/usr/lib/python2.7/dist-packages/cartopy/tests/test_img_transform.py is in python-cartopy 0.14.2+dfsg1-2build3.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | # (C) British Crown Copyright 2014 - 2016, Met Office
#
# This file is part of cartopy.
#
# cartopy 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.
#
# cartopy 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 cartopy. If not, see <https://www.gnu.org/licenses/>.
from __future__ import (absolute_import, division, print_function)
import numpy as np
from numpy.testing import assert_array_equal
import cartopy.img_transform as img_trans
import cartopy.crs as ccrs
def test_griding_data_std_range():
# Data which exists inside the standard projection bounds i.e.
# [-180, 180].
target_prj = ccrs.PlateCarree()
# create 3 data points
lats = np.array([65, 10, -45])
lons = np.array([-90, 0, 90])
data = np.array([1, 2, 3])
data_trans = ccrs.Geodetic()
target_x, target_y, extent = img_trans.mesh_projection(target_prj, 8, 4)
image = img_trans.regrid(data, lons, lats, data_trans, target_prj,
target_x, target_y,
mask_extrapolated=True)
# The expected image. n.b. on a map the data is reversed in the y axis.
expected = np.array([[3, 3, 3, 3, 3, 3, 3, 3],
[3, 1, 2, 2, 2, 3, 3, 3],
[1, 1, 1, 2, 2, 2, 3, 1],
[1, 1, 1, 1, 1, 1, 1, 1]], dtype=np.float64)
expected_mask = np.array(
[[True, True, True, True, True, True, True, True],
[True, False, False, False, False, False, False, True],
[True, False, False, False, False, False, False, True],
[True, True, True, True, True, True, True, True]])
assert_array_equal([-180, 180, -90, 90], extent)
assert_array_equal(expected, image)
assert_array_equal(expected_mask, image.mask)
def test_griding_data_outside_projection():
# Data which exists outside the standard projection e.g. [0, 360] rather
# than [-180, 180].
target_prj = ccrs.PlateCarree()
# create 3 data points
lats = np.array([65, 10, -45])
lons = np.array([120, 180, 240])
data = np.array([1, 2, 3])
data_trans = ccrs.Geodetic()
target_x, target_y, extent = img_trans.mesh_projection(target_prj, 8, 4)
image = img_trans.regrid(data, lons, lats, data_trans, target_prj,
target_x, target_y,
mask_extrapolated=True)
# The expected image. n.b. on a map the data is reversed in the y axis.
expected = np.array(
[[3, 3, 3, 3, 3, 3, 3, 3],
[3, 3, 3, 3, 3, 1, 2, 2],
[2, 2, 3, 1, 1, 1, 1, 2],
[1, 1, 1, 1, 1, 1, 1, 1]], dtype=np.float64)
expected_mask = np.array(
[[True, True, True, True, True, True, True, True],
[False, False, True, True, True, True, False, False],
[False, False, True, True, True, True, False, False],
[True, True, True, True, True, True, True, True]])
assert_array_equal([-180, 180, -90, 90], extent)
assert_array_equal(expected, image)
assert_array_equal(expected_mask, image.mask)
if __name__ == '__main__':
import nose
nose.runmodule(argv=['-sv', '--with-doctest'], exit=False)
|