示例#1
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 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()
示例#2
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 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])
示例#3
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 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()
示例#4
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 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])
示例#5
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 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])
示例#6
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    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
    }]
示例#9
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    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")