[pysal] 04/17: Imported Upstream version 1.11.0
Sebastiaan Couwenberg
sebastic at moszumanska.debian.org
Sat Jun 11 20:44:24 UTC 2016
This is an automated email from the git hooks/post-receive script.
sebastic pushed a commit to branch master
in repository pysal.
commit b4b8900c4c834ebeae4aa4f85a2f8433b5a3b48d
Author: Bas Couwenberg <sebastic at xs4all.nl>
Date: Sat Jun 11 17:11:49 2016 +0200
Imported Upstream version 1.11.0
---
.gitignore | 24 +
.travis.yml | 22 +-
.travis_testing.sh | 5 +
CHANGELOG.txt | 2423 +
README.md | 106 -
README.rst | 137 +
THANKS.txt | 1 +
doc/source/conf.py | 4 +-
doc/source/developers/py3k.rst | 107 +-
doc/source/developers/release.rst | 52 +-
doc/source/index.rst | 4 +-
doc/source/library/contrib/index.rst | 20 +-
doc/source/library/spreg/index.rst | 1 +
doc/source/references.rst | 1 +
figs/lisamaps.png | Bin 0 -> 501034 bytes
figs/lisamaps7.png | Bin 0 -> 501034 bytes
mk2 | 2 +
mk3 | 4 +
pysal/COPYING | 2 +-
pysal/__init__.py | 24 +-
pysal/common.py | 1 +
pysal/contrib/{README => README.rst} | 33 +-
pysal/contrib/clusterpy/__init__.py | 1 +
pysal/contrib/clusterpy/clusterpy.ipynb | 6735 +-
pysal/contrib/clusterpy/clusterpy_ext.py | 52 +-
pysal/contrib/githooks/pre-commit | 6 +
pysal/contrib/handler/Explaining the Handler.ipynb | 830 +
pysal/contrib/handler/__init__.py | 1 +
pysal/contrib/handler/handler.py | 101 +
pysal/contrib/handler/registry.py | 55 +
.../handler}/tests/test_diagnostics.py | 11 +-
.../handler}/tests/test_diagnostics_sp.py | 11 +-
.../handler}/tests/test_diagnostics_tsls.py | 11 +-
.../handler}/tests/test_error_sp.py | 210 +-
.../handler}/tests/test_error_sp_regimes.py | 33 +-
.../handler}/tests/test_error_sp_sparse.py | 212 +-
.../handler/tests/test_error_spet.py} | 22 +-
.../handler/tests/test_error_spet_regimes.py} | 12 +-
.../handler/tests/test_error_spet_sparse.py} | 24 +-
.../handler/tests/test_error_spom.py} | 24 +-
.../handler/tests/test_error_spom_regimes.py} | 36 +-
.../handler/tests/test_error_spom_sparse.py} | 72 +-
.../handler}/tests/test_ml_error.py | 43 +-
.../handler}/tests/test_ml_error_regimes.py | 66 +-
.../handler}/tests/test_ml_lag.py | 7 +-
.../handler}/tests/test_ml_lag_regimes.py | 10 +-
pysal/{spreg => contrib/handler}/tests/test_ols.py | 16 +-
.../handler}/tests/test_ols_regimes.py | 10 +-
.../handler}/tests/test_ols_sparse.py | 12 +-
.../handler}/tests/test_probit.py | 12 +-
.../handler}/tests/test_twosls.py | 7 +-
.../handler}/tests/test_twosls_regimes.py | 10 +-
.../handler}/tests/test_twosls_sp.py | 8 +-
.../handler}/tests/test_twosls_sp_regimes.py | 10 +-
.../handler}/tests/test_twosls_sp_sparse.py | 7 +-
.../handler}/tests/test_twosls_sparse.py | 7 +-
pysal/contrib/pdutilities/__init__.py | 4 +
pysal/contrib/pdutilities/dbf_utilities.py | 4 -
pysal/contrib/pdutilities/file_utilities.py | 65 +
pysal/contrib/pdutilities/shp_utilities.py | 21 +
pysal/contrib/viz/mapping.py | 63 +-
pysal/contrib/viz/mapping_guide.ipynb | 642 +-
pysal/core/IOHandlers/arcgis_swm.py | 4 +-
pysal/core/IOHandlers/csvWrapper.py | 5 +-
pysal/core/IOHandlers/pyDbfIO.py | 35 +-
pysal/core/IOHandlers/pyShpIO.py | 6 +-
pysal/core/IOHandlers/tests/test_csvWrapper.py | 10 +-
pysal/core/IOHandlers/tests/test_pyDbfIO.py | 50 +-
pysal/core/IOHandlers/wk1.py | 2 +-
pysal/core/Tables.py | 49 +-
pysal/core/util/shapefile.py | 32 +-
pysal/core/util/tests/test_shapefile.py | 27 +-
pysal/esda/mapclassify.py | 217 +-
pysal/esda/moran.py | 225 +-
pysal/esda/smoothing.py | 16 +-
pysal/esda/tests/test_mapclassify.py | 19 +-
pysal/esda/tests/test_moran.py | 23 +-
pysal/examples/10740/10740.dbf | Bin 0 -> 8188 bytes
pysal/examples/10740/10740.shp | Bin 0 -> 521068 bytes
pysal/examples/10740/10740.shx | Bin 0 -> 1660 bytes
pysal/examples/10740/10740_queen.gal | 391 +
pysal/examples/10740/10740_rook.gal | 391 +
pysal/examples/10740/README.md | 13 +
pysal/examples/Line/Line.dbf | Bin 0 -> 621 bytes
pysal/examples/Line/Line.prj | 1 +
pysal/examples/Line/Line.shp | Bin 0 -> 564 bytes
pysal/examples/Line/Line.shx | Bin 0 -> 132 bytes
pysal/examples/Line/README.md | 7 +
pysal/examples/Point/Point.dbf | Bin 0 -> 1236 bytes
pysal/examples/Point/Point.prj | 1 +
pysal/examples/Point/Point.shp | Bin 0 -> 352 bytes
pysal/examples/Point/Point.shx | Bin 0 -> 172 bytes
pysal/examples/Polygon/Polygon.dbf | Bin 0 -> 498 bytes
pysal/examples/Polygon/Polygon.prj | 1 +
pysal/examples/Polygon/Polygon.shp | Bin 0 -> 992 bytes
pysal/examples/Polygon/Polygon.shx | Bin 0 -> 124 bytes
pysal/examples/Polygon/README.md | 7 +
pysal/examples/README.txt | 5 +
pysal/examples/__init__.py | 77 +
pysal/examples/arcgis/README.md | 7 +
pysal/examples/arcgis/arcgis_ohio.dbf | Bin 0 -> 21876 bytes
pysal/examples/arcgis/arcgis_txt.txt | 9 +
pysal/examples/baltim/README.md | 16 +
pysal/examples/baltim/baltim.dbf | Bin 0 -> 31595 bytes
pysal/examples/baltim/baltim.shp | Bin 0 -> 6008 bytes
pysal/examples/baltim/baltim.shx | Bin 0 -> 1788 bytes
pysal/examples/baltim/baltim.tri.k12.kwt | 2744 +
pysal/examples/baltim/baltim_k4.gwt | 845 +
pysal/examples/baltim/baltim_q.gal | 423 +
pysal/examples/baltim/baltimore.geojson | 218 +
pysal/examples/book/README.md | 10 +
pysal/examples/book/book.gal | 33 +
pysal/examples/book/book.txt | 18 +
pysal/examples/burkitt/README.md | 11 +
pysal/examples/burkitt/burkitt.dbf | Bin 0 -> 7558 bytes
pysal/examples/burkitt/burkitt.shp | Bin 0 -> 5364 bytes
pysal/examples/burkitt/burkitt.shx | Bin 0 -> 1604 bytes
pysal/examples/calemp/README.md | 15 +
pysal/examples/calemp/calempdensity.csv | 59 +
pysal/examples/chicago/Chicago77.dbf | Bin 0 -> 16787 bytes
pysal/examples/chicago/Chicago77.shp | Bin 0 -> 687568 bytes
pysal/examples/chicago/Chicago77.shx | Bin 0 -> 716 bytes
pysal/examples/chicago/README.md | 11 +
pysal/examples/columbus/columbus.dbf | Bin 0 -> 10082 bytes
pysal/examples/columbus/columbus.gal | 99 +
pysal/examples/columbus/columbus.html | 132 +
pysal/examples/columbus/columbus.json | 104 +
pysal/examples/columbus/columbus.shp | Bin 0 -> 21980 bytes
pysal/examples/columbus/columbus.shx | Bin 0 -> 492 bytes
pysal/examples/desmith/README.md | 13 +
pysal/examples/desmith/desmith.gal | 21 +
pysal/examples/desmith/desmith.txt | 12 +
pysal/examples/examples.txt | 141 +
pysal/examples/geodanet/README.md | 26 +
pysal/examples/geodanet/crimes.dbf | Bin 0 -> 5551 bytes
pysal/examples/geodanet/crimes.prj | 1 +
pysal/examples/geodanet/crimes.sbn | Bin 0 -> 3228 bytes
pysal/examples/geodanet/crimes.sbx | Bin 0 -> 428 bytes
pysal/examples/geodanet/crimes.shp | Bin 0 -> 8136 bytes
pysal/examples/geodanet/crimes.shp.xml | 3 +
pysal/examples/geodanet/crimes.shx | Bin 0 -> 2396 bytes
pysal/examples/geodanet/schools.dbf | Bin 0 -> 146 bytes
pysal/examples/geodanet/schools.prj | 1 +
pysal/examples/geodanet/schools.sbn | Bin 0 -> 212 bytes
pysal/examples/geodanet/schools.sbx | Bin 0 -> 124 bytes
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 +
pysal/examples/geodanet/streets.sbn | Bin 0 -> 3388 bytes
pysal/examples/geodanet/streets.sbx | Bin 0 -> 540 bytes
pysal/examples/geodanet/streets.shp | Bin 0 -> 26044 bytes
pysal/examples/geodanet/streets.shx | Bin 0 -> 2444 bytes
pysal/examples/juvenile/README.md | 14 +
pysal/examples/juvenile/juvenile.dbf | Bin 0 -> 4834 bytes
pysal/examples/juvenile/juvenile.gwt | 2803 +
pysal/examples/juvenile/juvenile.shp | Bin 0 -> 4804 bytes
pysal/examples/juvenile/juvenile.shx | Bin 0 -> 1444 bytes
pysal/examples/mexico/README.md | 15 +
pysal/examples/mexico/mexico.csv | 33 +
pysal/examples/mexico/mexico.gal | 65 +
pysal/examples/nat/NAT.dbf | Bin 0 -> 2501092 bytes
pysal/examples/nat/NAT.shp | Bin 0 -> 1462216 bytes
pysal/examples/nat/NAT.shx | Bin 0 -> 24780 bytes
pysal/examples/nat/nat.geojson | 3092 +
pysal/examples/nat/nat_queen.gal | 6171 ++
pysal/examples/nat/nat_queen_old.gal | 6171 ++
pysal/examples/nat/nat_trian_k20.kwt | 64786 +++++++++++++++++++
pysal/examples/nat/natregimes.dbf | Bin 0 -> 2627705 bytes
pysal/examples/nat/natregimes.shp | Bin 0 -> 1462216 bytes
pysal/examples/nat/natregimes.shx | Bin 0 -> 24780 bytes
pysal/examples/networks/eberly_net.dbf | Bin 0 -> 1348 bytes
pysal/examples/networks/eberly_net.shp | Bin 0 -> 2652 bytes
pysal/examples/networks/eberly_net.shx | Bin 0 -> 332 bytes
.../networks/eberly_net_pts_offnetwork.dbf | Bin 0 -> 2197 bytes
.../networks/eberly_net_pts_offnetwork.shp | Bin 0 -> 2900 bytes
.../networks/eberly_net_pts_offnetwork.shx | Bin 0 -> 900 bytes
.../examples/networks/eberly_net_pts_onnetwork.dbf | Bin 0 -> 1275 bytes
.../examples/networks/eberly_net_pts_onnetwork.shp | Bin 0 -> 3180 bytes
.../examples/networks/eberly_net_pts_onnetwork.shx | Bin 0 -> 980 bytes
pysal/examples/networks/nonplanarsegments.dbf | Bin 0 -> 87 bytes
pysal/examples/networks/nonplanarsegments.prj | 1 +
pysal/examples/networks/nonplanarsegments.qpj | 1 +
pysal/examples/networks/nonplanarsegments.shp | Bin 0 -> 308 bytes
pysal/examples/networks/nonplanarsegments.shx | Bin 0 -> 116 bytes
pysal/examples/newHaven/new_haven_merged.dbf | Bin 0 -> 1320686 bytes
pysal/examples/newHaven/new_haven_merged.shp | Bin 0 -> 92304 bytes
pysal/examples/newHaven/new_haven_merged.shx | Bin 0 -> 26444 bytes
pysal/examples/newHaven/newhaven_nework.dbf | Bin 0 -> 206183 bytes
pysal/examples/newHaven/newhaven_nework.prj | 1 +
pysal/examples/newHaven/newhaven_nework.qpj | 1 +
pysal/examples/newHaven/newhaven_nework.shp | Bin 0 -> 358220 bytes
pysal/examples/newHaven/newhaven_nework.shx | Bin 0 -> 12396 bytes
pysal/examples/sacramento2/sacramentot2.dbf | Bin 0 -> 98149 bytes
pysal/examples/sacramento2/sacramentot2.gal | 807 +
pysal/examples/sacramento2/sacramentot2.sbn | Bin 0 -> 11172 bytes
pysal/examples/sacramento2/sacramentot2.sbx | Bin 0 -> 588 bytes
pysal/examples/sacramento2/sacramentot2.shp | Bin 0 -> 1587812 bytes
pysal/examples/sacramento2/sacramentot2.shx | Bin 0 -> 3324 bytes
pysal/examples/sids2/README.md | 14 +
pysal/examples/sids2/sids2.dbf | Bin 0 -> 23810 bytes
pysal/examples/sids2/sids2.gal | 201 +
pysal/examples/sids2/sids2.html | 124 +
pysal/examples/sids2/sids2.shp | Bin 0 -> 46196 bytes
pysal/examples/sids2/sids2.shx | Bin 0 -> 900 bytes
pysal/examples/sids2/sids2.swm | Bin 0 -> 7168 bytes
pysal/examples/snow_maps/SohoPeople.dbf | Bin 0 -> 3662 bytes
pysal/examples/snow_maps/SohoPeople.prj | 1 +
pysal/examples/snow_maps/SohoPeople.sbn | Bin 0 -> 3460 bytes
pysal/examples/snow_maps/SohoPeople.sbx | Bin 0 -> 380 bytes
pysal/examples/snow_maps/SohoPeople.shp | Bin 0 -> 9172 bytes
pysal/examples/snow_maps/SohoPeople.shx | Bin 0 -> 2692 bytes
pysal/examples/snow_maps/SohoWater.dbf | Bin 0 -> 157 bytes
pysal/examples/snow_maps/SohoWater.prj | 1 +
pysal/examples/snow_maps/SohoWater.sbn | Bin 0 -> 252 bytes
pysal/examples/snow_maps/SohoWater.sbx | Bin 0 -> 124 bytes
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/south.dbf | Bin 0 -> 1145962 bytes
pysal/examples/south/south.shp | Bin 0 -> 737448 bytes
pysal/examples/south/south.shx | Bin 0 -> 11396 bytes
pysal/examples/south/south_q.gal | 2825 +
pysal/examples/south/south_queen.gal | 2825 +
pysal/examples/stl/stl.gal | 157 +
pysal/examples/stl/stl_hom.csv | 79 +
pysal/examples/stl/stl_hom.dbf | Bin 0 -> 23280 bytes
pysal/examples/stl/stl_hom.html | 143 +
pysal/examples/stl/stl_hom.shp | Bin 0 -> 28276 bytes
pysal/examples/stl/stl_hom.shx | Bin 0 -> 724 bytes
pysal/examples/stl/stl_hom.txt | 80 +
pysal/examples/stl/stl_hom.wkt | 78 +
pysal/examples/stl/stl_hom_rook.gal | 157 +
pysal/examples/street_net_pts/street_net_pts.dbf | Bin 0 -> 3398 bytes
pysal/examples/street_net_pts/street_net_pts.prj | 1 +
pysal/examples/street_net_pts/street_net_pts.qpj | 1 +
pysal/examples/street_net_pts/street_net_pts.shp | Bin 0 -> 8584 bytes
pysal/examples/street_net_pts/street_net_pts.shx | Bin 0 -> 2524 bytes
pysal/examples/taz/taz.dbf | Bin 0 -> 485344 bytes
pysal/examples/taz/taz.shp | Bin 0 -> 5774828 bytes
pysal/examples/taz/taz.shx | Bin 0 -> 32972 bytes
pysal/examples/test_examples.py | 21 +
pysal/examples/us_income/README.md | 10 +
pysal/examples/us_income/spi_download.csv | 66 +
pysal/examples/us_income/states48.gal | 97 +
pysal/examples/us_income/us48.dbf | Bin 0 -> 4274 bytes
pysal/examples/us_income/us48.shp | Bin 0 -> 186476 bytes
pysal/examples/us_income/us48.shx | Bin 0 -> 484 bytes
pysal/examples/us_income/usjoin.csv | 49 +
pysal/examples/virginia/README.md | 10 +
pysal/examples/virginia/virginia.dbf | Bin 0 -> 11410 bytes
pysal/examples/virginia/virginia.gal | 273 +
pysal/examples/virginia/virginia.json | 143 +
pysal/examples/virginia/virginia.prj | 1 +
pysal/examples/virginia/virginia.shp | Bin 0 -> 71416 bytes
pysal/examples/virginia/virginia.shx | Bin 0 -> 1188 bytes
pysal/examples/virginia/virginia_queen.dat | 586 +
pysal/examples/virginia/virginia_queen.dbf | Bin 0 -> 11850 bytes
pysal/examples/virginia/virginia_queen.gal | 273 +
pysal/examples/virginia/virginia_queen.mat | Bin 0 -> 148160 bytes
pysal/examples/virginia/virginia_queen.mtx | 589 +
pysal/examples/virginia/virginia_queen.swm | Bin 0 -> 9232 bytes
pysal/examples/virginia/virginia_queen.txt | 137 +
pysal/examples/virginia/virginia_queen.wk1 | Bin 0 -> 314585 bytes
pysal/examples/virginia/virginia_rook.gal | 273 +
pysal/examples/wmat/geobugs_scot | 66 +
pysal/examples/wmat/lattice10x10.shp | Bin 0 -> 13700 bytes
pysal/examples/wmat/lattice10x10.shx | Bin 0 -> 900 bytes
pysal/examples/wmat/ohio.swm | Bin 0 -> 6978 bytes
pysal/examples/wmat/rook31.dbf | Bin 0 -> 161 bytes
pysal/examples/wmat/rook31.gal | 7 +
pysal/examples/wmat/rook31.shp | Bin 0 -> 556 bytes
pysal/examples/wmat/rook31.shx | Bin 0 -> 124 bytes
pysal/examples/wmat/spat-sym-us.mat | Bin 0 -> 416 bytes
pysal/examples/wmat/spat-sym-us.wk1 | Bin 0 -> 2221 bytes
pysal/examples/wmat/spdep_listw2WB_columbus | 59 +
pysal/examples/wmat/stata_full.txt | 57 +
pysal/examples/wmat/stata_sparse.txt | 57 +
pysal/examples/wmat/wmat.dat | 232 +
pysal/examples/wmat/wmat.mtx | 237 +
pysal/meta/wmd.py | 70 +-
pysal/network/tests/test_network.py | 5 +-
pysal/region/maxp.py | 26 +-
pysal/region/tests/test_maxp.py | 16 +-
pysal/spatial_dynamics/markov.py | 12 +-
pysal/spatial_dynamics/tests/test_markov.py | 11 +-
pysal/spreg/__init__.py | 3 +
pysal/spreg/diagnostics.py | 4 +-
pysal/spreg/diagnostics_sur.py | 221 +
pysal/spreg/ml_error.py | 83 +-
pysal/spreg/ml_error_regimes.py | 5 +-
pysal/spreg/ml_lag.py | 72 +-
pysal/spreg/ml_lag_regimes.py | 3 +
pysal/spreg/ols.py | 7 +-
pysal/spreg/opt.py | 92 +
pysal/spreg/optional_imports.ipynb | 278 +
pysal/spreg/probit.py | 322 +-
pysal/spreg/regimes.py | 6 +-
pysal/spreg/summary_output.py | 284 +-
pysal/spreg/sur.py | 697 +
pysal/spreg/sur_error.py | 549 +
pysal/spreg/sur_lag.py | 366 +
pysal/spreg/sur_utils.py | 363 +
pysal/spreg/tests/test_diagnostics.py | 52 +-
pysal/spreg/tests/test_diagnostics_sp.py | 44 +-
pysal/spreg/tests/test_diagnostics_tsls.py | 10 +-
pysal/spreg/tests/test_error_sp.py | 192 +-
pysal/spreg/tests/test_error_sp_het.py | 207 +-
pysal/spreg/tests/test_error_sp_het_regimes.py | 161 +-
pysal/spreg/tests/test_error_sp_het_sparse.py | 207 +-
pysal/spreg/tests/test_error_sp_hom.py | 229 +-
pysal/spreg/tests/test_error_sp_hom_regimes.py | 165 +-
pysal/spreg/tests/test_error_sp_regimes.py | 169 +-
pysal/spreg/tests/test_error_sp_sparse.py | 196 +-
pysal/spreg/tests/test_ml_error.py | 40 +-
pysal/spreg/tests/test_ml_error_regimes.py | 61 +-
pysal/spreg/tests/test_ml_lag.py | 35 +-
pysal/spreg/tests/test_ml_lag_regimes.py | 53 +-
pysal/spreg/tests/test_ols.py | 119 +-
pysal/spreg/tests/test_ols_regimes.py | 108 +-
pysal/spreg/tests/test_ols_sparse.py | 105 +-
pysal/spreg/tests/test_probit.py | 69 +-
pysal/spreg/tests/test_sur.py | 172 +
pysal/spreg/tests/test_sur_error.py | 120 +
pysal/spreg/tests/test_sur_lag.py | 176 +
pysal/spreg/tests/test_twosls.py | 117 +-
pysal/spreg/tests/test_twosls_regimes.py | 86 +-
pysal/spreg/tests/test_twosls_sp.py | 141 +-
pysal/spreg/tests/test_twosls_sp_regimes.py | 144 +-
pysal/spreg/tests/test_twosls_sp_sparse.py | 144 +-
pysal/spreg/tests/test_twosls_sparse.py | 121 +-
pysal/spreg/twosls.py | 17 +-
pysal/spreg/utils.py | 132 +-
pysal/version.py | 4 +-
pysal/weights/Distance.py | 12 +-
pysal/weights/_contW_binning.py | 10 +-
pysal/weights/tests/test_Distance.py | 69 +-
pysal/weights/tests/test_Wsets.py | 28 +-
pysal/weights/tests/test_user.py | 35 +-
pysal/weights/tests/test_util.py | 7 +
pysal/weights/tests/test_weights.py | 69 +-
pysal/weights/util.py | 22 +-
setup.py | 59 +-
tools/bump.py | 84 +
tools/spregsync | 47 +
touch.txt | 1 +
352 files changed, 114778 insertions(+), 6237 deletions(-)
diff --git a/.gitignore b/.gitignore
index b3b303c..6c82954 100644
--- a/.gitignore
+++ b/.gitignore
@@ -77,3 +77,27 @@ pysal/examples/south.prj
#Vi
*.swp
+
+.eggs/
+pysal/contrib/planar/
+pysal/esda/.ropeproject/
+pysal/esda/jenks_nb.ipynb
+pysal/examples/snow_maps/fake.dbf
+pysal/examples/snow_maps/fake.prj
+pysal/examples/snow_maps/fake.qpj
+pysal/examples/snow_maps/fake.shp
+pysal/examples/snow_maps/fake.shx
+pysal/examples/snow_maps/fixed.dbf
+pysal/examples/snow_maps/fixed.prj
+pysal/examples/snow_maps/fixed.qgs
+pysal/examples/snow_maps/fixed.qgs~
+pysal/examples/snow_maps/fixed.qpj
+pysal/examples/snow_maps/fixed.shp
+pysal/examples/snow_maps/fixed.shx
+pysal/examples/snow_maps/snow.qgs
+pysal/examples/snow_maps/snow.qgs~
+pysal/examples/snow_maps/soho_graph.dbf
+pysal/examples/snow_maps/soho_graph.prj
+pysal/examples/snow_maps/soho_graph.qpj
+pysal/examples/snow_maps/soho_graph.shp
+pysal/examples/snow_maps/soho_graph.shx
diff --git a/.travis.yml b/.travis.yml
index c24d869..82bf789 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -6,30 +6,26 @@ only:
python:
- 2.7
#- "2.6"
- # - 3.4
-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
+ - 3.4
before_install:
- wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
- chmod +x miniconda.sh
- - ./miniconda.sh -b
- - export PATH=/home/travis/miniconda/bin:$PATH
+ - ./miniconda.sh -b -p ./miniconda
+ - export PATH=`pwd`/miniconda/bin:$PATH
- conda update --yes conda
- conda create -y -q -n test-env python=$TRAVIS_PYTHON_VERSION
- source activate test-env
+ - chmod +x ./.travis_testing.sh
+ - if [[ $TRAVIS_PYTHON_VERSION == 3* ]]; then rm -rf pysal/contrib; 2to3 -nw pysal/ > /dev/null; fi
install:
- - conda install --yes numpy scipy nose pip
+ - conda install --yes numpy=1.10.2 scipy=0.16.0 nose pip
- pip install -r travis.txt
script:
- - python -c 'import numpy; print numpy.__version__'
- - python -c 'import scipy; print scipy.__version__'
+ - python -c 'import numpy; print(numpy.__version__)'
+ - python -c 'import scipy; print(scipy.__version__)'
- python setup.py install >/dev/null
- python -c 'import pysal'
#- python -c 'import multiprocessing as mp; print mp.cpu_count()'
@@ -38,7 +34,7 @@ script:
#- nosetests --processes=-1 --process-timeout=60 --verbosity=1 --ignore-files=collection --exclude-dir=pysal/contrib
#- nosetests
- echo "check_stable=False" >pysal/config.py
- - nosetests --with-coverage --cover-package=pysal
+ - ./.travis_testing.sh
#- cd doc; make pickle; make doctest
notifications:
email:
diff --git a/.travis_testing.sh b/.travis_testing.sh
new file mode 100644
index 0000000..78d5c1b
--- /dev/null
+++ b/.travis_testing.sh
@@ -0,0 +1,5 @@
+if [[ $TRAVIS_PYTHON_VERSION == 3* ]]; then
+ cd pysal; python -m 'nose';
+else
+ nosetests --with-coverage --cover-package=pysal;
+fi
diff --git a/CHANGELOG.txt b/CHANGELOG.txt
index 53ce244..1506e43 100644
--- a/CHANGELOG.txt
+++ b/CHANGELOG.txt
@@ -1,3 +1,2426 @@
+v<1.11.0>, 2016-01-27
+
+GitHub stats for 2015/01/31 - 2015/07/29
+
+These lists are automatically generated, and may be incomplete or contain duplicates.
+
+The following 13 authors contributed 216 commits.
+
+* Dani Arribas-Bel
+* David Folch
+* Levi John Wolf
+* Levi Wolf
+* Philip Stephens
+* Serge Rey
+* Sergio Rey
+* Wei Kang
+* jlaura
+* levi.john.wolf at gmail.com
+* ljw
+* ljwolf
+* pedrovma
+
+
+We closed a total of 86 issues, 33 pull requests and 53 regular issues;
+this is the full list (generated with the script
+:file:`tools/github_stats.py`):
+
+Pull Requests (33):
+
+* :ghpull:`724`: add synchronization tool
+* :ghpull:`733`: Fb/bump
+* :ghpull:`731`: Small docfixes
+* :ghpull:`730`: Contrib docs
+* :ghpull:`728`: B179
+* :ghpull:`727`: Geodf io
+* :ghpull:`725`: try pinning scipy,numpy
+* :ghpull:`723`: make sure to test all moran classes
+* :ghpull:`720`: Moving natural breaks to a cleaner kmeans implementation
+* :ghpull:`718`: force counts to be same length as bins
+* :ghpull:`714`: Dev
+* :ghpull:`715`: Heads
+* :ghpull:`713`: Enh712
+* :ghpull:`710`: Patsy/Pandas wrapper
+* :ghpull:`711`: Travis fixes
+* :ghpull:`706`: precommit hook
+* :ghpull:`707`: Keep dev updated with any bugfixes into master
+* :ghpull:`702`: fix for chi2 test 0 denominator and invocation of chi2 test in LISA_Markov
+* :ghpull:`704`: Allcloser
+* :ghpull:`703`: Swapping to Allclose and RTOL=.00005 in spreg
+* :ghpull:`701`: By col array
+* :ghpull:`700`: small optimization of bivariate moran motivated by #695
+* :ghpull:`696`: Pypi
+* :ghpull:`691`: Update doctest for one-off bug that was fixed with #690
+* :ghpull:`690`: fix for lisa markov one off for significance indicator
+* :ghpull:`689`: Clpy flex w
+* :ghpull:`688`: pep 8 edits
+* :ghpull:`687`: Change array assertions into allclose
+* :ghpull:`686`: Moran local bivariate
+* :ghpull:`684`: 591
+* :ghpull:`682`: release instructions updated
+* :ghpull:`681`: version bump for next dev cycle
+* :ghpull:`680`: Rel1.10
+
+Issues (53):
+
+* :ghissue:`705`: spreg check valve
+* :ghissue:`344`: Explore new dependency on ogr
+* :ghissue:`459`: Problem with bandwidth
+* :ghissue:`552`: Viz organization
+* :ghissue:`491`: Test np.allclose() for unit tests
+* :ghissue:`529`: Clarity needed on proper reference formatting in sphinx docs
+* :ghissue:`699`: Trouble importing pysal - ImportError: DLL load failed
+* :ghissue:`716`: `min_threshold_dist_from_shapefile` creating an island in some cases
+* :ghissue:`724`: add synchronization tool
+* :ghissue:`733`: Fb/bump
+* :ghissue:`731`: Small docfixes
+* :ghissue:`730`: Contrib docs
+* :ghissue:`719`: pysal not working with matplotlib v1.5 for plot_lisa_cluster, plot_choropleth, etc.
+* :ghissue:`728`: B179
+* :ghissue:`727`: Geodf io
+* :ghissue:`725`: try pinning scipy,numpy
+* :ghissue:`723`: make sure to test all moran classes
+* :ghissue:`720`: Moving natural breaks to a cleaner kmeans implementation
+* :ghissue:`717`: esda.mapclassify return problematic counts when there is 0 occurrence in the last class
+* :ghissue:`718`: force counts to be same length as bins
+* :ghissue:`714`: Dev
+* :ghissue:`712`: `block_weights` does not take argument `idVariable`
+* :ghissue:`715`: Heads
+* :ghissue:`713`: Enh712
+* :ghissue:`710`: Patsy/Pandas wrapper
+* :ghissue:`711`: Travis fixes
+* :ghissue:`706`: precommit hook
+* :ghissue:`708`: 2-3: is six a dependency or do we ship it?
+* :ghissue:`707`: Keep dev updated with any bugfixes into master
+* :ghissue:`702`: fix for chi2 test 0 denominator and invocation of chi2 test in LISA_Markov
+* :ghissue:`704`: Allcloser
+* :ghissue:`703`: Swapping to Allclose and RTOL=.00005 in spreg
+* :ghissue:`698`: Py3merge
+* :ghissue:`701`: By col array
+* :ghissue:`700`: small optimization of bivariate moran motivated by #695
+* :ghissue:`695`: Bivariate global moran's I formula
+* :ghissue:`683`: Py3 Conversion Project
+* :ghissue:`694`: Allclose in SPREG
+* :ghissue:`696`: Pypi
+* :ghissue:`691`: Update doctest for one-off bug that was fixed with #690
+* :ghissue:`693`: Trouble installation: No module named 'shapes'
+* :ghissue:`690`: fix for lisa markov one off for significance indicator
+* :ghissue:`689`: Clpy flex w
+* :ghissue:`688`: pep 8 edits
+* :ghissue:`685`: BV Lisa
+* :ghissue:`687`: Change array assertions into allclose
+* :ghissue:`686`: Moran local bivariate
+* :ghissue:`677`: Make meta importable from base
+* :ghissue:`684`: 591
+* :ghissue:`682`: release instructions updated
+* :ghissue:`679`: pysal.cg.sphere.fast_knn bug
+* :ghissue:`681`: version bump for next dev cycle
+* :ghissue:`680`: Rel1.10
+
+
+v<1.10.0>, 2015-07-29
+
+GitHub stats for 2015/01/31 - 2015/07/29
+
+These lists are automatically generated, and may be incomplete or contain duplicates.
+
+The following 20 authors contributed 334 commits.
+
+* Charlie Schmidt
+* Dani Arribas-Bel
+* Daniel Arribas-Bel
+* David C. Folch
+* David Folch
+* Jay
+* Levi John Wolf
+* Marynia
+* Philip Stephens
+* Serge Rey
+* Sergio Rey
+* Taylor Oshan
+* The Gitter Badger
+* Wei Kang
+* jay
+* jlaura
+* ljw
+* ljwolf
+* luc
+* pedrovma
+
+
+We closed a total of 156 issues, 58 pull requests and 98 regular issues;
+this is the full list (generated with the script
+:file:`tools/github_stats.py`):
+
+Pull Requests (58):
+
+* :ghpull:`675`: Update README.md
+* :ghpull:`673`: Adding init at pdutilities so they are importable
+* :ghpull:`672`: ENH: option to locate legend
+* :ghpull:`669`: add nonsudo travis
+* :ghpull:`666`: Cleaned up conflicts in ref branch
+* :ghpull:`664`: Lisa map
+* :ghpull:`663`: Examples
+* :ghpull:`661`: Reorganization of examples
+* :ghpull:`657`: Assuncao test division errors
+* :ghpull:`649`: Add a Gitter chat badge to README.md
+* :ghpull:`647`: Addresses 646
+* :ghpull:`645`: Update to weights module documentation for PySAL-REST
+* :ghpull:`644`: removed test print statements from df2dbf
+* :ghpull:`643`: using dtypes.name in df2dbf to avoid gotcha in type
+* :ghpull:`642`: Updating copyright year
+* :ghpull:`634`: allows non-symmetric distance matrices
+* :ghpull:`641`: turning off generatetree
+* :ghpull:`592`: adding check for version #591
+* :ghpull:`636`: vertical line point simulation
+* :ghpull:`639`: Snapping
+* :ghpull:`640`: Add users to travis
+* :ghpull:`627`: Networkrb
+* :ghpull:`631`: Fixing typoes in analysis.py
+* :ghpull:`626`: cast arrays over inconsistent kdtree return types
+* :ghpull:`620`: adding explicit check for random region contiguity
+* :ghpull:`619`: Fixing spreg's warnings
+* :ghpull:`618`: initial folder with dbf utilities using pandas
+* :ghpull:`616`: Adding isolation and theil indices to inequality._indices.py
+* :ghpull:`615`: Network docs
+* :ghpull:`614`: cleaning up pr testing
+* :ghpull:`613`: test coverage to 98% on network
+* :ghpull:`612`: small change for testing PR
+* :ghpull:`611`: stubbed in minimal tests
+* :ghpull:`607`: B603
+* :ghpull:`602`: Documentation Extraction Notebook
+* :ghpull:`606`: pct_nonzero was reporting a ratio not a percentage
+* :ghpull:`604`: Contribpush
+* :ghpull:`601`: Documentation Cleanup
+* :ghpull:`599`: Casting bugfix from #598
+* :ghpull:`600`: Updates for coveralls
+* :ghpull:`598`: IO in Python 3
+* :ghpull:`597`: Decoupling bbox from map_XXX_poly
+* :ghpull:`595`: Removed testing line in travis.yml and added a .coveragerc file to manag...
+* :ghpull:`590`: using numpy sum method
+* :ghpull:`589`: Wconstructor
+* :ghpull:`588`: Coveralls
+* :ghpull:`585`: Fisher Jenks bug in `plot_choropleth`
+* :ghpull:`584`: Alpha in plot chor
+* :ghpull:`583`: Fixed 576
+* :ghpull:`580`: working on #576
+* :ghpull:`578`: Fixes #577
+* :ghpull:`574`: Handle case where a region has a 0 share.
+* :ghpull:`571`: Dict to unique value mapper
+* :ghpull:`570`: numpy doc cleanup for weights module
+* :ghpull:`569`: folium viz scripts
+* :ghpull:`568`: inline with numpy doc spec (spatial_dynamics module)
+* :ghpull:`567`: New/masterbump
+* :ghpull:`566`: Fix for 1.9.0 missing file in setup.py
+
+Issues (98):
+
+* :ghissue:`675`: Update README.md
+* :ghissue:`658`: Travis.CI """Legacy""" architecture
+* :ghissue:`667`: Examples Not Found
+* :ghissue:`673`: Adding init at pdutilities so they are importable
+* :ghissue:`672`: ENH: option to locate legend
+* :ghissue:`669`: add nonsudo travis
+* :ghissue:`671`: Shapefile Read - PolygonM Attribute Error
+* :ghissue:`670`: examples README markdown files reformatting
+* :ghissue:`668`: Wconstructor
+* :ghissue:`666`: Cleaned up conflicts in ref branch
+* :ghissue:`664`: Lisa map
+* :ghissue:`662`: Pep8
+* :ghissue:`665`: Refs
+* :ghissue:`663`: Examples
+* :ghissue:`573`: Examples
+* :ghissue:`661`: Reorganization of examples
+* :ghissue:`656`: Assuncao rate improper division
+* :ghissue:`657`: Assuncao test division errors
+* :ghissue:`280`: handle multi-segment links in net_shp_io.py
+* :ghissue:`649`: Add a Gitter chat badge to README.md
+* :ghissue:`647`: Addresses 646
+* :ghissue:`646`: arc distance in knnW
+* :ghissue:`645`: Update to weights module documentation for PySAL-REST
+* :ghissue:`644`: removed test print statements from df2dbf
+* :ghissue:`643`: using dtypes.name in df2dbf to avoid gotcha in type
+* :ghissue:`603`: Polygon.contains_point does not correctly process multipart polygons.
+* :ghissue:`642`: Updating copyright year
+* :ghissue:`623`: reading road shapfiles into network
+* :ghissue:`608`: Scipy Sparse Graph
+* :ghissue:`621`: network distance speedup
+* :ghissue:`632`: network point snapping
+* :ghissue:`633`: point to point distances on network
+* :ghissue:`635`: simulating points on vertical lines
+* :ghissue:`634`: allows non-symmetric distance matrices
+* :ghissue:`641`: turning off generatetree
+* :ghissue:`637`: speedup distance computations
+* :ghissue:`592`: adding check for version #591
+* :ghissue:`628`: Re-enable doctests
+* :ghissue:`636`: vertical line point simulation
+* :ghissue:`639`: Snapping
+* :ghissue:`640`: Add users to travis
+* :ghissue:`638`: Add users to Travis
+* :ghissue:`627`: Networkrb
+* :ghissue:`622`: New network branch from clean master
+* :ghissue:`630`: NetworkG api is broken
+* :ghissue:`631`: Fixing typoes in analysis.py
+* :ghissue:`625`: Installation - Binstar and Anaconda
+* :ghissue:`624`: Network topology
+* :ghissue:`629`: changes to spreg tests for travis
+* :ghissue:`166`: pysal.esda.mapclassify.Fisher_Jenks - local variable 'best' referenced before assignment
+* :ghissue:`626`: cast arrays over inconsistent kdtree return types
+* :ghissue:`596`: [question] unsupervised classification
+* :ghissue:`620`: adding explicit check for random region contiguity
+* :ghissue:`617`: Random_Region not respecting contiguity constraint
+* :ghissue:`619`: Fixing spreg's warnings
+* :ghissue:`618`: initial folder with dbf utilities using pandas
+* :ghissue:`616`: Adding isolation and theil indices to inequality._indices.py
+* :ghissue:`615`: Network docs
+* :ghissue:`614`: cleaning up pr testing
+* :ghissue:`613`: test coverage to 98% on network
+* :ghissue:`612`: small change for testing PR
+* :ghissue:`611`: stubbed in minimal tests
+* :ghissue:`607`: B603
+* :ghissue:`602`: Documentation Extraction Notebook
+* :ghissue:`606`: pct_nonzero was reporting a ratio not a percentage
+* :ghissue:`605`: RTree Weights
+* :ghissue:`604`: Contribpush
+* :ghissue:`601`: Documentation Cleanup
+* :ghissue:`554`: Beginning documentation cleanup
+* :ghissue:`599`: Casting bugfix from #598
+* :ghissue:`600`: Updates for coveralls
+* :ghissue:`598`: IO in Python 3
+* :ghissue:`597`: Decoupling bbox from map_XXX_poly
+* :ghissue:`595`: Removed testing line in travis.yml and added a .coveragerc file to manag...
+* :ghissue:`586`: Look at using Coveralls
+* :ghissue:`590`: using numpy sum method
+* :ghissue:`589`: Wconstructor
+* :ghissue:`588`: Coveralls
+* :ghissue:`576`: Predecessor lists inconsistencies
+* :ghissue:`585`: Fisher Jenks bug in `plot_choropleth`
+* :ghissue:`584`: Alpha in plot chor
+* :ghissue:`583`: Fixed 576
+* :ghissue:`582`: Fixes #576
+* :ghissue:`581`: Network
+* :ghissue:`580`: working on #576
+* :ghissue:`575`: Network from Lattice
+* :ghissue:`578`: Fixes #577
+* :ghissue:`577`: bug in FileIO.cast
+* :ghissue:`574`: Handle case where a region has a 0 share.
+* :ghissue:`343`: Edge Segmentation
+* :ghissue:`571`: Dict to unique value mapper
+* :ghissue:`570`: numpy doc cleanup for weights module
+* :ghissue:`569`: folium viz scripts
+* :ghissue:`568`: inline with numpy doc spec (spatial_dynamics module)
+* :ghissue:`567`: New/masterbump
+* :ghissue:`564`: Bug in setup.py
+* :ghissue:`566`: Fix for 1.9.0 missing file in setup.py
+* :ghissue:`565`: Bsetup
+
+
+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.
+The following 13 authors contributed 216 commits.
+
+* Dani Arribas-Bel
+* David Folch
+* Levi John Wolf
+* Levi Wolf
+* Philip Stephens
+* Serge Rey
+* Sergio Rey
+* Wei Kang
+* jlaura
+* levi.john.wolf at gmail.com
+* ljw
+* ljwolf
+* pedrovma
+
+
+We closed a total of 86 issues, 33 pull requests and 53 regular issues;
+this is the full list (generated with the script
+:file:`tools/github_stats.py`):
+
+Pull Requests (33):
+
+* :ghpull:`724`: add synchronization tool
+* :ghpull:`733`: Fb/bump
+* :ghpull:`731`: Small docfixes
+* :ghpull:`730`: Contrib docs
+* :ghpull:`728`: B179
+* :ghpull:`727`: Geodf io
+* :ghpull:`725`: try pinning scipy,numpy
+* :ghpull:`723`: make sure to test all moran classes
+* :ghpull:`720`: Moving natural breaks to a cleaner kmeans implementation
+* :ghpull:`718`: force counts to be same length as bins
+* :ghpull:`714`: Dev
+* :ghpull:`715`: Heads
+* :ghpull:`713`: Enh712
+* :ghpull:`710`: Patsy/Pandas wrapper
+* :ghpull:`711`: Travis fixes
+* :ghpull:`706`: precommit hook
+* :ghpull:`707`: Keep dev updated with any bugfixes into master
+* :ghpull:`702`: fix for chi2 test 0 denominator and invocation of chi2 test in LISA_Markov
+* :ghpull:`704`: Allcloser
+* :ghpull:`703`: Swapping to Allclose and RTOL=.00005 in spreg
+* :ghpull:`701`: By col array
+* :ghpull:`700`: small optimization of bivariate moran motivated by #695
+* :ghpull:`696`: Pypi
+* :ghpull:`691`: Update doctest for one-off bug that was fixed with #690
+* :ghpull:`690`: fix for lisa markov one off for significance indicator
+* :ghpull:`689`: Clpy flex w
+* :ghpull:`688`: pep 8 edits
+* :ghpull:`687`: Change array assertions into allclose
+* :ghpull:`686`: Moran local bivariate
+* :ghpull:`684`: 591
+* :ghpull:`682`: release instructions updated
+* :ghpull:`681`: version bump for next dev cycle
+* :ghpull:`680`: Rel1.10
+
+Issues (53):
+
+* :ghissue:`705`: spreg check valve
+* :ghissue:`344`: Explore new dependency on ogr
+* :ghissue:`459`: Problem with bandwidth
+* :ghissue:`552`: Viz organization
+* :ghissue:`491`: Test np.allclose() for unit tests
+* :ghissue:`529`: Clarity needed on proper reference formatting in sphinx docs
+* :ghissue:`699`: Trouble importing pysal - ImportError: DLL load failed
+* :ghissue:`716`: `min_threshold_dist_from_shapefile` creating an island in some cases
+* :ghissue:`724`: add synchronization tool
+* :ghissue:`733`: Fb/bump
+* :ghissue:`731`: Small docfixes
+* :ghissue:`730`: Contrib docs
+* :ghissue:`719`: pysal not working with matplotlib v1.5 for plot_lisa_cluster, plot_choropleth, etc.
+* :ghissue:`728`: B179
+* :ghissue:`727`: Geodf io
+* :ghissue:`725`: try pinning scipy,numpy
+* :ghissue:`723`: make sure to test all moran classes
+* :ghissue:`720`: Moving natural breaks to a cleaner kmeans implementation
+* :ghissue:`717`: esda.mapclassify return problematic counts when there is 0 occurrence in the last class
+* :ghissue:`718`: force counts to be same length as bins
+* :ghissue:`714`: Dev
+* :ghissue:`712`: `block_weights` does not take argument `idVariable`
+* :ghissue:`715`: Heads
+* :ghissue:`713`: Enh712
+* :ghissue:`710`: Patsy/Pandas wrapper
+* :ghissue:`711`: Travis fixes
+* :ghissue:`706`: precommit hook
+* :ghissue:`708`: 2-3: is six a dependency or do we ship it?
+* :ghissue:`707`: Keep dev updated with any bugfixes into master
+* :ghissue:`702`: fix for chi2 test 0 denominator and invocation of chi2 test in LISA_Markov
+* :ghissue:`704`: Allcloser
+* :ghissue:`703`: Swapping to Allclose and RTOL=.00005 in spreg
+* :ghissue:`698`: Py3merge
+* :ghissue:`701`: By col array
+* :ghissue:`700`: small optimization of bivariate moran motivated by #695
+* :ghissue:`695`: Bivariate global moran's I formula
+* :ghissue:`683`: Py3 Conversion Project
+* :ghissue:`694`: Allclose in SPREG
+* :ghissue:`696`: Pypi
+* :ghissue:`691`: Update doctest for one-off bug that was fixed with #690
+* :ghissue:`693`: Trouble installation: No module named 'shapes'
+* :ghissue:`690`: fix for lisa markov one off for significance indicator
+* :ghissue:`689`: Clpy flex w
+* :ghissue:`688`: pep 8 edits
+* :ghissue:`685`: BV Lisa
+* :ghissue:`687`: Change array assertions into allclose
+* :ghissue:`686`: Moran local bivariate
+* :ghissue:`677`: Make meta importable from base
+* :ghissue:`684`: 591
+* :ghissue:`682`: release instructions updated
+* :ghissue:`679`: pysal.cg.sphere.fast_knn bug
+* :ghissue:`681`: version bump for next dev cycle
+* :ghissue:`680`: Rel1.10
+
v<1.10.0>, 2015-07-29
GitHub stats for 2015/01/31 - 2015/07/29
diff --git a/README.md b/README.md
deleted file mode 100644
index f76f76a..0000000
--- a/README.md
+++ /dev/null
@@ -1,106 +0,0 @@
-Python Spatial Analysis Library
-===============================
-
-[](https://gitter.im/pysal/pysal?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
-
-What is PySAL
---------------
-
-[](https://travis-ci.org/pysal/pysal)
-[](https://coveralls.io/r/pysal/pysal?branch=master)
-[](https://readthedocs.org/projects/pysal/?badge=latest)
-
-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, 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.rst b/README.rst
new file mode 100644
index 0000000..426b593
--- /dev/null
+++ b/README.rst
@@ -0,0 +1,137 @@
+Python Spatial Analysis Library
+=================================
+
+.. image:: https://travis-ci.org/pysal/pysal.svg
+ :target: https://travis-ci.org/pysal
+
+.. image:: https://coveralls.io/repos/pysal/pysal/badge.svg?branch=master
+ :target: https://coveralls.io/r/pysal/pysal?branch=master
+
+
+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.
+
+.. image:: https://farm2.staticflickr.com/1699/23937788493_1b9d147b9f_z.jpg
+ :width: 25%
+ :scale: 25%
+ :target: http://nbviewer.ipython.org/urls/gist.githubusercontent.com/darribas/657e0568df7a63362762/raw/pysal_lisa_maps.ipynb
+ :alt: LISA Maps of US County Homicide Rates
+
+*Above: Local Indicators of Spatial Association for Homicide Rates in US
+Counties 1990.*
+
+
+
+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 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 : https://github.com/pysal/pysal/
+.. _GeoDaX : https://geodacenter.asu.edu/software
+
+
+.. |build| image:: https://travis-ci.org/pysal/pysal.png
+ :scale: 100%
+ :align: middle
+ :target: https://travis-ci.org/pysal/pysal
+.. |cover| image:: https://coveralls.io/repos/pysal/pysal/badge.svg?branch=master
+ :scale: 50%
+ :align: top
+ :target: https://coveralls.io/r/pysal/pysal?branch=master
+.. |docs| image:: https://readthedocs.org/projects/pysal/badge/?verison=latest
+ :scale: 50%
+ :align: top
+ :target: http://pysal.readthedocs.org/en/latest/
+.. |talk| image:: https://badges.gitter.im/Join%20Chat.svg
+ :scale: 50%
+ :align: top
+ :target: https://gitter.im/pysal/pysal?
+
+
+
+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 Spatial econometrics and diagnostics
+* pysal.weights Spatial weights
+
+
+Installation
+============
+
+PySAL can be installed using pip:
+
+.. code-block:: bash
+
+ $ pip install pysal
+
+PySAL is also available through
+`Anaconda <https://www.continuum.io/downloads>`__ and `Enthought Canopy <https://www.enthought.com/products/canopy/>`__.
+
+Documentation
+=============
+
+For help on using PySAL, check out the following resources:
+
+* `User Guide <http://pysal.readthedocs.org/en/latest/users/index.html>`_
+* `Tutorials and Short Courses <https://github.com/pysal/notebooks/blob/master/courses.md>`_
+* `Notebooks <https://github.com/pysal/notebooks>`_
+* `User List <http://groups.google.com/group/openspace-list>`_
+
+
+
+Development
+===========
+
+PySAL development is hosted on github_.
+
+.. _github : https://github.com/pysal/pysal
+
+Discussions of development occurs on the
+`developer list <http://groups.google.com/group/pysal-dev>`_
+as well as gitter_.
+
+.. _gitter : https://gitter.im/pysal/pysal?
+
+Getting Involved
+================
+
+If you are interested in contributing to PySAL please see our
+`development guidelines <http://pysal.readthedocs.org/en/latest/developers/index.html>`_.
+
+
+Bug reports
+===========
+To search for or report bugs, please see PySAL's issues_.
+
+.. _issues : 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
index 78fdf99..89aaf75 100644
--- a/THANKS.txt
+++ b/THANKS.txt
@@ -22,6 +22,7 @@ Mark McCann
Taylor Oshan
Serge Rey
Charles R. Schmidt
+Alessandra Sozzi
Philip Stephens
Bohumil Svoma
Ran Wei
diff --git a/doc/source/conf.py b/doc/source/conf.py
index fb81629..c05a19c 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -57,9 +57,9 @@ copyright = u'2014-, PySAL Developers; 2009-13 Sergio Rey'
# built documents.
#
# The short X.Y version.
-version = '1.10.0'
+version = '1.11.0'
# The full version, including alpha/beta/rc tags.
-release = '1.10.0'
+release = '1.11.0'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
diff --git a/doc/source/developers/py3k.rst b/doc/source/developers/py3k.rst
index 057265f..271ac06 100644
--- a/doc/source/developers/py3k.rst
+++ b/doc/source/developers/py3k.rst
@@ -3,109 +3,6 @@ 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
-
+Starting with version 1.11, PySAL supports both the Python 2.x series
+(version 2.6 and 2.7) as well as Python 3.4 and newer.
diff --git a/doc/source/developers/release.rst b/doc/source/developers/release.rst
index 2902d93..953bf93 100644
--- a/doc/source/developers/release.rst
+++ b/doc/source/developers/release.rst
@@ -9,6 +9,7 @@ PySAL Release Management
Prepare the release
-------------------
+- Create a branch_ out of the `dev` branch
- Check all tests pass. See :doc:`testing`.
- Update CHANGELOG::
@@ -16,34 +17,53 @@ Prepare the release
- 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
+- Edit the file `version.py` to update MAJOR, MINOR, MICRO
+- Bump::
+
+ $ cd tools; python bump.py
+
- Commit all changes.
+- Push_ your branch up to your GitHub repos
+- On github issue a pull request, with a target of **upstream dev**.
+ Add a comment that this is for release.
+
Tag
---
-Make the Tag::
+With version 1.10 we changed the way the tags are created to reflect our
+policy_ of not pushing directly to upstream.
- $ git tag -a v1.4 -m 'my version 1.4'
+After you have issued a pull request, the project maintainer can `create the release`_ on GitHub.
+
+
+Make docs
+---------
- $ git push upstream v1.4
+As of version 1.6, docs are automatically compiled and hosted_.
-On each build machine, clone and checkout the newly created tag::
+Make and Upload and Test Distributions
+--------------------------------------
+
+On each build machine, clone and checkout the newly created tag (assuming that
+is `v1.10` in what follows)::
$ git clone http://github.com/pysal/pysal.git
+ $ cd pysal
$ git fetch --tags
- $ git checkout v1.4
+ $ git checkout v1.10
+
+- Make and upload_ to the **Testing** Python Package Index::
+
+ $ python setup.py sdist upload -r https://testpypi.python.org/pypi
+
+- Test that your package can install correctly::
+
+ $ pip install -i https://testpy.python.org/pypi
-Make docs
----------
-As of verison 1.6, docs are automatically compiled and hosted_.
+If all is good, proceed, otherwise fix.
-Make and Upload distributions
--------------------------------
- Make and upload_ to the Python Package Index in one shot!::
@@ -91,3 +111,7 @@ to announce the release.
.. _registered: http://docs.python.org/2.7/distutils/packageindex.html
.. _source: http://docs.python.org/distutils/sourcedist.html
.. _hosted: http://pysal.readthedocs.org
+.. _branch: https://github.com/pysal/pysal/wiki/GitHub-Standard-Operating-Procedures
+.. _policy: https://github.com/pysal/pysal/wiki/Example-git-config
+.. _create the release: https://help.github.com/articles/creating-releases/
+.. _Push: https://github.com/pysal/pysal/wiki/GitHub-Standard-Operating-Procedures
diff --git a/doc/source/index.rst b/doc/source/index.rst
index 3ff4313..4407ec8 100644
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -20,8 +20,8 @@ PySAL
.. sidebar:: Releases
- - `Stable 1.10.0 - July 2015 <users/installation.html>`_
- - `Development 1.11.0dev <http://github.com/pysal/pysal/>`_
+ - `Stable 1.11.0 (Released 2016-1-27) <users/installation.html>`_
+ - `Development <http://github.com/pysal/pysal/tree/dev>`_
PySAL is an open source library of spatial analysis functions written in
Python intended to support the development of high level applications.
diff --git a/doc/source/library/contrib/index.rst b/doc/source/library/contrib/index.rst
index 7fe43d8..b2a5a7a 100644
--- a/doc/source/library/contrib/index.rst
+++ b/doc/source/library/contrib/index.rst
@@ -50,7 +50,7 @@ Currently the following contribs are available:
5. Visualization -- Lightweight visualization layer (`Project page`_).
- - .. versionadded:: 1.7
+ - .. versionadded:: 1.5
- Path: pysal.contrib.viz
- Requires: `matplotlib`_
@@ -60,7 +60,24 @@ Currently the following contribs are available:
- Path: pysal.contrib.clusterpy
- Requires: `clusterpy`_
+ 7. Pandas utilities -- Tools to work with spatial file formats using `pandas`.
+ - .. versionadded:: 1.8
+ - Path: pysal.contrib.pdutilities
+ - Requires: `pandas`_
+
+ 8. Githooks -- Optional hooks for `git` to make development on `PySAL` easier
+
+ - .. versionadded:: 1.10
+ - Path: pysal.contrib.githooks (Note: not importable)
+ - Requires: `git`
+
+ 9. Handler -- A model ingester to standardize model extension
+
+ - .. versionadded:: 1.10
+ - Path: pysal.contrib.handler
+ - Requires: None
+ - Optional: `patsy`_
@@ -71,3 +88,4 @@ Currently the following contribs are available:
.. _project page: https://github.com/pysal/pysal/wiki/PySAL-Visualization-Project
.. _shapely: https://pypi.python.org/pypi/Shapely
.. _wxPython: http://www.wxpython.org/
+.. _patsy: https://github.com/pydata/patsy
diff --git a/doc/source/library/spreg/index.rst b/doc/source/library/spreg/index.rst
index 1116b6d..1559842 100644
--- a/doc/source/library/spreg/index.rst
+++ b/doc/source/library/spreg/index.rst
@@ -25,5 +25,6 @@
ml_error_regimes
ml_lag
ml_lag_regimes
+ sur
diff --git a/doc/source/references.rst b/doc/source/references.rst
index 03ad7a2..241405d 100644
--- a/doc/source/references.rst
+++ b/doc/source/references.rst
@@ -26,6 +26,7 @@ References
.. [Greene2003] Greene, W. H. (2003). Econometric analysis. Pearson Education India.
.. [Jacquez1996] Jacquez, G. M. (1996). A k nearest neighbour test for space–time interaction. Statistics in medicine, 15(18):1935–1949.
.. [Jarque1980] Jarque, C. M. and Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics letters, 6(3):255–259.
+.. [Jiang2013] Jian, B. (2013). Head/Tail breaks: A new classification scheme for data with a heavy-tailed distribution. The Professional Geographer, 65(3): 482-494.
.. [Kelejian1998] Kelejian, H. H. and 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 Estate Finance and Economics, 17(1):99–121.
.. [Kelejian1999] Kelejian, H. H. and Prucha, I. R. (1999). A generalized moments estimator for the autoregressive parameter in a spatial model. International Economic Review, 40:509–534.
.. [Kelejian2001] Kelejian, H. H. and Prucha, I. R. (2001). On the asymptotic distribution of the Moran I test statistic with applications. Journal of Econometrics, 104(2):219–257.
diff --git a/figs/lisamaps.png b/figs/lisamaps.png
new file mode 100644
index 0000000..9832bb7
Binary files /dev/null and b/figs/lisamaps.png differ
diff --git a/figs/lisamaps7.png b/figs/lisamaps7.png
new file mode 100644
index 0000000..9832bb7
Binary files /dev/null and b/figs/lisamaps7.png differ
diff --git a/mk2 b/mk2
new file mode 100755
index 0000000..c9e4a8b
--- /dev/null
+++ b/mk2
@@ -0,0 +1,2 @@
+git checkout py3conv_cleaning
+git stash
diff --git a/mk3 b/mk3
new file mode 100755
index 0000000..200e318
--- /dev/null
+++ b/mk3
@@ -0,0 +1,4 @@
+git branch -d 2to3
+git checkout -b 2to3
+python-modernize -nw -j 4 --future-unicode ./pysal
+2to3 -nw -j 4 ./pysal
diff --git a/pysal/COPYING b/pysal/COPYING
index 9125da2..ea898ef 100644
--- a/pysal/COPYING
+++ b/pysal/COPYING
@@ -1,4 +1,4 @@
-Copyright (c) 2009-13 Sergio J. Rey <sjsrey at gmail.com>
+Copyright (c) 2014- PySAL Developers, 2009-13 Sergio J. Rey <sjsrey at gmail.com>
All rights reserved.
Redistribution and use in source and binary forms, with or without
diff --git a/pysal/__init__.py b/pysal/__init__.py
index 5f20bc5..730c7e9 100644
--- a/pysal/__init__.py
+++ b/pysal/__init__.py
@@ -41,14 +41,14 @@ import pysal.cg
import pysal.core
from pysal.version import version
-from pysal.version import stable_release_date
-import urllib2, json
-import config
-import datetime
-import os, sys
+#from pysal.version import stable_release_date
+#import urllib2, json
+#import config
+#import datetime
+#import os, sys
# toplevel imports to be explicit
-from pysal.esda.moran import Moran, Moran_BV, Moran_BV_matrix, Moran_Local
+from pysal.esda.moran import Moran, Moran_BV, Moran_BV_matrix, Moran_Local, Moran_Local_BV
from pysal.esda.geary import Geary
from pysal.esda.join_counts import Join_Counts
from pysal.esda.gamma import Gamma
@@ -84,6 +84,11 @@ import pysal.spreg
import pysal.examples
from pysal.network.network import Network, NetworkG, NetworkK, NetworkF
+try:
+ import pandas
+ from pysal.contrib import pdutilities as pdio
+except ImportError:
+ print('Pandas adapters not loaded')
# Load the IOHandlers
from pysal.core import IOHandlers
@@ -100,6 +105,7 @@ MISSINGVALUE = None # used by fileIO to flag missing values.
# Load stale and other possible messages at import
+"""
base_path = os.path.split(pysal.__file__)[0]
config_path = os.path.join(base_path, 'config.py')
@@ -134,11 +140,11 @@ def check_version():
if today - delta > stable_release_date:
print("Your version of PySAL is %d days old.") % diff
print("There have likely been %d new release(s).") % releases
- query_yes_no("Disable this check? [Y/n]")
+ print("Suppress this by setting check_stable=False in config.py.")
+ #query_yes_no("Disable this check? [Y/n]")
else:
pass
-"""
def check_remote_version():
print("Checking web for last stable release....")
try:
@@ -150,9 +156,9 @@ def check_remote_version():
print(late)
except:
print("Machine is offline. I am unable to check for the latest version of PySAL")
-"""
if config.check_stable:
check_version()
else:
pass
+"""
diff --git a/pysal/common.py b/pysal/common.py
index 58e167a..d111997 100644
--- a/pysal/common.py
+++ b/pysal/common.py
@@ -16,6 +16,7 @@ except:
print 'scipy 0.7+ is required'
raise
+RTOL = .00001
import copy
import math
diff --git a/pysal/contrib/README b/pysal/contrib/README.rst
similarity index 66%
rename from pysal/contrib/README
rename to pysal/contrib/README.rst
index ad99aa4..1902edb 100644
--- a/pysal/contrib/README
+++ b/pysal/contrib/README.rst
@@ -34,24 +34,43 @@ Currently the following contribs are available:
- .. versionadded:: 1.3
- Path: pysal.contrib.weights_viewer.weights_viewer
- - Requires: wxPython
+ - Requires: `wxPython`_
3. Shapely Extension -- Exposes shapely methods as standalone functions
- .. versionadded:: 1.3
- Path: pysal.contrib.shapely_ext
- - Requires: shapely
+ - Requires: `shapely`_
4. Shared Perimeter Weights -- calculate shared perimeters weights.
- .. versionadded:: 1.3
- Path: pysal.contrib.shared_perimeter_weights
- - Requires: shapely
+ - Requires: `shapely`_
-5. Clusterpy Extension -- spatially constrained clustering algorithms.
+ 5. Visualization -- Lightweight visualization layer (`Project page`_).
- - .. versionadded:: 1.8
- - Path: pysal.contrib.clusterpy
- - Requires: clusterpy
+ - .. versionadded:: 1.5
+ - Path: pysal.contrib.viz
+ - Requires: `matplotlib`_
+ 6. Clusterpy -- Spatially constrained clustering.
+ - .. versionadded:: 1.8
+ - Path: pysal.contrib.clusterpy
+ - Requires: `clusterpy`_
+
+ 7. Pandas utilities -- Tools to work with spatial file formats using `pandas`.
+
+ - .. versionadded:: 1.8
+ - Path: pysal.contrib.pdutilities
+ - Requires: `pandas`_
+
+
+
+
+.. _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/pysal/contrib/clusterpy/__init__.py b/pysal/contrib/clusterpy/__init__.py
index 27bb912..079456a 100644
--- a/pysal/contrib/clusterpy/__init__.py
+++ b/pysal/contrib/clusterpy/__init__.py
@@ -10,3 +10,4 @@ else:
from clusterpy_ext import addRook2Layer
from clusterpy_ext import addQueen2Layer
from clusterpy_ext import addArray2Layer
+ from clusterpy_ext import addW2Layer
diff --git a/pysal/contrib/clusterpy/clusterpy.ipynb b/pysal/contrib/clusterpy/clusterpy.ipynb
index 9974d81..5762f97 100644
--- a/pysal/contrib/clusterpy/clusterpy.ipynb
+++ b/pysal/contrib/clusterpy/clusterpy.ipynb
@@ -1,3320 +1,3441 @@
{
- "metadata": {
- "name": "",
- "signature": "sha256:a138ff2bb82f0ff84b1c6ddbfcdd96a0d757ce0a91b7848a1d377b8e5b58d79b"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
{
- "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",
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- " 34: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],\n",
- " 35: [30L, 33L, 38L, 41L, 45L],\n",
- " 36: [42L],\n",
- " 37: [4L,\n",
- " 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",
- " 38: [30L, 33L, 35L, 41L, 45L],\n",
- " 39: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 40L, 43L, 44L, 46L, 47L, 48L],\n",
- " 40: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 43L, 44L, 46L, 47L, 48L],\n",
- " 41: [30L, 33L, 35L, 38L, 45L],\n",
- " 42: [36L],\n",
- " 43: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 44L, 46L, 47L, 48L],\n",
- " 44: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 46L, 47L, 48L],\n",
- " 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",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ClusterPy: Library of spatially constrained clustering algorithms\n",
+ "Populating the interactive namespace from numpy and matplotlib\n"
]
- },
- {
- "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"
+ }
+ ],
+ "source": [
+ "import pysal.contrib.clusterpy as cp\n",
+ "%pylab inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pysal as ps\n",
+ "from collections import Counter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "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: [6, 3, 6, 3, 6, 6, 3, 3, 2, 2, 0, 0, 0, 0, 9, 0, 2, 9, 0, 5, 8, 2, 5, 9, 9, 9, 1, 1, 1, 8, 4, 5, 7, 4, 7, 4, 8, 1, 4, 5, 5, 4, 8, 7, 7, 4, 5, 7, 7]\n",
+ "FINAL OF: 3458.63068498\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n"
+ ]
+ }
+ ],
+ "source": [
+ "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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Counter({0: 6, 1: 4, 2: 4, 3: 4, 4: 6, 5: 6, 6: 4, 7: 6, 8: 4, 9: 5})"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Counter(columbus.region2areas)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "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: [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: [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"
+ ]
+ }
+ ],
+ "source": [
+ "columbus.cluster('arisel', ['CRIME'], 5, wType='rook', inits=10, dissolve=0)\n",
+ "#calif.cluster('arisel', ['PCR2002'], 9, wType='rook', inits=10, dissolve=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['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_20150831012909']"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# regionalization solutions are added as a list of region ids at the end\n",
+ "columbus.fieldNames"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "warisel = ps.block_weights(columbus.region2areas)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{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",
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+ " 34: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],\n",
+ " 35: [30L, 33L, 38L, 41L, 45L],\n",
+ " 36: [42L],\n",
+ " 37: [4L,\n",
+ " 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",
+ " 38: [30L, 33L, 35L, 41L, 45L],\n",
+ " 39: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 40L, 43L, 44L, 46L, 47L, 48L],\n",
+ " 40: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 43L, 44L, 46L, 47L, 48L],\n",
+ " 41: [30L, 33L, 35L, 38L, 45L],\n",
+ " 42: [36L],\n",
+ " 43: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 44L, 46L, 47L, 48L],\n",
+ " 44: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 46L, 47L, 48L],\n",
+ " 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]}"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "wregimes.neighbors"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Attrribute data from a csv file and a W from a gal file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "mexico = cp.importCsvData(ps.examples.get_path('mexico.csv'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['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']"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mexico.fieldNames"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "w = ps.open(ps.examples.get_path('mexico.gal')).read()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "32"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "w.n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "cp.addRook2Layer(ps.examples.get_path('mexico.gal'), mexico)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{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]}"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mexico.Wrook"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "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: [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: [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"
+ ]
+ }
+ ],
+ "source": [
+ "mexico.cluster('arisel', ['pcgdp1940'], 5, wType='rook', inits=10, dissolve=0)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['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']"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mexico.fieldNames"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Getting variables\n",
+ "Variables successfully extracted\n"
]
},
{
- "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",
- " 'Y_1948',\n",
- " 'Y_1949',\n",
- " 'Y_1950',\n",
- " 'Y_1951',\n",
- " 'Y_1952',\n",
- " 'Y_1953',\n",
- " 'Y_1954',\n",
- " 'Y_1955',\n",
- " '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",
- " 'Y_1973',\n",
- " 'Y_1974',\n",
- " '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": 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",
- " 'Y_1953',\n",
- " 'Y_1954',\n",
- " 'Y_1955',\n",
- " '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",
- " 'Y_1973',\n",
- " 'Y_1974',\n",
- " '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",
- " 7: [518],\n",
- " 8: [347],\n",
- " 9: [507],\n",
- " 10: [948],\n",
- " 11: [607],\n",
- " 12: [581],\n",
- " 13: [532],\n",
- " 14: [393],\n",
- " 15: [414],\n",
- " 16: [601],\n",
- " 17: [768],\n",
- " 18: [906],\n",
- " 19: [790],\n",
- " 20: [599],\n",
- " 21: [286],\n",
- " 22: [621],\n",
- " 23: [592],\n",
- " 24: [596],\n",
- " 25: [868],\n",
- " 26: [686],\n",
- " 27: [918],\n",
- " 28: [410],\n",
- " 29: [1152],\n",
- " 30: [332],\n",
- " 31: [382],\n",
- " 32: [771],\n",
- " 33: [455],\n",
- " 34: [668],\n",
- " 35: [772],\n",
- " 36: [874],\n",
- " 37: [271],\n",
- " 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": []
+ "data": {
+ "text/plain": [
+ "{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]}"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mexico.getVars('pcgdp1940')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "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: [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: [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"
+ ]
+ }
+ ],
+ "source": [
+ "# 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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mexico.region2areas.index(2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[31, 13]"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mexico.Wrook[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Getting variables\n",
+ "Variables successfully extracted\n"
+ ]
},
{
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "Counter(regions)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
+ "data": {
+ "text/plain": [
+ "{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']}"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mexico.getVars('State')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "regions = np.array(mexico.region2areas)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "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])"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "regions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Counter({1: 16, 2: 12, 0: 2, 3: 1, 4: 1})"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Counter(regions)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Attrribute data from a csv file and an external `W` object"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "mexico = cp.importCsvData(ps.examples.get_path('mexico.csv'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "w = ps.open(ps.examples.get_path('mexico.gal')).read()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "cp.addW2Layer(w, mexico)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'0': ['31', '13'],\n",
+ " '1': ['2', '25'],\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",
+ " '2': ['1'],\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",
+ " '3': ['30', '22', '26'],\n",
+ " '30': ['3', '22'],\n",
+ " '31': ['18', '23', '10', '0', '13', '17', '9', '6'],\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']}"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mexico.Wrook"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Getting variables\n",
+ "Variables successfully extracted\n",
+ "Running original Arisel algorithm\n",
+ "Number of areas: 32\n",
+ "Number of regions: 5\n",
+ "Disconnected areas neighs: [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]\n",
+ "initial Solution: [-1, 2, -1, -1, -1, -1, -1, -1, 4, 1, -1, -1, -1, -1, -1, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, -1, -1, -1, -1, -1, -1]\n",
+ "initial O.F: 0.0\n",
+ "FINAL SOLUTION: [-1, 2, -1, -1, -1, -1, -1, -1, 4, 1, -1, -1, -1, -1, -1, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, -1, -1, -1, -1, -1, -1]\n",
+ "FINAL OF: 0.0\n",
+ "Done\n",
+ "Adding variables\n",
+ "Done\n"
+ ]
+ }
+ ],
+ "source": [
+ "mexico.cluster('arisel', ['pcgdp1940'], 5, wType='rook', inits=10, dissolve=0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Shapefile and mapping results with PySAL Viz"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "usf = ps.examples.get_path('us48.shp')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "us = cp.loadArcData(usf.split(\".\")[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{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]}"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "us.Wqueen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['ID',\n",
+ " 'AREA',\n",
+ " 'PERIMETER',\n",
+ " 'STATE_',\n",
+ " 'STATE_ID',\n",
+ " 'STATE_NAME',\n",
+ " 'STATE_FIPS',\n",
+ " 'SUB_REGION',\n",
+ " 'STATE_ABBR']"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "us.fieldNames"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "uscsv = ps.examples.get_path(\"usjoin.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "f = ps.open(uscsv)\n",
+ "pci = np.array([f.by_col[str(y)] for y in range(1929, 2010)]).T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "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]])"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pci"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "usy = cp.Layer()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "cp.addQueen2Layer(ps.examples.get_path('states48.gal'), usy)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "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"
+ ]
+ }
+ ],
+ "source": [
+ "names = [\"Y_%d\"%v for v in range(1929,2010)]\n",
+ "cp.addArray2Layer(pci, usy, names)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['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",
+ " 'Y_1953',\n",
+ " 'Y_1954',\n",
+ " 'Y_1955',\n",
+ " '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",
+ " 'Y_1973',\n",
+ " 'Y_1974',\n",
+ " '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']"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "names"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['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",
+ " 'Y_1953',\n",
+ " 'Y_1954',\n",
+ " 'Y_1955',\n",
+ " '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",
+ " 'Y_1973',\n",
+ " 'Y_1974',\n",
+ " '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']"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "usy.fieldNames"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Getting variables\n",
+ "Variables successfully extracted\n"
+ ]
},
{
- "cell_type": "code",
- "collapsed": false,
- "input": [],
- "language": "python",
- "metadata": {},
- "outputs": []
+ "data": {
+ "text/plain": [
+ "{0: [323],\n",
+ " 1: [600],\n",
+ " 2: [310],\n",
+ " 3: [991],\n",
+ " 4: [634],\n",
+ " 5: [1024],\n",
+ " 6: [1032],\n",
+ " 7: [518],\n",
+ " 8: [347],\n",
+ " 9: [507],\n",
+ " 10: [948],\n",
+ " 11: [607],\n",
+ " 12: [581],\n",
+ " 13: [532],\n",
+ " 14: [393],\n",
+ " 15: [414],\n",
+ " 16: [601],\n",
+ " 17: [768],\n",
+ " 18: [906],\n",
+ " 19: [790],\n",
+ " 20: [599],\n",
+ " 21: [286],\n",
+ " 22: [621],\n",
+ " 23: [592],\n",
+ " 24: [596],\n",
+ " 25: [868],\n",
+ " 26: [686],\n",
+ " 27: [918],\n",
+ " 28: [410],\n",
+ " 29: [1152],\n",
+ " 30: [332],\n",
+ " 31: [382],\n",
+ " 32: [771],\n",
+ " 33: [455],\n",
+ " 34: [668],\n",
+ " 35: [772],\n",
+ " 36: [874],\n",
+ " 37: [271],\n",
+ " 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]}"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "usy.getVars('Y_1929')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{}"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "usy.Wrook"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "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: [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: [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"
+ ]
+ }
+ ],
+ "source": [
+ "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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "us = cp.Layer()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "cp.addQueen2Layer(ps.examples.get_path('states48.gal'), us)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "uscsv = ps.examples.get_path(\"usjoin.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "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"
+ ]
+ }
+ ],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "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: [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: [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"
+ ]
+ }
+ ],
+ "source": [
+ "usy.cluster('arisel', ['Y_1980'], 8, wType='queen', inits=10, dissolve=0)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "us_alpha = cp.importCsvData(ps.examples.get_path('usjoin.csv'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Getting variables\n",
+ "Variables successfully extracted\n"
+ ]
+ }
+ ],
+ "source": [
+ "alpha_fips = us_alpha.getVars('STATE_FIPS')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{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]}"
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
- "metadata": {}
+ "source": [
+ "alpha_fips"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "dbf = ps.open(ps.examples.get_path('us48.dbf'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['AREA',\n",
+ " 'PERIMETER',\n",
+ " 'STATE_',\n",
+ " 'STATE_ID',\n",
+ " 'STATE_NAME',\n",
+ " 'STATE_FIPS',\n",
+ " 'SUB_REGION',\n",
+ " 'STATE_ABBR']"
+ ]
+ },
+ "execution_count": 53,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dbf.header"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "state_fips = dbf.by_col('STATE_FIPS')\n",
+ "names = dbf.by_col('STATE_NAME')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['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']"
+ ]
+ },
+ "execution_count": 55,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "names"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "state_fips = map(int, state_fips)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[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]"
+ ]
+ },
+ "execution_count": 57,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "state_fips"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# 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]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "regions = usy.region2areas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[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]"
+ ]
+ },
+ "execution_count": 60,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "regions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from pysal.contrib.viz import mapping as maps"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "shp = ps.examples.get_path('us48.shp')\n",
+ "regions = np.array(regions)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd8FNXax3/nzMz29ISQCiGUAKGXSJeOFKWKysUCCBaw\n36tXvXZFUHxt6LWCCqKiICgIUqT3GnpvIYUAaZttM3PO+8duQoCU3WSTbLz7/egH2J0588zsmWee\nec5TAD9+/Pjx48ePHz9+/Pjx48ePHz9+/Pjx48ePHz9+/Pjx48ePHz9+/Pjx48ePn7pDgEaT+fGQ\nITxAo7ECSKhtefz4KQta2wL48eMNOCD0TkhA59hYB4BOtS2PHz9lIXpxrMkA4gCoXhzTUxoCuAKg\n4IbPkwEcrHFpfJtI159ZtSpF2bQAcAxuziezwxEuEIIeDRoEbLlwoZtVln+qXvH8+Kkc3lK6kQA+\nG9g0 [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x149ca390>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "maps.plot_choropleth(shp, regions[reorder], 'unique_values')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "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: [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: [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"
+ ]
+ }
+ ],
+ "source": [
+ "usy.cluster('arisel', ['Y_1929'], 8, wType='queen', inits=10, dissolve=0)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "regions = usy.region2areas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "regions = np.array(regions)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd8FMX7xz8zW66nkZCEltBLgNCrgBTp+EXFhuWriKAi\nKijqzwKKBUEFFQVBBTtWRAREEBSQ3os06TWUkH5ly8zvj01CgJS75C65+L33C15J7nZnn92dfXbm\nmacAIUKECBEiRIgQIUKECBEiRIgQIUKECBEiRIgQIUKECBEiRIgQIUKECBEiRIgQlQeLzZ4y/IXJ\n3GKzuwDUrmh5QoQoClrRAoQI4Q8450JSu86o16ylAqBtRcsTIkRRiH5sawSAmgB0P7bpK4kAUgFk\nXfV5UwB7yl2a4CY29+e5CpWiaJoAOAAv+5PbmRNNqYDGrTs6DuzY0llxu74LrHghQpQOfyndWACz\nWlzX [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x1495def0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "maps.plot_choropleth(shp, regions[reorder], 'unique_values')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "regions = usy.region2areas\n",
+ "regions = np.array(regions)\n",
+ "maps.plot_choropleth(shp, regions[reorder], 'unique_values', title='All Years')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "ps.version"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "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')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "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')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "usy.cluster('arisel', names[40:], 8, wType='queen', inits=10, dissolve=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "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')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "Counter(regions)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "usy.getVars('Y_1929')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "usy.Wrook"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "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')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "regions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "Counter(regions)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "vars = names"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "vars.append('CONSTANT')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "vars"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "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')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "Counter(regions)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "south = cp.loadArcData(ps.examples.get_path(\"south.shp\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "south.fieldNames"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# uncomment if you have some time ;->\n",
+ "#south.cluster('arisel', ['HR70'], 20, wType='queen', inits=10, dissolve=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "#regions = south.region2areas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "shp = ps.examples.get_path('south.shp')\n",
+ "#maps.plot_choropleth(shp, np.array(regions), 'unique_values')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "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')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "Counter(regions)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": []
}
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/clusterpy/clusterpy_ext.py b/pysal/contrib/clusterpy/clusterpy_ext.py
index e48b202..dd3af80 100644
--- a/pysal/contrib/clusterpy/clusterpy_ext.py
+++ b/pysal/contrib/clusterpy/clusterpy_ext.py
@@ -4,7 +4,7 @@ import struct
__author__ = "Sergio Rey <sjsrey at gmail.com>"
-__ALL__= ['Layer', 'loadArcData', 'importCsvData', 'addRook2Layer', 'addQueen2Layer', 'addArray2Layer' ]
+__ALL__= ['Layer', 'loadArcData', 'importCsvData', 'addRook2Layer', 'addQueen2Layer', 'addArray2Layer', 'addW2Layer']
def _importArcData(filename):
@@ -270,15 +270,65 @@ def addArray2Layer(array, layer, names=None):
v[i] = array[i,j]
layer.addVariable([name], v)
+def addW2Layer(w, layer, contiguity='rook'):
+ '''
+ Attach a contiguity PySAL W object to a layer
+
+ NOTE: given clusterpy's requirements, this method only extracts the
+ `neighbors` dictionary.
+
+ ...
+
+ Parameters
+ ----------
+ w : ps.W
+ PySAL weights object
+ layer : clusterpy.Layer
+ Layer to attach the weights to
+ contiguity: str ['rook'|'queen']
+ Type of contguity expressed in `w`
+
+ Returns
+ -------
+ None
+
+ Example
+ -------
+ >>> import pysal as ps
+ >>> import pysal.contrib.clusterpy as cp
+ >>> w = ps.queen_from_shapefile(ps.examples.get_path('columbus.shp'))
+ >>> layer = cp.Layer()
+ >>> cp.addW2Layer(w, layer, contiguity='queen')
+ >>> layer.Wqueen[0]
+ [1, 2]
+ '''
+ if contiguity.upper()== "ROOK":
+ layer.Wrook = w.neighbors
+ elif contiguity.upper() == "QUEEN":
+ layer.Wqueen = w.neighbors
+ else:
+ print 'Unsupported contiguity type: ', contiguity
+ return None
+
if __name__ == '__main__':
import numpy as np
+ w = ps.queen_from_shapefile(ps.examples.get_path('columbus.shp'))
+ db = ps.open(ps.examples.get_path('columbus.dbf'))
+ vars = ['CRIME', 'HOVAL']
+ x = np.array([db.by_col(i) for i in vars]).T
+ layer = Layer()
+ _ = addArray2Layer(x, layer, names=vars)
+ _ = addW2Layer(w, layer)
+ layer.cluster('arisel', ['CRIME', 'CONSTANT'], 2, dissolve=0, std=0)
+ '''
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/githooks/pre-commit b/pysal/contrib/githooks/pre-commit
new file mode 100755
index 0000000..3f7a641
--- /dev/null
+++ b/pysal/contrib/githooks/pre-commit
@@ -0,0 +1,6 @@
+#!/bin/bash
+
+NOCHANGE='^pysal/spreg'
+git diff --cached --name-only | grep -E $NOCHANGE && \
+ echo "COMMIT REJECTED: Contains changes to pysal/spreg." && exit 1
+exit 0
diff --git a/pysal/contrib/handler/Explaining the Handler.ipynb b/pysal/contrib/handler/Explaining the Handler.ipynb
new file mode 100644
index 0000000..1b182d8
--- /dev/null
+++ b/pysal/contrib/handler/Explaining the Handler.ipynb
@@ -0,0 +1,830 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# What is the handler?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `spreg-handler` provides a unified interface to apply any specifed regression function in pysal to data, like a call to `lm` in `R`. \n",
+ "\n",
+ "This has 2 components. \n",
+ "\n",
+ "1. `registry.py`, which finds all of the valid model classes in `pysal.spreg`\n",
+ "2. `handler.py`, which proves *one* function to estimate all of those classes.\n",
+ "\n",
+ "Thanks to the `registry`, `handler.Model` can serve as the single point of access for a `patsy`/`pandas` interface logic, as well as anything else that we might want to add to regression classes without forcing it through inheritance.\n",
+ "\n",
+ "First, let's set up and estimate some models. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import handler as h\n",
+ "import pysal as ps\n",
+ "import geopandas as gpd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "df = gpd.read_file(ps.examples.get_path('columbus.json'))\n",
+ "dbf = ps.open(ps.examples.get_path('columbus.dbf'))\n",
+ "y = dbf.by_col_array(['HOVAL'])\n",
+ "X = dbf.by_col_array(['INC', 'CRIME'])\n",
+ "W = ps.open(ps.examples.get_path('columbus.gal')).read()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "original = ps.spreg.OLS(y,X,W, name_x=['INC', 'CRIME'], name_y='HOVAL')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The handler's default model is `OLS`. So, for a model of type `OLS`, no extra argument needs to be passed. However, for the sake of clarity, I'll pass the model specification argument, `mtype`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "handled = h.Model(y,\n",
+ " X,\n",
+ " W,\n",
+ " name_x=['INC', 'CRIME'], \n",
+ " name_y='HOVAL', \n",
+ " mtype='OLS')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "REGRESSION\n",
+ "----------\n",
+ "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES\n",
+ "-----------------------------------------\n",
+ "Data set : unknown\n",
+ "Weights matrix : unknown\n",
+ "Dependent Variable : HOVAL Number of Observations: 49\n",
+ "Mean dependent var : 38.4362 Number of Variables : 3\n",
+ "S.D. dependent var : 18.4661 Degrees of Freedom : 46\n",
+ "R-squared : 0.3495\n",
+ "Adjusted R-squared : 0.3212\n",
+ "Sum squared residual: 10647.015 F-statistic : 12.3582\n",
+ "Sigma-square : 231.457 Prob(F-statistic) : 5.064e-05\n",
+ "S.E. of regression : 15.214 Log likelihood : -201.368\n",
+ "Sigma-square ML : 217.286 Akaike info criterion : 408.735\n",
+ "S.E of regression ML: 14.7406 Schwarz criterion : 414.411\n",
+ "\n",
+ "------------------------------------------------------------------------------------\n",
+ " Variable Coefficient Std.Error t-Statistic Probability\n",
+ "------------------------------------------------------------------------------------\n",
+ " CONSTANT 46.4281827 13.1917570 3.5194844 0.0009867\n",
+ " CRIME -0.4848885 0.1826729 -2.6544086 0.0108745\n",
+ " INC 0.6289840 0.5359104 1.1736736 0.2465669\n",
+ "------------------------------------------------------------------------------------\n",
+ "\n",
+ "REGRESSION DIAGNOSTICS\n",
+ "MULTICOLLINEARITY CONDITION NUMBER 12.538\n",
+ "\n",
+ "TEST ON NORMALITY OF ERRORS\n",
+ "TEST DF VALUE PROB\n",
+ "Jarque-Bera 2 39.706 0.0000\n",
+ "\n",
+ "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
+ "RANDOM COEFFICIENTS\n",
+ "TEST DF VALUE PROB\n",
+ "Breusch-Pagan test 2 5.767 0.0559\n",
+ "Koenker-Bassett test 2 2.270 0.3214\n",
+ "================================ END OF REPORT =====================================\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(original.summary)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "REGRESSION\n",
+ "----------\n",
+ "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES\n",
+ "-----------------------------------------\n",
+ "Data set : unknown\n",
+ "Weights matrix : unknown\n",
+ "Dependent Variable : HOVAL Number of Observations: 49\n",
+ "Mean dependent var : 38.4362 Number of Variables : 3\n",
+ "S.D. dependent var : 18.4661 Degrees of Freedom : 46\n",
+ "R-squared : 0.3495\n",
+ "Adjusted R-squared : 0.3212\n",
+ "Sum squared residual: 10647.015 F-statistic : 12.3582\n",
+ "Sigma-square : 231.457 Prob(F-statistic) : 5.064e-05\n",
+ "S.E. of regression : 15.214 Log likelihood : -201.368\n",
+ "Sigma-square ML : 217.286 Akaike info criterion : 408.735\n",
+ "S.E of regression ML: 14.7406 Schwarz criterion : 414.411\n",
+ "\n",
+ "------------------------------------------------------------------------------------\n",
+ " Variable Coefficient Std.Error t-Statistic Probability\n",
+ "------------------------------------------------------------------------------------\n",
+ " CONSTANT 46.4281827 13.1917570 3.5194844 0.0009867\n",
+ " CRIME -0.4848885 0.1826729 -2.6544086 0.0108745\n",
+ " INC 0.6289840 0.5359104 1.1736736 0.2465669\n",
+ "------------------------------------------------------------------------------------\n",
+ "\n",
+ "REGRESSION DIAGNOSTICS\n",
+ "MULTICOLLINEARITY CONDITION NUMBER 12.538\n",
+ "\n",
+ "TEST ON NORMALITY OF ERRORS\n",
+ "TEST DF VALUE PROB\n",
+ "Jarque-Bera 2 39.706 0.0000\n",
+ "\n",
+ "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
+ "RANDOM COEFFICIENTS\n",
+ "TEST DF VALUE PROB\n",
+ "Breusch-Pagan test 2 5.767 0.0559\n",
+ "Koenker-Bassett test 2 2.270 0.3214\n",
+ "================================ END OF REPORT =====================================\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(handled.summary)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The long and short of it is that `Model` classes pass estimation to the function specified in `mtype`, and then contain the results in a reasonable way. \n",
+ "\n",
+ "In fact, the \"real\" `PySAL` model class sits under `handled._called`, so, at worst, we can just reference aspects of `handled` down to `_called`. I currently do this by iterating through `dir(handled._called)` and using `eval` to flatten all of `_called`'s attributes into `handled` at initialization. \n",
+ "\n",
+ "But, eventually, I am think about adding plotting, visual diagnostics, out of sample prediction, or other stuff to this wrapper. So, I will probably not duplicate the access points for intermediate computations, like `X'X`, `e`, or `TSLS`'s arcane-sounding `zthhthi`.\n",
+ "\n",
+ "I'd like to clean up this `Model` interface so that only X, Y, residuals, and some statistics are directly exposed. \n",
+ "\n",
+ "Keep in mind, since [assignment **never** copies data](https://youtu.be/_AEJHKGk9ns?t=296), and the original model sits in `handled._called`, this isn't actually a *loss* of information, just a *hiding*, which is a standard OOP principle. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Isn't this wastefully storing multiple copies of data in memory?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "No. Let's see where everything lives using the python built-in `id` function. \n",
+ "\n",
+ "Recall that the original model is stuffed into `Model._called`. So, if anything in there has a different memory address from what's being displayed by `Model`, the data is duplicated:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "__init__is in two different addresses.\n",
+ "\t Outer is at 0x7f11fbb2dcd0\n",
+ "\t Inner is at 0x7f11fbb92820\n"
+ ]
+ }
+ ],
+ "source": [
+ "for atname in dir(handled._called):\n",
+ " attr = eval(\"handled._called.{}\".format(atname))\n",
+ " composed_id = hex(id(attr))\n",
+ " outattr = eval(\"handled.{}\".format(atname))\n",
+ " outer_id = hex(id(outattr))\n",
+ " if composed_id != outer_id:\n",
+ " print(atname + \"is in two different addresses.\")\n",
+ " print(\"\\t Outer is at \" + outer_id +\"\\n\\t Inner is at \" + composed_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Only the `__init__` function exposed by `Model` is different from `Model._called`, which makes sense, since an `__init__` function can't write over itself. \n",
+ "\n",
+ "If we wanted to access `Model._called.__init__`, it's still there. This means we could implement some \"refit\" method, `Model.refit(y=Model.y, X=Model.X, ...)` which could use `Model._called.__init__` to revise estimates in `Model` in place or returning a new model.\n",
+ "\n",
+ "I don't know why we might want to do this, but it's kinda neat :)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# What does this buy us?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Regardless, all the stuff the wrapping `Model` class is parsed *around* the underlying PySAL classes. That is, the wrapper would only inject commands into the API. At minimum, it *is exactly* the underlying class. \n",
+ "\n",
+ "This is because it dispatches the arguments to the specified model type without knowing any special information about the function call. \n",
+ "\n",
+ "This means we can do some pretty cool things, while keeping the actual wrapper at ~40 LoC!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/ljw/.local/lib/python2.7/site-packages/scipy/optimize/_minimize.py:593: RuntimeWarning: Method 'bounded' does not support relative tolerance in x; defaulting to absolute tolerance.\n",
+ " \"defaulting to absolute tolerance.\", RuntimeWarning)\n"
+ ]
+ }
+ ],
+ "source": [
+ "ML = ps.spreg.ML_Lag(y,X,W)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "handled_ML = h.Model(y,X,W,mtype='ML_Lag')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "REGRESSION\n",
+ "----------\n",
+ "SUMMARY OF OUTPUT: MAXIMUM LIKELIHOOD SPATIAL LAG (METHOD = FULL)\n",
+ "-----------------------------------------------------------------\n",
+ "Data set : unknown\n",
+ "Weights matrix : unknown\n",
+ "Dependent Variable : dep_var Number of Observations: 49\n",
+ "Mean dependent var : 38.4362 Number of Variables : 4\n",
+ "S.D. dependent var : 18.4661 Degrees of Freedom : 45\n",
+ "Pseudo R-squared : 0.3639\n",
+ "Spatial Pseudo R-squared: 0.3384\n",
+ "Sigma-square ML : 212.490 Log likelihood : -200.903\n",
+ "S.E of regression : 14.577 Akaike info criterion : 409.807\n",
+ " Schwarz criterion : 417.374\n",
+ "\n",
+ "------------------------------------------------------------------------------------\n",
+ " Variable Coefficient Std.Error z-Statistic Probability\n",
+ "------------------------------------------------------------------------------------\n",
+ " CONSTANT 44.1882822 12.9020363 3.4249076 0.0006150\n",
+ " W_dep_var 0.0259466 0.0262601 0.9880621 0.3231223\n",
+ " var_1 0.5453705 0.5209327 1.0469117 0.2951403\n",
+ " var_2 -0.5186558 0.1768050 -2.9334902 0.0033517\n",
+ "------------------------------------------------------------------------------------\n",
+ "================================ END OF REPORT =====================================\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(ML.summary)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "REGRESSION\n",
+ "----------\n",
+ "SUMMARY OF OUTPUT: MAXIMUM LIKELIHOOD SPATIAL LAG (METHOD = FULL)\n",
+ "-----------------------------------------------------------------\n",
+ "Data set : unknown\n",
+ "Weights matrix : unknown\n",
+ "Dependent Variable : dep_var Number of Observations: 49\n",
+ "Mean dependent var : 38.4362 Number of Variables : 4\n",
+ "S.D. dependent var : 18.4661 Degrees of Freedom : 45\n",
+ "Pseudo R-squared : 0.3639\n",
+ "Spatial Pseudo R-squared: 0.3384\n",
+ "Sigma-square ML : 212.490 Log likelihood : -200.903\n",
+ "S.E of regression : 14.577 Akaike info criterion : 409.807\n",
+ " Schwarz criterion : 417.374\n",
+ "\n",
+ "------------------------------------------------------------------------------------\n",
+ " Variable Coefficient Std.Error z-Statistic Probability\n",
+ "------------------------------------------------------------------------------------\n",
+ " CONSTANT 44.1882822 12.9020363 3.4249076 0.0006150\n",
+ " W_dep_var 0.0259466 0.0262601 0.9880621 0.3231223\n",
+ " var_1 0.5453705 0.5209327 1.0469117 0.2951403\n",
+ " var_2 -0.5186558 0.1768050 -2.9334902 0.0033517\n",
+ "------------------------------------------------------------------------------------\n",
+ "================================ END OF REPORT =====================================\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(handled_ML.summary)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Intercepting formulas"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So, this is pretty neat, but gives us nothing above using *one* function to dispatch models. That's cool and R-like, but it's not necessarly better. Where it does add functionality is in its ability to intercept model formulas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "handled_eq = h.Model(\"HOVAL ~ INC + CRIME\", data=df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "REGRESSION\n",
+ "----------\n",
+ "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES\n",
+ "-----------------------------------------\n",
+ "Data set : unknown\n",
+ "Dependent Variable : dep_var Number of Observations: 49\n",
+ "Mean dependent var : 38.4362 Number of Variables : 3\n",
+ "S.D. dependent var : 18.4661 Degrees of Freedom : 46\n",
+ "R-squared : 0.3495\n",
+ "Adjusted R-squared : 0.3212\n",
+ "Sum squared residual: 10647.015 F-statistic : 12.3582\n",
+ "Sigma-square : 231.457 Prob(F-statistic) : 5.064e-05\n",
+ "S.E. of regression : 15.214 Log likelihood : -201.368\n",
+ "Sigma-square ML : 217.286 Akaike info criterion : 408.735\n",
+ "S.E of regression ML: 14.7406 Schwarz criterion : 414.411\n",
+ "\n",
+ "------------------------------------------------------------------------------------\n",
+ " Variable Coefficient Std.Error t-Statistic Probability\n",
+ "------------------------------------------------------------------------------------\n",
+ " CONSTANT 46.4281827 13.1917570 3.5194844 0.0009867\n",
+ " var_1 0.6289840 0.5359104 1.1736736 0.2465669\n",
+ " var_2 -0.4848885 0.1826729 -2.6544086 0.0108745\n",
+ "------------------------------------------------------------------------------------\n",
+ "\n",
+ "REGRESSION DIAGNOSTICS\n",
+ "MULTICOLLINEARITY CONDITION NUMBER 12.538\n",
+ "\n",
+ "TEST ON NORMALITY OF ERRORS\n",
+ "TEST DF VALUE PROB\n",
+ "Jarque-Bera 2 39.706 0.0000\n",
+ "\n",
+ "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
+ "RANDOM COEFFICIENTS\n",
+ "TEST DF VALUE PROB\n",
+ "Breusch-Pagan test 2 5.767 0.0559\n",
+ "Koenker-Bassett test 2 2.270 0.3214\n",
+ "================================ END OF REPORT =====================================\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(handled_eq.summary)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "That means `HOVAL`, `CRIME`, and `INC` all get drawn out of the dataframe using patsy and pushed into arrays. This works for any class, since we're just turning the equations into their consituent arrays. \n",
+ "\n",
+ "Where there is a possible bikeshedding point is over the syntax for TSLS-type models. Right now, I have it specified with (what I think is) a clear synatx reflecting the simultanous equations approach: \n",
+ "\n",
+ "`y ~ x1 + x2 || yend ~ xend1 + xend2`\n",
+ "\n",
+ "implies an equation where your exogenous relationship is `y ~ x1 + x2` and your endogenous relationship is `yend ~ xend1 + xend2`. \n",
+ "\n",
+ "For any simultaneous equation-type model, I would suggest using double pipe as the separator. Under the hood, I'm just using `string.split('||')`, since patsy doesn't use the double pipe. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "y = dbf.by_col_array(['CRIME'])\n",
+ "X = dbf.by_col_array(['INC'])\n",
+ "yend = dbf.by_col_array(['HOVAL'])\n",
+ "q = dbf.by_col_array(['DISCBD'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "tsls = ps.spreg.TSLS(y,X,yend,q,W)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "handledtsls = h.Model(y,X,yend,q,W,mtype='TSLS')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "handledtsls_eq = h.Model(\"CRIME ~ INC || HOVAL ~ DISCBD\", W, data=df, mtype='TSLS')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "REGRESSION\n",
+ "----------\n",
+ "SUMMARY OF OUTPUT: TWO STAGE LEAST SQUARES\n",
+ "------------------------------------------\n",
+ "Data set : unknown\n",
+ "Weights matrix : unknown\n",
+ "Dependent Variable : dep_var Number of Observations: 49\n",
+ "Mean dependent var : 35.1288 Number of Variables : 3\n",
+ "S.D. dependent var : 16.7321 Degrees of Freedom : 46\n",
+ "Pseudo R-squared : 0.2794\n",
+ "\n",
+ "------------------------------------------------------------------------------------\n",
+ " Variable Coefficient Std.Error z-Statistic Probability\n",
+ "------------------------------------------------------------------------------------\n",
+ " CONSTANT 88.4657958 15.1346096 5.8452645 0.0000000\n",
+ " endogenous_1 -1.5821659 0.7931892 -1.9946891 0.0460768\n",
+ " var_1 0.5200379 1.4146781 0.3676016 0.7131703\n",
+ "------------------------------------------------------------------------------------\n",
+ "Instrumented: endogenous_1\n",
+ "Instruments: instrument_1\n",
+ "================================ END OF REPORT =====================================\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(tsls.summary)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "REGRESSION\n",
+ "----------\n",
+ "SUMMARY OF OUTPUT: TWO STAGE LEAST SQUARES\n",
+ "------------------------------------------\n",
+ "Data set : unknown\n",
+ "Weights matrix : unknown\n",
+ "Dependent Variable : dep_var Number of Observations: 49\n",
+ "Mean dependent var : 35.1288 Number of Variables : 3\n",
+ "S.D. dependent var : 16.7321 Degrees of Freedom : 46\n",
+ "Pseudo R-squared : 0.2794\n",
+ "\n",
+ "------------------------------------------------------------------------------------\n",
+ " Variable Coefficient Std.Error z-Statistic Probability\n",
+ "------------------------------------------------------------------------------------\n",
+ " CONSTANT 88.4657958 15.1346096 5.8452645 0.0000000\n",
+ " endogenous_1 -1.5821659 0.7931892 -1.9946891 0.0460768\n",
+ " var_1 0.5200379 1.4146781 0.3676016 0.7131703\n",
+ "------------------------------------------------------------------------------------\n",
+ "Instrumented: endogenous_1\n",
+ "Instruments: instrument_1\n",
+ "================================ END OF REPORT =====================================\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(handledtsls.summary)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "REGRESSION\n",
+ "----------\n",
+ "SUMMARY OF OUTPUT: TWO STAGE LEAST SQUARES\n",
+ "------------------------------------------\n",
+ "Data set : unknown\n",
+ "Weights matrix : unknown\n",
+ "Dependent Variable : dep_var Number of Observations: 49\n",
+ "Mean dependent var : 35.1288 Number of Variables : 3\n",
+ "S.D. dependent var : 16.7321 Degrees of Freedom : 46\n",
+ "Pseudo R-squared : 0.2794\n",
+ "\n",
+ "------------------------------------------------------------------------------------\n",
+ " Variable Coefficient Std.Error z-Statistic Probability\n",
+ "------------------------------------------------------------------------------------\n",
+ " CONSTANT 88.4657958 15.1346096 5.8452645 0.0000000\n",
+ " endogenous_1 -1.5821659 0.7931892 -1.9946891 0.0460768\n",
+ " var_1 0.5200379 1.4146781 0.3676016 0.7131703\n",
+ "------------------------------------------------------------------------------------\n",
+ "Instrumented: endogenous_1\n",
+ "Instruments: instrument_1\n",
+ "================================ END OF REPORT =====================================\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(handledtsls_eq.summary)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This would also enable adding plotting capabilities to spatial regression models, like the standard four-plot output from plotting an `lm` in `R`, but wouldn't have to be hacked into each and every model class. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Pitfalls"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This wrapper is neat, but could lead us down a weird road.\n",
+ "\n",
+ "Right now, I clean up the mandatory arguments tuple `*args` such that only the arguments that match the type signature needed by PySAL get passed. This is to prevent runtime errors.\n",
+ "\n",
+ "To explain, let some function be defined using the default Python argument expansion (a.k.a. splatting) operator, `*`:\n",
+ "\n",
+ " def foo(*args, message='Hello'):\n",
+ " for arg in args:\n",
+ " print(message + args)\n",
+ " \n",
+ " >>> foo(\"world!\", \"users!\")\n",
+ " Hello world!\n",
+ " Hello users!\n",
+ " \n",
+ "Cool. Now, let's say we want to print our message right before we execute some function, `bar`. `bar` is a model class with a well-defined API. It requires two positional arguments of type `ndarray`, `y` and `X`, can accept an arbitrary number of keyword arguments, and returns `True` if a model is fit correctly. \n",
+ "\n",
+ "For the sake of argument (no pun intended), let's say that `foo` only needs the strings passed as positional arguments. Depending on your python version, we can wrap `bar` with `foo` this way:\n",
+ "\n",
+ " def foo(*args, message='Hello', **kwargs):\n",
+ " strargs = [arg for arg in args if isinstance(args, str)]\n",
+ " barargs = [arg for arg in args if arg not in strargs]\n",
+ " for arg in args:\n",
+ " print(message + arg)\n",
+ " return bar(*args, **kwargs)\n",
+ " \n",
+ "If we do this, arbitrary arguments could get passed to `foo` that, if `bar` isn't expecting them, will cause a `TypeError`. \n",
+ "\n",
+ " >>> foo(\"world!\", \"users!\", 12313, y, X)\n",
+ " Hello world!\n",
+ " Hello users!\n",
+ " -------------------------------------------\n",
+ " TypeError\n",
+ " return bar(*args, **kwargs)\n",
+ " \n",
+ " TypeError: bar() takes exactly 2 arguments (3 given)\n",
+ " \n",
+ "Some libraries (*ahem* Matplotlib) get around this by making almost every function take arbitrary arguments, and each function just peels off of `args` and `kwargs` what it needs. \n",
+ "\n",
+ "This isn't us, though. I would suggest that the standard be that we use input types or ordering of arguments to define how to wrap the underlying functions. This means that, if we know `bar` accepts a `y` and an `X` of type `ndarray` in that order, we pop off of a stack of arguments with the current scope's arguments at the top of the stack.\n",
+ "\n",
+ "Here, since we know that we're wrapping one function with two arguments, its arguments are at the bottom of the stack. \n",
+ "\n",
+ " def foo(*args, message=\"Hello\", **kwargs):\n",
+ " args.pop() = X\n",
+ " args.pop() = y\n",
+ " for arg in args:\n",
+ " print(message + arg)\n",
+ " return bar(y, X, **kwargs)\n",
+ " \n",
+ " >>> foo(\"world!\", \"users!\", y, X)\n",
+ " Hello world!\n",
+ " Hello users!\n",
+ " True #model fit successfully\n",
+ " \n",
+ "Of course, we could also get even more abstract. We could construct a list of arguments for the wrapped function based on the *types* expected by that function. If we can identify or construct subsequences of *args* that match the function we're wrapping, we're good to go. \n",
+ "\n",
+ "However, these strategies have the strange side effect that, if extra arguments are passed, no error is raised. This could be desirable, but may not be if the user expects the arguments to **do** something. \n",
+ "\n",
+ "The call with ignored arguments looks like this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "handledtsls_ignored = h.Model(\"CRIME ~ INC || HOVAL ~ DISCBD\",\n",
+ " True, #gets ignored\n",
+ " W, \n",
+ " 42, #gets ignored too\n",
+ " data=df, \n",
+ " mtype='GM_Lag')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "REGRESSION\n",
+ "----------\n",
+ "SUMMARY OF OUTPUT: SPATIAL TWO STAGE LEAST SQUARES\n",
+ "--------------------------------------------------\n",
+ "Data set : unknown\n",
+ "Weights matrix : unknown\n",
+ "Dependent Variable : dep_var Number of Observations: 49\n",
+ "Mean dependent var : 35.1288 Number of Variables : 4\n",
+ "S.D. dependent var : 16.7321 Degrees of Freedom : 45\n",
+ "Pseudo R-squared : 0.2377\n",
+ "Spatial Pseudo R-squared: 0.2477\n",
+ "\n",
+ "------------------------------------------------------------------------------------\n",
+ " Variable Coefficient Std.Error z-Statistic Probability\n",
+ "------------------------------------------------------------------------------------\n",
+ " CONSTANT 96.5182979 52.5305917 1.8373731 0.0661548\n",
+ " W_dep_var -0.0148182 0.0857203 -0.1728667 0.8627562\n",
+ " endogenous_1 -1.8097627 1.7652028 -1.0252436 0.3052483\n",
+ " var_1 0.7561942 2.3367036 0.3236158 0.7462289\n",
+ "------------------------------------------------------------------------------------\n",
+ "Instrumented: W_dep_var, endogenous_1\n",
+ "Instruments: W_instrument_1, W_var_1, instrument_1\n",
+ "================================ END OF REPORT =====================================\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(handledtsls_ignored.summary) #woah! no error or notice!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Those other positional arguments get ignored in the equation framework. \n",
+ "\n",
+ "Practically, this means you couldn't provide some variables in equations and some in vectors... you'd have to either have equations or vectors."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/handler/__init__.py b/pysal/contrib/handler/__init__.py
new file mode 100644
index 0000000..d08b80f
--- /dev/null
+++ b/pysal/contrib/handler/__init__.py
@@ -0,0 +1 @@
+from handler import *
diff --git a/pysal/contrib/handler/handler.py b/pysal/contrib/handler/handler.py
new file mode 100644
index 0000000..bb1f08c
--- /dev/null
+++ b/pysal/contrib/handler/handler.py
@@ -0,0 +1,101 @@
+import copy
+import registry as sr
+from pysal.weights import W
+try:
+ import patsy as p
+except:
+ p = None
+from numpy import array, ndarray, asarray
+from six import iteritems as diter
+
+#would like to just wrap this in the opt decorator...
+def pandashandler(formula_like, data):
+ """
+ process a pysal model signature and convert an equation/formula pair into a
+ pysal-specific object
+ """
+ if '||' in formula_like:
+ mu, inst = formula_like.split('||')
+ y, X = p.dmatrices(mu + '-1' , data=data)
+ yend, q = p.dmatrices(inst + '-1', data=data)
+ rargs = [y,X,yend,q]
+ rargs = [asarray(i) for i in rargs]
+ else:
+ y, X = p.dmatrices(formula_like + '-1', data=data)
+ rargs = [asarray(y), asarray(X)]
+
+ return rargs
+
+#def model(eqn, *args, data=df, **kwargs)
+class Model(object):
+ """
+ The model manager
+
+ arguments that sit above the pysal model API:
+
+ mtype : string mapping to the function called from spreg
+ fit : Bool denoting whether to actually apply the mtype to the data provided
+ immediately.
+
+ an example call would look like:
+
+ >>> Model(y,X,W, mtype='ML_Lag')
+ >>> Model(y,X,W, mytpe='OLS_Regimes')
+ """
+ def __init__(self, *args, **kwargs):
+ self._cache = {}
+ mtype = kwargs.pop('mtype', 'OLS')
+ self._mtype = mtype
+ self._mfunc = sr._everything[mtype]
+ self._fit = kwargs.pop('fit', True)
+
+ if isinstance(args[0], str):
+ formula = args[0]
+ data = kwargs.pop('data')
+ matrices = pandashandler(formula, data)
+ elif 'formula' in kwargs.keys() and 'data' in kwargs.keys():
+ formula = kwargs.pop('formula')
+ data = kwargs.pop('data')
+ matrices = pandashandler(formula, data)
+ else:
+ matrices = [arg for arg in args if not isinstance(arg, W)]
+
+ args = matrices + [arg for arg in args if isinstance(arg, W)]
+
+ if self._fit:
+ self._called = self._mfunc(*args, **kwargs)
+ for name in dir(self._called):
+ try:
+ exec('self.{n} = self._called.{n}'.format(n=name))
+ except:
+ print("Assigning {a} from {s} to {d} failed!".format(a=name,
+ s=self._called,
+ d=self))
+
+#need to still pass names down from formula into relevant pysal arguments
+
+if __name__ == '__main__':
+ import pysal as ps
+
+ dbf = ps.open(ps.examples.get_path('columbus.dbf'))
+ y, X = dbf.by_col_array(['HOVAL']), dbf.by_col_array(['INC', 'CRIME'])
+ Wcol = ps.open(ps.examples.get_path('columbus.gal')).read()
+ mod1 = sr.OLS(y,X)
+ hmod1 = Model(y,X)
+
+ mod2 = sr.OLS(y,X,Wcol)
+ hmod2 = Model(y,X,Wcol)
+
+ mod3 = sr.ML_Lag(y,X,Wcol)
+ hmod3 = Model(y,X,Wcol, mtype='ML_Lag')
+
+ mod4 = sr.ML_Error(y,X,Wcol)
+ hmod4 = Model(y,X,Wcol,mtype='ML_Error')
+
+ #real power comes from this, though
+# import geopandas as gpd
+#
+# df = gpd.read_file(ps.examples.get_path('columbus.dbf'))
+#
+# hmod1_pd = Model('HOVAL ~ INC + CRIME', data=data)
+# mod5 = sr.TSLS('HOVAL ~ INC + CRIME')
diff --git a/pysal/contrib/handler/registry.py b/pysal/contrib/handler/registry.py
new file mode 100644
index 0000000..8f25f89
--- /dev/null
+++ b/pysal/contrib/handler/registry.py
@@ -0,0 +1,55 @@
+import pysal
+from types import ModuleType, ClassType
+from six import iteritems as diter
+
+def _find_bcs():
+ classes = dict()
+ bcs = dict()
+ ucs = dict()
+ submods = dict()
+ for obname in dir(pysal.spreg):
+ ob = pysal.spreg.__dict__[obname]
+ if isinstance(ob, ModuleType):
+ if ob.__package__.startswith("pysal"):
+ submods.update({obname:ob})
+ elif isinstance(ob, ClassType):
+ classes.update({obname:ob})
+ if ob.__name__.startswith('Base'):
+ bcs.update({obname:ob})
+ for modname, mod in diter(submods):
+ basecands = dict()
+ for clname in dir(mod):
+ cl = mod.__dict__[clname]
+ if isinstance(cl, ClassType):
+ try:
+ if cl.__name__.startswith('Base'):
+ if cl not in bcs:
+ bcs.update({cl.__name__:cl})
+ else:
+ classes.update({cl.__name__:cl})
+ except:
+ pass
+ ucs.update({k:v for k,v in diter(classes) if (
+ any([issubclass(v, bc) for bc in bcs.values()])
+ and (k not in bcs))
+ or k.endswith('Regimes')})
+ return bcs, ucs
+
+base, user = _find_bcs()
+_everything = base.copy()
+_everything.update(user)
+
+for k,v in diter(base):
+ exec('{k} = {v}'.format(k=k,v=v))
+for k,v in diter(user):
+ exec('{k} = {v}'.format(k=k,v=v))
+
+__all__ = list()
+__all__.extend(base.keys())
+__all__.extend(user.keys())
+#regimes = {cls for cls in user if 'regimes' in cls.__module__}
+
+#if we go with something like a "base" and "user" submodule setup,
+#it'd be as simple as flattening the subclasses out into those submodules.
+#for name, cl in diter(_find_bcs()[0]):
+# exec('{n} = {cls}'.format(n=name, cls=cl))
diff --git a/pysal/spreg/tests/test_diagnostics.py b/pysal/contrib/handler/tests/test_diagnostics.py
similarity index 94%
copy from pysal/spreg/tests/test_diagnostics.py
copy to pysal/contrib/handler/tests/test_diagnostics.py
index 1904b58..97ef0cf 100644
--- a/pysal/spreg/tests/test_diagnostics.py
+++ b/pysal/contrib/handler/tests/test_diagnostics.py
@@ -2,8 +2,9 @@ import unittest
import numpy as np
import pysal
from pysal.spreg import diagnostics
-from pysal.spreg.ols import OLS
-
+from pysal.spreg.ols import OLS
+import pysal.contrib.handler as h
+from pysal.contrib.pdutilities.dbf_utilities import dbf2df
# create regression object used by all the tests below
db = pysal.open(pysal.examples.get_path("columbus.dbf"), "r")
@@ -13,7 +14,11 @@ X = []
X.append(db.by_col("INC"))
X.append(db.by_col("HOVAL"))
X = np.array(X).T
-reg = OLS(y,X)
+
+df = dbf2df(pysal.examples.get_path('columbus.dbf'))
+formula = 'CRIME ~ INC + HOVAL'
+
+reg = h.Model(formula, data=df)
class TestFStat(unittest.TestCase):
diff --git a/pysal/spreg/tests/test_diagnostics_sp.py b/pysal/contrib/handler/tests/test_diagnostics_sp.py
similarity index 95%
copy from pysal/spreg/tests/test_diagnostics_sp.py
copy to pysal/contrib/handler/tests/test_diagnostics_sp.py
index 3d9b8ff..fac2a6a 100644
--- a/pysal/spreg/tests/test_diagnostics_sp.py
+++ b/pysal/contrib/handler/tests/test_diagnostics_sp.py
@@ -1,10 +1,15 @@
import unittest
import numpy as np
import pysal
+from pysal.contrib.handler import Model
+from functools import partial
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.ols import OLS as OLS
+OLS = Model
+#from pysal.spreg.twosls import TSLS as TSLS
+TSLS = partial(Model, mtype='TSLS')
+#from pysal.spreg.twosls_sp import GM_Lag
+GM_Lag = partial(Model, mtype='GM_Lag')
from pysal.spreg.diagnostics_sp import LMtests, MoranRes, spDcache, AKtest
diff --git a/pysal/spreg/tests/test_diagnostics_tsls.py b/pysal/contrib/handler/tests/test_diagnostics_tsls.py
similarity index 86%
copy from pysal/spreg/tests/test_diagnostics_tsls.py
copy to pysal/contrib/handler/tests/test_diagnostics_tsls.py
index 4423b33..cba311c 100644
--- a/pysal/spreg/tests/test_diagnostics_tsls.py
+++ b/pysal/contrib/handler/tests/test_diagnostics_tsls.py
@@ -1,11 +1,16 @@
import unittest
import numpy as np
import pysal
+from pysal.contrib.handler import Model
+from functools import partial
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 pysal.spreg.ols import OLS as OLS
+OLS = Model
+#from pysal.spreg.twosls import TSLS as TSLS
+TSLS = partial(Model, mtype='TSLS')
+#from pysal.spreg.twosls_sp import GM_Lag
+GM_Lag = partial(Model, mtype='GM_Lag')
from scipy.stats import pearsonr
diff --git a/pysal/spreg/tests/test_error_sp.py b/pysal/contrib/handler/tests/test_error_sp.py
similarity index 61%
copy from pysal/spreg/tests/test_error_sp.py
copy to pysal/contrib/handler/tests/test_error_sp.py
index 1aba6db..9486e1f 100644
--- a/pysal/spreg/tests/test_error_sp.py
+++ b/pysal/contrib/handler/tests/test_error_sp.py
@@ -3,6 +3,16 @@ import scipy
import pysal
import numpy as np
from pysal.spreg import error_sp as SP
+from pysal.contrib.handler import Model
+from functools import partial
+BaseGM_Error = partial(Model, mtype='BaseGM_Error')
+GM_Error = partial(Model, mtype='GM_Error')
+
+BaseGM_Endog_Error = partial(Model, mtype='BaseGM_Endog_Error')
+GM_Endog_Error = partial(Model, mtype='GM_Endog_Error')
+
+BaseGM_Combo = partial(Model, mtype='BaseGM_Combo')
+GM_Combo = partial(Model, mtype='GM_Combo')
class TestBaseGMError(unittest.TestCase):
def setUp(self):
@@ -18,33 +28,33 @@ class TestBaseGMError(unittest.TestCase):
self.w.transform = 'r'
def test_model(self):
- reg = SP.BaseGM_Error(self.y, self.X, self.w.sparse)
+ reg = 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)
+ np.testing.assert_allclose(reg.betas,betas,4)
u = np.array([ 27.4739775])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,4)
predy = np.array([ 52.9930255])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,4)
n = 49
- self.assertAlmostEqual(reg.n,n,4)
+ np.testing.assert_allclose(reg.n,n,4)
k = 3
- self.assertAlmostEqual(reg.k,k,4)
+ np.testing.assert_allclose(reg.k,k,4)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,4)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ np.testing.assert_allclose(reg.x[0],x,4)
e = np.array([ 31.89620319])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e,4)
predy = np.array([ 52.9930255])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,4)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.vm,vm,4)
sig2 = 191.73716465732355
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,4)
class TestGMError(unittest.TestCase):
def setUp(self):
@@ -59,39 +69,39 @@ class TestGMError(unittest.TestCase):
self.w.transform = 'r'
def test_model(self):
- reg = SP.GM_Error(self.y, self.X, self.w)
+ reg = 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)
+ np.testing.assert_allclose(reg.betas,betas,4)
u = np.array([ 27.4739775])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,4)
predy = np.array([ 52.9930255])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,4)
n = 49
- self.assertAlmostEqual(reg.n,n,4)
+ np.testing.assert_allclose(reg.n,n,4)
k = 3
- self.assertAlmostEqual(reg.k,k,4)
+ np.testing.assert_allclose(reg.k,k,4)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,4)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ np.testing.assert_allclose(reg.x[0],x,4)
e = np.array([ 31.89620319])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e,4)
predy = np.array([ 52.9930255])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,4)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.vm,vm,4)
sig2 = 191.73716465732355
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,4)
pr2 = 0.3495097406012179
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.z_stat,z_stat,4)
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Maximum Likelihood requires SciPy version 11 or newer.")
@@ -114,39 +124,39 @@ class TestBaseGMEndogError(unittest.TestCase):
self.w.transform = 'r'
def test_model(self):
- reg = SP.BaseGM_Endog_Error(self.y, self.X, self.yd, self.q, self.w.sparse)
+ reg = 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)
+ np.testing.assert_allclose(reg.betas,betas,4)
u = np.array([ 26.55951566])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,4)
e = np.array([ 31.23925425])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e,4)
predy = np.array([ 53.9074875])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,4)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n)
k = 3
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,4)
x = np.array([ 1. , 19.531])
- np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ np.testing.assert_allclose(reg.x[0],x,4)
yend = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ np.testing.assert_allclose(reg.yend[0],yend,4)
z = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.z[0],z,4)
+ np.testing.assert_allclose(reg.z[0],z,4)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
#std_y
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.vm,vm,4)
sig2 = 192.50022721929574
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,4)
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Maximum Likelihood requires SciPy version 11 or newer.")
@@ -168,43 +178,43 @@ class TestGMEndogError(unittest.TestCase):
self.w.transform = 'r'
def test_model(self):
- reg = SP.GM_Endog_Error(self.y, self.X, self.yd, self.q, self.w)
+ reg = 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)
+ np.testing.assert_allclose(reg.betas,betas,4)
u = np.array([ 26.55951566])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,4)
e = np.array([ 31.23925425])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e,4)
predy = np.array([ 53.9074875])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,4)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n)
k = 3
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,4)
x = np.array([ 1. , 19.531])
- np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ np.testing.assert_allclose(reg.x[0],x,4)
yend = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ np.testing.assert_allclose(reg.yend[0],yend,4)
z = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.z[0],z,4)
+ np.testing.assert_allclose(reg.z[0],z,4)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.vm,vm,4)
pr2 = 0.346472557570858
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(reg.pr2,pr2)
sig2 = 192.50022721929574
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.z_stat,z_stat,4)
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Maximum Likelihood requires SciPy version 11 or newer.")
@@ -224,36 +234,36 @@ class TestBaseGMCombo(unittest.TestCase):
# 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)
+ reg = 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)
+ np.testing.assert_allclose(reg.betas,betas,4)
u = np.array([ 25.57932637])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,4)
e_filtered = np.array([ 31.65374945])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e_filtered,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e_filtered,4)
predy = np.array([ 54.88767663])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,4)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n)
k = 4
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,4)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ np.testing.assert_allclose(reg.x[0],x,4)
yend = np.array([ 35.4585005])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.z[0],z,4)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(np.diag(reg.vm),vm,4)
sig2 = 181.78650186468832
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,4)
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Maximum Likelihood requires SciPy version 11 or newer.")
@@ -270,48 +280,48 @@ class TestGMCombo(unittest.TestCase):
self.w.transform = 'r'
def test_model(self):
# Only spatial lag
- reg = SP.GM_Combo(self.y, self.X, w=self.w)
+ reg = 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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.betas,betas,4)
u = np.array([ 25.57932637])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,4)
e_filtered = np.array([ 31.65374945])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e_filtered,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e_filtered,4)
predy = np.array([ 54.88767685])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,4)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n)
k = 4
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,4)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ np.testing.assert_allclose(reg.x[0],x,4)
yend = np.array([ 35.4585005])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.z[0],z,4)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(np.diag(reg.vm),vm,4)
sig2 = 181.78650186468832
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,4)
pr2 = 0.3018280166937799
- self.assertAlmostEqual(reg.pr2,pr2,4)
+ np.testing.assert_allclose(reg.pr2,pr2,4)
pr2_e = 0.3561355586759414
- self.assertAlmostEqual(reg.pr2_e,pr2_e,4)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.z_stat,z_stat,4)
if __name__ == '__main__':
unittest.main()
diff --git a/pysal/spreg/tests/test_error_sp_regimes.py b/pysal/contrib/handler/tests/test_error_sp_regimes.py
similarity index 90%
copy from pysal/spreg/tests/test_error_sp_regimes.py
copy to pysal/contrib/handler/tests/test_error_sp_regimes.py
index 0754c72..5affcaf 100644
--- a/pysal/spreg/tests/test_error_sp_regimes.py
+++ b/pysal/contrib/handler/tests/test_error_sp_regimes.py
@@ -2,8 +2,19 @@ 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
+#from pysal.spreg import error_sp_regimes as SP
+#from pysal.spreg.error_sp import GM_Error, GM_Endog_Error, GM_Combo
+
+from functools import partial
+from pysal.contrib.handler import Model
+
+GM_Error = partial(Model, mtype='GM_Error')
+GM_Endog_Error = partial(Model, mtype='GM_Endog_Error')
+GM_Combo = partial(Model, mtype='GM_Combo')
+
+GM_Error_Regimes = partial(Model, mtype='GM_Error_Regimes')
+GM_Endog_Error_Regimes = partial(Model, mtype='GM_Endog_Error_Regimes')
+GM_Combo_Regimes = partial(Model, mtype='GM_Combo_Regimes')
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Maximum Likelihood requires SciPy version 11 or newer.")
@@ -45,7 +56,7 @@ class TestGM_Error_Regimes(unittest.TestCase):
self.w_a1.transform='r'
def test_model(self):
- reg = SP.GM_Error_Regimes(self.y, self.X, self.regimes, self.w)
+ reg = GM_Error_Regimes(self.y, self.X, self.regimes, self.w)
betas = np.array([[ 63.3443073 ],
[ -0.15468 ],
[ -1.52186509],
@@ -91,7 +102,7 @@ class TestGM_Error_Regimes(unittest.TestCase):
"""
def test_model_regi_error(self):
#Columbus:
- reg = SP.GM_Error_Regimes(self.y, self.X, self.regimes, self.w, regime_err_sep=True)
+ reg = GM_Error_Regimes(self.y, self.X, self.regimes, self.w, regime_err_sep=True)
betas = np.array([[ 60.45730439],
[ -0.17732134],
[ -1.30936328],
@@ -117,7 +128,7 @@ class TestGM_Error_Regimes(unittest.TestCase):
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)
+ model = 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))
@@ -126,7 +137,7 @@ class TestGM_Error_Regimes(unittest.TestCase):
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)
+ reg = 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)
@@ -178,7 +189,7 @@ class TestGM_Error_Regimes(unittest.TestCase):
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)
+ reg = 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],
@@ -205,7 +216,7 @@ class TestGM_Error_Regimes(unittest.TestCase):
#self.assertAlmostEqual(reg.chow.joint[0],chow_j)
np.testing.assert_allclose(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)
+ model = 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))
@@ -214,7 +225,7 @@ class TestGM_Error_Regimes(unittest.TestCase):
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)
+ reg = 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],
@@ -270,7 +281,7 @@ class TestGM_Error_Regimes(unittest.TestCase):
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)
+ reg = 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 ],
@@ -299,7 +310,7 @@ class TestGM_Error_Regimes(unittest.TestCase):
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)
+ model = 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))
diff --git a/pysal/spreg/tests/test_error_sp_sparse.py b/pysal/contrib/handler/tests/test_error_sp_sparse.py
similarity index 62%
copy from pysal/spreg/tests/test_error_sp_sparse.py
copy to pysal/contrib/handler/tests/test_error_sp_sparse.py
index d60c8eb..fb59ab6 100644
--- a/pysal/spreg/tests/test_error_sp_sparse.py
+++ b/pysal/contrib/handler/tests/test_error_sp_sparse.py
@@ -1,10 +1,20 @@
import unittest
import pysal
import numpy as np
-from pysal.spreg import error_sp as SP
+#from pysal.spreg import error_sp as SP
import scipy
from scipy import sparse
+from functools import partial
+from pysal.contrib.handler import Model
+
+GM_Error = partial(Model, mtype='GM_Error')
+GM_Endog_Error = partial(Model, mtype='GM_Endog_Error')
+GM_Combo = partial(Model, mtype='GM_Combo')
+BaseGM_Error = partial(Model, mtype='BaseGM_Error')
+BaseGM_Endog_Error = partial(Model, mtype='BaseGM_Endog_Error')
+BaseGM_Combo = partial(Model, mtype='BaseGM_Combo')
+
class TestBaseGMError(unittest.TestCase):
def setUp(self):
db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
@@ -20,33 +30,33 @@ class TestBaseGMError(unittest.TestCase):
self.w.transform = 'r'
def test_model(self):
- reg = SP.BaseGM_Error(self.y, self.X, self.w.sparse)
+ reg = 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)
+ np.allclose(reg.betas,betas,4)
u = np.array([ 27.4739775])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.allclose(reg.u[0],u,4)
predy = np.array([ 52.9930255])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.allclose(reg.predy[0],predy,4)
n = 49
- self.assertAlmostEqual(reg.n,n,4)
+ np.allclose(reg.n,n,4)
k = 3
- self.assertAlmostEqual(reg.k,k,4)
+ np.allclose(reg.k,k,4)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.allclose(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)
+ np.allclose(reg.x.toarray()[0],x,4)
e = np.array([ 31.89620319])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.allclose(reg.e_filtered[0],e,4)
predy = np.array([ 52.9930255])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.allclose(reg.predy[0],predy,4)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.allclose(reg.mean_y,my)
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.allclose(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)
+ np.allclose(reg.vm,vm,4)
sig2 = 191.73716465732355
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.allclose(reg.sig2,sig2,4)
class TestGMError(unittest.TestCase):
def setUp(self):
@@ -62,39 +72,39 @@ class TestGMError(unittest.TestCase):
self.w.transform = 'r'
def test_model(self):
- reg = SP.GM_Error(self.y, self.X, self.w)
+ reg = 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)
+ np.allclose(reg.betas,betas,4)
u = np.array([ 27.4739775])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.allclose(reg.u[0],u,4)
predy = np.array([ 52.9930255])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.allclose(reg.predy[0],predy,4)
n = 49
- self.assertAlmostEqual(reg.n,n,4)
+ np.allclose(reg.n,n,4)
k = 3
- self.assertAlmostEqual(reg.k,k,4)
+ np.allclose(reg.k,k,4)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.allclose(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)
+ np.allclose(reg.x.toarray()[0],x,4)
e = np.array([ 31.89620319])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.allclose(reg.e_filtered[0],e,4)
predy = np.array([ 52.9930255])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.allclose(reg.predy[0],predy,4)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.allclose(reg.mean_y,my)
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.allclose(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)
+ np.allclose(reg.vm,vm,4)
sig2 = 191.73716465732355
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.allclose(reg.sig2,sig2,4)
pr2 = 0.3495097406012179
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.allclose(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)
+ np.allclose(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)
+ np.allclose(reg.z_stat,z_stat,4)
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Maximum Likelihood requires SciPy version 11 or newer.")
@@ -118,39 +128,39 @@ class TestBaseGMEndogError(unittest.TestCase):
self.w.transform = 'r'
def test_model(self):
- reg = SP.BaseGM_Endog_Error(self.y, self.X, self.yd, self.q, self.w.sparse)
+ reg = 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)
+ np.allclose(reg.betas,betas,4)
u = np.array([ 26.55951566])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.allclose(reg.u[0],u,4)
e = np.array([ 31.23925425])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.allclose(reg.e_filtered[0],e,4)
predy = np.array([ 53.9074875])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.allclose(reg.predy[0],predy,4)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.allclose(reg.n,n)
k = 3
- self.assertAlmostEqual(reg.k,k)
+ np.allclose(reg.k,k)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.allclose(reg.y[0],y,4)
x = np.array([ 1. , 19.531])
- np.testing.assert_array_almost_equal(reg.x.toarray()[0],x,4)
+ np.allclose(reg.x.toarray()[0],x,4)
yend = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ np.allclose(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)
+ np.allclose(reg.z.toarray()[0],z,4)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.allclose(reg.mean_y,my)
#std_y
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.allclose(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)
+ np.allclose(reg.vm,vm,4)
sig2 = 192.5002
- self.assertAlmostEqual(round(reg.sig2,4),round(sig2,4),4)
+ np.allclose(round(reg.sig2,4),round(sig2,4),4)
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Maximum Likelihood requires SciPy version 11 or newer.")
@@ -173,43 +183,43 @@ class TestGMEndogError(unittest.TestCase):
self.w.transform = 'r'
def test_model(self):
- reg = SP.GM_Endog_Error(self.y, self.X, self.yd, self.q, self.w)
+ reg = 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)
+ np.allclose(reg.betas,betas,4)
u = np.array([ 26.55951566])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.allclose(reg.u[0],u,4)
e = np.array([ 31.23925425])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.allclose(reg.e_filtered[0],e,4)
predy = np.array([ 53.9074875])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.allclose(reg.predy[0],predy,4)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.allclose(reg.n,n)
k = 3
- self.assertAlmostEqual(reg.k,k)
+ np.allclose(reg.k,k)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.allclose(reg.y[0],y,4)
x = np.array([ 1. , 19.531])
- np.testing.assert_array_almost_equal(reg.x.toarray()[0],x,4)
+ np.allclose(reg.x.toarray()[0],x,4)
yend = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ np.allclose(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)
+ np.allclose(reg.z.toarray()[0],z,4)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.allclose(reg.mean_y,my)
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.allclose(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)
+ np.allclose(reg.vm,vm,4)
pr2 = 0.346472557570858
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.allclose(reg.pr2,pr2)
sig2 = 192.5002
- self.assertAlmostEqual(round(reg.sig2,4),round(sig2,4),4)
+ np.allclose(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)
+ np.allclose(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)
+ np.allclose(reg.z_stat,z_stat,4)
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Maximum Likelihood requires SciPy version 11 or newer.")
@@ -230,38 +240,38 @@ class TestBaseGMCombo(unittest.TestCase):
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)
+ reg = 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)
+ np.allclose(reg.betas,betas,4)
u = np.array([ 25.57932637])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.allclose(reg.u[0],u,4)
e_filtered = np.array([ 31.65374945])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e_filtered,4)
+ np.allclose(reg.e_filtered[0],e_filtered,4)
predy = np.array([ 54.88767663])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.allclose(reg.predy[0],predy,4)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.allclose(reg.n,n)
k = 4
- self.assertAlmostEqual(reg.k,k)
+ np.allclose(reg.k,k)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.allclose(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)
+ np.allclose(reg.x.toarray()[0],x,4)
yend = np.array([ 35.4585005])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ np.allclose(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)
+ np.allclose(reg.z.toarray()[0],z,4)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.allclose(reg.mean_y,my)
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.allclose(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)
+ np.allclose(reg.vm,vm,4)
sig2 = 181.78650186468832
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.allclose(reg.sig2,sig2,4)
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Maximum Likelihood requires SciPy version 11 or newer.")
@@ -279,51 +289,51 @@ class TestGMCombo(unittest.TestCase):
self.w.transform = 'r'
def test_model(self):
# Only spatial lag
- reg = SP.GM_Combo(self.y, self.X, w=self.w)
+ reg = 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)
+ np.allclose(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)
+ np.allclose(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)
+ np.allclose(reg.betas,betas,4)
u = np.array([ 25.57932637])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.allclose(reg.u[0],u,4)
e_filtered = np.array([ 31.65374945])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e_filtered,4)
+ np.allclose(reg.e_filtered[0],e_filtered,4)
predy = np.array([ 54.88767685])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.allclose(reg.predy[0],predy,4)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.allclose(reg.n,n)
k = 4
- self.assertAlmostEqual(reg.k,k)
+ np.allclose(reg.k,k)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.allclose(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)
+ np.allclose(reg.x.toarray()[0],x,4)
yend = np.array([ 35.4585005])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ np.allclose(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)
+ np.allclose(reg.z.toarray()[0],z,4)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.allclose(reg.mean_y,my)
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.allclose(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)
+ #np.allclose(reg.vm,vm,4)
np.testing.assert_allclose(reg.vm, vm, rtol=1e-05)
sig2 = 181.78650186468832
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.allclose(reg.sig2,sig2,4)
pr2 = 0.3018280166937799
- self.assertAlmostEqual(reg.pr2,pr2,4)
+ np.allclose(reg.pr2,pr2,4)
pr2_e = 0.3561355587000738
- self.assertAlmostEqual(reg.pr2_e,pr2_e,4)
+ np.allclose(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)
+ np.allclose(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)
+ np.allclose(reg.z_stat,z_stat,4)
if __name__ == '__main__':
start_suppress = np.get_printoptions()['suppress']
diff --git a/pysal/spreg/tests/test_error_sp_het.py b/pysal/contrib/handler/tests/test_error_spet.py
similarity index 95%
copy from pysal/spreg/tests/test_error_sp_het.py
copy to pysal/contrib/handler/tests/test_error_spet.py
index 1c2ce6e..4b677ff 100644
--- a/pysal/spreg/tests/test_error_sp_het.py
+++ b/pysal/contrib/handler/tests/test_error_spet.py
@@ -1,7 +1,15 @@
import unittest
import pysal
import numpy as np
-from pysal.spreg import error_sp_het as HET
+from pysal.contrib.handler import Model
+from functools import partial
+
+GM_Combo_Het = partial(Model, mtype='GM_Combo_Het')
+BaseGM_Combo_Het = partial(Model, mtype='BaseGM_Combo_Het')
+GM_Error_Het = partial(Model, mtype='GM_Error_Het')
+BaseGM_Error_Het = partial(Model, mtype='BaseGM_Error_Het')
+GM_Endog_Error_Het = partial(Model, mtype='GM_Endog_Error_Het')
+BaseGM_Endog_Error_Het = partial(Model, mtype='BaseGM_Endog_Error_Het')
class TestBaseGMErrorHet(unittest.TestCase):
def setUp(self):
@@ -17,7 +25,7 @@ class TestBaseGMErrorHet(unittest.TestCase):
self.w.transform = 'r'
def test_model(self):
- reg = HET.BaseGM_Error_Het(self.y, self.X, self.w.sparse, step1c=True)
+ reg = 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])
@@ -69,7 +77,7 @@ class TestGMErrorHet(unittest.TestCase):
self.w.transform = 'r'
def test_model(self):
- reg = HET.GM_Error_Het(self.y, self.X, self.w, step1c=True)
+ reg = 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])
@@ -136,7 +144,7 @@ class TestBaseGMEndogErrorHet(unittest.TestCase):
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)
+ reg = 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])
@@ -201,7 +209,7 @@ class TestGMEndogErrorHet(unittest.TestCase):
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)
+ reg = 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])
@@ -269,7 +277,7 @@ class TestBaseGMComboHet(unittest.TestCase):
# 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)
+ reg = 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])
@@ -337,7 +345,7 @@ class TestGMComboHet(unittest.TestCase):
def test_model(self):
# Only spatial lag
- reg = HET.GM_Combo_Het(self.y, self.X, w=self.w, step1c=True)
+ reg = 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])
diff --git a/pysal/spreg/tests/test_error_sp_het_regimes.py b/pysal/contrib/handler/tests/test_error_spet_regimes.py
similarity index 96%
copy from pysal/spreg/tests/test_error_sp_het_regimes.py
copy to pysal/contrib/handler/tests/test_error_spet_regimes.py
index 4eab0f5..111b241 100644
--- a/pysal/spreg/tests/test_error_sp_het_regimes.py
+++ b/pysal/contrib/handler/tests/test_error_spet_regimes.py
@@ -2,7 +2,17 @@ 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
+#from pysal.spreg.error_sp_het import GM_Error_Het, GM_Endog_Error_Het, GM_Combo_Het
+from pysal.contrib.handler import Model
+from functools import partial
+
+GM_Error_Het = partial(Model, mtype='GM_Error_Het')
+GM_Endog_Error_Het = partial(Model, mtype='GM_Endog_Error_Het')
+GM_Combo_Het = partial(Model, mtype='GM_Combo_Het')
+
+GM_Error_Het_Regimes = partial(Model, mtype='GM_Error_Het_Regimes')
+GM_Endog_Error_Het_Regimes = partial(Model, mtype='GM_Endog_Error_Het_Regimes')
+GM_Combo_Het_Regimes = partial(Model, mtyle='GM_Combo_Het_Regimes')
class TestGM_Error_Het_Regimes(unittest.TestCase):
def setUp(self):
diff --git a/pysal/spreg/tests/test_error_sp_het_sparse.py b/pysal/contrib/handler/tests/test_error_spet_sparse.py
similarity index 95%
copy from pysal/spreg/tests/test_error_sp_het_sparse.py
copy to pysal/contrib/handler/tests/test_error_spet_sparse.py
index ff8a80d..657f9c5 100644
--- a/pysal/spreg/tests/test_error_sp_het_sparse.py
+++ b/pysal/contrib/handler/tests/test_error_spet_sparse.py
@@ -2,7 +2,17 @@ import unittest
import pysal
import numpy as np
from scipy import sparse
-from pysal.spreg import error_sp_het as HET
+#from pysal.spreg import error_sp_het as HET
+from functools import partial
+from pysal.contrib.handler import Model
+
+GM_Error_Het = partial(Model, mtype='GM_Error_Het')
+GM_Endog_Error_Het = partial(Model, mtype='GM_Endog_Error_Het')
+GM_Combo_Het = partial(Model, mtype='GM_Combo_Het')
+
+BaseGM_Error_Het = partial(Model, mtype='BaseGM_Error_Het')
+BaseGM_Endog_Error_Het = partial(Model, mtype='BaseGM_Endog_Error_Het')
+BaseGM_Combo_Het = partial(Model, mtype='BaseGM_Combo_Het')
class TestBaseGMErrorHet(unittest.TestCase):
def setUp(self):
@@ -19,7 +29,7 @@ class TestBaseGMErrorHet(unittest.TestCase):
self.w.transform = 'r'
def test_model(self):
- reg = HET.BaseGM_Error_Het(self.y, self.X, self.w.sparse, step1c=True)
+ reg = 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])
@@ -72,7 +82,7 @@ class TestGMErrorHet(unittest.TestCase):
self.w.transform = 'r'
def test_model(self):
- reg = HET.GM_Error_Het(self.y, self.X, self.w, step1c=True)
+ reg = 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])
@@ -140,7 +150,7 @@ class TestBaseGMEndogErrorHet(unittest.TestCase):
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)
+ reg = 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])
@@ -206,7 +216,7 @@ class TestGMEndogErrorHet(unittest.TestCase):
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)
+ reg = 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])
@@ -275,7 +285,7 @@ class TestBaseGMComboHet(unittest.TestCase):
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)
+ reg = 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])
@@ -344,7 +354,7 @@ class TestGMComboHet(unittest.TestCase):
def test_model(self):
# Only spatial lag
- reg = HET.GM_Combo_Het(self.y, self.X, w=self.w, step1c=True)
+ reg = 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])
diff --git a/pysal/spreg/tests/test_error_sp_hom.py b/pysal/contrib/handler/tests/test_error_spom.py
similarity index 94%
copy from pysal/spreg/tests/test_error_sp_hom.py
copy to pysal/contrib/handler/tests/test_error_spom.py
index afc730d..320f3c2 100644
--- a/pysal/spreg/tests/test_error_sp_hom.py
+++ b/pysal/contrib/handler/tests/test_error_spom.py
@@ -4,8 +4,18 @@ Unittests for spreg.error_sp_hom module
'''
import unittest
import pysal
-from pysal.spreg import error_sp_hom as HOM
+#from pysal.spreg import error_sp_hom as HOM
import numpy as np
+from pysal.contrib.handler import Model
+from functools import partial
+
+GM_Combo_Hom = partial(Model, mtype='GM_Combo_Hom')
+GM_Error_Hom = partial(Model, mtype='GM_Error_Hom')
+GM_Endog_Error_Hom = partial(Model, mtype='GM_Endog_Error_Hom')
+
+BaseGM_Combo_Hom = partial(Model, mtype='BaseGM_Combo_Hom')
+BaseGM_Error_Hom = partial(Model, mtype='BaseGM_Error_Hom')
+BaseGM_Endog_Error_Hom = partial(Model, mtype='BaseGM_Endog_Error_Hom')
class BaseGM_Error_Hom_Tester(unittest.TestCase):
def setUp(self):
@@ -20,7 +30,7 @@ class BaseGM_Error_Hom_Tester(unittest.TestCase):
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')
+ reg = 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)
@@ -52,7 +62,7 @@ class GM_Error_Hom_Tester(unittest.TestCase):
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')
+ reg = 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)
@@ -102,7 +112,7 @@ class BaseGM_Endog_Error_Hom_Tester(unittest.TestCase):
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')
+ reg = 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)
@@ -157,7 +167,7 @@ class GM_Endog_Error_Hom_Tester(unittest.TestCase):
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')
+ reg = 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)
@@ -212,7 +222,7 @@ class BaseGM_Combo_Hom_Tester(unittest.TestCase):
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')
+ reg = 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)
@@ -259,7 +269,7 @@ class GM_Combo_Hom_Tester(unittest.TestCase):
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')
+ reg = 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)
diff --git a/pysal/spreg/tests/test_error_sp_hom_regimes.py b/pysal/contrib/handler/tests/test_error_spom_regimes.py
similarity index 87%
copy from pysal/spreg/tests/test_error_sp_hom_regimes.py
copy to pysal/contrib/handler/tests/test_error_spom_regimes.py
index 8c8e650..a082596 100644
--- a/pysal/spreg/tests/test_error_sp_hom_regimes.py
+++ b/pysal/contrib/handler/tests/test_error_spom_regimes.py
@@ -1,8 +1,22 @@
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
+#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
+
+from pysal.contrib.handler import Model
+from functools import partial
+
+GM_Error_Hom_Regimes = partial(Model, mtype='GM_Error_Hom_Regimes')
+GM_Endog_Error_Hom_Regimes = partial(Model, mtype='GM_Endog_Error_Hom_Regimes')
+GM_Combo_Hom_Regimes = partial(Model, mtype='GM_Combo_Hom_Regimes')
+BaseGM_Error_Hom_Regimes = partial(Model, mtype='BaseGM_Error_Hom_Regimes')
+BaseGM_Endog_Error_Hom_Regimes = partial(Model, mtype='BaseGM_Endog_Error_Hom_Regimes')
+BaseGM_Combo_Hom_Regimes = partial(Model, mtype='BaseGM_Combo_Hom_Regimes')
+
+GM_Error_Hom = partial(Model, mtype='GM_Error_Hom')
+GM_Endog_Error_Hom = partial(Model, mtype='GM_Endog_Error_Hom')
+GM_Combo_Hom = partial(Model, mtype='GM_Combo_Hom')
class TestGM_Error_Hom_Regimes(unittest.TestCase):
def setUp(self):
@@ -42,7 +56,7 @@ class TestGM_Error_Hom_Regimes(unittest.TestCase):
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')
+ reg = GM_Error_Hom_Regimes(self.y, self.X, self.regimes, self.w, A1='het')
betas = np.array([[ 62.95986466],
[ -0.15660795],
[ -1.49054832],
@@ -88,7 +102,7 @@ class TestGM_Error_Hom_Regimes(unittest.TestCase):
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')
+ model = 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))
@@ -96,7 +110,7 @@ class TestGM_Error_Hom_Regimes(unittest.TestCase):
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')
+ reg = 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],
@@ -121,7 +135,7 @@ class TestGM_Error_Hom_Regimes(unittest.TestCase):
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')
+ reg = 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],
@@ -172,7 +186,7 @@ class TestGM_Error_Hom_Regimes(unittest.TestCase):
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')
+ reg = 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],
@@ -199,7 +213,7 @@ class TestGM_Error_Hom_Regimes(unittest.TestCase):
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')
+ model = 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))
@@ -208,7 +222,7 @@ class TestGM_Error_Hom_Regimes(unittest.TestCase):
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')
+ reg = 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],
@@ -264,7 +278,7 @@ class TestGM_Error_Hom_Regimes(unittest.TestCase):
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')
+ reg = 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],
@@ -295,7 +309,7 @@ class TestGM_Error_Hom_Regimes(unittest.TestCase):
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')
+ model = 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))
diff --git a/pysal/spreg/tests/test_error_sp_hom.py b/pysal/contrib/handler/tests/test_error_spom_sparse.py
similarity index 86%
copy from pysal/spreg/tests/test_error_sp_hom.py
copy to pysal/contrib/handler/tests/test_error_spom_sparse.py
index afc730d..538ff62 100644
--- a/pysal/spreg/tests/test_error_sp_hom.py
+++ b/pysal/contrib/handler/tests/test_error_spom_sparse.py
@@ -4,9 +4,21 @@ Unittests for spreg.error_sp_hom module
'''
import unittest
import pysal
-from pysal.spreg import error_sp_hom as HOM
+#from pysal.spreg import error_sp_hom as HOM
+from scipy import sparse
import numpy as np
+from functools import partial
+from pysal.contrib.handler import Model
+
+GM_Error_Hom = partial(Model, mtype='GM_Error_Hom')
+GM_Endog_Error_Hom = partial(Model, mtype='GM_Endog_Error_Hom')
+GM_Combo_Hom = partial(Model, mtype='GM_Combo_Hom')
+
+BaseGM_Error_Hom = partial(Model, mtype='BaseGM_Error_Hom')
+BaseGM_Endog_Error_Hom = partial(Model, mtype='BaseGM_Endog_Error_Hom')
+BaseGM_Combo_Hom = partial(Model, mtype='BaseGM_Combo_Hom')
+
class BaseGM_Error_Hom_Tester(unittest.TestCase):
def setUp(self):
db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
@@ -17,13 +29,14 @@ class BaseGM_Error_Hom_Tester(unittest.TestCase):
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')
+ reg = 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)
+ 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)
@@ -49,13 +62,14 @@ class GM_Error_Hom_Tester(unittest.TestCase):
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')
+ reg = 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)
+ 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)
@@ -93,6 +107,7 @@ class BaseGM_Endog_Error_Hom_Tester(unittest.TestCase):
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
@@ -102,14 +117,14 @@ class BaseGM_Endog_Error_Hom_Tester(unittest.TestCase):
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')
+ reg = 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)
+ 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],z,7)
+ 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],h,7)
+ 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])
@@ -148,6 +163,7 @@ class GM_Endog_Error_Hom_Tester(unittest.TestCase):
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
@@ -157,14 +173,14 @@ class GM_Endog_Error_Hom_Tester(unittest.TestCase):
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')
+ reg = 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)
+ 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],z,7)
+ 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],h,7)
+ 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])
@@ -212,10 +228,11 @@ class BaseGM_Combo_Hom_Tester(unittest.TestCase):
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')
+ self.X = sparse.csr_matrix(self.X)
+ reg = 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)
+ 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)
@@ -226,9 +243,9 @@ class BaseGM_Combo_Hom_Tester(unittest.TestCase):
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)
+ 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],h,7)
+ 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])
@@ -241,9 +258,8 @@ class BaseGM_Combo_Hom_Tester(unittest.TestCase):
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)
+ 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)
@@ -256,13 +272,14 @@ class GM_Combo_Hom_Tester(unittest.TestCase):
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')
+ reg = 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)
+ 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)
@@ -273,13 +290,13 @@ class GM_Combo_Hom_Tester(unittest.TestCase):
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)
+ 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],h,7)
+ 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)
+ 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)
@@ -291,9 +308,8 @@ class GM_Combo_Hom_Tester(unittest.TestCase):
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)
+ 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]])
diff --git a/pysal/spreg/tests/test_ml_error.py b/pysal/contrib/handler/tests/test_ml_error.py
similarity index 66%
copy from pysal/spreg/tests/test_ml_error.py
copy to pysal/contrib/handler/tests/test_ml_error.py
index 385ee8e..4418ae4 100644
--- a/pysal/spreg/tests/test_ml_error.py
+++ b/pysal/contrib/handler/tests/test_ml_error.py
@@ -2,9 +2,14 @@ import unittest
import pysal
import scipy
import numpy as np
-from pysal.spreg.ml_error import ML_Error
+#from pysal.spreg.ml_error import ML_Error
from pysal.spreg import utils
+from functools import partial
+from pysal.contrib.handler import Model
+
+ML_Error = partial(Model, mtype='ML_Error')
+
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Max Likelihood requires SciPy version 11 or newer.")
class TestMLError(unittest.TestCase):
@@ -24,48 +29,48 @@ class TestMLError(unittest.TestCase):
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)
+ np.allclose(reg.betas,betas,4)
u = np.array([-5.97649777])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.allclose(reg.u[0],u,4)
predy = np.array([ 6.92258051])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.allclose(reg.predy[0],predy,4)
n = 1412
- self.assertAlmostEqual(reg.n,n,4)
+ np.allclose(reg.n,n,4)
k = 5
- self.assertAlmostEqual(reg.k,k,4)
+ np.allclose(reg.k,k,4)
y = np.array([ 0.94608274])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.allclose(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)
+ np.allclose(reg.x[0],x,4)
e = np.array([-4.92843327])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.allclose(reg.e_filtered[0],e,4)
my = 9.5492931620846928
- self.assertAlmostEqual(reg.mean_y,my)
+ np.allclose(reg.mean_y,my)
sy = 7.0388508798387219
- self.assertAlmostEqual(reg.std_y,sy)
+ np.allclose(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)
+ np.allclose(reg.vm.diagonal(),vm,4)
sig2 = [ 32.40685441]
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.allclose(reg.sig2,sig2,4)
pr2 = 0.3057664820364818
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.allclose(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)
+ np.allclose(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)
+ np.allclose(reg.z_stat,z_stat,4)
logll = -4471.407066887894
- self.assertAlmostEqual(reg.logll,logll,4)
+ np.allclose(reg.logll,logll,4)
aic = 8952.8141337757879
- self.assertAlmostEqual(reg.aic,aic,4)
+ np.allclose(reg.aic,aic,4)
schwarz = 8979.0779458660545
- self.assertAlmostEqual(reg.schwarz,schwarz,4)
+ np.allclose(reg.schwarz,schwarz,4)
if __name__ == '__main__':
unittest.main()
diff --git a/pysal/spreg/tests/test_ml_error_regimes.py b/pysal/contrib/handler/tests/test_ml_error_regimes.py
similarity index 71%
copy from pysal/spreg/tests/test_ml_error_regimes.py
copy to pysal/contrib/handler/tests/test_ml_error_regimes.py
index dd205e1..9f21b5e 100644
--- a/pysal/spreg/tests/test_ml_error_regimes.py
+++ b/pysal/contrib/handler/tests/test_ml_error_regimes.py
@@ -2,10 +2,16 @@ 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.ml_error_regimes import ML_Error_Regimes
+#from pysal.spreg.ml_error import ML_Error
from pysal.spreg import utils
+from functools import partial
+from pysal.contrib.handler import Model
+
+ML_Error_Regimes = partial(Model, mtype='ML_Error_Regimes')
+ML_Error = partial(Model, mtype='ML_Error')
+
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Max Likelihood requires SciPy version 11 or newer.")
class TestMLError(unittest.TestCase):
@@ -50,48 +56,48 @@ class TestMLError(unittest.TestCase):
[ -0.23710892],
[ 0.80581127],
[ 0.61770744]])
- np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ np.allclose(reg.betas,betas,4)
u = np.array([ 30.46599009])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.allclose(reg.u[0],u,4)
predy = np.array([ 16.53400991])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.allclose(reg.predy[0],predy,4)
n = 211
- self.assertAlmostEqual(reg.n,n,4)
+ np.allclose(reg.n,n,4)
k = 8
- self.assertAlmostEqual(reg.k,k,4)
+ np.allclose(reg.k,k,4)
y = np.array([ 47.])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.allclose(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)
+ np.allclose(reg.x[0],x,4)
e = np.array([ 34.69181334])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.allclose(reg.e_filtered[0],e,4)
my = 44.307180094786695
- self.assertAlmostEqual(reg.mean_y,my)
+ np.allclose(reg.mean_y,my)
sy = 23.606076835380495
- self.assertAlmostEqual(reg.std_y,sy)
+ np.allclose(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)
+ np.allclose(reg.vm.diagonal(),vm,4)
sig2 = np.array([[ 209.60639741]])
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.allclose(reg.sig2,sig2,4)
pr2 = 0.43600837301477025
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.allclose(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)
+ np.allclose(reg.std_err,std_err,4)
logll = -870.3331059537576
- self.assertAlmostEqual(reg.logll,logll,4)
+ np.allclose(reg.logll,logll,4)
aic = 1756.6662119075154
- self.assertAlmostEqual(reg.aic,aic,4)
+ np.allclose(reg.aic,aic,4)
schwarz = 1783.481076975324
- self.assertAlmostEqual(reg.schwarz,schwarz,4)
+ np.allclose(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)
+ np.allclose(reg.chow.regi,chow_r,4)
chow_j = 25.367913028011799
- self.assertAlmostEqual(reg.chow.joint[0],chow_j,4)
+ np.allclose(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,\
@@ -106,33 +112,33 @@ class TestMLError(unittest.TestCase):
[ -0.18803509],
[ 0.68956598],
[ 0.75599089]])
- np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ np.allclose(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)
+ np.allclose(reg.vm[0],vm,4)
u = np.array([ 31.97771505])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.allclose(reg.u[0],u,4)
predy = np.array([ 15.02228495])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.allclose(reg.predy[0],predy,4)
e = np.array([ 33.83065421])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.allclose(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)
+ np.allclose(reg.chow.regi,chow_r,4)
chow_j = 26.673798071789673
- self.assertAlmostEqual(reg.chow.joint[0],chow_j,4)
+ np.allclose(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)
+ np.allclose(model.betas,tbetas)
vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
- np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 4)
+ np.allclose(model.vm.diagonal(), vm, 4)
if __name__ == '__main__':
unittest.main()
diff --git a/pysal/spreg/tests/test_ml_lag.py b/pysal/contrib/handler/tests/test_ml_lag.py
similarity index 94%
copy from pysal/spreg/tests/test_ml_lag.py
copy to pysal/contrib/handler/tests/test_ml_lag.py
index 462db18..09c9df3 100644
--- a/pysal/spreg/tests/test_ml_lag.py
+++ b/pysal/contrib/handler/tests/test_ml_lag.py
@@ -2,9 +2,14 @@ import unittest
import pysal
import scipy
import numpy as np
-from pysal.spreg.ml_lag import ML_Lag
+#from pysal.spreg.ml_lag import ML_Lag
from pysal.spreg import utils
+from functools import partial
+from pysal.contrib.handler import Model
+
+ML_Lag = partial(Model, mtype='ML_Lag')
+
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Max Likelihood requires SciPy version 11 or newer.")
class TestMLError(unittest.TestCase):
diff --git a/pysal/spreg/tests/test_ml_lag_regimes.py b/pysal/contrib/handler/tests/test_ml_lag_regimes.py
similarity index 94%
copy from pysal/spreg/tests/test_ml_lag_regimes.py
copy to pysal/contrib/handler/tests/test_ml_lag_regimes.py
index dd77ce1..b99ed52 100644
--- a/pysal/spreg/tests/test_ml_lag_regimes.py
+++ b/pysal/contrib/handler/tests/test_ml_lag_regimes.py
@@ -2,10 +2,16 @@ 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.ml_lag_regimes import ML_Lag_Regimes
+#from pysal.spreg.ml_lag import ML_Lag
from pysal.spreg import utils
+from functools import partial
+from pysal.contrib.handler import Model
+
+ML_Lag = partial(Model, mtype='ML_Lag')
+ML_Lag_Regimes = partial(Model, mtype='ML_Lag_Regimes')
+
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Max Likelihood requires SciPy version 11 or newer.")
class TestMLError(unittest.TestCase):
diff --git a/pysal/spreg/tests/test_ols.py b/pysal/contrib/handler/tests/test_ols.py
similarity index 93%
copy from pysal/spreg/tests/test_ols.py
copy to pysal/contrib/handler/tests/test_ols.py
index f2495d9..daf466e 100644
--- a/pysal/spreg/tests/test_ols.py
+++ b/pysal/contrib/handler/tests/test_ols.py
@@ -2,7 +2,13 @@ import unittest
import numpy as np
import pysal
from pysal.spreg import utils
-import pysal.spreg as EC
+#import pysal.spreg as EC
+
+from functools import partial
+from pysal.contrib.handler import Model
+
+OLS = partial(Model, mtype='OLS')
+BaseOLS = partial(Model, mtype='BaseOLS')
PEGP = pysal.examples.get_path
@@ -19,7 +25,7 @@ class TestBaseOLS(unittest.TestCase):
def test_ols(self):
self.X = np.hstack((np.ones(self.y.shape),self.X))
- ols = EC.ols.BaseOLS(self.y,self.X)
+ 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],
@@ -29,7 +35,7 @@ class TestBaseOLS(unittest.TestCase):
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)
+ 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],
@@ -39,7 +45,7 @@ class TestBaseOLS(unittest.TestCase):
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)
+ 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],
@@ -48,7 +54,7 @@ class TestBaseOLS(unittest.TestCase):
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, \
+ ols = 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')
diff --git a/pysal/spreg/tests/test_ols_regimes.py b/pysal/contrib/handler/tests/test_ols_regimes.py
similarity index 96%
copy from pysal/spreg/tests/test_ols_regimes.py
copy to pysal/contrib/handler/tests/test_ols_regimes.py
index 20a9977..825f931 100644
--- a/pysal/spreg/tests/test_ols_regimes.py
+++ b/pysal/contrib/handler/tests/test_ols_regimes.py
@@ -1,8 +1,14 @@
import unittest
import numpy as np
import pysal
-from pysal.spreg.ols import OLS
-from pysal.spreg.ols_regimes import OLS_Regimes
+#from pysal.spreg.ols import OLS
+#from pysal.spreg.ols_regimes import OLS_Regimes
+
+from functools import partial
+from pysal.contrib.handler import Model
+
+OLS = partial(Model, mtype='OLS')
+OLS_Regimes = partial(Model, mtype='OLS_Regimes')
PEGP = pysal.examples.get_path
diff --git a/pysal/spreg/tests/test_ols_sparse.py b/pysal/contrib/handler/tests/test_ols_sparse.py
similarity index 94%
copy from pysal/spreg/tests/test_ols_sparse.py
copy to pysal/contrib/handler/tests/test_ols_sparse.py
index 1341c96..53cc2b0 100644
--- a/pysal/spreg/tests/test_ols_sparse.py
+++ b/pysal/contrib/handler/tests/test_ols_sparse.py
@@ -1,9 +1,15 @@
import unittest
import numpy as np
import pysal
-import pysal.spreg as EC
+#import pysal.spreg as EC
from scipy import sparse
+from pysal.contrib.handler import Model
+from functools import partial
+
+OLS = partial(Model, mtype='OLS')
+BaseOLS = partial(Model, mtype='BaseOLS')
+
PEGP = pysal.examples.get_path
class TestBaseOLS(unittest.TestCase):
@@ -20,7 +26,7 @@ class TestBaseOLS(unittest.TestCase):
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)
+ 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],
@@ -30,7 +36,7 @@ class TestBaseOLS(unittest.TestCase):
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, \
+ ols = 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)
diff --git a/pysal/spreg/tests/test_probit.py b/pysal/contrib/handler/tests/test_probit.py
similarity index 94%
copy from pysal/spreg/tests/test_probit.py
copy to pysal/contrib/handler/tests/test_probit.py
index 5c4e6dd..1546bd8 100644
--- a/pysal/spreg/tests/test_probit.py
+++ b/pysal/contrib/handler/tests/test_probit.py
@@ -1,7 +1,13 @@
import unittest
import pysal
import numpy as np
-from pysal.spreg import probit as PB
+#from pysal.spreg import probit as PB
+
+from pysal.contrib.handler import Model
+from functools import partial
+
+Probit = partial(Model, mtype='Probit')
+BaseProbit = partial(Model, mtype='BaseProbit')
class TestBaseProbit(unittest.TestCase):
def setUp(self):
@@ -18,7 +24,7 @@ class TestBaseProbit(unittest.TestCase):
self.w.transform = 'r'
def test_model(self):
- reg = PB.BaseProbit(self.y, self.X, w=self.w)
+ reg = 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])
@@ -68,7 +74,7 @@ class TestProbit(unittest.TestCase):
self.w.transform = 'r'
def test_model(self):
- reg = PB.Probit(self.y, self.X, w=self.w)
+ reg = 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])
diff --git a/pysal/spreg/tests/test_twosls.py b/pysal/contrib/handler/tests/test_twosls.py
similarity index 98%
copy from pysal/spreg/tests/test_twosls.py
copy to pysal/contrib/handler/tests/test_twosls.py
index 71021e3..e656dc4 100644
--- a/pysal/spreg/tests/test_twosls.py
+++ b/pysal/contrib/handler/tests/test_twosls.py
@@ -1,7 +1,12 @@
import unittest
import numpy as np
import pysal
-from pysal.spreg.twosls import BaseTSLS, TSLS
+#from pysal.spreg.twosls import BaseTSLS, TSLS
+from pysal.contrib.handler import Model
+from functools import partial
+
+BaseTSLS = partial(Model, mtype='BaseTSLS')
+TSLS = partial(Model, mtype='TSLS')
class TestBaseTSLS(unittest.TestCase):
def setUp(self):
diff --git a/pysal/spreg/tests/test_twosls_regimes.py b/pysal/contrib/handler/tests/test_twosls_regimes.py
similarity index 98%
copy from pysal/spreg/tests/test_twosls_regimes.py
copy to pysal/contrib/handler/tests/test_twosls_regimes.py
index 155bf03..62efd36 100644
--- a/pysal/spreg/tests/test_twosls_regimes.py
+++ b/pysal/contrib/handler/tests/test_twosls_regimes.py
@@ -1,8 +1,14 @@
import unittest
import numpy as np
import pysal
-from pysal.spreg.twosls_regimes import TSLS_Regimes
-from pysal.spreg.twosls import TSLS
+#from pysal.spreg.twosls_regimes import TSLS_Regimes
+#from pysal.spreg.twosls import TSLS
+
+from pysal.contrib.handler import Model
+from functools import partial
+
+TSLS_Regimes = partial(Model, mtype='TSLS_Regimes')
+TSLS = partial(Model, mtype='TSLS')
class TestTSLS(unittest.TestCase):
def setUp(self):
diff --git a/pysal/spreg/tests/test_twosls_sp.py b/pysal/contrib/handler/tests/test_twosls_sp.py
similarity index 99%
copy from pysal/spreg/tests/test_twosls_sp.py
copy to pysal/contrib/handler/tests/test_twosls_sp.py
index 914aef7..34a5fc4 100644
--- a/pysal/spreg/tests/test_twosls_sp.py
+++ b/pysal/contrib/handler/tests/test_twosls_sp.py
@@ -2,7 +2,13 @@ import unittest
import numpy as np
import pysal
import pysal.spreg.diagnostics as D
-from pysal.spreg.twosls_sp import BaseGM_Lag, GM_Lag
+#from pysal.spreg.twosls_sp import BaseGM_Lag, GM_Lag
+
+from pysal.contrib.handler import Model
+from functools import partial
+
+BaseGM_Lag = partial(Model, mtype='BaseGM_Lag')
+GM_Lag = partial(Model, mtype='GM_Lag')
class TestBaseGMLag(unittest.TestCase):
def setUp(self):
diff --git a/pysal/spreg/tests/test_twosls_sp_regimes.py b/pysal/contrib/handler/tests/test_twosls_sp_regimes.py
similarity index 98%
copy from pysal/spreg/tests/test_twosls_sp_regimes.py
copy to pysal/contrib/handler/tests/test_twosls_sp_regimes.py
index b311b17..1a524ff 100644
--- a/pysal/spreg/tests/test_twosls_sp_regimes.py
+++ b/pysal/contrib/handler/tests/test_twosls_sp_regimes.py
@@ -1,9 +1,15 @@
import unittest
import numpy as np
import pysal
-from pysal.spreg.twosls_sp_regimes import GM_Lag_Regimes
+#from pysal.spreg.twosls_sp_regimes import GM_Lag_Regimes
from pysal.spreg import utils
-from pysal.spreg.twosls_sp import GM_Lag
+#from pysal.spreg.twosls_sp import GM_Lag
+
+from pysal.contrib.handler import Model
+from functools import partial
+
+GM_Lag_Regimes = partial(Model, mtype='GM_Lag_Regimes')
+GM_Lag = partial(Model, mtype='GM_Lag')
class TestGMLag_Regimes(unittest.TestCase):
def setUp(self):
diff --git a/pysal/spreg/tests/test_twosls_sp_sparse.py b/pysal/contrib/handler/tests/test_twosls_sp_sparse.py
similarity index 99%
copy from pysal/spreg/tests/test_twosls_sp_sparse.py
copy to pysal/contrib/handler/tests/test_twosls_sp_sparse.py
index e6669e0..4dac949 100644
--- a/pysal/spreg/tests/test_twosls_sp_sparse.py
+++ b/pysal/contrib/handler/tests/test_twosls_sp_sparse.py
@@ -1,10 +1,15 @@
import unittest
import numpy as np
import pysal
-from pysal.spreg.twosls_sp import BaseGM_Lag, GM_Lag
+#from pysal.spreg.twosls_sp import BaseGM_Lag, GM_Lag
import pysal.spreg.diagnostics as D
from scipy import sparse as SP
+from functools import partial
+from pysal.contrib.handler import Model
+
+GM_Lag = partial(Model, mtype='GM_Lag')
+BaseGM_Lag = partial(Model, mtype='BaseGM_Lag')
class TestBaseGMLag(unittest.TestCase):
def setUp(self):
diff --git a/pysal/spreg/tests/test_twosls_sparse.py b/pysal/contrib/handler/tests/test_twosls_sparse.py
similarity index 98%
copy from pysal/spreg/tests/test_twosls_sparse.py
copy to pysal/contrib/handler/tests/test_twosls_sparse.py
index 654dd5a..d3c8f10 100644
--- a/pysal/spreg/tests/test_twosls_sparse.py
+++ b/pysal/contrib/handler/tests/test_twosls_sparse.py
@@ -1,9 +1,14 @@
import unittest
import numpy as np
import pysal
-from pysal.spreg.twosls import TSLS, BaseTSLS
+#from pysal.spreg.twosls import TSLS, BaseTSLS
from scipy import sparse as SP
+from pysal.contrib.handler import Model
+from functools import partial
+
+TSLS = partial(Model, mtype='TSLS')
+BaseTSLS = partial(Model, mtype='BaseTSLS')
class TestBaseTSLS(unittest.TestCase):
def setUp(self):
diff --git a/pysal/contrib/pdutilities/__init__.py b/pysal/contrib/pdutilities/__init__.py
index e69de29..4df967d 100644
--- a/pysal/contrib/pdutilities/__init__.py
+++ b/pysal/contrib/pdutilities/__init__.py
@@ -0,0 +1,4 @@
+import dbf_utilities as dbio
+import shp_utilities as shio
+import file_utilities as fio
+from file_utilities import read_files, write_files
diff --git a/pysal/contrib/pdutilities/dbf_utilities.py b/pysal/contrib/pdutilities/dbf_utilities.py
index afe08f2..9395751 100644
--- a/pysal/contrib/pdutilities/dbf_utilities.py
+++ b/pysal/contrib/pdutilities/dbf_utilities.py
@@ -106,9 +106,6 @@ def df2dbf(df, dbf_path, my_specs=None):
db.close()
return dbf_path
-
-
-
def dbf2df(dbf_path, index=None, cols=False, incl_index=False):
'''
Read a dbf file as a pandas.DataFrame, optionally selecting the index
@@ -218,4 +215,3 @@ def dta2dbf(dta_path,dbf_path):
db = pd.read_stata(dta_path)
dp = df2dbf(db,dbf_path)
return dp
-
diff --git a/pysal/contrib/pdutilities/file_utilities.py b/pysal/contrib/pdutilities/file_utilities.py
new file mode 100644
index 0000000..ad45a5e
--- /dev/null
+++ b/pysal/contrib/pdutilities/file_utilities.py
@@ -0,0 +1,65 @@
+import pysal as ps
+import os
+from shp_utilities import shp2series, series2shp
+from dbf_utilities import dbf2df, df2dbf
+
+
+def read_files(filepath, **kwargs):
+ """
+ Reads a dbf/shapefile pair, squashing geometries into a "geometry" column.
+ """
+ #keyword arguments wrapper will strip all around dbf2df's required arguments
+ geomcol = kwargs.pop('geomcol', 'geometry')
+ weights = kwargs.pop('weights', '')
+
+ dbf_path, shp_path = _pairpath(filepath)
+
+ df = dbf2df(dbf_path, **kwargs)
+ df[geomcol] = shp2series(shp_path)
+
+ if weights != '' and isinstance(weights, str):
+ if weights.lower() in ['rook', 'queen']:
+ if weights.lower() == 'rook':
+ df.W = ps.rook_from_shapefile(shp_path)
+ else:
+ df.W = ps.queen_from_shapefile(shp_path)
+ else:
+ try:
+ W_path = os.path.splitext(dbf_path)[0] + '.' + weights
+ df.W = ps.open(W_path).read()
+ except IOError:
+ print('Weights construction failed! Passing on weights')
+
+ return df
+
+def write_files(df, filepath, **kwargs):
+ """
+ writes dataframes with potential geometric components out to files.
+ """
+ geomcol = kwargs.pop('geomcol', 'geometry')
+ weights = kwargs.pop('weights', 'gal')
+
+ dbf_path, shp_path = _pairpath(filepath)
+
+ if geomcol not in df.columns:
+ return df2dbf(df, dbf_path, **kwargs)
+ else:
+ shp_out = series2shp(df[geomcol], shp_path)
+ not_geom = [x for x in df.columns if x != geomcol]
+ dbf_out = df2dbf(df[not_geom], dbf_path, **kwargs)
+ if hasattr(df, 'W'):
+ W_path = os.path.splitext(filepath)[0] + '.' + weights
+ ps.open(W_path, 'w').write(df.W)
+ else:
+ W_path = 'no weights written'
+ return dbf_out, shp_out, W_path
+
+
+
+def _pairpath(filepath):
+ """
+ return dbf/shp paths for any shp, dbf, or basepath passed to function.
+ """
+ base = os.path.splitext(filepath)[0]
+ return base + '.dbf', base + '.shp'
+
diff --git a/pysal/contrib/pdutilities/shp_utilities.py b/pysal/contrib/pdutilities/shp_utilities.py
new file mode 100644
index 0000000..7aaec2d
--- /dev/null
+++ b/pysal/contrib/pdutilities/shp_utilities.py
@@ -0,0 +1,21 @@
+import pandas as pd
+import pysal as ps
+
+def shp2series(filepath):
+ """
+ reads a shapefile, stuffing each shape into an element of a Pandas Series
+ """
+ f = ps.open(filepath)
+ s = pd.Series(poly for poly in f)
+ f.close()
+ return s
+
+def series2shp(series, filepath):
+ """
+ writes a series of pysal polygons to a file
+ """
+ f = ps.open(filepath, 'w')
+ for poly in series:
+ f.write(poly)
+ f.close()
+ return filepath
diff --git a/pysal/contrib/viz/mapping.py b/pysal/contrib/viz/mapping.py
index ebfc231..cfb38f6 100644
--- a/pysal/contrib/viz/mapping.py
+++ b/pysal/contrib/viz/mapping.py
@@ -64,7 +64,9 @@ def map_point_shp(shp, which='all', bbox=None):
pts.append(pt)
pts = np.array(pts)
sc = plt.scatter(pts[:, 0], pts[:, 1])
- _ = _add_axes2col(sc, bbox)
+ #print(sc.get_axes().get_xlim())
+ #_ = _add_axes2col(sc, bbox)
+ #print(sc.get_axes().get_xlim())
return sc
def map_line_shp(shp, which='all', bbox=None):
@@ -117,7 +119,7 @@ def map_line_shp(shp, which='all', bbox=None):
rows.append(i)
i += 1
lc = LineCollection(patches)
- _ = _add_axes2col(lc, bbox)
+ #_ = _add_axes2col(lc, bbox)
lc.shp2dbf_row = rows
return lc
@@ -173,13 +175,13 @@ def map_poly_shp(shp, which='all', bbox=None):
rows.append(i)
i += 1
pc = PolyCollection(patches)
- _ = _add_axes2col(pc, bbox)
+ #_ = _add_axes2col(pc, bbox)
pc.shp2dbf_row = rows
return pc
# Mid-level pieces
-def setup_ax(polyCos_list, ax=None):
+def setup_ax(polyCos_list, bboxs, ax=None):
'''
Generate an Axes object for a list of collections
...
@@ -189,6 +191,9 @@ def setup_ax(polyCos_list, ax=None):
polyCos_list: list
List of Matplotlib collections (e.g. an object from
map_poly_shp)
+ bboxs : list
+ List of lists, each containing the bounding box of the
+ respective polyCo, expressed as [xmin, ymin, xmax, ymax]
ax : AxesSubplot
(Optional) Pre-existing axes to which append the collections
and setup
@@ -201,28 +206,16 @@ def setup_ax(polyCos_list, ax=None):
'''
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())
+
+ for polyCo, bbox in zip(polyCos_list, bboxs):
ax.add_collection(polyCo)
+ polyCo.axes.set_xlim((bbox[0], bbox[2]))
+ polyCo.axes.set_ylim((bbox[1], bbox[3]))
+ abboxs = np.array(bboxs)
+ ax.set_xlim((abboxs[:, 0].min(), \
+ abboxs[:, 2].max()))
+ ax.set_ylim((abboxs[:, 1].min(), \
+ abboxs[:, 3].max()))
ax.set_frame_on(False)
ax.axes.get_yaxis().set_visible(False)
ax.axes.get_xaxis().set_visible(False)
@@ -512,6 +505,7 @@ def _expand_values(values, shp2dbf_row):
# High-level pieces
+
def plot_poly_lines(shp_link, savein=None, poly_col='none'):
'''
Quick plotting of shapefiles
@@ -528,15 +522,17 @@ def plot_poly_lines(shp_link, savein=None, poly_col='none'):
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)
+ ax = setup_ax([patchco], [shp.bbox])
+ fig.add_axes(ax)
+
if savein:
plt.savefig(savein)
else:
+ print('callng plt.show()')
plt.show()
return None
@@ -633,7 +629,7 @@ def plot_choropleth(shp_link, values, type, k=5, cmap=None,
map_obj.set_alpha(alpha)
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
- ax = setup_ax([map_obj], ax)
+ ax = setup_ax([map_obj], [shp.bbox], ax)
if title:
ax.set_title(title)
if type=='quantiles' or type=='fisher_jenks' or type=='equal_interval':
@@ -655,8 +651,8 @@ lisa_clrs = {1: '#FF0000', 2: '#66CCFF', 3: '#003399', 4: '#CD5C5C', \
lisa_lbls = {1: 'HH', 2: 'LH', 3: 'LL', 4: 'HL', \
0: 'Non-significant'}
-def plot_lisa_cluster(shp_link, lisa, p_thres=0.01, shp_type='poly',
- title='', legend=True, savein=None, figsize=None, dpi=300, alpha=1.,
+def plot_lisa_cluster(shp_link, lisa, p_thres=0.01, shp_type='poly',
+ title='', legend=True, savein=None, figsize=None, dpi=300, alpha=1.,
leg_loc=0):
'''
Plot LISA cluster maps easily
@@ -710,7 +706,7 @@ def plot_lisa_cluster(shp_link, lisa, p_thres=0.01, shp_type='poly',
# Figure
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
- ax = setup_ax([lisa_obj], ax)
+ ax = setup_ax([lisa_obj], [shp.bbox], ax)
# Legend
if legend:
boxes, labels = lisa_legend_components(lisa, p_thres)
@@ -722,7 +718,7 @@ def plot_lisa_cluster(shp_link, lisa, p_thres=0.01, shp_type='poly',
else:
plt.show()
return None
-
+
if __name__ == '__main__':
@@ -783,11 +779,10 @@ if __name__ == '__main__':
#ax = setup_ax([pc], ax)
plt.show()
'''
-
+
shp_link = ps.examples.get_path('columbus.shp')
values = np.array(ps.open(ps.examples.get_path('columbus.dbf')).by_col('HOVAL'))
w = ps.queen_from_shapefile(shp_link)
lisa = ps.Moran_Local(values, w, permutations=999)
_ = plot_lisa_cluster(shp_link, lisa)
#_ = plot_choropleth(shp_link, values, 'fisher_jenks')
-
diff --git a/pysal/contrib/viz/mapping_guide.ipynb b/pysal/contrib/viz/mapping_guide.ipynb
index 10f14a2..e057820 100644
--- a/pysal/contrib/viz/mapping_guide.ipynb
+++ b/pysal/contrib/viz/mapping_guide.ipynb
@@ -1,327 +1,371 @@
{
- "metadata": {
- "name": "",
- "signature": "sha256:d5daafa6a69a7136921bdccec96c48e0b8deaee29eaea96718e9a2c2ee5bb488"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
{
- "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": 1
- },
- {
- "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+/AAAIABJREFUeJzsnXl8VNXd/z93m30yM9khYSdsokCPgiLihtalrq1Fa/tY\nW2vt81TbPvhYq9XWpdan9udTW+tWta51q4q7iICsBuEQdghrEiD7Mklmv9vvjzvBEGYye0LgvF+v\n+xpy77nnnAmZz5z7Pd+F03UdDAaDwRgY+MGeAIPBYJxIMNFlMBiMAYSJLoPBYAwgTHQZDAZjAGGi\ny2AwGAMIE10Gg8EYQJjoMhgMxgDCRJfBYDAGECa6DAaDMYAw0WUwGIwBhIkug8FgDCBMdBkMBmMA\nYaLLYDAYAwgTXQaDwRhAmOgyGAzGAMJEl8FgMAYQJroMBoMxgDDRZTAYjAFEHOwJMBiZ8tpr23ft\n2WMNi6KuC [...]
- "text": [
- "<matplotlib.figure.Figure at 0x108f26e90>"
- ]
- }
- ],
- "prompt_number": 2
- },
- {
- "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",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "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 *"
+ ]
+ },
+ {
+ "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",
+ "* Serge Rey `<sjsrey 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",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
{
- "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",
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXl8nFW9/z/n2WZNJnuapktautIF2gMtLQVkF5BVrgrX\nhcumoKKC4gJcr/jzqlfv5QrXqwjuqCBeFEFEtkKhpbQ9tIU26b6lSZo9M5n9eZ5zfn88Ewgly6yZ\npD3v1+th0mee55zvhMxnznzPdyFCCEgkEolkbFCKbYBEIpEcT0jRlUgkkjFEiq5EIpGMIVJ0JRKJ\nZAyRoiuRSCRjiBRdiUQiGUO0YhsgkWQCY0x9+umKLffe2/CErgvNMLim60LVdaFpmtB0nWuaJlRN\nE5qqQnMeB/4t1LPP7lt01VVdKymlR4r9WiTHJ1J0JRMKSqm9du1um3MiEgliJhKKmcn9y5eHJgGw\nCmSe [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x1060f0190>"
+ ]
+ },
"metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xd4VFX6wPHvmVTIhIQSGKQXaQMKIo5iQXTXxuIKFnTF\nlt3fqljWggULanTFXVHX7q4aFHsBC+gqqCsWNKAiYhQUESkyoYXAAClkzu+PM2NCyPR7596ZnM/z\n+Jgyc+8bkrw5c8573iOklGiapmnJ4bA6AE3TtJZEJ11N07Qk0klX0zQtiXTS1TRNSyKddDVN05JI\nJ11N07Qk0kk3xTiFGH6mED/M6yh2z9tfVDiFeNXqmDRNi55OuinmMag9HjrdXcUHR+RTMKQVU62O\nSdO06Omkm0KeFaIV8EIWXD6/Ro5pnUHZZ276WB2XpmnR00k3tfwT+BaYGXj/VeBU68LRNC1WQm8D\nTg3PCjEGe [...]
- "text": [
- "<matplotlib.figure.Figure at 0x108f267d0>"
- ]
- }
- ],
- "prompt_number": 3
- },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "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], [shp.bbox, shp.bbox, shp.bbox])\n",
+ "fig.add_axes(ax)\n",
+ "show()"
+ ]
+ },
+ {
+ "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",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
{
- "cell_type": "markdown",
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xd4VFX6wPHvmVTIJBAgMEgHgVAFAUdRRNR1saACanR1\nLdldC4qg2LAgomt0RX8iCu6qsSuoYMGKFcFgQEQElBKqgKGGJENLSM7vjzMxAZOp9869Mzmf5/Ex\n5eaeF0jenDn3Pe8RUko0TdO0yHBYHYCmaVp9opOupmlaBOmkq2maFkE66WqapkWQTrqapmkRpJOu\npmlaBOmkG2WcQvR7UIg1W246+8Dcj+/Z7hTiHatj0jQtcDrpRpk9UDYWWoz5aMlXpwzu0ajXce0n\nWB2TpmmBE3pzRPQoE6IBsBh4LFHKl2HWPGAyjJxjcWiapgVIz3Sjy3+AFcAr3vffAUZaF46macHS\nSTdK [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x10a03d150>"
+ ]
+ },
"metadata": {},
- "source": [
- "\n",
- "* `base_choropleth_classif`"
- ]
- },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "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], [net.bbox, net.bbox])\n",
+ "fig.add_axes(ax)\n",
+ "show()"
+ ]
+ },
+ {
+ "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",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Higher-level component\n",
- "\n",
- "This currently includes the following end-user functions:"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "callng plt.show()\n"
]
},
{
- "cell_type": "markdown",
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXeYJFXVh9/ZHIFlyTmjICAccs6gkkRAFEGQoGAAUcH4\nIUo28SEZQRERPgQEJEnO+QfskmFJCwsLLJtz/P44t3Z6Z7tnOlTonr3v89TTPT3dVbdnqk7de8Lv\ntM2bN49IJBKJ5EOPogcQiUQiixLR6EYikUiORKMbiUQiORKNbiQSieRINLqRSCSSI9HoRiKRSI70\nKnoAkUgtSFoJeB7YAugL9Kvx8Ujgy2b2RO6Dj0SIRjfSenwADABGm9mUWj8s6QvAnNRHFYlUSXQv\nRFoKM5sLvAWsWecuehKNbqRAotGNtCJvAmvV+dlodCOFEt0LkVZkBCnOdCW1hdd7AjPNLNbGRzIj\nGt1I [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x10a03ddd0>"
+ ]
+ },
"metadata": {},
- "source": [
- "* `plot_poly_lines`: very quick shapefile plotting."
- ]
- },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "maps.plot_poly_lines(ps.examples.get_path('columbus.shp'))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* `plot_choropleth`: for quick plotting of several types of chocopleths."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
{
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "maps.plot_poly_lines(ps.examples.get_path('columbus.shp'))"
- ],
- "language": "python",
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAD8CAYAAADUv3dIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4XNWZ/99z2/SiKSqjLluWbLkb44oxGEyHtcG0xGwg\nC4TNLoTd7D6/JDybUDaQhJKEkJAlZAkJsc2mOIbY2IBjg417lW1Zkq0ujUZlerlz2/n9oZEjbLXp\nEj6f55lnNPfeOffMaOY7733PWxDGGAgEAoGQGahsT4BAIBAuJ4joEggEQgYhoksgEAgZhIgugUAg\nZBAiugQCgZBBiOgSCARCBiGiS8gICKGvIIQ+TeP4uxBCX03X+ARCqiCiS/iigGM3AmFCQ0SXQCAQ\nMggRXULKQQgVI4T+hBDqQQj1IYRehYusUITQTxBCbQghH0LoMEJo+ZB9V8a2+RBC3Qihl2Lb1Qih\n38XG [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x10a429d10>"
+ ]
+ },
"metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXeYJFXVh9/ZHIFlyTmjICAccs6gkkRAFEGQoGAAUcH4\nIUo28SEZQRERPgQEJEnO+QfskmFJCwsLLJtz/P44t3Z6Z7tnOlTonr3v89TTPT3dVbdnqk7de8Lv\ntM2bN49IJBKJ5EOPogcQiUQiixLR6EYikUiORKMbiUQiORKNbiQSieRINLqRSCSSI9HoRiKRSI70\nKnoAkUgtSFoJeB7YAugL9Kvx8Ujgy2b2RO6Dj0SIRjfSenwADABGm9mUWj8s6QvAnNRHFYlUSXQv\nRFoKM5sLvAWsWecuehKNbqRAotGNtCJvAmvV+dlodCOFEt0LkVZkBCnOdCW1hdd7AjPNLNbGRzIj\nGt1IKzIC+ [...]
- "text": [
- "<matplotlib.figure.Figure at 0x104465cd0>"
- ]
- }
- ],
- "prompt_number": 4
+ "output_type": "display_data"
},
{
- "cell_type": "markdown",
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAD8CAYAAADUv3dIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXd8JMd173tOVXdPTgAGOQO72F1sDlwuc86kAi0xKNFK\ntuxn05btq+vr+961pesrB9mSrGfJlmQFW7RoZVESc07LZdjIzQlY5DAJkzpV1f0D2OUGpBlMALj1\n/XzmM6m7+gww/ZvqUyegEAIkEolEUhpIuQ2QSCSSiwkpuhKJRFJCpOhKJBJJCZGiK5FIJCVEiq5E\nIpGUECm6EolEUkKk6EoKBiJ+AxH/Z7ntyAdE5IjYXm47JO9+UMbpSiSTogsAnUKIE+W2RfLuRs50\nJRKJpIRI0ZWcw/mX2Yj4PUT8wtTjaxCxHxE/i4gjiDiIiA9Mt+3U8z+b2qYfET9+9tiI+DwifuKs\nbR9A [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x10a6373d0>"
+ ]
+ },
"metadata": {},
- "source": [
- "* `plot_choropleth`: for quick plotting of several types of chocopleths."
- ]
+ "output_type": "display_data"
},
{
- "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', 'equal_interval', 'fisher_jenks']\n",
- "for typ in types:\n",
- " maps.plot_choropleth(shp_link, values, typ, title=typ)"
- ],
- "language": "python",
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADxCAYAAABoIWSWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXmcVNWZ//95zl1qr+rqfQW6oVkFQRYV3MAVEaMmqHEy\nJpkk40STTDJL/JloYnTMYszka5yJ0YmaaDTGuIG4awSD4oIgQrM3NN1N71t1bbfucs7vj6qGBlm6\nq6t6kft+ve6rbt/lnFPVVZ966jnPeR4SQsDGxsbGZnhgIz0AGxsbm5MJW3RtbGxshhFbdG1sbGyG\nEVt0bWxsbIYRW3RtbGxshhFbdG1sbGyGEVt0bT6TENFLRPSPqf2vENHfR3pMNjYAII/0AGxshgoR\n3Q5gohDiH/uOCSEuHbkR2dgcG9vStbGxsRlGbNG1yShENIeINhJRLxE9mdruPNpPfCLiRFSV2l9G\nRJuI [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x10a7770d0>"
+ ]
+ },
"metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAD8CAYAAADUv3dIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4XNWZ/99z2/SiKSqjLluWbLkb44oxGEyHtcG0xGwg\nC4TNLoTd7D6/JDybUDaQhJKEkJAlZAkJsc2mOIbY2IBjg417lW1Zkq0ujUZlerlz2/n9oZEjbLXp\nEj6f55lnNPfeOffMaOY7733PWxDGGAgEAoGQGahsT4BAIBAuJ4joEggEQgYhoksgEAgZhIgugUAg\nZBAiugQCgZBBiOgSCARCBiGiS8gICKGvIIQ+TeP4uxBCX03X+ARCqiCiS/iigGM3AmFCQ0SXQCAQ\nMggRXULKQQgVI4T+hBDqQQj1IYRehYusUITQTxBCbQghH0LoMEJo+ZB9V8a2+RBC3Qihl2Lb1Qih\n38XG9CCED [...]
- "text": [
- "<matplotlib.figure.Figure at 0x109255310>"
- ]
- },
- {
- "metadata": {},
- "output_type": "display_data",
- "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAD8CAYAAADUv3dIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXl8XFd9//39nrvMvkqjfZdseZPXeE1CVkgIJCwBAhT4\nhVACpU9/dKPlaWna0qfPj19ftH1Kf6yhUEohlFAge+IkDtnjON432bKtxdpGo9Hsd+7ce885zx+S\njRdJ1oxGM1J83q+XPJqZe8/9jjzzmXO+57sg5xwEAoFAUBpIuQ0QCASCqwkhugKBQFBChOgKBAJB\nCRGiKxAIBCVEiK5AIBCUECG6AoFAUEKE6AqKBiJ+GxG/Um47CgERGSK2ldsOwdsfFHG6AsGk6AJA\nB+f8TLltEby9ETNdgUAgKCFCdAUXcekyGxH/HRH/bur3GxFxEBH/GBHDiDiMiPdOd+zU/S9NHTOI\niPddODYi/ [...]
- "text": [
- "<matplotlib.figure.Figure at 0x109411750>"
- ]
- },
- {
- "metadata": {},
- "output_type": "display_data",
- "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADxCAYAAABoIWSWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXmcVNWZ//95zl1qr+rqfQW6oVkFQRYV3MAVEaMmqHEy\nJpkk40STTDJL/JloYnTMYszka5yJ0YmaaDTGuIG4awSD4oIgQrM3NN1N71t1bbfucs7vj6qGBlm6\nq6t6kft+ve6rbt/lnFPVVZ966jnPeR4SQsDGxsbGZnhgIz0AGxsbm5MJW3RtbGxshhFbdG1sbGyG\nEVt0bWxsbIYRW3RtbGxshhFbdG1sbGyGEVt0bT6TENFLRPSPqf2vENHfR3pMNjYAII/0AGxshgoR\n3Q5gohDiH/uOCSEuHbkR2dgcG9vStbGxsRlGbNG1yShENIeINhJRLxE9mdruPNpPfCLiRFSV2l9G\nRJuIKERE9 [...]
- "text": [
- "<matplotlib.figure.Figure at 0x108f3fe10>"
- ]
- },
- {
- "metadata": {},
- "output_type": "display_data",
- "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADxCAYAAABoIWSWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXl8VNX5/z/PuffO3NkymSRkT8hCCJFdIAqICCiIiN+6\noNRvf2oXa+vX7v3q1+61tYu1dWvrbqutuOGC4gJKBYsFlUU0IQQI2SD7MpPZ5y7n98dMMLJmmUkC\n3PfrdV9z5y7nnBmYT577nOc8D3HOYWBgYGAwPLCRHoCBgYHBmYQhugYGBgbDiCG6BgYGBsOIIboG\nBgYGw4ghugYGBgbDiCG6BgYGBsOIIboGowYi2khEXz3JNf9NROuGa0xDgYj+TkS/GulxGIwuDNE1\nGE3w2Hb8Czh/mnO+pD+NEdENRPTvuIxscJz08xiceRiia2BwHIhIjEczcWjD4DTCEF2DY0JE2UT0\nIhG1EdEBI [...]
- "text": [
- "<matplotlib.figure.Figure at 0x1095ed550>"
- ]
- },
- {
- "metadata": {},
- "output_type": "display_data",
- "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADxCAYAAABoIWSWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXecVOX1/z/nuWXutJ2d7Y2yC0sRaSKooCKoICJ+jYoh\n/vJVU4xp3/TEr4lpJqao+Ro1iSVqEhNL7ChGRY1gVFRARJbOsg22t9lpd+be+zy/P2YWV+qWmVlW\n7vv1uq+Zufc+ZYblM8+cc55zSAgBGxsbG5vMwIZ7AjY2NjYnErbo2tjY2GQQW3RtbGxsMogtujY2\nNjYZxBZdGxsbmwxii66NjY1NBrFF12ZAENFEIvqAiHqIyCKiH/WjTS0RnZuBuVUR0dkp6OdnRPT3\nVMzJxuZg5OGegM2I4wcAXhNCzBhAG5E80ooQ4uRUdZWifmxsDsFe6doMlDEAtg3HwESUqUUCZWgc\nmxMQW3Rt+ [...]
- "text": [
- "<matplotlib.figure.Figure at 0x1099be190>"
- ]
- }
- ],
- "prompt_number": 5
+ "output_type": "display_data"
},
{
- "cell_type": "markdown",
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADxCAYAAABoIWSWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXl8VNX5/z/PuffO3NkymSRkT8hCCJFdIAqICCiIiN+6\noNRvf2oXa+vX7v3q1+61tYu1dWvrbqutuOGC4gJKBYsFlUU0IQQI2SD7MpPZ5y7n98dMMLJmmUkC\n3PfrdV9z5y7nnBmYT577nOc8D3HOYWBgYGAwPLCRHoCBgYHBmYQhugYGBgbDiCG6BgYGBsOIIboG\nBgYGw4ghugYGBgbDiCG6BgYGBsOIIboGowYi2khEXz3JNf9NROuGa0xDgYj+TkS/GulxGIwuDNE1\nGE3w2Hb8Czh/mnO+pD+NEdENRPTvuIxscJz08xiceRiia2BwHIhIjEczcWjD4DTCEF2DY0JE2UT0\nIhG1 [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x10a936cd0>"
+ ]
+ },
"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"
- ]
+ "output_type": "display_data"
},
{
- "cell_type": "markdown",
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADxCAYAAABoIWSWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXecVOX1/z/nuWXutJ2d7Y2yC0sRaSKooCKoICJ+jYoh\n/vJVU4xp3/TEr4lpJqao+Ro1iSVqEhNL7ChGRY1gVFRARJbOsg22t9lpd+be+zy/P2YWV+qWmVlW\n7vv1uq+Zufc+ZYblM8+cc55zSAgBGxsbG5vMwIZ7AjY2NjYnErbo2tjY2GQQW3RtbGxsMogtujY2\nNjYZxBZdGxsbmwxii66NjY1NBrFF12ZAENFEIvqAiHqIyCKiH/WjTS0RnZuBuVUR0dkp6OdnRPT3\nVMzJxuZg5OGegM2I4wcAXhNCzBhAG5E80ooQ4uRUdZWifmxsDsFe6doMlDEAtg3HwESUqUUCZWgc\nmxMQ [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x10aa48810>"
+ ]
+ },
"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."
- ]
+ "output_type": "display_data"
}
],
- "metadata": {}
+ "source": [
+ "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', 'equal_interval', 'fisher_jenks']\n",
+ "for typ in types:\n",
+ " maps.plot_choropleth(shp_link, values, typ, title=typ)"
+ ]
+ },
+ {
+ "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 t [...]
+ "\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",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
}
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/core/IOHandlers/arcgis_swm.py b/pysal/core/IOHandlers/arcgis_swm.py
index 488b2d7..1fd96c9 100644
--- a/pysal/core/IOHandlers/arcgis_swm.py
+++ b/pysal/core/IOHandlers/arcgis_swm.py
@@ -107,6 +107,7 @@ class ArcGISSwmIO(FileIO.FileIO):
raise StopIteration
header01 = self.file.readline()
+ header01 = header01.decode()
id_var, srs = header01[:-1].split(';')
self.varName = id_var
self.header_len = len(header01) + 8
@@ -191,7 +192,8 @@ class ArcGISSwmIO(FileIO.FileIO):
if useIdIndex:
id2i = obj.id2i
obj = remap_ids(obj, id2i)
- self.file.write('%s;Unknown\n' % self.varName)
+ unk = str('%s;Unknown\n' % self.varName).encode()
+ self.file.write(unk)
self.file.write(pack('<l', obj.n))
self.file.write(pack('<l', obj.transform.upper() == 'R'))
for obs in obj.weights:
diff --git a/pysal/core/IOHandlers/csvWrapper.py b/pysal/core/IOHandlers/csvWrapper.py
index 2ef0e0b..8049d65 100644
--- a/pysal/core/IOHandlers/csvWrapper.py
+++ b/pysal/core/IOHandlers/csvWrapper.py
@@ -6,13 +6,12 @@ __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):
"""
diff --git a/pysal/core/IOHandlers/pyDbfIO.py b/pysal/core/IOHandlers/pyDbfIO.py
index 0677b7d..5aecc6f 100644
--- a/pysal/core/IOHandlers/pyDbfIO.py
+++ b/pysal/core/IOHandlers/pyDbfIO.py
@@ -54,26 +54,28 @@ class DBF(pysal.core.Tables.DataTable):
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
+ numrec, lenheader = struct.unpack('<xxxxLH22x', f.read(32)) #from dbf file standards
+ numfields = (lenheader - 33) // 32 #each field is 32 bytes
self.n_records = numrec
self.n_fields = numfields
self.field_info = [('DeletionFlag', 'C', 1, 0)]
record_size = 1
- fmt = 's'
+ fmt = 's' #each record is a string
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
+ '<11sc4xBB14x', f.read(32)) #again, check struct for fmt def.
+ name = name.decode() #forces to unicode in 2, to str in 3
+ typ = typ.decode()
+ name = name.replace('\0', '') #same as NULs, \x00
+ #eliminate NULs from string
self._col_index[name] = (idx, record_size)
idx += 1
- fmt += '%ds' % size
+ fmt += '%ds' % size #alt: str(size) + 's'
record_size += size
self.field_info.append((name, typ, size, deci))
- terminator = f.read(1)
+ terminator = f.read(1).decode()
assert terminator == '\r'
self.header_size = self.f.tell()
self.record_size = record_size
@@ -115,6 +117,7 @@ class DBF(pysal.core.Tables.DataTable):
col = [0] * self.n_records
for i in xrange(self.n_records):
value = f.read(size)
+ value = value.decode()
f.seek(gap, 1)
if typ == 'N':
value = value.replace('\0', '').lstrip()
@@ -145,7 +148,7 @@ class DBF(pysal.core.Tables.DataTable):
value = pysal.MISSINGVALUE
else:
value = float(value)
- if isinstance(value, str):
+ if isinstance(value, str) or isinstance(value, unicode):
value = value.rstrip()
col[i] = value
self.seek(prevPos)
@@ -155,6 +158,7 @@ class DBF(pysal.core.Tables.DataTable):
self.seek(i)
rec = list(struct.unpack(
self.record_fmt, self.f.read(self.record_size)))
+ rec = [entry.decode() for entry in rec]
if rec[0] != ' ':
return self.read_record(i + 1)
result = []
@@ -191,7 +195,7 @@ class DBF(pysal.core.Tables.DataTable):
value = pysal.MISSINGVALUE
else:
value = float(value)
- if isinstance(value, str):
+ if isinstance(value, str) or isinstance(value, unicode):
value = value.rstrip()
result.append(value)
return result
@@ -215,7 +219,7 @@ class DBF(pysal.core.Tables.DataTable):
if len(obj) != len(self.header):
raise TypeError("Rows must contains %d fields" % len(self.header))
self.numrec += 1
- self.f.write(' ') # deletion flag
+ self.f.write(' '.encode()) # deletion flag
for (typ, size, deci), value in itertools.izip(self.field_spec, obj):
if value is None:
if typ == 'C':
@@ -239,7 +243,7 @@ class DBF(pysal.core.Tables.DataTable):
except:
print value, len(value), size
raise
- self.f.write(value)
+ self.f.write(value.encode())
self.pos += 1
def flush(self):
@@ -251,7 +255,7 @@ class DBF(pysal.core.Tables.DataTable):
if self.mode == 'w':
self.flush()
# End of file
- self.f.write('\x1A')
+ self.f.write('\x1A'.encode())
self.f.close()
pysal.core.Tables.DataTable.close(self)
@@ -279,11 +283,14 @@ class DBF(pysal.core.Tables.DataTable):
self.f.write(hdr)
# field specs
for name, (typ, size, deci) in itertools.izip(self.header, self.field_spec):
+ typ = typ.encode()
name = name.ljust(11, '\x00')
+ name = name.encode()
fld = struct.pack('<11sc4xBB14x', name, typ, size, deci)
self.f.write(fld)
# terminator
- self.f.write('\r')
+ term = '\r'.encode()
+ self.f.write(term)
if self.f.tell() != POS and not self.FIRST_WRITE:
self.f.seek(POS)
diff --git a/pysal/core/IOHandlers/pyShpIO.py b/pysal/core/IOHandlers/pyShpIO.py
index d1a2c55..b123bda 100644
--- a/pysal/core/IOHandlers/pyShpIO.py
+++ b/pysal/core/IOHandlers/pyShpIO.py
@@ -70,7 +70,7 @@ class PurePyShpWrapper(pysal.core.FileIO.FileIO):
self.__create()
def __len__(self):
- if self.dataObj:
+ if self.dataObj != None:
return len(self.dataObj)
else:
return 0
@@ -127,7 +127,7 @@ class PurePyShpWrapper(pysal.core.FileIO.FileIO):
rec['NumParts'] = len(shape.parts)
all_parts = shape.parts
partsIndex = [0]
- for l in map(len, all_parts)[:-1]:
+ for l in [len(part) for part in all_parts][:-1]:
partsIndex.append(partsIndex[-1] + l)
rec['Parts Index'] = partsIndex
verts = sum(all_parts, [])
@@ -156,7 +156,7 @@ class PurePyShpWrapper(pysal.core.FileIO.FileIO):
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)
+ is_cw = [pysal.cg.is_clockwise(part) for part in 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:
diff --git a/pysal/core/IOHandlers/tests/test_csvWrapper.py b/pysal/core/IOHandlers/tests/test_csvWrapper.py
index 70d0d52..2ceb46b 100644
--- a/pysal/core/IOHandlers/tests/test_csvWrapper.py
+++ b/pysal/core/IOHandlers/tests/test_csvWrapper.py
@@ -2,7 +2,9 @@ import unittest
import pysal
import tempfile
import os
+from sys import version as V
+PY3 = int(V[0]) > 2
class test_csvWrapper(unittest.TestCase):
def setUp(self):
@@ -42,8 +44,12 @@ class test_csvWrapper(unittest.TestCase):
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])
+ if PY3:
+ for i, pt in enumerate(self.obj.__next__()[0].vertices):
+ self.assertEquals(pt[:], verts[i])
+ else:
+ 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:
diff --git a/pysal/core/IOHandlers/tests/test_pyDbfIO.py b/pysal/core/IOHandlers/tests/test_pyDbfIO.py
index df0c7cf..10a8c2b 100644
--- a/pysal/core/IOHandlers/tests/test_pyDbfIO.py
+++ b/pysal/core/IOHandlers/tests/test_pyDbfIO.py
@@ -1,64 +1,66 @@
+
+
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)
+ self.assertEqual(len(self.dbObj), 195)
- def test_cast(self):
- self.assertEquals(self.dbObj._spec, [])
- self.dbObj.cast('FIPSSTCO', float)
- self.assertEquals(self.dbObj._spec[1], float)
def test_tell(self):
- self.assertEquals(self.dbObj.tell(), 0)
+ self.assertEqual(self.dbObj.tell(), 0)
self.dbObj.read(1)
- self.assertEquals(self.dbObj.tell(), 1)
+ self.assertEqual(self.dbObj.tell(), 1)
self.dbObj.read(50)
- self.assertEquals(self.dbObj.tell(), 51)
+ self.assertEqual(self.dbObj.tell(), 51)
self.dbObj.read()
- self.assertEquals(self.dbObj.tell(), 195)
+ self.assertEqual(self.dbObj.tell(), 195)
+
+ def test_cast(self):
+ self.assertEqual(self.dbObj._spec, [])
+ self.dbObj.cast('FIPSSTCO', float)
+ self.assertEqual(self.dbObj._spec[1], float)
def test_seek(self):
self.dbObj.seek(0)
- self.assertEquals(self.dbObj.tell(), 0)
+ self.assertEqual(self.dbObj.tell(), 0)
self.dbObj.seek(55)
- self.assertEquals(self.dbObj.tell(), 55)
+ self.assertEqual(self.dbObj.tell(), 55)
self.dbObj.read(1)
- self.assertEquals(self.dbObj.tell(), 56)
+ self.assertEqual(self.dbObj.tell(), 56)
def test_read(self):
self.dbObj.seek(0)
objs = self.dbObj.read()
- self.assertEquals(len(objs), 195)
+ self.assertEqual(len(objs), 195)
self.dbObj.seek(0)
objsB = list(self.dbObj)
- self.assertEquals(len(objsB), 195)
+ self.assertEqual(len(objsB), 195)
for rowA, rowB in zip(objs, objsB):
- self.assertEquals(rowA, rowB)
+ self.assertEqual(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.assertEqual(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.assertEqual(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.assertEqual(db32, [33, '35001', '000500', '35001000500', '5'])
self.dbObj.seek(0)
- self.assertEquals(self.dbObj.next(), db0)
+ self.assertEqual(next(self.dbObj), db0)
self.dbObj.seek(57)
- self.assertEquals(self.dbObj.next(), db57)
+ self.assertEqual(next(self.dbObj), db57)
self.dbObj.seek(32)
- self.assertEquals(self.dbObj.next(), db32)
+ self.assertEqual(next(self.dbObj), db32)
def test_write(self):
f = tempfile.NamedTemporaryFile(suffix='.dbf')
@@ -80,7 +82,7 @@ class test_DBF(unittest.TestCase):
#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.assertEqual(orig.read(n), copy.read(n))
#self.assertEquals(orig.read(1), copy.read(1)) # last byte may fail
orig.close()
copy.close()
@@ -107,7 +109,7 @@ class test_DBF(unittest.TestCase):
db.write(rec)
db.close()
db2 = pysal.core.IOHandlers.pyDbfIO.DBF(fname, 'r')
- self.assertEquals(records, db2.read())
+ self.assertEqual(records, db2.read())
os.remove(fname)
diff --git a/pysal/core/IOHandlers/wk1.py b/pysal/core/IOHandlers/wk1.py
index fccca98..fd19049 100644
--- a/pysal/core/IOHandlers/wk1.py
+++ b/pysal/core/IOHandlers/wk1.py
@@ -306,7 +306,7 @@ class Wk1IO(FileIO.FileIO):
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, "'"))
+ f.write(pack('<2H1c', 41, 1, "'".encode()))
id2i = obj.id2i
for i, w_i in enumerate(obj):
diff --git a/pysal/core/Tables.py b/pysal/core/Tables.py
index 04c8a26..5fc03aa 100644
--- a/pysal/core/Tables.py
+++ b/pysal/core/Tables.py
@@ -48,14 +48,14 @@ class DataTable(FileIO.FileIO):
else:
raise AttributeError('Field: % s does not exist in header' % key)
- def by_col_array(self, variable_names):
+ def by_col_array(self, *args):
"""
- Return columns of table as a numpy array
+ Return columns of table as a numpy array.
Parameters
----------
- variable_names: list of strings of length k
+ *args: any number of strings of length k
names of variables to extract
Returns
@@ -69,12 +69,22 @@ class DataTable(FileIO.FileIO):
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.
+ If only strings are passed to the function, then an array with those
+ columns will be constructed.
+
+ If only one list of strings is passed, the output is identical to those
+ strings being passed.
+
+ If at least one list is passed and other strings or lists are passed,
+ this returns a tuple containing arrays constructed from each positional
+ argument.
+
Examples
--------
>>> import pysal as ps
>>> dbf = ps.open(ps.examples.get_path('NAT.dbf'))
- >>> hr = dbf.by_col_array(['HR70', 'HR80'])
+ >>> hr = dbf.by_col_array('HR70', 'HR80')
>>> hr[0:5]
array([[ 0. , 8.85582713],
[ 0. , 17.20874204],
@@ -98,7 +108,7 @@ class DataTable(FileIO.FileIO):
Numpy only supports homogeneous arrays. See Notes above.
- >>> hr = dbf.by_col_array(['STATE_NAME', 'HR80'])
+ >>> hr = dbf.by_col_array('STATE_NAME', 'HR80')
>>> hr[0:5]
array([['Minnesota', '8.8558271343'],
['Washington', '17.208742041'],
@@ -107,10 +117,35 @@ class DataTable(FileIO.FileIO):
['Washington', '7.77000777']],
dtype='|S20')
+ >>> y, X = dbf.by_col_array('STATE_NAME', ['HR80', 'HR70'])
+ >>> y[0:5]
+ array([['Minnesota'],
+ ['Washington'],
+ ['Washington'],
+ ['Washington'],
+ ['Washington']],
+ dtype='|S20')
+ >>> X[0:5]
+ array([[ 8.85582713, 0. ],
+ [ 17.20874204, 0. ],
+ [ 3.4507747 , 1.91515848],
+ [ 3.26381409, 1.28864319],
+ [ 7.77000777, 0. ]])
"""
- lst = [self._get_col(variable) for variable in variable_names]
- return np.array(lst).T
+ if any([isinstance(arg, list) for arg in args]):
+ results = []
+ for namelist in args:
+ if isinstance(namelist, str):
+ results.append([self._get_col(namelist)])
+ else:
+ results.append([self._get_col(vbl) for vbl in namelist])
+ if len(results) == 1:
+ return np.array(results[0]).T
+ else:
+ return tuple(np.array(lst).T for lst in results)
+ else:
+ return np.array([self._get_col(name) for name in args]).T
def __getitem__(self, key):
""" DataTables fully support slicing in 2D,
diff --git a/pysal/core/util/shapefile.py b/pysal/core/util/shapefile.py
index 973b152..ef7d113 100644
--- a/pysal/core/util/shapefile.py
+++ b/pysal/core/util/shapefile.py
@@ -15,10 +15,18 @@ 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
+
+PY3 = int(sys.version[0]) > 2
+
+if PY3:
+ import io
+else:
+ from cStringIO import StringIO
+
if sys.byteorder == 'little':
SYS_BYTE_ORDER = '<'
else:
@@ -31,6 +39,14 @@ __all__ = ['shp_file', 'shx_file']
#SHAPEFILE Globals
+def bufferIO(buf):
+ """
+ Helper function for 2-3 compatibility
+ """
+ if PY3:
+ return io.BytesIO(buf)
+ else:
+ return StringIO(buf)
def struct2arrayinfo(struct):
struct = list(struct)
@@ -336,11 +352,11 @@ class shp_file:
self.__isreadable()
if shpId + 1 > self.__numRecords:
raise IndexError
- fPosition, bytes = self._shx.index[shpId]
+ fPosition, byts = 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(bufferIO(self.fileObj.read(byts)))
#return self.shape.unpack(self.fileObj.read(bytes))
def __update_bbox(self, s):
@@ -378,13 +394,13 @@ class shp_file:
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(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.header['File Length'] = self.__file_Length // 2
self.__seek(0)
self.fileObj.write(_packDict(HEADERSTRUCT, self.header))
self.fileObj.close()
@@ -444,7 +460,7 @@ class shx_file:
self.__isreadable()
self.fileObj = open(self.fileName + '.shx', 'rb')
self._header = _unpackDict(UHEADERSTRUCT, self.fileObj)
- self.numRecords = numRecords = (self._header['File Length'] - 50) / 4
+ self.numRecords = numRecords = (self._header['File Length'] - 50) // 4
index = {}
fmt = '>%di' % (2 * numRecords)
size = calcsize(fmt)
@@ -515,13 +531,13 @@ class shx_file:
if self.__mode == 'w':
self.__iswritable()
header['File Length'] = (
- self.numRecords * calcsize('>ii') + 100) / 2
+ 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])
+ values.extend([off // 2, size // 2])
self.fileObj.write(pack(fmt, *values))
self.fileObj.close()
diff --git a/pysal/core/util/tests/test_shapefile.py b/pysal/core/util/tests/test_shapefile.py
index 06fed2b..a9eee1b 100644
--- a/pysal/core/util/tests/test_shapefile.py
+++ b/pysal/core/util/tests/test_shapefile.py
@@ -1,9 +1,24 @@
import unittest
-from cStringIO import StringIO
+
+import sys
+PY3 = int(sys.version[0]) > 2
+if PY3:
+ import io
+else:
+ 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
+def bufferIO(buf):
+ """
+ Temp stringIO function to force compat
+ """
+ if PY3:
+ return io.BytesIO(buf)
+ else:
+ return StringIO(buf)
class TestNoneMax(unittest.TestCase):
def test_none_max(self):
@@ -152,7 +167,7 @@ class TestPoint(unittest.TestCase):
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")
+ dat = bufferIO(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))
@@ -172,7 +187,7 @@ class TestPolyLine(unittest.TestCase):
self.assertEqual(expected, PolyLine.pack(record))
def test_unpack(self):
- dat = StringIO(b"""\x03\x00\x00\x00\xc0\x46\x52\x3a\xdd\x8a\x82\
+ dat = bufferIO(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\
@@ -197,7 +212,7 @@ class TestPointZ(unittest.TestCase):
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@")
+ dat = bufferIO(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))
@@ -214,7 +229,7 @@ class TestPolyLineZ(unittest.TestCase):
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$@\x00\x00\x00\x [...]
+ dat = bufferIO(b"""\r\x00\x00\x00\xc0FR:\xdd\x8a\x82\xbf=\xc1e\xce\xc7\x92\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4~?k@\x7f`\\\x88\xd0\xbf\x01\x00\x00\x00\x03\x00\x00\x00\x00\x00\x00\x00\xc0FR:\xdd\x8a\x82\xbfk@\x7f`\\\x88\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4~?=\xc1e\xce\xc7\x92\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4~?=\xc1e\xce\xc7\x92\xd0\xbf\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00$@\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))
@@ -241,7 +256,7 @@ class TestPolygonZ(unittest.TestCase):
'Mmax': 4,
'Marray': [2, 4, 2, 2]
}
- dat = StringIO(PolygonZ.pack(record))
+ dat = bufferIO(PolygonZ.pack(record))
self.assertEqual(record, PolygonZ.unpack(dat))
diff --git a/pysal/esda/mapclassify.py b/pysal/esda/mapclassify.py
index 97c526c..bc91801 100644
--- a/pysal/esda/mapclassify.py
+++ b/pysal/esda/mapclassify.py
@@ -1,6 +1,8 @@
"""
A module of classification schemes for choropleth mapping.
"""
+
+
__author__ = "Sergio J. Rey"
__all__ = ['Map_Classifier', 'quantile', 'Box_Plot', 'Equal_Interval',
@@ -8,7 +10,7 @@ __all__ = ['Map_Classifier', 'quantile', 'Box_Plot', 'Equal_Interval',
'Jenks_Caspall_Forced', 'Jenks_Caspall_Sampled',
'Max_P_Classifier', 'Maximum_Breaks', 'Natural_Breaks',
'Quantiles', 'Percentiles', 'Std_Mean', 'User_Defined',
- 'gadf', 'K_classifiers']
+ 'gadf', 'K_classifiers', 'HeadTail_Breaks']
K = 5 # default number of classes in any map scheme with this as an argument
@@ -17,6 +19,19 @@ import scipy.stats as stats
import scipy as sp
import copy
import sys
+from scipy.cluster.vq import kmeans as KMEANS
+
+
+def headTail_breaks(values, cuts):
+ """
+ head tail breaks helper function
+ """
+ values = np.array(values)
+ mean = np.mean(values)
+ cuts.append(mean)
+ if (len(values) > 1):
+ return headTail_breaks(values[values >= mean], cuts)
+ return cuts
def quantile(y, k=4):
@@ -237,7 +252,7 @@ def bin1d(x, bins):
k -= 1
l, r = cuts.pop(-1)
binIds += (x > l) * (x <= r) * k
- counts = np.bincount(binIds)
+ counts = np.bincount(binIds, minlength=len(bins))
return (binIds, counts)
@@ -252,9 +267,35 @@ def load_example():
return cal
-def natural_breaks(values, k=5, itmax=100):
+def _kmeans(y, k=5):
+ """
+ Helper function to do kmeans in one dimension
+ """
+
+ y = y * 1. # KMEANS needs float or double dtype
+ centroids = KMEANS(y, k)[0]
+ centroids.sort()
+ try:
+ class_ids = np.abs(y - centroids).argmin(axis=1)
+ except:
+ class_ids = np.abs(y[:, np.newaxis] - centroids).argmin(axis=1)
+
+ uc = np.unique(class_ids)
+ cuts = np.array([y[class_ids == c].max() for c in uc])
+ y_cent = np.zeros_like(y)
+ for c in uc:
+ y_cent[class_ids == c] = centroids[c]
+ diffs = y - y_cent
+ diffs *= diffs
+
+ return class_ids, cuts, diffs.sum(), centroids
+
+
+def natural_breaks(values, k=5):
"""
natural breaks helper function
+
+ Jenks natural breaks is kmeans in one dimension
"""
values = np.array(values)
uv = np.unique(values)
@@ -263,38 +304,12 @@ def natural_breaks(values, k=5, itmax=100):
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
+ kres = _kmeans(values, k)
+ sids = kres[-1] # centroids
+ fit = kres[-2]
+ class_ids = kres[0]
+ cuts = kres[1]
+ return (sids, class_ids, fit, cuts)
def _fisher_jenks_means(values, classes=5, sort=True):
@@ -371,10 +386,10 @@ class Map_Classifier:
"""
Abstract class for all map classifications [Slocum2008]_
- 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:
+ 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::
@@ -385,14 +400,13 @@ class Map_Classifier:
-
-
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.HeadTail_Breaks`
* :class:`~pysal.esda.mapclassify.Jenks_Caspall`
* :class:`~pysal.esda.mapclassify.Jenks_Caspall_Forced`
* :class:`~pysal.esda.mapclassify.Jenks_Caspall_Sampled`
@@ -531,6 +545,68 @@ class Map_Classifier:
return table
+class HeadTail_Breaks(Map_Classifier):
+ """
+ Head/tail Breaks Map Classification for Heavy-tailed Distributions
+
+ Parameters
+ ----------
+ y : array
+ (n,1), values to classify
+ 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()
+ >>> htb = HeadTail_Breaks(cal)
+ >>> htb.k
+ 3
+ >>> htb.counts
+ array([50, 7, 1])
+ >>> htb.bins
+ array([ 125.92810345, 811.26 , 4111.45 ])
+ >>> np.random.seed(123456)
+ >>> x = np.random.lognormal(3, 1, 1000)
+ >>> htb = HeadTail_Breaks(x)
+ >>> htb.bins
+ array([ 32.26204423, 72.50205622, 128.07150107, 190.2899093 ,
+ 264.82847377, 457.88157946, 576.76046949])
+ >>> htb.counts
+ array([695, 209, 62, 22, 10, 1, 1])
+
+ Notes
+ -----
+
+ Head/tail Breaks is a relatively new classification method developed
+ and introduced by [Jiang2013]_ for data with a heavy-tailed distribution.
+
+
+ Based on contributions by Alessandra Sozzi <alessandra.sozzi at gmail.com>.
+
+ """
+ def __init__(self, y):
+ Map_Classifier.__init__(self, y)
+ self.name = 'HeadTail_Breaks'
+
+ def _set_bins(self):
+
+ x = self.y.copy()
+ bins = []
+ bins = headTail_breaks(x, bins)
+ self.bins = np.array(bins)
+ self.k = len(self.bins)
+
+
class Equal_Interval(Map_Classifier):
"""
Equal Interval Classification
@@ -977,20 +1053,20 @@ class Natural_Breaks(Map_Classifier):
Examples
--------
- >>> import numpy as np
- >>> np.random.seed(10)
- >>> cal = load_example()
- >>> nb = Natural_Breaks(cal, k = 5)
+ >>> import numpy
+ >>> import pysal
+ >>> numpy.random.seed(123456)
+ >>> cal = pysal.esda.mapclassify.load_example()
+ >>> nb = pysal.Natural_Breaks(cal, k=5)
>>> nb.k
5
>>> nb.counts
- array([14, 13, 14, 10, 7])
+ array([41, 9, 6, 1, 1])
>>> nb.bins
- array([ 1.81000000e+00, 7.60000000e+00, 2.98200000e+01,
- 1.81270000e+02, 4.11145000e+03])
- >>> x = np.array([1] * 50)
+ array([ 29.82, 110.74, 370.5 , 722.85, 4111.45])
+ >>> x = numpy.array([1] * 50)
>>> x[-1] = 20
- >>> nb = Natural_Breaks(x, k = 5, initial = 0)
+ >>> nb = pysal.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
@@ -1002,11 +1078,10 @@ class Natural_Breaks(Map_Classifier):
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
+ 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.
+ multiple runs of Natural_Breaks on the same data, set initial to a higher
+ value.
"""
@@ -1020,16 +1095,29 @@ class Natural_Breaks(Map_Classifier):
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]
+ values = np.array(x)
+ 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
+ uv.sort()
+ # we set the bins equal to the sorted unique values and ramp k
+ # downwards. no need to call kmeans.
+ self.bins = uv
+ self.k = k
+ else:
+ # find an initial solution and then try to find an improvement
+ res0 = natural_breaks(x, k)
+ fit = res0[2]
+ for i in xrange(self.initial):
+ res = natural_breaks(x, k)
+ fit_i = res[2]
+ if fit_i < fit:
+ res0 = res
+ self.bins = np.array(res0[-1])
+ self.k = len(self.bins)
class Fisher_Jenks(Map_Classifier):
@@ -1882,7 +1970,6 @@ def opt_part(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
diff --git a/pysal/esda/moran.py b/pysal/esda/moran.py
index bd75770..c04862f 100644
--- a/pysal/esda/moran.py
+++ b/pysal/esda/moran.py
@@ -2,16 +2,16 @@
Moran's I Spatial Autocorrelation Statistics
"""
-__author__ = "Sergio J. Rey <srey at asu.edu>"
+__author__ = "Sergio J. Rey <srey at asu.edu>, \
+ Dani Arribas-Bel <daniel.arribas.bel at gmail.com>"
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"]
+ "Moran_Local_BV", "Moran_Rate", "Moran_Local_Rate"]
-np.seterr(invalid='ignore')
PERMUTATIONS = 999
@@ -128,11 +128,9 @@ class Moran:
>>> mi_1.p_norm
5.7916539074498452e-05
-
- 5.7916539074498452e-05
"""
def __init__(self, y, w, transformation="r", permutations=PERMUTATIONS,
- two_tailed=True):
+ two_tailed=True):
self.y = y
w.transform = transformation
self.w = w
@@ -153,7 +151,6 @@ class Moran:
self.p_norm *= 2.
self.p_rand *= 2.
-
if permutations:
sim = [self.__calc(np.random.permutation(self.z))
for i in xrange(permutations)]
@@ -175,7 +172,6 @@ class Moran:
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 = (z * z).sum()
@@ -312,6 +308,8 @@ class Moran_BV:
zx = (x - x.mean()) / x.std(ddof=1)
self.zx = zx
self.zy = zy
+ n = x.shape[0]
+ self.den = n - 1. # zx'zx = zy'zy = n-1
w.transform = transformation
self.w = w
self.I = self.__calc(zy)
@@ -336,7 +334,6 @@ class Moran_BV:
def __calc(self, zy):
wzy = slag(self.w, zy)
self.num = (self.zx * wzy).sum()
- self.den = (zy * zy).sum()
return self.num / self.den
@@ -602,7 +599,8 @@ class Moran_Local:
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 = 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])
@@ -611,7 +609,7 @@ class Moran_Local:
moved into unittests that are conditional on architectures
"""
def __init__(self, y, w, transformation="r", permutations=PERMUTATIONS,
- geoda_quads=False):
+ geoda_quads=False):
self.y = y
n = len(y)
self.n = n
@@ -694,8 +692,197 @@ class Moran_Local:
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
+ self.q = self.quads[0] * pp + self.quads[1] * np + self.quads[2] * nn \
+ + self.quads[3] * pn
+
+
+class Moran_Local_BV:
+ """Bivariate Local Moran Statistics
+
+
+ Parameters
+ ----------
+ x : array
+ x-axis variable
+ y : array
+ (n,1), wy will be on y axis
+ w : W
+ weight instance assumed to be aligned with y
+ transformation : {'R', 'B', 'D', 'U', 'V'}
+ 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
+ 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
+ ----------
+
+ 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
+ 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("sids2.gal")).read()
+ >>> f = ps.open(ps.examples.get_path("sids2.dbf"))
+ >>> x = np.array(f.by_col['SIDR79'])
+ >>> y = np.array(f.by_col['SIDR74'])
+ >>> lm = ps.Moran_Local_BV(x, y, w, transformation = "r", \
+ permutations = 99)
+ >>> lm.q[:10]
+ array([3, 4, 3, 4, 2, 1, 4, 4, 2, 4])
+ >>> lm.p_z_sim[0]
+ 0.0017240031348827456
+ >>> lm = ps.Moran_Local_BV(x, y, w, transformation = "r", \
+ permutations = 99, geoda_quads=True)
+ >>> lm.q[:10]
+ array([2, 4, 2, 4, 3, 1, 4, 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, x, y, w, transformation="r", permutations=PERMUTATIONS,
+ geoda_quads=False):
+ self.y = y
+ n = len(y)
+ self.n = n
+ self.n_1 = n - 1
+ zx = x - x.mean()
+ zy = y - y.mean()
+ # setting for floating point noise
+ orig_settings = np.seterr()
+ np.seterr(all="ignore")
+ sx = x.std()
+ zx /= sx
+ sy = y.std()
+ zy /= sy
+ np.seterr(**orig_settings)
+ self.zx = zx
+ self.zy = zy
+ w.transform = transformation
+ self.w = w
+ self.permutations = permutations
+ self.den = (zx * zx).sum()
+ self.Is = self.calc(self.w, self.zx, self.zy)
+ 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 = above.sum(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, zx, zy):
+ zly = slag(w, zy)
+ return self.n_1 * self.zx * zly / 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.
+
+ """
+ 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]
+
+ zx = self.zx
+ zy = self.zy
+ for i in xrange(self.w.n):
+ idsi = ids[ids != i]
+ np.random.shuffle(idsi)
+ tmp = zy[idsi[rids[:, 0:wc[i]]]]
+ lisas[i] = zx[i] * (w[i] * tmp).sum(1)
+ self.rlisas = (n_1 / self.den) * lisas
+
+ def __quads(self):
+ zl = slag(self.w, self.zy)
+ zp = self.zx > 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):
@@ -809,17 +996,3 @@ class Moran_Local_Rate(Moran_Local):
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
index 6774a83..4bd9f90 100644
--- a/pysal/esda/smoothing.py
+++ b/pysal/esda/smoothing.py
@@ -151,7 +151,7 @@ def sum_by_n(d, w, n):
"""
t = len(d)
- h = t / n
+ h = t // n #must be floor!
d = d * w
return np.array([sum(d[i: i + h]) for i in range(0, t, h)])
@@ -205,7 +205,7 @@ def crude_age_standardization(e, b, n):
"""
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)
+ age_weight = b * 1.0 / b_by_n.repeat(len(e) // n)
return sum_by_n(r, age_weight, n)
@@ -269,13 +269,13 @@ def direct_age_standardization(e, b, s, n, alpha=0.05):
[0.023744019138755977, 0.026650717703349279]
"""
- age_weight = (1.0 / b) * (s * 1.0 / sum_by_n(s, 1.0, n).repeat(len(s) / n))
+ 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)]
+ 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)
@@ -902,7 +902,7 @@ class Age_Adjusted_Smoother:
"""
def __init__(self, e, b, w, s, alpha=0.05):
t = len(e)
- h = t / w.n
+ h = t // w.n
w.transform = 'b'
e_n, b_n = [], []
for i in range(h):
@@ -1194,8 +1194,8 @@ class Spatial_Filtering:
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]
+ 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:
diff --git a/pysal/esda/tests/test_mapclassify.py b/pysal/esda/tests/test_mapclassify.py
index 0bca060..d8ebc7e 100644
--- a/pysal/esda/tests/test_mapclassify.py
+++ b/pysal/esda/tests/test_mapclassify.py
@@ -109,7 +109,7 @@ class TestNaturalBreaks(unittest.TestCase):
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]))
+ nb.counts, np.array([41, 9, 6, 1, 1]))
def test_Natural_Breaks_stability(self):
for i in range(10):
@@ -125,6 +125,23 @@ class TestNaturalBreaks(unittest.TestCase):
self.assertEquals(len(nb.counts), 5)
+
+class TestHeadTailBreaks(unittest.TestCase):
+ def setUp(self):
+ x = range(1,1000)
+ y = []
+ for i in x:
+ y.append(i**(-2))
+ self.V = np.array(y)
+
+ def test_HeadTail_Breaks(self):
+ htb = HeadTail_Breaks(self.V)
+ self.assertEquals(htb.k, 4)
+ self.assertEquals(len(htb.counts), 4)
+ np.testing.assert_array_almost_equal(
+ htb.counts, np.array([975, 21, 2, 1]))
+
+
class TestMapClassifier(unittest.TestCase):
def test_Map_Classifier(self):
# map__classifier = Map_Classifier(y)
diff --git a/pysal/esda/tests/test_moran.py b/pysal/esda/tests/test_moran.py
index 9701c75..f61e962 100644
--- a/pysal/esda/tests/test_moran.py
+++ b/pysal/esda/tests/test_moran.py
@@ -13,7 +13,7 @@ class Moran_Tester(unittest.TestCase):
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)
+ self.assertAlmostEquals(mi.p_norm, 0.00013573931385468807)
def test_sids(self):
w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
@@ -67,6 +67,23 @@ class Moran_Local_Tester(unittest.TestCase):
self.assertAlmostEquals(lm.VI_sim, 0.2067126047680822)
+class Moran_Local_BV_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.x = np.array(f.by_col['SIDR79'])
+ self.y = np.array(f.by_col['SIDR74'])
+
+ def test_Moran_Local_BV(self):
+ lm = moran.Moran_Local_BV(self.x, self.y, self.w,
+ transformation="r", permutations=99)
+ self.assertAlmostEquals(lm.Is[0], 1.4649221250620736)
+ self.assertAlmostEquals(lm.z_sim[0], 2.9246889997781773)
+ self.assertAlmostEquals(lm.p_z_sim[0], 0.0017240031348827456)
+ self.assertAlmostEquals(lm.VI_sim, 0.24983591065175745)
+
+
class Moran_Local_Rate_Tester(unittest.TestCase):
def setUp(self):
np.random.seed(10)
@@ -84,7 +101,9 @@ class Moran_Local_Rate_Tester(unittest.TestCase):
suite = unittest.TestSuite()
-test_classes = [Moran_Tester, Moran_BV_matrix_Tester, Moran_Local_Tester]
+test_classes = [Moran_Tester, Moran_Rate_Tester,
+ Moran_BV_matrix_Tester, Moran_Local_Tester,
+ Moran_Local_BV_Tester, Moran_Local_Rate_Tester]
for i in test_classes:
a = unittest.TestLoader().loadTestsFromTestCase(i)
suite.addTest(a)
diff --git a/pysal/examples/10740/10740.dbf b/pysal/examples/10740/10740.dbf
new file mode 100644
index 0000000..4dc3bd5
Binary files /dev/null and b/pysal/examples/10740/10740.dbf differ
diff --git a/pysal/examples/10740/10740.shp b/pysal/examples/10740/10740.shp
new file mode 100644
index 0000000..34ee937
Binary files /dev/null and b/pysal/examples/10740/10740.shp differ
diff --git a/pysal/examples/10740/10740.shx b/pysal/examples/10740/10740.shx
new file mode 100644
index 0000000..9f72583
Binary files /dev/null and b/pysal/examples/10740/10740.shx differ
diff --git a/pysal/examples/10740/10740_queen.gal b/pysal/examples/10740/10740_queen.gal
new file mode 100644
index 0000000..72047ac
--- /dev/null
+++ b/pysal/examples/10740/10740_queen.gal
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diff --git a/pysal/examples/10740/10740_rook.gal b/pysal/examples/10740/10740_rook.gal
new file mode 100644
index 0000000..90f3237
--- /dev/null
+++ b/pysal/examples/10740/10740_rook.gal
@@ -0,0 +1,391 @@
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diff --git a/pysal/examples/10740/README.md b/pysal/examples/10740/README.md
new file mode 100644
index 0000000..ef6eccc
--- /dev/null
+++ b/pysal/examples/10740/README.md
@@ -0,0 +1,13 @@
+10740
+=====
+
+Albuquerque Census 2000 Tract Data
+----------------------------------
+
+Albuquerque, New Mexico, 2000 Census Tracts (n = 141)
+
+* 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
diff --git a/pysal/examples/Line/Line.dbf b/pysal/examples/Line/Line.dbf
new file mode 100644
index 0000000..b3f7f90
Binary files /dev/null and b/pysal/examples/Line/Line.dbf differ
diff --git a/pysal/examples/Line/Line.prj b/pysal/examples/Line/Line.prj
new file mode 100644
index 0000000..a30c00a
--- /dev/null
+++ b/pysal/examples/Line/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/Line.shp b/pysal/examples/Line/Line.shp
new file mode 100644
index 0000000..6e27301
Binary files /dev/null and b/pysal/examples/Line/Line.shp differ
diff --git a/pysal/examples/Line/Line.shx b/pysal/examples/Line/Line.shx
new file mode 100644
index 0000000..b27a051
Binary files /dev/null and b/pysal/examples/Line/Line.shx differ
diff --git a/pysal/examples/Line/README.md b/pysal/examples/Line/README.md
new file mode 100644
index 0000000..ea83d38
--- /dev/null
+++ b/pysal/examples/Line/README.md
@@ -0,0 +1,7 @@
+Point
+=====
+
+Point Shapefile
+---------------
+
+Used for testing
diff --git a/pysal/examples/Point/Point.dbf b/pysal/examples/Point/Point.dbf
new file mode 100644
index 0000000..13e7563
Binary files /dev/null and b/pysal/examples/Point/Point.dbf differ
diff --git a/pysal/examples/Point/Point.prj b/pysal/examples/Point/Point.prj
new file mode 100644
index 0000000..a30c00a
--- /dev/null
+++ b/pysal/examples/Point/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/Point.shp b/pysal/examples/Point/Point.shp
new file mode 100644
index 0000000..846c2b3
Binary files /dev/null and b/pysal/examples/Point/Point.shp differ
diff --git a/pysal/examples/Point/Point.shx b/pysal/examples/Point/Point.shx
new file mode 100644
index 0000000..8a9f027
Binary files /dev/null and b/pysal/examples/Point/Point.shx differ
diff --git a/pysal/examples/Polygon/Polygon.dbf b/pysal/examples/Polygon/Polygon.dbf
new file mode 100644
index 0000000..d925117
Binary files /dev/null and b/pysal/examples/Polygon/Polygon.dbf differ
diff --git a/pysal/examples/Polygon/Polygon.prj b/pysal/examples/Polygon/Polygon.prj
new file mode 100644
index 0000000..a30c00a
--- /dev/null
+++ b/pysal/examples/Polygon/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/Polygon.shp b/pysal/examples/Polygon/Polygon.shp
new file mode 100644
index 0000000..3ad50c2
Binary files /dev/null and b/pysal/examples/Polygon/Polygon.shp differ
diff --git a/pysal/examples/Polygon/Polygon.shx b/pysal/examples/Polygon/Polygon.shx
new file mode 100644
index 0000000..eb12139
Binary files /dev/null and b/pysal/examples/Polygon/Polygon.shx differ
diff --git a/pysal/examples/Polygon/README.md b/pysal/examples/Polygon/README.md
new file mode 100644
index 0000000..3f3a53b
--- /dev/null
+++ b/pysal/examples/Polygon/README.md
@@ -0,0 +1,7 @@
+Polygon
+=======
+
+Polygon Shapefile
+-----------------
+
+Used for testing
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..7cd42bd
--- /dev/null
+++ b/pysal/examples/__init__.py
@@ -0,0 +1,77 @@
+import os
+import pysal
+
+__all__ = ['get_path']
+
+base = os.path.split(pysal.__file__)[0]
+example_dir = os.path.join(base, "examples")
+dirs = next(os.walk(example_dir))[1]
+file_2_dir = {}
+
+for d in dirs:
+ tmp = os.path.join(example_dir, d)
+ files_in_tmp = os.listdir(tmp)
+ for f in files_in_tmp:
+ file_2_dir[f] = tmp
+
+
+def get_path(example_name):
+ """
+ Get path of PySAL or example folders
+ """
+ if type(example_name) != str:
+ try:
+ example_name = str(example_name)
+ except:
+ raise KeyError('Cannot coerce requested example name to string')
+ if example_name in dirs:
+ return os.path.join(example_dir, example_name, example_name)
+ elif example_name in file_2_dir:
+ d = file_2_dir[example_name]
+ return os.path.join(d, example_name)
+ elif example_name == "":
+ return os.path.join(base, 'examples', example_name)
+ else:
+ raise KeyError(example_name + ' not found in PySAL built-in examples.')
+
+
+def available(verbose=False):
+ """
+ List available datasets in pysal.examples
+ """
+ base = get_path('')
+ examples = [os.path.join(get_path(''), d) for d in os.listdir(base)]
+ examples = [d for d in examples if os.path.isdir(d) and '__' not in d]
+ if not verbose:
+ return [os.path.split(d)[-1] for d in examples]
+ examples = [os.path.join(dty, 'README.md') for dty in examples]
+ descs = [_read_example(path) for path in examples]
+ return [{desc['name']:desc['description'] for desc in descs}]
+
+
+def _read_example(pth):
+ try:
+ with open(pth, 'r') as io:
+ title = io.readline().strip('\n')
+ io.readline() # titling
+ io.readline() # pad
+ short = io.readline().strip('\n')
+ io.readline() # subtitling
+ io.readline() # pad
+ rest = io.readlines()
+ rest = [l.strip('\n') for l in rest if l.strip('\n') != '']
+ d = {'name': title, 'description': short, 'explanation': rest}
+ except IOError:
+ basename = os.path.split(pth)[-2]
+ dirname = os.path.split(basename)[-1]
+ d = {'name': dirname, 'description': None, 'explanation': None}
+ return d
+
+
+def explain(name): # would be nice to use pandas for display here
+ """
+ Explain a dataset by name
+ """
+ path = os.path.split(pysal.examples.get_path(name))[0]
+ fpath = os.path.join(path, 'README.md')
+ return _read_example(fpath)
diff --git a/pysal/examples/arcgis/README.md b/pysal/examples/arcgis/README.md
new file mode 100644
index 0000000..8204b83
--- /dev/null
+++ b/pysal/examples/arcgis/README.md
@@ -0,0 +1,7 @@
+arcgis
+======
+
+arcgis testing files
+--------------------
+
+Files used for internal testing
diff --git a/pysal/examples/arcgis/arcgis_ohio.dbf b/pysal/examples/arcgis/arcgis_ohio.dbf
new file mode 100644
index 0000000..34cef0a
Binary files /dev/null and b/pysal/examples/arcgis/arcgis_ohio.dbf differ
diff --git a/pysal/examples/arcgis/arcgis_txt.txt b/pysal/examples/arcgis/arcgis_txt.txt
new file mode 100644
index 0000000..1063e70
--- /dev/null
+++ b/pysal/examples/arcgis/arcgis_txt.txt
@@ -0,0 +1,9 @@
+StationID
+1 1 0.0
+1 2 0.1
+1 3 0.14286
+2 1 0.1
+2 3 0.05
+3 1 0.16667
+3 2 0.06667
+3 3 0.0
\ No newline at end of file
diff --git a/pysal/examples/baltim/README.md b/pysal/examples/baltim/README.md
new file mode 100644
index 0000000..00949c5
--- /dev/null
+++ b/pysal/examples/baltim/README.md
@@ -0,0 +1,16 @@
+baltim
+======
+
+Baltimore house sales prices and hedonics
+-----------------------------------------
+
+* baltim.dbf attribute data
+* baltim.shp shape file
+* baltim.shx spatial index file
+* baltim.tri.k12.kwt Kernel weights using a triangular kernel with 12 neares
+ neighbors
+* baltim_k4.gwt Nearest neighbor weights (4nn)
+* baltim_q.gal Queen contiguity file
+* baltimore.geojson
+
+Point data, n=211, k= 17.
diff --git a/pysal/examples/baltim/baltim.dbf b/pysal/examples/baltim/baltim.dbf
new file mode 100644
index 0000000..be9d815
Binary files /dev/null and b/pysal/examples/baltim/baltim.dbf differ
diff --git a/pysal/examples/baltim/baltim.shp b/pysal/examples/baltim/baltim.shp
new file mode 100644
index 0000000..f867886
Binary files /dev/null and b/pysal/examples/baltim/baltim.shp differ
diff --git a/pysal/examples/baltim/baltim.shx b/pysal/examples/baltim/baltim.shx
new file mode 100644
index 0000000..8414722
Binary files /dev/null and b/pysal/examples/baltim/baltim.shx differ
diff --git a/pysal/examples/baltim/baltim.tri.k12.kwt b/pysal/examples/baltim/baltim.tri.k12.kwt
new file mode 100644
index 0000000..1343b39
--- /dev/null
+++ b/pysal/examples/baltim/baltim.tri.k12.kwt
@@ -0,0 +1,2744 @@
+0 211 baltim.shp STATION
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diff --git a/pysal/examples/baltim/baltim_k4.gwt b/pysal/examples/baltim/baltim_k4.gwt
new file mode 100644
index 0000000..1f77dc6
--- /dev/null
+++ b/pysal/examples/baltim/baltim_k4.gwt
@@ -0,0 +1,845 @@
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new file mode 100644
index 0000000..1c8c631
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+]
+}
diff --git a/pysal/examples/book/README.md b/pysal/examples/book/README.md
new file mode 100644
index 0000000..b9dc8b9
--- /dev/null
+++ b/pysal/examples/book/README.md
@@ -0,0 +1,10 @@
+book
+====
+
+Synthetic data to illustrate spatial weights
+--------------------------------------------
+
+* book.gal: rook contiguity for regular lattice
+* book.txt: attribute data for regular lattice
+
+Source: Anselin, L. and S.J. Rey (in progress) Spatial Econometrics: Foundations.
diff --git a/pysal/examples/book/book.gal b/pysal/examples/book/book.gal
new file mode 100644
index 0000000..4d6fc1d
--- /dev/null
+++ b/pysal/examples/book/book.gal
@@ -0,0 +1,33 @@
+16
+1 2
+2 5
+2 3
+1 3 6
+3 3
+2 4 7
+4 2
+3 8
+5 3
+1 6 9
+6 4
+2 5 7 10
+7 4
+3 6 8 11
+8 3
+4 7 12
+9 3
+5 10 13
+10 4
+6 9 11 14
+11 4
+7 10 12 15
+12 3
+8 11 16
+13 2
+9 14
+14 3
+10 13 15
+15 3
+11 14 16
+16 2
+12 15
diff --git a/pysal/examples/book/book.txt b/pysal/examples/book/book.txt
new file mode 100644
index 0000000..87bd9c9
--- /dev/null
+++ b/pysal/examples/book/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
+2,41,-4.375,19.140625,3.663635e+02,74.921875
+3,38,-7.375,54.390625,2.958340e+03,67.296875
+4,45,-0.375,0.140625,1.977539e-02,4.031250
+5,39,-6.375,40.640625,1.651660e+03,45.421875
+6,42,-3.375,11.390625,1.297463e+02,38.812500
+7,41,-4.375,19.140625,3.663635e+02,50.312500
+8,42,-3.375,11.390625,1.297463e+02,-2.953125
+9,48,2.625,6.890625,4.748071e+01,-2.953125
+10,49,3.625,13.140625,1.726760e+02,27.187500
+11,48,2.625,6.890625,4.748071e+01,24.937500
+12,51,5.625,31.640625,1.001129e+03,49.921875
+13,47,1.625,2.640625,6.972900e+00,13.406250
+14,51,5.625,31.640625,1.001129e+03,55.546875
+15,50,4.625,21.390625,4.575588e+02,82.671875
+16,55,9.625,92.640625,8.582285e+03,98.6562509
diff --git a/pysal/examples/burkitt/README.md b/pysal/examples/burkitt/README.md
new file mode 100644
index 0000000..de37cf8
--- /dev/null
+++ b/pysal/examples/burkitt/README.md
@@ -0,0 +1,11 @@
+burkitt
+=======
+
+Burkitt's lymphoma in the Western Nile district of Uganda
+---------------------------------------------------------
+
+ * burkitt.dbf attribute file
+ * burkitt.shp shapefile
+ * burkitt.shx spatial index
+
+ Point data, n=188, k=6
diff --git a/pysal/examples/burkitt/burkitt.dbf b/pysal/examples/burkitt/burkitt.dbf
new file mode 100644
index 0000000..e7ecd09
Binary files /dev/null and b/pysal/examples/burkitt/burkitt.dbf differ
diff --git a/pysal/examples/burkitt/burkitt.shp b/pysal/examples/burkitt/burkitt.shp
new file mode 100644
index 0000000..bcd3d72
Binary files /dev/null and b/pysal/examples/burkitt/burkitt.shp differ
diff --git a/pysal/examples/burkitt/burkitt.shx b/pysal/examples/burkitt/burkitt.shx
new file mode 100644
index 0000000..459563f
Binary files /dev/null and b/pysal/examples/burkitt/burkitt.shx differ
diff --git a/pysal/examples/calemp/README.md b/pysal/examples/calemp/README.md
new file mode 100644
index 0000000..76c0e9c
--- /dev/null
+++ b/pysal/examples/calemp/README.md
@@ -0,0 +1,15 @@
+calemp
+======
+
+Employment density for California counties
+------------------------------------------
+
+* calempdensity.csv: data on employment and employment density in California
+ counties.
+
+Polygon data, n=58, k=11.
+
+Source: Anselin, L. and S.J. Rey (in progress) Spatial Econometrics: Foundations.
+
+
+
diff --git a/pysal/examples/calemp/calempdensity.csv b/pysal/examples/calemp/calempdensity.csv
new file mode 100644
index 0000000..bfe8734
--- /dev/null
+++ b/pysal/examples/calemp/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"
+"05000US06001","06001","Alameda County, California","Alameda County, California","00","06",630171,630171,5,1910.1,329.92
+"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
+"05000US06067","06067","Sacramento County, California","Sacramento County, California","00","06",480346,480346,5,2501.1,192.05
+"05000US06069","06069","San Benito County, California","San Benito County, California","00","06",12163,12163,2,3597.9,3.38
+"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/chicago/Chicago77.dbf b/pysal/examples/chicago/Chicago77.dbf
new file mode 100644
index 0000000..cfa0934
Binary files /dev/null and b/pysal/examples/chicago/Chicago77.dbf differ
diff --git a/pysal/examples/chicago/Chicago77.shp b/pysal/examples/chicago/Chicago77.shp
new file mode 100644
index 0000000..ee30858
Binary files /dev/null and b/pysal/examples/chicago/Chicago77.shp differ
diff --git a/pysal/examples/chicago/Chicago77.shx b/pysal/examples/chicago/Chicago77.shx
new file mode 100644
index 0000000..56f35b4
Binary files /dev/null and b/pysal/examples/chicago/Chicago77.shx differ
diff --git a/pysal/examples/chicago/README.md b/pysal/examples/chicago/README.md
new file mode 100644
index 0000000..2184228
--- /dev/null
+++ b/pysal/examples/chicago/README.md
@@ -0,0 +1,11 @@
+chicago
+=======
+
+Chicago neighborhoods
+--------------------
+
+ * Chicago77.dbf attribute file
+ * Chicago77.shp shapefile
+ * Chicago77.shx spatial index
+
+Polygons, n=77, k=11
diff --git a/pysal/examples/columbus/columbus.dbf b/pysal/examples/columbus/columbus.dbf
new file mode 100644
index 0000000..f850bda
Binary files /dev/null and b/pysal/examples/columbus/columbus.dbf differ
diff --git a/pysal/examples/columbus/columbus.gal b/pysal/examples/columbus/columbus.gal
new file mode 100644
index 0000000..b8edabc
--- /dev/null
+++ b/pysal/examples/columbus/columbus.gal
@@ -0,0 +1,99 @@
+49
+1 2
+2 3
+2 3
+4 3 1
+3 4
+5 4 2 1
+4 4
+8 3 5 2
+5 8
+16 15 11 8 9 6 3 4
+6 2
+9 5
+7 4
+14 13 12 8
+8 6
+13 12 11 5 4 7
+9 8
+26 25 22 20 15 10 6 5
+10 4
+22 20 17 9
+11 5
+16 15 12 5 8
+12 6
+16 14 13 11 8 7
+13 4
+14 12 7 8
+14 6
+19 12 13 16 18 7
+15 6
+25 16 26 5 9 11
+16 8
+25 24 18 15 5 11 12 14
+17 3
+23 20 10
+18 4
+24 19 16 14
+19 3
+18 24 14
+20 10
+35 33 27 22 23 32 40 17 10 9
+21 3
+34 24 30
+22 6
+28 27 26 20 10 9
+23 3
+32 17 20
+24 7
+30 29 25 16 18 21 19
+25 8
+30 29 15 26 28 16 24 9
+26 6
+29 28 22 9 25 15
+27 4
+33 28 20 22
+28 9
+38 37 29 27 33 35 22 26 25
+29 7
+37 30 28 38 24 25 26
+30 5
+37 29 24 25 21
+31 3
+39 36 34
+32 4
+41 40 23 20
+33 4
+35 20 27 28
+34 4
+42 36 21 31
+35 7
+44 43 38 40 20 33 28
+36 5
+46 39 34 42 31
+37 6
+45 38 43 28 29 30
+38 6
+43 35 44 28 37 29
+39 3
+46 36 31
+40 5
+47 41 32 35 20
+41 3
+47 32 40
+42 2
+34 36
+43 6
+48 45 35 44 38 37
+44 5
+49 48 35 43 38
+45 4
+48 49 37 43
+46 2
+36 39
+47 2
+40 41
+48 4
+49 44 43 45
+49 3
+44 48 45
diff --git a/pysal/examples/columbus/columbus.html b/pysal/examples/columbus/columbus.html
new file mode 100644
index 0000000..af8b1ac
--- /dev/null
+++ b/pysal/examples/columbus/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/columbus.json b/pysal/examples/columbus/columbus.json
new file mode 100644
index 0000000..579b7c3
--- /dev/null
+++ b/pysal/examples/columbus/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": [...]
+,
+{ "type": "Feature", "properties": { "AREA": 0.283079, "PERIMETER": 2.335634, "COLUMBUS_": 7.0, "COLUMBUS_I": 8.0, "POLYID": 6.0, "NEIG": 8, "HOVAL": 28.75, "INC": 16.028999, "CRIME": 26.066658, "OPEN": 0.563075, "PLUMB": 0.25413, "DISCBD": 3.78, "X": 43.75, "Y": 39.279999, "NSA": 1.0, "NSB": 1.0, "EW": 1.0, "CP": 0.0, "THOUS": 1000.0, "NEIGNO": 1008.0 }, "bbox": [ 9.333296775817871, 13.272419929504395, 10.180600166320801, 13.698240280151367 ], "geometry": { "type": "Polygon", "coordinat [...]
+,
+{ "type": "Feature", "properties": { "AREA": 0.257084, "PERIMETER": 2.554577, "COLUMBUS_": 8.0, "COLUMBUS_I": 4.0, "POLYID": 7.0, "NEIG": 4, "HOVAL": 75.0, "INC": 8.438, "CRIME": 0.178269, "OPEN": 0.0, "PLUMB": 2.402402, "DISCBD": 2.74, "X": 33.360001, "Y": 38.41, "NSA": 1.0, "NSB": 1.0, "EW": 0.0, "CP": 0.0, "THOUS": 1000.0, "NEIGNO": 1004.0 }, "bbox": [ 7.801973819732666, 12.942020416259766, 8.456572532653809, 13.644510269165039 ], "geometry": { "type": "Polygon", "coordinates": [ [ [ [...]
+,
+{ "type": "Feature", "properties": { "AREA": 0.204954, "PERIMETER": 2.139524, "COLUMBUS_": 9.0, "COLUMBUS_I": 3.0, "POLYID": 8.0, "NEIG": 3, "HOVAL": 37.125, "INC": 11.337, "CRIME": 38.425858, "OPEN": 3.483478, "PLUMB": 2.739726, "DISCBD": 2.89, "X": 36.709999, "Y": 38.709999, "NSA": 1.0, "NSB": 1.0, "EW": 0.0, "CP": 0.0, "THOUS": 1000.0, "NEIGNO": 1003.0 }, "bbox": [ 8.10498046875, 13.104069709777832, 8.733969688415527, 13.644430160522461 ], "geometry": { "type": "Polygon", "coordinates [...]
+,
+{ "type": "Feature", "properties": { "AREA": 0.500755, "PERIMETER": 3.169707, "COLUMBUS_": 10.0, "COLUMBUS_I": 18.0, "POLYID": 9.0, "NEIG": 18, "HOVAL": 52.599998, "INC": 17.586, "CRIME": 30.515917, "OPEN": 0.527488, "PLUMB": 0.890736, "DISCBD": 3.17, "X": 43.439999, "Y": 35.919998, "NSA": 1.0, "NSB": 1.0, "EW": 1.0, "CP": 0.0, "THOUS": 1000.0, "NEIGNO": 1018.0 }, "bbox": [ 9.124277114868164, 12.595190048217773, 10.095430374145508, 13.298540115356445 ], "geometry": { "type": "Polygon", " [...]
+,
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+{ "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/columbus.shp b/pysal/examples/columbus/columbus.shp
new file mode 100644
index 0000000..d6dde43
Binary files /dev/null and b/pysal/examples/columbus/columbus.shp differ
diff --git a/pysal/examples/columbus/columbus.shx b/pysal/examples/columbus/columbus.shx
new file mode 100644
index 0000000..7086b6f
Binary files /dev/null and b/pysal/examples/columbus/columbus.shx differ
diff --git a/pysal/examples/desmith/README.md b/pysal/examples/desmith/README.md
new file mode 100644
index 0000000..2ffd8ef
--- /dev/null
+++ b/pysal/examples/desmith/README.md
@@ -0,0 +1,13 @@
+desmith
+=======
+
+Small data set to illustrate Moran's I statistic
+------------------------------------------------
+
+Figure 5-31 of de Smith, Goodchild and Longley (2015)
+
+Polygons, n=12, k=1
+
+Source: <http://www.spatialanalysisonline.com/>
+
+Used with permission.
diff --git a/pysal/examples/desmith/desmith.gal b/pysal/examples/desmith/desmith.gal
new file mode 100644
index 0000000..43bb702
--- /dev/null
+++ b/pysal/examples/desmith/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/desmith.txt b/pysal/examples/desmith/desmith.txt
new file mode 100644
index 0000000..2b0194a
--- /dev/null
+++ b/pysal/examples/desmith/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/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/geodanet/README.md b/pysal/examples/geodanet/README.md
new file mode 100644
index 0000000..f8b53da
--- /dev/null
+++ b/pysal/examples/geodanet/README.md
@@ -0,0 +1,26 @@
+geodanet
+========
+
+Datasets from geodanet for network analysis
+-------------------------------------------
+
+* crimes.dbf attribute data for crime point set
+* crimes.prj projection
+* crimes.sbn spatial index
+* crimes.sbx spatial index
+* crimes.shp shapefile for crime point data
+* crimes.shp.xml metadata
+* crimes.shx shape index
+* schools.dbf attribute data for schools point set
+* schools.prj projection
+* schools.sbn spatial index
+* schools.sbx spatial index
+* schools.shp spatial index
+* schools.shp.xml metadata
+* schools.shx shape index
+* streets.dbf attribute data for street polyline
+* streets.prj projection
+* streets.sbn spatial index
+* streets.sbx spatial index
+* streets.shp shapefile
+* streets.shx shape index
diff --git a/pysal/examples/geodanet/crimes.dbf b/pysal/examples/geodanet/crimes.dbf
new file mode 100644
index 0000000..523c2c8
Binary files /dev/null and b/pysal/examples/geodanet/crimes.dbf differ
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
Binary files /dev/null and b/pysal/examples/geodanet/schools.dbf differ
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
Binary files /dev/null and b/pysal/examples/geodanet/schools.sbn differ
diff --git a/pysal/examples/geodanet/schools.sbx b/pysal/examples/geodanet/schools.sbx
new file mode 100644
index 0000000..abadf28
Binary files /dev/null and b/pysal/examples/geodanet/schools.sbx differ
diff --git a/pysal/examples/geodanet/schools.shp b/pysal/examples/geodanet/schools.shp
new file mode 100644
index 0000000..b71949e
Binary files /dev/null and b/pysal/examples/geodanet/schools.shp differ
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
Binary files /dev/null and b/pysal/examples/geodanet/streets.sbn differ
diff --git a/pysal/examples/geodanet/streets.sbx b/pysal/examples/geodanet/streets.sbx
new file mode 100644
index 0000000..5fc350e
Binary files /dev/null and b/pysal/examples/geodanet/streets.sbx differ
diff --git a/pysal/examples/geodanet/streets.shp b/pysal/examples/geodanet/streets.shp
new file mode 100644
index 0000000..b5809a7
Binary files /dev/null and b/pysal/examples/geodanet/streets.shp differ
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/README.md b/pysal/examples/juvenile/README.md
new file mode 100644
index 0000000..a9e1066
--- /dev/null
+++ b/pysal/examples/juvenile/README.md
@@ -0,0 +1,14 @@
+juvenile
+========
+
+Residences of juvenile offenders in Cardiff, UK
+-----------------------------------------------
+
+ * juvenile.dbf attribute file
+ * juvenile.gwt spatial weights
+ * juvenile.shp shapefile
+ * juvenile.shx spatial index
+
+Point data, unknown coordinates, n=168, k=3.
+
+Source: http://geodacenter.org/downloads/data-files/juvenile.zip
diff --git a/pysal/examples/juvenile/juvenile.dbf b/pysal/examples/juvenile/juvenile.dbf
new file mode 100644
index 0000000..c5e8e3f
Binary files /dev/null and b/pysal/examples/juvenile/juvenile.dbf differ
diff --git a/pysal/examples/juvenile/juvenile.gwt b/pysal/examples/juvenile/juvenile.gwt
new file mode 100644
index 0000000..6d9f409
--- /dev/null
+++ b/pysal/examples/juvenile/juvenile.gwt
@@ -0,0 +1,2803 @@
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diff --git a/pysal/examples/juvenile/juvenile.shp b/pysal/examples/juvenile/juvenile.shp
new file mode 100644
index 0000000..00f1c8c
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diff --git a/pysal/examples/juvenile/juvenile.shx b/pysal/examples/juvenile/juvenile.shx
new file mode 100644
index 0000000..b22e112
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diff --git a/pysal/examples/mexico/README.md b/pysal/examples/mexico/README.md
new file mode 100644
index 0000000..5fab293
--- /dev/null
+++ b/pysal/examples/mexico/README.md
@@ -0,0 +1,15 @@
+mexico
+======
+
+Mexican states regional income 1940-2000
+----------------------------------------
+
+Data used in Rey, S.J. and M.L. Sastre Gutierrez. (2010) "Interregional inequality
+dynamics in Mexico." Spatial Economic Analysis, 5: 277-298
+
+* mexico.csv: attribute data
+* mexico.gal: spatial weights in GAL format
+
+Polygon data, n=32, k=13
+
+
diff --git a/pysal/examples/mexico/mexico.csv b/pysal/examples/mexico/mexico.csv
new file mode 100644
index 0000000..14f87ac
--- /dev/null
+++ b/pysal/examples/mexico/mexico.csv
@@ -0,0 +1,33 @@
+State,pcgdp1940,pcgdp1950,pcgdp1960,pcgdp1970,pcgdp1980,pcgdp1990,pcgdp2000,hanson03,hanson98,esquivel99,inegi,inegi2
+Aguascalientes,10384.000,6234.000,8714.000,16078.000,21022.000,20787.000,27782.000,2.000,2.000,3.000,4.000,4.000
+Baja California,22361.000,20977.000,17865.000,25321.000,29283.000,26839.000,29855.000,1.000,1.000,5.000,1.000,1.000
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diff --git a/pysal/examples/nat/NAT.shx b/pysal/examples/nat/NAT.shx
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diff --git a/pysal/examples/nat/nat.geojson b/pysal/examples/nat/nat.geojson
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diff --git a/pysal/examples/nat/nat_queen.gal b/pysal/examples/nat/nat_queen.gal
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diff --git a/pysal/examples/nat/nat_queen_old.gal b/pysal/examples/nat/nat_queen_old.gal
new file mode 100644
index 0000000..555db04
--- /dev/null
+++ b/pysal/examples/nat/nat_queen_old.gal
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diff --git a/pysal/examples/nat/nat_trian_k20.kwt b/pysal/examples/nat/nat_trian_k20.kwt
new file mode 100644
index 0000000..7c08a95
--- /dev/null
+++ b/pysal/examples/nat/nat_trian_k20.kwt
@@ -0,0 +1,64786 @@
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diff --git a/pysal/examples/networks/nonplanarsegments.prj b/pysal/examples/networks/nonplanarsegments.prj
new file mode 100644
index 0000000..a30c00a
--- /dev/null
+++ b/pysal/examples/networks/nonplanarsegments.prj
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/pysal/examples/networks/nonplanarsegments.qpj b/pysal/examples/networks/nonplanarsegments.qpj
new file mode 100644
index 0000000..5fbc831
--- /dev/null
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new file mode 100644
index 0000000..a30c00a
--- /dev/null
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\ No newline at end of file
diff --git a/pysal/examples/newHaven/newhaven_nework.qpj b/pysal/examples/newHaven/newhaven_nework.qpj
new file mode 100644
index 0000000..5fbc831
--- /dev/null
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diff --git a/pysal/examples/sacramento2/sacramentot2.gal b/pysal/examples/sacramento2/sacramentot2.gal
new file mode 100644
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diff --git a/pysal/examples/sids2/README.md b/pysal/examples/sids2/README.md
new file mode 100644
index 0000000..bacdf3c
--- /dev/null
+++ b/pysal/examples/sids2/README.md
@@ -0,0 +1,14 @@
+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
+
+
+Source: http://geodacenter.org/downloads/data-files/sids2.zip
diff --git a/pysal/examples/sids2/sids2.dbf b/pysal/examples/sids2/sids2.dbf
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new file mode 100644
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diff --git a/pysal/examples/sids2/sids2.html b/pysal/examples/sids2/sids2.html
new file mode 100644
index 0000000..7936caa
--- /dev/null
+++ b/pysal/examples/sids2/sids2.html
@@ -0,0 +1,124 @@
+<?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>
diff --git a/pysal/examples/sids2/sids2.shp b/pysal/examples/sids2/sids2.shp
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diff --git a/pysal/examples/snow_maps/SohoPeople.dbf b/pysal/examples/snow_maps/SohoPeople.dbf
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diff --git a/pysal/examples/snow_maps/SohoPeople.prj b/pysal/examples/snow_maps/SohoPeople.prj
new file mode 100644
index 0000000..5c6f76d
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+++ b/pysal/examples/snow_maps/SohoPeople.prj
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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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diff --git a/pysal/examples/snow_maps/SohoWater.dbf b/pysal/examples/snow_maps/SohoWater.dbf
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diff --git a/pysal/examples/snow_maps/SohoWater.prj b/pysal/examples/snow_maps/SohoWater.prj
new file mode 100644
index 0000000..5c6f76d
--- /dev/null
+++ b/pysal/examples/snow_maps/SohoWater.prj
@@ -0,0 +1 @@
+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
--- /dev/null
+++ b/pysal/examples/snow_maps/Soho_Network.prj
@@ -0,0 +1 @@
+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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diff --git a/pysal/examples/south/south_q.gal b/pysal/examples/south/south_q.gal
new file mode 100644
index 0000000..8188264
--- /dev/null
+++ b/pysal/examples/south/south_q.gal
@@ -0,0 +1,2825 @@
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diff --git a/pysal/examples/south/south_queen.gal b/pysal/examples/south/south_queen.gal
new file mode 100644
index 0000000..c0758a8
--- /dev/null
+++ b/pysal/examples/south/south_queen.gal
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diff --git a/pysal/examples/stl/stl.gal b/pysal/examples/stl/stl.gal
new file mode 100644
index 0000000..be30409
--- /dev/null
+++ b/pysal/examples/stl/stl.gal
@@ -0,0 +1,157 @@
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diff --git a/pysal/examples/stl/stl_hom.csv b/pysal/examples/stl/stl_hom.csv
new file mode 100644
index 0000000..f21c203
--- /dev/null
+++ b/pysal/examples/stl/stl_hom.csv
@@ -0,0 +1,79 @@
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diff --git a/pysal/examples/stl/stl_hom.dbf b/pysal/examples/stl/stl_hom.dbf
new file mode 100644
index 0000000..b3cde10
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diff --git a/pysal/examples/stl/stl_hom.html b/pysal/examples/stl/stl_hom.html
new file mode 100644
index 0000000..e37239d
--- /dev/null
+++ b/pysal/examples/stl/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/stl_hom.shp b/pysal/examples/stl/stl_hom.shp
new file mode 100644
index 0000000..577420f
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diff --git a/pysal/examples/stl/stl_hom.shx b/pysal/examples/stl/stl_hom.shx
new file mode 100644
index 0000000..81eda1d
Binary files /dev/null and b/pysal/examples/stl/stl_hom.shx differ
diff --git a/pysal/examples/stl/stl_hom.txt b/pysal/examples/stl/stl_hom.txt
new file mode 100644
index 0000000..cf94cc8
--- /dev/null
+++ b/pysal/examples/stl/stl_hom.txt
@@ -0,0 +1,80 @@
+78,4
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+17119,6.715531,7.973957,83
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+29221,8.400979,3.263947,8
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+17055,7.543185,3.295762,16
+29186,5.125642,7.249679,4
+29187,4.428286,3.041846,10
+29169,3.429187,1.618018,7
+17077,2.610668,4.910801,8
+29157,7.23493,1.991457,6
+17199,6.842239,3.146192,20
+29065,1.42849,7.26665,1
+29093,9.329751,3.110904,5
+29123,5.436063,2.980271,3
+29215,6.577835,3.866767,7
+17181,4.392081,1.868408,4
+29179,5.856001,12.577034,2
+29017,5.763799,7.803599,3
+29031,2.651026,3.47149,8
+29203,7.79403,4.334822,3
+29223,0,8.451537,0
diff --git a/pysal/examples/stl/stl_hom.wkt b/pysal/examples/stl/stl_hom.wkt
new file mode 100644
index 0000000..a436b5b
--- /dev/null
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diff --git a/pysal/examples/stl/stl_hom_rook.gal b/pysal/examples/stl/stl_hom_rook.gal
new file mode 100644
index 0000000..7d8e0f8
--- /dev/null
+++ b/pysal/examples/stl/stl_hom_rook.gal
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+32 4
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+33 8
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+34 5
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+35 7
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+36 6
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+37 6
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+38 4
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+40 3
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+54 53 43 31 42
+45 7
+60 59 51 37 35 43 34
+46 7
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+62 5
+68 66 56 52 58
+63 5
+64 57 67 49 51
+64 6
+71 70 63 67 51 60
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+66 6
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+67 7
+76 75 71 66 57 64 63
+68 3
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+74 71 78 64 60 69 59
+71 5
+78 75 67 64 70
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diff --git a/pysal/examples/street_net_pts/street_net_pts.dbf b/pysal/examples/street_net_pts/street_net_pts.dbf
new file mode 100644
index 0000000..622c92c
Binary files /dev/null and b/pysal/examples/street_net_pts/street_net_pts.dbf differ
diff --git a/pysal/examples/street_net_pts/street_net_pts.prj b/pysal/examples/street_net_pts/street_net_pts.prj
new file mode 100644
index 0000000..a30c00a
--- /dev/null
+++ b/pysal/examples/street_net_pts/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/street_net_pts.qpj b/pysal/examples/street_net_pts/street_net_pts.qpj
new file mode 100644
index 0000000..5fbc831
--- /dev/null
+++ b/pysal/examples/street_net_pts/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/street_net_pts.shp b/pysal/examples/street_net_pts/street_net_pts.shp
new file mode 100644
index 0000000..977293c
Binary files /dev/null and b/pysal/examples/street_net_pts/street_net_pts.shp differ
diff --git a/pysal/examples/street_net_pts/street_net_pts.shx b/pysal/examples/street_net_pts/street_net_pts.shx
new file mode 100644
index 0000000..841b1c2
Binary files /dev/null and b/pysal/examples/street_net_pts/street_net_pts.shx differ
diff --git a/pysal/examples/taz/taz.dbf b/pysal/examples/taz/taz.dbf
new file mode 100644
index 0000000..29e6756
Binary files /dev/null and b/pysal/examples/taz/taz.dbf differ
diff --git a/pysal/examples/taz/taz.shp b/pysal/examples/taz/taz.shp
new file mode 100644
index 0000000..df6d3c6
Binary files /dev/null and b/pysal/examples/taz/taz.shp differ
diff --git a/pysal/examples/taz/taz.shx b/pysal/examples/taz/taz.shx
new file mode 100644
index 0000000..c3d9972
Binary files /dev/null and b/pysal/examples/taz/taz.shx differ
diff --git a/pysal/examples/test_examples.py b/pysal/examples/test_examples.py
new file mode 100644
index 0000000..c5e44f3
--- /dev/null
+++ b/pysal/examples/test_examples.py
@@ -0,0 +1,21 @@
+import unittest
+import pysal.examples as ex
+
+
+class Example_Tester(unittest.TestCase):
+ def test_get_path(self):
+ pathparts = ex.get_path('').split('/')
+ self.localpath = '/'.join(pathparts[-3:])
+ self.assertEquals(self.localpath, 'pysal/examples/')
+
+ def test_parser(self):
+ for example in ex.available():
+ self.extext = ex.explain(example)
+ # make sure names are returned
+ self.assertIsNotNone(self.extext['name'])
+ # Make sure there's an empty line between titling and description
+ if self.extext['description'] is not None:
+ self.assertNotIn('----', self.extext['description'])
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/examples/us_income/README.md b/pysal/examples/us_income/README.md
new file mode 100644
index 0000000..d076762
--- /dev/null
+++ b/pysal/examples/us_income/README.md
@@ -0,0 +1,10 @@
+us_income
+=========
+
+Per-capita income for the lower 47 US states 1929-2010
+------------------------------------------------------
+
+ * us48.shp: shapefile
+ * us48.dbf: dbf for shapefile
+ * us48.shx: index for shapefile
+ * usjoin.csv: attribute data (comma delimited file)
diff --git a/pysal/examples/us_income/spi_download.csv b/pysal/examples/us_income/spi_download.csv
new file mode 100644
index 0000000..a547490
--- /dev/null
+++ b/pysal/examples/us_income/spi_download.csv
@@ -0,0 +1,66 @@
+"Per capita personal income 2/","FIPS","AreaName","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 "
+"400","00","United States 3/",3836 ,4084 ,4340 ,4717 ,5230 ,5708 ,6172 ,6754 ,7402 ,8243 ,9138 ,10091 ,11209 ,11901 ,12583 ,13807 ,14637 ,15338 ,16137 ,17244 ,18402 ,19354 ,19818 ,20799 ,21385 ,22297 ,23262 ,24442 ,25654 ,27258 ,28333 ,30318 ,31149 ,31470 ,32284 ,33899 ,35447 ,37728 ,39430 ,40208
+"400","01","Alabama",2734 ,2962 ,3206 ,3526 ,3943 ,4336 ,4766 ,5313 ,5794 ,6464 ,7139 ,7825 ,8659 ,9152 ,9772 ,10752 ,11504 ,12080 ,12735 ,13639 ,14776 ,15618 ,16337 ,17264 ,17766 ,18656 ,19551 ,20245 ,21118 ,22217 ,22961 ,24070 ,25097 ,25816 ,26753 ,28405 ,29889 ,31484 ,32883 ,33768
+"400","02","Alaska 3/",4769 ,5248 ,5583 ,5940 ,6805 ,8130 ,10666 ,12109 ,12388 ,12495 ,13199 ,14975 ,16528 ,18819 ,18843 ,19395 ,20104 ,19673 ,19244 ,19848 ,21525 ,22594 ,23092 ,23706 ,24478 ,25186 ,25778 ,26179 ,27197 ,27943 ,28538 ,30534 ,32274 ,33160 ,33543 ,34898 ,36812 ,38898 ,41153 ,44039
+"400","04","Arizona",3495 ,3829 ,4131 ,4473 ,4904 ,5301 ,5535 ,6061 ,6615 ,7532 ,8509 ,9484 ,10582 ,10882 ,11630 ,12803 ,13636 ,14335 ,14896 ,15583 ,16281 ,16806 ,17253 ,17762 ,18371 ,19385 ,20164 ,21159 ,22231 ,23722 ,24583 ,26261 ,26937 ,27192 ,27859 ,29567 ,31563 ,33498 ,34413 ,34335
+"400","05","Arkansas",2616 ,2840 ,3088 ,3409 ,3978 ,4368 ,4668 ,5157 ,5657 ,6469 ,7020 ,7521 ,8508 ,8947 ,9463 ,10486 ,11128 ,11625 ,12023 ,12855 ,13727 ,14402 ,15103 ,16204 ,16692 ,17496 ,18260 ,19170 ,19846 ,20798 ,21556 ,22578 ,23883 ,24299 ,25475 ,26905 ,27981 ,29573 ,31646 ,32397
+"400","06","California",4532 ,4801 ,5027 ,5451 ,5944 ,6557 ,7136 ,7835 ,8572 ,9572 ,10719 ,11928 ,13144 ,13742 ,14517 ,15913 ,16777 ,17526 ,18447 ,19515 ,20448 ,21380 ,21734 ,22439 ,22744 ,23448 ,24498 ,25788 ,27063 ,29195 ,30679 ,33394 ,33869 ,34006 ,34922 ,36830 ,38670 ,41404 ,43221 ,43641
+"400","08","Colorado",3687 ,4040 ,4399 ,4774 ,5283 ,5837 ,6322 ,6880 ,7535 ,8485 ,9502 ,10714 ,12078 ,12904 ,13538 ,14669 ,15267 ,15713 ,16205 ,17072 ,18398 ,19377 ,20123 ,21102 ,22152 ,23237 ,24575 ,25964 ,27402 ,29174 ,30919 ,33979 ,35305 ,35032 ,35160 ,36649 ,38539 ,40912 ,42444 ,42985
+"400","09","Connecticut",4821 ,5071 ,5295 ,5692 ,6226 ,6794 ,7252 ,7878 ,8684 ,9656 ,10855 ,12321 ,13780 ,14849 ,15737 ,17496 ,18635 ,19895 ,21573 ,23557 ,25397 ,26198 ,26430 ,28287 ,29051 ,29891 ,31366 ,32835 ,34877 ,37226 ,38718 ,41921 ,43614 ,43346 ,43730 ,46417 ,48485 ,52702 ,55609 ,56272
+"400","10","Delaware",4406 ,4594 ,4888 ,5297 ,5858 ,6336 ,6740 ,7345 ,7892 ,8624 ,9482 ,10756 ,11827 ,12684 ,13468 ,14748 ,15900 ,16589 ,17604 ,18819 ,20559 ,21209 ,22073 ,22500 ,22885 ,23487 ,24409 ,25808 ,26574 ,28397 ,29072 ,31007 ,32410 ,33212 ,33879 ,35753 ,37062 ,39168 ,40068 ,40519
+"400","11","District of Columbia",4483 ,4970 ,5497 ,6026 ,6470 ,7252 ,8034 ,8728 ,9639 ,10367 ,11224 ,12218 ,13381 ,14570 ,15379 ,16986 ,18051 ,18932 ,20046 ,22157 ,23843 ,26015 ,27333 ,28694 ,29883 ,30804 ,31291 ,32981 ,34807 ,36503 ,37093 ,40485 ,44870 ,45442 ,47529 ,51458 ,55268 ,60080 ,63881 ,66119
+"400","12","Florida",3658 ,3998 ,4282 ,4696 ,5217 ,5591 ,5901 ,6360 ,6972 ,7841 ,8731 ,9921 ,11101 ,11682 ,12640 ,13718 ,14643 ,15391 ,16193 ,17291 ,18734 ,19437 ,19776 ,20474 ,21197 ,21919 ,23014 ,24050 ,24919 ,26453 ,27329 ,29079 ,29834 ,30530 ,31364 ,33659 ,35769 ,38308 ,39204 ,39267
+"400","13","Georgia",3149 ,3379 ,3650 ,4023 ,4481 ,4854 ,5157 ,5687 ,6200 ,6951 ,7656 ,8408 ,9393 ,10041 ,10857 ,12148 ,13052 ,13917 ,14679 ,15721 ,16658 ,17563 ,18110 ,19139 ,19866 ,20945 ,22023 ,23340 ,24287 ,25680 ,26772 ,28531 ,29205 ,29272 ,29683 ,30639 ,32176 ,33473 ,34650 ,34893
+"400","15","Hawaii 3/",4532 ,5077 ,5319 ,5671 ,6128 ,6911 ,7396 ,7880 ,8338 ,9111 ,10098 ,11394 ,12235 ,12780 ,13883 ,14909 ,15591 ,16288 ,17118 ,18496 ,20285 ,21818 ,22763 ,24014 ,24566 ,24847 ,25160 ,25253 ,25892 ,26546 ,27467 ,29073 ,29506 ,30533 ,31520 ,33787 ,35851 ,38520 ,40907 ,42055
+"400","16","Idaho",3264 ,3539 ,3739 ,4133 ,4686 ,5353 ,5573 ,6100 ,6479 ,7253 ,7796 ,8637 ,9378 ,9622 ,10331 ,10988 ,11497 ,11774 ,12428 ,13437 ,14632 ,15603 ,16015 ,17063 ,18110 ,18865 ,19665 ,20525 ,20961 ,22234 ,23269 ,24684 ,25656 ,26029 ,26472 ,28453 ,29642 ,31668 ,32905 ,33074
+"400","17","Illinois",4333 ,4568 ,4867 ,5262 ,5892 ,6452 ,7008 ,7620 ,8359 ,9222 ,10140 ,10980 ,12150 ,12868 ,13420 ,14760 ,15524 ,16304 ,17236 ,18551 ,19672 ,20835 ,21148 ,22553 ,23068 ,24181 ,25382 ,26806 ,28130 ,29746 ,30619 ,32636 ,33183 ,33696 ,34569 ,35957 ,37168 ,39549 ,41569 ,42347
+"400","18","Indiana",3692 ,3791 ,4086 ,4439 ,5083 ,5423 ,5837 ,6504 ,7167 ,7950 ,8726 ,9353 ,10287 ,10673 ,11184 ,12402 ,13065 ,13739 ,14522 ,15424 ,16668 ,17454 ,17865 ,19099 ,19885 ,20973 ,21644 ,22655 ,23607 ,25169 ,25899 ,27461 ,28054 ,28534 ,29588 ,30645 ,31302 ,32881 ,33756 ,34605
+"400","19","Iowa",3665 ,3878 ,4025 ,4498 ,5409 ,5603 ,6231 ,6596 ,7267 ,8379 ,8994 ,9573 ,10840 ,11236 ,11529 ,12815 ,13370 ,13993 ,14786 ,15206 ,16484 ,17350 ,17700 ,18789 ,18700 ,20367 ,21006 ,22787 ,23747 ,24898 ,25539 ,27295 ,27900 ,28832 ,29444 ,31674 ,32306 ,33853 ,35699 ,37402
+"400","20","Kansas",3555 ,3824 ,4152 ,4619 ,5275 ,5710 ,6206 ,6716 ,7279 ,8020 ,9119 ,9939 ,11197 ,12056 ,12475 ,13629 ,14312 ,14957 ,15567 ,16207 ,17008 ,18034 ,18605 ,19710 ,20371 ,21235 ,21870 ,23255 ,24504 ,26032 ,26826 ,28479 ,29670 ,29759 ,30822 ,31918 ,33130 ,35756 ,37389 ,38820
+"400","21","Kentucky",2964 ,3176 ,3383 ,3705 ,4132 ,4595 ,4940 ,5457 ,6075 ,6758 ,7603 ,8113 ,8992 ,9552 ,9861 ,11035 ,11503 ,11819 ,12485 ,13553 ,14530 ,15360 ,16142 ,17150 ,17554 ,18308 ,18978 ,19982 ,21021 ,22244 ,23032 ,24786 ,25336 ,25838 ,26348 ,27518 ,28557 ,30129 ,31206 ,32076
+"400","22","Louisiana",2886 ,3089 ,3310 ,3576 ,3977 ,4493 ,4958 ,5549 ,6107 ,6912 ,7744 ,8767 ,9991 ,10561 ,10871 ,11595 ,12024 ,11998 ,12219 ,13029 ,13963 ,15171 ,15931 ,16857 ,17559 ,18641 ,19367 ,20155 ,21088 ,22119 ,22458 ,23570 ,25373 ,25948 ,26708 ,28066 ,30100 ,33750 ,35363 ,36424
+"400","23","Maine",3138 ,3413 ,3590 ,3858 ,4308 ,4747 ,5026 ,5698 ,6116 ,6700 ,7412 ,8333 ,9164 ,9901 ,10548 ,11628 ,12462 ,13406 ,14434 ,15593 ,16683 ,17211 ,17457 ,18214 ,18690 ,19552 ,20372 ,21507 ,22566 ,24171 ,25151 ,26697 ,28204 ,28898 ,29939 ,31474 ,32022 ,33735 ,35078 ,36457
+"400","24","Maryland",4199 ,4558 ,4881 ,5281 ,5805 ,6368 ,6890 ,7534 ,8174 ,9030 ,9971 ,11164 ,12335 ,13334 ,14281 ,15701 ,16935 ,17932 ,19067 ,20468 ,21733 ,22681 ,23282 ,24112 ,24805 ,25780 ,26618 ,27689 ,29000 ,30742 ,32216 ,34681 ,36272 ,37164 ,38212 ,40625 ,42601 ,45121 ,47050 ,48378
+"400","25","Massachusetts",4185 ,4472 ,4743 ,5102 ,5541 ,6011 ,6453 ,6993 ,7611 ,8422 ,9371 ,10570 ,11744 ,12892 ,13942 ,15639 ,16798 ,18003 ,19397 ,21127 ,22095 ,22797 ,23314 ,24422 ,25182 ,26393 ,27662 ,29279 ,30911 ,33006 ,34671 ,38213 ,39500 ,39512 ,40161 ,42123 ,43897 ,47330 ,49885 ,51254
+"400","26","Michigan",4147 ,4198 ,4501 ,4970 ,5552 ,5926 ,6307 ,7092 ,7950 ,8800 ,9629 ,10291 ,11075 ,11522 ,12284 ,13588 ,14685 ,15520 ,15964 ,16894 ,18072 ,18719 ,19129 ,20179 ,21046 ,22593 ,23428 ,24279 ,25349 ,26903 ,27858 ,29392 ,29977 ,30188 ,31214 ,31650 ,32265 ,33198 ,34188 ,34949
+"400","27","Minnesota",3775 ,4050 ,4270 ,4626 ,5421 ,5824 ,6231 ,6725 ,7538 ,8420 ,9312 ,10229 ,11258 ,12015 ,12649 ,14250 ,15023 ,15810 ,16737 ,17351 ,18744 ,19710 ,20129 ,21306 ,21601 ,23003 ,24144 ,25871 ,27095 ,29273 ,30562 ,32598 ,33345 ,34076 ,35289 ,37079 ,37991 ,40015 ,41764 ,43037
+"400","28","Mississippi",2403 ,2628 ,2855 ,3194 ,3597 ,3920 ,4207 ,4746 ,5233 ,5768 ,6491 ,7005 ,7842 ,8231 ,8570 ,9377 ,9857 ,10174 ,10799 ,11566 ,12499 ,13117 ,13749 ,14651 ,15426 ,16512 ,17176 ,18079 ,18880 ,19947 ,20555 ,21556 ,22819 ,23148 ,23999 ,25169 ,26836 ,28010 ,29549 ,30399
+"400","29","Missouri",3569 ,3855 ,4118 ,4453 ,4935 ,5280 ,5759 ,6313 ,6986 ,7753 ,8632 ,9306 ,10383 ,11110 ,11808 ,13011 ,13836 ,14471 ,15139 ,15926 ,16928 ,17582 ,18320 ,19327 ,19951 ,21035 ,21832 ,22901 ,24104 ,25419 ,26218 ,27892 ,28624 ,29266 ,30239 ,31435 ,32278 ,34062 ,35308 ,36631
+"400","30","Montana",3294 ,3624 ,3790 ,4350 ,5000 ,5367 ,5814 ,6202 ,6622 ,7655 ,8192 ,9038 ,10187 ,10628 ,11054 ,11617 ,11762 ,12350 ,12848 ,13241 ,14569 ,15346 ,16250 ,16859 ,17787 ,17989 ,18546 ,19261 ,20033 ,21459 ,22045 ,23470 ,25315 ,25685 ,27000 ,28613 ,30141 ,32204 ,33948 ,34644
+"400","31","Nebraska",3571 ,3793 ,4119 ,4525 ,5259 ,5452 ,6182 ,6445 ,6961 ,8030 ,8646 ,9155 ,10579 ,11361 ,11749 ,13040 ,13756 ,14232 ,14947 ,15915 ,16790 ,17948 ,18523 ,19403 ,19836 ,21024 ,22008 ,23853 ,24359 ,25859 ,27017 ,28600 ,29906 ,30329 ,32141 ,33279 ,34331 ,35726 ,37908 ,39150
+"400","32","Nevada",4530 ,4932 ,5218 ,5553 ,6093 ,6479 ,7029 ,7731 ,8519 ,9718 ,10639 ,11679 ,12711 ,13104 ,13600 ,14501 ,15310 ,15992 ,16713 ,18052 ,19165 ,20042 ,20777 ,22099 ,22833 ,23892 ,24914 ,26239 ,27118 ,28624 ,29650 ,30985 ,31186 ,31336 ,32710 ,35350 ,38231 ,39376 ,41145 ,41182
+"400","33","New Hampshire",3745 ,3883 ,4091 ,4411 ,4863 ,5262 ,5602 ,6249 ,6866 ,7730 ,8686 ,9816 ,10985 ,11979 ,13015 ,14455 ,15663 ,16819 ,18088 ,19361 ,20235 ,20236 ,21056 ,21861 ,22311 ,23642 ,24845 ,26649 ,27546 ,29664 ,31036 ,34089 ,34716 ,35126 ,35699 ,37612 ,38412 ,40999 ,42831 ,43623
+"400","34","New Jersey",4500 ,4813 ,5113 ,5513 ,6027 ,6561 ,7053 ,7699 ,8439 ,9359 ,10372 ,11676 ,12986 ,13997 ,15027 ,16506 ,17571 ,18618 ,19952 ,21763 ,23235 ,24354 ,24754 ,26270 ,26799 ,27593 ,29022 ,30613 ,32326 ,34212 ,35360 ,38666 ,39680 ,39964 ,40504 ,42406 ,43994 ,47655 ,50265 ,51358
+"400","35","New Mexico",2920 ,3189 ,3416 ,3748 ,4119 ,4551 ,5050 ,5520 ,6059 ,6802 ,7545 ,8331 ,9286 ,9916 ,10434 ,11233 ,11959 ,12217 ,12638 ,13227 ,14009 ,14823 ,15577 ,16260 ,17039 ,17772 ,18617 ,19289 ,19968 ,21059 ,21461 ,22752 ,24796 ,25063 ,25773 ,27300 ,28931 ,30587 ,32163 ,33430
+"400","36","New York",4573 ,4868 ,5166 ,5528 ,5963 ,6474 ,6970 ,7469 ,8132 ,8928 ,9819 ,10985 ,12260 ,13321 ,14267 ,15727 ,16761 ,17833 ,18978 ,20720 ,22202 ,23710 ,23685 ,24693 ,25089 ,25807 ,27106 ,28497 ,30012 ,31416 ,32625 ,34629 ,35458 ,35417 ,36165 ,38398 ,40678 ,43973 ,47612 ,48753
+"400","37","North Carolina",3046 ,3273 ,3500 ,3890 ,4353 ,4734 ,5048 ,5581 ,6041 ,6744 ,7401 ,8183 ,9142 ,9711 ,10500 ,11771 ,12592 ,13393 ,14241 ,15341 ,16454 ,17194 ,17691 ,18886 ,19704 ,20630 ,21615 ,22714 ,23945 ,25301 ,26326 ,27916 ,28394 ,28479 ,28979 ,30586 ,32066 ,33640 ,34952 ,35344
+"400","38","North Dakota",3099 ,3257 ,3715 ,4412 ,6207 ,6136 ,6390 ,6206 ,6421 ,8095 ,8290 ,7894 ,10300 ,11009 ,11417 ,12268 ,12728 ,13025 ,13589 ,12653 ,14371 ,15866 ,16167 ,17639 ,17696 ,19156 ,19004 ,21279 ,20854 ,23177 ,23502 ,25625 ,26699 ,27369 ,29761 ,30339 ,32353 ,33602 ,36695 ,39870
+"400","39","Ohio",3911 ,4088 ,4322 ,4688 ,5209 ,5724 ,6101 ,6751 ,7492 ,8283 ,9172 ,10022 ,10922 ,11470 ,12173 ,13437 ,14249 ,14873 ,15529 ,16549 ,17672 ,18638 ,19013 ,20025 ,20676 ,21818 ,22653 ,23545 ,24912 ,26418 ,27293 ,28694 ,29280 ,29855 ,30698 ,31617 ,32498 ,34093 ,35307 ,36021
+"400","40","Oklahoma",3204 ,3475 ,3710 ,4016 ,4515 ,4976 ,5493 ,5974 ,6570 ,7343 ,8395 ,9487 ,10943 ,11816 ,11737 ,12618 ,13171 ,13312 ,13455 ,14166 ,15192 ,16077 ,16434 ,17269 ,17772 ,18427 ,18973 ,19936 ,20899 ,21949 ,22757 ,24606 ,26228 ,26232 ,26929 ,28810 ,30492 ,33280 ,34336 ,35985
+"400","41","Oregon",3675 ,3927 ,4197 ,4605 ,5109 ,5701 ,6185 ,6898 ,7526 ,8409 ,9295 ,10086 ,10802 ,11116 ,11849 ,12811 ,13429 ,14059 ,14724 ,15776 ,16956 ,17895 ,18469 ,19201 ,20077 ,21219 ,22531 ,23751 ,24854 ,26016 ,27016 ,28719 ,29238 ,29761 ,30549 ,31598 ,32488 ,34623 ,35712 ,36297
+"400","42","Pennsylvania",3804 ,4069 ,4289 ,4678 ,5155 ,5692 ,6182 ,6796 ,7473 ,8262 ,9142 ,10040 ,11100 ,11880 ,12448 ,13468 ,14318 ,15017 ,15857 ,17067 ,18412 ,19433 ,20171 ,21050 ,21655 ,22355 ,23226 ,24384 ,25566 ,27367 ,28348 ,30111 ,30704 ,31506 ,32427 ,33852 ,34978 ,37326 ,39058 ,40140
+"400","44","Rhode Island",3847 ,4098 ,4285 ,4613 ,4955 ,5385 ,5851 ,6402 ,6975 ,7636 ,8497 ,9645 ,10733 ,11540 ,12389 ,13629 ,14538 ,15445 ,16387 ,17909 ,19389 ,19821 ,19981 ,20820 ,21600 ,22199 ,23364 ,24299 ,25621 ,26945 ,27741 ,29485 ,31166 ,32158 ,33469 ,35090 ,36233 ,38392 ,40219 ,41368
+"400","45","South Carolina",2821 ,3055 ,3265 ,3594 ,4016 ,4451 ,4730 ,5255 ,5668 ,6301 ,6988 ,7736 ,8606 ,9078 ,9790 ,10900 ,11590 ,12194 ,12939 ,13906 ,14931 ,15844 ,16256 ,17010 ,17651 ,18579 ,19384 ,20359 ,21287 ,22573 ,23550 ,25082 ,25653 ,26080 ,26704 ,27933 ,29270 ,31031 ,32065 ,32666
+"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
+"400","47","Tennessee",2957 ,3176 ,3439 ,3800 ,4284 ,4685 ,5026 ,5570 ,6089 ,6862 ,7555 ,8227 ,9120 ,9696 ,10293 ,11413 ,12152 ,12895 ,13754 ,14783 ,15718 ,16574 ,17242 ,18527 ,19331 ,20283 ,21339 ,22136 ,23031 ,24462 ,25370 ,26692 ,27535 ,28143 ,29026 ,30297 ,31360 ,32986 ,34287 ,34976
+"400","48","Texas",3364 ,3628 ,3840 ,4175 ,4659 ,5170 ,5738 ,6347 ,6943 ,7856 ,8832 ,9870 ,11320 ,11965 ,12348 ,13377 ,14110 ,14182 ,14453 ,15245 ,16165 ,17260 ,17763 ,18765 ,19413 ,20161 ,21070 ,22260 ,23812 ,25376 ,26399 ,28504 ,29166 ,28935 ,29581 ,31073 ,33172 ,35275 ,36829 ,37774
+"400","49","Utah",3105 ,3389 ,3649 ,3971 ,4316 ,4738 ,5173 ,5755 ,6344 ,7055 ,7792 ,8492 ,9347 ,9953 ,10506 ,11371 ,11926 ,12322 ,12652 ,13162 ,13941 ,14847 ,15479 ,16135 ,16845 ,17775 ,18765 ,19899 ,21001 ,22188 ,22943 ,24519 ,25536 ,25648 ,25830 ,26827 ,28599 ,30320 ,31739 ,31944
+"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
+"400","51","Virginia",3560 ,3792 ,4091 ,4486 ,4971 ,5485 ,5960 ,6549 ,7193 ,8025 ,8950 ,10107 ,11227 ,12095 ,12993 ,14298 ,15284 ,16188 ,17191 ,18442 ,19614 ,20312 ,20953 ,21842 ,22596 ,23534 ,24360 ,25354 ,26695 ,28199 ,29617 ,31641 ,33263 ,33776 ,35029 ,36912 ,38980 ,41367 ,43275 ,44224
+"400","53","Washington",4085 ,4189 ,4361 ,4713 ,5284 ,5892 ,6535 ,7165 ,7797 ,8820 ,9847 ,10810 ,11834 ,12435 ,13144 ,13972 ,14619 ,15422 ,16090 ,17055 ,18405 ,19637 ,20583 ,21581 ,22139 ,22981 ,23778 ,25280 ,26749 ,28821 ,30521 ,32407 ,32950 ,33107 ,33869 ,35986 ,36773 ,39623 ,42020 ,42857
+"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
+"400","92","Mideast",4308 ,4606 ,4889 ,5272 ,5742 ,6273 ,6771 ,7346 ,8026 ,8845 ,9758 ,10874 ,12081 ,13048 ,13901 ,15241 ,16257 ,17209 ,18303 ,19874 ,21301 ,22542 ,22899 ,23939 ,24460 ,25217 ,26357 ,27691 ,29125 ,30776 ,31932 ,34183 ,35133 ,35509 ,36317 ,38317 ,40137 ,43156 ,45859 ,47001
+"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/us_income/states48.gal b/pysal/examples/us_income/states48.gal
new file mode 100644
index 0000000..ed8fbfa
--- /dev/null
+++ b/pysal/examples/us_income/states48.gal
@@ -0,0 +1,97 @@
+48
+0 4
+7 8 21 39
+1 5
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diff --git a/pysal/examples/us_income/us48.dbf b/pysal/examples/us_income/us48.dbf
new file mode 100644
index 0000000..a289e04
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diff --git a/pysal/examples/us_income/us48.shp b/pysal/examples/us_income/us48.shp
new file mode 100644
index 0000000..1d032dd
Binary files /dev/null and b/pysal/examples/us_income/us48.shp differ
diff --git a/pysal/examples/us_income/us48.shx b/pysal/examples/us_income/us48.shx
new file mode 100644
index 0000000..aa683fc
Binary files /dev/null and b/pysal/examples/us_income/us48.shx differ
diff --git a/pysal/examples/us_income/usjoin.csv b/pysal/examples/us_income/usjoin.csv
new file mode 100644
index 0000000..24ab198
--- /dev/null
+++ b/pysal/examples/us_income/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/README.md b/pysal/examples/virginia/README.md
new file mode 100644
index 0000000..5703feb
--- /dev/null
+++ b/pysal/examples/virginia/README.md
@@ -0,0 +1,10 @@
+virginia
+========
+
+Virginia counties shapefile
+---------------------------
+
+ * virginia.shp: Shapefile
+ * virginia.shx: shapefile index
+ * virginia.dbf: attributes
+ * virginia.prj: shapefile projection
diff --git a/pysal/examples/virginia/virginia.dbf b/pysal/examples/virginia/virginia.dbf
new file mode 100644
index 0000000..7ed087e
Binary files /dev/null and b/pysal/examples/virginia/virginia.dbf differ
diff --git a/pysal/examples/virginia/virginia.gal b/pysal/examples/virginia/virginia.gal
new file mode 100644
index 0000000..c8ccf1c
--- /dev/null
+++ b/pysal/examples/virginia/virginia.gal
@@ -0,0 +1,273 @@
+0 136 virginia POLY_ID
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diff --git a/pysal/examples/virginia/virginia.json b/pysal/examples/virginia/virginia.json
new file mode 100644
index 0000000..b933151
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diff --git a/pysal/examples/virginia/virginia.prj b/pysal/examples/virginia/virginia.prj
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diff --git a/pysal/examples/virginia/virginia_queen.dbf b/pysal/examples/virginia/virginia_queen.dbf
new file mode 100644
index 0000000..b9183d5
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diff --git a/pysal/examples/virginia/virginia_queen.gal b/pysal/examples/virginia/virginia_queen.gal
new file mode 100644
index 0000000..4bf9e5c
--- /dev/null
+++ b/pysal/examples/virginia/virginia_queen.gal
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diff --git a/pysal/examples/virginia/virginia_queen.mat b/pysal/examples/virginia/virginia_queen.mat
new file mode 100644
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diff --git a/pysal/examples/virginia/virginia_queen.mtx b/pysal/examples/virginia/virginia_queen.mtx
new file mode 100644
index 0000000..59a405a
--- /dev/null
+++ b/pysal/examples/virginia/virginia_queen.mtx
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diff --git a/pysal/examples/virginia/virginia_queen.swm b/pysal/examples/virginia/virginia_queen.swm
new file mode 100644
index 0000000..7595a0e
Binary files /dev/null and b/pysal/examples/virginia/virginia_queen.swm differ
diff --git a/pysal/examples/virginia/virginia_queen.txt b/pysal/examples/virginia/virginia_queen.txt
new file mode 100644
index 0000000..d6fecc8
--- /dev/null
+++ b/pysal/examples/virginia/virginia_queen.txt
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+51590 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 0.0 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 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 0.0 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 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 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 0.0 0. [...]
+51520 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 0.0 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 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 0.0 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 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 0.0 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. [...]
diff --git a/pysal/examples/virginia/virginia_queen.wk1 b/pysal/examples/virginia/virginia_queen.wk1
new file mode 100644
index 0000000..ceba7b7
Binary files /dev/null and b/pysal/examples/virginia/virginia_queen.wk1 differ
diff --git a/pysal/examples/virginia/virginia_rook.gal b/pysal/examples/virginia/virginia_rook.gal
new file mode 100644
index 0000000..08f0381
--- /dev/null
+++ b/pysal/examples/virginia/virginia_rook.gal
@@ -0,0 +1,273 @@
+0 136 virginia POLY_ID
+1 4
+7 5 4 3
+2 4
+9 8 6 3
+3 4
+8 7 2 1
+4 1
+1
+5 4
+16 15 7 1
+6 6
+14 12 11 10 9 2
+7 6
+16 13 8 5 3 1
+8 7
+19 13 9 21 2 3 7
+9 6
+21 18 17 6 2 8
+10 3
+14 11 6
+11 2
+10 6
+12 1
+6
+13 5
+20 16 8 19 7
+14 2
+10 6
+15 6
+25 23 24 31 16 5
+16 6
+24 20 13 7 5 15
+17 2
+18 9
+18 2
+9 17
+19 6
+26 20 21 27 8 13
+20 5
+24 19 26 13 16
+21 7
+33 29 27 28 9 19 8
+22 2
+32 23
+23 8
+41 39 38 34 32 31 15 22
+24 5
+31 20 26 16 15
+25 1
+15
+26 6
+36 31 19 27 24 20
+27 7
+44 36 33 29 21 26 19
+28 4
+35 33 30 21
+29 2
+21 27
+30 4
+43 37 35 28
+31 9
+56 45 41 40 36 24 26 23 15
+32 4
+47 23 39 22
+33 7
+44 35 46 48 27 28 21
+34 1
+23
+35 6
+46 37 57 30 28 33
+36 6
+49 45 44 27 26 31
+37 4
+51 43 30 35
+38 1
+23
+39 9
+64 59 55 54 52 47 41 23 32
+40 1
+31
+41 6
+68 52 56 31 23 39
+42 1
+69
+43 3
+51 37 30
+44 7
+60 49 48 63 33 27 36
+45 5
+58 56 36 49 31
+46 7
+76 63 48 66 57 35 33
+47 6
+62 55 53 50 39 32
+48 4
+63 46 44 33
+49 6
+61 58 44 60 36 45
+50 1
+47
+51 2
+37 43
+52 6
+77 75 64 68 41 39
+53 1
+47
+54 1
+39
+55 5
+78 62 64 39 47
+56 6
+79 68 58 45 31 41
+57 3
+66 46 35
+58 6
+79 61 72 56 49 45
+59 1
+39
+60 6
+67 65 63 73 44 49
+61 4
+72 67 49 58
+62 5
+81 78 74 55 47
+63 6
+76 73 46 48 60 44
+64 9
+105 99 82 78 77 75 52 39 55
+65 2
+67 60
+66 4
+76 71 57 46
+67 10
+96 93 91 86 72 73 84 60 65 61
+68 6
+94 77 79 56 41 52
+69 1
+42
+70 3
+104 89 83
+71 1
+66
+72 6
+90 79 93 67 61 58
+73 6
+95 84 76 63 67 60
+74 4
+97 88 81 62
+75 3
+77 52 64
+76 7
+92 80 98 66 46 63 73
+77 7
+105 68 94 111 64 75 52
+78 8
+106 87 85 81 64 99 55 62
+79 7
+107 94 72 90 58 56 68
+80 5
+109 101 98 92 76
+81 6
+102 97 106 78 74 62
+82 1
+64
+83 4
+113 104 88 70
+84 7
+108 96 95 93 86 73 67
+85 1
+78
+86 2
+84 67
+87 1
+78
+88 5
+113 110 97 74 83
+89 3
+100 104 70
+90 5
+112 107 93 72 79
+91 2
+96 67
+92 2
+76 80
+93 8
+121 112 96 108 84 67 90 72
+94 6
+111 122 107 79 68 77
+95 5
+114 108 103 84 73
+96 4
+84 93 91 67
+97 7
+118 110 102 81 106 74 88
+98 3
+109 80 76
+99 6
+127 125 106 105 64 78
+100 5
+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/geobugs_scot b/pysal/examples/wmat/geobugs_scot
new file mode 100644
index 0000000..d3c326e
--- /dev/null
+++ b/pysal/examples/wmat/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/wmat/lattice10x10.shp b/pysal/examples/wmat/lattice10x10.shp
new file mode 100755
index 0000000..47ad243
Binary files /dev/null and b/pysal/examples/wmat/lattice10x10.shp differ
diff --git a/pysal/examples/wmat/lattice10x10.shx b/pysal/examples/wmat/lattice10x10.shx
new file mode 100755
index 0000000..60dea1d
Binary files /dev/null and b/pysal/examples/wmat/lattice10x10.shx differ
diff --git a/pysal/examples/wmat/ohio.swm b/pysal/examples/wmat/ohio.swm
new file mode 100644
index 0000000..f258e5d
Binary files /dev/null and b/pysal/examples/wmat/ohio.swm differ
diff --git a/pysal/examples/wmat/rook31.dbf b/pysal/examples/wmat/rook31.dbf
new file mode 100644
index 0000000..fddc13c
Binary files /dev/null and b/pysal/examples/wmat/rook31.dbf differ
diff --git a/pysal/examples/wmat/rook31.gal b/pysal/examples/wmat/rook31.gal
new file mode 100644
index 0000000..c9d91b8
--- /dev/null
+++ b/pysal/examples/wmat/rook31.gal
@@ -0,0 +1,7 @@
+0 3 rook31 POLY_ID
+1 1
+2
+2 2
+3 1
+3 1
+2
diff --git a/pysal/examples/wmat/rook31.shp b/pysal/examples/wmat/rook31.shp
new file mode 100644
index 0000000..3f2ec3f
Binary files /dev/null and b/pysal/examples/wmat/rook31.shp differ
diff --git a/pysal/examples/wmat/rook31.shx b/pysal/examples/wmat/rook31.shx
new file mode 100644
index 0000000..ba074d4
Binary files /dev/null and b/pysal/examples/wmat/rook31.shx differ
diff --git a/pysal/examples/wmat/spat-sym-us.mat b/pysal/examples/wmat/spat-sym-us.mat
new file mode 100644
index 0000000..678f0c0
Binary files /dev/null and b/pysal/examples/wmat/spat-sym-us.mat differ
diff --git a/pysal/examples/wmat/spat-sym-us.wk1 b/pysal/examples/wmat/spat-sym-us.wk1
new file mode 100644
index 0000000..9c9bd51
Binary files /dev/null and b/pysal/examples/wmat/spat-sym-us.wk1 differ
diff --git a/pysal/examples/wmat/spdep_listw2WB_columbus b/pysal/examples/wmat/spdep_listw2WB_columbus
new file mode 100644
index 0000000..958bada
--- /dev/null
+++ b/pysal/examples/wmat/spdep_listw2WB_columbus
@@ -0,0 +1,59 @@
+list(adj = c(2, 3, 1, 3, 4, 1, 2, 4, 5, 2, 3, 5, 8, 3, 4, 6,
+8, 9, 11, 15, 5, 9, 8, 12, 13, 14, 4, 5, 7, 11, 12, 13, 5, 6,
+10, 15, 20, 22, 25, 26, 9, 17, 20, 22, 5, 8, 12, 15, 16, 7, 8,
+11, 13, 14, 16, 7, 8, 12, 14, 7, 12, 13, 16, 18, 19, 5, 9, 11,
+16, 25, 26, 11, 12, 14, 15, 18, 24, 25, 10, 20, 23, 14, 16, 19,
+24, 14, 18, 24, 9, 10, 17, 22, 23, 27, 32, 33, 35, 40, 24, 30,
+34, 9, 10, 20, 26, 27, 28, 17, 20, 32, 16, 18, 19, 21, 25, 29,
+30, 9, 15, 16, 24, 26, 28, 29, 9, 15, 22, 25, 28, 29, 20, 22,
+28, 33, 22, 25, 26, 27, 29, 33, 35, 37, 38, 24, 25, 26, 28, 30,
+37, 38, 21, 24, 29, 37, 34, 36, 20, 23, 40, 41, 20, 27, 28, 35,
+21, 31, 36, 42, 20, 28, 33, 38, 40, 43, 44, 31, 34, 39, 42, 46,
+28, 29, 30, 38, 43, 45, 28, 29, 35, 37, 43, 44, 36, 46, 20, 32,
+35, 41, 47, 32, 40, 47, 34, 36, 35, 37, 38, 44, 45, 48, 35, 38,
+43, 48, 49, 37, 43, 48, 49, 36, 39, 40, 41, 43, 44, 45, 49, 44,
+45, 48), weights = c(0.5, 0.5, 0.333333333333333, 0.333333333333333,
+0.333333333333333, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25,
+0.25, 0.142857142857143, 0.142857142857143, 0.142857142857143,
+0.142857142857143, 0.142857142857143, 0.142857142857143, 0.142857142857143,
+0.5, 0.5, 0.25, 0.25, 0.25, 0.25, 0.166666666666667, 0.166666666666667,
+0.166666666666667, 0.166666666666667, 0.166666666666667, 0.166666666666667,
+0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.25,
+0.25, 0.25, 0.25, 0.2, 0.2, 0.2, 0.2, 0.2, 0.166666666666667,
+0.166666666666667, 0.166666666666667, 0.166666666666667, 0.166666666666667,
+0.166666666666667, 0.25, 0.25, 0.25, 0.25, 0.166666666666667,
+0.166666666666667, 0.166666666666667, 0.166666666666667, 0.166666666666667,
+0.166666666666667, 0.166666666666667, 0.166666666666667, 0.166666666666667,
+0.166666666666667, 0.166666666666667, 0.166666666666667, 0.142857142857143,
+0.142857142857143, 0.142857142857143, 0.142857142857143, 0.142857142857143,
+0.142857142857143, 0.142857142857143, 0.333333333333333, 0.333333333333333,
+0.333333333333333, 0.25, 0.25, 0.25, 0.25, 0.333333333333333,
+0.333333333333333, 0.333333333333333, 0.1, 0.1, 0.1, 0.1, 0.1,
+0.1, 0.1, 0.1, 0.1, 0.1, 0.333333333333333, 0.333333333333333,
+0.333333333333333, 0.166666666666667, 0.166666666666667, 0.166666666666667,
+0.166666666666667, 0.166666666666667, 0.166666666666667, 0.333333333333333,
+0.333333333333333, 0.333333333333333, 0.142857142857143, 0.142857142857143,
+0.142857142857143, 0.142857142857143, 0.142857142857143, 0.142857142857143,
+0.142857142857143, 0.142857142857143, 0.142857142857143, 0.142857142857143,
+0.142857142857143, 0.142857142857143, 0.142857142857143, 0.142857142857143,
+0.166666666666667, 0.166666666666667, 0.166666666666667, 0.166666666666667,
+0.166666666666667, 0.166666666666667, 0.25, 0.25, 0.25, 0.25,
+0.111111111111111, 0.111111111111111, 0.111111111111111, 0.111111111111111,
+0.111111111111111, 0.111111111111111, 0.111111111111111, 0.111111111111111,
+0.111111111111111, 0.142857142857143, 0.142857142857143, 0.142857142857143,
+0.142857142857143, 0.142857142857143, 0.142857142857143, 0.142857142857143,
+0.25, 0.25, 0.25, 0.25, 0.5, 0.5, 0.25, 0.25, 0.25, 0.25, 0.25,
+0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.142857142857143,
+0.142857142857143, 0.142857142857143, 0.142857142857143, 0.142857142857143,
+0.142857142857143, 0.142857142857143, 0.2, 0.2, 0.2, 0.2, 0.2,
+0.166666666666667, 0.166666666666667, 0.166666666666667, 0.166666666666667,
+0.166666666666667, 0.166666666666667, 0.166666666666667, 0.166666666666667,
+0.166666666666667, 0.166666666666667, 0.166666666666667, 0.166666666666667,
+0.5, 0.5, 0.2, 0.2, 0.2, 0.2, 0.2, 0.333333333333333, 0.333333333333333,
+0.333333333333333, 0.5, 0.5, 0.166666666666667, 0.166666666666667,
+0.166666666666667, 0.166666666666667, 0.166666666666667, 0.166666666666667,
+0.2, 0.2, 0.2, 0.2, 0.2, 0.25, 0.25, 0.25, 0.25, 0.5, 0.5, 0.5,
+0.5, 0.25, 0.25, 0.25, 0.25, 0.333333333333333, 0.333333333333333,
+0.333333333333333), num = c(2, 3, 4, 4, 7, 2, 4, 6, 8, 4, 5,
+6, 4, 6, 6, 7, 3, 4, 3, 10, 3, 6, 3, 7, 7, 6, 4, 9, 7, 4, 2,
+4, 4, 4, 7, 5, 6, 6, 2, 5, 3, 2, 6, 5, 4, 2, 2, 4, 3))
\ No newline at end of file
diff --git a/pysal/examples/wmat/stata_full.txt b/pysal/examples/wmat/stata_full.txt
new file mode 100644
index 0000000..b4bc27c
--- /dev/null
+++ b/pysal/examples/wmat/stata_full.txt
@@ -0,0 +1,57 @@
+56
+1 0 0 0 0 0 0 .125 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 .125 0 0 0 0 0 .125 0 .125 .125 0 0
+2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .125 0 0 0 0 0 0 0 0 .125 .125 0 0 .125 0 0 0 0 0 .125 0 0 0 0 0 0 0 0 0 .125 0 0 0 0 0 0 0 0
+3 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 .125 0 0 .125 0 0 0 0 0 .125 0 0 0 0
+4 0 0 0 0 0 .125 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 .125 0 0 0 0 0 0 0 0 0 0 0 0 0
+5 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 0 0 0 0 0 0 0 0 0 0 0 0 0
+6 0 0 0 .125 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 .125 0 0 0 .125 0 0 .125 0 0 0 0 0 .125 0 0 0 0 0 .125 0 0 0 0 0 0 0
+7 .125 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 0 0 0 0 0 0 0 0 .125 .125 0 0
+8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .125 0 0 0 0 0 .125 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .125 0 0 0 0 0 .125 0 0 0 0 0 0 0 0 0 0 0 .125
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diff --git a/pysal/examples/wmat/stata_sparse.txt b/pysal/examples/wmat/stata_sparse.txt
new file mode 100644
index 0000000..5d50281
--- /dev/null
+++ b/pysal/examples/wmat/stata_sparse.txt
@@ -0,0 +1,57 @@
+56
+1 7 45 51 53 54
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+3 43 46 52
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+5
+6 4 30 34 37 43 49
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+10
+11
+12
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+15
+16 8 22 29 32 38 40
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diff --git a/pysal/examples/wmat/wmat.dat b/pysal/examples/wmat/wmat.dat
new file mode 100644
index 0000000..c604f0c
--- /dev/null
+++ b/pysal/examples/wmat/wmat.dat
@@ -0,0 +1,232 @@
+ 2.0000 1.0000 0.2500
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\ No newline at end of file
diff --git a/pysal/examples/wmat/wmat.mtx b/pysal/examples/wmat/wmat.mtx
new file mode 100644
index 0000000..b6e8eb5
--- /dev/null
+++ b/pysal/examples/wmat/wmat.mtx
@@ -0,0 +1,237 @@
+%%MatrixMarket matrix coordinate real general
+%==================================================
+% This is a test file generated from wmat.dat file.
+% ==================================================
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+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/meta/wmd.py b/pysal/meta/wmd.py
index 3cef3dd..e1bd3d0 100644
--- a/pysal/meta/wmd.py
+++ b/pysal/meta/wmd.py
@@ -1,3 +1,9 @@
+import pysal as ps
+import json
+from six.moves import urllib as urllib
+import copy
+import numpy as np
+
"""
Weights Meta Data
@@ -19,13 +25,7 @@ TODO
- 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):
"""
@@ -48,12 +48,13 @@ def wmd_reader(fileName):
meta_data = json.load(fp)
global fullmeta
fullmeta = {}
- fullmeta['root'] = copy.deepcopy(meta_data)
+ 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):
@@ -89,7 +90,8 @@ class WMD(ps.W):
_wmd_writer(self, fileName, data=data)
######################### Private functions #########################
-
+
+
def _wmd_writer(wmd_object, fileName, data=False):
try:
with open(fileName, 'w') as f:
@@ -98,12 +100,13 @@ def _wmd_writer(wmd_object, fileName, data=False):
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=(',', ': '))
+ f,
+ indent=4,
+ separators=(',', ': '))
except:
print 'wmd_writer failed.'
+
def _block(arg_dict):
"""
General handler for block weights
@@ -115,7 +118,10 @@ def _block(arg_dict):
>>> 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'}}}
+ {'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']
@@ -124,7 +130,6 @@ def _block(arg_dict):
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)
@@ -136,10 +141,11 @@ def _block(arg_dict):
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}
+ w.meta_data['parameters'] = {'block_variable': var_name}
return w
+
def _contiguity(arg_dict):
"""
General handler for building contiguity weights from shapefiles
@@ -151,7 +157,9 @@ def _contiguity(arg_dict):
>>> 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'}}
+ {'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:
@@ -165,29 +173,30 @@ def _contiguity(arg_dict):
elif weight_type == 'queen':
w = ps.queen_from_shapefile(uri)
else:
- print "Unsupported contiguity criterion: ",weight_type
+ 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
+ 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)
+ 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)
+ 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 }
+ 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["input1"] = {"type": 'shp', 'uri': uri}
w.meta_data["transform"] = w.transform
- w.meta_data["weight_type"] = weight_type
+ 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
@@ -199,7 +208,11 @@ def _kernel(arg_dict):
>>> 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}}}
+ {'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}}}
@@ -212,8 +225,8 @@ def _kernel(arg_dict):
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)
+ 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 = {}
@@ -240,6 +253,7 @@ def _kernel(arg_dict):
w.meta_data['parameters'] = arg_dict['parameters']
return w
+
def _distance(arg_dict):
"""
General handler for distance based weights obtained from shapefiles
diff --git a/pysal/network/tests/test_network.py b/pysal/network/tests/test_network.py
index f5e850d..c604d89 100644
--- a/pysal/network/tests/test_network.py
+++ b/pysal/network/tests/test_network.py
@@ -4,7 +4,7 @@ import unittest
import numpy as np
import pysal as ps
-from .. import util
+from pysal.network import util
class TestNetwork(unittest.TestCase):
@@ -130,3 +130,6 @@ class TestNetworkUtils(unittest.TestCase):
self.distance, self.pred = util.dijkstra(self.ntw, self.ntw.edge_lengths, 0)
self.assertAlmostEqual(self.distance[196], 5505.668247, places=4)
self.assertEqual(self.pred[196], 133)
+
+if __name__ == 'main':
+ unittest.main()
diff --git a/pysal/region/maxp.py b/pysal/region/maxp.py
index b097ae5..f9f9f2c 100644
--- a/pysal/region/maxp.py
+++ b/pysal/region/maxp.py
@@ -5,15 +5,13 @@ 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>"
+__author__ = "Serge Rey <srey at asu.edu>, David Folch <dfolch at fsu.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"]
@@ -75,10 +73,8 @@ class Maxp:
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
@@ -89,10 +85,8 @@ class Maxp:
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))
@@ -104,7 +98,7 @@ class Maxp:
>>> min([len(region) for region in solution.regions])
3
>>> solution.regions[0]
- [4, 14, 5, 24, 3]
+ [76, 66, 56]
>>>
"""
@@ -184,7 +178,7 @@ class Maxp:
potential.extend(neighbors)
if potential:
# add a random neighbor
- neigID = random.randint(0, len(potential) - 1)
+ neigID = np.random.randint(0, len(potential))
neigAdd = potential.pop(neigID)
region.append(neigAdd)
# remove it from candidates
@@ -218,7 +212,7 @@ class Maxp:
candidates.append(region)
if candidates:
# add enclave to random region
- regID = random.randint(0, len(candidates) - 1)
+ regID = np.random.randint(0, len(candidates))
rid = candidates[regID]
regions[rid].append(enclave)
a2r[enclave] = rid
@@ -379,10 +373,8 @@ class Maxp:
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))
@@ -397,7 +389,7 @@ class Maxp:
>>> solution.inference(nperm=9)
>>> solution.pvalue
- 0.2
+ 0.1
"""
ids = self.w.id_order
@@ -456,10 +448,8 @@ class Maxp:
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))
@@ -552,10 +542,8 @@ class Maxp_LISA(Maxp):
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
@@ -571,9 +559,9 @@ class Maxp_LISA(Maxp):
>>> p=np.ones(w.n)
>>> mpl=pysal.region.Maxp_LISA(w,z,p,floor=3,floor_variable=p)
>>> mpl.p
- 31
+ 30
>>> mpl.regions[0]
- [99, 89, 98]
+ [99, 98, 89]
"""
def __init__(self, w, z, y, floor, floor_variable, initial=100):
diff --git a/pysal/region/tests/test_maxp.py b/pysal/region/tests/test_maxp.py
index 18a66cf..3f6721c 100644
--- a/pysal/region/tests/test_maxp.py
+++ b/pysal/region/tests/test_maxp.py
@@ -2,12 +2,10 @@
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):
@@ -15,26 +13,29 @@ class Test_Maxp(unittest.TestCase):
z = np.random.random_sample((w.n, 2))
p = np.ones((w.n, 1), float)
floor = 3
+ np.random.seed(111)
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])
+ self.assertEquals(solution.p, 28)
+ self.assertEquals(solution.regions[0], [51, 61, 71])
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
+ np.random.seed(111)
solution = pysal.region.Maxp(
w, z, floor, floor_variable=p, initial=100)
solution.inference(nperm=9)
- self.assertAlmostEquals(solution.pvalue, 0.20000000000000001, 10)
+ self.assertAlmostEquals(solution.pvalue, 0.30000000000000001, 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
+ np.random.seed(111)
solution = pysal.region.Maxp(
w, z, floor, floor_variable=p, initial=100)
solution.cinference(nperm=9, maxiter=100)
@@ -44,9 +45,10 @@ class Test_Maxp(unittest.TestCase):
w = pysal.lat2W(10, 10)
z = np.random.random_sample((w.n, 2))
p = np.ones(w.n)
+ np.random.seed(111)
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])
+ self.assertEquals(mpl.p, 30)
+ self.assertEquals(mpl.regions[0], [99, 89, 79, 88, 78])
suite = unittest.TestLoader().loadTestsFromTestCase(Test_Maxp)
diff --git a/pysal/spatial_dynamics/markov.py b/pysal/spatial_dynamics/markov.py
index 7fe854f..f415340 100644
--- a/pysal/spatial_dynamics/markov.py
+++ b/pysal/spatial_dynamics/markov.py
@@ -594,7 +594,7 @@ def chi2(T1, T2):
[ 1., 92., 815., 51.],
[ 1., 0., 60., 903.]])
>>> chi2(T1,T2)
- (23.422628044813656, 0.0053137895983268457, 9)
+ (23.397284414732951, 0.0053631167048613371, 9)
Notes
-----
@@ -616,7 +616,7 @@ def chi2(T1, T2):
rs1nz = rs1 > 0
dof1 = sum(rs1nz)
dof2 = sum(rs2nz)
- rs2 = rs2 + rs2nz
+ rs2 = rs2 + (rs2 == 0)
dof = (dof1 - 1) * (dof2 - 1)
p = np.diag(1 / rs2) * np.matrix(T2)
E = np.diag(rs1) * np.matrix(p)
@@ -808,7 +808,7 @@ class LISA_Markov(Markov):
>>> lm_random.significant_moves
array([[11, 11, 11, ..., 59, 59, 59],
[54, 54, 54, ..., 54, 55, 59],
- [11, 11, 11, ..., 11, 59, 59],
+ [11, 11, 11, ..., 27, 59, 59],
...,
[54, 54, 54, ..., 54, 54, 54],
[49, 49, 49, ..., 54, 54, 54],
@@ -824,7 +824,7 @@ class LISA_Markov(Markov):
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'
+ 'Chi2: 1059.678, p: 0.00, dof: 9'
Actual transitions of LISAs
@@ -894,7 +894,7 @@ class LISA_Markov(Markov):
origin = q[:, t]
dest = q[:, t + 1]
p_origin = pb[:, t]
- p_dest = pb[:, t]
+ p_dest = pb[:, t + 1]
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])
@@ -923,7 +923,7 @@ class LISA_Markov(Markov):
t1 = np.diag(trans) * kp
t2 = self.transitions
t1 = t1.getA()
- self.chi_2 = pysal.spatial_dynamics.markov.chi2(t1, t2)
+ self.chi_2 = pysal.spatial_dynamics.markov.chi2(t2, t1)
self.expected_t = t1
self.permutations = permutations
diff --git a/pysal/spatial_dynamics/tests/test_markov.py b/pysal/spatial_dynamics/tests/test_markov.py
index 8e10daf..8164674 100644
--- a/pysal/spatial_dynamics/tests/test_markov.py
+++ b/pysal/spatial_dynamics/tests/test_markov.py
@@ -64,11 +64,12 @@ class test_chi2(unittest.TestCase):
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()
+ chi = np.matrix([[4.05598541e+01, 6.44644317e-04, 1.60000000e+01],
+ [5.54751974e+01, 2.97033748e-06, 1.60000000e+01],
+ [1.77528996e+01, 3.38563882e-01, 1.60000000e+01],
+ [4.00390961e+01, 7.68422046e-04, 1.60000000e+01],
+ [4.67966803e+01, 7.32512065e-05,
+ 1.60000000e+01]]).getA()
obs = np.matrix(sm.chi2).getA()
np.testing.assert_array_almost_equal(obs, chi)
obs = np.matrix(
diff --git a/pysal/spreg/__init__.py b/pysal/spreg/__init__.py
index 0587fab..f5bc705 100644
--- a/pysal/spreg/__init__.py
+++ b/pysal/spreg/__init__.py
@@ -18,3 +18,6 @@ from ml_lag import *
from ml_lag_regimes import *
from ml_error import *
from ml_error_regimes import *
+from sur import *
+from sur_error import *
+from sur_lag import *
diff --git a/pysal/spreg/diagnostics.py b/pysal/spreg/diagnostics.py
index 19ab1b2..45f6e1d 100644
--- a/pysal/spreg/diagnostics.py
+++ b/pysal/spreg/diagnostics.py
@@ -906,10 +906,10 @@ def white(reg):
# Compute cross-products and squares of the regression variables
if type(X).__name__ == 'ndarray':
- A = np.zeros((n, (k * (k + 1)) / 2))
+ 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))
+ A = SP.lil_matrix((n, (k * (k + 1)) // 2))
else:
raise Exception, "unknown X type, %s" % type(X).__name__
counter = 0
diff --git a/pysal/spreg/diagnostics_sur.py b/pysal/spreg/diagnostics_sur.py
new file mode 100644
index 0000000..71f5541
--- /dev/null
+++ b/pysal/spreg/diagnostics_sur.py
@@ -0,0 +1,221 @@
+"""
+Diagnostics for SUR and 3SLS estimation
+"""
+
+__author__= "Luc Anselin lanselin at gmail.com, \
+ Pedro V. Amaral pedrovma at gmail.com"
+
+
+import numpy as np
+import scipy.stats as stats
+import numpy.linalg as la
+from sur_utils import sur_dict2mat,sur_mat2dict,sur_corr
+from regimes import buildR1var,wald_test
+
+
+__all__ = ['sur_setp','sur_lrtest','sur_lmtest','lam_setp','surLMe']
+
+
+def sur_setp(bigB,varb):
+ ''' Utility to compute standard error, t and p-value
+
+ Parameters
+ ----------
+ bigB : dictionary of regression coefficient estimates,
+ one vector by equation
+ varb : variance-covariance matrix of coefficients
+
+ Returns
+ -------
+ surinfdict : dictionary with standard error, t-value, and
+ p-value array, one for each equation
+
+ '''
+ vvb = varb.diagonal()
+ n_eq = len(bigB.keys())
+ bigK = np.zeros((n_eq,1),dtype=np.int_)
+ for r in range(n_eq):
+ bigK[r] = bigB[r].shape[0]
+ b = sur_dict2mat(bigB)
+ se = np.sqrt(vvb)
+ se.resize(len(se),1)
+ t = np.divide(b,se)
+ tp = stats.norm.sf(abs(t))*2
+ surinf = np.hstack((se,t,tp))
+ surinfdict = sur_mat2dict(surinf,bigK)
+ return surinfdict
+
+def lam_setp(lam,vm):
+ """Standard errors, t-test and p-value for lambda in SUR Error ML
+
+ Parameters
+ ----------
+ lam : n_eq x 1 array with ML estimates for spatial error
+ autoregressive coefficient
+ vm : n_eq x n_eq subset of variance-covariance matrix for
+ lambda and Sigma in SUR Error ML
+ (needs to be subset from full vm)
+
+ Returns
+ -------
+ : tuple with arrays for standard error, t-value and p-value
+ (each element in the tuple is an n_eq x 1 array)
+
+ """
+ vvb = vm.diagonal()
+ se = np.sqrt(vvb)
+ se.resize(len(se),1)
+ t = np.divide(lam,se)
+ tp = stats.norm.sf(abs(t))*2
+ return (se,t,tp)
+
+def sur_lrtest(n,n_eq,ldetS0,ldetS1):
+ ''' Likelihood Ratio test on off-diagonal elements of Sigma
+
+ Parameters
+ ----------
+ n : cross-sectional dimension (number of observations for an equation)
+ n_eq : number of equations
+ ldetS0 : log determinant of Sigma for OLS case
+ ldetS1 : log determinant of Sigma for SUR case (should be iterated)
+
+ Returns
+ -------
+ (lrtest,M,pvalue) : tupel with value of test statistic (lrtest),
+ degrees of freedom (M, as an integer)
+ p-value
+
+ '''
+ M = n_eq * (n_eq - 1)/2.0
+ lrtest = n * (ldetS0 - ldetS1)
+ pvalue = stats.chi2.sf(lrtest,M)
+ return (lrtest,int(M),pvalue)
+
+
+def sur_lmtest(n,n_eq,sig):
+ ''' Lagrange Multiplier test on off-diagonal elements of Sigma
+
+ Parameters
+ ----------
+ n : cross-sectional dimension (number of observations for an equation)
+ n_eq : number of equations
+ sig : inter-equation covariance matrix for null model (OLS)
+
+ Returns
+ -------
+ (lmtest,M,pvalue) : tupel with value of test statistic (lmtest),
+ degrees of freedom (M, as an integer)
+ p-value
+
+ '''
+ R = sur_corr(sig)
+ tr = np.trace(np.dot(R.T,R))
+ M = n_eq * (n_eq - 1)/2.0
+ lmtest = (n/2.0) * (tr - n_eq)
+ pvalue = stats.chi2.sf(lmtest,M)
+ return (lmtest,int(M),pvalue)
+
+
+def surLMe(n_eq,WS,bigE,sig):
+ """Lagrange Multiplier test on error spatial autocorrelation in SUR
+
+ Parameters
+ ----------
+ n_eq : number of equations
+ WS : spatial weights matrix in sparse form
+ bigE : n x n_eq matrix of residuals by equation
+ sig : cross-equation error covariance matrix
+
+ Returns
+ -------
+ (LMe,n_eq,pvalue) : tupel with value of statistic (LMe), degrees
+ of freedom (n_eq) and p-value
+
+ """
+ # spatially lagged residuals
+ WbigE = WS * bigE
+ # score
+ EWE = np.dot(bigE.T,WbigE)
+ sigi = la.inv(sig)
+ SEWE = sigi * EWE
+ score = SEWE.sum(axis=1)
+ score.resize(n_eq,1)
+ # trace terms
+ WW = WS * WS
+ trWW = np.sum(WW.diagonal())
+ WTW = WS.T * WS
+ trWtW = np.sum(WTW.diagonal())
+ # denominator
+ SiS = sigi * sig
+ Tii = trWW * np.identity(n_eq)
+ tSiS = trWtW * SiS
+ denom = Tii + tSiS
+ idenom = la.inv(denom)
+ # test statistic
+ LMe = np.dot(np.dot(score.T,idenom),score)[0][0]
+ pvalue = stats.chi2.sf(LMe,n_eq)
+ return (LMe,n_eq,pvalue)
+
+def sur_chow(n_eq,bigK,bSUR,varb):
+ """test on constancy of regression coefficients across equations in
+ a SUR specification
+
+ Note: requires a previous check on constancy of number of coefficients
+ across equations; no other checks are carried out, so it is possible
+ that the results are meaningless if the variables are not listed in
+ the same order in each equation.
+
+ Parameters
+ ----------
+ n_eq : integer, number of equations
+ bigK : array with the number of variables by equation (includes constant)
+ bSUR : dictionary with the SUR regression coefficients by equation
+ varb : array with the variance-covariance matrix for the SUR regression
+ coefficients
+
+ Returns
+ -------
+ test : a list with for each coefficient (in order) a tuple with the
+ value of the test statistic, the degrees of freedom, and the
+ p-value
+
+ """
+ kr = bigK[0][0]
+ test = []
+ bb = sur_dict2mat(bSUR)
+ kf = 0
+ nr = n_eq
+ df = n_eq - 1
+ for i in range(kr):
+ Ri = buildR1var(i,kr,kf,0,nr)
+ tt,p = wald_test(bb,Ri,np.zeros((df,1)),varb)
+ test.append((tt,df,p))
+ return test
+
+def sur_joinrho(n_eq,bigK,bSUR,varb):
+ """Test on joint significance of spatial autoregressive coefficient in SUR
+
+ Parameters
+ ----------
+ n_eq : integer, number of equations
+ bigK : n_eq x 1 array with number of variables by equation
+ (includes constant term, exogenous and endogeneous and
+ spatial lag)
+ bSUR : dictionary with regression coefficients by equation, with
+ the spatial autoregressive term as last
+ varb : variance-covariance matrix for regression coefficients
+
+ Returns
+ -------
+ : tuple with test statistic, degrees of freedom, p-value
+
+ """
+ bb = sur_dict2mat(bSUR)
+ R = np.zeros((n_eq,varb.shape[0]))
+ q = np.zeros((n_eq,1))
+ kc = -1
+ for i in range(n_eq):
+ kc = kc + bigK[i]
+ R[i,kc] = 1
+ w,p = wald_test(bb,R,q,varb)
+ return (w,n_eq,p)
\ No newline at end of file
diff --git a/pysal/spreg/ml_error.py b/pysal/spreg/ml_error.py
index e0e8539..ce26a97 100644
--- a/pysal/spreg/ml_error.py
+++ b/pysal/spreg/ml_error.py
@@ -2,12 +2,16 @@
ML Estimation of Spatial Error Model
"""
-__author__ = "Luc Anselin luc.anselin at asu.edu, Serge Rey srey at asu.edu"
+__author__ = "Luc Anselin luc.anselin at asu.edu,\
+ Serge Rey srey at asu.edu, \
+ Levi Wolf levi.john.wolf at asu.edu"
import numpy as np
import numpy.linalg as la
+from scipy import sparse as sp
+from scipy.sparse.linalg import splu as SuperLU
import pysal as ps
-from pysal.spreg.utils import RegressionPropsY, RegressionPropsVM
+from utils import RegressionPropsY, RegressionPropsVM
import diagnostics as DIAG
import user_output as USER
import summary_output as SUMMARY
@@ -39,6 +43,8 @@ class BaseML_Error(RegressionPropsY, RegressionPropsVM, REGI.Regimes_Frame):
Spatial weights sparse matrix
method : string
if 'full', brute force calculation (full matrix expressions)
+ if 'ord', Ord eigenvalue calculation
+ if 'LU', LU decomposition for sparse matrices
epsilon : float
tolerance criterion in mimimize_scalar function and inverse_product
regimes_att : dictionary
@@ -157,19 +163,29 @@ class BaseML_Error(RegressionPropsY, RegressionPropsVM, REGI.Regimes_Frame):
self.n, self.k = self.x.shape
self.method = method
self.epsilon = epsilon
- W = w.full()[0]
+
+ #W = w.full()[0] #wait to build pending what is needed
+ #Wsp = w.sparse
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':
+ if methodML in ['FULL', 'LU', 'ORD']:
+ if methodML == 'FULL':
+ W = w.full()[0] # need dense here
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 == 'LU':
+ I = sp.identity(w.n)
+ Wsp = w.sparse # need sparse here
+ res = minimize_scalar(err_c_loglik_sp, 0.0, bounds=(-1.0,1.0),
+ args=(self.n, self.y, ylag,
+ self.x, xlag, I, Wsp),
+ method='bounded', tol=epsilon)
elif methodML == 'ORD':
# check on symmetry structure
if w.asymmetry(intrinsic=False) == []:
@@ -177,6 +193,7 @@ class BaseML_Error(RegressionPropsY, RegressionPropsVM, REGI.Regimes_Frame):
WW = ww.todense()
evals = la.eigvalsh(WW)
else:
+ W = w.full()[0] # need dense here
evals = la.eigvals(W)
res = minimize_scalar(
err_c_loglik_ord, 0.0, bounds=(-1.0, 1.0),
@@ -247,7 +264,6 @@ class BaseML_Error(RegressionPropsY, RegressionPropsVM, REGI.Regimes_Frame):
self.vm = np.vstack((vv, vv1))
- self._cache = {}
def get_x_lag(self, w, regimes_att):
if regimes_att:
@@ -277,7 +293,8 @@ class ML_Error(BaseML_Error):
Spatial weights sparse matrix
method : string
if 'full', brute force calculation (full matrix expressions)
- ir 'ord', Ord eigenvalue method
+ if 'ord', Ord eigenvalue method
+ if 'LU', LU sparse matrix decomposition
epsilon : float
tolerance criterion in mimimize_scalar function and inverse_product
spat_diag : boolean
@@ -431,21 +448,18 @@ class ML_Error(BaseML_Error):
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)
+ 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)
def err_c_loglik(lam, n, y, ylag, x, xlag, W):
@@ -468,9 +482,32 @@ def err_c_loglik(lam, n, y, ylag, x, xlag, W):
clik = nlsig2 - jacob
return clik
+def err_c_loglik_sp(lam, n, y, ylag, x, xlag, I, Wsp):
+ # concentrated log-lik for error model, no constants, LU
+ if isinstance(lam, np.ndarray):
+ if lam.shape == (1,1):
+ lam = lam[0][0] #why does the interior value change?
+ 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 = I - lam * Wsp
+ LU = SuperLU(a.tocsc())
+ jacob = np.sum(np.log(np.abs(LU.U.diagonal())))
+ # 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
+ # concentrated log-lik for error model, no constants, eigenvalues
ys = y - lam * ylag
xs = x - lam * xlag
ysys = np.dot(ys.T, ys)
diff --git a/pysal/spreg/ml_error_regimes.py b/pysal/spreg/ml_error_regimes.py
index f75c0f7..58a9f5b 100644
--- a/pysal/spreg/ml_error_regimes.py
+++ b/pysal/spreg/ml_error_regimes.py
@@ -52,6 +52,8 @@ class ML_Error_Regimes(BaseML_Error, REGI.Regimes_Frame):
Spatial weights sparse matrix
method : string
if 'full', brute force calculation (full matrix expressions)
+ if 'ord', Ord eigenvalue computation
+ if 'LU', LU sparse matrix decomposition
epsilon : float
tolerance criterion in mimimize_scalar function and inverse_product
regime_err_sep : boolean
@@ -112,6 +114,8 @@ class ML_Error_Regimes(BaseML_Error, REGI.Regimes_Frame):
method : string
log Jacobian method
if 'full': brute force (full matrix computations)
+ if 'ord', Ord eigenvalue computation
+ if 'LU', LU sparse matrix decomposition
epsilon : float
tolerance criterion used in minimize_scalar function and inverse_product
mean_y : float
@@ -317,7 +321,6 @@ class ML_Error_Regimes(BaseML_Error, REGI.Regimes_Frame):
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)
diff --git a/pysal/spreg/ml_lag.py b/pysal/spreg/ml_lag.py
index c890024..dd322b1 100644
--- a/pysal/spreg/ml_lag.py
+++ b/pysal/spreg/ml_lag.py
@@ -2,13 +2,16 @@
ML Estimation of Spatial Lag Model
"""
-__author__ = "Luc Anselin luc.anselin at asu.edu, Serge Rey srey at asu.edu"
+__author__ = "Luc Anselin luc.anselin at asu.edu, \
+ Serge Rey srey at asu.edu, \
+ Levi Wolf levi.john.wolf at gmail.com"
import numpy as np
import numpy.linalg as la
+from scipy import sparse as sp
+from scipy.sparse.linalg import splu as SuperLU
import pysal as ps
-from pysal.spreg.utils import RegressionPropsY, RegressionPropsVM, inverse_prod
-from utils import spdot
+from utils import RegressionPropsY, RegressionPropsVM, inverse_prod, spdot
import diagnostics as DIAG
import user_output as USER
import summary_output as SUMMARY
@@ -40,6 +43,7 @@ class BaseML_Lag(RegressionPropsY, RegressionPropsVM):
method : string
if 'full', brute force calculation (full matrix expressions)
if 'ord', Ord eigenvalue method
+ if 'LU', LU sparse matrix decomposition
epsilon : float
tolerance criterion in mimimize_scalar function and inverse_product
@@ -174,7 +178,8 @@ class BaseML_Lag(RegressionPropsY, RegressionPropsVM):
self.n, self.k = self.x.shape
self.method = method
self.epsilon = epsilon
- W = w.full()[0]
+ #W = w.full()[0]
+ #Wsp = w.sparse
ylag = ps.lag_spatial(w, y)
# b0, b1, e0 and e1
xtx = spdot(self.x.T, self.x)
@@ -187,12 +192,19 @@ class BaseML_Lag(RegressionPropsY, RegressionPropsVM):
e1 = ylag - spdot(x, b1)
methodML = method.upper()
# call minimizer using concentrated log-likelihood to get rho
- if methodML in ['FULL', 'ORD']:
+ if methodML in ['FULL', 'LU', 'ORD']:
if methodML == 'FULL':
+ W = w.full()[0] # moved here
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 == 'LU':
+ I = sp.identity(w.n)
+ Wsp = w.sparse # moved here
+ res = minimize_scalar(lag_c_loglik_sp, 0.0, bounds=(-1.0,1.0),
+ args=(self.n, e0, e1, I, Wsp),
+ method='bounded', tol=epsilon)
elif methodML == 'ORD':
# check on symmetry structure
if w.asymmetry(intrinsic=False) == []:
@@ -200,6 +212,7 @@ class BaseML_Lag(RegressionPropsY, RegressionPropsVM):
WW = ww.todense()
evals = la.eigvalsh(WW)
else:
+ W = w.full()[0] # moved here
evals = la.eigvals(W)
res = minimize_scalar(lag_c_loglik_ord, 0.0, bounds=(-1.0, 1.0),
args=(
@@ -207,7 +220,7 @@ class BaseML_Lag(RegressionPropsY, RegressionPropsVM):
tol=epsilon)
else:
# program will crash, need to catch
- print "{0} is an unsupported method".format(methodML)
+ print("{0} is an unsupported method".format(methodML))
self = None
return
@@ -232,7 +245,6 @@ class BaseML_Lag(RegressionPropsY, RegressionPropsVM):
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
@@ -528,24 +540,21 @@ class ML_Lag(BaseML_Lag):
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)
+ 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)
def lag_c_loglik(rho, n, e0, e1, W):
# concentrated log-lik for lag model, no constants, brute force
@@ -559,6 +568,19 @@ def lag_c_loglik(rho, n, e0, e1, W):
clik = nlsig2 - jacob
return clik
+def lag_c_loglik_sp(rho, n, e0, e1, I, Wsp):
+ # concentrated log-lik for lag model, sparse algebra
+ if isinstance(rho, np.ndarray):
+ if rho.shape == (1,1):
+ rho = rho[0][0] #why does the interior value change?
+ er = e0 - rho * e1
+ sig2 = np.dot(er.T, er) / n
+ nlsig2 = (n / 2.0) * np.log(sig2)
+ a = I - rho * Wsp
+ LU = SuperLU(a.tocsc())
+ jacob = np.sum(np.log(np.abs(LU.U.diagonal())))
+ clike = nlsig2 - jacob
+ return clike
def lag_c_loglik_ord(rho, n, e0, e1, evals):
# concentrated log-lik for lag model, no constants, Ord eigenvalue method
diff --git a/pysal/spreg/ml_lag_regimes.py b/pysal/spreg/ml_lag_regimes.py
index 514c0d5..9c03ae7 100644
--- a/pysal/spreg/ml_lag_regimes.py
+++ b/pysal/spreg/ml_lag_regimes.py
@@ -53,6 +53,7 @@ class ML_Lag_Regimes(BaseML_Lag, REGI.Regimes_Frame):
method : string
if 'full', brute force calculation (full matrix expressions)
if 'ord', Ord eigenvalue method
+ if 'LU', LU sparse matrix decomposition
epsilon : float
tolerance criterion in mimimize_scalar function and inverse_product
regime_lag_sep: boolean
@@ -111,6 +112,8 @@ class ML_Lag_Regimes(BaseML_Lag, REGI.Regimes_Frame):
method : string
log Jacobian method
if 'full': brute force (full matrix computations)
+ if 'ord', Ord eigenvalue method
+ if 'LU', LU sparse matrix decomposition
epsilon : float
tolerance criterion used in minimize_scalar function and inverse_product
mean_y : float
diff --git a/pysal/spreg/ols.py b/pysal/spreg/ols.py
index 10710d3..482e117 100644
--- a/pysal/spreg/ols.py
+++ b/pysal/spreg/ols.py
@@ -113,15 +113,14 @@ class BaseOLS(RegressionPropsY, RegressionPropsVM):
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
+ if robust is not None:
+ self.vm = ROBUST.robust_vm(reg=self, gwk=gwk, sig2n_k=sig2n_k)
+
class OLS(BaseOLS):
diff --git a/pysal/spreg/opt.py b/pysal/spreg/opt.py
new file mode 100644
index 0000000..ac423d7
--- /dev/null
+++ b/pysal/spreg/opt.py
@@ -0,0 +1,92 @@
+import copy
+
+def simport(modname):
+ """
+ Safely import a module without raising an error.
+
+ Parameters
+ -----------
+ modname : str
+ module name needed to import
+
+ Returns
+ --------
+ tuple of (True, Module) or (False, None) depending on whether the import
+ succeeded.
+
+ Notes
+ ------
+ Wrapping this function around an iterative context or a with context would
+ allow the module to be used without necessarily attaching it permanently in
+ the global namespace:
+
+ >>> for t,mod in simport('pandas'):
+ if t:
+ mod.DataFrame()
+ else:
+ #do alternative behavior here
+ del mod #or don't del, your call
+
+ instead of:
+
+ >>> t, mod = simport('pandas')
+ >>> if t:
+ mod.DataFrame()
+ else:
+ #do alternative behavior here
+
+ The first idiom makes it work kind of a like a with statement.
+ """
+ try:
+ exec('import {}'.format(modname))
+ return True, eval(modname)
+ except:
+ return False, None
+
+def requires(*args, **kwargs):
+ """
+ Decorator to wrap functions with extra dependencies:
+
+ Arguments
+ ---------
+ args : list
+ list of strings containing module to import
+ verbose : bool
+ boolean describing whether to print a warning message on import
+ failure
+ Returns
+ -------
+ Original function is all arg in args are importable, otherwise returns a
+ function that passes.
+ """
+ v = kwargs.pop('verbose', True)
+ wanted = copy.deepcopy(args)
+ def inner(function):
+ available = [simport(arg)[0] for arg in args]
+ if all(available):
+ return function
+ else:
+ def passer(*args,**kwargs):
+ if v:
+ missing = [arg for i, arg in enumerate(wanted) if not available[i]]
+ print('missing dependencies: {d}'.format(d=missing))
+ print('not running {}'.format(function.__name__))
+ else:
+ pass
+ return passer
+ return inner
+
+if __name__ == '__main__':
+
+ @requires('pandas')
+ def test():
+ import pandas
+ print('ASDF')
+
+ @requires('thisisnotarealmodule')
+ def test2():
+ print('you shouldnt see this')
+
+ test()
+ test2()
+
diff --git a/pysal/spreg/optional_imports.ipynb b/pysal/spreg/optional_imports.ipynb
new file mode 100644
index 0000000..bf3095a
--- /dev/null
+++ b/pysal/spreg/optional_imports.ipynb
@@ -0,0 +1,278 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This details how the `requires` decorator can be used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from opt import requires"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The function takes an arbitrary number of strings that describe the dependencies introduced by the class or function. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "@requires('pandas')\n",
+ "def test():\n",
+ " import pandas\n",
+ " print('yay pandas version {}'.format(pandas.__version__))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "yay pandas version 0.16.2\n"
+ ]
+ }
+ ],
+ "source": [
+ "test()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So, neat. What if we didn't have the module needed?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "@requires(\"notarealmodule\")\n",
+ "def test2():\n",
+ " print(\"you shouldn't see this\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "missing dependencies: ['notarealmodule']\n",
+ "not running test2\n"
+ ]
+ }
+ ],
+ "source": [
+ "test2()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So, if the requirements aren't met, your original function is replaced with a new function.\n",
+ "\n",
+ "it looks kind of like this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def passer():\n",
+ " if verbose:\n",
+ " missing = [arg for i,arg in enumerate(args) if not available[i]]\n",
+ " print(\"missing dependencies: {d}\".format(d=missing))\n",
+ " print(\"not running {}\".format(function.__name__))\n",
+ " else:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "By deafult, the function is replaced with a verbose version. If you pass `verbose=False`, then the function gets replaced with a nonprinting version. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "@requires(\"notarealmodule\", verbose=False)\n",
+ "def test3():\n",
+ " print(\"you shouldn't see this, either\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "test3()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The cool thing is, this works on class definitions as well:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "@requires(\"notarealmodule\")\n",
+ "class OLS_mock(object):\n",
+ " def __init__(self, *args, **kwargs):\n",
+ " for arg in args:\n",
+ " print(arg)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "missing dependencies: ['notarealmodule']\n",
+ "not running OLS_mock\n"
+ ]
+ }
+ ],
+ "source": [
+ "OLS_mock(1,2,3,4,5, w='Tom')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "@requires(\"pymc3\")\n",
+ "class BayesianHLM(object):\n",
+ " def __init__(self, *args, **kwargs):\n",
+ " print(arg)for arg in args[0:]:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1\n",
+ "2\n",
+ "3\n",
+ "4\n",
+ "5\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<__main__.BayesianHLM at 0x7fe3a32f9e90>"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "BayesianHLM(1,2,3,4,5, w='Tom')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "I think this could make more sense for `spreg` dependencies, where the extra stuff might be reasonable at a function or class level, rather than at the module level. \n",
+ "\n",
+ "In addition, it could be useful for any plans for a viz setup, where maybe a projected map would require `cartopy`, but an unprojected map could just use `matplotlib`. This would let us do this function-by-function and print or raise reasonable fail states. "
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/spreg/probit.py b/pysal/spreg/probit.py
index f4c0549..e1a7ad5 100644
--- a/pysal/spreg/probit.py
+++ b/pysal/spreg/probit.py
@@ -14,7 +14,7 @@ from utils import spdot, spbroadcast
__all__ = ["Probit"]
-class BaseProbit:
+class BaseProbit(object):
"""
Probit class to do all the computations
@@ -137,18 +137,41 @@ class BaseProbit:
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:
+ try:
+ return self._cache['vm']
+ except AttributeError:
+ self._cache = {}
+ H = self.hessian(self.betas)
+ self._cache['vm'] = -la.inv(H)
+ except KeyError:
H = self.hessian(self.betas)
self._cache['vm'] = -la.inv(H)
return self._cache['vm']
+
+ @vm.setter
+ def vm(self, val):
+ try:
+ self._cache['vm'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['vm'] = val
- @property
+ @property #could this get packaged into a separate function or something? It feels weird to duplicate this.
def z_stat(self):
- if 'z_stat' not in self._cache:
+ try:
+ return self._cache['z_stat']
+ except AttributeError:
+ 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()
+ except KeyError:
variance = self.vm.diagonal()
zStat = self.betas.reshape(len(self.betas),) / np.sqrt(variance)
rs = {}
@@ -157,16 +180,46 @@ class BaseProbit:
self._cache['z_stat'] = rs.values()
return self._cache['z_stat']
+ @z_stat.setter
+ def z_stat(self, val):
+ try:
+ self._cache['z_stat'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['z_stat'] = val
+
@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']
+ try:
+ return self._cache['slopes_std_err']
+ except AttributeError:
+ self._cache = {}
+ self._cache['slopes_std_err'] = np.sqrt(self.slopes_vm.diagonal())
+ except KeyError:
+ self._cache['slopes_std_err'] = np.sqrt(self.slopes_vm.diagonal())
+ return self._cache['slopes_std_err']
+
+ @slopes_std_err.setter
+ def slopes_std_err(self, val):
+ try:
+ self._cache['slopes_std_err'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['slopes_std_err'] = val
@property
def slopes_z_stat(self):
- if 'slopes_z_stat' not in self._cache:
+ try:
+ return self._cache['slopes_z_stat']
+ except AttributeError:
+ 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()
+ except KeyError:
zStat = self.slopes.reshape(
len(self.slopes),) / self.slopes_std_err
rs = {}
@@ -175,65 +228,184 @@ class BaseProbit:
self._cache['slopes_z_stat'] = rs.values()
return self._cache['slopes_z_stat']
+ @slopes_z_stat.setter
+ def slopes_z_stat(self, val):
+ try:
+ self._cache['slopes_z_stat'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['slopes_z_stat'] = val
+
@property
def xmean(self):
- if 'xmean' not in self._cache:
+ try:
+ return self._cache['xmean']
+ except AttributeError:
+ self._cache = {}
+ try: #why is this try-accept? can x be a list??
+ self._cache['xmean'] = np.reshape(sum(self.x) / self.n, (self.k, 1))
+ except:
+ self._cache['xmean'] = np.reshape(sum(self.x).toarray() / self.n, (self.k, 1))
+ except KeyError:
try:
self._cache['xmean'] = np.reshape(sum(self.x) / self.n, (self.k, 1))
except:
self._cache['xmean'] = np.reshape(sum(self.x).toarray() / self.n, (self.k, 1))
return self._cache['xmean']
+ @xmean.setter
+ def xmean(self, val):
+ try:
+ self._cache['xmean'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['xmean'] = val
+
@property
def xb(self):
- if 'xb' not in self._cache:
+ try:
+ return self._cache['xb']
+ except AttributeError:
+ self._cache = {}
+ self._cache['xb'] = spdot(self.x, self.betas)
+ except KeyError:
self._cache['xb'] = spdot(self.x, self.betas)
return self._cache['xb']
+ @xb.setter
+ def xb(self, val):
+ try:
+ self._cache['xb'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['xb'] = val
+
@property
def predy(self):
- if 'predy' not in self._cache:
+ try:
+ return self._cache['predy']
+ except AttributeError:
+ self._cache = {}
+ self._cache['predy'] = norm.cdf(self.xb)
+ except KeyError:
self._cache['predy'] = norm.cdf(self.xb)
return self._cache['predy']
+
+ @predy.setter
+ def predy(self, val):
+ try:
+ self._cache['predy'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['predy'] = val
@property
def predpc(self):
- if 'predpc' not in self._cache:
+ try:
+ return self._cache['predpc']
+ except AttributeError:
+ 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(100.0 * np.sum(predpc) / self.n)
+ except KeyError:
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)
+ self._cache['predpc'] = float(100.0 * np.sum(predpc) / self.n)
return self._cache['predpc']
+ @predpc.setter
+ def predpc(self, val):
+ try:
+ self._cache['predpc'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['predpc'] = val
+
@property
def phiy(self):
- if 'phiy' not in self._cache:
+ try:
+ return self._cache['phiy']
+ except AttributeError:
+ self._cache = {}
+ self._cache['phiy'] = norm.pdf(self.xb)
+ except KeyError:
self._cache['phiy'] = norm.pdf(self.xb)
return self._cache['phiy']
+
+ @phiy.setter
+ def phiy(self, val):
+ try:
+ self._cache['phiy'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['phiy'] = val
@property
def scale(self):
- if 'scale' not in self._cache:
+ try:
+ return self._cache['scale']
+ except AttributeError:
+ self._cache = {}
+ if self.scalem == 'phimean':
+ self._cache['scale'] = float(1.0 * np.sum(self.phiy) / self.n)
+ elif self.scalem == 'xmean':
+ self._cache['scale'] = float(norm.pdf(np.dot(self.xmean.T, self.betas)))
+ except KeyError:
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)))
+ self._cache['scale'] = float(norm.pdf(np.dot(self.xmean.T, self.betas)))
return self._cache['scale']
+ @scale.setter
+ def scale(self, val):
+ try:
+ self._cache['scale'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['scale'] = val
+
@property
def slopes(self):
- if 'slopes' not in self._cache:
- # Disregard the presence of dummies.
+ try:
+ return self._cache['slopes']
+ except AttributeError:
+ self._cache = {}
+ self._cache['slopes'] = self.betas[1:] * self.scale
+ except KeyError:
self._cache['slopes'] = self.betas[1:] * self.scale
return self._cache['slopes']
+ @slopes.setter
+ def slopes(self, val):
+ try:
+ self._cache['slopes'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['slopes'] = val
+
@property
def slopes_vm(self):
- if 'slopes_vm' not in self._cache:
+ try:
+ return self._cache['slopes_vm']
+ except AttributeError:
+ self._cache = {}
+ x = self.xmean
+ b = self.betas
+ dfdb = np.eye(self.k) - spdot(b.T, x) * spdot(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:]
+ except KeyError:
x = self.xmean
b = self.betas
dfdb = np.eye(self.k) - spdot(b.T, x) * spdot(b, x.T)
@@ -242,51 +414,145 @@ class BaseProbit:
self._cache['slopes_vm'] = slopes_vm[1:, 1:]
return self._cache['slopes_vm']
+ @slopes_vm.setter
+ def slopes_vm(self, val):
+ try:
+ self._cache['slopes_vm'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['slopes_vm'] = val
+
@property
def LR(self):
- if 'LR' not in self._cache:
+ try:
+ return self._cache['LR']
+ except AttributeError:
+ 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))
+ except KeyError:
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']
+ @LR.setter
+ def LR(self, val):
+ try:
+ self._cache['LR'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['LR'] = val
+
@property
def u_naive(self):
- if 'u_naive' not in self._cache:
+ try:
+ return self._cache['u_naive']
+ except AttributeError:
+ self._cache = {}
+ self._cache['u_naive'] = self.y - self.predy
+ except KeyError:
u_naive = self.y - self.predy
self._cache['u_naive'] = u_naive
return self._cache['u_naive']
+ @u_naive.setter
+ def u_naive(self, val):
+ try:
+ self._cache['u_naive'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['u_naive'] = val
+
@property
def u_gen(self):
- if 'u_gen' not in self._cache:
+ try:
+ return self._cache['u_gen']
+ except AttributeError:
+ self._cache = {}
+ Phi_prod = self.predy * (1 - self.predy)
+ u_gen = self.phiy * (self.u_naive / Phi_prod)
+ self._cache['u_gen'] = u_gen
+ except KeyError:
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']
+
+ @u_gen.setter
+ def u_gen(self, val):
+ try:
+ self._cache['u_gen'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['u_gen'] = val
@property
def Pinkse_error(self):
- if 'Pinkse_error' not in self._cache:
+ try:
+ return self._cache['Pinkse_error']
+ except AttributeError:
+ self._cache = {}
+ self._cache['Pinkse_error'], self._cache[
+ 'KP_error'], self._cache['PS_error'] = sp_tests(self)
+ except KeyError:
self._cache['Pinkse_error'], self._cache[
'KP_error'], self._cache['PS_error'] = sp_tests(self)
return self._cache['Pinkse_error']
+ @Pinkse_error.setter
+ def Pinkse_error(self, val):
+ try:
+ self._cache['Pinkse_error'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['Pinkse_error'] = val
+
@property
def KP_error(self):
- if 'KP_error' not in self._cache:
+ try:
+ return self._cache['KP_error']
+ except AttributeError:
+ self._cache = {}
+ self._cache['Pinkse_error'], self._cache[
+ 'KP_error'], self._cache['PS_error'] = sp_tests(self)
+ except KeyError:
self._cache['Pinkse_error'], self._cache[
'KP_error'], self._cache['PS_error'] = sp_tests(self)
return self._cache['KP_error']
+ @KP_error.setter
+ def KP_error(self, val):
+ try:
+ self._cache['KP_error'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['KP_error'] = val
+
@property
def PS_error(self):
- if 'PS_error' not in self._cache:
+ try:
+ return self._cache['PS_error']
+ except AttributeError:
+ self._cache = {}
+ self._cache['Pinkse_error'], self._cache[
+ 'KP_error'], self._cache['PS_error'] = sp_tests(self)
+ except KeyError:
self._cache['Pinkse_error'], self._cache[
'KP_error'], self._cache['PS_error'] = sp_tests(self)
return self._cache['PS_error']
+ @PS_error.setter
+ def PS_error(self, val):
+ try:
+ self._cache['PS_error'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['PS_error'] = val
+
def par_est(self):
start = np.dot(la.inv(spdot(self.x.T, self.x)),
spdot(self.x.T, self.y))
diff --git a/pysal/spreg/regimes.py b/pysal/spreg/regimes.py
index bbffdde..1896a2b 100644
--- a/pysal/spreg/regimes.py
+++ b/pysal/spreg/regimes.py
@@ -279,6 +279,8 @@ 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
+
+ Note: needs a placeholder for kryd in builR1var, set to 0
...
@@ -298,7 +300,8 @@ def buildR(kr, kf, nr):
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)))
+ return np.vstack(tuple(map(buildR1var, np.arange(kr), [kr] * kr, [kf] * kr,\
+ [0] * kr, [nr] * kr)))
def buildR1var(vari, kr, kf, kryd, nr):
@@ -319,6 +322,7 @@ def buildR1var(vari, kr, kf, kryd, nr):
kf : int
Number of variables that do not vary across regimes ("fixed" or
global)
+ kryd : Number of endogenous variables varying across regimes
nr : int
Number of regimes
diff --git a/pysal/spreg/summary_output.py b/pysal/spreg/summary_output.py
index 26f9c0e..70b160e 100644
--- a/pysal/spreg/summary_output.py
+++ b/pysal/spreg/summary_output.py
@@ -644,6 +644,43 @@ def Probit(reg, vm, w, spat_diag):
summary(reg=reg, vm=vm, instruments=False,
short_intro=True, spat_diag=spat_diag)
+def SUR(reg, nonspat_diag=True, spat_diag=False, regimes=False,\
+ tsls=False, lambd=False, surlm=False):
+ reg.__summary = {}
+ # compute diagnostics and organize summary output
+ reg.__summary['summary_zt'] = 'z'
+ reg.__summary['summary_std_err'] = None
+ if not tsls:
+ try:
+ sum_str = "%-20s:%12.3f %-22s:%12d\n" % (
+ 'Log likelihood (SUR)', reg.llik, 'Number of Iterations', reg.niter)
+ except:
+ sum_str = "%-20s:%12.3f\n" % (
+ 'Log likelihood (SUR)', reg.llik)
+ summary_add_other_top(reg, sum_str)
+ if lambd:
+ sum_str = "%-20s:%12.3f %-22s:%12.3f\n" % (
+ 'Log likel. (error)', reg.errllik, 'Log likel. (SUR error)', reg.surerrllik)
+ summary_add_other_top(reg, sum_str)
+ # build coefficients table body
+ summary_coefs_sur(reg, lambd=lambd)
+ if tsls:
+ for i in range(1,reg.n_eq+1):
+ summary_coefs_instruments(reg, sur=i)
+ #if regimes:
+ # summary_regimes(reg)
+ summary_warning(reg)
+ summary_diag_sur(reg, nonspat_diag=nonspat_diag, spat_diag=spat_diag, tsls=tsls, lambd=lambd)
+ if surlm and spat_diag: # only in classic SUR
+ sum_str = summary_spat_diag_intro(no_mi=True)
+ sum_str += "%-27s %2d %12.3f %6.4f\n" % (
+ "Lagrange Multiplier (error)", reg.lmEtest[1], reg.lmEtest[0], reg.lmEtest[2])
+ summary_add_other_end(reg, sum_str)
+
+ summary_errorcorr(reg)
+ summary_SUR(reg=reg)
+
+
##############################################################################
@@ -677,7 +714,6 @@ def beta_diag(reg, robust):
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)
@@ -766,7 +802,7 @@ def summary(reg, vm, instruments, short_intro=False, nonspat_diag=False, spat_di
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:
+ for m in sorted(multireg):
mreg = multireg[m]
summary += "----------\n\n"
summary += summary_intro(mreg, short_intro)
@@ -819,6 +855,40 @@ def summary_multi(reg, multireg, vm, instruments, short_intro=False, nonspat_dia
summary += summary_close()
reg.summary = summary
+def summary_SUR(reg, short_intro=True):
+ summary = summary_open()
+ summary += summary_intro(reg, short_intro, sur=True)
+ summary += "%-20s:%12d %-22s:%12d\n" % (
+ 'Number of Equations', reg.n_eq, 'Number of Observations', reg.n)
+ try:
+ summary += reg.__summary['summary_other_top']
+ except:
+ pass
+ summary += "----------\n\n"
+ for m in reg.name_bigy.keys():
+ summary += summary_sur_mid(reg, m)
+ #summary += mreg.__summary['summary_r2']
+ #if nonspat_diag:
+ # summary += mreg.__summary['summary_nonspat_diag_1']
+ summary += summary_coefs_intro(reg)
+ summary += reg.__summary[m]['summary_coefs']
+ summary += "------------------------------------------------------------------------------------\n"
+ try:
+ summary += reg.__summary[m]['summary_coefs_instruments']
+ except:
+ pass
+ try:
+ summary += reg.__summary['summary_other_mid']
+ except:
+ pass
+ summary += "\n"
+ try:
+ summary += reg.__summary['summary_other_end']
+ except:
+ pass
+ summary += summary_close()
+ reg.summary = summary
+
def _get_var_indices(reg, lambd=False):
try:
@@ -846,7 +916,7 @@ def _get_var_indices(reg, lambd=False):
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()
+ #name_reg.sort()
if reg.constant_regi == 'many':
indices += [j] + [var_names.index(ind) for ind in name_reg]
else:
@@ -856,12 +926,15 @@ def _get_var_indices(reg, lambd=False):
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()
+ #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()
+ #indices = [0] + (np.argsort(var_names[1:last_v]) + 1).tolist()
+ indices = range(len(var_names[1:last_v])+1)
return var_names, indices
+
+
##############################################################################
@@ -882,15 +955,18 @@ def summary_open(multi=False):
return strSummary
-def summary_intro(reg, short): # extra space d
+def summary_intro(reg, short, sur=False): # 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:
+ try:
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)
+ except:
+ pass
+ if not sur:
+ 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)
@@ -899,6 +975,18 @@ def summary_intro(reg, short): # extra space d
#strSummary += '\n'
return strSummary
+def summary_sur_mid(reg, eq):
+ strSummary = "SUMMARY OF EQUATION " + str(eq+1) + "\n"
+ strSummary += "-" * (len(strSummary) - 1) + "\n"
+ n_var = int(reg.bigK[reg.name_bigy.keys().index(eq)])
+ strSummary += "%-20s:%12s %-22s:%12d\n" % (
+ 'Dependent Variable', reg.name_bigy[eq], 'Number of Variables', n_var)
+ strSummary += "%-20s:%12.4f %-22s:%12d\n" % (
+ 'Mean dependent var', np.mean(reg.bigy[eq]), 'Degrees of Freedom', reg.n - n_var)
+ strSummary += "%-20s:%12.4f\n" % (
+ 'S.D. dependent var', np.std(reg.bigy[eq]))
+ return strSummary
+
def summary_coefs_intro(reg):
strSummary = "\n"
@@ -924,8 +1012,42 @@ def summary_coefs_allx(reg, zt_stat, lambd=False):
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_sur(reg, lambd=False):
+ try:
+ betas = reg.bSUR
+ inf = reg.sur_inf
+ except:
+ betas = reg.b3SLS
+ inf = reg.tsls_inf
+ for eq in reg.name_bigy.keys():
+ reg.__summary[eq] = {}
+ strSummary = ""
+ for i in range(len(reg.name_bigX[eq])):
+ strSummary += "%20s %12.7f %12.7f %12.7f %12.7f\n" \
+ % (reg.name_bigX[eq][i], betas[eq][i][0], inf[eq][i][0],\
+ inf[eq][i][1], inf[eq][i][2])
+ try:
+ i += 1
+ for j in range(len(reg.name_bigyend[eq])):
+ strSummary += "%20s %12.7f %12.7f %12.7f %12.7f\n" \
+ % (reg.name_bigyend[eq][j], betas[eq][i+j][0], inf[eq][i+j][0],\
+ inf[eq][i+j][1], inf[eq][i+j][2])
+ except:
+ pass
+ if lambd:
+ pos = reg.name_bigy.keys().index(eq)
+ try:
+ strSummary += "%20s %12.7f %12.7f %12.7f %12.7f\n" \
+ % ("lambda_"+str(eq+1), reg.lamsur[pos], reg.lamsetp[0][pos][0],\
+ reg.lamsetp[1][pos][0], reg.lamsetp[2][pos][0])
+ except:
+ strSummary += "%20s %12.7f \n" \
+ % ("lambda_"+str(eq+1), reg.lamsur[pos])
+
+ reg.__summary[eq]['summary_coefs'] = strSummary
def summary_coefs_somex(reg, zt_stat):
"""This is a special case needed for models that do not have inference on
@@ -938,7 +1060,9 @@ def summary_coefs_somex(reg, zt_stat):
% (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)
@@ -946,7 +1070,7 @@ def summary_coefs_yend(reg, zt_stat, lambd=False):
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):
@@ -962,41 +1086,60 @@ def summary_coefs_lambda(reg, zt_stat):
'summary_coefs'] += "%20s %12.7f \n" % (name_var[-1], reg.betas[-1][0])
-def summary_coefs_instruments(reg):
+def summary_coefs_instruments(reg, sur=None):
"""Generates a list of the instruments used.
"""
+ if not sur:
+ name_q = reg.name_q
+ name_yend = reg.name_yend
+ else:
+ eq = reg.name_bigy.keys()[sur-1]
+ name_q = reg.name_bigq[eq]
+ name_yend = reg.name_bigyend[eq]
insts = "Instruments: "
- for name in sorted(reg.name_q):
+ for name in sorted(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):
+ for name in sorted(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
+ if not sur:
+ reg.__summary['summary_coefs_instruments'] = inst2
+ else:
+ reg.__summary[eq]['summary_coefs_instruments'] = inst2
def summary_iteration(reg): # extra space d
"""Reports the number of iterations computed.
"""
try:
+ niter = reg.niter
+ except:
+ niter = reg.iteration
+ try:
if reg.step1c:
step1c = 'Yes'
else:
step1c = 'No'
txt = "%-20s:%12s %-22s:%12s\n" % (
- 'N. of iterations', reg.iteration, 'Step1c computed', step1c)
+ 'N. of iterations', niter, 'Step1c computed', step1c)
except:
- txt = "%-20s:%12s\n" % ('N. of iterations', reg.iteration)
+ txt = "%-20s:%12s\n" % ('N. of iterations', niter)
+
+ summary_add_other_top(reg, txt)
+
+
+def summary_add_other_top(reg, sum_str):
try:
- reg.__summary['summary_other_top'] += txt
+ reg.__summary['summary_other_top'] += sum_str
except:
- reg.__summary['summary_other_top'] = txt
+ reg.__summary['summary_other_top'] = sum_str
def summary_regimes(reg, chow=True):
@@ -1011,7 +1154,8 @@ def summary_regimes(reg, chow=True):
if chow:
summary_chow(reg)
-
+'''
+deprecated
def summary_sur(reg, u_cov=False):
"""Lists the equation ID variable used.
"""
@@ -1037,7 +1181,7 @@ def summary_sur(reg, u_cov=False):
'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"
@@ -1088,6 +1232,7 @@ def summary_coefs_slopes(reg):
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:
@@ -1147,10 +1292,13 @@ def summary_nonspat_diag_2(reg):
return strSummary
-def summary_spat_diag_intro():
+def summary_spat_diag_intro(no_mi=False):
strSummary = ""
strSummary += "\nDIAGNOSTICS FOR SPATIAL DEPENDENCE\n"
- strSummary += "TEST MI/DF VALUE PROB\n"
+ if no_mi:
+ strSummary += "TEST DF VALUE PROB\n"
+ else:
+ strSummary += "TEST MI/DF VALUE PROB\n"
return strSummary
@@ -1190,6 +1338,82 @@ def summary_spat_diag_probit(reg):
"Pinkse-Slade (error)", 1, reg.PS_error[0], reg.PS_error[1])
return strSummary
+def summary_diag_sur(reg, nonspat_diag, spat_diag, tsls, lambd, rho=False):
+ if tsls:
+ try:
+ if reg.joinrho != None:
+ rho = True
+ except:
+ pass
+ if spat_diag and rho:
+ strSummary = ""
+ strSummary += "\nREGRESSION DIAGNOSTICS\n"
+ #strSummary += "----------------------\n"
+ strSummary += " TEST DF VALUE PROB\n"
+ strSummary += "%41s %2d %10.3f %6.4f\n" % (
+ "Joint significance (rho)", reg.joinrho[1], reg.joinrho[0], reg.joinrho[2])
+ summary_add_other_end(reg, strSummary)
+ else:
+ if nonspat_diag or (spat_diag and lambd):
+ strSummary = ""
+ strSummary += "\nREGRESSION DIAGNOSTICS\n"
+ #strSummary += "----------------------\n"
+ strSummary += " TEST DF VALUE PROB\n"
+ if nonspat_diag:
+ try:
+ strSummary += "%41s %2d %10.3f %6.4f\n" % (
+ "LM test on Sigma", reg.lmtest[1], reg.lmtest[0], reg.lmtest[2])
+ except:
+ pass
+ strSummary += "%41s %2d %10.3f %6.4f\n" % (
+ "LR test on Sigma", reg.lrtest[1], reg.lrtest[0], reg.lrtest[2])
+ if lambd and spat_diag:
+ strSummary += "%41s %2d %10.3f %6.4f\n" % (
+ "LR test on lambda", reg.likrlambda[1], reg.likrlambda[0], reg.likrlambda[2])
+ if reg.vm != None:
+ strSummary += "%41s %2d %10.3f %6.4f\n" % (
+ "Joint significance (lambda)", reg.joinlam[1], reg.joinlam[0], reg.joinlam[2])
+ summary_add_other_end(reg, strSummary)
+ chow_lamb = False
+ try:
+ if reg.lamtest != None:
+ chow_lamb = True
+ except:
+ pass
+ if nonspat_diag or (spat_diag and chow_lamb):
+ if reg.surchow != None:
+ strChow = "\nOTHER DIAGNOSTICS - CHOW TEST\n"
+ strChow += " VARIABLES DF VALUE PROB\n"
+ if nonspat_diag:
+ kx = len(reg.surchow)
+ if tsls:
+ kx += -len(reg.name_bigyend[reg.name_bigyend.keys()[0]])
+ names_chow = {}
+ for k in range(len(reg.surchow)):
+ names_chow[k] = ""
+ if k < kx:
+ for i in reg.name_bigX:
+ names_chow[k] += reg.name_bigX[i][k] + ", "
+ else:
+ for j in reg.name_bigyend:
+ names_chow[k] += reg.name_bigyend[j][k-kx] + ", "
+ if len(names_chow[k])>42:
+ names_chow[k] = names_chow[k][0:38] + "..."
+ else:
+ names_chow[k] = names_chow[k][:-2]
+ for k in range(len(reg.surchow)):
+ strChow += "%41s %2d %10.3f %6.4f\n" % (
+ names_chow[k], reg.surchow[k][1], reg.surchow[k][0], reg.surchow[k][2])
+ if spat_diag and chow_lamb:
+ strChow += "%41s %2d %10.3f %6.4f\n" % (
+ "lambda", reg.lamtest[1], reg.lamtest[0], reg.lamtest[2])
+ summary_add_other_end(reg, strChow)
+
+def summary_add_other_end(reg, strSummary):
+ try:
+ reg.__summary['summary_other_end'] += strSummary
+ except:
+ reg.__summary['summary_other_end'] = strSummary
def summary_vm(reg, instruments):
strVM = "\n"
@@ -1210,6 +1434,20 @@ def summary_vm(reg, instruments):
strVM += "\n"
return strVM
+def summary_errorcorr(reg):
+ str_ucv = "\n"
+ str_ucv += "ERROR CORRELATION MATRIX\n"
+ #str_ucv += "------------------------\n"
+ for i in range(reg.n_eq):
+ str_ucv += "%12s" %("EQUATION " + str(i+1))
+ str_ucv += "\n"
+ nrow = reg.corr.shape[0]
+ ncol = reg.corr.shape[1]
+ for i in range(nrow):
+ for j in range(ncol):
+ str_ucv += "%12.6f" % (reg.corr[i][j])
+ str_ucv += "\n"
+ summary_add_other_end(reg, str_ucv)
def summary_pred(reg):
strPred = "\n\n"
diff --git a/pysal/spreg/sur.py b/pysal/spreg/sur.py
new file mode 100644
index 0000000..c881adb
--- /dev/null
+++ b/pysal/spreg/sur.py
@@ -0,0 +1,697 @@
+"""
+SUR and 3SLS estimation
+"""
+
+__author__= "Luc Anselin lanselin at gmail.com, \
+ Pedro V. Amaral pedrovma at gmail.com"
+
+
+import numpy as np
+import numpy.linalg as la
+import scipy.stats as stats
+import summary_output as SUMMARY
+import user_output as USER
+from sur_utils import sur_dict2mat,sur_mat2dict,sur_corr,\
+ sur_crossprod,sur_est,sur_resids,check_k
+from diagnostics_sur import sur_setp,sur_lrtest,sur_lmtest,surLMe,sur_chow
+
+
+__all__ = ['SUR','ThreeSLS']
+
+
+class BaseSUR():
+ """ Base class for SUR estimation, both two step as well as iterated
+
+ Parameters
+ ----------
+
+ bigy : dictionary with vector for dependent variable by equation
+ bigX : dictionary with matrix of explanatory variables by equation
+ (note, already includes constant term)
+ iter : whether or not to use iterated estimation
+ default = False
+ maxiter : maximum iterations; default = 5
+ epsilon : precision criterion to end iterations
+ default = 0.00001
+ verbose : flag to print out iteration number and value of log det(sig)
+ at the beginning and the end of the iteration
+
+
+ Attributes
+ ----------
+
+ bigy : dictionary with y values
+ bigX : dictionary with X values
+ bigXX : dictionary with X_t'X_r cross-products
+ bigXy : dictionary with X_t'y_r cross-products
+ n_eq : number of equations
+ n : number of observations in each cross-section
+ bigK : vector with number of explanatory variables (including constant)
+ for each equation
+ bOLS : dictionary with OLS regression coefficients for each equation
+ olsE : N x n_eq array with OLS residuals for each equation
+ bSUR : dictionary with SUR regression coefficients for each equation
+ varb : variance-covariance matrix
+ bigE : N x n_eq array with SUR residuals for each equation
+ sig : Sigma matrix of inter-equation error covariances
+ ldetS1 : log det(Sigma) for SUR model
+ resids : n by n_eq array of residuals
+ sig_ols : Sigma matrix for OLS residuals
+ ldetS0 : log det(Sigma) for null model (OLS by equation, diagonals only)
+ niter : number of iterations (=0 for iter=False)
+ corr : inter-equation SUR error correlation matrix
+ llik : log-likelihood (including the constant pi)
+
+
+ Methods
+ -------
+
+ sur_ols : OLS estimation by equation
+
+ """
+ def __init__(self,bigy,bigX,iter=False,maxiter=5,epsilon=0.00001,verbose=False):
+ # setting up the cross-products
+ self.bigy = bigy
+ self.bigX = bigX
+ self.n_eq = len(bigy.keys())
+ self.n = bigy[0].shape[0]
+ self.bigK = np.zeros((self.n_eq,1),dtype=np.int_)
+ for r in range(self.n_eq):
+ self.bigK[r] = self.bigX[r].shape[1]
+ self.bigXX,self.bigXy = sur_crossprod(self.bigX,self.bigy)
+ # OLS regression by equation, sets up initial residuals
+ self.sur_ols() # creates self.bOLS and self.olsE
+ # SUR estimation using OLS residuals - two step estimation
+ self.bSUR,self.varb,self.sig = sur_est(self.bigXX,self.bigXy,self.olsE,self.bigK)
+ resids = sur_resids(self.bigy,self.bigX,self.bSUR) # matrix of residuals
+ # Sigma and log det(Sigma) for null model
+ self.sig_ols = self.sig
+ sols = np.diag(np.diag(self.sig))
+ self.ldetS0 = np.log(np.diag(sols)).sum()
+ det0 = self.ldetS0
+ # setup for iteration
+ det1 = la.slogdet(self.sig)[1]
+ self.ldetS1 = det1
+ #self.niter = 0
+ if iter: # iterated FGLS aka ML
+ n_iter = 0
+ while np.abs(det1-det0) > epsilon and n_iter <= maxiter:
+ n_iter += 1
+ det0 = det1
+ self.bSUR,self.varb,self.sig = sur_est(self.bigXX,self.bigXy,\
+ resids,self.bigK)
+ resids = sur_resids(self.bigy,self.bigX,self.bSUR)
+ det1 = la.slogdet(self.sig)[1]
+ if verbose:
+ print (n_iter,det0,det1)
+ self.bigE = sur_resids(self.bigy,self.bigX,self.bSUR)
+ self.ldetS1 = det1
+ self.niter = n_iter
+ else:
+ self.niter = 1
+ self.bigE = resids
+ self.corr = sur_corr(self.sig)
+ lik = self.n_eq * (1.0 + np.log(2.0*np.pi)) + self.ldetS1
+ self.llik = - (self.n / 2.0) * lik
+
+
+ def sur_ols(self):
+ '''OLS estimation of SUR equations
+
+ Parameters
+ ----------
+
+ self : BaseSUR object
+
+ Creates
+ -------
+
+ self.bOLS : dictionary with regression coefficients for each equation
+ self.olsE : N x n_eq array with OLS residuals for each equation
+
+ '''
+ self.bOLS = {}
+ for r in range(self.n_eq):
+ self.bOLS[r] = np.dot(la.inv(self.bigXX[(r,r)]),self.bigXy[(r,r)])
+ self.olsE = sur_resids(self.bigy,self.bigX,self.bOLS)
+
+
+
+
+class SUR(BaseSUR):
+ """ User class for SUR estimation, both two step as well as iterated
+
+ Parameters
+ ----------
+
+ bigy : dictionary with vector for dependent variable by equation
+ bigX : dictionary with matrix of explanatory variables by equation
+ (note, already includes constant term)
+ w : spatial weights object, default = None
+ nonspat_diag : boolean; flag for non-spatial diagnostics, default = True
+ spat_diag : boolean; flag for spatial diagnostics, default = False
+ iter : boolean; whether or not to use iterated estimation
+ default = False
+ maxiter : integer; maximum iterations; default = 5
+ epsilon : float; precision criterion to end iterations
+ default = 0.00001
+ verbose : boolean; flag to print out iteration number and value
+ of log det(sig) at the beginning and the end of the iteration
+ name_bigy : dictionary with name of dependent variable for each equation
+ default = None, but should be specified
+ is done when sur_stackxy is used
+ name_bigX : dictionary with names of explanatory variables for each
+ equation
+ default = None, but should be specified
+ is done when sur_stackxy is used
+ name_ds : string; name for the data set
+ name_w : string; name for the weights file
+
+
+ Attributes
+ ----------
+
+ bigy : dictionary with y values
+ bigX : dictionary with X values
+ bigXX : dictionary with X_t'X_r cross-products
+ bigXy : dictionary with X_t'y_r cross-products
+ n_eq : number of equations
+ n : number of observations in each cross-section
+ bigK : vector with number of explanatory variables (including constant)
+ for each equation
+ bOLS : dictionary with OLS regression coefficients for each equation
+ olsE : N x n_eq array with OLS residuals for each equation
+ bSUR : dictionary with SUR regression coefficients for each equation
+ varb : variance-covariance matrix
+ sig : Sigma matrix of inter-equation error covariances
+ ldetS1 : log det(Sigma) for SUR model
+ bigE : n by n_eq array of residuals
+ sig_ols : Sigma matrix for OLS residuals (diagonal)
+ ldetS0 : log det(Sigma) for null model (OLS by equation)
+ niter : number of iterations (=0 for iter=False)
+ corr : inter-equation error correlation matrix
+ llik : log-likelihood (including the constant pi)
+ sur_inf : dictionary with standard error, asymptotic t and p-value,
+ one for each equation
+ lrtest : Likelihood Ratio test on off-diagonal elements of sigma
+ (tupel with test,df,p-value)
+ lmtest : Lagrange Multipler test on off-diagonal elements of sigma
+ (tupel with test,df,p-value)
+ lmEtest : Lagrange Multiplier test on error spatial autocorrelation in SUR
+ surchow : list with tuples for Chow test on regression coefficients
+ each tuple contains test value, degrees of freedom, p-value
+ name_bigy : dictionary with name of dependent variable for each equation
+ name_bigX : dictionary with names of explanatory variables for each
+ equation
+ name_ds : string; name for the data set
+ name_w : string; name for the weights file
+
+
+ Examples
+ --------
+
+ First import pysal to load the spatial analysis tools.
+
+ >>> 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.
+
+ >>> db = pysal.open(pysal.examples.get_path("NAT.dbf"),'r')
+
+ The specification of the model to be estimated can be provided as lists.
+ Each equation should be listed separately. In this example, equation 1
+ has HR80 as dependent variable and PS80 and UE80 as exogenous regressors.
+ For equation 2, HR90 is the dependent variable, and PS90 and UE90 the
+ exogenous regressors.
+
+ >>> y_var = ['HR80','HR90']
+ >>> x_var = [['PS80','UE80'],['PS90','UE90']]
+
+ Although not required for this method, we can load a weights matrix file
+ to allow for spatial diagnostics.
+
+ >>> w = pysal.queen_from_shapefile(pysal.examples.get_path("NAT.shp"))
+ >>> w.transform='r'
+
+ The SUR method requires data to be provided as dictionaries. PySAL
+ provides the tool sur_dictxy to create these dictionaries from the
+ list of variables. The line below will create four dictionaries
+ containing respectively the dependent variables (bigy), the regressors
+ (bigX), the dependent variables' names (bigyvars) and regressors' names
+ (bigXvars). All these will be created from th database (db) and lists
+ of variables (y_var and x_var) created above.
+
+ >>> bigy,bigX,bigyvars,bigXvars = pysal.spreg.sur_utils.sur_dictxy(db,y_var,x_var)
+
+ We can now run the regression and then have a summary of the output by typing:
+ 'print(reg.summary)'
+
+ >>> reg = SUR(bigy,bigX,w=w,name_bigy=bigyvars,name_bigX=bigXvars,spat_diag=True,name_ds="nat")
+ >>> print(reg.summary)
+ REGRESSION
+ ----------
+ SUMMARY OF OUTPUT: SEEMINGLY UNRELATED REGRESSIONS (SUR)
+ --------------------------------------------------------
+ Data set : nat
+ Weights matrix : unknown
+ Number of Equations : 2 Number of Observations: 3085
+ Log likelihood (SUR): -19902.966 Number of Iterations : 1
+ ----------
+ <BLANKLINE>
+ SUMMARY OF EQUATION 1
+ ---------------------
+ Dependent Variable : HR80 Number of Variables : 3
+ Mean dependent var : 6.9276 Degrees of Freedom : 3082
+ S.D. dependent var : 6.8251
+ <BLANKLINE>
+ ------------------------------------------------------------------------------------
+ Variable Coefficient Std.Error z-Statistic Probability
+ ------------------------------------------------------------------------------------
+ Constant_1 5.1390718 0.2624673 19.5798587 0.0000000
+ PS80 0.6776481 0.1219578 5.5564132 0.0000000
+ UE80 0.2637240 0.0343184 7.6846277 0.0000000
+ ------------------------------------------------------------------------------------
+ <BLANKLINE>
+ SUMMARY OF EQUATION 2
+ ---------------------
+ Dependent Variable : HR90 Number of Variables : 3
+ Mean dependent var : 6.1829 Degrees of Freedom : 3082
+ S.D. dependent var : 6.6403
+ <BLANKLINE>
+ ------------------------------------------------------------------------------------
+ Variable Coefficient Std.Error z-Statistic Probability
+ ------------------------------------------------------------------------------------
+ Constant_2 3.6139403 0.2534996 14.2561949 0.0000000
+ PS90 1.0260715 0.1121662 9.1477755 0.0000000
+ UE90 0.3865499 0.0341996 11.3027760 0.0000000
+ ------------------------------------------------------------------------------------
+ <BLANKLINE>
+ <BLANKLINE>
+ REGRESSION DIAGNOSTICS
+ TEST DF VALUE PROB
+ LM test on Sigma 1 680.168 0.0000
+ LR test on Sigma 1 768.385 0.0000
+ <BLANKLINE>
+ OTHER DIAGNOSTICS - CHOW TEST
+ VARIABLES DF VALUE PROB
+ Constant_1, Constant_2 1 26.729 0.0000
+ PS80, PS90 1 8.241 0.0041
+ UE80, UE90 1 9.384 0.0022
+ <BLANKLINE>
+ DIAGNOSTICS FOR SPATIAL DEPENDENCE
+ TEST DF VALUE PROB
+ Lagrange Multiplier (error) 2 1333.625 0.0000
+ <BLANKLINE>
+ ERROR CORRELATION MATRIX
+ EQUATION 1 EQUATION 2
+ 1.000000 0.469548
+ 0.469548 1.000000
+ ================================ END OF REPORT =====================================
+ """
+
+ def __init__(self,bigy,bigX,w=None,nonspat_diag=True,spat_diag=False,vm=False,\
+ iter=False,maxiter=5,epsilon=0.00001,verbose=False,\
+ name_bigy=None,name_bigX=None,name_ds=None,name_w=None):
+
+ #need checks on match between bigy, bigX dimensions
+ # init moved here before name check
+ BaseSUR.__init__(self,bigy=bigy,bigX=bigX,iter=iter,\
+ maxiter=maxiter,epsilon=epsilon,verbose=verbose)
+
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_w = USER.set_name_w(name_w, w)
+ #initialize names - should be generated by sur_stack
+ if name_bigy:
+ self.name_bigy = name_bigy
+ else: # need to construct y names
+ self.name_bigy = {}
+ for r in range(self.n_eq):
+ yn = 'dep_var_' + str(r)
+ self.name_bigy[r] = yn
+ if name_bigX:
+ self.name_bigX = name_bigX
+ else: # need to construct x names
+ self.name_bigX = {}
+ for r in range(self.n_eq):
+ k = self.bigX[r].shape[1] - 1
+ name_x = ['var_' + str(i + 1) + "_" + str(r) for i in range(k)]
+ ct = 'Constant_' + str(r) # NOTE: constant always included in X
+ name_x.insert(0, ct)
+ self.name_bigX[r] = name_x
+
+ #inference
+ self.sur_inf = sur_setp(self.bSUR,self.varb)
+
+ if nonspat_diag:
+ #LR test on off-diagonal elements of Sigma
+ self.lrtest = sur_lrtest(self.n,self.n_eq,self.ldetS0,self.ldetS1)
+
+ #LM test on off-diagonal elements of Sigma
+ self.lmtest = sur_lmtest(self.n,self.n_eq,self.sig_ols)
+ else:
+ self.lrtest = None
+ self.lmtest = None
+
+ #LM test on spatial error autocorrelation
+ if spat_diag:
+ if not w:
+ raise Exception, "Error: spatial weights needed"
+ WS = w.sparse
+ self.lmEtest = surLMe(self.n_eq,WS,self.bigE,self.sig)
+ else:
+ self.lmEtest = None
+
+ #LM test on spatial lag autocorrelation
+
+ # test on constancy of coefficients across equations
+ if check_k(self.bigK): # only for equal number of variables
+ self.surchow = sur_chow(self.n_eq,self.bigK,self.bSUR,self.varb)
+ else:
+ self.surchow = None
+
+ #Listing of the results
+ self.title = "SEEMINGLY UNRELATED REGRESSIONS (SUR)"
+ SUMMARY.SUR(reg=self, nonspat_diag=nonspat_diag, spat_diag=spat_diag, surlm=True)
+
+class BaseThreeSLS():
+ """ Base class for 3SLS estimation, two step
+
+ Parameters
+ ----------
+
+ bigy : dictionary with vector for dependent variable by equation
+ bigX : dictionary with matrix of explanatory variables by equation
+ (note, already includes constant term)
+ bigyend : dictionary with matrix of endogenous variables by equation
+ bigq : dictionary with matrix of instruments by equation
+
+
+ Attributes
+ ----------
+
+ bigy : dictionary with y values
+ bigZ : dictionary with matrix of exogenous and endogenous variables
+ for each equation
+ bigZHZH : dictionary with matrix of cross products Zhat_r'Zhat_s
+ bigZHy : dictionary with matrix of cross products Zhat_r'y_end_s
+ n_eq : number of equations
+ n : number of observations in each cross-section
+ bigK : vector with number of explanatory variables (including constant,
+ exogenous and endogenous) for each equation
+ b2SLS : dictionary with 2SLS regression coefficients for each equation
+ tslsE : N x n_eq array with OLS residuals for each equation
+ b3SLS : dictionary with 3SLS regression coefficients for each equation
+ varb : variance-covariance matrix
+ sig : Sigma matrix of inter-equation error covariances
+ bigE : n by n_eq array of residuals
+ corr : inter-equation 3SLS error correlation matrix
+
+
+ Methods
+ -------
+
+ tsls_2sls : 2SLS estimation by equation
+
+ """
+ def __init__(self,bigy,bigX,bigyend,bigq):
+ # setting up the cross-products
+ self.bigy = bigy
+ self.n_eq = len(bigy.keys())
+ self.n = bigy[0].shape[0]
+ # dictionary with exog and endog, Z
+ self.bigZ = {}
+ for r in range(self.n_eq):
+ self.bigZ[r] = np.hstack((bigX[r],bigyend[r]))
+ # number of explanatory variables by equation
+ self.bigK = np.zeros((self.n_eq,1),dtype=np.int_)
+ for r in range(self.n_eq):
+ self.bigK[r] = self.bigZ[r].shape[1]
+ # dictionary with instruments, H
+ bigH = {}
+ for r in range(self.n_eq):
+ bigH[r] = np.hstack((bigX[r],bigq[r]))
+ # dictionary with instrumental variables, X and yend_predicted, Z-hat
+ bigZhat = {}
+ for r in range(self.n_eq):
+ try:
+ HHi = la.inv(np.dot(bigH[r].T,bigH[r]))
+ except:
+ raise Exception, "ERROR: singular cross product matrix, check instruments"
+ Hye = np.dot(bigH[r].T,bigyend[r])
+ yp = np.dot(bigH[r],np.dot(HHi,Hye))
+ bigZhat[r] = np.hstack((bigX[r],yp))
+ self.bigZHZH,self.bigZHy = sur_crossprod(bigZhat,self.bigy)
+
+ # 2SLS regression by equation, sets up initial residuals
+ self.sur_2sls() # creates self.b2SLS and self.tslsE
+
+ self.b3SLS,self.varb,self.sig = sur_est(self.bigZHZH,self.bigZHy,self.tslsE,self.bigK)
+ self.bigE = sur_resids(self.bigy,self.bigZ,self.b3SLS) # matrix of residuals
+
+ # inter-equation correlation matrix
+ self.corr = sur_corr(self.sig)
+
+
+ def sur_2sls(self):
+ '''2SLS estimation of SUR equations
+
+ Parameters
+ ----------
+
+ self : BaseSUR object
+
+ Creates
+ -------
+
+ self.b2SLS : dictionary with regression coefficients for each equation
+ self.tslsE : N x n_eq array with OLS residuals for each equation
+
+ '''
+ self.b2SLS = {}
+ for r in range(self.n_eq):
+ self.b2SLS[r] = np.dot(la.inv(self.bigZHZH[(r,r)]),self.bigZHy[(r,r)])
+ self.tslsE = sur_resids(self.bigy,self.bigZ,self.b2SLS)
+
+
+class ThreeSLS(BaseThreeSLS):
+ """ User class for 3SLS estimation
+
+ Parameters
+ ----------
+
+ bigy : dictionary with vector for dependent variable by equation
+ bigX : dictionary with matrix of explanatory variables by equation
+ (note, already includes constant term)
+ bigyend : dictionary with matrix of endogenous variables by equation
+ bigq : dictionary with matrix of instruments by equation
+ nonspat_diag : boolean; flag for non-spatial diagnostics, default = True
+ name_bigy : dictionary with name of dependent variable for each equation
+ default = None, but should be specified
+ is done when sur_stackxy is used
+ name_bigX : dictionary with names of explanatory variables for each
+ equation
+ default = None, but should be specified
+ is done when sur_stackxy is used
+ name_bigyend : dictionary with names of endogenous variables for each
+ equation
+ default = None, but should be specified
+ is done when sur_stackZ is used
+ name_bigq : dictionary with names of instrumental variables for each
+ equations
+ default = None, but should be specified
+ is done when sur_stackZ is used
+ name_ds : string; name for the data set
+
+
+ Attributes
+ ----------
+
+ bigy : dictionary with y values
+ bigZ : dictionary with matrix of exogenous and endogenous variables
+ for each equation
+ bigZHZH : dictionary with matrix of cross products Zhat_r'Zhat_s
+ bigZHy : dictionary with matrix of cross products Zhat_r'y_end_s
+ n_eq : number of equations
+ n : number of observations in each cross-section
+ bigK : vector with number of explanatory variables (including constant,
+ exogenous and endogenous) for each equation
+ b2SLS : dictionary with 2SLS regression coefficients for each equation
+ tslsE : N x n_eq array with OLS residuals for each equation
+ b3SLS : dictionary with 3SLS regression coefficients for each equation
+ varb : variance-covariance matrix
+ sig : Sigma matrix of inter-equation error covariances
+ bigE : n by n_eq array of residuals
+ corr : inter-equation 3SLS error correlation matrix
+ tsls_inf : dictionary with standard error, asymptotic t and p-value,
+ one for each equation
+ surchow : list with tuples for Chow test on regression coefficients
+ each tuple contains test value, degrees of freedom, p-value
+ name_ds : string; name for the data set
+ name_bigy : dictionary with name of dependent variable for each equation
+ name_bigX : dictionary with names of explanatory variables for each
+ equation
+ name_bigyend : dictionary with names of endogenous variables for each
+ equation
+ name_bigq : dictionary with names of instrumental variables for each
+ equations
+
+
+ Examples
+ --------
+
+ First import pysal to load the spatial analysis tools.
+
+ >>> 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.
+
+ >>> db = pysal.open(pysal.examples.get_path("NAT.dbf"),'r')
+
+ The specification of the model to be estimated can be provided as lists.
+ Each equation should be listed separately. In this example, equation 1
+ has HR80 as dependent variable, PS80 and UE80 as exogenous regressors,
+ RD80 as endogenous regressor and FP79 as additional instrument.
+ For equation 2, HR90 is the dependent variable, PS90 and UE90 the
+ exogenous regressors, RD90 as endogenous regressor and FP99 as
+ additional instrument
+
+ >>> y_var = ['HR80','HR90']
+ >>> x_var = [['PS80','UE80'],['PS90','UE90']]
+ >>> yend_var = [['RD80'],['RD90']]
+ >>> q_var = [['FP79'],['FP89']]
+
+ The SUR method requires data to be provided as dictionaries. PySAL
+ provides two tools to create these dictionaries from the list of variables:
+ sur_dictxy and sur_dictZ. The tool sur_dictxy can be used to create the
+ dictionaries for Y and X, and sur_dictZ for endogenous variables (yend) and
+ additional instruments (q).
+
+ >>> bigy,bigX,bigyvars,bigXvars = pysal.spreg.sur_utils.sur_dictxy(db,y_var,x_var)
+ >>> bigyend,bigyendvars = pysal.spreg.sur_utils.sur_dictZ(db,yend_var)
+ >>> bigq,bigqvars = pysal.spreg.sur_utils.sur_dictZ(db,q_var)
+
+ We can now run the regression and then have a summary of the output by typing:
+ print(reg.summary)
+
+ Alternatively, we can just check the betas and standard errors, asymptotic t
+ and p-value of the parameters:
+
+ >>> reg = ThreeSLS(bigy,bigX,bigyend,bigq,name_bigy=bigyvars,name_bigX=bigXvars,name_bigyend=bigyendvars,name_bigq=bigqvars,name_ds="NAT")
+ >>> reg.b3SLS
+ {0: array([[ 6.92426353],
+ [ 1.42921826],
+ [ 0.00049435],
+ [ 3.5829275 ]]), 1: array([[ 7.62385875],
+ [ 1.65031181],
+ [-0.21682974],
+ [ 3.91250428]])}
+
+ >>> reg.tsls_inf
+ {0: array([[ 0.23220853, 29.81916157, 0. ],
+ [ 0.10373417, 13.77770036, 0. ],
+ [ 0.03086193, 0.01601807, 0.98721998],
+ [ 0.11131999, 32.18584124, 0. ]]), 1: array([[ 0.28739415, 26.52753638, 0. ],
+ [ 0.09597031, 17.19606554, 0. ],
+ [ 0.04089547, -5.30204786, 0.00000011],
+ [ 0.13586789, 28.79638723, 0. ]])}
+
+ """
+
+ def __init__(self,bigy,bigX,bigyend,bigq,nonspat_diag=True,\
+ name_bigy=None,name_bigX=None,name_bigyend=None,name_bigq=None,\
+ name_ds=None):
+
+ #need checks on match between bigy, bigX dimensions
+ BaseThreeSLS.__init__(self,bigy=bigy,bigX=bigX,bigyend=bigyend,\
+ bigq=bigq)
+
+ self.name_ds = USER.set_name_ds(name_ds)
+ #initialize names - should be generated by sur_stack
+ if name_bigy:
+ self.name_bigy = name_bigy
+ else: # need to construct y names
+ self.name_bigy = {}
+ for r in range(self.n_eq):
+ yn = 'dep_var_' + str(r+1)
+ self.name_bigy[r] = yn
+ if name_bigX:
+ self.name_bigX = name_bigX
+ else: # need to construct x names
+ self.name_bigX = {}
+ for r in range(self.n_eq):
+ k = bigX[r].shape[1] - 1
+ name_x = ['var_' + str(i + 1) + "_" + str(r+1) for i in range(k)]
+ ct = 'Constant_' + str(r+1) # NOTE: constant always included in X
+ name_x.insert(0, ct)
+ self.name_bigX[r] = name_x
+ if name_bigyend:
+ self.name_bigyend = name_bigyend
+ else: # need to construct names
+ self.name_bigyend = {}
+ for r in range(self.n_eq):
+ ky = bigyend[r].shape[1]
+ name_ye = ['end_' + str(i + 1) + "_" + str(r+1) for i in range(ky)]
+ self.name_bigyend[r] = name_ye
+ if name_bigq:
+ self.name_bigq = name_bigq
+ else: # need to construct names
+ self.name_bigq = {}
+ for r in range(self.n_eq):
+ ki = bigq[r].shape[1]
+ name_i = ['inst_' + str(i + 1) + "_" + str(r+1) for i in range(ki)]
+ self.name_bigq[r] = name_i
+
+ #inference
+ self.tsls_inf = sur_setp(self.b3SLS,self.varb)
+
+ # test on constancy of coefficients across equations
+ if check_k(self.bigK): # only for equal number of variables
+ self.surchow = sur_chow(self.n_eq,self.bigK,self.b3SLS,self.varb)
+ else:
+ self.surchow = None
+
+ #Listing of the results
+ self.title = "THREE STAGE LEAST SQUARES (3SLS)"
+ SUMMARY.SUR(reg=self, tsls=True, nonspat_diag=nonspat_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
+ from sur_utils import sur_dictxy,sur_dictZ
+
+ db = pysal.open(pysal.examples.get_path('NAT.dbf'), 'r')
+ y_var = ['HR80','HR90']
+ x_var = [['PS80','UE80'],['PS90','UE90']]
+ #Example SUR
+ #"""
+ w = pysal.queen_from_shapefile(pysal.examples.get_path("NAT.shp"))
+ w.transform='r'
+ bigy0,bigX0,bigyvars0,bigXvars0 = sur_dictxy(db,y_var,x_var)
+ reg0 = SUR(bigy0,bigX0,w=w,name_bigy=bigyvars0,name_bigX=bigXvars0,\
+ spat_diag=True,name_ds="nat")
+ print reg0.summary
+ """
+ #Example 3SLS
+ yend_var = [['RD80'],['RD90']]
+ q_var = [['FP79'],['FP89']]
+
+ bigy1,bigX1,bigyvars1,bigXvars1 = sur_dictxy(db,y_var,x_var)
+ bigyend1,bigyendvars1 = sur_dictZ(db,yend_var)
+ bigq1,bigqvars1 = sur_dictZ(db,q_var)
+
+ reg1 = ThreeSLS(bigy1,bigX1,bigyend1,bigq1,name_ds="nat")
+
+ print reg1.summary
+ #"""
diff --git a/pysal/spreg/sur_error.py b/pysal/spreg/sur_error.py
new file mode 100644
index 0000000..94adb3e
--- /dev/null
+++ b/pysal/spreg/sur_error.py
@@ -0,0 +1,549 @@
+"""
+Spatial Error SUR estimation
+"""
+
+__author__= "Luc Anselin lanselin at gmail.com, \
+ Pedro V. Amaral pedrovma at gmail.com"
+
+
+import numpy as np
+import pysal
+import numpy.linalg as la
+import scipy.stats as stats
+import summary_output as SUMMARY
+import user_output as USER
+from scipy.sparse.linalg import splu as SuperLU
+from scipy.optimize import minimize_scalar, minimize
+from scipy import sparse as sp
+
+from ml_error import err_c_loglik_sp
+from sur_utils import sur_dictxy,sur_corr,sur_dict2mat,\
+ sur_crossprod,sur_est,sur_resids,filter_dict,\
+ check_k
+from sur import BaseSUR
+from diagnostics_sur import sur_setp, lam_setp, sur_chow
+from regimes import buildR,wald_test
+
+__all__ = ["BaseSURerrorML","SURerrorML"]
+
+
+class BaseSURerrorML():
+ """Base class for SUR Error estimation by Maximum Likelihood
+
+ requires: scipy.optimize.minimize_scalar and
+ scipy.optimize.minimize
+
+ Parameters
+ ----------
+ bigy : dictionary with vectors of dependent variable, one for
+ each equation
+ bigX : dictionary with matrices of explanatory variables,
+ one for each equation
+ w : spatial weights object
+ epsilon : convergence criterion for ML iterations
+ default 0.0000001
+
+ Attributes
+ ----------
+ n : number of observations in each cross-section
+ n2 : n/2
+ n_eq : number of equations
+ bigy : dictionary with vectors of dependent variable, one for
+ each equation
+ bigX : dictionary with matrices of explanatory variables,
+ one for each equation
+ bigK : n_eq x 1 array with number of explanatory variables
+ by equation
+ bigylag : spatially lagged dependent variable
+ bigXlag : spatially lagged explanatory variable
+ lamols : spatial autoregressive coefficients from equation by
+ equation ML-Error estimation
+ clikerr : concentrated log-likelihood from equation by equation
+ ML-Error estimation (no constant)
+ bSUR0 : SUR estimation for betas without spatial autocorrelation
+ llik : log-likelihood for classic SUR estimation (includes constant)
+ lamsur : spatial autoregressive coefficient in ML SUR Error
+ bSUR : beta coefficients in ML SUR Error
+ varb : variance of beta coefficients in ML SUR Error
+ sig : error variance-covariance matrix in ML SUR Error
+ corr : error correlation matrix
+ bigE : n by n_eq matrix of vectors of residuals for each equation
+ cliksurerr : concentrated log-likelihood from ML SUR Error (no constant)
+
+ """
+ def __init__(self,bigy,bigX,w,epsilon=0.0000001):
+ # setting up constants
+ self.n = w.n
+ self.n2 = self.n / 2.0
+ self.n_eq = len(bigy.keys())
+ WS = w.sparse
+ I = sp.identity(self.n)
+ # variables
+ self.bigy = bigy
+ self.bigX = bigX
+ # number of variables by equation
+ self.bigK = np.zeros((self.n_eq,1),dtype=np.int_)
+ for r in range(self.n_eq):
+ self.bigK[r] = self.bigX[r].shape[1]
+ # spatially lagged variables
+ self.bigylag = {}
+ for r in range(self.n_eq):
+ self.bigylag[r] = WS*self.bigy[r]
+ # note: unlike WX as instruments, this includes the constant
+ self.bigXlag = {}
+ for r in range(self.n_eq):
+ self.bigXlag[r] = WS*self.bigX[r]
+
+ # spatial parameter starting values
+ lam = np.zeros((self.n_eq,1)) # initialize as an array
+ fun0 = 0.0
+ fun1 = 0.0
+ for r in range(self.n_eq):
+ res = minimize_scalar(err_c_loglik_sp, 0.0, bounds=(-1.0,1.0),
+ args=(self.n, self.bigy[r], self.bigylag[r],
+ self.bigX[r], self.bigXlag[r], I, WS),
+ method='bounded', options={'xatol':epsilon})
+ lam[r]= res.x
+ fun1 += res.fun
+ self.lamols = lam
+ self.clikerr = -fun1 #negative because use in min
+
+ # SUR starting values
+ reg0 = BaseSUR(self.bigy,self.bigX,iter=True)
+ bigE = reg0.bigE
+ self.bSUR0 = reg0.bSUR
+ self.llik = reg0.llik # as is, includes constant
+
+ # iteration
+ lambdabounds = [ (-1.0,+1.0) for i in range(self.n_eq)]
+ while abs(fun0 - fun1) > epsilon:
+ fun0 = fun1
+ sply = filter_dict(lam,self.bigy,self.bigylag)
+ splX = filter_dict(lam,self.bigX,self.bigXlag)
+ WbigE = WS * bigE
+ splbigE = bigE - WbigE * lam.T
+ splXX,splXy = sur_crossprod(splX,sply)
+ b1,varb1,sig1 = sur_est(splXX,splXy,splbigE,self.bigK)
+ bigE = sur_resids(self.bigy,self.bigX,b1)
+ res = minimize(clik,lam,args=(self.n,self.n2,self.n_eq,\
+ bigE,I,WS),method='L-BFGS-B',\
+ bounds=lambdabounds)
+ lam = res.x
+ lam.resize((self.n_eq,1))
+ fun1 = res.fun
+ self.lamsur = lam
+ self.bSUR = b1
+ self.varb = varb1
+ self.sig = sig1
+ self.corr = sur_corr(self.sig)
+ self.bigE = bigE
+ self.cliksurerr = -fun1 #negative because use in min, no constant
+
+class SURerrorML(BaseSURerrorML):
+ """User class for SUR Error estimation by Maximum Likelihood
+
+ Parameters
+ ----------
+ bigy : dictionary with vectors of dependent variable, one for
+ each equation
+ bigX : dictionary with matrices of explanatory variables,
+ one for each equation
+ w : spatial weights object
+ epsilon : convergence criterion for ML iterations
+ default 0.0000001
+ nonspat_diag : boolean; flag for non-spatial diagnostics, default = True
+ spat_diag : boolean; flag for spatial diagnostics, default = False
+ vm : boolean; flag for asymptotic variance for lambda and Sigma,
+ default = False
+ name_bigy : dictionary with name of dependent variable for each equation
+ default = None, but should be specified
+ is done when sur_stackxy is used
+ name_bigX : dictionary with names of explanatory variables for each
+ equation
+ default = None, but should be specified
+ is done when sur_stackxy is used
+ name_ds : string; name for the data set
+ name_w : string; name for the weights file
+
+ Attributes
+ ----------
+ n : number of observations in each cross-section
+ n2 : n/2
+ n_eq : number of equations
+ bigy : dictionary with vectors of dependent variable, one for
+ each equation
+ bigX : dictionary with matrices of explanatory variables,
+ one for each equation
+ bigK : n_eq x 1 array with number of explanatory variables
+ by equation
+ bigylag : spatially lagged dependent variable
+ bigXlag : spatially lagged explanatory variable
+ lamols : spatial autoregressive coefficients from equation by
+ equation ML-Error estimation
+ clikerr : concentrated log-likelihood from equation by equation
+ ML-Error estimation (no constant)
+ bSUR0 : SUR estimation for betas without spatial autocorrelation
+ llik : log-likelihood for classic SUR estimation (includes constant)
+ lamsur : spatial autoregressive coefficient in ML SUR Error
+ bSUR : beta coefficients in ML SUR Error
+ varb : variance of beta coefficients in ML SUR Error
+ sig : error variance-covariance matrix in ML SUR Error
+ bigE : n by n_eq matrix of vectors of residuals for each equation
+ cliksurerr : concentrated log-likelihood from ML SUR Error (no constant)
+ sur_inf : inference for regression coefficients, stand. error, t, p
+ errllik : log-likelihood for error model without SUR (with constant)
+ surerrllik : log-likelihood for SUR error model (with constant)
+ lrtest : likelihood ratio test for off-diagonal Sigma elements
+ likrlambda : likelihood ratio test on spatial autoregressive coefficients
+ vm : asymptotic variance matrix for lambda and Sigma (only for vm=True)
+ lamsetp : inference for lambda, stand. error, t, p (only for vm=True)
+ lamtest : tuple with test for constancy of lambda across equations
+ (test value, degrees of freedom, p-value)
+ joinlam : tuple with test for joint significance of lambda across
+ equations (test value, degrees of freedom, p-value)
+ surchow : list with tuples for Chow test on regression coefficients
+ each tuple contains test value, degrees of freedom, p-value
+ name_bigy : dictionary with name of dependent variable for each equation
+ name_bigX : dictionary with names of explanatory variables for each
+ equation
+ name_ds : string; name for the data set
+ name_w : string; name for the weights file
+
+
+ Examples
+ --------
+
+ First import pysal to load the spatial analysis tools.
+
+ >>> 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.
+
+ >>> db = pysal.open(pysal.examples.get_path("NAT.dbf"),'r')
+
+ The specification of the model to be estimated can be provided as lists.
+ Each equation should be listed separately. Equation 1 has HR80 as dependent
+ variable, and PS80 and UE80 as exogenous regressors.
+ For equation 2, HR90 is the dependent variable, and PS90 and UE90 the
+ exogenous regressors.
+
+ >>> y_var = ['HR80','HR90']
+ >>> x_var = [['PS80','UE80'],['PS90','UE90']]
+ >>> yend_var = [['RD80'],['RD90']]
+ >>> q_var = [['FP79'],['FP89']]
+
+ The SUR method requires data to be provided as dictionaries. PySAL
+ provides the tool sur_dictxy to create these dictionaries from the
+ list of variables. The line below will create four dictionaries
+ containing respectively the dependent variables (bigy), the regressors
+ (bigX), the dependent variables' names (bigyvars) and regressors' names
+ (bigXvars). All these will be created from th database (db) and lists
+ of variables (y_var and x_var) created above.
+
+ >>> bigy,bigX,bigyvars,bigXvars = pysal.spreg.sur_utils.sur_dictxy(db,y_var,x_var)
+
+ To run a spatial error model, we need to specify the spatial weights matrix.
+ To do that, we can open an already existing gal file or create a new one.
+ In this example, we will create a new one from NAT.shp and transform it to
+ row-standardized.
+
+ >>> w = pysal.queen_from_shapefile(pysal.examples.get_path("NAT.shp"))
+ >>> w.transform='r'
+
+ We can now run the regression and then have a summary of the output by typing:
+ print(reg.summary)
+
+ Alternatively, we can just check the betas and standard errors, asymptotic t
+ and p-value of the parameters:
+
+ >>> reg = SURerrorML(bigy,bigX,w=w,name_bigy=bigyvars,name_bigX=bigXvars,name_ds="NAT",name_w="nat_queen")
+ >>> reg.bSUR
+ {0: array([[ 4.0222855 ],
+ [ 0.88489646],
+ [ 0.42402853]]), 1: array([[ 3.04923009],
+ [ 1.10972634],
+ [ 0.47075682]])}
+
+ >>> reg.sur_inf
+ {0: array([[ 0.36692181, 10.96224141, 0. ],
+ [ 0.14129077, 6.26294579, 0. ],
+ [ 0.04267954, 9.93517021, 0. ]]), 1: array([[ 0.33139969, 9.20106497, 0. ],
+ [ 0.13352591, 8.31094371, 0. ],
+ [ 0.04004097, 11.756878 , 0. ]])}
+
+ """
+ def __init__(self,bigy,bigX,w,nonspat_diag=True,spat_diag=False,vm=False,\
+ epsilon=0.0000001,\
+ name_bigy=None,name_bigX=None,name_ds=None,name_w=None):
+
+ #need checks on match between bigy, bigX dimensions
+
+ # moved init here
+ BaseSURerrorML.__init__(self,bigy=bigy,bigX=bigX,w=w,epsilon=epsilon)
+
+ # check on variable names for listing results
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_w = USER.set_name_w(name_w, w)
+ #initialize names - should be generated by sur_stack
+ if name_bigy:
+ self.name_bigy = name_bigy
+ else: # need to construct y names
+ self.name_bigy = {}
+ for r in range(self.n_eq):
+ yn = 'dep_var_' + str(r)
+ self.name_bigy[r] = yn
+ if name_bigX:
+ self.name_bigX = name_bigX
+ else: # need to construct x names
+ self.name_bigX = {}
+ for r in range(self.n_eq):
+ k = self.bigX[r].shape[1] - 1
+ name_x = ['var_' + str(i + 1) + "_" + str(r+1) for i in range(k)]
+ ct = 'Constant_' + str(r+1) # NOTE: constant always included in X
+ name_x.insert(0, ct)
+ self.name_bigX[r] = name_x
+
+
+
+
+ #inference
+ self.sur_inf = sur_setp(self.bSUR,self.varb)
+
+ # adjust concentrated log lik for constant
+ const = -self.n2 * (self.n_eq * (1.0 + np.log(2.0*np.pi)))
+ self.errllik = const + self.clikerr
+ self.surerrllik = const + self.cliksurerr
+
+ # LR test on off-diagonal sigma
+ if nonspat_diag:
+ M = self.n_eq * (self.n_eq - 1)/2.0
+ likrodiag = 2.0 * (self.surerrllik - self.errllik)
+ plik1 = stats.chisqprob(likrodiag, M)
+ self.lrtest = (likrodiag,int(M),plik1)
+ else:
+ self.lrtest = None
+
+ # LR test on spatial autoregressive coefficients
+ if spat_diag:
+ liklambda = 2.0 * (self.surerrllik - self.llik)
+ plik2 = stats.chisqprob(liklambda, self.n_eq)
+ self.likrlambda = (liklambda,self.n_eq,plik2)
+ else:
+ self.likrlambda = None
+
+ # asymptotic variance for spatial coefficient
+ if vm:
+ self.vm = surerrvm(self.n,self.n_eq,w,self.lamsur,self.sig)
+ vlam = self.vm[:self.n_eq,:self.n_eq]
+ self.lamsetp = lam_setp(self.lamsur,vlam)
+ # test on constancy of lambdas
+ R = buildR(kr=1,kf=0,nr=self.n_eq)
+ w,p = wald_test(self.lamsur,R,np.zeros((R.shape[0],1)),vlam)
+ self.lamtest = (w,R.shape[0],p)
+ if spat_diag: # test on joint significance of lambdas
+ Rj = np.identity(self.n_eq)
+ wj,pj = wald_test(self.lamsur,Rj,np.zeros((Rj.shape[0],1)),vlam)
+ self.joinlam = (wj,Rj.shape[0],pj)
+ else:
+ self.joinlam = None
+ else:
+ self.vm = None
+ self.lamsetp = None
+ self.lamtest = None
+ self.joinlam = None
+
+ # test on constancy of regression coefficients across equations
+ if check_k(self.bigK): # only for equal number of variables
+ self.surchow = sur_chow(self.n_eq,self.bigK,self.bSUR,self.varb)
+ else:
+ self.surchow = None
+
+ # listing of results
+ self.title = "SEEMINGLY UNRELATED REGRESSIONS (SUR) - SPATIAL ERROR MODEL"
+ SUMMARY.SUR(reg=self, nonspat_diag=nonspat_diag, spat_diag=spat_diag, lambd=True)
+
+
+
+def jacob(lam,n_eq,I,WS):
+ """Log-Jacobian for SUR Error model
+
+ Parameters
+ ----------
+ lam : n_eq by 1 array of spatial autoregressive parameters
+ n_eq : number of equations
+ I : sparse Identity matrix
+ WS : sparse spatial weights matrix
+
+ Returns
+ -------
+ logjac : the log Jacobian
+
+ """
+ logjac = 0.0
+ for r in range(n_eq):
+ lami = lam[r]
+ lamWS = WS.multiply(lami)
+ B = (I - lamWS).tocsc()
+ LU = SuperLU(B)
+ jj = np.sum(np.log(np.abs(LU.U.diagonal())))
+ logjac += jj
+ return logjac
+
+def clik(lam,n,n2,n_eq,bigE,I,WS):
+ """ Concentrated (negative) log-likelihood for SUR Error model
+
+ Parameters
+ ----------
+ lam : n_eq x 1 array of spatial autoregressive parameters
+ n : number of observations in each cross-section
+ n2 : n/2
+ n_eq : number of equations
+ bigE : n by n_eq matrix with vectors of residuals for
+ each equation
+ I : sparse Identity matrix
+ WS : sparse spatial weights matrix
+
+ Returns
+ -------
+ -clik : negative (for minimize) of the concentrated
+ log-likelihood function
+
+ """
+ WbigE = WS * bigE
+ spfbigE = bigE - WbigE * lam.T
+ sig = np.dot(spfbigE.T,spfbigE) / n
+ ldet = la.slogdet(sig)[1]
+ logjac = jacob(lam,n_eq,I,WS)
+ clik = - n2 * ldet + logjac
+ return -clik # negative for minimize
+
+def surerrvm(n,n_eq,w,lam,sig):
+ """Asymptotic variance matrix for lambda and Sigma in
+ ML SUR Error estimation
+
+ Source: Anselin (1988), Chapter 10.
+
+ Parameters
+ ----------
+ n : scalar, number of cross-sectional observations
+ n_eq : scalar, number of equations
+ w : spatial weights object
+ lam : n_eq by 1 vector with spatial autoregressive coefficients
+ sig : n_eq by n_eq matrix with cross-equation error covariances
+
+ Returns
+ -------
+ vm : asymptotic variance-covariance matrix for spatial autoregressive
+ coefficients and the upper triangular elements of Sigma
+ n_eq + n_eq x (n_eq + 1) / 2 coefficients
+
+
+ """
+ # inverse Sigma
+ sigi = la.inv(sig)
+ sisi = sigi * sig
+ # elements of Psi_lam,lam
+ # trace terms
+ trDi = np.zeros((n_eq,1))
+ trDDi = np.zeros((n_eq,1))
+ trDTDi = np.zeros((n_eq,1))
+ trDTiDj = np.zeros((n_eq,n_eq))
+ WS = w.sparse
+ I = sp.identity(n)
+ for i in range(n_eq):
+ lami = lam[i][0]
+ lamWS = WS.multiply(lami)
+ B = (I - lamWS)
+ bb = B.todense()
+ Bi = la.inv(bb)
+ D = WS * Bi
+ trDi[i] = np.trace(D)
+ DD = np.dot(D,D)
+ trDDi[i] = np.trace(DD)
+ DD = np.dot(D.T,D)
+ trDTDi[i] = np.trace(DD)
+ for j in range(i+1,n_eq):
+ lamj = lam[j][0]
+ lamWS = WS.multiply(lamj)
+ B = (I - lamWS)
+ bb = B.todense()
+ Bi = la.inv(bb)
+ Dj = WS * Bi
+ DD = np.dot(D.T,Dj)
+ trDTiDj[i,j] = np.trace(DD)
+ trDTiDj[j,i] = trDTiDj[i,j]
+ np.fill_diagonal(trDTiDj,trDTDi)
+
+ sisjT = sisi * trDTiDj
+ Vll = np.diagflat(trDDi) + sisjT
+
+ # elements of Psi_lam_sig
+ P = int(n_eq * (n_eq + 1)/2) #force ints to be ints
+ tlist = [ (i,j) for i in range(n_eq) for j in range(i,n_eq) ]
+ zog = sigi * trDi
+ Vlsig = np.zeros((n_eq,P))
+ for i in range(n_eq):
+ for j in range(n_eq):
+ if i > j:
+ jj = tlist.index((j,i))
+ else:
+ jj = tlist.index((i,j))
+ Vlsig[i,jj] = zog[i,j]
+
+ # top of Psi
+ vtop = np.hstack((Vll,Vlsig))
+
+ # elements of Psi_sig_sig
+
+ Vsig = np.zeros((P,P))
+ for ij in range(P):
+ i,j = tlist[ij]
+ for hk in range(P):
+ h,k = tlist[hk]
+ if i == j:
+ if h == k:
+ Vsig[ij,hk] = 0.5 * (sigi[i,h]**2)
+ else: # h not equal to k
+ Vsig[ij,hk] = sigi[i,h]*sigi[i,k]
+ else: # i not equal to j
+ if h == k:
+ Vsig[ij,hk] = sigi[i,h]*sigi[j,h]
+ else: # h not equal to k
+ Vsig[ij,hk] = sigi[i,h]*sigi[j,k] + sigi[i,k]*sigi[j,h]
+ Vsig = n * Vsig
+
+ # bottom of Psi
+ vbottom = np.hstack((Vlsig.T,Vsig))
+
+ # all of Psi
+ vbig = np.vstack((vtop,vbottom))
+
+ # inverse of Psi
+ vm = la.inv(vbig)
+
+ return vm
+
+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 sur_utils import sur_dictxy,sur_dictZ
+
+ db = pysal.open(pysal.examples.get_path('NAT.dbf'), 'r')
+ y_var = ['HR80','HR90']
+ x_var = [['PS80','UE80'],['PS90','UE90']]
+ w = pysal.queen_from_shapefile(pysal.examples.get_path("NAT.shp"))
+ w.transform='r'
+ bigy0,bigX0,bigyvars0,bigXvars0 = sur_dictxy(db,y_var,x_var)
+ reg0 = SURerrorML(bigy0,bigX0,w,name_bigy=bigyvars0,name_bigX=bigXvars0,\
+ name_w="natqueen",name_ds="natregimes",vm=False,nonspat_diag=True,spat_diag=True)
+ #print reg0.summary
diff --git a/pysal/spreg/sur_lag.py b/pysal/spreg/sur_lag.py
new file mode 100644
index 0000000..80f601a
--- /dev/null
+++ b/pysal/spreg/sur_lag.py
@@ -0,0 +1,366 @@
+"""
+Spatial Lag SUR estimation
+"""
+
+__author__= "Luc Anselin lanselin at gmail.com, \
+ Pedro V. Amaral pedrovma at gmail.com"
+
+
+import numpy as np
+import pysal
+import summary_output as SUMMARY
+import user_output as USER
+from sur import BaseThreeSLS
+from diagnostics_sur import sur_setp, sur_chow, sur_joinrho
+from sur_utils import check_k
+
+__all__ = ["SURlagIV"]
+
+class SURlagIV(BaseThreeSLS):
+ """ User class for spatial lag estimation using IV
+
+ Parameters
+ ----------
+
+ bigy : dictionary with vector for dependent variable by equation
+ bigX : dictionary with matrix of explanatory variables by equation
+ (note, already includes constant term)
+ bigyend : dictionary with matrix of endogenous variables by equation
+ (optional)
+ bigq : dictionary with matrix of instruments by equation
+ (optional)
+ w : spatial weights object, required
+ vm : boolean
+ listing of full variance-covariance matrix, default = False
+ w_lags : integer
+ order of spatial lags for WX instruments, default = 1
+ lag_q : boolean
+ flag to apply spatial lag to other instruments,
+ default = True
+ nonspat_diag : boolean; flag for non-spatial diagnostics, default = True
+ spat_diag : boolean; flag for spatial diagnostics, default = False
+ name_bigy : dictionary with name of dependent variable for each equation
+ default = None, but should be specified
+ is done when sur_stackxy is used
+ name_bigX : dictionary with names of explanatory variables for each
+ equation
+ default = None, but should be specified
+ is done when sur_stackxy is used
+ name_bigyend : dictionary with names of endogenous variables for each
+ equation
+ default = None, but should be spedified
+ is done when sur_stackZ is used
+ name_bigq : dictionary with names of instrumental variables for each
+ equations
+ default = None, but should be specified
+ is done when sur_stackZ is used
+ name_ds : string; name for the data set
+ name_w : string; name for the spatial weights
+
+
+ Attributes
+ ----------
+
+ w : spatial weights object
+ bigy : dictionary with y values
+ bigZ : dictionary with matrix of exogenous and endogenous variables
+ for each equation
+ bigyend : dictionary with matrix of endogenous variables for each
+ equation; contains Wy only if no other endogenous specified
+ bigq : dictionary with matrix of instrumental variables for each
+ equation; contains WX only if no other endogenous specified
+ bigZHZH : dictionary with matrix of cross products Zhat_r'Zhat_s
+ bigZHy : dictionary with matrix of cross products Zhat_r'y_end_s
+ n_eq : number of equations
+ n : number of observations in each cross-section
+ bigK : vector with number of explanatory variables (including constant,
+ exogenous and endogenous) for each equation
+ b2SLS : dictionary with 2SLS regression coefficients for each equation
+ tslsE : N x n_eq array with OLS residuals for each equation
+ b3SLS : dictionary with 3SLS regression coefficients for each equation
+ varb : variance-covariance matrix
+ sig : Sigma matrix of inter-equation error covariances
+ resids : n by n_eq array of residuals
+ corr : inter-equation 3SLS error correlation matrix
+ tsls_inf : dictionary with standard error, asymptotic t and p-value,
+ one for each equation
+ joinrho : test on joint significance of spatial autoregressive coefficient
+ tuple with test statistic, degrees of freedom, p-value
+ surchow : list with tuples for Chow test on regression coefficients
+ each tuple contains test value, degrees of freedom, p-value
+ name_w : string; name for the spatial weights
+ name_ds : string; name for the data set
+ name_bigy : dictionary with name of dependent variable for each equation
+ name_bigX : dictionary with names of explanatory variables for each
+ equation
+ name_bigyend : dictionary with names of endogenous variables for each
+ equation
+ name_bigq : dictionary with names of instrumental variables for each
+ equations
+
+
+ Examples
+ --------
+
+ First import pysal to load the spatial analysis tools.
+
+ >>> 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.
+
+ >>> db = pysal.open(pysal.examples.get_path("NAT.dbf"),'r')
+
+ The specification of the model to be estimated can be provided as lists.
+ Each equation should be listed separately. Although not required,
+ in this example we will specify additional endogenous regressors.
+ Equation 1 has HR80 as dependent variable, PS80 and UE80 as exogenous regressors,
+ RD80 as endogenous regressor and FP79 as additional instrument.
+ For equation 2, HR90 is the dependent variable, PS90 and UE90 the
+ exogenous regressors, RD90 as endogenous regressor and FP99 as
+ additional instrument
+
+ >>> y_var = ['HR80','HR90']
+ >>> x_var = [['PS80','UE80'],['PS90','UE90']]
+ >>> yend_var = [['RD80'],['RD90']]
+ >>> q_var = [['FP79'],['FP89']]
+
+ The SUR method requires data to be provided as dictionaries. PySAL
+ provides two tools to create these dictionaries from the list of variables:
+ sur_dictxy and sur_dictZ. The tool sur_dictxy can be used to create the
+ dictionaries for Y and X, and sur_dictZ for endogenous variables (yend) and
+ additional instruments (q).
+
+ >>> bigy,bigX,bigyvars,bigXvars = pysal.spreg.sur_utils.sur_dictxy(db,y_var,x_var)
+ >>> bigyend,bigyendvars = pysal.spreg.sur_utils.sur_dictZ(db,yend_var)
+ >>> bigq,bigqvars = pysal.spreg.sur_utils.sur_dictZ(db,q_var)
+
+ To run a spatial lag model, we need to specify the spatial weights matrix.
+ To do that, we can open an already existing gal file or create a new one.
+ In this example, we will create a new one from NAT.shp and transform it to
+ row-standardized.
+
+ >>> w = pysal.queen_from_shapefile(pysal.examples.get_path("NAT.shp"))
+ >>> w.transform='r'
+
+ We can now run the regression and then have a summary of the output by typing:
+ print(reg.summary)
+
+ Alternatively, we can just check the betas and standard errors, asymptotic t
+ and p-value of the parameters:
+
+ >>> reg = SURlagIV(bigy,bigX,bigyend,bigq,w=w,name_bigy=bigyvars,name_bigX=bigXvars,name_bigyend=bigyendvars,name_bigq=bigqvars,name_ds="NAT",name_w="nat_queen")
+ >>> reg.b3SLS
+ {0: array([[ 6.95472387],
+ [ 1.44044301],
+ [-0.00771893],
+ [ 3.65051153],
+ [ 0.00362663]]), 1: array([[ 5.61101925],
+ [ 1.38716801],
+ [-0.15512029],
+ [ 3.1884457 ],
+ [ 0.25832185]])}
+
+ >>> reg.tsls_inf
+ {0: array([[ 0.49128435, 14.15620899, 0. ],
+ [ 0.11516292, 12.50787151, 0. ],
+ [ 0.03204088, -0.2409087 , 0.80962588],
+ [ 0.1876025 , 19.45875745, 0. ],
+ [ 0.05450628, 0.06653605, 0.94695106]]), 1: array([[ 0.44969956, 12.47726211, 0. ],
+ [ 0.10440241, 13.28674277, 0. ],
+ [ 0.04150243, -3.73761961, 0.00018577],
+ [ 0.19133145, 16.66451427, 0. ],
+ [ 0.04394024, 5.87893596, 0. ]])}
+ """
+
+ def __init__(self,bigy,bigX,bigyend=None,bigq=None,w=None,vm=False,\
+ w_lags=1, lag_q=True, nonspat_diag=True, spat_diag=False,\
+ name_bigy=None,name_bigX=None,name_bigyend=None,\
+ name_bigq=None,name_ds=None,name_w=None):
+ if w == None:
+ raise Exception, "Spatial weights required for SUR-Lag"
+ self.w = w
+ WS = w.sparse
+ self.name_ds = USER.set_name_ds(name_ds)
+ self.name_w = USER.set_name_w(name_w, w)
+ if bigyend and not(bigq):
+ raise Exception, "Instruments needed when endogenous variables"
+ #initialize
+ self.bigy = bigy
+ self.n_eq = len(self.bigy.keys())
+ if name_bigy:
+ self.name_bigy = name_bigy
+ else: # need to construct y names
+ self.name_bigy = {}
+ for r in range(self.n_eq):
+ yn = 'dep_var_' + str(r+1)
+ self.name_bigy[r] = yn
+ self.bigX = bigX
+ if name_bigX:
+ self.name_bigX = name_bigX
+ else: # need to construct x names
+ self.name_bigX = {}
+ for r in range(self.n_eq):
+ k = self.bigX[r].shape[1] - 1
+ name_x = ['var_' + str(i + 1) + "_" + str(r+1) for i in range(k)]
+ ct = 'Constant_' + str(r+1) # NOTE: constant always included in X
+ name_x.insert(0, ct)
+ self.name_bigX[r] = name_x
+ if bigyend: # check on other endogenous
+ self.bigyend = bigyend
+ if name_bigyend:
+ self.name_bigyend = name_bigyend
+ else: # need to construct names
+ self.name_bigyend = {}
+ for r in range(self.n_eq):
+ ky = self.bigyend[r].shape[1]
+ name_ye = ['end_' + str(i + 1) + "_" + str(r+1) for i in range(ky)]
+ self.name_bigyend[r] = name_ye
+ if bigq: # check on instruments
+ self.bigq = bigq
+ if name_bigq:
+ self.name_bigq = name_bigq
+ else: # need to construct names
+ self.name_bigq = {}
+ for r in range(self.n_eq):
+ ki = self.bigq[r].shape[1]
+ name_i = ['inst_' + str(i + 1) + "_" + str(r+1) for i in range(ki)]
+ self.name_bigq[r] = name_i
+
+ #spatial lag dependent variable
+ bigylag = {}
+ for r in range(self.n_eq):
+ bigylag[r] = WS*self.bigy[r]
+ if bigyend:
+ self.bigyend=bigyend
+ for r in range(self.n_eq):
+ self.bigyend[r] = np.hstack((self.bigyend[r],bigylag[r]))
+ # adjust variable names
+ for r in range(self.n_eq):
+ wyname = "W_" + self.name_bigy[r]
+ self.name_bigyend[r].append(wyname)
+ else: # no other endogenous variables
+ self.bigyend={}
+ for r in range(self.n_eq):
+ self.bigyend[r] = bigylag[r]
+ # variable names
+ self.name_bigyend = {}
+ for r in range(self.n_eq):
+ wyname = ["W_" + self.name_bigy[r]]
+ self.name_bigyend[r] = wyname
+
+ #spatially lagged exogenous variables
+ bigwx = {}
+ wxnames = {}
+ if w_lags == 1:
+ for r in range(self.n_eq):
+ bigwx[r] = WS* self.bigX[r][:,1:]
+ wxnames[r] = [ "W_" + i for i in self.name_bigX[r][1:]]
+ if bigq: # other instruments
+ if lag_q: # also lags for instruments
+ bigwq = {}
+ for r in range(self.n_eq):
+ bigwq = WS* self.bigq[r]
+ self.bigq[r] = np.hstack((self.bigq[r],bigwx[r],bigwq))
+ wqnames = [ "W_" + i for i in self.name_bigq[r]]
+ wxnames[r] = wxnames[r] + wqnames
+ self.name_bigq[r] = self.name_bigq[r] + wxnames[r]
+ else: # no lags for other instruments
+ for r in range(self.n_eq):
+ self.bigq[r] = np.hstack((self.bigq[r],bigwx[r]))
+ self.name_bigq[r] = self.name_bigq[r] + wxnames[r]
+ else: #no other instruments only wx
+ self.bigq = {}
+ self.name_bigq = {}
+ for r in range(self.n_eq):
+ self.bigq[r]=bigwx[r]
+ self.name_bigq[r] = wxnames[r]
+ elif w_lags > 1: # higher order lags for WX
+ for r in range(self.n_eq):
+ bigwxwork = WS* self.bigX[r][:,1:]
+ bigwx[r] = bigwxwork
+ nameswork = [ "W_" + i for i in self.name_bigX[r][1:]]
+ wxnames[r] = nameswork
+ for i in range(1,w_lags):
+ bigwxwork = WS*bigwxwork
+ bigwx[r] = np.hstack((bigwx[r],bigwxwork))
+ nameswork = [ "W" + i for i in nameswork ]
+ wxnames[r] = wxnames[r] + nameswork
+ if bigq: # other instruments
+ if lag_q: # lags for other instruments
+ wq = {}
+ wqnames = {}
+ for r in range(self.n_eq):
+ bigwq = WS* self.bigq[r]
+ wqnameswork = [ "W_" + i for i in self.name_bigq[r]]
+ wqnames[r] = wqnameswork
+ wq[r] = bigwq
+ for i in range(1,w_lags):
+ bigwq = WS* bigwq
+ wq[r] = np.hstack((wq[r],bigwq))
+ wqnameswork = [ "W" + i for i in wqnameswork ]
+ wqnames[r] = wqnames[r] + wqnameswork
+ self.bigq[r] = np.hstack((self.bigq[r],bigwx[r],wq[r]))
+ self.name_bigq[r] = self.name_bigq[r] + wxnames[r] + wqnames[r]
+
+ else: # no lags for other instruments
+ for r in range(self.n_eq):
+ self.bigq[r] = np.hstack((self.bigq[r],bigwx[r]))
+ self.name_bigq[r] = self.name_bigq[r] + wxnames[r]
+ else: # no other instruments only wx
+ self.bigq = {}
+ self.name_bigq = {}
+ for r in range(self.n_eq):
+ self.bigq[r] = bigwx[r]
+ self.name_bigq[r] = wxnames[r]
+
+ else:
+ raise Exception, "Lag order must be 1 or higher"
+
+ BaseThreeSLS.__init__(self,bigy=self.bigy,bigX=self.bigX,bigyend=self.bigyend,\
+ bigq=self.bigq)
+
+ #inference
+ self.tsls_inf = sur_setp(self.b3SLS,self.varb)
+
+ # test on joint significance of spatial coefficients
+ if spat_diag:
+ self.joinrho = sur_joinrho(self.n_eq,self.bigK,self.b3SLS,self.varb)
+ else:
+ self.joinrho = None
+
+ # test on constancy of coefficients across equations
+ if check_k(self.bigK): # only for equal number of variables
+ self.surchow = sur_chow(self.n_eq,self.bigK,self.b3SLS,self.varb)
+ else:
+ self.surchow = None
+
+ #list results
+ self.title = "SEEMINGLY UNRELATED REGRESSIONS (SUR) - SPATIAL LAG MODEL"
+ SUMMARY.SUR(reg=self, tsls=True, spat_diag=spat_diag, nonspat_diag=nonspat_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
+ from sur_utils import sur_dictxy
+
+ db = pysal.open(pysal.examples.get_path('NAT.dbf'), 'r')
+ w = pysal.queen_from_shapefile(pysal.examples.get_path("NAT.shp"))
+ w.transform='r'
+ y_var0 = ['HR80','HR90']
+ x_var0 = [['PS80','UE80'],['PS90','UE90']]
+
+ bigy0,bigX0,bigyvars0,bigXvars0 = sur_dictxy(db,y_var0,x_var0)
+
+ reg = SURlagIV(bigy0,bigX0,w=w,name_bigy=bigyvars0,name_bigX=bigXvars0,\
+ name_ds="NAT",name_w="nat_queen",nonspat_diag=True,spat_diag=True)
+ print reg.summary
diff --git a/pysal/spreg/sur_utils.py b/pysal/spreg/sur_utils.py
new file mode 100644
index 0000000..099fe54
--- /dev/null
+++ b/pysal/spreg/sur_utils.py
@@ -0,0 +1,363 @@
+"""
+Utilities for SUR and 3SLS estimation
+"""
+
+__author__= "Luc Anselin lanselin at gmail.com, \
+ Pedro V. Amaral pedrovma at gmail.com"
+
+
+import numpy as np
+import numpy.linalg as la
+
+__all__ = ['sur_dictxy','sur_dictZ','sur_mat2dict','sur_dict2mat',\
+ 'sur_corr','sur_crossprod','sur_est','sur_resids',\
+ 'filter_dict','check_k']
+
+
+def sur_dictxy(db,y_vars,x_vars,space_id=None,time_id=None):
+ """Stack X and y matrices and variable names as dictionaries by equation
+
+ Parameters
+ ----------
+ db : data object created by pysal.open
+ y_vars : list of lists with variable name(s) for dependent var
+ (Note must be a list of lists, even in splm case)
+ x_vars : list of lists with variable names for explanatory vars
+ space_id : variable with space ID, used for splm format
+ time_id : variable with time ID, used for splm format
+
+ Return
+ ------
+ (bigy,bigX,bigy_vars,bigX_vars)
+ : tuple with dictionaries for y and X, one for
+ each equation, bigy and bigX
+ Note: bigX already includes the constant term
+ dictionaries with y and X variables, by equation
+ (includes constant for X)
+ """
+ c = "Constant"
+ if (len(y_vars) > 1): # old format
+ n_eq = len(y_vars)
+ y = np.array([db.by_col(name) for name in y_vars]).T
+ n = y.shape[0]
+ bigy = {}
+ bigy_vars = dict((r,y_vars[r]) for r in range(n_eq))
+ bigy = dict((r,np.resize(y[:,r],(n,1))) for r in range(n_eq))
+ if not(len(x_vars) == n_eq): #CHANGE into exception
+ print "Error: mismatch variable lists"
+ bigX = {}
+ bigX_vars = {}
+ for r in range(n_eq):
+ litx = np.array([db.by_col(name) for name in x_vars[r]]).T
+ ic = c + "_" + str(r+1)
+ x_vars[r].insert(0,ic)
+ litxc = np.hstack((np.ones((n, 1)), litx))
+ bigX[r] = litxc
+ bigX_vars[r] = x_vars[r]
+ k = litxc.shape[1]
+ return (bigy,bigX,bigy_vars,bigX_vars)
+ elif (len(y_vars) == 1): #splm format
+ if not(time_id): #CHANGE into exception
+ print "Error: time id must be specified"
+ y = np.array([db.by_col(name) for name in y_vars]).T
+ bign = y.shape[0]
+ tt = np.array([db.by_col(name) for name in time_id]).T
+ tt1 = set([val for sublist in tt.tolist() for val in sublist])
+ n_eq = len(tt1)
+ tt2 = list(tt1)
+ tt2.sort()
+ tt3 = [str(int(a)+1) for a in tt2]
+ n = bign/n_eq
+ longx = np.array([db.by_col(name) for name in x_vars[0]]).T
+ longxc = np.hstack((np.ones((bign, 1)), longx))
+ xvars = x_vars[0][:]
+ xvars.insert(0,c)
+ bigy = {}
+ bigX = {}
+ bigy_vars = {}
+ bigX_vars = {}
+ for r in range(n_eq):
+ k0 = r * n
+ ke = r * n + n
+ bigy[r] = y[k0:ke,:]
+ bigy_vars[r] = y_vars[0] + "_" + tt3[r]
+ bigX[r] = longxc[k0:ke,:]
+ bxvars = [i + "_" + tt3[r] for i in xvars]
+ bigX_vars[r] = bxvars
+ return (bigy,bigX,bigy_vars,bigX_vars)
+ else:
+ print "error message, but should never be here"
+
+
+def sur_dictZ(db,z_vars,form="spreg",const=False,space_id=None,time_id=None):
+ """Generic stack data matrices and variable names as dictionaries by equation
+
+ Parameters
+ ----------
+ db : data object created by pysal.open
+ varnames : list of lists with variable name(s)
+ (Note must be a list of lists, even in splm case)
+ form : format used for data set
+ default="spreg": cross-sectional format
+ form="plm" : plm (R) compatible using space and time id
+ const : boolean
+ flag for constant term, default = "False"
+ space_id : variable with space ID, used for plm format
+ time_id : variable with time ID, used for plm format
+
+ Return
+ ------
+ (bigZ,bigZ_names) : tuple with dictionaries variables and variable
+ names, one for each equation
+ Note: bigX already includes the constant term
+
+ """
+ c = "Constant"
+ if form == "spreg": # old format
+ n_eq = len(z_vars)
+ bigZ = {}
+ bigZ_names = {}
+ for r in range(n_eq):
+ litz = np.array([db.by_col(name) for name in z_vars[r]]).T
+ if const:
+ ic = c + "_" + str(r+1)
+ z_vars[r].insert(0,ic)
+ litz = np.hstack((np.ones((litz.shape[0], 1)), litz))
+ bigZ[r] = litz
+ bigZ_names[r] = z_vars[r]
+ return (bigZ,bigZ_names)
+
+ elif (form == "plm"): #plm format
+ if not(time_id): #CHANGE into exception
+ raise Exception, "Error: time id must be specified for plm format"
+ tt = np.array([db.by_col(name) for name in time_id]).T
+ bign = tt.shape[0]
+ tt1 = set([val for sublist in tt.tolist() for val in sublist])
+ n_eq = len(tt1)
+ tt2 = list(tt1)
+ tt2.sort()
+ tt3 = [str(int(a)+1) for a in tt2]
+ n = bign/n_eq
+ longz = np.array([db.by_col(name) for name in z_vars[0]]).T
+ zvars = z_vars[0][:]
+ if const:
+ longz = np.hstack((np.ones((bign, 1)), longz))
+ zvars.insert(0,c)
+ bigZ = {}
+ bigZ_names = {}
+ for r in range(n_eq):
+ k0 = r * n
+ ke = r * n + n
+ bigZ[r] = longz[k0:ke,:]
+ bzvars = [i + "_" + tt3[r] for i in zvars]
+ bigZ_names[r] = bzvars
+ return (bigZ,bigZ_names)
+ else:
+ raise Exception, "you should never be here"
+
+
+
+def sur_mat2dict(mat,ndim):
+ """ Utility to convert a vector or matrix to a dictionary with ndim keys,
+ one for each equation
+
+ Parameters
+ ----------
+ mat : vector or matrix with elements to be converted
+ ndim : vector with number of elements (rows) to belong to each
+ dict
+
+ Returns
+ -------
+ dicts : dictionary with len(ndim) keys, from 0 to len(ndim)-1
+
+
+ """
+ kwork = np.vstack((np.zeros((1,1),dtype=np.int_),ndim))
+ dicts = {}
+ ki = 0
+ for r in range(1,len(kwork)):
+ ki = ki + kwork[r-1][0]
+ ke = ki + kwork[r][0]
+ dicts[r-1] = mat[ki:ke,:]
+ return(dicts)
+
+def sur_dict2mat(dicts):
+ """ Utility to stack the elements of a dictionary of vectors
+
+ Parameters
+ ----------
+ dicts : dictionary of vectors or matrices with same number
+ of columns (no checks yet!)
+
+ Returns
+ -------
+ mat : a vector or matrix of vertically stacked vectors
+
+
+ """
+ n_dicts = len(dicts.keys())
+ mat = np.vstack((dicts[t] for t in range(n_dicts)))
+ return(mat)
+
+def sur_corr(sig):
+ """SUR error correlation matrix
+
+ Parameters
+ ----------
+
+ sig : Sigma cross-equation covariance matrix
+
+ Returns
+ -------
+
+ corr : correlation matrix corresponding to sig
+
+ """
+ v = sig.diagonal()
+ s = np.sqrt(v)
+ s.resize(len(s),1)
+ sxs = np.dot(s,s.T)
+ corr = sig / sxs
+ return corr
+
+def sur_crossprod(bigZ,bigy):
+ '''Creates dictionaries of cross products by time period for both SUR and 3SLS
+
+ Parameters
+ ----------
+
+ bigZ : dictionary with matrix of explanatory variables,
+ including constant, exogenous and endogenous, one
+ for each equation
+ bigy : dictionary with vectors of dependent variable, one
+ for each equation
+
+
+ Returns
+ -------
+
+ bigZy : dictionary of all r,s cross-products
+ of Z_r'y_s
+ bigZZ : dictionary of all r,s cross-products
+ of Z_r'Z_s
+ '''
+ bigZZ = {}
+ n_eq = len(bigy.keys())
+ for r in range(n_eq):
+ for t in range(n_eq):
+ bigZZ[(r,t)] = np.dot(bigZ[r].T,bigZ[t])
+ bigZy = {}
+ for r in range(n_eq):
+ for t in range(n_eq):
+ bigZy[(r,t)] = np.dot(bigZ[r].T,bigy[t])
+ return bigZZ,bigZy
+
+
+def sur_est(bigXX,bigXy,bigE,bigK):
+ ''' Basic SUR estimation equations for both SUR and 3SLS
+
+
+ Parameters
+ ----------
+
+ bigXX : dictionary of cross-product matrices X_t'X_r
+ (created by sur_crossprod)
+ bigXy : dictionary of cross-product matrices X_t'y_r
+ (created by sur_crossprod)
+ bigE : n by n_eq array of residuals
+
+ Returns
+ -------
+
+ bSUR : dictionary with regression coefficients by equation
+ varb : variance-covariance matrix for the regression coefficients
+ sig : residual covariance matrix (using previous residuals)
+
+ '''
+ n = bigE.shape[0]
+ n_eq = bigE.shape[1]
+ sig = np.dot(bigE.T,bigE) / n
+ sigi = la.inv(sig)
+ sigiXX = {}
+ for r in range(n_eq):
+ for t in range(n_eq):
+ sigiXX[(r,t)] = bigXX[(r,t)]*sigi[r,t]
+ sigiXy = {}
+ for r in range(n_eq):
+ sxy=0.0
+ for t in range(n_eq):
+ sxy = sxy + sigi[r,t]*bigXy[(r,t)]
+ sigiXy[r] = sxy
+ xsigy = np.vstack((sigiXy[t] for t in range(n_eq)))
+ xsigx = np.vstack(((np.hstack(sigiXX[(r,t)] for t in range(n_eq))) for r in range(n_eq)))
+ varb = la.inv(xsigx)
+ beta = np.dot(varb,xsigy)
+ bSUR = sur_mat2dict(beta,bigK)
+ return bSUR, varb, sig
+
+
+def sur_resids(bigy,bigX,beta):
+ ''' Computation of a matrix with residuals by equation
+
+ Parameters
+ ----------
+
+ bigy : dictionary with vector of dependent variable, one for each equation
+ bigX : dictionary with matrix of explanatory variables, one for
+ each equation
+ beta : dictionary with estimation coefficients by
+ equation
+
+ Returns
+ -------
+ bigE : a n x n_eq matrix of vectors of residuals
+
+ '''
+ n_eq = len(bigy.keys())
+ bigE = np.hstack((bigy[r] - np.dot(bigX[r],beta[r])) for r in range(n_eq))
+ return(bigE)
+
+
+def filter_dict(lam,bigZ,bigZlag):
+ """Dictionary of spatially filtered variables for use in SUR
+
+ Parameters
+ ----------
+ lam : n_eq x 1 array of spatial autoregressive parameters
+ bigZ : dictionary of vector or matrix of variables, one for
+ each equation
+ bigZlag : dictionaly of vector or matrix of spatially lagged
+ variables, one for each equation
+
+ Returns
+ -------
+ Zfilt : dictionary with spatially filtered variables
+ Z - lam*WZ, one for each equation
+
+ """
+ n_eq = lam.shape[0]
+ if not(len(bigZ.keys()) == n_eq and len(bigZlag.keys()) == n_eq):
+ raise Exception, "Error: incompatible dimensions"
+ Zfilt = {}
+ for r in range(n_eq):
+ lami = lam[r][0]
+ Zfilt[r] = bigZ[r] - lami*bigZlag[r]
+ return Zfilt
+
+def check_k(bigK):
+ """Check on equality of number of variables by equation
+
+ Parameter
+ ---------
+ bigK : n_eq x 1 array of number of variables (includes constant)
+
+ Returns
+ -------
+ True/False : result of test
+ """
+ kk = bigK.flatten()
+ k = kk[0]
+ check = np.equal(k,kk)
+ return all(check)
+
diff --git a/pysal/spreg/tests/test_diagnostics.py b/pysal/spreg/tests/test_diagnostics.py
index 1904b58..a1fe5db 100644
--- a/pysal/spreg/tests/test_diagnostics.py
+++ b/pysal/spreg/tests/test_diagnostics.py
@@ -2,7 +2,8 @@ import unittest
import numpy as np
import pysal
from pysal.spreg import diagnostics
-from pysal.spreg.ols import OLS
+from pysal.spreg.ols import OLS
+from pysal.common import RTOL
# create regression object used by all the tests below
@@ -19,9 +20,9 @@ reg = OLS(y,X)
class TestFStat(unittest.TestCase):
def test_f_stat(self):
obs = diagnostics.f_stat(reg)
- exp = (28.385629224695, 0.000000009341)
+ exp = (28.385629224695, 0.000000009340747)
for i in range(2):
- self.assertAlmostEquals(obs[i],exp[i])
+ np.testing.assert_allclose(obs[i],exp[i], RTOL)
class TestTStat(unittest.TestCase):
def test_t_stat(self):
@@ -31,81 +32,81 @@ class TestTStat(unittest.TestCase):
(-2.6544086427176916, 0.010874504909754612)]
for i in range(3):
for j in range(2):
- self.assertAlmostEquals(obs[i][j],exp[i][j])
+ np.testing.assert_allclose(obs[i][j],exp[i][j], RTOL)
class TestR2(unittest.TestCase):
def test_r2(self):
obs = diagnostics.r2(reg)
exp = 0.55240404083742334
- self.assertAlmostEquals(obs,exp)
+ np.testing.assert_allclose(obs,exp, RTOL)
class TestAr2(unittest.TestCase):
def test_ar2(self):
obs = diagnostics.ar2(reg)
exp = 0.5329433469607896
- self.assertAlmostEquals(obs,exp)
+ np.testing.assert_allclose(obs,exp, RTOL)
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)
+ np.testing.assert_allclose(obs,exp, RTOL)
class TestLogLikelihood(unittest.TestCase):
def test_log_likelihood(self):
obs = diagnostics.log_likelihood(reg)
exp = -187.3772388121491
- self.assertAlmostEquals(obs,exp)
+ np.testing.assert_allclose(obs,exp, RTOL)
class TestAkaike(unittest.TestCase):
def test_akaike(self):
obs = diagnostics.akaike(reg)
exp = 380.7544776242982
- self.assertAlmostEquals(obs,exp)
+ np.testing.assert_allclose(obs,exp, RTOL)
class TestSchwarz(unittest.TestCase):
def test_schwarz(self):
obs = diagnostics.schwarz(reg)
exp = 386.42993851863008
- self.assertAlmostEquals(obs,exp)
+ np.testing.assert_allclose(obs,exp, RTOL)
class TestConditionIndex(unittest.TestCase):
def test_condition_index(self):
obs = diagnostics.condition_index(reg)
exp = 6.541827751444
- self.assertAlmostEquals(obs,exp)
+ np.testing.assert_allclose(obs,exp, RTOL)
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'])
+ np.testing.assert_equal(obs['df'],exp['df'])
+ np.testing.assert_allclose(obs['jb'],exp['jb'], RTOL)
+ np.testing.assert_allclose(obs['pvalue'],exp['pvalue'], RTOL)
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'])
+ np.testing.assert_equal(obs['df'],exp['df'])
+ np.testing.assert_allclose(obs['bp'],exp['bp'])
+ np.testing.assert_allclose(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'])
+ np.testing.assert_equal(obs['df'],exp['df'])
+ np.testing.assert_allclose(obs['wh'],exp['wh'], RTOL)
+ np.testing.assert_allclose(obs['pvalue'],exp['pvalue'], RTOL)
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'])
+ np.testing.assert_equal(obs['df'],exp['df'])
+ np.testing.assert_allclose(obs['kb'],exp['kb'], RTOL)
+ np.testing.assert_allclose(obs['pvalue'],exp['pvalue'], RTOL)
class TestVif(unittest.TestCase):
def test_vif(self):
@@ -115,14 +116,15 @@ class TestVif(unittest.TestCase):
(1.3331174971891973, 0.75012142748740707)]
for i in range(1,3):
for j in range(2):
- self.assertAlmostEquals(obs[i][j],exp[i][j])
+ np.testing.assert_allclose(obs[i][j],exp[i][j], RTOL)
class TestConstantCheck(unittest.TestCase):
def test_constant_check(self):
obs = diagnostics.constant_check(reg.x)
exp = True
- self.assertEquals(obs,exp)
+ np.testing.assert_equal(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
index 3d9b8ff..27147a7 100644
--- a/pysal/spreg/tests/test_diagnostics_sp.py
+++ b/pysal/spreg/tests/test_diagnostics_sp.py
@@ -6,7 +6,7 @@ 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
-
+from pysal.common import RTOL
class TestLMtests(unittest.TestCase):
def setUp(self):
@@ -28,27 +28,27 @@ class TestLMtests(unittest.TestCase):
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)
+ np.testing.assert_allclose(lms.lme, lme, RTOL)
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)
+ np.testing.assert_allclose(lms.lml, lml, RTOL)
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)
+ np.testing.assert_allclose(lms.rlme, rlme, RTOL)
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)
+ np.testing.assert_allclose(lms.rlml, rlml, RTOL)
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)
+ np.testing.assert_allclose(lms.sarma, sarma, RTOL)
class TestMoranRes(unittest.TestCase):
@@ -70,19 +70,19 @@ class TestMoranRes(unittest.TestCase):
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)
+ np.testing.assert_allclose(m.I, 0.17130999999999999, RTOL)
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)
+ np.testing.assert_allclose(m.vI, 0.0081304900000000001, RTOL)
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)
+ np.testing.assert_allclose(m.eI, -0.034522999999999998, RTOL)
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)
+ np.testing.assert_allclose(m.zI, 2.2827389999999999, RTOL)
class TestAKTest(unittest.TestCase):
@@ -111,27 +111,27 @@ class TestAKTest(unittest.TestCase):
def test_gen_mi(self):
ak = AKtest(self.reg, self.w)
- np.testing.assert_array_almost_equal(ak.mi, 0.2232672865437263, decimal=6)
+ np.testing.assert_allclose(ak.mi, 0.2232672865437263, RTOL)
def test_gen_ak(self):
ak = AKtest(self.reg, self.w)
- np.testing.assert_array_almost_equal(ak.ak, 4.6428948758930852, decimal=6)
+ np.testing.assert_allclose(ak.ak, 4.6428948758930852, RTOL)
def test_gen_p(self):
ak = AKtest(self.reg, self.w)
- np.testing.assert_array_almost_equal(ak.p, 0.031182360054340875, decimal=6)
+ np.testing.assert_allclose(ak.p, 0.031182360054340875, RTOL)
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)
+ np.testing.assert_allclose(ak.mi, 0.2232672865437263, RTOL)
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)
+ np.testing.assert_allclose(ak.ak, 1.1575928784397795, RTOL)
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)
+ np.testing.assert_allclose(ak.p, 0.28196531619791054, RTOL)
class TestSpDcache(unittest.TestCase):
def setUp(self):
@@ -152,27 +152,27 @@ class TestSpDcache(unittest.TestCase):
def test_j(self):
cache = spDcache(self.ols, self.w)
- np.testing.assert_array_almost_equal(cache.j[0][0], 0.62330311259039439, decimal=6)
+ np.testing.assert_allclose(cache.j[0][0], 0.62330311259039439, RTOL)
def test_t(self):
cache = spDcache(self.ols, self.w)
- np.testing.assert_array_almost_equal(cache.t, 22.751186696900984, decimal=6)
+ np.testing.assert_allclose(cache.t, 22.751186696900984, RTOL)
def test_trA(self):
cache = spDcache(self.ols, self.w)
- np.testing.assert_array_almost_equal(cache.trA, 1.5880426389276328, decimal=6)
+ np.testing.assert_allclose(cache.trA, 1.5880426389276328, RTOL)
def test_utwuDs(self):
cache = spDcache(self.ols, self.w)
- np.testing.assert_array_almost_equal(cache.utwuDs[0][0], 8.3941977502916068, decimal=6)
+ np.testing.assert_allclose(cache.utwuDs[0][0], 8.3941977502916068, RTOL)
def test_utwyDs(self):
cache = spDcache(self.ols, self.w)
- np.testing.assert_array_almost_equal(cache.utwyDs[0][0], 5.475255215067957, decimal=6)
+ np.testing.assert_allclose(cache.utwyDs[0][0], 5.475255215067957, RTOL)
def test_wu(self):
cache = spDcache(self.ols, self.w)
- np.testing.assert_array_almost_equal(cache.wu[0][0], -10.681344941514411, decimal=6)
+ np.testing.assert_allclose(cache.wu[0][0], -10.681344941514411, RTOL)
if __name__ == '__main__':
diff --git a/pysal/spreg/tests/test_diagnostics_tsls.py b/pysal/spreg/tests/test_diagnostics_tsls.py
index 4423b33..77ec17d 100644
--- a/pysal/spreg/tests/test_diagnostics_tsls.py
+++ b/pysal/spreg/tests/test_diagnostics_tsls.py
@@ -7,7 +7,7 @@ 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
-
+from pysal.common import RTOL
# create regression object used by the apatial tests
db = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
@@ -45,21 +45,19 @@ class TestTStat(unittest.TestCase):
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])
+ np.testing.assert_allclose(obs, exp, RTOL)
class TestPr2Aspatial(unittest.TestCase):
def test_pr2_aspatial(self):
obs = diagnostics_tsls.pr2_aspatial(reg)
exp = 0.2793613712817381
- self.assertAlmostEquals(obs,exp)
+ np.testing.assert_allclose(obs,exp, RTOL)
class TestPr2Spatial(unittest.TestCase):
def test_pr2_spatial(self):
obs = diagnostics_tsls.pr2_spatial(regsp)
exp = 0.29964855438065163
- self.assertAlmostEquals(obs,exp)
+ np.testing.assert_allclose(obs,exp, RTOL)
if __name__ == '__main__':
diff --git a/pysal/spreg/tests/test_error_sp.py b/pysal/spreg/tests/test_error_sp.py
index 1aba6db..798a3e4 100644
--- a/pysal/spreg/tests/test_error_sp.py
+++ b/pysal/spreg/tests/test_error_sp.py
@@ -3,6 +3,7 @@ import scipy
import pysal
import numpy as np
from pysal.spreg import error_sp as SP
+from pysal.common import RTOL
class TestBaseGMError(unittest.TestCase):
def setUp(self):
@@ -20,31 +21,31 @@ class TestBaseGMError(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 27.4739775])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 52.9930255])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n,4)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 3
- self.assertAlmostEqual(reg.k,k,4)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
e = np.array([ 31.89620319])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
predy = np.array([ 52.9930255])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my,RTOL)
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(reg.std_y,sy,RTOL)
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
sig2 = 191.73716465732355
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
class TestGMError(unittest.TestCase):
def setUp(self):
@@ -61,37 +62,37 @@ class TestGMError(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 27.4739775])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 52.9930255])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n,4)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 3
- self.assertAlmostEqual(reg.k,k,4)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
e = np.array([ 31.89620319])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
predy = np.array([ 52.9930255])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my,RTOL)
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(reg.std_y,sy,RTOL)
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
sig2 = 191.73716465732355
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
pr2 = 0.3495097406012179
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Maximum Likelihood requires SciPy version 11 or newer.")
@@ -116,37 +117,39 @@ class TestBaseGMEndogError(unittest.TestCase):
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)
+ #raising a warning causes a failure...
+ print('Running reduced precision test in L120 of test_error_sp.py')
+ np.testing.assert_allclose(reg.betas,betas,RTOL+.0001)
u = np.array([ 26.55951566])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
e = np.array([ 31.23925425])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
predy = np.array([ 53.9074875])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 3
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531])
- np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
yend = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ np.testing.assert_allclose(reg.yend[0],yend,RTOL)
z = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.z[0],z,4)
+ np.testing.assert_allclose(reg.z[0],z,RTOL)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my,RTOL)
#std_y
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(reg.std_y,sy,RTOL)
#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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
sig2 = 192.50022721929574
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Maximum Likelihood requires SciPy version 11 or newer.")
@@ -170,41 +173,42 @@ class TestGMEndogError(unittest.TestCase):
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)
+ print('Running reduced precision test in L175 of test_error_sp.py')
+ np.testing.assert_allclose(reg.betas,betas,RTOL +.0001)
u = np.array([ 26.55951566])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
e = np.array([ 31.23925425])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
predy = np.array([ 53.9074875])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 3
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531])
- np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
yend = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ np.testing.assert_allclose(reg.yend[0],yend,RTOL)
z = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.z[0],z,4)
+ np.testing.assert_allclose(reg.z[0],z,RTOL)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my,RTOL)
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(reg.std_y,sy,RTOL)
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
pr2 = 0.346472557570858
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(reg.pr2,pr2,RTOL)
sig2 = 192.50022721929574
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
std_err = np.array([ 23.003401 , 0.73500657, 0.38075777])
- np.testing.assert_array_almost_equal(reg.std_err,std_err,4)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Maximum Likelihood requires SciPy version 11 or newer.")
@@ -226,34 +230,34 @@ class TestBaseGMCombo(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 25.57932637])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
e_filtered = np.array([ 31.65374945])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e_filtered,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e_filtered,RTOL)
predy = np.array([ 54.88767663])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 4
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
yend = np.array([ 35.4585005])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ np.testing.assert_allclose(reg.yend[0],yend,RTOL)
z = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
- np.testing.assert_array_almost_equal(reg.z[0],z,4)
+ np.testing.assert_allclose(reg.z[0],z,RTOL)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my,RTOL)
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(reg.std_y,sy,RTOL)
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)
+ np.testing.assert_allclose(np.diag(reg.vm),vm,RTOL)
sig2 = 181.78650186468832
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Maximum Likelihood requires SciPy version 11 or newer.")
@@ -272,46 +276,46 @@ class TestGMCombo(unittest.TestCase):
# 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)
+ np.testing.assert_allclose(reg.e_pred[0],e_reduced,RTOL)
predy_e = np.array([ 52.28082782])
- np.testing.assert_array_almost_equal(reg.predy_e[0],predy_e,4)
+ np.testing.assert_allclose(reg.predy_e[0],predy_e,RTOL)
betas = np.array([[ 57.61123515],[ 0.73441313], [ -0.59459416], [ -0.21762921], [ 0.54732051]])
- np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 25.57932637])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
e_filtered = np.array([ 31.65374945])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e_filtered,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e_filtered,RTOL)
predy = np.array([ 54.88767685])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 4
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
yend = np.array([ 35.4585005])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ np.testing.assert_allclose(reg.yend[0],yend,RTOL)
z = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
- np.testing.assert_array_almost_equal(reg.z[0],z,4)
+ np.testing.assert_allclose(reg.z[0],z,RTOL)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(np.diag(reg.vm),vm,RTOL)
sig2 = 181.78650186468832
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
pr2 = 0.3018280166937799
- self.assertAlmostEqual(reg.pr2,pr2,4)
+ np.testing.assert_allclose(reg.pr2,pr2,RTOL)
pr2_e = 0.3561355586759414
- self.assertAlmostEqual(reg.pr2_e,pr2_e,4)
+ np.testing.assert_allclose(reg.pr2_e,pr2_e,RTOL)
std_err = np.array([ 22.85692222, 0.48786559, 0.17914356, 0.46449318])
- np.testing.assert_array_almost_equal(reg.std_err,std_err,4)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
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
index 1c2ce6e..cefaad7 100644
--- a/pysal/spreg/tests/test_error_sp_het.py
+++ b/pysal/spreg/tests/test_error_sp_het.py
@@ -2,6 +2,7 @@ import unittest
import pysal
import numpy as np
from pysal.spreg import error_sp_het as HET
+from pysal.common import RTOL
class TestBaseGMErrorHet(unittest.TestCase):
def setUp(self):
@@ -19,29 +20,29 @@ class TestBaseGMErrorHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 27.38122697])
- np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
ef = np.array([ 32.29765975])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
+ np.testing.assert_allclose(reg.e_filtered[0],ef,RTOL)
predy = np.array([ 53.08577603])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n)
k = 3
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
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)
+ np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
stdy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,stdy)
+ np.testing.assert_allclose(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,
@@ -50,11 +51,11 @@ class TestBaseGMErrorHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
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)
+ np.testing.assert_allclose(reg.xtx,xtx,RTOL)
class TestGMErrorHet(unittest.TestCase):
def setUp(self):
@@ -71,29 +72,29 @@ class TestGMErrorHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 27.38122697])
- np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
ef = np.array([ 32.29765975])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
+ np.testing.assert_allclose(reg.e_filtered[0],ef,RTOL)
predy = np.array([ 53.08577603])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n)
k = 3
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
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)
+ np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
stdy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,stdy)
+ np.testing.assert_allclose(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,
@@ -102,20 +103,20 @@ class TestGMErrorHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
pr2 = 0.34951013222581306
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.std_err,stde,RTOL)
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)
+ np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
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)
+ np.testing.assert_allclose(reg.xtx,xtx,RTOL)
class TestBaseGMEndogErrorHet(unittest.TestCase):
def setUp(self):
@@ -138,37 +139,37 @@ class TestBaseGMEndogErrorHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 26.51812895])
- np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
ef = np.array([ 31.46604707])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
+ np.testing.assert_allclose(reg.e_filtered[0],ef,RTOL)
predy = np.array([ 53.94887405])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n)
k = 3
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531])
- np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
yend = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ np.testing.assert_allclose(reg.yend[0],yend,RTOL)
q = np.array([ 5.03])
- np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ np.testing.assert_allclose(reg.q[0],q,RTOL)
z = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.z[0],z,7)
+ np.testing.assert_allclose(reg.z[0],z,RTOL)
h = np.array([ 1. , 19.531, 5.03 ])
- np.testing.assert_array_almost_equal(reg.h[0],h,7)
+ np.testing.assert_allclose(reg.h[0],h,RTOL)
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)
+ np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
stdy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,stdy)
+ np.testing.assert_allclose(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,
@@ -177,11 +178,11 @@ class TestBaseGMEndogErrorHet(unittest.TestCase):
-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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
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)
+ np.testing.assert_allclose(reg.hth,hth,RTOL)
class TestGMEndogErrorHet(unittest.TestCase):
def setUp(self):
@@ -203,35 +204,35 @@ class TestGMEndogErrorHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 26.51812895])
- np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 53.94887405])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n)
k = 3
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531])
- np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
yend = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ np.testing.assert_allclose(reg.yend[0],yend,RTOL)
q = np.array([ 5.03])
- np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ np.testing.assert_allclose(reg.q[0],q,RTOL)
z = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.z[0],z,7)
+ np.testing.assert_allclose(reg.z[0],z,RTOL)
h = np.array([ 1. , 19.531, 5.03 ])
- np.testing.assert_array_almost_equal(reg.h[0],h,7)
+ np.testing.assert_allclose(reg.h[0],h,RTOL)
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)
+ np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
stdy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,stdy)
+ np.testing.assert_allclose(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,
@@ -240,18 +241,18 @@ class TestGMEndogErrorHet(unittest.TestCase):
-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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
pr2 = 0.34648011338954804
- self.assertAlmostEqual(reg.pr2,pr2,7)
+ np.testing.assert_allclose(reg.pr2,pr2,RTOL)
std_err = np.array([ 28.89009873, 0.77309965, 0.46798299,
0.17767558])
- np.testing.assert_array_almost_equal(reg.std_err,std_err,6)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
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)
+ np.testing.assert_allclose(reg.hth,hth,RTOL)
class TestBaseGMComboHet(unittest.TestCase):
def setUp(self):
@@ -271,35 +272,35 @@ class TestBaseGMComboHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 25.65156033])
- np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
ef = np.array([ 31.87664403])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
+ np.testing.assert_allclose(reg.e_filtered[0],ef,RTOL)
predy = np.array([ 54.81544267])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n)
k = 4
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
yend = np.array([ 35.4585005])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ np.testing.assert_allclose(reg.yend[0],yend,RTOL)
q = np.array([ 18.594 , 24.7142675])
- np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ np.testing.assert_allclose(reg.q[0],q,RTOL)
z = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
- np.testing.assert_array_almost_equal(reg.z[0],z,7)
+ np.testing.assert_allclose(reg.z[0],z,RTOL)
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)
+ np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
stdy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,stdy,7)
+ np.testing.assert_allclose(reg.std_y,stdy,RTOL)
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,
@@ -310,7 +311,7 @@ class TestBaseGMComboHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
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,
@@ -321,7 +322,7 @@ class TestBaseGMComboHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.hth,hth,RTOL)
class TestGMComboHet(unittest.TestCase):
def setUp(self):
@@ -339,39 +340,39 @@ class TestGMComboHet(unittest.TestCase):
# 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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 25.65156033])
- np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
ef = np.array([ 31.87664403])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
+ np.testing.assert_allclose(reg.e_filtered[0],ef,RTOL)
ep = np.array([ 28.30648145])
- np.testing.assert_array_almost_equal(reg.e_pred[0],ep,7)
+ np.testing.assert_allclose(reg.e_pred[0],ep,RTOL)
pe = np.array([ 52.16052155])
- np.testing.assert_array_almost_equal(reg.predy_e[0],pe,7)
+ np.testing.assert_allclose(reg.predy_e[0],pe,RTOL)
predy = np.array([ 54.81544267])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n)
k = 4
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
yend = np.array([ 35.4585005])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ np.testing.assert_allclose(reg.yend[0],yend,RTOL)
q = np.array([ 18.594 , 24.7142675])
- np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ np.testing.assert_allclose(reg.q[0],q,RTOL)
z = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
- np.testing.assert_array_almost_equal(reg.z[0],z,7)
+ np.testing.assert_allclose(reg.z[0],z,RTOL)
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)
+ np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
stdy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,stdy)
+ np.testing.assert_allclose(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,
@@ -382,15 +383,15 @@ class TestGMComboHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
pr2 = 0.3001582877472412
- self.assertAlmostEqual(reg.pr2,pr2,7)
+ np.testing.assert_allclose(reg.pr2,pr2,RTOL)
pr2_e = 0.35613102283621967
- self.assertAlmostEqual(reg.pr2_e,pr2_e,7)
+ np.testing.assert_allclose(reg.pr2_e,pr2_e,RTOL)
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)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
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,
@@ -401,7 +402,7 @@ class TestGMComboHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.hth,hth,RTOL)
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
index 4eab0f5..9a6d80b 100644
--- a/pysal/spreg/tests/test_error_sp_het_regimes.py
+++ b/pysal/spreg/tests/test_error_sp_het_regimes.py
@@ -3,6 +3,7 @@ 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
+from pysal.common import RTOL
class TestGM_Error_Het_Regimes(unittest.TestCase):
def setUp(self):
@@ -38,8 +39,8 @@ class TestGM_Error_Het_Regimes(unittest.TestCase):
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.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):
@@ -51,40 +52,40 @@ class TestGM_Error_Het_Regimes(unittest.TestCase):
[ -0.3358993 ],
[ -0.82129289],
[ 0.54662719]])
- np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([-2.19031456])
- np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 17.91629456])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n,6)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 6
- self.assertAlmostEqual(reg.k,k,6)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.y[0],y,6)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([[ 0. , 0. , 0. , 1. , 80.467003, 19.531 ]])
- np.testing.assert_array_almost_equal(reg.x[0].toarray(),x,6)
+ np.testing.assert_allclose(reg.x[0].toarray(),x,RTOL)
e = np.array([ 2.77847355])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,6)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
my = 35.128823897959187
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my, RTOL)
sy = 16.732092091229699
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(reg.std_y,sy, RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0],vm,RTOL)
pr2 = 0.5515791216043385
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(reg.pr2,pr2, RTOL)
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)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 0.72341901308525713
- self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ np.testing.assert_allclose(reg.chow.joint[0],chow_j, RTOL)
def test_model_regi_error(self):
#Columbus:
@@ -97,31 +98,31 @@ class TestGM_Error_Het_Regimes(unittest.TestCase):
[ -0.31841139],
[ -1.27502813],
[ 0.11515312]])
- np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0],vm,RTOL)
u = np.array([-0.05357818])
- np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 15.77955818])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
e = np.array([ 0.70542044])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,6)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 4.7467070503995412
- self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ np.testing.assert_allclose(reg.chow.joint[0],chow_j, RTOL)
#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)
+ np.testing.assert_allclose(model.betas,tbetas, RTOL)
vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
- np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 6)
+ np.testing.assert_allclose(model.vm.diagonal(), vm, RTOL)
def test_model_endog(self):
reg = SP.GM_Endog_Error_Het_Regimes(self.y, self.X2, self.yd, self.q, self.regimes, self.w)
@@ -132,44 +133,44 @@ class TestGM_Error_Het_Regimes(unittest.TestCase):
[ -3.20196286],
[ -1.13672283],
[ 0.2174965 ]])
- np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 20.50716917])
- np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
e = np.array([ 25.13517175])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,6)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
predy = np.array([-4.78118917])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n, RTOL)
k = 6
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k, RTOL)
y = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.y[0],y,6)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([[ 0. , 0. , 1. , 19.531]])
- np.testing.assert_array_almost_equal(reg.x[0].toarray(),x,6)
+ np.testing.assert_allclose(reg.x[0].toarray(),x,RTOL)
yend = np.array([[ 0. , 80.467003]])
- np.testing.assert_array_almost_equal(reg.yend[0].toarray(),yend,6)
+ np.testing.assert_allclose(reg.yend[0].toarray(),yend,RTOL)
z = np.array([[ 0. , 0. , 1. , 19.531 , 0. ,
80.467003]])
- np.testing.assert_array_almost_equal(reg.z[0].toarray(),z,6)
+ np.testing.assert_allclose(reg.z[0].toarray(),z,RTOL)
my = 35.128823897959187
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my, RTOL)
sy = 16.732092091229699
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(reg.std_y,sy, RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0],vm,RTOL)
pr2 = 0.19776512679331681
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 1.2329971019087163
- self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ np.testing.assert_allclose(reg.chow.joint[0],chow_j, RTOL)
def test_model_endog_regi_error(self):
#Columbus:
@@ -182,32 +183,32 @@ class TestGM_Error_Het_Regimes(unittest.TestCase):
[ 5.48294187e-01],
[ -1.28432891e+00],
[ 3.57661629e-02]])
- np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0],vm,RTOL)
u = np.array([ 25.73781918])
- np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([-10.01183918])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
e = np.array([ 26.5449135])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,6)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 1.3756768204399892
- self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ np.testing.assert_allclose(reg.chow.joint[0],chow_j, RTOL)
#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)
+ np.testing.assert_allclose(model.betas,tbetas, RTOL)
vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
- np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 6)
+ np.testing.assert_allclose(model.vm.diagonal(), vm, RTOL)
def test_model_combo(self):
reg = SP.GM_Combo_Het_Regimes(self.y, self.X2, self.regimes, self.yd, self.q, w=self.w)
@@ -219,48 +220,48 @@ class TestGM_Error_Het_Regimes(unittest.TestCase):
[ -2.21328949e-01],
[ 6.41902155e-01],
[ -2.45714919e-02]])
- np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 0.94039246])
- np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
e_filtered = np.array([ 0.8737864])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e_filtered,5)
+ np.testing.assert_allclose(reg.e_filtered[0],e_filtered,RTOL)
predy_e = np.array([ 18.68732105])
- np.testing.assert_array_almost_equal(reg.predy_e[0],predy_e,6)
+ np.testing.assert_allclose(reg.predy_e[0],predy_e,RTOL)
predy = np.array([ 14.78558754])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n, RTOL)
k = 7
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k, RTOL)
y = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.y[0],y,6)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([[ 0. , 0. , 1. , 19.531]])
- np.testing.assert_array_almost_equal(reg.x[0].toarray(),x,6)
+ np.testing.assert_allclose(reg.x[0].toarray(),x,RTOL)
yend = np.array([[ 0. , 80.467003 , 24.7142675]])
- np.testing.assert_array_almost_equal(reg.yend[0].toarray(),yend,6)
+ np.testing.assert_allclose(reg.yend[0].toarray(),yend,RTOL)
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)
+ np.testing.assert_allclose(reg.z[0].toarray(),z,RTOL)
my = 35.128823897959187
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my, RTOL)
sy = 16.732092091229699
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(reg.std_y,sy, RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0],vm,RTOL)
pr2 = 0.6504148883602958
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(reg.pr2,pr2,RTOL)
pr2_e = 0.527136896994038
- self.assertAlmostEqual(reg.pr2_e,pr2_e)
+ np.testing.assert_allclose(reg.pr2_e,pr2_e,RTOL)
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)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 0.78070369988354349
- self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ np.testing.assert_allclose(reg.chow.joint[0],chow_j,RTOL)
def test_model_combo_regi_error(self):
#Columbus:
@@ -275,33 +276,33 @@ class TestGM_Error_Het_Regimes(unittest.TestCase):
[ -0.49175217],
[ 0.65733173],
[ -0.07713581]])
- np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0],vm,RTOL)
u = np.array([ 7.81039418])
- np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 7.91558582])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
e = np.array([ 7.22996911])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,6)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 1.1316136604755913
- self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ np.testing.assert_allclose(reg.chow.joint[0],chow_j,RTOL)
#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)
+ np.testing.assert_allclose(model.betas,tbetas,RTOL)
vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
-
+ #was this supposed to get tested?
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
index ff8a80d..1370b37 100644
--- a/pysal/spreg/tests/test_error_sp_het_sparse.py
+++ b/pysal/spreg/tests/test_error_sp_het_sparse.py
@@ -3,6 +3,7 @@ import pysal
import numpy as np
from scipy import sparse
from pysal.spreg import error_sp_het as HET
+from pysal.common import RTOL
class TestBaseGMErrorHet(unittest.TestCase):
def setUp(self):
@@ -21,29 +22,29 @@ class TestBaseGMErrorHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 27.38122697])
- np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
ef = np.array([ 32.29765975])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
+ np.testing.assert_allclose(reg.e_filtered[0],ef,RTOL)
predy = np.array([ 53.08577603])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n)
k = 3
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x[0].toarray()[0],x,7)
+ np.testing.assert_allclose(reg.x[0].toarray()[0],x,RTOL)
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)
+ np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
stdy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,stdy)
+ np.testing.assert_allclose(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,
@@ -52,11 +53,11 @@ class TestBaseGMErrorHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
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)
+ np.testing.assert_allclose(reg.xtx,xtx,RTOL)
class TestGMErrorHet(unittest.TestCase):
def setUp(self):
@@ -74,29 +75,29 @@ class TestGMErrorHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 27.38122697])
- np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
ef = np.array([ 32.29765975])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
+ np.testing.assert_allclose(reg.e_filtered[0],ef,RTOL)
predy = np.array([ 53.08577603])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n)
k = 3
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x[0].toarray()[0],x,7)
+ np.testing.assert_allclose(reg.x[0].toarray()[0],x,RTOL)
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)
+ np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
stdy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,stdy)
+ np.testing.assert_allclose(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,
@@ -105,20 +106,20 @@ class TestGMErrorHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
pr2 = 0.34951013222581306
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.std_err,stde,RTOL)
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)
+ np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
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)
+ np.testing.assert_allclose(reg.xtx,xtx,RTOL)
class TestBaseGMEndogErrorHet(unittest.TestCase):
def setUp(self):
@@ -142,37 +143,37 @@ class TestBaseGMEndogErrorHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 26.51812895])
- np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
ef = np.array([ 31.46604707])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
+ np.testing.assert_allclose(reg.e_filtered[0],ef,RTOL)
predy = np.array([ 53.94887405])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n)
k = 3
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531])
- np.testing.assert_array_almost_equal(reg.x[0].toarray()[0],x,7)
+ np.testing.assert_allclose(reg.x[0].toarray()[0],x,RTOL)
yend = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ np.testing.assert_allclose(reg.yend[0],yend,RTOL)
q = np.array([ 5.03])
- np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ np.testing.assert_allclose(reg.q[0],q,RTOL)
z = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.z[0].toarray()[0],z,7)
+ np.testing.assert_allclose(reg.z[0].toarray()[0],z,RTOL)
h = np.array([ 1. , 19.531, 5.03 ])
- np.testing.assert_array_almost_equal(reg.h[0].toarray()[0],h,7)
+ np.testing.assert_allclose(reg.h[0].toarray()[0],h,RTOL)
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)
+ np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
stdy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,stdy)
+ np.testing.assert_allclose(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,
@@ -181,11 +182,11 @@ class TestBaseGMEndogErrorHet(unittest.TestCase):
-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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
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)
+ np.testing.assert_allclose(reg.hth,hth,RTOL)
class TestGMEndogErrorHet(unittest.TestCase):
def setUp(self):
@@ -208,35 +209,35 @@ class TestGMEndogErrorHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 26.51812895])
- np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 53.94887405])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n)
k = 3
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531])
- np.testing.assert_array_almost_equal(reg.x[0].toarray()[0],x,7)
+ np.testing.assert_allclose(reg.x[0].toarray()[0],x,RTOL)
yend = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ np.testing.assert_allclose(reg.yend[0],yend,RTOL)
q = np.array([ 5.03])
- np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ np.testing.assert_allclose(reg.q[0],q,RTOL)
z = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.z[0].toarray()[0],z,7)
+ np.testing.assert_allclose(reg.z[0].toarray()[0],z,RTOL)
h = np.array([ 1. , 19.531, 5.03 ])
- np.testing.assert_array_almost_equal(reg.h[0].toarray()[0],h,7)
+ np.testing.assert_allclose(reg.h[0].toarray()[0],h,RTOL)
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)
+ np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
stdy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,stdy)
+ np.testing.assert_allclose(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,
@@ -245,18 +246,18 @@ class TestGMEndogErrorHet(unittest.TestCase):
-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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
pr2 = 0.34648011338954804
- self.assertAlmostEqual(reg.pr2,pr2,7)
+ np.testing.assert_allclose(reg.pr2,pr2,RTOL)
std_err = np.array([ 28.89009873, 0.77309965, 0.46798299,
0.17767558])
- np.testing.assert_array_almost_equal(reg.std_err,std_err,6)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
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)
+ np.testing.assert_allclose(reg.hth,hth,RTOL)
class TestBaseGMComboHet(unittest.TestCase):
def setUp(self):
@@ -277,35 +278,35 @@ class TestBaseGMComboHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 25.65156033])
- np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
ef = np.array([ 31.87664403])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
+ np.testing.assert_allclose(reg.e_filtered[0],ef,RTOL)
predy = np.array([ 54.81544267])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n)
k = 4
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x[0].toarray()[0],x,7)
+ np.testing.assert_allclose(reg.x[0].toarray()[0],x,RTOL)
yend = np.array([ 35.4585005])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ np.testing.assert_allclose(reg.yend[0],yend,RTOL)
q = np.array([ 18.594 , 24.7142675])
- np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ np.testing.assert_allclose(reg.q[0],q,RTOL)
z = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
- np.testing.assert_array_almost_equal(reg.z[0].toarray()[0],z,7)
+ np.testing.assert_allclose(reg.z[0].toarray()[0],z,RTOL)
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)
+ np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
stdy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,stdy,7)
+ np.testing.assert_allclose(reg.std_y,stdy,RTOL)
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,
@@ -316,7 +317,7 @@ class TestBaseGMComboHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
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,
@@ -327,7 +328,7 @@ class TestBaseGMComboHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.hth,hth,RTOL)
class TestGMComboHet(unittest.TestCase):
def setUp(self):
@@ -346,39 +347,39 @@ class TestGMComboHet(unittest.TestCase):
# 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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 25.65156033])
- np.testing.assert_array_almost_equal(reg.u[0],u,7)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
ef = np.array([ 31.87664403])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
+ np.testing.assert_allclose(reg.e_filtered[0],ef,RTOL)
ep = np.array([ 28.30648145])
- np.testing.assert_array_almost_equal(reg.e_pred[0],ep,7)
+ np.testing.assert_allclose(reg.e_pred[0],ep,RTOL)
pe = np.array([ 52.16052155])
- np.testing.assert_array_almost_equal(reg.predy_e[0],pe,7)
+ np.testing.assert_allclose(reg.predy_e[0],pe,RTOL)
predy = np.array([ 54.81544267])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n)
k = 4
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,7)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x[0].toarray()[0],x,7)
+ np.testing.assert_allclose(reg.x[0].toarray()[0],x,RTOL)
yend = np.array([ 35.4585005])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ np.testing.assert_allclose(reg.yend[0],yend,RTOL)
q = np.array([ 18.594 , 24.7142675])
- np.testing.assert_array_almost_equal(reg.q[0].toarray()[0],q,7)
+ np.testing.assert_allclose(reg.q[0].toarray()[0],q,RTOL)
z = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
- np.testing.assert_array_almost_equal(reg.z[0].toarray()[0],z,7)
+ np.testing.assert_allclose(reg.z[0].toarray()[0],z,RTOL)
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)
+ np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
stdy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,stdy)
+ np.testing.assert_allclose(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,
@@ -389,15 +390,15 @@ class TestGMComboHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
pr2 = 0.3001582877472412
- self.assertAlmostEqual(reg.pr2,pr2,7)
+ np.testing.assert_allclose(reg.pr2,pr2,RTOL)
pr2_e = 0.35613102283621967
- self.assertAlmostEqual(reg.pr2_e,pr2_e,7)
+ np.testing.assert_allclose(reg.pr2_e,pr2_e,RTOL)
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)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
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,
@@ -408,7 +409,7 @@ class TestGMComboHet(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.hth,hth,RTOL)
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
index afc730d..777f800 100644
--- a/pysal/spreg/tests/test_error_sp_hom.py
+++ b/pysal/spreg/tests/test_error_sp_hom.py
@@ -6,6 +6,7 @@ import unittest
import pysal
from pysal.spreg import error_sp_hom as HOM
import numpy as np
+from pysal.common import RTOL
class BaseGM_Error_Hom_Tester(unittest.TestCase):
def setUp(self):
@@ -21,24 +22,24 @@ class BaseGM_Error_Hom_Tester(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.y[0],np.array([80.467003]),RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
+ np.testing.assert_allclose(reg.u[0],np.array([27.466734]),RTOL)
+ np.testing.assert_allclose(reg.e_filtered[0],np.array([ 32.37298547]),RTOL)
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)
+ np.testing.assert_string_equal(reg.iter_stop,i_s)
+ np.testing.assert_allclose(reg.predy[0],np.array([ 53.000269]),RTOL)
+ np.testing.assert_allclose(reg.n,49,RTOL)
+ np.testing.assert_allclose(reg.k,3,RTOL)
sig2 = 189.94459439729718
- self.assertAlmostEqual(reg.sig2,sig2)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
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)
+ np.testing.assert_allclose(reg.xtx,xtx,RTOL)
class GM_Error_Hom_Tester(unittest.TestCase):
def setUp(self):
@@ -53,35 +54,35 @@ class GM_Error_Hom_Tester(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.y[0],np.array([80.467003]),RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
+ np.testing.assert_allclose(reg.u[0],np.array([27.46673388]),RTOL)
+ np.testing.assert_allclose(reg.e_filtered[0],np.array([ 32.37298547]),RTOL)
+ np.testing.assert_allclose(reg.predy[0],np.array([ 53.00026912]),RTOL)
+ np.testing.assert_allclose(reg.n,49,RTOL)
+ np.testing.assert_allclose(reg.k,3,RTOL)
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
i_s = 'Maximum number of iterations reached.'
- self.assertAlmostEqual(reg.iter_stop,i_s,7)
- self.assertAlmostEqual(reg.iteration,1,7)
+ np.testing.assert_string_equal(reg.iter_stop,i_s)
+ np.testing.assert_allclose(reg.iteration,1,RTOL)
my = 38.436224469387746
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
std_y = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,std_y)
+ np.testing.assert_allclose(reg.std_y,std_y)
pr2 = 0.34950977055969729
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(reg.pr2,pr2)
sig2 = 189.94459439729718
- self.assertAlmostEqual(reg.sig2,sig2)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
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)
+ np.testing.assert_allclose(reg.xtx,xtx,RTOL)
class BaseGM_Endog_Error_Hom_Tester(unittest.TestCase):
@@ -103,42 +104,42 @@ class BaseGM_Endog_Error_Hom_Tester(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.y[0],np.array([ 80.467003]),RTOL)
x = np.array([ 1. , 19.531])
- np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
z = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.z[0],z,7)
+ np.testing.assert_allclose(reg.z[0],z,RTOL)
h = np.array([ 1. , 19.531, 5.03 ])
- np.testing.assert_array_almost_equal(reg.h[0],h,7)
+ np.testing.assert_allclose(reg.h[0],h,RTOL)
yend = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ np.testing.assert_allclose(reg.yend[0],yend,RTOL)
q = np.array([ 5.03])
- np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ np.testing.assert_allclose(reg.q[0],q,RTOL)
betas = np.array([[ 55.36575166], [ 0.46432416], [ -0.66904404], [ 0.43205526]])
- np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
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)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
+ np.testing.assert_allclose(reg.e_filtered[0],np.array([ 31.74114306]),RTOL)
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)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
+ np.testing.assert_allclose(reg.n,49,RTOL)
+ np.testing.assert_allclose(reg.k,3,RTOL)
sig2 = 190.59435238060928
- self.assertAlmostEqual(reg.sig2,sig2)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
i_s = 'Maximum number of iterations reached.'
- self.assertAlmostEqual(reg.iter_stop,i_s,7)
+ np.testing.assert_string_equal(reg.iter_stop,i_s)
its = 1
- self.assertAlmostEqual(reg.iteration,its,7)
+ np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
std_y = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,std_y)
+ np.testing.assert_allclose(reg.std_y,std_y)
sig2 = 0
- #self.assertAlmostEqual(reg.sig2,sig2)
+ #np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.hth,hth,RTOL)
class GM_Endog_Error_Hom_Tester(unittest.TestCase):
def setUp(self):
@@ -158,45 +159,45 @@ class GM_Endog_Error_Hom_Tester(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.y[0],np.array([ 80.467003]),RTOL)
x = np.array([ 1. , 19.531])
- np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
z = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.z[0],z,7)
+ np.testing.assert_allclose(reg.z[0],z,RTOL)
h = np.array([ 1. , 19.531, 5.03 ])
- np.testing.assert_array_almost_equal(reg.h[0],h,7)
+ np.testing.assert_allclose(reg.h[0],h,RTOL)
yend = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ np.testing.assert_allclose(reg.yend[0],yend,RTOL)
q = np.array([ 5.03])
- np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ np.testing.assert_allclose(reg.q[0],q,RTOL)
betas = np.array([[ 55.36575166], [ 0.46432416], [ -0.66904404], [ 0.43205526]])
- np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
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)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
+ np.testing.assert_allclose(reg.e_filtered[0],np.array([ 31.74114306]),RTOL)
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)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
+ np.testing.assert_allclose(reg.n,49,RTOL)
+ np.testing.assert_allclose(reg.k,3,RTOL)
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
i_s = 'Maximum number of iterations reached.'
- self.assertAlmostEqual(reg.iter_stop,i_s,7)
+ np.testing.assert_string_equal(reg.iter_stop,i_s)
its = 1
- self.assertAlmostEqual(reg.iteration,its,7)
+ np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
std_y = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,std_y)
+ np.testing.assert_allclose(reg.std_y,std_y)
pr2 = 0.34647366525657419
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(reg.pr2,pr2)
sig2 = 190.59435238060928
- self.assertAlmostEqual(reg.sig2,sig2)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
class BaseGM_Combo_Hom_Tester(unittest.TestCase):
@@ -213,39 +214,39 @@ class BaseGM_Combo_Hom_Tester(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.y[0],np.array([80.467003]),RTOL)
x = np.array([ 1. , 19.531])
- np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
+ np.testing.assert_allclose(reg.u[0],np.array([34.3450723]),RTOL)
+ np.testing.assert_allclose(reg.e_filtered[0],np.array([ 36.6149682]),RTOL)
+ np.testing.assert_allclose(reg.predy[0],np.array([ 46.1219307]),RTOL)
+ np.testing.assert_allclose(reg.n,49,RTOL)
+ np.testing.assert_allclose(reg.k,3,RTOL)
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
z = np.array([ 1. , 19.531 , 35.4585005])
- np.testing.assert_array_almost_equal(reg.z[0],z,7)
+ np.testing.assert_allclose(reg.z[0],z,RTOL)
h = np.array([ 1. , 19.531, 18.594])
- np.testing.assert_array_almost_equal(reg.h[0],h,7)
+ np.testing.assert_allclose(reg.h[0],h,RTOL)
yend = np.array([ 35.4585005])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ np.testing.assert_allclose(reg.yend[0],yend,RTOL)
q = np.array([ 18.594])
- np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ np.testing.assert_allclose(reg.q[0],q,RTOL)
i_s = 'Maximum number of iterations reached.'
- self.assertAlmostEqual(reg.iter_stop,i_s,7)
+ np.testing.assert_string_equal(reg.iter_stop,i_s)
its = 1
- self.assertAlmostEqual(reg.iteration,its,7)
+ np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
std_y = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,std_y)
+ np.testing.assert_allclose(reg.std_y,std_y)
sig2 = 232.22680651270042
- #self.assertAlmostEqual(reg.sig2,sig2)
+ #np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.hth,hth,RTOL)
class GM_Combo_Hom_Tester(unittest.TestCase):
@@ -260,46 +261,46 @@ class GM_Combo_Hom_Tester(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.y[0],np.array([80.467003]),RTOL)
x = np.array([ 1. , 19.531])
- np.testing.assert_array_almost_equal(reg.x[0],x,7)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
+ np.testing.assert_allclose(reg.u[0],np.array([34.3450723]),RTOL)
+ np.testing.assert_allclose(reg.e_filtered[0],np.array([ 36.6149682]),RTOL)
+ np.testing.assert_allclose(reg.e_pred[0],np.array([ 32.90372983]),RTOL)
+ np.testing.assert_allclose(reg.predy[0],np.array([ 46.1219307]),RTOL)
+ np.testing.assert_allclose(reg.predy_e[0],np.array([47.56327317]),RTOL)
+ np.testing.assert_allclose(reg.n,49,RTOL)
+ np.testing.assert_allclose(reg.k,3,RTOL)
z = np.array([ 1. , 19.531 , 35.4585005])
- np.testing.assert_array_almost_equal(reg.z[0],z,7)
+ np.testing.assert_allclose(reg.z[0],z,RTOL)
h = np.array([ 1. , 19.531, 18.594])
- np.testing.assert_array_almost_equal(reg.h[0],h,7)
+ np.testing.assert_allclose(reg.h[0],h,RTOL)
yend = np.array([ 35.4585005])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
+ np.testing.assert_allclose(reg.yend[0],yend,RTOL)
q = np.array([ 18.594])
- np.testing.assert_array_almost_equal(reg.q[0],q,7)
+ np.testing.assert_allclose(reg.q[0],q,RTOL)
i_s = 'Maximum number of iterations reached.'
- self.assertAlmostEqual(reg.iter_stop,i_s,7)
- self.assertAlmostEqual(reg.iteration,1,7)
+ np.testing.assert_string_equal(reg.iter_stop,i_s)
+ np.testing.assert_allclose(reg.iteration,1,RTOL)
my = 38.436224469387746
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
std_y = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,std_y)
+ np.testing.assert_allclose(reg.std_y,std_y)
pr2 = 0.28379825632694394
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(reg.pr2,pr2)
pr2_e = 0.25082892555141506
- self.assertAlmostEqual(reg.pr2_e,pr2_e)
+ np.testing.assert_allclose(reg.pr2_e,pr2_e)
sig2 = 232.22680651270042
- #self.assertAlmostEqual(reg.sig2, sig2)
+ #np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
suite = unittest.TestSuite()
test_classes = [BaseGM_Error_Hom_Tester, GM_Error_Hom_Tester,\
diff --git a/pysal/spreg/tests/test_error_sp_hom_regimes.py b/pysal/spreg/tests/test_error_sp_hom_regimes.py
index 8c8e650..f9de3cc 100644
--- a/pysal/spreg/tests/test_error_sp_hom_regimes.py
+++ b/pysal/spreg/tests/test_error_sp_hom_regimes.py
@@ -3,6 +3,7 @@ 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
+from pysal.common import RTOL
class TestGM_Error_Hom_Regimes(unittest.TestCase):
def setUp(self):
@@ -37,8 +38,8 @@ class TestGM_Error_Hom_Regimes(unittest.TestCase):
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.regi_a = [0]*(n//2) + [1]*(n//2) ##must be floos!
+ self.w_a1 = pysal.lat2W(latt//2,latt)
self.w_a1.transform='r'
def test_model(self):
@@ -50,41 +51,41 @@ class TestGM_Error_Hom_Regimes(unittest.TestCase):
[ -0.3358993 ],
[ -0.82129289],
[ 0.54033921]])
- np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([-2.19031456])
- np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 17.91629456])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n,6)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 6
- self.assertAlmostEqual(reg.k,k,6)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.y[0],y,6)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([[ 0. , 0. , 0. , 1. , 80.467003, 19.531 ]])
- np.testing.assert_array_almost_equal(reg.x[0].toarray(),x,6)
+ np.testing.assert_allclose(reg.x[0].toarray(),x,RTOL)
e = np.array([ 2.72131648])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,6)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
my = 35.128823897959187
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my,RTOL)
sy = 16.732092091229699
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(reg.std_y,sy,RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0],vm,RTOL)
sig2 = 96.96108341267626
- self.assertAlmostEqual(reg.sig2,sig2,5)
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
pr2 = 0.5515791216023577
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(reg.pr2,pr2,RTOL)
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)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 0.92133276766189676
- self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ np.testing.assert_allclose(reg.chow.joint[0],chow_j,RTOL)
def test_model_regi_error(self):
#Artficial:
@@ -92,9 +93,9 @@ class TestGM_Error_Hom_Regimes(unittest.TestCase):
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)
+ np.testing.assert_allclose(model.betas,tbetas,RTOL)
vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
- np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 6)
+ np.testing.assert_allclose(model.vm.diagonal(), vm, RTOL)
#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],
@@ -103,22 +104,22 @@ class TestGM_Error_Hom_Regimes(unittest.TestCase):
[ 61.4526885 ],
[ -1.90700858],
[ 0.1102755 ]])
- np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
vm = np.array([ 45.57956967, -1.65365774, 0. , 0. ,
0. , 0. ])
- np.testing.assert_array_almost_equal(reg.vm[0],vm,6)
+ np.testing.assert_allclose(reg.vm[0],vm,RTOL)
u = np.array([-8.48092392])
- np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 24.20690392])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
e = np.array([-8.33982604])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,6)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 1.7914221673031792
- self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ np.testing.assert_allclose(reg.chow.joint[0],chow_j,RTOL)
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')
@@ -129,46 +130,46 @@ class TestGM_Error_Hom_Regimes(unittest.TestCase):
[ -3.20196286],
[ -1.13672283],
[ 0.22178164]])
- np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 20.50716917])
- np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
e = np.array([ 25.22635318])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,6)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
predy = np.array([-4.78118917])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 6
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.y[0],y,6)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([[ 0. , 0. , 1. , 19.531]])
- np.testing.assert_array_almost_equal(reg.x[0].toarray(),x,6)
+ np.testing.assert_allclose(reg.x[0].toarray(),x,RTOL)
yend = np.array([[ 0. , 80.467003]])
- np.testing.assert_array_almost_equal(reg.yend[0].toarray(),yend,6)
+ np.testing.assert_allclose(reg.yend[0].toarray(),yend,RTOL)
z = np.array([[ 0. , 0. , 1. , 19.531 , 0. ,
80.467003]])
- np.testing.assert_array_almost_equal(reg.z[0].toarray(),z,6)
+ np.testing.assert_allclose(reg.z[0].toarray(),z,RTOL)
my = 35.128823897959187
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my,RTOL)
sy = 16.732092091229699
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(reg.std_y,sy,RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0],vm,RTOL)
pr2 = 0.19776512679498906
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(reg.pr2,pr2,RTOL)
sig2 = 644.23810259214
- self.assertAlmostEqual(reg.sig2,sig2,5)
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
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)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 1.248410480025556
- self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ np.testing.assert_allclose(reg.chow.joint[0],chow_j,RTOL)
def test_model_endog_regi_error(self):
#Columbus:
@@ -181,31 +182,31 @@ class TestGM_Error_Hom_Regimes(unittest.TestCase):
[ 5.48294187e-01],
[ -1.28432891e+00],
[ 2.98658172e-02]])
- np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0],vm,RTOL)
u = np.array([ 25.73781918])
- np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([-10.01183918])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
e = np.array([26.41176711])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,6)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 1.4485058522307526
- self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ np.testing.assert_allclose(reg.chow.joint[0],chow_j,RTOL)
#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)
+ np.testing.assert_allclose(model.betas,tbetas)
vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
- np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 6)
+ np.testing.assert_allclose(model.vm.diagonal(), vm, RTOL)
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')
@@ -217,50 +218,50 @@ class TestGM_Error_Hom_Regimes(unittest.TestCase):
[ -0.22132895],
[ 0.64190215],
[ -0.07314671]])
- np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 0.94039246])
- np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
e_filtered = np.array([ 0.74211331])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e_filtered,5)
+ np.testing.assert_allclose(reg.e_filtered[0],e_filtered,RTOL)
predy_e = np.array([ 18.68732105])
- np.testing.assert_array_almost_equal(reg.predy_e[0],predy_e,6)
+ np.testing.assert_allclose(reg.predy_e[0],predy_e,RTOL)
predy = np.array([ 14.78558754])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 7
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.y[0],y,6)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([[ 0. , 0. , 1. , 19.531]])
- np.testing.assert_array_almost_equal(reg.x[0].toarray(),x,6)
+ np.testing.assert_allclose(reg.x[0].toarray(),x,RTOL)
yend = np.array([[ 0. , 80.467003 , 24.7142675]])
- np.testing.assert_array_almost_equal(reg.yend[0].toarray(),yend,6)
+ np.testing.assert_allclose(reg.yend[0].toarray(),yend,RTOL)
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)
+ np.testing.assert_allclose(reg.z[0].toarray(),z,RTOL)
my = 35.128823897959187
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my,RTOL)
sy = 16.732092091229699
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(reg.std_y,sy,RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0],vm,RTOL)
sig2 = 95.57694234438294
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
pr2 = 0.6504148883591536
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(reg.pr2,pr2,RTOL)
pr2_e = 0.5271368969923579
- self.assertAlmostEqual(reg.pr2_e,pr2_e)
+ np.testing.assert_allclose(reg.pr2_e,pr2_e,RTOL)
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)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 0.59098559257602923
- self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ np.testing.assert_allclose(reg.chow.joint[0],chow_j,RTOL)
def test_model_combo_regi_error(self):
#Columbus:
@@ -275,31 +276,31 @@ class TestGM_Error_Hom_Regimes(unittest.TestCase):
[ -4.91752171e-01],
[ 6.57331733e-01],
[ -2.68716241e-02]])
- np.testing.assert_array_almost_equal(reg.betas,betas,6)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0],vm,RTOL)
u = np.array([ 7.81039418])
- np.testing.assert_array_almost_equal(reg.u[0],u,6)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 7.91558582])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
e = np.array([ 7.60819283])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,6)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 0.74134991257940286
- self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ np.testing.assert_allclose(reg.chow.joint[0],chow_j,RTOL)
#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)
+ np.testing.assert_allclose(model.betas,tbetas)
vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
diff --git a/pysal/spreg/tests/test_error_sp_regimes.py b/pysal/spreg/tests/test_error_sp_regimes.py
index 0754c72..d4f89eb 100644
--- a/pysal/spreg/tests/test_error_sp_regimes.py
+++ b/pysal/spreg/tests/test_error_sp_regimes.py
@@ -4,6 +4,7 @@ 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
+from pysal.common import RTOL
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Maximum Likelihood requires SciPy version 11 or newer.")
@@ -40,8 +41,8 @@ class TestGM_Error_Regimes(unittest.TestCase):
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.regi_a = [0]*(n//2) + [1]*(n//2) ##must be floors!
+ self.w_a1 = pysal.lat2W(latt//2,latt)
self.w_a1.transform='r'
def test_model(self):
@@ -53,41 +54,41 @@ class TestGM_Error_Regimes(unittest.TestCase):
[ -0.33550084],
[ -0.85076108],
[ 0.38671608]])
- np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([-2.06177251])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 17.78775251])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n,4)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 6
- self.assertAlmostEqual(reg.k,k,4)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([[ 0. , 0. , 0. , 1. , 80.467003, 19.531 ]])
- np.testing.assert_array_almost_equal(reg.x[0].toarray(),x,4)
+ np.testing.assert_allclose(reg.x[0].toarray(),x,RTOL)
e = np.array([ 1.40747232])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
my = 35.128823897959187
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my,RTOL)
sy = 16.732092091229699
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(reg.std_y,sy,RTOL)
vm = np.array([ 50.55875289, -0.14444487, -2.05735489, 0. ,
0. , 0. ])
- np.testing.assert_array_almost_equal(reg.vm[0],vm,4)
+ np.testing.assert_allclose(reg.vm[0],vm,RTOL)
sig2 = 102.13050615267227
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
pr2 = 0.5525102200608539
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(reg.pr2,pr2,RTOL)
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)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 0.81985446000130979
- self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ np.testing.assert_allclose(reg.chow.joint[0],chow_j,RTOL)
"""
def test_model_regi_error(self):
#Columbus:
@@ -100,80 +101,82 @@ class TestGM_Error_Regimes(unittest.TestCase):
[ -0.31845995],
[ -1.29047149],
[ 0.08092997]])
- np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
vm = np.array([ 39.33656288, -0.08420799, -1.50350999, 0. ,
0. , 0. ])
- np.testing.assert_array_almost_equal(reg.vm[0],vm,4)
+ np.testing.assert_allclose(reg.vm[0],vm,RTOL)
u = np.array([ 0.00698341])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 15.71899659])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
e = np.array([ 0.53685671])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 0.70119418251625387
- self.assertAlmostEqual(reg.chow.joint[0],chow_j,4)
+ np.testing.assert_allclose(reg.chow.joint[0],chow_j,RTOL)
#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)
+ np.testing.assert_allclose(model.betas,tbetas,RTOL)
vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
- np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 4)
+ np.testing.assert_allclose(model.vm.diagonal(), vm,RTOL)
"""
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)
+ print('Runining higher-tolerance test on L133 of test_error_sp_regimes.py')
+ np.testing.assert_allclose(reg.betas.T,betas,RTOL + .0001)
u = np.array([ 20.89660904])
- #np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ #np.testing.assert_allclose(reg.u[0],u,RTOL)
np.testing.assert_allclose(reg.u[0],u,rtol=1e-05)
e = np.array([ 25.21818724])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
predy = np.array([-5.17062904])
- #np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ #np.testing.assert_allclose(reg.predy[0],predy,RTOL)
np.testing.assert_allclose(reg.predy[0],predy,rtol=1e-03)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n)
k = 6
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k)
y = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([[ 0. , 0. , 1. , 19.531]])
- np.testing.assert_array_almost_equal(reg.x[0].toarray(),x,4)
+ np.testing.assert_allclose(reg.x[0].toarray(),x,RTOL)
yend = np.array([[ 0. , 80.467003]])
- np.testing.assert_array_almost_equal(reg.yend[0].toarray(),yend,4)
+ np.testing.assert_allclose(reg.yend[0].toarray(),yend,RTOL)
z = np.array([[ 0. , 0. , 1. , 19.531 , 0. ,
80.467003]])
- np.testing.assert_array_almost_equal(reg.z[0].toarray(),z,4)
+ np.testing.assert_allclose(reg.z[0].toarray(),z,RTOL)
my = 35.128823897959187
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
sy = 16.732092091229699
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(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)
+ #np.testing.assert_allclose(reg.vm[0],vm,RTOL)
np.allclose(reg.vm, vm)
pr2 = 0.19623994206233333
- self.assertAlmostEqual(reg.pr2,pr2,4)
+ np.testing.assert_allclose(reg.pr2,pr2,RTOL)
sig2 = 649.4011
- #self.assertAlmostEqual(round(reg.sig2,4),round(sig2,4),4)
+ #np.testing.assert_allclose(round(reg.sig2,RTOL),round(sig2,RTOL),RTOL)
np.testing.assert_allclose(sig2, reg.sig2, rtol=1e-05)
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)
+ #np.testing.assert_allclose(reg.std_err,std_err,RTOL)
np.testing.assert_allclose(reg.std_err, std_err, rtol=1e-05)
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)
+ print('Running higher-tolerance tests on L176 of test_error_sp_regimes.py')
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL+.0001)
chow_j = 1.2885590185243503
- #self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ #np.testing.assert_allclose(reg.chow.joint[0],chow_j)
np.testing.assert_allclose(reg.chow.joint[0], chow_j, rtol=1e-05)
def test_model_endog_regi_error(self):
@@ -187,36 +190,36 @@ class TestGM_Error_Regimes(unittest.TestCase):
[ 5.68908920e-01],
[ -1.28824699e+00],
[ 6.70387351e-02]])
- np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
vm = np.array([ 791.86679123, 140.12967794, -81.37581255, 0. ,
0. , 0. ])
- np.testing.assert_array_almost_equal(reg.vm[0],vm,4)
+ np.testing.assert_allclose(reg.vm[0],vm,RTOL)
u = np.array([ 25.80361497])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([-10.07763497])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
e = np.array([ 27.32251813])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 1.1184631131987004
- #self.assertAlmostEqual(reg.chow.joint[0],chow_j)
- np.testing.assert_allclose(reg.chow.joint[0], chow_j)
+ #np.testing.assert_allclose(reg.chow.joint[0],chow_j)
+ np.testing.assert_allclose(reg.chow.joint[0], chow_j,RTOL)
#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)
+ np.testing.assert_allclose(model.betas,tbetas,RTOL)
vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
- np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 4)
+ np.testing.assert_allclose(model.vm.diagonal(), vm,RTOL)
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)
+ np.testing.assert_allclose(reg.predy_e[0],predy_e,RTOL)
betas = np.array([[ 36.44798052],
[ -0.7974482 ],
[ 30.53782661],
@@ -225,48 +228,48 @@ class TestGM_Error_Regimes(unittest.TestCase):
[ -0.21736652],
[ 0.64801059],
[ -0.16601265]])
- np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 0.84393304])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
e_filtered = np.array([ 0.4040027])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e_filtered,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e_filtered,RTOL)
predy = np.array([ 14.88204696])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 7
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([[ 0. , 0. , 1. , 19.531]])
- np.testing.assert_array_almost_equal(reg.x[0].toarray(),x,4)
+ np.testing.assert_allclose(reg.x[0].toarray(),x,RTOL)
yend = np.array([[ 0. , 80.467003 , 24.7142675]])
- np.testing.assert_array_almost_equal(reg.yend[0].toarray(),yend,4)
+ np.testing.assert_allclose(reg.yend[0].toarray(),yend,RTOL)
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)
+ np.testing.assert_allclose(reg.z[0].toarray(),z,RTOL)
my = 35.128823897959187
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my,RTOL)
sy = 16.732092091229699
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(reg.std_y,sy,RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0],vm,RTOL)
sig2 = 94.98610921110007
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
pr2 = 0.6493586702255537
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(reg.pr2,pr2,RTOL)
pr2_e = 0.5255332447240576
- self.assertAlmostEqual(reg.pr2_e,pr2_e)
+ np.testing.assert_allclose(reg.pr2_e,pr2_e,RTOL)
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)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 0.64531386285872072
- self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ np.testing.assert_allclose(reg.chow.joint[0],chow_j,RTOL)
def test_model_combo_regi_error(self):
#Columbus:
@@ -281,29 +284,29 @@ class TestGM_Error_Regimes(unittest.TestCase):
[ -0.48972903],
[ 0.65883658],
[ -0.17174845]])
- np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0],vm,RTOL)
u = np.array([ 7.73968703])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 7.98629297])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
e = np.array([ 6.45052714])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 0.28479988992843119
- self.assertAlmostEqual(reg.chow.joint[0],chow_j)
+ np.testing.assert_allclose(reg.chow.joint[0],chow_j, RTOL)
#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)
+ np.testing.assert_allclose(model.betas,tbetas)
vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
if __name__ == '__main__':
diff --git a/pysal/spreg/tests/test_error_sp_sparse.py b/pysal/spreg/tests/test_error_sp_sparse.py
index d60c8eb..898ef24 100644
--- a/pysal/spreg/tests/test_error_sp_sparse.py
+++ b/pysal/spreg/tests/test_error_sp_sparse.py
@@ -1,10 +1,12 @@
import unittest
import pysal
import numpy as np
-from pysal.spreg import error_sp as SP
import scipy
from scipy import sparse
+from pysal.spreg import error_sp as SP
+from pysal.common import RTOL
+
class TestBaseGMError(unittest.TestCase):
def setUp(self):
db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
@@ -22,31 +24,31 @@ class TestBaseGMError(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 27.4739775])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 52.9930255])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n,4)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 3
- self.assertAlmostEqual(reg.k,k,4)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x.toarray()[0],x,4)
+ np.testing.assert_allclose(reg.x.toarray()[0],x,RTOL)
e = np.array([ 31.89620319])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
predy = np.array([ 52.9930255])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my,RTOL)
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(reg.std_y,sy,RTOL)
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
sig2 = 191.73716465732355
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
class TestGMError(unittest.TestCase):
def setUp(self):
@@ -64,37 +66,37 @@ class TestGMError(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 27.4739775])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 52.9930255])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n,4)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 3
- self.assertAlmostEqual(reg.k,k,4)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x.toarray()[0],x,4)
+ np.testing.assert_allclose(reg.x.toarray()[0],x,RTOL)
e = np.array([ 31.89620319])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
predy = np.array([ 52.9930255])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my,RTOL)
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(reg.std_y,sy,RTOL)
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
sig2 = 191.73716465732355
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
pr2 = 0.3495097406012179
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(reg.pr2,pr2,RTOL)
std_err = np.array([ 12.32416094, 0.4989716 , 0.1785863 ])
- np.testing.assert_array_almost_equal(reg.std_err,std_err,4)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Maximum Likelihood requires SciPy version 11 or newer.")
@@ -120,37 +122,38 @@ class TestBaseGMEndogError(unittest.TestCase):
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)
+ print('Running reduced-precision test in L125 of test_error_sp_sparse.py')
+ np.testing.assert_allclose(reg.betas,betas,RTOL + .0001)
u = np.array([ 26.55951566])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
e = np.array([ 31.23925425])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
predy = np.array([ 53.9074875])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 3
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531])
- np.testing.assert_array_almost_equal(reg.x.toarray()[0],x,4)
+ np.testing.assert_allclose(reg.x.toarray()[0],x,RTOL)
yend = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ np.testing.assert_allclose(reg.yend[0],yend,RTOL)
z = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.z.toarray()[0],z,4)
+ np.testing.assert_allclose(reg.z.toarray()[0],z,RTOL)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my,RTOL)
#std_y
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(reg.std_y,sy,RTOL)
#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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
sig2 = 192.5002
- self.assertAlmostEqual(round(reg.sig2,4),round(sig2,4),4)
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Maximum Likelihood requires SciPy version 11 or newer.")
@@ -175,41 +178,42 @@ class TestGMEndogError(unittest.TestCase):
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)
+ print('Running reduced-tolernace test in L181 of test_error_sp_sparse.py')
+ np.testing.assert_allclose(reg.betas,betas,RTOL + .0001)
u = np.array([ 26.55951566])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
e = np.array([ 31.23925425])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
predy = np.array([ 53.9074875])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 3
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531])
- np.testing.assert_array_almost_equal(reg.x.toarray()[0],x,4)
+ np.testing.assert_allclose(reg.x.toarray()[0],x,RTOL)
yend = np.array([ 15.72598])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ np.testing.assert_allclose(reg.yend[0],yend,RTOL)
z = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.z.toarray()[0],z,4)
+ np.testing.assert_allclose(reg.z.toarray()[0],z,RTOL)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my,RTOL)
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(reg.std_y,sy,RTOL)
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
pr2 = 0.346472557570858
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(reg.pr2,pr2,RTOL)
sig2 = 192.5002
- self.assertAlmostEqual(round(reg.sig2,4),round(sig2,4),4)
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
std_err = np.array([ 23.003401 , 0.73500657, 0.38075777])
- np.testing.assert_array_almost_equal(reg.std_err,std_err,4)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Maximum Likelihood requires SciPy version 11 or newer.")
@@ -232,36 +236,36 @@ class TestBaseGMCombo(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 25.57932637])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
e_filtered = np.array([ 31.65374945])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e_filtered,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e_filtered,RTOL)
predy = np.array([ 54.88767663])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 4
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x.toarray()[0],x,4)
+ np.testing.assert_allclose(reg.x.toarray()[0],x,RTOL)
yend = np.array([ 35.4585005])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ np.testing.assert_allclose(reg.yend[0],yend,RTOL)
z = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
- np.testing.assert_array_almost_equal(reg.z.toarray()[0],z,4)
+ np.testing.assert_allclose(reg.z.toarray()[0],z,RTOL)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my,RTOL)
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(reg.std_y,sy,RTOL)
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
sig2 = 181.78650186468832
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Maximum Likelihood requires SciPy version 11 or newer.")
@@ -281,49 +285,49 @@ class TestGMCombo(unittest.TestCase):
# 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)
+ np.testing.assert_allclose(reg.e_pred[0],e_reduced,RTOL)
predy_e = np.array([ 52.28082782])
- np.testing.assert_array_almost_equal(reg.predy_e[0],predy_e,4)
+ np.testing.assert_allclose(reg.predy_e[0],predy_e,RTOL)
betas = np.array([[ 57.61123515],[ 0.73441313], [ -0.59459416], [ -0.21762921], [ 0.54732051]])
- np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 25.57932637])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
e_filtered = np.array([ 31.65374945])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e_filtered,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e_filtered,RTOL)
predy = np.array([ 54.88767685])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 4
- self.assertAlmostEqual(reg.k,k)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 80.467003])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x.toarray()[0],x,4)
+ np.testing.assert_allclose(reg.x.toarray()[0],x,RTOL)
yend = np.array([ 35.4585005])
- np.testing.assert_array_almost_equal(reg.yend[0],yend,4)
+ np.testing.assert_allclose(reg.yend[0],yend,RTOL)
z = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
- np.testing.assert_array_almost_equal(reg.z.toarray()[0],z,4)
+ np.testing.assert_allclose(reg.z.toarray()[0],z,RTOL)
my = 38.43622446938776
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my,RTOL)
sy = 18.466069465206047
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(reg.std_y,sy,RTOL)
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)
- np.testing.assert_allclose(reg.vm, vm, rtol=1e-05)
+ #np.testing.assert_allclose(reg.vm,vm,RTOL)
+ np.testing.assert_allclose(reg.vm, vm, RTOL)
sig2 = 181.78650186468832
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
pr2 = 0.3018280166937799
- self.assertAlmostEqual(reg.pr2,pr2,4)
+ np.testing.assert_allclose(reg.pr2,pr2,RTOL)
pr2_e = 0.3561355587000738
- self.assertAlmostEqual(reg.pr2_e,pr2_e,4)
+ np.testing.assert_allclose(reg.pr2_e,pr2_e,RTOL)
std_err = np.array([ 22.85692222, 0.48786559, 0.17914356, 0.46449318])
- np.testing.assert_array_almost_equal(reg.std_err,std_err,4)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
if __name__ == '__main__':
start_suppress = np.get_printoptions()['suppress']
diff --git a/pysal/spreg/tests/test_ml_error.py b/pysal/spreg/tests/test_ml_error.py
index 385ee8e..bebdde1 100644
--- a/pysal/spreg/tests/test_ml_error.py
+++ b/pysal/spreg/tests/test_ml_error.py
@@ -4,6 +4,7 @@ import scipy
import numpy as np
from pysal.spreg.ml_error import ML_Error
from pysal.spreg import utils
+from pysal.common import RTOL
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Max Likelihood requires SciPy version 11 or newer.")
@@ -24,48 +25,49 @@ class TestMLError(unittest.TestCase):
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)
+ print('Running higher-tolerance tests on line L28 of test_ml_error.py')
+ np.testing.assert_allclose(reg.betas,betas,RTOL + .0001)
u = np.array([-5.97649777])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 6.92258051])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 1412
- self.assertAlmostEqual(reg.n,n,4)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 5
- self.assertAlmostEqual(reg.k,k,4)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 0.94608274])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , -0.39902838, 0.89645344, 6.85780705, 7.2636377 ])
- np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
e = np.array([-4.92843327])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
my = 9.5492931620846928
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
sy = 7.0388508798387219
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.vm.diagonal(),vm,RTOL)
+ sig2 = np.array([[ 32.40685441]])
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
pr2 = 0.3057664820364818
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
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)
+ np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
logll = -4471.407066887894
- self.assertAlmostEqual(reg.logll,logll,4)
+ np.testing.assert_allclose(reg.logll,logll,RTOL)
aic = 8952.8141337757879
- self.assertAlmostEqual(reg.aic,aic,4)
+ np.testing.assert_allclose(reg.aic,aic,RTOL)
schwarz = 8979.0779458660545
- self.assertAlmostEqual(reg.schwarz,schwarz,4)
+ np.testing.assert_allclose(reg.schwarz,schwarz,RTOL)
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
index dd205e1..1694512 100644
--- a/pysal/spreg/tests/test_ml_error_regimes.py
+++ b/pysal/spreg/tests/test_ml_error_regimes.py
@@ -5,6 +5,7 @@ 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
+from pysal.common import RTOL
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Max Likelihood requires SciPy version 11 or newer.")
@@ -34,8 +35,8 @@ class TestMLError(unittest.TestCase):
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.regi_a = [0]*(n//2) + [1]*(n//2) #must be floor!
+ self.w_a1 = pysal.lat2W(latt//2,latt)
self.w_a1.transform='r'
def test_model1(self):
@@ -50,48 +51,48 @@ class TestMLError(unittest.TestCase):
[ -0.23710892],
[ 0.80581127],
[ 0.61770744]])
- np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 30.46599009])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 16.53400991])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 211
- self.assertAlmostEqual(reg.n,n,4)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 8
- self.assertAlmostEqual(reg.k,k,4)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 47.])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 4. , 148. , 11.25, 0. , 0. , 0. , 0. ])
- np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
e = np.array([ 34.69181334])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
my = 44.307180094786695
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my,RTOL)
sy = 23.606076835380495
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(reg.std_y,sy,RTOL)
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)
+ np.testing.assert_allclose(reg.vm.diagonal(),vm,RTOL)
sig2 = np.array([[ 209.60639741]])
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
pr2 = 0.43600837301477025
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(reg.pr2,pr2, RTOL)
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)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
logll = -870.3331059537576
- self.assertAlmostEqual(reg.logll,logll,4)
+ np.testing.assert_allclose(reg.logll,logll,RTOL)
aic = 1756.6662119075154
- self.assertAlmostEqual(reg.aic,aic,4)
+ np.testing.assert_allclose(reg.aic,aic,RTOL)
schwarz = 1783.481076975324
- self.assertAlmostEqual(reg.schwarz,schwarz,4)
+ np.testing.assert_allclose(reg.schwarz,schwarz,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 25.367913028011799
- self.assertAlmostEqual(reg.chow.joint[0],chow_j,4)
+ np.testing.assert_allclose(reg.chow.joint[0],chow_j,RTOL)
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,\
@@ -106,33 +107,33 @@ class TestMLError(unittest.TestCase):
[ -0.18803509],
[ 0.68956598],
[ 0.75599089]])
- np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0],vm,RTOL)
u = np.array([ 31.97771505])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 15.02228495])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
e = np.array([ 33.83065421])
- np.testing.assert_array_almost_equal(reg.e_filtered[0],e,4)
+ np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 26.673798071789673
- self.assertAlmostEqual(reg.chow.joint[0],chow_j,4)
+ np.testing.assert_allclose(reg.chow.joint[0],chow_j,RTOL)
#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)
+ np.testing.assert_allclose(model.betas,tbetas)
vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
- np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 4)
+ np.testing.assert_allclose(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
index 462db18..b8c669b 100644
--- a/pysal/spreg/tests/test_ml_lag.py
+++ b/pysal/spreg/tests/test_ml_lag.py
@@ -4,6 +4,7 @@ import scipy
import numpy as np
from pysal.spreg.ml_lag import ML_Lag
from pysal.spreg import utils
+from pysal.common import RTOL
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Max Likelihood requires SciPy version 11 or newer.")
@@ -30,39 +31,39 @@ class TestMLError(unittest.TestCase):
[-0.20103955],
[ 0.65462382],
[ 0.62351143]])
- np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 47.51218398])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([-0.51218398])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 211
- self.assertAlmostEqual(reg.n,n,4)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 5
- self.assertAlmostEqual(reg.k,k,4)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 47.])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 4. , 148. , 11.25])
- np.testing.assert_array_almost_equal(reg.x[0],x,4)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
e = np.array([ 41.99251608])
- np.testing.assert_array_almost_equal(reg.e_pred[0],e,4)
+ np.testing.assert_allclose(reg.e_pred[0],e,RTOL)
my = 44.307180094786695
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
sy = 23.606076835380495
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.vm.diagonal(),vm,RTOL)
sig2 = 216.27525647243797
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
pr2 = 0.6133020721559487
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
logll = -875.92771143484833
- self.assertAlmostEqual(reg.logll,logll,4)
+ np.testing.assert_allclose(reg.logll,logll,RTOL)
aic = 1761.8554228696967
- self.assertAlmostEqual(reg.aic,aic,4)
+ np.testing.assert_allclose(reg.aic,aic,RTOL)
schwarz = 1778.614713537077
- self.assertAlmostEqual(reg.schwarz,schwarz,4)
+ np.testing.assert_allclose(reg.schwarz,schwarz,RTOL)
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
index dd77ce1..4b19966 100644
--- a/pysal/spreg/tests/test_ml_lag_regimes.py
+++ b/pysal/spreg/tests/test_ml_lag_regimes.py
@@ -5,6 +5,7 @@ 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
+from pysal.common import RTOL
@unittest.skipIf(int(scipy.__version__.split(".")[1]) < 11,
"Max Likelihood requires SciPy version 11 or newer.")
@@ -36,49 +37,49 @@ class TestMLError(unittest.TestCase):
[ -0.17021393],
[ 0.81941371],
[ 0.53850323]])
- np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 32.73718478])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 14.26281522])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 211
- self.assertAlmostEqual(reg.n,n,4)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 9
- self.assertAlmostEqual(reg.k,k,4)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 47.])
- np.testing.assert_array_almost_equal(reg.y[0],y,4)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([[ 1. , 4. , 148. , 11.25, 0. , 0. , 0. ,
0. ]])
- np.testing.assert_array_almost_equal(reg.x[0].toarray(),x,4)
+ np.testing.assert_allclose(reg.x[0].toarray(),x,RTOL)
e = np.array([ 29.45407124])
- np.testing.assert_array_almost_equal(reg.e_pred[0],e,4)
+ np.testing.assert_allclose(reg.e_pred[0],e,RTOL)
my = 44.307180094786695
- self.assertAlmostEqual(reg.mean_y,my)
+ np.testing.assert_allclose(reg.mean_y,my)
sy = 23.606076835380495
- self.assertAlmostEqual(reg.std_y,sy)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.vm.diagonal(),vm,RTOL)
sig2 = 200.04433357145007
- self.assertAlmostEqual(reg.sig2,sig2,4)
+ np.testing.assert_allclose(reg.sig2,sig2,RTOL)
pr2 = 0.6404460298085746
- self.assertAlmostEqual(reg.pr2,pr2)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.std_err,std_err,RTOL)
logll = -864.98505596489736
- self.assertAlmostEqual(reg.logll,logll,4)
+ np.testing.assert_allclose(reg.logll,logll,RTOL)
aic = 1747.9701119297947
- self.assertAlmostEqual(reg.aic,aic,4)
+ np.testing.assert_allclose(reg.aic,aic,RTOL)
schwarz = 1778.1368351310794
- self.assertAlmostEqual(reg.schwarz,schwarz,4)
+ np.testing.assert_allclose(reg.schwarz,schwarz,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 21.648337464039283
- self.assertAlmostEqual(reg.chow.joint[0],chow_j,4)
+ np.testing.assert_allclose(reg.chow.joint[0],chow_j,RTOL)
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,\
@@ -93,25 +94,25 @@ class TestMLError(unittest.TestCase):
[-0.18207394],
[ 0.71129227],
[ 0.66753263]])
- np.testing.assert_array_almost_equal(reg.betas,betas,4)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0],vm,RTOL)
u = np.array([ 34.03630518])
- np.testing.assert_array_almost_equal(reg.u[0],u,4)
+ np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 12.96369482])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,4)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
e = np.array([ 32.46466912])
- np.testing.assert_array_almost_equal(reg.e_pred[0],e,4)
+ np.testing.assert_allclose(reg.e_pred[0],e,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi,chow_r,RTOL)
chow_j = 21.978012275873063
- self.assertAlmostEqual(reg.chow.joint[0],chow_j,4)
+ np.testing.assert_allclose(reg.chow.joint[0],chow_j,RTOL)
if __name__ == '__main__':
unittest.main()
diff --git a/pysal/spreg/tests/test_ols.py b/pysal/spreg/tests/test_ols.py
index f2495d9..431c908 100644
--- a/pysal/spreg/tests/test_ols.py
+++ b/pysal/spreg/tests/test_ols.py
@@ -1,8 +1,9 @@
import unittest
import numpy as np
import pysal
-from pysal.spreg import utils
import pysal.spreg as EC
+from pysal.spreg import utils
+from pysal.common import RTOL
PEGP = pysal.examples.get_path
@@ -20,106 +21,106 @@ class TestBaseOLS(unittest.TestCase):
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([[
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(ols.vm, vm,RTOL)
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([[
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(ols.vm, vm,RTOL)
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([[
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(ols.vm, vm,RTOL)
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.testing.assert_allclose(ols.aic, \
+ 408.73548964604873 ,RTOL)
+ np.testing.assert_allclose(ols.ar2, \
+ 0.32123239427957662 ,RTOL)
+ np.testing.assert_allclose(ols.betas, \
np.array([[ 46.42818268], [ 0.62898397], \
- [ -0.48488854]]), 7)
+ [ -0.48488854]]),RTOL)
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)
+ np.testing.assert_allclose(bp, ols_bp,RTOL)
+ np.testing.assert_allclose(ols.f_stat, \
+ (12.358198885356581, 5.0636903313953024e-05),RTOL)
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)
+ np.testing.assert_allclose(ols_jb,jb,RTOL)
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_allclose(ols_white,white,RTOL)
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_allclose(ols.koenker_bassett[key], kb[key],RTOL)
+ np.testing.assert_allclose(ols.lm_error, \
+ (4.1508117035117893, 0.041614570655392716),RTOL)
+ np.testing.assert_allclose(ols.lm_lag, \
+ (0.98279980617162233, 0.32150855529063727),RTOL)
+ np.testing.assert_allclose(ols.lm_sarma, \
+ (4.3222725729143736, 0.11519415308749938),RTOL)
+ np.testing.assert_allclose(ols.logll, \
+ -201.3677448230244 ,RTOL)
+ np.testing.assert_allclose(ols.mean_y, \
+ 38.436224469387746,RTOL)
+ np.testing.assert_allclose(ols.moran_res[0], \
+ 0.20373540938,RTOL)
+ np.testing.assert_allclose(ols.moran_res[1], \
+ 2.59180452208,RTOL)
+ np.testing.assert_allclose(ols.moran_res[2], \
+ 0.00954740031251,RTOL)
+ np.testing.assert_allclose(ols.mulColli, \
+ 12.537554873824675 ,RTOL)
+ np.testing.assert_allclose(ols.n, 49, RTOL)
+ np.testing.assert_string_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_string_equal(ols.name_w, 'unknown')
+ np.testing.assert_equal(ols.name_x, ['CONSTANT', 'income','crime'])
+ np.testing.assert_string_equal(ols.name_y, 'home value')
+ np.testing.assert_allclose(ols.predy[3], np.array([
+ 33.53969014]),RTOL)
+ np.testing.assert_allclose(ols.r2, \
+ 0.34951437785126105 ,RTOL)
+ np.testing.assert_allclose(ols.rlm_error, \
+ (3.3394727667427513, 0.067636278225568919),RTOL)
+ np.testing.assert_allclose(ols.rlm_lag, \
+ (0.17146086940258459, 0.67881673703455414),RTOL)
np.testing.assert_equal(ols.robust, 'unadjusted')
- np.testing.assert_array_almost_equal(ols.schwarz, \
+ np.testing.assert_allclose(ols.schwarz, \
414.41095054038061,7 )
- np.testing.assert_array_almost_equal(ols.sig2, \
+ np.testing.assert_allclose(ols.sig2, \
231.4568494392652,7 )
- np.testing.assert_array_almost_equal(ols.sig2ML, \
+ np.testing.assert_allclose(ols.sig2ML, \
217.28602192257551,7 )
- np.testing.assert_array_almost_equal(ols.sig2n, \
- 217.28602192257551, 7)
+ np.testing.assert_allclose(ols.sig2n, \
+ 217.28602192257551,RTOL)
- 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)
+ np.testing.assert_allclose(ols.t_stat[2][0], \
+ -2.65440864272,RTOL)
+ np.testing.assert_allclose(ols.t_stat[2][1], \
+ 0.0108745049098,RTOL)
if __name__ == '__main__':
unittest.main()
diff --git a/pysal/spreg/tests/test_ols_regimes.py b/pysal/spreg/tests/test_ols_regimes.py
index 20a9977..0b46c65 100644
--- a/pysal/spreg/tests/test_ols_regimes.py
+++ b/pysal/spreg/tests/test_ols_regimes.py
@@ -3,6 +3,7 @@ import numpy as np
import pysal
from pysal.spreg.ols import OLS
from pysal.spreg.ols_regimes import OLS_Regimes
+from pysal.common import RTOL
PEGP = pysal.examples.get_path
@@ -22,21 +23,21 @@ class TestOLS_regimes(unittest.TestCase):
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],\
+ #np.testing.assert_allclose(ols.aic, 408.73548964604873 ,RTOL)
+ np.testing.assert_allclose(ols.ar2,0.50761700679873101 ,RTOL)
+ np.testing.assert_allclose(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_allclose(ols.vm[0], vm,RTOL)
+ np.testing.assert_allclose(ols.lm_error, \
+ (5.92970357, 0.01488775),RTOL)
+ np.testing.assert_allclose(ols.lm_lag, \
+ (8.78315751, 0.00304024),RTOL)
+ np.testing.assert_allclose(ols.lm_sarma, \
+ (8.89955982, 0.01168114),RTOL)
+ np.testing.assert_allclose(ols.mean_y, \
+ 35.1288238979591,RTOL)
np.testing.assert_equal(ols.k, 6)
np.testing.assert_equal(ols.kf, 0)
np.testing.assert_equal(ols.kr, 3)
@@ -47,23 +48,23 @@ class TestOLS_regimes(unittest.TestCase):
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_allclose(ols.predy[3], np.array([
+ 51.05003696]),RTOL)
+ np.testing.assert_allclose(ols.r2, \
+ 0.55890690192386316 ,RTOL)
+ np.testing.assert_allclose(ols.rlm_error, \
+ (0.11640231, 0.73296972),RTOL)
+ np.testing.assert_allclose(ols.rlm_lag, \
+ (2.96985625, 0.08482939),RTOL)
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.testing.assert_allclose(ols.sig2, \
+ 137.84897351821013,RTOL)
+ np.testing.assert_allclose(ols.sig2n, \
+ 120.96950737312316,RTOL)
+ np.testing.assert_allclose(ols.t_stat[2][0], \
+ -0.43342216706091791,RTOL)
+ np.testing.assert_allclose(ols.t_stat[2][1], \
+ 0.66687472578594531,RTOL)
np.set_printoptions(suppress=start_suppress)
"""
def test_OLS_regi(self):
@@ -77,49 +78,50 @@ class TestOLS_regimes(unittest.TestCase):
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)
+ np.testing.assert_allclose(model.betas,tbetas,RTOL)
vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
- np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 6)
+ np.testing.assert_allclose(model.vm.diagonal(), vm,RTOL)
#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)
+ np.testing.assert_allclose(reg.multi[0].aic, 192.96044303402897 ,RTOL)
tbetas = np.array([[ 68.78670869],
[ -1.9864167 ],
[ -0.10887962],
[ 67.73579559],
[ -1.36937552],
[ -0.31792362]])
- np.testing.assert_array_almost_equal(tbetas, reg.betas)
+ np.testing.assert_allclose(tbetas, reg.betas,RTOL)
vm = np.array([ 41.68828023, -1.83582717, -0.17053478, 0. ,
0. , 0. ])
- np.testing.assert_array_almost_equal(reg.vm[0], vm, 6)
+ np.testing.assert_allclose(reg.vm[0], vm,RTOL)
u_3 = np.array([[ 0.31781838],
[-5.6905584 ],
[-6.8819715 ]])
- np.testing.assert_array_almost_equal(reg.u[0:3], u_3, 7)
+ np.testing.assert_allclose(reg.u[0:3], u_3,RTOL)
predy_3 = np.array([[ 15.40816162],
[ 24.4923124 ],
[ 37.5087525 ]])
- np.testing.assert_array_almost_equal(reg.predy[0:3], predy_3, 7)
+ np.testing.assert_allclose(reg.predy[0:3], predy_3,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi, chow_regi,RTOL)
+ np.testing.assert_allclose(reg.chow.joint[0], 0.67787986791767096,RTOL)
"""
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]]))
+ np.testing.assert_allclose(ols.betas,np.array([[ -0.24385565], [ \
+ -0.26335026], [ 68.89701137], [ -1.67389685]]), RTOL)
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_allclose(ols.vm[0], vm,RTOL)
+ np.testing.assert_allclose(ols.lm_error, \
+ (5.62668744, 0.01768903),RTOL)
+ np.testing.assert_allclose(ols.lm_lag, \
+ (9.43264957, 0.00213156),RTOL)
+ np.testing.assert_allclose(ols.mean_y, \
+ 35.12882389795919,RTOL)
np.testing.assert_equal(ols.kf, 2)
np.testing.assert_equal(ols.kr, 1)
np.testing.assert_equal(ols.n, 49)
@@ -129,15 +131,15 @@ class TestOLS_regimes(unittest.TestCase):
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_allclose(ols.predy[3], np.array([
+ 52.65974636]),RTOL)
+ np.testing.assert_allclose(ols.r2, \
+ 0.5525561183786056 ,RTOL)
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.testing.assert_allclose(ols.t_stat[2][0], \
+ 13.848705206463748,RTOL)
+ np.testing.assert_allclose(ols.t_stat[2][1], \
+ 7.776650625274256e-18,RTOL)
np.set_printoptions(suppress=start_suppress)
if __name__ == '__main__':
diff --git a/pysal/spreg/tests/test_ols_sparse.py b/pysal/spreg/tests/test_ols_sparse.py
index 1341c96..03d4017 100644
--- a/pysal/spreg/tests/test_ols_sparse.py
+++ b/pysal/spreg/tests/test_ols_sparse.py
@@ -3,6 +3,7 @@ import numpy as np
import pysal
import pysal.spreg as EC
from scipy import sparse
+from pysal.common import RTOL
PEGP = pysal.examples.get_path
@@ -21,12 +22,12 @@ class TestBaseOLS(unittest.TestCase):
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([[
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(ols.vm, vm,RTOL)
def test_OLS(self):
self.X = sparse.csr_matrix(self.X)
@@ -34,74 +35,74 @@ class TestBaseOLS(unittest.TestCase):
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.testing.assert_allclose(ols.aic, \
+ 408.73548964604873 ,RTOL)
+ np.testing.assert_allclose(ols.ar2, \
+ 0.32123239427957662 ,RTOL)
+ np.testing.assert_allclose(ols.betas, \
np.array([[ 46.42818268], [ 0.62898397], \
- [ -0.48488854]]), 7)
+ [ -0.48488854]]),RTOL)
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)
+ np.testing.assert_allclose(bp, ols_bp,RTOL)
+ np.testing.assert_allclose(ols.f_stat, \
+ (12.358198885356581, 5.0636903313953024e-05),RTOL)
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)
+ np.testing.assert_allclose(ols_jb,jb,RTOL)
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_allclose(ols_white,white,RTOL)
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_allclose(ols.koenker_bassett[key], kb[key],RTOL)
+ np.testing.assert_allclose(ols.lm_error, \
+ (4.1508117035117893, 0.041614570655392716),RTOL)
+ np.testing.assert_allclose(ols.lm_lag, \
+ (0.98279980617162233, 0.32150855529063727),RTOL)
+ np.testing.assert_allclose(ols.lm_sarma, \
+ (4.3222725729143736, 0.11519415308749938),RTOL)
+ np.testing.assert_allclose(ols.logll, \
+ -201.3677448230244 ,RTOL)
+ np.testing.assert_allclose(ols.mean_y, \
+ 38.436224469387746,RTOL)
+ np.testing.assert_allclose(ols.moran_res[0], \
+ 0.20373540938,RTOL)
+ np.testing.assert_allclose(ols.moran_res[1], \
+ 2.59180452208,RTOL)
+ np.testing.assert_allclose(ols.moran_res[2], \
+ 0.00954740031251,RTOL)
+ np.testing.assert_allclose(ols.mulColli, \
+ 12.537554873824675 ,RTOL)
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_allclose(ols.predy[3], np.array([
+ 33.53969014]),RTOL)
+ np.testing.assert_allclose(ols.r2, \
+ 0.34951437785126105 ,RTOL)
+ np.testing.assert_allclose(ols.rlm_error, \
+ (3.3394727667427513, 0.067636278225568919),RTOL)
+ np.testing.assert_allclose(ols.rlm_lag, \
+ (0.17146086940258459, 0.67881673703455414),RTOL)
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_allclose(ols.schwarz, \
+ 414.41095054038061,RTOL)
+ np.testing.assert_allclose(ols.sig2, \
+ 231.4568494392652,RTOL)
+ np.testing.assert_allclose(ols.sig2ML, \
+ 217.28602192257551,RTOL)
+ np.testing.assert_allclose(ols.sig2n, \
+ 217.28602192257551,RTOL)
- 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)
+ np.testing.assert_allclose(ols.t_stat[2][0], \
+ -2.65440864272,RTOL)
+ np.testing.assert_allclose(ols.t_stat[2][1], \
+ 0.0108745049098,RTOL)
if __name__ == '__main__':
unittest.main()
diff --git a/pysal/spreg/tests/test_probit.py b/pysal/spreg/tests/test_probit.py
index 5c4e6dd..3875219 100644
--- a/pysal/spreg/tests/test_probit.py
+++ b/pysal/spreg/tests/test_probit.py
@@ -2,6 +2,7 @@ import unittest
import pysal
import numpy as np
from pysal.spreg import probit as PB
+from pysal.common import RTOL
class TestBaseProbit(unittest.TestCase):
def setUp(self):
@@ -20,39 +21,39 @@ class TestBaseProbit(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
predy = np.array([ 0.00174739])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n,6)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 3
- self.assertAlmostEqual(reg.k,k,6)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 0.])
- np.testing.assert_array_almost_equal(reg.y[0],y,6)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 80.467003])
- np.testing.assert_array_almost_equal(reg.x[0],x,6)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
xmean = np.array([[ 1. ], [ 14.37493876], [ 38.43622447 ]])
- np.testing.assert_array_almost_equal(reg.xmean,xmean,6)
+ np.testing.assert_allclose(reg.xmean,xmean,RTOL)
predpc = 85.714285714285708
- self.assertAlmostEqual(reg.predpc,predpc,5)
+ np.testing.assert_allclose(reg.predpc,predpc,RTOL)
logl = -20.06009093055782
- self.assertAlmostEqual(reg.logl,logl,5)
+ np.testing.assert_allclose(reg.logl,logl,RTOL)
scale = 0.23309310130643665
- self.assertAlmostEqual(reg.scale,scale,5)
+ np.testing.assert_allclose(reg.scale,scale,RTOL)
slopes = np.array([[-0.04653776], [-0.00687944]])
- np.testing.assert_array_almost_equal(reg.slopes,slopes,6)
+ np.testing.assert_allclose(reg.slopes,slopes,RTOL)
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)
+ np.testing.assert_allclose(reg.slopes_vm,slopes_vm,RTOL)
LR = 25.317683245671716
- self.assertAlmostEqual(reg.LR[0],LR,5)
+ np.testing.assert_allclose(reg.LR[0],LR,RTOL)
Pinkse_error = 2.9632385352516728
- self.assertAlmostEqual(reg.Pinkse_error[0],Pinkse_error,5)
+ np.testing.assert_allclose(reg.Pinkse_error[0],Pinkse_error,RTOL)
KP_error = 1.6509224700582124
- self.assertAlmostEqual(reg.KP_error[0],KP_error,5)
+ np.testing.assert_allclose(reg.KP_error[0],KP_error,RTOL)
PS_error = 2.3732463777623511
- self.assertAlmostEqual(reg.PS_error[0],PS_error,5)
+ np.testing.assert_allclose(reg.PS_error[0],PS_error,RTOL)
class TestProbit(unittest.TestCase):
def setUp(self):
@@ -70,39 +71,39 @@ class TestProbit(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas,betas,RTOL)
predy = np.array([ 0.00174739])
- np.testing.assert_array_almost_equal(reg.predy[0],predy,6)
+ np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
- self.assertAlmostEqual(reg.n,n,6)
+ np.testing.assert_allclose(reg.n,n,RTOL)
k = 3
- self.assertAlmostEqual(reg.k,k,6)
+ np.testing.assert_allclose(reg.k,k,RTOL)
y = np.array([ 0.])
- np.testing.assert_array_almost_equal(reg.y[0],y,6)
+ np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 80.467003])
- np.testing.assert_array_almost_equal(reg.x[0],x,6)
+ np.testing.assert_allclose(reg.x[0],x,RTOL)
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)
+ np.testing.assert_allclose(reg.vm,vm,RTOL)
xmean = np.array([[ 1. ], [ 14.37493876], [ 38.43622447 ]])
- np.testing.assert_array_almost_equal(reg.xmean,xmean,6)
+ np.testing.assert_allclose(reg.xmean,xmean,RTOL)
predpc = 85.714285714285708
- self.assertAlmostEqual(reg.predpc,predpc,5)
+ np.testing.assert_allclose(reg.predpc,predpc,RTOL)
logl = -20.06009093055782
- self.assertAlmostEqual(reg.logl,logl,5)
+ np.testing.assert_allclose(reg.logl,logl,RTOL)
scale = 0.23309310130643665
- self.assertAlmostEqual(reg.scale,scale,5)
+ np.testing.assert_allclose(reg.scale,scale,RTOL)
slopes = np.array([[-0.04653776], [-0.00687944]])
- np.testing.assert_array_almost_equal(reg.slopes,slopes,6)
+ np.testing.assert_allclose(reg.slopes,slopes,RTOL)
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)
+ np.testing.assert_allclose(reg.slopes_vm,slopes_vm,RTOL)
LR = 25.317683245671716
- self.assertAlmostEqual(reg.LR[0],LR,5)
+ np.testing.assert_allclose(reg.LR[0],LR,RTOL)
Pinkse_error = 2.9632385352516728
- self.assertAlmostEqual(reg.Pinkse_error[0],Pinkse_error,5)
+ np.testing.assert_allclose(reg.Pinkse_error[0],Pinkse_error,RTOL)
KP_error = 1.6509224700582124
- self.assertAlmostEqual(reg.KP_error[0],KP_error,5)
+ np.testing.assert_allclose(reg.KP_error[0],KP_error,RTOL)
PS_error = 2.3732463777623511
- self.assertAlmostEqual(reg.PS_error[0],PS_error,5)
+ np.testing.assert_allclose(reg.PS_error[0],PS_error,RTOL)
if __name__ == '__main__':
unittest.main()
diff --git a/pysal/spreg/tests/test_sur.py b/pysal/spreg/tests/test_sur.py
new file mode 100644
index 0000000..76342fd
--- /dev/null
+++ b/pysal/spreg/tests/test_sur.py
@@ -0,0 +1,172 @@
+import unittest
+import numpy as np
+import pysal
+from pysal.spreg.sur_utils import sur_dictxy,sur_dictZ
+from pysal.spreg.sur import SUR,ThreeSLS
+from pysal.common import RTOL
+
+PEGP = pysal.examples.get_path
+
+def dict_compare(actual, desired, rtol):
+ for i in actual.keys():
+ np.testing.assert_allclose(actual[i],desired[i],rtol)
+
+
+class Test_SUR(unittest.TestCase):
+ def setUp(self):
+ self.db = pysal.open(pysal.examples.get_path('NAT.dbf'),'r')
+ self.w = pysal.queen_from_shapefile(pysal.examples.get_path("NAT.shp"))
+ self.w.transform = 'r'
+
+
+ def test_SUR(self): #2 equations, same K in each, two-step estimation
+ y_var0 = ['HR80','HR90']
+ x_var0 = [['PS80','UE80'],['PS90','UE90']]
+ bigy0,bigX0,bigyvars0,bigXvars0 = sur_dictxy(self.db,y_var0,x_var0)
+ reg = SUR(bigy0,bigX0,name_bigy=bigyvars0,name_bigX=bigXvars0)
+
+ dict_compare(reg.bOLS,{0: np.array([[ 5.39719146],[ 0.6973813 ],\
+ [ 0.22566378]]), 1: np.array([[ 1.80829725],[ 1.03504143],[ 0.6582483 ]])},RTOL)
+ dict_compare(reg.bSUR,{0: np.array([[ 5.13907179],[ 0.67764814],\
+ [ 0.26372397]]), 1: np.array([[ 3.61394031],[ 1.02607147],[ 0.38654993]])},RTOL)
+ dict_compare(reg.sur_inf,{0: np.array([[ 2.62467257e-01, 1.95798587e+01, 2.29656805e-85],\
+ [ 1.21957836e-01, 5.55641325e+00, 2.75374482e-08],\
+ [ 3.43183797e-02, 7.68462769e+00, 1.53442563e-14]]),\
+ 1: np.array([[ 2.53499643e-01, 1.42561949e+01, 4.10220329e-46],\
+ [ 1.12166227e-01, 9.14777552e+00, 5.81179115e-20],\
+ [ 3.41995564e-02, 1.13027760e+01, 1.27134462e-29]])},RTOL)
+ np.testing.assert_allclose(reg.corr,np.array([[ 1. , 0.46954842],\
+ [ 0.46954842, 1. ]]),RTOL)
+ np.testing.assert_allclose(reg.surchow,[(26.72917022127309, 1, 2.3406126054510838e-07),\
+ (8.2409218385398244, 1, 0.0040956326095295649),\
+ (9.3837654127686712, 1, 0.002189154327032255)],RTOL)
+
+ def test_SUR_iter(self): #2 equations, same K in each, iterated estimation, spatial test
+ y_var0 = ['HR80','HR90']
+ x_var0 = [['PS80','UE80'],['PS90','UE90']]
+ bigy0,bigX0,bigyvars0,bigXvars0 = sur_dictxy(self.db,y_var0,x_var0)
+ reg = SUR(bigy0,bigX0,w=self.w,nonspat_diag=True,spat_diag=True,iter=True,verbose=False,\
+ name_bigy=bigyvars0,name_bigX=bigXvars0)
+
+ dict_compare(reg.bOLS,{0: np.array([[ 5.39719146],[ 0.6973813 ],\
+ [ 0.22566378]]), 1: np.array([[ 1.80829725],[ 1.03504143],[ 0.6582483 ]])},RTOL)
+ dict_compare(reg.bSUR,{0: np.array([[ 5.18423225],[ 0.67757925],\
+ [ 0.25706498]]), 1: np.array([[ 3.79731807],[ 1.02411196],[ 0.35895674]])},RTOL)
+ dict_compare(reg.sur_inf,{0: np.array([[ 2.59392406e-01, 1.99860602e+01, 7.28237551e-89],\
+ [ 1.21911330e-01, 5.55796781e+00, 2.72933704e-08],\
+ [ 3.38051365e-02, 7.60431727e+00, 2.86411830e-14]]),\
+ 1: np.array([[ 2.53108919e-01, 1.50027035e+01, 7.04886598e-51],\
+ [ 1.13329850e-01, 9.03655976e+00, 1.61679985e-19],\
+ [ 3.40440433e-02, 1.05438928e+01, 5.42075621e-26]])},RTOL)
+ np.testing.assert_allclose(reg.corr,np.array([[ 1. , 0.5079133],
+ [ 0.5079133, 1. ]]),RTOL)
+ np.testing.assert_allclose(reg.surchow,[(23.457203761752844, 1, 1.2772356421778157e-06),\
+ (8.6998292497532859, 1, 0.0031823985960753743),\
+ (6.8426866249389589, 1, 0.0089004881389025351)],RTOL)
+ np.testing.assert_allclose(reg.llik,-19860.067987395596)
+ np.testing.assert_allclose(reg.lmtest,(680.16759754291365, 1, 6.144389240997126e-150))
+ np.testing.assert_allclose(reg.lrtest, (854.18095147295708, 1, 8.966465468792485e-188))
+ np.testing.assert_allclose(reg.lmEtest,(1270.9424750801545, 2, 1.0431532803839709e-276))
+
+ def test_SUR_3eq(self): #3 equations, different K, iterated estimation, spatial test
+ y_var1 = ['HR60','HR70','HR80']
+ x_var1 = [['RD60','PS60'],['RD70','PS70','UE70'],['RD80','PS80']]
+ bigy1,bigX1,bigyvars1,bigXvars1 = sur_dictxy(self.db,y_var1,x_var1)
+ reg = SUR(bigy1,bigX1,w=self.w,spat_diag=True,iter=True,verbose=False,\
+ name_bigy=bigyvars1,name_bigX=bigXvars1)
+
+ dict_compare(reg.bOLS,{0: np.array([[ 4.50407527],[ 2.50426531],\
+ [ 0.50130802]]), 1: np.array([[ 7.41171812],[ 4.0021532 ],[ 1.32168167],\
+ [-0.22786048]]), 2: np.array([[ 6.92761614],[ 3.90531039],[ 1.47413939]])},RTOL)
+ dict_compare(reg.bSUR,{0: np.array([[ 4.50407527],[ 2.39199682],\
+ [ 0.52723694]]), 1: np.array([[ 7.44509818],
+ [ 3.74968571],[ 1.28811685],[-0.23526451]]), 2: np.array([[ 6.92761614],\
+ [ 3.65423052],[ 1.38247611]])},RTOL)
+ dict_compare(reg.sur_inf,{0: np.array([[ 9.16019177e-002, 4.91700980e+001, 0.00000000e+000],\
+ [ 9.18832357e-002, 2.60330060e+001, 2.09562528e-149],\
+ [ 9.31668754e-002, 5.65906002e+000, 1.52204326e-008]]),\
+ 1: np.array([[ 2.31085029e-001, 3.22180031e+001, 9.87752395e-228],\
+ [ 1.14421850e-001, 3.27707138e+001, 1.53941252e-235],\
+ [ 1.14799399e-001, 1.12205888e+001, 3.23111806e-029],\
+ [ 4.47806286e-002, -5.25371170e+000, 1.49064159e-007]]),\
+ 2: np.array([[ 1.00643767e-001, 6.88330371e+001, 0.00000000e+000],\
+ [ 1.00599909e-001, 3.63243917e+001, 6.66811571e-289],\
+ [ 1.02053898e-001, 1.35465291e+001, 8.30659234e-042]])},RTOL)
+ np.testing.assert_allclose(reg.corr,np.array([[ 1. , 0.34470181, 0.25096458],\
+ [ 0.34470181, 1. , 0.33527277],[ 0.25096458, 0.33527277, 1. ]]),RTOL)
+ np.testing.assert_allclose(reg.llik,-28695.767676078722)
+ np.testing.assert_allclose(reg.lmtest,(882.43543942655947, 3, 5.7128374010751484e-191))
+ np.testing.assert_allclose(reg.lrtest, (818.30409875688747, 3, 4.6392724270549021e-177))
+ np.testing.assert_allclose(reg.lmEtest,(696.64318511682916, 3, 1.1218163246066135e-150))
+
+ def test_3SLS(self): # two equations, one endog, one instrument, same k
+ y_var1 = ['HR80','HR90']
+ x_var1 = [['PS80','UE80'],['PS90','UE90']]
+ yend_var1 = [['RD80'],['RD90']]
+ q_var1 = [['FP79'],['FP89']]
+ bigy1,bigX1,bigyvars1,bigXvars1 = sur_dictxy(self.db,y_var1,x_var1)
+ bigyend1,bigyendvars1 = sur_dictZ(self.db,yend_var1)
+ bigq1,bigqvars1 = sur_dictZ(self.db,q_var1)
+ reg = ThreeSLS(bigy1,bigX1,bigyend1,bigq1)
+
+ dict_compare(reg.b3SLS,{0: np.array([[ 6.92426353e+00],[ 1.42921826e+00],[ 4.94348442e-04],\
+ [ 3.58292750e+00]]), 1: np.array([[ 7.62385875],[ 1.65031181],[-0.21682974],[ 3.91250428]])},RTOL)
+ dict_compare(reg.tsls_inf,{0: np.array([[ 2.32208525e-001, 2.98191616e+001, 2.20522747e-195],\
+ [ 1.03734166e-001, 1.37777004e+001, 3.47155373e-043],\
+ [ 3.08619277e-002, 1.60180675e-002, 9.87219978e-001],\
+ [ 1.11319989e-001, 3.21858412e+001, 2.78527634e-227]]),\
+ 1: np.array([[ 2.87394149e-001, 2.65275364e+001, 4.66554915e-155],\
+ [ 9.59703138e-002, 1.71960655e+001, 2.84185085e-066],\
+ [ 4.08954707e-002, -5.30204786e+000, 1.14510807e-007],\
+ [ 1.35867887e-001, 2.87963872e+001, 2.38043782e-182]])},RTOL)
+ np.testing.assert_allclose(reg.corr,np.array([[ 1. , 0.26404959],
+ [ 0.26404959, 1. ]]),RTOL)
+ np.testing.assert_allclose(reg.surchow,[(4.398001850528483, 1, 0.035981064325265613),\
+ (3.3042403886525147, 1, 0.069101286634542139),\
+ (21.712902666281863, 1, 3.1665430446850281e-06),\
+ (4.4286185200127388, 1, 0.035341101907069621)],RTOL)
+
+ def test_3SLS_uneqK(self): # Three equations, unequal K, two endog variables, three instruments
+ y_var2 = ['HR60','HR70','HR80']
+ x_var2 = [['RD60','PS60'],['RD70','PS70','MA70'],['RD80','PS80']]
+ yend_var2 = [['UE60','DV60'],['UE70','DV70'],['UE80','DV80']]
+ q_var2 = [['FH60','FP59','GI59'],['FH70','FP69','GI69'],['FH80','FP79','GI79']]
+ bigy2,bigX2,bigyvars2,bigXvars2 = sur_dictxy(self.db,y_var2,x_var2)
+ bigyend2,bigyendvars2 = sur_dictZ(self.db,yend_var2)
+ bigq2,bigqvars2 = sur_dictZ(self.db,q_var2)
+ reg = ThreeSLS(bigy2,bigX2,bigyend2,bigq2,name_bigy=bigyvars2,\
+ name_bigX=bigXvars2,name_bigyend=bigyendvars2,\
+ name_bigq=bigqvars2,name_ds="natregimes")
+
+ dict_compare(reg.b2SLS,{0: np.array([[-2.04160355],[ 4.5438992 ],[ 1.65007567],[-0.73163458],\
+ [ 5.43071683]]), 1: np.array([[ 17.26252005],[ 5.17297895],[ 1.2893243 ],[ -0.38349609],\
+ [ -2.17689289],[ 4.31713382]]), 2: np.array([[-7.6809159 ],[ 3.88957396],[ 0.49973258],\
+ [ 0.36476446],[ 2.63375234]])},RTOL)
+ dict_compare(reg.b3SLS,{0: np.array([[-1.56830297],[ 4.07805179],[ 1.49694849],[-0.5376807 ],\
+ [ 4.65487154]]), 1: np.array([[ 16.13792395],[ 4.97265632],[ 1.31962844],[ -0.32122485],\
+ [ -2.12407425],[ 3.91227737]]), 2: np.array([[-6.7283657 ],[ 3.79206731],[ 0.52278922],\
+ [ 0.33447996],[ 2.47158609]])},RTOL)
+ dict_compare(reg.tsls_inf,{0: np.array([[ 9.95215966e-01, -1.57584185e+00, 1.15062254e-01],\
+ [ 2.26574971e-01, 1.79986861e+01, 1.99495587e-72],\
+ [ 1.60939740e-01, 9.30129807e+00, 1.38741353e-20],\
+ [ 1.19040839e-01, -4.51677511e+00, 6.27885257e-06],\
+ [ 5.32942876e-01, 8.73427857e+00, 2.45216107e-18]]),\
+ 1: np.array([[ 1.59523920e+000, 1.01163035e+001, 4.67748637e-024],\
+ [ 1.87013008e-001, 2.65898954e+001, 8.88419907e-156],\
+ [ 1.44410869e-001, 9.13801331e+000, 6.36101069e-020],\
+ [ 3.46429228e-002, -9.27245233e+000, 1.81914372e-020],\
+ [ 2.49627824e-001, -8.50896434e+000, 1.75493796e-017],\
+ [ 4.19425249e-001, 9.32771068e+000, 1.08182251e-020]]),\
+ 2: np.array([[ 1.09143600e+000, -6.16469102e+000, 7.06208998e-010],\
+ [ 1.27908896e-001, 2.96466268e+001, 3.74870055e-193],\
+ [ 1.32436222e-001, 3.94747912e+000, 7.89784041e-005],\
+ [ 8.81489692e-002, 3.79448524e+000, 1.47950082e-004],\
+ [ 1.95538678e-001, 1.26398834e+001, 1.27242486e-036]])},RTOL)
+ np.testing.assert_allclose(reg.corr,np.array([[ 1. , 0.31819323, 0.20428789],\
+ [ 0.31819323, 1. , 0.12492191],[ 0.20428789, 0.12492191, 1. ]]),RTOL)
+
+
+
+if __name__ == '__main__':
+ unittest.main()
+
diff --git a/pysal/spreg/tests/test_sur_error.py b/pysal/spreg/tests/test_sur_error.py
new file mode 100644
index 0000000..b0d56be
--- /dev/null
+++ b/pysal/spreg/tests/test_sur_error.py
@@ -0,0 +1,120 @@
+import unittest
+import numpy as np
+import pysal
+from pysal.spreg.sur_utils import sur_dictxy,sur_dictZ
+from pysal.spreg.sur_error import SURerrorML
+from pysal.common import RTOL
+
+PEGP = pysal.examples.get_path
+
+def dict_compare(actual, desired, rtol):
+ for i in actual.keys():
+ np.testing.assert_allclose(actual[i],desired[i],rtol)
+
+
+class Test_SUR_error(unittest.TestCase):
+ def setUp(self):
+ self.db = pysal.open(pysal.examples.get_path('NAT.dbf'),'r')
+ self.w = pysal.queen_from_shapefile(pysal.examples.get_path("NAT.shp"))
+ self.w.transform = 'r'
+
+
+ def test_error(self): #2 equations
+ y_var0 = ['HR80','HR90']
+ x_var0 = [['PS80','UE80'],['PS90','UE90']]
+ bigy0,bigX0,bigyvars0,bigXvars0 = sur_dictxy(self.db,y_var0,x_var0)
+ reg = SURerrorML(bigy0,bigX0,self.w,\
+ name_bigy=bigyvars0,name_bigX=bigXvars0,spat_diag=True,\
+ name_w="natqueen",name_ds="natregimes",nonspat_diag=False)
+
+ dict_compare(reg.bSUR0,{0: np.array([[ 5.18423225],[ 0.67757925],
+ [ 0.25706498]]), 1: np.array([[ 3.79731807],[ 1.02411196],[ 0.35895674]])},RTOL)
+ dict_compare(reg.bSUR,{0: np.array([[ 4.0222855 ],[ 0.88489646],[ 0.42402853]]),\
+ 1: np.array([[ 3.04923009],[ 1.10972634],[ 0.47075682]])},RTOL)
+ dict_compare(reg.sur_inf,{0: np.array([[ 3.669218e-01, 1.096224e+01, 5.804195e-28],\
+ [ 1.412908e-01, 6.262946e+00, 3.777726e-10],\
+ [ 4.267954e-02, 9.935169e+00, 2.926783e-23]]),\
+ 1: np.array([[ 3.31399691e-01, 9.20106497e+00, 3.54419478e-20],\
+ [ 1.33525912e-01, 8.31094371e+00, 9.49439563e-17],\
+ [ 4.00409716e-02, 1.17568780e+01, 6.50970965e-32]])},RTOL)
+ np.testing.assert_allclose(reg.lamols,np.array([[ 0.60205035],[ 0.56056348]]),RTOL)
+ np.testing.assert_allclose(reg.lamsur,np.array([[ 0.54361986],[ 0.50445451]]),RTOL)
+ np.testing.assert_allclose(reg.corr,np.array([[ 1. , 0.31763719],\
+ [ 0.31763719, 1. ]]),RTOL)
+ np.testing.assert_allclose(reg.surchow,[(5.1073696860799931, 1, 0.023824413482255974),\
+ (1.9524745281321374, 1, 0.16232044613203933),\
+ (0.79663667463065702, 1, 0.37210085476281407)],RTOL)
+ np.testing.assert_allclose(reg.llik,-19860.067987395596)
+ np.testing.assert_allclose(reg.errllik,-19497.031128906794)
+ np.testing.assert_allclose(reg.surerrllik,-19353.052023136348)
+ np.testing.assert_allclose(reg.likrlambda, (1014.0319285186415, 2, 6.3938800607190098e-221))
+
+ def test_error_vm(self): #Asymptotic variance matrix
+ y_var0 = ['HR80','HR90']
+ x_var0 = [['PS80','UE80'],['PS90','UE90']]
+ bigy0,bigX0,bigyvars0,bigXvars0 = sur_dictxy(self.db,y_var0,x_var0)
+ reg = SURerrorML(bigy0,bigX0,self.w,spat_diag=True,vm=True,\
+ name_bigy=bigyvars0,name_bigX=bigXvars0,\
+ name_w="natqueen",name_ds="natregimes")
+
+ dict_compare(reg.bSUR,{0: np.array([[ 4.0222855 ],[ 0.88489646],[ 0.42402853]]),\
+ 1: np.array([[ 3.04923009],[ 1.10972634],[ 0.47075682]])},RTOL)
+ dict_compare(reg.sur_inf,{0: np.array([[ 3.669218e-01, 1.096224e+01, 5.804195e-28],\
+ [ 1.412908e-01, 6.262946e+00, 3.777726e-10],\
+ [ 4.267954e-02, 9.935169e+00, 2.926783e-23]]),\
+ 1: np.array([[ 3.31399691e-01, 9.20106497e+00, 3.54419478e-20],\
+ [ 1.33525912e-01, 8.31094371e+00, 9.49439563e-17],\
+ [ 4.00409716e-02, 1.17568780e+01, 6.50970965e-32]])},RTOL)
+ np.testing.assert_allclose(reg.vm, np.array([[ 4.14625293e-04, 2.38494923e-05, -3.48748935e-03,\
+ -5.55994101e-04, -1.63239040e-04],[ 2.38494923e-05, 4.53642714e-04, -2.00602452e-04,\
+ -5.46893937e-04, -3.10498019e-03],[ -3.48748935e-03, -2.00602452e-04, 7.09989591e-01,\
+ 2.11105214e-01, 6.39785285e-02],[ -5.55994101e-04, -5.46893937e-04, 2.11105214e-01,\
+ 3.42890248e-01, 1.91931389e-01],[ -1.63239040e-04, -3.10498019e-03, 6.39785285e-02,\
+ 1.91931389e-01, 5.86933821e-01]]),RTOL)
+ np.testing.assert_allclose(reg.lamsetp,(np.array([[ 0.02036235],\
+ [ 0.02129889]]), np.array([[ 26.69730489],[ 23.68454458]]), np.array([[ 5.059048e-157],\
+ [ 5.202838e-124]])),RTOL)
+ np.testing.assert_allclose(reg.joinlam,(1207.81269, 2, 5.330924e-263))
+ np.testing.assert_allclose(reg.surchow,[(5.1073696860799931, 1, 0.023824413482255974),
+ (1.9524745281321374, 1, 0.16232044613203933),
+ (0.79663667463065702, 1, 0.37210085476281407)],RTOL)
+ np.testing.assert_allclose(reg.likrlambda,(1014.0319285186415, 2, 6.3938800607190098e-221))
+ np.testing.assert_allclose(reg.lrtest, (287.95821154104488, 1, 1.3849971230596533e-64))
+ np.testing.assert_allclose(reg.lamtest, (1.8693306894921564, 1, 0.17155175615429052))
+
+ def test_error_3eq(self): #Three equation example, unequal K
+ y_var1 = ['HR60','HR70','HR80']
+ x_var1 = [['RD60','PS60'],['RD70','PS70','UE70'],['RD80','PS80']]
+ bigy1,bigX1,bigyvars1,bigXvars1 = sur_dictxy(self.db,y_var1,x_var1)
+ reg = SURerrorML(bigy1,bigX1,self.w,name_bigy=bigyvars1,name_bigX=bigXvars1,\
+ name_w="natqueen",name_ds="natregimes")
+
+ dict_compare(reg.bSUR0,{0: np.array([[ 4.50407527],[ 2.39199682],[ 0.52723694]]), 1: np.array([[ 7.44509818],\
+ [ 3.74968571],[ 1.28811685],[-0.23526451]]), 2: np.array([[ 6.92761614],[ 3.65423052],\
+ [ 1.38247611]])},RTOL)
+ dict_compare(reg.bSUR,{0: np.array([[ 4.474891 ],[ 2.19004379],[ 0.59110509]]), 1: np.array([[ 7.15676612],\
+ [ 3.49581077],[ 1.12846288],[-0.17133968]]), 2: np.array([[ 6.91550936],[ 3.69351192],\
+ [ 1.40395543]])},RTOL)
+ dict_compare(reg.sur_inf,{0: np.array([[ 1.345557e-001, 3.325679e+001, 1.628656e-242],\
+ [ 1.205317e-001, 1.816985e+001, 8.943986e-074],\
+ [ 1.092657e-001, 5.409796e+000, 6.309653e-008]]),\
+ 1: np.array([[ 2.957692e-001, 2.419713e+001, 2.384951e-129],\
+ [ 1.482144e-001, 2.358618e+001, 5.343318e-123],\
+ [ 1.344687e-001, 8.392014e+000, 4.778835e-017],\
+ [ 5.378335e-002, -3.185738e+000, 1.443854e-003]]),\
+ 2: np.array([[ 1.500528e-001, 4.608718e+001, 0.000000e+000],\
+ [ 1.236340e-001, 2.987457e+001, 4.210941e-196],\
+ [ 1.194989e-001, 1.174869e+001, 7.172248e-032]])},RTOL)
+ np.testing.assert_allclose(reg.lamols,np.array([[ 0.4248829 ],[ 0.46428101],[ 0.42823999]]),RTOL)
+ np.testing.assert_allclose(reg.lamsur,np.array([[ 0.36137603],[ 0.38321666],[ 0.37183716]]),RTOL)
+ np.testing.assert_allclose(reg.corr,np.array([[ 1., 0.24563253, 0.14986527],\
+ [ 0.24563253, 1., 0.25945021],[ 0.14986527, 0.25945021, 1. ]]),RTOL)
+ np.testing.assert_allclose(reg.llik,-28695.767676078722)
+ np.testing.assert_allclose(reg.errllik,-28593.569427945633)
+ np.testing.assert_allclose(reg.surerrllik,-28393.703607018397)
+ np.testing.assert_allclose(reg.lrtest,(399.7316418544724, 3, 2.5309501580053097e-86))
+
+
+if __name__ == '__main__':
+ unittest.main()
+
diff --git a/pysal/spreg/tests/test_sur_lag.py b/pysal/spreg/tests/test_sur_lag.py
new file mode 100644
index 0000000..f1d1c7d
--- /dev/null
+++ b/pysal/spreg/tests/test_sur_lag.py
@@ -0,0 +1,176 @@
+import unittest
+import numpy as np
+import pysal
+from pysal.spreg.sur_utils import sur_dictxy,sur_dictZ
+from pysal.spreg.sur_lag import SURlagIV
+from pysal.common import RTOL
+
+PEGP = pysal.examples.get_path
+
+def dict_compare(actual, desired, rtol):
+ for i in actual.keys():
+ np.testing.assert_allclose(actual[i],desired[i],rtol)
+
+
+class Test_SURlagIV(unittest.TestCase):
+ def setUp(self):
+ self.db = pysal.open(pysal.examples.get_path('NAT.dbf'),'r')
+ self.w = pysal.queen_from_shapefile(pysal.examples.get_path("NAT.shp"))
+ self.w.transform = 'r'
+
+
+ def test_3SLS(self): #2 equations, same K in each
+ y_var0 = ['HR80','HR90']
+ x_var0 = [['PS80','UE80'],['PS90','UE90']]
+ bigy0,bigX0,bigyvars0,bigXvars0 = sur_dictxy(self.db,y_var0,x_var0)
+ reg = SURlagIV(bigy0,bigX0,w=self.w,name_bigy=bigyvars0,name_bigX=bigXvars0,\
+ name_ds="NAT",name_w="nat_queen")
+
+ dict_compare(reg.b3SLS,{0: np.array([[ 4.79766641],[ 0.66900706],[ 0.45430715],\
+ [-0.13665465]]), 1: np.array([[ 2.27972563],[ 0.99252289],[ 0.52280565],[ 0.06909469]])},RTOL)
+ dict_compare(reg.tsls_inf,{0: np.array([[ 4.55824001e+00, 1.05252606e+00, 2.92558259e-01],\
+ [ 3.54744447e-01, 1.88588453e+00, 5.93105171e-02],\
+ [ 7.79071951e-02, 5.83138887e+00, 5.49679157e-09],\
+ [ 6.74318852e-01, -2.02655838e-01, 8.39404043e-01]]),\
+ 1: np.array([[ 3.90351092e-01, 5.84019280e+00, 5.21404469e-09],\
+ [ 1.21674079e-01, 8.15722547e+00, 3.42808098e-16],\
+ [ 4.47686969e-02, 1.16779288e+01, 1.65273681e-31],\
+ [ 7.99640809e-02, 8.64071585e-01, 3.87548567e-01]])},RTOL)
+ np.testing.assert_allclose(reg.corr,np.array([[ 1. , 0.525751],
+ [ 0.525751, 1. ]]),RTOL)
+ np.testing.assert_allclose(reg.surchow,[(0.3178787640240518, 1, 0.57288522734425285),\
+ (1.0261877219299562, 1, 0.31105574708021311),\
+ (0.76852435750330428, 1, 0.38067394159083323),\
+ (0.099802260814129934, 1, 0.75206705793155604)],RTOL)
+
+ def test_3SLS_3eq(self): #Three equations, no endogenous
+ y_var1 = ['HR60','HR70','HR80']
+ x_var1 = [['RD60','PS60'],['RD70','PS70','UE70'],['RD80','PS80']]
+ bigy1,bigX1,bigyvars1,bigXvars1 = sur_dictxy(self.db,y_var1,x_var1)
+ reg = SURlagIV(bigy1,bigX1,w=self.w,name_bigy=bigyvars1,name_bigX=bigXvars1,\
+ name_ds="NAT",name_w="nat_queen")
+
+ dict_compare(reg.b2SLS,{0: np.array([[ 2.42754085],[ 1.48928052],[ 0.33812558],\
+ [ 0.45567848]]), 1: np.array([[ 4.83887747],[ 2.86272903],[ 0.96950417],\
+ [-0.12928124],[ 0.33328525]]), 2: np.array([[ 6.69407561],[ 3.81449588],\
+ [ 1.44603996],[ 0.03355501]])},RTOL)
+ dict_compare(reg.b3SLS,{0: np.array([[ 2.1646724 ],[ 1.31916307],[ 0.3398716 ],
+ [ 0.51336281]]), 1: np.array([[ 4.87587006],[ 2.68927603],
+ [ 0.94945336],[-0.145607 ],[ 0.33901794]]), 2: np.array([[ 6.48848271],
+ [ 3.53936913],[ 1.34731149],[ 0.06309451]])},RTOL)
+ dict_compare(reg.tsls_inf,{0: np.array([[ 3.51568531e-01, 6.15718476e+00, 7.40494437e-10],\
+ [ 1.86875349e-01, 7.05905340e+00, 1.67640650e-12],\
+ [ 9.04557549e-02, 3.75732426e+00, 1.71739894e-04],\
+ [ 7.48661202e-02, 6.85707782e+00, 7.02833502e-12]]),\
+ 1: np.array([[ 4.72342840e-01, 1.03227352e+01, 5.56158073e-25],\
+ [ 2.12539934e-01, 1.26530388e+01, 1.07629786e-36],\
+ [ 1.21325632e-01, 7.82566179e+00, 5.04993280e-15],\
+ [ 4.61662438e-02, -3.15397123e+00, 1.61064963e-03],\
+ [ 5.41804741e-02, 6.25719766e+00, 3.91956530e-10]]),\
+ 2: np.array([[ 3.36526688e-001, 1.92807374e+001, 7.79572152e-083],\
+ [ 1.59012676e-001, 2.22584087e+001, 9.35079396e-110],\
+ [ 1.08370073e-001, 1.24325052e+001, 1.74091603e-035],\
+ [ 4.61776859e-002, 1.36634202e+000, 1.71831639e-001]])},RTOL)
+
+ reg = SURlagIV(bigy1,bigX1,w=self.w,w_lags=2,name_bigy=bigyvars1,name_bigX=bigXvars1,\
+ name_ds="NAT",name_w="nat_queen")
+
+ dict_compare(reg.b3SLS,{0: np.array([[ 1.77468937],[ 1.14510457],[ 0.30768813],\
+ [ 0.5989414 ]]), 1: np.array([[ 4.26823484],[ 2.43651351],[ 0.8683601 ],[-0.12672555],\
+ [ 0.4208373 ]]), 2: np.array([[ 6.02334209],[ 3.38056146],[ 1.30003556],[ 0.12992573]])},RTOL)
+ dict_compare(reg.tsls_inf,{0: np.array([[ 3.27608281e-01, 5.41710779e+00, 6.05708284e-08],\
+ [ 1.76245578e-01, 6.49721025e+00, 8.18230736e-11],\
+ [ 8.95068772e-02, 3.43759205e+00, 5.86911195e-04],\
+ [ 6.94610221e-02, 8.62269771e+00, 6.53949186e-18]]),\
+ 1: np.array([[ 4.52225005e-01, 9.43829906e+00, 3.78879655e-21],\
+ [ 2.03807701e-01, 1.19549629e+01, 6.11608551e-33],\
+ [ 1.19004906e-01, 7.29684281e+00, 2.94598624e-13],\
+ [ 4.57552474e-02, -2.76963964e+00, 5.61183429e-03],\
+ [ 5.13101239e-02, 8.20183745e+00, 2.36740266e-16]]),\
+ 2: np.array([[ 3.27580342e-001, 1.83873735e+001, 1.65820984e-075],\
+ [ 1.55771577e-001, 2.17020429e+001, 1.96247435e-104],\
+ [ 1.06817752e-001, 1.21705946e+001, 4.45822889e-034],\
+ [ 4.48871540e-002, 2.89449691e+000, 3.79766647e-003]])},RTOL)
+
+ def test_3SLS_3eq_end(self): #Three equations, two endogenous, three instruments
+ y_var2 = ['HR60','HR70','HR80']
+ x_var2 = [['RD60','PS60'],['RD70','PS70','MA70'],['RD80','PS80']]
+ yend_var2 = [['UE60','DV60'],['UE70','DV70'],['UE80','DV80']]
+ q_var2 = [['FH60','FP59','GI59'],['FH70','FP69','GI69'],['FH80','FP79','GI79']]
+ bigy2,bigX2,bigyvars2,bigXvars2 = sur_dictxy(self.db,y_var2,x_var2)
+ bigyend2,bigyendvars2 = sur_dictZ(self.db,yend_var2)
+ bigq2,bigqvars2 = sur_dictZ(self.db,q_var2)
+ reg = SURlagIV(bigy2,bigX2,bigyend2,bigq2,w=self.w,name_bigy=bigyvars2,name_bigX=bigXvars2,\
+ name_bigyend=bigyendvars2,name_bigq=bigqvars2,spat_diag=True,name_ds="NAT",name_w="nat_queen")
+
+ dict_compare(reg.b2SLS,{0: np.array([[-2.36265226],[ 1.69785946],[ 0.65777251],[-0.07519173],[ 2.15755822],\
+ [ 0.69200015]]), 1: np.array([[ 8.13716008],[ 3.28583832],[ 0.90311859],[-0.21702098],[-1.04365606],\
+ [ 2.8597322 ],[ 0.39935589]]), 2: np.array([[-5.8117312 ],[ 3.49934818],[ 0.56523782],[ 0.09653315],\
+ [ 2.31166815],[ 0.20602185]])},RTOL)
+ dict_compare(reg.b3SLS,{0: np.array([[-2.33115839],[ 1.43097732],[ 0.57312948],[ 0.03474891],[ 1.78825098],\
+ [ 0.7145636 ]]), 1: np.array([[ 8.34932294],[ 3.28396774],[ 0.95119978],[-0.19323687],[-1.1750583 ],\
+ [ 2.75925141],[ 0.38544424]]), 2: np.array([[-5.2395274 ],[ 3.38941755],[ 0.55897901],[ 0.08212108],\
+ [ 2.19387428],[ 0.21582944]])},RTOL)
+ dict_compare(reg.tsls_inf,{0: np.array([[ 7.31246733e-01, -3.18792315e+00, 1.43298614e-03],\
+ [ 2.07089585e-01, 6.90994348e+00, 4.84846854e-12],\
+ [ 1.15296751e-01, 4.97090750e+00, 6.66402399e-07],\
+ [ 8.75272616e-02, 3.97006755e-01, 6.91362479e-01],\
+ [ 3.10638495e-01, 5.75669472e+00, 8.57768262e-09],\
+ [ 5.40333500e-02, 1.32244919e+01, 6.33639937e-40]]),\
+ 1: np.array([[ 1.71703190e+00, 4.86264870e+00, 1.15825305e-06],\
+ [ 2.79253520e-01, 1.17598079e+01, 6.28772226e-32],\
+ [ 1.27575632e-01, 7.45596763e+00, 8.92106480e-14],\
+ [ 3.31742265e-02, -5.82490950e+00, 5.71435564e-09],\
+ [ 2.19785746e-01, -5.34638083e+00, 8.97303096e-08],\
+ [ 3.29882178e-01, 8.36435430e+00, 6.04450321e-17],\
+ [ 5.54968909e-02, 6.94533032e+00, 3.77575814e-12]]),\
+ 2: np.array([[ 9.77398092e-01, -5.36068920e+00, 8.29050465e-08],\
+ [ 1.67632600e-01, 2.02193222e+01, 6.61862485e-91],\
+ [ 1.24321379e-01, 4.49624202e+00, 6.91650078e-06],\
+ [ 6.94834624e-02, 1.18187957e+00, 2.37253491e-01],\
+ [ 1.68013780e-01, 1.30577045e+01, 5.74336064e-39],\
+ [ 4.16751208e-02, 5.17885587e+00, 2.23250870e-07]])},RTOL)
+ np.testing.assert_allclose(reg.joinrho,(215.897034, 3, 1.54744730e-46))
+
+ def test_3SLS_3eq_2or(self): # Second order spatial lags, no instrument lags
+ y_var2 = ['HR60','HR70','HR80']
+ x_var2 = [['RD60','PS60'],['RD70','PS70','MA70'],['RD80','PS80']]
+ yend_var2 = [['UE60','DV60'],['UE70','DV70'],['UE80','DV80']]
+ q_var2 = [['FH60','FP59','GI59'],['FH70','FP69','GI69'],['FH80','FP79','GI79']]
+
+ bigy2,bigX2,bigyvars2,bigXvars2 = sur_dictxy(self.db,y_var2,x_var2)
+ bigyend2,bigyendvars2 = sur_dictZ(self.db,yend_var2)
+ bigq2,bigqvars2 = sur_dictZ(self.db,q_var2)
+ reg = SURlagIV(bigy2,bigX2,bigyend2,bigq2,w=self.w,w_lags=2,lag_q=False,\
+ name_bigy=bigyvars2,name_bigX=bigXvars2,\
+ name_bigyend=bigyendvars2,name_bigq=bigqvars2,\
+ name_ds="NAT",name_w="nat_queen")
+
+ dict_compare(reg.b3SLS,{0: np.array([[-2.40071969],[ 1.2933015 ],[ 0.53165876],[ 0.04883189],[ 1.6663233 ],\
+ [ 0.76473297]]), 1: np.array([[ 7.24987963],[ 2.96110365],[ 0.86322179],[-0.17847268],[-1.1332928 ],\
+ [ 2.69573919],[ 0.48295237]]), 2: np.array([[-7.55692635],[ 3.17561152],[ 0.37487877],[ 0.1816544 ],\
+ [ 2.45768258],[ 0.27716717]])},RTOL)
+ dict_compare(reg.tsls_inf,{0: np.array([[ 7.28635609e-01, -3.29481522e+00, 9.84864177e-04],\
+ [ 2.44756930e-01, 5.28402406e+00, 1.26376643e-07],\
+ [ 1.26021571e-01, 4.21879172e+00, 2.45615028e-05],\
+ [ 1.03323393e-01, 4.72612122e-01, 6.36489932e-01],\
+ [ 3.48694501e-01, 4.77874843e+00, 1.76389726e-06],\
+ [ 6.10435763e-02, 1.25276568e+01, 5.26966810e-36]]),\
+ 1: np.array([[ 1.76286536e+00, 4.11255436e+00, 3.91305295e-05],\
+ [ 2.78649343e-01, 1.06266306e+01, 2.24061686e-26],\
+ [ 1.28607242e-01, 6.71207766e+00, 1.91872523e-11],\
+ [ 3.21721548e-02, -5.54742685e+00, 2.89904383e-08],\
+ [ 2.09773378e-01, -5.40246249e+00, 6.57322045e-08],\
+ [ 3.06806758e-01, 8.78644007e+00, 1.54373978e-18],\
+ [ 5.88231798e-02, 8.21023915e+00, 2.20748374e-16]]),\
+ 2: np.array([[ 1.10429601e+00, -6.84320712e+00, 7.74395589e-12],\
+ [ 1.81002635e-01, 1.75445597e+01, 6.54581911e-69],\
+ [ 1.33983129e-01, 2.79795505e+00, 5.14272697e-03],\
+ [ 7.56814009e-02, 2.40025154e+00, 1.63838090e-02],\
+ [ 1.83365858e-01, 1.34031635e+01, 5.79398038e-41],\
+ [ 4.61324726e-02, 6.00807101e+00, 1.87743612e-09]])},RTOL)
+
+
+if __name__ == '__main__':
+ unittest.main()
+
diff --git a/pysal/spreg/tests/test_twosls.py b/pysal/spreg/tests/test_twosls.py
index 71021e3..cd99ab5 100644
--- a/pysal/spreg/tests/test_twosls.py
+++ b/pysal/spreg/tests/test_twosls.py
@@ -2,6 +2,7 @@ import unittest
import numpy as np
import pysal
from pysal.spreg.twosls import BaseTSLS, TSLS
+from pysal.common import RTOL
class TestBaseTSLS(unittest.TestCase):
def setUp(self):
@@ -22,84 +23,85 @@ class TestBaseTSLS(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
h_0 = np.array([ 1. , 19.531, 5.03 ])
- np.testing.assert_array_almost_equal(reg.h[0], h_0)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.hth, hth,RTOL)
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)
+ np.testing.assert_allclose(reg.hthi, hthi,RTOL)
self.assertEqual(reg.k, 3)
self.assertEqual(reg.kstar, 1)
- self.assertAlmostEqual(reg.mean_y, 35.128823897959187, 7)
+ np.testing.assert_allclose(reg.mean_y, 35.128823897959187,RTOL)
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)
+ np.testing.assert_allclose(reg.pfora1a2, pfora1a2,RTOL)
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)
+ np.testing.assert_allclose(reg.predy[0:5], predy_5,RTOL)
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)
+ np.testing.assert_allclose(reg.sig2n_k, 587.56797852699822,RTOL)
+ np.testing.assert_allclose(reg.sig2n, 551.5944288212637,RTOL)
+ np.testing.assert_allclose(reg.sig2, 551.5944288212637,RTOL)
+ np.testing.assert_allclose(reg.std_y, 16.732092091229699,RTOL)
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)
+ np.testing.assert_allclose(reg.u[0:5], u_5,RTOL)
+ np.testing.assert_allclose(reg.utu, 27028.127012241919,RTOL)
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)
+ np.testing.assert_allclose(reg.varb, varb,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
x_0 = np.array([ 1. , 19.531])
- np.testing.assert_array_almost_equal(reg.x[0], x_0, 7)
+ np.testing.assert_allclose(reg.x[0], x_0,RTOL)
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)
+ np.testing.assert_allclose(reg.y[0:5], y_5,RTOL)
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)
+ np.testing.assert_allclose(reg.yend[0:5], yend_5,RTOL)
z_0 = np.array([ 1. , 19.531 , 80.467003])
- np.testing.assert_array_almost_equal(reg.z[0], z_0, 7)
+ np.testing.assert_allclose(reg.z[0], z_0,RTOL)
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_allclose(reg.zthhthi, zthhthi, RTOL)
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
class TestTSLS(unittest.TestCase):
def setUp(self):
@@ -119,105 +121,106 @@ class TestTSLS(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
h_0 = np.array([ 1. , 19.531, 5.03 ])
- np.testing.assert_array_almost_equal(reg.h[0], h_0)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.hth, hth,RTOL)
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)
+ np.testing.assert_allclose(reg.hthi, hthi,RTOL)
self.assertEqual(reg.k, 3)
self.assertEqual(reg.kstar, 1)
- self.assertAlmostEqual(reg.mean_y, 35.128823897959187, 7)
+ np.testing.assert_allclose(reg.mean_y, 35.128823897959187,RTOL)
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)
+ np.testing.assert_allclose(reg.pfora1a2, pfora1a2,RTOL)
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)
+ np.testing.assert_allclose(reg.predy[0:5], predy_5,RTOL)
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)
+ np.testing.assert_allclose(reg.sig2n_k, 587.56797852699822,RTOL)
+ np.testing.assert_allclose(reg.sig2n, 551.5944288212637,RTOL)
+ np.testing.assert_allclose(reg.sig2, 551.5944288212637,RTOL)
+ np.testing.assert_allclose(reg.std_y, 16.732092091229699,RTOL)
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)
+ np.testing.assert_allclose(reg.u[0:5], u_5,RTOL)
+ np.testing.assert_allclose(reg.utu, 27028.127012241919,RTOL)
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)
+ np.testing.assert_allclose(reg.varb, varb,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
x_0 = np.array([ 1. , 19.531])
- np.testing.assert_array_almost_equal(reg.x[0], x_0, 7)
+ np.testing.assert_allclose(reg.x[0], x_0,RTOL)
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)
+ np.testing.assert_allclose(reg.y[0:5], y_5,RTOL)
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)
+ np.testing.assert_allclose(reg.yend[0:5], yend_5,RTOL)
z_0 = np.array([ 1. , 19.531 , 80.467003])
- np.testing.assert_array_almost_equal(reg.z[0], z_0, 7)
+ np.testing.assert_allclose(reg.z[0], z_0,RTOL)
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_allclose(reg.zthhthi, zthhthi,RTOL)
np.testing.assert_array_almost_equal(reg.zthhthi, zthhthi, 7)
- self.assertAlmostEqual(reg.pr2, 0.27936137128173893, 7)
+ np.testing.assert_allclose(reg.pr2, 0.27936137128173893,RTOL)
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)
+ np.testing.assert_allclose(reg.z_stat, z_stat,RTOL)
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
ak_test = np.array([ 1.16816972, 0.27977763])
- np.testing.assert_array_almost_equal(reg.ak_test, ak_test, 7)
+ np.testing.assert_allclose(reg.ak_test, ak_test,RTOL)
def test_names(self):
w = pysal.queen_from_shapefile(pysal.examples.get_path('columbus.shp'))
@@ -234,11 +237,11 @@ class TestTSLS(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
self.assertListEqual(reg.name_x, ['CONSTANT']+name_x)
self.assertListEqual(reg.name_yend, name_yend)
self.assertListEqual(reg.name_q, name_q)
diff --git a/pysal/spreg/tests/test_twosls_regimes.py b/pysal/spreg/tests/test_twosls_regimes.py
index 155bf03..a5862f7 100644
--- a/pysal/spreg/tests/test_twosls_regimes.py
+++ b/pysal/spreg/tests/test_twosls_regimes.py
@@ -3,6 +3,7 @@ import numpy as np
import pysal
from pysal.spreg.twosls_regimes import TSLS_Regimes
from pysal.spreg.twosls import TSLS
+from pysal.common import RTOL
class TestTSLS(unittest.TestCase):
def setUp(self):
@@ -24,9 +25,9 @@ class TestTSLS(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.h[0]*np.eye(6), h_0, RTOL)
hth = np.array([[ 25. , 416.378999 , 0. , 0. ,\
76.03 , 0. ],\
[ 416.378999 , 7831.05477839, 0. , 0. ,\
@@ -39,7 +40,7 @@ class TestTSLS(unittest.TestCase):
291.9749 , 0. ],\
[ 0. , 0. , 63.72 , 827.47378 ,\
0. , 206.6102 ]])
- np.testing.assert_array_almost_equal(reg.hth, hth, 7)
+ np.testing.assert_allclose(reg.hth, hth,RTOL)
hthi = np.array([[ 0.3507855 , -0.0175615 , 0. , 0. , -0.00601601,\
0. ],\
[-0.0175615 , 0.00194521, -0. , -0. , -0.00487844,\
@@ -52,10 +53,10 @@ class TestTSLS(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.hthi, hthi,RTOL)
+ self.assertEqual(reg.k,6)
self.assertEqual(reg.kstar, 2)
- self.assertAlmostEqual(reg.mean_y, 35.128823897959187, 7)
+ np.testing.assert_allclose(reg.mean_y, 35.128823897959187,RTOL)
np.testing.assert_equal(reg.kf, 0)
np.testing.assert_equal(reg.kr, 3)
np.testing.assert_equal(reg.n, 49)
@@ -72,18 +73,18 @@ class TestTSLS(unittest.TestCase):
1.01810757, 0. ],\
[ 0. , 0. , -5.42403871, -0.6641704 ,\
0. , 0.34027606]])
- np.testing.assert_array_almost_equal(reg.pfora1a2, pfora1a2, 7)
+ np.testing.assert_allclose(reg.pfora1a2, pfora1a2,RTOL)
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)
+ np.testing.assert_allclose(reg.predy[0:5], predy_5,RTOL)
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)
+ np.testing.assert_allclose(reg.sig2n_k, 990.00750983736714,RTOL)
+ np.testing.assert_allclose(reg.sig2n, 868.78210046952631,RTOL)
+ np.testing.assert_allclose(reg.sig2, 990.00750983736714,RTOL)
+ np.testing.assert_allclose(reg.std_y, 16.732092091229699,RTOL)
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)
+ np.testing.assert_allclose(reg.u[0:5], u_5,RTOL)
+ np.testing.assert_allclose(reg.utu, 42570.322923006788,RTOL)
varb = np.array([[ 0.50015831, 0.07969376, 0. , 0. , -0.04760541,\
0. ],\
[ 0.07969376, 0.06523527, 0. , 0. , -0.03105915,\
@@ -96,7 +97,7 @@ class TestTSLS(unittest.TestCase):
-0. ],\
[-0. , -0. , -0.02117969, -0.00259344, -0. ,\
0.0013287 ]])
- np.testing.assert_array_almost_equal(reg.varb, varb, 7)
+ np.testing.assert_allclose(reg.varb, varb,RTOL)
vm = np.array([[ 495.16048523, 78.89742341, 0. , 0. ,\
-47.12971066, 0. ],\
[ 78.89742341, 64.58341083, 0. , 0. ,\
@@ -109,15 +110,15 @@ class TestTSLS(unittest.TestCase):
14.89456384, 0. ],\
[ 0. , 0. , -20.96804956, -2.56752553,\
0. , 1.3154267 ]])
- np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
x_0 = np.array([[ 0. , 0. , 1. , 19.531]])
- np.testing.assert_array_almost_equal(reg.x[0]*np.eye(4), x_0, 7)
+ np.testing.assert_allclose(reg.x[0]*np.eye(4), x_0,RTOL)
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)
+ np.testing.assert_allclose(reg.y[0:5], y_5,RTOL)
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)
+ np.testing.assert_allclose(reg.yend[0:3]*np.eye(2), yend_3,RTOL)
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)
+ np.testing.assert_allclose(reg.z[0]*np.eye(6), z_0,RTOL)
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,\
@@ -130,27 +131,28 @@ class TestTSLS(unittest.TestCase):
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_allclose(reg.zthhthi, zthhthi, RTOL) #why does this fail??
np.testing.assert_array_almost_equal(reg.zthhthi, zthhthi, 7)
- self.assertAlmostEqual(reg.pr2, 0.17729324026706564, 7)
+ np.testing.assert_allclose(reg.pr2, 0.17729324026706564,RTOL)
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)
+ np.testing.assert_allclose(np.array(reg.z_stat), z_stat,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi, chow_regi,RTOL)
+ np.testing.assert_allclose(reg.chow.joint[0], 1.1353790779820598,RTOL)
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
vm = np.array([[ 495.16048523, 78.89742341, 0. , 0. ,\
-47.12971066, 0. ],\
[ 78.89742341, 64.58341083, 0. , 0. ,\
@@ -163,13 +165,13 @@ class TestTSLS(unittest.TestCase):
14.89456384, 0. ],\
[ 0. , 0. , -20.96804956, -2.56752553,\
0. , 1.3154267 ]])
- np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
vm = np.array([[ 495.16048523, 78.89742341, 0. , 0. ,\
-47.12971066, 0. ],\
[ 78.89742341, 64.58341083, 0. , 0. ,\
@@ -182,9 +184,9 @@ class TestTSLS(unittest.TestCase):
14.89456384, 0. ],\
[ 0. , 0. , -20.96804956, -2.56752553,\
0. , 1.3154267 ]])
- np.testing.assert_array_almost_equal(reg.vm, vm, 7)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
ak_test = np.array([ 0.69774552, 0.40354227])
- np.testing.assert_array_almost_equal(reg.ak_test, ak_test, 7)
+ np.testing.assert_allclose(reg.ak_test, ak_test,RTOL)
def test_names(self):
w = pysal.queen_from_shapefile(pysal.examples.get_path('columbus.shp'))
@@ -202,7 +204,7 @@ class TestTSLS(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
vm = np.array([[ 522.75813101, 120.64940697, -15.60303241, -0.976389 ,\
-67.15556574, 0.64553579],\
[ 122.83491674, 122.62303068, -5.52270916, 0.05023488,\
@@ -215,7 +217,7 @@ class TestTSLS(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
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'])
@@ -234,14 +236,14 @@ class TestTSLS(unittest.TestCase):
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)
+ regi = [0]*(n//2) + [1]*(n//2) ##must be floor!
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)
+ 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)
+ np.testing.assert_allclose(model.betas,tbetas)
vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
- np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 6)
+ np.testing.assert_allclose(model.vm.diagonal(), vm,RTOL)
#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],
@@ -250,23 +252,23 @@ class TestTSLS(unittest.TestCase):
[ 0.49775429],
[ -3.72663211],
[ -1.27451485],])
- np.testing.assert_array_almost_equal(tbetas, reg.betas)
+ np.testing.assert_allclose(tbetas, reg.betas,RTOL)
vm = np.array([ 495.16048523, 78.89742341, 0. , 0. ,
-47.12971066, 0. ])
- np.testing.assert_array_almost_equal(reg.vm[0], vm, 6)
+ np.testing.assert_allclose(reg.vm[0], vm,RTOL)
u_3 = np.array([[ 25.57676372],
[-17.94922796],
[-26.71588759]])
- np.testing.assert_array_almost_equal(reg.u[0:3], u_3, 7)
+ np.testing.assert_allclose(reg.u[0:3], u_3,RTOL)
predy_3 = np.array([[ -9.85078372],
[ 36.75098196],
[ 57.34266859]])
- np.testing.assert_array_almost_equal(reg.predy[0:3], predy_3, 7)
+ np.testing.assert_allclose(reg.predy[0:3], predy_3,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi, chow_regi,RTOL)
+ np.testing.assert_allclose(reg.chow.joint[0], 1.1353790779821029,RTOL)
if __name__ == '__main__':
start_suppress = np.get_printoptions()['suppress']
diff --git a/pysal/spreg/tests/test_twosls_sp.py b/pysal/spreg/tests/test_twosls_sp.py
index 914aef7..0ac98d4 100644
--- a/pysal/spreg/tests/test_twosls_sp.py
+++ b/pysal/spreg/tests/test_twosls_sp.py
@@ -3,6 +3,7 @@ import numpy as np
import pysal
import pysal.spreg.diagnostics as D
from pysal.spreg.twosls_sp import BaseGM_Lag, GM_Lag
+from pysal.common import RTOL
class TestBaseGMLag(unittest.TestCase):
def setUp(self):
@@ -22,52 +23,52 @@ class TestBaseGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.hth[0], hth,RTOL)
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)
+ np.testing.assert_allclose(reg.hthi[0], hthi,RTOL)
self.assertEqual(reg.k, 4)
self.assertEqual(reg.kstar, 1)
- self.assertAlmostEqual(reg.mean_y, 38.436224469387746, 7)
+ np.testing.assert_allclose(reg.mean_y, 38.436224469387746,RTOL)
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)
+ np.testing.assert_allclose(reg.pfora1a2[0], pfora1a2,RTOL)
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)
+ np.testing.assert_allclose(reg.predy[0:5], predy_5,RTOL)
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)
+ np.testing.assert_allclose(reg.q[0], q_5)
+ np.testing.assert_allclose(reg.sig2n_k, 234.54258763039289,RTOL)
+ np.testing.assert_allclose(reg.sig2n, 215.39625394627919,RTOL)
+ np.testing.assert_allclose(reg.sig2, 215.39625394627919,RTOL)
+ np.testing.assert_allclose(reg.std_y, 18.466069465206047,RTOL)
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)
+ np.testing.assert_allclose(reg.u[0:5], u_5,RTOL)
+ np.testing.assert_allclose(reg.utu, 10554.41644336768,RTOL)
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)
+ np.testing.assert_allclose(reg.varb, varb,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
x_0 = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x[0], x_0, 7)
+ np.testing.assert_allclose(reg.x[0], x_0,RTOL)
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)
+ np.testing.assert_allclose(reg.y[0:5], y_5,RTOL)
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)
+ np.testing.assert_allclose(reg.yend[0:5], yend_5,RTOL)
z_0 = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
- np.testing.assert_array_almost_equal(reg.z[0], z_0, 7)
+ np.testing.assert_allclose(reg.z[0], z_0,RTOL)
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,
@@ -76,6 +77,7 @@ class TestBaseGMLag(unittest.TestCase):
-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_allclose(reg.zthhthi, zthhthi, RTOL) WHYYYY
np.testing.assert_array_almost_equal(reg.zthhthi, zthhthi, 7)
def test_init_white_(self):
@@ -88,10 +90,10 @@ class TestBaseGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(dbetas, se_betas)
def test_init_hac_(self):
w_lags = 2
@@ -104,10 +106,10 @@ class TestBaseGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(dbetas, se_betas)
def test_init_discbd(self):
w_lags = 2
@@ -121,10 +123,10 @@ class TestBaseGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(dbetas, se_betas)
def test_n_k(self):
w_lags = 2
@@ -136,12 +138,12 @@ class TestBaseGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
def test_lag_q(self):
w_lags = 2
@@ -155,10 +157,10 @@ class TestBaseGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(dbetas, se_betas)
@@ -177,59 +179,59 @@ class TestGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.e_pred[0:5], e_5,RTOL)
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.hth[0], hth,RTOL)
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)
+ np.testing.assert_allclose(reg.hthi[0], hthi,RTOL)
self.assertEqual(reg.k, 4)
self.assertEqual(reg.kstar, 1)
- self.assertAlmostEqual(reg.mean_y, 38.436224469387746, 7)
+ np.testing.assert_allclose(reg.mean_y, 38.436224469387746,RTOL)
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)
+ np.testing.assert_allclose(reg.pr2, 0.3551928222612527,RTOL)
+ np.testing.assert_allclose(reg.pr2_e, 0.34763857386174174,RTOL)
+ np.testing.assert_allclose(reg.pfora1a2[0], pfora1a2,RTOL)
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)
+ np.testing.assert_allclose(reg.predy[0:5], predy_5,RTOL)
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)
+ np.testing.assert_allclose(reg.predy_e[0:5], predy_e_5,RTOL)
q_5 = np.array([ 18.594 , 24.7142675 , 13.72216667, 27.82929567])
- np.testing.assert_array_almost_equal(reg.q[0], q_5)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.sig2n_k, 234.54258763039289,RTOL)
+ np.testing.assert_allclose(reg.sig2n, 215.39625394627919,RTOL)
+ np.testing.assert_allclose(reg.sig2, 215.39625394627919,RTOL)
+ np.testing.assert_allclose(reg.std_y, 18.466069465206047,RTOL)
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)
+ np.testing.assert_allclose(reg.u[0:5], u_5,RTOL)
+ np.testing.assert_allclose(reg.utu, 10554.41644336768,RTOL)
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)
+ np.testing.assert_allclose(reg.varb, varb,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
x_0 = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x[0], x_0, 7)
+ np.testing.assert_allclose(reg.x[0], x_0,RTOL)
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)
+ np.testing.assert_allclose(reg.y[0:5], y_5,RTOL)
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)
+ np.testing.assert_allclose(reg.yend[0:5], yend_5,RTOL)
z_0 = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
- np.testing.assert_array_almost_equal(reg.z[0], z_0, 7)
+ np.testing.assert_allclose(reg.z[0], z_0,RTOL)
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,
@@ -238,6 +240,7 @@ class TestGMLag(unittest.TestCase):
-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_allclose(reg.zthhthi, zthhthi RTOL) #another issue with rtol
np.testing.assert_array_almost_equal(reg.zthhthi, zthhthi, 7)
def test_init_white_(self):
@@ -247,10 +250,10 @@ class TestGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(dbetas, se_betas)
def test_init_hac_(self):
X = []
@@ -260,10 +263,10 @@ class TestGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(dbetas, se_betas)
def test_init_discbd(self):
X = np.array(self.db.by_col("INC"))
@@ -274,10 +277,10 @@ class TestGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(dbetas, se_betas)
def test_n_k(self):
X = []
@@ -286,12 +289,12 @@ class TestGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
def test_lag_q(self):
X = np.array(self.db.by_col("INC"))
@@ -302,10 +305,10 @@ class TestGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(dbetas, se_betas)
def test_spatial(self):
X = np.array(self.db.by_col("INC"))
@@ -317,14 +320,14 @@ class TestGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
ak_test = np.array([ 2.52597326, 0.11198567])
- np.testing.assert_array_almost_equal(reg.ak_test, ak_test, 7)
+ np.testing.assert_allclose(reg.ak_test, ak_test,RTOL)
def test_names(self):
X = np.array(self.db.by_col("INC"))
@@ -347,12 +350,12 @@ class TestGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
self.assertListEqual(reg.name_x, ['CONSTANT']+name_x)
name_yend.append('W_crime')
self.assertListEqual(reg.name_yend, name_yend)
diff --git a/pysal/spreg/tests/test_twosls_sp_regimes.py b/pysal/spreg/tests/test_twosls_sp_regimes.py
index b311b17..8f814a9 100644
--- a/pysal/spreg/tests/test_twosls_sp_regimes.py
+++ b/pysal/spreg/tests/test_twosls_sp_regimes.py
@@ -4,6 +4,7 @@ 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
+from pysal.common import RTOL
class TestGMLag_Regimes(unittest.TestCase):
def setUp(self):
@@ -29,87 +30,88 @@ class TestGMLag_Regimes(unittest.TestCase):
[ -0.81498302],
[ -0.28391409],
[ 0.4736163 ]])
- np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.e_pred[0:5], e_5,RTOL)
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.mean_y, 35.128823897959187,RTOL)
self.assertEqual(reg.n, 49)
- self.assertAlmostEqual(reg.pr2, 0.6572182131915739, 7)
- self.assertAlmostEqual(reg.pr2_e, 0.5779687278635434, 7)
+ np.testing.assert_allclose(reg.pr2, 0.6572182131915739,RTOL)
+ np.testing.assert_allclose(reg.pr2_e, 0.5779687278635434,RTOL)
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)
+ np.testing.assert_allclose(reg.pfora1a2[0], pfora1a2,RTOL)
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)
+ np.testing.assert_allclose(reg.predy[0:5], predy_5,RTOL)
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)
+ np.testing.assert_allclose(reg.predy_e[0:5], predy_e_5,RTOL)
q_5 = np.array([[ 0. , 0. , 18.594 , 35.4585005]])
- np.testing.assert_array_almost_equal(reg.q[0]*np.eye(4), q_5)
+ np.testing.assert_allclose(reg.q[0]*np.eye(4), q_5, RTOL)
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)
+ np.testing.assert_allclose(reg.sig2n_k, 109.76462904625834,RTOL)
+ np.testing.assert_allclose(reg.sig2n, 94.08396775393571,RTOL)
+ np.testing.assert_allclose(reg.sig2, 109.76462904625834,RTOL)
+ np.testing.assert_allclose(reg.std_y, 16.732092091229699,RTOL)
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)
+ np.testing.assert_allclose(reg.u[0:5], u_5,RTOL)
+ np.testing.assert_allclose(reg.utu, 4610.11441994285,RTOL)
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)
+ np.testing.assert_allclose(reg.varb[0], varb,RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0], vm,RTOL)
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)
+ np.testing.assert_allclose(reg.x[0]*np.eye(6), x_0,RTOL)
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)
+ np.testing.assert_allclose(reg.y[0:5], y_5,RTOL)
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)
+ np.testing.assert_allclose(reg.yend[0:5]*np.array([[1]]), yend_5,RTOL)
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)
+ np.testing.assert_allclose(reg.z[0]*np.eye(7), z_0,RTOL)
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)
+ # np.testing.assert_allclose(reg.zthhthi[0], zthhthi, RTOL)
+ np.testing.assert_array_almost_equal(reg.zthhthi[0], zthhthi)
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)
+ np.testing.assert_allclose(reg.chow.regi, chow_regi,RTOL)
+ np.testing.assert_allclose(reg.chow.joint[0], 0.82409867601863462,RTOL)
def test_init_discbd(self):
#Matches SpaceStat.
@@ -127,31 +129,31 @@ class TestGMLag_Regimes(unittest.TestCase):
[ -0.68305796],
[ -0.37106077],
[ 0.55809516]])
- np.testing.assert_array_almost_equal(tbetas, reg.betas)
+ np.testing.assert_allclose(tbetas, reg.betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0], vm,RTOL)
e_3 = np.array([[-0.33142796],
[-9.51719607],
[-7.86272153]])
- np.testing.assert_array_almost_equal(reg.e_pred[0:3], e_3, 7)
+ np.testing.assert_allclose(reg.e_pred[0:3], e_3,RTOL)
u_3 = np.array([[ 4.51839601],
[-5.67363147],
[-5.1927562 ]])
- np.testing.assert_array_almost_equal(reg.u[0:3], u_3, 7)
+ np.testing.assert_allclose(reg.u[0:3], u_3,RTOL)
predy_3 = np.array([[ 11.20758399],
[ 24.47538547],
[ 35.8195372 ]])
- np.testing.assert_array_almost_equal(reg.predy[0:3], predy_3, 7)
+ np.testing.assert_allclose(reg.predy[0:3], predy_3,RTOL)
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)
+ np.testing.assert_allclose(reg.predy_e[0:3], predy_e_3,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi, chow_regi,RTOL)
+ np.testing.assert_allclose(reg.chow.joint[0], 0.31248100032096549,RTOL)
def test_lag_q(self):
X = np.array(self.db.by_col("INC"))
@@ -168,15 +170,15 @@ class TestGMLag_Regimes(unittest.TestCase):
[ -0.28494432],
[ -0.2294271 ],
[ 0.62996544]])
- np.testing.assert_array_almost_equal(tbetas, reg.betas)
+ np.testing.assert_allclose(tbetas, reg.betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0], vm,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi, chow_regi,RTOL)
+ np.testing.assert_allclose(reg.chow.joint[0], 0.54288190938307757,RTOL)
def test_all_regi(self):
X = np.array(self.db.by_col("INC"))
@@ -188,31 +190,31 @@ class TestGMLag_Regimes(unittest.TestCase):
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)
+ np.testing.assert_allclose(tbetas, reg.betas.T,RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0], vm,RTOL)
e_3 = np.array([[-2.66997799],
[-7.69786264],
[-4.39412782]])
- np.testing.assert_array_almost_equal(reg.e_pred[0:3], e_3, 7)
+ np.testing.assert_allclose(reg.e_pred[0:3], e_3,RTOL)
u_3 = np.array([[ 1.13879007],
[-3.76873198],
[-1.89671717]])
- np.testing.assert_array_almost_equal(reg.u[0:3], u_3, 7)
+ np.testing.assert_allclose(reg.u[0:3], u_3,RTOL)
predy_3 = np.array([[ 14.58718993],
[ 22.57048598],
[ 32.52349817]])
- np.testing.assert_array_almost_equal(reg.predy[0:3], predy_3, 7)
+ np.testing.assert_allclose(reg.predy[0:3], predy_3,RTOL)
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)
+ np.testing.assert_allclose(reg.predy_e[0:3], predy_e_3,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi, chow_regi,RTOL)
+ np.testing.assert_allclose(reg.chow.joint[0], 0.88634854058300516,RTOL)
def test_all_regi_sig2(self):
#Artficial:
@@ -225,16 +227,16 @@ class TestGMLag_Regimes(unittest.TestCase):
latt = int(np.sqrt(n))
w = pysal.lat2W(latt,latt)
w.transform='r'
- regi = [0]*(n/2) + [1]*(n/2)
+ 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 = 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)
+ 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)
+ np.testing.assert_allclose(model.betas,tbetas)
vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
- np.testing.assert_array_almost_equal(model.vm.diagonal(), vm, 6)
+ np.testing.assert_allclose(model.vm.diagonal(), vm,RTOL)
#Columbus:
X = np.array(self.db.by_col("INC"))
X = np.reshape(X, (49,1))
@@ -251,32 +253,32 @@ class TestGMLag_Regimes(unittest.TestCase):
[ -0.12304063],
[ -0.46840307],
[ 0.67108156]])
- np.testing.assert_array_almost_equal(tbetas, reg.betas)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.vm[0], vm,RTOL)
e_3 = np.array([[ -1.32209547],
[-13.15611199],
[-11.62357696]])
- np.testing.assert_array_almost_equal(reg.e_pred[0:3], e_3, 7)
+ np.testing.assert_allclose(reg.e_pred[0:3], e_3,RTOL)
u_3 = np.array([[ 6.99250069],
[-7.5665856 ],
[-7.04753328]])
- np.testing.assert_array_almost_equal(reg.u[0:3], u_3, 7)
+ np.testing.assert_allclose(reg.u[0:3], u_3,RTOL)
predy_3 = np.array([[ 8.73347931],
[ 26.3683396 ],
[ 37.67431428]])
- np.testing.assert_array_almost_equal(reg.predy[0:3], predy_3, 7)
+ np.testing.assert_allclose(reg.predy[0:3], predy_3,RTOL)
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)
+ np.testing.assert_allclose(reg.predy_e[0:3], predy_e_3,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi, chow_regi,RTOL)
+ np.testing.assert_allclose(reg.chow.joint[0], 0.38417230022512161,RTOL)
def test_fixed_const(self):
X = np.array(self.db.by_col("INC"))
@@ -292,30 +294,30 @@ class TestGMLag_Regimes(unittest.TestCase):
[ -0.45793559],
[ -0.24216904],
[ 0.62500602]])
- np.testing.assert_array_almost_equal(tbetas, reg.betas)
+ np.testing.assert_allclose(tbetas, reg.betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0], vm,RTOL)
e_3 = np.array([[ 0.17317815],
[-5.53766328],
[-3.82889307]])
- np.testing.assert_array_almost_equal(reg.e_pred[0:3], e_3, 7)
+ np.testing.assert_allclose(reg.e_pred[0:3], e_3,RTOL)
u_3 = np.array([[ 3.10025518],
[-1.83150689],
[-1.49598494]])
- np.testing.assert_array_almost_equal(reg.u[0:3], u_3, 7)
+ np.testing.assert_allclose(reg.u[0:3], u_3,RTOL)
predy_3 = np.array([[ 12.62572482],
[ 20.63326089],
[ 32.12276594]])
- np.testing.assert_array_almost_equal(reg.predy[0:3], predy_3, 7)
+ np.testing.assert_allclose(reg.predy[0:3], predy_3,RTOL)
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)
+ np.testing.assert_allclose(reg.predy_e[0:3], predy_e_3,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi, chow_regi,RTOL)
+ np.testing.assert_allclose(reg.chow.joint[0], 12.017256217621382,RTOL)
def test_names(self):
y_var = 'CRIME'
@@ -334,15 +336,15 @@ class TestGMLag_Regimes(unittest.TestCase):
[ -0.28494432],
[ -0.2294271 ],
[ 0.62996544]])
- np.testing.assert_array_almost_equal(reg.betas, betas, 7)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm[0], vm,RTOL)
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)
+ np.testing.assert_allclose(reg.chow.regi, chow_regi,RTOL)
+ np.testing.assert_allclose(reg.chow.joint[0], 0.63336222761359162,RTOL)
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'])
diff --git a/pysal/spreg/tests/test_twosls_sp_sparse.py b/pysal/spreg/tests/test_twosls_sp_sparse.py
index e6669e0..65152cd 100644
--- a/pysal/spreg/tests/test_twosls_sp_sparse.py
+++ b/pysal/spreg/tests/test_twosls_sp_sparse.py
@@ -1,10 +1,10 @@
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
-
+from pysal.spreg.twosls_sp import BaseGM_Lag, GM_Lag
+from pysal.common import RTOL
class TestBaseGMLag(unittest.TestCase):
def setUp(self):
@@ -24,52 +24,52 @@ class TestBaseGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.hth[0], hth,RTOL)
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)
+ np.testing.assert_allclose(reg.hthi[0], hthi,RTOL)
self.assertEqual(reg.k, 4)
self.assertEqual(reg.kstar, 1)
- self.assertAlmostEqual(reg.mean_y, 38.436224469387746, 7)
+ np.testing.assert_allclose(reg.mean_y, 38.436224469387746,RTOL)
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)
+ np.testing.assert_allclose(reg.pfora1a2[0], pfora1a2,RTOL)
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)
+ np.testing.assert_allclose(reg.predy[0:5], predy_5,RTOL)
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)
+ np.testing.assert_allclose(reg.q[0], q_5)
+ np.testing.assert_allclose(reg.sig2n_k, 234.54258763039289,RTOL)
+ np.testing.assert_allclose(reg.sig2n, 215.39625394627919,RTOL)
+ np.testing.assert_allclose(reg.sig2, 215.39625394627919,RTOL)
+ np.testing.assert_allclose(reg.std_y, 18.466069465206047,RTOL)
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)
+ np.testing.assert_allclose(reg.u[0:5], u_5,RTOL)
+ np.testing.assert_allclose(reg.utu, 10554.41644336768,RTOL)
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)
+ np.testing.assert_allclose(reg.varb, varb,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
x_0 = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x.toarray()[0], x_0, 7)
+ np.testing.assert_allclose(reg.x.toarray()[0], x_0,RTOL)
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)
+ np.testing.assert_allclose(reg.y[0:5], y_5,RTOL)
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)
+ np.testing.assert_allclose(reg.yend[0:5], yend_5,RTOL)
z_0 = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
- np.testing.assert_array_almost_equal(reg.z.toarray()[0], z_0, 7)
+ np.testing.assert_allclose(reg.z.toarray()[0], z_0,RTOL)
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,
@@ -78,6 +78,7 @@ class TestBaseGMLag(unittest.TestCase):
-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_allclose(reg.zthhthi, zthhthi,RTOL)
np.testing.assert_array_almost_equal(reg.zthhthi, zthhthi, 7)
def test_init_white_(self):
@@ -90,10 +91,10 @@ class TestBaseGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(dbetas, se_betas)
def test_init_hac_(self):
X = []
@@ -106,10 +107,10 @@ class TestBaseGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(dbetas, se_betas)
def test_init_discbd(self):
X = np.array(self.db.by_col("INC"))
@@ -123,10 +124,10 @@ class TestBaseGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(dbetas, se_betas)
def test_n_k(self):
X = []
@@ -138,12 +139,12 @@ class TestBaseGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
def test_lag_q(self):
X = np.array(self.db.by_col("INC"))
@@ -157,10 +158,10 @@ class TestBaseGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(dbetas, se_betas)
@@ -180,59 +181,59 @@ class TestGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.e_pred[0:5], e_5,RTOL)
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.hth[0], hth,RTOL)
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)
+ np.testing.assert_allclose(reg.hthi[0], hthi,RTOL)
self.assertEqual(reg.k, 4)
self.assertEqual(reg.kstar, 1)
- self.assertAlmostEqual(reg.mean_y, 38.436224469387746, 7)
+ np.testing.assert_allclose(reg.mean_y, 38.436224469387746,RTOL)
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)
+ np.testing.assert_allclose(reg.pr2, 0.3551928222612527,RTOL)
+ np.testing.assert_allclose(reg.pr2_e, 0.34763857386174174,RTOL)
+ np.testing.assert_allclose(reg.pfora1a2[0], pfora1a2,RTOL)
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)
+ np.testing.assert_allclose(reg.predy[0:5], predy_5,RTOL)
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)
+ np.testing.assert_allclose(reg.predy_e[0:5], predy_e_5,RTOL)
q_5 = np.array([ 18.594 , 24.7142675 , 13.72216667, 27.82929567])
- np.testing.assert_array_almost_equal(reg.q.toarray()[0], q_5)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.sig2n_k, 234.54258763039289,RTOL)
+ np.testing.assert_allclose(reg.sig2n, 215.39625394627919,RTOL)
+ np.testing.assert_allclose(reg.sig2, 215.39625394627919,RTOL)
+ np.testing.assert_allclose(reg.std_y, 18.466069465206047,RTOL)
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)
+ np.testing.assert_allclose(reg.u[0:5], u_5,RTOL)
+ np.testing.assert_allclose(reg.utu, 10554.41644336768,RTOL)
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)
+ np.testing.assert_allclose(reg.varb, varb,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
x_0 = np.array([ 1. , 19.531 , 15.72598])
- np.testing.assert_array_almost_equal(reg.x.toarray()[0], x_0, 7)
+ np.testing.assert_allclose(reg.x.toarray()[0], x_0,RTOL)
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)
+ np.testing.assert_allclose(reg.y[0:5], y_5,RTOL)
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)
+ np.testing.assert_allclose(reg.yend[0:5], yend_5,RTOL)
z_0 = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
- np.testing.assert_array_almost_equal(reg.z.toarray()[0], z_0, 7)
+ np.testing.assert_allclose(reg.z.toarray()[0], z_0,RTOL)
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,
@@ -241,6 +242,7 @@ class TestGMLag(unittest.TestCase):
-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_allclose(reg.zthhthi, zthhthi,RTOL)
np.testing.assert_array_almost_equal(reg.zthhthi, zthhthi, 7)
def test_init_white_(self):
@@ -251,10 +253,10 @@ class TestGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(dbetas, se_betas)
def test_init_hac_(self):
X = []
@@ -265,10 +267,10 @@ class TestGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(dbetas, se_betas)
def test_init_discbd(self):
X = np.array(self.db.by_col("INC"))
@@ -280,10 +282,10 @@ class TestGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(dbetas, se_betas)
def test_n_k(self):
X = []
@@ -293,12 +295,12 @@ class TestGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
def test_lag_q(self):
X = np.array(self.db.by_col("INC"))
@@ -310,10 +312,10 @@ class TestGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(dbetas, se_betas)
def test_spatial(self):
X = np.array(self.db.by_col("INC"))
@@ -326,14 +328,14 @@ class TestGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
ak_test = np.array([ 2.52597326, 0.11198567])
- np.testing.assert_array_almost_equal(reg.ak_test, ak_test, 7)
+ np.testing.assert_allclose(reg.ak_test, ak_test,RTOL)
def test_names(self):
X = np.array(self.db.by_col("INC"))
@@ -357,12 +359,12 @@ class TestGMLag(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
self.assertListEqual(reg.name_x, ['CONSTANT']+name_x)
name_yend.append('W_crime')
self.assertListEqual(reg.name_yend, name_yend)
diff --git a/pysal/spreg/tests/test_twosls_sparse.py b/pysal/spreg/tests/test_twosls_sparse.py
index 654dd5a..68509a0 100644
--- a/pysal/spreg/tests/test_twosls_sparse.py
+++ b/pysal/spreg/tests/test_twosls_sparse.py
@@ -3,7 +3,7 @@ import numpy as np
import pysal
from pysal.spreg.twosls import TSLS, BaseTSLS
from scipy import sparse as SP
-
+from pysal.common import RTOL
class TestBaseTSLS(unittest.TestCase):
def setUp(self):
@@ -25,84 +25,84 @@ class TestBaseTSLS(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
h_0 = np.array([ 1. , 19.531, 5.03 ])
- np.testing.assert_array_almost_equal(reg.h.toarray()[0], h_0)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.hth, hth,RTOL)
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)
+ np.testing.assert_allclose(reg.hthi, hthi,RTOL)
self.assertEqual(reg.k, 3)
self.assertEqual(reg.kstar, 1)
- self.assertAlmostEqual(reg.mean_y, 35.128823897959187, 7)
+ np.testing.assert_allclose(reg.mean_y, 35.128823897959187,RTOL)
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)
+ np.testing.assert_allclose(reg.pfora1a2, pfora1a2,RTOL)
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)
+ np.testing.assert_allclose(reg.predy[0:5], predy_5,RTOL)
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)
+ np.testing.assert_allclose(reg.sig2n_k, 587.56797852699822,RTOL)
+ np.testing.assert_allclose(reg.sig2n, 551.5944288212637,RTOL)
+ np.testing.assert_allclose(reg.sig2, 551.5944288212637,RTOL)
+ np.testing.assert_allclose(reg.std_y, 16.732092091229699,RTOL)
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)
+ np.testing.assert_allclose(reg.u[0:5], u_5,RTOL)
+ np.testing.assert_allclose(reg.utu, 27028.127012241919,RTOL)
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)
+ np.testing.assert_allclose(reg.varb, varb,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
x_0 = np.array([ 1. , 19.531])
- np.testing.assert_array_almost_equal(reg.x.toarray()[0], x_0, 7)
+ np.testing.assert_allclose(reg.x.toarray()[0], x_0,RTOL)
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)
+ np.testing.assert_allclose(reg.y[0:5], y_5,RTOL)
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)
+ np.testing.assert_allclose(reg.yend[0:5], yend_5,RTOL)
z_0 = np.array([ 1. , 19.531 , 80.467003])
- np.testing.assert_array_almost_equal(reg.z.toarray()[0], z_0, 7)
+ np.testing.assert_allclose(reg.z.toarray()[0], z_0,RTOL)
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_allclose(reg.zthhthi, zthhthi,RTOL)
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
class TestTSLS(unittest.TestCase):
def setUp(self):
@@ -123,105 +123,106 @@ class TestTSLS(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
h_0 = np.array([ 1. , 19.531, 5.03 ])
- np.testing.assert_array_almost_equal(reg.h.toarray()[0], h_0)
+ np.testing.assert_allclose(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)
+ np.testing.assert_allclose(reg.hth, hth,RTOL)
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)
+ np.testing.assert_allclose(reg.hthi, hthi,RTOL)
self.assertEqual(reg.k, 3)
self.assertEqual(reg.kstar, 1)
- self.assertAlmostEqual(reg.mean_y, 35.128823897959187, 7)
+ np.testing.assert_allclose(reg.mean_y, 35.128823897959187,RTOL)
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)
+ np.testing.assert_allclose(reg.pfora1a2, pfora1a2,RTOL)
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)
+ np.testing.assert_allclose(reg.predy[0:5], predy_5,RTOL)
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)
+ np.testing.assert_allclose(reg.sig2n_k, 587.56797852699822,RTOL)
+ np.testing.assert_allclose(reg.sig2n, 551.5944288212637,RTOL)
+ np.testing.assert_allclose(reg.sig2, 551.5944288212637,RTOL)
+ np.testing.assert_allclose(reg.std_y, 16.732092091229699,RTOL)
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)
+ np.testing.assert_allclose(reg.u[0:5], u_5,RTOL)
+ np.testing.assert_allclose(reg.utu, 27028.127012241919,RTOL)
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)
+ np.testing.assert_allclose(reg.varb, varb,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
x_0 = np.array([ 1. , 19.531])
- np.testing.assert_array_almost_equal(reg.x.toarray()[0], x_0, 7)
+ np.testing.assert_allclose(reg.x.toarray()[0], x_0,RTOL)
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)
+ np.testing.assert_allclose(reg.y[0:5], y_5,RTOL)
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)
+ np.testing.assert_allclose(reg.yend[0:5], yend_5,RTOL)
z_0 = np.array([ 1. , 19.531 , 80.467003])
- np.testing.assert_array_almost_equal(reg.z.toarray()[0], z_0, 7)
+ np.testing.assert_allclose(reg.z.toarray()[0], z_0,RTOL)
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)
+ # np.testing.assert_allclose(reg.zthhthi, zthhthi,RTOL)
+ np.testing.assert_array_almost_equal(reg.zthhthi, zthhthi,7)
+ np.testing.assert_allclose(reg.pr2, 0.27936137128173893,RTOL)
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)
+ np.testing.assert_allclose(reg.z_stat, z_stat,RTOL)
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
ak_test = np.array([ 1.16816972, 0.27977763])
- np.testing.assert_array_almost_equal(reg.ak_test, ak_test, 7)
+ np.testing.assert_allclose(reg.ak_test, ak_test,RTOL)
def test_names(self):
w = pysal.queen_from_shapefile(pysal.examples.get_path('columbus.shp'))
@@ -238,11 +239,11 @@ class TestTSLS(unittest.TestCase):
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)
+ np.testing.assert_allclose(reg.betas, betas,RTOL)
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)
+ np.testing.assert_allclose(reg.vm, vm,RTOL)
self.assertListEqual(reg.name_x, ['CONSTANT']+name_x)
self.assertListEqual(reg.name_yend, name_yend)
self.assertListEqual(reg.name_q, name_q)
diff --git a/pysal/spreg/twosls.py b/pysal/spreg/twosls.py
index a00e7ab..4f2f1b5 100755
--- a/pysal/spreg/twosls.py
+++ b/pysal/spreg/twosls.py
@@ -185,7 +185,6 @@ class BaseTSLS(RegressionPropsY, RegressionPropsVM):
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:
@@ -200,10 +199,24 @@ class BaseTSLS(RegressionPropsY, RegressionPropsVM):
@property
def vm(self):
- if 'vm' not in self._cache:
+ try:
+ return self._cache['vm']
+ except AttributeError:
+ self._cache = {}
+ self._cache['vm'] = np.dot(self.sig2, self.varb)
+ except KeyError:
self._cache['vm'] = np.dot(self.sig2, self.varb)
return self._cache['vm']
+ @vm.setter
+ def vm(self, val):
+ try:
+ self._cache['vm'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['vm'] = val
+ except KeyError:
+ self._cache['vm'] = val
class TSLS(BaseTSLS):
diff --git a/pysal/spreg/utils.py b/pysal/spreg/utils.py
index a1f2b6c..3d455d4 100755
--- a/pysal/spreg/utils.py
+++ b/pysal/spreg/utils.py
@@ -5,17 +5,19 @@ 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"
+ Daniel Arribas-Bel darribas at asu.edu,\
+ Levi Wolf levi.john.wolf at gmail.com"
import numpy as np
from scipy import sparse as SP
+from scipy.sparse import linalg as SPla
import scipy.optimize as op
import numpy.linalg as la
from pysal import lag_spatial
import copy
-class RegressionPropsY:
+class RegressionPropsY(object):
"""
Helper class that adds common regression properties to any regression
@@ -36,18 +38,48 @@ class RegressionPropsY:
@property
def mean_y(self):
- if 'mean_y' not in self._cache:
+ try:
+ return self._cache['mean_y']
+ except AttributeError:
+ self._cache = {}
+ self._cache['mean_y'] = np.mean(self.y)
+ except KeyError:
self._cache['mean_y'] = np.mean(self.y)
return self._cache['mean_y']
+
+ @mean_y.setter
+ def mean_y(self, val):
+ try:
+ self._cache['mean_y'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['mean_y'] = val
+ except KeyError:
+ self._cache['mean_y'] = val
@property
def std_y(self):
- if 'std_y' not in self._cache:
+ try:
+ return self._cache['std_y']
+ except AttributeError:
+ self._cache = {}
+ self._cache['std_y'] = np.std(self.y, ddof=1)
+ except KeyError:
self._cache['std_y'] = np.std(self.y, ddof=1)
return self._cache['std_y']
+
+ @std_y.setter
+ def std_y(self, val):
+ try:
+ self._cache['std_y'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['std_y'] = val
+ except KeyError:
+ self._cache['std_y'] = val
-class RegressionPropsVM:
+class RegressionPropsVM(object):
"""
Helper class that adds common regression properties to any regression
@@ -72,27 +104,88 @@ class RegressionPropsVM:
@property
def utu(self):
- if 'utu' not in self._cache:
+ try:
+ return self._cache['utu']
+ except AttributeError:
+ self._cache = {}
+ self._cache['utu'] = np.sum(self.u ** 2)
+ except KeyError:
self._cache['utu'] = np.sum(self.u ** 2)
return self._cache['utu']
+ @utu.setter
+ def utu(self, val):
+ try:
+ self._cache['utu'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['utu'] = val
+ except KeyError:
+ self._cache['utu'] = val
+
@property
def sig2n(self):
- if 'sig2n' not in self._cache:
+ try:
+ return self._cache['sig2n']
+ except AttributeError:
+ self._cache = {}
+ self._cache['sig2n'] = self.utu / self.n
+ except KeyError:
self._cache['sig2n'] = self.utu / self.n
return self._cache['sig2n']
+ @sig2n.setter
+ def sig2n(self, val):
+ try:
+ self._cache['sig2n'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['sig2n'] = val
+ except KeyError:
+ self._cache['sig2n'] = val
+
@property
def sig2n_k(self):
- if 'sig2n_k' not in self._cache:
+ try:
+ return self._cache['sig2n_k']
+ except AttributeError:
+ self._cache = {}
+ self._cache['sig2n_k'] = self.utu / (self.n - self.k)
+ except KeyError:
self._cache['sig2n_k'] = self.utu / (self.n - self.k)
return self._cache['sig2n_k']
+
+ @sig2n_k.setter
+ def sig2n_k(self, val):
+ try:
+ self._cache['sig2n_k'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['sig2n_k'] = val
+ except KeyError:
+ self._cache['sig2n_k'] = val
@property
def vm(self):
- if 'vm' not in self._cache:
+ try:
+ return self._cache['vm']
+ except AttributeError:
+ self._cache = {}
self._cache['vm'] = np.dot(self.sig2, self.xtxi)
- return self._cache['vm']
+ except KeyError:
+ self._cache['vm'] = np.dot(self.sig2, self.xtxi)
+ finally:
+ return self._cache['vm']
+
+ @vm.setter
+ def vm(self, val):
+ try:
+ self._cache['vm'] = val
+ except AttributeError:
+ self._cache = {}
+ self._cache['vm'] = val
+ except KeyError:
+ self._cache['vm'] = val
def get_A1_het(S):
@@ -781,6 +874,25 @@ def set_warn(reg, warn):
else:
pass
+def splogdet(spmat):
+ ''' Large dimension log determinant computation. '''
+ #symmetric = np.allclose(spmat.T, spmat) #could branch for cholmod
+ symmetric = False
+ if symmetric:
+ #CHOLMOD could be used like:
+ #from scikits.sparse import cholmod as CHOLMOD
+ #det = np.sum(np.log(np.abs(CHOLMOD.cholesky(spmat).D())))
+ pass #leave in for scaffolding.
+ else:
+ if isinstance(spmat, SP.csc_matrix) or isinstance(spmat, SP.csr_matrix):
+ LU = SPla.splu(spmat)
+ det = np.sum(np.log(np.abs(LU.U.diagonal())))
+ elif SP.isspmatrix(spmat):
+ return spdet(spmat.tocsc())
+ else:
+ det = la.slogdet(spmat)
+ return det
+
def RegressionProps_basic(reg, betas=None, predy=None, u=None, sig2=None, sig2n_k=None, vm=None):
''' Set props based on arguments passed. '''
diff --git a/pysal/version.py b/pysal/version.py
index fe891b7..11da3db 100644
--- a/pysal/version.py
+++ b/pysal/version.py
@@ -1,3 +1,3 @@
import datetime
-version = "1.10.0"
-stable_release_date = datetime.date(2015, 7, 29)
+version = "1.11.0"
+stable_release_date = datetime.date(2016, 1, 27)
diff --git a/pysal/weights/Distance.py b/pysal/weights/Distance.py
index 1296365..1727b88 100644
--- a/pysal/weights/Distance.py
+++ b/pysal/weights/Distance.py
@@ -170,13 +170,13 @@ class Kernel(W):
Attributes
----------
weights : dict
- Dictionary keyed by id with a list of weights for each neighbor
+ Dictionary keyed by id with a list of weights for each neighbor
neighbors : dict
- of lists of neighbors keyed by observation id
+ of lists of neighbors keyed by observation id
bandwidth : array
- array of bandwidths
+ array of bandwidths
Examples
--------
@@ -338,7 +338,7 @@ class Kernel(W):
for i, nids in enumerate(self.neigh):
di, ni = kdtq(self.data[i], k=len(nids))
if not isinstance(di, np.ndarray):
- di = np.asarray([di] * len(nids))
+ di = np.asarray([di] * len(nids))
ni = np.asarray([ni] * len(nids))
zi = np.array([dict(zip(ni, di))[nid] for nid in nids]) / bw[i]
z.append(zi)
@@ -392,10 +392,10 @@ class DistanceBand(W):
Attributes
----------
weights : dict
- of neighbor weights keyed by observation id
+ of neighbor weights keyed by observation id
neighbors : dict
- of neighbors keyed by observation id
+ of neighbors keyed by observation id
Examples
--------
diff --git a/pysal/weights/_contW_binning.py b/pysal/weights/_contW_binning.py
index af81b76..b88bd1c 100644
--- a/pysal/weights/_contW_binning.py
+++ b/pysal/weights/_contW_binning.py
@@ -61,9 +61,9 @@ class ContiguityWeights_binning:
# bucket size
if (numPoly < SHP_SMALL):
- bucketmin = numPoly / BUCK_SM + 2
+ bucketmin = numPoly // BUCK_SM + 2
else:
- bucketmin = numPoly / BUCK_LG + 2
+ bucketmin = numPoly // BUCK_LG + 2
# print 'bucketmin: ', bucketmin
# bucket length
lengthx = ((shapebox[2] + DELTA) - shapebox[0]) / bucketmin
@@ -220,9 +220,9 @@ class ContiguityWeightsPolygons:
# bucket size
if (numPoly < SHP_SMALL):
- bucketmin = numPoly / BUCK_SM + 2
+ bucketmin = numPoly // BUCK_SM + 2
else:
- bucketmin = numPoly / BUCK_LG + 2
+ bucketmin = numPoly // BUCK_LG + 2
# print 'bucketmin: ', bucketmin
# bucket length
lengthx = ((shapebox[2] + DELTA) - shapebox[0]) / bucketmin
@@ -359,7 +359,7 @@ if __name__ == "__main__":
print "using " + str(fname)
print "time elapsed for rook... using bins: " + str(t1 - t0)
- from _contW_rtree import ContiguityWeights_rtree
+ from pysal.weights._contW_rtree import ContiguityWeights_rtree
t0 = time.time()
rt = ContiguityWeights_rtree(pysal.open(fname), ROOK)
diff --git a/pysal/weights/tests/test_Distance.py b/pysal/weights/tests/test_Distance.py
index 5294069..c60f74f 100644
--- a/pysal/weights/tests/test_Distance.py
+++ b/pysal/weights/tests/test_Distance.py
@@ -20,8 +20,8 @@ class TestDistanceWeights(unittest.TestCase):
pts = [i.centroid for i in pysal.open(self.polyShp)]
kd = pysal.cg.kdtree.KDTree(pts)
wnn4 = pysal.knnW(kd, 4)
- self.assertEqual(wnn4.neighbors[0], [2,1,3,7])
- self.assertEqual(wnn4.neighbors[7], [3,6,12,11])
+ self.assertEqual(set(wnn4.neighbors[0]), set([2,1,3,7]))
+ self.assertEqual(set(wnn4.neighbors[7]), set([3,6,12,11]))
def test_knnW_arc(self):
pts = [x.centroid for x in pysal.open(self.arcShp)]
@@ -32,15 +32,15 @@ class TestDistanceWeights(unittest.TestCase):
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(
+ 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])
+ wds = {kw.neighbors[0][i]: v for i, v in enumerate(kw.weights[0])}
+ self.assertEqual(wds, {0: 1, 1: 0.500000049999995, 3: 0.4409830615267465})
kw15 = pysal.Kernel(self.points, bandwidth=15.0)
self.assertEqual(kw15[0], {0: 1.0, 1: 0.33333333333333337,
3: 0.2546440075000701})
@@ -48,10 +48,9 @@ class TestDistanceWeights(unittest.TestCase):
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])
+ wds = {kwa.neighbors[0][i]: v for i, v in enumerate(kwa.weights[0])}
+ self.assertEqual(wds, {0: 1.0, 1: 0.59999999999999998,
+ 3: 0.55278640450004202, 4: 0.10557280900008403})
self.assertEqual(kwa.bandwidth[0], 25.)
self.assertEqual(kwa.bandwidth[1], 15.)
self.assertEqual(kwa.bandwidth[2], 25.)
@@ -59,44 +58,50 @@ class TestDistanceWeights(unittest.TestCase):
self.assertEqual(kwa.bandwidth[4], 14.5)
self.assertEqual(kwa.bandwidth[5], 25.)
kwea = pysal.Kernel(self.points, fixed=False)
+ wds = {kwea.neighbors[0][i]: v for i, v in enumerate(kw.weights[0])}
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]])
+ np.allclose(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])
+ wds = {kweag.neighbors[0][i]: v for i, v in enumerate(kweag.weights[0])}
+ self.assertEqual(wds, {0: 0.3989422804014327,
+ 1: 0.26741902915776961,
+ 3: 0.24197074871621341})
l = kweag.bandwidth.tolist()
- self.assertEqual(l, [[11.180341005532938], [11.180341005532938],
- [20.000002000000002], [11.180341005532938],
- [14.142137037944515], [18.027758180095585]])
+ np.allclose(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
- ]))
+ wds = {kw.neighbors[1][i]: v for i, v in enumerate(kw.weights[1])}
+ self.assertEqual(wds, {4: 0.0070787731484506233,
+ 2: 0.2052478782400463,
+ 3: 0.23051223027663237,
+ 1: 1.0})
kwa = pysal.adaptive_kernelW_from_shapefile(self.polyShp)
- self.assertEqual(kwa.weights[0], [1.0, 0.03178906767736345,
- 9.9999990066379496e-08])
+ wds = {kwa.neighbors[0][i]: v for i, v in enumerate(kwa.weights[0])}
+ self.assertEqual(wds, {0: 1.0, 2: 0.03178906767736345,
+ 1: 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])
+ wds = {wid.neighbors[0][i]: v for i, v in enumerate(wid.weights[0])}
+ self.assertEqual(wds, {1: 0.10000000000000001,
+ 3: 0.089442719099991588})
wid2 = pysal.threshold_continuousW_from_array(
self.points, 11.2, alpha=-2.0)
- self.assertEqual(wid2.weights[0], [0.01, 0.0079999999999999984])
+ wds = {wid2.neighbors[0][i]: v for i, v in enumerate(wid2.weights[0])}
+ self.assertEqual(wds, {1: 0.01, 3: 0.0079999999999999984})
w = pysal.threshold_continuousW_from_shapefile(
self.polyShp, 0.62, idVariable="POLYID")
- self.assertEqual(w.weights[1], [1.6702346893743334,
- 1.7250729841938093])
+ wds = {w.neighbors[1][i]: v for i, v in enumerate(w.weights[1])}
+ self.assertEqual(wds, {2: 1.6702346893743334,
+ 3: 1.7250729841938093})
def test_DistanceBand(self):
""" see issue #126 """
diff --git a/pysal/weights/tests/test_Wsets.py b/pysal/weights/tests/test_Wsets.py
index 6c15030..9bef022 100644
--- a/pysal/weights/tests/test_Wsets.py
+++ b/pysal/weights/tests/test_Wsets.py
@@ -12,9 +12,9 @@ class TestWsets(unittest.TestCase):
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])
+ self.assertEqual(set(w1.neighbors[15]), set([11, 14]))
+ self.assertEqual(set(w2.neighbors[15]), set([11, 14, 19]))
+ self.assertEqual(set(w3.neighbors[15]), set([19, 11, 14]))
def test_w_intersection(self):
"""Unit test"""
@@ -22,9 +22,9 @@ class TestWsets(unittest.TestCase):
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])
+ self.assertEqual(set(w1.neighbors[15]), set([11, 14]))
+ self.assertEqual(set(w2.neighbors[15]), set([11, 14, 19]))
+ self.assertEqual(set(w3.neighbors[15]), set([19, 11, 14]))
def test_w_difference(self):
"""Unit test"""
@@ -32,9 +32,9 @@ class TestWsets(unittest.TestCase):
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])
+ self.assertEqual(set(w1.neighbors[15]), set([10, 11, 14]))
+ self.assertEqual(set(w2.neighbors[15]), set([11, 14]))
+ self.assertEqual(set(w3.neighbors[15]), set([10]))
def test_w_symmetric_difference(self):
"""Unit test"""
@@ -43,9 +43,9 @@ class TestWsets(unittest.TestCase):
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])
+ self.assertEqual(set(w1.neighbors[15]), set([10, 11, 14]))
+ self.assertEqual(set(w2.neighbors[15]), set([11, 14, 19]))
+ self.assertEqual(set(w3.neighbors[15]), set([10, 19]))
def test_w_subset(self):
"""Unit test"""
@@ -53,8 +53,8 @@ class TestWsets(unittest.TestCase):
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])
+ self.assertEqual(set(w1.neighbors[15]), set([11, 14, 19]))
+ self.assertEqual(set(w2.neighbors[15]), set([11, 14]))
suite = unittest.TestLoader().loadTestsFromTestCase(TestWsets)
diff --git a/pysal/weights/tests/test_user.py b/pysal/weights/tests/test_user.py
index 7011dad..c6d37c1 100644
--- a/pysal/weights/tests/test_user.py
+++ b/pysal/weights/tests/test_user.py
@@ -71,23 +71,25 @@ class Testuser(unittest.TestCase):
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])
+ wds = {wid.neighbors[0][i]: v for i, v in enumerate(wid.weights[0])}
+ self.assertEqual(wds, {1: 0.10000000000000001,
+ 3: 0.089442719099991588})
wid2 = pysal.threshold_continuousW_from_array(points, 11.2, alpha=-2.0)
+ wds = {wid2.neighbors[0][i]: v for i, v in enumerate(wid2.weights[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])
+ wds = {w.neighbors[1][i]:v for i,v in enumerate(w.weights[1])}
+ self.assertEquals(wds, {2:1.6702346893743334, 3:1.7250729841938093})
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])
+ wds = {kw.neighbors[0][i]:v for i,v in enumerate(kw.weights[0])}
+ self.assertEquals(wds, {0:1.0, 1:0.50000004999999503,
+ 3:0.44098306152674649})
np.testing.assert_array_almost_equal(
kw.bandwidth, np.array([[20.000002],
[20.000002],
@@ -117,18 +119,18 @@ class Testuser(unittest.TestCase):
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])
+ wds = {kwa.neighbors[0][i]: v for i, v in enumerate(kwa.weights[0])}
+ self.assertEqual(wds, {0: 1.0, 1: 0.59999999999999998,
+ 3: 0.55278640450004202,
+ 4: 0.10557280900008403})
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])
+ wds = {kweag.neighbors[0][i]: v for i, v in enumerate(kweag.weights[0])}
+ self.assertEqual(wds, {0: 0.3989422804014327, 1: 0.26741902915776961,
+ 3: 0.24197074871621341})
np.testing.assert_array_almost_equal(kweag.bandwidth,
np.array([[11.18034101],
[11.18034101],
@@ -140,8 +142,9 @@ class Testuser(unittest.TestCase):
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])
+ wds = {kwa.neighbors[0][i]: v for i, v in enumerate(kwa.weights[0])}
+ self.assertEquals(wds, {0: 1.0, 2: 0.03178906767736345,
+ 1: 9.9999990066379496e-08})
np.testing.assert_array_almost_equal(kwa.bandwidth[:3],
np.array([[0.59871832],
[0.59871832],
diff --git a/pysal/weights/tests/test_util.py b/pysal/weights/tests/test_util.py
index 24cd3b1..65253bc 100644
--- a/pysal/weights/tests/test_util.py
+++ b/pysal/weights/tests/test_util.py
@@ -55,6 +55,13 @@ class Testutil(unittest.TestCase):
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)
+ ids = ['id-%i'%i for i in range(len(regimes))]
+ w = pysal.block_weights(regimes, ids=np.array(ids))
+ w0 = {'id-1': 1.0}
+ self.assertEquals(w['id-0'], w0)
+ w = pysal.block_weights(regimes, ids=ids)
+ w0 = {'id-1': 1.0}
+ self.assertEquals(w['id-0'], w0)
def test_comb(self):
x = range(4)
diff --git a/pysal/weights/tests/test_weights.py b/pysal/weights/tests/test_weights.py
index 91596d4..b790b64 100644
--- a/pysal/weights/tests/test_weights.py
+++ b/pysal/weights/tests/test_weights.py
@@ -10,10 +10,12 @@ class TestW(unittest.TestCase):
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.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)
@@ -41,7 +43,12 @@ class TestW(unittest.TestCase):
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)])
+ 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)
@@ -89,15 +96,19 @@ class TestW(unittest.TestCase):
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:
+ 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]}
+ 4: [8, 0, 2, 6], 5: [1, 3, 7], 6: [4, 0, 8], 7: [3, 1, 5],
+ 8: [6, 2, 4]}
+ wneighbs = {k: {neighb: weights[k][i] for i, neighb in enumerate(v)}
+ for k, v in neighbors.iteritems()}
w2 = pysal.higher_order(self.w3x3, 2)
- self.assertEqual(w2.neighbors, neighbors)
- self.assertEqual(w2.weights, weights)
+ test_wneighbs = {k: {ne: weights[k][i] for i, ne in enumerate(v)}
+ for k, v in w2.neighbors.iteritems()}
+ self.assertEqual(test_wneighbs, wneighbs)
def test_histogram(self):
hist = [(0, 1), (1, 1), (2, 4), (3, 20), (4, 57), (5, 44), (6, 36),
@@ -226,10 +237,12 @@ class Test_WSP_Back_To_W(unittest.TestCase):
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.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)
@@ -259,7 +272,12 @@ class Test_WSP_Back_To_W(unittest.TestCase):
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)])
+ 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(), [])
@@ -306,15 +324,19 @@ class Test_WSP_Back_To_W(unittest.TestCase):
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]}
+ 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]}
+ 4: [8, 0, 2, 6], 5: [1, 3, 7], 6: [4, 0, 8], 7: [3, 1, 5],
+ 8: [6, 2, 4]}
+ wneighbs = {k: {neighb: weights[k][i] for i, neighb in enumerate(v)}
+ for k, v in neighbors.iteritems()}
w2 = pysal.higher_order(self.w3x3, 2)
- self.assertEqual(w2.neighbors, neighbors)
- self.assertEqual(w2.weights, weights)
+ test_wneighbs = {k: {ne: w2.weights[k][i] for i, ne in enumerate(v)}
+ for k, v in w2.neighbors.iteritems()}
+ self.assertEqual(test_wneighbs, wneighbs)
def test_histogram(self):
hist = [(0, 1), (1, 1), (2, 4), (3, 20), (4, 57), (5, 44), (6, 36),
@@ -424,7 +446,6 @@ class Test_WSP_Back_To_W(unittest.TestCase):
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)
diff --git a/pysal/weights/util.py b/pysal/weights/util.py
index d822a25..5ddfe94 100644
--- a/pysal/weights/util.py
+++ b/pysal/weights/util.py
@@ -64,7 +64,7 @@ def hexLat2W(nrows=5, ncols=5):
return lat2W(nrows, ncols)
n = nrows * ncols
- rid = [i / ncols for i in xrange(n)]
+ rid = [i // ncols for i in xrange(n)]
cid = [i % ncols for i in xrange(n)]
r1 = nrows - 1
c1 = ncols - 1
@@ -147,7 +147,7 @@ def lat2W(nrows=5, ncols=5, rook=True, id_type='int'):
n = nrows * ncols
r1 = nrows - 1
c1 = ncols - 1
- rid = [i / ncols for i in xrange(n)]
+ rid = [i // ncols for i in xrange(n)] #must be floor!
cid = [i % ncols for i in xrange(n)]
w = {}
r = below = 0
@@ -251,7 +251,7 @@ def regime_weights(regimes):
-def block_weights(regimes):
+def block_weights(regimes, ids=None, sparse=False):
"""
Construct spatial weights for regime neighbors.
@@ -262,8 +262,13 @@ def block_weights(regimes):
Parameters
----------
- regimes : list, array
- ids of which regime an observation belongs to
+ regimes : list, array
+ ids of which regime an observation belongs to
+ ids : list, array
+ Ordered sequence of IDs for the observations
+ sparse : boolean
+ If True return WSP instance
+ If False return W instance
Returns
-------
@@ -300,7 +305,12 @@ def block_weights(regimes):
members = NPNZ(regimes == rid)[0]
for member in members:
neighbors[member] = members[NPNZ(members != member)[0]].tolist()
- return pysal.weights.W(neighbors)
+ w = pysal.weights.W(neighbors)
+ if ids is not None:
+ w.remap_ids(ids)
+ if sparse:
+ w = pysal.weights.WSP(w.sparse, id_order=ids)
+ return w
def comb(items, n=None):
diff --git a/setup.py b/setup.py
index 79fa7c3..ccad7df 100644
--- a/setup.py
+++ b/setup.py
@@ -1,25 +1,19 @@
# coding: utf-8
+from setuptools import setup, find_packages
+
try:
- from setuptools import setup, find_packages
+ from distutils.command.build_py import build_py_2to3 as build_py
except ImportError:
- from distutils.core import setup
+ from distutils.command.build_py import build_py
-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:
+with open('README.rst') as file:
long_description = file.read()
MAJOR = 1
-MINOR = 10
+MINOR = 11
MICRO = 0
ISRELEASED = False
VERSION = '%d.%d.%d' % (MAJOR, MINOR, MICRO)
@@ -33,39 +27,6 @@ if os.path.exists('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()
@@ -84,7 +45,7 @@ def setup_package():
setup(
name='PySAL',
- version=dversion,
+ version=VERSION,
description="A library of spatial analysis functions.",
long_description=long_description,
maintainer="PySAL Developers",
@@ -107,12 +68,14 @@ def setup_package():
'Programming Language :: Python',
'Programming Language :: Python :: 2.5',
'Programming Language :: Python :: 2.6',
- 'Programming Language :: Python :: 2.7'
+ 'Programming Language :: Python :: 2.7',
+ 'Programming Language :: Python :: 3.4'
],
packages=find_packages(exclude=[".meta", "*.meta.*", "meta.*",
"meta"]),
package_data={'pysal': list(example_data_files)},
- requires=['scipy']
+ requires=['scipy'],
+ cmdclass= {'build_py': build_py}
)
diff --git a/tools/bump.py b/tools/bump.py
new file mode 100644
index 0000000..f96609a
--- /dev/null
+++ b/tools/bump.py
@@ -0,0 +1,84 @@
+"""
+Update version numbers in a release branch
+"""
+import datetime
+import os
+import sys
+import inspect
+
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(currentdir)
+sys.path.insert(0, parentdir)
+
+from pysal.version import version
+
+MAJOR, MINOR, MICRO = map(int, version.split("."))
+today = datetime.date.today()
+year = str(today.year)
+day = str(today.day)
+month = str(today.month)
+
+version = "{}.{}.{}".format(MAJOR, MINOR, MICRO)
+num = {"MAJOR": MAJOR, "MINOR": MINOR, "MICRO": MICRO}
+
+with open('../pysal/version.py') as v_file:
+ lines = v_file.readlines()
+ v_line = lines[1]
+ d_line = lines[2]
+ v_line = v_line.split()
+ v_line[-1] = '"' + version + '"'
+ v_line = (" ").join(v_line)+"\n"
+ di = d_line.index('datetime')
+ s0 = d_line[di:].strip()
+ s1 = s0[:]
+ s1 = s1.split()
+ s1[0] = s1[0][:-5]+year+","
+ s1[1] = month+","
+ s1[2] = day+")"
+ s1 = " ".join(s1)
+ d_line = d_line.replace(s0, s1)
+ lines[1] = v_line
+ lines[2] = d_line
+
+with open("../pysal/version.py", 'w') as v_file:
+ v_file.write("".join(lines))
+
+with open("../setup.py") as s_file:
+ lines = s_file.readlines()
+ for i, line in enumerate(lines):
+ if line[:5] in num:
+ v = num[line[:5]]
+ line = "{} = {}\n".format(line[:5], v)
+ lines[i] = line
+
+with open("../setup.py", 'w') as s_file:
+ s_file.write("".join(lines))
+
+with open("../doc/source/conf.py") as c_file:
+ lines = c_file.readlines()
+ for i, line in enumerate(lines):
+ tok = line[:9]
+ if tok == 'version =':
+ lines[i] = "version = '{}'\n".format(version)
+ print lines[i]
+ elif tok == 'release =':
+ lines[i] = "release = '{}'\n".format(version)
+ print lines[i]
+
+with open("../doc/source/conf.py", 'w') as c_file:
+ c_file.write("".join(lines))
+
+with open("../doc/source/index.rst") as i_file:
+ lines = i_file.readlines()
+ for i, line in enumerate(lines):
+ if line[:13] == ' - `Stable':
+ a = ' - `Stable {}'.format(version)
+ b = '(Released {}-{}-{})'.format(year, month, day)
+ lines[i] = '{} {} <users/installation.html>`_\n'.format(a, b)
+ if line[:13] == ' - `Develo':
+ lines[i] = (' - `Development'
+ ' <http://github.com/pysal/pysal/tree/dev>`'
+ '_\n')
+
+with open("../doc/source/index.rst", 'w') as i_file:
+ i_file.write("".join(lines))
diff --git a/tools/spregsync b/tools/spregsync
new file mode 100755
index 0000000..cfef84e
--- /dev/null
+++ b/tools/spregsync
@@ -0,0 +1,47 @@
+#!/usr/bin/python2
+
+import subprocess as sb
+import shutil
+import sys
+
+try:
+ import click
+except ImportError:
+ print('Interface built with Click: \n\tclick.pocoo.org')
+ print('Install click to use (available via pip/conda)')
+ sys.exit()
+
+ at click.command()
+ at click.option('--dry_run', is_flag=True, help='avoid running copy operation')
+ at click.option('--verbose', is_flag=True, help='print files being copied')
+ at click.option('-v', is_flag=True, help='print files being copied')
+ at click.argument('source', nargs=1)
+ at click.argument('destination', nargs=1)
+def sync(source, destination, dry_run, verbose, v):
+ """
+ Uses git ls-files to build a list of files that DESTINATION needs to
+ synchronize from SOURCE.
+
+ All files tracked by git in DESTINATION get copied from SOURCE to
+ DESTINATION.
+ """
+ verbose = v or verbose
+ proc = sb.Popen('git ls-files {}'.format(destination), shell=True, stdout=sb.PIPE)
+ file_list = [s.strip().split('/')[2:] for s in proc.stdout.readlines()]
+ file_list = ['/'.join(s) for s in file_list]
+ sources = ['/'.join((source, s)) for s in file_list]
+ destinations = ['/'.join((destination, s)) for s in file_list]
+ if dry_run:
+ if verbose:
+ print('Discovered these files:')
+ for f in file_list:
+ print(f)
+ return 0
+ else:
+ for src,dst in zip(sources, destinations):
+ if verbose:
+ print("copying: {} to {}".format(src,dst))
+ shutil.copy2(src,dst)
+
+if __name__ == "__main__":
+ sync()
diff --git a/touch.txt b/touch.txt
new file mode 100644
index 0000000..8bd6648
--- /dev/null
+++ b/touch.txt
@@ -0,0 +1 @@
+asdf
--
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