test_enhancements.py

# Copyright (c) 2017-2023 Satpy developers
#
# This file is part of satpy.
#
# satpy is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software
# Foundation, either version 3 of the License, or (at your option) any later
# version.
#
# satpy 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 General Public License for more details.
#
# You should have received a copy of the GNU General Public License along with
# satpy.  If not, see <http://www.gnu.org/licenses/>.
"""Unit testing the enhancements functions, e.g. cira_stretch."""

import contextlib
import os
from tempfile import NamedTemporaryFile
from unittest import mock

import dask.array as da
import numpy as np
import pytest
import xarray as xr

from satpy.enhancements import create_colormap, on_dask_array, on_separate_bands, using_map_blocks

# NOTE:
# The following fixtures are not defined in this file, but are used and injected by Pytest:
# - tmp_path


def run_and_check_enhancement(func, data, expected, **kwargs):
    """Perform basic checks that apply to multiple tests."""
    from trollimage.xrimage import XRImage

    pre_attrs = data.attrs
    img = XRImage(data)
    func(img, **kwargs)

    assert isinstance(img.data.data, da.Array)
    old_keys = set(pre_attrs.keys())
    # It is OK to have "enhancement_history" added
    new_keys = set(img.data.attrs.keys()) - {"enhancement_history"}
    assert old_keys == new_keys

    res_data_arr = img.data
    assert isinstance(res_data_arr, xr.DataArray)
    assert isinstance(res_data_arr.data, da.Array)
    res_data = res_data_arr.data.compute()  # mimics what xrimage geotiff writing does
    assert not isinstance(res_data, da.Array)
    np.testing.assert_allclose(res_data, expected, atol=1.e-6, rtol=0)


def identical_decorator(func):
    """Decorate but do nothing."""
    return func


class TestEnhancementStretch:
    """Class for testing enhancements in satpy.enhancements."""

    def setup_method(self):
        """Create test data used by every test."""
        data = np.arange(-210, 790, 100).reshape((2, 5)) * 0.95
        data[0, 0] = np.nan  # one bad value for testing
        crefl_data = np.arange(-210, 790, 100).reshape((2, 5)) * 0.95
        crefl_data /= 5.605
        crefl_data[0, 0] = np.nan  # one bad value for testing
        crefl_data[0, 1] = 0.
        self.ch1 = xr.DataArray(da.from_array(data, chunks=2), dims=('y', 'x'), attrs={'test': 'test'})
        self.ch2 = xr.DataArray(da.from_array(crefl_data, chunks=2), dims=('y', 'x'), attrs={'test': 'test'})
        rgb_data = np.stack([data, data, data])
        self.rgb = xr.DataArray(da.from_array(rgb_data, chunks=(3, 2, 2)),
                                dims=('bands', 'y', 'x'),
                                coords={'bands': ['R', 'G', 'B']})

    @pytest.mark.parametrize(
        ("decorator", "exp_call_cls"),
        [
            (identical_decorator, xr.DataArray),
            (on_dask_array, da.Array),
            (using_map_blocks, np.ndarray),
        ],
    )
    @pytest.mark.parametrize("input_data_name", ["ch1", "ch2", "rgb"])
    def test_apply_enhancement(self, input_data_name, decorator, exp_call_cls):
        """Test the 'apply_enhancement' utility function."""

        def _enh_func(img):
            def _calc_func(data):
                assert isinstance(data, exp_call_cls)
                return data

            decorated_func = decorator(_calc_func)
            return decorated_func(img.data)

        in_data = getattr(self, input_data_name)
        exp_data = in_data.values
        if "bands" not in in_data.coords:
            exp_data = exp_data[np.newaxis, :, :]
        run_and_check_enhancement(_enh_func, in_data, exp_data)

    def test_cira_stretch(self):
        """Test applying the cira_stretch."""
        from satpy.enhancements import cira_stretch

        expected = np.array([[
            [np.nan, -7.04045974, -7.04045974, 0.79630132, 0.95947296],
            [1.05181359, 1.11651012, 1.16635571, 1.20691137, 1.24110186]]])
        run_and_check_enhancement(cira_stretch, self.ch1, expected)

    def test_reinhard(self):
        """Test the reinhard algorithm."""
        from satpy.enhancements import reinhard_to_srgb
        expected = np.array([[[np.nan, 0., 0., 0.93333793, 1.29432402],
                              [1.55428709, 1.76572249, 1.94738635, 2.10848544, 2.25432809]],

                             [[np.nan, 0., 0., 0.93333793, 1.29432402],
                              [1.55428709, 1.76572249, 1.94738635, 2.10848544, 2.25432809]],

                             [[np.nan, 0., 0., 0.93333793, 1.29432402],
                              [1.55428709, 1.76572249, 1.94738635, 2.10848544, 2.25432809]]])
        run_and_check_enhancement(reinhard_to_srgb, self.rgb, expected)

    def test_lookup(self):
        """Test the lookup enhancement function."""
        from satpy.enhancements import lookup
        expected = np.array([[
            [0., 0., 0., 0.333333, 0.705882],
            [1., 1., 1., 1., 1.]]])
        lut = np.arange(256.)
        run_and_check_enhancement(lookup, self.ch1, expected, luts=lut)

        expected = np.array([[[0., 0., 0., 0.333333, 0.705882],
                              [1., 1., 1., 1., 1.]],
                             [[0., 0., 0., 0.333333, 0.705882],
                              [1., 1., 1., 1., 1.]],
                             [[0., 0., 0., 0.333333, 0.705882],
                              [1., 1., 1., 1., 1.]]])
        lut = np.arange(256.)
        lut = np.vstack((lut, lut, lut)).T
        run_and_check_enhancement(lookup, self.rgb, expected, luts=lut)

    def test_colorize(self):
        """Test the colorize enhancement function."""
        from trollimage.colormap import brbg

        from satpy.enhancements import colorize
        expected = np.array([
            [[np.nan, 3.29411723e-01, 3.29411723e-01, 3.21825881e-08, 3.21825881e-08],
             [3.21825881e-08, 3.21825881e-08, 3.21825881e-08, 3.21825881e-08, 3.21825881e-08]],
            [[np.nan, 1.88235327e-01, 1.88235327e-01, 2.35294109e-01, 2.35294109e-01],
             [2.35294109e-01, 2.35294109e-01, 2.35294109e-01, 2.35294109e-01, 2.35294109e-01]],
            [[np.nan, 1.96078164e-02, 1.96078164e-02, 1.88235281e-01, 1.88235281e-01],
             [1.88235281e-01, 1.88235281e-01, 1.88235281e-01, 1.88235281e-01, 1.88235281e-01]]])
        run_and_check_enhancement(colorize, self.ch1, expected, palettes=brbg)

    def test_palettize(self):
        """Test the palettize enhancement function."""
        from trollimage.colormap import brbg

        from satpy.enhancements import palettize
        expected = np.array([[[10, 0, 0, 10, 10], [10, 10, 10, 10, 10]]])
        run_and_check_enhancement(palettize, self.ch1, expected, palettes=brbg)

    def test_three_d_effect(self):
        """Test the three_d_effect enhancement function."""
        from satpy.enhancements import three_d_effect
        expected = np.array([[
            [np.nan, np.nan, -389.5, -294.5, 826.5],
            [np.nan, np.nan, 85.5, 180.5, 1301.5]]])
        run_and_check_enhancement(three_d_effect, self.ch1, expected)

    def test_piecewise_linear_stretch(self):
        """Test the piecewise_linear_stretch enhancement function."""
        from satpy.enhancements import piecewise_linear_stretch
        expected = np.array([[
            [np.nan, 0., 0., 0.44378, 0.631734],
            [0.737562, 0.825041, 0.912521, 1., 1.]]])
        run_and_check_enhancement(piecewise_linear_stretch,
                                  self.ch2 / 100.0,
                                  expected,
                                  xp=[0., 25., 55., 100., 255.],
                                  fp=[0., 90., 140., 175., 255.],
                                  reference_scale_factor=255,
                                  )

    def test_btemp_threshold(self):
        """Test applying the cira_stretch."""
        from satpy.enhancements import btemp_threshold

        expected = np.array([[
            [np.nan, 0.946207, 0.892695, 0.839184, 0.785672],
            [0.73216, 0.595869, 0.158745, -0.278379, -0.715503]]])
        run_and_check_enhancement(btemp_threshold, self.ch1, expected,
                                  min_in=-200, max_in=500, threshold=350)

    def test_merge_colormaps(self):
        """Test merging colormaps."""
        from trollimage.colormap import Colormap

        from satpy.enhancements import _merge_colormaps as mcp
        from satpy.enhancements import create_colormap
        ret_map = mock.MagicMock()

        create_colormap_mock = mock.Mock(wraps=create_colormap)
        cmap1 = Colormap((1, (1., 1., 1.)))
        kwargs = {'palettes': cmap1}

        with mock.patch('satpy.enhancements.create_colormap', create_colormap_mock):
            res = mcp(kwargs)
        assert res is cmap1
        create_colormap_mock.assert_not_called()
        create_colormap_mock.reset_mock()
        ret_map.reset_mock()

        cmap1 = {'colors': 'blues', 'min_value': 0,
                 'max_value': 1}
        kwargs = {'palettes': [cmap1]}
        with mock.patch('satpy.enhancements.create_colormap', create_colormap_mock), \
                mock.patch('trollimage.colormap.blues', ret_map):
            _ = mcp(kwargs)
        create_colormap_mock.assert_called_once()
        ret_map.reverse.assert_not_called()
        ret_map.set_range.assert_called_with(0, 1)
        create_colormap_mock.reset_mock()
        ret_map.reset_mock()

        cmap2 = {'colors': 'blues', 'min_value': 2,
                 'max_value': 3, 'reverse': True}
        kwargs = {'palettes': [cmap2]}
        with mock.patch('trollimage.colormap.blues', ret_map):
            _ = mcp(kwargs)
        ret_map.reverse.assert_called_once()
        ret_map.set_range.assert_called_with(2, 3)
        create_colormap_mock.reset_mock()
        ret_map.reset_mock()

        kwargs = {'palettes': [cmap1, cmap2]}
        with mock.patch('trollimage.colormap.blues', ret_map):
            _ = mcp(kwargs)
        ret_map.__add__.assert_called_once()

    def tearDown(self):
        """Clean up."""


@contextlib.contextmanager
def closed_named_temp_file(**kwargs):
    """Named temporary file context manager that closes the file after creation.

    This helps with Windows systems which can get upset with opening or
    deleting a file that is already open.

    """
    try:
        with NamedTemporaryFile(delete=False, **kwargs) as tmp_cmap:
            yield tmp_cmap.name
    finally:
        os.remove(tmp_cmap.name)


def _write_cmap_to_file(cmap_filename, cmap_data):
    ext = os.path.splitext(cmap_filename)[1]
    if ext in (".npy",):
        np.save(cmap_filename, cmap_data)
    elif ext in (".npz",):
        np.savez(cmap_filename, cmap_data)
    else:
        np.savetxt(cmap_filename, cmap_data, delimiter=",")


def _generate_cmap_test_data(color_scale, colormap_mode):
    cmap_data = np.array([
        [1, 0, 0],
        [1, 1, 0],
        [1, 1, 1],
        [0, 0, 1],
    ], dtype=np.float64)
    if len(colormap_mode) != 3:
        _cmap_data = cmap_data
        cmap_data = np.empty((cmap_data.shape[0], len(colormap_mode)),
                             dtype=np.float64)
        if colormap_mode.startswith("V") or colormap_mode.endswith("A"):
            cmap_data[:, 0] = np.array([128, 130, 132, 134]) / 255.0
            cmap_data[:, -3:] = _cmap_data
        if colormap_mode.startswith("V") and colormap_mode.endswith("A"):
            cmap_data[:, 1] = np.array([128, 130, 132, 134]) / 255.0
    if color_scale is None or color_scale == 255:
        cmap_data = (cmap_data * 255).astype(np.uint8)
    return cmap_data


class TestColormapLoading:
    """Test utilities used with colormaps."""

    @pytest.mark.parametrize("color_scale", [None, 1.0])
    @pytest.mark.parametrize("colormap_mode", ["RGB", "VRGB", "VRGBA"])
    @pytest.mark.parametrize("extra_kwargs",
                             [
                                 {},
                                 {"min_value": 50, "max_value": 100},
                             ])
    @pytest.mark.parametrize("filename_suffix", [".npy", ".npz", ".csv"])
    def test_cmap_from_file(self, color_scale, colormap_mode, extra_kwargs, filename_suffix):
        """Test that colormaps can be loaded from a binary file."""
        # create the colormap file on disk
        with closed_named_temp_file(suffix=filename_suffix) as cmap_filename:
            cmap_data = _generate_cmap_test_data(color_scale, colormap_mode)
            _write_cmap_to_file(cmap_filename, cmap_data)

            unset_first_value = 128.0 / 255.0 if colormap_mode.startswith("V") else 0.0
            unset_last_value = 134.0 / 255.0 if colormap_mode.startswith("V") else 1.0
            if (color_scale is None or color_scale == 255) and colormap_mode.startswith("V"):
                unset_first_value *= 255
                unset_last_value *= 255
            if "min_value" in extra_kwargs:
                unset_first_value = extra_kwargs["min_value"]
                unset_last_value = extra_kwargs["max_value"]

            first_color = [1.0, 0.0, 0.0]
            if colormap_mode == "VRGBA":
                first_color = [128.0 / 255.0] + first_color

            kwargs1 = {"filename": cmap_filename}
            kwargs1.update(extra_kwargs)
            if color_scale is not None:
                kwargs1["color_scale"] = color_scale

            cmap = create_colormap(kwargs1)
            assert cmap.colors.shape[0] == 4
            np.testing.assert_equal(cmap.colors[0], first_color)
            assert cmap.values.shape[0] == 4
            assert cmap.values[0] == unset_first_value
            assert cmap.values[-1] == unset_last_value

    def test_cmap_vrgb_as_rgba(self):
        """Test that data created as VRGB still reads as RGBA."""
        with closed_named_temp_file(suffix=".npy") as cmap_filename:
            cmap_data = _generate_cmap_test_data(None, "VRGB")
            np.save(cmap_filename, cmap_data)
            cmap = create_colormap({'filename': cmap_filename, 'colormap_mode': "RGBA"})
            assert cmap.colors.shape[0] == 4
            assert cmap.colors.shape[1] == 4  # RGBA
            np.testing.assert_equal(cmap.colors[0], [128 / 255., 1.0, 0, 0])
            assert cmap.values.shape[0] == 4
            assert cmap.values[0] == 0
            assert cmap.values[-1] == 1.0

    @pytest.mark.parametrize(
        ("real_mode", "forced_mode"),
        [
            ("VRGBA", "RGBA"),
            ("VRGBA", "VRGB"),
            ("RGBA", "RGB"),
        ]
    )
    @pytest.mark.parametrize("filename_suffix", [".npy", ".csv"])
    def test_cmap_bad_mode(self, real_mode, forced_mode, filename_suffix):
        """Test that reading colormaps with the wrong mode fails."""
        with closed_named_temp_file(suffix=filename_suffix) as cmap_filename:
            cmap_data = _generate_cmap_test_data(None, real_mode)
            _write_cmap_to_file(cmap_filename, cmap_data)
            # Force colormap_mode VRGBA to RGBA and we should see an exception
            with pytest.raises(ValueError):
                create_colormap({'filename': cmap_filename, 'colormap_mode': forced_mode})

    def test_cmap_from_file_bad_shape(self):
        """Test that unknown array shape causes an error."""
        from satpy.enhancements import create_colormap

        # create the colormap file on disk
        with closed_named_temp_file(suffix='.npy') as cmap_filename:
            np.save(cmap_filename, np.array([
                [0],
                [64],
                [128],
                [255],
            ]))

            with pytest.raises(ValueError):
                create_colormap({'filename': cmap_filename})

    def test_cmap_from_config_path(self, tmp_path):
        """Test loading a colormap relative to a config path."""
        import satpy
        from satpy.enhancements import create_colormap

        cmap_dir = tmp_path / "colormaps"
        cmap_dir.mkdir()
        cmap_filename = cmap_dir / "my_colormap.npy"
        cmap_data = _generate_cmap_test_data(None, "RGBA")
        np.save(cmap_filename, cmap_data)
        with satpy.config.set(config_path=[tmp_path]):
            rel_cmap_filename = os.path.join("colormaps", "my_colormap.npy")
            cmap = create_colormap({'filename': rel_cmap_filename, 'colormap_mode': "RGBA"})
            assert cmap.colors.shape[0] == 4
            assert cmap.colors.shape[1] == 4  # RGBA
            np.testing.assert_equal(cmap.colors[0], [128 / 255., 1.0, 0, 0])
            assert cmap.values.shape[0] == 4
            assert cmap.values[0] == 0
            assert cmap.values[-1] == 1.0

    def test_cmap_from_trollimage(self):
        """Test that colormaps in trollimage can be loaded."""
        from satpy.enhancements import create_colormap
        cmap = create_colormap({'colors': 'pubu'})
        from trollimage.colormap import pubu
        np.testing.assert_equal(cmap.colors, pubu.colors)
        np.testing.assert_equal(cmap.values, pubu.values)

    def test_cmap_no_colormap(self):
        """Test that being unable to create a colormap raises an error."""
        from satpy.enhancements import create_colormap
        with pytest.raises(ValueError):
            create_colormap({})

    def test_cmap_list(self):
        """Test that colors can be a list/tuple."""
        from satpy.enhancements import create_colormap
        colors = [
            [0, 0, 1],
            [1, 0, 1],
            [0, 1, 1],
            [1, 1, 1],
        ]
        values = [2, 4, 6, 8]
        cmap = create_colormap({'colors': colors, 'color_scale': 1})
        assert cmap.colors.shape[0] == 4
        np.testing.assert_equal(cmap.colors[0], [0.0, 0.0, 1.0])
        assert cmap.values.shape[0] == 4
        assert cmap.values[0] == 0
        assert cmap.values[-1] == 1.0

        cmap = create_colormap({'colors': colors, 'color_scale': 1, 'values': values})
        assert cmap.colors.shape[0] == 4
        np.testing.assert_equal(cmap.colors[0], [0.0, 0.0, 1.0])
        assert cmap.values.shape[0] == 4
        assert cmap.values[0] == 2
        assert cmap.values[-1] == 8


def test_on_separate_bands():
    """Test the `on_separate_bands` decorator."""

    def func(array, index, gain=2):
        return xr.DataArray(np.ones(array.shape, dtype=array.dtype) * index * gain,
                            coords=array.coords, dims=array.dims, attrs=array.attrs)

    separate_func = on_separate_bands(func)
    arr = xr.DataArray(np.zeros((3, 10, 10)), dims=['bands', 'y', 'x'], coords={"bands": ["R", "G", "B"]})
    assert separate_func(arr).shape == arr.shape
    assert all(separate_func(arr, gain=1).values[:, 0, 0] == [0, 1, 2])


def test_using_map_blocks():
    """Test the `using_map_blocks` decorator."""

    def func(np_array, block_info=None):
        value = block_info[0]['chunk-location'][-1]
        return np.ones(np_array.shape) * value

    map_blocked_func = using_map_blocks(func)
    arr = xr.DataArray(da.zeros((3, 10, 10), dtype=int, chunks=5), dims=['bands', 'y', 'x'])
    res = map_blocked_func(arr)
    assert res.shape == arr.shape
    assert res[0, 0, 0].compute() != res[0, 9, 9].compute()


def test_on_dask_array():
    """Test the `on_dask_array` decorator."""

    def func(dask_array):
        if not isinstance(dask_array, da.core.Array):
            pytest.fail("Array is not a dask array")
        return dask_array

    dask_func = on_dask_array(func)
    arr = xr.DataArray(da.zeros((3, 10, 10), dtype=int, chunks=5), dims=['bands', 'y', 'x'])
    res = dask_func(arr)
    assert res.shape == arr.shape


@pytest.fixture
def fake_area():
    """Return a fake 2×2 area."""
    from pyresample.geometry import create_area_def
    return create_area_def("wingertsberg", 4087, area_extent=[-2_000, -2_000, 2_000, 2_000], shape=(2, 2))


_nwcsaf_geo_props = {
    'cma_geo': ("geo", "cma", None, 'cma_pal', None, 'cloudmask', 'CMA', "uint8"),
    'cma_pps': ("pps", "cma", None, 'cma_pal', None, 'cloudmask', 'CMA', "uint8"),
    'cma_extended_pps': ("pps", "cma_extended", None, 'cma_extended_pal', None,
                         'cloudmask_extended', 'CMA', "uint8"),
    'cmaprob_pps': ("pps", "cmaprob", None, 'cmaprob_pal', None, 'cloudmask_probability',
                    'CMAPROB', "uint8"),
    'ct_geo': ("geo", "ct", None, 'ct_pal', None, 'cloudtype', 'CT', "uint8"),
    'ct_pps': ("pps", "ct", None, 'ct_pal', None, 'cloudtype', 'CT', "uint8"),
    'ctth_alti_geo': ("geo", "ctth_alti", None, 'ctth_alti_pal', None, 'cloud_top_height',
                      'CTTH', "float64"),
    'ctth_alti_pps': ("pps", "ctth_alti", None, 'ctth_alti_pal', "ctth_status_flag",
                      'cloud_top_height', 'CTTH', "float64"),
    'ctth_pres_geo': ("geo", "ctth_pres", None, 'ctth_pres_pal', None, 'cloud_top_pressure',
                      'CTTH', "float64"),
    'ctth_pres_pps': ("pps", "ctth_pres", None, 'ctth_pres_pal', None, 'cloud_top_pressure',
                      'CTTH', "float64"),
    'ctth_tempe_geo': ("geo", "ctth_tempe", None, 'ctth_tempe_pal', None, 'cloud_top_temperature',
                       'CTTH', "float64"),
    'ctth_tempe_pps': ("pps", "ctth_tempe", None, 'ctth_tempe_pal', None, 'cloud_top_temperature',
                       'CTTH', "float64"),
    'cmic_phase_geo': ("geo", "cmic_phase", None, 'cmic_phase_pal', None, 'cloud_top_phase',
                       'CMIC', "uint8"),
    'cmic_phase_pps': ("pps", "cmic_phase", None, 'cmic_phase_pal', "cmic_status_flag", 'cloud_top_phase',
                       'CMIC', "uint8"),
    'cmic_reff_geo': ("geo", "cmic_reff", None, 'cmic_reff_pal', None, 'cloud_drop_effective_radius',
                      'CMIC', "float64"),
    'cmic_reff_pps': ("pps", "cmic_reff", "cmic_cre", 'cmic_cre_pal', "cmic_status_flag",
                      'cloud_drop_effective_radius', 'CMIC', "float64"),
    'cmic_cot_geo': ("geo", "cmic_cot", None, 'cmic_cot_pal', None, 'cloud_optical_thickness',
                     'CMIC', "float64"),
    'cmic_cot_pps': ("pps", "cmic_cot", None, 'cmic_cot_pal', None, 'cloud_optical_thickness',
                     'CMIC', "float64"),
    'cmic_cwp_pps': ("pps", "cmic_cwp", None, 'cmic_cwp_pal', None, 'cloud_water_path',
                     'CMIC', "float64"),
    'cmic_lwp_geo': ("geo", "cmic_lwp", None, 'cmic_lwp_pal', None, 'cloud_liquid_water_path',
                     'CMIC', "float64"),
    'cmic_lwp_pps': ("pps", "cmic_lwp", None, 'cmic_lwp_pal', None, 'liquid_water_path',
                     'CMIC', "float64"),
    'cmic_iwp_geo': ("geo", "cmic_iwp", None, 'cmic_iwp_pal', None, 'cloud_ice_water_path',
                     'CMIC', "float64"),
    'cmic_iwp_pps': ("pps", "cmic_iwp", None, 'cmic_iwp_pal', None, 'ice_water_path',
                     'CMIC', "float64"),
    'pc': ("geo", "pc", None, 'pc_pal', None, 'precipitation_probability', 'PC', "uint8"),
    'crr': ("geo", "crr", None, 'crr_pal', None, 'convective_rain_rate', 'CRR', "uint8"),
    'crr_accum': ("geo", "crr_accum", None, 'crr_pal', None,
                  'convective_precipitation_hourly_accumulation', 'CRR', "uint8"),
    'ishai_tpw': ("geo", "ishai_tpw", None, 'ishai_tpw_pal', None, 'total_precipitable_water',
                  'iSHAI', "float64"),
    'ishai_shw': ("geo", "ishai_shw", None, 'ishai_shw_pal', None, 'showalter_index',
                  'iSHAI', "float64"),
    'ishai_li': ("geo", "ishai_li", None, 'ishai_li_pal', None, 'lifted_index',
                 'iSHAI', "float64"),
    'ci_prob30': ("geo", "ci_prob30", None, 'ci_pal', None, 'convection_initiation_prob30',
                  'CI', "float64"),
    'ci_prob60': ("geo", "ci_prob60", None, 'ci_pal', None, 'convection_initiation_prob60',
                  'CI', "float64"),
    'ci_prob90': ("geo", "ci_prob90", None, 'ci_pal', None, 'convection_initiation_prob90',
                  'CI', "float64"),
    'asii_turb_trop_prob': ("geo", "asii_turb_trop_prob", None, 'asii_turb_prob_pal', None,
                            'asii_prob', 'ASII-NG', "float64"),
    'MapCellCatType': ("geo", "MapCellCatType", None, 'MapCellCatType_pal', None,
                       'rdt_cell_type', 'RDT-CW', "uint8"),
}


@pytest.mark.parametrize(
    "data",
    ['cma_geo', 'cma_pps', 'cma_extended_pps', 'cmaprob_pps', 'ct_geo',
     'ct_pps', 'ctth_alti_geo', 'ctth_alti_pps', 'ctth_pres_geo',
     'ctth_pres_pps', 'ctth_tempe_geo', 'ctth_tempe_pps',
     'cmic_phase_geo', 'cmic_phase_pps', 'cmic_reff_geo',
     'cmic_reff_pps', 'cmic_cot_geo', 'cmic_cot_pps', 'cmic_cwp_pps',
     'cmic_lwp_geo', 'cmic_lwp_pps', 'cmic_iwp_geo', 'cmic_iwp_pps',
     'pc', 'crr', 'crr_accum', 'ishai_tpw', 'ishai_shw', 'ishai_li',
     'ci_prob30', 'ci_prob60', 'ci_prob90', 'asii_turb_trop_prob',
     'MapCellCatType']
)
def test_nwcsaf_comps(fake_area, tmp_path, data):
    """Test loading NWCSAF composites."""
    from satpy.writers import get_enhanced_image

    from ... import Scene
    (flavour, dvname, altname, palettename, statusname, comp, filelabel, dtp) = _nwcsaf_geo_props[data]
    rng = (0, 100) if dtp == "uint8" else (-100, 1000)
    if flavour == "geo":
        fn = f"S_NWC_{filelabel:s}_MSG2_MSG-N-VISIR_20220124T094500Z.nc"
        reader = "nwcsaf-geo"
        id_ = {"satellite_identifier": "MSG4"}
    else:
        fn = f"S_NWC_{filelabel:s}_noaa20_00000_20230301T1200213Z_20230301T1201458Z.nc"
        reader = "nwcsaf-pps_nc"
        id_ = {"platform": "NOAA-20"}
    fk = tmp_path / fn
    # create a minimally fake netCDF file, otherwise satpy won't load the
    # composite
    ds = xr.Dataset(
        coords={"nx": [0], "ny": [0]},
        attrs={
            "source": "satpy unit test",
            "time_coverage_start": "0001-01-01T00:00:00Z",
            "time_coverage_end": "0001-01-01T01:00:00Z",
        }
    )
    ds.attrs.update(id_)
    ds.to_netcdf(fk)
    sc = Scene(filenames=[os.fspath(fk)], reader=[reader])
    sc[palettename] = xr.DataArray(
        da.tile(da.arange(256), [3, 1]).T,
        dims=("pal02_colors", "pal_RGB"))
    fake_alti = da.linspace(rng[0], rng[1], 4, chunks=2, dtype=dtp).reshape(2, 2)
    ancvars = [sc[palettename]]
    if statusname is not None:
        sc[statusname] = xr.DataArray(
            da.zeros(shape=(2, 2), dtype="uint8"),
            attrs={
                "area": fake_area,
                "_FillValue": 123},
            dims=("y", "x"))
        ancvars.append(sc[statusname])
    sc[dvname] = xr.DataArray(
        fake_alti,
        dims=("y", "x"),
        attrs={
            "area": fake_area,
            "scaled_FillValue": 123,
            "ancillary_variables": ancvars,
            "valid_range": rng})

    def _fake_get_varname(info, info_type="file_key"):
        return altname or dvname

    with mock.patch("satpy.readers.nwcsaf_nc.NcNWCSAF._get_varname_in_file") as srnN_:
        srnN_.side_effect = _fake_get_varname
        sc.load([comp])
    im = get_enhanced_image(sc[comp])
    if flavour == "geo":
        assert im.mode == "P"
        np.testing.assert_array_equal(im.data.coords["bands"], ["P"])
        if dtp == "float64":
            np.testing.assert_allclose(
                im.data.sel(bands="P"),
                ((fake_alti - rng[0]) * (255 / np.ptp(rng))).round())
        else:
            np.testing.assert_allclose(im.data.sel(bands="P"), fake_alti)


class TestTCREnhancement:
    """Test the AHI enhancement functions."""

    def setup_method(self):
        """Create test data."""
        data = da.arange(-100, 1000, 110).reshape(2, 5)
        rgb_data = np.stack([data, data, data])
        self.rgb = xr.DataArray(rgb_data, dims=('bands', 'y', 'x'),
                                coords={'bands': ['R', 'G', 'B']},
                                attrs={'platform_name': 'Himawari-8'})

    def test_jma_true_color_reproduction(self):
        """Test the jma_true_color_reproduction enhancement."""
        from trollimage.xrimage import XRImage

        from satpy.enhancements import jma_true_color_reproduction

        expected = [[[-109.93, 10.993, 131.916, 252.839, 373.762],
                     [494.685, 615.608, 736.531, 857.454, 978.377]],

                    [[-97.73, 9.773, 117.276, 224.779, 332.282],
                     [439.785, 547.288, 654.791, 762.294, 869.797]],

                    [[-93.29, 9.329, 111.948, 214.567, 317.186],
                     [419.805, 522.424, 625.043, 727.662, 830.281]]]

        img = XRImage(self.rgb)
        jma_true_color_reproduction(img)

        np.testing.assert_almost_equal(img.data.compute(), expected)

        self.rgb.attrs['platform_name'] = None
        img = XRImage(self.rgb)
        with pytest.raises(ValueError, match="Missing platform name."):
            jma_true_color_reproduction(img)

        self.rgb.attrs['platform_name'] = 'Fakesat'
        img = XRImage(self.rgb)
        with pytest.raises(KeyError, match="No conversion matrix found for platform Fakesat"):
            jma_true_color_reproduction(img)