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
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
|
# Copyright Crown and Cartopy Contributors
#
# This file is part of Cartopy and is released under the BSD 3-clause license.
# See LICENSE in the root of the repository for full licensing details.
import types
import matplotlib.colors as colors
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import pytest
import shapely.geometry as sgeom
from cartopy import config
import cartopy.crs as ccrs
import cartopy.io.img_tiles as cimgt
from cartopy.tests.conftest import _HAS_PYKDTREE_OR_SCIPY
import cartopy.tests.test_img_tiles as ctest_tiles
if not _HAS_PYKDTREE_OR_SCIPY:
pytest.skip('pykdtree or scipy is required', allow_module_level=True)
NATURAL_EARTH_IMG = (config["repo_data_dir"] / 'raster' / 'natural_earth'
/ '50-natural-earth-1-downsampled.png')
REGIONAL_IMG = (config['repo_data_dir'] / 'raster' / 'sample'
/ 'Miriam.A2012270.2050.2km.jpg')
# We have an exceptionally large tolerance for the web_tiles test.
# The basemap changes on a regular basis (for seasons) and we really only
# care that it is putting images onto the map which are roughly correct.
@pytest.mark.natural_earth
@pytest.mark.network
@pytest.mark.mpl_image_compare(filename='web_tiles.png', tolerance=5.91)
def test_web_tiles():
extent = [-15, 0.1, 50, 60]
target_domain = sgeom.Polygon([[extent[0], extent[1]],
[extent[2], extent[1]],
[extent[2], extent[3]],
[extent[0], extent[3]],
[extent[0], extent[1]]])
map_prj = cimgt.GoogleTiles().crs
fig = plt.figure()
ax = fig.add_subplot(2, 2, 1, projection=map_prj)
gt = cimgt.GoogleTiles()
gt._image_url = types.MethodType(ctest_tiles.GOOGLE_IMAGE_URL_REPLACEMENT,
gt)
img, extent, origin = gt.image_for_domain(target_domain, 1)
ax.imshow(np.array(img), extent=extent, transform=gt.crs,
interpolation='bilinear', origin=origin)
ax.coastlines(color='white')
ax = fig.add_subplot(2, 2, 2, projection=map_prj)
qt = cimgt.QuadtreeTiles()
img, extent, origin = qt.image_for_domain(target_domain, 1)
ax.imshow(np.array(img), extent=extent, transform=qt.crs,
interpolation='bilinear', origin=origin)
ax.coastlines(color='white')
ax = fig.add_subplot(2, 2, 3, projection=map_prj)
osm = cimgt.OSM()
img, extent, origin = osm.image_for_domain(target_domain, 1)
ax.imshow(np.array(img), extent=extent, transform=osm.crs,
interpolation='bilinear', origin=origin)
ax.coastlines()
return fig
@pytest.mark.natural_earth
@pytest.mark.network
@pytest.mark.mpl_image_compare(filename='image_merge.png', tolerance=0.03)
def test_image_merge():
# tests the basic image merging functionality
tiles = []
for i in range(1, 4):
for j in range(0, 3):
tiles.append((i, j, 2))
gt = cimgt.GoogleTiles()
gt._image_url = types.MethodType(ctest_tiles.GOOGLE_IMAGE_URL_REPLACEMENT,
gt)
images_to_merge = []
for tile in tiles:
img, extent, origin = gt.get_image(tile)
img = np.array(img)
x = np.linspace(extent[0], extent[1], img.shape[1], endpoint=False)
y = np.linspace(extent[2], extent[3], img.shape[0], endpoint=False)
images_to_merge.append([img, x, y, origin])
img, extent, origin = cimgt._merge_tiles(images_to_merge)
ax = plt.axes(projection=gt.crs)
ax.set_global()
ax.coastlines()
ax.imshow(img, origin=origin, extent=extent, alpha=0.5)
return ax.figure
@pytest.mark.mpl_image_compare(filename='imshow_natural_earth_ortho.png')
def test_imshow():
source_proj = ccrs.PlateCarree()
img = plt.imread(NATURAL_EARTH_IMG)
# Convert the image to a byte array, rather than float, which is the
# form that JPG images would be loaded with imread.
img = (img * 255).astype('uint8')
ax = plt.axes(projection=ccrs.Orthographic())
ax.imshow(img, transform=source_proj,
extent=[-180, 180, -90, 90])
return ax.figure
@pytest.mark.natural_earth
@pytest.mark.mpl_image_compare(filename='imshow_regional_projected.png',
tolerance=1.97)
def test_imshow_projected():
source_proj = ccrs.PlateCarree()
img_extent = (-120.67660000000001, -106.32104523100001,
13.2301484511245, 30.766899999999502)
img = plt.imread(REGIONAL_IMG)
ax = plt.axes(projection=ccrs.LambertConformal())
ax.set_extent(img_extent, crs=source_proj)
ax.coastlines(resolution='50m')
ax.imshow(img, extent=img_extent, transform=source_proj)
return ax.figure
def test_imshow_wrapping():
ax = plt.axes(projection=ccrs.PlateCarree(central_longitude=0.0))
# Set the extent outside of the current projection domain to ensure
# it is wrapped back to the (-180, 180) extent of the projection
ax.imshow(np.random.random((10, 10)), transform=ccrs.PlateCarree(),
extent=(0, 360, -90, 90))
assert ax.get_xlim() == (-180, 180)
def test_imshow_arguments():
"""Smoke test for imshow argument passing in the fast-path"""
ax = plt.axes(projection=ccrs.PlateCarree())
# Set the regrid_shape parameter to ensure it isn't passed to Axes.imshow()
# in the fast-path call to super()
with pytest.warns(UserWarning, match="ignoring regrid_shape"):
ax.imshow(np.random.random((10, 10)), transform=ccrs.PlateCarree(),
extent=(-180, 180, -90, 90), regrid_shape=500)
def test_imshow_rgba():
# tests that the alpha of a RGBA array passed to imshow is set to 0
# instead of masked
z = np.full((100, 100), 0.5)
cmap = plt.get_cmap()
norm = colors.Normalize(vmin=0, vmax=1)
z1 = cmap(norm(z))
plt_crs = ccrs.LambertAzimuthalEqualArea()
latlon_crs = ccrs.PlateCarree()
ax = plt.axes(projection=plt_crs)
ax.set_extent([-30, -20, 60, 70], crs=latlon_crs)
img = ax.imshow(z1, extent=[-26, -24, 64, 66], transform=latlon_crs)
assert sum(img.get_array().data[:, 0, 3]) == 0
def test_imshow_rgba_alpha():
# test that alpha channel from RGBA is not skipped
dy, dx = (3, 4)
ax = plt.axes(projection=ccrs.Orthographic(-120, 45))
# Create RGBA Image with random data and linspace alpha
RGBA = np.linspace(0, 255 * 31, dx * dy * 4,
dtype=np.uint8).reshape((dy, dx, 4))
alpha = np.array([0, 85, 170, 255])
RGBA[:, :, 3] = alpha
img = ax.imshow(RGBA, transform=ccrs.PlateCarree())
assert np.all(np.unique(img.get_array().data[:, :, 3]) == alpha)
def test_imshow_rgb():
# tests that the alpha of a RGB array passed to imshow is set to 0
# instead of masked
z = np.full((100, 100, 3), 0.5)
plt_crs = ccrs.LambertAzimuthalEqualArea()
latlon_crs = ccrs.PlateCarree()
ax = plt.axes(projection=plt_crs)
ax.set_extent([-30, -20, 60, 70], crs=latlon_crs)
img = ax.imshow(z, extent=[-26, -24, 64, 66], transform=latlon_crs)
assert sum(img.get_array().data[:, 0, 3]) == 0
@pytest.mark.mpl_image_compare(filename='imshow_natural_earth_ortho.png')
def test_stock_img():
ax = plt.axes(projection=ccrs.Orthographic())
ax.stock_img()
return ax.figure
@pytest.mark.mpl_image_compare(filename='imshow_natural_earth_ortho.png')
def test_pil_Image():
img = Image.open(NATURAL_EARTH_IMG)
source_proj = ccrs.PlateCarree()
ax = plt.axes(projection=ccrs.Orthographic())
ax.imshow(img, transform=source_proj,
extent=[-180, 180, -90, 90])
return ax.figure
@pytest.mark.mpl_image_compare(filename='imshow_natural_earth_ortho.png')
def test_background_img():
ax = plt.axes(projection=ccrs.Orthographic())
ax.background_img(name='ne_shaded', resolution='low')
return ax.figure
def test_alpha_2d_warp():
# tests that both image and alpha arrays (if alpha is 2D) are warped
plt_crs = ccrs.Geostationary(central_longitude=-155.)
fig = plt.figure(figsize=(5, 5))
ax = fig.add_subplot(1, 1, 1, projection=plt_crs)
latlon_crs = ccrs.PlateCarree()
coords = [-162., -148., 17.5, 23.]
ax.set_extent(coords, crs=latlon_crs)
fake_data = np.zeros([100, 100])
fake_alphas = np.zeros(fake_data.shape)
image = ax.imshow(fake_data, extent=coords, transform=latlon_crs,
alpha=fake_alphas)
image_data = image.get_array()
image_alpha = image.get_alpha()
assert image_data.shape == image_alpha.shape
|
