def test_to_image_2D(self, mock_geoimage):
"""
Conversion to image
"""
# 2D
data = np.arange(25).reshape((5, 5))
p = dataset.Dataset(data, mode="L", fill_value=0, palette=[0, 1, 2, 3, 4, 5])
to_image(p)
np.testing.assert_array_equal(data, mock_geoimage.call_args[0][0][0])
mock_geoimage.reset_mock()
def test_to_image_3D(self, mock_geoimage):
"""Conversion to image."""
# 3D
data = np.arange(75).reshape((3, 5, 5))
p = xr.DataArray(data, dims=['bands', 'y', 'x'])
p['bands'] = ['R', 'G', 'B']
to_image(p)
np.testing.assert_array_equal(data[0], mock_geoimage.call_args[0][0][0])
np.testing.assert_array_equal(data[1], mock_geoimage.call_args[0][0][1])
np.testing.assert_array_equal(data[2], mock_geoimage.call_args[0][0][2])
def test_to_image_2D(self, mock_geoimage):
"""
Conversion to image
"""
# 2D
data = np.arange(25).reshape((5, 5))
p = projectable.Projectable(data, mode="L", fill_value=0, palette=[0, 1, 2, 3, 4, 5])
to_image(p)
np.testing.assert_array_equal(data, mock_geoimage.call_args[0][0][0])
mock_geoimage.reset_mock()
def test_to_image_3D(self, mock_geoimage):
"""
Conversion to image
"""
# 3D
data = np.arange(75).reshape((3, 5, 5))
p = dataset.Dataset(data)
to_image(p)
np.testing.assert_array_equal(data[0], mock_geoimage.call_args[0][0][0])
np.testing.assert_array_equal(data[1], mock_geoimage.call_args[0][0][1])
np.testing.assert_array_equal(data[2], mock_geoimage.call_args[0][0][2])
def test_to_image_3D(self, mock_geoimage):
"""
Conversion to image
"""
# 3D
data = np.arange(75).reshape((3, 5, 5))
p = projectable.Projectable(data)
to_image(p)
np.testing.assert_array_equal(data[0], mock_geoimage.call_args[0][0][0])
np.testing.assert_array_equal(data[1], mock_geoimage.call_args[0][0][1])
np.testing.assert_array_equal(data[2], mock_geoimage.call_args[0][0][2])
def test_to_image_2D(self, mock_geoimage):
"""Conversion to image."""
# 2D
data = np.arange(25).reshape((5, 5))
p = xr.DataArray(data, attrs=dict(mode="L", fill_value=0,
palette=[0, 1, 2, 3, 4, 5]),
dims=['y', 'x'])
to_image(p)
np.testing.assert_array_equal(
data, mock_geoimage.call_args[0][0].values)
mock_geoimage.reset_mock()
def get_image(self, sector_def):
local = self.scene.resample(sector_def)
img = to_image(local[self.product].squeeze())
self.enhance_image(img)
img = add_overlay(img,
area=sector_def,
coast_dir=COAST_DIR,
color=GOLDENROD,
fill_value=0)
pilimg = img.pil_image()
self.decorate_pilimg(pilimg)
return pilimg
global_scene.load([10.8])
area = "EuropeCanaryS95"
local_scene = global_scene.resample(area)
import numpy as np
from satpy.composites import BWCompositor
from satpy.enhancements import colorize
from satpy.writers import to_image
arr = np.array([[0, 0, 0], [255, 0, 0]])
np.save("/tmp/binary_colormap.npy", arr)
compositor = BWCompositor("test", standard_name="colorized_ir_clouds")
composite = compositor((local_scene[10.8], ))
img = to_image(composite)
kwargs = {
"palettes": [{
"filename": "/tmp/binary_colormap.npy",
"min_value": 223.15,
"max_value": 303.15
}]
}
kwargs = {
"palettes": [{
"colors": "spectral",
"min_value": 223.15,
"max_value": 303.15
}]
maia_scene = Scene(reader='maia', filenames=[fnmaia])
print(maia_scene.available_dataset_ids())
maia_scene.load(["CloudType", "ct", "cma", "cma_conf",
'opaq_cloud', "CloudTopPres",
"CloudTopTemp", "Alt_surface"])
# CloudType is a bit field containing the actual "ct" with values
# from 0 to 20 which can be interpreted according to the cpool colormap
# "ct" can be display in black and white:
maia_scene.show("ct")
# but it is better to palettize the image:
# step 1: creation of the palette
mycolors = []
for i in range(21):
mycolors.append(hex_to_rgb(cpool[i]))
arr = np.array(mycolors)
np.save("/tmp/binary_maia_ct_colormap.npy", arr)
# step2: creation of the composite
compositor = BWCompositor("test", standard_name="maia_ct")
composite = compositor((maia_scene["ct"],))
kwargs = {"palettes": [
{"filename": "/tmp/binary_maia_ct_colormap.npy",
"min_value": 0, "max_value": 20}]}
img = to_image(composite)
palettize(img, ** kwargs)
img.show()
img.pil_save("ct.png")
