[pyresample] 01/16: Imported Upstream version 1.1.3
Antonio Valentino
a_valentino-guest at moszumanska.debian.org
Sat Jul 11 19:52:36 UTC 2015
This is an automated email from the git hooks/post-receive script.
a_valentino-guest pushed a commit to branch master
in repository pyresample.
commit ff538ddebdeaf9ab64c07b0afd980a184871fbd6
Author: Antonio Valentino <antonio.valentino at tiscali.it>
Date: Sat Jul 11 11:12:05 2015 +0000
Imported Upstream version 1.1.3
---
.travis.yml | 18 +
LICENSE.txt | 829 ++++---------------
PKG-INFO | 16 -
README | 4 +-
README.md | 39 +
docs/source/_static/images/time_vs_nproc_1-12.png | Bin 0 -> 35330 bytes
docs/source/geo_def.rst | 14 +-
docs/source/multi.rst | 3 +
pyresample.egg-info/PKG-INFO | 16 -
pyresample.egg-info/SOURCES.txt | 63 --
pyresample.egg-info/dependency_links.txt | 1 -
pyresample.egg-info/not-zip-safe | 1 -
pyresample.egg-info/requires.txt | 9 -
pyresample.egg-info/top_level.txt | 1 -
pyresample/__init__.py | 51 +-
pyresample/_multi_proc.py | 137 ++--
pyresample/_spatial_mp.py | 202 ++---
pyresample/data_reduce.py | 198 ++---
pyresample/geo_filter.py | 64 +-
pyresample/geometry.py | 442 +++++-----
pyresample/grid.py | 153 ++--
pyresample/image.py | 155 ++--
pyresample/kd_tree.py | 593 +++++++-------
pyresample/plot.py | 177 ++--
pyresample/spherical_geometry.py | 92 ++-
pyresample/test/__init__.py | 46 ++
{test => pyresample/test}/test_files/areas.cfg | 0
{test => pyresample/test}/test_files/mask_grid.dat | 0
.../test}/test_files/mask_test_data.dat | 0
.../test}/test_files/mask_test_fill_value.dat | 0
.../test}/test_files/mask_test_full_fill.dat | 0
.../test}/test_files/mask_test_full_fill_multi.dat | 0
.../test}/test_files/mask_test_mask.dat | 0
.../test}/test_files/mask_test_nearest_data.dat | 0
.../test}/test_files/mask_test_nearest_mask.dat | 0
.../test}/test_files/quick_mask_test.dat | 0
.../test}/test_files/ssmis_swath.npz | Bin
pyresample/test/test_geometry.py | 646 +++++++++++++++
pyresample/test/test_grid.py | 201 +++++
pyresample/test/test_image.py | 225 +++++
pyresample/test/test_kd_tree.py | 907 +++++++++++++++++++++
pyresample/test/test_plot.py | 87 ++
.../test}/test_spherical_geometry.py | 53 +-
{test => pyresample/test}/test_swath.py | 75 +-
{test => pyresample/test}/test_utils.py | 56 +-
pyresample/utils.py | 213 +++--
pyresample/version.py | 28 +-
requirements.txt | 2 +
setup.cfg | 5 -
setup.py | 59 +-
test/test_geometry.py | 513 ------------
test/test_grid.py | 177 ----
test/test_image.py | 202 -----
test/test_kd_tree.py | 836 -------------------
test/test_plot.py | 67 --
55 files changed, 3833 insertions(+), 3843 deletions(-)
diff --git a/.travis.yml b/.travis.yml
new file mode 100644
index 0000000..c137efd
--- /dev/null
+++ b/.travis.yml
@@ -0,0 +1,18 @@
+language: python
+python:
+- '2.6'
+- '2.7'
+- '3.2'
+- '3.3'
+- '3.4'
+before_install:
+- sudo add-apt-repository ppa:ubuntugis/ppa -y
+- sudo apt-get update -qq
+- sudo apt-get install libfreetype6-dev
+- sudo apt-get install libgeos-3.3.8 libgeos-c1 libgeos-dev
+install:
+- pip install -r requirements.txt
+- pip install -e ".[pykdtree]"
+- pip install coveralls
+script: coverage run --source=pyresample setup.py test
+after_success: coveralls
diff --git a/LICENSE.txt b/LICENSE.txt
index 94a9ed0..e3acbd5 100644
--- a/LICENSE.txt
+++ b/LICENSE.txt
@@ -1,674 +1,165 @@
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+
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+
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+of the GNU Lesser General Public License from time to time. Such new
+versions will be similar in spirit to the present version, but may
+differ in detail to address new problems or concerns.
+
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+Library as you received it specifies that a certain numbered version
+of the GNU Lesser General Public License "or any later version"
+applies to it, you have the option of following the terms and
+conditions either of that published version or of any later version
+published by the Free Software Foundation. If the Library as you
+received it does not specify a version number of the GNU Lesser
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diff --git a/PKG-INFO b/PKG-INFO
deleted file mode 100644
index 9e9ceba..0000000
--- a/PKG-INFO
+++ /dev/null
@@ -1,16 +0,0 @@
-Metadata-Version: 1.1
-Name: pyresample
-Version: 1.1.0
-Summary: Resampling of remote sensing data in Python
-Home-page: UNKNOWN
-Author: Esben S. Nielsen
-Author-email: esn at dmi.dk
-License: UNKNOWN
-Description: UNKNOWN
-Platform: UNKNOWN
-Classifier: Development Status :: 5 - Production/Stable
-Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
-Classifier: Programming Language :: Python
-Classifier: Operating System :: OS Independent
-Classifier: Intended Audience :: Science/Research
-Classifier: Topic :: Scientific/Engineering
diff --git a/README b/README
deleted file mode 100644
index 5c5095c..0000000
--- a/README
+++ /dev/null
@@ -1,3 +0,0 @@
-Python package for geospatial resampling
-
-Look at http://code.google.com/p/pyresample/ and http://pytroll.org/ for more information.
diff --git a/README b/README
new file mode 120000
index 0000000..42061c0
--- /dev/null
+++ b/README
@@ -0,0 +1 @@
+README.md
\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..52ad6d1
--- /dev/null
+++ b/README.md
@@ -0,0 +1,39 @@
+[](https://travis-ci.org/mraspaud/pyresample)
+
+Python package for geospatial resampling
+----------------------------------------
+
+Resampling (reprojection) of geospatial image data in Python.
+Pyresample uses a kd-tree approach for resampling.
+Pyresample is designed for resampling of remote sensing data and supports resampling from both fixed grids and geolocated swath data.
+Several types of resampling are supported including nearest neighbour, gaussian weighting and weighting with a user defined radial function.
+Pyresample works with Numpy arrays including support for masked arrays.
+Support for parallel resampling using multiple processor cores.
+Plotting capablity using Basemap. As of v0.8.0 [pykdtree](https://github.com/storpipfugl/pykdtree) can be used to speed up processing.
+
+Pyresample is tested with Python 2.6, 2.7, 3.2, 3.3, and 3.4.
+
+Note: For numpy >= 1.6.2 use pyresample >= 0.7.13
+
+[Documentation](https://pyresample.readthedocs.org/en/latest/)
+Look at [pytroll.org](http://pytroll.org/) for more information.
+
+
+===News===
+ * *2015-02-03*: Pyresample-1.1.3 released. Switch to LGPLv3.
+
+ * *2014-12-17*: Pyresample-1.1.2 released. Fix to allow tests to run on travis.
+
+ * *2014-12-10*: Pyresample-1.1.1 released. Wrapping of longitudes and latitudes is now implemented.
+
+ * *2013-10-23*: Pyresample-1.1.0 released. Added option for calculating uncertainties for weighted kd-tree resampling. From now on pyresample will adhere to [http://semver.org/ semantic versioning].
+
+ * *2013-07-03*: Pyresample-1.0.0 released. Minor API change to the geometry.py module as the boundary variable is removed and replaced by proj_x_coords and proj_y_coords. Caching scheme removed from projection coordinate calculation in geometry.py as it introduced excessive complications. The numexpr package is now used for minor bottleneck optimization if available. Version number bumped to 1.0.0 as pyresample has been running stable in production environments for several years now.
+
+ * *2013-03-20*: Pyresample-0.8.0 released. Enables use of pykdtree. Fixes projection handling for 'latlong' projection.
+
+ * *2013-01-21*: Pyresample-0.7.13 released. Fixes numpy incompability introduced with numpy v1.6.2
+
+ * *2012-10-18*: Pyresample-0.7.12 released. Better integration with Basemap with support for plotting using globe projections (geos, ortho and nspere). Documentation updated with correct description of the epsilon parameter.
+
+ * *2012-07-03*: Pyresample-0.7.11 released. Support for plotting in Plate Carree projection and bugfixes for meridians and parallels in plots. Added utils.fwhm2sigma convenience function for use in Gauss resampling.
diff --git a/docs/source/_static/images/time_vs_nproc_1-12.png b/docs/source/_static/images/time_vs_nproc_1-12.png
new file mode 100644
index 0000000..dd3c41d
Binary files /dev/null and b/docs/source/_static/images/time_vs_nproc_1-12.png differ
diff --git a/docs/source/geo_def.rst b/docs/source/geo_def.rst
index 68c6e92..3031b93 100644
--- a/docs/source/geo_def.rst
+++ b/docs/source/geo_def.rst
@@ -3,6 +3,18 @@ Geometry definitions
The module **pyresample.geometry** contains classes for describing different kinds of types
of remote sensing data geometries. The use of the different classes is described below.
+Remarks
+-------
+
+All longitudes and latitudes provided to **pyresample.geometry** must be in degrees.
+Longitudes must additionally be in the [-180;+180[ validity range.
+
+As of version 1.1.1, the **pyresample.geometry** contructors will check the range of
+longitude values, send a warning if some of them fall outside validity range,
+and automatically correct the invalid values into [-180;+180[.
+
+Use function **utils.wrap_longitudes** for wrapping longitudes yourself.
+
AreaDefinition
--------------
@@ -309,4 +321,4 @@ It can be tested if a (lon, lat) point is inside a GeometryDefinition
... x_size, y_size, area_extent)
>>> print (0, -90) in area_def
True
-
\ No newline at end of file
+
diff --git a/docs/source/multi.rst b/docs/source/multi.rst
index 564d141..f7e294d 100644
--- a/docs/source/multi.rst
+++ b/docs/source/multi.rst
@@ -34,3 +34,6 @@ Example of resampling in parallel using 4 processes:
Note: Do not use more processes than available processor cores. As there is a process creation overhead
there might be neglible performance improvement using say 8 compared to 4 processor cores.
Test on the actual system to determine the most sensible number of processes to use.
+
+Here is an example of the performance for a varying number of processors on a 64-bit ubuntu 14.04, 32 GB RAM, 2 x Intel Xeon E5-2630 with 6 physical cores each:
+ .. image:: _static/images/time_vs_nproc_1-12.png
diff --git a/pyresample.egg-info/PKG-INFO b/pyresample.egg-info/PKG-INFO
deleted file mode 100644
index 9e9ceba..0000000
--- a/pyresample.egg-info/PKG-INFO
+++ /dev/null
@@ -1,16 +0,0 @@
-Metadata-Version: 1.1
-Name: pyresample
-Version: 1.1.0
-Summary: Resampling of remote sensing data in Python
-Home-page: UNKNOWN
-Author: Esben S. Nielsen
-Author-email: esn at dmi.dk
-License: UNKNOWN
-Description: UNKNOWN
-Platform: UNKNOWN
-Classifier: Development Status :: 5 - Production/Stable
-Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
-Classifier: Programming Language :: Python
-Classifier: Operating System :: OS Independent
-Classifier: Intended Audience :: Science/Research
-Classifier: Topic :: Scientific/Engineering
diff --git a/pyresample.egg-info/SOURCES.txt b/pyresample.egg-info/SOURCES.txt
deleted file mode 100644
index 99a3d5a..0000000
--- a/pyresample.egg-info/SOURCES.txt
+++ /dev/null
@@ -1,63 +0,0 @@
-LICENSE.txt
-MANIFEST.in
-README
-setup.py
-docs/Makefile
-docs/source/API.rst
-docs/source/conf.py
-docs/source/data_reduce.rst
-docs/source/geo_def.rst
-docs/source/geo_filter.rst
-docs/source/grid.rst
-docs/source/index.rst
-docs/source/installation.rst
-docs/source/multi.rst
-docs/source/plot.rst
-docs/source/preproc.rst
-docs/source/swath.rst
-docs/source/_static/images/tb37_multi.png
-docs/source/_static/images/tb37v_bmng.png
-docs/source/_static/images/tb37v_ortho.png
-docs/source/_static/images/tb37v_pc.png
-docs/source/_static/images/tb37v_quick.png
-docs/source/_static/images/uncert_conc_nh.png
-docs/source/_static/images/uncert_count_nh.png
-docs/source/_static/images/uncert_stddev_nh.png
-pyresample/__init__.py
-pyresample/_multi_proc.py
-pyresample/_spatial_mp.py
-pyresample/data_reduce.py
-pyresample/geo_filter.py
-pyresample/geometry.py
-pyresample/grid.py
-pyresample/image.py
-pyresample/kd_tree.py
-pyresample/plot.py
-pyresample/spherical_geometry.py
-pyresample/utils.py
-pyresample/version.py
-pyresample.egg-info/PKG-INFO
-pyresample.egg-info/SOURCES.txt
-pyresample.egg-info/dependency_links.txt
-pyresample.egg-info/not-zip-safe
-pyresample.egg-info/requires.txt
-pyresample.egg-info/top_level.txt
-test/test_geometry.py
-test/test_grid.py
-test/test_image.py
-test/test_kd_tree.py
-test/test_plot.py
-test/test_spherical_geometry.py
-test/test_swath.py
-test/test_utils.py
-test/test_files/areas.cfg
-test/test_files/mask_grid.dat
-test/test_files/mask_test_data.dat
-test/test_files/mask_test_fill_value.dat
-test/test_files/mask_test_full_fill.dat
-test/test_files/mask_test_full_fill_multi.dat
-test/test_files/mask_test_mask.dat
-test/test_files/mask_test_nearest_data.dat
-test/test_files/mask_test_nearest_mask.dat
-test/test_files/quick_mask_test.dat
-test/test_files/ssmis_swath.npz
\ No newline at end of file
diff --git a/pyresample.egg-info/dependency_links.txt b/pyresample.egg-info/dependency_links.txt
deleted file mode 100644
index 8b13789..0000000
--- a/pyresample.egg-info/dependency_links.txt
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/pyresample.egg-info/not-zip-safe b/pyresample.egg-info/not-zip-safe
deleted file mode 100644
index 8b13789..0000000
--- a/pyresample.egg-info/not-zip-safe
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/pyresample.egg-info/requires.txt b/pyresample.egg-info/requires.txt
deleted file mode 100644
index 37520a3..0000000
--- a/pyresample.egg-info/requires.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-pyproj
-numpy
-configobj
-
-[numexpr]
-numexpr
-
-[pykdtree]
-pykdtree
\ No newline at end of file
diff --git a/pyresample.egg-info/top_level.txt b/pyresample.egg-info/top_level.txt
deleted file mode 100644
index 2c78f3a..0000000
--- a/pyresample.egg-info/top_level.txt
+++ /dev/null
@@ -1 +0,0 @@
-pyresample
diff --git a/pyresample/__init__.py b/pyresample/__init__.py
index 375e914..7a79e45 100644
--- a/pyresample/__init__.py
+++ b/pyresample/__init__.py
@@ -1,42 +1,47 @@
-#pyresample, Resampling of remote sensing image data in python
-#
-#Copyright (C) 2010 Esben S. Nielsen
+# pyresample, Resampling of remote sensing image data in python
#
-#This program is free software: you can redistribute it and/or modify
-#it under the terms of the GNU General Public License as published by
-#the Free Software Foundation, either version 3 of the License, or
-#(at your option) any later version.
+# Copyright (C) 2010, 2014, 2015 Esben S. Nielsen
#
-#This program is distributed in the hope that it will be useful,
-#but WITHOUT ANY WARRANTY; without even the implied warranty of
-#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-#GNU General Public License for more details.
+# This program is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Lesser General Public License as published by the Free
+# Software Foundation, either version 3 of the License, or (at your option) any
+# later version.
#
-#You should have received a copy of the GNU General Public License
-# along with this program. If not, see <http://www.gnu.org/licenses/>.
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License along
+# with this program. If not, see <http://www.gnu.org/licenses/>.
+
+from __future__ import absolute_import
+
+from pyresample.version import __version__
+from pyresample import geometry
+from pyresample import grid
+from pyresample import image
+from pyresample import kd_tree
+from pyresample import utils
+from pyresample import plot
-import grid
-import image
-import kd_tree
-import utils
-import version
-import plot
+__all__ = ['grid', 'image', 'kd_tree',
+ 'utils', 'plot', 'geo_filter', 'geometry']
-__version__ = version.__version__
def get_capabilities():
cap = {}
try:
from pykdtree.kdtree import KDTree
- cap['pykdtree'] = True
+ cap['pykdtree'] = True
except ImportError:
cap['pykdtree'] = False
try:
import numexpr
- cap['numexpr'] = True
+ cap['numexpr'] = True
except ImportError:
- cap['numexpr'] = False
+ cap['numexpr'] = False
return cap
diff --git a/pyresample/_multi_proc.py b/pyresample/_multi_proc.py
index b1d1957..e40c293 100644
--- a/pyresample/_multi_proc.py
+++ b/pyresample/_multi_proc.py
@@ -1,37 +1,40 @@
-#pyresample, Resampling of remote sensing image data in python
-#
-#Copyright (C) 2010 Esben S. Nielsen
+# pyresample, Resampling of remote sensing image data in python
#
-#This program is free software: you can redistribute it and/or modify
-#it under the terms of the GNU General Public License as published by
-#the Free Software Foundation, either version 3 of the License, or
-#(at your option) any later version.
+# Copyright (C) 2010, 2015 Esben S. Nielsen
#
-#This program is distributed in the hope that it will be useful,
-#but WITHOUT ANY WARRANTY; without even the implied warranty of
-#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-#GNU General Public License for more details.
+# This program is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Lesser General Public License as published by the Free
+# Software Foundation, either version 3 of the License, or (at your option) any
+# later version.
#
-#You should have received a copy of the GNU General Public License
-# along with this program. If not, see <http://www.gnu.org/licenses/>.
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License along
+# with this program. If not, see <http://www.gnu.org/licenses/>.
-import ctypes
+from __future__ import absolute_import
+import ctypes
import multiprocessing as mp
+
import numpy as np
+
class Scheduler(object):
-
+
def __init__(self, ndata, nprocs, chunk=None, schedule='guided'):
- if not schedule in ['guided','dynamic', 'static']:
- raise ValueError, 'unknown scheduling strategy'
+ if not schedule in ['guided', 'dynamic', 'static']:
+ raise ValueError('unknown scheduling strategy')
self._ndata = mp.RawValue(ctypes.c_int, ndata)
self._start = mp.RawValue(ctypes.c_int, 0)
self._lock = mp.Lock()
self._schedule = schedule
self._nprocs = nprocs
if schedule == 'guided' or schedule == 'dynamic':
- min_chunk = ndata // (10*nprocs)
+ min_chunk = ndata // (10 * nprocs)
if chunk:
min_chunk = chunk
min_chunk = max(min_chunk, 1)
@@ -42,63 +45,55 @@ class Scheduler(object):
min_chunk = max(chunk, min_chunk)
min_chunk = max(min_chunk, 1)
self._chunk = min_chunk
-
- def __iter__(self):
- return self
- def next(self):
- self._lock.acquire()
- ndata = self._ndata.value
- nprocs = self._nprocs
- start = self._start.value
- if self._schedule == 'guided':
- _chunk = ndata // nprocs
- chunk = max(self._chunk, _chunk)
- else:
- chunk = self._chunk
- if ndata:
- if chunk > ndata:
- s0 = start
- s1 = start + ndata
- self._ndata.value = 0
+ def __iter__(self):
+ while True:
+ self._lock.acquire()
+ ndata = self._ndata.value
+ nprocs = self._nprocs
+ start = self._start.value
+ if self._schedule == 'guided':
+ _chunk = ndata // nprocs
+ chunk = max(self._chunk, _chunk)
+ else:
+ chunk = self._chunk
+ if ndata:
+ if chunk > ndata:
+ s0 = start
+ s1 = start + ndata
+ self._ndata.value = 0
+ else:
+ s0 = start
+ s1 = start + chunk
+ self._ndata.value = ndata - chunk
+ self._start.value = start + chunk
+ self._lock.release()
+ yield slice(s0, s1)
else:
- s0 = start
- s1 = start + chunk
- self._ndata.value = ndata - chunk
- self._start.value = start + chunk
- self._lock.release()
- return slice(s0, s1)
- else:
- self._lock.release()
- raise StopIteration
+ self._lock.release()
+ raise StopIteration
def shmem_as_ndarray(raw_array):
_ctypes_to_numpy = {
- ctypes.c_char : np.int8,
- ctypes.c_wchar : np.int16,
- ctypes.c_byte : np.int8,
- ctypes.c_ubyte : np.uint8,
- ctypes.c_short : np.int16,
- ctypes.c_ushort : np.uint16,
- ctypes.c_int : np.int32,
- ctypes.c_uint : np.int32,
- ctypes.c_long : np.int32,
- ctypes.c_ulong : np.int32,
- ctypes.c_float : np.float32,
- ctypes.c_double : np.float64
- }
- address = raw_array._wrapper.get_address()
- size = raw_array._wrapper.get_size()
+ ctypes.c_char: np.int8,
+ ctypes.c_wchar: np.int16,
+ ctypes.c_byte: np.int8,
+ ctypes.c_ubyte: np.uint8,
+ ctypes.c_short: np.int16,
+ ctypes.c_ushort: np.uint16,
+ ctypes.c_int: np.int32,
+ ctypes.c_uint: np.int32,
+ ctypes.c_long: np.int32,
+ ctypes.c_ulong: np.int32,
+ ctypes.c_float: np.float32,
+ ctypes.c_double: np.float64
+ }
dtype = _ctypes_to_numpy[raw_array._type_]
- class Dummy(object): pass
- d = Dummy()
- d.__array_interface__ = {
- 'data' : (address, False),
- 'typestr' : np.dtype(np.uint8).str,
- 'descr' : np.dtype(np.uint8).descr,
- 'shape' : (size,),
- 'strides' : None,
- 'version' : 3
- }
- return np.asarray(d).view(dtype=dtype)
\ No newline at end of file
+
+ # The following works too, but occasionally raises
+ # RuntimeWarning: Item size computed from the PEP 3118 buffer format string does not match the actual item size.
+ # and appears to be slower.
+ # return np.ctypeslib.as_array(raw_array)
+
+ return np.frombuffer(raw_array, dtype=dtype)
diff --git a/pyresample/_spatial_mp.py b/pyresample/_spatial_mp.py
index 78a0c0d..56f96f4 100644
--- a/pyresample/_spatial_mp.py
+++ b/pyresample/_spatial_mp.py
@@ -1,19 +1,21 @@
-#pyresample, Resampling of remote sensing image data in python
-#
-#Copyright (C) 2010, 2013 Esben S. Nielsen, Martin Raspaud
+# pyresample, Resampling of remote sensing image data in python
#
-#This program is free software: you can redistribute it and/or modify
-#it under the terms of the GNU General Public License as published by
-#the Free Software Foundation, either version 3 of the License, or
-#(at your option) any later version.
+# Copyright (C) 2010, 2013, 2015 Esben S. Nielsen, Martin Raspaud
#
-#This program is distributed in the hope that it will be useful,
-#but WITHOUT ANY WARRANTY; without even the implied warranty of
-#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-#GNU General Public License for more details.
+# This program is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Lesser General Public License as published by the Free
+# Software Foundation, either version 3 of the License, or (at your option) any
+# later version.
#
-#You should have received a copy of the GNU General Public License
-# along with this program. If not, see <http://www.gnu.org/licenses/>.
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License along
+# with this program. If not, see <http://www.gnu.org/licenses/>.
+
+from __future__ import absolute_import
import ctypes
@@ -27,16 +29,17 @@ try:
except ImportError:
ne = None
-from _multi_proc import shmem_as_ndarray, Scheduler
+from ._multi_proc import shmem_as_ndarray, Scheduler
-#Earth radius
+# Earth radius
R = 6370997.0
class cKDTree_MP(object):
+
''' Multiprocessing cKDTree subclass, shared memory '''
- def __init__(self, data, leafsize=10, nprocs=2, chunk=None,\
+ def __init__(self, data, leafsize=10, nprocs=2, chunk=None,
schedule='guided'):
'''
Same as cKDTree.__init__ except that an internal copy
@@ -49,34 +52,34 @@ class cKDTree_MP(object):
self.n, self.m = data.shape
# Allocate shared memory for data
- self.shmem_data = mp.RawArray(ctypes.c_double, self.n*self.m)
-
+ self.shmem_data = mp.RawArray(ctypes.c_double, self.n * self.m)
+
# View shared memory as ndarray, and copy over the data.
# The RawArray objects have information about the dtype and
# buffer size.
_data = shmem_as_ndarray(self.shmem_data).reshape((self.n, self.m))
_data[:,:] = data
-
+
# Initialize parent, we must do this last because
# cKDTree stores a reference to the data array. We pass in
# the copy in shared memory rather than the origial data.
self.leafsize = leafsize
self._nprocs = nprocs
self._chunk = chunk
- self._schedule = schedule
-
+ self._schedule = schedule
+
def query(self, x, k=1, eps=0, p=2, distance_upper_bound=np.inf):
'''
Same as cKDTree.query except parallelized with multiple
processes and shared memory.
'''
-
+
# allocate shared memory for x and result
nx = x.shape[0]
- shmem_x = mp.RawArray(ctypes.c_double, nx*self.m)
- shmem_d = mp.RawArray(ctypes.c_double, nx*k)
- shmem_i = mp.RawArray(ctypes.c_int, nx*k)
-
+ shmem_x = mp.RawArray(ctypes.c_double, nx * self.m)
+ shmem_d = mp.RawArray(ctypes.c_double, nx * k)
+ shmem_i = mp.RawArray(ctypes.c_int, nx * k)
+
# view shared memory as ndarrays
_x = shmem_as_ndarray(shmem_x).reshape((nx, self.m))
if k == 1:
@@ -85,84 +88,84 @@ class cKDTree_MP(object):
else:
_d = shmem_as_ndarray(shmem_d).reshape((nx, k))
_i = shmem_as_ndarray(shmem_i).reshape((nx, k))
-
+
# copy x to shared memory
_x[:] = x
-
- # set up a scheduler to load balance the query
- scheduler = Scheduler(nx, self._nprocs, chunk=self._chunk,\
+
+ # set up a scheduler to load balance the query
+ scheduler = Scheduler(nx, self._nprocs, chunk=self._chunk,
schedule=self._schedule)
# query with multiple processes
- query_args = [scheduler, self.shmem_data, self.n, self.m,\
- self.leafsize, shmem_x, nx, shmem_d, shmem_i,\
+ query_args = [scheduler, self.shmem_data, self.n, self.m,
+ self.leafsize, shmem_x, nx, shmem_d, shmem_i,
k, eps, p, distance_upper_bound]
-
+
_run_jobs(_parallel_query, query_args, self._nprocs)
# return results (private memory)
return _d.copy(), _i.copy()
-
+
class Proj(pyproj.Proj):
- def __call__(self, data1, data2, inverse=False, radians=False,\
+ def __call__(self, data1, data2, inverse=False, radians=False,
errcheck=False, nprocs=1):
if self.is_latlong():
return data1, data2
-
- return super(Proj, self).__call__(data1, data2, inverse=inverse,\
+
+ return super(Proj, self).__call__(data1, data2, inverse=inverse,
radians=radians, errcheck=errcheck)
class Proj_MP(pyproj.Proj):
-
+
def __init__(self, *args, **kwargs):
self._args = args
self._kwargs = kwargs
-
- def __call__(self, data1, data2, inverse=False, radians=False,\
+
+ def __call__(self, data1, data2, inverse=False, radians=False,
errcheck=False, nprocs=2, chunk=None, schedule='guided'):
if self.is_latlong():
return data1, data2
-
+
grid_shape = data1.shape
n = data1.size
-
- #Create shared memory
+
+ # Create shared memory
shmem_data1 = mp.RawArray(ctypes.c_double, n)
shmem_data2 = mp.RawArray(ctypes.c_double, n)
shmem_res1 = mp.RawArray(ctypes.c_double, n)
shmem_res2 = mp.RawArray(ctypes.c_double, n)
-
+
# view shared memory as ndarrays
_data1 = shmem_as_ndarray(shmem_data1)
_data2 = shmem_as_ndarray(shmem_data2)
_res1 = shmem_as_ndarray(shmem_res1)
_res2 = shmem_as_ndarray(shmem_res2)
-
+
# copy input data to shared memory
_data1[:] = data1.ravel()
_data2[:] = data2.ravel()
-
- # set up a scheduler to load balance the query
+
+ # set up a scheduler to load balance the query
scheduler = Scheduler(n, nprocs, chunk=chunk, schedule=schedule)
-
+
# Projection with multiple processes
- proj_call_args = [scheduler, shmem_data1, shmem_data2, shmem_res1,\
- shmem_res2, self._args, self._kwargs, inverse,\
+ proj_call_args = [scheduler, shmem_data1, shmem_data2, shmem_res1,
+ shmem_res2, self._args, self._kwargs, inverse,
radians, errcheck]
-
+
_run_jobs(_parallel_proj, proj_call_args, nprocs)
return _res1.copy().reshape(grid_shape), _res2.copy().reshape(grid_shape)
class Cartesian(object):
-
+
def __init__(self, *args, **kwargs):
pass
-
+
def transform_lonlats(self, lons, lats):
-
+
coords = np.zeros((lons.size, 3), dtype=lons.dtype)
deg2rad = lons.dtype.type(np.pi / 180)
if ne:
@@ -170,13 +173,14 @@ class Cartesian(object):
coords[:, 1] = ne.evaluate("R*cos(lats*deg2rad)*sin(lons*deg2rad)")
coords[:, 2] = ne.evaluate("R*sin(lats*deg2rad)")
else:
- coords[:, 0] = R*np.cos(lats*deg2rad)*np.cos(lons*deg2rad)
- coords[:, 1] = R*np.cos(lats*deg2rad)*np.sin(lons*deg2rad)
- coords[:, 2] = R*np.sin(lats*deg2rad)
+ coords[:, 0] = R * np.cos(lats * deg2rad) * np.cos(lons * deg2rad)
+ coords[:, 1] = R * np.cos(lats * deg2rad) * np.sin(lons * deg2rad)
+ coords[:, 2] = R * np.sin(lats * deg2rad)
return coords
-
+
Cartesian_MP = Cartesian
+
def _run_jobs(target, args, nprocs):
"""Run process pool
"""
@@ -185,25 +189,29 @@ def _run_jobs(target, args, nprocs):
# access to these values are serialized automatically
ierr = mp.Value(ctypes.c_int, 0)
err_msg = mp.Array(ctypes.c_char, 1024)
-
+
args.extend((ierr, err_msg))
-
+
pool = [mp.Process(target=target, args=args) for n in range(nprocs)]
- for p in pool: p.start()
- for p in pool: p.join()
+ for p in pool:
+ p.start()
+ for p in pool:
+ p.join()
if ierr.value != 0:
- raise RuntimeError,\
- ('%d errors in worker processes. Last one reported:\n%s'%\
- (ierr.value, err_msg.value))
-
+ raise RuntimeError('%d errors in worker processes. Last one reported:\n%s' %
+ (ierr.value, err_msg.value.decode()))
+
# This is executed in an external process:
-def _parallel_query(scheduler, # scheduler for load balancing
- data, ndata, ndim, leafsize, # data needed to reconstruct the kd-tree
- x, nx, d, i, # query data and results
- k, eps, p, dub, # auxillary query parameters
- ierr, err_msg): # return values (0 on success)
-
- try:
+
+
+def _parallel_query(scheduler, # scheduler for load balancing
+ # data needed to reconstruct the kd-tree
+ data, ndata, ndim, leafsize,
+ x, nx, d, i, # query data and results
+ k, eps, p, dub, # auxillary query parameters
+ ierr, err_msg): # return values (0 on success)
+
+ try:
# View shared memory as ndarrays.
_data = shmem_as_ndarray(data).reshape((ndata, ndim))
_x = shmem_as_ndarray(x).reshape((nx, ndim))
@@ -223,17 +231,18 @@ def _parallel_query(scheduler, # scheduler for load balancing
for s in scheduler:
if k == 1:
_d[s], _i[s] = kdtree.query(_x[s,:], k=1, eps=eps, p=p,\
- distance_upper_bound=dub)
+ distance_upper_bound=dub)
else:
_d[s,:], _i[s,:] = kdtree.query(_x[s,:], k=k, eps=eps, p=p,\
distance_upper_bound=dub)
# An error occured, increment the return value ierr.
# Access to ierr is serialized by multiprocessing.
- except Exception, e:
+ except Exception as e:
ierr.value += 1
- err_msg.value = e.message
-
-def _parallel_proj(scheduler, data1, data2, res1, res2, proj_args, proj_kwargs,\
+ err_msg.value = str(e).encode()
+
+
+def _parallel_proj(scheduler, data1, data2, res1, res2, proj_args, proj_kwargs,
inverse, radians, errcheck, ierr, err_msg):
try:
# View shared memory as ndarrays.
@@ -241,35 +250,38 @@ def _parallel_proj(scheduler, data1, data2, res1, res2, proj_args, proj_kwargs,\
_data2 = shmem_as_ndarray(data2)
_res1 = shmem_as_ndarray(res1)
_res2 = shmem_as_ndarray(res2)
-
- #Initialise pyproj
+
+ # Initialise pyproj
proj = pyproj.Proj(*proj_args, **proj_kwargs)
-
- #Reproject data segment
+
+ # Reproject data segment
for s in scheduler:
- _res1[s], _res2[s] = proj(_data1[s], _data2[s], inverse=inverse,\
- radians=radians, errcheck=errcheck)
-
+ _res1[s], _res2[s] = proj(_data1[s], _data2[s], inverse=inverse,
+ radians=radians, errcheck=errcheck)
+
# An error occured, increment the return value ierr.
# Access to ierr is serialized by multiprocessing.
- except Exception, e:
+ except Exception as e:
ierr.value += 1
- err_msg.value = e.message
-
+ err_msg.value = str(e).encode()
+
+
def _parallel_transform(scheduler, lons, lats, n, coords, ierr, err_msg):
try:
# View shared memory as ndarrays.
_lons = shmem_as_ndarray(lons)
_lats = shmem_as_ndarray(lats)
_coords = shmem_as_ndarray(coords).reshape((n, 3))
-
- #Transform to cartesian coordinates
+
+ # Transform to cartesian coordinates
for s in scheduler:
- _coords[s, 0] = R*np.cos(np.radians(_lats[s]))*np.cos(np.radians(_lons[s]))
- _coords[s, 1] = R*np.cos(np.radians(_lats[s]))*np.sin(np.radians(_lons[s]))
- _coords[s, 2] = R*np.sin(np.radians(_lats[s]))
+ _coords[s, 0] = R * \
+ np.cos(np.radians(_lats[s])) * np.cos(np.radians(_lons[s]))
+ _coords[s, 1] = R * \
+ np.cos(np.radians(_lats[s])) * np.sin(np.radians(_lons[s]))
+ _coords[s, 2] = R * np.sin(np.radians(_lats[s]))
# An error occured, increment the return value ierr.
# Access to ierr is serialized by multiprocessing.
- except Exception, e:
+ except Exception as e:
ierr.value += 1
- err_msg.value = e.message
+ err_msg.value = str(e).encode()
diff --git a/pyresample/data_reduce.py b/pyresample/data_reduce.py
index fc5c5f8..693f410 100644
--- a/pyresample/data_reduce.py
+++ b/pyresample/data_reduce.py
@@ -1,33 +1,36 @@
-#pyresample, Resampling of remote sensing image data in python
-#
-#Copyright (C) 2010 Esben S. Nielsen
+# pyresample, Resampling of remote sensing image data in python
#
-#This program is free software: you can redistribute it and/or modify
-#it under the terms of the GNU General Public License as published by
-#the Free Software Foundation, either version 3 of the License, or
-#(at your option) any later version.
+# Copyright (C) 2010, 2015 Esben S. Nielsen
#
-#This program is distributed in the hope that it will be useful,
-#but WITHOUT ANY WARRANTY; without even the implied warranty of
-#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-#GNU General Public License for more details.
+# This program is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Lesser General Public License as published by the Free
+# Software Foundation, either version 3 of the License, or (at your option) any
+# later version.
#
-#You should have received a copy of the GNU General Public License
-# along with this program. If not, see <http://www.gnu.org/licenses/>.
+# This program is distributed in the hope that it will be useful, but WITHOUT
+# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
+# details.
+#
+# You should have received a copy of the GNU Lesser General Public License along
+# with this program. If not, see <http://www.gnu.org/licenses/>.
"""Reduce data sets based on geographical information"""
+from __future__ import absolute_import
+
import numpy as np
-#Earth radius
+# Earth radius
R = 6370997.0
-def swath_from_cartesian_grid(cart_grid, lons, lats, data,
+
+def swath_from_cartesian_grid(cart_grid, lons, lats, data,
radius_of_influence):
"""Makes coarse data reduction of swath data by comparison with
cartesian grid
-
+
:Parameters:
chart_grid : numpy array
Grid of area cartesian coordinates
@@ -39,26 +42,27 @@ def swath_from_cartesian_grid(cart_grid, lons, lats, data,
Swath data
radius_of_influence : float
Cut off distance in meters
-
+
:Returns:
(lons, lats, data) : list of numpy arrays
Reduced swath data and coordinate set
"""
-
- valid_index = get_valid_index_from_cartesian_grid(cart_grid, lons, lats,
+
+ valid_index = get_valid_index_from_cartesian_grid(cart_grid, lons, lats,
radius_of_influence)
lons = lons[valid_index]
lats = lats[valid_index]
data = data[valid_index]
-
+
return lons, lats, data
-def get_valid_index_from_cartesian_grid(cart_grid, lons, lats,
+
+def get_valid_index_from_cartesian_grid(cart_grid, lons, lats,
radius_of_influence):
"""Calculates relevant data indices using coarse data reduction of swath
data by comparison with cartesian grid
-
+
:Parameters:
chart_grid : numpy array
Grid of area cartesian coordinates
@@ -70,40 +74,41 @@ def get_valid_index_from_cartesian_grid(cart_grid, lons, lats,
Swath data
radius_of_influence : float
Cut off distance in meters
-
+
:Returns:
valid_index : numpy array
Boolean array of same size as lons and lats indicating relevant indices
"""
-
+
def _get_lons(x, y):
- return np.rad2deg(np.arccos(x/np.sqrt(x**2 + y**2)))*np.sign(y)
-
+ return np.rad2deg(np.arccos(x / np.sqrt(x ** 2 + y ** 2))) * np.sign(y)
+
def _get_lats(z):
- return 90 - np.rad2deg(np.arccos(z/R))
-
- #Get sides of target grid and transform to lon lats
- lons_side1 = _get_lons(cart_grid[0, :, 0], cart_grid[0, :, 1])
+ return 90 - np.rad2deg(np.arccos(z / R))
+
+ # Get sides of target grid and transform to lon lats
+ lons_side1 = _get_lons(cart_grid[0, :, 0], cart_grid[0, :, 1])
lons_side2 = _get_lons(cart_grid[:, -1, 0], cart_grid[:, -1, 1])
lons_side3 = _get_lons(cart_grid[-1, ::-1, 0], cart_grid[-1, ::-1, 1])
lons_side4 = _get_lons(cart_grid[::-1, 0, 0], cart_grid[::-1, 0, 1])
-
+
lats_side1 = _get_lats(cart_grid[0, :, 2])
lats_side2 = _get_lats(cart_grid[:, -1, 2])
lats_side3 = _get_lats(cart_grid[-1, ::-1, 2])
lats_side4 = _get_lats(cart_grid[::-1, 0, 2])
-
+
valid_index = _get_valid_index(lons_side1, lons_side2, lons_side3, lons_side4,
lats_side1, lats_side2, lats_side3, lats_side4,
lons, lats, radius_of_influence)
-
+
return valid_index
-def swath_from_lonlat_grid(grid_lons, grid_lats, lons, lats, data,\
+
+def swath_from_lonlat_grid(grid_lons, grid_lats, lons, lats, data,
radius_of_influence):
"""Makes coarse data reduction of swath data by comparison with
lon lat grid
-
+
:Parameters:
grid_lons : numpy array
Grid of area lons
@@ -117,25 +122,27 @@ def swath_from_lonlat_grid(grid_lons, grid_lats, lons, lats, data,\
Swath data
radius_of_influence : float
Cut off distance in meters
-
+
:Returns:
(lons, lats, data) : list of numpy arrays
Reduced swath data and coordinate set
"""
-
- valid_index = get_valid_index_from_lonlat_grid(grid_lons, grid_lats, lons, lats, radius_of_influence)
+
+ valid_index = get_valid_index_from_lonlat_grid(
+ grid_lons, grid_lats, lons, lats, radius_of_influence)
lons = lons[valid_index]
lats = lats[valid_index]
data = data[valid_index]
-
+
return lons, lats, data
-def swath_from_lonlat_boundaries(boundary_lons, boundary_lats, lons, lats, data,\
- radius_of_influence):
+
+def swath_from_lonlat_boundaries(boundary_lons, boundary_lats, lons, lats, data,
+ radius_of_influence):
"""Makes coarse data reduction of swath data by comparison with
lon lat boundary
-
+
:Parameters:
boundary_lons : numpy array
Grid of area lons
@@ -149,26 +156,26 @@ def swath_from_lonlat_boundaries(boundary_lons, boundary_lats, lons, lats, data,
Swath data
radius_of_influence : float
Cut off distance in meters
-
+
:Returns:
(lons, lats, data) : list of numpy arrays
Reduced swath data and coordinate set
"""
-
- valid_index = get_valid_index_from_lonlat_boundaries(boundary_lons,
+
+ valid_index = get_valid_index_from_lonlat_boundaries(boundary_lons,
boundary_lats, lons, lats, radius_of_influence)
lons = lons[valid_index]
lats = lats[valid_index]
data = data[valid_index]
-
+
return lons, lats, data
def get_valid_index_from_lonlat_grid(grid_lons, grid_lats, lons, lats, radius_of_influence):
"""Calculates relevant data indices using coarse data reduction of swath
data by comparison with lon lat grid
-
+
:Parameters:
chart_grid : numpy array
Grid of area cartesian coordinates
@@ -180,64 +187,66 @@ def get_valid_index_from_lonlat_grid(grid_lons, grid_lats, lons, lats, radius_of
Swath data
radius_of_influence : float
Cut off distance in meters
-
+
:Returns:
valid_index : numpy array
Boolean array of same size as lon and lat indicating relevant indices
"""
-
- #Get sides of target grid
- lons_side1 = grid_lons[0, :]
+
+ # Get sides of target grid
+ lons_side1 = grid_lons[0, :]
lons_side2 = grid_lons[:, -1]
lons_side3 = grid_lons[-1, ::-1]
lons_side4 = grid_lons[::-1, 0]
- lats_side1 = grid_lats[0, :]
+ lats_side1 = grid_lats[0, :]
lats_side2 = grid_lats[:, -1]
lats_side3 = grid_lats[-1, :]
lats_side4 = grid_lats[:, 0]
-
+
valid_index = _get_valid_index(lons_side1, lons_side2, lons_side3, lons_side4,
lats_side1, lats_side2, lats_side3, lats_side4,
lons, lats, radius_of_influence)
-
+
return valid_index
+
def get_valid_index_from_lonlat_boundaries(boundary_lons, boundary_lats, lons, lats, radius_of_influence):
"""Find relevant indices from grid boundaries using the
winding number theorem"""
-
- valid_index = _get_valid_index(boundary_lons.side1, boundary_lons.side2,
+
+ valid_index = _get_valid_index(boundary_lons.side1, boundary_lons.side2,
boundary_lons.side3, boundary_lons.side4,
- boundary_lats.side1, boundary_lats.side2,
+ boundary_lats.side1, boundary_lats.side2,
boundary_lats.side3, boundary_lats.side4,
lons, lats, radius_of_influence)
-
+
return valid_index
-
+
+
def _get_valid_index(lons_side1, lons_side2, lons_side3, lons_side4,
lats_side1, lats_side2, lats_side3, lats_side4,
lons, lats, radius_of_influence):
"""Find relevant indices from grid boundaries using the
winding number theorem"""
-
- #Coarse reduction of data based on extrema analysis of the boundary
- #lon lat values of the target grid
+
+ # Coarse reduction of data based on extrema analysis of the boundary
+ # lon lat values of the target grid
illegal_lons = (((lons_side1 < -180) | (lons_side1 > 180)).any() or
((lons_side2 < -180) | (lons_side2 > 180)).any() or
((lons_side3 < -180) | (lons_side3 > 180)).any() or
((lons_side4 < -180) | (lons_side4 > 180)).any())
-
+
illegal_lats = (((lats_side1 < -90) | (lats_side1 > 90)).any() or
((lats_side2 < -90) | (lats_side2 > 90)).any() or
((lats_side3 < -90) | (lats_side3 > 90)).any() or
((lats_side4 < -90) | (lats_side4 > 90)).any())
-
+
if illegal_lons or illegal_lats:
- #Grid boundaries are not safe to operate on
- return np.ones(lons.size, dtype=np.bool)
-
- #Find sum angle sum of grid boundary
+ # Grid boundaries are not safe to operate on
+ return np.ones(lons.size, dtype=np.bool)
+
+ # Find sum angle sum of grid boundary
angle_sum = 0
for side in (lons_side1, lons_side2, lons_side3, lons_side4):
prev = None
@@ -246,12 +255,12 @@ def _get_valid_index(lons_side1, lons_side2, lons_side3, lons_side4,
if prev:
delta = lon - prev
if abs(delta) > 180:
- delta = (abs(delta)-360) * (delta//abs(delta))
+ delta = (abs(delta) - 360) * (delta // abs(delta))
angle_sum += delta
side_sum += delta
prev = lon
-
- #Buffer min and max lon and lat of interest with radius of interest
+
+ # Buffer min and max lon and lat of interest with radius of interest
lat_min = min(lats_side1.min(), lats_side2.min(), lats_side3.min(),
lats_side4.min())
lat_min_buffered = lat_min - float(radius_of_influence) / R
@@ -261,42 +270,43 @@ def _get_valid_index(lons_side1, lons_side2, lons_side3, lons_side4,
max_angle_s2 = max(abs(lats_side2.max()), abs(lats_side2.min()))
max_angle_s4 = max(abs(lats_side4.max()), abs(lats_side4.min()))
- lon_min_buffered = (lons_side4.min() -
- float(radius_of_influence) /
- (np.sin(np.radians(max_angle_s4)) * R))
-
- lon_max_buffered = (lons_side2.max() +
- float(radius_of_influence) /
- (np.sin(np.radians(max_angle_s2)) * R))
-
- #From the winding number theorem follows:
- #angle_sum possiblilities:
+ lon_min_buffered = (lons_side4.min() -
+ float(radius_of_influence) /
+ (np.sin(np.radians(max_angle_s4)) * R))
+
+ lon_max_buffered = (lons_side2.max() +
+ float(radius_of_influence) /
+ (np.sin(np.radians(max_angle_s2)) * R))
+
+ # From the winding number theorem follows:
+ # angle_sum possiblilities:
#-360: area covers north pole
# 360: area covers south pole
# 0: area covers no poles
- #else: area covers both poles
+ # else: area covers both poles
if round(angle_sum) == -360:
- #Covers NP
- valid_index = (lats >= lat_min_buffered)
+ # Covers NP
+ valid_index = (lats >= lat_min_buffered)
elif round(angle_sum) == 360:
- #Covers SP
- valid_index = (lats <= lat_max_buffered)
+ # Covers SP
+ valid_index = (lats <= lat_max_buffered)
elif round(angle_sum) == 0:
- #Covers no poles
+ # Covers no poles
valid_lats = (lats >= lat_min_buffered) * (lats <= lat_max_buffered)
if lons_side2.min() > lons_side4.max():
- #No date line crossing
- valid_lons = (lons >= lon_min_buffered) * (lons <= lon_max_buffered)
+ # No date line crossing
+ valid_lons = (lons >= lon_min_buffered) * \
+ (lons <= lon_max_buffered)
else:
- #Date line crossing
+ # Date line crossing
seg1 = (lons >= lon_min_buffered) * (lons <= 180)
seg2 = (lons <= lon_max_buffered) * (lons >= -180)
- valid_lons = seg1 + seg2
-
- valid_index = valid_lats * valid_lons
+ valid_lons = seg1 + seg2
+
+ valid_index = valid_lats * valid_lons
else:
- #Covers both poles don't reduce
+ # Covers both poles don't reduce
valid_index = np.ones(lons.size, dtype=np.bool)
return valid_index
diff --git a/pyresample/geo_filter.py b/pyresample/geo_filter.py
index e6fec3e..31317f6 100644
--- a/pyresample/geo_filter.py
+++ b/pyresample/geo_filter.py
@@ -1,11 +1,13 @@
import numpy as np
-import _spatial_mp
-import geometry
+from . import _spatial_mp
+from . import geometry
+
class GridFilter(object):
+
"""Geographic filter from a grid
-
+
:Parameters:
grid_ll_x : float
Projection x coordinate of lower left corner of lower left pixel
@@ -18,59 +20,59 @@ class GridFilter(object):
proj4_string : string
mask : numpy array
Mask as boolean numpy array
-
+
"""
-
+
def __init__(self, area_def, filter, nprocs=1):
self.area_def = area_def
self._filter = filter.astype(np.bool)
self.nprocs = nprocs
-
+
def get_valid_index(self, geometry_def):
"""Calculates valid_index array based on lons and lats
-
+
:Parameters:
lons : numpy array
lats : numpy array
-
+
:Returns:
Boolean numpy array of same shape as lons and lats
-
+
"""
-
+
lons = geometry_def.lons[:]
lats = geometry_def.lats[:]
-
- #Get projection coords
+
+ # Get projection coords
if self.nprocs > 1:
proj = _spatial_mp.Proj_MP(**self.area_def.proj_dict)
else:
proj = _spatial_mp.Proj(**self.area_def.proj_dict)
-
+
x_coord, y_coord = proj(lons, lats, nprocs=self.nprocs)
-
- #Find array indices of coordinates
- target_x = ((x_coord / self.area_def.pixel_size_x) +
+
+ # Find array indices of coordinates
+ target_x = ((x_coord / self.area_def.pixel_size_x) +
self.area_def.pixel_offset_x).astype(np.int32)
- target_y = (self.area_def.pixel_offset_y -
- (y_coord / self.area_def.pixel_size_y)).astype(np.int32)
-
- #Create mask for pixels outside array (invalid pixels)
+ target_y = (self.area_def.pixel_offset_y -
+ (y_coord / self.area_def.pixel_size_y)).astype(np.int32)
+
+ # Create mask for pixels outside array (invalid pixels)
target_x_valid = (target_x >= 0) & (target_x < self.area_def.x_size)
target_y_valid = (target_y >= 0) & (target_y < self.area_def.y_size)
-
- #Set index of invalid pixels to 0
- target_x[np.invert(target_x_valid)] = 0
+
+ # Set index of invalid pixels to 0
+ target_x[np.invert(target_x_valid)] = 0
target_y[np.invert(target_y_valid)] = 0
-
- #Find mask
+
+ # Find mask
filter = self._filter[target_y, target_x]
-
- #Remove invalid pixels
+
+ # Remove invalid pixels
filter = (filter & target_x_valid & target_y_valid).astype(np.bool)
-
+
return filter
-
+
def filter(self, geometry_def, data):
lons = geometry_def.lons[:]
lats = geometry_def.lats[:]
@@ -79,8 +81,6 @@ class GridFilter(object):
lats_f = lats[valid_index]
data_f = data[valid_index]
geometry_def_f = \
- geometry.CoordinateDefinition(lons_f, lats_f,
+ geometry.CoordinateDefinition(lons_f, lats_f,
nprocs=geometry_def.nprocs)
return geometry_def_f, data_f
-
-
diff --git a/pyresample/geometry.py b/pyresample/geometry.py
index 6593c94..fa4b977 100644
--- a/pyresample/geometry.py
+++ b/pyresample/geometry.py
@@ -1,26 +1,34 @@
-#pyresample, Resampling of remote sensing image data in python
-#
-#Copyright (C) 2010, 2013 Esben S. Nielsen
+# pyresample, Resampling of remote sensing image data in python
#
-#This program is free software: you can redistribute it and/or modify
-#it under the terms of the GNU General Public License as published by
-#the Free Software Foundation, either version 3 of the License, or
-#(at your option) any later version.
+# Copyright (C) 2010-2015
#
-#This program is distributed in the hope that it will be useful,
-#but WITHOUT ANY WARRANTY; without even the implied warranty of
-#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-#GNU General Public License for more details.
+# Authors:
+# Esben S. Nielsen
+# Thomas Lavergne
#
-#You should have received a copy of the GNU General Public License
-# along with this program. If not, see <http://www.gnu.org/licenses/>.
+# This program is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Lesser General Public License as published by the Free
+# Software Foundation, either version 3 of the License, or (at your option) any
+# later version.
+#
+# This program is distributed in the hope that it will be useful, but WITHOUT
+# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
+# details.
+#
+# You should have received a copy of the GNU Lesser General Public License along
+# with this program. If not, see <http://www.gnu.org/licenses/>.
"""Classes for geometry operations"""
-import weakref
+
+from __future__ import absolute_import
+
+import warnings
import numpy as np
-import _spatial_mp
+from . import utils
+from . import _spatial_mp
class DimensionError(Exception):
@@ -28,9 +36,10 @@ class DimensionError(Exception):
class Boundary(object):
+
"""Container for geometry boundary.
Labelling starts in upper left corner and proceeds clockwise"""
-
+
def __init__(self, side1, side2, side3, side4):
self.side1 = side1
self.side2 = side2
@@ -39,22 +48,39 @@ class Boundary(object):
class BaseDefinition(object):
+
"""Base class for geometry definitions"""
-
+
def __init__(self, lons=None, lats=None, nprocs=1):
if type(lons) != type(lats):
raise TypeError('lons and lats must be of same type')
elif lons is not None:
if lons.shape != lats.shape:
raise ValueError('lons and lats must have same shape')
-
+
self.nprocs = nprocs
- self.lons = lons
- self.lats = lats
-
+ # check the latitutes
+ if lats is not None and ((lats.min() < -90. or lats.max() > +90.)):
+ # throw exception
+ raise ValueError(
+ 'Some latitudes are outside the [-90.;+90] validity range')
+ else:
+ self.lats = lats
+
+ # check the longitudes
+ if lons is not None and ((lons.min() < -180. or lons.max() >= +180.)):
+ # issue warning
+ warnings.warn('All geometry objects expect longitudes in the [-180:+180[ range. ' +
+ 'We will now automatically wrap your longitudes into [-180:+180[, and continue. ' +
+ 'To avoid this warning next time, use routine utils.wrap_longitudes().')
+ # wrap longitudes to [-180;+180[
+ self.lons = utils.wrap_longitudes(lons)
+ else:
+ self.lons = lons
+
self.cartesian_coords = None
-
+
def __eq__(self, other):
"""Test for approximate equality"""
@@ -76,53 +102,53 @@ class BaseDefinition(object):
np.allclose(self_lats, other_lats, atol=1e-6,
rtol=5e-9))
except (AttributeError, ValueError):
- return False
+ return False
def __ne__(self, other):
"""Test for approximate equality"""
-
+
return not self.__eq__(other)
-
+
def get_lonlat(self, row, col):
"""Retrieve lon and lat of single pixel
-
+
:Parameters:
row : int
col : int
-
+
:Returns:
(lon, lat) : tuple of floats
"""
-
+
if self.ndim != 2:
raise DimensionError(('operation undefined '
'for %sD geometry ') % self.ndim)
elif self.lons is None or self.lats is None:
raise ValueError('lon/lat values are not defined')
return self.lons[row, col], self.lats[row, col]
-
+
def get_lonlats(self, data_slice=None, **kwargs):
"""Base method for lon lat retrieval with slicing"""
-
+
if self.lons is None or self.lats is None:
raise ValueError('lon/lat values are not defined')
elif data_slice is None:
return self.lons, self.lats
else:
return self.lons[data_slice], self.lats[data_slice]
-
+
def get_boundary_lonlats(self):
- """Returns Boundary objects"""
-
- side1 = self.get_lonlats(data_slice=(0, slice(None)))
- side2 = self.get_lonlats(data_slice=(slice(None), -1))
- side3 = self.get_lonlats(data_slice=(-1, slice(None)))
- side4 = self.get_lonlats(data_slice=(slice(None), 0))
- return Boundary(side1[0], side2[0], side3[0][::-1], side4[0][::-1]), Boundary(side1[1], side2[1], side3[1][::-1], side4[1][::-1])
-
+ """Returns Boundary objects"""
+
+ side1 = self.get_lonlats(data_slice=(0, slice(None)))
+ side2 = self.get_lonlats(data_slice=(slice(None), -1))
+ side3 = self.get_lonlats(data_slice=(-1, slice(None)))
+ side4 = self.get_lonlats(data_slice=(slice(None), 0))
+ return Boundary(side1[0], side2[0], side3[0][::-1], side4[0][::-1]), Boundary(side1[1], side2[1], side3[1][::-1], side4[1][::-1])
+
def get_cartesian_coords(self, nprocs=None, data_slice=None, cache=False):
"""Retrieve cartesian coordinates of geometry definition
-
+
:Parameters:
nprocs : int, optional
Number of processor cores to be used.
@@ -131,45 +157,45 @@ class BaseDefinition(object):
Calculate only cartesian coordnates for the defined slice
cache : bool, optional
Store result the result. Requires data_slice to be None
-
+
:Returns:
cartesian_coords : numpy array
"""
if self.cartesian_coords is None:
- #Coordinates are not cached
+ # Coordinates are not cached
if nprocs is None:
nprocs = self.nprocs
-
+
if data_slice is None:
- #Use full slice
+ # Use full slice
data_slice = slice(None)
-
+
lons, lats = self.get_lonlats(nprocs=nprocs, data_slice=data_slice)
-
+
if nprocs > 1:
cartesian = _spatial_mp.Cartesian_MP(nprocs)
else:
cartesian = _spatial_mp.Cartesian()
-
- cartesian_coords = cartesian.transform_lonlats(np.ravel(lons),
+
+ cartesian_coords = cartesian.transform_lonlats(np.ravel(lons),
np.ravel(lats))
-
+
if isinstance(lons, np.ndarray) and lons.ndim > 1:
- #Reshape to correct shape
- cartesian_coords = cartesian_coords.reshape(lons.shape[0],
+ # Reshape to correct shape
+ cartesian_coords = cartesian_coords.reshape(lons.shape[0],
lons.shape[1], 3)
-
+
if cache and data_slice is None:
- self.cartesian_coords = cartesian_coords
+ self.cartesian_coords = cartesian_coords
else:
- #Coordinates are cached
+ # Coordinates are cached
if data_slice is None:
cartesian_coords = self.cartesian_coords
else:
cartesian_coords = self.cartesian_coords[data_slice]
-
- return cartesian_coords
+
+ return cartesian_coords
@property
def corners(self):
@@ -180,7 +206,7 @@ class BaseDefinition(object):
Coordinate(*self.get_lonlat(0, -1)),
Coordinate(*self.get_lonlat(-1, -1)),
Coordinate(*self.get_lonlat(-1, 0))]
-
+
def __contains__(self, point):
"""Is a point inside the 4 corners of the current area? This uses
great circle arcs as area boundaries.
@@ -197,28 +223,28 @@ class BaseDefinition(object):
"""Tests if the current area overlaps the *other* area. This is based
solely on the corners of areas, assuming the boundaries to be great
circles.
-
+
:Parameters:
other : object
Instance of subclass of BaseDefinition
-
+
:Returns:
overlaps : bool
"""
from pyresample.spherical_geometry import Arc
-
+
self_corners = self.corners
other_corners = other.corners
-
+
for i in self_corners:
if i in other:
return True
for i in other_corners:
if i in self:
return True
-
+
self_arc1 = Arc(self_corners[0], self_corners[1])
self_arc2 = Arc(self_corners[1], self_corners[2])
self_arc3 = Arc(self_corners[2], self_corners[3])
@@ -246,11 +272,11 @@ class BaseDefinition(object):
def intersection(self, other):
"""Returns the corners of the intersection polygon of the current area
with *other*.
-
+
:Parameters:
other : object
Instance of subclass of BaseDefinition
-
+
:Returns:
(corner1, corner2, corner3, corner4) : tuple of points
"""
@@ -259,25 +285,25 @@ class BaseDefinition(object):
def overlap_rate(self, other):
"""Get how much the current area overlaps an *other* area.
-
+
:Parameters:
other : object
Instance of subclass of BaseDefinition
-
+
:Returns:
overlap_rate : float
"""
-
+
from pyresample.spherical_geometry import get_polygon_area
other_area = other.get_area()
inter_area = get_polygon_area(self.intersection(other))
return inter_area / other_area
-
class CoordinateDefinition(BaseDefinition):
+
"""Base class for geometry definitions defined by lons and lats only"""
-
+
def __init__(self, lons, lats, nprocs=1):
if lons.shape == lats.shape and lons.dtype == lats.dtype:
self.shape = lons.shape
@@ -286,21 +312,21 @@ class CoordinateDefinition(BaseDefinition):
self.dtype = lons.dtype
else:
raise ValueError(('%s must be created with either '
- 'lon/lats of the same shape with same dtype') %
+ 'lon/lats of the same shape with same dtype') %
self.__class__.__name__)
super(CoordinateDefinition, self).__init__(lons, lats, nprocs)
-
+
def concatenate(self, other):
if self.ndim != other.ndim:
raise DimensionError(('Unable to concatenate %sD and %sD '
'geometries') % (self.ndim, other.ndim))
- klass = _get_highest_level_class(self, other)
+ klass = _get_highest_level_class(self, other)
lons = np.concatenate((self.lons, other.lons))
lats = np.concatenate((self.lats, other.lats))
nprocs = min(self.nprocs, other.nprocs)
return klass(lons, lats, nprocs=nprocs)
-
- def append(self, other):
+
+ def append(self, other):
if self.ndim != other.ndim:
raise DimensionError(('Unable to append %sD and %sD '
'geometries') % (self.ndim, other.ndim))
@@ -310,27 +336,28 @@ class CoordinateDefinition(BaseDefinition):
self.size = self.lons.size
def __str__(self):
- #Rely on numpy's object printing
- return ('Shape: %s\nLons: %s\nLats: %s') % (str(self.shape),
+ # Rely on numpy's object printing
+ return ('Shape: %s\nLons: %s\nLats: %s') % (str(self.shape),
str(self.lons),
str(self.lats))
-
+
class GridDefinition(CoordinateDefinition):
+
"""Grid defined by lons and lats
-
+
:Parameters:
lons : numpy array
lats : numpy array
nprocs : int, optional
Number of processor cores to be used for calculations.
-
+
:Attributes:
shape : tuple
Grid shape as (rows, cols)
size : int
Number of elements in grid
-
+
Properties:
lons : object
Grid lons
@@ -339,25 +366,26 @@ class GridDefinition(CoordinateDefinition):
cartesian_coords : object
Grid cartesian coordinates
"""
-
+
def __init__(self, lons, lats, nprocs=1):
if lons.shape != lats.shape:
raise ValueError('lon and lat grid must have same shape')
elif lons.ndim != 2:
raise ValueError('2 dimensional lon lat grid expected')
-
+
super(GridDefinition, self).__init__(lons, lats, nprocs)
class SwathDefinition(CoordinateDefinition):
+
"""Swath defined by lons and lats
-
+
:Parameters:
lons : numpy array
lats : numpy array
nprocs : int, optional
Number of processor cores to be used for calculations.
-
+
:Attributes:
shape : tuple
Swath shape
@@ -365,7 +393,7 @@ class SwathDefinition(CoordinateDefinition):
Number of elements in swath
ndims : int
Swath dimensions
-
+
Properties:
lons : object
Swath lons
@@ -374,7 +402,7 @@ class SwathDefinition(CoordinateDefinition):
cartesian_coords : object
Swath cartesian coordinates
"""
-
+
def __init__(self, lons, lats, nprocs=1):
if lons.shape != lats.shape:
raise ValueError('lon and lat arrays must have same shape')
@@ -383,7 +411,8 @@ class SwathDefinition(CoordinateDefinition):
super(SwathDefinition, self).__init__(lons, lats, nprocs)
-class AreaDefinition(BaseDefinition):
+class AreaDefinition(BaseDefinition):
+
"""Holds definition of an area.
:Parameters:
@@ -407,7 +436,7 @@ class AreaDefinition(BaseDefinition):
Grid lons
lats : numpy array, optional
Grid lats
-
+
:Attributes:
area_id : str
ID of area
@@ -441,7 +470,7 @@ class AreaDefinition(BaseDefinition):
pixel_offset_y : float
y offset between projection center and upper left corner of upper
left pixel in units of pixels..
-
+
Properties:
proj4_string : str
Projection defined as Proj.4 string
@@ -456,8 +485,7 @@ class AreaDefinition(BaseDefinition):
projection_y_coords : object
Grid projection y coordinate
"""
-
-
+
def __init__(self, area_id, name, proj_id, proj_dict, x_size, y_size,
area_extent, nprocs=1, lons=None, lats=None, dtype=np.float64):
if not isinstance(proj_dict, dict):
@@ -481,69 +509,69 @@ class AreaDefinition(BaseDefinition):
self.pixel_size_y = (area_extent[3] - area_extent[1]) / float(y_size)
self.proj_dict = proj_dict
self.area_extent = tuple(area_extent)
-
+
# Calculate area_extent in lon lat
proj = _spatial_mp.Proj(**proj_dict)
- corner_lons, corner_lats = proj((area_extent[0], area_extent[2]),
- (area_extent[1], area_extent[3]),
+ corner_lons, corner_lats = proj((area_extent[0], area_extent[2]),
+ (area_extent[1], area_extent[3]),
inverse=True)
- self.area_extent_ll = (corner_lons[0], corner_lats[0],
+ self.area_extent_ll = (corner_lons[0], corner_lats[0],
corner_lons[1], corner_lats[1])
-
- #Calculate projection coordinates of center of upper left pixel
+
+ # Calculate projection coordinates of center of upper left pixel
self.pixel_upper_left = \
- (float(area_extent[0]) +
- float(self.pixel_size_x) / 2,
- float(area_extent[3]) -
- float(self.pixel_size_y) / 2)
-
- #Pixel_offset defines the distance to projection center from origen (UL)
- #of image in units of pixels.
+ (float(area_extent[0]) +
+ float(self.pixel_size_x) / 2,
+ float(area_extent[3]) -
+ float(self.pixel_size_y) / 2)
+
+ # Pixel_offset defines the distance to projection center from origen (UL)
+ # of image in units of pixels.
self.pixel_offset_x = -self.area_extent[0] / self.pixel_size_x
self.pixel_offset_y = self.area_extent[3] / self.pixel_size_y
-
+
self.projection_x_coords = None
self.projection_y_coords = None
self.dtype = dtype
-
+
def __str__(self):
- #We need a sorted dictionary for a unique hash of str(self)
+ # We need a sorted dictionary for a unique hash of str(self)
proj_dict = self.proj_dict
- proj_str = ('{' +
- ', '.join(["'%s': '%s'"%(str(k), str(proj_dict[k]))
+ proj_str = ('{' +
+ ', '.join(["'%s': '%s'" % (str(k), str(proj_dict[k]))
for k in sorted(proj_dict.keys())]) +
'}')
return ('Area ID: %s\nName: %s\nProjection ID: %s\n'
'Projection: %s\nNumber of columns: %s\nNumber of rows: %s\n'
- 'Area extent: %s') % (self.area_id, self.name, self.proj_id,
- proj_str, self.x_size, self.y_size,
+ 'Area extent: %s') % (self.area_id, self.name, self.proj_id,
+ proj_str, self.x_size, self.y_size,
self.area_extent)
-
+
__repr__ = __str__
-
+
def __eq__(self, other):
"""Test for equality"""
-
+
try:
return ((self.proj_dict == other.proj_dict) and
(self.shape == other.shape) and
(self.area_extent == other.area_extent))
except AttributeError:
return super(AreaDefinition, self).__eq__(other)
-
+
def __ne__(self, other):
"""Test for equality"""
-
+
return not self.__eq__(other)
-
+
def get_xy_from_lonlat(self, lon, lat):
"""Retrieve closest x and y coordinates (column, row indices) for the
specified geolocation (lon,lat) if inside area. If lon,lat is a point a
ValueError is raised if the return point is outside the area domain. If
lon,lat is a tuple of sequences of longitudes and latitudes, a tuple of
masked arrays are returned.
-
+
:Input:
lon : point or sequence (list or array) of longitudes
lat : point or sequence (list or array) of latitudes
@@ -557,13 +585,13 @@ class AreaDefinition(BaseDefinition):
if isinstance(lat, list):
lat = np.array(lat)
- if ((isinstance(lon, np.ndarray) and
- not isinstance(lat, np.ndarray)) or
- (not isinstance(lon, np.ndarray) and
+ if ((isinstance(lon, np.ndarray) and
+ not isinstance(lat, np.ndarray)) or
+ (not isinstance(lon, np.ndarray) and
isinstance(lat, np.ndarray))):
- raise ValueError("Both lon and lat needs to be of " +
+ raise ValueError("Both lon and lat needs to be of " +
"the same type and have the same dimensions!")
-
+
if isinstance(lon, np.ndarray) and isinstance(lat, np.ndarray):
if lon.shape != lat.shape:
raise ValueError("lon and lat is not of the same shape!")
@@ -571,44 +599,44 @@ class AreaDefinition(BaseDefinition):
pobj = _spatial_mp.Proj(self.proj4_string)
upl_x = self.area_extent[0]
upl_y = self.area_extent[3]
- xscale = abs(self.area_extent[2] -
+ xscale = abs(self.area_extent[2] -
self.area_extent[0]) / float(self.x_size)
- yscale = abs(self.area_extent[1] -
+ yscale = abs(self.area_extent[1] -
self.area_extent[3]) / float(self.y_size)
-
+
xm_, ym_ = pobj(lon, lat)
x__ = (xm_ - upl_x) / xscale
y__ = (upl_y - ym_) / yscale
if isinstance(x__, np.ndarray) and isinstance(y__, np.ndarray):
- mask = (((x__ < 0 ) | (x__ > self.x_size)) |
- ((y__ < 0) | (y__ > self.y_size)))
- return (np.ma.masked_array(x__.astype('int'), mask=mask,
+ mask = (((x__ < 0) | (x__ > self.x_size)) |
+ ((y__ < 0) | (y__ > self.y_size)))
+ return (np.ma.masked_array(x__.astype('int'), mask=mask,
fill_value=-1),
np.ma.masked_array(y__.astype('int'), mask=mask,
fill_value=-1))
else:
if ((x__ < 0 or x__ > self.x_size) or
- (y__ < 0 or y__ > self.y_size)):
+ (y__ < 0 or y__ > self.y_size)):
raise ValueError('Point outside area:( %f %f)' % (x__, y__))
return int(x__), int(y__)
def get_lonlat(self, row, col):
"""Retrieves lon and lat values of single point in area grid
-
+
:Parameters:
row : int
col : int
-
+
:Returns:
(lon, lat) : tuple of floats
"""
-
+
return self.get_lonlats(nprocs=None, data_slice=(row, col))
-
+
def get_proj_coords(self, data_slice=None, cache=False, dtype=None):
"""Get projection coordinates of grid
-
+
:Parameters:
data_slice : slice object, optional
Calculate only coordinates for specified slice
@@ -619,18 +647,18 @@ class AreaDefinition(BaseDefinition):
(target_x, target_y) : tuple of numpy arrays
Grids of area x- and y-coordinates in projection units
"""
-
+
def get_val(val, sub_val, max):
- #Get value with substitution and wrapping
+ # Get value with substitution and wrapping
if val is None:
return sub_val
else:
if val < 0:
- #Wrap index
+ # Wrap index
return max + val
else:
return val
-
+
if self.projection_x_coords is not None and self.projection_y_coords is not None:
# Projection coords are cached
if data_slice is None:
@@ -644,79 +672,84 @@ class AreaDefinition(BaseDefinition):
if dtype is None:
dtype = self.dtype
- #create coordinates of local area as ndarrays
+ # create coordinates of local area as ndarrays
if data_slice is None or data_slice == slice(None):
- #Full slice
+ # Full slice
rows = self.y_size
cols = self.x_size
row_start = 0
col_start = 0
- else:
+ else:
if isinstance(data_slice, slice):
- #Row slice
+ # Row slice
row_start = get_val(data_slice.start, 0, self.y_size)
col_start = 0
- rows = get_val(data_slice.stop, self.y_size, self.y_size) - row_start
+ rows = get_val(
+ data_slice.stop, self.y_size, self.y_size) - row_start
cols = self.x_size
elif isinstance(data_slice[0], slice) and isinstance(data_slice[1], slice):
- #Block slice
+ # Block slice
row_start = get_val(data_slice[0].start, 0, self.y_size)
col_start = get_val(data_slice[1].start, 0, self.x_size)
- rows = get_val(data_slice[0].stop, self.y_size, self.y_size) - row_start
- cols = get_val(data_slice[1].stop, self.x_size, self.x_size) - col_start
+ rows = get_val(
+ data_slice[0].stop, self.y_size, self.y_size) - row_start
+ cols = get_val(
+ data_slice[1].stop, self.x_size, self.x_size) - col_start
elif isinstance(data_slice[0], slice):
- #Select from col
+ # Select from col
is_1d_select = True
row_start = get_val(data_slice[0].start, 0, self.y_size)
col_start = get_val(data_slice[1], 0, self.x_size)
- rows = get_val(data_slice[0].stop, self.y_size, self.y_size) - row_start
+ rows = get_val(
+ data_slice[0].stop, self.y_size, self.y_size) - row_start
cols = 1
elif isinstance(data_slice[1], slice):
- #Select from row
+ # Select from row
is_1d_select = True
row_start = get_val(data_slice[0], 0, self.y_size)
col_start = get_val(data_slice[1].start, 0, self.x_size)
rows = 1
- cols = get_val(data_slice[1].stop, self.x_size, self.x_size) - col_start
+ cols = get_val(
+ data_slice[1].stop, self.x_size, self.x_size) - col_start
else:
- #Single element select
+ # Single element select
is_single_value = True
-
- row_start = get_val(data_slice[0], 0, self.y_size)
+
+ row_start = get_val(data_slice[0], 0, self.y_size)
col_start = get_val(data_slice[1], 0, self.x_size)
-
+
rows = 1
- cols = 1
-
- #Calculate coordinates
- target_x = np.fromfunction(lambda i, j: (j + col_start) *
- self.pixel_size_x +
+ cols = 1
+
+ # Calculate coordinates
+ target_x = np.fromfunction(lambda i, j: (j + col_start) *
+ self.pixel_size_x +
self.pixel_upper_left[0],
- (rows,
+ (rows,
cols), dtype=dtype)
-
- target_y = np.fromfunction(lambda i, j:
- self.pixel_upper_left[1] -
+
+ target_y = np.fromfunction(lambda i, j:
+ self.pixel_upper_left[1] -
(i + row_start) * self.pixel_size_y,
- (rows,
+ (rows,
cols), dtype=dtype)
-
+
if is_single_value:
- #Return single values
+ # Return single values
target_x = float(target_x)
target_y = float(target_y)
elif is_1d_select:
- #Reshape to 1D array
+ # Reshape to 1D array
target_x = target_x.reshape((target_x.size,))
target_y = target_y.reshape((target_y.size,))
-
+
if cache and data_slice is None:
# Cache the result if requested
self.projection_x_coords = target_x
self.projection_y_coords = target_y
return target_x, target_y
-
+
@property
def proj_x_coords(self):
return self.get_proj_coords(data_slice=(0, slice(None)))[0]
@@ -725,10 +758,26 @@ class AreaDefinition(BaseDefinition):
def proj_y_coords(self):
return self.get_proj_coords(data_slice=(slice(None), 0))[1]
+ @property
+ def outer_boundary_corners(self):
+ """Returns the lon,lat of the outer edges of the corner points
+ """
+ from pyresample.spherical_geometry import Coordinate
+ proj = _spatial_mp.Proj(**self.proj_dict)
+
+ corner_lons, corner_lats = proj((self.area_extent[0], self.area_extent[2],
+ self.area_extent[2], self.area_extent[0]),
+ (self.area_extent[3], self.area_extent[3],
+ self.area_extent[1], self.area_extent[1]),
+ inverse=True)
+ return [Coordinate(corner_lons[0], corner_lats[0]),
+ Coordinate(corner_lons[1], corner_lats[1]),
+ Coordinate(corner_lons[2], corner_lats[2]),
+ Coordinate(corner_lons[3], corner_lats[3])]
def get_lonlats(self, nprocs=None, data_slice=None, cache=False, dtype=None):
"""Returns lon and lat arrays of area.
-
+
:Parameters:
nprocs : int, optional
Number of processor cores to be used.
@@ -741,7 +790,7 @@ class AreaDefinition(BaseDefinition):
:Returns:
(lons, lats) : tuple of numpy arrays
Grids of area lons and and lats
- """
+ """
if dtype is None:
dtype = self.dtype
@@ -750,53 +799,54 @@ class AreaDefinition(BaseDefinition):
#Data is not cached
if nprocs is None:
nprocs = self.nprocs
-
- #Proj.4 definition of target area projection
+
+ # Proj.4 definition of target area projection
if nprocs > 1:
target_proj = _spatial_mp.Proj_MP(**self.proj_dict)
else:
target_proj = _spatial_mp.Proj(**self.proj_dict)
-
- #Get coordinates of local area as ndarrays
- target_x, target_y = self.get_proj_coords(data_slice=data_slice, dtype=dtype)
-
- #Get corresponding longitude and latitude values
+
+ # Get coordinates of local area as ndarrays
+ target_x, target_y = self.get_proj_coords(
+ data_slice=data_slice, dtype=dtype)
+
+ # Get corresponding longitude and latitude values
lons, lats = target_proj(target_x, target_y, inverse=True,
nprocs=nprocs)
lons = np.asanyarray(lons, dtype=dtype)
lats = np.asanyarray(lats, dtype=dtype)
-
+
if cache and data_slice is None:
# Cache the result if requested
self.lons = lons
self.lats = lats
- #Free memory
+ # Free memory
del(target_x)
del(target_y)
else:
#Data is cached
if data_slice is None:
- #Full slice
+ # Full slice
lons = self.lons
lats = self.lats
else:
lons = self.lons[data_slice]
lats = self.lats[data_slice]
-
+
return lons, lats
@property
def proj4_string(self):
"""Returns projection definition as Proj.4 string"""
-
+
items = self.proj_dict.items()
- return '+' + ' +'.join([ t[0] + '=' + t[1] for t in items])
-
+ return '+' + ' +'.join([t[0] + '=' + t[1] for t in items])
+
def _get_slice(segments, shape):
"""Generator for segmenting a 1D or 2D array"""
-
+
if not (1 <= len(shape) <= 2):
raise ValueError('Cannot segment array of shape: %s' % str(shape))
else:
@@ -812,19 +862,21 @@ def _get_slice(segments, shape):
start_idx = end_idx
end_idx = min(start_idx + slice_length, size)
+
def _flatten_cartesian_coords(cartesian_coords):
"""Flatten array to (n, 3) shape"""
-
- shape = cartesian_coords.shape
+
+ shape = cartesian_coords.shape
if len(shape) > 2:
- cartesian_coords = cartesian_coords.reshape(shape[0] *
+ cartesian_coords = cartesian_coords.reshape(shape[0] *
shape[1], 3)
return cartesian_coords
+
def _get_highest_level_class(obj1, obj2):
- if (not issubclass(obj1.__class__, obj2.__class__) or
- not issubclass(obj2.__class__, obj1.__class__)):
- raise TypeError('No common superclass for %s and %s' %
+ if (not issubclass(obj1.__class__, obj2.__class__) or
+ not issubclass(obj2.__class__, obj1.__class__)):
+ raise TypeError('No common superclass for %s and %s' %
(obj1.__class__, obj2.__class__))
if obj1.__class__ == obj2.__class__:
@@ -833,6 +885,4 @@ def _get_highest_level_class(obj1, obj2):
klass = obj2.__class__
else:
klass = obj1.__class__
- return klass
-
-
+ return klass
diff --git a/pyresample/grid.py b/pyresample/grid.py
index d93494c..52d1033 100644
--- a/pyresample/grid.py
+++ b/pyresample/grid.py
@@ -1,27 +1,34 @@
-#pyresample, Resampling of remote sensing image data in python
-#
-#Copyright (C) 2010 Esben S. Nielsen
+# pyresample, Resampling of remote sensing image data in python
#
-#This program is free software: you can redistribute it and/or modify
-#it under the terms of the GNU General Public License as published by
-#the Free Software Foundation, either version 3 of the License, or
+# Copyright (C) 2010, 2014, 2015 Esben S. Nielsen
+#
+# This program is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Lesser General Public License as published by the Free
+# Software Foundation, either version 3 of the License, or
#(at your option) any later version.
#
-#This program is distributed in the hope that it will be useful,
-#but WITHOUT ANY WARRANTY; without even the implied warranty of
-#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-#GNU General Public License for more details.
+# This program is distributed in the hope that it will be useful, but WITHOUT
+# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
+# details.
#
-#You should have received a copy of the GNU General Public License
-# along with this program. If not, see <http://www.gnu.org/licenses/>.
+# You should have received a copy of the GNU Lesser General Public License along
+# with this program. If not, see <http://www.gnu.org/licenses/>.
"""Resample image from one projection to another
using nearest neighbour method in cartesian projection coordinate systems"""
+from __future__ import absolute_import
+
import numpy as np
-import geometry
-import _spatial_mp
+from . import geometry
+from . import _spatial_mp
+
+try:
+ range = xrange
+except NameError:
+ pass
def get_image_from_linesample(row_indices, col_indices, source_image,
@@ -39,42 +46,42 @@ def get_image_from_linesample(row_indices, col_indices, source_image,
Set undetermined pixels to this value.
If fill_value is None a masked array is returned
with undetermined pixels masked
-
+
:Returns:
image_data : numpy array
Resampled image
"""
-
- #mask out non valid row and col indices
+
+ # mask out non valid row and col indices
row_mask = (row_indices >= 0) * (row_indices < source_image.shape[0])
col_mask = (col_indices >= 0) * (col_indices < source_image.shape[1])
valid_rows = row_indices * row_mask
valid_cols = col_indices * col_mask
- #free memory
+ # free memory
del(row_indices)
del(col_indices)
-
- #get valid part of image
+
+ # get valid part of image
target_image = source_image[valid_rows, valid_cols]
-
- #free memory
+
+ # free memory
del(valid_rows)
del(valid_cols)
- #create mask for valid data points
+ # create mask for valid data points
valid_data = row_mask * col_mask
if valid_data.ndim != target_image.ndim:
for i in range(target_image.ndim - valid_data.ndim):
valid_data = np.expand_dims(valid_data, axis=valid_data.ndim)
-
- #free memory
+
+ # free memory
del(row_mask)
del(col_mask)
-
- #fill the non valid part of the image
+
+ # fill the non valid part of the image
if fill_value is not None:
- target_filled = (target_image * valid_data +
+ target_filled = (target_image * valid_data +
(1 - valid_data) * fill_value)
else:
if np.ma.is_masked(target_image):
@@ -82,12 +89,13 @@ def get_image_from_linesample(row_indices, col_indices, source_image,
else:
mask = (1 - valid_data)
target_filled = np.ma.array(target_image, mask=mask)
-
+
return target_filled.astype(target_image.dtype)
-
+
+
def get_linesample(lons, lats, source_area_def, nprocs=1):
"""Returns index row and col arrays for resampling
-
+
:Parameters:
lons : numpy array
Lons. Dimensions must match lats
@@ -97,31 +105,32 @@ def get_linesample(lons, lats, source_area_def, nprocs=1):
Source definition as AreaDefinition object
nprocs : int, optional
Number of processor cores to be used
-
+
:Returns:
(row_indices, col_indices) : tuple of numpy arrays
Arrays for resampling area by array indexing
"""
-
- #Proj.4 definition of source area projection
+
+ # Proj.4 definition of source area projection
if nprocs > 1:
source_proj = _spatial_mp.Proj_MP(**source_area_def.proj_dict)
else:
source_proj = _spatial_mp.Proj(**source_area_def.proj_dict)
- #get cartesian projection values from longitude and latitude
+ # get cartesian projection values from longitude and latitude
source_x, source_y = source_proj(lons, lats, nprocs=nprocs)
- #Find corresponding pixels (element by element conversion of ndarrays)
- source_pixel_x = (source_area_def.pixel_offset_x + \
+ # Find corresponding pixels (element by element conversion of ndarrays)
+ source_pixel_x = (source_area_def.pixel_offset_x +
source_x / source_area_def.pixel_size_x).astype(np.int32)
-
- source_pixel_y = (source_area_def.pixel_offset_y - \
+
+ source_pixel_y = (source_area_def.pixel_offset_y -
source_y / source_area_def.pixel_size_y).astype(np.int32)
-
+
return source_pixel_y, source_pixel_x
-
-def get_image_from_lonlats(lons, lats, source_area_def, source_image_data,
+
+
+def get_image_from_lonlats(lons, lats, source_area_def, source_image_data,
fill_value=0, nprocs=1):
"""Samples from image based on lon lat arrays
using nearest neighbour method in cartesian projection coordinate systems.
@@ -141,20 +150,21 @@ def get_image_from_lonlats(lons, lats, source_area_def, source_image_data,
with undetermined pixels masked
nprocs : int, optional
Number of processor cores to be used
-
+
:Returns:
image_data : numpy array
Resampled image data
"""
- source_pixel_y, source_pixel_x = get_linesample(lons, lats,
- source_area_def,
+ source_pixel_y, source_pixel_x = get_linesample(lons, lats,
+ source_area_def,
nprocs=nprocs)
- #Return target image
+ # Return target image
return get_image_from_linesample(source_pixel_y, source_pixel_x,
source_image_data, fill_value)
+
def get_resampled_image(target_area_def, source_area_def, source_image_data,
fill_value=0, nprocs=1, segments=None):
"""Resamples image using nearest neighbour method in cartesian
@@ -176,12 +186,12 @@ def get_resampled_image(target_area_def, source_area_def, source_image_data,
segments : {int, None} optional
Number of segments to use when resampling.
If set to None an estimate will be calculated.
-
+
:Returns:
image_data : numpy array
Resampled image data
"""
-
+
if not isinstance(target_area_def, geometry.AreaDefinition):
raise TypeError('target_area_def must be of type AreaDefinition')
if not isinstance(source_area_def, geometry.AreaDefinition):
@@ -191,7 +201,7 @@ def get_resampled_image(target_area_def, source_area_def, source_image_data,
raise TypeError('source_image must be of type ndarray'
' or a masked array.')
- #Calculate number of segments if needed
+ # Calculate number of segments if needed
if segments is None:
rows = target_area_def.y_size
cut_off = 500
@@ -199,37 +209,32 @@ def get_resampled_image(target_area_def, source_area_def, source_image_data,
segments = int(rows / cut_off)
else:
segments = 1
-
-
+
if segments > 1:
- #Iterate through segments
- for i, target_slice in enumerate(geometry._get_slice(segments,
- target_area_def.shape)):
-
- #Select data from segment with slice
- lons, lats = target_area_def.get_lonlats(nprocs=nprocs, data_slice=target_slice)
-
- #Calculate partial result
- next_result = get_image_from_lonlats(lons, lats, source_area_def,
- source_image_data,
+ # Iterate through segments
+ for i, target_slice in enumerate(geometry._get_slice(segments,
+ target_area_def.shape)):
+
+ # Select data from segment with slice
+ lons, lats = target_area_def.get_lonlats(
+ nprocs=nprocs, data_slice=target_slice)
+
+ # Calculate partial result
+ next_result = get_image_from_lonlats(lons, lats, source_area_def,
+ source_image_data,
fill_value, nprocs)
-
- #Build result iteratively
+
+ # Build result iteratively
if i == 0:
- #First iteration
+ # First iteration
result = next_result
- else:
+ else:
result = np.row_stack((result, next_result))
-
+
return result
else:
- #Get lon lat arrays of target area
+ # Get lon lat arrays of target area
lons, lats = target_area_def.get_lonlats(nprocs)
- #Get target image
- return get_image_from_lonlats(lons, lats, source_area_def,
+ # Get target image
+ return get_image_from_lonlats(lons, lats, source_area_def,
source_image_data, fill_value, nprocs)
-
-
-
-
-
diff --git a/pyresample/image.py b/pyresample/image.py
index b813874..5622a3f 100644
--- a/pyresample/image.py
+++ b/pyresample/image.py
@@ -1,31 +1,34 @@
-#pyresample, Resampling of remote sensing image data in python
-#
-#Copyright (C) 2010 Esben S. Nielsen
+# pyresample, Resampling of remote sensing image data in python
#
-#This program is free software: you can redistribute it and/or modify
-#it under the terms of the GNU General Public License as published by
-#the Free Software Foundation, either version 3 of the License, or
-#(at your option) any later version.
+# Copyright (C) 2010, 2015 Esben S. Nielsen
#
-#This program is distributed in the hope that it will be useful,
-#but WITHOUT ANY WARRANTY; without even the implied warranty of
-#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-#GNU General Public License for more details.
+# This program is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Lesser General Public License as published by the Free
+# Software Foundation, either version 3 of the License, or (at your option) any
+# later version.
#
-#You should have received a copy of the GNU General Public License
-# along with this program. If not, see <http://www.gnu.org/licenses/>.
+# This program is distributed in the hope that it will be useful, but WITHOUT
+# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
+# details.
+#
+# You should have received a copy of the GNU Lesser General Public License along
+# with this program. If not, see <http://www.gnu.org/licenses/>.
"""Handles resampling of images with assigned geometry definitions"""
+from __future__ import absolute_import
+
import numpy as np
-import geometry, grid, kd_tree
+from . import geometry, grid, kd_tree
class ImageContainer(object):
+
"""Holds image with geometry definition.
Allows indexing with linesample arrays.
-
+
:Parameters:
image_data : numpy array
Image data
@@ -37,7 +40,7 @@ class ImageContainer(object):
with undetermined pixels masked
nprocs : int, optional
Number of processor cores to be used
-
+
:Attributes:
image_data : numpy array
Image data
@@ -48,21 +51,21 @@ class ImageContainer(object):
nprocs : int
Number of processor cores to be used for geometry operations
"""
-
+
def __init__(self, image_data, geo_def, fill_value=0, nprocs=1):
if not isinstance(image_data, (np.ndarray, np.ma.core.MaskedArray)):
raise TypeError('image_data must be either an ndarray'
' or a masked array')
- elif ((image_data.ndim > geo_def.ndim + 1) or
+ elif ((image_data.ndim > geo_def.ndim + 1) or
(image_data.ndim < geo_def.ndim)):
- raise ValueError(('Unexpected number of dimensions for '
- 'image_data: ') % image_data.ndim)
+ raise ValueError(('Unexpected number of dimensions for '
+ 'image_data: ') % image_data.ndim)
for i, size in enumerate(geo_def.shape):
if image_data.shape[i] != size:
raise ValueError(('Size mismatch for image_data. Expected '
'size %s for dimension %s and got %s') %
- (size, i, image_data.shape[i]))
-
+ (size, i, image_data.shape[i]))
+
self.shape = geo_def.shape
self.size = geo_def.size
self.ndim = geo_def.ndim
@@ -73,18 +76,18 @@ class ImageContainer(object):
self.channels = 1
self.geo_def = geo_def
self.fill_value = fill_value
- self.nprocs = nprocs
-
+ self.nprocs = nprocs
+
def __str__(self):
- return 'Image:\n %s'%self.image_data.__str__()
+ return 'Image:\n %s' % self.image_data.__str__()
- def __repr__(self):
+ def __repr__(self):
return self.image_data.__repr__()
-
+
def resample(self, target_geo_def):
"""Base method for resampling"""
-
- raise NotImplementedError('Method "resample" is not implemented '
+
+ raise NotImplementedError('Method "resample" is not implemented '
'in class %s' % self.__class__.__name__)
def get_array_from_linesample(self, row_indices, col_indices):
@@ -95,32 +98,33 @@ class ImageContainer(object):
Row indices. Dimensions must match col_indices
col_indices : numpy array
Col indices. Dimensions must match row_indices
-
+
:Returns:
image_data : numpy_array
Resampled image data
"""
-
+
if self.geo_def.ndim != 2:
- raise TypeError('Resampling from linesamples only makes sense '
+ raise TypeError('Resampling from linesamples only makes sense '
'on 2D data')
-
+
return grid.get_image_from_linesample(row_indices, col_indices,
- self.image_data,
+ self.image_data,
self.fill_value)
-
+
def get_array_from_neighbour_info(self, *args, **kwargs):
"""Base method for resampling from preprocessed data."""
-
+
raise NotImplementedError('Method "get_array_from_neighbour_info" is '
- 'not implemented in class %s' %
+ 'not implemented in class %s' %
self.__class__.__name__)
class ImageContainerQuick(ImageContainer):
+
"""Holds image with area definition. '
Allows quick resampling within area.
-
+
:Parameters:
image_data : numpy array
Image data
@@ -135,7 +139,7 @@ class ImageContainerQuick(ImageContainer):
segments : {int, None}
Number of segments to use when resampling.
If set to None an estimate will be calculated
-
+
:Attributes:
image_data : numpy array
Image data
@@ -151,29 +155,29 @@ class ImageContainerQuick(ImageContainer):
Number of segments to use when resampling
"""
- def __init__(self, image_data, geo_def, fill_value=0, nprocs=1,
+ def __init__(self, image_data, geo_def, fill_value=0, nprocs=1,
segments=None):
if not isinstance(geo_def, geometry.AreaDefinition):
raise TypeError('area_def must be of type '
- 'geometry.AreaDefinition')
- super(ImageContainerQuick, self).__init__(image_data, geo_def,
- fill_value=fill_value,
+ 'geometry.AreaDefinition')
+ super(ImageContainerQuick, self).__init__(image_data, geo_def,
+ fill_value=fill_value,
nprocs=nprocs)
self.segments = segments
def resample(self, target_area_def):
"""Resamples image to area definition using nearest neighbour
approach in projection coordinates.
-
+
:Parameters:
target_area_def : object
Target area definition as AreaDefinition object
-
+
:Returns:
image_container : object
ImageContainerQuick object of resampled area
- """
-
+ """
+
resampled_image = grid.get_resampled_image(target_area_def,
self.geo_def,
self.image_data,
@@ -181,15 +185,16 @@ class ImageContainerQuick(ImageContainer):
nprocs=self.nprocs,
segments=self.segments)
- return ImageContainerQuick(resampled_image, target_area_def,
+ return ImageContainerQuick(resampled_image, target_area_def,
fill_value=self.fill_value,
nprocs=self.nprocs, segments=self.segments)
-
+
class ImageContainerNearest(ImageContainer):
+
"""Holds image with geometry definition.
Allows nearest neighbour resampling to new geometry definition.
-
+
:Parameters:
image_data : numpy array
Image data
@@ -212,7 +217,7 @@ class ImageContainerNearest(ImageContainer):
segments : {int, None}
Number of segments to use when resampling.
If set to None an estimate will be calculated
-
+
:Attributes:
image_data : numpy array
Image data
@@ -232,50 +237,50 @@ class ImageContainerNearest(ImageContainer):
Number of segments to use when resampling
"""
- def __init__(self, image_data, geo_def, radius_of_influence, epsilon=0,
+ def __init__(self, image_data, geo_def, radius_of_influence, epsilon=0,
fill_value=0, reduce_data=True, nprocs=1, segments=None):
- super(ImageContainerNearest, self).__init__(image_data, geo_def,
- fill_value=fill_value,
+ super(ImageContainerNearest, self).__init__(image_data, geo_def,
+ fill_value=fill_value,
nprocs=nprocs)
self.radius_of_influence = radius_of_influence
self.epsilon = epsilon
self.reduce_data = reduce_data
self.segments = segments
-
+
def resample(self, target_geo_def):
"""Resamples image to area definition using nearest neighbour
approach
-
+
:Parameters:
target_geo_def : object
Target geometry definition
-
+
:Returns:
image_container : object
ImageContainerNearest object of resampled geometry
"""
-
+
if self.image_data.ndim > 2 and self.ndim > 1:
- image_data = self.image_data.reshape(self.image_data.shape[0] *
- self.image_data.shape[1],
+ image_data = self.image_data.reshape(self.image_data.shape[0] *
+ self.image_data.shape[1],
self.image_data.shape[2])
else:
image_data = self.image_data.ravel()
-
+
resampled_image = \
- kd_tree.resample_nearest(self.geo_def,
- image_data,
- target_geo_def,
- self.radius_of_influence,
- epsilon=self.epsilon,
- fill_value=self.fill_value,
- nprocs=self.nprocs,
- reduce_data=self.reduce_data,
- segments=self.segments)
- return ImageContainerNearest(resampled_image, target_geo_def,
- self.radius_of_influence,
+ kd_tree.resample_nearest(self.geo_def,
+ image_data,
+ target_geo_def,
+ self.radius_of_influence,
+ epsilon=self.epsilon,
+ fill_value=self.fill_value,
+ nprocs=self.nprocs,
+ reduce_data=self.reduce_data,
+ segments=self.segments)
+ return ImageContainerNearest(resampled_image, target_geo_def,
+ self.radius_of_influence,
epsilon=self.epsilon,
- fill_value=self.fill_value,
- reduce_data=self.reduce_data,
+ fill_value=self.fill_value,
+ reduce_data=self.reduce_data,
nprocs=self.nprocs,
- segments=self.segments)
\ No newline at end of file
+ segments=self.segments)
diff --git a/pyresample/kd_tree.py b/pyresample/kd_tree.py
index 4302a52..ded8587 100644
--- a/pyresample/kd_tree.py
+++ b/pyresample/kd_tree.py
@@ -1,30 +1,40 @@
-#pyresample, Resampling of remote sensing image data in python
-#
-#Copyright (C) 2010 Esben S. Nielsen
+# pyresample, Resampling of remote sensing image data in python
#
-#This program is free software: you can redistribute it and/or modify
-#it under the terms of the GNU General Public License as published by
-#the Free Software Foundation, either version 3 of the License, or
+# Copyright (C) 2010, 2014, 2015 Esben S. Nielsen
+# Adam.Dybbroe
+#
+# This program is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Lesser General Public License as published by the Free
+# Software Foundation, either version 3 of the License, or
#(at your option) any later version.
#
-#This program is distributed in the hope that it will be useful,
-#but WITHOUT ANY WARRANTY; without even the implied warranty of
-#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-#GNU General Public License for more details.
+# This program is distributed in the hope that it will be useful, but WITHOUT
+# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
+# details.
#
-#You should have received a copy of the GNU General Public License
-# along with this program. If not, see <http://www.gnu.org/licenses/>.
+# You should have received a copy of the GNU Lesser General Public License along
+# with this program. If not, see <http://www.gnu.org/licenses/>.
+
+"""Handles reprojection of geolocated data. Several types of resampling are
+supported"""
-"""Handles reprojection of geolocated data. Several types of resampling are supported"""
+from __future__ import absolute_import
import types
import warnings
+import sys
import numpy as np
-import geometry
-import data_reduce
-import _spatial_mp
+from . import geometry
+from . import data_reduce
+from . import _spatial_mp
+
+if sys.version < '3':
+ range = xrange
+else:
+ long = int
kd_tree_name = None
try:
@@ -33,19 +43,22 @@ try:
except ImportError:
try:
import scipy.spatial as sp
- kd_tree_name = 'scipy.spatial'
+ kd_tree_name = 'scipy.spatial'
except ImportError:
raise ImportError('Either pykdtree or scipy must be available')
-
+
+
class EmptyResult(Exception):
pass
+
def which_kdtree():
"""Returns the name of the kdtree used for resampling
"""
-
+
return kd_tree_name
+
def resample_nearest(source_geo_def, data, target_geo_def,
radius_of_influence, epsilon=0,
fill_value=0, reduce_data=True, nprocs=1, segments=None):
@@ -76,17 +89,18 @@ def resample_nearest(source_geo_def, data, target_geo_def,
segments : {int, None}
Number of segments to use when resampling.
If set to None an estimate will be calculated
-
+
:Returns:
data : numpy array
Source data resampled to target geometry
"""
-
+
return _resample(source_geo_def, data, target_geo_def, 'nn',
radius_of_influence, neighbours=1,
epsilon=epsilon, fill_value=fill_value,
reduce_data=reduce_data, nprocs=nprocs, segments=segments)
+
def resample_gauss(source_geo_def, data, target_geo_def,
radius_of_influence, sigmas, neighbours=8, epsilon=0,
fill_value=0, reduce_data=True, nprocs=1, segments=None, with_uncert=False):
@@ -125,7 +139,7 @@ def resample_gauss(source_geo_def, data, target_geo_def,
If set to None an estimate will be calculated
with_uncert : bool, optional
Calculate uncertainty estimates
-
+
:Returns:
data : numpy array (default)
Source data resampled to target geometry
@@ -137,37 +151,37 @@ def resample_gauss(source_geo_def, data, target_geo_def,
"""
def gauss(sigma):
- #Return gauss function object
- return lambda r: np.exp(-r**2 / float(sigma)**2)
-
- #Build correct sigma argument
+ # Return gauss function object
+ return lambda r: np.exp(-r ** 2 / float(sigma) ** 2)
+
+ # Build correct sigma argument
is_multi_channel = False
try:
sigmas.__iter__()
sigma_list = sigmas
is_multi_channel = True
except:
- sigma_list = [sigmas]
-
-
+ sigma_list = [sigmas]
+
for sigma in sigma_list:
if not isinstance(sigma, (long, int, float)):
- raise TypeError('sigma must be number')
-
- #Get gauss function objects
+ raise TypeError('sigma must be number')
+
+ # Get gauss function objects
if is_multi_channel:
- weight_funcs = map(gauss, sigma_list)
+ weight_funcs = list(map(gauss, sigma_list))
else:
weight_funcs = gauss(sigmas)
-
+
return _resample(source_geo_def, data, target_geo_def, 'custom',
radius_of_influence, neighbours=neighbours,
epsilon=epsilon, weight_funcs=weight_funcs, fill_value=fill_value,
reduce_data=reduce_data, nprocs=nprocs, segments=segments, with_uncert=with_uncert)
+
def resample_custom(source_geo_def, data, target_geo_def,
radius_of_influence, weight_funcs, neighbours=8,
- epsilon=0, fill_value=0, reduce_data=True, nprocs=1,
+ epsilon=0, fill_value=0, reduce_data=True, nprocs=1,
segments=None, with_uncert=False):
"""Resamples data using kd-tree custom radial weighting neighbour approach
@@ -203,7 +217,7 @@ def resample_custom(source_geo_def, data, target_geo_def,
segments : {int, None}
Number of segments to use when resampling.
If set to None an estimate will be calculated
-
+
:Returns:
data : numpy array (default)
Source data resampled to target geometry
@@ -217,47 +231,49 @@ def resample_custom(source_geo_def, data, target_geo_def,
try:
for weight_func in weight_funcs:
if not isinstance(weight_func, types.FunctionType):
- raise TypeError('weight_func must be function object')
+ raise TypeError('weight_func must be function object')
except:
if not isinstance(weight_funcs, types.FunctionType):
raise TypeError('weight_func must be function object')
-
+
return _resample(source_geo_def, data, target_geo_def, 'custom',
radius_of_influence, neighbours=neighbours,
epsilon=epsilon, weight_funcs=weight_funcs,
fill_value=fill_value, reduce_data=reduce_data,
nprocs=nprocs, segments=segments, with_uncert=with_uncert)
+
def _resample(source_geo_def, data, target_geo_def, resample_type,
- radius_of_influence, neighbours=8, epsilon=0, weight_funcs=None,
- fill_value=0, reduce_data=True, nprocs=1, segments=None, with_uncert=False):
- """Resamples swath using kd-tree approach"""
-
+ radius_of_influence, neighbours=8, epsilon=0, weight_funcs=None,
+ fill_value=0, reduce_data=True, nprocs=1, segments=None, with_uncert=False):
+ """Resamples swath using kd-tree approach"""
+
valid_input_index, valid_output_index, index_array, distance_array = \
- get_neighbour_info(source_geo_def,
- target_geo_def,
- radius_of_influence,
- neighbours=neighbours,
- epsilon=epsilon,
- reduce_data=reduce_data,
- nprocs=nprocs,
- segments=segments)
-
-
- return get_sample_from_neighbour_info(resample_type,
- target_geo_def.shape,
- data, valid_input_index,
- valid_output_index,
- index_array,
- distance_array=distance_array,
- weight_funcs=weight_funcs,
- fill_value=fill_value,
- with_uncert=with_uncert)
-
-def get_neighbour_info(source_geo_def, target_geo_def, radius_of_influence,
- neighbours=8, epsilon=0, reduce_data=True, nprocs=1, segments=None):
+ get_neighbour_info(source_geo_def,
+ target_geo_def,
+ radius_of_influence,
+ neighbours=neighbours,
+ epsilon=epsilon,
+ reduce_data=reduce_data,
+ nprocs=nprocs,
+ segments=segments)
+
+ return get_sample_from_neighbour_info(resample_type,
+ target_geo_def.shape,
+ data, valid_input_index,
+ valid_output_index,
+ index_array,
+ distance_array=distance_array,
+ weight_funcs=weight_funcs,
+ fill_value=fill_value,
+ with_uncert=with_uncert)
+
+
+def get_neighbour_info(source_geo_def, target_geo_def, radius_of_influence,
+ neighbours=8, epsilon=0, reduce_data=True,
+ nprocs=1, segments=None):
"""Returns neighbour info
-
+
:Parameters:
source_geo_def : object
Geometry definition of source
@@ -282,7 +298,7 @@ def get_neighbour_info(source_geo_def, target_geo_def, radius_of_influence,
segments : {int, None}
Number of segments to use when resampling.
If set to None an estimate will be calculated
-
+
:Returns:
(valid_input_index, valid_output_index,
index_array, distance_array) : tuple of numpy arrays
@@ -290,7 +306,7 @@ def get_neighbour_info(source_geo_def, target_geo_def, radius_of_influence,
"""
if source_geo_def.size < neighbours:
- warnings.warn('Searching for %s neighbours in %s data points' %
+ warnings.warn('Searching for %s neighbours in %s data points' %
(neighbours, source_geo_def.size))
if segments is None:
@@ -299,155 +315,157 @@ def get_neighbour_info(source_geo_def, target_geo_def, radius_of_influence,
segments = int(target_geo_def.size / cut_off)
else:
segments = 1
-
- #Find reduced input coordinate set
- valid_input_index, source_lons, source_lats = _get_valid_input_index(source_geo_def, target_geo_def,
- reduce_data,
- radius_of_influence,
- nprocs=nprocs)
-
- #Create kd-tree
+
+ # Find reduced input coordinate set
+ valid_input_index, source_lons, source_lats = _get_valid_input_index(source_geo_def, target_geo_def,
+ reduce_data,
+ radius_of_influence,
+ nprocs=nprocs)
+
+ # Create kd-tree
try:
- resample_kdtree = _create_resample_kdtree(source_lons, source_lats,
- valid_input_index,
+ resample_kdtree = _create_resample_kdtree(source_lons, source_lats,
+ valid_input_index,
nprocs=nprocs)
except EmptyResult:
- #Handle if all input data is reduced away
- valid_output_index, index_array, distance_array = \
- _create_empty_info(source_geo_def, target_geo_def, neighbours)
- return (valid_input_index, valid_output_index, index_array,
- distance_array)
-
+ # Handle if all input data is reduced away
+ valid_output_index, index_array, distance_array = \
+ _create_empty_info(source_geo_def, target_geo_def, neighbours)
+ return (valid_input_index, valid_output_index, index_array,
+ distance_array)
+
if segments > 1:
- #Iterate through segments
- for i, target_slice in enumerate(geometry._get_slice(segments,
- target_geo_def.shape)):
+ # Iterate through segments
+ for i, target_slice in enumerate(geometry._get_slice(segments,
+ target_geo_def.shape)):
- #Query on slice of target coordinates
+ # Query on slice of target coordinates
next_voi, next_ia, next_da = \
- _query_resample_kdtree(resample_kdtree, source_geo_def,
- target_geo_def,
- radius_of_influence, target_slice,
- neighbours=neighbours,
- epsilon=epsilon,
- reduce_data=reduce_data,
- nprocs=nprocs)
-
- #Build result iteratively
+ _query_resample_kdtree(resample_kdtree, source_geo_def,
+ target_geo_def,
+ radius_of_influence, target_slice,
+ neighbours=neighbours,
+ epsilon=epsilon,
+ reduce_data=reduce_data,
+ nprocs=nprocs)
+
+ # Build result iteratively
if i == 0:
- #First iteration
+ # First iteration
valid_output_index = next_voi
index_array = next_ia
distance_array = next_da
- else:
+ else:
valid_output_index = np.append(valid_output_index, next_voi)
if neighbours > 1:
index_array = np.row_stack((index_array, next_ia))
distance_array = np.row_stack((distance_array, next_da))
else:
index_array = np.append(index_array, next_ia)
- distance_array = np.append(distance_array, next_da)
+ distance_array = np.append(distance_array, next_da)
else:
- #Query kd-tree with full target coordinate set
+ # Query kd-tree with full target coordinate set
full_slice = slice(None)
valid_output_index, index_array, distance_array = \
- _query_resample_kdtree(resample_kdtree, source_geo_def,
- target_geo_def,
- radius_of_influence, full_slice,
- neighbours=neighbours,
- epsilon=epsilon,
- reduce_data=reduce_data,
- nprocs=nprocs)
-
+ _query_resample_kdtree(resample_kdtree, source_geo_def,
+ target_geo_def,
+ radius_of_influence, full_slice,
+ neighbours=neighbours,
+ epsilon=epsilon,
+ reduce_data=reduce_data,
+ nprocs=nprocs)
+
# Check if number of neighbours is potentially too low
if neighbours > 1:
if not np.all(np.isinf(distance_array[:, -1])):
warnings.warn(('Possible more than %s neighbours '
- 'within %s m for some data points') %
+ 'within %s m for some data points') %
(neighbours, radius_of_influence))
-
- return valid_input_index, valid_output_index, index_array, distance_array
-def _get_valid_input_index(source_geo_def, target_geo_def, reduce_data,
+ return valid_input_index, valid_output_index, index_array, distance_array
+
+
+def _get_valid_input_index(source_geo_def, target_geo_def, reduce_data,
radius_of_influence, nprocs=1):
"""Find indices of reduced inputput data"""
-
+
source_lons, source_lats = source_geo_def.get_lonlats(nprocs=nprocs)
source_lons = source_lons.ravel()
source_lats = source_lats.ravel()
-
+
if source_lons.size == 0 or source_lats.size == 0:
raise ValueError('Cannot resample empty data set')
elif source_lons.size != source_lats.size or \
source_lons.shape != source_lats.shape:
raise ValueError('Mismatch between lons and lats')
-
- #Remove illegal values
- valid_data = ((source_lons >= -180) & (source_lons <= 180) &
+
+ # Remove illegal values
+ valid_data = ((source_lons >= -180) & (source_lons <= 180) &
(source_lats <= 90) & (source_lats >= -90))
valid_input_index = np.ones(source_geo_def.size, dtype=np.bool)
-
+
if reduce_data:
- #Reduce dataset
- if (isinstance(source_geo_def, geometry.CoordinateDefinition) and
- isinstance(target_geo_def, (geometry.GridDefinition,
- geometry.AreaDefinition))) or \
- (isinstance(source_geo_def, (geometry.GridDefinition,
+ # Reduce dataset
+ if (isinstance(source_geo_def, geometry.CoordinateDefinition) and
+ isinstance(target_geo_def, (geometry.GridDefinition,
+ geometry.AreaDefinition))) or \
+ (isinstance(source_geo_def, (geometry.GridDefinition,
geometry.AreaDefinition)) and
- isinstance(target_geo_def, (geometry.GridDefinition,
+ isinstance(target_geo_def, (geometry.GridDefinition,
geometry.AreaDefinition))):
- #Resampling from swath to grid or from grid to grid
+ # Resampling from swath to grid or from grid to grid
lonlat_boundary = target_geo_def.get_boundary_lonlats()
valid_input_index = \
data_reduce.get_valid_index_from_lonlat_boundaries(
- lonlat_boundary[0],
- lonlat_boundary[1],
- source_lons, source_lats,
- radius_of_influence)
-
- #Combine reduced and legal values
+ lonlat_boundary[0],
+ lonlat_boundary[1],
+ source_lons, source_lats,
+ radius_of_influence)
+
+ # Combine reduced and legal values
valid_input_index = (valid_data & valid_input_index)
-
-
+
if(isinstance(valid_input_index, np.ma.core.MaskedArray)):
- #Make sure valid_input_index is not a masked array
+ # Make sure valid_input_index is not a masked array
valid_input_index = valid_input_index.filled(False)
-
+
return valid_input_index, source_lons, source_lats
-def _get_valid_output_index(source_geo_def, target_geo_def, target_lons,
+
+def _get_valid_output_index(source_geo_def, target_geo_def, target_lons,
target_lats, reduce_data, radius_of_influence):
"""Find indices of reduced output data"""
-
+
valid_output_index = np.ones(target_lons.size, dtype=np.bool)
-
+
if reduce_data:
- if isinstance(source_geo_def, (geometry.GridDefinition,
- geometry.AreaDefinition)) and \
- isinstance(target_geo_def, geometry.CoordinateDefinition):
- #Resampling from grid to swath
+ if isinstance(source_geo_def, (geometry.GridDefinition,
+ geometry.AreaDefinition)) and \
+ isinstance(target_geo_def, geometry.CoordinateDefinition):
+ # Resampling from grid to swath
lonlat_boundary = source_geo_def.get_boundary_lonlats()
valid_output_index = \
data_reduce.get_valid_index_from_lonlat_boundaries(
- lonlat_boundary[0],
- lonlat_boundary[1],
- target_lons,
- target_lats,
- radius_of_influence)
+ lonlat_boundary[0],
+ lonlat_boundary[1],
+ target_lons,
+ target_lats,
+ radius_of_influence)
valid_output_index = valid_output_index.astype(np.bool)
-
- #Remove illegal values
- valid_out = ((target_lons >= -180) & (target_lons <= 180) &
- (target_lats <= 90) & (target_lats >= -90))
-
- #Combine reduced and legal values
+
+ # Remove illegal values
+ valid_out = ((target_lons >= -180) & (target_lons <= 180) &
+ (target_lats <= 90) & (target_lats >= -90))
+
+ # Combine reduced and legal values
valid_output_index = (valid_output_index & valid_out)
-
+
return valid_output_index
-
+
+
def _create_resample_kdtree(source_lons, source_lats, valid_input_index, nprocs=1):
"""Set up kd tree on input"""
-
+
"""
if not isinstance(source_geo_def, geometry.BaseDefinition):
raise TypeError('source_geo_def must be of geometry type')
@@ -457,100 +475,104 @@ def _create_resample_kdtree(source_lons, source_lats, valid_input_index, nprocs=
input_coords = geometry._flatten_cartesian_coords(source_cartesian_coords)
input_coords = input_coords[valid_input_index]
"""
-
+
source_lons_valid = source_lons[valid_input_index]
source_lats_valid = source_lats[valid_input_index]
-
+
if nprocs > 1:
cartesian = _spatial_mp.Cartesian_MP(nprocs)
else:
cartesian = _spatial_mp.Cartesian()
- input_coords = cartesian.transform_lonlats(source_lons_valid, source_lats_valid)
-
+ input_coords = cartesian.transform_lonlats(
+ source_lons_valid, source_lats_valid)
+
if input_coords.size == 0:
raise EmptyResult('No valid data points in input data')
- #Build kd-tree on input
+ # Build kd-tree on input
if kd_tree_name == 'pykdtree':
resample_kdtree = KDTree(input_coords)
- elif nprocs > 1:
+ elif nprocs > 1:
resample_kdtree = _spatial_mp.cKDTree_MP(input_coords,
nprocs=nprocs)
else:
resample_kdtree = sp.cKDTree(input_coords)
-
+
return resample_kdtree
-def _query_resample_kdtree(resample_kdtree, source_geo_def, target_geo_def,
- radius_of_influence, data_slice,
- neighbours=8, epsilon=0, reduce_data=True, nprocs=1):
+
+def _query_resample_kdtree(resample_kdtree, source_geo_def, target_geo_def,
+ radius_of_influence, data_slice,
+ neighbours=8, epsilon=0, reduce_data=True, nprocs=1):
"""Query kd-tree on slice of target coordinates"""
- #Check validity of input
+ # Check validity of input
if not isinstance(target_geo_def, geometry.BaseDefinition):
- raise TypeError('target_geo_def must be of geometry type')
+ raise TypeError('target_geo_def must be of geometry type')
elif not isinstance(radius_of_influence, (long, int, float)):
raise TypeError('radius_of_influence must be number')
elif not isinstance(neighbours, int):
raise TypeError('neighbours must be integer')
elif not isinstance(epsilon, (long, int, float)):
raise TypeError('epsilon must be number')
-
- #Get sliced target coordinates
- target_lons, target_lats = target_geo_def.get_lonlats(nprocs=nprocs,
- data_slice=data_slice, dtype=source_geo_def.dtype)
-
- #Find indiced of reduced target coordinates
- valid_output_index = _get_valid_output_index(source_geo_def,
- target_geo_def,
- target_lons.ravel(),
- target_lats.ravel(),
- reduce_data,
+
+ # Get sliced target coordinates
+ target_lons, target_lats = target_geo_def.get_lonlats(nprocs=nprocs,
+ data_slice=data_slice, dtype=source_geo_def.dtype)
+
+ # Find indiced of reduced target coordinates
+ valid_output_index = _get_valid_output_index(source_geo_def,
+ target_geo_def,
+ target_lons.ravel(),
+ target_lats.ravel(),
+ reduce_data,
radius_of_influence)
- #Get cartesian target coordinates and select reduced set
+ # Get cartesian target coordinates and select reduced set
if nprocs > 1:
cartesian = _spatial_mp.Cartesian_MP(nprocs)
else:
cartesian = _spatial_mp.Cartesian()
-
- target_lons_valid = target_lons.ravel()[valid_output_index]
+
+ target_lons_valid = target_lons.ravel()[valid_output_index]
target_lats_valid = target_lats.ravel()[valid_output_index]
-
- output_coords = cartesian.transform_lonlats(target_lons_valid, target_lats_valid)
-
- #Query kd-tree
- distance_array, index_array = resample_kdtree.query(output_coords,
+
+ output_coords = cartesian.transform_lonlats(
+ target_lons_valid, target_lats_valid)
+
+ # Query kd-tree
+ distance_array, index_array = resample_kdtree.query(output_coords,
k=neighbours,
eps=epsilon,
- distance_upper_bound=
- radius_of_influence)
-
+ distance_upper_bound=radius_of_influence)
+
return valid_output_index, index_array, distance_array
+
def _create_empty_info(source_geo_def, target_geo_def, neighbours):
"""Creates dummy info for empty result set"""
-
+
valid_output_index = np.ones(target_geo_def.size, dtype=np.bool)
if neighbours > 1:
- index_array = (np.ones((target_geo_def.size, neighbours),
+ index_array = (np.ones((target_geo_def.size, neighbours),
dtype=np.int32) * source_geo_def.size)
distance_array = np.ones((target_geo_def.size, neighbours))
else:
- index_array = (np.ones(target_geo_def.size, dtype=np.int32) *
+ index_array = (np.ones(target_geo_def.size, dtype=np.int32) *
source_geo_def.size)
distance_array = np.ones(target_geo_def.size)
-
- return valid_output_index, index_array, distance_array
-
-def get_sample_from_neighbour_info(resample_type, output_shape, data,
- valid_input_index, valid_output_index,
- index_array, distance_array=None,
- weight_funcs=None, fill_value=0,
+
+ return valid_output_index, index_array, distance_array
+
+
+def get_sample_from_neighbour_info(resample_type, output_shape, data,
+ valid_input_index, valid_output_index,
+ index_array, distance_array=None,
+ weight_funcs=None, fill_value=0,
with_uncert=False):
"""Resamples swath based on neighbour info
-
+
:Parameters:
resample_type : {'nn', 'custom'}
'nn': Use nearest neighbour resampling
@@ -578,7 +600,7 @@ def get_sample_from_neighbour_info(resample_type, output_shape, data,
Set undetermined pixels to this value.
If fill_value is None a masked array is returned
with undetermined pixels masked
-
+
:Returns:
result : numpy array
Source data resampled to target geometry
@@ -588,36 +610,36 @@ def get_sample_from_neighbour_info(resample_type, output_shape, data,
data = data.reshape(data.shape[0] * data.shape[1], data.shape[2])
elif data.shape[0] != valid_input_index.size:
data = data.ravel()
-
+
if valid_input_index.size != data.shape[0]:
raise ValueError('Mismatch between geometry and dataset')
-
+
is_multi_channel = (data.ndim > 1)
valid_input_size = valid_input_index.sum()
valid_output_size = valid_output_index.sum()
-
- #Handle empty result set
- if valid_input_size == 0 or valid_output_size == 0:
+
+ # Handle empty result set
+ if valid_input_size == 0 or valid_output_size == 0:
if is_multi_channel:
output_shape = list(output_shape)
output_shape.append(data.shape[1])
-
+
if fill_value is None:
- #Use masked array for fill values
- return np.ma.array(np.zeros(output_shape, data.dtype),
+ # Use masked array for fill values
+ return np.ma.array(np.zeros(output_shape, data.dtype),
mask=np.ones(output_shape, dtype=np.bool))
else:
- #Return fill vaues for all pixels
- return np.ones(output_shape, dtype=data.dtype) * fill_value
+ # Return fill vaues for all pixels
+ return np.ones(output_shape, dtype=data.dtype) * fill_value
- #Get size of output and reduced input
+ # Get size of output and reduced input
input_size = valid_input_size
if len(output_shape) > 1:
output_size = output_shape[0] * output_shape[1]
else:
output_size = output_shape[0]
-
- #Check validity of input
+
+ # Check validity of input
if not isinstance(data, np.ndarray):
raise TypeError('data must be numpy array')
elif valid_input_index.ndim != 1:
@@ -625,64 +647,63 @@ def get_sample_from_neighbour_info(resample_type, output_shape, data,
elif data.shape[0] != valid_input_index.size:
raise TypeError('Not the same number of datapoints in '
'valid_input_index and data')
-
+
valid_types = ('nn', 'custom')
if not resample_type in valid_types:
raise TypeError('Invalid resampling type: %s' % resample_type)
-
+
if resample_type == 'custom' and weight_funcs is None:
raise ValueError('weight_funcs must be supplied when using '
- 'custom resampling')
-
+ 'custom resampling')
+
if not isinstance(fill_value, (long, int, float)) and fill_value is not None:
raise TypeError('fill_value must be number or None')
-
+
if index_array.ndim == 1:
neighbours = 1
else:
neighbours = index_array.shape[1]
if resample_type == 'nn':
- raise ValueError('index_array contains more neighbours than '
+ raise ValueError('index_array contains more neighbours than '
'just the nearest')
-
- #Reduce data
- new_data = data[valid_input_index]
-
- #Nearest neighbour resampling should conserve data type
- #Get data type
+
+ # Reduce data
+ new_data = data[valid_input_index]
+
+ # Nearest neighbour resampling should conserve data type
+ # Get data type
conserve_input_data_type = False
if resample_type == 'nn':
conserve_input_data_type = True
input_data_type = new_data.dtype
-
- #Handle masked array input
+
+ # Handle masked array input
is_masked_data = False
if np.ma.is_masked(new_data):
- #Add the mask as channels to the dataset
+ # Add the mask as channels to the dataset
is_masked_data = True
new_data = np.column_stack((new_data.data, new_data.mask))
- if new_data.ndim > 1: # Multiple channels or masked input
+ if new_data.ndim > 1: # Multiple channels or masked input
output_shape = list(output_shape)
output_shape.append(new_data.shape[1])
-
- #Prepare weight_funcs argument for handeling mask data
+ # Prepare weight_funcs argument for handeling mask data
if weight_funcs is not None and is_masked_data:
if is_multi_channel:
weight_funcs = weight_funcs * 2
else:
weight_funcs = (weight_funcs,) * 2
-
- #Handle request for masking intead of using fill values
+
+ # Handle request for masking intead of using fill values
use_masked_fill_value = False
if fill_value is None:
use_masked_fill_value = True
fill_value = _get_fill_mask_value(new_data.dtype)
-
- #Resample based on kd-tree query result
+
+ # Resample based on kd-tree query result
if resample_type == 'nn' or neighbours == 1:
- #Get nearest neighbour using array indexing
+ # Get nearest neighbour using array indexing
index_mask = (index_array == input_size)
new_index_array = np.where(index_mask, 0, index_array)
result = new_data[new_index_array]
@@ -691,12 +712,12 @@ def get_sample_from_neighbour_info(resample_type, output_shape, data,
# Calculate result using weighting.
# Note: the code below has low readability in order
# to avoid looping over numpy arrays
-
- #Get neighbours and masks of valid indices
+
+ # Get neighbours and masks of valid indices
ch_neighbour_list = []
index_mask_list = []
- for i in range(neighbours): # Iterate over number of neighbours
- # Make working copy neighbour index and
+ for i in range(neighbours): # Iterate over number of neighbours
+ # Make working copy neighbour index and
# set out of bounds indices to zero
index_ni = index_array[:, i].copy()
index_mask_ni = (index_ni == input_size)
@@ -704,65 +725,67 @@ def get_sample_from_neighbour_info(resample_type, output_shape, data,
# Get channel data for the corresponing indices
ch_ni = new_data[index_ni]
- ch_neighbour_list.append(ch_ni)
+ ch_neighbour_list.append(ch_ni)
index_mask_list.append(index_mask_ni)
-
- #Calculate weights
+
+ # Calculate weights
weight_list = []
- for i in range(neighbours): # Iterate over number of neighbours
+ for i in range(neighbours): # Iterate over number of neighbours
# Make working copy of neighbour distances and
# set out of bounds distance to 1 in order to avoid numerical Inf
distance = distance_array[:, i].copy()
distance[index_mask_list[i]] = 1
-
- if new_data.ndim > 1: # More than one channel in data set.
+
+ if new_data.ndim > 1: # More than one channel in data set.
# Calculate weights for each channel
weights = []
num_weights = valid_output_index.sum()
num_channels = new_data.shape[1]
- for j in range(num_channels):
+ for j in range(num_channels):
calc_weight = weight_funcs[j](distance)
- # Turn a scalar weight into a numpy array
+ # Turn a scalar weight into a numpy array
# (no effect if calc_weight already is an array)
expanded_calc_weight = np.ones(num_weights) * calc_weight
weights.append(expanded_calc_weight)
- # Collect weights for all channels for neighbour number
+ # Collect weights for all channels for neighbour number
weight_list.append(np.column_stack(weights))
- else: # Only one channel
+ else: # Only one channel
weights = weight_funcs(distance)
weight_list.append(weights)
-
+
result = 0
norm = 0
count = 0
norm_sqr = 0
stddev = 0
- # Calculate result
- for i in range(neighbours): # Iterate over number of neighbours
+ # Calculate result
+ for i in range(neighbours): # Iterate over number of neighbours
# Find invalid indices to be masked of from calculation
- if new_data.ndim > 1: # More than one channel in data set.
- inv_index_mask = np.expand_dims(np.invert(index_mask_list[i]), axis=1)
- else: # Only one channel
+ if new_data.ndim > 1: # More than one channel in data set.
+ inv_index_mask = np.expand_dims(
+ np.invert(index_mask_list[i]), axis=1)
+ else: # Only one channel
inv_index_mask = np.invert(index_mask_list[i])
-
- #Aggregate result and norm
+
+ # Aggregate result and norm
weights_tmp = inv_index_mask * weight_list[i]
result += weights_tmp * ch_neighbour_list[i]
norm += weights_tmp
- #Normalize result and set fillvalue
+ # Normalize result and set fillvalue
result_valid_index = (norm > 0)
result[result_valid_index] /= norm[result_valid_index]
if with_uncert: # Calculate uncertainties
# 2. pass to calculate standard deviation
- for i in range(neighbours): # Iterate over number of neighbours
+ for i in range(neighbours): # Iterate over number of neighbours
# Find invalid indices to be masked of from calculation
- if new_data.ndim > 1: # More than one channel in data set.
- inv_index_mask = np.expand_dims(np.invert(index_mask_list[i]), axis=1)
- else: # Only one channel
+ if new_data.ndim > 1: # More than one channel in data set.
+ inv_index_mask = np.expand_dims(
+ np.invert(index_mask_list[i]), axis=1)
+ else: # Only one channel
inv_index_mask = np.invert(index_mask_list[i])
# Aggregate stddev information
@@ -776,20 +799,21 @@ def get_sample_from_neighbour_info(resample_type, output_shape, data,
new_valid_index = (count > 1)
v1 = norm[new_valid_index]
v2 = norm_sqr[new_valid_index]
- stddev[new_valid_index] = np.sqrt((v1 / (v1 ** 2 - v2)) * stddev[new_valid_index])
+ stddev[new_valid_index] = np.sqrt(
+ (v1 / (v1 ** 2 - v2)) * stddev[new_valid_index])
stddev[~new_valid_index] = np.NaN
- #Add fill values
- result[np.invert(result_valid_index)] = fill_value
+ # Add fill values
+ result[np.invert(result_valid_index)] = fill_value
# Create full result
- if new_data.ndim > 1: # More than one channel
+ if new_data.ndim > 1: # More than one channel
output_raw_shape = ((output_size, new_data.shape[1]))
- else: # One channel
+ else: # One channel
output_raw_shape = output_size
full_result = np.ones(output_raw_shape) * fill_value
- full_result[valid_output_index] = result
+ full_result[valid_output_index] = result
result = full_result
if with_uncert: # Add fill values for uncertainty
@@ -799,29 +823,29 @@ def get_sample_from_neighbour_info(resample_type, output_shape, data,
full_count[valid_output_index] = count
stddev = full_stddev
count = full_count
-
+
stddev = stddev.reshape(output_shape)
count = count.reshape(output_shape)
- if is_masked_data: # Ignore uncert computation of masks
+ if is_masked_data: # Ignore uncert computation of masks
stddev = _remask_data(stddev, is_to_be_masked=False)
count = _remask_data(count, is_to_be_masked=False)
# Set masks for invalid stddev
stddev = np.ma.array(stddev, mask=np.isnan(stddev))
- #Reshape resampled data to correct shape
+ # Reshape resampled data to correct shape
result = result.reshape(output_shape)
- #Remap mask channels to create masked output
+ # Remap mask channels to create masked output
if is_masked_data:
result = _remask_data(result)
-
- #Create masking of fill values
+
+ # Create masking of fill values
if use_masked_fill_value:
result = np.ma.masked_equal(result, fill_value)
-
- #Set output data type to input data type if relevant
+
+ # Set output data type to input data type if relevant
if conserve_input_data_type:
result = result.astype(input_data_type)
@@ -833,9 +857,10 @@ def get_sample_from_neighbour_info(resample_type, output_shape, data,
else:
return result
+
def _get_fill_mask_value(data_dtype):
"""Returns the maximum value of dtype"""
-
+
if issubclass(data_dtype.type, np.floating):
fill_value = np.finfo(data_dtype.type).max
elif issubclass(data_dtype.type, np.integer):
@@ -845,13 +870,14 @@ def _get_fill_mask_value(data_dtype):
data_dtype.type)
return fill_value
+
def _remask_data(data, is_to_be_masked=True):
"""Interprets half the array as mask for the other half"""
-
+
channels = data.shape[-1]
if is_to_be_masked:
- mask = data[..., (channels // 2):]
- #All pixels affected by masked pixels are masked out
+ mask = data[..., (channels // 2):]
+ # All pixels affected by masked pixels are masked out
mask = (mask != 0)
data = np.ma.array(data[..., :(channels // 2)], mask=mask)
else:
@@ -860,4 +886,3 @@ def _remask_data(data, is_to_be_masked=True):
if data.shape[-1] == 1:
data = data.reshape(data.shape[:-1])
return data
-
diff --git a/pyresample/plot.py b/pyresample/plot.py
index 86e1eed..72ca5d6 100644
--- a/pyresample/plot.py
+++ b/pyresample/plot.py
@@ -1,81 +1,104 @@
+# pyresample, Resampling of remote sensing image data in python
+#
+# Copyright (C) 2010-2015
+#
+# Authors:
+# Esben S. Nielsen
+# Thomas Lavergne
+#
+# This program is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Lesser General Public License as published by the Free
+# Software Foundation, either version 3 of the License, or (at your option) any
+# later version.
+#
+# This program is distributed in the hope that it will be useful, but WITHOUT
+# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
+# details.
+#
+# You should have received a copy of the GNU Lesser General Public License along
+# with this program. If not, see <http://www.gnu.org/licenses/>.
+
+from __future__ import absolute_import
import numpy as np
def ellps2axis(ellps_name):
"""Get semi-major and semi-minor axis from ellipsis definition
-
+
:Parameters:
ellps_name : str
Standard name of ellipsis
-
+
:Returns:
(a, b) : semi-major and semi-minor axis
"""
-
- ellps = {'helmert': {'a': 6378200.0, 'b': 6356818.1696278909},
- 'intl': {'a': 6378388.0, 'b': 6356911.9461279465},
- 'merit': {'a': 6378137.0, 'b': 6356752.2982159676},
- 'wgs72': {'a': 6378135.0, 'b': 6356750.5200160937},
- 'sphere': {'a': 6370997.0, 'b': 6370997.0},
- 'clrk66': {'a': 6378206.4000000004, 'b': 6356583.7999999998},
- 'nwl9d': {'a': 6378145.0, 'b': 6356759.7694886839},
- 'lerch': {'a': 6378139.0, 'b': 6356754.2915103417},
- 'evrstss': {'a': 6377298.5559999999, 'b': 6356097.5503008962},
- 'evrst30': {'a': 6377276.3449999997, 'b': 6356075.4131402401},
- 'mprts': {'a': 6397300.0, 'b': 6363806.2827225132},
- 'krass': {'a': 6378245.0, 'b': 6356863.0187730473},
- 'walbeck': {'a': 6376896.0, 'b': 6355834.8466999996},
- 'kaula': {'a': 6378163.0, 'b': 6356776.9920869097},
- 'wgs66': {'a': 6378145.0, 'b': 6356759.7694886839},
- 'evrst56': {'a': 6377301.2429999998, 'b': 6356100.2283681016},
- 'new_intl': {'a': 6378157.5, 'b': 6356772.2000000002},
- 'airy': {'a': 6377563.3959999997, 'b': 6356256.9100000001},
- 'bessel': {'a': 6377397.1550000003, 'b': 6356078.9628181886},
- 'seasia': {'a': 6378155.0, 'b': 6356773.3205000004},
- 'aust_sa': {'a': 6378160.0, 'b': 6356774.7191953054},
- 'wgs84': {'a': 6378137.0, 'b': 6356752.3142451793},
- 'hough': {'a': 6378270.0, 'b': 6356794.3434343431},
- 'wgs60': {'a': 6378165.0, 'b': 6356783.2869594367},
- 'engelis': {'a': 6378136.0499999998, 'b': 6356751.3227215428},
- 'apl4.9': {'a': 6378137.0, 'b': 6356751.796311819},
- 'andrae': {'a': 6377104.4299999997, 'b': 6355847.4152333336},
- 'sgs85': {'a': 6378136.0, 'b': 6356751.301568781},
- 'delmbr': {'a': 6376428.0, 'b': 6355957.9261637237},
- 'fschr60m': {'a': 6378155.0, 'b': 6356773.3204827355},
- 'iau76': {'a': 6378140.0, 'b': 6356755.2881575283},
- 'plessis': {'a': 6376523.0, 'b': 6355863.0},
- 'cpm': {'a': 6375738.7000000002, 'b': 6356666.221912113},
- 'fschr68': {'a': 6378150.0, 'b': 6356768.3372443849},
- 'mod_airy': {'a': 6377340.1890000002, 'b': 6356034.4460000005},
- 'grs80': {'a': 6378137.0, 'b': 6356752.3141403561},
- 'bess_nam': {'a': 6377483.8650000002, 'b': 6356165.3829663256},
- 'fschr60': {'a': 6378166.0, 'b': 6356784.2836071067},
- 'clrk80': {'a': 6378249.1449999996, 'b': 6356514.9658284895},
- 'evrst69': {'a': 6377295.6639999999, 'b': 6356094.6679152036},
- 'grs67': {'a': 6378160.0, 'b': 6356774.5160907144},
+
+ ellps = {'helmert': {'a': 6378200.0, 'b': 6356818.1696278909},
+ 'intl': {'a': 6378388.0, 'b': 6356911.9461279465},
+ 'merit': {'a': 6378137.0, 'b': 6356752.2982159676},
+ 'wgs72': {'a': 6378135.0, 'b': 6356750.5200160937},
+ 'sphere': {'a': 6370997.0, 'b': 6370997.0},
+ 'clrk66': {'a': 6378206.4000000004, 'b': 6356583.7999999998},
+ 'nwl9d': {'a': 6378145.0, 'b': 6356759.7694886839},
+ 'lerch': {'a': 6378139.0, 'b': 6356754.2915103417},
+ 'evrstss': {'a': 6377298.5559999999, 'b': 6356097.5503008962},
+ 'evrst30': {'a': 6377276.3449999997, 'b': 6356075.4131402401},
+ 'mprts': {'a': 6397300.0, 'b': 6363806.2827225132},
+ 'krass': {'a': 6378245.0, 'b': 6356863.0187730473},
+ 'walbeck': {'a': 6376896.0, 'b': 6355834.8466999996},
+ 'kaula': {'a': 6378163.0, 'b': 6356776.9920869097},
+ 'wgs66': {'a': 6378145.0, 'b': 6356759.7694886839},
+ 'evrst56': {'a': 6377301.2429999998, 'b': 6356100.2283681016},
+ 'new_intl': {'a': 6378157.5, 'b': 6356772.2000000002},
+ 'airy': {'a': 6377563.3959999997, 'b': 6356256.9100000001},
+ 'bessel': {'a': 6377397.1550000003, 'b': 6356078.9628181886},
+ 'seasia': {'a': 6378155.0, 'b': 6356773.3205000004},
+ 'aust_sa': {'a': 6378160.0, 'b': 6356774.7191953054},
+ 'wgs84': {'a': 6378137.0, 'b': 6356752.3142451793},
+ 'hough': {'a': 6378270.0, 'b': 6356794.3434343431},
+ 'wgs60': {'a': 6378165.0, 'b': 6356783.2869594367},
+ 'engelis': {'a': 6378136.0499999998, 'b': 6356751.3227215428},
+ 'apl4.9': {'a': 6378137.0, 'b': 6356751.796311819},
+ 'andrae': {'a': 6377104.4299999997, 'b': 6355847.4152333336},
+ 'sgs85': {'a': 6378136.0, 'b': 6356751.301568781},
+ 'delmbr': {'a': 6376428.0, 'b': 6355957.9261637237},
+ 'fschr60m': {'a': 6378155.0, 'b': 6356773.3204827355},
+ 'iau76': {'a': 6378140.0, 'b': 6356755.2881575283},
+ 'plessis': {'a': 6376523.0, 'b': 6355863.0},
+ 'cpm': {'a': 6375738.7000000002, 'b': 6356666.221912113},
+ 'fschr68': {'a': 6378150.0, 'b': 6356768.3372443849},
+ 'mod_airy': {'a': 6377340.1890000002, 'b': 6356034.4460000005},
+ 'grs80': {'a': 6378137.0, 'b': 6356752.3141403561},
+ 'bess_nam': {'a': 6377483.8650000002, 'b': 6356165.3829663256},
+ 'fschr60': {'a': 6378166.0, 'b': 6356784.2836071067},
+ 'clrk80': {'a': 6378249.1449999996, 'b': 6356514.9658284895},
+ 'evrst69': {'a': 6377295.6639999999, 'b': 6356094.6679152036},
+ 'grs67': {'a': 6378160.0, 'b': 6356774.5160907144},
'evrst48': {'a': 6377304.0630000001, 'b': 6356103.0389931547}}
try:
ellps_axis = ellps[ellps_name.lower()]
a = ellps_axis['a']
b = ellps_axis['b']
- except KeyError, e:
+ except KeyError as e:
raise ValueError(('Could not determine semi-major and semi-minor axis '
- 'of specified ellipsis %s') % ellps_name)
+ 'of specified ellipsis %s') % ellps_name)
return a, b
+
def area_def2basemap(area_def, **kwargs):
"""Get Basemap object from AreaDefinition
-
+
:Parameters:
area_def : object
geometry.AreaDefinition object
**kwargs: Keyword arguments
Additional initialization arguments for Basemap
-
+
:Returns:
bmap : Basemap object
"""
-
+
from mpl_toolkits.basemap import Basemap
try:
a, b = ellps2axis(area_def.proj_dict['ellps'])
@@ -92,8 +115,8 @@ def area_def2basemap(area_def, **kwargs):
# Default to WGS84 ellipsoid
a, b = ellps2axis('wgs84')
rsphere = (a, b)
-
- # Add projection specific basemap args to args passed to function
+
+ # Add projection specific basemap args to args passed to function
basemap_args = kwargs
basemap_args['rsphere'] = rsphere
@@ -114,25 +137,26 @@ def area_def2basemap(area_def, **kwargs):
basemap_args['projection'] = 'cyl'
else:
basemap_args['projection'] = area_def.proj_dict['proj']
-
+
# Try adding potentially remaining args
- for key in ('lon_0', 'lat_0', 'lon_1', 'lat_1', 'lon_2', 'lat_2',
+ for key in ('lon_0', 'lat_0', 'lon_1', 'lat_1', 'lon_2', 'lat_2',
'lat_ts'):
try:
basemap_args[key] = float(area_def.proj_dict[key])
except KeyError:
pass
- return Basemap(**basemap_args)
-
-def _get_quicklook(area_def, data, vmin=None, vmax=None,
- label='Variable (units)', num_meridians=45,
+ return Basemap(**basemap_args)
+
+
+def _get_quicklook(area_def, data, vmin=None, vmax=None,
+ label='Variable (units)', num_meridians=45,
num_parallels=10, coast_res='c'):
"""Get default Basemap matplotlib plot
"""
-
+
if area_def.shape != data.shape:
- raise ValueError('area_def shape %s does not match data shape %s' %
+ raise ValueError('area_def shape %s does not match data shape %s' %
(list(area_def.shape), list(data.shape)))
import matplotlib.pyplot as plt
bmap = area_def2basemap(area_def, resolution=coast_res)
@@ -144,14 +168,15 @@ def _get_quicklook(area_def, data, vmin=None, vmax=None,
if not (np.ma.isMaskedArray(data) and data.mask.all()):
col = bmap.imshow(data, origin='upper', vmin=vmin, vmax=vmax)
plt.colorbar(col, shrink=0.5, pad=0.05).set_label(label)
-
+
return plt
-
-def show_quicklook(area_def, data, vmin=None, vmax=None,
- label='Variable (units)', num_meridians=45,
+
+
+def show_quicklook(area_def, data, vmin=None, vmax=None,
+ label='Variable (units)', num_meridians=45,
num_parallels=10, coast_res='c'):
"""Display default quicklook plot
-
+
:Parameters:
area_def : object
geometry.AreaDefinition object
@@ -169,22 +194,23 @@ def show_quicklook(area_def, data, vmin=None, vmax=None,
Number of parallels to plot on the globe
coast_res : {'c', 'l', 'i', 'h', 'f'}, optional
Resolution of coastlines
-
+
:Returns:
bmap : Basemap object
"""
-
- plt = _get_quicklook(area_def, data, vmin=vmin, vmax=vmax,
- label=label, num_meridians=num_meridians,
+
+ plt = _get_quicklook(area_def, data, vmin=vmin, vmax=vmax,
+ label=label, num_meridians=num_meridians,
num_parallels=num_parallels, coast_res=coast_res)
plt.show()
plt.close()
-
-def save_quicklook(filename, area_def, data, vmin=None, vmax=None,
- label='Variable (units)', num_meridians=45,
+
+
+def save_quicklook(filename, area_def, data, vmin=None, vmax=None,
+ label='Variable (units)', num_meridians=45,
num_parallels=10, coast_res='c', backend='AGG'):
"""Display default quicklook plot
-
+
:Parameters:
filename : str
path to output file
@@ -207,12 +233,11 @@ def save_quicklook(filename, area_def, data, vmin=None, vmax=None,
backend : str, optional
matplotlib backend to use'
"""
-
+
import matplotlib
matplotlib.use(backend, warn=False)
- plt = _get_quicklook(area_def, data, vmin=vmin, vmax=vmax,
- label=label, num_meridians=num_meridians,
+ plt = _get_quicklook(area_def, data, vmin=vmin, vmax=vmax,
+ label=label, num_meridians=num_meridians,
num_parallels=num_parallels, coast_res=coast_res)
plt.savefig(filename, bbox_inches='tight')
plt.close()
-
diff --git a/pyresample/spherical_geometry.py b/pyresample/spherical_geometry.py
index 637c9d0..e332fb2 100644
--- a/pyresample/spherical_geometry.py
+++ b/pyresample/spherical_geometry.py
@@ -1,30 +1,39 @@
-#pyresample, Resampling of remote sensing image data in python
-#
-#Copyright (C) 2010 Martin Raspaud
+# pyresample, Resampling of remote sensing image data in python
#
-#This program is free software: you can redistribute it and/or modify
-#it under the terms of the GNU General Public License as published by
-#the Free Software Foundation, either version 3 of the License, or
-#(at your option) any later version.
+# Copyright (C) 2010, 2015 Martin Raspaud
#
-#This program is distributed in the hope that it will be useful,
-#but WITHOUT ANY WARRANTY; without even the implied warranty of
-#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-#GNU General Public License for more details.
+# This program is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Lesser General Public License as published by the Free
+# Software Foundation, either version 3 of the License, or (at your option) any
+# later version.
#
-#You should have received a copy of the GNU General Public License
-# along with this program. If not, see <http://www.gnu.org/licenses/>.
+# This program is distributed in the hope that it will be useful, but WITHOUT
+# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
+# details.
+#
+# You should have received a copy of the GNU Lesser General Public License along
+# with this program. If not, see <http://www.gnu.org/licenses/>.
"""Classes for spherical geometry operations"""
+from __future__ import absolute_import
+
import math
import numpy as np
+try:
+ range = xrange
+except NameError:
+ pass
+
EPSILON = 0.0000001
# FIXME: this has not been tested with R != 1
+
class Coordinate(object):
+
"""Point on earth in terms of lat and lon.
"""
lat = None
@@ -32,7 +41,7 @@ class Coordinate(object):
x__ = None
y__ = None
z__ = None
-
+
def __init__(self, lon=None, lat=None,
x__=None, y__=None, z__=None, R__=1):
@@ -40,7 +49,7 @@ class Coordinate(object):
if lat is not None and lon is not None:
if not(-180 <= lon <= 180 and -90 <= lat <= 90):
raise ValueError('Illegal (lon, lat) coordinates: (%s, %s)'
- % (lon, lat))
+ % (lon, lat))
self.lat = math.radians(lat)
self.lon = math.radians(lon)
self._update_cart()
@@ -57,15 +66,14 @@ class Coordinate(object):
self.x__ = math.cos(self.lat) * math.cos(self.lon)
self.y__ = math.cos(self.lat) * math.sin(self.lon)
self.z__ = math.sin(self.lat)
-
def _update_lonlat(self):
"""Convert cartesian to lon/lat.
"""
-
+
self.lat = math.degrees(math.asin(self.z__ / self.R__))
self.lon = math.degrees(math.atan2(self.y__, self.x__))
-
+
def __ne__(self, other):
if(abs(self.lat - other.lat) < EPSILON and
abs(self.lon - other.lon) < EPSILON):
@@ -78,7 +86,7 @@ class Coordinate(object):
def __str__(self):
return str((math.degrees(self.lon), math.degrees(self.lat)))
-
+
def __repr__(self):
return str((math.degrees(self.lon), math.degrees(self.lat)))
@@ -138,7 +146,7 @@ class Coordinate(object):
x__ = self.y__ * point.z__ - self.z__ * point.y__
y__ = self.z__ * point.x__ - self.x__ * point.z__
z__ = self.x__ * point.y__ - self.y__ * point.x__
-
+
return Coordinate(x__=x__, y__=y__, z__=z__)
def dot(self, point):
@@ -148,7 +156,9 @@ class Coordinate(object):
self.y__ * point.y__ +
self.z__ * point.z__)
+
class Arc(object):
+
"""An arc of the great circle between two points.
"""
start = None
@@ -167,9 +177,9 @@ class Arc(object):
val = 1
elif val < -1:
val = -1
-
+
return math.acos(val)
-
+
def __eq__(self, other):
if(self.start == other.start and self.end == other.end):
return 1
@@ -206,26 +216,25 @@ class Arc(object):
ua_ = a__.cross(b__)
ub_ = a__.cross(c__)
- val = ua_.dot(ub_) / (ua_.norm() * ub_.norm())
+ val = ua_.dot(ub_) / (ua_.norm() * ub_.norm())
if abs(val - 1) < EPSILON:
angle = 0
elif abs(val + 1) < EPSILON:
angle = math.pi
else:
- angle = math.acos(val)
+ angle = math.acos(val)
n__ = ua_.normalize()
if n__.dot(c__) > 0:
return -angle
else:
return angle
-
+
def intersections(self, other_arc):
"""Gives the two intersections of the greats circles defined by the
current arc and *other_arc*.
"""
-
-
+
if self.end.lon - self.start.lon > math.pi:
self.end.lon -= 2 * math.pi
if other_arc.end.lon - other_arc.start.lon > math.pi:
@@ -234,7 +243,7 @@ class Arc(object):
self.end.lon += 2 * math.pi
if other_arc.end.lon - other_arc.start.lon < -math.pi:
other_arc.end.lon += 2 * math.pi
-
+
ea_ = self.start.cross2cart(self.end).normalize()
eb_ = other_arc.start.cross2cart(other_arc.end).normalize()
@@ -259,14 +268,12 @@ class Arc(object):
two points.
"""
-
for i in self.intersections(other_arc):
a__ = self.start
b__ = self.end
c__ = other_arc.start
d__ = other_arc.end
-
ab_ = a__.distance(b__)
cd_ = c__.distance(d__)
@@ -275,36 +282,39 @@ class Arc(object):
return i
return None
+
def modpi(val):
"""Puts *val* between -pi and pi.
"""
return (val + math.pi) % (2 * math.pi) - math.pi
+
def get_polygon_area(corners):
"""Get the area of the convex area defined by *corners*.
"""
# We assume the earth is spherical !!!
# Should be the radius of the earth at the observed position
R = 1
-
+
c1_ = corners[0]
area = 0
-
+
for idx in range(1, len(corners) - 1):
b1_ = Arc(c1_, corners[idx])
b2_ = Arc(c1_, corners[idx + 1])
b3_ = Arc(corners[idx], corners[idx + 1])
e__ = (abs(b1_.angle(b2_)) +
- abs(b2_.angle(b3_)) +
- abs(b3_.angle(b1_)))
+ abs(b2_.angle(b3_)) +
+ abs(b3_.angle(b1_)))
area += R ** 2 * e__ - math.pi
return area
+
def get_intersections(b__, boundaries):
"""Get the intersections of *b__* with *boundaries*.
Returns both the intersection coordinates and the concerned boundaries.
"""
-
+
intersections = []
bounds = []
for other_b in boundaries:
@@ -313,7 +323,8 @@ def get_intersections(b__, boundaries):
intersections.append(inter)
bounds.append(other_b)
return intersections, bounds
-
+
+
def get_first_intersection(b__, boundaries):
"""Get the first intersection on *b__* with *boundaries*.
"""
@@ -325,6 +336,7 @@ def get_first_intersection(b__, boundaries):
return intersections[indices[0]]
return None
+
def get_next_intersection(p__, b__, boundaries):
"""Get the next intersection from the intersection of arcs *p__* and *b__*
along segment *b__* with *boundaries*.
@@ -339,6 +351,7 @@ def get_next_intersection(p__, b__, boundaries):
return intersections[indices[1]], bounds[indices[1]]
return None, None
+
def point_inside(point, corners):
"""Is a point inside the 4 corners ? This uses great circle arcs as area
boundaries.
@@ -347,7 +360,7 @@ def point_inside(point, corners):
arc2 = Arc(corners[1], corners[2])
arc3 = Arc(corners[2], corners[3])
arc4 = Arc(corners[3], corners[0])
-
+
arc5 = Arc(corners[1], point)
arc6 = Arc(corners[3], point)
@@ -358,10 +371,11 @@ def point_inside(point, corners):
angle2bis = modpi(arc3.angle(arc6))
return (np.sign(angle1) == np.sign(angle1bis) and
- abs(angle1) > abs(angle1bis) and
+ abs(angle1) > abs(angle1bis) and
np.sign(angle2) == np.sign(angle2bis) and
abs(angle2) > abs(angle2bis))
+
def intersection_polygon(area_corners, segment_corners):
"""Get the intersection polygon between two areas.
"""
@@ -411,5 +425,3 @@ def intersection_polygon(area_corners, segment_corners):
b__ = b2_
boundaries, other_boundaries = other_boundaries, boundaries
return poly[:-1]
-
-
diff --git a/pyresample/test/__init__.py b/pyresample/test/__init__.py
new file mode 100644
index 0000000..1fe0950
--- /dev/null
+++ b/pyresample/test/__init__.py
@@ -0,0 +1,46 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2014, 2015 Martin Raspaud
+
+# Author(s):
+
+# Martin Raspaud <martin.raspaud at smhi.se>
+
+# This program is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Lesser General Public License as published by the Free
+# Software Foundation, either version 3 of the License, or (at your option) any
+# later version.
+
+# This program is distributed in the hope that it will be useful, but WITHOUT
+# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
+# details.
+
+# You should have received a copy of the GNU Lesser General Public License along
+# with this program. If not, see <http://www.gnu.org/licenses/>.
+
+"""The test base.
+"""
+
+
+from pyresample.test import test_geometry, test_grid, test_image, test_kd_tree, test_plot, test_spherical_geometry, test_swath, test_utils
+
+import unittest
+
+
+def suite():
+ """The global test suite.
+ """
+ mysuite = unittest.TestSuite()
+ # Use the unittests also
+ mysuite.addTests(test_geometry.suite())
+ mysuite.addTests(test_grid.suite())
+ mysuite.addTests(test_image.suite())
+ mysuite.addTests(test_kd_tree.suite())
+ mysuite.addTests(test_plot.suite())
+ mysuite.addTests(test_spherical_geometry.suite())
+ mysuite.addTests(test_swath.suite())
+ mysuite.addTests(test_utils.suite())
+
+ return mysuite
diff --git a/test/test_files/areas.cfg b/pyresample/test/test_files/areas.cfg
similarity index 100%
rename from test/test_files/areas.cfg
rename to pyresample/test/test_files/areas.cfg
diff --git a/test/test_files/mask_grid.dat b/pyresample/test/test_files/mask_grid.dat
similarity index 100%
rename from test/test_files/mask_grid.dat
rename to pyresample/test/test_files/mask_grid.dat
diff --git a/test/test_files/mask_test_data.dat b/pyresample/test/test_files/mask_test_data.dat
similarity index 100%
rename from test/test_files/mask_test_data.dat
rename to pyresample/test/test_files/mask_test_data.dat
diff --git a/test/test_files/mask_test_fill_value.dat b/pyresample/test/test_files/mask_test_fill_value.dat
similarity index 100%
rename from test/test_files/mask_test_fill_value.dat
rename to pyresample/test/test_files/mask_test_fill_value.dat
diff --git a/test/test_files/mask_test_full_fill.dat b/pyresample/test/test_files/mask_test_full_fill.dat
similarity index 100%
rename from test/test_files/mask_test_full_fill.dat
rename to pyresample/test/test_files/mask_test_full_fill.dat
diff --git a/test/test_files/mask_test_full_fill_multi.dat b/pyresample/test/test_files/mask_test_full_fill_multi.dat
similarity index 100%
rename from test/test_files/mask_test_full_fill_multi.dat
rename to pyresample/test/test_files/mask_test_full_fill_multi.dat
diff --git a/test/test_files/mask_test_mask.dat b/pyresample/test/test_files/mask_test_mask.dat
similarity index 100%
rename from test/test_files/mask_test_mask.dat
rename to pyresample/test/test_files/mask_test_mask.dat
diff --git a/test/test_files/mask_test_nearest_data.dat b/pyresample/test/test_files/mask_test_nearest_data.dat
similarity index 100%
rename from test/test_files/mask_test_nearest_data.dat
rename to pyresample/test/test_files/mask_test_nearest_data.dat
diff --git a/test/test_files/mask_test_nearest_mask.dat b/pyresample/test/test_files/mask_test_nearest_mask.dat
similarity index 100%
rename from test/test_files/mask_test_nearest_mask.dat
rename to pyresample/test/test_files/mask_test_nearest_mask.dat
diff --git a/test/test_files/quick_mask_test.dat b/pyresample/test/test_files/quick_mask_test.dat
similarity index 100%
rename from test/test_files/quick_mask_test.dat
rename to pyresample/test/test_files/quick_mask_test.dat
diff --git a/test/test_files/ssmis_swath.npz b/pyresample/test/test_files/ssmis_swath.npz
similarity index 100%
rename from test/test_files/ssmis_swath.npz
rename to pyresample/test/test_files/ssmis_swath.npz
diff --git a/pyresample/test/test_geometry.py b/pyresample/test/test_geometry.py
new file mode 100644
index 0000000..bba33ee
--- /dev/null
+++ b/pyresample/test/test_geometry.py
@@ -0,0 +1,646 @@
+from __future__ import with_statement
+
+import sys
+import unittest
+
+import numpy as np
+
+import warnings
+if sys.version_info < (2, 6):
+ warnings.simplefilter("ignore")
+else:
+ warnings.simplefilter("always")
+
+from pyresample import geometry, geo_filter
+
+
+def tmp(f):
+ f.tmp = True
+ return f
+
+
+class Test(unittest.TestCase):
+
+ """Unit testing the geometry and geo_filter modules"""
+
+ def assert_raises(self, exception, call_able, *args):
+ """assertRaises() has changed from py2.6 to 2.7! Here is an attempt to
+ cover both"""
+ import sys
+ if sys.version_info < (2, 7):
+ self.assertRaises(exception, call_able, *args)
+ else:
+ with self.assertRaises(exception):
+ call_able(*args)
+
+ def test_lonlat_precomp(self):
+ area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
+ {'a': '6378144.0',
+ 'b': '6356759.0',
+ 'lat_0': '50.00',
+ 'lat_ts': '50.00',
+ 'lon_0': '8.00',
+ 'proj': 'stere'},
+ 800,
+ 800,
+ [-1370912.72,
+ -909968.64000000001,
+ 1029087.28,
+ 1490031.3600000001])
+ lons, lats = area_def.get_lonlats()
+ area_def2 = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
+ {'a': '6378144.0',
+ 'b': '6356759.0',
+ 'lat_0': '50.00',
+ 'lat_ts': '50.00',
+ 'lon_0': '8.00',
+ 'proj': 'stere'},
+ 800,
+ 800,
+ [-1370912.72,
+ -909968.64000000001,
+ 1029087.28,
+ 1490031.3600000001],
+ lons=lons, lats=lats)
+ lon, lat = area_def.get_lonlat(400, 400)
+ self.assertAlmostEqual(lon, 5.5028467120975835,
+ msg='lon retrieval from precomputated grid failed')
+ self.assertAlmostEqual(lat, 52.566998432390619,
+ msg='lat retrieval from precomputated grid failed')
+
+ @tmp
+ def test_cartesian(self):
+ area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
+ {'a': '6378144.0',
+ 'b': '6356759.0',
+ 'lat_0': '50.00',
+ 'lat_ts': '50.00',
+ 'lon_0': '8.00',
+ 'proj': 'stere'},
+ 800,
+ 800,
+ [-1370912.72,
+ -909968.64000000001,
+ 1029087.28,
+ 1490031.3600000001])
+ cart_coords = area_def.get_cartesian_coords()
+ exp = 5872039989466.8457031
+ self.assertTrue((cart_coords.sum() - exp) < 1e-7 * exp,
+ msg='Calculation of cartesian coordinates failed')
+
+ def test_base_lat_invalid(self):
+
+ lons = np.arange(-135., +135, 20.)
+ lats = np.ones_like(lons) * 70.
+ lats[0] = -95
+ lats[1] = +95
+ self.assertRaises(
+ ValueError, geometry.BaseDefinition, lons=lons, lats=lats)
+
+ def test_base_lon_wrapping(self):
+
+ lons1 = np.arange(-135., +135, 50.)
+ lats = np.ones_like(lons1) * 70.
+
+ with warnings.catch_warnings(record=True) as w1:
+ base_def1 = geometry.BaseDefinition(lons1, lats)
+ self.assertFalse(
+ len(w1) != 0, 'Got warning <%s>, but was not expecting one' % w1)
+
+ lons2 = np.where(lons1 < 0, lons1 + 360, lons1)
+ with warnings.catch_warnings(record=True) as w2:
+ base_def2 = geometry.BaseDefinition(lons2, lats)
+ self.assertFalse(
+ len(w2) != 1, 'Failed to trigger a warning on longitude wrapping')
+ self.assertFalse(('-180:+180' not in str(w2[0].message)),
+ 'Failed to trigger correct warning about longitude wrapping')
+
+ self.assertFalse(
+ base_def1 != base_def2, 'longitude wrapping to [-180:+180] did not work')
+
+ with warnings.catch_warnings(record=True) as w3:
+ base_def3 = geometry.BaseDefinition(None, None)
+ self.assertFalse(
+ len(w3) != 0, 'Got warning <%s>, but was not expecting one' % w3)
+
+ self.assert_raises(ValueError, base_def3.get_lonlats)
+
+ def test_base_type(self):
+ lons1 = np.arange(-135., +135, 50.)
+ lats = np.ones_like(lons1) * 70.
+
+ # Test dtype is preserved without longitude wrapping
+ basedef = geometry.BaseDefinition(lons1, lats)
+ lons, _ = basedef.get_lonlats()
+ self.assertEqual(lons.dtype, lons1.dtype,
+ "BaseDefinition did not maintain dtype of longitudes (in:%s out:%s)" %
+ (lons1.dtype, lons.dtype,))
+
+ lons1_ints = lons1.astype('int')
+ basedef = geometry.BaseDefinition(lons1_ints, lats)
+ lons, _ = basedef.get_lonlats()
+ self.assertEqual(lons.dtype, lons1_ints.dtype,
+ "BaseDefinition did not maintain dtype of longitudes (in:%s out:%s)" %
+ (lons1_ints.dtype, lons.dtype,))
+
+ # Test dtype is preserved with automatic longitude wrapping
+ lons2 = np.where(lons1 < 0, lons1 + 360, lons1)
+ with warnings.catch_warnings(record=True) as w:
+ basedef = geometry.BaseDefinition(lons2, lats)
+
+ lons, _ = basedef.get_lonlats()
+ self.assertEqual(lons.dtype, lons2.dtype,
+ "BaseDefinition did not maintain dtype of longitudes (in:%s out:%s)" %
+ (lons2.dtype, lons.dtype,))
+
+ lons2_ints = lons2.astype('int')
+ with warnings.catch_warnings(record=True) as w:
+ basedef = geometry.BaseDefinition(lons2_ints, lats)
+
+ lons, _ = basedef.get_lonlats()
+ self.assertEqual(lons.dtype, lons2_ints.dtype,
+ "BaseDefinition did not maintain dtype of longitudes (in:%s out:%s)" %
+ (lons2_ints.dtype, lons.dtype,))
+
+ def test_swath(self):
+ lons1 = np.fromfunction(lambda y, x: 3 + (10.0 / 100) * x, (5000, 100))
+ lats1 = np.fromfunction(
+ lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100))
+
+ swath_def = geometry.SwathDefinition(lons1, lats1)
+
+ lons2, lats2 = swath_def.get_lonlats()
+
+ self.assertFalse(id(lons1) != id(lons2) or id(lats1) != id(lats2),
+ msg='Caching of swath coordinates failed')
+
+ def test_swath_wrap(self):
+ lons1 = np.fromfunction(lambda y, x: 3 + (10.0 / 100) * x, (5000, 100))
+ lats1 = np.fromfunction(
+ lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100))
+
+ lons1 += 180.
+ if (sys.version_info < (2, 6) or
+ (sys.version_info >= (3, 0) and sys.version_info < (3, 4))):
+ swath_def = geometry.BaseDefinition(lons1, lats1)
+ else:
+ with warnings.catch_warnings(record=True) as w1:
+ swath_def = geometry.BaseDefinition(lons1, lats1)
+ self.assertFalse(
+ len(w1) != 1, 'Failed to trigger a warning on longitude wrapping')
+ self.assertFalse(('-180:+180' not in str(w1[0].message)),
+ 'Failed to trigger correct warning about longitude wrapping')
+
+ lons2, lats2 = swath_def.get_lonlats()
+
+ self.assertTrue(id(lons1) != id(lons2),
+ msg='Caching of swath coordinates failed with longitude wrapping')
+
+ self.assertTrue(lons2.min() > -180 and lons2.max() < 180,
+ 'Wrapping of longitudes failed for SwathDefinition')
+
+ def test_area_equal(self):
+ area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
+ {'a': '6378144.0',
+ 'b': '6356759.0',
+ 'lat_0': '50.00',
+ 'lat_ts': '50.00',
+ 'lon_0': '8.00',
+ 'proj': 'stere'},
+ 800,
+ 800,
+ [-1370912.72,
+ -909968.64000000001,
+ 1029087.28,
+ 1490031.3600000001])
+ area_def2 = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
+ {'a': '6378144.0',
+ 'b': '6356759.0',
+ 'lat_0': '50.00',
+ 'lat_ts': '50.00',
+ 'lon_0': '8.00',
+ 'proj': 'stere'},
+ 800,
+ 800,
+ [-1370912.72,
+ -909968.64000000001,
+ 1029087.28,
+ 1490031.3600000001])
+ self.assertFalse(
+ area_def != area_def2, 'area_defs are not equal as expected')
+
+ def test_not_area_equal(self):
+ area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
+ {'a': '6378144.0',
+ 'b': '6356759.0',
+ 'lat_0': '50.00',
+ 'lat_ts': '50.00',
+ 'lon_0': '8.00',
+ 'proj': 'stere'},
+ 800,
+ 800,
+ [-1370912.72,
+ -909968.64000000001,
+ 1029087.28,
+ 1490031.3600000001])
+
+ msg_area = geometry.AreaDefinition('msg_full', 'Full globe MSG image 0 degrees',
+ 'msg_full',
+ {'a': '6378169.0',
+ 'b': '6356584.0',
+ 'h': '35785831.0',
+ 'lon_0': '0',
+ 'proj': 'geos'},
+ 3712,
+ 3712,
+ [-5568742.4000000004,
+ -5568742.4000000004,
+ 5568742.4000000004,
+ 5568742.4000000004]
+ )
+ self.assertFalse(
+ area_def == msg_area, 'area_defs are not expected to be equal')
+
+ def test_swath_equal(self):
+ lons = np.array([1.2, 1.3, 1.4, 1.5])
+ lats = np.array([65.9, 65.86, 65.82, 65.78])
+ swath_def = geometry.SwathDefinition(lons, lats)
+ swath_def2 = geometry.SwathDefinition(lons, lats)
+ self.assertFalse(
+ swath_def != swath_def2, 'swath_defs are not equal as expected')
+
+ def test_swath_not_equal(self):
+ lats1 = np.array([65.9, 65.86, 65.82, 65.78])
+ lons = np.array([1.2, 1.3, 1.4, 1.5])
+ lats2 = np.array([65.91, 65.85, 65.80, 65.75])
+ swath_def = geometry.SwathDefinition(lons, lats1)
+ swath_def2 = geometry.SwathDefinition(lons, lats2)
+ self.assertFalse(
+ swath_def == swath_def2, 'swath_defs are not expected to be equal')
+
+ def test_swath_equal_area(self):
+ area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
+ {'a': '6378144.0',
+ 'b': '6356759.0',
+ 'lat_0': '50.00',
+ 'lat_ts': '50.00',
+ 'lon_0': '8.00',
+ 'proj': 'stere'},
+ 800,
+ 800,
+ [-1370912.72,
+ -909968.64000000001,
+ 1029087.28,
+ 1490031.3600000001])
+
+ swath_def = geometry.SwathDefinition(*area_def.get_lonlats())
+
+ self.assertFalse(
+ swath_def != area_def, "swath_def and area_def should be equal")
+
+ area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
+ {'a': '6378144.0',
+ 'b': '6356759.0',
+ 'lat_0': '50.00',
+ 'lat_ts': '50.00',
+ 'lon_0': '8.00',
+ 'proj': 'stere'},
+ 800,
+ 800,
+ [-1370912.72,
+ -909968.64000000001,
+ 1029087.28,
+ 1490031.3600000001])
+
+ self.assertFalse(
+ area_def != swath_def, "swath_def and area_def should be equal")
+
+ def test_swath_not_equal_area(self):
+ area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
+ {'a': '6378144.0',
+ 'b': '6356759.0',
+ 'lat_0': '50.00',
+ 'lat_ts': '50.00',
+ 'lon_0': '8.00',
+ 'proj': 'stere'},
+ 800,
+ 800,
+ [-1370912.72,
+ -909968.64000000001,
+ 1029087.28,
+ 1490031.3600000001])
+
+ lons = np.array([1.2, 1.3, 1.4, 1.5])
+ lats = np.array([65.9, 65.86, 65.82, 65.78])
+ swath_def = geometry.SwathDefinition(lons, lats)
+
+ self.assertFalse(
+ swath_def == area_def, "swath_def and area_def should be different")
+
+ area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
+ {'a': '6378144.0',
+ 'b': '6356759.0',
+ 'lat_0': '50.00',
+ 'lat_ts': '50.00',
+ 'lon_0': '8.00',
+ 'proj': 'stere'},
+ 800,
+ 800,
+ [-1370912.72,
+ -909968.64000000001,
+ 1029087.28,
+ 1490031.3600000001])
+
+ self.assertFalse(
+ area_def == swath_def, "swath_def and area_def should be different")
+
+ def test_concat_1d(self):
+ lons1 = np.array([1, 2, 3])
+ lats1 = np.array([1, 2, 3])
+ lons2 = np.array([4, 5, 6])
+ lats2 = np.array([4, 5, 6])
+ swath_def1 = geometry.SwathDefinition(lons1, lats1)
+ swath_def2 = geometry.SwathDefinition(lons2, lats2)
+ swath_def_concat = swath_def1.concatenate(swath_def2)
+ expected = np.array([1, 2, 3, 4, 5, 6])
+ self.assertTrue(np.array_equal(swath_def_concat.lons, expected) and
+ np.array_equal(swath_def_concat.lons, expected),
+ 'Failed to concatenate 1D swaths')
+
+ def test_concat_2d(self):
+ lons1 = np.array([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
+ lats1 = np.array([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
+ lons2 = np.array([[4, 5, 6], [6, 7, 8]])
+ lats2 = np.array([[4, 5, 6], [6, 7, 8]])
+ swath_def1 = geometry.SwathDefinition(lons1, lats1)
+ swath_def2 = geometry.SwathDefinition(lons2, lats2)
+ swath_def_concat = swath_def1.concatenate(swath_def2)
+ expected = np.array(
+ [[1, 2, 3], [3, 4, 5], [5, 6, 7], [4, 5, 6], [6, 7, 8]])
+ self.assertTrue(np.array_equal(swath_def_concat.lons, expected) and
+ np.array_equal(swath_def_concat.lons, expected),
+ 'Failed to concatenate 2D swaths')
+
+ def test_append_1d(self):
+ lons1 = np.array([1, 2, 3])
+ lats1 = np.array([1, 2, 3])
+ lons2 = np.array([4, 5, 6])
+ lats2 = np.array([4, 5, 6])
+ swath_def1 = geometry.SwathDefinition(lons1, lats1)
+ swath_def2 = geometry.SwathDefinition(lons2, lats2)
+ swath_def1.append(swath_def2)
+ expected = np.array([1, 2, 3, 4, 5, 6])
+ self.assertTrue(np.array_equal(swath_def1.lons, expected) and
+ np.array_equal(swath_def1.lons, expected),
+ 'Failed to append 1D swaths')
+
+ def test_append_2d(self):
+ lons1 = np.array([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
+ lats1 = np.array([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
+ lons2 = np.array([[4, 5, 6], [6, 7, 8]])
+ lats2 = np.array([[4, 5, 6], [6, 7, 8]])
+ swath_def1 = geometry.SwathDefinition(lons1, lats1)
+ swath_def2 = geometry.SwathDefinition(lons2, lats2)
+ swath_def1.append(swath_def2)
+ expected = np.array(
+ [[1, 2, 3], [3, 4, 5], [5, 6, 7], [4, 5, 6], [6, 7, 8]])
+ self.assertTrue(np.array_equal(swath_def1.lons, expected) and
+ np.array_equal(swath_def1.lons, expected),
+ 'Failed to append 2D swaths')
+
+ def test_grid_filter_valid(self):
+ lons = np.array([-170, -30, 30, 170])
+ lats = np.array([20, -40, 50, -80])
+ swath_def = geometry.SwathDefinition(lons, lats)
+ filter_area = geometry.AreaDefinition('test', 'test', 'test',
+ {'proj': 'eqc', 'lon_0': 0.0,
+ 'lat_0': 0.0},
+ 8, 8,
+ (-20037508.34, -10018754.17, 20037508.34, 10018754.17))
+ filter = np.array([[1, 1, 1, 1, 0, 0, 0, 0],
+ [1, 1, 1, 1, 0, 0, 0, 0],
+ [1, 1, 1, 1, 0, 0, 0, 0],
+ [1, 1, 1, 1, 0, 0, 0, 0],
+ [0, 0, 0, 0, 1, 1, 1, 1],
+ [0, 0, 0, 0, 1, 1, 1, 1],
+ [0, 0, 0, 0, 1, 1, 1, 1],
+ [0, 0, 0, 0, 1, 1, 1, 1],
+ ])
+ grid_filter = geo_filter.GridFilter(filter_area, filter)
+ valid_index = grid_filter.get_valid_index(swath_def)
+ expected = np.array([1, 0, 0, 1])
+ self.assertTrue(
+ np.array_equal(valid_index, expected), 'Failed to find grid filter')
+
+ def test_grid_filter(self):
+ lons = np.array([-170, -30, 30, 170])
+ lats = np.array([20, -40, 50, -80])
+ swath_def = geometry.SwathDefinition(lons, lats)
+ data = np.array([1, 2, 3, 4])
+ filter_area = geometry.AreaDefinition('test', 'test', 'test',
+ {'proj': 'eqc', 'lon_0': 0.0,
+ 'lat_0': 0.0},
+ 8, 8,
+ (-20037508.34, -10018754.17, 20037508.34, 10018754.17))
+ filter = np.array([[1, 1, 1, 1, 0, 0, 0, 0],
+ [1, 1, 1, 1, 0, 0, 0, 0],
+ [1, 1, 1, 1, 0, 0, 0, 0],
+ [1, 1, 1, 1, 0, 0, 0, 0],
+ [0, 0, 0, 0, 1, 1, 1, 1],
+ [0, 0, 0, 0, 1, 1, 1, 1],
+ [0, 0, 0, 0, 1, 1, 1, 1],
+ [0, 0, 0, 0, 1, 1, 1, 1],
+ ])
+ grid_filter = geo_filter.GridFilter(filter_area, filter)
+ swath_def_f, data_f = grid_filter.filter(swath_def, data)
+ expected = np.array([1, 4])
+ self.assertTrue(
+ np.array_equal(data_f, expected), 'Failed grid filtering data')
+ expected_lons = np.array([-170, 170])
+ expected_lats = np.array([20, -80])
+ self.assertTrue(np.array_equal(swath_def_f.lons[:], expected_lons)
+ and np.array_equal(swath_def_f.lats[:], expected_lats),
+ 'Failed finding grid filtering lon lats')
+
+ def test_grid_filter2D(self):
+ lons = np.array([[-170, -30, 30, 170],
+ [-170, -30, 30, 170]])
+ lats = np.array([[20, -40, 50, -80],
+ [25, -35, 55, -75]])
+ swath_def = geometry.SwathDefinition(lons, lats)
+ data1 = np.ones((2, 4))
+ data2 = np.ones((2, 4)) * 2
+ data3 = np.ones((2, 4)) * 3
+ data = np.dstack((data1, data2, data3))
+ filter_area = geometry.AreaDefinition('test', 'test', 'test',
+ {'proj': 'eqc', 'lon_0': 0.0,
+ 'lat_0': 0.0},
+ 8, 8,
+ (-20037508.34, -10018754.17, 20037508.34, 10018754.17))
+ filter = np.array([[1, 1, 1, 1, 0, 0, 0, 0],
+ [1, 1, 1, 1, 0, 0, 0, 0],
+ [1, 1, 1, 1, 0, 0, 0, 0],
+ [1, 1, 1, 1, 0, 0, 0, 0],
+ [0, 0, 0, 0, 1, 1, 1, 1],
+ [0, 0, 0, 0, 1, 1, 1, 1],
+ [0, 0, 0, 0, 1, 1, 1, 1],
+ [0, 0, 0, 0, 1, 1, 1, 1],
+ ])
+ grid_filter = geo_filter.GridFilter(filter_area, filter, nprocs=2)
+ swath_def_f, data_f = grid_filter.filter(swath_def, data)
+ expected = np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]])
+ self.assertTrue(
+ np.array_equal(data_f, expected), 'Failed 2D grid filtering data')
+ expected_lons = np.array([-170, 170, -170, 170])
+ expected_lats = np.array([20, -80, 25, -75])
+ self.assertTrue(np.array_equal(swath_def_f.lons[:], expected_lons)
+ and np.array_equal(swath_def_f.lats[:], expected_lats),
+ 'Failed finding 2D grid filtering lon lats')
+
+ def test_boundary(self):
+ area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
+ {'a': '6378144.0',
+ 'b': '6356759.0',
+ 'lat_0': '50.00',
+ 'lat_ts': '50.00',
+ 'lon_0': '8.00',
+ 'proj': 'stere'},
+ 10,
+ 10,
+ [-1370912.72,
+ -909968.64000000001,
+ 1029087.28,
+ 1490031.3600000001])
+ proj_x_boundary, proj_y_boundary = area_def.proj_x_coords, area_def.proj_y_coords
+ expected_x = np.array([-1250912.72, -1010912.72, -770912.72,
+ -530912.72, -290912.72, -50912.72, 189087.28,
+ 429087.28, 669087.28, 909087.28])
+ expected_y = np.array([1370031.36, 1130031.36, 890031.36, 650031.36,
+ 410031.36, 170031.36, -69968.64, -309968.64,
+ -549968.64, -789968.64])
+ self.assertTrue(np.allclose(proj_x_boundary, expected_x),
+ 'Failed to find projection x coords')
+ self.assertTrue(np.allclose(proj_y_boundary, expected_y),
+ 'Failed to find projection y coords')
+
+ def test_area_extent_ll(self):
+ area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
+ {'a': '6378144.0',
+ 'b': '6356759.0',
+ 'lat_0': '50.00',
+ 'lat_ts': '50.00',
+ 'lon_0': '8.00',
+ 'proj': 'stere'},
+ 10,
+ 10,
+ [-1370912.72,
+ -909968.64000000001,
+ 1029087.28,
+ 1490031.3600000001])
+ self.assertAlmostEqual(sum(area_def.area_extent_ll),
+ 122.06448093539757, 5,
+ 'Failed to get lon and lats of area extent')
+
+ @tmp
+ def test_latlong_area(self):
+ area_def = geometry.AreaDefinition('', '', '',
+ {'proj': 'latlong'},
+ 360, 180,
+ [-180, -90, 180, 90])
+ lons, lats = area_def.get_lonlats()
+ self.assertEqual(lons[0, 0], -179.5)
+ self.assertEqual(lats[0, 0], 89.5)
+
+ def test_get_xy_from_lonlat(self):
+ """Test the function get_xy_from_lonlat"""
+ from pyresample import utils
+ area_id = 'test'
+ area_name = 'Test area with 2x2 pixels'
+ proj_id = 'test'
+ x_size = 2
+ y_size = 2
+ area_extent = [1000000, 0, 1050000, 50000]
+ proj_dict = {"proj": 'laea',
+ 'lat_0': '60',
+ 'lon_0': '0',
+ 'a': '6371228.0', 'units': 'm'}
+ area_def = utils.get_area_def(area_id,
+ area_name,
+ proj_id,
+ proj_dict,
+ x_size, y_size,
+ area_extent)
+ import pyproj
+ p__ = pyproj.Proj(proj_dict)
+ lon_ul, lat_ul = p__(1000000, 50000, inverse=True)
+ lon_ur, lat_ur = p__(1050000, 50000, inverse=True)
+ lon_ll, lat_ll = p__(1000000, 0, inverse=True)
+ lon_lr, lat_lr = p__(1050000, 0, inverse=True)
+
+ eps_lonlat = 0.01
+ eps_meters = 100
+ x__, y__ = area_def.get_xy_from_lonlat(lon_ul + eps_lonlat,
+ lat_ul - eps_lonlat)
+ x_expect, y_expect = 0, 0
+ self.assertEqual(x__, x_expect)
+ self.assertEqual(y__, y_expect)
+ x__, y__ = area_def.get_xy_from_lonlat(lon_ur - eps_lonlat,
+ lat_ur - eps_lonlat)
+ self.assertEqual(x__, 1)
+ self.assertEqual(y__, 0)
+ x__, y__ = area_def.get_xy_from_lonlat(lon_ll + eps_lonlat,
+ lat_ll + eps_lonlat)
+ self.assertEqual(x__, 0)
+ self.assertEqual(y__, 1)
+ x__, y__ = area_def.get_xy_from_lonlat(lon_lr - eps_lonlat,
+ lat_lr + eps_lonlat)
+ self.assertEqual(x__, 1)
+ self.assertEqual(y__, 1)
+
+ lon, lat = p__(1025000 - eps_meters, 25000 - eps_meters, inverse=True)
+ x__, y__ = area_def.get_xy_from_lonlat(lon, lat)
+ self.assertEqual(x__, 0)
+ self.assertEqual(y__, 1)
+
+ lon, lat = p__(1025000 + eps_meters, 25000 - eps_meters, inverse=True)
+ x__, y__ = area_def.get_xy_from_lonlat(lon, lat)
+ self.assertEqual(x__, 1)
+ self.assertEqual(y__, 1)
+
+ lon, lat = p__(1025000 - eps_meters, 25000 + eps_meters, inverse=True)
+ x__, y__ = area_def.get_xy_from_lonlat(lon, lat)
+ self.assertEqual(x__, 0)
+ self.assertEqual(y__, 0)
+
+ lon, lat = p__(1025000 + eps_meters, 25000 + eps_meters, inverse=True)
+ x__, y__ = area_def.get_xy_from_lonlat(lon, lat)
+ self.assertEqual(x__, 1)
+ self.assertEqual(y__, 0)
+
+ lon, lat = p__(999000, -10, inverse=True)
+ self.assert_raises(ValueError, area_def.get_xy_from_lonlat, lon, lat)
+ self.assert_raises(ValueError, area_def.get_xy_from_lonlat, 0., 0.)
+
+ # Test getting arrays back:
+ lons = [lon_ll + eps_lonlat, lon_ur - eps_lonlat]
+ lats = [lat_ll + eps_lonlat, lat_ur - eps_lonlat]
+ x__, y__ = area_def.get_xy_from_lonlat(lons, lats)
+
+ x_expects = np.array([0, 1])
+ y_expects = np.array([1, 0])
+ self.assertTrue((x__.data == x_expects).all())
+ self.assertTrue((y__.data == y_expects).all())
+
+
+def suite():
+ """The test suite.
+ """
+ loader = unittest.TestLoader()
+ mysuite = unittest.TestSuite()
+ mysuite.addTest(loader.loadTestsFromTestCase(Test))
+
+ return mysuite
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pyresample/test/test_grid.py b/pyresample/test/test_grid.py
new file mode 100644
index 0000000..9a7f53d
--- /dev/null
+++ b/pyresample/test/test_grid.py
@@ -0,0 +1,201 @@
+import copy
+import unittest
+
+import numpy as np
+
+from pyresample import grid, geometry, utils
+
+
+def mp(f):
+ f.mp = True
+ return f
+
+
+def tmp(f):
+ f.tmp = True
+ return f
+
+
+class Test(unittest.TestCase):
+
+ area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
+ {'a': '6378144.0',
+ 'b': '6356759.0',
+ 'lat_0': '50.00',
+ 'lat_ts': '50.00',
+ 'lon_0': '8.00',
+ 'proj': 'stere'},
+ 800,
+ 800,
+ [-1370912.72,
+ -909968.64000000001,
+ 1029087.28,
+ 1490031.3600000001])
+
+ area_def2 = geometry.AreaDefinition('areaD2', 'Europe (3km, HRV, VTC)', 'areaD2',
+ {'a': '6378144.0',
+ 'b': '6356759.0',
+ 'lat_0': '50.00',
+ 'lat_ts': '50.00',
+ 'lon_0': '8.00',
+ 'proj': 'stere'},
+ 5,
+ 5,
+ [-1370912.72,
+ -909968.64000000001,
+ 1029087.28,
+ 1490031.3600000001])
+
+ msg_area = geometry.AreaDefinition('msg_full', 'Full globe MSG image 0 degrees',
+ 'msg_full',
+ {'a': '6378169.0',
+ 'b': '6356584.0',
+ 'h': '35785831.0',
+ 'lon_0': '0',
+ 'proj': 'geos'},
+ 3712,
+ 3712,
+ [-5568742.4000000004,
+ -5568742.4000000004,
+ 5568742.4000000004,
+ 5568742.4000000004]
+ )
+
+ def test_linesample(self):
+ data = np.fromfunction(lambda y, x: y * x, (40, 40))
+ rows = np.array([[1, 2], [3, 4]])
+ cols = np.array([[25, 26], [27, 28]])
+ res = grid.get_image_from_linesample(rows, cols, data)
+ expected = np.array([[25., 52.], [81., 112.]])
+ self.assertTrue(np.array_equal(res, expected), 'Linesample failed')
+
+ def test_linesample_multi(self):
+ data1 = np.fromfunction(lambda y, x: y * x, (40, 40))
+ data2 = np.fromfunction(lambda y, x: 2 * y * x, (40, 40))
+ data3 = np.fromfunction(lambda y, x: 3 * y * x, (40, 40))
+ data = np.zeros((40, 40, 3))
+ data[:, :, 0] = data1
+ data[:, :, 1] = data2
+ data[:, :, 2] = data3
+ rows = np.array([[1, 2], [3, 4]])
+ cols = np.array([[25, 26], [27, 28]])
+ res = grid.get_image_from_linesample(rows, cols, data)
+ expected = np.array([[[25., 50., 75.],
+ [52., 104., 156.]],
+ [[81., 162., 243.],
+ [112., 224., 336.]]])
+ self.assertTrue(np.array_equal(res, expected), 'Linesample failed')
+
+ def test_from_latlon(self):
+ data = np.fromfunction(lambda y, x: y * x, (800, 800))
+ lons = np.fromfunction(lambda y, x: x, (10, 10))
+ lats = np.fromfunction(lambda y, x: 50 - (5.0 / 10) * y, (10, 10))
+ #source_def = grid.AreaDefinition.get_from_area_def(self.area_def)
+ source_def = self.area_def
+ res = grid.get_image_from_lonlats(lons, lats, source_def, data)
+ expected = np.array([[129276., 141032., 153370., 165804., 178334., 190575.,
+ 202864., 214768., 226176., 238080.],
+ [133056., 146016., 158808., 171696., 184320., 196992.,
+ 209712., 222480., 234840., 247715.],
+ [137026., 150150., 163370., 177215., 190629., 203756.,
+ 217464., 230256., 243048., 256373.],
+ [140660., 154496., 168714., 182484., 196542., 210650.,
+ 224257., 238464., 251712., 265512.],
+ [144480., 158484., 173148., 187912., 202776., 217358.,
+ 231990., 246240., 259920., 274170.],
+ [147968., 163261., 178398., 193635., 208616., 223647.,
+ 238728., 253859., 268584., 283898.],
+ [151638., 167121., 182704., 198990., 214775., 230280.,
+ 246442., 261617., 276792., 292574.],
+ [154980., 171186., 187860., 204016., 220542., 237120.,
+ 253125., 269806., 285456., 301732.],
+ [158500., 175536., 192038., 209280., 226626., 243697.,
+ 260820., 277564., 293664., 310408.],
+ [161696., 179470., 197100., 214834., 232320., 250236.,
+ 267448., 285090., 302328., 320229.]])
+ self.assertTrue(
+ np.array_equal(res, expected), 'Sampling from lat lon failed')
+
+ def test_proj_coords(self):
+ #res = grid.get_proj_coords(self.area_def2)
+ res = self.area_def2.get_proj_coords()
+ cross_sum = res[0].sum() + res[1].sum()
+ expected = 2977965.9999999963
+ self.assertAlmostEqual(
+ cross_sum, expected, msg='Calculation of proj coords failed')
+
+ def test_latlons(self):
+ #res = grid.get_lonlats(self.area_def2)
+ res = self.area_def2.get_lonlats()
+ cross_sum = res[0].sum() + res[1].sum()
+ expected = 1440.8280578215431
+ self.assertAlmostEqual(
+ cross_sum, expected, msg='Calculation of lat lons failed')
+
+ @mp
+ def test_latlons_mp(self):
+ #res = grid.get_lonlats(self.area_def2, nprocs=2)
+ res = self.area_def2.get_lonlats(nprocs=2)
+ cross_sum = res[0].sum() + res[1].sum()
+ expected = 1440.8280578215431
+ self.assertAlmostEqual(
+ cross_sum, expected, msg='Calculation of lat lons failed')
+
+ def test_resampled_image(self):
+ data = np.fromfunction(lambda y, x: y * x * 10 ** -6, (3712, 3712))
+ target_def = self.area_def
+ source_def = self.msg_area
+ res = grid.get_resampled_image(
+ target_def, source_def, data, segments=1)
+ cross_sum = res.sum()
+ expected = 399936.39392500359
+ self.assertAlmostEqual(
+ cross_sum, expected, msg='Resampling of image failed')
+
+ @tmp
+ def test_generate_linesample(self):
+ data = np.fromfunction(lambda y, x: y * x * 10 ** -6, (3712, 3712))
+ row_indices, col_indices = utils.generate_quick_linesample_arrays(self.msg_area,
+ self.area_def)
+ res = data[row_indices, col_indices]
+ cross_sum = res.sum()
+ expected = 399936.39392500359
+ self.assertAlmostEqual(
+ cross_sum, expected, msg='Generate linesample failed')
+ self.assertFalse(row_indices.dtype != np.uint16 or col_indices.dtype != np.uint16,
+ 'Generate linesample failed. Downcast to uint16 expected')
+
+ @mp
+ def test_resampled_image_mp(self):
+ data = np.fromfunction(lambda y, x: y * x * 10 ** -6, (3712, 3712))
+ target_def = self.area_def
+ source_def = self.msg_area
+ res = grid.get_resampled_image(
+ target_def, source_def, data, nprocs=2, segments=1)
+ cross_sum = res.sum()
+ expected = 399936.39392500359
+ self.assertAlmostEqual(
+ cross_sum, expected, msg='Resampling of image mp failed')
+
+ def test_single_lonlat(self):
+ lon, lat = self.area_def.get_lonlat(400, 400)
+ self.assertAlmostEqual(
+ lon, 5.5028467120975835, msg='Resampling of single lon failed')
+ self.assertAlmostEqual(
+ lat, 52.566998432390619, msg='Resampling of single lat failed')
+
+ def test_proj4_string(self):
+ proj4_string = self.area_def.proj4_string
+ expected_string = '+a=6378144.0 +b=6356759.0 +lat_ts=50.00 +lon_0=8.00 +proj=stere +lat_0=50.00'
+ self.assertEqual(
+ frozenset(proj4_string.split()), frozenset(expected_string.split()))
+
+
+def suite():
+ """The test suite.
+ """
+ loader = unittest.TestLoader()
+ mysuite = unittest.TestSuite()
+ mysuite.addTest(loader.loadTestsFromTestCase(Test))
+
+ return mysuite
diff --git a/pyresample/test/test_image.py b/pyresample/test/test_image.py
new file mode 100644
index 0000000..8342583
--- /dev/null
+++ b/pyresample/test/test_image.py
@@ -0,0 +1,225 @@
+import os
+import unittest
+
+import numpy
+
+from pyresample import image, geometry, grid, utils
+
+
+def mask(f):
+ f.mask = True
+ return f
+
+
+def tmp(f):
+ f.tmp = True
+ return f
+
+
+class Test(unittest.TestCase):
+
+ area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
+ {'a': '6378144.0',
+ 'b': '6356759.0',
+ 'lat_0': '50.00',
+ 'lat_ts': '50.00',
+ 'lon_0': '8.00',
+ 'proj': 'stere'},
+ 800,
+ 800,
+ [-1370912.72,
+ -909968.64000000001,
+ 1029087.28,
+ 1490031.3600000001])
+
+ msg_area = geometry.AreaDefinition('msg_full', 'Full globe MSG image 0 degrees',
+ 'msg_full',
+ {'a': '6378169.0',
+ 'b': '6356584.0',
+ 'h': '35785831.0',
+ 'lon_0': '0',
+ 'proj': 'geos'},
+ 3712,
+ 3712,
+ [-5568742.4000000004,
+ -5568742.4000000004,
+ 5568742.4000000004,
+ 5568742.4000000004]
+ )
+
+ msg_area_resize = geometry.AreaDefinition('msg_full', 'Full globe MSG image 0 degrees',
+ 'msg_full',
+ {'a': '6378169.0',
+ 'b': '6356584.0',
+ 'h': '35785831.0',
+ 'lon_0': '0',
+ 'proj': 'geos'},
+ 928,
+ 928,
+ [-5568742.4000000004,
+ -5568742.4000000004,
+ 5568742.4000000004,
+ 5568742.4000000004]
+ )
+
+ @tmp
+ def test_image(self):
+ data = numpy.fromfunction(lambda y, x: y * x * 10 ** -6, (3712, 3712))
+ msg_con = image.ImageContainerQuick(data, self.msg_area, segments=1)
+ area_con = msg_con.resample(self.area_def)
+ res = area_con.image_data
+ cross_sum = res.sum()
+ expected = 399936.39392500359
+ self.assertAlmostEqual(
+ cross_sum, expected, msg='ImageContainer resampling quick failed')
+
+ @tmp
+ def test_image_segments(self):
+ data = numpy.fromfunction(lambda y, x: y * x * 10 ** -6, (3712, 3712))
+ msg_con = image.ImageContainerQuick(data, self.msg_area, segments=8)
+ area_con = msg_con.resample(self.area_def)
+ res = area_con.image_data
+ cross_sum = res.sum()
+ expected = 399936.39392500359
+ self.assertAlmostEqual(
+ cross_sum, expected, msg='ImageContainer resampling quick segments failed')
+
+ def test_return_type(self):
+ data = numpy.ones((3712, 3712)).astype('int')
+ msg_con = image.ImageContainerQuick(data, self.msg_area, segments=1)
+ area_con = msg_con.resample(self.area_def)
+ res = area_con.image_data
+ self.assertTrue(
+ data.dtype is res.dtype, msg='Failed to maintain input data type')
+
+ @mask
+ def test_masked_image(self):
+ data = numpy.zeros((3712, 3712))
+ mask = numpy.zeros((3712, 3712))
+ mask[:, 1865:] = 1
+ data_masked = numpy.ma.array(data, mask=mask)
+ msg_con = image.ImageContainerQuick(
+ data_masked, self.msg_area, segments=1)
+ area_con = msg_con.resample(self.area_def)
+ res = area_con.image_data
+ resampled_mask = res.mask.astype('int')
+ expected = numpy.fromfile(os.path.join(os.path.dirname(__file__), 'test_files', 'mask_grid.dat'),
+ sep=' ').reshape((800, 800))
+ self.assertTrue(numpy.array_equal(
+ resampled_mask, expected), msg='Failed to resample masked array')
+
+ @mask
+ def test_masked_image_fill(self):
+ data = numpy.zeros((3712, 3712))
+ mask = numpy.zeros((3712, 3712))
+ mask[:, 1865:] = 1
+ data_masked = numpy.ma.array(data, mask=mask)
+ msg_con = image.ImageContainerQuick(data_masked, self.msg_area,
+ fill_value=None, segments=1)
+ area_con = msg_con.resample(self.area_def)
+ res = area_con.image_data
+ resampled_mask = res.mask.astype('int')
+ expected = numpy.fromfile(os.path.join(os.path.dirname(__file__), 'test_files', 'mask_grid.dat'),
+ sep=' ').reshape((800, 800))
+ self.assertTrue(numpy.array_equal(
+ resampled_mask, expected), msg='Failed to resample masked array')
+
+ def test_nearest_neighbour(self):
+ data = numpy.fromfunction(lambda y, x: y * x * 10 ** -6, (3712, 3712))
+ msg_con = image.ImageContainerNearest(
+ data, self.msg_area, 50000, segments=1)
+ area_con = msg_con.resample(self.area_def)
+ res = area_con.image_data
+ cross_sum = res.sum()
+ expected = 399936.783062
+ self.assertAlmostEqual(cross_sum, expected,
+ msg='ImageContainer resampling nearest neighbour failed')
+
+ def test_nearest_resize(self):
+ data = numpy.fromfunction(lambda y, x: y * x * 10 ** -6, (3712, 3712))
+ msg_con = image.ImageContainerNearest(
+ data, self.msg_area, 50000, segments=1)
+ area_con = msg_con.resample(self.msg_area_resize)
+ res = area_con.image_data
+ cross_sum = res.sum()
+ expected = 2212023.0175830
+ self.assertAlmostEqual(cross_sum, expected,
+ msg='ImageContainer resampling nearest neighbour failed')
+
+ def test_nearest_neighbour_multi(self):
+ data1 = numpy.fromfunction(lambda y, x: y * x * 10 ** -6, (3712, 3712))
+ data2 = numpy.fromfunction(
+ lambda y, x: y * x * 10 ** -6, (3712, 3712)) * 2
+ data = numpy.dstack((data1, data2))
+ msg_con = image.ImageContainerNearest(
+ data, self.msg_area, 50000, segments=1)
+ area_con = msg_con.resample(self.area_def)
+ res = area_con.image_data
+ cross_sum1 = res[:, :, 0].sum()
+ expected1 = 399936.783062
+ self.assertAlmostEqual(cross_sum1, expected1,
+ msg='ImageContainer resampling nearest neighbour multi failed')
+ cross_sum2 = res[:, :, 1].sum()
+ expected2 = 399936.783062 * 2
+ self.assertAlmostEqual(cross_sum2, expected2,
+ msg='ImageContainer resampling nearest neighbour multi failed')
+
+ def test_nearest_neighbour_multi_preproc(self):
+ data1 = numpy.fromfunction(lambda y, x: y * x * 10 ** -6, (3712, 3712))
+ data2 = numpy.fromfunction(
+ lambda y, x: y * x * 10 ** -6, (3712, 3712)) * 2
+ data = numpy.dstack((data1, data2))
+ msg_con = image.ImageContainer(data, self.msg_area)
+ #area_con = msg_con.resample_area_nearest_neighbour(self.area_def, 50000)
+ row_indices, col_indices = \
+ utils.generate_nearest_neighbour_linesample_arrays(self.msg_area,
+ self.area_def,
+ 50000)
+ res = msg_con.get_array_from_linesample(row_indices, col_indices)
+ cross_sum1 = res[:, :, 0].sum()
+ expected1 = 399936.783062
+ self.assertAlmostEqual(cross_sum1, expected1,
+ msg='ImageContainer resampling nearest neighbour multi preproc failed')
+ cross_sum2 = res[:, :, 1].sum()
+ expected2 = 399936.783062 * 2
+ self.assertAlmostEqual(cross_sum2, expected2,
+ msg='ImageContainer resampling nearest neighbour multi preproc failed')
+
+ def test_nearest_swath(self):
+ data = numpy.fromfunction(lambda y, x: y * x, (50, 10))
+ lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ swath_con = image.ImageContainerNearest(
+ data, swath_def, 50000, segments=1)
+ area_con = swath_con.resample(self.area_def)
+ res = area_con.image_data
+ cross_sum = res.sum()
+ expected = 15874591.0
+ self.assertEqual(cross_sum, expected,
+ msg='ImageContainer swath resampling nearest failed')
+
+ def test_nearest_swath_segments(self):
+ data = numpy.fromfunction(lambda y, x: y * x, (50, 10))
+ data = numpy.dstack(3 * (data,))
+ lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ swath_con = image.ImageContainerNearest(
+ data, swath_def, 50000, segments=2)
+ area_con = swath_con.resample(self.area_def)
+ res = area_con.image_data
+ cross_sum = res.sum()
+ expected = 3 * 15874591.0
+ self.assertEqual(cross_sum, expected,
+ msg='ImageContainer swath segments resampling nearest failed')
+
+
+def suite():
+ """The test suite.
+ """
+ loader = unittest.TestLoader()
+ mysuite = unittest.TestSuite()
+ mysuite.addTest(loader.loadTestsFromTestCase(Test))
+
+ return mysuite
diff --git a/pyresample/test/test_kd_tree.py b/pyresample/test/test_kd_tree.py
new file mode 100644
index 0000000..6859bf8
--- /dev/null
+++ b/pyresample/test/test_kd_tree.py
@@ -0,0 +1,907 @@
+from __future__ import with_statement
+
+import os
+import sys
+import unittest
+
+import warnings
+if sys.version_info < (2, 6):
+ warnings.simplefilter("ignore")
+else:
+ warnings.simplefilter("always")
+
+import numpy
+
+from pyresample import kd_tree, utils, geometry, data_reduce
+
+
+def mp(f):
+ f.mp = True
+ return f
+
+
+def quick(f):
+ f.quick = True
+ return f
+
+
+def tmp(f):
+ f.tmp = True
+ return f
+
+
+class Test(unittest.TestCase):
+
+ area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
+ {'a': '6378144.0',
+ 'b': '6356759.0',
+ 'lat_0': '50.00',
+ 'lat_ts': '50.00',
+ 'lon_0': '8.00',
+ 'proj': 'stere'},
+ 800,
+ 800,
+ [-1370912.72,
+ -909968.64000000001,
+ 1029087.28,
+ 1490031.3600000001])
+
+ tdata = numpy.array([1, 2, 3])
+ tlons = numpy.array([11.280789, 12.649354, 12.080402])
+ tlats = numpy.array([56.011037, 55.629675, 55.641535])
+ tswath = geometry.SwathDefinition(lons=tlons, lats=tlats)
+ tgrid = geometry.CoordinateDefinition(lons=numpy.array([12.562036]),
+ lats=numpy.array([55.715613]))
+
+ def test_nearest_base(self):
+ res = kd_tree.resample_nearest(self.tswath,
+ self.tdata.ravel(), self.tgrid,
+ 100000, reduce_data=False, segments=1)
+ self.assertTrue(res[0] == 2, 'Failed to calculate nearest neighbour')
+
+ @tmp
+ def test_gauss_base(self):
+ if (sys.version_info < (2, 6) or
+ (sys.version_info >= (3, 0) and sys.version_info < (3, 4))):
+
+ res = kd_tree.resample_gauss(self.tswath,
+ self.tdata.ravel(), self.tgrid,
+ 50000, 25000, reduce_data=False, segments=1)
+ else:
+ with warnings.catch_warnings(record=True) as w:
+ res = kd_tree.resample_gauss(self.tswath,
+ self.tdata.ravel(), self.tgrid,
+ 50000, 25000, reduce_data=False, segments=1)
+ self.assertFalse(
+ len(w) != 1, 'Failed to create neighbour warning')
+ self.assertFalse(('Searching' not in str(
+ w[0].message)), 'Failed to create correct neighbour warning')
+ self.assertAlmostEqual(res[0], 2.2020729, 5,
+ 'Failed to calculate gaussian weighting')
+
+ def test_custom_base(self):
+ def wf(dist):
+ return 1 - dist / 100000.0
+
+ if (sys.version_info < (2, 6) or
+ (sys.version_info >= (3, 0) and sys.version_info < (3, 4))):
+ res = kd_tree.resample_custom(self.tswath,
+ self.tdata.ravel(), self.tgrid,
+ 50000, wf, reduce_data=False, segments=1)
+ else:
+ with warnings.catch_warnings(record=True) as w:
+ res = kd_tree.resample_custom(self.tswath,
+ self.tdata.ravel(), self.tgrid,
+ 50000, wf, reduce_data=False, segments=1)
+ self.assertFalse(
+ len(w) != 1, 'Failed to create neighbour warning')
+ self.assertFalse(('Searching' not in str(
+ w[0].message)), 'Failed to create correct neighbour warning')
+ self.assertAlmostEqual(res[0], 2.4356757, 5,
+ 'Failed to calculate custom weighting')
+
+ @tmp
+ def test_gauss_uncert(self):
+ sigma = utils.fwhm2sigma(41627.730557884883)
+ if (sys.version_info < (2, 6) or
+ (sys.version_info >= (3, 0) and sys.version_info < (3, 4))):
+ res, stddev, count = kd_tree.resample_gauss(self.tswath, self.tdata,
+ self.tgrid, 100000, sigma,
+ with_uncert=True)
+ else:
+ with warnings.catch_warnings(record=True) as w:
+ res, stddev, count = kd_tree.resample_gauss(self.tswath, self.tdata,
+ self.tgrid, 100000, sigma,
+ with_uncert=True)
+ self.assertFalse(
+ len(w) != 1, 'Failed to create neighbour warning')
+ self.assertFalse(('Searching' not in str(
+ w[0].message)), 'Failed to create correct neighbour warning')
+
+ expected_res = 2.20206560694
+ expected_stddev = 0.707115076173
+ expected_count = 3
+ self.assertAlmostEqual(res[0], expected_res, 5,
+ 'Failed to calculate gaussian weighting with uncertainty')
+ self.assertAlmostEqual(stddev[0], expected_stddev, 5,
+ 'Failed to calculate uncertainty for gaussian weighting')
+ self.assertEqual(
+ count[0], expected_count, 'Wrong data point count for gaussian weighting with uncertainty')
+
+ @tmp
+ def test_custom_uncert(self):
+ def wf(dist):
+ return 1 - dist / 100000.0
+
+ if (sys.version_info < (2, 6) or
+ (sys.version_info >= (3, 0) and sys.version_info < (3, 4))):
+ res, stddev, counts = kd_tree.resample_custom(self.tswath,
+ self.tdata, self.tgrid,
+ 100000, wf, with_uncert=True)
+ else:
+ with warnings.catch_warnings(record=True) as w:
+ res, stddev, counts = kd_tree.resample_custom(self.tswath,
+ self.tdata, self.tgrid,
+ 100000, wf, with_uncert=True)
+ self.assertFalse(
+ len(w) != 1, 'Failed to create neighbour warning')
+ self.assertFalse(('Searching' not in str(
+ w[0].message)), 'Failed to create correct neighbour warning')
+
+ self.assertAlmostEqual(res[0], 2.32193149, 5,
+ 'Failed to calculate custom weighting with uncertainty')
+ self.assertAlmostEqual(stddev[0], 0.81817972, 5,
+ 'Failed to calculate custom for gaussian weighting')
+ self.assertEqual(
+ counts[0], 3, 'Wrong data point count for custom weighting with uncertainty')
+
+ def test_nearest(self):
+ data = numpy.fromfunction(lambda y, x: y * x, (50, 10))
+ lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ res = kd_tree.resample_nearest(swath_def, data.ravel(),
+ self.area_def, 50000, segments=1)
+ cross_sum = res.sum()
+ expected = 15874591.0
+ self.assertEqual(cross_sum, expected,
+ msg='Swath resampling nearest failed')
+
+ def test_nearest_1d(self):
+ data = numpy.fromfunction(lambda x, y: x * y, (800, 800))
+ lons = numpy.fromfunction(lambda x: 3 + x / 100., (500,))
+ lats = numpy.fromfunction(lambda x: 75 - x / 10., (500,))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ res = kd_tree.resample_nearest(self.area_def, data.ravel(),
+ swath_def, 50000, segments=1)
+ cross_sum = res.sum()
+ expected = 35821299.0
+ self.assertEqual(res.shape, (500,),
+ msg='Swath resampling nearest 1d failed')
+ self.assertEqual(cross_sum, expected,
+ msg='Swath resampling nearest 1d failed')
+
+ def test_nearest_empty(self):
+ data = numpy.fromfunction(lambda y, x: y * x, (50, 10))
+ lons = numpy.fromfunction(lambda y, x: 165 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ res = kd_tree.resample_nearest(swath_def, data.ravel(),
+ self.area_def, 50000, segments=1)
+ cross_sum = res.sum()
+ expected = 0
+ self.assertEqual(cross_sum, expected,
+ msg='Swath resampling nearest empty failed')
+
+ def test_nearest_empty_multi(self):
+ data = numpy.fromfunction(lambda y, x: y * x, (50, 10))
+ lons = numpy.fromfunction(lambda y, x: 165 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ data_multi = numpy.column_stack((data.ravel(), data.ravel(),
+ data.ravel()))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ res = kd_tree.resample_nearest(swath_def, data_multi,
+ self.area_def, 50000, segments=1)
+ self.assertEqual(res.shape, (800, 800, 3),
+ msg='Swath resampling nearest empty multi failed')
+
+ def test_nearest_empty_multi_masked(self):
+ data = numpy.fromfunction(lambda y, x: y * x, (50, 10))
+ lons = numpy.fromfunction(lambda y, x: 165 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ data_multi = numpy.column_stack((data.ravel(), data.ravel(),
+ data.ravel()))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ res = kd_tree.resample_nearest(swath_def, data_multi,
+ self.area_def, 50000, segments=1,
+ fill_value=None)
+ self.assertEqual(res.shape, (800, 800, 3),
+ msg='Swath resampling nearest empty multi masked failed')
+
+ def test_nearest_empty_masked(self):
+ data = numpy.fromfunction(lambda y, x: y * x, (50, 10))
+ lons = numpy.fromfunction(lambda y, x: 165 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ res = kd_tree.resample_nearest(swath_def, data.ravel(),
+ self.area_def, 50000, segments=1,
+ fill_value=None)
+ cross_sum = res.mask.sum()
+ expected = res.size
+ self.assertTrue(cross_sum == expected,
+ msg='Swath resampling nearest empty masked failed')
+
+ def test_nearest_segments(self):
+ data = numpy.fromfunction(lambda y, x: y * x, (50, 10))
+ lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ res = kd_tree.resample_nearest(swath_def, data.ravel(),
+ self.area_def, 50000, segments=2)
+ cross_sum = res.sum()
+ expected = 15874591.0
+ self.assertEqual(cross_sum, expected,
+ msg='Swath resampling nearest segments failed')
+
+ def test_nearest_remap(self):
+ data = numpy.fromfunction(lambda y, x: y * x, (50, 10))
+ lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ res = kd_tree.resample_nearest(swath_def, data.ravel(),
+ self.area_def, 50000, segments=1)
+ remap = kd_tree.resample_nearest(self.area_def, res.ravel(),
+ swath_def, 5000, segments=1)
+ cross_sum = remap.sum()
+ expected = 22275.0
+ self.assertEqual(cross_sum, expected,
+ msg='Grid remapping nearest failed')
+
+ def test_nearest_mp(self):
+ data = numpy.fromfunction(lambda y, x: y * x, (50, 10))
+ lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ res = kd_tree.resample_nearest(swath_def, data.ravel(),
+ self.area_def, 50000, nprocs=2, segments=1)
+ cross_sum = res.sum()
+ expected = 15874591.0
+ self.assertEqual(cross_sum, expected,
+ msg='Swath resampling mp nearest failed')
+
+ def test_nearest_multi(self):
+ data = numpy.fromfunction(lambda y, x: y * x, (50, 10))
+ lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ data_multi = numpy.column_stack((data.ravel(), data.ravel(),
+ data.ravel()))
+ res = kd_tree.resample_nearest(swath_def, data_multi,
+ self.area_def, 50000, segments=1)
+ cross_sum = res.sum()
+ expected = 3 * 15874591.0
+ self.assertEqual(cross_sum, expected,
+ msg='Swath multi channel resampling nearest failed')
+
+ def test_nearest_multi_unraveled(self):
+ data = numpy.fromfunction(lambda y, x: y * x, (50, 10))
+ lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ data_multi = numpy.dstack((data, data, data))
+ res = kd_tree.resample_nearest(swath_def, data_multi,
+ self.area_def, 50000, segments=1)
+ cross_sum = res.sum()
+ expected = 3 * 15874591.0
+ self.assertEqual(cross_sum, expected,
+ msg='Swath multi channel resampling nearest failed')
+
+ def test_gauss_sparse(self):
+ data = numpy.fromfunction(lambda y, x: y * x, (50, 10))
+ lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ res = kd_tree.resample_gauss(swath_def, data.ravel(),
+ self.area_def, 50000, 25000, fill_value=-1, segments=1)
+ cross_sum = res.sum()
+ expected = 15387753.9852
+ self.assertAlmostEqual(cross_sum, expected, places=3,
+ msg='Swath gauss sparse nearest failed')
+
+ def test_gauss(self):
+ data = numpy.fromfunction(lambda y, x: (y + x) * 10 ** -5, (5000, 100))
+ lons = numpy.fromfunction(
+ lambda y, x: 3 + (10.0 / 100) * x, (5000, 100))
+ lats = numpy.fromfunction(
+ lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ if (sys.version_info < (2, 6) or
+ (sys.version_info >= (3, 0) and sys.version_info < (3, 4))):
+ res = kd_tree.resample_gauss(swath_def, data.ravel(),
+ self.area_def, 50000, 25000, segments=1)
+ else:
+ with warnings.catch_warnings(record=True) as w:
+ res = kd_tree.resample_gauss(swath_def, data.ravel(),
+ self.area_def, 50000, 25000, segments=1)
+ self.assertFalse(
+ len(w) != 1, 'Failed to create neighbour radius warning')
+ self.assertFalse(('Possible more' not in str(
+ w[0].message)), 'Failed to create correct neighbour radius warning')
+ cross_sum = res.sum()
+ expected = 4872.81050892
+ self.assertAlmostEqual(cross_sum, expected,
+ msg='Swath resampling gauss failed')
+
+ def test_gauss_fwhm(self):
+ data = numpy.fromfunction(lambda y, x: (y + x) * 10 ** -5, (5000, 100))
+ lons = numpy.fromfunction(
+ lambda y, x: 3 + (10.0 / 100) * x, (5000, 100))
+ lats = numpy.fromfunction(
+ lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ if (sys.version_info < (2, 6) or
+ (sys.version_info >= (3, 0) and sys.version_info < (3, 4))):
+ res = kd_tree.resample_gauss(swath_def, data.ravel(),
+ self.area_def, 50000, utils.fwhm2sigma(41627.730557884883), segments=1)
+ else:
+ with warnings.catch_warnings(record=True) as w:
+ res = kd_tree.resample_gauss(swath_def, data.ravel(),
+ self.area_def, 50000, utils.fwhm2sigma(41627.730557884883), segments=1)
+ self.assertFalse(
+ len(w) != 1, 'Failed to create neighbour radius warning')
+ self.assertFalse(('Possible more' not in str(
+ w[0].message)), 'Failed to create correct neighbour radius warning')
+ cross_sum = res.sum()
+ expected = 4872.81050892
+ self.assertAlmostEqual(cross_sum, expected,
+ msg='Swath resampling gauss failed')
+
+ def test_gauss_multi(self):
+ data = numpy.fromfunction(lambda y, x: (y + x) * 10 ** -6, (5000, 100))
+ lons = numpy.fromfunction(
+ lambda y, x: 3 + (10.0 / 100) * x, (5000, 100))
+ lats = numpy.fromfunction(
+ lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ data_multi = numpy.column_stack((data.ravel(), data.ravel(),
+ data.ravel()))
+ if (sys.version_info < (2, 6) or
+ (sys.version_info >= (3, 0) and sys.version_info < (3, 4))):
+ res = kd_tree.resample_gauss(swath_def, data_multi,
+ self.area_def, 50000, [25000, 15000, 10000], segments=1)
+ else:
+ with warnings.catch_warnings(record=True) as w:
+ res = kd_tree.resample_gauss(swath_def, data_multi,
+ self.area_def, 50000, [25000, 15000, 10000], segments=1)
+ self.assertFalse(
+ len(w) != 1, 'Failed to create neighbour radius warning')
+ self.assertFalse(('Possible more' not in str(
+ w[0].message)), 'Failed to create correct neighbour radius warning')
+ cross_sum = res.sum()
+ expected = 1461.84313918
+ self.assertAlmostEqual(cross_sum, expected,
+ msg='Swath multi channel resampling gauss failed')
+
+ @tmp
+ def test_gauss_multi_uncert(self):
+ data = numpy.fromfunction(lambda y, x: (y + x) * 10 ** -6, (5000, 100))
+ lons = numpy.fromfunction(
+ lambda y, x: 3 + (10.0 / 100) * x, (5000, 100))
+ lats = numpy.fromfunction(
+ lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ data_multi = numpy.column_stack((data.ravel(), data.ravel(),
+ data.ravel()))
+ if (sys.version_info < (2, 6) or
+ (sys.version_info >= (3, 0) and sys.version_info < (3, 4))):
+ res, stddev, counts = kd_tree.resample_gauss(swath_def, data_multi,
+ self.area_def, 50000, [
+ 25000, 15000, 10000],
+ segments=1, with_uncert=True)
+ else:
+ with warnings.catch_warnings(record=True) as w:
+ res, stddev, counts = kd_tree.resample_gauss(swath_def, data_multi,
+ self.area_def, 50000, [
+ 25000, 15000, 10000],
+ segments=1, with_uncert=True)
+ self.assertFalse(
+ len(w) != 1, 'Failed to create neighbour radius warning')
+ self.assertFalse(('Possible more' not in str(
+ w[0].message)), 'Failed to create correct neighbour radius warning')
+ cross_sum = res.sum()
+ cross_sum_stddev = stddev.sum()
+ cross_sum_counts = counts.sum()
+ expected = 1461.84313918
+ expected_stddev = 0.446204424799
+ expected_counts = 4934802.0
+ self.assertTrue(res.shape == stddev.shape and stddev.shape ==
+ counts.shape and counts.shape == (800, 800, 3))
+ self.assertAlmostEqual(cross_sum, expected,
+ msg='Swath multi channel resampling gauss failed on data')
+ self.assertAlmostEqual(cross_sum_stddev, expected_stddev,
+ msg='Swath multi channel resampling gauss failed on stddev')
+ self.assertAlmostEqual(cross_sum_counts, expected_counts,
+ msg='Swath multi channel resampling gauss failed on counts')
+
+ def test_gauss_multi_mp(self):
+ data = numpy.fromfunction(lambda y, x: (y + x) * 10 ** -6, (5000, 100))
+ lons = numpy.fromfunction(
+ lambda y, x: 3 + (10.0 / 100) * x, (5000, 100))
+ lats = numpy.fromfunction(
+ lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ data_multi = numpy.column_stack((data.ravel(), data.ravel(),
+ data.ravel()))
+ if (sys.version_info < (2, 6) or
+ (sys.version_info >= (3, 0) and sys.version_info < (3, 4))):
+ res = kd_tree.resample_gauss(swath_def, data_multi,
+ self.area_def, 50000, [
+ 25000, 15000, 10000],
+ nprocs=2, segments=1)
+ else:
+ with warnings.catch_warnings(record=True) as w:
+ res = kd_tree.resample_gauss(swath_def, data_multi,
+ self.area_def, 50000, [
+ 25000, 15000, 10000],
+ nprocs=2, segments=1)
+ self.assertFalse(
+ len(w) != 1, 'Failed to create neighbour radius warning')
+ self.assertFalse(('Possible more' not in str(
+ w[0].message)), 'Failed to create correct neighbour radius warning')
+ cross_sum = res.sum()
+ expected = 1461.84313918
+ self.assertAlmostEqual(cross_sum, expected,
+ msg='Swath multi channel resampling gauss failed')
+
+ def test_gauss_multi_mp_segments(self):
+ data = numpy.fromfunction(lambda y, x: (y + x) * 10 ** -6, (5000, 100))
+ lons = numpy.fromfunction(
+ lambda y, x: 3 + (10.0 / 100) * x, (5000, 100))
+ lats = numpy.fromfunction(
+ lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ data_multi = numpy.column_stack((data.ravel(), data.ravel(),
+ data.ravel()))
+ if (sys.version_info < (2, 6) or
+ (sys.version_info >= (3, 0) and sys.version_info < (3, 4))):
+ res = kd_tree.resample_gauss(swath_def, data_multi,
+ self.area_def, 50000, [
+ 25000, 15000, 10000],
+ nprocs=2, segments=1)
+ else:
+ with warnings.catch_warnings(record=True) as w:
+ res = kd_tree.resample_gauss(swath_def, data_multi,
+ self.area_def, 50000, [
+ 25000, 15000, 10000],
+ nprocs=2, segments=1)
+ self.assertFalse(
+ len(w) != 1, 'Failed to create neighbour radius warning')
+ self.assertFalse(('Possible more' not in str(
+ w[0].message)), 'Failed to create correct neighbour radius warning')
+ cross_sum = res.sum()
+ expected = 1461.84313918
+ self.assertAlmostEqual(cross_sum, expected,
+ msg='Swath multi channel segments resampling gauss failed')
+
+ def test_gauss_multi_mp_segments_empty(self):
+ data = numpy.fromfunction(lambda y, x: (y + x) * 10 ** -6, (5000, 100))
+ lons = numpy.fromfunction(
+ lambda y, x: 165 + (10.0 / 100) * x, (5000, 100))
+ lats = numpy.fromfunction(
+ lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ data_multi = numpy.column_stack((data.ravel(), data.ravel(),
+ data.ravel()))
+ res = kd_tree.resample_gauss(swath_def, data_multi,
+ self.area_def, 50000, [
+ 25000, 15000, 10000],
+ nprocs=2, segments=1)
+ cross_sum = res.sum()
+ self.assertTrue(cross_sum == 0,
+ msg=('Swath multi channel segments empty '
+ 'resampling gauss failed'))
+
+ def test_custom(self):
+ def wf(dist):
+ return 1 - dist / 100000.0
+
+ data = numpy.fromfunction(lambda y, x: (y + x) * 10 ** -5, (5000, 100))
+ lons = numpy.fromfunction(
+ lambda y, x: 3 + (10.0 / 100) * x, (5000, 100))
+ lats = numpy.fromfunction(
+ lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ if (sys.version_info < (2, 6) or
+ (sys.version_info >= (3, 0) and sys.version_info < (3, 4))):
+ res = kd_tree.resample_custom(swath_def, data.ravel(),
+ self.area_def, 50000, wf, segments=1)
+ else:
+ with warnings.catch_warnings(record=True) as w:
+ res = kd_tree.resample_custom(swath_def, data.ravel(),
+ self.area_def, 50000, wf, segments=1)
+ self.assertFalse(
+ len(w) != 1, 'Failed to create neighbour radius warning')
+ self.assertFalse(('Possible more' not in str(
+ w[0].message)), 'Failed to create correct neighbour radius warning')
+ cross_sum = res.sum()
+ expected = 4872.81050729
+ self.assertAlmostEqual(cross_sum, expected,
+ msg='Swath custom resampling failed')
+
+ def test_custom_multi(self):
+ def wf1(dist):
+ return 1 - dist / 100000.0
+
+ def wf2(dist):
+ return 1
+
+ def wf3(dist):
+ return numpy.cos(dist) ** 2
+
+ data = numpy.fromfunction(lambda y, x: (y + x) * 10 ** -6, (5000, 100))
+ lons = numpy.fromfunction(
+ lambda y, x: 3 + (10.0 / 100) * x, (5000, 100))
+ lats = numpy.fromfunction(
+ lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ data_multi = numpy.column_stack((data.ravel(), data.ravel(),
+ data.ravel()))
+ if (sys.version_info < (2, 6) or
+ (sys.version_info >= (3, 0) and sys.version_info < (3, 4))):
+ res = kd_tree.resample_custom(swath_def, data_multi,
+ self.area_def, 50000, [wf1, wf2, wf3], segments=1)
+ else:
+ with warnings.catch_warnings(record=True) as w:
+ res = kd_tree.resample_custom(swath_def, data_multi,
+ self.area_def, 50000, [wf1, wf2, wf3], segments=1)
+ self.assertFalse(
+ len(w) != 1, 'Failed to create neighbour radius warning')
+ self.assertFalse(('Possible more' not in str(
+ w[0].message)), 'Failed to create correct neighbour radius warning')
+ cross_sum = res.sum()
+ expected = 1461.842980746
+ self.assertAlmostEqual(cross_sum, expected,
+ msg='Swath multi channel custom resampling failed')
+
+ def test_reduce(self):
+ data = numpy.fromfunction(lambda y, x: (y + x), (1000, 1000))
+ lons = numpy.fromfunction(
+ lambda y, x: -180 + (360.0 / 1000) * x, (1000, 1000))
+ lats = numpy.fromfunction(
+ lambda y, x: -90 + (180.0 / 1000) * y, (1000, 1000))
+ grid_lons, grid_lats = self.area_def.get_lonlats()
+ lons, lats, data = data_reduce.swath_from_lonlat_grid(grid_lons, grid_lats,
+ lons, lats, data,
+ 7000)
+ cross_sum = data.sum()
+ expected = 20514375.0
+ self.assertAlmostEqual(cross_sum, expected, msg='Reduce data failed')
+
+ def test_reduce_boundary(self):
+ data = numpy.fromfunction(lambda y, x: (y + x), (1000, 1000))
+ lons = numpy.fromfunction(
+ lambda y, x: -180 + (360.0 / 1000) * x, (1000, 1000))
+ lats = numpy.fromfunction(
+ lambda y, x: -90 + (180.0 / 1000) * y, (1000, 1000))
+ boundary_lonlats = self.area_def.get_boundary_lonlats()
+ lons, lats, data = data_reduce.swath_from_lonlat_boundaries(boundary_lonlats[0],
+ boundary_lonlats[
+ 1],
+ lons, lats, data,
+ 7000)
+ cross_sum = data.sum()
+ expected = 20514375.0
+ self.assertAlmostEqual(cross_sum, expected, msg='Reduce data failed')
+
+ def test_cartesian_reduce(self):
+ data = numpy.fromfunction(lambda y, x: (y + x), (1000, 1000))
+ lons = numpy.fromfunction(
+ lambda y, x: -180 + (360.0 / 1000) * x, (1000, 1000))
+ lats = numpy.fromfunction(
+ lambda y, x: -90 + (180.0 / 1000) * y, (1000, 1000))
+ #grid = utils.generate_cartesian_grid(self.area_def)
+ grid = self.area_def.get_cartesian_coords()
+ lons, lats, data = data_reduce.swath_from_cartesian_grid(grid, lons, lats, data,
+ 7000)
+ cross_sum = data.sum()
+ expected = 20514375.0
+ self.assertAlmostEqual(
+ cross_sum, expected, msg='Cartesian reduce data failed')
+
+ def test_area_con_reduce(self):
+ data = numpy.fromfunction(lambda y, x: (y + x), (1000, 1000))
+ lons = numpy.fromfunction(
+ lambda y, x: -180 + (360.0 / 1000) * x, (1000, 1000))
+ lats = numpy.fromfunction(
+ lambda y, x: -90 + (180.0 / 1000) * y, (1000, 1000))
+ grid_lons, grid_lats = self.area_def.get_lonlats()
+ valid_index = data_reduce.get_valid_index_from_lonlat_grid(grid_lons, grid_lats,
+ lons, lats, 7000)
+ data = data[valid_index]
+ cross_sum = data.sum()
+ expected = 20514375.0
+ self.assertAlmostEqual(cross_sum, expected, msg='Reduce data failed')
+
+ def test_area_con_cartesian_reduce(self):
+ data = numpy.fromfunction(lambda y, x: (y + x), (1000, 1000))
+ lons = numpy.fromfunction(
+ lambda y, x: -180 + (360.0 / 1000) * x, (1000, 1000))
+ lats = numpy.fromfunction(
+ lambda y, x: -90 + (180.0 / 1000) * y, (1000, 1000))
+ cart_grid = self.area_def.get_cartesian_coords()
+ valid_index = data_reduce.get_valid_index_from_cartesian_grid(cart_grid,
+ lons, lats, 7000)
+ data = data[valid_index]
+ cross_sum = data.sum()
+ expected = 20514375.0
+ self.assertAlmostEqual(
+ cross_sum, expected, msg='Cartesian reduce data failed')
+
+ def test_masked_nearest(self):
+ data = numpy.ones((50, 10))
+ data[:, 5:] = 2
+ lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ mask = numpy.ones((50, 10))
+ mask[:, :5] = 0
+ masked_data = numpy.ma.array(data, mask=mask)
+ res = kd_tree.resample_nearest(swath_def, masked_data.ravel(),
+ self.area_def, 50000, segments=1)
+ expected_mask = numpy.fromfile(os.path.join(os.path.dirname(__file__),
+ 'test_files',
+ 'mask_test_nearest_mask.dat'),
+ sep=' ').reshape((800, 800))
+ expected_data = numpy.fromfile(os.path.join(os.path.dirname(__file__),
+ 'test_files',
+ 'mask_test_nearest_data.dat'),
+ sep=' ').reshape((800, 800))
+ self.assertTrue(numpy.array_equal(expected_mask, res.mask),
+ msg='Resampling of swath mask failed')
+ self.assertTrue(numpy.array_equal(expected_data, res.data),
+ msg='Resampling of swath masked data failed')
+
+ def test_masked_nearest_1d(self):
+ data = numpy.ones((800, 800))
+ data[:400, :] = 2
+ lons = numpy.fromfunction(lambda x: 3 + x / 100., (500,))
+ lats = numpy.fromfunction(lambda x: 75 - x / 10., (500,))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ mask = numpy.ones((800, 800))
+ mask[400:, :] = 0
+ masked_data = numpy.ma.array(data, mask=mask)
+ res = kd_tree.resample_nearest(self.area_def, masked_data.ravel(),
+ swath_def, 50000, segments=1)
+ self.assertEqual(res.mask.sum(), 108,
+ msg='Swath resampling masked nearest 1d failed')
+
+ def test_masked_gauss(self):
+ data = numpy.ones((50, 10))
+ data[:, 5:] = 2
+ lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ mask = numpy.ones((50, 10))
+ mask[:, :5] = 0
+ masked_data = numpy.ma.array(data, mask=mask)
+ res = kd_tree.resample_gauss(swath_def, masked_data.ravel(),
+ self.area_def, 50000, 25000, segments=1)
+ expected_mask = numpy.fromfile(os.path.join(os.path.dirname(__file__),
+ 'test_files',
+ 'mask_test_mask.dat'),
+ sep=' ').reshape((800, 800))
+ expected_data = numpy.fromfile(os.path.join(os.path.dirname(__file__),
+ 'test_files',
+ 'mask_test_data.dat'),
+ sep=' ').reshape((800, 800))
+ expected = expected_data.sum()
+ cross_sum = res.data.sum()
+
+ self.assertTrue(numpy.array_equal(expected_mask, res.mask),
+ msg='Gauss resampling of swath mask failed')
+ self.assertAlmostEqual(cross_sum, expected, places=3,
+ msg='Gauss resampling of swath masked data failed')
+
+ def test_masked_fill_float(self):
+ data = numpy.ones((50, 10))
+ lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ res = kd_tree.resample_nearest(swath_def, data.ravel(),
+ self.area_def, 50000, fill_value=None, segments=1)
+ expected_fill_mask = numpy.fromfile(os.path.join(os.path.dirname(__file__),
+ 'test_files',
+ 'mask_test_fill_value.dat'),
+ sep=' ').reshape((800, 800))
+ fill_mask = res.mask
+ self.assertTrue(numpy.array_equal(fill_mask, expected_fill_mask),
+ msg='Failed to create fill mask on float data')
+
+ def test_masked_fill_int(self):
+ data = numpy.ones((50, 10)).astype('int')
+ lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ res = kd_tree.resample_nearest(swath_def, data.ravel(),
+ self.area_def, 50000, fill_value=None, segments=1)
+ expected_fill_mask = numpy.fromfile(os.path.join(os.path.dirname(__file__),
+ 'test_files',
+ 'mask_test_fill_value.dat'),
+ sep=' ').reshape((800, 800))
+ fill_mask = res.mask
+ self.assertTrue(numpy.array_equal(fill_mask, expected_fill_mask),
+ msg='Failed to create fill mask on integer data')
+
+ def test_masked_full(self):
+ data = numpy.ones((50, 10))
+ data[:, 5:] = 2
+ mask = numpy.ones((50, 10))
+ mask[:, :5] = 0
+ masked_data = numpy.ma.array(data, mask=mask)
+ lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ res = kd_tree.resample_nearest(swath_def,
+ masked_data.ravel(
+ ), self.area_def, 50000,
+ fill_value=None, segments=1)
+ expected_fill_mask = numpy.fromfile(os.path.join(os.path.dirname(__file__),
+ 'test_files',
+ 'mask_test_full_fill.dat'),
+ sep=' ').reshape((800, 800))
+ fill_mask = res.mask
+
+ self.assertTrue(numpy.array_equal(fill_mask, expected_fill_mask),
+ msg='Failed to create fill mask on masked data')
+
+ def test_masked_full_multi(self):
+ data = numpy.ones((50, 10))
+ data[:, 5:] = 2
+ mask1 = numpy.ones((50, 10))
+ mask1[:, :5] = 0
+ mask2 = numpy.ones((50, 10))
+ mask2[:, 5:] = 0
+ mask3 = numpy.ones((50, 10))
+ mask3[:25, :] = 0
+ data_multi = numpy.column_stack(
+ (data.ravel(), data.ravel(), data.ravel()))
+ mask_multi = numpy.column_stack(
+ (mask1.ravel(), mask2.ravel(), mask3.ravel()))
+ masked_data = numpy.ma.array(data_multi, mask=mask_multi)
+ lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ res = kd_tree.resample_nearest(swath_def,
+ masked_data, self.area_def, 50000,
+ fill_value=None, segments=1)
+ expected_fill_mask = numpy.fromfile(os.path.join(os.path.dirname(__file__),
+ 'test_files',
+ 'mask_test_full_fill_multi.dat'),
+ sep=' ').reshape((800, 800, 3))
+ fill_mask = res.mask
+ cross_sum = res.sum()
+ expected = 357140.0
+ self.assertAlmostEqual(cross_sum, expected,
+ msg='Failed to resample masked data')
+ self.assertTrue(numpy.array_equal(fill_mask, expected_fill_mask),
+ msg='Failed to create fill mask on masked data')
+
+ def test_nearest_from_sample(self):
+ data = numpy.fromfunction(lambda y, x: y * x, (50, 10))
+ lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ valid_input_index, valid_output_index, index_array, distance_array = \
+ kd_tree.get_neighbour_info(swath_def,
+ self.area_def,
+ 50000, neighbours=1, segments=1)
+ res = kd_tree.get_sample_from_neighbour_info('nn', (800, 800), data.ravel(),
+ valid_input_index, valid_output_index,
+ index_array)
+ cross_sum = res.sum()
+ expected = 15874591.0
+ self.assertEqual(cross_sum, expected,
+ msg='Swath resampling from neighbour info nearest failed')
+
+ def test_custom_multi_from_sample(self):
+ def wf1(dist):
+ return 1 - dist / 100000.0
+
+ def wf2(dist):
+ return 1
+
+ def wf3(dist):
+ return numpy.cos(dist) ** 2
+
+ data = numpy.fromfunction(lambda y, x: (y + x) * 10 ** -6, (5000, 100))
+ lons = numpy.fromfunction(
+ lambda y, x: 3 + (10.0 / 100) * x, (5000, 100))
+ lats = numpy.fromfunction(
+ lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ data_multi = numpy.column_stack((data.ravel(), data.ravel(),
+ data.ravel()))
+
+ if (sys.version_info < (2, 6) or
+ (sys.version_info >= (3, 0) and sys.version_info < (3, 4))):
+ valid_input_index, valid_output_index, index_array, distance_array = \
+ kd_tree.get_neighbour_info(swath_def,
+ self.area_def,
+ 50000, segments=1)
+ else:
+ with warnings.catch_warnings(record=True) as w:
+ valid_input_index, valid_output_index, index_array, distance_array = \
+ kd_tree.get_neighbour_info(swath_def,
+ self.area_def,
+ 50000, segments=1)
+ self.assertFalse(
+ len(w) != 1, 'Failed to create neighbour radius warning')
+ self.assertFalse(('Possible more' not in str(
+ w[0].message)), 'Failed to create correct neighbour radius warning')
+
+ res = kd_tree.get_sample_from_neighbour_info('custom', (800, 800),
+ data_multi,
+ valid_input_index, valid_output_index,
+ index_array, distance_array,
+ weight_funcs=[wf1, wf2, wf3])
+
+ cross_sum = res.sum()
+
+ expected = 1461.842980746
+ self.assertAlmostEqual(cross_sum, expected,
+ msg='Swath multi channel custom resampling from neighbour info failed 1')
+ res = kd_tree.get_sample_from_neighbour_info('custom', (800, 800),
+ data_multi,
+ valid_input_index, valid_output_index,
+ index_array, distance_array,
+ weight_funcs=[wf1, wf2, wf3])
+
+ # Look for error where input data has been manipulated
+ cross_sum = res.sum()
+ expected = 1461.842980746
+ self.assertAlmostEqual(cross_sum, expected,
+ msg='Swath multi channel custom resampling from neighbour info failed 2')
+
+ def test_masked_multi_from_sample(self):
+ data = numpy.ones((50, 10))
+ data[:, 5:] = 2
+ mask1 = numpy.ones((50, 10))
+ mask1[:, :5] = 0
+ mask2 = numpy.ones((50, 10))
+ mask2[:, 5:] = 0
+ mask3 = numpy.ones((50, 10))
+ mask3[:25, :] = 0
+ data_multi = numpy.column_stack(
+ (data.ravel(), data.ravel(), data.ravel()))
+ mask_multi = numpy.column_stack(
+ (mask1.ravel(), mask2.ravel(), mask3.ravel()))
+ masked_data = numpy.ma.array(data_multi, mask=mask_multi)
+ lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
+ lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
+ swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
+ valid_input_index, valid_output_index, index_array, distance_array = \
+ kd_tree.get_neighbour_info(swath_def,
+ self.area_def,
+ 50000, neighbours=1, segments=1)
+ res = kd_tree.get_sample_from_neighbour_info('nn', (800, 800),
+ masked_data,
+ valid_input_index,
+ valid_output_index, index_array,
+ fill_value=None)
+ expected_fill_mask = numpy.fromfile(os.path.join(os.path.dirname(__file__),
+ 'test_files',
+ 'mask_test_full_fill_multi.dat'),
+ sep=' ').reshape((800, 800, 3))
+ fill_mask = res.mask
+ self.assertTrue(numpy.array_equal(fill_mask, expected_fill_mask),
+ msg='Failed to create fill mask on masked data')
+
+
+def suite():
+ """The test suite.
+ """
+ loader = unittest.TestLoader()
+ mysuite = unittest.TestSuite()
+ mysuite.addTest(loader.loadTestsFromTestCase(Test))
+
+ return mysuite
diff --git a/pyresample/test/test_plot.py b/pyresample/test/test_plot.py
new file mode 100644
index 0000000..f2cd261
--- /dev/null
+++ b/pyresample/test/test_plot.py
@@ -0,0 +1,87 @@
+import unittest
+import os
+
+import numpy as np
+
+from pyresample import plot, geometry, utils, kd_tree
+
+try:
+ import matplotlib
+ matplotlib.use('Agg')
+except ImportError:
+ pass # Postpone fail to individual tests
+
+
+def tmp(f):
+ f.tmp = True
+ return f
+
+
+class Test(unittest.TestCase):
+
+ filename = os.path.abspath(os.path.join(os.path.dirname(__file__),
+ 'test_files', 'ssmis_swath.npz'))
+ data = np.load(filename)['data']
+ lons = data[:, 0].astype(np.float64)
+ lats = data[:, 1].astype(np.float64)
+ tb37v = data[:, 2].astype(np.float64)
+
+ # screen out the fill values
+ fvalue = -10000000000.0
+ valid_fov = (lons != fvalue) * (lats != fvalue) * (tb37v != fvalue)
+ lons = lons[valid_fov]
+ lats = lats[valid_fov]
+ tb37v = tb37v[valid_fov]
+
+ def test_ellps2axis(self):
+ a, b = plot.ellps2axis('WGS84')
+ self.assertAlmostEqual(a, 6378137.0,
+ msg='Failed to get semi-major axis of ellipsis')
+ self.assertAlmostEqual(b, 6356752.3142451793,
+ msg='Failed to get semi-minor axis of ellipsis')
+
+ def test_area_def2basemap(self):
+ area_def = utils.parse_area_file(os.path.join(os.path.dirname(__file__),
+ 'test_files', 'areas.cfg'), 'ease_sh')[0]
+ bmap = plot.area_def2basemap(area_def)
+ self.assertTrue(bmap.rmajor == bmap.rminor and
+ bmap.rmajor == 6371228.0,
+ 'Failed to create Basemap object')
+
+ def test_plate_carreeplot(self):
+ area_def = utils.parse_area_file(os.path.join(os.path.dirname(__file__),
+ 'test_files', 'areas.cfg'), 'pc_world')[0]
+ swath_def = geometry.SwathDefinition(self.lons, self.lats)
+ result = kd_tree.resample_nearest(swath_def, self.tb37v, area_def,
+ radius_of_influence=20000,
+ fill_value=None)
+ plt = plot._get_quicklook(area_def, result, num_meridians=0,
+ num_parallels=0)
+
+ def test_easeplot(self):
+ area_def = utils.parse_area_file(os.path.join(os.path.dirname(__file__),
+ 'test_files', 'areas.cfg'), 'ease_sh')[0]
+ swath_def = geometry.SwathDefinition(self.lons, self.lats)
+ result = kd_tree.resample_nearest(swath_def, self.tb37v, area_def,
+ radius_of_influence=20000,
+ fill_value=None)
+ plt = plot._get_quicklook(area_def, result)
+
+ def test_orthoplot(self):
+ area_def = utils.parse_area_file(os.path.join(os.path.dirname(__file__),
+ 'test_files', 'areas.cfg'), 'ortho')[0]
+ swath_def = geometry.SwathDefinition(self.lons, self.lats)
+ result = kd_tree.resample_nearest(swath_def, self.tb37v, area_def,
+ radius_of_influence=20000,
+ fill_value=None)
+ plt = plot._get_quicklook(area_def, result)
+
+
+def suite():
+ """The test suite.
+ """
+ loader = unittest.TestLoader()
+ mysuite = unittest.TestSuite()
+ mysuite.addTest(loader.loadTestsFromTestCase(Test))
+
+ return mysuite
diff --git a/test/test_spherical_geometry.py b/pyresample/test/test_spherical_geometry.py
similarity index 97%
rename from test/test_spherical_geometry.py
rename to pyresample/test/test_spherical_geometry.py
index 098b16a..169df45 100644
--- a/test/test_spherical_geometry.py
+++ b/pyresample/test/test_spherical_geometry.py
@@ -9,8 +9,10 @@ from pyresample import geometry
class TestOverlap(unittest.TestCase):
+
"""Testing overlapping functions in pyresample.
"""
+
def assert_raises(self, exception, call_able, *args):
"""assertRaises() has changed from py2.6 to 2.7! Here is an attempt to
cover both"""
@@ -27,7 +29,7 @@ class TestOverlap(unittest.TestCase):
lons = np.array([[-11, 11], [-11, 11]])
lats = np.array([[11, 11], [-11, -11]])
area = geometry.SwathDefinition(lons, lats)
-
+
point = Coordinate(0, 0)
self.assertTrue(point in area)
@@ -35,7 +37,6 @@ class TestOverlap(unittest.TestCase):
point = Coordinate(0, 12)
self.assertFalse(point in area)
-
lons = np.array([[-179, 179], [-179, 179]])
lats = np.array([[1, 1], [-1, -1]])
area = geometry.SwathDefinition(lons, lats)
@@ -67,7 +68,7 @@ class TestOverlap(unittest.TestCase):
lons1 = np.array([[0, 90], [-90, 180]])
lats1 = np.array([[89, 89], [89, 89]])
area1 = geometry.SwathDefinition(lons1, lats1)
-
+
lons2 = np.array([[45, 135], [-45, -135]])
lats2 = np.array([[89, 89], [89, 89]])
area2 = geometry.SwathDefinition(lons2, lats2)
@@ -78,14 +79,14 @@ class TestOverlap(unittest.TestCase):
lons1 = np.array([[0, 45], [135, 90]])
lats1 = np.array([[89, 89], [89, 89]])
area1 = geometry.SwathDefinition(lons1, lats1)
-
+
lons2 = np.array([[180, -135], [-45, -90]])
lats2 = np.array([[89, 89], [89, 89]])
area2 = geometry.SwathDefinition(lons2, lats2)
self.assertFalse(area1.overlaps(area2))
self.assertFalse(area2.overlaps(area1))
-
+
lons1 = np.array([[-1, 1], [-1, 1]])
lats1 = np.array([[1, 1], [-1, -1]])
area1 = geometry.SwathDefinition(lons1, lats1)
@@ -96,7 +97,6 @@ class TestOverlap(unittest.TestCase):
self.assertTrue(area1.overlaps(area2))
self.assertTrue(area2.overlaps(area1))
-
lons1 = np.array([[-1, 0], [-1, 0]])
lats1 = np.array([[1, 2], [-1, 0]])
@@ -105,11 +105,10 @@ class TestOverlap(unittest.TestCase):
lons2 = np.array([[1, 2], [1, 2]])
lats2 = np.array([[1, 2], [-1, 0]])
area2 = geometry.SwathDefinition(lons2, lats2)
-
+
self.assertFalse(area1.overlaps(area2))
self.assertFalse(area2.overlaps(area1))
-
def test_overlap_rate(self):
"""Test how much two areas overlap.
"""
@@ -124,23 +123,22 @@ class TestOverlap(unittest.TestCase):
self.assertAlmostEqual(area1.overlap_rate(area2), 0.25, 3)
self.assertAlmostEqual(area2.overlap_rate(area1), 0.25, 3)
-
+
lons1 = np.array([[82.829699999999974, 36.888300000000001],
[98.145499999999984, 2.8773]])
lats1 = np.array([[60.5944, 52.859999999999999],
[80.395899999999997, 66.7547]])
area1 = geometry.SwathDefinition(lons1, lats1)
-
+
lons2 = np.array([[7.8098183315148422, 26.189349044600252],
[7.8098183315148422, 26.189349044600252]])
lats2 = np.array([[62.953206630716465, 62.953206630716465],
[53.301561187195546, 53.301561187195546]])
area2 = geometry.SwathDefinition(lons2, lats2)
-
self.assertAlmostEqual(area1.overlap_rate(area2), 0.07, 2)
self.assertAlmostEqual(area2.overlap_rate(area1), 0.012, 3)
-
+
lons1 = np.array([[82.829699999999974, 36.888300000000001],
[98.145499999999984, 2.8773]])
lats1 = np.array([[60.5944, 52.859999999999999],
@@ -153,13 +151,12 @@ class TestOverlap(unittest.TestCase):
[57.304862819933433, 57.304862819933433]])
area2 = geometry.SwathDefinition(lons2, lats2)
-
self.assertAlmostEqual(area1.overlap_rate(area2), 0.5, 2)
self.assertAlmostEqual(area2.overlap_rate(area1), 0.068, 3)
-
class TestSphereGeometry(unittest.TestCase):
+
"""Testing sphere geometry from this module.
"""
@@ -207,7 +204,6 @@ class TestSphereGeometry(unittest.TestCase):
self.assertAlmostEqual(arc4.angle(arc2), math.pi,
msg="this should be pi")
-
p5_ = Coordinate(base + 1, base + 1)
p6_ = Coordinate(base + 1, base - 1)
p7_ = Coordinate(base - 1, base - 1)
@@ -238,14 +234,12 @@ class TestSphereGeometry(unittest.TestCase):
self.assertAlmostEqual(arc1.angle(arc6), 3 * math.pi / 4, 3,
msg="this should be 3pi/4")
-
c0_ = Coordinate(180, 0)
c1_ = Coordinate(180, 1)
c2_ = Coordinate(-179, 0)
c3_ = Coordinate(-180, -1)
c4_ = Coordinate(179, 0)
-
arc1 = Arc(c0_, c1_)
arc2 = Arc(c0_, c2_)
arc3 = Arc(c0_, c3_)
@@ -300,15 +294,14 @@ class TestSphereGeometry(unittest.TestCase):
self.assertAlmostEqual(arc2.angle(arc1), -math.pi / 2,
msg="this should be -pi/2")
- self.assertAlmostEqual(Arc(c1_, c2_).angle(arc1), math.pi/4, 3,
+ self.assertAlmostEqual(Arc(c1_, c2_).angle(arc1), math.pi / 4, 3,
msg="this should be pi/4")
-
- self.assertAlmostEqual(Arc(c4_, c3_).angle(arc4), -math.pi/4, 3,
- msg="this should be -pi/4")
- self.assertAlmostEqual(Arc(c1_, c4_).angle(arc1), -math.pi/4, 3,
+ self.assertAlmostEqual(Arc(c4_, c3_).angle(arc4), -math.pi / 4, 3,
msg="this should be -pi/4")
+ self.assertAlmostEqual(Arc(c1_, c4_).angle(arc1), -math.pi / 4, 3,
+ msg="this should be -pi/4")
def test_intersects(self):
"""Test if two arcs intersect.
@@ -347,8 +340,6 @@ class TestSphereGeometry(unittest.TestCase):
self.assertTrue(arc35.intersects(arc24))
-
-
p0_ = Coordinate(180, 0)
p1_ = Coordinate(180, 1)
p2_ = Coordinate(-179, 0)
@@ -389,7 +380,7 @@ class TestSphereGeometry(unittest.TestCase):
p1_ = Coordinate(0, 89)
p2_ = Coordinate(90, 89)
p3_ = Coordinate(180, 89)
- p4_ = Coordinate(-90, 89)
+ p4_ = Coordinate(-90, 89)
p5_ = Coordinate(45, 89)
p6_ = Coordinate(135, 89)
@@ -420,8 +411,16 @@ class TestSphereGeometry(unittest.TestCase):
self.assertTrue(arc35.intersects(arc24))
+def suite():
+ """The test suite.
+ """
+ loader = unittest.TestLoader()
+ mysuite = unittest.TestSuite()
+ mysuite.addTest(loader.loadTestsFromTestCase(TestOverlap))
+ mysuite.addTest(loader.loadTestsFromTestCase(TestSphereGeometry))
-if __name__ == '__main__':
- unittest.main()
+ return mysuite
+if __name__ == '__main__':
+ unittest.main()
diff --git a/test/test_swath.py b/pyresample/test/test_swath.py
similarity index 54%
rename from test/test_swath.py
rename to pyresample/test/test_swath.py
index bc19e59..ab0c4c1 100644
--- a/test/test_swath.py
+++ b/pyresample/test/test_swath.py
@@ -15,48 +15,71 @@ def tmp(f):
f.tmp = True
return f
+
class Test(unittest.TestCase):
-
- filename = os.path.abspath(os.path.join(os.path.dirname(__file__),
- 'test_files', 'ssmis_swath.npz'))
+
+ filename = os.path.abspath(os.path.join(os.path.dirname(__file__),
+ 'test_files', 'ssmis_swath.npz'))
data = np.load(filename)['data']
lons = data[:, 0].astype(np.float64)
lats = data[:, 1].astype(np.float64)
tb37v = data[:, 2].astype(np.float64)
-
- @tmp
+
+ # screen out the fill values
+ fvalue = -10000000000.0
+ valid_fov = (lons != fvalue) * (lats != fvalue) * (tb37v != fvalue)
+ lons = lons[valid_fov]
+ lats = lats[valid_fov]
+ tb37v = tb37v[valid_fov]
+
+ @tmp
def test_self_map(self):
swath_def = geometry.SwathDefinition(lons=self.lons, lats=self.lats)
if sys.version_info < (2, 6):
- res = kd_tree.resample_gauss(swath_def, self.tb37v.copy(), swath_def,
+ res = kd_tree.resample_gauss(swath_def, self.tb37v.copy(), swath_def,
radius_of_influence=70000, sigmas=56500)
else:
with warnings.catch_warnings(record=True) as w:
- res = kd_tree.resample_gauss(swath_def, self.tb37v.copy(), swath_def,
+ res = kd_tree.resample_gauss(swath_def, self.tb37v.copy(), swath_def,
radius_of_influence=70000, sigmas=56500)
- self.failIf(len(w) != 1, 'Failed to create neighbour radius warning')
- self.failIf(('Possible more' not in str(w[0].message)), 'Failed to create correct neighbour radius warning')
-
- self.assertAlmostEqual(res.sum() / 100., 668848.082208, 1,
- msg='Failed self mapping swath for 1 channel')
-
+ self.assertFalse(
+ len(w) != 1, 'Failed to create neighbour radius warning')
+ self.assertFalse(('Possible more' not in str(
+ w[0].message)), 'Failed to create correct neighbour radius warning')
+
+ self.assertAlmostEqual(res.sum() / 100., 668848.082208, 1,
+ msg='Failed self mapping swath for 1 channel')
+
def test_self_map_multi(self):
data = np.column_stack((self.tb37v, self.tb37v, self.tb37v))
swath_def = geometry.SwathDefinition(lons=self.lons, lats=self.lats)
- if sys.version_info < (2, 6):
- res = kd_tree.resample_gauss(swath_def, data, swath_def,
+
+ if (sys.version_info < (2, 6) or
+ (sys.version_info >= (3, 0) and sys.version_info < (3, 4))):
+ res = kd_tree.resample_gauss(swath_def, data, swath_def,
radius_of_influence=70000, sigmas=[56500, 56500, 56500])
else:
with warnings.catch_warnings(record=True) as w:
- res = kd_tree.resample_gauss(swath_def, data, swath_def,
+ res = kd_tree.resample_gauss(swath_def, data, swath_def,
radius_of_influence=70000, sigmas=[56500, 56500, 56500])
- self.failIf(len(w) != 1, 'Failed to create neighbour radius warning')
- self.failIf(('Possible more' not in str(w[0].message)), 'Failed to create correct neighbour radius warning')
-
- self.assertAlmostEqual(res[:, 0].sum() / 100., 668848.082208, 1,
- msg='Failed self mapping swath multi for channel 1')
- self.assertAlmostEqual(res[:, 1].sum() / 100., 668848.082208, 1,
- msg='Failed self mapping swath multi for channel 2')
- self.assertAlmostEqual(res[:, 2].sum() / 100., 668848.082208, 1,
- msg='Failed self mapping swath multi for channel 3')
-
+ self.assertFalse(
+ len(w) != 1, 'Failed to create neighbour radius warning')
+ self.assertFalse(('Possible more' not in str(
+ w[0].message)), 'Failed to create correct neighbour radius warning')
+
+ self.assertAlmostEqual(res[:, 0].sum() / 100., 668848.082208, 1,
+ msg='Failed self mapping swath multi for channel 1')
+ self.assertAlmostEqual(res[:, 1].sum() / 100., 668848.082208, 1,
+ msg='Failed self mapping swath multi for channel 2')
+ self.assertAlmostEqual(res[:, 2].sum() / 100., 668848.082208, 1,
+ msg='Failed self mapping swath multi for channel 3')
+
+
+def suite():
+ """The test suite.
+ """
+ loader = unittest.TestLoader()
+ mysuite = unittest.TestSuite()
+ mysuite.addTest(loader.loadTestsFromTestCase(Test))
+
+ return mysuite
diff --git a/test/test_utils.py b/pyresample/test/test_utils.py
similarity index 55%
rename from test/test_utils.py
rename to pyresample/test/test_utils.py
index 40bd699..6274667 100644
--- a/test/test_utils.py
+++ b/pyresample/test/test_utils.py
@@ -3,51 +3,73 @@ import unittest
from pyresample import utils
+import numpy as np
def tmp(f):
f.tmp = True
return f
+
class Test(unittest.TestCase):
def test_area_parser(self):
- ease_nh, ease_sh = utils.parse_area_file(os.path.join(os.path.dirname(__file__),
- 'test_files',
+ ease_nh, ease_sh = utils.parse_area_file(os.path.join(os.path.dirname(__file__),
+ 'test_files',
'areas.cfg'), 'ease_nh', 'ease_sh')
-
- nh_found = (ease_nh.__str__() =="""Area ID: ease_nh
+
+ nh_found = (ease_nh.__str__() == """Area ID: ease_nh
Name: Arctic EASE grid
Projection ID: ease_nh
Projection: {'a': '6371228.0', 'lat_0': '90', 'lon_0': '0', 'proj': 'laea', 'units': 'm'}
Number of columns: 425
Number of rows: 425
Area extent: (-5326849.0625, -5326849.0625, 5326849.0625, 5326849.0625)""")
-
- sh_found = (ease_sh.__str__() =="""Area ID: ease_sh
+
+ sh_found = (ease_sh.__str__() == """Area ID: ease_sh
Name: Antarctic EASE grid
Projection ID: ease_sh
Projection: {'a': '6371228.0', 'lat_0': '-90', 'lon_0': '0', 'proj': 'laea', 'units': 'm'}
Number of columns: 425
Number of rows: 425
Area extent: (-5326849.0625, -5326849.0625, 5326849.0625, 5326849.0625)""")
-
- self.assertTrue(nh_found and sh_found, msg='Failed to parse areas correctly')
-
+
+ self.assertTrue(
+ nh_found and sh_found, msg='Failed to parse areas correctly')
+
def test_load_area(self):
- ease_nh = utils.load_area(os.path.join(os.path.dirname(__file__),
- 'test_files',
- 'areas.cfg'), 'ease_nh')
- nh_found = (ease_nh.__str__() =="""Area ID: ease_nh
+ ease_nh = utils.load_area(os.path.join(os.path.dirname(__file__),
+ 'test_files',
+ 'areas.cfg'), 'ease_nh')
+ nh_found = (ease_nh.__str__() == """Area ID: ease_nh
Name: Arctic EASE grid
Projection ID: ease_nh
Projection: {'a': '6371228.0', 'lat_0': '90', 'lon_0': '0', 'proj': 'laea', 'units': 'm'}
Number of columns: 425
Number of rows: 425
Area extent: (-5326849.0625, -5326849.0625, 5326849.0625, 5326849.0625)""")
- self.assertTrue(nh_found, msg='Failed to load area correctly')
-
+ self.assertTrue(nh_found, msg='Failed to load area correctly')
+
def test_not_found_exception(self):
- self.assertRaises(utils.AreaNotFound, utils.parse_area_file,
- os.path.join(os.path.dirname(__file__), 'test_files', 'areas.cfg'),
+ self.assertRaises(utils.AreaNotFound, utils.parse_area_file,
+ os.path.join(
+ os.path.dirname(__file__), 'test_files', 'areas.cfg'),
'no_area')
+ def test_wrap_longitudes(self):
+ # test that we indeed wrap to [-180:+180[
+ step = 60
+ lons = np.arange(-360,360+step,step)
+ self.assertTrue((lons.min() < -180) and (lons.max() >= 180) and (+180 in lons))
+ wlons = utils.wrap_longitudes(lons)
+ self.assertFalse((wlons.min() < -180) or (wlons.max() >= 180) or (+180 in wlons))
+
+
+
+def suite():
+ """The test suite.
+ """
+ loader = unittest.TestLoader()
+ mysuite = unittest.TestSuite()
+ mysuite.addTest(loader.loadTestsFromTestCase(Test))
+
+ return mysuite
diff --git a/pyresample/utils.py b/pyresample/utils.py
index 336848c..647f5c2 100644
--- a/pyresample/utils.py
+++ b/pyresample/utils.py
@@ -1,77 +1,86 @@
-#pyresample, Resampling of remote sensing image data in python
-#
-#Copyright (C) 2010 Esben S. Nielsen
+# pyresample, Resampling of remote sensing image data in python
#
-#This program is free software: you can redistribute it and/or modify
-#it under the terms of the GNU General Public License as published by
-#the Free Software Foundation, either version 3 of the License, or
-#(at your option) any later version.
+# Copyright (C) 2010-2015
#
-#This program is distributed in the hope that it will be useful,
-#but WITHOUT ANY WARRANTY; without even the implied warranty of
-#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-#GNU General Public License for more details.
+# Authors:
+# Esben S. Nielsen
+# Thomas Lavergne
#
-#You should have received a copy of the GNU General Public License
-# along with this program. If not, see <http://www.gnu.org/licenses/>.
+# This program is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Lesser General Public License as published by the Free
+# Software Foundation, either version 3 of the License, or (at your option) any
+# later version.
+#
+# This program is distributed in the hope that it will be useful, but WITHOUT
+# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
+# details.
+#
+# You should have received a copy of the GNU Lesser General Public License along
+# with this program. If not, see <http://www.gnu.org/licenses/>.
"""Utility functions for pyresample"""
+from __future__ import absolute_import
+
import numpy as np
from configobj import ConfigObj
-import geometry, grid, kd_tree
-import _spatial_mp
+import pyresample as pr
+
class AreaNotFound(Exception):
+
"""Exception raised when specified are is no found in file"""
pass
+
def load_area(area_file_name, *regions):
"""Load area(s) from area file
-
+
:Parameters:
area_file_name : str
Path to area definition file
regions : str argument list
Regions to parse. If no regions are specified all
regions in the file are returned
-
+
:Returns:
area_defs : object or list
If one area name is specified a single AreaDefinition object is returned
If several area names are specified a list of AreaDefinition objects is returned
-
+
:Raises:
AreaNotFound
If a specified area name is not found
"""
-
+
area_list = parse_area_file(area_file_name, *regions)
if len(area_list) == 1:
return area_list[0]
else:
return area_list
+
def parse_area_file(area_file_name, *regions):
"""Parse area information from area file
-
+
:Parameters:
area_file_name : str
Path to area definition file
regions : str argument list
Regions to parse. If no regions are specified all
regions in the file are returned
-
+
:Returns:
area_defs : list
List of AreaDefinition objects
-
+
:Raises:
AreaNotFound
If a specified area is not found
"""
-
+
area_file = open(area_file_name, 'r')
area_list = list(regions)
if len(area_list) == 0:
@@ -80,19 +89,19 @@ def parse_area_file(area_file_name, *regions):
else:
select_all_areas = False
area_defs = [None for i in area_list]
-
- #Extract area from file
+
+ # Extract area from file
in_area = False
for line in area_file.readlines():
if not in_area:
if 'REGION' in line:
area_id = line.replace('REGION:', ''). \
- replace('{', '').strip()
+ replace('{', '').strip()
if area_id in area_list or select_all_areas:
in_area = True
area_content = ''
elif '};' in line:
- in_area = False
+ in_area = False
if select_all_areas:
area_defs.append(_create_area(area_id, area_content))
else:
@@ -102,15 +111,16 @@ def parse_area_file(area_file_name, *regions):
area_content += line
area_file.close()
-
- #Check if all specified areas were found
+
+ # Check if all specified areas were found
if not select_all_areas:
for i, area in enumerate(area_defs):
if area is None:
- raise AreaNotFound('Area "%s" not found in file "%s"'%
- (area_list[i], area_file_name))
+ raise AreaNotFound('Area "%s" not found in file "%s"' %
+ (area_list[i], area_file_name))
return area_defs
+
def _create_area(area_id, area_content):
"""Parse area configuration"""
@@ -119,30 +129,34 @@ def _create_area(area_id, area_content):
for line in config_obj.splitlines()])
config = config_obj.dict()
config['REGION'] = area_id
+
try:
- config['NAME'].__iter__()
+ string_types = basestring
+ except NameError:
+ string_types = str
+ if not isinstance(config['NAME'], string_types):
config['NAME'] = ', '.join(config['NAME'])
- except:
- config['NAME'] = ''.join(config['NAME'])
+
config['XSIZE'] = int(config['XSIZE'])
config['YSIZE'] = int(config['YSIZE'])
config['AREA_EXTENT'][0] = config['AREA_EXTENT'][0].replace('(', '')
config['AREA_EXTENT'][3] = config['AREA_EXTENT'][3].replace(')', '')
-
+
for i, val in enumerate(config['AREA_EXTENT']):
config['AREA_EXTENT'][i] = float(val)
-
+
config['PCS_DEF'] = _get_proj4_args(config['PCS_DEF'])
-
- return geometry.AreaDefinition(config['REGION'], config['NAME'],
- config['PCS_ID'], config['PCS_DEF'],
- config['XSIZE'], config['YSIZE'],
- config['AREA_EXTENT'])
+
+ return pr.geometry.AreaDefinition(config['REGION'], config['NAME'],
+ config['PCS_ID'], config['PCS_DEF'],
+ config['XSIZE'], config['YSIZE'],
+ config['AREA_EXTENT'])
+
def get_area_def(area_id, area_name, proj_id, proj4_args, x_size, y_size,
area_extent):
"""Construct AreaDefinition object from arguments
-
+
:Parameters:
area_id : str
ID of area
@@ -158,19 +172,20 @@ def get_area_def(area_id, area_name, proj_id, proj4_args, x_size, y_size,
Number of pixel in y dimension
area_extent : list
Area extent as a list of ints (LL_x, LL_y, UR_x, UR_y)
-
+
:Returns:
area_def : object
AreaDefinition object
"""
-
+
proj_dict = _get_proj4_args(proj4_args)
- return geometry.AreaDefinition(area_id, area_name, proj_id, proj_dict, x_size,
- y_size, area_extent)
+ return pr.geometry.AreaDefinition(area_id, area_name, proj_id, proj_dict, x_size,
+ y_size, area_extent)
+
def generate_quick_linesample_arrays(source_area_def, target_area_def, nprocs=1):
"""Generate linesample arrays for quick grid resampling
-
+
:Parameters:
source_area_def : object
Source area definition as AreaDefinition object
@@ -182,28 +197,29 @@ def generate_quick_linesample_arrays(source_area_def, target_area_def, nprocs=1)
:Returns:
(row_indices, col_indices) : tuple of numpy arrays
"""
- if not (isinstance(source_area_def, geometry.AreaDefinition) and
- isinstance(target_area_def, geometry.AreaDefinition)):
+ if not (isinstance(source_area_def, pr.geometry.AreaDefinition) and
+ isinstance(target_area_def, pr.geometry.AreaDefinition)):
raise TypeError('source_area_def and target_area_def must be of type '
'geometry.AreaDefinition')
-
+
lons, lats = target_area_def.get_lonlats(nprocs)
-
- source_pixel_y, source_pixel_x = grid.get_linesample(lons, lats,
- source_area_def,
- nprocs=nprocs)
-
- source_pixel_x = _downcast_index_array(source_pixel_x,
- source_area_def.shape[1])
- source_pixel_y = _downcast_index_array(source_pixel_y,
+
+ source_pixel_y, source_pixel_x = pr.grid.get_linesample(lons, lats,
+ source_area_def,
+ nprocs=nprocs)
+
+ source_pixel_x = _downcast_index_array(source_pixel_x,
+ source_area_def.shape[1])
+ source_pixel_y = _downcast_index_array(source_pixel_y,
source_area_def.shape[0])
-
+
return source_pixel_y, source_pixel_x
-def generate_nearest_neighbour_linesample_arrays(source_area_def, target_area_def,
+
+def generate_nearest_neighbour_linesample_arrays(source_area_def, target_area_def,
radius_of_influence, nprocs=1):
"""Generate linesample arrays for nearest neighbour grid resampling
-
+
:Parameters:
source_area_def : object
Source area definition as AreaDefinition object
@@ -217,29 +233,29 @@ def generate_nearest_neighbour_linesample_arrays(source_area_def, target_area_de
:Returns:
(row_indices, col_indices) : tuple of numpy arrays
"""
-
- if not (isinstance(source_area_def, geometry.AreaDefinition) and
- isinstance(target_area_def, geometry.AreaDefinition)):
+
+ if not (isinstance(source_area_def, pr.geometry.AreaDefinition) and
+ isinstance(target_area_def, pr.geometry.AreaDefinition)):
raise TypeError('source_area_def and target_area_def must be of type '
'geometry.AreaDefinition')
-
+
valid_input_index, valid_output_index, index_array, distance_array = \
- kd_tree.get_neighbour_info(source_area_def,
- target_area_def,
- radius_of_influence,
- neighbours=1,
- nprocs=nprocs)
- #Enumerate rows and cols
- rows = np.fromfunction(lambda i, j: i, source_area_def.shape,
+ pr.kd_tree.get_neighbour_info(source_area_def,
+ target_area_def,
+ radius_of_influence,
+ neighbours=1,
+ nprocs=nprocs)
+ # Enumerate rows and cols
+ rows = np.fromfunction(lambda i, j: i, source_area_def.shape,
dtype=np.int32).ravel()
- cols = np.fromfunction(lambda i, j: j, source_area_def.shape,
+ cols = np.fromfunction(lambda i, j: j, source_area_def.shape,
dtype=np.int32).ravel()
-
- #Reduce to match resampling data set
+
+ # Reduce to match resampling data set
rows_valid = rows[valid_input_index]
cols_valid = cols[valid_input_index]
-
- #Get result using array indexing
+
+ # Get result using array indexing
number_of_valid_points = valid_input_index.sum()
index_mask = (index_array == number_of_valid_points)
index_array[index_mask] = 0
@@ -247,21 +263,22 @@ def generate_nearest_neighbour_linesample_arrays(source_area_def, target_area_de
col_sample = cols_valid[index_array]
row_sample[index_mask] = -1
col_sample[index_mask] = -1
-
- #Reshape to correct shape
+
+ # Reshape to correct shape
row_indices = row_sample.reshape(target_area_def.shape)
col_indices = col_sample.reshape(target_area_def.shape)
-
- row_indices = _downcast_index_array(row_indices,
+
+ row_indices = _downcast_index_array(row_indices,
source_area_def.shape[0])
- col_indices = _downcast_index_array(col_indices,
+ col_indices = _downcast_index_array(col_indices,
source_area_def.shape[1])
-
+
return row_indices, col_indices
+
def fwhm2sigma(fwhm):
"""Calculate sigma for gauss function from FWHM (3 dB level)
-
+
:Parameters:
fwhm : float
FWHM of gauss function (3 dB level of beam footprint)
@@ -269,15 +286,16 @@ def fwhm2sigma(fwhm):
:Returns:
sigma : float
sigma for use in resampling gauss function
-
+
"""
-
+
return fwhm / (2 * np.sqrt(np.log(2)))
-
+
+
def _get_proj4_args(proj4_args):
"""Create dict from proj4 args
"""
-
+
if isinstance(proj4_args, str):
proj_config = ConfigObj(proj4_args.replace('+', '').split())
else:
@@ -288,10 +306,25 @@ def _get_proj4_args(proj4_args):
def _downcast_index_array(index_array, size):
"""Try to downcast array to uint16
"""
-
+
if size <= np.iinfo(np.uint16).max:
mask = (index_array < 0) | (index_array >= size)
index_array[mask] = size
index_array = index_array.astype(np.uint16)
return index_array
-
+
+
+def wrap_longitudes(lons):
+ """Wrap longitudes to the [-180:+180[ validity range (preserves dtype)
+
+ :Parameters:
+ lons : numpy array
+ Longitudes in degrees
+
+ :Returns:
+ lons : numpy array
+ Longitudes wrapped into [-180:+180[ validity range
+
+ """
+ lons_wrap = (lons + 180) % (360) - 180
+ return lons_wrap.astype(lons.dtype)
diff --git a/pyresample/version.py b/pyresample/version.py
index a278cb1..d891594 100644
--- a/pyresample/version.py
+++ b/pyresample/version.py
@@ -1,18 +1,18 @@
-#pyresample, Resampling of remote sensing image data in python
-#
-#Copyright (C) 2010 Esben S. Nielsen
+# pyresample, Resampling of remote sensing image data in python
#
-#This program is free software: you can redistribute it and/or modify
-#it under the terms of the GNU General Public License as published by
-#the Free Software Foundation, either version 3 of the License, or
-#(at your option) any later version.
+# Copyright (C) 2010, 2014, 2015 Esben S. Nielsen
#
-#This program is distributed in the hope that it will be useful,
-#but WITHOUT ANY WARRANTY; without even the implied warranty of
-#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-#GNU General Public License for more details.
+# This program is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Lesser General Public License as published by the Free
+# Software Foundation, either version 3 of the License, or (at your option) any
+# later version.
#
-#You should have received a copy of the GNU General Public License
-# along with this program. If not, see <http://www.gnu.org/licenses/>.
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License along
+# with this program. If not, see <http://www.gnu.org/licenses/>.
-__version__ = '1.1.0'
+__version__ = '1.1.3'
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..4498b6b
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,2 @@
+git+https://github.com/matplotlib/matplotlib.git
+git+https://github.com/matplotlib/basemap.git
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index 861a9f5..0000000
--- a/setup.cfg
+++ /dev/null
@@ -1,5 +0,0 @@
-[egg_info]
-tag_build =
-tag_date = 0
-tag_svn_revision = 0
-
diff --git a/setup.py b/setup.py
index 6b27c76..18ea8cd 100644
--- a/setup.py
+++ b/setup.py
@@ -1,30 +1,33 @@
-#pyresample, Resampling of remote sensing image data in python
-#
-#Copyright (C) 2012 Esben S. Nielsen
+# pyresample, Resampling of remote sensing image data in python
#
-#This program is free software: you can redistribute it and/or modify
-#it under the terms of the GNU General Public License as published by
-#the Free Software Foundation, either version 3 of the License, or
+# Copyright (C) 2012, 2014, 2015 Esben S. Nielsen
+#
+# This program is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Lesser General Public License as published by the Free
+# Software Foundation, either version 3 of the License, or
#(at your option) any later version.
#
-#This program is distributed in the hope that it will be useful,
-#but WITHOUT ANY WARRANTY; without even the implied warranty of
-#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-#GNU General Public License for more details.
+# This program is distributed in the hope that it will be useful, but WITHOUT
+# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
+# details.
#
-#You should have received a copy of the GNU General Public License
-# along with this program. If not, see <http://www.gnu.org/licenses/>.
+# You should have received a copy of the GNU Lesser General Public License along
+# with this program. If not, see <http://www.gnu.org/licenses/>.
+# workaround python bug: http://bugs.python.org/issue15881#msg170215
+import multiprocessing
from setuptools import setup
import sys
-import os
import imp
version = imp.load_source('pyresample.version', 'pyresample/version.py')
requirements = ['pyproj', 'numpy', 'configobj']
-extras_require = {'pykdtree': ['pykdtree'], 'numexpr': ['numexpr']}
+extras_require = {'pykdtree': ['pykdtree'],
+ 'numexpr': ['numexpr'],
+ 'quicklook': ['matplotlib', 'basemap']}
if sys.version_info < (2, 6):
# multiprocessing is not in the standard library
@@ -33,22 +36,20 @@ if sys.version_info < (2, 6):
setup(name='pyresample',
version=version.__version__,
description='Resampling of remote sensing data in Python',
- author='Esben S. Nielsen',
- author_email='esn at dmi.dk',
- package_dir = {'pyresample': 'pyresample'},
- packages = ['pyresample'],
+ author='Thomas Lavergne',
+ author_email='t.lavergne at met.no',
+ package_dir={'pyresample': 'pyresample'},
+ packages=['pyresample'],
install_requires=requirements,
- extras_require = extras_require,
- zip_safe = False,
+ extras_require=extras_require,
+ test_suite='pyresample.test.suite',
+ zip_safe=False,
classifiers=[
- 'Development Status :: 5 - Production/Stable',
- 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
- 'Programming Language :: Python',
- 'Operating System :: OS Independent',
- 'Intended Audience :: Science/Research',
- 'Topic :: Scientific/Engineering'
+ 'Development Status :: 5 - Production/Stable',
+ 'License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)',
+ 'Programming Language :: Python',
+ 'Operating System :: OS Independent',
+ 'Intended Audience :: Science/Research',
+ 'Topic :: Scientific/Engineering'
]
)
-
-
-
diff --git a/test/test_geometry.py b/test/test_geometry.py
deleted file mode 100644
index e806a32..0000000
--- a/test/test_geometry.py
+++ /dev/null
@@ -1,513 +0,0 @@
-from __future__ import with_statement
-
-import unittest
-
-import numpy as np
-
-from pyresample import geometry, geo_filter
-
-
-def tmp(f):
- f.tmp = True
- return f
-
-class Test(unittest.TestCase):
- """Unit testing the geometry and geo_filter modules"""
- def assert_raises(self, exception, call_able, *args):
- """assertRaises() has changed from py2.6 to 2.7! Here is an attempt to
- cover both"""
- import sys
- if sys.version_info < (2, 7):
- self.assertRaises(exception, call_able, *args)
- else:
- with self.assertRaises(exception):
- call_able(*args)
-
-
- def test_lonlat_precomp(self):
- area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
- {'a': '6378144.0',
- 'b': '6356759.0',
- 'lat_0': '50.00',
- 'lat_ts': '50.00',
- 'lon_0': '8.00',
- 'proj': 'stere'},
- 800,
- 800,
- [-1370912.72,
- -909968.64000000001,
- 1029087.28,
- 1490031.3600000001])
- lons, lats = area_def.get_lonlats()
- area_def2 = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
- {'a': '6378144.0',
- 'b': '6356759.0',
- 'lat_0': '50.00',
- 'lat_ts': '50.00',
- 'lon_0': '8.00',
- 'proj': 'stere'},
- 800,
- 800,
- [-1370912.72,
- -909968.64000000001,
- 1029087.28,
- 1490031.3600000001],
- lons=lons, lats=lats)
- lon, lat = area_def.get_lonlat(400, 400)
- self.assertAlmostEqual(lon, 5.5028467120975835,
- msg='lon retrieval from precomputated grid failed')
- self.assertAlmostEqual(lat, 52.566998432390619,
- msg='lat retrieval from precomputated grid failed')
-
- @tmp
- def test_cartesian(self):
- area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
- {'a': '6378144.0',
- 'b': '6356759.0',
- 'lat_0': '50.00',
- 'lat_ts': '50.00',
- 'lon_0': '8.00',
- 'proj': 'stere'},
- 800,
- 800,
- [-1370912.72,
- -909968.64000000001,
- 1029087.28,
- 1490031.3600000001])
- cart_coords = area_def.get_cartesian_coords()
- exp = 5872039989466.8457031
- self.assertTrue((cart_coords.sum() - exp) < 1e-7 * exp,
- msg='Calculation of cartesian coordinates failed')
-
- def test_swath(self):
- lons1 = np.fromfunction(lambda y, x: 3 + (10.0/100)*x, (5000, 100))
- lats1 = np.fromfunction(lambda y, x: 75 - (50.0/5000)*y, (5000, 100))
-
- swath_def = geometry.SwathDefinition(lons1, lats1)
-
- lons2, lats2 = swath_def.get_lonlats()
-
- self.failIf(id(lons1) != id(lons2) or id(lats1) != id(lats2),
- msg='Caching of swath coordinates failed')
-
- def test_area_equal(self):
- area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
- {'a': '6378144.0',
- 'b': '6356759.0',
- 'lat_0': '50.00',
- 'lat_ts': '50.00',
- 'lon_0': '8.00',
- 'proj': 'stere'},
- 800,
- 800,
- [-1370912.72,
- -909968.64000000001,
- 1029087.28,
- 1490031.3600000001])
- area_def2 = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
- {'a': '6378144.0',
- 'b': '6356759.0',
- 'lat_0': '50.00',
- 'lat_ts': '50.00',
- 'lon_0': '8.00',
- 'proj': 'stere'},
- 800,
- 800,
- [-1370912.72,
- -909968.64000000001,
- 1029087.28,
- 1490031.3600000001])
- self.failIf(area_def != area_def2, 'area_defs are not equal as expected')
-
- def test_not_area_equal(self):
- area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
- {'a': '6378144.0',
- 'b': '6356759.0',
- 'lat_0': '50.00',
- 'lat_ts': '50.00',
- 'lon_0': '8.00',
- 'proj': 'stere'},
- 800,
- 800,
- [-1370912.72,
- -909968.64000000001,
- 1029087.28,
- 1490031.3600000001])
-
- msg_area = geometry.AreaDefinition('msg_full', 'Full globe MSG image 0 degrees',
- 'msg_full',
- {'a': '6378169.0',
- 'b': '6356584.0',
- 'h': '35785831.0',
- 'lon_0': '0',
- 'proj': 'geos'},
- 3712,
- 3712,
- [-5568742.4000000004,
- -5568742.4000000004,
- 5568742.4000000004,
- 5568742.4000000004]
- )
- self.failIf(area_def == msg_area, 'area_defs are not expected to be equal')
-
- def test_swath_equal(self):
- lons = np.array([1.2, 1.3, 1.4, 1.5])
- lats = np.array([65.9, 65.86, 65.82, 65.78])
- swath_def = geometry.SwathDefinition(lons, lats)
- swath_def2 = geometry.SwathDefinition(lons, lats)
- self.failIf(swath_def != swath_def2, 'swath_defs are not equal as expected')
-
- def test_swath_not_equal(self):
- lats1 = np.array([65.9, 65.86, 65.82, 65.78])
- lons = np.array([1.2, 1.3, 1.4, 1.5])
- lats2 = np.array([65.91, 65.85, 65.80, 65.75])
- swath_def = geometry.SwathDefinition(lons, lats1)
- swath_def2 = geometry.SwathDefinition(lons, lats2)
- self.failIf(swath_def == swath_def2, 'swath_defs are not expected to be equal')
-
- def test_swath_equal_area(self):
- area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
- {'a': '6378144.0',
- 'b': '6356759.0',
- 'lat_0': '50.00',
- 'lat_ts': '50.00',
- 'lon_0': '8.00',
- 'proj': 'stere'},
- 800,
- 800,
- [-1370912.72,
- -909968.64000000001,
- 1029087.28,
- 1490031.3600000001])
-
- swath_def = geometry.SwathDefinition(*area_def.get_lonlats())
-
- self.failIf(swath_def != area_def, "swath_def and area_def should be equal")
-
- area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
- {'a': '6378144.0',
- 'b': '6356759.0',
- 'lat_0': '50.00',
- 'lat_ts': '50.00',
- 'lon_0': '8.00',
- 'proj': 'stere'},
- 800,
- 800,
- [-1370912.72,
- -909968.64000000001,
- 1029087.28,
- 1490031.3600000001])
-
- self.failIf(area_def != swath_def, "swath_def and area_def should be equal")
-
- def test_swath_not_equal_area(self):
- area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
- {'a': '6378144.0',
- 'b': '6356759.0',
- 'lat_0': '50.00',
- 'lat_ts': '50.00',
- 'lon_0': '8.00',
- 'proj': 'stere'},
- 800,
- 800,
- [-1370912.72,
- -909968.64000000001,
- 1029087.28,
- 1490031.3600000001])
-
- lons = np.array([1.2, 1.3, 1.4, 1.5])
- lats = np.array([65.9, 65.86, 65.82, 65.78])
- swath_def = geometry.SwathDefinition(lons, lats)
-
- self.failIf(swath_def == area_def, "swath_def and area_def should be different")
-
- area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
- {'a': '6378144.0',
- 'b': '6356759.0',
- 'lat_0': '50.00',
- 'lat_ts': '50.00',
- 'lon_0': '8.00',
- 'proj': 'stere'},
- 800,
- 800,
- [-1370912.72,
- -909968.64000000001,
- 1029087.28,
- 1490031.3600000001])
-
- self.failIf(area_def == swath_def, "swath_def and area_def should be different")
-
- def test_concat_1d(self):
- lons1 = np.array([1, 2, 3])
- lats1 = np.array([1, 2, 3])
- lons2 = np.array([4, 5, 6])
- lats2 = np.array([4, 5, 6])
- swath_def1 = geometry.SwathDefinition(lons1, lats1)
- swath_def2 = geometry.SwathDefinition(lons2, lats2)
- swath_def_concat = swath_def1.concatenate(swath_def2)
- expected = np.array([1, 2, 3, 4, 5, 6])
- self.assertTrue(np.array_equal(swath_def_concat.lons, expected) and
- np.array_equal(swath_def_concat.lons, expected),
- 'Failed to concatenate 1D swaths')
-
- def test_concat_2d(self):
- lons1 = np.array([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
- lats1 = np.array([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
- lons2 = np.array([[4, 5, 6], [6, 7, 8]])
- lats2 = np.array([[4, 5, 6], [6, 7, 8]])
- swath_def1 = geometry.SwathDefinition(lons1, lats1)
- swath_def2 = geometry.SwathDefinition(lons2, lats2)
- swath_def_concat = swath_def1.concatenate(swath_def2)
- expected = np.array([[1, 2, 3], [3, 4, 5], [5, 6, 7], [4, 5, 6], [6, 7, 8]])
- self.assertTrue(np.array_equal(swath_def_concat.lons, expected) and
- np.array_equal(swath_def_concat.lons, expected),
- 'Failed to concatenate 2D swaths')
-
- def test_append_1d(self):
- lons1 = np.array([1, 2, 3])
- lats1 = np.array([1, 2, 3])
- lons2 = np.array([4, 5, 6])
- lats2 = np.array([4, 5, 6])
- swath_def1 = geometry.SwathDefinition(lons1, lats1)
- swath_def2 = geometry.SwathDefinition(lons2, lats2)
- swath_def1.append(swath_def2)
- expected = np.array([1, 2, 3, 4, 5, 6])
- self.assertTrue(np.array_equal(swath_def1.lons, expected) and
- np.array_equal(swath_def1.lons, expected),
- 'Failed to append 1D swaths')
-
- def test_append_2d(self):
- lons1 = np.array([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
- lats1 = np.array([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
- lons2 = np.array([[4, 5, 6], [6, 7, 8]])
- lats2 = np.array([[4, 5, 6], [6, 7, 8]])
- swath_def1 = geometry.SwathDefinition(lons1, lats1)
- swath_def2 = geometry.SwathDefinition(lons2, lats2)
- swath_def1.append(swath_def2)
- expected = np.array([[1, 2, 3], [3, 4, 5], [5, 6, 7], [4, 5, 6], [6, 7, 8]])
- self.assertTrue(np.array_equal(swath_def1.lons, expected) and
- np.array_equal(swath_def1.lons, expected),
- 'Failed to append 2D swaths')
-
- def test_grid_filter_valid(self):
- lons = np.array([-170, -30, 30, 170])
- lats = np.array([20, -40, 50, -80])
- swath_def = geometry.SwathDefinition(lons, lats)
- filter_area = geometry.AreaDefinition('test', 'test', 'test',
- {'proj' : 'eqc', 'lon_0' : 0.0, 'lat_0' : 0.0},
- 8, 8,
- (-20037508.34, -10018754.17, 20037508.34, 10018754.17))
- filter = np.array([[1, 1, 1, 1, 0, 0, 0, 0],
- [1, 1, 1, 1, 0, 0, 0, 0],
- [1, 1, 1, 1, 0, 0, 0, 0],
- [1, 1, 1, 1, 0, 0, 0, 0],
- [0, 0, 0, 0, 1, 1, 1, 1],
- [0, 0, 0, 0, 1, 1, 1, 1],
- [0, 0, 0, 0, 1, 1, 1, 1],
- [0, 0, 0, 0, 1, 1, 1, 1],
- ])
- grid_filter = geo_filter.GridFilter(filter_area, filter)
- valid_index = grid_filter.get_valid_index(swath_def)
- expected = np.array([1, 0, 0, 1])
- self.assertTrue(np.array_equal(valid_index, expected), 'Failed to find grid filter')
-
- def test_grid_filter(self):
- lons = np.array([-170, -30, 30, 170])
- lats = np.array([20, -40, 50, -80])
- swath_def = geometry.SwathDefinition(lons, lats)
- data = np.array([1, 2, 3, 4])
- filter_area = geometry.AreaDefinition('test', 'test', 'test',
- {'proj' : 'eqc', 'lon_0' : 0.0, 'lat_0' : 0.0},
- 8, 8,
- (-20037508.34, -10018754.17, 20037508.34, 10018754.17))
- filter = np.array([[1, 1, 1, 1, 0, 0, 0, 0],
- [1, 1, 1, 1, 0, 0, 0, 0],
- [1, 1, 1, 1, 0, 0, 0, 0],
- [1, 1, 1, 1, 0, 0, 0, 0],
- [0, 0, 0, 0, 1, 1, 1, 1],
- [0, 0, 0, 0, 1, 1, 1, 1],
- [0, 0, 0, 0, 1, 1, 1, 1],
- [0, 0, 0, 0, 1, 1, 1, 1],
- ])
- grid_filter = geo_filter.GridFilter(filter_area, filter)
- swath_def_f, data_f = grid_filter.filter(swath_def, data)
- expected = np.array([1, 4])
- self.assertTrue(np.array_equal(data_f, expected), 'Failed grid filtering data')
- expected_lons = np.array([-170, 170])
- expected_lats = np.array([20, -80])
- self.assertTrue(np.array_equal(swath_def_f.lons[:], expected_lons)
- and np.array_equal(swath_def_f.lats[:], expected_lats),
- 'Failed finding grid filtering lon lats')
-
- def test_grid_filter2D(self):
- lons = np.array([[-170, -30, 30, 170],
- [-170, -30, 30, 170]])
- lats = np.array([[20, -40, 50, -80],
- [25, -35, 55, -75]])
- swath_def = geometry.SwathDefinition(lons, lats)
- data1 = np.ones((2, 4))
- data2 = np.ones((2, 4)) * 2
- data3 = np.ones((2, 4)) * 3
- data = np.dstack((data1, data2, data3))
- filter_area = geometry.AreaDefinition('test', 'test', 'test',
- {'proj' : 'eqc', 'lon_0' : 0.0, 'lat_0' : 0.0},
- 8, 8,
- (-20037508.34, -10018754.17, 20037508.34, 10018754.17))
- filter = np.array([[1, 1, 1, 1, 0, 0, 0, 0],
- [1, 1, 1, 1, 0, 0, 0, 0],
- [1, 1, 1, 1, 0, 0, 0, 0],
- [1, 1, 1, 1, 0, 0, 0, 0],
- [0, 0, 0, 0, 1, 1, 1, 1],
- [0, 0, 0, 0, 1, 1, 1, 1],
- [0, 0, 0, 0, 1, 1, 1, 1],
- [0, 0, 0, 0, 1, 1, 1, 1],
- ])
- grid_filter = geo_filter.GridFilter(filter_area, filter, nprocs=2)
- swath_def_f, data_f = grid_filter.filter(swath_def, data)
- expected = np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]])
- self.assertTrue(np.array_equal(data_f, expected), 'Failed 2D grid filtering data')
- expected_lons = np.array([-170, 170, -170, 170])
- expected_lats = np.array([20, -80, 25, -75])
- self.assertTrue(np.array_equal(swath_def_f.lons[:], expected_lons)
- and np.array_equal(swath_def_f.lats[:], expected_lats),
- 'Failed finding 2D grid filtering lon lats')
-
- def test_boundary(self):
- area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
- {'a': '6378144.0',
- 'b': '6356759.0',
- 'lat_0': '50.00',
- 'lat_ts': '50.00',
- 'lon_0': '8.00',
- 'proj': 'stere'},
- 10,
- 10,
- [-1370912.72,
- -909968.64000000001,
- 1029087.28,
- 1490031.3600000001])
- proj_x_boundary, proj_y_boundary = area_def.proj_x_coords, area_def.proj_y_coords
- expected_x = np.array([-1250912.72, -1010912.72, -770912.72,
- -530912.72, -290912.72, -50912.72, 189087.28,
- 429087.28, 669087.28, 909087.28])
- expected_y = np.array([1370031.36, 1130031.36, 890031.36, 650031.36,
- 410031.36, 170031.36, -69968.64, -309968.64,
- -549968.64, -789968.64])
- self.assertTrue(np.allclose(proj_x_boundary, expected_x),
- 'Failed to find projection x coords')
- self.assertTrue(np.allclose(proj_y_boundary, expected_y),
- 'Failed to find projection y coords')
-
-
- def test_area_extent_ll(self):
- area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
- {'a': '6378144.0',
- 'b': '6356759.0',
- 'lat_0': '50.00',
- 'lat_ts': '50.00',
- 'lon_0': '8.00',
- 'proj': 'stere'},
- 10,
- 10,
- [-1370912.72,
- -909968.64000000001,
- 1029087.28,
- 1490031.3600000001])
- self.assertAlmostEqual(sum(area_def.area_extent_ll),
- 122.06448093539757, 5,
- 'Failed to get lon and lats of area extent')
-
- @tmp
- def test_latlong_area(self):
- area_def = geometry.AreaDefinition('', '', '',
- {'proj': 'latlong'},
- 360, 180,
- [-180, -90, 180, 90])
- lons, lats = area_def.get_lonlats()
- self.assertEqual(lons[0, 0], -179.5)
- self.assertEqual(lats[0, 0], 89.5)
-
-
- def test_get_xy_from_lonlat(self):
- """Test the function get_xy_from_lonlat"""
- from pyresample import utils
- area_id = 'test'
- area_name = 'Test area with 2x2 pixels'
- proj_id = 'test'
- x_size = 2
- y_size = 2
- area_extent = [1000000, 0, 1050000, 50000]
- proj_dict = {"proj": 'laea',
- 'lat_0': '60',
- 'lon_0': '0',
- 'a': '6371228.0', 'units': 'm'}
- area_def = utils.get_area_def(area_id,
- area_name,
- proj_id,
- proj_dict,
- x_size, y_size,
- area_extent)
- import pyproj
- p__ = pyproj.Proj(proj_dict)
- lon_ul, lat_ul = p__(1000000, 50000, inverse=True)
- lon_ur, lat_ur = p__(1050000, 50000, inverse=True)
- lon_ll, lat_ll = p__(1000000, 0, inverse=True)
- lon_lr, lat_lr = p__(1050000, 0, inverse=True)
-
- eps_lonlat = 0.01
- eps_meters = 100
- x__, y__ = area_def.get_xy_from_lonlat(lon_ul + eps_lonlat,
- lat_ul - eps_lonlat)
- x_expect, y_expect = 0, 0
- self.assertEqual(x__, x_expect)
- self.assertEqual(y__, y_expect)
- x__, y__ = area_def.get_xy_from_lonlat(lon_ur - eps_lonlat,
- lat_ur - eps_lonlat)
- self.assertEqual(x__, 1)
- self.assertEqual(y__, 0)
- x__, y__ = area_def.get_xy_from_lonlat(lon_ll + eps_lonlat,
- lat_ll + eps_lonlat)
- self.assertEqual(x__, 0)
- self.assertEqual(y__, 1)
- x__, y__ = area_def.get_xy_from_lonlat(lon_lr - eps_lonlat,
- lat_lr + eps_lonlat)
- self.assertEqual(x__, 1)
- self.assertEqual(y__, 1)
-
- lon, lat = p__(1025000 - eps_meters, 25000 - eps_meters, inverse=True)
- x__, y__ = area_def.get_xy_from_lonlat(lon, lat)
- self.assertEqual(x__, 0)
- self.assertEqual(y__, 1)
-
- lon, lat = p__(1025000 + eps_meters, 25000 - eps_meters, inverse=True)
- x__, y__ = area_def.get_xy_from_lonlat(lon, lat)
- self.assertEqual(x__, 1)
- self.assertEqual(y__, 1)
-
- lon, lat = p__(1025000 - eps_meters, 25000 + eps_meters, inverse=True)
- x__, y__ = area_def.get_xy_from_lonlat(lon, lat)
- self.assertEqual(x__, 0)
- self.assertEqual(y__, 0)
-
- lon, lat = p__(1025000 + eps_meters, 25000 + eps_meters, inverse=True)
- x__, y__ = area_def.get_xy_from_lonlat(lon, lat)
- self.assertEqual(x__, 1)
- self.assertEqual(y__, 0)
-
- lon, lat = p__(999000, -10, inverse=True)
- self.assert_raises(ValueError, area_def.get_xy_from_lonlat, lon, lat)
- self.assert_raises(ValueError, area_def.get_xy_from_lonlat, 0., 0.)
-
- # Test getting arrays back:
- lons = [lon_ll + eps_lonlat, lon_ur - eps_lonlat]
- lats = [lat_ll + eps_lonlat, lat_ur - eps_lonlat]
- x__, y__ = area_def.get_xy_from_lonlat(lons, lats)
-
- x_expects = np.array([0, 1])
- y_expects = np.array([1, 0])
- self.assertTrue((x__.data == x_expects).all())
- self.assertTrue((y__.data == y_expects).all())
-
-
-if __name__ == '__main__':
- unittest.main()
diff --git a/test/test_grid.py b/test/test_grid.py
deleted file mode 100644
index 8238b99..0000000
--- a/test/test_grid.py
+++ /dev/null
@@ -1,177 +0,0 @@
-import copy
-import unittest
-
-import numpy as np
-
-from pyresample import grid, geometry, utils
-
-
-def mp(f):
- f.mp = True
- return f
-
-def tmp(f):
- f.tmp = True
- return f
-
-class Test(unittest.TestCase):
-
- area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
- {'a': '6378144.0',
- 'b': '6356759.0',
- 'lat_0': '50.00',
- 'lat_ts': '50.00',
- 'lon_0': '8.00',
- 'proj': 'stere'},
- 800,
- 800,
- [-1370912.72,
- -909968.64000000001,
- 1029087.28,
- 1490031.3600000001])
-
- area_def2 = geometry.AreaDefinition('areaD2', 'Europe (3km, HRV, VTC)', 'areaD2',
- {'a': '6378144.0',
- 'b': '6356759.0',
- 'lat_0': '50.00',
- 'lat_ts': '50.00',
- 'lon_0': '8.00',
- 'proj': 'stere'},
- 5,
- 5,
- [-1370912.72,
- -909968.64000000001,
- 1029087.28,
- 1490031.3600000001])
-
- msg_area = geometry.AreaDefinition('msg_full', 'Full globe MSG image 0 degrees',
- 'msg_full',
- {'a': '6378169.0',
- 'b': '6356584.0',
- 'h': '35785831.0',
- 'lon_0': '0',
- 'proj': 'geos'},
- 3712,
- 3712,
- [-5568742.4000000004,
- -5568742.4000000004,
- 5568742.4000000004,
- 5568742.4000000004]
- )
-
- def test_linesample(self):
- data = np.fromfunction(lambda y, x: y*x, (40, 40))
- rows = np.array([[1, 2], [3, 4]])
- cols = np.array([[25, 26], [27, 28]])
- res = grid.get_image_from_linesample(rows, cols, data)
- expected = np.array([[25., 52.], [81., 112.]])
- self.assertTrue(np.array_equal(res, expected), 'Linesample failed')
-
- def test_linesample_multi(self):
- data1 = np.fromfunction(lambda y, x: y*x, (40, 40))
- data2 = np.fromfunction(lambda y, x: 2*y*x, (40, 40))
- data3 = np.fromfunction(lambda y, x: 3*y*x, (40, 40))
- data = np.zeros((40, 40, 3))
- data[:, :, 0] = data1
- data[:, :, 1] = data2
- data[:, :, 2] = data3
- rows = np.array([[1, 2], [3, 4]])
- cols = np.array([[25, 26], [27, 28]])
- res = grid.get_image_from_linesample(rows, cols, data)
- expected = np.array([[[25., 50., 75.],
- [52., 104., 156.]],
- [[81., 162., 243.],
- [ 112., 224., 336.]]])
- self.assertTrue(np.array_equal(res, expected), 'Linesample failed')
-
- def test_from_latlon(self):
- data = np.fromfunction(lambda y, x: y*x, (800, 800))
- lons = np.fromfunction(lambda y, x: x, (10, 10))
- lats = np.fromfunction(lambda y, x: 50 - (5.0/10)*y, (10, 10))
- #source_def = grid.AreaDefinition.get_from_area_def(self.area_def)
- source_def = self.area_def
- res = grid.get_image_from_lonlats(lons, lats, source_def, data)
- expected = np.array([[ 129276., 141032., 153370., 165804., 178334., 190575.,
- 202864., 214768., 226176., 238080.],
- [ 133056., 146016., 158808., 171696., 184320., 196992.,
- 209712., 222480., 234840., 247715.],
- [ 137026., 150150., 163370., 177215., 190629., 203756.,
- 217464., 230256., 243048., 256373.],
- [ 140660., 154496., 168714., 182484., 196542., 210650.,
- 224257., 238464., 251712., 265512.],
- [ 144480., 158484., 173148., 187912., 202776., 217358.,
- 231990., 246240., 259920., 274170.],
- [ 147968., 163261., 178398., 193635., 208616., 223647.,
- 238728., 253859., 268584., 283898.],
- [ 151638., 167121., 182704., 198990., 214775., 230280.,
- 246442., 261617., 276792., 292574.],
- [ 154980., 171186., 187860., 204016., 220542., 237120.,
- 253125., 269806., 285456., 301732.],
- [ 158500., 175536., 192038., 209280., 226626., 243697.,
- 260820., 277564., 293664., 310408.],
- [ 161696., 179470., 197100., 214834., 232320., 250236.,
- 267448., 285090., 302328., 320229.]])
- self.assertTrue(np.array_equal(res, expected), 'Sampling from lat lon failed')
-
- def test_proj_coords(self):
- #res = grid.get_proj_coords(self.area_def2)
- res = self.area_def2.get_proj_coords()
- cross_sum = res[0].sum() + res[1].sum()
- expected = 2977965.9999999963
- self.assertAlmostEqual(cross_sum, expected, msg='Calculation of proj coords failed')
-
- def test_latlons(self):
- #res = grid.get_lonlats(self.area_def2)
- res = self.area_def2.get_lonlats()
- cross_sum = res[0].sum() + res[1].sum()
- expected = 1440.8280578215431
- self.assertAlmostEqual(cross_sum, expected, msg='Calculation of lat lons failed')
-
- @mp
- def test_latlons_mp(self):
- #res = grid.get_lonlats(self.area_def2, nprocs=2)
- res = self.area_def2.get_lonlats(nprocs=2)
- cross_sum = res[0].sum() + res[1].sum()
- expected = 1440.8280578215431
- self.assertAlmostEqual(cross_sum, expected, msg='Calculation of lat lons failed')
-
- def test_resampled_image(self):
- data = np.fromfunction(lambda y, x: y*x*10**-6, (3712, 3712))
- target_def = self.area_def
- source_def = self.msg_area
- res = grid.get_resampled_image(target_def, source_def, data, segments=1)
- cross_sum = res.sum()
- expected = 399936.39392500359
- self.assertAlmostEqual(cross_sum, expected, msg='Resampling of image failed')
-
- @tmp
- def test_generate_linesample(self):
- data = np.fromfunction(lambda y, x: y*x*10**-6, (3712, 3712))
- row_indices, col_indices = utils.generate_quick_linesample_arrays(self.msg_area,
- self.area_def)
- res = data[row_indices, col_indices]
- cross_sum = res.sum()
- expected = 399936.39392500359
- self.assertAlmostEqual(cross_sum, expected, msg='Generate linesample failed')
- self.failIf(row_indices.dtype != np.uint16 or col_indices.dtype != np.uint16,
- 'Generate linesample failed. Downcast to uint16 expected')
-
- @mp
- def test_resampled_image_mp(self):
- data = np.fromfunction(lambda y, x: y*x*10**-6, (3712, 3712))
- target_def = self.area_def
- source_def = self.msg_area
- res = grid.get_resampled_image(target_def, source_def, data, nprocs=2, segments=1)
- cross_sum = res.sum()
- expected = 399936.39392500359
- self.assertAlmostEqual(cross_sum, expected, msg='Resampling of image mp failed')
-
- def test_single_lonlat(self):
- lon, lat = self.area_def.get_lonlat(400, 400)
- self.assertAlmostEqual(lon, 5.5028467120975835, msg='Resampling of single lon failed')
- self.assertAlmostEqual(lat, 52.566998432390619, msg='Resampling of single lat failed')
-
- def test_proj4_string(self):
- proj4_string = self.area_def.proj4_string
- self.assertEqual(proj4_string, '+a=6378144.0 +b=6356759.0 +lat_ts=50.00 +lon_0=8.00 +proj=stere +lat_0=50.00')
-
diff --git a/test/test_image.py b/test/test_image.py
deleted file mode 100644
index a8471a1..0000000
--- a/test/test_image.py
+++ /dev/null
@@ -1,202 +0,0 @@
-import os
-import unittest
-
-import numpy
-
-from pyresample import image, geometry, grid, utils
-
-def mask(f):
- f.mask = True
- return f
-
-def tmp(f):
- f.tmp = True
- return f
-
-
-class Test(unittest.TestCase):
-
- area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
- {'a': '6378144.0',
- 'b': '6356759.0',
- 'lat_0': '50.00',
- 'lat_ts': '50.00',
- 'lon_0': '8.00',
- 'proj': 'stere'},
- 800,
- 800,
- [-1370912.72,
- -909968.64000000001,
- 1029087.28,
- 1490031.3600000001])
-
- msg_area = geometry.AreaDefinition('msg_full', 'Full globe MSG image 0 degrees',
- 'msg_full',
- {'a': '6378169.0',
- 'b': '6356584.0',
- 'h': '35785831.0',
- 'lon_0': '0',
- 'proj': 'geos'},
- 3712,
- 3712,
- [-5568742.4000000004,
- -5568742.4000000004,
- 5568742.4000000004,
- 5568742.4000000004]
- )
-
- msg_area_resize = geometry.AreaDefinition('msg_full', 'Full globe MSG image 0 degrees',
- 'msg_full',
- {'a': '6378169.0',
- 'b': '6356584.0',
- 'h': '35785831.0',
- 'lon_0': '0',
- 'proj': 'geos'},
- 928,
- 928,
- [-5568742.4000000004,
- -5568742.4000000004,
- 5568742.4000000004,
- 5568742.4000000004]
- )
-
- @tmp
- def test_image(self):
- data = numpy.fromfunction(lambda y, x: y*x*10**-6, (3712, 3712))
- msg_con = image.ImageContainerQuick(data, self.msg_area, segments=1)
- area_con = msg_con.resample(self.area_def)
- res = area_con.image_data
- cross_sum = res.sum()
- expected = 399936.39392500359
- self.assertAlmostEqual(cross_sum, expected, msg='ImageContainer resampling quick failed')
-
- @tmp
- def test_image_segments(self):
- data = numpy.fromfunction(lambda y, x: y*x*10**-6, (3712, 3712))
- msg_con = image.ImageContainerQuick(data, self.msg_area, segments=8)
- area_con = msg_con.resample(self.area_def)
- res = area_con.image_data
- cross_sum = res.sum()
- expected = 399936.39392500359
- self.assertAlmostEqual(cross_sum, expected, msg='ImageContainer resampling quick segments failed')
-
- def test_return_type(self):
- data = numpy.ones((3712, 3712)).astype('int')
- msg_con = image.ImageContainerQuick(data, self.msg_area, segments=1)
- area_con = msg_con.resample(self.area_def)
- res = area_con.image_data
- self.assertTrue(data.dtype is res.dtype, msg='Failed to maintain input data type')
-
- @mask
- def test_masked_image(self):
- data = numpy.zeros((3712, 3712))
- mask = numpy.zeros((3712, 3712))
- mask[:, 1865:] = 1
- data_masked = numpy.ma.array(data, mask=mask)
- msg_con = image.ImageContainerQuick(data_masked, self.msg_area, segments=1)
- area_con = msg_con.resample(self.area_def)
- res = area_con.image_data
- resampled_mask = res.mask.astype('int')
- expected = numpy.fromfile(os.path.join(os.path.dirname(__file__), 'test_files', 'mask_grid.dat'),
- sep=' ').reshape((800, 800))
- self.assertTrue(numpy.array_equal(resampled_mask, expected), msg='Failed to resample masked array')
-
- @mask
- def test_masked_image_fill(self):
- data = numpy.zeros((3712, 3712))
- mask = numpy.zeros((3712, 3712))
- mask[:, 1865:] = 1
- data_masked = numpy.ma.array(data, mask=mask)
- msg_con = image.ImageContainerQuick(data_masked, self.msg_area,
- fill_value=None, segments=1)
- area_con = msg_con.resample(self.area_def)
- res = area_con.image_data
- resampled_mask = res.mask.astype('int')
- expected = numpy.fromfile(os.path.join(os.path.dirname(__file__), 'test_files', 'mask_grid.dat'),
- sep=' ').reshape((800, 800))
- self.assertTrue(numpy.array_equal(resampled_mask, expected), msg='Failed to resample masked array')
-
- def test_nearest_neighbour(self):
- data = numpy.fromfunction(lambda y, x: y*x*10**-6, (3712, 3712))
- msg_con = image.ImageContainerNearest(data, self.msg_area, 50000, segments=1)
- area_con = msg_con.resample(self.area_def)
- res = area_con.image_data
- cross_sum = res.sum()
- expected = 399936.783062
- self.assertAlmostEqual(cross_sum, expected,
- msg='ImageContainer resampling nearest neighbour failed')
-
- def test_nearest_resize(self):
- data = numpy.fromfunction(lambda y, x: y*x*10**-6, (3712, 3712))
- msg_con = image.ImageContainerNearest(data, self.msg_area, 50000, segments=1)
- area_con = msg_con.resample(self.msg_area_resize)
- res = area_con.image_data
- cross_sum = res.sum()
- expected = 2212023.0175830
- self.assertAlmostEqual(cross_sum, expected,
- msg='ImageContainer resampling nearest neighbour failed')
-
- def test_nearest_neighbour_multi(self):
- data1 = numpy.fromfunction(lambda y, x: y*x*10**-6, (3712, 3712))
- data2 = numpy.fromfunction(lambda y, x: y*x*10**-6, (3712, 3712)) * 2
- data = numpy.dstack((data1, data2))
- msg_con = image.ImageContainerNearest(data, self.msg_area, 50000, segments=1)
- area_con = msg_con.resample(self.area_def)
- res = area_con.image_data
- cross_sum1 = res[:, :, 0].sum()
- expected1 = 399936.783062
- self.assertAlmostEqual(cross_sum1, expected1,
- msg='ImageContainer resampling nearest neighbour multi failed')
- cross_sum2 = res[:, :, 1].sum()
- expected2 = 399936.783062 * 2
- self.assertAlmostEqual(cross_sum2, expected2,
- msg='ImageContainer resampling nearest neighbour multi failed')
-
- def test_nearest_neighbour_multi_preproc(self):
- data1 = numpy.fromfunction(lambda y, x: y*x*10**-6, (3712, 3712))
- data2 = numpy.fromfunction(lambda y, x: y*x*10**-6, (3712, 3712)) * 2
- data = numpy.dstack((data1, data2))
- msg_con = image.ImageContainer(data, self.msg_area)
- #area_con = msg_con.resample_area_nearest_neighbour(self.area_def, 50000)
- row_indices, col_indices = \
- utils.generate_nearest_neighbour_linesample_arrays(self.msg_area,
- self.area_def,
- 50000)
- res = msg_con.get_array_from_linesample(row_indices, col_indices)
- cross_sum1 = res[:, :, 0].sum()
- expected1 = 399936.783062
- self.assertAlmostEqual(cross_sum1, expected1,
- msg='ImageContainer resampling nearest neighbour multi preproc failed')
- cross_sum2 = res[:, :, 1].sum()
- expected2 = 399936.783062 * 2
- self.assertAlmostEqual(cross_sum2, expected2,
- msg='ImageContainer resampling nearest neighbour multi preproc failed')
-
- def test_nearest_swath(self):
- data = numpy.fromfunction(lambda y, x: y*x, (50, 10))
- lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- swath_con = image.ImageContainerNearest(data, swath_def, 50000, segments=1)
- area_con = swath_con.resample(self.area_def)
- res = area_con.image_data
- cross_sum = res.sum()
- expected = 15874591.0
- self.assertEqual(cross_sum, expected,\
- msg='ImageContainer swath resampling nearest failed')
-
- def test_nearest_swath_segments(self):
- data = numpy.fromfunction(lambda y, x: y*x, (50, 10))
- data = numpy.dstack(3 * (data,))
- lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- swath_con = image.ImageContainerNearest(data, swath_def, 50000, segments=2)
- area_con = swath_con.resample(self.area_def)
- res = area_con.image_data
- cross_sum = res.sum()
- expected = 3 * 15874591.0
- self.assertEqual(cross_sum, expected,\
- msg='ImageContainer swath segments resampling nearest failed')
-
-
diff --git a/test/test_kd_tree.py b/test/test_kd_tree.py
deleted file mode 100644
index cdd5ef4..0000000
--- a/test/test_kd_tree.py
+++ /dev/null
@@ -1,836 +0,0 @@
-from __future__ import with_statement
-
-import os
-import sys
-import unittest
-
-import warnings
-if sys.version_info < (2, 6):
- warnings.simplefilter("ignore")
-else:
- warnings.simplefilter("always")
-
-import numpy
-
-from pyresample import kd_tree, utils, geometry, data_reduce
-
-
-def mp(f):
- f.mp = True
- return f
-
-def quick(f):
- f.quick = True
- return f
-
-def tmp(f):
- f.tmp = True
- return f
-
-class Test(unittest.TestCase):
-
- area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
- {'a': '6378144.0',
- 'b': '6356759.0',
- 'lat_0': '50.00',
- 'lat_ts': '50.00',
- 'lon_0': '8.00',
- 'proj': 'stere'},
- 800,
- 800,
- [-1370912.72,
- -909968.64000000001,
- 1029087.28,
- 1490031.3600000001])
-
- tdata = numpy.array([1, 2, 3])
- tlons = numpy.array([11.280789, 12.649354, 12.080402])
- tlats = numpy.array([56.011037, 55.629675, 55.641535])
- tswath = geometry.SwathDefinition(lons=tlons, lats=tlats)
- tgrid = geometry.CoordinateDefinition(lons=numpy.array([12.562036]),
- lats=numpy.array([55.715613]))
-
-
- def test_nearest_base(self):
- res = kd_tree.resample_nearest(self.tswath,\
- self.tdata.ravel(), self.tgrid,\
- 100000, reduce_data=False, segments=1)
- self.assertTrue(res[0] == 2, 'Failed to calculate nearest neighbour')
-
- @tmp
- def test_gauss_base(self):
- if sys.version_info < (2, 6):
- res = kd_tree.resample_gauss(self.tswath, \
- self.tdata.ravel(), self.tgrid,\
- 50000, 25000, reduce_data=False, segments=1)
- else:
- with warnings.catch_warnings(record=True) as w:
- res = kd_tree.resample_gauss(self.tswath, \
- self.tdata.ravel(), self.tgrid,\
- 50000, 25000, reduce_data=False, segments=1)
- self.failIf(len(w) != 1, 'Failed to create neighbour warning')
- self.failIf(('Searching' not in str(w[0].message)), 'Failed to create correct neighbour warning')
- self.assertAlmostEqual(res[0], 2.2020729, 5, \
- 'Failed to calculate gaussian weighting')
-
-
- def test_custom_base(self):
- def wf(dist):
- return 1 - dist/100000.0
-
- if sys.version_info < (2, 6):
- res = kd_tree.resample_custom(self.tswath,\
- self.tdata.ravel(), self.tgrid,\
- 50000, wf, reduce_data=False, segments=1)
- else:
- with warnings.catch_warnings(record=True) as w:
- res = kd_tree.resample_custom(self.tswath,\
- self.tdata.ravel(), self.tgrid,\
- 50000, wf, reduce_data=False, segments=1)
- self.failIf(len(w) != 1, 'Failed to create neighbour warning')
- self.failIf(('Searching' not in str(w[0].message)), 'Failed to create correct neighbour warning')
- self.assertAlmostEqual(res[0], 2.4356757, 5,\
- 'Failed to calculate custom weighting')
-
- @tmp
- def test_gauss_uncert(self):
- sigma = utils.fwhm2sigma(41627.730557884883)
- if sys.version_info < (2, 6):
- res, stddev, count = kd_tree.resample_gauss(self.tswath, self.tdata,
- self.tgrid, 100000, sigma,
- with_uncert=True)
- else:
- with warnings.catch_warnings(record=True) as w:
- res, stddev, count = kd_tree.resample_gauss(self.tswath, self.tdata,
- self.tgrid, 100000, sigma,
- with_uncert=True)
- self.failIf(len(w) != 1, 'Failed to create neighbour warning')
- self.failIf(('Searching' not in str(w[0].message)), 'Failed to create correct neighbour warning')
-
- expected_res = 2.20206560694
- expected_stddev = 0.707115076173
- expected_count = 3
- self.assertAlmostEqual(res[0], expected_res, 5, \
- 'Failed to calculate gaussian weighting with uncertainty')
- self.assertAlmostEqual(stddev[0], expected_stddev, 5, \
- 'Failed to calculate uncertainty for gaussian weighting')
- self.assertEqual(count[0], expected_count, 'Wrong data point count for gaussian weighting with uncertainty')
-
- @tmp
- def test_custom_uncert(self):
- def wf(dist):
- return 1 - dist/100000.0
-
- if sys.version_info < (2, 6):
- res, stddev, counts = kd_tree.resample_custom(self.tswath,
- self.tdata, self.tgrid,
- 100000, wf, with_uncert=True)
- else:
- with warnings.catch_warnings(record=True) as w:
- res, stddev, counts = kd_tree.resample_custom(self.tswath,
- self.tdata, self.tgrid,
- 100000, wf, with_uncert=True)
- self.failIf(len(w) != 1, 'Failed to create neighbour warning')
- self.failIf(('Searching' not in str(w[0].message)), 'Failed to create correct neighbour warning')
-
- self.assertAlmostEqual(res[0], 2.32193149, 5, \
- 'Failed to calculate custom weighting with uncertainty')
- self.assertAlmostEqual(stddev[0], 0.81817972, 5, \
- 'Failed to calculate custom for gaussian weighting')
- self.assertEqual(counts[0], 3, 'Wrong data point count for custom weighting with uncertainty')
-
-
-
- def test_nearest(self):
- data = numpy.fromfunction(lambda y, x: y*x, (50, 10))
- lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- res = kd_tree.resample_nearest(swath_def, data.ravel(),\
- self.area_def, 50000, segments=1)
- cross_sum = res.sum()
- expected = 15874591.0
- self.assertEqual(cross_sum, expected,\
- msg='Swath resampling nearest failed')
-
-
- def test_nearest_1d(self):
- data = numpy.fromfunction(lambda x, y: x * y, (800, 800))
- lons = numpy.fromfunction(lambda x: 3 + x / 100. , (500,))
- lats = numpy.fromfunction(lambda x: 75 - x / 10., (500,))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- res = kd_tree.resample_nearest(self.area_def, data.ravel(),
- swath_def, 50000, segments=1)
- cross_sum = res.sum()
- expected = 35821299.0
- self.assertEqual(res.shape, (500,),
- msg='Swath resampling nearest 1d failed')
- self.assertEqual(cross_sum, expected,
- msg='Swath resampling nearest 1d failed')
-
-
- def test_nearest_empty(self):
- data = numpy.fromfunction(lambda y, x: y*x, (50, 10))
- lons = numpy.fromfunction(lambda y, x: 165 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- res = kd_tree.resample_nearest(swath_def, data.ravel(),\
- self.area_def, 50000, segments=1)
- cross_sum = res.sum()
- expected = 0
- self.assertEqual(cross_sum, expected,\
- msg='Swath resampling nearest empty failed')
-
-
- def test_nearest_empty_multi(self):
- data = numpy.fromfunction(lambda y, x: y*x, (50, 10))
- lons = numpy.fromfunction(lambda y, x: 165 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- data_multi = numpy.column_stack((data.ravel(), data.ravel(),\
- data.ravel()))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- res = kd_tree.resample_nearest(swath_def, data_multi,\
- self.area_def, 50000, segments=1)
- self.assertEqual(res.shape, (800, 800, 3),\
- msg='Swath resampling nearest empty multi failed')
-
-
- def test_nearest_empty_multi_masked(self):
- data = numpy.fromfunction(lambda y, x: y*x, (50, 10))
- lons = numpy.fromfunction(lambda y, x: 165 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- data_multi = numpy.column_stack((data.ravel(), data.ravel(),\
- data.ravel()))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- res = kd_tree.resample_nearest(swath_def, data_multi,\
- self.area_def, 50000, segments=1,
- fill_value=None)
- self.assertEqual(res.shape, (800, 800, 3),
- msg='Swath resampling nearest empty multi masked failed')
-
-
- def test_nearest_empty_masked(self):
- data = numpy.fromfunction(lambda y, x: y*x, (50, 10))
- lons = numpy.fromfunction(lambda y, x: 165 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- res = kd_tree.resample_nearest(swath_def, data.ravel(),\
- self.area_def, 50000, segments=1,
- fill_value=None)
- cross_sum = res.mask.sum()
- expected = res.size
- self.assertTrue(cross_sum == expected,
- msg='Swath resampling nearest empty masked failed')
-
-
- def test_nearest_segments(self):
- data = numpy.fromfunction(lambda y, x: y*x, (50, 10))
- lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- res = kd_tree.resample_nearest(swath_def, data.ravel(),\
- self.area_def, 50000, segments=2)
- cross_sum = res.sum()
- expected = 15874591.0
- self.assertEqual(cross_sum, expected,\
- msg='Swath resampling nearest segments failed')
-
-
- def test_nearest_remap(self):
- data = numpy.fromfunction(lambda y, x: y*x, (50, 10))
- lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- res = kd_tree.resample_nearest(swath_def, data.ravel(),\
- self.area_def, 50000, segments=1)
- remap = kd_tree.resample_nearest(self.area_def, res.ravel(),\
- swath_def, 5000, segments=1)
- cross_sum = remap.sum()
- expected = 22275.0
- self.assertEqual(cross_sum, expected,\
- msg='Grid remapping nearest failed')
-
-
- def test_nearest_mp(self):
- data = numpy.fromfunction(lambda y, x: y*x, (50, 10))
- lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- res = kd_tree.resample_nearest(swath_def, data.ravel(),\
- self.area_def, 50000, nprocs=2, segments=1)
- cross_sum = res.sum()
- expected = 15874591.0
- self.assertEqual(cross_sum, expected,\
- msg='Swath resampling mp nearest failed')
-
-
- def test_nearest_multi(self):
- data = numpy.fromfunction(lambda y, x: y*x, (50, 10))
- lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- data_multi = numpy.column_stack((data.ravel(), data.ravel(),\
- data.ravel()))
- res = kd_tree.resample_nearest(swath_def, data_multi,\
- self.area_def, 50000, segments=1)
- cross_sum = res.sum()
- expected = 3 * 15874591.0
- self.assertEqual(cross_sum, expected,\
- msg='Swath multi channel resampling nearest failed')
-
-
- def test_nearest_multi_unraveled(self):
- data = numpy.fromfunction(lambda y, x: y*x, (50, 10))
- lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- data_multi = numpy.dstack((data, data, data))
- res = kd_tree.resample_nearest(swath_def, data_multi,\
- self.area_def, 50000, segments=1)
- cross_sum = res.sum()
- expected = 3 * 15874591.0
- self.assertEqual(cross_sum, expected,\
- msg='Swath multi channel resampling nearest failed')
-
-
- def test_gauss_sparse(self):
- data = numpy.fromfunction(lambda y, x: y*x, (50, 10))
- lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- res = kd_tree.resample_gauss(swath_def, data.ravel(),\
- self.area_def, 50000, 25000, fill_value=-1, segments=1)
- cross_sum = res.sum()
- expected = 15387753.9852
- self.assertAlmostEqual(cross_sum, expected, places=3,\
- msg='Swath gauss sparse nearest failed')
-
-
- def test_gauss(self):
- data = numpy.fromfunction(lambda y, x: (y + x)*10**-5, (5000, 100))
- lons = numpy.fromfunction(lambda y, x: 3 + (10.0/100)*x, (5000, 100))
- lats = numpy.fromfunction(lambda y, x: 75 - (50.0/5000)*y, (5000, 100))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- if sys.version_info < (2, 6):
- res = kd_tree.resample_gauss(swath_def, data.ravel(),\
- self.area_def, 50000, 25000, segments=1)
- else:
- with warnings.catch_warnings(record=True) as w:
- res = kd_tree.resample_gauss(swath_def, data.ravel(),\
- self.area_def, 50000, 25000, segments=1)
- self.failIf(len(w) != 1, 'Failed to create neighbour radius warning')
- self.failIf(('Possible more' not in str(w[0].message)), 'Failed to create correct neighbour radius warning')
- cross_sum = res.sum()
- expected = 4872.81050892
- self.assertAlmostEqual(cross_sum, expected,\
- msg='Swath resampling gauss failed')
-
-
- def test_gauss_fwhm(self):
- data = numpy.fromfunction(lambda y, x: (y + x)*10**-5, (5000, 100))
- lons = numpy.fromfunction(lambda y, x: 3 + (10.0/100)*x, (5000, 100))
- lats = numpy.fromfunction(lambda y, x: 75 - (50.0/5000)*y, (5000, 100))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- if sys.version_info < (2, 6):
- res = kd_tree.resample_gauss(swath_def, data.ravel(),\
- self.area_def, 50000, utils.fwhm2sigma(41627.730557884883), segments=1)
- else:
- with warnings.catch_warnings(record=True) as w:
- res = kd_tree.resample_gauss(swath_def, data.ravel(),\
- self.area_def, 50000, utils.fwhm2sigma(41627.730557884883), segments=1)
- self.failIf(len(w) != 1, 'Failed to create neighbour radius warning')
- self.failIf(('Possible more' not in str(w[0].message)), 'Failed to create correct neighbour radius warning')
- cross_sum = res.sum()
- expected = 4872.81050892
- self.assertAlmostEqual(cross_sum, expected,\
- msg='Swath resampling gauss failed')
-
-
- def test_gauss_multi(self):
- data = numpy.fromfunction(lambda y, x: (y + x)*10**-6, (5000, 100))
- lons = numpy.fromfunction(lambda y, x: 3 + (10.0/100)*x, (5000, 100))
- lats = numpy.fromfunction(lambda y, x: 75 - (50.0/5000)*y, (5000, 100))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- data_multi = numpy.column_stack((data.ravel(), data.ravel(),\
- data.ravel()))
- if sys.version_info < (2, 6):
- res = kd_tree.resample_gauss(swath_def, data_multi,\
- self.area_def, 50000, [25000, 15000, 10000], segments=1)
- else:
- with warnings.catch_warnings(record=True) as w:
- res = kd_tree.resample_gauss(swath_def, data_multi,\
- self.area_def, 50000, [25000, 15000, 10000], segments=1)
- self.failIf(len(w) != 1, 'Failed to create neighbour radius warning')
- self.failIf(('Possible more' not in str(w[0].message)), 'Failed to create correct neighbour radius warning')
- cross_sum = res.sum()
- expected = 1461.84313918
- self.assertAlmostEqual(cross_sum, expected,\
- msg='Swath multi channel resampling gauss failed')
-
- @tmp
- def test_gauss_multi_uncert(self):
- data = numpy.fromfunction(lambda y, x: (y + x)*10**-6, (5000, 100))
- lons = numpy.fromfunction(lambda y, x: 3 + (10.0/100)*x, (5000, 100))
- lats = numpy.fromfunction(lambda y, x: 75 - (50.0/5000)*y, (5000, 100))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- data_multi = numpy.column_stack((data.ravel(), data.ravel(),\
- data.ravel()))
- if sys.version_info < (2, 6):
- res, stddev, counts = kd_tree.resample_gauss(swath_def, data_multi,\
- self.area_def, 50000, [25000, 15000, 10000],
- segments=1, with_uncert=True)
- else:
- with warnings.catch_warnings(record=True) as w:
- res, stddev, counts = kd_tree.resample_gauss(swath_def, data_multi,\
- self.area_def, 50000, [25000, 15000, 10000],
- segments=1, with_uncert=True)
- self.failIf(len(w) != 1, 'Failed to create neighbour radius warning')
- self.failIf(('Possible more' not in str(w[0].message)), 'Failed to create correct neighbour radius warning')
- cross_sum = res.sum()
- cross_sum_stddev = stddev.sum()
- cross_sum_counts = counts.sum()
- expected = 1461.84313918
- expected_stddev = 0.446204424799
- expected_counts = 4934802.0
- self.assertTrue(res.shape == stddev.shape and stddev.shape == counts.shape and counts.shape == (800, 800, 3))
- self.assertAlmostEqual(cross_sum, expected,
- msg='Swath multi channel resampling gauss failed on data')
- self.assertAlmostEqual(cross_sum_stddev, expected_stddev,
- msg='Swath multi channel resampling gauss failed on stddev')
- self.assertAlmostEqual(cross_sum_counts, expected_counts,
- msg='Swath multi channel resampling gauss failed on counts')
-
-
- def test_gauss_multi_mp(self):
- data = numpy.fromfunction(lambda y, x: (y + x)*10**-6, (5000, 100))
- lons = numpy.fromfunction(lambda y, x: 3 + (10.0/100)*x, (5000, 100))
- lats = numpy.fromfunction(lambda y, x: 75 - (50.0/5000)*y, (5000, 100))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- data_multi = numpy.column_stack((data.ravel(), data.ravel(),\
- data.ravel()))
- if sys.version_info < (2, 6):
- res = kd_tree.resample_gauss(swath_def, data_multi,\
- self.area_def, 50000, [25000, 15000, 10000],\
- nprocs=2, segments=1)
- else:
- with warnings.catch_warnings(record=True) as w:
- res = kd_tree.resample_gauss(swath_def, data_multi,\
- self.area_def, 50000, [25000, 15000, 10000],\
- nprocs=2, segments=1)
- self.failIf(len(w) != 1, 'Failed to create neighbour radius warning')
- self.failIf(('Possible more' not in str(w[0].message)), 'Failed to create correct neighbour radius warning')
- cross_sum = res.sum()
- expected = 1461.84313918
- self.assertAlmostEqual(cross_sum, expected,\
- msg='Swath multi channel resampling gauss failed')
-
-
- def test_gauss_multi_mp_segments(self):
- data = numpy.fromfunction(lambda y, x: (y + x)*10**-6, (5000, 100))
- lons = numpy.fromfunction(lambda y, x: 3 + (10.0/100)*x, (5000, 100))
- lats = numpy.fromfunction(lambda y, x: 75 - (50.0/5000)*y, (5000, 100))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- data_multi = numpy.column_stack((data.ravel(), data.ravel(),\
- data.ravel()))
- if sys.version_info < (2, 6):
- res = kd_tree.resample_gauss(swath_def, data_multi,\
- self.area_def, 50000, [25000, 15000, 10000],\
- nprocs=2, segments=1)
- else:
- with warnings.catch_warnings(record=True) as w:
- res = kd_tree.resample_gauss(swath_def, data_multi,\
- self.area_def, 50000, [25000, 15000, 10000],\
- nprocs=2, segments=1)
- self.failIf(len(w) != 1, 'Failed to create neighbour radius warning')
- self.failIf(('Possible more' not in str(w[0].message)), 'Failed to create correct neighbour radius warning')
- cross_sum = res.sum()
- expected = 1461.84313918
- self.assertAlmostEqual(cross_sum, expected,\
- msg='Swath multi channel segments resampling gauss failed')
-
-
- def test_gauss_multi_mp_segments_empty(self):
- data = numpy.fromfunction(lambda y, x: (y + x)*10**-6, (5000, 100))
- lons = numpy.fromfunction(lambda y, x: 165 + (10.0/100)*x, (5000, 100))
- lats = numpy.fromfunction(lambda y, x: 75 - (50.0/5000)*y, (5000, 100))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- data_multi = numpy.column_stack((data.ravel(), data.ravel(),\
- data.ravel()))
- res = kd_tree.resample_gauss(swath_def, data_multi,\
- self.area_def, 50000, [25000, 15000, 10000],\
- nprocs=2, segments=1)
- cross_sum = res.sum()
- self.assertTrue(cross_sum == 0,
- msg=('Swath multi channel segments empty '
- 'resampling gauss failed'))
-
-
- def test_custom(self):
- def wf(dist):
- return 1 - dist/100000.0
-
- data = numpy.fromfunction(lambda y, x: (y + x)*10**-5, (5000, 100))
- lons = numpy.fromfunction(lambda y, x: 3 + (10.0/100)*x, (5000, 100))
- lats = numpy.fromfunction(lambda y, x: 75 - (50.0/5000)*y, (5000, 100))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- if sys.version_info < (2, 6):
- res = kd_tree.resample_custom(swath_def, data.ravel(),\
- self.area_def, 50000, wf, segments=1)
- else:
- with warnings.catch_warnings(record=True) as w:
- res = kd_tree.resample_custom(swath_def, data.ravel(),\
- self.area_def, 50000, wf, segments=1)
- self.failIf(len(w) != 1, 'Failed to create neighbour radius warning')
- self.failIf(('Possible more' not in str(w[0].message)), 'Failed to create correct neighbour radius warning')
- cross_sum = res.sum()
- expected = 4872.81050729
- self.assertAlmostEqual(cross_sum, expected,\
- msg='Swath custom resampling failed')
-
-
- def test_custom_multi(self):
- def wf1(dist):
- return 1 - dist/100000.0
-
- def wf2(dist):
- return 1
-
- def wf3(dist):
- return numpy.cos(dist)**2
-
- data = numpy.fromfunction(lambda y, x: (y + x)*10**-6, (5000, 100))
- lons = numpy.fromfunction(lambda y, x: 3 + (10.0/100)*x, (5000, 100))
- lats = numpy.fromfunction(lambda y, x: 75 - (50.0/5000)*y, (5000, 100))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- data_multi = numpy.column_stack((data.ravel(), data.ravel(),\
- data.ravel()))
- if sys.version_info < (2, 6):
- res = kd_tree.resample_custom(swath_def, data_multi,\
- self.area_def, 50000, [wf1, wf2, wf3], segments=1)
- else:
- with warnings.catch_warnings(record=True) as w:
- res = kd_tree.resample_custom(swath_def, data_multi,\
- self.area_def, 50000, [wf1, wf2, wf3], segments=1)
- self.failIf(len(w) != 1, 'Failed to create neighbour radius warning')
- self.failIf(('Possible more' not in str(w[0].message)), 'Failed to create correct neighbour radius warning')
- cross_sum = res.sum()
- expected = 1461.842980746
- self.assertAlmostEqual(cross_sum, expected,\
- msg='Swath multi channel custom resampling failed')
-
- def test_reduce(self):
- data = numpy.fromfunction(lambda y, x: (y + x), (1000, 1000))
- lons = numpy.fromfunction(lambda y, x: -180 + (360.0/1000)*x, (1000, 1000))
- lats = numpy.fromfunction(lambda y, x: -90 + (180.0/1000)*y, (1000, 1000))
- grid_lons, grid_lats = self.area_def.get_lonlats()
- lons, lats, data = data_reduce.swath_from_lonlat_grid(grid_lons, grid_lats,
- lons, lats, data,
- 7000)
- cross_sum = data.sum()
- expected = 20514375.0
- self.assertAlmostEqual(cross_sum, expected, msg='Reduce data failed')
-
- def test_reduce_boundary(self):
- data = numpy.fromfunction(lambda y, x: (y + x), (1000, 1000))
- lons = numpy.fromfunction(lambda y, x: -180 + (360.0/1000)*x, (1000, 1000))
- lats = numpy.fromfunction(lambda y, x: -90 + (180.0/1000)*y, (1000, 1000))
- boundary_lonlats = self.area_def.get_boundary_lonlats()
- lons, lats, data = data_reduce.swath_from_lonlat_boundaries(boundary_lonlats[0],
- boundary_lonlats[1],
- lons, lats, data,
- 7000)
- cross_sum = data.sum()
- expected = 20514375.0
- self.assertAlmostEqual(cross_sum, expected, msg='Reduce data failed')
-
- def test_cartesian_reduce(self):
- data = numpy.fromfunction(lambda y, x: (y + x), (1000, 1000))
- lons = numpy.fromfunction(lambda y, x: -180 + (360.0/1000)*x, (1000, 1000))
- lats = numpy.fromfunction(lambda y, x: -90 + (180.0/1000)*y, (1000, 1000))
- #grid = utils.generate_cartesian_grid(self.area_def)
- grid = self.area_def.get_cartesian_coords()
- lons, lats, data = data_reduce.swath_from_cartesian_grid(grid, lons, lats, data,
- 7000)
- cross_sum = data.sum()
- expected = 20514375.0
- self.assertAlmostEqual(cross_sum, expected, msg='Cartesian reduce data failed')
-
- def test_area_con_reduce(self):
- data = numpy.fromfunction(lambda y, x: (y + x), (1000, 1000))
- lons = numpy.fromfunction(lambda y, x: -180 + (360.0/1000)*x, (1000, 1000))
- lats = numpy.fromfunction(lambda y, x: -90 + (180.0/1000)*y, (1000, 1000))
- grid_lons, grid_lats = self.area_def.get_lonlats()
- valid_index = data_reduce.get_valid_index_from_lonlat_grid(grid_lons, grid_lats,
- lons, lats, 7000)
- data = data[valid_index]
- cross_sum = data.sum()
- expected = 20514375.0
- self.assertAlmostEqual(cross_sum, expected, msg='Reduce data failed')
-
- def test_area_con_cartesian_reduce(self):
- data = numpy.fromfunction(lambda y, x: (y + x), (1000, 1000))
- lons = numpy.fromfunction(lambda y, x: -180 + (360.0/1000)*x, (1000, 1000))
- lats = numpy.fromfunction(lambda y, x: -90 + (180.0/1000)*y, (1000, 1000))
- cart_grid = self.area_def.get_cartesian_coords()
- valid_index = data_reduce.get_valid_index_from_cartesian_grid(cart_grid,
- lons, lats, 7000)
- data = data[valid_index]
- cross_sum = data.sum()
- expected = 20514375.0
- self.assertAlmostEqual(cross_sum, expected, msg='Cartesian reduce data failed')
-
- def test_masked_nearest(self):
- data = numpy.ones((50, 10))
- data[:, 5:] = 2
- lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- mask = numpy.ones((50, 10))
- mask[:, :5] = 0
- masked_data = numpy.ma.array(data, mask=mask)
- res = kd_tree.resample_nearest(swath_def, masked_data.ravel(),
- self.area_def, 50000, segments=1)
- expected_mask = numpy.fromfile(os.path.join(os.path.dirname(__file__),
- 'test_files',
- 'mask_test_nearest_mask.dat'),
- sep=' ').reshape((800, 800))
- expected_data = numpy.fromfile(os.path.join(os.path.dirname(__file__),
- 'test_files',
- 'mask_test_nearest_data.dat'),
- sep=' ').reshape((800, 800))
- self.assertTrue(numpy.array_equal(expected_mask, res.mask),
- msg='Resampling of swath mask failed')
- self.assertTrue(numpy.array_equal(expected_data, res.data),
- msg='Resampling of swath masked data failed')
-
- def test_masked_nearest_1d(self):
- data = numpy.ones((800, 800))
- data[:400, :] = 2
- lons = numpy.fromfunction(lambda x: 3 + x / 100. , (500,))
- lats = numpy.fromfunction(lambda x: 75 - x / 10., (500,))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- mask = numpy.ones((800, 800))
- mask[400:, :] = 0
- masked_data = numpy.ma.array(data, mask=mask)
- res = kd_tree.resample_nearest(self.area_def, masked_data.ravel(),
- swath_def, 50000, segments=1)
- self.assertEqual(res.mask.sum(), 108,
- msg='Swath resampling masked nearest 1d failed')
-
-
- def test_masked_gauss(self):
- data = numpy.ones((50, 10))
- data[:, 5:] = 2
- lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- mask = numpy.ones((50, 10))
- mask[:, :5] = 0
- masked_data = numpy.ma.array(data, mask=mask)
- res = kd_tree.resample_gauss(swath_def, masked_data.ravel(),\
- self.area_def, 50000, 25000, segments=1)
- expected_mask = numpy.fromfile(os.path.join(os.path.dirname(__file__),
- 'test_files',
- 'mask_test_mask.dat'),
- sep=' ').reshape((800, 800))
- expected_data = numpy.fromfile(os.path.join(os.path.dirname(__file__),
- 'test_files',
- 'mask_test_data.dat'),
- sep=' ').reshape((800, 800))
- expected = expected_data.sum()
- cross_sum = res.data.sum()
-
- self.assertTrue(numpy.array_equal(expected_mask, res.mask),
- msg='Gauss resampling of swath mask failed')
- self.assertAlmostEqual(cross_sum, expected, places=3,\
- msg='Gauss resampling of swath masked data failed')
-
-
- def test_masked_fill_float(self):
- data = numpy.ones((50, 10))
- lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- res = kd_tree.resample_nearest(swath_def, data.ravel(),
- self.area_def, 50000, fill_value=None, segments=1)
- expected_fill_mask = numpy.fromfile(os.path.join(os.path.dirname(__file__),
- 'test_files',
- 'mask_test_fill_value.dat'),
- sep=' ').reshape((800, 800))
- fill_mask = res.mask
- self.assertTrue(numpy.array_equal(fill_mask, expected_fill_mask),
- msg='Failed to create fill mask on float data')
-
- def test_masked_fill_int(self):
- data = numpy.ones((50, 10)).astype('int')
- lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- res = kd_tree.resample_nearest(swath_def, data.ravel(),
- self.area_def, 50000, fill_value=None, segments=1)
- expected_fill_mask = numpy.fromfile(os.path.join(os.path.dirname(__file__),
- 'test_files',
- 'mask_test_fill_value.dat'),
- sep=' ').reshape((800, 800))
- fill_mask = res.mask
- self.assertTrue(numpy.array_equal(fill_mask, expected_fill_mask),
- msg='Failed to create fill mask on integer data')
-
- def test_masked_full(self):
- data = numpy.ones((50, 10))
- data[:, 5:] = 2
- mask = numpy.ones((50, 10))
- mask[:, :5] = 0
- masked_data = numpy.ma.array(data, mask=mask)
- lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- res = kd_tree.resample_nearest(swath_def,
- masked_data.ravel(), self.area_def, 50000,
- fill_value=None, segments=1)
- expected_fill_mask = numpy.fromfile(os.path.join(os.path.dirname(__file__),
- 'test_files',
- 'mask_test_full_fill.dat'),
- sep=' ').reshape((800, 800))
- fill_mask = res.mask
-
- self.assertTrue(numpy.array_equal(fill_mask, expected_fill_mask),
- msg='Failed to create fill mask on masked data')
-
- def test_masked_full_multi(self):
- data = numpy.ones((50, 10))
- data[:, 5:] = 2
- mask1 = numpy.ones((50, 10))
- mask1[:, :5] = 0
- mask2 = numpy.ones((50, 10))
- mask2[:, 5:] = 0
- mask3 = numpy.ones((50, 10))
- mask3[:25, :] = 0
- data_multi = numpy.column_stack((data.ravel(), data.ravel(), data.ravel()))
- mask_multi = numpy.column_stack((mask1.ravel(), mask2.ravel(), mask3.ravel()))
- masked_data = numpy.ma.array(data_multi, mask=mask_multi)
- lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- res = kd_tree.resample_nearest(swath_def,
- masked_data, self.area_def, 50000,
- fill_value=None, segments=1)
- expected_fill_mask = numpy.fromfile(os.path.join(os.path.dirname(__file__),
- 'test_files',
- 'mask_test_full_fill_multi.dat'),
- sep=' ').reshape((800, 800, 3))
- fill_mask = res.mask
- cross_sum = res.sum()
- expected = 357140.0
- self.assertAlmostEqual(cross_sum, expected,\
- msg='Failed to resample masked data')
- self.assertTrue(numpy.array_equal(fill_mask, expected_fill_mask),
- msg='Failed to create fill mask on masked data')
-
- def test_nearest_from_sample(self):
- data = numpy.fromfunction(lambda y, x: y*x, (50, 10))
- lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- valid_input_index, valid_output_index, index_array, distance_array = \
- kd_tree.get_neighbour_info(swath_def,
- self.area_def,
- 50000, neighbours=1, segments=1)
- res = kd_tree.get_sample_from_neighbour_info('nn', (800, 800), data.ravel(),
- valid_input_index, valid_output_index,
- index_array)
- cross_sum = res.sum()
- expected = 15874591.0
- self.assertEqual(cross_sum, expected,\
- msg='Swath resampling from neighbour info nearest failed')
-
-
- def test_custom_multi_from_sample(self):
- def wf1(dist):
- return 1 - dist/100000.0
-
- def wf2(dist):
- return 1
-
- def wf3(dist):
- return numpy.cos(dist)**2
-
- data = numpy.fromfunction(lambda y, x: (y + x)*10**-6, (5000, 100))
- lons = numpy.fromfunction(lambda y, x: 3 + (10.0/100)*x, (5000, 100))
- lats = numpy.fromfunction(lambda y, x: 75 - (50.0/5000)*y, (5000, 100))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- data_multi = numpy.column_stack((data.ravel(), data.ravel(),\
- data.ravel()))
-
- if sys.version_info < (2, 6):
- valid_input_index, valid_output_index, index_array, distance_array = \
- kd_tree.get_neighbour_info(swath_def,
- self.area_def,
- 50000, segments=1)
- else:
- with warnings.catch_warnings(record=True) as w:
- valid_input_index, valid_output_index, index_array, distance_array = \
- kd_tree.get_neighbour_info(swath_def,
- self.area_def,
- 50000, segments=1)
- self.failIf(len(w) != 1, 'Failed to create neighbour radius warning')
- self.failIf(('Possible more' not in str(w[0].message)), 'Failed to create correct neighbour radius warning')
-
- res = kd_tree.get_sample_from_neighbour_info('custom', (800, 800),
- data_multi,
- valid_input_index, valid_output_index,
- index_array, distance_array,
- weight_funcs=[wf1, wf2, wf3])
-
- cross_sum = res.sum()
-
- expected = 1461.842980746
- self.assertAlmostEqual(cross_sum, expected,\
- msg='Swath multi channel custom resampling from neighbour info failed 1')
- res = kd_tree.get_sample_from_neighbour_info('custom', (800, 800),
- data_multi,
- valid_input_index, valid_output_index,
- index_array, distance_array,
- weight_funcs=[wf1, wf2, wf3])
-
- # Look for error where input data has been manipulated
- cross_sum = res.sum()
- expected = 1461.842980746
- self.assertAlmostEqual(cross_sum, expected,\
- msg='Swath multi channel custom resampling from neighbour info failed 2')
-
-
- def test_masked_multi_from_sample(self):
- data = numpy.ones((50, 10))
- data[:, 5:] = 2
- mask1 = numpy.ones((50, 10))
- mask1[:, :5] = 0
- mask2 = numpy.ones((50, 10))
- mask2[:, 5:] = 0
- mask3 = numpy.ones((50, 10))
- mask3[:25, :] = 0
- data_multi = numpy.column_stack((data.ravel(), data.ravel(), data.ravel()))
- mask_multi = numpy.column_stack((mask1.ravel(), mask2.ravel(), mask3.ravel()))
- masked_data = numpy.ma.array(data_multi, mask=mask_multi)
- lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
- lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
- swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
- valid_input_index, valid_output_index, index_array, distance_array = \
- kd_tree.get_neighbour_info(swath_def,
- self.area_def,
- 50000, neighbours=1, segments=1)
- res = kd_tree.get_sample_from_neighbour_info('nn', (800, 800),
- masked_data,
- valid_input_index,
- valid_output_index, index_array,
- fill_value=None)
- expected_fill_mask = numpy.fromfile(os.path.join(os.path.dirname(__file__),
- 'test_files',
- 'mask_test_full_fill_multi.dat'),
- sep=' ').reshape((800, 800, 3))
- fill_mask = res.mask
- self.assertTrue(numpy.array_equal(fill_mask, expected_fill_mask),
- msg='Failed to create fill mask on masked data')
-
-
-
diff --git a/test/test_plot.py b/test/test_plot.py
deleted file mode 100644
index d513f6e..0000000
--- a/test/test_plot.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import unittest
-import os
-
-import numpy as np
-
-import pyresample as pr
-
-try:
- import matplotlib
- matplotlib.use('Agg')
-except ImportError:
- pass # Postpone fail to individual tests
-
-def tmp(f):
- f.tmp = True
- return f
-
-class Test(unittest.TestCase):
-
- filename = os.path.abspath(os.path.join(os.path.dirname(__file__),
- 'test_files', 'ssmis_swath.npz'))
- data = np.load(filename)['data']
- lons = data[:, 0].astype(np.float64)
- lats = data[:, 1].astype(np.float64)
- tb37v = data[:, 2].astype(np.float64)
-
- def test_ellps2axis(self):
- a, b = pr.plot.ellps2axis('WGS84')
- self.assertAlmostEqual(a, 6378137.0,
- msg='Failed to get semi-major axis of ellipsis')
- self.assertAlmostEqual(b, 6356752.3142451793,
- msg='Failed to get semi-minor axis of ellipsis')
-
- def test_area_def2basemap(self):
- area_def = pr.utils.parse_area_file(os.path.join(os.path.dirname(__file__),
- 'test_files', 'areas.cfg'), 'ease_sh')[0]
- bmap = pr.plot.area_def2basemap(area_def)
- self.assertTrue(bmap.rmajor == bmap.rminor and
- bmap.rmajor == 6371228.0,
- 'Failed to create Basemap object')
-
- def test_plate_carreeplot(self):
- area_def = pr.utils.parse_area_file(os.path.join(os.path.dirname(__file__),
- 'test_files', 'areas.cfg'), 'pc_world')[0]
- swath_def = pr.geometry.SwathDefinition(self.lons, self.lats)
- result = pr.kd_tree.resample_nearest(swath_def, self.tb37v, area_def,
- radius_of_influence=20000,
- fill_value=None)
- plt = pr.plot._get_quicklook(area_def, result, num_meridians=0,
- num_parallels=0)
- def test_easeplot(self):
- area_def = pr.utils.parse_area_file(os.path.join(os.path.dirname(__file__),
- 'test_files', 'areas.cfg'), 'ease_sh')[0]
- swath_def = pr.geometry.SwathDefinition(self.lons, self.lats)
- result = pr.kd_tree.resample_nearest(swath_def, self.tb37v, area_def,
- radius_of_influence=20000,
- fill_value=None)
- plt = pr.plot._get_quicklook(area_def, result)
-
- def test_orthoplot(self):
- area_def = pr.utils.parse_area_file(os.path.join(os.path.dirname(__file__),
- 'test_files', 'areas.cfg'), 'ortho')[0]
- swath_def = pr.geometry.SwathDefinition(self.lons, self.lats)
- result = pr.kd_tree.resample_nearest(swath_def, self.tb37v, area_def,
- radius_of_influence=20000,
- fill_value=None)
- plt = pr.plot._get_quicklook(area_def, result)
--
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