbel[0][0:4])
+
+ def test_full(self):
+ neighbors = {'first': ['second'], 'second': ['first',
+ 'third'], 'third': ['second']}
+ weights = {'first': [1], 'second': [1, 1], 'third': [1]}
+ w = pysal.W(neighbors, weights)
+ wf, ids = pysal.full(w)
+ wfo = np.array([[0., 1., 0.], [1., 0., 1.], [0., 1., 0.]])
+ np.testing.assert_array_almost_equal(wfo, wf, decimal=8)
+ idso = ['first', 'second', 'third']
+ self.assertEquals(idso, ids)
+
+ def test_full2W(self):
+ a = np.zeros((4, 4))
+ for i in range(len(a)):
+ for j in range(len(a[i])):
+ if i != j:
+ a[i, j] = np.random.random(1)
+ w = pysal.weights.util.full2W(a)
+ np.testing.assert_array_equal(w.full()[0], a)
+ ids = ['myID0', 'myID1', 'myID2', 'myID3']
+ w = pysal.weights.util.full2W(a, ids=ids)
+ np.testing.assert_array_equal(w.full()[0], a)
+ w.full()[0] == a
+
+ def test_WSP2W(self):
+ sp = pysal.weights.lat2SW(2, 5)
+ wsp = pysal.weights.WSP(sp)
+ w = pysal.weights.WSP2W(wsp)
+ self.assertEquals(w.n, 10)
+ self.assertEquals(w[0], {1: 1, 5: 1})
+ w = pysal.open(pysal.examples.get_path('sids2.gal'), 'r').read()
+ wsp = pysal.weights.WSP(w.sparse, w.id_order)
+ w = pysal.weights.WSP2W(wsp)
+ self.assertEquals(w.n, 100)
+ self.assertEquals(w['37135'], {'37001': 1.0, '37033': 1.0,
+ '37037': 1.0, '37063': 1.0, '37145': 1.0})
+
+ def test_insert_diagonal(self):
+ w1 = pysal.weights.insert_diagonal(self.w)
+ r1 = {0: 1.0, 1: 1.0, 4: 1.0, 101: 1.0, 85: 1.0, 5: 1.0}
+ self.assertEquals(w1[0], r1)
+ w1 = pysal.weights.insert_diagonal(self.w, 20)
+ r1 = {0: 20, 1: 1.0, 4: 1.0, 101: 1.0, 85: 1.0, 5: 1.0}
+ self.assertEquals(w1[0], r1)
+ diag = np.arange(100, 100 + self.w.n)
+ w1 = pysal.weights.insert_diagonal(self.w, diag)
+ r1 = {0: 100, 1: 1.0, 4: 1.0, 101: 1.0, 85: 1.0, 5: 1.0}
+ self.assertEquals(w1[0], r1)
+
+ def test_remap_ids(self):
+ w = pysal.lat2W(3, 2)
+ wid_order = [0, 1, 2, 3, 4, 5]
+ self.assertEquals(wid_order, w.id_order)
+ wneighbors0 = [2, 1]
+ self.assertEquals(wneighbors0, w.neighbors[0])
+ old_to_new = {0: 'a', 1: 'b', 2: 'c', 3: 'd', 4: 'e', 5: 'f'}
+ w_new = pysal.remap_ids(w, old_to_new)
+ w_newid_order = ['a', 'b', 'c', 'd', 'e', 'f']
+ self.assertEquals(w_newid_order, w_new.id_order)
+ w_newdneighborsa = ['c', 'b']
+ self.assertEquals(w_newdneighborsa, w_new.neighbors['a'])
+
+ def test_get_ids(self):
+ polyids = pysal.weights.util.get_ids(
+ pysal.examples.get_path('columbus.shp'), "POLYID")
+ polyids5 = [1, 2, 3, 4, 5]
+ self.assertEquals(polyids5, polyids[:5])
+
+ def test_get_points_array_from_shapefile(self):
+ xy = pysal.weights.util.get_points_array_from_shapefile(
+ pysal.examples.get_path('juvenile.shp'))
+ xy3 = np.array([[94., 93.], [80., 95.], [79., 90.]])
+ np.testing.assert_array_almost_equal(xy3, xy[:3], decimal=8)
+ xy = pysal.weights.util.get_points_array_from_shapefile(
+ pysal.examples.get_path('columbus.shp'))
+ xy3 = np.array([[8.82721847, 14.36907602], [8.33265837,
+ 14.03162401], [9.01226541, 13.81971908]])
+ np.testing.assert_array_almost_equal(xy3, xy[:3], decimal=8)
+
+ def test_min_threshold_distance(self):
+ x, y = np.indices((5, 5))
+ x.shape = (25, 1)
+ y.shape = (25, 1)
+ data = np.hstack([x, y])
+ mint = 1.0
+ self.assertEquals(
+ mint, pysal.weights.util.min_threshold_distance(data))
+
+suite = unittest.TestLoader().loadTestsFromTestCase(Testutil)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/weights/tests/test_weights.py b/pysal/weights/tests/test_weights.py
new file mode 100644
index 0000000..66d6a7b
--- /dev/null
+++ b/pysal/weights/tests/test_weights.py
@@ -0,0 +1,451 @@
+import unittest
+import pysal
+import numpy as np
+
+NPTA3E = np.testing.assert_array_almost_equal
+
+
+class TestW(unittest.TestCase):
+ def setUp(self):
+ from pysal import rook_from_shapefile
+ self.w = rook_from_shapefile(pysal.examples.get_path('10740.shp'))
+
+ self.neighbors = {0: [3, 1], 1: [0, 4, 2], 2: [1, 5], 3: [0, 6, 4], 4: [1, 3,
+ 7, 5], 5: [2, 4, 8], 6: [3, 7], 7: [4, 6, 8], 8: [5, 7]}
+ self.weights = {0: [1, 1], 1: [1, 1, 1], 2: [1, 1], 3: [1, 1, 1], 4: [1, 1,
+ 1, 1], 5: [1, 1, 1], 6: [1, 1], 7: [1, 1, 1], 8: [1, 1]}
+
+ self.w3x3 = pysal.lat2W(3, 3)
+
+ def test_W(self):
+ w = pysal.W(self.neighbors, self.weights)
+ self.assertEqual(w.pct_nonzero, 0.29629629629629628)
+
+ def test___getitem__(self):
+ self.assertEqual(
+ self.w[0], {1: 1.0, 4: 1.0, 101: 1.0, 85: 1.0, 5: 1.0})
+
+ def test___init__(self):
+ w = pysal.W(self.neighbors, self.weights)
+ self.assertEqual(w.pct_nonzero, 0.29629629629629628)
+
+ def test___iter__(self):
+ w = pysal.lat2W(3, 3)
+ res = {}
+ for i, wi in enumerate(w):
+ res[i] = wi
+ self.assertEqual(res[0], (0, {1: 1.0, 3: 1.0}))
+ self.assertEqual(res[8], (8, {5: 1.0, 7: 1.0}))
+
+ def test_asymmetries(self):
+ w = pysal.lat2W(3, 3)
+ w.transform = 'r'
+ result = w.asymmetry()
+ self.assertEqual(result, [(0, 1), (0, 3), (1, 0), (1, 2), (1, 4), (2, 1), (2, 5), (3, 0), (3, 4), (3, 6), (4, 1), (4, 3), (4, 5), (4, 7), (5, 2), (5, 4), (5, 8), (6, 3), (6, 7), (7, 4), (7, 6), (7, 8), (8, 5), (8, 7)])
+
+ def test_asymmetry(self):
+ w = pysal.lat2W(3, 3)
+ self.assertEqual(w.asymmetry(), [])
+ w.transform = 'r'
+ self.assertFalse(w.asymmetry() == [])
+
+ def test_cardinalities(self):
+ w = pysal.lat2W(3, 3)
+ self.assertEqual(w.cardinalities, {0: 2, 1: 3, 2: 2, 3: 3, 4: 4, 5: 3,
+ 6: 2, 7: 3, 8: 2})
+
+ def test_diagW2(self):
+ NPTA3E(self.w3x3.diagW2, np.array([2., 3., 2., 3., 4., 3., 2.,
+ 3., 2.]))
+
+ def test_diagWtW(self):
+ NPTA3E(self.w3x3.diagW2, np.array([2., 3., 2., 3., 4., 3., 2.,
+ 3., 2.]))
+
+ def test_diagWtW_WW(self):
+ NPTA3E(self.w3x3.diagWtW_WW, np.array([4., 6., 4., 6., 8.,
+ 6., 4., 6., 4.]))
+
+ def test_full(self):
+ wf = np.array([[0., 1., 0., 1., 0., 0., 0., 0., 0.],
+ [1., 0., 1., 0., 1., 0., 0., 0., 0.],
+ [0., 1., 0., 0., 0., 1., 0., 0., 0.],
+ [1., 0., 0., 0., 1., 0., 1., 0., 0.],
+ [0., 1., 0., 1., 0., 1., 0., 1., 0.],
+ [0., 0., 1., 0., 1., 0., 0., 0., 1.],
+ [0., 0., 0., 1., 0., 0., 0., 1., 0.],
+ [0., 0., 0., 0., 1., 0., 1., 0., 1.],
+ [0., 0., 0., 0., 0., 1., 0., 1., 0.]])
+ ids = range(9)
+
+ wf1, ids1 = self.w3x3.full()
+ NPTA3E(wf1, wf)
+ self.assertEqual(ids1, ids)
+
+ def test_get_transform(self):
+ self.assertEqual(self.w3x3.transform, 'O')
+ self.w3x3.transform = 'r'
+ self.assertEqual(self.w3x3.transform, 'R')
+ self.w3x3.transform = 'b'
+
+ def test_higher_order(self):
+ weights = {0: [1.0, 1.0, 1.0], 1: [1.0, 1.0, 1.0], 2: [1.0, 1.0, 1.0], 3: [1.0, 1.0,
+ 1.0], 4: [1.0, 1.0, 1.0, 1.0], 5: [1.0, 1.0, 1.0], 6: [1.0, 1.0, 1.0], 7:
+ [1.0, 1.0, 1.0], 8: [1.0, 1.0, 1.0]}
+ neighbors = {0: [4, 6, 2], 1: [3, 5, 7], 2: [8, 0, 4], 3: [7, 1, 5],
+ 4: [8, 0, 2, 6], 5: [1, 3, 7], 6: [4, 0, 8], 7: [3, 1, 5], 8:
+ [6, 2, 4]}
+ w2 = pysal.higher_order(self.w3x3, 2)
+ self.assertEqual(w2.neighbors, neighbors)
+ self.assertEqual(w2.weights, weights)
+
+ def test_histogram(self):
+ hist = [(0, 1), (1, 1), (2, 4), (3, 20), (4, 57), (5, 44), (6, 36),
+ (7, 15), (8, 7), (9, 1), (10, 6), (11, 0), (12, 2), (13, 0),
+ (14, 0), (15, 1)]
+ self.assertEqual(self.w.histogram, hist)
+
+ def test_id2i(self):
+ id2i = {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8}
+ self.assertEqual(self.w3x3.id2i, id2i)
+
+ def test_id_order_set(self):
+ w = pysal.W(neighbors={'a': ['b'], 'b': ['a', 'c'], 'c': ['b']})
+ self.assertFalse(w.id_order_set)
+
+ def test_islands(self):
+ w = pysal.W(neighbors={'a': ['b'], 'b': ['a', 'c'], 'c':
+ ['b'], 'd': []})
+ self.assertEqual(w.islands, ['d'])
+ self.assertEqual(self.w3x3.islands, [])
+
+ def test_max_neighbors(self):
+ w = pysal.W(neighbors={'a': ['b'], 'b': ['a', 'c'], 'c':
+ ['b'], 'd': []})
+ self.assertEqual(w.max_neighbors, 2)
+ self.assertEqual(self.w3x3.max_neighbors, 4)
+
+ def test_mean_neighbors(self):
+ w = pysal.lat2W()
+ self.assertEqual(w.mean_neighbors, 3.2)
+
+ def test_min_neighbors(self):
+ w = pysal.lat2W()
+ self.assertEqual(w.min_neighbors, 2)
+
+ def test_n(self):
+ w = pysal.lat2W()
+ self.assertEqual(w.n, 25)
+
+ def test_neighbor_offsets(self):
+ d = {0: [3, 1],
+ 1: [0, 4, 2],
+ 2: [1, 5],
+ 3: [0, 6, 4],
+ 4: [1, 3, 7, 5],
+ 5: [2, 4, 8],
+ 6: [3, 7],
+ 7: [4, 6, 8],
+ 8: [5, 7]}
+
+ self.assertEqual(self.w3x3.neighbor_offsets, d)
+
+ def test_nonzero(self):
+ self.assertEquals(self.w3x3.nonzero, 24)
+
+ def test_order(self):
+ w = pysal.lat2W(3, 3)
+ o = {0: [-1, 1, 2, 1, 2, 3, 2, 3, 0],
+ 1: [1, -1, 1, 2, 1, 2, 3, 2, 3],
+ 2: [2, 1, -1, 3, 2, 1, 0, 3, 2],
+ 3: [1, 2, 3, -1, 1, 2, 1, 2, 3],
+ 4: [2, 1, 2, 1, -1, 1, 2, 1, 2],
+ 5: [3, 2, 1, 2, 1, -1, 3, 2, 1],
+ 6: [2, 3, 0, 1, 2, 3, -1, 1, 2],
+ 7: [3, 2, 3, 2, 1, 2, 1, -1, 1],
+ 8: [0, 3, 2, 3, 2, 1, 2, 1, -1]}
+ self.assertEquals(pysal.order(w), o)
+
+ def test_pct_nonzero(self):
+ self.assertEqual(self.w3x3.pct_nonzero, 0.29629629629629628)
+
+ def test_s0(self):
+ self.assertEqual(self.w3x3.s0, 24.0)
+
+ def test_s1(self):
+ self.assertEqual(self.w3x3.s1, 48.0)
+
+ def test_s2(self):
+ self.assertEqual(self.w3x3.s2, 272.0)
+
+ def test_s2array(self):
+ s2a = np.array([[16.], [36.], [16.], [36.],
+ [64.], [36.], [16.], [36.], [16.]])
+ NPTA3E(self.w3x3.s2array, s2a)
+
+ def test_sd(self):
+ self.assertEquals(self.w3x3.sd, 0.66666666666666663)
+
+ def test_set_transform(self):
+ w = pysal.lat2W(2, 2)
+ self.assertEqual(w.transform, 'O')
+ self.assertEquals(w.weights[0], [1.0, 1.0])
+ w.transform = 'r'
+ self.assertEquals(w.weights[0], [0.5, 0.5])
+
+ def test_shimbel(self):
+ d = {0: [-1, 1, 2, 1, 2, 3, 2, 3, 4],
+ 1: [1, -1, 1, 2, 1, 2, 3, 2, 3],
+ 2: [2, 1, -1, 3, 2, 1, 4, 3, 2],
+ 3: [1, 2, 3, -1, 1, 2, 1, 2, 3],
+ 4: [2, 1, 2, 1, -1, 1, 2, 1, 2],
+ 5: [3, 2, 1, 2, 1, -1, 3, 2, 1],
+ 6: [2, 3, 4, 1, 2, 3, -1, 1, 2],
+ 7: [3, 2, 3, 2, 1, 2, 1, -1, 1],
+ 8: [4, 3, 2, 3, 2, 1, 2, 1, -1]}
+ self.assertEquals(pysal.shimbel(self.w3x3), d)
+
+ def test_sparse(self):
+ self.assertEqual(self.w3x3.sparse.nnz, 24)
+
+ def test_trcW2(self):
+ self.assertEqual(self.w3x3.trcW2, 24.)
+
+ def test_trcWtW(self):
+ self.assertEqual(self.w3x3.trcWtW, 24.)
+
+ def test_trcWtW_WW(self):
+ self.assertEqual(self.w3x3.trcWtW_WW, 48.)
+
+
+class Test_WSP_Back_To_W(unittest.TestCase):
+ # Test to make sure we get back to the same W functionality
+ def setUp(self):
+ from pysal import rook_from_shapefile
+ self.w = rook_from_shapefile(pysal.examples.get_path('10740.shp'))
+ wsp = pysal.weights.WSP(self.w.sparse, self.w.id_order)
+ self.w = pysal.weights.WSP2W(wsp)
+
+ self.neighbors = {0: [3, 1], 1: [0, 4, 2], 2: [1, 5], 3: [0, 6, 4], 4: [1, 3,
+ 7, 5], 5: [2, 4, 8], 6: [3, 7], 7: [4, 6, 8], 8: [5, 7]}
+ self.weights = {0: [1, 1], 1: [1, 1, 1], 2: [1, 1], 3: [1, 1, 1], 4: [1, 1,
+ 1, 1], 5: [1, 1, 1], 6: [1, 1], 7: [1, 1, 1], 8: [1, 1]}
+
+ self.w3x3 = pysal.lat2W(3, 3)
+ w3x3 = pysal.weights.WSP(self.w3x3.sparse, self.w3x3.id_order)
+ self.w3x3 = pysal.weights.WSP2W(w3x3)
+
+ def test_W(self):
+ w = pysal.W(self.neighbors, self.weights)
+ self.assertEqual(w.pct_nonzero, 0.29629629629629628)
+
+ def test___getitem__(self):
+ self.assertEqual(
+ self.w[0], {1: 1.0, 4: 1.0, 101: 1.0, 85: 1.0, 5: 1.0})
+
+ def test___init__(self):
+ w = pysal.W(self.neighbors, self.weights)
+ self.assertEqual(w.pct_nonzero, 0.29629629629629628)
+
+ def test___iter__(self):
+ w = pysal.lat2W(3, 3)
+ res = {}
+ for i, wi in enumerate(w):
+ res[i] = wi
+ self.assertEqual(res[0], (0, {1: 1.0, 3: 1.0}))
+ self.assertEqual(res[8], (8, {5: 1.0, 7: 1.0}))
+
+ def test_asymmetries(self):
+ w = pysal.lat2W(3, 3)
+ w.transform = 'r'
+ result = w.asymmetry()
+ self.assertEqual(result, [(0, 1), (0, 3), (1, 0), (1, 2), (1, 4), (2, 1), (2, 5), (3, 0), (3, 4), (3, 6), (4, 1), (4, 3), (4, 5), (4, 7), (5, 2), (5, 4), (5, 8), (6, 3), (6, 7), (7, 4), (7, 6), (7, 8), (8, 5), (8, 7)])
+ def test_asymmetry(self):
+ w = pysal.lat2W(3, 3)
+ self.assertEqual(w.asymmetry(), [])
+ w.transform = 'r'
+ self.assertFalse(w.asymmetry() == [])
+
+ def test_cardinalities(self):
+ w = pysal.lat2W(3, 3)
+ self.assertEqual(w.cardinalities, {0: 2, 1: 3, 2: 2, 3: 3, 4: 4, 5: 3,
+ 6: 2, 7: 3, 8: 2})
+
+ def test_diagW2(self):
+ NPTA3E(self.w3x3.diagW2, np.array([2., 3., 2., 3., 4., 3., 2.,
+ 3., 2.]))
+
+ def test_diagWtW(self):
+ NPTA3E(self.w3x3.diagW2, np.array([2., 3., 2., 3., 4., 3., 2.,
+ 3., 2.]))
+
+ def test_diagWtW_WW(self):
+ NPTA3E(self.w3x3.diagWtW_WW, np.array([4., 6., 4., 6., 8.,
+ 6., 4., 6., 4.]))
+
+ def test_full(self):
+ wf = np.array([[0., 1., 0., 1., 0., 0., 0., 0., 0.],
+ [1., 0., 1., 0., 1., 0., 0., 0., 0.],
+ [0., 1., 0., 0., 0., 1., 0., 0., 0.],
+ [1., 0., 0., 0., 1., 0., 1., 0., 0.],
+ [0., 1., 0., 1., 0., 1., 0., 1., 0.],
+ [0., 0., 1., 0., 1., 0., 0., 0., 1.],
+ [0., 0., 0., 1., 0., 0., 0., 1., 0.],
+ [0., 0., 0., 0., 1., 0., 1., 0., 1.],
+ [0., 0., 0., 0., 0., 1., 0., 1., 0.]])
+ ids = range(9)
+
+ wf1, ids1 = self.w3x3.full()
+ NPTA3E(wf1, wf)
+ self.assertEqual(ids1, ids)
+
+ def test_get_transform(self):
+ self.assertEqual(self.w3x3.transform, 'O')
+ self.w3x3.transform = 'r'
+ self.assertEqual(self.w3x3.transform, 'R')
+ self.w3x3.transform = 'b'
+
+ def test_higher_order(self):
+ weights = {0: [1.0, 1.0, 1.0], 1: [1.0, 1.0, 1.0], 2: [1.0, 1.0, 1.0], 3: [1.0, 1.0,
+ 1.0], 4: [1.0, 1.0, 1.0, 1.0], 5: [1.0, 1.0, 1.0], 6: [1.0, 1.0, 1.0], 7:
+ [1.0, 1.0, 1.0], 8: [1.0, 1.0, 1.0]}
+ neighbors = {0: [4, 6, 2], 1: [3, 5, 7], 2: [8, 0, 4], 3: [7, 1, 5],
+ 4: [8, 0, 2, 6], 5: [1, 3, 7], 6: [4, 0, 8], 7: [3, 1, 5], 8:
+ [6, 2, 4]}
+ w2 = pysal.higher_order(self.w3x3, 2)
+ self.assertEqual(w2.neighbors, neighbors)
+ self.assertEqual(w2.weights, weights)
+
+ def test_histogram(self):
+ hist = [(0, 1), (1, 1), (2, 4), (3, 20), (4, 57), (5, 44), (6, 36),
+ (7, 15), (8, 7), (9, 1), (10, 6), (11, 0), (12, 2), (13, 0),
+ (14, 0), (15, 1)]
+ self.assertEqual(self.w.histogram, hist)
+
+ def test_id2i(self):
+ id2i = {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8}
+ self.assertEqual(self.w3x3.id2i, id2i)
+
+ def test_id_order_set(self):
+ w = pysal.W(neighbors={'a': ['b'], 'b': ['a', 'c'], 'c': ['b']})
+ self.assertFalse(w.id_order_set)
+
+ def test_islands(self):
+ w = pysal.W(neighbors={'a': ['b'], 'b': ['a', 'c'], 'c':
+ ['b'], 'd': []})
+ self.assertEqual(w.islands, ['d'])
+ self.assertEqual(self.w3x3.islands, [])
+
+ def test_max_neighbors(self):
+ w = pysal.W(neighbors={'a': ['b'], 'b': ['a', 'c'], 'c':
+ ['b'], 'd': []})
+ self.assertEqual(w.max_neighbors, 2)
+ self.assertEqual(self.w3x3.max_neighbors, 4)
+
+ def test_mean_neighbors(self):
+ w = pysal.lat2W()
+ self.assertEqual(w.mean_neighbors, 3.2)
+
+ def test_min_neighbors(self):
+ w = pysal.lat2W()
+ self.assertEqual(w.min_neighbors, 2)
+
+ def test_n(self):
+ w = pysal.lat2W()
+ self.assertEqual(w.n, 25)
+
+ def test_nonzero(self):
+ self.assertEquals(self.w3x3.nonzero, 24)
+
+ def test_order(self):
+ w = pysal.lat2W(3, 3)
+ o = {0: [-1, 1, 2, 1, 2, 3, 2, 3, 0],
+ 1: [1, -1, 1, 2, 1, 2, 3, 2, 3],
+ 2: [2, 1, -1, 3, 2, 1, 0, 3, 2],
+ 3: [1, 2, 3, -1, 1, 2, 1, 2, 3],
+ 4: [2, 1, 2, 1, -1, 1, 2, 1, 2],
+ 5: [3, 2, 1, 2, 1, -1, 3, 2, 1],
+ 6: [2, 3, 0, 1, 2, 3, -1, 1, 2],
+ 7: [3, 2, 3, 2, 1, 2, 1, -1, 1],
+ 8: [0, 3, 2, 3, 2, 1, 2, 1, -1]}
+ self.assertEquals(pysal.order(w), o)
+
+ def test_pct_nonzero(self):
+ self.assertEqual(self.w3x3.pct_nonzero, 0.29629629629629628)
+
+ def test_s0(self):
+ self.assertEqual(self.w3x3.s0, 24.0)
+
+ def test_s1(self):
+ self.assertEqual(self.w3x3.s1, 48.0)
+
+ def test_s2(self):
+ self.assertEqual(self.w3x3.s2, 272.0)
+
+ def test_s2array(self):
+ s2a = np.array([[16.], [36.], [16.], [36.],
+ [64.], [36.], [16.], [36.], [16.]])
+ NPTA3E(self.w3x3.s2array, s2a)
+
+ def test_sd(self):
+ self.assertEquals(self.w3x3.sd, 0.66666666666666663)
+
+ def test_set_transform(self):
+ w = pysal.lat2W(2, 2)
+ self.assertEqual(w.transform, 'O')
+ self.assertEquals(w.weights[0], [1.0, 1.0])
+ w.transform = 'r'
+ self.assertEquals(w.weights[0], [0.5, 0.5])
+
+ def test_shimbel(self):
+ d = {0: [-1, 1, 2, 1, 2, 3, 2, 3, 4],
+ 1: [1, -1, 1, 2, 1, 2, 3, 2, 3],
+ 2: [2, 1, -1, 3, 2, 1, 4, 3, 2],
+ 3: [1, 2, 3, -1, 1, 2, 1, 2, 3],
+ 4: [2, 1, 2, 1, -1, 1, 2, 1, 2],
+ 5: [3, 2, 1, 2, 1, -1, 3, 2, 1],
+ 6: [2, 3, 4, 1, 2, 3, -1, 1, 2],
+ 7: [3, 2, 3, 2, 1, 2, 1, -1, 1],
+ 8: [4, 3, 2, 3, 2, 1, 2, 1, -1]}
+ self.assertEquals(pysal.shimbel(self.w3x3), d)
+
+ def test_sparse(self):
+ self.assertEqual(self.w3x3.sparse.nnz, 24)
+
+ def test_trcW2(self):
+ self.assertEqual(self.w3x3.trcW2, 24.)
+
+ def test_trcWtW(self):
+ self.assertEqual(self.w3x3.trcWtW, 24.)
+
+ def test_trcWtW_WW(self):
+ self.assertEqual(self.w3x3.trcWtW_WW, 48.)
+
+
+class TestWSP(unittest.TestCase):
+ def setUp(self):
+ from pysal import rook_from_shapefile
+ self.w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
+ self.wsp = pysal.weights.WSP(self.w.sparse, self.w.id_order)
+ w3x3 = pysal.lat2W(3, 3)
+ self.w3x3 = pysal.weights.WSP(w3x3.sparse)
+
+ def test_WSP(self):
+ self.assertEquals(self.w.id_order, self.wsp.id_order)
+ self.assertEquals(self.w.n, self.wsp.n)
+ np.testing.assert_array_equal(
+ self.w.sparse.todense(), self.wsp.sparse.todense())
+
+ def test_diagWtW_WW(self):
+ NPTA3E(self.w3x3.diagWtW_WW, np.array([4., 6., 4., 6., 8.,
+ 6., 4., 6., 4.]))
+
+ def test_trcWtW_WW(self):
+ self.assertEqual(self.w3x3.trcWtW_WW, 48.)
+
+ def test_s0(self):
+ self.assertEqual(self.w3x3.s0, 24.0)
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/weights/user.py b/pysal/weights/user.py
new file mode 100644
index 0000000..133b8c2
--- /dev/null
+++ b/pysal/weights/user.py
@@ -0,0 +1,1123 @@
+"""
+Convenience functions for the construction of spatial weights based on
+contiguity and distance criteria
+"""
+
+__author__ = "Sergio J. Rey <srey at asu.edu> "
+__all__ = ['queen_from_shapefile', 'rook_from_shapefile', 'knnW_from_array', 'knnW_from_shapefile', 'threshold_binaryW_from_array', 'threshold_binaryW_from_shapefile', 'threshold_continuousW_from_array', 'threshold_continuousW_from_shapefile', 'kernelW', 'kernelW_from_shapefile', 'adaptive_kernelW', 'adaptive_kernelW_from_shapefile', 'min_threshold_dist_from_shapefile', 'build_lattice_shapefile']
+
+import pysal
+from Contiguity import buildContiguity
+from Distance import knnW, Kernel, DistanceBand
+from util import get_ids, get_points_array_from_shapefile, min_threshold_distance
+import numpy as np
+
+def queen_from_shapefile(shapefile, idVariable=None, sparse=False):
+ """
+ Queen contiguity weights from a polygon shapefile
+
+ Parameters
+ ----------
+
+ shapefile : string
+ name of polygon shapefile including suffix.
+ idVariable : string
+ name of a column in the shapefile's DBF to use for ids.
+ sparse : boolean
+ If True return WSP instance
+ If False return W instance
+ Returns
+ -------
+
+ w : W
+ instance of spatial weights
+
+ Examples
+ --------
+ >>> wq=queen_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ >>> "%.3f"%wq.pct_nonzero
+ '0.098'
+ >>> wq=queen_from_shapefile(pysal.examples.get_path("columbus.shp"),"POLYID")
+ >>> "%.3f"%wq.pct_nonzero
+ '0.098'
+ >>> wq=queen_from_shapefile(pysal.examples.get_path("columbus.shp"), sparse=True)
+ >>> pct_sp = wq.sparse.nnz *1. / wq.n**2
+ >>> "%.3f"%pct_sp
+ '0.098'
+
+ Notes
+ -----
+
+ Queen contiguity defines as neighbors any pair of polygons that share at
+ least one vertex in their polygon definitions.
+
+ See Also
+ --------
+ :class:`pysal.weights.W`
+
+ """
+ shp = pysal.open(shapefile)
+ w = buildContiguity(shp, criterion='queen')
+ if idVariable:
+ ids = get_ids(shapefile, idVariable)
+ w.remap_ids(ids)
+ else:
+ ids = None
+ shp.close()
+ w.set_shapefile(shapefile, idVariable)
+
+ if sparse:
+ w = pysal.weights.WSP(w.sparse, id_order=ids)
+
+ return w
+
+
+def rook_from_shapefile(shapefile, idVariable=None, sparse=False):
+ """
+ Rook contiguity weights from a polygon shapefile
+
+ Parameters
+ ----------
+
+ shapefile : string
+ name of polygon shapefile including suffix.
+ sparse : boolean
+ If True return WSP instance
+ If False return W instance
+
+ Returns
+ -------
+
+ w : W
+ instance of spatial weights
+
+ Examples
+ --------
+ >>> wr=rook_from_shapefile(pysal.examples.get_path("columbus.shp"), "POLYID")
+ >>> "%.3f"%wr.pct_nonzero
+ '0.083'
+ >>> wr=rook_from_shapefile(pysal.examples.get_path("columbus.shp"), sparse=True)
+ >>> pct_sp = wr.sparse.nnz *1. / wr.n**2
+ >>> "%.3f"%pct_sp
+ '0.083'
+
+ Notes
+ -----
+
+ Rook contiguity defines as neighbors any pair of polygons that share a
+ common edge in their polygon definitions.
+
+ See Also
+ --------
+ :class:`pysal.weights.W`
+
+ """
+ shp = pysal.open(shapefile)
+ w = buildContiguity(shp, criterion='rook')
+ if idVariable:
+ ids = get_ids(shapefile, idVariable)
+ w.remap_ids(ids)
+ else:
+ ids = None
+ shp.close()
+ w.set_shapefile(shapefile, idVariable)
+
+ if sparse:
+ w = pysal.weights.WSP(w.sparse, id_order=ids)
+
+
+ return w
+
+
+def spw_from_gal(galfile):
+ """
+ Sparse scipy matrix for w from a gal file
+
+ Parameters
+ ----------
+
+ galfile: string
+ name of gal file including suffix
+
+ Returns
+ -------
+
+ spw : scipy sparse matrix in CSR format
+
+ ids : array
+ identifiers for rows/cols of spw
+
+ Examples
+ --------
+
+ >>> spw = pysal.weights.user.spw_from_gal(pysal.examples.get_path("sids2.gal"))
+ >>> spw.sparse.nnz
+ 462
+ """
+
+ return pysal.open(galfile, 'r').read(sparse=True)
+
+# Distance based weights
+
+
+def knnW_from_array(array, k=2, p=2, ids=None, radius=None):
+ """
+ Nearest neighbor weights from a numpy array
+
+ Parameters
+ ----------
+
+ data : array (n,m)
+ attribute data, n observations on m attributes
+ k : int
+ number of nearest neighbors
+ p : float
+ Minkowski p-norm distance metric parameter:
+ 1<=p<=infinity
+ 2: Euclidean distance
+ 1: Manhattan distance
+ ids : list
+ identifiers to attach to each observation
+ radius : If supplied arc_distances will be calculated
+ based on the given radius. p will be ignored.
+
+ Returns
+ -------
+
+ w : W instance
+ Weights object with binary weights
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> x,y=np.indices((5,5))
+ >>> x.shape=(25,1)
+ >>> y.shape=(25,1)
+ >>> data=np.hstack([x,y])
+ >>> wnn2=knnW_from_array(data,k=2)
+ >>> wnn4=knnW_from_array(data,k=4)
+ >>> set([1, 5, 6, 2]) == set(wnn4.neighbors[0])
+ True
+ >>> set([0, 1, 10, 6]) == set(wnn4.neighbors[5])
+ True
+ >>> set([1, 5]) == set(wnn2.neighbors[0])
+ True
+ >>> set([0,6]) == set(wnn2.neighbors[5])
+ True
+ >>> "%.2f"%wnn2.pct_nonzero
+ '0.08'
+ >>> wnn4.pct_nonzero
+ 0.16
+ >>> wnn4=knnW_from_array(data,k=4)
+ >>> set([ 1,5,6,2]) == set(wnn4.neighbors[0])
+ True
+ >>> wnn4=knnW_from_array(data,k=4)
+ >>> wnn3e=knnW(data,p=2,k=3)
+ >>> set([1,5,6]) == set(wnn3e.neighbors[0])
+ True
+ >>> wnn3m=knnW(data,p=1,k=3)
+ >>> set([1,5,2]) == set(wnn3m.neighbors[0])
+ True
+
+ Notes
+ -----
+
+ Ties between neighbors of equal distance are arbitrarily broken.
+
+ See Also
+ --------
+ :class:`pysal.weights.W`
+
+ """
+ if radius is not None:
+ array = pysal.cg.KDTree(array, distance_metric='Arc', radius=radius)
+ return knnW(array, k=k, p=p, ids=ids)
+
+
+def knnW_from_shapefile(shapefile, k=2, p=2, idVariable=None, radius=None):
+ """
+ Nearest neighbor weights from a shapefile
+
+ Parameters
+ ----------
+
+ shapefile : string
+ shapefile name with shp suffix
+ k : int
+ number of nearest neighbors
+ p : float
+ Minkowski p-norm distance metric parameter:
+ 1<=p<=infinity
+ 2: Euclidean distance
+ 1: Manhattan distance
+ idVariable : string
+ name of a column in the shapefile's DBF to use for ids
+ radius : If supplied arc_distances will be calculated
+ based on the given radius. p will be ignored.
+
+ Returns
+ -------
+
+ w : W instance
+ Weights object with binary weights
+
+ Examples
+ --------
+
+ Polygon shapefile
+
+ >>> wc=knnW_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ >>> "%.4f"%wc.pct_nonzero
+ '0.0408'
+ >>> set([2,1]) == set(wc.neighbors[0])
+ True
+ >>> wc3=pysal.knnW_from_shapefile(pysal.examples.get_path("columbus.shp"),k=3)
+ >>> set(wc3.neighbors[0]) == set([2,1,3])
+ True
+ >>> set(wc3.neighbors[2]) == set([4,3,0])
+ True
+
+ 1 offset rather than 0 offset
+
+ >>> wc3_1=knnW_from_shapefile(pysal.examples.get_path("columbus.shp"),k=3,idVariable="POLYID")
+ >>> set([4,3,2]) == set(wc3_1.neighbors[1])
+ True
+ >>> wc3_1.weights[2]
+ [1.0, 1.0, 1.0]
+ >>> set([4,1,8]) == set(wc3_1.neighbors[2])
+ True
+
+
+ Point shapefile
+
+ >>> w=knnW_from_shapefile(pysal.examples.get_path("juvenile.shp"))
+ >>> w.pct_nonzero
+ 0.011904761904761904
+ >>> w1=knnW_from_shapefile(pysal.examples.get_path("juvenile.shp"),k=1)
+ >>> "%.3f"%w1.pct_nonzero
+ '0.006'
+ >>>
+
+ Notes
+ -----
+
+ Supports polygon or point shapefiles. For polygon shapefiles, distance is
+ based on polygon centroids. Distances are defined using coordinates in
+ shapefile which are assumed to be projected and not geographical
+ coordinates.
+
+ Ties between neighbors of equal distance are arbitrarily broken.
+
+ See Also
+ --------
+ :class:`pysal.weights.W`
+
+ """
+
+ data = get_points_array_from_shapefile(shapefile)
+ if radius is not None:
+ data = pysal.cg.KDTree(data, distance_metric='Arc', radius=radius)
+ if idVariable:
+ ids = get_ids(shapefile, idVariable)
+ return knnW(data, k=k, p=p, ids=ids)
+ return knnW(data, k=k, p=p)
+
+
+def threshold_binaryW_from_array(array, threshold, p=2, radius=None):
+ """
+ Binary weights based on a distance threshold
+
+ Parameters
+ ----------
+
+ array : array (n,m)
+ attribute data, n observations on m attributes
+ threshold : float
+ distance band
+ p : float
+ Minkowski p-norm distance metric parameter:
+ 1<=p<=infinity
+ 2: Euclidean distance
+ 1: Manhattan distance
+ radius : If supplied arc_distances will be calculated
+ based on the given radius. p will be ignored.
+
+ Returns
+ -------
+
+ w : W instance
+ Weights object with binary weights
+
+ Examples
+ --------
+ >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+ >>> w=threshold_binaryW_from_array(points,threshold=11.2)
+ WARNING: there is one disconnected observation (no neighbors)
+ Island id: [2]
+ >>> w.weights
+ {0: [1, 1], 1: [1, 1], 2: [], 3: [1, 1], 4: [1], 5: [1]}
+ >>> w.neighbors
+ {0: [1, 3], 1: [0, 3], 2: [], 3: [1, 0], 4: [5], 5: [4]}
+ >>>
+ """
+ if radius is not None:
+ array = pysal.cg.KDTree(array, distance_metric='Arc', radius=radius)
+ return DistanceBand(array, threshold=threshold, p=p)
+
+
+def threshold_binaryW_from_shapefile(shapefile, threshold, p=2, idVariable=None, radius=None):
+ """
+ Threshold distance based binary weights from a shapefile
+
+ Parameters
+ ----------
+
+ shapefile : string
+ shapefile name with shp suffix
+ threshold : float
+ distance band
+ p : float
+ Minkowski p-norm distance metric parameter:
+ 1<=p<=infinity
+ 2: Euclidean distance
+ 1: Manhattan distance
+ idVariable : string
+ name of a column in the shapefile's DBF to use for ids
+ radius : If supplied arc_distances will be calculated
+ based on the given radius. p will be ignored.
+
+ Returns
+ -------
+
+ w : W instance
+ Weights object with binary weights
+
+ Examples
+ --------
+ >>> w = threshold_binaryW_from_shapefile(pysal.examples.get_path("columbus.shp"),0.62,idVariable="POLYID")
+ >>> w.weights[1]
+ [1, 1]
+
+ Notes
+ -----
+ Supports polygon or point shapefiles. For polygon shapefiles, distance is
+ based on polygon centroids. Distances are defined using coordinates in
+ shapefile which are assumed to be projected and not geographical
+ coordinates.
+
+ """
+ data = get_points_array_from_shapefile(shapefile)
+ if radius is not None:
+ data = pysal.cg.KDTree(data, distance_metric='Arc', radius=radius)
+ if idVariable:
+ ids = get_ids(shapefile, idVariable)
+ w = DistanceBand(data, threshold=threshold, p=p)
+ w.remap_ids(ids)
+ return w
+ return threshold_binaryW_from_array(data, threshold, p=p)
+
+
+def threshold_continuousW_from_array(array, threshold, p=2,
+ alpha=-1, radius=None):
+
+ """
+ Continuous weights based on a distance threshold
+
+ Parameters
+ ----------
+
+ array : array (n,m)
+ attribute data, n observations on m attributes
+ threshold : float
+ distance band
+ p : float
+ Minkowski p-norm distance metric parameter:
+ 1<=p<=infinity
+ 2: Euclidean distance
+ 1: Manhattan distance
+ alpha : float
+ distance decay parameter for weight (default -1.0)
+ if alpha is positive the weights will not decline with
+ distance.
+ radius : If supplied arc_distances will be calculated
+ based on the given radius. p will be ignored.
+
+ Returns
+ -------
+
+ w : W instance
+ Weights object with continuous weights
+
+ Examples
+ --------
+
+ inverse distance weights
+
+ >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+ >>> wid=threshold_continuousW_from_array(points,11.2)
+ WARNING: there is one disconnected observation (no neighbors)
+ Island id: [2]
+ >>> wid.weights[0]
+ [0.10000000000000001, 0.089442719099991588]
+
+ gravity weights
+
+ >>> wid2=threshold_continuousW_from_array(points,11.2,alpha=-2.0)
+ WARNING: there is one disconnected observation (no neighbors)
+ Island id: [2]
+ >>> wid2.weights[0]
+ [0.01, 0.0079999999999999984]
+
+ """
+ if radius is not None:
+ array = pysal.cg.KDTree(array, distance_metric='Arc', radius=radius)
+ w = DistanceBand(
+ array, threshold=threshold, p=p, alpha=alpha, binary=False)
+ return w
+
+
+def threshold_continuousW_from_shapefile(shapefile, threshold, p=2,
+ alpha=-1, idVariable=None, radius=None):
+ """
+ Threshold distance based continuous weights from a shapefile
+
+ Parameters
+ ----------
+
+ shapefile : string
+ shapefile name with shp suffix
+ threshold : float
+ distance band
+ p : float
+ Minkowski p-norm distance metric parameter:
+ 1<=p<=infinity
+ 2: Euclidean distance
+ 1: Manhattan distance
+ alpha : float
+ distance decay parameter for weight (default -1.0)
+ if alpha is positive the weights will not decline with
+ distance.
+ idVariable : string
+ name of a column in the shapefile's DBF to use for ids
+ radius : If supplied arc_distances will be calculated
+ based on the given radius. p will be ignored.
+
+ Returns
+ -------
+
+ w : W instance
+ Weights object with continuous weights
+
+ Examples
+ --------
+ >>> w = threshold_continuousW_from_shapefile(pysal.examples.get_path("columbus.shp"),0.62,idVariable="POLYID")
+ >>> w.weights[1]
+ [1.6702346893743334, 1.7250729841938093]
+
+ Notes
+ -----
+ Supports polygon or point shapefiles. For polygon shapefiles, distance is
+ based on polygon centroids. Distances are defined using coordinates in
+ shapefile which are assumed to be projected and not geographical
+ coordinates.
+
+ """
+ data = get_points_array_from_shapefile(shapefile)
+ if radius is not None:
+ data = pysal.cg.KDTree(data, distance_metric='Arc', radius=radius)
+ if idVariable:
+ ids = get_ids(shapefile, idVariable)
+ w = DistanceBand(data, threshold=threshold, p=p, alpha=alpha, binary=False)
+ w.remap_ids(ids)
+ else:
+ w = threshold_continuousW_from_array(data, threshold, p=p, alpha=alpha)
+ w.set_shapefile(shapefile,idVariable)
+ return w
+
+
+# Kernel Weights
+
+
+def kernelW(points, k=2, function='triangular', fixed=True,
+ radius=None, diagonal=False):
+ """
+ Kernel based weights
+
+ Parameters
+ ----------
+
+ points : array (n,k)
+ n observations on k characteristics used to measure
+ distances between the n objects
+ k : int
+ the number of nearest neighbors to use for determining
+ bandwidth. Bandwidth taken as :math:`h_i=max(dknn) \\forall i`
+ where :math:`dknn` is a vector of k-nearest neighbor
+ distances (the distance to the kth nearest neighbor for each
+ observation).
+ function : string {'triangular','uniform','quadratic','epanechnikov',
+ 'quartic','bisquare','gaussian'}
+
+ .. math::
+
+ z_{i,j} = d_{i,j}/h_i
+
+ triangular
+
+ .. math::
+
+ K(z) = (1 - |z|) \ if |z| \le 1
+
+ uniform
+
+ .. math::
+
+ K(z) = |z| \ if |z| \le 1
+
+ quadratic
+
+ .. math::
+
+ K(z) = (3/4)(1-z^2) \ if |z| \le 1
+
+ epanechnikov
+
+ .. math::
+
+ K(z) = (1-z^2) \ if |z| \le 1
+
+ quartic
+
+ .. math::
+
+ K(z) = (15/16)(1-z^2)^2 \ if |z| \le 1
+
+ bisquare
+
+ .. math::
+
+ K(z) = (1-z^2)^2 \ if |z| \le 1
+
+ gaussian
+
+ .. math::
+
+ K(z) = (2\pi)^{(-1/2)} exp(-z^2 / 2)
+
+ fixed : binary
+ If true then :math:`h_i=h \\forall i`. If false then
+ bandwidth is adaptive across observations.
+ radius : If supplied arc_distances will be calculated
+ based on the given radius. p will be ignored.
+ diagonal : boolean
+ If true, set diagonal weights = 1.0, if false (default)
+ diagonal weights are set to value according to kernel
+ function
+
+ Returns
+ -------
+
+ w : W
+ instance of spatial weights
+
+ Examples
+ --------
+ >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+ >>> kw=kernelW(points)
+ >>> kw.weights[0]
+ [1.0, 0.500000049999995, 0.4409830615267465]
+ >>> kw.neighbors[0]
+ [0, 1, 3]
+ >>> kw.bandwidth
+ array([[ 20.000002],
+ [ 20.000002],
+ [ 20.000002],
+ [ 20.000002],
+ [ 20.000002],
+ [ 20.000002]])
+
+ use different k
+
+ >>> kw=kernelW(points,k=3)
+ >>> kw.neighbors[0]
+ [0, 1, 3, 4]
+ >>> kw.bandwidth
+ array([[ 22.36068201],
+ [ 22.36068201],
+ [ 22.36068201],
+ [ 22.36068201],
+ [ 22.36068201],
+ [ 22.36068201]])
+
+ Diagonals to 1.0
+
+ >>> kq = kernelW(points,function='gaussian')
+ >>> kq.weights
+ {0: [0.3989422804014327, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 0.3989422804014327, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 0.3989422804014327, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 0.3989422804014327, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 0.3989422804014327, 0.35206533556593145], 5: [0.26575287272131043, 0.3520653 [...]
+ >>> kqd = kernelW(points, function='gaussian', diagonal=True)
+ >>> kqd.weights
+ {0: [1.0, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 1.0, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 1.0, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 1.0, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 1.0, 0.35206533556593145], 5: [0.26575287272131043, 0.35206533556593145, 1.0]}
+
+ """
+ if radius is not None:
+ points = pysal.cg.KDTree(points, distance_metric='Arc', radius=radius)
+ return Kernel(points, function=function, k=k, fixed=fixed,
+ diagonal=diagonal)
+
+
+def kernelW_from_shapefile(shapefile, k=2, function='triangular',
+ idVariable=None, fixed=True, radius=None, diagonal=False):
+ """
+ Kernel based weights
+
+ Parameters
+ ----------
+
+ shapefile : string
+ shapefile name with shp suffix
+ k : int
+ the number of nearest neighbors to use for determining
+ bandwidth. Bandwidth taken as :math:`h_i=max(dknn) \\forall i`
+ where :math:`dknn` is a vector of k-nearest neighbor
+ distances (the distance to the kth nearest neighbor for each
+ observation).
+ function : string {'triangular','uniform','quadratic','epanechnikov',
+ 'quartic','bisquare','gaussian'}
+
+ .. math::
+
+ z_{i,j} = d_{i,j}/h_i
+
+ triangular
+
+ .. math::
+
+ K(z) = (1 - |z|) \ if |z| \le 1
+
+ uniform
+
+ .. math::
+
+ K(z) = |z| \ if |z| \le 1
+
+ quadratic
+
+ .. math::
+
+ K(z) = (3/4)(1-z^2) \ if |z| \le 1
+
+ epanechnikov
+
+ .. math::
+
+ K(z) = (1-z^2) \ if |z| \le 1
+
+ quartic
+
+ .. math::
+
+ K(z) = (15/16)(1-z^2)^2 \ if |z| \le 1
+
+ bisquare
+
+ .. math::
+
+ K(z) = (1-z^2)^2 \ if |z| \le 1
+
+ gaussian
+
+ .. math::
+
+ K(z) = (2\pi)^{(-1/2)} exp(-z^2 / 2)
+ idVariable : string
+ name of a column in the shapefile's DBF to use for ids
+
+ fixed : binary
+ If true then :math:`h_i=h \\forall i`. If false then
+ bandwidth is adaptive across observations.
+ radius : If supplied arc_distances will be calculated
+ based on the given radius. p will be ignored.
+ diagonal : boolean
+ If true, set diagonal weights = 1.0, if false (default)
+ diagonal weights are set to value according to kernel
+ function
+
+ Returns
+ -------
+
+ w : W
+ instance of spatial weights
+
+ Examples
+ --------
+ >>> kw = pysal.kernelW_from_shapefile(pysal.examples.get_path("columbus.shp"),idVariable='POLYID', function = 'gaussian')
+
+ >>> kwd = pysal.kernelW_from_shapefile(pysal.examples.get_path("columbus.shp"),idVariable='POLYID', function = 'gaussian', diagonal = True)
+ >>> set(kw.neighbors[1]) == set([4, 2, 3, 1])
+ True
+ >>> set(kwd.neighbors[1]) == set([4, 2, 3, 1])
+ True
+ >>>
+ >>> set(kw.weights[1]) == set( [0.2436835517263174, 0.29090631630909874, 0.29671172124745776, 0.3989422804014327])
+ True
+ >>> set(kwd.weights[1]) == set( [0.2436835517263174, 0.29090631630909874, 0.29671172124745776, 1.0])
+ True
+
+
+ Notes
+ -----
+ Supports polygon or point shapefiles. For polygon shapefiles, distance is
+ based on polygon centroids. Distances are defined using coordinates in
+ shapefile which are assumed to be projected and not geographical
+ coordinates.
+
+ """
+ points = get_points_array_from_shapefile(shapefile)
+ if radius is not None:
+ points = pysal.cg.KDTree(points, distance_metric='Arc', radius=radius)
+ if idVariable:
+ ids = get_ids(shapefile, idVariable)
+ return Kernel(points, function=function, k=k, ids=ids, fixed=fixed,
+ diagonal = diagonal)
+ return kernelW(points, k=k, function=function, fixed=fixed,
+ diagonal=diagonal)
+
+
+def adaptive_kernelW(points, bandwidths=None, k=2, function='triangular',
+ radius=None, diagonal=False):
+ """
+ Kernel weights with adaptive bandwidths
+
+ Parameters
+ ----------
+
+ points : array (n,k)
+ n observations on k characteristics used to measure
+ distances between the n objects
+ bandwidths : float or array-like (optional)
+ the bandwidth :math:`h_i` for the kernel.
+ if no bandwidth is specified k is used to determine the
+ adaptive bandwidth
+ k : int
+ the number of nearest neighbors to use for determining
+ bandwidth. For fixed bandwidth, :math:`h_i=max(dknn) \\forall i`
+ where :math:`dknn` is a vector of k-nearest neighbor
+ distances (the distance to the kth nearest neighbor for each
+ observation). For adaptive bandwidths, :math:`h_i=dknn_i`
+ function : string {'triangular','uniform','quadratic','quartic','gaussian'}
+ kernel function defined as follows with
+
+ .. math::
+
+ z_{i,j} = d_{i,j}/h_i
+
+ triangular
+
+ .. math::
+
+ K(z) = (1 - |z|) \ if |z| \le 1
+
+ uniform
+
+ .. math::
+
+ K(z) = |z| \ if |z| \le 1
+
+ quadratic
+
+ .. math::
+
+ K(z) = (3/4)(1-z^2) \ if |z| \le 1
+
+ quartic
+
+ .. math::
+
+ K(z) = (15/16)(1-z^2)^2 \ if |z| \le 1
+
+ gaussian
+
+ .. math::
+
+ K(z) = (2\pi)^{(-1/2)} exp(-z^2 / 2)
+
+ radius : If supplied arc_distances will be calculated
+ based on the given radius. p will be ignored.
+ diagonal : boolean
+ If true, set diagonal weights = 1.0, if false (default)
+ diagonal weights are set to value according to kernel
+ function
+ Returns
+ -------
+
+ w : W
+ instance of spatial weights
+
+ Examples
+ --------
+
+ User specified bandwidths
+
+ >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+ >>> bw=[25.0,15.0,25.0,16.0,14.5,25.0]
+ >>> kwa=adaptive_kernelW(points,bandwidths=bw)
+ >>> kwa.weights[0]
+ [1.0, 0.6, 0.552786404500042, 0.10557280900008403]
+ >>> kwa.neighbors[0]
+ [0, 1, 3, 4]
+ >>> kwa.bandwidth
+ array([[ 25. ],
+ [ 15. ],
+ [ 25. ],
+ [ 16. ],
+ [ 14.5],
+ [ 25. ]])
+
+ Endogenous adaptive bandwidths
+
+ >>> kwea=adaptive_kernelW(points)
+ >>> kwea.weights[0]
+ [1.0, 0.10557289844279438, 9.99999900663795e-08]
+ >>> kwea.neighbors[0]
+ [0, 1, 3]
+ >>> kwea.bandwidth
+ array([[ 11.18034101],
+ [ 11.18034101],
+ [ 20.000002 ],
+ [ 11.18034101],
+ [ 14.14213704],
+ [ 18.02775818]])
+
+ Endogenous adaptive bandwidths with Gaussian kernel
+
+ >>> kweag=adaptive_kernelW(points,function='gaussian')
+ >>> kweag.weights[0]
+ [0.3989422804014327, 0.2674190291577696, 0.2419707487162134]
+ >>> kweag.bandwidth
+ array([[ 11.18034101],
+ [ 11.18034101],
+ [ 20.000002 ],
+ [ 11.18034101],
+ [ 14.14213704],
+ [ 18.02775818]])
+
+ with diagonal
+
+ >>> kweag = pysal.adaptive_kernelW(points, function='gaussian')
+ >>> kweagd = pysal.adaptive_kernelW(points, function='gaussian', diagonal=True)
+ >>> kweag.neighbors[0]
+ [0, 1, 3]
+ >>> kweagd.neighbors[0]
+ [0, 1, 3]
+ >>> kweag.weights[0]
+ [0.3989422804014327, 0.2674190291577696, 0.2419707487162134]
+ >>> kweagd.weights[0]
+ [1.0, 0.2674190291577696, 0.2419707487162134]
+
+ """
+ if radius is not None:
+ points = pysal.cg.KDTree(points, distance_metric='Arc', radius=radius)
+ return Kernel(points, bandwidth=bandwidths, fixed=False, k=k,
+ function=function, diagonal=diagonal)
+
+
+def adaptive_kernelW_from_shapefile(shapefile, bandwidths=None, k=2, function='triangular',
+ idVariable=None, radius=None,
+ diagonal = False):
+ """
+ Kernel weights with adaptive bandwidths
+
+ Parameters
+ ----------
+
+ shapefile : string
+ shapefile name with shp suffix
+ bandwidths : float or array-like (optional)
+ the bandwidth :math:`h_i` for the kernel.
+ if no bandwidth is specified k is used to determine the
+ adaptive bandwidth
+ k : int
+ the number of nearest neighbors to use for determining
+ bandwidth. For fixed bandwidth, :math:`h_i=max(dknn) \\forall i`
+ where :math:`dknn` is a vector of k-nearest neighbor
+ distances (the distance to the kth nearest neighbor for each
+ observation). For adaptive bandwidths, :math:`h_i=dknn_i`
+ function : string {'triangular','uniform','quadratic','quartic','gaussian'}
+ kernel function defined as follows with
+
+ .. math::
+
+ z_{i,j} = d_{i,j}/h_i
+
+ triangular
+
+ .. math::
+
+ K(z) = (1 - |z|) \ if |z| \le 1
+
+ uniform
+
+ .. math::
+
+ K(z) = |z| \ if |z| \le 1
+
+ quadratic
+
+ .. math::
+
+ K(z) = (3/4)(1-z^2) \ if |z| \le 1
+
+ quartic
+
+ .. math::
+
+ K(z) = (15/16)(1-z^2)^2 \ if |z| \le 1
+
+ gaussian
+
+ .. math::
+
+ K(z) = (2\pi)^{(-1/2)} exp(-z^2 / 2)
+ idVariable : string
+ name of a column in the shapefile's DBF to use for ids
+ radius : If supplied arc_distances will be calculated
+ based on the given radius. p will be ignored.
+ diagonal : boolean
+ If true, set diagonal weights = 1.0, if false (default)
+ diagonal weights are set to value according to kernel
+ function
+
+ Returns
+ -------
+
+ w : W
+ instance of spatial weights
+
+ Examples
+ --------
+ >>> kwa = pysal.adaptive_kernelW_from_shapefile(pysal.examples.get_path("columbus.shp"), function='gaussian')
+ >>> kwad = pysal.adaptive_kernelW_from_shapefile(pysal.examples.get_path("columbus.shp"), function='gaussian', diagonal=True)
+ >>> kwa.neighbors[0]
+ [0, 2, 1]
+ >>> kwad.neighbors[0]
+ [0, 2, 1]
+ >>> kwa.weights[0]
+ [0.3989422804014327, 0.24966013701844503, 0.2419707487162134]
+ >>> kwad.weights[0]
+ [1.0, 0.24966013701844503, 0.2419707487162134]
+ >>>
+
+ Notes
+ -----
+ Supports polygon or point shapefiles. For polygon shapefiles, distance is
+ based on polygon centroids. Distances are defined using coordinates in
+ shapefile which are assumed to be projected and not geographical
+ coordinates.
+
+ """
+ points = get_points_array_from_shapefile(shapefile)
+ if radius is not None:
+ points = pysal.cg.KDTree(points, distance_metric='Arc', radius=radius)
+ if idVariable:
+ ids = get_ids(shapefile, idVariable)
+ return Kernel(points, bandwidth=bandwidths, fixed=False, k=k,
+ function=function, ids=ids, diagonal=diagonal)
+ return adaptive_kernelW(points, bandwidths=bandwidths, k=k,
+ function=function, diagonal=diagonal)
+
+
+def min_threshold_dist_from_shapefile(shapefile, radius=None, p=2):
+ """
+ Kernel weights with adaptive bandwidths
+
+ Parameters
+ ----------
+
+ shapefile : string
+ shapefile name with shp suffix
+ radius : If supplied arc_distances will be calculated
+ based on the given radius. p will be ignored.
+ p : float
+ Minkowski p-norm distance metric parameter:
+ 1<=p<=infinity
+ 2: Euclidean distance
+ 1: Manhattan distance
+
+ Returns
+ -------
+ d : float
+ minimum nearest neighbor distance between the n observations
+
+ Examples
+ --------
+ >>> md = min_threshold_dist_from_shapefile(pysal.examples.get_path("columbus.shp"))
+ >>> md
+ 0.61886415807685413
+ >>> min_threshold_dist_from_shapefile(pysal.examples.get_path("stl_hom.shp"), pysal.cg.sphere.RADIUS_EARTH_MILES)
+ 31.846942936393717
+
+ Notes
+ -----
+ Supports polygon or point shapefiles. For polygon shapefiles, distance is
+ based on polygon centroids. Distances are defined using coordinates in
+ shapefile which are assumed to be projected and not geographical
+ coordinates.
+
+ """
+ points = get_points_array_from_shapefile(shapefile)
+ if radius is not None:
+ points = pysal.cg.KDTree(points, distance_metric='Arc', radius=radius)
+ return min_threshold_distance(points,p)
+
+
+def build_lattice_shapefile(nrows, ncols, outFileName):
+ """
+ Build a lattice shapefile with nrows rows and ncols cols
+
+ Parameters
+ ----------
+
+ nrows : int
+ Number of rows
+ ncols : int
+ Number of cols
+ outFileName : str
+ shapefile name with shp suffix
+
+ Returns
+ -------
+ None
+ """
+ if not outFileName.endswith('.shp'):
+ raise ValueError("outFileName must end with .shp")
+ o = pysal.open(outFileName, 'w')
+ dbf_name = outFileName.split(".")[0] + ".dbf"
+ d = pysal.open(dbf_name, 'w')
+ d.header = [ 'ID' ]
+ d.field_spec = [ ('N', 8, 0) ]
+ c = 0
+ for i in xrange(nrows):
+ for j in xrange(ncols):
+ ll = i, j
+ ul = i, j + 1
+ ur = i + 1, j + 1
+ lr = i + 1, j
+ o.write(pysal.cg.Polygon([ll, ul, ur, lr, ll]))
+ d.write([c])
+ c += 1
+ d.close()
+ o.close()
+
+def _test():
+ import doctest
+ # the following line could be used to define an alternative to the '<BLANKLINE>' flag
+ #doctest.BLANKLINE_MARKER = 'something better than <BLANKLINE>'
+ start_suppress = np.get_printoptions()['suppress']
+ np.set_printoptions(suppress=True)
+ doctest.testmod()
+ np.set_printoptions(suppress=start_suppress)
+
+if __name__ == '__main__':
+ _test()
+
+
diff --git a/pysal/weights/util.py b/pysal/weights/util.py
new file mode 100644
index 0000000..572df3f
--- /dev/null
+++ b/pysal/weights/util.py
@@ -0,0 +1,1197 @@
+import pysal
+from pysal.common import *
+import pysal.weights
+import numpy as np
+from scipy import sparse, float32
+import scipy.spatial
+import os
+import operator
+import scipy
+
+__all__ = ['lat2W', 'block_weights', 'comb', 'order', 'higher_order',
+ 'shimbel', 'remap_ids', 'full2W', 'full', 'WSP2W',
+ 'insert_diagonal', 'get_ids', 'get_points_array_from_shapefile',
+ 'min_threshold_distance', 'lat2SW', 'w_local_cluster',
+ 'higher_order_sp', 'hexLat2W', 'regime_weights']
+
+
+def hexLat2W(nrows=5, ncols=5):
+ """
+ Create a W object for a hexagonal lattice.
+
+ Parameters
+ ----------
+
+ nrows : int
+ number of rows
+ ncols : int
+ number of columns
+
+ Returns
+ -------
+
+ w : W
+ instance of spatial weights class W
+
+ Notes
+ -----
+
+ Observations are row ordered: first k observations are in row 0, next k in row 1, and so on.
+
+ Construction is based on shifting every other column of a regular lattice
+ down 1/2 of a cell.
+
+ Examples
+ --------
+
+ >>> import pysal as ps
+ >>> w = ps.lat2W()
+ >>> w.neighbors[1]
+ [0, 6, 2]
+ >>> w.neighbors[21]
+ [16, 20, 22]
+ >>> wh = ps.hexLat2W()
+ >>> wh.neighbors[1]
+ [0, 6, 2, 5, 7]
+ >>> wh.neighbors[21]
+ [16, 20, 22]
+ >>>
+ """
+
+ if nrows == 1 or ncols == 1:
+ print "Hexagon lattice requires at least 2 rows and columns"
+ print "Returning a linear contiguity structure"
+ return lat2W(nrows, ncols)
+
+ n = nrows * ncols
+ rid = [i / ncols for i in xrange(n)]
+ cid = [i % ncols for i in xrange(n)]
+ r1 = nrows - 1
+ c1 = ncols - 1
+
+ w = lat2W(nrows, ncols).neighbors
+ for i in xrange(n):
+ odd = cid[i] % 2
+ if odd:
+ if rid[i] < r1: # odd col index above last row
+ # new sw neighbor
+ if cid[i] > 0:
+ j = i + ncols - 1
+ w[i] = w.get(i, []) + [j]
+ # new se neighbor
+ if cid[i] < c1:
+ j = i + ncols + 1
+ w[i] = w.get(i, []) + [j]
+
+ else: # even col
+ # nw
+ jnw = [i - ncols - 1]
+ # ne
+ jne = [i - ncols + 1]
+ if rid[i] > 0:
+ w[i]
+ if cid[i] == 0:
+ w[i] = w.get(i, []) + jne
+ elif cid[i] == c1:
+ w[i] = w.get(i, []) + jnw
+ else:
+ w[i] = w.get(i, []) + jne
+ w[i] = w.get(i, []) + jnw
+
+
+ return pysal.weights.W(w)
+
+
+def lat2W(nrows=5, ncols=5, rook=True, id_type='int'):
+ """
+ Create a W object for a regular lattice.
+
+ Parameters
+ ----------
+
+ nrows : int
+ number of rows
+ ncols : int
+ number of columns
+ rook : boolean
+ type of contiguity. Default is rook. For queen, rook =False
+ id_type : string
+ string defining the type of IDs to use in the final W object;
+ options are 'int' (0, 1, 2 ...; default), 'float' (0.0,
+ 1.0, 2.0, ...) and 'string' ('id0', 'id1', 'id2', ...)
+
+ Returns
+ -------
+
+ w : W
+ instance of spatial weights class W
+
+ Notes
+ -----
+
+ Observations are row ordered: first k observations are in row 0, next k in row 1, and so on.
+
+ Examples
+ --------
+
+ >>> from pysal import lat2W
+ >>> w9 = lat2W(3,3)
+ >>> "%.3f"%w9.pct_nonzero
+ '0.296'
+ >>> w9[0]
+ {1: 1.0, 3: 1.0}
+ >>> w9[3]
+ {0: 1.0, 4: 1.0, 6: 1.0}
+ >>>
+ """
+ n = nrows * ncols
+ r1 = nrows - 1
+ c1 = ncols - 1
+ rid = [i / ncols for i in xrange(n)]
+ cid = [i % ncols for i in xrange(n)]
+ w = {}
+ r = below = 0
+ for i in xrange(n - 1):
+ if rid[i] < r1:
+ below = rid[i] + 1
+ r = below * ncols + cid[i]
+ w[i] = w.get(i, []) + [r]
+ w[r] = w.get(r, []) + [i]
+ if cid[i] < c1:
+ right = cid[i] + 1
+ c = rid[i] * ncols + right
+ w[i] = w.get(i, []) + [c]
+ w[c] = w.get(c, []) + [i]
+ if not rook:
+ # southeast bishop
+ if cid[i] < c1 and rid[i] < r1:
+ r = (rid[i] + 1) * ncols + 1 + cid[i]
+ w[i] = w.get(i, []) + [r]
+ w[r] = w.get(r, []) + [i]
+ # southwest bishop
+ if cid[i] > 0 and rid[i] < r1:
+ r = (rid[i] + 1) * ncols - 1 + cid[i]
+ w[i] = w.get(i, []) + [r]
+ w[r] = w.get(r, []) + [i]
+
+ neighbors = {}
+ weights = {}
+ for key in w:
+ weights[key] = [1.] * len(w[key])
+ ids = range(n)
+ if id_type == 'string':
+ ids = ['id' + str(i) for i in ids]
+ elif id_type == 'float':
+ ids = [i * 1. for i in ids]
+ if id_type == 'string' or id_type == 'float':
+ id_dict = dict(zip(range(n), ids))
+ alt_w = {}
+ alt_weights = {}
+ for i in w:
+ values = [id_dict[j] for j in w[i]]
+ key = id_dict[i]
+ alt_w[key] = values
+ alt_weights[key] = weights[i]
+ w = alt_w
+ weights = alt_weights
+ return pysal.weights.W(w, weights, ids=ids, id_order=ids[:])
+
+def regime_weights(regimes):
+ """
+ Construct spatial weights for regime neighbors.
+
+ Block contiguity structures are relevant when defining neighbor relations
+ based on membership in a regime. For example, all counties belonging to
+ the same state could be defined as neighbors, in an analysis of all
+ counties in the US.
+
+ Parameters
+ ----------
+ regimes : list or array
+ ids of which regime an observation belongs to
+
+ Returns
+ -------
+
+ W : spatial weights instance
+
+ Examples
+ --------
+
+ >>> from pysal import regime_weights
+ >>> import numpy as np
+ >>> regimes = np.ones(25)
+ >>> regimes[range(10,20)] = 2
+ >>> regimes[range(21,25)] = 3
+ >>> regimes
+ array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,
+ 2., 2., 2., 2., 2., 2., 2., 1., 3., 3., 3., 3.])
+ >>> w = regime_weights(regimes)
+ PendingDepricationWarning: regime_weights will be renamed to block_weights in PySAL 2.0
+ >>> w.weights[0]
+ [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+ >>> w.neighbors[0]
+ [1, 2, 3, 4, 5, 6, 7, 8, 9, 20]
+ >>> regimes = ['n','n','s','s','e','e','w','w','e']
+ >>> n = len(regimes)
+ >>> w = regime_weights(regimes)
+ PendingDepricationWarning: regime_weights will be renamed to block_weights in PySAL 2.0
+ >>> w.neighbors
+ {0: [1], 1: [0], 2: [3], 3: [2], 4: [5, 8], 5: [4, 8], 6: [7], 7: [6], 8: [4, 5]}
+
+ Notes
+ -----
+ regime_weights will be deprecated in PySAL 2.0 and renamed to block_weights.
+
+ """
+ msg = "PendingDepricationWarning: regime_weights will be "
+ msg += "renamed to block_weights in PySAL 2.0"
+ print msg
+ return block_weights(regimes)
+
+
+
+def block_weights(regimes):
+ """
+ Construct spatial weights for regime neighbors.
+
+ Block contiguity structures are relevant when defining neighbor relations
+ based on membership in a regime. For example, all counties belonging to
+ the same state could be defined as neighbors, in an analysis of all
+ counties in the US.
+
+ Parameters
+ ----------
+ regimes : list or array
+ ids of which regime an observation belongs to
+
+ Returns
+ -------
+
+ W : spatial weights instance
+
+ Examples
+ --------
+
+ >>> from pysal import block_weights
+ >>> import numpy as np
+ >>> regimes = np.ones(25)
+ >>> regimes[range(10,20)] = 2
+ >>> regimes[range(21,25)] = 3
+ >>> regimes
+ array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,
+ 2., 2., 2., 2., 2., 2., 2., 1., 3., 3., 3., 3.])
+ >>> w = block_weights(regimes)
+ >>> w.weights[0]
+ [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+ >>> w.neighbors[0]
+ [1, 2, 3, 4, 5, 6, 7, 8, 9, 20]
+ >>> regimes = ['n','n','s','s','e','e','w','w','e']
+ >>> n = len(regimes)
+ >>> w = block_weights(regimes)
+ >>> w.neighbors
+ {0: [1], 1: [0], 2: [3], 3: [2], 4: [5, 8], 5: [4, 8], 6: [7], 7: [6], 8: [4, 5]}
+ """
+ rids = np.unique(regimes)
+ neighbors = {}
+ NPNZ = np.nonzero
+ regimes = np.array(regimes)
+ for rid in rids:
+ members = NPNZ(regimes == rid)[0]
+ for member in members:
+ neighbors[member] = members[NPNZ(members != member)[0]].tolist()
+ return pysal.weights.W(neighbors)
+
+
+def comb(items, n=None):
+ """
+ Combinations of size n taken from items
+
+ Parameters
+ ----------
+
+ items : sequence
+ n : integer
+ size of combinations to take from items
+
+ Returns
+ -------
+
+ implicit : generator
+ combinations of size n taken from items
+
+ Examples
+ --------
+ >>> x = range(4)
+ >>> for c in comb(x, 2):
+ ... print c
+ ...
+ [0, 1]
+ [0, 2]
+ [0, 3]
+ [1, 2]
+ [1, 3]
+ [2, 3]
+
+ """
+ if n is None:
+ n = len(items)
+ for i in range(len(items)):
+ v = items[i:i + 1]
+ if n == 1:
+ yield v
+ else:
+ rest = items[i + 1:]
+ for c in comb(rest, n - 1):
+ yield v + c
+
+
+def order(w, kmax=3):
+ """
+ Determine the non-redundant order of contiguity up to a specific
+ order.
+
+ Parameters
+ ----------
+
+ w : W
+ spatial weights object
+
+ kmax : int
+ maximum order of contiguity
+
+ Returns
+ -------
+
+ info : dictionary
+ observation id is the key, value is a list of contiguity
+ orders with a negative 1 in the ith position
+
+ Notes
+ -----
+ Implements the algorithm in Anselin and Smirnov (1996) [1]_
+
+ Examples
+ --------
+ >>> from pysal import rook_from_shapefile as rfs
+ >>> w = rfs(pysal.examples.get_path('10740.shp'))
+ WARNING: there is one disconnected observation (no neighbors)
+ Island id: [163]
+ >>> w3 = order(w, kmax = 3)
+ >>> w3[1][0:5]
+ [1, -1, 1, 2, 1]
+
+ """
+ #ids = w.neighbors.keys()
+ ids = w.id_order
+ info = {}
+ for id_ in ids:
+ s = [0] * w.n
+ s[ids.index(id_)] = -1
+ for j in w.neighbors[id_]:
+ s[ids.index(j)] = 1
+ k = 1
+ while k < kmax:
+ knext = k + 1
+ if s.count(k):
+ # get neighbors of order k
+ js = [ids[j] for j, val in enumerate(s) if val == k]
+ # get first order neighbors for order k neighbors
+ for j in js:
+ next_neighbors = w.neighbors[j]
+ for neighbor in next_neighbors:
+ nid = ids.index(neighbor)
+ if s[nid] == 0:
+ s[nid] = knext
+ k = knext
+ info[id_] = s
+ return info
+
+
+def higher_order(w, k=2):
+ """
+ Contiguity weights object of order k
+
+ Parameters
+ ----------
+
+ w : W
+ spatial weights object
+ k : int
+ order of contiguity
+
+ Returns
+ -------
+
+ implicit : W
+ spatial weights object
+
+ Notes
+ -----
+ Proper higher order neighbors are returned such that i and j are k-order
+ neighbors iff the shortest path from i-j is of length k.
+
+ Examples
+ --------
+ >>> from pysal import lat2W, higher_order
+ >>> w10 = lat2W(10, 10)
+ >>> w10_2 = higher_order(w10, 2)
+ >>> w10_2[0]
+ {2: 1.0, 11: 1.0, 20: 1.0}
+ >>> w5 = lat2W()
+ >>> w5[0]
+ {1: 1.0, 5: 1.0}
+ >>> w5[1]
+ {0: 1.0, 2: 1.0, 6: 1.0}
+ >>> w5_2 = higher_order(w5,2)
+ >>> w5_2[0]
+ {10: 1.0, 2: 1.0, 6: 1.0}
+ """
+ return higher_order_sp(w, k)
+
+
+def higher_order_sp(w, k=2, shortest_path=True, diagonal=False):
+ """
+ Contiguity weights for either a sparse W or pysal.weights.W for order k
+
+ Parameters
+ ==========
+
+ w: [W instance | scipy.sparse.csr.csr_instance]
+
+ k: Order of contiguity
+
+ shortest_path: Boolean
+
+ True: i,j and k-order neighbors if the shortest path for
+ i,j is k
+
+ False: i,j are k-order neighbors if there is a path from
+ i,j of length k
+
+ diagonal: Boolean
+
+ False: remove k-order (i,j) joins when i==j
+
+ True: keep k-order (i,j) joins when i==j
+
+ Returns
+ -------
+
+ wk: [W instance | WSP instance] type matches type of w argument
+
+
+ Notes
+ -----
+ Lower order contiguities are removed.
+
+ Examples
+ --------
+
+ >>> import pysal
+ >>> w25 = pysal.lat2W(5,5)
+ >>> w25.n
+ 25
+ >>> w25[0]
+ {1: 1.0, 5: 1.0}
+ >>> w25_2 = pysal.weights.util.higher_order_sp(w25, 2)
+ >>> w25_2[0]
+ {10: 1.0, 2: 1.0, 6: 1.0}
+ >>> w25_2 = pysal.weights.util.higher_order_sp(w25, 2, diagonal=True)
+ >>> w25_2[0]
+ {0: 1.0, 10: 1.0, 2: 1.0, 6: 1.0}
+ >>> w25_3 = pysal.weights.util.higher_order_sp(w25, 3)
+ >>> w25_3[0]
+ {15: 1.0, 3: 1.0, 11: 1.0, 7: 1.0}
+ >>> w25_3 = pysal.weights.util.higher_order_sp(w25, 3, shortest_path=False)
+ >>> w25_3[0]
+ {1: 1.0, 3: 1.0, 5: 1.0, 7: 1.0, 11: 1.0, 15: 1.0}
+
+ """
+ tw = type(w)
+ id_order = None
+ if tw == pysal.weights.weights.W:
+ id_order = w.id_order
+ w = w.sparse
+ elif tw != scipy.sparse.csr.csr_matrix:
+ print "Unsupported sparse argument."
+ return None
+
+ wk = w**k
+ rk, ck = wk.nonzero()
+ sk = set(zip(rk, ck))
+
+ if shortest_path:
+ for j in range(1, k):
+ wj = w**j
+ rj, cj = wj.nonzero()
+ sj = set(zip(rj, cj))
+ sk.difference_update(sj)
+
+ if not diagonal:
+ sk = set([(i,j) for i,j in sk if i!=j])
+
+ if id_order:
+ d = dict([(i,[]) for i in id_order])
+ for pair in sk:
+ k, v = pair
+ k = id_order[k]
+ v = id_order[v]
+ d[k].append(v)
+ return pysal.W(neighbors=d)
+ else:
+ d = {}
+ for pair in sk:
+ k, v = pair
+ if k in d:
+ d[k].append(v)
+ else:
+ d[k] = [v]
+ return pysal.weights.WSP(pysal.W(neighbors=d).sparse)
+
+
+def w_local_cluster(w):
+ """
+ Local clustering coefficients for each unit as a node in a graph. [ws]_
+
+ Parameters
+ ----------
+
+ w : W
+ spatial weights object
+
+ Returns
+ -------
+
+ c : array (w.n,1)
+ local clustering coefficients
+
+ Notes
+ -----
+
+ The local clustering coefficient :math:`c_i` quantifies how close the
+ neighbors of observation :math:`i` are to being a clique:
+
+ .. math::
+
+ c_i = | \{w_{j,k}\} |/ (k_i(k_i - 1)): j,k \in N_i
+
+ where :math:`N_i` is the set of neighbors to :math:`i`, :math:`k_i =
+ |N_i|` and :math:`\{w_{j,k}\}` is the set of non-zero elements of the
+ weights between pairs in :math:`N_i`.
+
+ References
+ ----------
+
+ .. [ws] Watts, D.J. and S.H. Strogatz (1988) "Collective dynamics of 'small-world' networks". Nature, 393: 440-442.
+
+ Examples
+ --------
+
+ >>> w = pysal.lat2W(3,3, rook=False)
+ >>> w_local_cluster(w)
+ array([[ 1. ],
+ [ 0.6 ],
+ [ 1. ],
+ [ 0.6 ],
+ [ 0.42857143],
+ [ 0.6 ],
+ [ 1. ],
+ [ 0.6 ],
+ [ 1. ]])
+
+ """
+
+ c = np.zeros((w.n, 1), float)
+ w.transformation = 'b'
+ for i, id in enumerate(w.id_order):
+ ki = max(w.cardinalities[id], 1) # deal with islands
+ Ni = w.neighbors[id]
+ wi = pysal.w_subset(w, Ni).full()[0]
+ c[i] = wi.sum() / (ki * (ki - 1))
+ return c
+
+
+def shimbel(w):
+ """
+ Find the Shimbel matrix for first order contiguity matrix.
+
+ Parameters
+ ----------
+ w : W
+ spatial weights object
+ Returns
+ -------
+
+ info : list of lists
+ one list for each observation which stores the shortest
+ order between it and each of the the other observations.
+
+ Examples
+ --------
+ >>> from pysal import lat2W, shimbel
+ >>> w5 = lat2W()
+ >>> w5_shimbel = shimbel(w5)
+ >>> w5_shimbel[0][24]
+ 8
+ >>> w5_shimbel[0][0:4]
+ [-1, 1, 2, 3]
+ >>>
+ """
+ info = {}
+ ids = w.id_order
+ for id in ids:
+ s = [0] * w.n
+ s[ids.index(id)] = -1
+ for j in w.neighbors[id]:
+ s[ids.index(j)] = 1
+ k = 1
+ flag = s.count(0)
+ while flag:
+ p = -1
+ knext = k + 1
+ for j in range(s.count(k)):
+ neighbor = s.index(k, p + 1)
+ p = neighbor
+ next_neighbors = w.neighbors[ids[p]]
+ for neighbor in next_neighbors:
+ nid = ids.index(neighbor)
+ if s[nid] == 0:
+ s[nid] = knext
+ k = knext
+ flag = s.count(0)
+ info[id] = s
+ return info
+
+
+def full(w):
+ """
+ Generate a full numpy array
+
+ Parameters
+ ----------
+ w : W
+ spatial weights object
+
+ Returns
+ -------
+
+ implicit : tuple
+ first element being the full numpy array and second element
+ keys being the ids associated with each row in the array.
+
+ Examples
+ --------
+ >>> from pysal import W, full
+ >>> neighbors = {'first':['second'],'second':['first','third'],'third':['second']}
+ >>> weights = {'first':[1],'second':[1,1],'third':[1]}
+ >>> w = W(neighbors, weights)
+ >>> wf, ids = full(w)
+ >>> wf
+ array([[ 0., 1., 0.],
+ [ 1., 0., 1.],
+ [ 0., 1., 0.]])
+ >>> ids
+ ['first', 'second', 'third']
+ """
+ wfull = np.zeros([w.n, w.n], dtype=float)
+ keys = w.neighbors.keys()
+ if w.id_order:
+ keys = w.id_order
+ for i, key in enumerate(keys):
+ n_i = w.neighbors[key]
+ w_i = w.weights[key]
+ for j, wij in zip(n_i, w_i):
+ c = keys.index(j)
+ wfull[i, c] = wij
+ return (wfull, keys)
+
+
+def full2W(m, ids=None):
+ '''
+ Create a PySAL W object from a full array
+ ...
+
+ Parameters
+ ----------
+ m : array
+ nxn array with the full weights matrix
+ ids : list
+ User ids assumed to be aligned with m
+
+ Returns
+ -------
+ w : W
+ PySAL weights object
+
+ Examples
+ --------
+ >>> import pysal as ps
+ >>> import numpy as np
+
+ Create an array of zeros
+
+ >>> a = np.zeros((4, 4))
+
+ For loop to fill it with random numbers
+
+ >>> for i in range(len(a)):
+ ... for j in range(len(a[i])):
+ ... if i!=j:
+ ... a[i, j] = np.random.random(1)
+
+ Create W object
+
+ >>> w = ps.weights.util.full2W(a)
+ >>> w.full()[0] == a
+ array([[ True, True, True, True],
+ [ True, True, True, True],
+ [ True, True, True, True],
+ [ True, True, True, True]], dtype=bool)
+
+ Create list of user ids
+
+ >>> ids = ['myID0', 'myID1', 'myID2', 'myID3']
+ >>> w = ps.weights.util.full2W(a, ids=ids)
+ >>> w.full()[0] == a
+ array([[ True, True, True, True],
+ [ True, True, True, True],
+ [ True, True, True, True],
+ [ True, True, True, True]], dtype=bool)
+ '''
+ if m.shape[0] != m.shape[1]:
+ raise ValueError('Your array is not square')
+ neighbors, weights = {}, {}
+ for i in xrange(m.shape[0]):
+ # for i, row in enumerate(m):
+ row = m[i]
+ if ids:
+ i = ids[i]
+ ngh = list(row.nonzero()[0])
+ weights[i] = list(row[ngh])
+ ngh = list(ngh)
+ if ids:
+ ngh = [ids[j] for j in ngh]
+ neighbors[i] = ngh
+ return pysal.W(neighbors, weights, id_order=ids)
+
+
+def WSP2W(wsp, silent_island_warning=False):
+ """
+ Convert a pysal WSP object (thin weights matrix) to a pysal W object.
+
+ Parameters
+ ----------
+ wsp : WSP
+ PySAL sparse weights object
+ silent_island_warning : boolean
+ Switch to turn off (default on) print statements
+ for every observation with islands
+
+ Returns
+ -------
+ w : W
+ PySAL weights object
+
+ Examples
+ --------
+ >>> import pysal
+
+ Build a 10x10 scipy.sparse matrix for a rectangular 2x5 region of cells
+ (rook contiguity), then construct a PySAL sparse weights object (wsp).
+
+ >>> sp = pysal.weights.lat2SW(2, 5)
+ >>> wsp = pysal.weights.WSP(sp)
+ >>> wsp.n
+ 10
+ >>> print wsp.sparse[0].todense()
+ [[0 1 0 0 0 1 0 0 0 0]]
+
+ Convert this sparse weights object to a standard PySAL weights object.
+
+ >>> w = pysal.weights.WSP2W(wsp)
+ >>> w.n
+ 10
+ >>> print w.full()[0][0]
+ [ 0. 1. 0. 0. 0. 1. 0. 0. 0. 0.]
+
+ """
+ wsp.sparse
+ indices = wsp.sparse.indices
+ data = wsp.sparse.data
+ indptr = wsp.sparse.indptr
+ id_order = wsp.id_order
+ if id_order:
+ # replace indices with user IDs
+ indices = [id_order[i] for i in indices]
+ else:
+ id_order = range(wsp.n)
+ neighbors, weights = {}, {}
+ start = indptr[0]
+ for i in xrange(wsp.n):
+ oid = id_order[i]
+ end = indptr[i + 1]
+ neighbors[oid] = indices[start:end]
+ weights[oid] = data[start:end]
+ start = end
+ ids = copy.copy(wsp.id_order)
+ w = pysal.W(neighbors, weights, ids,
+ silent_island_warning=silent_island_warning)
+ w._sparse = copy.deepcopy(wsp.sparse)
+ w._cache['sparse'] = w._sparse
+ return w
+
+
+def insert_diagonal(w, diagonal=1.0, wsp=False):
+ """
+ Returns a new weights object with values inserted along the main diagonal.
+
+ Parameters
+ ----------
+ w : W
+ Spatial weights object
+
+ diagonal : float, int or array
+ Defines the value(s) to which the weights matrix diagonal should
+ be set. If a constant is passed then each element along the
+ diagonal will get this value (default is 1.0). An array of length
+ w.n can be passed to set explicit values to each element along
+ the diagonal (assumed to be in the same order as w.id_order).
+
+ wsp : boolean
+ If True return a thin weights object of the type WSP, if False
+ return the standard W object.
+
+ Returns
+ -------
+ w : W
+ Spatial weights object
+
+ Examples
+ --------
+ >>> import pysal
+ >>> import numpy as np
+
+ Build a basic rook weights matrix, which has zeros on the diagonal, then
+ insert ones along the diagonal.
+
+ >>> w = pysal.lat2W(5, 5, id_type='string')
+ >>> w_const = pysal.weights.insert_diagonal(w)
+ >>> w['id0']
+ {'id5': 1.0, 'id1': 1.0}
+ >>> w_const['id0']
+ {'id5': 1.0, 'id0': 1.0, 'id1': 1.0}
+
+ Insert different values along the main diagonal.
+
+ >>> diag = np.arange(100, 125)
+ >>> w_var = pysal.weights.insert_diagonal(w, diag)
+ >>> w_var['id0']
+ {'id5': 1.0, 'id0': 100.0, 'id1': 1.0}
+
+ """
+ w_new = copy.deepcopy(w.sparse)
+ w_new = w_new.tolil()
+ if issubclass(type(diagonal), np.ndarray):
+ if w.n != diagonal.shape[0]:
+ raise Exception("shape of w and diagonal do not match")
+ w_new.setdiag(diagonal)
+ elif operator.isNumberType(diagonal):
+ w_new.setdiag([diagonal] * w.n)
+ else:
+ raise Exception("Invalid value passed to diagonal")
+ w_out = pysal.weights.WSP(w_new, copy.copy(w.id_order))
+ if wsp:
+ return w_out
+ else:
+ return WSP2W(w_out)
+
+
+def remap_ids(w, old2new, id_order=[]):
+ """
+ Remaps the IDs in a spatial weights object
+
+ Parameters
+ ----------
+ w : W
+ Spatial weights object
+
+ old2new : dictionary
+ Dictionary where the keys are the IDs in w (i.e. "old IDs") and
+ the values are the IDs to replace them (i.e. "new IDs")
+
+ id_order : list
+ An ordered list of new IDs, which defines the order of observations when
+ iterating over W. If not set then the id_order in w will be
+ used.
+
+ Returns
+ -------
+
+ implicit : W
+ Spatial weights object with new IDs
+
+ Examples
+ --------
+ >>> from pysal import lat2W, remap_ids
+ >>> w = lat2W(3,2)
+ >>> w.id_order
+ [0, 1, 2, 3, 4, 5]
+ >>> w.neighbors[0]
+ [2, 1]
+ >>> old_to_new = {0:'a', 1:'b', 2:'c', 3:'d', 4:'e', 5:'f'}
+ >>> w_new = remap_ids(w, old_to_new)
+ >>> w_new.id_order
+ ['a', 'b', 'c', 'd', 'e', 'f']
+ >>> w_new.neighbors['a']
+ ['c', 'b']
+
+ """
+ if not isinstance(w, pysal.weights.W):
+ raise Exception("w must be a spatial weights object")
+ new_neigh = {}
+ new_weights = {}
+ for key, value in w.neighbors.iteritems():
+ new_values = [old2new[i] for i in value]
+ new_key = old2new[key]
+ new_neigh[new_key] = new_values
+ new_weights[new_key] = copy.copy(w.weights[key])
+ if id_order:
+ return pysal.weights.W(new_neigh, new_weights, id_order)
+ else:
+ if w.id_order:
+ id_order = [old2new[i] for i in w.id_order]
+ return pysal.weights.W(new_neigh, new_weights, id_order)
+ else:
+ return pysal.weights.W(new_neigh, new_weights)
+
+
+def get_ids(shapefile, idVariable):
+ """
+ Gets the IDs from the DBF file that moves with a given shape file
+
+ Parameters
+ ----------
+ shapefile : string
+ name of a shape file including suffix
+ idVariable : string
+ name of a column in the shapefile's DBF to use for ids
+
+ Returns
+ -------
+ ids : list
+ a list of IDs
+
+ Examples
+ --------
+ >>> from pysal.weights.util import get_ids
+ >>> polyids = get_ids(pysal.examples.get_path("columbus.shp"), "POLYID")
+ >>> polyids[:5]
+ [1, 2, 3, 4, 5]
+ """
+
+ try:
+ dbname = os.path.splitext(shapefile)[0] + '.dbf'
+ db = pysal.open(dbname)
+ var = db.by_col[idVariable]
+ db.close()
+ return var
+ except IOError:
+ msg = 'The shapefile "%s" appears to be missing its DBF file. The DBF file "%s" could not be found.' % (
+ shapefile, dbname)
+ raise IOError(msg)
+ except AttributeError:
+ msg = 'The variable "%s" was not found in the DBF file. The DBF contains the following variables: %s.' % (
+ idVariable, ','.join(db.header))
+ raise KeyError(msg)
+
+
+def get_points_array_from_shapefile(shapefile):
+ """
+ Gets a data array of x and y coordinates from a given shape file
+
+ Parameters
+ ----------
+ shapefile : string
+ name of a shape file including suffix
+
+ Returns
+ -------
+ points : array (n, 2)
+ a data array of x and y coordinates
+
+ Notes
+ -----
+ If the given shape file includes polygons,
+ this function returns x and y coordinates of the polygons' centroids
+
+ Examples
+ --------
+ Point shapefile
+
+ >>> from pysal.weights.util import get_points_array_from_shapefile
+ >>> xy = get_points_array_from_shapefile(pysal.examples.get_path('juvenile.shp'))
+ >>> xy[:3]
+ array([[ 94., 93.],
+ [ 80., 95.],
+ [ 79., 90.]])
+
+ Polygon shapefile
+
+ >>> xy = get_points_array_from_shapefile(pysal.examples.get_path('columbus.shp'))
+ >>> xy[:3]
+ array([[ 8.82721847, 14.36907602],
+ [ 8.33265837, 14.03162401],
+ [ 9.01226541, 13.81971908]])
+ """
+
+ f = pysal.open(shapefile)
+ if f.type.__name__ == 'Polygon':
+ data = np.array([shape.centroid for shape in f])
+ elif f.type.__name__ == 'Point':
+ data = np.array([shape for shape in f])
+ f.close()
+ return data
+
+
+def min_threshold_distance(data, p=2):
+ """
+ Get the maximum nearest neighbor distance
+
+ Parameters
+ ----------
+
+ data : array (n,k) or KDTree where KDtree.data is array (n,k)
+ n observations on k attributes
+ p : float
+ Minkowski p-norm distance metric parameter:
+ 1<=p<=infinity
+ 2: Euclidean distance
+ 1: Manhattan distance
+
+ Returns
+ -------
+ nnd : float
+ maximum nearest neighbor distance between the n observations
+
+ Examples
+ --------
+ >>> from pysal.weights.util import min_threshold_distance
+ >>> import numpy as np
+ >>> x, y = np.indices((5, 5))
+ >>> x.shape = (25, 1)
+ >>> y.shape = (25, 1)
+ >>> data = np.hstack([x, y])
+ >>> min_threshold_distance(data)
+ 1.0
+
+ """
+ if issubclass(type(data), scipy.spatial.KDTree):
+ kd = data
+ data = kd.data
+ else:
+ kd = KDTree(data)
+ nn = kd.query(data, k=2, p=p)
+ nnd = nn[0].max(axis=0)[1]
+ return nnd
+
+
+def lat2SW(nrows=3, ncols=5, criterion="rook", row_st=False):
+ """
+ Create a sparse W matrix for a regular lattice.
+
+ Parameters
+ ----------
+
+ nrows : int
+ number of rows
+ ncols : int
+ number of columns
+ rook : "rook", "queen", or "bishop"
+ type of contiguity. Default is rook.
+ row_st : boolean
+ If True, the created sparse W object is row-standardized so
+ every row sums up to one. Defaults to False.
+
+ Returns
+ -------
+
+ w : scipy.sparse.dia_matrix
+ instance of a scipy sparse matrix
+
+ Notes
+ -----
+
+ Observations are row ordered: first k observations are in row 0, next k in row 1, and so on.
+ This method directly creates the W matrix using the strucuture of the contiguity type.
+
+ Examples
+ --------
+
+ >>> from pysal import weights
+ >>> w9 = weights.lat2SW(3,3)
+ >>> w9[0,1]
+ 1
+ >>> w9[3,6]
+ 1
+ >>> w9r = weights.lat2SW(3,3, row_st=True)
+ >>> w9r[3,6]
+ 0.33333333333333331
+ """
+ n = nrows * ncols
+ diagonals = []
+ offsets = []
+ if criterion == "rook" or criterion == "queen":
+ d = np.ones((1, n))
+ for i in range(ncols - 1, n, ncols):
+ d[0, i] = 0
+ diagonals.append(d)
+ offsets.append(-1)
+
+ d = np.ones((1, n))
+ diagonals.append(d)
+ offsets.append(-ncols)
+
+ if criterion == "queen" or criterion == "bishop":
+ d = np.ones((1, n))
+ for i in range(0, n, ncols):
+ d[0, i] = 0
+ diagonals.append(d)
+ offsets.append(-(ncols - 1))
+
+ d = np.ones((1, n))
+ for i in range(ncols - 1, n, ncols):
+ d[0, i] = 0
+ diagonals.append(d)
+ offsets.append(-(ncols + 1))
+ data = np.concatenate(diagonals)
+ offsets = np.array(offsets)
+ m = sparse.dia_matrix((data, offsets), shape=(n, n), dtype=np.int8)
+ m = m + m.T
+ if row_st:
+ m = sparse.spdiags(1. / m.sum(1).T, 0, *m.shape) * m
+ return m
+
+
+def write_gal(file, k=10):
+ f = open(file, 'w')
+ n = k * k
+ f.write("0 %d" % n)
+ for i in xrange(n):
+ row = i / k
+ col = i % k
+ neighs = [i - i, i + 1, i - k, i + k]
+ neighs = [j for j in neighs if j >= 0 and j < n]
+ f.write("\n%d %d\n" % (i, len(neighs)))
+ f.write(" ".join(map(str, neighs)))
+ f.close()
+
+if __name__ == "__main__":
+ from pysal import lat2W
+
+ assert (lat2W(5, 5).sparse.todense() == lat2SW(5, 5).todense()).all()
+ assert (lat2W(5, 3).sparse.todense() == lat2SW(5, 3).todense()).all()
+ assert (lat2W(5, 3, rook=False).sparse.todense() == lat2SW(5, 3,
+ 'queen').todense()).all()
+ assert (lat2W(50, 50, rook=False).sparse.todense() == lat2SW(50,
+ 50, 'queen').todense()).all()
diff --git a/pysal/weights/weights.py b/pysal/weights/weights.py
new file mode 100644
index 0000000..4a0237d
--- /dev/null
+++ b/pysal/weights/weights.py
@@ -0,0 +1,1066 @@
+__all__ = ['W', 'WSP']
+__author__ = "Sergio J. Rey <srey at asu.edu> "
+
+import pysal
+import math
+import numpy as np
+import scipy.sparse
+from os.path import basename as BASENAME
+from pysal.weights import util
+
+
+class W(object):
+ """
+ Spatial weights
+
+ Parameters
+ ----------
+ neighbors : dictionary
+ key is region ID, value is a list of neighbor IDS
+ Example: {'a':['b'],'b':['a','c'],'c':['b']}
+ weights = None : dictionary
+ key is region ID, value is a list of edge weights
+ If not supplied all edge weights are assumed to have a weight of 1.
+ Example: {'a':[0.5],'b':[0.5,1.5],'c':[1.5]}
+ id_order = None : list
+ An ordered list of ids, defines the order of
+ observations when iterating over W if not set,
+ lexicographical ordering is used to iterate and the
+ id_order_set property will return False. This can be
+ set after creation by setting the 'id_order' property.
+ silent_island_warning : boolean
+ By default PySAL will print a warning if the
+ dataset contains any disconnected observations or
+ islands. To silence this warning set this
+ parameter to True.
+ ids = None : list
+ values to use for keys of the neighbors and weights dicts
+
+ Attributes
+ ----------
+
+ asymmetries
+ cardinalities
+ diagW2
+ diagWtW
+ diagWtW_WW
+ histogram
+ id2i
+ id_order
+ id_order_set
+ islands
+ max_neighbors
+ mean_neighbors
+ min_neighbors
+ n
+ neighbor_offsets
+ nonzero
+ pct_nonzero
+ s0
+ s1
+ s2
+ s2array
+ sd
+ sparse
+ trcW2
+ trcWtW
+ trcWtW_WW
+ transform
+
+ Examples
+ --------
+ >>> from pysal import W, lat2W
+ >>> neighbors = {0: [3, 1], 1: [0, 4, 2], 2: [1, 5], 3: [0, 6, 4], 4: [1, 3, 7, 5], 5: [2, 4, 8], 6: [3, 7], 7: [4, 6, 8], 8: [5, 7]}
+ >>> weights = {0: [1, 1], 1: [1, 1, 1], 2: [1, 1], 3: [1, 1, 1], 4: [1, 1, 1, 1], 5: [1, 1, 1], 6: [1, 1], 7: [1, 1, 1], 8: [1, 1]}
+ >>> w = W(neighbors, weights)
+ >>> "%.3f"%w.pct_nonzero
+ '0.296'
+
+ Read from external gal file
+
+ >>> import pysal
+ >>> w = pysal.open(pysal.examples.get_path("stl.gal")).read()
+ >>> w.n
+ 78
+ >>> "%.3f"%w.pct_nonzero
+ '0.065'
+
+ Set weights implicitly
+
+ >>> neighbors = {0: [3, 1], 1: [0, 4, 2], 2: [1, 5], 3: [0, 6, 4], 4: [1, 3, 7, 5], 5: [2, 4, 8], 6: [3, 7], 7: [4, 6, 8], 8: [5, 7]}
+ >>> w = W(neighbors)
+ >>> "%.3f"%w.pct_nonzero
+ '0.296'
+ >>> w = lat2W(100, 100)
+ >>> w.trcW2
+ 39600.0
+ >>> w.trcWtW
+ 39600.0
+ >>> w.transform='r'
+ >>> w.trcW2
+ 2530.7222222222586
+ >>> w.trcWtW
+ 2533.6666666666774
+
+ Cardinality Histogram
+
+ >>> w=pysal.rook_from_shapefile(pysal.examples.get_path("sacramentot2.shp"))
+ >>> w.histogram
+ [(1, 1), (2, 6), (3, 33), (4, 103), (5, 114), (6, 73), (7, 35), (8, 17), (9, 9), (10, 4), (11, 4), (12, 3), (13, 0), (14, 1)]
+
+ Disconnected observations (islands)
+
+ >>> w = pysal.W({1:[0],0:[1],2:[], 3:[]})
+ WARNING: there are 2 disconnected observations
+ Island ids: [2, 3]
+
+ """
+
+ def __init__(self, neighbors, weights=None, id_order=None,
+ silent_island_warning=False, ids=None):
+ self.silent_island_warning = silent_island_warning
+ self.transformations = {}
+ self.neighbors = neighbors
+ if not weights:
+ weights = {}
+ for key in neighbors:
+ weights[key] = [1.] * len(neighbors[key])
+ self.weights = weights
+ self.transformations['O'] = self.weights.copy() # original weights
+ self.transform = 'O'
+ if id_order is None:
+ self._id_order = self.neighbors.keys()
+ self._id_order.sort()
+ self._id_order_set = False
+ else:
+ self._id_order = id_order
+ self._id_order_set = True
+ self._reset()
+ self._n = len(self.weights)
+ if self.islands and not self.silent_island_warning:
+ ni = len(self.islands)
+ if ni == 1:
+ print "WARNING: there is one disconnected observation (no neighbors)"
+ print "Island id: ", self.islands
+ else:
+ print "WARNING: there are %d disconnected observations" % ni
+ print "Island ids: ", self.islands
+
+ def _reset(self):
+ """
+ Reset properties
+ """
+ self._cache = {}
+
+ @property
+ def sparse(self):
+ """
+ Sparse matrix object
+
+ For any matrix manipulations required for w, w.sparse should be
+ used. This is based on scipy.sparse.
+ """
+ if 'sparse' not in self._cache:
+ self._sparse = self._build_sparse()
+ self._cache['sparse'] = self._sparse
+ return self._sparse
+
+ def _build_sparse(self):
+ """
+ construct the sparse attribute
+ """
+
+ row = []
+ col = []
+ data = []
+ id2i = self.id2i
+ for i, neigh_list in self.neighbor_offsets.iteritems():
+ card = self.cardinalities[i]
+ row.extend([id2i[i]] * card)
+ col.extend(neigh_list)
+ data.extend(self.weights[i])
+ row = np.array(row)
+ col = np.array(col)
+ data = np.array(data)
+ s = scipy.sparse.csr_matrix((data, (row, col)), shape=(self.n, self.n))
+ return s
+
+ @property
+ def id2i(self):
+ """
+ Dictionary where the key is an ID and the value is that ID's
+ index in W.id_order.
+ """
+ if 'id2i' not in self._cache:
+ self._id2i = {}
+ for i, id_i in enumerate(self._id_order):
+ self._id2i[id_i] = i
+ self._id2i = self._id2i
+ self._cache['id2i'] = self._id2i
+ return self._id2i
+
+ @property
+ def n(self):
+ """
+ number of units
+ """
+ if "n" not in self._cache:
+ self._n = len(self.neighbors)
+ self._cache['n'] = self._n
+ return self._n
+
+ @property
+ def s0(self):
+ """
+ float
+
+ .. math::
+
+ s0=\sum_i \sum_j w_{i,j}
+
+ """
+ if 's0' not in self._cache:
+ self._s0 = self.sparse.sum()
+ self._cache['s0'] = self._s0
+ return self._s0
+
+ @property
+ def s1(self):
+ """
+ float
+
+ .. math::
+
+ s1=1/2 \sum_i \sum_j (w_{i,j} + w_{j,i})^2
+
+ """
+ if 's1' not in self._cache:
+ t = self.sparse.transpose()
+ t = t + self.sparse
+ t2 = t.multiply(t) # element-wise square
+ self._s1 = t2.sum() / 2.
+ self._cache['s1'] = self._s1
+ return self._s1
+
+ @property
+ def s2array(self):
+ """
+ individual elements comprising s2
+
+ See Also
+ --------
+ s2
+
+ """
+ if 's2array' not in self._cache:
+ s = self.sparse
+ self._s2array = np.array(s.sum(1) + s.sum(0).transpose()) ** 2
+ self._cache['s2array'] = self._s2array
+ return self._s2array
+
+ @property
+ def s2(self):
+ """
+ float
+
+ .. math::
+
+ s2=\sum_j (\sum_i w_{i,j} + \sum_i w_{j,i})^2
+
+ """
+ if 's2' not in self._cache:
+ self._s2 = self.s2array.sum()
+ self._cache['s2'] = self._s2
+ return self._s2
+
+ @property
+ def trcW2(self):
+ """
+ Trace of :math:`WW`
+
+ See Also
+ --------
+ diagW2
+
+ """
+ if 'trcW2' not in self._cache:
+ self._trcW2 = self.diagW2.sum()
+ self._cache['trcw2'] = self._trcW2
+ return self._trcW2
+
+ @property
+ def diagW2(self):
+ """
+ Diagonal of :math:`WW` : array
+
+ See Also
+ --------
+ trcW2
+
+ """
+ if 'diagw2' not in self._cache:
+ self._diagW2 = (self.sparse * self.sparse).diagonal()
+ self._cache['diagW2'] = self._diagW2
+ return self._diagW2
+
+ @property
+ def diagWtW(self):
+ """
+ Diagonal of :math:`W^{'}W` : array
+
+ See Also
+ --------
+ trcWtW
+
+ """
+ if 'diagWtW' not in self._cache:
+ self._diagWtW = (self.sparse.transpose() * self.sparse).diagonal()
+ self._cache['diagWtW'] = self._diagWtW
+ return self._diagWtW
+
+ @property
+ def trcWtW(self):
+ """
+ Trace of :math:`W^{'}W` : float
+
+ See Also
+ --------
+ diagWtW
+
+ """
+ if 'trcWtW' not in self._cache:
+ self._trcWtW = self.diagWtW.sum()
+ self._cache['trcWtW'] = self._trcWtW
+ return self._trcWtW
+
+ @property
+ def diagWtW_WW(self):
+ """
+ diagonal of :math:`W^{'}W + WW`
+ """
+ if 'diagWtW_WW' not in self._cache:
+ wt = self.sparse.transpose()
+ w = self.sparse
+ self._diagWtW_WW = (wt * w + w * w).diagonal()
+ self._cache['diagWtW_WW'] = self._diagWtW_WW
+ return self._diagWtW_WW
+
+ @property
+ def trcWtW_WW(self):
+ """
+ trace of :math:`W^{'}W + WW`
+ """
+ if 'trcWtW_WW' not in self._cache:
+ self._trcWtW_WW = self.diagWtW_WW.sum()
+ self._cache['trcWtW_WW'] = self._trcWtW_WW
+ return self._trcWtW_WW
+
+ @property
+ def pct_nonzero(self):
+ """
+ percentage of nonzero weights
+ """
+ if 'pct_nonzero' not in self._cache:
+ self._pct_nonzero = self.sparse.nnz / (1. * self._n ** 2)
+ self._cache['pct_nonzero'] = self._pct_nonzero
+ return self._pct_nonzero
+
+ @property
+ def cardinalities(self):
+ """
+ number of neighbors for each observation : dict
+ """
+ if 'cardinalities' not in self._cache:
+ c = {}
+ for i in self._id_order:
+ c[i] = len(self.neighbors[i])
+ self._cardinalities = c
+ self._cache['cardinalities'] = self._cardinalities
+ return self._cardinalities
+
+ @property
+ def max_neighbors(self):
+ """
+ largest number of neighbors
+ """
+ if 'max_neighbors' not in self._cache:
+ self._max_neighbors = max(self.cardinalities.values())
+ self._cache['max_neighbors'] = self._max_neighbors
+ return self._max_neighbors
+
+ @property
+ def mean_neighbors(self):
+ """
+ average number of neighbors
+ """
+ if 'mean_neighbors' not in self._cache:
+ self._mean_neighbors = np.mean(self.cardinalities.values())
+ self._cache['mean_neighbors'] = self._mean_neighbors
+ return self._mean_neighbors
+
+ @property
+ def min_neighbors(self):
+ """
+ minimum number of neighbors
+ """
+ if 'min_neighbors' not in self._cache:
+ self._min_neighbors = min(self.cardinalities.values())
+ self._cache['min_neighbors'] = self._min_neighbors
+ return self._min_neighbors
+
+ @property
+ def nonzero(self):
+ """
+ number of nonzero weights
+ """
+ if 'nonzero' not in self._cache:
+ self._nonzero = self.sparse.nnz
+ self._cache['nonzero'] = self._nonzero
+ return self._nonzero
+
+ @property
+ def sd(self):
+ """
+ standard deviation of number of neighbors : float
+ """
+ if 'sd' not in self._cache:
+ self._sd = np.std(self.cardinalities.values())
+ self._cache['sd'] = self._sd
+ return self._sd
+
+ @property
+ def asymmetries(self):
+ """
+ list of id pairs with asymmetric weights
+ """
+ if 'asymmetries' not in self._cache:
+ self._asymmetries = self.asymmetry()
+ self._cache['asymmetries'] = self._asymmetries
+ return self._asymmetries
+
+ @property
+ def islands(self):
+ """
+ list of ids without any neighbors
+ """
+ if 'islands' not in self._cache:
+ self._islands = [i for i,
+ c in self.cardinalities.items() if c == 0]
+ self._cache['islands'] = self._islands
+ return self._islands
+
+ @property
+ def histogram(self):
+ """
+ cardinality histogram as a dictionary, key is the id, value is the
+ number of neighbors for that unit
+ """
+ if 'histogram' not in self._cache:
+ ct, bin = np.histogram(self.cardinalities.values(),
+ range(self.min_neighbors, self.max_neighbors + 2))
+ self._histogram = zip(bin, ct)
+ self._cache['histogram'] = self._histogram
+ return self._histogram
+
+ def __getitem__(self, key):
+ """
+ Allow a dictionary like interaction with the weights class.
+
+ Examples
+ --------
+ >>> from pysal import rook_from_shapefile as rfs
+ >>> w = rfs(pysal.examples.get_path("10740.shp"))
+ WARNING: there is one disconnected observation (no neighbors)
+ Island id: [163]
+ >>> w[163]
+ {}
+ >>> w[0]
+ {1: 1.0, 4: 1.0, 101: 1.0, 85: 1.0, 5: 1.0}
+ """
+ return dict(zip(self.neighbors[key], self.weights[key]))
+
+ def __iter__(self):
+ """
+ Support iteration over weights
+
+ Examples
+ --------
+ >>> import pysal
+ >>> w=pysal.lat2W(3,3)
+ >>> for i,wi in enumerate(w):
+ ... print i,wi
+ ...
+ 0 (0, {1: 1.0, 3: 1.0})
+ 1 (1, {0: 1.0, 2: 1.0, 4: 1.0})
+ 2 (2, {1: 1.0, 5: 1.0})
+ 3 (3, {0: 1.0, 4: 1.0, 6: 1.0})
+ 4 (4, {1: 1.0, 3: 1.0, 5: 1.0, 7: 1.0})
+ 5 (5, {8: 1.0, 2: 1.0, 4: 1.0})
+ 6 (6, {3: 1.0, 7: 1.0})
+ 7 (7, {8: 1.0, 4: 1.0, 6: 1.0})
+ 8 (8, {5: 1.0, 7: 1.0})
+ >>>
+ """
+ for i in self._id_order:
+ yield i, dict(zip(self.neighbors[i], self.weights[i]))
+
+ def remap_ids(self, new_ids):
+ '''
+ In place modification throughout `W` of id values from `w.id_order` to
+ `new_ids` in all
+ ...
+
+ Arguments
+ ---------
+
+ new_ids : list/ndarray
+ Aligned list of new ids to be inserted. Note that first
+ element of new_ids will replace first element of
+ w.id_order, second element of new_ids replaces second
+ element of w.id_order and so on.
+
+ Example
+ -------
+
+ >>> import pysal as ps
+ >>> w = ps.lat2W(3, 3)
+ >>> w.id_order
+ [0, 1, 2, 3, 4, 5, 6, 7, 8]
+ >>> w.neighbors[0]
+ [3, 1]
+ >>> new_ids = ['id%i'%id for id in w.id_order]
+ >>> _ = w.remap_ids(new_ids)
+ >>> w.id_order
+ ['id0', 'id1', 'id2', 'id3', 'id4', 'id5', 'id6', 'id7', 'id8']
+ >>> w.neighbors['id0']
+ ['id3', 'id1']
+ '''
+ old_ids = self._id_order
+ if len(old_ids) != len(new_ids):
+ raise Exception("W.remap_ids: length of `old_ids` does not match \
+ that of new_ids")
+ if len(set(new_ids)) != len(new_ids):
+ raise Exception("W.remap_ids: list `new_ids` contains duplicates")
+ else:
+ new_neighbors = {}
+ new_weights = {}
+ old_transformations = self.transformations['O'].copy()
+ new_transformations = {}
+ for o,n in zip(old_ids, new_ids):
+ o_neighbors = self.neighbors[o]
+ o_weights = self.weights[o]
+ n_neighbors = [ new_ids[old_ids.index(j)] for j in o_neighbors]
+ new_neighbors[n] = n_neighbors
+ new_weights[n] = o_weights[:]
+ new_transformations[n] = old_transformations[o]
+ self.neighbors = new_neighbors
+ self.weights = new_weights
+ self.transformations["O"] = new_transformations
+
+ id_order = [ self._id_order.index(o) for o in old_ids]
+ for i,id_ in enumerate(id_order):
+ self.id_order[id_] = new_ids[i]
+
+ self._reset()
+
+ def __set_id_order(self, ordered_ids):
+ """
+ Set the iteration order in w.
+
+ W can be iterated over. On construction the iteration order is set to
+ the lexicographic order of the keys in the w.weights dictionary. If a specific order
+ is required it can be set with this method.
+
+ Parameters
+ ----------
+
+ ordered_ids : sequence
+ identifiers for observations in specified order
+
+ Notes
+ -----
+
+ ordered_ids is checked against the ids implied by the keys in
+ w.weights. If they are not equivalent sets an exception is raised and
+ the iteration order is not changed.
+
+ Examples
+ --------
+
+ >>> import pysal
+ >>> w=pysal.lat2W(3,3)
+ >>> for i,wi in enumerate(w):
+ ... print i,wi
+ ...
+ 0 (0, {1: 1.0, 3: 1.0})
+ 1 (1, {0: 1.0, 2: 1.0, 4: 1.0})
+ 2 (2, {1: 1.0, 5: 1.0})
+ 3 (3, {0: 1.0, 4: 1.0, 6: 1.0})
+ 4 (4, {1: 1.0, 3: 1.0, 5: 1.0, 7: 1.0})
+ 5 (5, {8: 1.0, 2: 1.0, 4: 1.0})
+ 6 (6, {3: 1.0, 7: 1.0})
+ 7 (7, {8: 1.0, 4: 1.0, 6: 1.0})
+ 8 (8, {5: 1.0, 7: 1.0})
+ >>> w.id_order
+ [0, 1, 2, 3, 4, 5, 6, 7, 8]
+ >>> w.id_order=range(8,-1,-1)
+ >>> w.id_order
+ [8, 7, 6, 5, 4, 3, 2, 1, 0]
+ >>> for i,w_i in enumerate(w):
+ ... print i,w_i
+ ...
+ 0 (8, {5: 1.0, 7: 1.0})
+ 1 (7, {8: 1.0, 4: 1.0, 6: 1.0})
+ 2 (6, {3: 1.0, 7: 1.0})
+ 3 (5, {8: 1.0, 2: 1.0, 4: 1.0})
+ 4 (4, {1: 1.0, 3: 1.0, 5: 1.0, 7: 1.0})
+ 5 (3, {0: 1.0, 4: 1.0, 6: 1.0})
+ 6 (2, {1: 1.0, 5: 1.0})
+ 7 (1, {0: 1.0, 2: 1.0, 4: 1.0})
+ 8 (0, {1: 1.0, 3: 1.0})
+ >>>
+
+ """
+ if set(self._id_order) == set(ordered_ids):
+ self._id_order = ordered_ids
+ self._id_order_set = True
+ self._reset()
+ else:
+ raise Exception('ordered_ids do not align with W ids')
+
+ def __get_id_order(self):
+ """
+ Returns the ids for the observations in the order in which they
+ would be encountered if iterating over the weights .
+ """
+ return self._id_order
+
+ id_order = property(__get_id_order, __set_id_order)
+
+ @property
+ def id_order_set(self):
+ """
+ Returns True if user has set id_order, False if not.
+
+ Examples
+ --------
+ >>> from pysal import lat2W
+ >>> w=lat2W()
+ >>> w.id_order_set
+ True
+ """
+ return self._id_order_set
+
+ @property
+ def neighbor_offsets(self):
+ """
+ Given the current id_order, neighbor_offsets[id] is the offsets of the
+ id's neighbors in id_order
+
+ Examples
+ --------
+ >>> from pysal import W
+ >>> neighbors={'c': ['b'], 'b': ['c', 'a'], 'a': ['b']}
+ >>> weights ={'c': [1.0], 'b': [1.0, 1.0], 'a': [1.0]}
+ >>> w=W(neighbors,weights)
+ >>> w.id_order = ['a','b','c']
+ >>> w.neighbor_offsets['b']
+ [2, 0]
+ >>> w.id_order = ['b','a','c']
+ >>> w.neighbor_offsets['b']
+ [2, 1]
+ """
+ if "neighbors_0" not in self._cache:
+ self.__neighbors_0 = {}
+ id2i = self.id2i
+ for j, neigh_list in self.neighbors.iteritems():
+ self.__neighbors_0[j] = [id2i[neigh] for neigh in neigh_list]
+ self._cache['neighbors_0'] = self.__neighbors_0
+ return self.__neighbors_0
+
+ def get_transform(self):
+ """
+ Getter for transform property
+
+ Returns
+ -------
+ transformation : string (or none)
+
+ Examples
+ --------
+ >>> from pysal import lat2W
+ >>> w=lat2W()
+ >>> w.weights[0]
+ [1.0, 1.0]
+ >>> w.transform
+ 'O'
+ >>> w.transform='r'
+ >>> w.weights[0]
+ [0.5, 0.5]
+ >>> w.transform='b'
+ >>> w.weights[0]
+ [1.0, 1.0]
+ >>>
+ """
+ return self._transform
+
+ def set_transform(self, value="B"):
+ """
+ Transformations of weights.
+
+ Notes
+ -----
+
+ Transformations are applied only to the value of the weights at
+ instantiation. Chaining of transformations cannot be done on a W
+ instance.
+
+ Parameters
+ ----------
+ transform : string (not case sensitive)
+
+ .. table::
+
+ ================ ======================================================
+ transform string value
+ ================ ======================================================
+ B Binary
+ R Row-standardization (global sum=n)
+ D Double-standardization (global sum=1)
+ V Variance stabilizing
+ O Restore original transformation (from instantiation)
+ ================ ======================================================
+
+ Examples
+ --------
+ >>> from pysal import lat2W
+ >>> w=lat2W()
+ >>> w.weights[0]
+ [1.0, 1.0]
+ >>> w.transform
+ 'O'
+ >>> w.transform='r'
+ >>> w.weights[0]
+ [0.5, 0.5]
+ >>> w.transform='b'
+ >>> w.weights[0]
+ [1.0, 1.0]
+ >>>
+ """
+ value = value.upper()
+ self._transform = value
+ if value in self.transformations:
+ self.weights = self.transformations[value]
+ self._reset()
+ else:
+ if value == "R":
+ # row standardized weights
+ weights = {}
+ self.weights = self.transformations['O']
+ for i in self.weights:
+ wijs = self.weights[i]
+ row_sum = sum(wijs) * 1.0
+ if row_sum == 0.0:
+ if not self.silent_island_warning:
+ print 'WARNING: ', i, ' is an island (no neighbors)'
+ weights[i] = [wij / row_sum for wij in wijs]
+ weights = weights
+ self.transformations[value] = weights
+ self.weights = weights
+ self._reset()
+ elif value == "D":
+ # doubly-standardized weights
+ # update current chars before doing global sum
+ self._reset()
+ s0 = self.s0
+ ws = 1.0 / s0
+ weights = {}
+ self.weights = self.transformations['O']
+ for i in self.weights:
+ wijs = self.weights[i]
+ weights[i] = [wij * ws for wij in wijs]
+ weights = weights
+ self.transformations[value] = weights
+ self.weights = weights
+ self._reset()
+ elif value == "B":
+ # binary transformation
+ weights = {}
+ self.weights = self.transformations['O']
+ for i in self.weights:
+ wijs = self.weights[i]
+ weights[i] = [1.0 for wij in wijs]
+ weights = weights
+ self.transformations[value] = weights
+ self.weights = weights
+ self._reset()
+ elif value == "V":
+ # variance stabilizing
+ weights = {}
+ q = {}
+ k = self.cardinalities
+ s = {}
+ Q = 0.0
+ self.weights = self.transformations['O']
+ for i in self.weights:
+ wijs = self.weights[i]
+ q[i] = math.sqrt(sum([wij * wij for wij in wijs]))
+ s[i] = [wij / q[i] for wij in wijs]
+ Q += sum([si for si in s[i]])
+ nQ = self.n / Q
+ for i in self.weights:
+ weights[i] = [w * nQ for w in s[i]]
+ weights = weights
+ self.transformations[value] = weights
+ self.weights = weights
+ self._reset()
+ elif value == "O":
+ # put weights back to original transformation
+ weights = {}
+ original = self.transformations[value]
+ self.weights = original
+ self._reset()
+ else:
+ print 'unsupported weights transformation'
+
+ transform = property(get_transform, set_transform)
+
+ def asymmetry(self, intrinsic=True):
+ """
+ Asymmetry check
+
+ Parameters
+ ----------
+ intrinsic: boolean (default=True)
+
+ intrinsic symmetry:
+ :math:`w_{i,j} == w_{j,i}`
+
+ if intrisic is False:
+ symmetry is defined as :math:`i \in N_j \ AND \ j \in N_i` where
+ :math:`N_j` is the set of neighbors for j.
+
+ Returns
+ -------
+ asymmetries : list
+ empty if no asymmetries are found
+ if asymmetries, then a list of (i,j) tuples is returned
+
+ Examples
+ --------
+
+ >>> from pysal import lat2W
+ >>> w=lat2W(3,3)
+ >>> w.asymmetry()
+ []
+ >>> w.transform='r'
+ >>> w.asymmetry()
+ [(0, 1), (0, 3), (1, 0), (1, 2), (1, 4), (2, 1), (2, 5), (3, 0), (3, 4), (3, 6), (4, 1), (4, 3), (4, 5), (4, 7), (5, 2), (5, 4), (5, 8), (6, 3), (6, 7), (7, 4), (7, 6), (7, 8), (8, 5), (8, 7)]
+ >>> result = w.asymmetry(intrinsic=False)
+ >>> result
+ []
+ >>> neighbors={0:[1,2,3], 1:[1,2,3], 2:[0,1], 3:[0,1]}
+ >>> weights={0:[1,1,1], 1:[1,1,1], 2:[1,1], 3:[1,1]}
+ >>> w=W(neighbors,weights)
+ >>> w.asymmetry()
+ [(0, 1), (1, 0)]
+ """
+
+ if intrinsic:
+ wd = self.sparse.transpose() - self.sparse
+ else:
+ transform = self.transform
+ self.transform = 'b'
+ wd = self.sparse.transpose() - self.sparse
+ self.transform = transform
+
+ ids = np.nonzero(wd)
+ if len(ids[0]) == 0:
+ return []
+ else:
+ ijs = zip(ids[0], ids[1])
+ ijs.sort()
+ return ijs
+
+ def full(self):
+ """
+ Generate a full numpy array
+
+ Returns
+ -------
+
+ implicit : tuple
+ first element being the full numpy array and second element
+ keys being the ids associated with each row in the array.
+
+ Examples
+ --------
+ >>> from pysal import W
+ >>> neighbors={'first':['second'],'second':['first','third'],'third':['second']}
+ >>> weights={'first':[1],'second':[1,1],'third':[1]}
+ >>> w=W(neighbors,weights)
+ >>> wf,ids=w.full()
+ >>> wf
+ array([[ 0., 1., 0.],
+ [ 1., 0., 1.],
+ [ 0., 1., 0.]])
+ >>> ids
+ ['first', 'second', 'third']
+
+ See also
+ --------
+ full
+ """
+ return util.full(self)
+
+ def towsp(self):
+ '''
+ Generate a WSP object
+
+ Returns
+ -------
+
+ implicit : pysal.WSP
+ Thin W class
+
+ Examples
+ --------
+ >>> import pysal as ps
+ >>> from pysal import W
+ >>> neighbors={'first':['second'],'second':['first','third'],'third':['second']}
+ >>> weights={'first':[1],'second':[1,1],'third':[1]}
+ >>> w=W(neighbors,weights)
+ >>> wsp=w.towsp()
+ >>> isinstance(wsp, ps.weights.weights.WSP)
+ True
+ >>> wsp.n
+ 3
+ >>> wsp.s0
+ 4
+
+ See also
+ --------
+ WSP
+ '''
+ return WSP(self.sparse, self._id_order)
+
+ def set_shapefile(self, shapefile, idVariable=None, full=False):
+ """
+ Adding meta data for writing headers of gal and gwt files
+
+ Parameters
+ ----------
+
+ shapefile : (string)
+ shapefile name used to construct weights
+
+ idVariable : (string)
+ name of attribute in shapefile to associate with ids in the weights
+
+ full : (boolean)
+ True - write out entire path for shapefile, False
+ (default) only base of shapefile without extension
+
+ """
+ if full:
+ self._shpName = shapefile
+ else:
+ self._shpName = BASENAME(shapefile).split(".")[0]
+
+ self._varName = idVariable
+
+
+class WSP(object):
+
+ """
+ Thin W class for spreg
+
+ Parameters
+ ----------
+
+ sparse : scipy sparse object
+ NxN object from scipy.sparse
+
+ id_order : list
+ An ordered list of ids, assumed to match the ordering in
+ sparse.
+
+ Attributes
+ ----------
+
+ n
+ s0
+ trcWtW_WW
+
+ Examples
+ --------
+
+ From GAL information
+
+ >>> import scipy.sparse
+ >>> import pysal
+ >>> rows = [0, 1, 1, 2, 2, 3]
+ >>> cols = [1, 0, 2, 1, 3, 3]
+ >>> weights = [1, 0.75, 0.25, 0.9, 0.1, 1]
+ >>> sparse = scipy.sparse.csr_matrix((weights, (rows, cols)), shape=(4,4))
+ >>> w = pysal.weights.WSP(sparse)
+ >>> w.s0
+ 4.0
+ >>> w.trcWtW_WW
+ 6.3949999999999996
+ >>> w.n
+ 4
+
+ """
+
+ def __init__(self, sparse, id_order=None):
+ if not scipy.sparse.issparse(sparse):
+ raise ValueError("must pass a scipy sparse object")
+ rows, cols = sparse.shape
+ if rows != cols:
+ raise ValueError("Weights object must be square")
+ self.sparse = sparse.tocsr()
+ self.n = sparse.shape[0]
+ if id_order:
+ if len(id_order) != self.n:
+ raise ValueError(
+ "Number of values in id_order must match shape of sparse")
+ self.id_order = id_order
+ self._cache = {}
+
+ @property
+ def s0(self):
+ """
+ float
+
+ .. math::
+
+ s0=\sum_i \sum_j w_{i,j}
+
+ """
+ if 's0' not in self._cache:
+ self._s0 = self.sparse.sum()
+ self._cache['s0'] = self._s0
+ return self._s0
+
+ @property
+ def trcWtW_WW(self):
+ """
+ trace of :math:`W^{'}W + WW`
+ """
+ if 'trcWtW_WW' not in self._cache:
+ self._trcWtW_WW = self.diagWtW_WW.sum()
+ self._cache['trcWtW_WW'] = self._trcWtW_WW
+ return self._trcWtW_WW
+
+ @property
+ def diagWtW_WW(self):
+ """
+ diagonal of :math:`W^{'}W + WW`
+ """
+ if 'diagWtW_WW' not in self._cache:
+ wt = self.sparse.transpose()
+ w = self.sparse
+ self._diagWtW_WW = (wt * w + w * w).diagonal()
+ self._cache['diagWtW_WW'] = self._diagWtW_WW
+ return self._diagWtW_WW
diff --git a/pysal/weights/weights_from_geojson.ipynb b/pysal/weights/weights_from_geojson.ipynb
new file mode 100644
index 0000000..e9febb5
--- /dev/null
+++ b/pysal/weights/weights_from_geojson.ipynb
@@ -0,0 +1,311 @@
+{
+ "metadata": {
+ "name": "weights_from_geojson"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Prototyping the construction of PySAL weights from geojson. Supports local files, uri, and serialized objects\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "Author: Sergio Rey <sjsrey at gmail.com>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import geojson\n",
+ "import pysal as ps\n",
+ "import urllib2 as urllib"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "class PolygonCollection:\n",
+ " def __init__(self, polygons, bbox=None):\n",
+ " \"\"\"\n",
+ "\n",
+ " Parameters\n",
+ " ==========\n",
+ " polygons: dict\n",
+ " key is polygon Id, value is PySAL Polygon object\n",
+ " bbox: list (optional)\n",
+ " [left, lower, right, upper]\n",
+ "\n",
+ " Notes\n",
+ " =====\n",
+ " bbox is supported in geojson specification at both the feature and feature collection level. However, not all geojson writers generate the bbox at the feature collection level. \n",
+ " In those cases, the bbox property will be set on initial access.\n",
+ "\n",
+ " \"\"\"\n",
+ " \n",
+ " self.type=ps.cg.Polygon\n",
+ " self.n = len(polygons)\n",
+ " self.polygons = polygons\n",
+ " if bbox is None:\n",
+ " self._bbox = None\n",
+ " else:\n",
+ " self._bbox = bbox\n",
+ " \n",
+ " @property\n",
+ " def bbox(self):\n",
+ " bboxes = np.array([self.polygons[p].bbox for p in self.polygons])\n",
+ " mins = bboxes.min(axis=0)\n",
+ " maxs = bboxes.max(axis=0)\n",
+ " self._bbox = [ mins[0], mins[1], maxs[2], maxs[3] ]\n",
+ " return self._bbox\n",
+ " \n",
+ " \n",
+ " def __getitem__(self, index):\n",
+ " return self.polygons[index]\n",
+ " "
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "def queen_geojson(gjobj):\n",
+ " \"\"\"\n",
+ " Constructs a PySAL queen contiguity W from a geojson object\n",
+ "\n",
+ " \"\"\"\n",
+ " polys = []\n",
+ " ids = []\n",
+ " i = 0\n",
+ " for feature in gjobj['features']:\n",
+ " polys.append(ps.cg.asShape(geojson.Polygon(feature['geometry']['coordinates'])))\n",
+ " ids.append(i)\n",
+ " i += 1\n",
+ " polygons = PolygonCollection(dict(zip(ids,polys)))\n",
+ " neighbors = ps.weights.Contiguity.ContiguityWeightsPolygons(polygons).w\n",
+ " return ps.W(neighbors)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "def queen_from_geojson(uri):\n",
+ " gjobj = geojson.load(urllib.urlopen(uri))\n",
+ " return queen_geojson(gjobj)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "def queen_from_geojsons(s):\n",
+ " return queen_geojson(info)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "def queen_from_geojsonf(fileName):\n",
+ " fp = open(fileName)\n",
+ " obj = geojson.load(fp)\n",
+ " fp.close()\n",
+ " return queen_geojson(obj)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# from a uri\n",
+ "uri = \"http://toae.org/pub/columbus.json\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "wq = queen_from_geojson(uri)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "wq.n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "pyout",
+ "prompt_number": 9,
+ "text": [
+ "49"
+ ]
+ }
+ ],
+ "prompt_number": 9
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# from a local file\n",
+ "wf = queen_from_geojsonf(ps.examples.get_path(\"columbus.json\"))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 10
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "wf.n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "pyout",
+ "prompt_number": 11,
+ "text": [
+ "49"
+ ]
+ }
+ ],
+ "prompt_number": 11
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# from a serialized geojson object\n",
+ "\n",
+ "fp = open(ps.examples.get_path(\"columbus.json\"))\n",
+ "info = geojson.load(fp)\n",
+ "fp.close()\n",
+ "infos = geojson.dumps(info)\n",
+ "ws = queen_from_geojsons(infos)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 12
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "ws.n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "pyout",
+ "prompt_number": 13,
+ "text": [
+ "49"
+ ]
+ }
+ ],
+ "prompt_number": 13
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "wf.neighbors == ws.neighbors"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "pyout",
+ "prompt_number": 14,
+ "text": [
+ "True"
+ ]
+ }
+ ],
+ "prompt_number": 14
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "wq.neighbors == wf.neighbors"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "pyout",
+ "prompt_number": 15,
+ "text": [
+ "True"
+ ]
+ }
+ ],
+ "prompt_number": 15
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+}
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..f3aca17
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1 @@
+scipy>=0.11
diff --git a/rtd.txt b/rtd.txt
new file mode 100644
index 0000000..b9d419a
--- /dev/null
+++ b/rtd.txt
@@ -0,0 +1 @@
+sphinxcontrib-napoleon
diff --git a/setup.cfg b/setup.cfg
new file mode 100644
index 0000000..520e354
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,6 @@
+[nosetests]
+ignore-files=collection
+exclude-dir=pysal/contrib
+
+[wheel]
+universal=1
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..dcfa1bd
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,120 @@
+# coding: utf-8
+
+try:
+ from setuptools import setup, find_packages
+except ImportError:
+ from distutils.core import setup
+
+import sys
+import shutil
+import os
+if sys.version_info[0] < 3:
+ import __builtin__ as builtins
+else:
+ import builtins
+
+from pysal.version import version as dversion
+
+with open('README.txt') as file:
+ long_description = file.read()
+
+MAJOR = 1
+MINOR = 9
+MICRO = 1
+ISRELEASED = False
+VERSION = '%d.%d.%d' % (MAJOR, MINOR, MICRO)
+
+
+# BEFORE importing distutils, remove MANIFEST. distutils doesn't properly
+# update it when the contents of directories change.
+if os.path.exists('MANIFEST'):
+ os.remove('MANIFEST')
+
+
+def setup_package():
+
+ # Perform 2to3 if needed
+ local_path = os.path.dirname(os.path.abspath(sys.argv[0])) # get cwd
+ src_path = local_path
+
+ if sys.version_info[0] == 3:
+ src_path = os.path.join(local_path, 'build', 'py3k')
+ sys.path.insert(0, os.path.join(local_path, 'tools'))
+ import py3tool
+ print("Converting to Python3 via 2to3...")
+ py3tool.sync_2to3('pysal', os.path.join(src_path, 'pysal'))
+
+ site_cfg = os.path.join(local_path, 'site.cfg')
+ if os.path.isfile(site_cfg):
+ shutil.copy(site_cfg, src_path)
+
+ # Ugly hack to make pip work with Python 3, see #1857.
+ # Explanation: pip messes with __file__ which interacts badly with the
+ # change in directory due to the 2to3 conversion. Therefore we restore
+ # __file__ to what it would have been otherwise.
+ global __file__
+ __file__ = os.path.join(os.curdir, os.path.basename(__file__))
+ if '--egg-base' in sys.argv:
+ # Change pip-egg-info entry to absolute path, so pip can find it
+ # after changing directory.
+ idx = sys.argv.index('--egg-base')
+ if sys.argv[idx + 1] == 'pip-egg-info':
+ sys.argv[idx + 1] = os.path.join(local_path, 'pip-egg-info')
+
+ old_path = os.getcwd()
+ os.chdir(src_path)
+ sys.path.insert(0, src_path)
+
+
+ # get all file endings and copy whole file names without a file suffix
+ # assumes nested directories are only down one level
+ example_data_files = set()
+ for i in os.listdir("pysal/examples"):
+ if i.endswith(('py', 'pyc')):
+ continue
+ if not os.path.isdir("pysal/examples/" + i):
+ if "." in i:
+ glob_name = "examples/*." + i.split(".")[-1]
+ else:
+ glob_name = "examples/" + i
+ else:
+ glob_name = "examples/" + i + "/*"
+
+ example_data_files.add(glob_name)
+
+ setup(
+ name='PySAL',
+ version=dversion,
+ description="A library of spatial analysis functions.",
+ long_description=long_description,
+ maintainer="PySAL Developers",
+ maintainer_email='pysal-dev at googlegroups.com',
+ url='http://pysal.org',
+ download_url='https://pypi.python.org/pypi/PySAL',
+ license='BSD',
+ py_modules=['pysal'],
+ test_suite='nose.collector',
+ tests_require=['nose'],
+ keywords='spatial statistics',
+ classifiers=[
+ 'Development Status :: 5 - Production/Stable',
+ 'Intended Audience :: Science/Research',
+ 'Intended Audience :: Developers',
+ 'Intended Audience :: Education',
+ 'Topic :: Scientific/Engineering',
+ 'Topic :: Scientific/Engineering :: GIS',
+ 'License :: OSI Approved :: BSD License',
+ 'Programming Language :: Python',
+ 'Programming Language :: Python :: 2.5',
+ 'Programming Language :: Python :: 2.6',
+ 'Programming Language :: Python :: 2.7'
+ ],
+ packages=find_packages(exclude=[".meta", "*.meta.*", "meta.*",
+ "meta"]),
+ package_data={'pysal': list(example_data_files)},
+ requires=['scipy']
+ )
+
+
+if __name__ == '__main__':
+ setup_package()
diff --git a/tools/cron.py b/tools/cron.py
new file mode 100644
index 0000000..948376d
--- /dev/null
+++ b/tools/cron.py
@@ -0,0 +1,16 @@
+#!/usr/bin/python
+"""Sets and unsets a lock on the local pysal repository."""
+import os, sys
+
+#check lock
+if os.path.exists('/tmp/pysal.lock'):
+ print "LOCK IN PLACE, another process is running perhaps?"
+ sys.exit(1)
+else:
+ lck = open('/tmp/pysal.lock','w')
+ lck.write('%d'%os.getpid())
+ lck.close()
+ lck = True
+ os.system('/Users/stephens/Dropbox/work/Projects/pysal/trunk/tools/test.sh')
+ os.remove('/tmp/pysal.lock')
+
diff --git a/tools/docs.sh b/tools/docs.sh
new file mode 100644
index 0000000..55d0517
--- /dev/null
+++ b/tools/docs.sh
@@ -0,0 +1,39 @@
+#!/bin/bash -
+
+if [ -f /tmp/pysal.lock ]
+then
+#check repo for changes
+cd /home/pstephen/pysal/
+svn cleanup
+a=$(svnversion)
+svn update -r $((a+1)) 2>&1
+
+if [ $? = "0" ]
+then
+b=$(svnversion)
+else exit
+fi
+
+if [ "$a" != "$b" ]
+then
+export PYTHONPATH=/home/pstephen/pysal/
+find /home/pstephen/pysal/pysal -name "*.pyc" -exec rm '{}' ';'
+
+# run coverage and copy to server
+/usr/local/bin/coverage html -d /home/pstephen/coverage pysal/*.py pysal/cg/*.py pysal/esda/*.py pysal/inequality/*.py pysal/network/*.py pysal/region/*.py pysal/spatial_dynamics/*.py pysal/spreg/*.py pysal/weights/*.py pysal/core/*.py pysal/core/IOHandlers/*.py pysal/core/util/*.py
+rsync -r --delete /home/pstephen/coverage/ stephens at geodacenter.org:~/coverage
+
+# build new docs and copy to server
+cd /home/pstephen/pysal/doc
+/usr/bin/make clean
+/usr/bin/sphinx-build -Q -b html -d build/doctrees source build/html
+rsync -r --delete /home/pstephen/pysal/doc/build/html/ stephens at geodacenter.org:~/dev
+
+# build source installer and copy to server
+cd /home/pstephen/pysal/
+/usr/bin/python setup.py sdist
+rsync -r --delete /home/pstephen/pysal/dist/pysal* stephens at geodacenter.org:~/tmp/builds
+
+else echo "`date`, "$a", "$b", Repo unchanged." > /tmp/pysal.log 2>&1
+fi
+fi
diff --git a/tools/github_stats.py b/tools/github_stats.py
new file mode 100644
index 0000000..9fd5263
--- /dev/null
+++ b/tools/github_stats.py
@@ -0,0 +1,193 @@
+#!/usr/bin/env python
+"""Simple tools to query github.com and gather stats about issues.
+"""
+#-----------------------------------------------------------------------------
+# Imports
+#-----------------------------------------------------------------------------
+
+from __future__ import print_function
+
+import json
+import re
+import sys
+
+from datetime import datetime, timedelta
+from subprocess import check_output
+from urllib2 import urlopen
+
+#-----------------------------------------------------------------------------
+# Globals
+#-----------------------------------------------------------------------------
+
+ISO8601 = "%Y-%m-%dT%H:%M:%SZ"
+PER_PAGE = 100
+
+element_pat = re.compile(r'<(.+?)>')
+rel_pat = re.compile(r'rel=[\'"](\w+)[\'"]')
+
+#-----------------------------------------------------------------------------
+# Functions
+#-----------------------------------------------------------------------------
+
+def parse_link_header(headers):
+ link_s = headers.get('link', '')
+ urls = element_pat.findall(link_s)
+ rels = rel_pat.findall(link_s)
+ d = {}
+ for rel,url in zip(rels, urls):
+ d[rel] = url
+ return d
+
+def get_paged_request(url):
+ """get a full list, handling APIv3's paging"""
+ results = []
+ while url:
+ print("fetching %s" % url, file=sys.stderr)
+ f = urlopen(url)
+ results.extend(json.load(f))
+ links = parse_link_header(f.headers)
+ url = links.get('next')
+ return results
+
+def get_issues(project="pysal/pysal", state="closed", pulls=False):
+ """Get a list of the issues from the Github API."""
+ which = 'pulls' if pulls else 'issues'
+ url = "https://api.github.com/repos/%s/%s?state=%s&per_page=%i" % (project, which, state, PER_PAGE)
+ return get_paged_request(url)
+
+
+def _parse_datetime(s):
+ """Parse dates in the format returned by the Github API."""
+ if s:
+ return datetime.strptime(s, ISO8601)
+ else:
+ return datetime.fromtimestamp(0)
+
+
+def issues2dict(issues):
+ """Convert a list of issues to a dict, keyed by issue number."""
+ idict = {}
+ for i in issues:
+ idict[i['number']] = i
+ return idict
+
+
+def is_pull_request(issue):
+ """Return True if the given issue is a pull request."""
+ return 'pull_request_url' in issue
+
+
+def issues_closed_since(period=timedelta(days=365), project="pysal/pysal", pulls=False):
+ """Get all issues closed since a particular point in time. period
+can either be a datetime object, or a timedelta object. In the
+latter case, it is used as a time before the present."""
+
+ which = 'pulls' if pulls else 'issues'
+
+ if isinstance(period, timedelta):
+ period = datetime.now() - period
+ url = "https://api.github.com/repos/%s/%s?state=closed&sort=updated&since=%s&per_page=%i" % (project, which, period.strftime(ISO8601), PER_PAGE)
+ allclosed = get_paged_request(url)
+ # allclosed = get_issues(project=project, state='closed', pulls=pulls, since=period)
+ filtered = [i for i in allclosed if _parse_datetime(i['closed_at']) > period]
+
+ # exclude rejected PRs
+ if pulls:
+ filtered = [ pr for pr in filtered if pr['merged_at'] ]
+
+ return filtered
+
+
+def sorted_by_field(issues, field='closed_at', reverse=False):
+ """Return a list of issues sorted by closing date date."""
+ return sorted(issues, key = lambda i:i[field], reverse=reverse)
+
+
+def report(issues, show_urls=False):
+ """Summary report about a list of issues, printing number and title.
+ """
+ # titles may have unicode in them, so we must encode everything below
+ if show_urls:
+ for i in issues:
+ role = 'ghpull' if 'merged_at' in i else 'ghissue'
+ print('* :%s:`%d`: %s' % (role, i['number'],
+ i['title'].encode('utf-8')))
+ else:
+ for i in issues:
+ print('* %d: %s' % (i['number'], i['title'].encode('utf-8')))
+
+#-----------------------------------------------------------------------------
+# Main script
+#-----------------------------------------------------------------------------
+
+if __name__ == "__main__":
+ # Whether to add reST urls for all issues in printout.
+ show_urls = True
+
+ # By default, search one month back
+ tag = None
+ if len(sys.argv) > 1:
+ try:
+ days = int(sys.argv[1])
+ except:
+ tag = sys.argv[1]
+ else:
+ tag = check_output(['git', 'describe', '--abbrev=0']).strip()
+
+ if tag:
+ cmd = ['git', 'log', '-1', '--format=%ai', tag]
+ tagday, tz = check_output(cmd).strip().rsplit(' ', 1)
+ since = datetime.strptime(tagday, "%Y-%m-%d %H:%M:%S")
+ else:
+ since = datetime.now() - timedelta(days=days)
+
+ print("fetching GitHub stats since %s (tag: %s)" % (since, tag), file=sys.stderr)
+ # turn off to play interactively without redownloading, use %run -i
+ if 1:
+ issues = issues_closed_since(since, pulls=False)
+ pulls = issues_closed_since(since, pulls=True)
+
+ # For regular reports, it's nice to show them in reverse chronological order
+ issues = sorted_by_field(issues, reverse=True)
+ pulls = sorted_by_field(pulls, reverse=True)
+
+ n_issues, n_pulls = map(len, (issues, pulls))
+ n_total = n_issues + n_pulls
+
+ # Print summary report we can directly include into release notes.
+ print()
+ since_day = since.strftime("%Y/%m/%d")
+ today = datetime.today().strftime("%Y/%m/%d")
+ print(".. _github-stats:")
+ print('Github stats')
+ print('============')
+ print()
+ print("GitHub stats for %s - %s (tag: %s)" % (since_day, today, tag))
+ print()
+ print("These lists are automatically generated, and may be incomplete or contain duplicates.")
+ print()
+ if tag:
+ # print git info, in addition to GitHub info:
+ since_tag = tag+'..'
+ cmd = ['git', 'log', '--oneline', since_tag]
+ ncommits = len(check_output(cmd).splitlines())
+
+ author_cmd = ['git', 'log', '--format=* %aN', since_tag]
+ all_authors = check_output(author_cmd).splitlines()
+ unique_authors = sorted(set(all_authors))
+
+ print("The following %i authors contributed %i commits." % (len(unique_authors), ncommits))
+ print()
+ print('\n'.join(unique_authors))
+ print()
+
+ print()
+ print("We closed a total of %d issues, %d pull requests and %d regular issues;\n"
+ "this is the full list (generated with the script \n"
+ ":file:`tools/github_stats.py`):" % (n_total, n_pulls, n_issues))
+ print()
+ print('Pull Requests (%d):\n' % n_pulls)
+ report(pulls, show_urls)
+ print()
+ print('Issues (%d):\n' % n_issues)
+ report(issues, show_urls)
diff --git a/tools/py3tool.py b/tools/py3tool.py
new file mode 100644
index 0000000..23a16a4
--- /dev/null
+++ b/tools/py3tool.py
@@ -0,0 +1,345 @@
+#!/usr/bin/env python3
+# -*- python -*-
+"""
+%prog SUBMODULE...
+
+Hack to pipe submodules of Numpy through 2to3 and build them in-place
+one-by-one.
+
+Example usage:
+
+ python3 tools/py3tool.py testing distutils core
+
+This will copy files to _py3k/numpy, add a dummy __init__.py and
+version.py on the top level, and copy and 2to3 the files of the three
+submodules.
+
+When running py3tool again, only changed files are re-processed, which
+makes the test-bugfix cycle faster.
+
+"""
+from optparse import OptionParser
+import shutil
+import os
+import sys
+import re
+import subprocess
+import fnmatch
+
+if os.environ.get('USE_2TO3CACHE'):
+ import lib2to3cache
+
+BASE = os.path.normpath(os.path.join(os.path.dirname(__file__), '..'))
+TEMP = os.path.normpath(os.path.join(BASE, '_py3k'))
+
+SCRIPT_2TO3 = os.path.join(BASE, 'tools', '2to3.py')
+
+EXTRA_2TO3_FLAGS = {
+ '*/setup.py': '-x import',
+ #'numpy/core/code_generators/generate_umath.py': '-x import',
+ #'numpy/core/code_generators/generate_numpy_api.py': '-x import',
+ #'numpy/core/code_generators/generate_ufunc_api.py': '-x import',
+ #'numpy/core/defchararray.py': '-x unicode',
+ #'numpy/compat/py3k.py': '-x unicode',
+ #'numpy/ma/timer_comparison.py': 'skip',
+ #'numpy/distutils/system_info.py': '-x reduce',
+ #'numpy/f2py/auxfuncs.py': '-x reduce',
+ #'numpy/lib/arrayterator.py': '-x reduce',
+ #'numpy/lib/tests/test_arrayterator.py': '-x reduce',
+ #'numpy/ma/core.py': '-x reduce',
+ #'numpy/ma/tests/test_core.py': '-x reduce',
+ #'numpy/ma/tests/test_old_ma.py': '-x reduce',
+ #'numpy/ma/timer_comparison.py': '-x reduce',
+ #'numpy/oldnumeric/ma.py': '-x reduce',
+}
+
+def main():
+ p = OptionParser(usage=__doc__.strip())
+ p.add_option("--clean", "-c", action="store_true",
+ help="clean source directory")
+ options, args = p.parse_args()
+
+ if not args:
+ p.error('no submodules given')
+ else:
+ dirs = ['scipy/%s' % x for x in map(os.path.basename, args)]
+
+ # Prepare
+ if not os.path.isdir(TEMP):
+ os.makedirs(TEMP)
+
+ # Set up dummy files (for building only submodules)
+ dummy_files = {
+ '__init__.py': 'from scipy.version import version as __version__',
+ 'version.py': 'version = "0.8.0.dev"'
+ }
+
+ for fn, content in dummy_files.items():
+ fn = os.path.join(TEMP, 'scipy', fn)
+ if not os.path.isfile(fn):
+ try:
+ os.makedirs(os.path.dirname(fn))
+ except OSError:
+ pass
+ f = open(fn, 'wb+')
+ f.write(content.encode('ascii'))
+ f.close()
+
+ # Environment
+ pp = [os.path.abspath(TEMP)]
+ def getenv():
+ env = dict(os.environ)
+ env.update({'PYTHONPATH': ':'.join(pp)})
+ return env
+
+ # Copy
+ for d in dirs:
+ src = os.path.join(BASE, d)
+ dst = os.path.join(TEMP, d)
+
+ # Run 2to3
+ sync_2to3(dst=dst,
+ src=src,
+ patchfile=os.path.join(TEMP, os.path.basename(d) + '.patch'),
+ clean=options.clean)
+
+ # Run setup.py, falling back to Pdb post-mortem on exceptions
+ setup_py = os.path.join(dst, 'setup.py')
+ if os.path.isfile(setup_py):
+ code = """\
+import pdb, sys, traceback
+p = pdb.Pdb()
+try:
+ import __main__
+ __main__.__dict__.update({
+ "__name__": "__main__", "__file__": "setup.py",
+ "__builtins__": __builtins__})
+ fp = open("setup.py", "rb")
+ try:
+ exec(compile(fp.read(), "setup.py", 'exec'))
+ finally:
+ fp.close()
+except SystemExit:
+ raise
+except:
+ traceback.print_exc()
+ t = sys.exc_info()[2]
+ p.interaction(None, t)
+"""
+ ret = subprocess.call([sys.executable, '-c', code,
+ 'build_ext', '-i'],
+ cwd=dst,
+ env=getenv())
+ if ret != 0:
+ raise RuntimeError("Build failed.")
+
+ # Run nosetests
+ subprocess.call(['nosetests3', '-v', d], cwd=TEMP)
+
+def custom_mangling(filename):
+ import_mangling = [
+ os.path.join('cluster', '__init__.py'),
+ os.path.join('cluster', 'hierarchy.py'),
+ os.path.join('cluster', 'vq.py'),
+ os.path.join('fftpack', 'basic.py'),
+ os.path.join('fftpack', 'pseudo_diffs.py'),
+ os.path.join('integrate', 'odepack.py'),
+ os.path.join('integrate', 'quadpack.py'),
+ os.path.join('integrate', '_ode.py'),
+ os.path.join('interpolate', 'fitpack.py'),
+ os.path.join('interpolate', 'fitpack2.py'),
+ os.path.join('interpolate', 'interpolate.py'),
+ os.path.join('interpolate', 'interpolate_wrapper.py'),
+ os.path.join('interpolate', 'ndgriddata.py'),
+ os.path.join('io', 'array_import.py'),
+ os.path.join('io', '__init__.py'),
+ os.path.join('io', 'matlab', 'miobase.py'),
+ os.path.join('io', 'matlab', 'mio4.py'),
+ os.path.join('io', 'matlab', 'mio5.py'),
+ os.path.join('io', 'matlab', 'mio5_params.py'),
+ os.path.join('linalg', 'basic.py'),
+ os.path.join('linalg', 'decomp.py'),
+ os.path.join('linalg', 'lapack.py'),
+ os.path.join('linalg', 'flinalg.py'),
+ os.path.join('linalg', 'iterative.py'),
+ os.path.join('linalg', 'misc.py'),
+ os.path.join('lib', 'blas', '__init__.py'),
+ os.path.join('lib', 'lapack', '__init__.py'),
+ os.path.join('ndimage', 'filters.py'),
+ os.path.join('ndimage', 'fourier.py'),
+ os.path.join('ndimage', 'interpolation.py'),
+ os.path.join('ndimage', 'measurements.py'),
+ os.path.join('ndimage', 'morphology.py'),
+ os.path.join('optimize', 'minpack.py'),
+ os.path.join('optimize', 'zeros.py'),
+ os.path.join('optimize', 'lbfgsb.py'),
+ os.path.join('optimize', 'cobyla.py'),
+ os.path.join('optimize', 'slsqp.py'),
+ os.path.join('optimize', 'nnls.py'),
+ os.path.join('signal', '__init__.py'),
+ os.path.join('signal', 'bsplines.py'),
+ os.path.join('signal', 'signaltools.py'),
+ os.path.join('signal', 'fir_filter_design.py'),
+ os.path.join('special', '__init__.py'),
+ os.path.join('special', 'add_newdocs.py'),
+ os.path.join('special', 'basic.py'),
+ os.path.join('special', 'orthogonal.py'),
+ os.path.join('spatial', '__init__.py'),
+ os.path.join('spatial', 'distance.py'),
+ os.path.join('sparse', 'linalg', 'isolve', 'iterative.py'),
+ os.path.join('sparse', 'linalg', 'dsolve', 'linsolve.py'),
+ os.path.join('sparse', 'linalg', 'dsolve', 'umfpack', 'umfpack.py'),
+ os.path.join('sparse', 'linalg', 'eigen', 'arpack', 'arpack.py'),
+ os.path.join('sparse', 'linalg', 'eigen', 'arpack', 'speigs.py'),
+ os.path.join('sparse', 'linalg', 'iterative', 'isolve', 'iterative.py'),
+ os.path.join('stats', 'stats.py'),
+ os.path.join('stats', 'distributions.py'),
+ os.path.join('stats', 'morestats.py'),
+ os.path.join('stats', 'kde.py'),
+ os.path.join('stats', 'mstats_basic.py'),
+ ]
+
+ if any(filename.endswith(x) for x in import_mangling):
+ print(filename)
+ f = open(filename, 'r', encoding='utf-8')
+ text = f.read()
+ f.close()
+ for mod in ['_vq', '_hierarchy_wrap', '_fftpack', 'convolve',
+ '_flinalg', 'fblas', 'flapack', 'cblas', 'clapack',
+ 'calc_lwork', '_cephes', 'specfun', 'orthogonal_eval',
+ 'lambertw', 'ckdtree', '_distance_wrap', '_logit',
+ '_minpack', '_zeros', '_lbfgsb', '_cobyla', '_slsqp',
+ '_nnls',
+ 'sigtools', 'spline', 'spectral',
+ '_fitpack', 'dfitpack', '_interpolate',
+ '_odepack', '_quadpack', 'vode', '_dop',
+ 'vonmises_cython',
+ 'futil', 'mvn',
+ '_nd_image',
+ 'numpyio',
+ '_superlu', '_arpack', '_iterative', '_umfpack',
+ 'interpnd',
+ 'mio_utils', 'mio5_utils', 'streams'
+ ]:
+ text = re.sub(r'^(\s*)import %s' % mod,
+ r'\1from . import %s' % mod,
+ text, flags=re.M)
+ text = re.sub(r'^(\s*)from %s import' % mod,
+ r'\1from .%s import' % mod,
+ text, flags=re.M)
+ #text = text.replace('from matrixlib', 'from .matrixlib')
+ f = open(filename, 'w', encoding='utf-8')
+ f.write(text)
+ f.close()
+
+def walk_sync(dir1, dir2, _seen=None):
+ if _seen is None:
+ seen = {}
+ else:
+ seen = _seen
+
+ if not dir1.endswith(os.path.sep):
+ dir1 = dir1 + os.path.sep
+
+ # Walk through stuff (which we haven't yet gone through) in dir1
+ for root, dirs, files in os.walk(dir1):
+ sub = root[len(dir1):]
+ if sub in seen:
+ dirs = [x for x in dirs if x not in seen[sub][0]]
+ files = [x for x in files if x not in seen[sub][1]]
+ seen[sub][0].extend(dirs)
+ seen[sub][1].extend(files)
+ else:
+ seen[sub] = (dirs, files)
+ if not dirs and not files:
+ continue
+ yield os.path.join(dir1, sub), os.path.join(dir2, sub), dirs, files
+
+ if _seen is None:
+ # Walk through stuff (which we haven't yet gone through) in dir2
+ for root2, root1, dirs, files in walk_sync(dir2, dir1, _seen=seen):
+ yield root1, root2, dirs, files
+
+def sync_2to3(src, dst, patchfile=None, clean=False):
+ import lib2to3.main
+ from io import StringIO
+
+ to_convert = []
+
+ for src_dir, dst_dir, dirs, files in walk_sync(src, dst):
+ for fn in dirs + files:
+ src_fn = os.path.join(src_dir, fn)
+ dst_fn = os.path.join(dst_dir, fn)
+
+ # skip temporary etc. files
+ if fn.startswith('.#') or fn.endswith('~'):
+ continue
+
+ # remove non-existing
+ if os.path.exists(dst_fn) and not os.path.exists(src_fn):
+ if clean:
+ if os.path.isdir(dst_fn):
+ shutil.rmtree(dst_fn)
+ else:
+ os.unlink(dst_fn)
+ continue
+
+ # make directories
+ if os.path.isdir(src_fn):
+ if not os.path.isdir(dst_fn):
+ os.makedirs(dst_fn)
+ continue
+
+ dst_dir = os.path.dirname(dst_fn)
+ if os.path.isfile(dst_fn) and not os.path.isdir(dst_dir):
+ os.makedirs(dst_dir)
+
+ # don't replace up-to-date files
+ try:
+ if os.path.isfile(dst_fn) and \
+ os.stat(dst_fn).st_mtime >= os.stat(src_fn).st_mtime:
+ continue
+ except OSError:
+ pass
+
+ # copy file
+ shutil.copyfile(src_fn, dst_fn)
+
+ # add .py files to 2to3 list
+ if dst_fn.endswith('.py'):
+ to_convert.append((src_fn, dst_fn))
+
+ # run 2to3
+ flag_sets = {}
+ for fn, dst_fn in to_convert:
+ flag = ''
+ for pat, opt in EXTRA_2TO3_FLAGS.items():
+ if fnmatch.fnmatch(fn, pat):
+ flag = opt
+ break
+ flag_sets.setdefault(flag, []).append(dst_fn)
+
+ if patchfile:
+ p = open(patchfile, 'wb+')
+ else:
+ p = open(os.devnull, 'wb')
+
+ for flags, filenames in flag_sets.items():
+ if flags == 'skip':
+ continue
+
+ _old_stdout = sys.stdout
+ try:
+ sys.stdout = StringIO()
+ lib2to3.main.main("lib2to3.fixes", ['-w', '-n'] + flags.split()+filenames)
+ finally:
+ sys.stdout = _old_stdout
+
+ for fn, dst_fn in to_convert:
+ # perform custom mangling
+ custom_mangling(dst_fn)
+
+ p.close()
+
+if __name__ == "__main__":
+ main()
diff --git a/tools/test.sh b/tools/test.sh
new file mode 100644
index 0000000..6f47437
--- /dev/null
+++ b/tools/test.sh
@@ -0,0 +1,69 @@
+#!/bin/bash -
+
+if [ -f /tmp/pysal.lock ]
+ then
+ cd /Users/stephens/tmp/pysal/
+ svn cleanup
+ a=$(svnversion)
+ svn update -r $((a+1)) 2>&1
+
+ if [ $? = "0" ]
+ then
+ b=$(svnversion)
+ else exit
+ fi
+
+ if [ "$a" != "$b" ]
+ then
+ #cd pysal
+ #rm -rf /tmp/pysal
+ #svn checkout http://pysal.googlecode.com/svn/trunk pysal
+ export PYTHONPATH=/Users/stephens/tmp/pysal
+ export PATH=/Library/Frameworks/EPD64.framework/Versions/Current/bin:$PATH
+ find pysal -name "*.pyc" -exec rm '{}' ';'
+
+ # setup message header
+ #echo "to: pas at asu.edu" > /tmp/report.txt
+ echo "to: phil.stphns at gmail.com" > /tmp/report.txt
+ echo "from: phil.stphns at gmail.com" >> /tmp/report.txt
+ printf "Subject: PySAL Unittest Results for revision $b" >> /tmp/report.txt
+ echo "" >> /tmp/report.txt
+
+
+ cd /Users/stephens/tmp/pysal/
+ svn log -r $(svnversion) >> /tmp/report.txt
+ echo "" >> /tmp/report.txt
+
+ # print system information
+ python -c 'import os,sys, numpy, scipy; print sys.platform, sys.version; print "Scipy version:", scipy.__version__; print "Numpy version:", numpy.__version__' >> /tmp/report.txt
+ echo "" >> /tmp/report.txt
+
+ echo "Full Coverage Report --> http://pysal.geodacenter.org/coverage/index.html" >> /tmp/report.txt
+ echo "" >> /tmp/report.txt
+
+ # execute pep8 stats
+ echo "" >> /tmp/report.txt
+ echo "PEP 8 Stats" >> /tmp/report.txt
+ pep8 --statistics -qq . >> /tmp/report.txt
+ echo "" >> /tmp/report.txt
+
+ # execute nose test framework
+ nosetests pysal/ >> /tmp/report.txt 2>&1
+ #nosetests --with-coverage --cover-html --cover-package=pysal --cover-html-dir=/tmp/coverage pysal/ >> /tmp/report.txt 2>&1
+ #rsync -r --delete /tmp/coverage/ stephens at geodacenter.org:~/coverage
+ echo "" >> /tmp/report.txt
+
+ # execute sphinx doctest framework
+ cd /Users/stephens/tmp/pysal/doc/
+ /usr/bin/make clean
+ sphinx-build -b doctest -d build/doctrees source build/doctest >> /tmp/report.txt 2>/dev/null
+ echo "" >> /tmp/report.txt
+
+
+ # remove instances of a single period on a line which causes sendmail to send now
+ sed "s/^\./\.\./g" /tmp/report.txt > /tmp/report.eml
+ /usr/sbin/sendmail -t < /tmp/report.eml
+
+ else echo "`date`, "$a", "$b", Repo unchanged." > /tmp/pysal.log 2>&1
+ fi
+fi
diff --git a/travis.txt b/travis.txt
new file mode 100644
index 0000000..b7d4c0a
--- /dev/null
+++ b/travis.txt
@@ -0,0 +1,6 @@
+nose-exclude
+nose-progressive
+sphinx
+sphinxcontrib-napoleon
+networkx
+Shapely
--
Alioth's /usr/local/bin/git-commit-notice on /srv/git.debian.org/git/pkg-grass/pysal.git
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