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
|
# (C) British Crown Copyright 2011 - 2020, 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 operator
import os
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pytest
from cartopy import config
from cartopy.tests.mpl import MPL_VERSION, ImageTesting
import cartopy.crs as ccrs
import cartopy.img_transform as im_trans
from functools import reduce
class TestRegrid(object):
def test_array_dims(self):
# Source data
source_nx = 100
source_ny = 100
source_x = np.linspace(-180.0,
180.0,
source_nx).astype(np.float64)
source_y = np.linspace(-90, 90.0, source_ny).astype(np.float64)
source_x, source_y = np.meshgrid(source_x, source_y)
data = np.arange(source_nx * source_ny,
dtype=np.int32).reshape(source_ny, source_nx)
source_cs = ccrs.Geodetic()
# Target grid
target_nx = 23
target_ny = 45
target_proj = ccrs.PlateCarree()
target_x, target_y, extent = im_trans.mesh_projection(target_proj,
target_nx,
target_ny)
# Perform regrid
new_array = im_trans.regrid(data, source_x, source_y, source_cs,
target_proj, target_x, target_y)
# Check dimensions of return array
assert new_array.shape == target_x.shape
assert new_array.shape == target_y.shape
assert new_array.shape == (target_ny, target_nx)
def test_different_dims(self):
# Source data
source_nx = 100
source_ny = 100
source_x = np.linspace(-180.0, 180.0,
source_nx).astype(np.float64)
source_y = np.linspace(-90, 90.0,
source_ny).astype(np.float64)
source_x, source_y = np.meshgrid(source_x, source_y)
data = np.arange(source_nx * source_ny,
dtype=np.int32).reshape(source_ny, source_nx)
source_cs = ccrs.Geodetic()
# Target grids (different shapes)
target_x_shape = (23, 45)
target_y_shape = (23, 44)
target_x = np.arange(reduce(operator.mul, target_x_shape),
dtype=np.float64).reshape(target_x_shape)
target_y = np.arange(reduce(operator.mul, target_y_shape),
dtype=np.float64).reshape(target_y_shape)
target_proj = ccrs.PlateCarree()
# Attempt regrid
with pytest.raises(ValueError):
im_trans.regrid(data, source_x, source_y, source_cs,
target_proj, target_x, target_y)
if MPL_VERSION < '2':
# Changes in zooming in old versions.
regrid_tolerance = 2.5
elif MPL_VERSION < '2.0.1':
regrid_tolerance = 0.5
elif MPL_VERSION < '2.1.0':
# Bug in latest Matplotlib that we don't consider correct.
regrid_tolerance = 4.78
else:
# Bug in latest Matplotlib that we don't consider correct.
regrid_tolerance = 5.55
@pytest.mark.natural_earth
@ImageTesting(['regrid_image'],
tolerance=regrid_tolerance)
def test_regrid_image():
# Source data
fname = os.path.join(config["repo_data_dir"], 'raster', 'natural_earth',
'50-natural-earth-1-downsampled.png')
nx = 720
ny = 360
source_proj = ccrs.PlateCarree()
source_x, source_y, _ = im_trans.mesh_projection(source_proj, nx, ny)
data = plt.imread(fname)
# Flip vertically to match source_x/source_y orientation
data = data[::-1]
# Target grid
target_nx = 300
target_ny = 300
target_proj = ccrs.InterruptedGoodeHomolosine()
target_x, target_y, target_extent = im_trans.mesh_projection(target_proj,
target_nx,
target_ny)
# Perform regrid
new_array = im_trans.regrid(data, source_x, source_y, source_proj,
target_proj, target_x, target_y)
# Plot
plt.figure(figsize=(10, 10))
gs = mpl.gridspec.GridSpec(nrows=4, ncols=1,
hspace=1.5, wspace=0.5)
# Set up axes and title
ax = plt.subplot(gs[0], projection=target_proj)
plt.imshow(new_array, origin='lower', extent=target_extent)
ax.coastlines()
# Plot each color slice (tests masking)
cmaps = {'red': 'Reds', 'green': 'Greens', 'blue': 'Blues'}
for i, color in enumerate(['red', 'green', 'blue']):
ax = plt.subplot(gs[i + 1], projection=target_proj)
plt.imshow(new_array[:, :, i], extent=target_extent, origin='lower',
cmap=cmaps[color])
ax.coastlines()
# Tighten up layout
gs.tight_layout(plt.gcf())
|
