diff --git a/lib/matplotlib/__init__.py b/lib/matplotlib/__init__.py index dc8c004a598b..13bfa81d9ffa 100644 --- a/lib/matplotlib/__init__.py +++ b/lib/matplotlib/__init__.py @@ -129,6 +129,8 @@ "interactive", "is_interactive", "colormaps", + "multivar_colormaps", + "bivar_colormaps", "color_sequences", ] @@ -1543,4 +1545,6 @@ def validate_backend(s): # workaround: we must defer colormaps import to after loading rcParams, because # colormap creation depends on rcParams from matplotlib.cm import _colormaps as colormaps # noqa: E402 +from matplotlib.cm import _multivar_colormaps as multivar_colormaps # noqa: E402 +from matplotlib.cm import _bivar_colormaps as bivar_colormaps # noqa: E402 from matplotlib.colors import _color_sequences as color_sequences # noqa: E402 diff --git a/lib/matplotlib/__init__.pyi b/lib/matplotlib/__init__.pyi index 05dc927dc6c9..5b6797d3a7da 100644 --- a/lib/matplotlib/__init__.pyi +++ b/lib/matplotlib/__init__.pyi @@ -116,4 +116,6 @@ def _preprocess_data( ) -> Callable: ... from matplotlib.cm import _colormaps as colormaps # noqa: E402 +from matplotlib.cm import _multivar_colormaps as multivar_colormaps # noqa: E402 +from matplotlib.cm import _bivar_colormaps as bivar_colormaps # noqa: E402 from matplotlib.colors import _color_sequences as color_sequences # noqa: E402 diff --git a/lib/matplotlib/_cm_bivar.py b/lib/matplotlib/_cm_bivar.py new file mode 100644 index 000000000000..53c0d48d7d6c --- /dev/null +++ b/lib/matplotlib/_cm_bivar.py @@ -0,0 +1,1312 @@ +# auto-generated by https://github.com/trygvrad/multivariate_colormaps +# date: 2024-05-24 + +import numpy as np +from matplotlib.colors import SegmentedBivarColormap + +BiPeak = np.array( + [0.000, 0.674, 0.931, 0.000, 0.680, 0.922, 0.000, 0.685, 0.914, 0.000, + 0.691, 0.906, 0.000, 0.696, 0.898, 0.000, 0.701, 0.890, 0.000, 0.706, + 0.882, 0.000, 0.711, 0.875, 0.000, 0.715, 0.867, 0.000, 0.720, 0.860, + 0.000, 0.725, 0.853, 0.000, 0.729, 0.845, 0.000, 0.733, 0.838, 0.000, + 0.737, 0.831, 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0.532, 0.975, 0.500, 0.516, 0.974, 0.497, 0.500, + 0.974, 0.493, 0.483, 0.973, 0.489, 0.467, 0.971, 0.485, 0.450, 0.970, + 0.480, 0.433, 0.968, 0.476, 0.415, 0.966, 0.471, 0.397, 0.964, 0.466, + 0.380, 0.962, 0.461, 0.362, 0.959, 0.456, 0.343, 0.956, 0.451, 0.325, + 0.953, 0.446, 0.306, 0.950, 0.440, 0.287, 0.947, 0.435, 0.268, 0.943, + 0.429, 0.248, 0.939, 0.423, 0.228, 0.936, 0.417, 0.207, 0.931, 0.411, + 0.185, 0.927, 0.405, 0.162, 0.923, 0.399, 0.138, 0.918, 0.393, 0.111, + 0.809, 0.354, 0.960, 0.814, 0.364, 0.951, 0.819, 0.375, 0.942, 0.825, + 0.384, 0.934, 0.830, 0.393, 0.925, 0.835, 0.402, 0.917, 0.841, 0.410, + 0.908, 0.846, 0.418, 0.900, 0.852, 0.426, 0.892, 0.857, 0.433, 0.884, + 0.863, 0.440, 0.876, 0.868, 0.446, 0.868, 0.873, 0.452, 0.860, 0.879, + 0.458, 0.852, 0.884, 0.464, 0.843, 0.889, 0.469, 0.835, 0.894, 0.474, + 0.827, 0.899, 0.478, 0.818, 0.904, 0.483, 0.810, 0.909, 0.486, 0.801, + 0.914, 0.490, 0.792, 0.919, 0.493, 0.783, 0.923, 0.496, 0.774, 0.928, + 0.499, 0.764, 0.932, 0.501, 0.755, 0.936, 0.503, 0.745, 0.940, 0.505, + 0.734, 0.944, 0.506, 0.724, 0.948, 0.507, 0.713, 0.951, 0.508, 0.701, + 0.954, 0.508, 0.690, 0.957, 0.508, 0.678, 0.960, 0.508, 0.666, 0.962, + 0.507, 0.653, 0.965, 0.507, 0.640, 0.967, 0.505, 0.627, 0.968, 0.504, + 0.613, 0.970, 0.502, 0.599, 0.971, 0.500, 0.585, 0.972, 0.498, 0.570, + 0.973, 0.495, 0.555, 0.973, 0.493, 0.540, 0.973, 0.490, 0.525, 0.973, + 0.486, 0.509, 0.973, 0.483, 0.493, 0.972, 0.479, 0.476, 0.971, 0.475, + 0.460, 0.970, 0.471, 0.443, 0.969, 0.467, 0.426, 0.967, 0.463, 0.409, + 0.965, 0.458, 0.391, 0.963, 0.453, 0.374, 0.961, 0.449, 0.356, 0.958, + 0.444, 0.338, 0.956, 0.438, 0.319, 0.953, 0.433, 0.301, 0.949, 0.428, + 0.282, 0.946, 0.422, 0.263, 0.943, 0.417, 0.243, 0.939, 0.411, 0.223, + 0.935, 0.405, 0.202, 0.931, 0.399, 0.181, 0.927, 0.393, 0.158, 0.923, + 0.387, 0.134, 0.918, 0.381, 0.107, + ]).reshape((65, 65, 3)) + +BiOrangeBlue = np.array( + [0.000, 0.000, 0.000, 0.000, 0.062, 0.125, 0.000, 0.125, 0.250, 0.000, + 0.188, 0.375, 0.000, 0.250, 0.500, 0.000, 0.312, 0.625, 0.000, 0.375, + 0.750, 0.000, 0.438, 0.875, 0.000, 0.500, 1.000, 0.125, 0.062, 0.000, + 0.125, 0.125, 0.125, 0.125, 0.188, 0.250, 0.125, 0.250, 0.375, 0.125, + 0.312, 0.500, 0.125, 0.375, 0.625, 0.125, 0.438, 0.750, 0.125, 0.500, + 0.875, 0.125, 0.562, 1.000, 0.250, 0.125, 0.000, 0.250, 0.188, 0.125, + 0.250, 0.250, 0.250, 0.250, 0.312, 0.375, 0.250, 0.375, 0.500, 0.250, + 0.438, 0.625, 0.250, 0.500, 0.750, 0.250, 0.562, 0.875, 0.250, 0.625, + 1.000, 0.375, 0.188, 0.000, 0.375, 0.250, 0.125, 0.375, 0.312, 0.250, + 0.375, 0.375, 0.375, 0.375, 0.438, 0.500, 0.375, 0.500, 0.625, 0.375, + 0.562, 0.750, 0.375, 0.625, 0.875, 0.375, 0.688, 1.000, 0.500, 0.250, + 0.000, 0.500, 0.312, 0.125, 0.500, 0.375, 0.250, 0.500, 0.438, 0.375, + 0.500, 0.500, 0.500, 0.500, 0.562, 0.625, 0.500, 0.625, 0.750, 0.500, + 0.688, 0.875, 0.500, 0.750, 1.000, 0.625, 0.312, 0.000, 0.625, 0.375, + 0.125, 0.625, 0.438, 0.250, 0.625, 0.500, 0.375, 0.625, 0.562, 0.500, + 0.625, 0.625, 0.625, 0.625, 0.688, 0.750, 0.625, 0.750, 0.875, 0.625, + 0.812, 1.000, 0.750, 0.375, 0.000, 0.750, 0.438, 0.125, 0.750, 0.500, + 0.250, 0.750, 0.562, 0.375, 0.750, 0.625, 0.500, 0.750, 0.688, 0.625, + 0.750, 0.750, 0.750, 0.750, 0.812, 0.875, 0.750, 0.875, 1.000, 0.875, + 0.438, 0.000, 0.875, 0.500, 0.125, 0.875, 0.562, 0.250, 0.875, 0.625, + 0.375, 0.875, 0.688, 0.500, 0.875, 0.750, 0.625, 0.875, 0.812, 0.750, + 0.875, 0.875, 0.875, 0.875, 0.938, 1.000, 1.000, 0.500, 0.000, 1.000, + 0.562, 0.125, 1.000, 0.625, 0.250, 1.000, 0.688, 0.375, 1.000, 0.750, + 0.500, 1.000, 0.812, 0.625, 1.000, 0.875, 0.750, 1.000, 0.938, 0.875, + 1.000, 1.000, 1.000, + ]).reshape((9, 9, 3)) + +cmaps = { + "BiPeak": SegmentedBivarColormap( + BiPeak, 256, "square", (.5, .5), name="BiPeak"), + "BiOrangeBlue": SegmentedBivarColormap( + BiOrangeBlue, 256, "square", (0, 0), name="BiOrangeBlue"), + "BiCone": SegmentedBivarColormap(BiPeak, 256, "circle", (.5, .5), name="BiCone"), +} diff --git a/lib/matplotlib/_cm_multivar.py b/lib/matplotlib/_cm_multivar.py new file mode 100644 index 000000000000..610d7c40935b --- /dev/null +++ b/lib/matplotlib/_cm_multivar.py @@ -0,0 +1,166 @@ +# auto-generated by https://github.com/trygvrad/multivariate_colormaps +# date: 2024-05-28 + +from .colors import LinearSegmentedColormap, MultivarColormap +import matplotlib as mpl +_LUTSIZE = mpl.rcParams['image.lut'] + +_2VarAddA0_data = [[0.000, 0.000, 0.000], + [0.020, 0.026, 0.031], + [0.049, 0.068, 0.085], + [0.075, 0.107, 0.135], + [0.097, 0.144, 0.183], + [0.116, 0.178, 0.231], + [0.133, 0.212, 0.279], + [0.148, 0.244, 0.326], + [0.161, 0.276, 0.374], + [0.173, 0.308, 0.422], + [0.182, 0.339, 0.471], + [0.190, 0.370, 0.521], + [0.197, 0.400, 0.572], + [0.201, 0.431, 0.623], + [0.204, 0.461, 0.675], + [0.204, 0.491, 0.728], + [0.202, 0.520, 0.783], + [0.197, 0.549, 0.838], + [0.187, 0.577, 0.895]] + +_2VarAddA1_data = [[0.000, 0.000, 0.000], + [0.030, 0.023, 0.018], + [0.079, 0.060, 0.043], + [0.125, 0.093, 0.065], + [0.170, 0.123, 0.083], + [0.213, 0.151, 0.098], + [0.255, 0.177, 0.110], + [0.298, 0.202, 0.120], + [0.341, 0.226, 0.128], + [0.384, 0.249, 0.134], + [0.427, 0.271, 0.138], + [0.472, 0.292, 0.141], + [0.517, 0.313, 0.142], + [0.563, 0.333, 0.141], + [0.610, 0.353, 0.139], + [0.658, 0.372, 0.134], + [0.708, 0.390, 0.127], + [0.759, 0.407, 0.118], + [0.813, 0.423, 0.105]] + +_2VarSubA0_data = [[1.000, 1.000, 1.000], + [0.959, 0.973, 0.986], + [0.916, 0.948, 0.974], + [0.874, 0.923, 0.965], + [0.832, 0.899, 0.956], + [0.790, 0.875, 0.948], + [0.748, 0.852, 0.940], + [0.707, 0.829, 0.934], + [0.665, 0.806, 0.927], + [0.624, 0.784, 0.921], + [0.583, 0.762, 0.916], + [0.541, 0.740, 0.910], + [0.500, 0.718, 0.905], + [0.457, 0.697, 0.901], + [0.414, 0.675, 0.896], + [0.369, 0.652, 0.892], + [0.320, 0.629, 0.888], + [0.266, 0.604, 0.884], + [0.199, 0.574, 0.881]] + +_2VarSubA1_data = [[1.000, 1.000, 1.000], + [0.982, 0.967, 0.955], + [0.966, 0.935, 0.908], + [0.951, 0.902, 0.860], + [0.937, 0.870, 0.813], + [0.923, 0.838, 0.765], + [0.910, 0.807, 0.718], + [0.898, 0.776, 0.671], + [0.886, 0.745, 0.624], + [0.874, 0.714, 0.577], + [0.862, 0.683, 0.530], + [0.851, 0.653, 0.483], + [0.841, 0.622, 0.435], + [0.831, 0.592, 0.388], + [0.822, 0.561, 0.340], + [0.813, 0.530, 0.290], + [0.806, 0.498, 0.239], + [0.802, 0.464, 0.184], + [0.801, 0.426, 0.119]] + +_3VarAddA0_data = [[0.000, 0.000, 0.000], + [0.018, 0.023, 0.028], + [0.040, 0.056, 0.071], + [0.059, 0.087, 0.110], + [0.074, 0.114, 0.147], + [0.086, 0.139, 0.183], + [0.095, 0.163, 0.219], + [0.101, 0.187, 0.255], + [0.105, 0.209, 0.290], + [0.107, 0.230, 0.326], + [0.105, 0.251, 0.362], + [0.101, 0.271, 0.398], + [0.091, 0.291, 0.434], + [0.075, 0.309, 0.471], + [0.046, 0.325, 0.507], + [0.021, 0.341, 0.546], + [0.021, 0.363, 0.584], + [0.022, 0.385, 0.622], + [0.023, 0.408, 0.661]] + +_3VarAddA1_data = [[0.000, 0.000, 0.000], + [0.020, 0.024, 0.016], + [0.047, 0.058, 0.034], + [0.072, 0.088, 0.048], + [0.093, 0.116, 0.059], + [0.113, 0.142, 0.067], + [0.131, 0.167, 0.071], + [0.149, 0.190, 0.074], + [0.166, 0.213, 0.074], + [0.182, 0.235, 0.072], + [0.198, 0.256, 0.068], + [0.215, 0.276, 0.061], + [0.232, 0.296, 0.051], + [0.249, 0.314, 0.037], + [0.270, 0.330, 0.018], + [0.288, 0.347, 0.000], + [0.302, 0.369, 0.000], + [0.315, 0.391, 0.000], + [0.328, 0.414, 0.000]] + +_3VarAddA2_data = [[0.000, 0.000, 0.000], + [0.029, 0.020, 0.023], + [0.072, 0.045, 0.055], + [0.111, 0.067, 0.084], + [0.148, 0.085, 0.109], + [0.184, 0.101, 0.133], + [0.219, 0.115, 0.155], + [0.254, 0.127, 0.176], + [0.289, 0.138, 0.195], + [0.323, 0.147, 0.214], + [0.358, 0.155, 0.232], + [0.393, 0.161, 0.250], + [0.429, 0.166, 0.267], + [0.467, 0.169, 0.283], + [0.507, 0.168, 0.298], + [0.546, 0.168, 0.313], + [0.580, 0.172, 0.328], + [0.615, 0.175, 0.341], + [0.649, 0.178, 0.355]] + +cmaps = { + name: LinearSegmentedColormap.from_list(name, data, _LUTSIZE) for name, data in [ + ('2VarAddA0', _2VarAddA0_data), + ('2VarAddA1', _2VarAddA1_data), + ('2VarSubA0', _2VarSubA0_data), + ('2VarSubA1', _2VarSubA1_data), + ('3VarAddA0', _3VarAddA0_data), + ('3VarAddA1', _3VarAddA1_data), + ('3VarAddA2', _3VarAddA2_data), + ]} + +cmap_families = { + '2VarAddA': MultivarColormap([cmaps[f'2VarAddA{i}'] for i in range(2)], + 'sRGB_add', name='2VarAddA'), + '2VarSubA': MultivarColormap([cmaps[f'2VarSubA{i}'] for i in range(2)], + 'sRGB_sub', name='2VarSubA'), + '3VarAddA': MultivarColormap([cmaps[f'3VarAddA{i}'] for i in range(3)], + 'sRGB_add', name='3VarAddA'), +} diff --git a/lib/matplotlib/cm.py b/lib/matplotlib/cm.py index f5bc455df1f7..025cb84db1d7 100644 --- a/lib/matplotlib/cm.py +++ b/lib/matplotlib/cm.py @@ -24,6 +24,8 @@ from matplotlib import _api, colors, cbook, scale from matplotlib._cm import datad from matplotlib._cm_listed import cmaps as cmaps_listed +from matplotlib._cm_multivar import cmap_families as multivar_cmaps +from matplotlib._cm_bivar import cmaps as bivar_cmaps _LUTSIZE = mpl.rcParams['image.lut'] @@ -238,6 +240,10 @@ def get_cmap(self, cmap): _colormaps = ColormapRegistry(_gen_cmap_registry()) globals().update(_colormaps) +_multivar_colormaps = ColormapRegistry(multivar_cmaps) + +_bivar_colormaps = ColormapRegistry(bivar_cmaps) + # This is an exact copy of pyplot.get_cmap(). It was removed in 3.9, but apparently # caused more user trouble than expected. Re-added for 3.9.1 and extended the diff --git a/lib/matplotlib/cm.pyi b/lib/matplotlib/cm.pyi index be8f10b39cb6..40e841d829ab 100644 --- a/lib/matplotlib/cm.pyi +++ b/lib/matplotlib/cm.pyi @@ -18,6 +18,8 @@ class ColormapRegistry(Mapping[str, colors.Colormap]): def get_cmap(self, cmap: str | colors.Colormap) -> colors.Colormap: ... _colormaps: ColormapRegistry = ... +_multivar_colormaps: ColormapRegistry = ... +_bivar_colormaps: ColormapRegistry = ... def get_cmap(name: str | colors.Colormap | None = ..., lut: int | None = ...) -> colors.Colormap: ... diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py index 5f40e7b0fb9a..7f29861d48e5 100644 --- a/lib/matplotlib/colors.py +++ b/lib/matplotlib/colors.py @@ -54,7 +54,7 @@ import matplotlib as mpl import numpy as np -from matplotlib import _api, _cm, cbook, scale +from matplotlib import _api, _cm, cbook, scale, _image from ._color_data import BASE_COLORS, TABLEAU_COLORS, CSS4_COLORS, XKCD_COLORS @@ -87,6 +87,7 @@ def __delitem__(self, key): _colors_full_map = _ColorMapping(_colors_full_map) _REPR_PNG_SIZE = (512, 64) +_BIVAR_REPR_PNG_SIZE = 256 def get_named_colors_mapping(): @@ -704,6 +705,7 @@ def __init__(self, name, N=256): self._i_over = self.N + 1 self._i_bad = self.N + 2 self._isinit = False + self.n_variates = 1 #: When this colormap exists on a scalar mappable and colorbar_extend #: is not False, colorbar creation will pick up ``colorbar_extend`` as #: the default value for the ``extend`` keyword in the @@ -723,7 +725,7 @@ def __call__(self, X, alpha=None, bytes=False): alpha : float or array-like or None Alpha must be a scalar between 0 and 1, a sequence of such floats with shape matching X, or None. - bytes : bool + bytes : bool, default: False If False (default), the returned RGBA values will be floats in the interval ``[0, 1]`` otherwise they will be `numpy.uint8`\s in the interval ``[0, 255]``. @@ -733,6 +735,36 @@ def __call__(self, X, alpha=None, bytes=False): Tuple of RGBA values if X is scalar, otherwise an array of RGBA values with a shape of ``X.shape + (4, )``. """ + rgba, mask = self._get_rgba_and_mask(X, alpha=alpha, bytes=bytes) + if not np.iterable(X): + rgba = tuple(rgba) + return rgba + + def _get_rgba_and_mask(self, X, alpha=None, bytes=False): + r""" + Parameters + ---------- + X : float or int, `~numpy.ndarray` or scalar + The data value(s) to convert to RGBA. + For floats, *X* should be in the interval ``[0.0, 1.0]`` to + return the RGBA values ``X*100`` percent along the Colormap line. + For integers, *X* should be in the interval ``[0, Colormap.N)`` to + return RGBA values *indexed* from the Colormap with index ``X``. + alpha : float or array-like or None + Alpha must be a scalar between 0 and 1, a sequence of such + floats with shape matching X, or None. + bytes : bool, default: False + If False (default), the returned RGBA values will be floats in the + interval ``[0, 1]`` otherwise they will be `numpy.uint8`\s in the + interval ``[0, 255]``. + + Returns + ------- + colors : np.ndarray + Array of RGBA values with a shape of ``X.shape + (4, )``. + mask : np.ndarray + Boolean array with True where the input is ``np.nan`` or masked. + """ if not self._isinit: self._init() @@ -776,9 +808,7 @@ def __call__(self, X, alpha=None, bytes=False): if (lut[-1] == 0).all(): rgba[mask_bad] = (0, 0, 0, 0) - if not np.iterable(X): - rgba = tuple(rgba) - return rgba + return rgba, mask_bad def __copy__(self): cls = self.__class__ @@ -1226,6 +1256,864 @@ def reversed(self, name=None): return new_cmap +class MultivarColormap: + """ + Class for holding multiple `~matplotlib.colors.Colormap` for use in a + `~matplotlib.cm.ScalarMappable` object + """ + def __init__(self, colormaps, combination_mode, name='multivariate colormap'): + """ + Parameters + ---------- + colormaps: list or tuple of `~matplotlib.colors.Colormap` objects + The individual colormaps that are combined + combination_mode: str, 'sRGB_add' or 'sRGB_sub' + Describe how colormaps are combined in sRGB space + + - If 'sRGB_add' -> Mixing produces brighter colors + `sRGB = sum(colors)` + - If 'sRGB_sub' -> Mixing produces darker colors + `sRGB = 1 - sum(1 - colors)` + name : str, optional + The name of the colormap family. + """ + self.name = name + + if not np.iterable(colormaps) \ + or len(colormaps) == 1 \ + or isinstance(colormaps, str): + raise ValueError("A MultivarColormap must have more than one colormap.") + colormaps = list(colormaps) # ensure cmaps is a list, i.e. not a tuple + for i, cmap in enumerate(colormaps): + if isinstance(cmap, str): + colormaps[i] = mpl.colormaps[cmap] + elif not isinstance(cmap, Colormap): + raise ValueError("colormaps must be a list of objects that subclass" + " Colormap or a name found in the colormap registry.") + + self._colormaps = colormaps + _api.check_in_list(['sRGB_add', 'sRGB_sub'], combination_mode=combination_mode) + self._combination_mode = combination_mode + self.n_variates = len(colormaps) + self._rgba_bad = (0.0, 0.0, 0.0, 0.0) # If bad, don't paint anything. + + def __call__(self, X, alpha=None, bytes=False, clip=True): + r""" + Parameters + ---------- + X : tuple (X0, X1, ...) of length equal to the number of colormaps + X0, X1 ...: + float or int, `~numpy.ndarray` or scalar + The data value(s) to convert to RGBA. + For floats, *Xi...* should be in the interval ``[0.0, 1.0]`` to + return the RGBA values ``X*100`` percent along the Colormap line. + For integers, *Xi...* should be in the interval ``[0, self[i].N)`` to + return RGBA values *indexed* from colormap [i] with index ``Xi``, where + self[i] is colormap i. + alpha : float or array-like or None + Alpha must be a scalar between 0 and 1, a sequence of such + floats with shape matching *Xi*, or None. + bytes : bool, default: False + If False (default), the returned RGBA values will be floats in the + interval ``[0, 1]`` otherwise they will be `numpy.uint8`\s in the + interval ``[0, 255]``. + clip : bool, default: True + If True, clip output to 0 to 1 + + Returns + ------- + Tuple of RGBA values if X[0] is scalar, otherwise an array of + RGBA values with a shape of ``X.shape + (4, )``. + """ + + if len(X) != len(self): + raise ValueError( + f'For the selected colormap the data must have a first dimension ' + f'{len(self)}, not {len(X)}') + rgba, mask_bad = self[0]._get_rgba_and_mask(X[0], bytes=False) + for c, xx in zip(self[1:], X[1:]): + sub_rgba, sub_mask_bad = c._get_rgba_and_mask(xx, bytes=False) + rgba[..., :3] += sub_rgba[..., :3] # add colors + rgba[..., 3] *= sub_rgba[..., 3] # multiply alpha + mask_bad |= sub_mask_bad + + if self.combination_mode == 'sRGB_sub': + rgba[..., :3] -= len(self) - 1 + + rgba[mask_bad] = self.get_bad() + + if clip: + rgba = np.clip(rgba, 0, 1) + + if alpha is not None: + if clip: + alpha = np.clip(alpha, 0, 1) + if np.shape(alpha) not in [(), np.shape(X[0])]: + raise ValueError( + f"alpha is array-like but its shape {np.shape(alpha)} does " + f"not match that of X[0] {np.shape(X[0])}") + rgba[..., -1] *= alpha + + if bytes: + if not clip: + raise ValueError( + "clip cannot be false while bytes is true" + " as uint8 does not support values below 0" + " or above 255.") + rgba = (rgba * 255).astype('uint8') + + if not np.iterable(X[0]): + rgba = tuple(rgba) + + return rgba + + def copy(self): + """Return a copy of the multivarcolormap.""" + return self.__copy__() + + def __copy__(self): + cls = self.__class__ + cmapobject = cls.__new__(cls) + cmapobject.__dict__.update(self.__dict__) + cmapobject._colormaps = [cm.copy() for cm in self._colormaps] + cmapobject._rgba_bad = np.copy(self._rgba_bad) + return cmapobject + + def __eq__(self, other): + if not isinstance(other, MultivarColormap): + return False + if len(self) != len(other): + return False + for c0, c1 in zip(self, other): + if c0 != c1: + return False + if not all(self._rgba_bad == other._rgba_bad): + return False + if self.combination_mode != other.combination_mode: + return False + return True + + def __getitem__(self, item): + return self._colormaps[item] + + def __iter__(self): + for c in self._colormaps: + yield c + + def __len__(self): + return len(self._colormaps) + + def __str__(self): + return self.name + + def get_bad(self): + """Get the color for masked values.""" + return np.array(self._rgba_bad) + + def resampled(self, lutshape): + """ + Return a new colormap with *lutshape* entries. + + Parameters + ---------- + lutshape : tuple of (`int`, `None`) + The tuple must have a length matching the number of variates. + For each element in the tuple, if `int`, the corresponding colorbar + is resampled, if `None`, the corresponding colorbar is not resampled. + + Returns + ------- + MultivarColormap + """ + + if not np.iterable(lutshape) or len(lutshape) != len(self): + raise ValueError(f"lutshape must be of length {len(self)}") + new_cmap = self.copy() + for i, s in enumerate(lutshape): + if s is not None: + new_cmap._colormaps[i] = self[i].resampled(s) + return new_cmap + + def with_extremes(self, *, bad=None, under=None, over=None): + """ + Return a copy of the `MultivarColormap` with modified out-of-range attributes. + + The *bad* keyword modifies the copied `MultivarColormap` while *under* and + *over* modifies the attributes of the copied component colormaps. + Note that *under* and *over* colors are subject to the mixing rules determined + by the *combination_mode*. + + Parameters + ---------- + bad: :mpltype:`color`, default: None + If Matplotlib color, the bad value is set accordingly in the copy + + under tuple of :mpltype:`color`, default: None + If tuple, the `under` value of each component is set with the values + from the tuple. + + over tuple of :mpltype:`color`, default: None + If tuple, the `over` value of each component is set with the values + from the tuple. + + Returns + ------- + MultivarColormap + copy of self with attributes set + + """ + new_cm = self.copy() + if bad is not None: + new_cm._rgba_bad = to_rgba(bad) + if under is not None: + if not np.iterable(under) or len(under) != len(new_cm): + raise ValueError("*under* must contain a color for each scalar colormap" + f" i.e. be of length {len(new_cm)}.") + else: + for c, b in zip(new_cm, under): + c.set_under(b) + if over is not None: + if not np.iterable(over) or len(over) != len(new_cm): + raise ValueError("*over* must contain a color for each scalar colormap" + f" i.e. be of length {len(new_cm)}.") + else: + for c, b in zip(new_cm, over): + c.set_over(b) + return new_cm + + @property + def combination_mode(self): + return self._combination_mode + + def _repr_png_(self): + """Generate a PNG representation of the Colormap.""" + X = np.tile(np.linspace(0, 1, _REPR_PNG_SIZE[0]), + (_REPR_PNG_SIZE[1], 1)) + pixels = np.zeros((_REPR_PNG_SIZE[1]*len(self), _REPR_PNG_SIZE[0], 4), + dtype=np.uint8) + for i, c in enumerate(self): + pixels[i*_REPR_PNG_SIZE[1]:(i+1)*_REPR_PNG_SIZE[1], :] = c(X, bytes=True) + png_bytes = io.BytesIO() + title = self.name + ' multivariate colormap' + author = f'Matplotlib v{mpl.__version__}, https://matplotlib.org' + pnginfo = PngInfo() + pnginfo.add_text('Title', title) + pnginfo.add_text('Description', title) + pnginfo.add_text('Author', author) + pnginfo.add_text('Software', author) + Image.fromarray(pixels).save(png_bytes, format='png', pnginfo=pnginfo) + return png_bytes.getvalue() + + def _repr_html_(self): + """Generate an HTML representation of the MultivarColormap.""" + return ''.join([c._repr_html_() for c in self._colormaps]) + + +class BivarColormap: + """ + Base class for all bivariate to RGBA mappings. + + Designed as a drop-in replacement for Colormap when using a 2D + lookup table. To be used with `~matplotlib.cm.ScalarMappable`. + """ + + def __init__(self, N=256, M=256, shape='square', origin=(0, 0), + name='bivariate colormap'): + """ + Parameters + ---------- + N : int, default: 256 + The number of RGB quantization levels along the first axis. + M : int, default: 256 + The number of RGB quantization levels along the second axis. + shape : {'square', 'circle', 'ignore', 'circleignore'} + + - 'square' each variate is clipped to [0,1] independently + - 'circle' the variates are clipped radially to the center + of the colormap, and a circular mask is applied when the colormap + is displayed + - 'ignore' the variates are not clipped, but instead assigned the + 'outside' color + - 'circleignore' a circular mask is applied, but the data is not + clipped and instead assigned the 'outside' color + + origin : (float, float), default: (0,0) + The relative origin of the colormap. Typically (0, 0), for colormaps + that are linear on both axis, and (.5, .5) for circular colormaps. + Used when getting 1D colormaps from 2D colormaps. + name : str, optional + The name of the colormap. + """ + + self.name = name + self.N = int(N) # ensure that N is always int + self.M = int(M) + _api.check_in_list(['square', 'circle', 'ignore', 'circleignore'], shape=shape) + self._shape = shape + self._rgba_bad = (0.0, 0.0, 0.0, 0.0) # If bad, don't paint anything. + self._rgba_outside = (1.0, 0.0, 1.0, 1.0) + self._isinit = False + self.n_variates = 2 + self._origin = (float(origin[0]), float(origin[1])) + '''#: When this colormap exists on a scalar mappable and colorbar_extend + #: is not False, colorbar creation will pick up ``colorbar_extend`` as + #: the default value for the ``extend`` keyword in the + #: `matplotlib.colorbar.Colorbar` constructor. + self.colorbar_extend = False''' + + def __call__(self, X, alpha=None, bytes=False): + r""" + Parameters + ---------- + X : tuple (X0, X1), X0 and X1: float or int `~numpy.ndarray` or scalar + The data value(s) to convert to RGBA. + + - For floats, *X* should be in the interval ``[0.0, 1.0]`` to + return the RGBA values ``X*100`` percent along the Colormap. + - For integers, *X* should be in the interval ``[0, Colormap.N)`` to + return RGBA values *indexed* from the Colormap with index ``X``. + + alpha : float or array-like or None, default: None + Alpha must be a scalar between 0 and 1, a sequence of such + floats with shape matching X0, or None. + bytes : bool, default: False + If False (default), the returned RGBA values will be floats in the + interval ``[0, 1]`` otherwise they will be `numpy.uint8`\s in the + interval ``[0, 255]``. + + Returns + ------- + Tuple of RGBA values if X is scalar, otherwise an array of + RGBA values with a shape of ``X.shape + (4, )``. + """ + + if len(X) != 2: + raise ValueError( + f'For a `BivarColormap` the data must have a first dimension ' + f'2, not {len(X)}') + + if not self._isinit: + self._init() + + X0 = np.ma.array(X[0], copy=True) + X1 = np.ma.array(X[1], copy=True) + # clip to shape of colormap, circle square, etc. + self._clip((X0, X1)) + + # Native byteorder is faster. + if not X0.dtype.isnative: + X0 = X0.byteswap().view(X0.dtype.newbyteorder()) + if not X1.dtype.isnative: + X1 = X1.byteswap().view(X1.dtype.newbyteorder()) + + if X0.dtype.kind == "f": + X0 *= self.N + # xa == 1 (== N after multiplication) is not out of range. + X0[X0 == self.N] = self.N - 1 + + if X1.dtype.kind == "f": + X1 *= self.M + # xa == 1 (== N after multiplication) is not out of range. + X1[X1 == self.M] = self.M - 1 + + # Pre-compute the masks before casting to int (which can truncate) + mask_outside = (X0 < 0) | (X1 < 0) | (X0 >= self.N) | (X1 >= self.M) + # If input was masked, get the bad mask from it; else mask out nans. + mask_bad_0 = X0.mask if np.ma.is_masked(X0) else np.isnan(X0) + mask_bad_1 = X1.mask if np.ma.is_masked(X1) else np.isnan(X1) + mask_bad = mask_bad_0 | mask_bad_1 + + with np.errstate(invalid="ignore"): + # We need this cast for unsigned ints as well as floats + X0 = X0.astype(int) + X1 = X1.astype(int) + + # Set masked values to zero + # The corresponding rgb values will be replaced later + for X_part in [X0, X1]: + X_part[mask_outside] = 0 + X_part[mask_bad] = 0 + + rgba = self._lut[X0, X1] + if np.isscalar(X[0]): + rgba = np.copy(rgba) + rgba[mask_outside] = self._rgba_outside + rgba[mask_bad] = self._rgba_bad + if bytes: + rgba = (rgba * 255).astype(np.uint8) + if alpha is not None: + alpha = np.clip(alpha, 0, 1) + if bytes: + alpha *= 255 # Will be cast to uint8 upon assignment. + if np.shape(alpha) not in [(), np.shape(X0)]: + raise ValueError( + f"alpha is array-like but its shape {np.shape(alpha)} does " + f"not match that of X[0] {np.shape(X0)}") + rgba[..., -1] = alpha + # If the "bad" color is all zeros, then ignore alpha input. + if (np.array(self._rgba_bad) == 0).all(): + rgba[mask_bad] = (0, 0, 0, 0) + + if not np.iterable(X[0]): + rgba = tuple(rgba) + return rgba + + @property + def lut(self): + """ + For external access to the lut, i.e. for displaying the cmap. + For circular colormaps this returns a lut with a circular mask. + + Internal functions (such as to_rgb()) should use _lut + which stores the lut without a circular mask + A lut without the circular mask is needed in to_rgb() because the + conversion from floats to ints results in some some pixel-requests + just outside of the circular mask + + """ + if not self._isinit: + self._init() + lut = np.copy(self._lut) + if self.shape == 'circle' or self.shape == 'circleignore': + n = np.linspace(-1, 1, self.N) + m = np.linspace(-1, 1, self.M) + radii_sqr = (n**2)[:, np.newaxis] + (m**2)[np.newaxis, :] + mask_outside = radii_sqr > 1 + lut[mask_outside, 3] = 0 + return lut + + def __copy__(self): + cls = self.__class__ + cmapobject = cls.__new__(cls) + cmapobject.__dict__.update(self.__dict__) + + cmapobject._rgba_outside = np.copy(self._rgba_outside) + cmapobject._rgba_bad = np.copy(self._rgba_bad) + cmapobject._shape = self.shape + if self._isinit: + cmapobject._lut = np.copy(self._lut) + return cmapobject + + def __eq__(self, other): + if not isinstance(other, BivarColormap): + return False + # To compare lookup tables the Colormaps have to be initialized + if not self._isinit: + self._init() + if not other._isinit: + other._init() + if not np.array_equal(self._lut, other._lut): + return False + if not np.array_equal(self._rgba_bad, other._rgba_bad): + return False + if not np.array_equal(self._rgba_outside, other._rgba_outside): + return False + if self.shape != other.shape: + return False + return True + + def get_bad(self): + """Get the color for masked values.""" + return self._rgba_bad + + def get_outside(self): + """Get the color for out-of-range values.""" + return self._rgba_outside + + def resampled(self, lutshape, transposed=False): + """ + Return a new colormap with *lutshape* entries. + + Note that this function does not move the origin. + + Parameters + ---------- + lutshape : tuple of ints or None + The tuple must be of length 2, and each entry is either an int or None. + + - If an int, the corresponding axis is resampled. + - If negative the corresponding axis is resampled in reverse + - If -1, the axis is inverted + - If 1 or None, the corresponding axis is not resampled. + + transposed : bool, default: False + if True, the axes are swapped after resampling + + Returns + ------- + BivarColormap + """ + + if not np.iterable(lutshape) or len(lutshape) != 2: + raise ValueError("lutshape must be of length 2") + lutshape = [lutshape[0], lutshape[1]] + if lutshape[0] is None or lutshape[0] == 1: + lutshape[0] = self.N + if lutshape[1] is None or lutshape[1] == 1: + lutshape[1] = self.M + + inverted = [False, False] + if lutshape[0] < 0: + inverted[0] = True + lutshape[0] = -lutshape[0] + if lutshape[0] == 1: + lutshape[0] = self.N + if lutshape[1] < 0: + inverted[1] = True + lutshape[1] = -lutshape[1] + if lutshape[1] == 1: + lutshape[1] = self.M + x_0, x_1 = np.mgrid[0:1:(lutshape[0] * 1j), 0:1:(lutshape[1] * 1j)] + if inverted[0]: + x_0 = x_0[::-1, :] + if inverted[1]: + x_1 = x_1[:, ::-1] + + # we need to use shape = 'square' while resampling the colormap. + # if the colormap has shape = 'circle' we would otherwise get *outside* in the + # resampled colormap + shape_memory = self._shape + self._shape = 'square' + if transposed: + new_lut = self((x_1, x_0)) + new_cmap = BivarColormapFromImage(new_lut, name=self.name, + shape=shape_memory, + origin=self.origin[::-1]) + else: + new_lut = self((x_0, x_1)) + new_cmap = BivarColormapFromImage(new_lut, name=self.name, + shape=shape_memory, + origin=self.origin) + self._shape = shape_memory + + new_cmap._rgba_bad = self._rgba_bad + new_cmap._rgba_outside = self._rgba_outside + return new_cmap + + def reversed(self, axis_0=True, axis_1=True): + """ + Reverses both or one of the axis. + """ + r_0 = -1 if axis_0 else 1 + r_1 = -1 if axis_1 else 1 + return self.resampled((r_0, r_1)) + + def transposed(self): + """ + Transposes the colormap by swapping the order of the axis + """ + return self.resampled((None, None), transposed=True) + + def with_extremes(self, *, bad=None, outside=None, shape=None, origin=None): + """ + Return a copy of the `BivarColormap` with modified attributes. + + Note that the *outside* color is only relevant if `shape` = 'ignore' + or 'circleignore'. + + Parameters + ---------- + bad : None or :mpltype:`color` + If Matplotlib color, the *bad* value is set accordingly in the copy + + outside : None or :mpltype:`color` + If Matplotlib color and shape is 'ignore' or 'circleignore', values + *outside* the colormap are colored accordingly in the copy + + shape : {'square', 'circle', 'ignore', 'circleignore'} + + - If 'square' each variate is clipped to [0,1] independently + - If 'circle' the variates are clipped radially to the center + of the colormap, and a circular mask is applied when the colormap + is displayed + - If 'ignore' the variates are not clipped, but instead assigned the + *outside* color + - If 'circleignore' a circular mask is applied, but the data is not + clipped and instead assigned the *outside* color + + origin : (float, float) + The relative origin of the colormap. Typically (0, 0), for colormaps + that are linear on both axis, and (.5, .5) for circular colormaps. + Used when getting 1D colormaps from 2D colormaps. + + Returns + ------- + BivarColormap + copy of self with attributes set + """ + new_cm = self.copy() + if bad is not None: + new_cm._rgba_bad = to_rgba(bad) + if outside is not None: + new_cm._rgba_outside = to_rgba(outside) + if shape is not None: + _api.check_in_list(['square', 'circle', 'ignore', 'circleignore'], + shape=shape) + new_cm._shape = shape + if origin is not None: + new_cm._origin = (float(origin[0]), float(origin[1])) + + return new_cm + + def _init(self): + """Generate the lookup table, ``self._lut``.""" + raise NotImplementedError("Abstract class only") + + @property + def shape(self): + return self._shape + + @property + def origin(self): + return self._origin + + def _clip(self, X): + """ + For internal use when applying a BivarColormap to data. + i.e. cm.ScalarMappable().to_rgba() + Clips X[0] and X[1] according to 'self.shape'. + X is modified in-place. + + Parameters + ---------- + X: np.array + array of floats or ints to be clipped + shape : {'square', 'circle', 'ignore', 'circleignore'} + + - If 'square' each variate is clipped to [0,1] independently + - If 'circle' the variates are clipped radially to the center + of the colormap. + It is assumed that a circular mask is applied when the colormap + is displayed + - If 'ignore' the variates are not clipped, but instead assigned the + 'outside' color + - If 'circleignore' a circular mask is applied, but the data is not clipped + and instead assigned the 'outside' color + + """ + if self.shape == 'square': + for X_part, mx in zip(X, (self.N, self.M)): + X_part[X_part < 0] = 0 + if X_part.dtype.kind == "f": + X_part[X_part > 1] = 1 + else: + X_part[X_part >= mx] = mx - 1 + + elif self.shape == 'ignore': + for X_part, mx in zip(X, (self.N, self.M)): + X_part[X_part < 0] = -1 + if X_part.dtype.kind == "f": + X_part[X_part > 1] = -1 + else: + X_part[X_part >= mx] = -1 + + elif self.shape == 'circle' or self.shape == 'circleignore': + for X_part in X: + if X_part.dtype.kind != "f": + raise NotImplementedError( + "Circular bivariate colormaps are only" + " implemented for use with with floats") + radii_sqr = (X[0] - 0.5)**2 + (X[1] - 0.5)**2 + mask_outside = radii_sqr > 0.25 + if self.shape == 'circle': + overextend = 2 * np.sqrt(radii_sqr[mask_outside]) + X[0][mask_outside] = (X[0][mask_outside] - 0.5) / overextend + 0.5 + X[1][mask_outside] = (X[1][mask_outside] - 0.5) / overextend + 0.5 + else: + X[0][mask_outside] = -1 + X[1][mask_outside] = -1 + + def __getitem__(self, item): + """Creates and returns a colorbar along the selected axis""" + if not self._isinit: + self._init() + if item == 0: + origin_1_as_int = int(self._origin[1]*self.M) + if origin_1_as_int > self.M-1: + origin_1_as_int = self.M-1 + one_d_lut = self._lut[:, origin_1_as_int] + new_cmap = ListedColormap(one_d_lut, name=f'{self.name}_0', N=self.N) + + elif item == 1: + origin_0_as_int = int(self._origin[0]*self.N) + if origin_0_as_int > self.N-1: + origin_0_as_int = self.N-1 + one_d_lut = self._lut[origin_0_as_int, :] + new_cmap = ListedColormap(one_d_lut, name=f'{self.name}_1', N=self.M) + else: + raise KeyError(f"only 0 or 1 are" + f" valid keys for BivarColormap, not {item!r}") + new_cmap._rgba_bad = self._rgba_bad + if self.shape in ['ignore', 'circleignore']: + new_cmap.set_over(self._rgba_outside) + new_cmap.set_under(self._rgba_outside) + return new_cmap + + def _repr_png_(self): + """Generate a PNG representation of the BivarColormap.""" + if not self._isinit: + self._init() + pixels = self.lut + if pixels.shape[0] < _BIVAR_REPR_PNG_SIZE: + pixels = np.repeat(pixels, + repeats=_BIVAR_REPR_PNG_SIZE//pixels.shape[0], + axis=0)[:256, :] + if pixels.shape[1] < _BIVAR_REPR_PNG_SIZE: + pixels = np.repeat(pixels, + repeats=_BIVAR_REPR_PNG_SIZE//pixels.shape[1], + axis=1)[:, :256] + pixels = (pixels[::-1, :, :] * 255).astype(np.uint8) + png_bytes = io.BytesIO() + title = self.name + ' BivarColormap' + author = f'Matplotlib v{mpl.__version__}, https://matplotlib.org' + pnginfo = PngInfo() + pnginfo.add_text('Title', title) + pnginfo.add_text('Description', title) + pnginfo.add_text('Author', author) + pnginfo.add_text('Software', author) + Image.fromarray(pixels).save(png_bytes, format='png', pnginfo=pnginfo) + return png_bytes.getvalue() + + def _repr_html_(self): + """Generate an HTML representation of the Colormap.""" + png_bytes = self._repr_png_() + png_base64 = base64.b64encode(png_bytes).decode('ascii') + def color_block(color): + hex_color = to_hex(color, keep_alpha=True) + return (f'
') + + return ('
' + f'{self.name} ' + '
' + '
' + '
' + '
' + f'{color_block(self.get_outside())} outside' + '
' + '
' + f'bad {color_block(self.get_bad())}' + '
') + + def copy(self): + """Return a copy of the colormap.""" + return self.__copy__() + + +class SegmentedBivarColormap(BivarColormap): + """ + BivarColormap object generated by supersampling a regular grid. + + Parameters + ---------- + patch : np.array + Patch is required to have a shape (k, l, 3), and will get supersampled + to a lut of shape (N, N, 4). + N : int + The number of RGB quantization levels along each axis. + shape : {'square', 'circle', 'ignore', 'circleignore'} + + - If 'square' each variate is clipped to [0,1] independently + - If 'circle' the variates are clipped radially to the center + of the colormap, and a circular mask is applied when the colormap + is displayed + - If 'ignore' the variates are not clipped, but instead assigned the + 'outside' color + - If 'circleignore' a circular mask is applied, but the data is not clipped + + origin : (float, float) + The relative origin of the colormap. Typically (0, 0), for colormaps + that are linear on both axis, and (.5, .5) for circular colormaps. + Used when getting 1D colormaps from 2D colormaps. + + name : str, optional + The name of the colormap. + """ + + def __init__(self, patch, N=256, shape='square', origin=(0, 0), + name='segmented bivariate colormap'): + _api.check_shape((None, None, 3), patch=patch) + self.patch = patch + super().__init__(N, N, shape, origin, name=name) + + def _init(self): + s = self.patch.shape + _patch = np.empty((s[0], s[1], 4)) + _patch[:, :, :3] = self.patch + _patch[:, :, 3] = 1 + transform = mpl.transforms.Affine2D().translate(-0.5, -0.5)\ + .scale(self.N / (s[1] - 1), self.N / (s[0] - 1)) + self._lut = np.empty((self.N, self.N, 4)) + + _image.resample(_patch, self._lut, transform, _image.BILINEAR, + resample=False, alpha=1) + self._isinit = True + + +class BivarColormapFromImage(BivarColormap): + """ + BivarColormap object generated by supersampling a regular grid. + + Parameters + ---------- + lut : nparray of shape (N, M, 3) or (N, M, 4) + The look-up-table + shape: {'square', 'circle', 'ignore', 'circleignore'} + + - If 'square' each variate is clipped to [0,1] independently + - If 'circle' the variates are clipped radially to the center + of the colormap, and a circular mask is applied when the colormap + is displayed + - If 'ignore' the variates are not clipped, but instead assigned the + 'outside' color + - If 'circleignore' a circular mask is applied, but the data is not clipped + + origin: (float, float) + The relative origin of the colormap. Typically (0, 0), for colormaps + that are linear on both axis, and (.5, .5) for circular colormaps. + Used when getting 1D colormaps from 2D colormaps. + name : str, optional + The name of the colormap. + + """ + + def __init__(self, lut, shape='square', origin=(0, 0), name='from image'): + # We can allow for a PIL.Image as input in the following way, but importing + # matplotlib.image.pil_to_array() results in a circular import + # For now, this function only accepts numpy arrays. + # i.e.: + # if isinstance(Image, lut): + # lut = image.pil_to_array(lut) + lut = np.array(lut, copy=True) + if lut.ndim != 3 or lut.shape[2] not in (3, 4): + raise ValueError("The lut must be an array of shape (n, m, 3) or (n, m, 4)", + " or a PIL.image encoded as RGB or RGBA") + + if lut.dtype == np.uint8: + lut = lut.astype(np.float32)/255 + if lut.shape[2] == 3: + new_lut = np.empty((lut.shape[0], lut.shape[1], 4), dtype=lut.dtype) + new_lut[:, :, :3] = lut + new_lut[:, :, 3] = 1. + lut = new_lut + self._lut = lut + super().__init__(lut.shape[0], lut.shape[1], shape, origin, name=name) + + def _init(self): + self._isinit = True + + class Normalize: """ A class which, when called, maps values within the interval diff --git a/lib/matplotlib/colors.pyi b/lib/matplotlib/colors.pyi index 514801b714b8..6941e3d5d176 100644 --- a/lib/matplotlib/colors.pyi +++ b/lib/matplotlib/colors.pyi @@ -138,6 +138,101 @@ class ListedColormap(Colormap): def resampled(self, lutsize: int) -> ListedColormap: ... def reversed(self, name: str | None = ...) -> ListedColormap: ... +class MultivarColormap: + name: str + n_variates: int + def __init__(self, colormaps: list[Colormap], combination_mode: Literal['sRGB_add', 'sRGB_sub'], name: str = ...) -> None: ... + @overload + def __call__( + self, X: Sequence[Sequence[float]] | np.ndarray, alpha: ArrayLike | None = ..., bytes: bool = ..., clip: bool = ... + ) -> np.ndarray: ... + @overload + def __call__( + self, X: Sequence[float], alpha: float | None = ..., bytes: bool = ..., clip: bool = ... + ) -> tuple[float, float, float, float]: ... + @overload + def __call__( + self, X: ArrayLike, alpha: ArrayLike | None = ..., bytes: bool = ..., clip: bool = ... + ) -> tuple[float, float, float, float] | np.ndarray: ... + def copy(self) -> MultivarColormap: ... + def __copy__(self) -> MultivarColormap: ... + def __eq__(self, other: Any) -> bool: ... + def __getitem__(self, item: int) -> Colormap: ... + def __iter__(self) -> Iterator[Colormap]: ... + def __len__(self) -> int: ... + def get_bad(self) -> np.ndarray: ... + def resampled(self, lutshape: Sequence[int | None]) -> MultivarColormap: ... + def with_extremes( + self, + *, + bad: ColorType | None = ..., + under: Sequence[ColorType] | None = ..., + over: Sequence[ColorType] | None = ... + ) -> MultivarColormap: ... + @property + def combination_mode(self) -> str: ... + def _repr_html_(self) -> str: ... + def _repr_png_(self) -> bytes: ... + +class BivarColormap: + name: str + N: int + M: int + n_variates: int + def __init__( + self, N: int = ..., M: int | None = ..., shape: Literal['square', 'circle', 'ignore', 'circleignore'] = ..., + origin: Sequence[float] = ..., name: str = ... + ) -> None: ... + @overload + def __call__( + self, X: Sequence[Sequence[float]] | np.ndarray, alpha: ArrayLike | None = ..., bytes: bool = ... + ) -> np.ndarray: ... + @overload + def __call__( + self, X: Sequence[float], alpha: float | None = ..., bytes: bool = ... + ) -> tuple[float, float, float, float]: ... + @overload + def __call__( + self, X: ArrayLike, alpha: ArrayLike | None = ..., bytes: bool = ... + ) -> tuple[float, float, float, float] | np.ndarray: ... + @property + def lut(self) -> np.ndarray: ... + @property + def shape(self) -> str: ... + @property + def origin(self) -> tuple[float, float]: ... + def copy(self) -> BivarColormap: ... + def __copy__(self) -> BivarColormap: ... + def __getitem__(self, item: int) -> Colormap: ... + def __eq__(self, other: Any) -> bool: ... + def get_bad(self) -> np.ndarray: ... + def get_outside(self) -> np.ndarray: ... + def resampled(self, lutshape: Sequence[int | None], transposed: bool = ...) -> BivarColormap: ... + def transposed(self) -> BivarColormap: ... + def reversed(self, axis_0: bool = ..., axis_1: bool = ...) -> BivarColormap: ... + def with_extremes( + self, + *, + bad: ColorType | None = ..., + outside: ColorType | None = ..., + shape: str | None = ..., + origin: None | Sequence[float] = ..., + ) -> MultivarColormap: ... + def _repr_html_(self) -> str: ... + def _repr_png_(self) -> bytes: ... + +class SegmentedBivarColormap(BivarColormap): + def __init__( + self, patch: np.ndarray, N: int = ..., shape: Literal['square', 'circle', 'ignore', 'circleignore'] = ..., + origin: Sequence[float] = ..., name: str = ... + ) -> None: ... + +class BivarColormapFromImage(BivarColormap): + def __init__( + self, lut: np.ndarray, shape: Literal['square', 'circle', 'ignore', 'circleignore'] = ..., + origin: Sequence[float] = ..., name: str = ... + ) -> None: ... + class Normalize: callbacks: cbook.CallbackRegistry def __init__( diff --git a/lib/matplotlib/meson.build b/lib/matplotlib/meson.build index c4b66fc1b336..657adfd4e835 100644 --- a/lib/matplotlib/meson.build +++ b/lib/matplotlib/meson.build @@ -4,7 +4,9 @@ python_sources = [ '_animation_data.py', '_blocking_input.py', '_cm.py', + '_cm_bivar.py', '_cm_listed.py', + '_cm_multivar.py', '_color_data.py', '_constrained_layout.py', '_docstring.py', diff --git a/lib/matplotlib/tests/baseline_images/test_multivariate_colormaps/bivariate_cmap_shapes.png b/lib/matplotlib/tests/baseline_images/test_multivariate_colormaps/bivariate_cmap_shapes.png new file mode 100644 index 000000000000..f22b446fc84b Binary files /dev/null and b/lib/matplotlib/tests/baseline_images/test_multivariate_colormaps/bivariate_cmap_shapes.png differ diff --git a/lib/matplotlib/tests/baseline_images/test_multivariate_colormaps/multivar_alpha_mixing.png b/lib/matplotlib/tests/baseline_images/test_multivariate_colormaps/multivar_alpha_mixing.png new file mode 100644 index 000000000000..415dfda291dc Binary files /dev/null and b/lib/matplotlib/tests/baseline_images/test_multivariate_colormaps/multivar_alpha_mixing.png differ diff --git a/lib/matplotlib/tests/meson.build b/lib/matplotlib/tests/meson.build index 107066636f31..1dcf3791638a 100644 --- a/lib/matplotlib/tests/meson.build +++ b/lib/matplotlib/tests/meson.build @@ -57,6 +57,7 @@ python_sources = [ 'test_marker.py', 'test_mathtext.py', 'test_matplotlib.py', + 'test_multivariate_colormaps.py', 'test_mlab.py', 'test_offsetbox.py', 'test_patches.py', diff --git a/lib/matplotlib/tests/test_multivariate_colormaps.py b/lib/matplotlib/tests/test_multivariate_colormaps.py new file mode 100644 index 000000000000..81a2e6adeb35 --- /dev/null +++ b/lib/matplotlib/tests/test_multivariate_colormaps.py @@ -0,0 +1,564 @@ +import numpy as np +from numpy.testing import assert_array_equal, assert_allclose +import matplotlib.pyplot as plt +from matplotlib.testing.decorators import (image_comparison, + remove_ticks_and_titles) +import matplotlib as mpl +import pytest +from pathlib import Path +from io import BytesIO +from PIL import Image +import base64 + + +@image_comparison(["bivariate_cmap_shapes.png"]) +def test_bivariate_cmap_shapes(): + x_0 = np.repeat(np.linspace(-0.1, 1.1, 10, dtype='float32')[None, :], 10, axis=0) + x_1 = x_0.T + + fig, axes = plt.subplots(1, 4, figsize=(10, 2)) + + # shape = 'square' + cmap = mpl.bivar_colormaps['BiPeak'] + axes[0].imshow(cmap((x_0, x_1)), interpolation='nearest') + + # shape = 'circle' + cmap = mpl.bivar_colormaps['BiCone'] + axes[1].imshow(cmap((x_0, x_1)), interpolation='nearest') + + # shape = 'ignore' + cmap = mpl.bivar_colormaps['BiPeak'] + cmap = cmap.with_extremes(shape='ignore') + axes[2].imshow(cmap((x_0, x_1)), interpolation='nearest') + + # shape = circleignore + cmap = mpl.bivar_colormaps['BiCone'] + cmap = cmap.with_extremes(shape='circleignore') + axes[3].imshow(cmap((x_0, x_1)), interpolation='nearest') + remove_ticks_and_titles(fig) + + +def test_multivar_creation(): + # test creation of a custom multivariate colorbar + blues = mpl.colormaps['Blues'] + cmap = mpl.colors.MultivarColormap((blues, 'Oranges'), 'sRGB_sub') + y, x = np.mgrid[0:3, 0:3]/2 + im = cmap((y, x)) + res = np.array([[[0.96862745, 0.94509804, 0.92156863, 1], + [0.96004614, 0.53504037, 0.23277201, 1], + [0.46666667, 0.1372549, 0.01568627, 1]], + [[0.41708574, 0.64141484, 0.75980008, 1], + [0.40850442, 0.23135717, 0.07100346, 1], + [0, 0, 0, 1]], + [[0.03137255, 0.14901961, 0.34117647, 1], + [0.02279123, 0, 0, 1], + [0, 0, 0, 1]]]) + assert_allclose(im, res, atol=0.01) + + with pytest.raises(ValueError, match="colormaps must be a list of"): + cmap = mpl.colors.MultivarColormap((blues, [blues]), 'sRGB_sub') + with pytest.raises(ValueError, match="A MultivarColormap must"): + cmap = mpl.colors.MultivarColormap('blues', 'sRGB_sub') + with pytest.raises(ValueError, match="A MultivarColormap must"): + cmap = mpl.colors.MultivarColormap((blues), 'sRGB_sub') + + +@image_comparison(["multivar_alpha_mixing.png"]) +def test_multivar_alpha_mixing(): + # test creation of a custom colormap using 'rainbow' + # and a colormap that goes from alpha = 1 to alpha = 0 + rainbow = mpl.colormaps['rainbow'] + alpha = np.zeros((256, 4)) + alpha[:, 3] = np.linspace(1, 0, 256) + alpha_cmap = mpl.colors.LinearSegmentedColormap.from_list('from_list', alpha) + + cmap = mpl.colors.MultivarColormap((rainbow, alpha_cmap), 'sRGB_add') + y, x = np.mgrid[0:10, 0:10]/9 + im = cmap((y, x)) + + fig, ax = plt.subplots() + ax.imshow(im, interpolation='nearest') + remove_ticks_and_titles(fig) + + +def test_multivar_cmap_call(): + cmap = mpl.multivar_colormaps['2VarAddA'] + assert_array_equal(cmap((0.0, 0.0)), (0, 0, 0, 1)) + assert_array_equal(cmap((1.0, 1.0)), (1, 1, 1, 1)) + assert_allclose(cmap((0.0, 0.0), alpha=0.1), (0, 0, 0, 0.1), atol=0.1) + + cmap = mpl.multivar_colormaps['2VarSubA'] + assert_array_equal(cmap((0.0, 0.0)), (1, 1, 1, 1)) + assert_allclose(cmap((1.0, 1.0)), (0, 0, 0, 1), atol=0.1) + + # check outside and bad + cs = cmap([(0., 0., 0., 1.2, np.nan), (0., 1.2, np.nan, 0., 0., )]) + assert_allclose(cs, [[1., 1., 1., 1.], + [0.801, 0.426, 0.119, 1.], + [0., 0., 0., 0.], + [0.199, 0.574, 0.881, 1.], + [0., 0., 0., 0.]]) + + assert_array_equal(cmap((0.0, 0.0), bytes=True), (255, 255, 255, 255)) + + with pytest.raises(ValueError, match="alpha is array-like but its shape"): + cs = cmap([(0, 5, 9), (0, 0, 0)], alpha=(0.5, 0.3)) + + with pytest.raises(ValueError, match="For the selected colormap the data"): + cs = cmap([(0, 5, 9), (0, 0, 0), (0, 0, 0)]) + + with pytest.raises(ValueError, match="clip cannot be false"): + cs = cmap([(0, 5, 9), (0, 0, 0)], bytes=True, clip=False) + # Tests calling a multivariate colormap with integer values + cmap = mpl.multivar_colormaps['2VarSubA'] + + # call only integers + cs = cmap([(0, 50, 100, 0, 0, 300), (0, 0, 0, 50, 100, 300)]) + res = np.array([[1, 1, 1, 1], + [0.85176471, 0.91029412, 0.96023529, 1], + [0.70452941, 0.82764706, 0.93358824, 1], + [0.94358824, 0.88505882, 0.83511765, 1], + [0.89729412, 0.77417647, 0.66823529, 1], + [0, 0, 0, 1]]) + assert_allclose(cs, res, atol=0.01) + + # call only integers, wrong byte order + swapped_dt = np.dtype(int).newbyteorder() + cs = cmap([np.array([0, 50, 100, 0, 0, 300], dtype=swapped_dt), + np.array([0, 0, 0, 50, 100, 300], dtype=swapped_dt)]) + assert_allclose(cs, res, atol=0.01) + + # call mix floats integers + # check calling with bytes = True + cs = cmap([(0, 50, 100, 0, 0, 300), (0, 0, 0, 50, 100, 300)], bytes=True) + res = np.array([[255, 255, 255, 255], + [217, 232, 244, 255], + [179, 211, 238, 255], + [240, 225, 212, 255], + [228, 197, 170, 255], + [0, 0, 0, 255]]) + assert_allclose(cs, res, atol=0.01) + + cs = cmap([(0, 50, 100, 0, 0, 300), (0, 0, 0, 50, 100, 300)], alpha=0.5) + res = np.array([[1, 1, 1, 0.5], + [0.85176471, 0.91029412, 0.96023529, 0.5], + [0.70452941, 0.82764706, 0.93358824, 0.5], + [0.94358824, 0.88505882, 0.83511765, 0.5], + [0.89729412, 0.77417647, 0.66823529, 0.5], + [0, 0, 0, 0.5]]) + assert_allclose(cs, res, atol=0.01) + # call with tuple + assert_allclose(cmap((100, 120), bytes=True, alpha=0.5), + [149, 142, 136, 127], atol=0.01) + + # alpha and bytes + cs = cmap([(0, 5, 9, 0, 0, 10), (0, 0, 0, 5, 11, 12)], bytes=True, alpha=0.5) + res = np.array([[0, 0, 255, 127], + [141, 0, 255, 127], + [255, 0, 255, 127], + [0, 115, 255, 127], + [0, 255, 255, 127], + [255, 255, 255, 127]]) + + # bad alpha shape + with pytest.raises(ValueError, match="alpha is array-like but its shape"): + cs = cmap([(0, 5, 9), (0, 0, 0)], bytes=True, alpha=(0.5, 0.3)) + + cmap = cmap.with_extremes(bad=(1, 1, 1, 1)) + cs = cmap([(0., 1.1, np.nan), (0., 1.2, 1.)]) + res = np.array([[1., 1., 1., 1.], + [0., 0., 0., 1.], + [1., 1., 1., 1.]]) + assert_allclose(cs, res, atol=0.01) + + # call outside with tuple + assert_allclose(cmap((300, 300), bytes=True, alpha=0.5), + [0, 0, 0, 127], atol=0.01) + with pytest.raises(ValueError, + match="For the selected colormap the data must have"): + cs = cmap((0, 5, 9)) + + # test over/under + cmap = mpl.multivar_colormaps['2VarAddA'] + with pytest.raises(ValueError, match='i.e. be of length 2'): + cmap.with_extremes(over=0) + with pytest.raises(ValueError, match='i.e. be of length 2'): + cmap.with_extremes(under=0) + + cmap = cmap.with_extremes(under=[(0, 0, 0, 0)]*2) + assert_allclose((0, 0, 0, 0), cmap((-1., 0)), atol=1e-2) + cmap = cmap.with_extremes(over=[(0, 0, 0, 0)]*2) + assert_allclose((0, 0, 0, 0), cmap((2., 0)), atol=1e-2) + + +def test_multivar_bad_mode(): + cmap = mpl.multivar_colormaps['2VarSubA'] + with pytest.raises(ValueError, match="is not a valid value for"): + cmap = mpl.colors.MultivarColormap(cmap[:], 'bad') + + +def test_multivar_resample(): + cmap = mpl.multivar_colormaps['3VarAddA'] + cmap_resampled = cmap.resampled((None, 10, 3)) + + assert_allclose(cmap_resampled[1](0.25), (0.093, 0.116, 0.059, 1.0)) + assert_allclose(cmap_resampled((0, 0.25, 0)), (0.093, 0.116, 0.059, 1.0)) + assert_allclose(cmap_resampled((1, 0.25, 1)), (0.417271, 0.264624, 0.274976, 1.), + atol=0.01) + + with pytest.raises(ValueError, match="lutshape must be of length"): + cmap = cmap.resampled(4) + + +def test_bivar_cmap_call_tuple(): + cmap = mpl.bivar_colormaps['BiOrangeBlue'] + assert_allclose(cmap((1.0, 1.0)), (1, 1, 1, 1), atol=0.01) + assert_allclose(cmap((0.0, 0.0)), (0, 0, 0, 1), atol=0.1) + assert_allclose(cmap((0.0, 0.0), alpha=0.1), (0, 0, 0, 0.1), atol=0.1) + + +def test_bivar_cmap_call(): + """ + Tests calling a bivariate colormap with integer values + """ + im = np.ones((10, 12, 4)) + im[:, :, 0] = np.linspace(0, 1, 10)[:, np.newaxis] + im[:, :, 1] = np.linspace(0, 1, 12)[np.newaxis, :] + cmap = mpl.colors.BivarColormapFromImage(im) + + # call only integers + cs = cmap([(0, 5, 9, 0, 0, 10), (0, 0, 0, 5, 11, 12)]) + res = np.array([[0, 0, 1, 1], + [0.556, 0, 1, 1], + [1, 0, 1, 1], + [0, 0.454, 1, 1], + [0, 1, 1, 1], + [1, 1, 1, 1]]) + assert_allclose(cs, res, atol=0.01) + # call only integers, wrong byte order + swapped_dt = np.dtype(int).newbyteorder() + cs = cmap([np.array([0, 5, 9, 0, 0, 10], dtype=swapped_dt), + np.array([0, 0, 0, 5, 11, 12], dtype=swapped_dt)]) + assert_allclose(cs, res, atol=0.01) + + # call mix floats integers + cmap = cmap.with_extremes(outside=(1, 0, 0, 0)) + cs = cmap([(0.5, 0), (0, 3)]) + res = np.array([[0.555, 0, 1, 1], + [0, 0.2727, 1, 1]]) + assert_allclose(cs, res, atol=0.01) + + # check calling with bytes = True + cs = cmap([(0, 5, 9, 0, 0, 10), (0, 0, 0, 5, 11, 12)], bytes=True) + res = np.array([[0, 0, 255, 255], + [141, 0, 255, 255], + [255, 0, 255, 255], + [0, 115, 255, 255], + [0, 255, 255, 255], + [255, 255, 255, 255]]) + assert_allclose(cs, res, atol=0.01) + + # test alpha + cs = cmap([(0, 5, 9, 0, 0, 10), (0, 0, 0, 5, 11, 12)], alpha=0.5) + res = np.array([[0, 0, 1, 0.5], + [0.556, 0, 1, 0.5], + [1, 0, 1, 0.5], + [0, 0.454, 1, 0.5], + [0, 1, 1, 0.5], + [1, 1, 1, 0.5]]) + assert_allclose(cs, res, atol=0.01) + # call with tuple + assert_allclose(cmap((10, 12), bytes=True, alpha=0.5), + [255, 255, 255, 127], atol=0.01) + + # alpha and bytes + cs = cmap([(0, 5, 9, 0, 0, 10), (0, 0, 0, 5, 11, 12)], bytes=True, alpha=0.5) + res = np.array([[0, 0, 255, 127], + [141, 0, 255, 127], + [255, 0, 255, 127], + [0, 115, 255, 127], + [0, 255, 255, 127], + [255, 255, 255, 127]]) + + # bad alpha shape + with pytest.raises(ValueError, match="alpha is array-like but its shape"): + cs = cmap([(0, 5, 9), (0, 0, 0)], bytes=True, alpha=(0.5, 0.3)) + + # set shape to 'ignore'. + # final point is outside colormap and should then receive + # the 'outside' (in this case [1,0,0,0]) + # also test 'bad' (in this case [1,1,1,0]) + cmap = cmap.with_extremes(outside=(1, 0, 0, 0), bad=(1, 1, 1, 0), shape='ignore') + cs = cmap([(0., 1.1, np.nan), (0., 1.2, 1.)]) + res = np.array([[0, 0, 1, 1], + [1, 0, 0, 0], + [1, 1, 1, 0]]) + assert_allclose(cs, res, atol=0.01) + # call outside with tuple + assert_allclose(cmap((10, 12), bytes=True, alpha=0.5), + [255, 0, 0, 127], atol=0.01) + # with integers + cs = cmap([(0, 10), (0, 12)]) + res = np.array([[0, 0, 1, 1], + [1, 0, 0, 0]]) + assert_allclose(cs, res, atol=0.01) + + with pytest.raises(ValueError, + match="For a `BivarColormap` the data must have"): + cs = cmap((0, 5, 9)) + + cmap = cmap.with_extremes(shape='circle') + with pytest.raises(NotImplementedError, + match="only implemented for use with with floats"): + cs = cmap([(0, 5, 9, 0, 0, 9), (0, 0, 0, 5, 11, 11)]) + + # test origin + cmap = mpl.bivar_colormaps['BiOrangeBlue'].with_extremes(origin=(0.5, 0.5)) + assert_allclose(cmap[0](0.5), + (0.50244140625, 0.5024222412109375, 0.50244140625, 1)) + assert_allclose(cmap[1](0.5), + (0.50244140625, 0.5024222412109375, 0.50244140625, 1)) + cmap = mpl.bivar_colormaps['BiOrangeBlue'].with_extremes(origin=(1, 1)) + assert_allclose(cmap[0](1.), + (0.99853515625, 0.9985467529296875, 0.99853515625, 1.0)) + assert_allclose(cmap[1](1.), + (0.99853515625, 0.9985467529296875, 0.99853515625, 1.0)) + with pytest.raises(KeyError, + match="only 0 or 1 are valid keys"): + cs = cmap[2] + + +def test_bivar_getitem(): + """Test __getitem__ on BivarColormap""" + xA = ([.0, .25, .5, .75, 1., -1, 2], [.5]*7) + xB = ([.5]*7, [.0, .25, .5, .75, 1., -1, 2]) + + cmaps = mpl.bivar_colormaps['BiPeak'] + assert_array_equal(cmaps(xA), cmaps[0](xA[0])) + assert_array_equal(cmaps(xB), cmaps[1](xB[1])) + + cmaps = cmaps.with_extremes(shape='ignore') + assert_array_equal(cmaps(xA), cmaps[0](xA[0])) + assert_array_equal(cmaps(xB), cmaps[1](xB[1])) + + xA = ([.0, .25, .5, .75, 1., -1, 2], [.0]*7) + xB = ([.0]*7, [.0, .25, .5, .75, 1., -1, 2]) + cmaps = mpl.bivar_colormaps['BiOrangeBlue'] + assert_array_equal(cmaps(xA), cmaps[0](xA[0])) + assert_array_equal(cmaps(xB), cmaps[1](xB[1])) + + cmaps = cmaps.with_extremes(shape='ignore') + assert_array_equal(cmaps(xA), cmaps[0](xA[0])) + assert_array_equal(cmaps(xB), cmaps[1](xB[1])) + + +def test_bivar_cmap_bad_shape(): + """ + Tests calling a bivariate colormap with integer values + """ + cmap = mpl.bivar_colormaps['BiCone'] + _ = cmap.lut + with pytest.raises(ValueError, + match="is not a valid value for shape"): + cmap.with_extremes(shape='bad_shape') + + with pytest.raises(ValueError, + match="is not a valid value for shape"): + mpl.colors.BivarColormapFromImage(np.ones((3, 3, 4)), + shape='bad_shape') + + +def test_bivar_cmap_bad_lut(): + """ + Tests calling a bivariate colormap with integer values + """ + with pytest.raises(ValueError, + match="The lut must be an array of shape"): + cmap = mpl.colors.BivarColormapFromImage(np.ones((3, 3, 5))) + + +def test_bivar_cmap_from_image(): + """ + This tests the creation and use of a bivariate colormap + generated from an image + """ + + data_0 = np.arange(6).reshape((2, 3))/5 + data_1 = np.arange(6).reshape((3, 2)).T/5 + + # bivariate colormap from array + cim = np.ones((10, 12, 3)) + cim[:, :, 0] = np.arange(10)[:, np.newaxis]/10 + cim[:, :, 1] = np.arange(12)[np.newaxis, :]/12 + + cmap = mpl.colors.BivarColormapFromImage(cim) + im = cmap((data_0, data_1)) + res = np.array([[[0, 0, 1, 1], + [0.2, 0.33333333, 1, 1], + [0.4, 0.75, 1, 1]], + [[0.6, 0.16666667, 1, 1], + [0.8, 0.58333333, 1, 1], + [0.9, 0.91666667, 1, 1]]]) + assert_allclose(im, res, atol=0.01) + + # input as unit8 + cim = np.ones((10, 12, 3))*255 + cim[:, :, 0] = np.arange(10)[:, np.newaxis]/10*255 + cim[:, :, 1] = np.arange(12)[np.newaxis, :]/12*255 + + cmap = mpl.colors.BivarColormapFromImage(cim.astype(np.uint8)) + im = cmap((data_0, data_1)) + res = np.array([[[0, 0, 1, 1], + [0.2, 0.33333333, 1, 1], + [0.4, 0.75, 1, 1]], + [[0.6, 0.16666667, 1, 1], + [0.8, 0.58333333, 1, 1], + [0.9, 0.91666667, 1, 1]]]) + assert_allclose(im, res, atol=0.01) + + # bivariate colormap from array + png_path = Path(__file__).parent / "baseline_images/pngsuite/basn2c16.png" + cim = Image.open(png_path) + cim = np.asarray(cim.convert('RGBA')) + + cmap = mpl.colors.BivarColormapFromImage(cim) + im = cmap((data_0, data_1), bytes=True) + res = np.array([[[255, 255, 0, 255], + [156, 206, 0, 255], + [49, 156, 49, 255]], + [[206, 99, 0, 255], + [99, 49, 107, 255], + [0, 0, 255, 255]]]) + assert_allclose(im, res, atol=0.01) + + +def test_bivar_resample(): + cmap = mpl.bivar_colormaps['BiOrangeBlue'].resampled((2, 2)) + assert_allclose(cmap((0.25, 0.25)), (0, 0, 0, 1), atol=1e-2) + + cmap = mpl.bivar_colormaps['BiOrangeBlue'].resampled((-2, 2)) + assert_allclose(cmap((0.25, 0.25)), (1., 0.5, 0., 1.), atol=1e-2) + + cmap = mpl.bivar_colormaps['BiOrangeBlue'].resampled((2, -2)) + assert_allclose(cmap((0.25, 0.25)), (0., 0.5, 1., 1.), atol=1e-2) + + cmap = mpl.bivar_colormaps['BiOrangeBlue'].resampled((-2, -2)) + assert_allclose(cmap((0.25, 0.25)), (1, 1, 1, 1), atol=1e-2) + + cmap = mpl.bivar_colormaps['BiOrangeBlue'].reversed() + assert_allclose(cmap((0.25, 0.25)), (0.748535, 0.748547, 0.748535, 1.), atol=1e-2) + cmap = mpl.bivar_colormaps['BiOrangeBlue'].transposed() + assert_allclose(cmap((0.25, 0.25)), (0.252441, 0.252422, 0.252441, 1.), atol=1e-2) + + with pytest.raises(ValueError, match="lutshape must be of length"): + cmap = cmap.resampled(4) + + +def test_bivariate_repr_png(): + cmap = mpl.bivar_colormaps['BiCone'] + png = cmap._repr_png_() + assert len(png) > 0 + img = Image.open(BytesIO(png)) + assert img.width > 0 + assert img.height > 0 + assert 'Title' in img.text + assert 'Description' in img.text + assert 'Author' in img.text + assert 'Software' in img.text + + +def test_bivariate_repr_html(): + cmap = mpl.bivar_colormaps['BiCone'] + html = cmap._repr_html_() + assert len(html) > 0 + png = cmap._repr_png_() + assert base64.b64encode(png).decode('ascii') in html + assert cmap.name in html + assert html.startswith('') + + +def test_multivariate_repr_png(): + cmap = mpl.multivar_colormaps['3VarAddA'] + png = cmap._repr_png_() + assert len(png) > 0 + img = Image.open(BytesIO(png)) + assert img.width > 0 + assert img.height > 0 + assert 'Title' in img.text + assert 'Description' in img.text + assert 'Author' in img.text + assert 'Software' in img.text + + +def test_multivariate_repr_html(): + cmap = mpl.multivar_colormaps['3VarAddA'] + html = cmap._repr_html_() + assert len(html) > 0 + for c in cmap: + png = c._repr_png_() + assert base64.b64encode(png).decode('ascii') in html + assert cmap.name in html + assert html.startswith('') + + +def test_bivar_eq(): + """ + Tests equality between multivariate colormaps + """ + cmap_0 = mpl.bivar_colormaps['BiPeak'] + + cmap_1 = mpl.bivar_colormaps['BiPeak'] + assert (cmap_0 == cmap_1) is True + + cmap_1 = mpl.multivar_colormaps['2VarAddA'] + assert (cmap_0 == cmap_1) is False + + cmap_1 = mpl.bivar_colormaps['BiCone'] + assert (cmap_0 == cmap_1) is False + + cmap_1 = mpl.bivar_colormaps['BiPeak'] + cmap_1 = cmap_1.with_extremes(bad='k') + assert (cmap_0 == cmap_1) is False + + cmap_1 = mpl.bivar_colormaps['BiPeak'] + cmap_1 = cmap_1.with_extremes(outside='k') + assert (cmap_0 == cmap_1) is False + + cmap_1 = mpl.bivar_colormaps['BiPeak'] + cmap_1._init() + cmap_1._lut *= 0.5 + assert (cmap_0 == cmap_1) is False + + cmap_1 = mpl.bivar_colormaps['BiPeak'] + cmap_1 = cmap_1.with_extremes(shape='ignore') + assert (cmap_0 == cmap_1) is False + + +def test_multivar_eq(): + """ + Tests equality between multivariate colormaps + """ + cmap_0 = mpl.multivar_colormaps['2VarAddA'] + + cmap_1 = mpl.multivar_colormaps['2VarAddA'] + assert (cmap_0 == cmap_1) is True + + cmap_1 = mpl.bivar_colormaps['BiPeak'] + assert (cmap_0 == cmap_1) is False + + cmap_1 = mpl.colors.MultivarColormap([cmap_0[0]]*2, + 'sRGB_add') + assert (cmap_0 == cmap_1) is False + + cmap_1 = mpl.multivar_colormaps['3VarAddA'] + assert (cmap_0 == cmap_1) is False + + cmap_1 = mpl.multivar_colormaps['2VarAddA'] + cmap_1 = cmap_1.with_extremes(bad='k') + assert (cmap_0 == cmap_1) is False + + cmap_1 = mpl.multivar_colormaps['2VarAddA'] + cmap_1 = mpl.colors.MultivarColormap(cmap_1[:], 'sRGB_sub') + assert (cmap_0 == cmap_1) is False