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
|
# Copyright Cartopy Contributors
#
# This file is part of Cartopy and is released under the LGPL license.
# See COPYING and COPYING.LESSER in the root of the repository for full
# licensing details.
import operator
import os
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pytest
from cartopy import config
import cartopy.crs as ccrs
import cartopy.img_transform as im_trans
from functools import reduce
class TestRegrid:
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)
# Bug in latest Matplotlib that we don't consider correct.
@pytest.mark.natural_earth
@pytest.mark.mpl_image_compare(filename='regrid_image.png', tolerance=5.55)
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(emphasis='land')
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
fig = 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 = fig.add_subplot(gs[0], projection=target_proj)
ax.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 = fig.add_subplot(gs[i + 1], projection=target_proj)
ax.imshow(new_array[:, :, i], extent=target_extent, origin='lower',
cmap=cmaps[color])
ax.coastlines()
# Tighten up layout
gs.tight_layout(fig)
return fig
|
