'
+ '
'
+ 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 : nparray of shape (k, k, 3)
+ This patch gets supersamples to a lut of shape (N, M, 4)
+ N : int
+ The number of RGB quantization levels along each axis.
+ shape: str 'square' or 'circle' or 'ignore' or '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'):
+ 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[0] - 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: str 'square' or 'circle' or 'ignore' or '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 unput 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
@@ -1827,6 +2726,28 @@ def autoscale_None(self, A):
return Norm
+def _get_mask(X):
+ """
+ Helper function to get the mask. Marked values and np.nan are
+ true in the output
+
+ Parameters
+ ----------
+ X : iterable of np.arrays
+ each array must be numpy array or masked array of shape
+ (n, m) or (n)
+
+ Returns
+ -------
+ mask, bool or boolean np.array of shape (n,m) or (n)
+ """
+ mask_bad = X[0].mask if np.ma.is_masked(X[0]) else np.isnan(X[0])
+ if len(X) > 1:
+ for xx in X[1:]:
+ mask_bad |= xx.mask if np.ma.is_masked(xx) else np.isnan(xx)
+ return mask_bad
+
+
def _create_empty_object_of_class(cls):
return cls.__new__(cls)
@@ -2167,7 +3088,7 @@ def inverse(self, value):
class NoNorm(Normalize):
"""
Dummy replacement for `Normalize`, for the case where we want to use
- indices directly in a `~matplotlib.cm.ScalarMappable`.
+ indices directly in a `~matplotlib.cm.VectorMappable`.
"""
def __call__(self, value, clip=None):
if np.iterable(value):
diff --git a/lib/matplotlib/colors.pyi b/lib/matplotlib/colors.pyi
index 514801b714b8..824081d20a8d 100644
--- a/lib/matplotlib/colors.pyi
+++ b/lib/matplotlib/colors.pyi
@@ -138,6 +138,96 @@ class ListedColormap(Colormap):
def resampled(self, lutsize: int) -> ListedColormap: ...
def reversed(self, name: str | None = ...) -> ListedColormap: ...
+class MultivarColormap:
+ name: str
+ colormaps: list[Colormap]
+ n_variates: int
+ def __init__(self, colormaps: list[Colormap], combination_mode: str, 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 __getitem__(self, item: int) -> Colormap: ...
+ def __iter__(self) -> 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: str = ..., 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 __getitem__(self, item: int) -> Colormap: ...
+ def __eq__(self, other) -> bool: ...
+ def get_bad(self) -> np.ndarray: ...
+ def get_outside(self) -> np.ndarray: ...
+ def copy(self) -> BivarColormap: ...
+ def resampled(self, lutshape: Sequence[int | None], transposed: bool = ...) -> BivarColormap: ...
+ def transposed(self) -> BivarColormap: ...
+ def reversed(self, axis_0: bool = True, axis_1: bool = True) -> 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: str = ..., origin: Sequence[float] = ..., name: str = ...
+ ) -> None: ...
+
+class BivarColormapFromImage(BivarColormap):
+ def __init__(self, lut: np.ndarray, shape: str = ..., origin: Sequence[float] = ..., name: str = ...
+ ) -> None: ...
+
class Normalize:
callbacks: cbook.CallbackRegistry
def __init__(
diff --git a/lib/matplotlib/contour.py b/lib/matplotlib/contour.py
index 0e6068c64b62..8613ba4e6ec1 100644
--- a/lib/matplotlib/contour.py
+++ b/lib/matplotlib/contour.py
@@ -180,13 +180,16 @@ def clabel(self, levels=None, *,
self._label_font_props = font_manager.FontProperties(size=fontsize)
if colors is None:
- self.labelMappable = self
+ self.label_colorizer = self._get_colorizer
self.labelCValueList = np.take(self.cvalues, self.labelIndiceList)
else:
cmap = mcolors.ListedColormap(colors, N=len(self.labelLevelList))
self.labelCValueList = list(range(len(self.labelLevelList)))
- self.labelMappable = cm.ScalarMappable(cmap=cmap,
- norm=mcolors.NoNorm())
+ # self.labelMappable = cm.Colorizer(cmap=cmap,
+ # norm=mcolors.NoNorm())
+ # if not hasattr(self, 'colorizer'):
+ self.label_colorizer = lambda: cm.Colorizer(cmap=cmap,
+ norm=mcolors.NoNorm())
self.labelXYs = []
@@ -506,7 +509,7 @@ def add_label(self, x, y, rotation, lev, cvalue):
rotation=rotation,
horizontalalignment='center', verticalalignment='center',
zorder=self._clabel_zorder,
- color=self.labelMappable.to_rgba(cvalue, alpha=self.get_alpha()),
+ color=self.label_colorizer().to_rgba(cvalue, alpha=self.get_alpha()),
fontproperties=self._label_font_props,
clip_box=self.axes.bbox)
if self._use_clabeltext:
@@ -850,15 +853,16 @@ def __init__(self, ax, *args,
self.labelTexts = []
self.labelCValues = []
- self.set_cmap(cmap)
+ self.colorizer.cmap = cmap
if norm is not None:
- self.set_norm(norm)
- with self.norm.callbacks.blocked(signal="changed"):
- if vmin is not None:
- self.norm.vmin = vmin
- if vmax is not None:
- self.norm.vmax = vmax
- self.norm._changed()
+ self.colorizer.norm = norm
+ # with self.colorizer.norm.callbacks.blocked(signal="changed"):
+ # if vmin is not None:
+ # self.colorizer.vmin = vmin
+ # if vmax is not None:
+ # self.colorizer.vmax = vmax
+ # self.colorizer.norm._changed()
+ self.colorizer.set_clim(vmin, vmax)
self._process_colors()
if self._paths is None:
@@ -907,7 +911,7 @@ def __init__(self, ax, *args,
[subp.codes for subp in p._iter_connected_components()]
for p in self.get_paths()])
tcolors = _api.deprecated("3.8")(property(lambda self: [
- (tuple(rgba),) for rgba in self.to_rgba(self.cvalues, self.alpha)]))
+ (tuple(rgba),) for rgba in self.colorizer.to_rgba(self.cvalues, self.alpha)]))
tlinewidths = _api.deprecated("3.8")(property(lambda self: [
(w,) for w in self.get_linewidths()]))
alpha = property(lambda self: self.get_alpha())
@@ -1103,18 +1107,18 @@ def _get_lowers_and_uppers(self):
def changed(self):
if not hasattr(self, "cvalues"):
self._process_colors() # Sets cvalues.
- # Force an autoscale immediately because self.to_rgba() calls
+ # Force an autoscale immediately because self.colorizer.to_rgba() calls
# autoscale_None() internally with the data passed to it,
# so if vmin/vmax are not set yet, this would override them with
# content from *cvalues* rather than levels like we want
- self.norm.autoscale_None(self.levels)
+ self.colorizer.autoscale_None(np.array(self.levels))
self.set_array(self.cvalues)
self.update_scalarmappable()
alphas = np.broadcast_to(self.get_alpha(), len(self.cvalues))
for label, cv, alpha in zip(self.labelTexts, self.labelCValues, alphas):
label.set_alpha(alpha)
- label.set_color(self.labelMappable.to_rgba(cv))
- super().changed()
+ label.set_color(self.colorizer.to_rgba(cv))
+ # super().colorizer.changed()
def _autolev(self, N):
"""
@@ -1244,7 +1248,7 @@ def _process_colors(self):
of the regions.
"""
- self.monochrome = self.cmap.monochrome
+ self.monochrome = self.colorizer.cmap.monochrome
if self.colors is not None:
# Generate integers for direct indexing.
i0, i1 = 0, len(self.levels)
@@ -1256,14 +1260,14 @@ def _process_colors(self):
if self.extend in ('both', 'max'):
i1 += 1
self.cvalues = list(range(i0, i1))
- self.set_norm(mcolors.NoNorm())
+ self.colorizer.norm = mcolors.NoNorm()
else:
self.cvalues = self.layers
- self.norm.autoscale_None(self.levels)
+ self.colorizer.autoscale_None(np.array(self.levels))
self.set_array(self.cvalues)
self.update_scalarmappable()
if self.extend in ('both', 'max', 'min'):
- self.norm.clip = False
+ self.colorizer.clip = False
def _process_linewidths(self, linewidths):
Nlev = len(self.levels)
diff --git a/lib/matplotlib/figure.py b/lib/matplotlib/figure.py
index 41d4b6078223..925030908614 100644
--- a/lib/matplotlib/figure.py
+++ b/lib/matplotlib/figure.py
@@ -3069,7 +3069,7 @@ def figimage(self, X, xo=0, yo=0, alpha=None, norm=None, cmap=None,
im.set_array(X)
im.set_alpha(alpha)
if norm is None:
- im.set_clim(vmin, vmax)
+ im.colorizer.set_clim(vmin, vmax)
self.images.append(im)
im._remove_method = self.images.remove
self.stale = True
diff --git a/lib/matplotlib/figure.pyi b/lib/matplotlib/figure.pyi
index 27366e83bc4d..f8db25eccd4c 100644
--- a/lib/matplotlib/figure.pyi
+++ b/lib/matplotlib/figure.pyi
@@ -15,7 +15,7 @@ from matplotlib.backend_bases import (
)
from matplotlib.colors import Colormap, Normalize
from matplotlib.colorbar import Colorbar
-from matplotlib.cm import ScalarMappable
+from matplotlib.cm import ScalarMappable, VectorMappable
from matplotlib.gridspec import GridSpec, SubplotSpec, SubplotParams as SubplotParams
from matplotlib.image import _ImageBase, FigureImage
from matplotlib.layout_engine import LayoutEngine
@@ -158,7 +158,7 @@ class FigureBase(Artist):
) -> Text: ...
def colorbar(
self,
- mappable: ScalarMappable,
+ mappable: VectorMappable,
cax: Axes | None = ...,
ax: Axes | Iterable[Axes] | None = ...,
use_gridspec: bool = ...,
@@ -205,7 +205,7 @@ class FigureBase(Artist):
def add_subfigure(self, subplotspec: SubplotSpec, **kwargs) -> SubFigure: ...
def sca(self, a: Axes) -> Axes: ...
def gca(self) -> Axes: ...
- def _gci(self) -> ScalarMappable | None: ...
+ def _gci(self) -> VectorMappable | None: ...
def _process_projection_requirements(
self, *, axes_class=None, polar=False, projection=None, **kwargs
) -> tuple[type[Axes], dict[str, Any]]: ...
diff --git a/lib/matplotlib/image.py b/lib/matplotlib/image.py
index 95994201b94e..a5c2f26b39ff 100644
--- a/lib/matplotlib/image.py
+++ b/lib/matplotlib/image.py
@@ -229,14 +229,16 @@ def _rgb_to_rgba(A):
return rgba
-class _ImageBase(martist.Artist, cm.ScalarMappable):
+class _ImageBase(martist.ColorizingArtist, cm.ColorizerShim):
"""
Base class for images.
interpolation and cmap default to their rc settings
- cmap is a colors.Colormap instance
- norm is a colors.Normalize instance to map luminance to 0-1
+ cmap is a colors.Colormap, colors.MultivarColormap or
+ colors.BivarColormap instance
+ norm is a colors.Normalize instance to map luminance to 0-1 or a
+ sequence of colors.Normalize objects
extent is data axes (left, right, bottom, top) for making image plots
registered with data plots. Default is to label the pixel
@@ -258,8 +260,10 @@ def __init__(self, ax,
interpolation_stage=None,
**kwargs
):
- martist.Artist.__init__(self)
- cm.ScalarMappable.__init__(self, norm, cmap)
+
+ martist.ColorizingArtist.__init__(self, norm, cmap)
+ # martist.Artist.__init__(self)
+ # cm.ScalarMappable.__init__(self, norm, cmap)
if origin is None:
origin = mpl.rcParams['image.origin']
_api.check_in_list(["upper", "lower"], origin=origin)
@@ -292,11 +296,12 @@ def get_size(self):
def get_shape(self):
"""
- Return the shape of the image as tuple (numrows, numcols, channels).
+ Return the shape of the image as tuple.
+ (numrows, numcols, channels) if RGB/RGBA
+ (numrows, numcols) if scalar or multivariate data
"""
if self._A is None:
raise RuntimeError('You must first set the image array')
-
return self._A.shape
def set_alpha(self, alpha):
@@ -331,7 +336,42 @@ def changed(self):
Call this whenever the mappable is changed so observers can update.
"""
self._imcache = None
- cm.ScalarMappable.changed(self)
+ martist.ColorizingArtist.changed(self) # cm.ScalarMappable.changed(self)
+
+ def _resample_and_norm(self, A, norm, out_shape, out_mask, t):
+ """
+ Parameters
+ ----------
+ A : 2D numpy array to be resampled
+ norm : cm.Normalize object
+ out_shape : tuple of ints (out_height, out_width)
+ out_mask : 2D numpy array of shape (out_height, out_width)
+ t : Affine2D transform
+
+ Returns
+ -------
+ resampled_arr2D : resampled and normalized 2D numpy array
+ """
+ if A.dtype.kind == 'f': # Float dtype: scale to same dtype.
+ scaled_dtype = np.dtype("f8" if A.dtype.itemsize > 4 else "f4")
+ if scaled_dtype.itemsize < A.dtype.itemsize:
+ _api.warn_external(f"Casting input data from {A.dtype}"
+ f" to {scaled_dtype} for imshow.")
+ else: # Int dtype, likely.
+ # TODO slice input array first
+ # Scale to appropriately sized float: use float32 if the
+ # dynamic range is small, to limit the memory footprint.
+ da = A.max().astype("f8") - A.min().astype("f8")
+ scaled_dtype = "f8" if da > 1e8 else "f4"
+ # resample the input data to the correct resolution and shape
+ A_resampled = _resample(self, A.astype(scaled_dtype), out_shape, t)
+ # if using NoNorm, cast back to the original datatype
+ if isinstance(norm, mcolors.NoNorm):
+ A_resampled = A_resampled.astype(A.dtype)
+ # mask and run through the norm
+ resampled_masked = np.ma.masked_array(A_resampled, out_mask)
+ output = norm(resampled_masked)
+ return output
def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
unsampled=False, round_to_pixel_border=True):
@@ -388,7 +428,6 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
"this method is called.")
clipped_bbox = Bbox.intersection(out_bbox, clip_bbox)
-
if clipped_bbox is None:
return None, 0, 0, None
@@ -450,84 +489,85 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
interpolation_stage = 'rgba'
else:
interpolation_stage = 'data'
-
- if A.ndim == 2 and interpolation_stage == 'data':
- # if we are a 2D array, then we are running through the
- # norm + colormap transformation. However, in general the
- # input data is not going to match the size on the screen so we
- # have to resample to the correct number of pixels
-
- if A.dtype.kind == 'f': # Float dtype: scale to same dtype.
- scaled_dtype = np.dtype("f8" if A.dtype.itemsize > 4 else "f4")
- if scaled_dtype.itemsize < A.dtype.itemsize:
- _api.warn_external(f"Casting input data from {A.dtype}"
- f" to {scaled_dtype} for imshow.")
- else: # Int dtype, likely.
- # TODO slice input array first
- # Scale to appropriately sized float: use float32 if the
- # dynamic range is small, to limit the memory footprint.
- da = A.max().astype("f8") - A.min().astype("f8")
- scaled_dtype = "f8" if da > 1e8 else "f4"
-
- # resample the input data to the correct resolution and shape
- A_resampled = _resample(self, A.astype(scaled_dtype), out_shape, t)
-
- # if using NoNorm, cast back to the original datatype
- if isinstance(self.norm, mcolors.NoNorm):
- A_resampled = A_resampled.astype(A.dtype)
-
- # Compute out_mask (what screen pixels include "bad" data
- # pixels) and out_alpha (to what extent screen pixels are
- # covered by data pixels: 0 outside the data extent, 1 inside
- # (even for bad data), and intermediate values at the edges).
- mask = (np.where(A.mask, np.float32(np.nan), np.float32(1))
- if A.mask.shape == A.shape # nontrivial mask
- else np.ones_like(A, np.float32))
- # we always have to interpolate the mask to account for
- # non-affine transformations
- out_alpha = _resample(self, mask, out_shape, t, resample=True)
- del mask # Make sure we don't use mask anymore!
- out_mask = np.isnan(out_alpha)
- out_alpha[out_mask] = 1
- # Apply the pixel-by-pixel alpha values if present
- alpha = self.get_alpha()
- if alpha is not None and np.ndim(alpha) > 0:
- out_alpha *= _resample(self, alpha, out_shape, t, resample=True)
- # mask and run through the norm
- resampled_masked = np.ma.masked_array(A_resampled, out_mask)
- output = self.norm(resampled_masked)
- else:
- if A.ndim == 2: # interpolation_stage = 'rgba'
- self.norm.autoscale_None(A)
- A = self.to_rgba(A)
- alpha = self._get_scalar_alpha()
+ scalar_alpha = self._get_scalar_alpha()
+ if A.ndim == 2:
+ if interpolation_stage == 'rgba':
+ # run norm -> colormap transformation and then rescale
+ self.colorizer.autoscale_None(A)
+ unscaled_rgba = self.colorizer.to_rgba(A)
+ output = _resample( # resample rgba channels
+ self, unscaled_rgba, out_shape, t, alpha=scalar_alpha)
+ # transforming to bytes *after* resampling gives improved results
+ if output.dtype.kind == 'f':
+ output = (output * 255).astype(np.uint8)
+ else: # if _interpolation_stage != 'rgba':
+ # In general the input data is not going to match the size on the
+ # screen so we have to resample to the correct number of pixels
+
+ # First compute out_mask (what screen pixels include "bad" data
+ # pixels) and out_alpha (to what extent screen pixels are
+ # covered by data pixels: 0 outside the data extent, 1 inside
+ # (even for bad data), and intermediate values at the edges).
+ mask = mcolors._get_mask(cm._iterable_variates_in_data(A))
+ if mask.shape == A.shape:
+ # we always have to interpolate the mask to account for
+ # non-affine transformations
+ # To get all pixels where partially covered by the mask
+ # we run _resample with an array with np.nan
+ nan_mask = np.where(mask, np.float32(np.nan), np.float32(1))
+ out_alpha = _resample(self, nan_mask, out_shape, t,
+ resample=True)
+ else:
+ out_alpha = np.ones(out_shape, np.float32)
+ del mask, nan_mask # Make sure we don't use mask anymore!
+ out_mask = np.isnan(out_alpha)
+ out_alpha[out_mask] = 1
+ # Apply the pixel-by-pixel alpha values if present
+ alpha = self.get_alpha()
+ if alpha is not None and np.ndim(alpha) > 0:
+ out_alpha *= _resample(self, alpha, out_shape,
+ t, resample=True)
+ # Resample and norm data
+ if self.colorizer.cmap.n_variates == 1:
+ normed_resampled =\
+ self._resample_and_norm(A,
+ self.colorizer._norm[0],
+ out_shape,
+ out_mask, t)
+ else:
+ normed_resampled = [self._resample_and_norm(a,
+ self.colorizer._norm[i],
+ out_shape,
+ out_mask, t)
+ for i, a in
+ enumerate(cm._iterable_variates_in_data(A))]
+ output = self.colorizer.cmap(normed_resampled, bytes=True)
+ # Apply alpha *after* if the input was greyscale without a mask
+ alpha_channel = output[:, :, 3]
+ alpha_channel[:] = ( # Assignment will cast to uint8.
+ alpha_channel.astype(np.float32) * out_alpha * scalar_alpha)
+
+ else: # A.ndim == 3
if A.shape[2] == 3:
# No need to resample alpha or make a full array; NumPy will expand
# this out and cast to uint8 if necessary when it's assigned to the
# alpha channel below.
- output_alpha = (255 * alpha) if A.dtype == np.uint8 else alpha
+ output_alpha = (255 * scalar_alpha) if A.dtype == np.uint8 \
+ else scalar_alpha
else:
output_alpha = _resample( # resample alpha channel
- self, A[..., 3], out_shape, t, alpha=alpha)
+ self, A[..., 3], out_shape, t, alpha=scalar_alpha)
+
output = _resample( # resample rgb channels
- self, _rgb_to_rgba(A[..., :3]), out_shape, t, alpha=alpha)
+ self, _rgb_to_rgba(A[..., :3]), out_shape, t, alpha=scalar_alpha)
output[..., 3] = output_alpha # recombine rgb and alpha
-
- # output is now either a 2D array of normed (int or float) data
- # or an RGBA array of re-sampled input
- output = self.to_rgba(output, bytes=True, norm=False)
+ if output.dtype.kind == 'f':
+ output = (output * 255).astype(np.uint8)
# output is now a correctly sized RGBA array of uint8
-
- # Apply alpha *after* if the input was greyscale without a mask
- if A.ndim == 2:
- alpha = self._get_scalar_alpha()
- alpha_channel = output[:, :, 3]
- alpha_channel[:] = ( # Assignment will cast to uint8.
- alpha_channel.astype(np.float32) * out_alpha * alpha)
-
- else:
+ else: # if unsampled:
if self._imcache is None:
- self._imcache = self.to_rgba(A, bytes=True, norm=(A.ndim == 2))
+ self._imcache = self.colorizer.to_rgba(A, bytes=True,
+ norm=(A.ndim == 2))
output = self._imcache
# Subset the input image to only the part that will be displayed.
@@ -622,41 +662,61 @@ def contains(self, mouseevent):
def write_png(self, fname):
"""Write the image to png file *fname*."""
- im = self.to_rgba(self._A[::-1] if self.origin == 'lower' else self._A,
- bytes=True, norm=True)
+ im = self.colorizer.to_rgba(
+ self._A[::-1] if self.origin == 'lower' else self._A,
+ bytes=True, norm=True)
PIL.Image.fromarray(im).save(fname, format="png")
@staticmethod
- def _normalize_image_array(A):
+ def _normalize_image_array(A, n_variates):
"""
Check validity of image-like input *A* and normalize it to a format suitable for
Image subclasses.
"""
- A = cbook.safe_masked_invalid(A, copy=True)
- if A.dtype != np.uint8 and not np.can_cast(A.dtype, float, "same_kind"):
- raise TypeError(f"Image data of dtype {A.dtype} cannot be "
- f"converted to float")
- if A.ndim == 3 and A.shape[-1] == 1:
- A = A.squeeze(-1) # If just (M, N, 1), assume scalar and apply colormap.
- if not (A.ndim == 2 or A.ndim == 3 and A.shape[-1] in [3, 4]):
- raise TypeError(f"Invalid shape {A.shape} for image data")
- if A.ndim == 3:
- # If the input data has values outside the valid range (after
- # normalisation), we issue a warning and then clip X to the bounds
- # - otherwise casting wraps extreme values, hiding outliers and
- # making reliable interpretation impossible.
- high = 255 if np.issubdtype(A.dtype, np.integer) else 1
- if A.min() < 0 or high < A.max():
- _log.warning(
- 'Clipping input data to the valid range for imshow with '
- 'RGB data ([0..1] for floats or [0..255] for integers). '
- 'Got range [%s..%s].',
- A.min(), A.max()
- )
- A = np.clip(A, 0, high)
- # Cast unsupported integer types to uint8
- if A.dtype != np.uint8 and np.issubdtype(A.dtype, np.integer):
- A = A.astype(np.uint8)
+ if n_variates == 1:
+ A = cbook.safe_masked_invalid(A, copy=True)
+ if A.dtype != np.uint8 and not np.can_cast(A.dtype, float, "same_kind"):
+ raise TypeError(f"Image data of dtype {A.dtype} cannot be "
+ f"converted to float")
+ if A.ndim == 3 and A.shape[-1] == 1:
+ A = A.squeeze(-1) # If just (M, N, 1), assume scalar and apply colormap
+ if not (A.ndim == 2 or A.ndim == 3 and A.shape[-1] in [3, 4]):
+ if A.ndim == 3 and A.shape[0] == 2:
+ raise TypeError(f"Invalid shape {A.shape} for image data."
+ " For multivariate data a valid colormap must be"
+ " explicitly declared, for example"
+ f" cmap='BiOrangeBlue' or cmap='2VarAddA'")
+ if A.ndim == 3 and A.shape[0] > 2 and A.shape[0] <= 8:
+ raise TypeError(f"Invalid shape {A.shape} for image data."
+ " For multivariate data a multivariate colormap"
+ " must be explicitly declared, for example"
+ f" cmap='{A.shape[0]}VarAddA'")
+ raise TypeError(f"Invalid shape {A.shape} for image data")
+ if A.ndim == 3:
+ # If the input data has values outside the valid range (after
+ # normalisation), we issue a warning and then clip X to the bounds
+ # - otherwise casting wraps extreme values, hiding outliers and
+ # making reliable interpretation impossible.
+ high = 255 if np.issubdtype(A.dtype, np.integer) else 1
+ if A.min() < 0 or high < A.max():
+ _log.warning(
+ 'Clipping input data to the valid range for imshow with '
+ 'RGB data ([0..1] for floats or [0..255] for integers). '
+ 'Got range [%s..%s].',
+ A.min(), A.max()
+ )
+ A = np.clip(A, 0, high)
+ # Cast unsupported integer types to uint8
+ if A.dtype != np.uint8 and np.issubdtype(A.dtype, np.integer):
+ A = A.astype(np.uint8)
+ else: # n_variates > 1
+ A = cm._ensure_multivariate_data(n_variates, A)
+
+ A = cbook.safe_masked_invalid(A, copy=True)
+ for key in A.dtype.fields:
+ if not np.can_cast(A[key].dtype, float, "same_kind"):
+ raise TypeError(f"Image data of dtype {A.dtype} cannot be "
+ f"converted to a sequence of floats")
return A
def set_data(self, A):
@@ -671,7 +731,7 @@ def set_data(self, A):
"""
if isinstance(A, PIL.Image.Image):
A = pil_to_array(A) # Needed e.g. to apply png palette.
- self._A = self._normalize_image_array(A)
+ self._A = self._normalize_image_array(A, self.colorizer.cmap.n_variates)
self._imcache = None
self.stale = True
@@ -812,10 +872,12 @@ class AxesImage(_ImageBase):
Parameters
----------
ax : `~matplotlib.axes.Axes`
- The Axes the image will belong to.
- cmap : str or `~matplotlib.colors.Colormap`, default: :rc:`image.cmap`
+ The axes the image will belong to.
+ cmap : str or `~matplotlib.colors.Colormap`, or \
+ `~matplotlib.colors.MultivarColormap` or \
+ `~matplotlib.colors.BivarColormap`, default: :rc:`image.cmap`
The Colormap instance or registered colormap name used to map scalar
- data to colors.
+ or multivariate data to colors.
norm : str or `~matplotlib.colors.Normalize`
Maps luminance to 0-1.
interpolation : str, default: :rc:`image.interpolation`
@@ -1020,7 +1082,7 @@ def make_image(self, renderer, magnification=1.0, unsampled=False):
A = self._A
if A.ndim == 2:
if A.dtype != np.uint8:
- A = self.to_rgba(A, bytes=True)
+ A = self.colorizer.to_rgba(A, bytes=True)
else:
A = np.repeat(A[:, :, np.newaxis], 4, 2)
A[:, :, 3] = 255
@@ -1098,7 +1160,7 @@ def set_data(self, x, y, A):
(M, N) `~numpy.ndarray` or masked array of values to be
colormapped, or (M, N, 3) RGB array, or (M, N, 4) RGBA array.
"""
- A = self._normalize_image_array(A)
+ A = self._normalize_image_array(A, self.colorizer.cmap.n_variates)
x = np.array(x, np.float32)
y = np.array(y, np.float32)
if not (x.ndim == y.ndim == 1 and A.shape[:2] == y.shape + x.shape):
@@ -1212,7 +1274,7 @@ def make_image(self, renderer, magnification=1.0, unsampled=False):
raise ValueError('unsampled not supported on PColorImage')
if self._imcache is None:
- A = self.to_rgba(self._A, bytes=True)
+ A = self.colorizer.to_rgba(self._A, bytes=True)
self._imcache = np.pad(A, [(1, 1), (1, 1), (0, 0)], "constant")
padded_A = self._imcache
bg = mcolors.to_rgba(self.axes.patch.get_facecolor(), 0)
@@ -1258,7 +1320,7 @@ def set_data(self, x, y, A):
- (M, N, 3): RGB array
- (M, N, 4): RGBA array
"""
- A = self._normalize_image_array(A)
+ A = self._normalize_image_array(A, self.colorizer.cmap.n_variates)
x = np.arange(0., A.shape[1] + 1) if x is None else np.array(x, float).ravel()
y = np.arange(0., A.shape[0] + 1) if y is None else np.array(y, float).ravel()
if A.shape[:2] != (y.size - 1, x.size - 1):
@@ -1351,7 +1413,9 @@ def make_image(self, renderer, magnification=1.0, unsampled=False):
def set_data(self, A):
"""Set the image array."""
- cm.ScalarMappable.set_array(self, A)
+ A = self._normalize_image_array(A, self.colorizer.cmap.n_variates)
+ martist.ColorizingArtist.set_array(self, A)
+ # cm.ScalarMappable.set_array(self, A)
self.stale = True
@@ -1582,7 +1646,7 @@ def imsave(fname, arr, vmin=None, vmax=None, cmap=None, format=None,
# as is, saving a few operations.
rgba = arr
else:
- sm = cm.ScalarMappable(cmap=cmap)
+ sm = cm.Colorizer(cmap=cmap)
sm.set_clim(vmin, vmax)
rgba = sm.to_rgba(arr, bytes=True)
if pil_kwargs is None:
diff --git a/lib/matplotlib/image.pyi b/lib/matplotlib/image.pyi
index f4a90ed94386..ff64334d59ca 100644
--- a/lib/matplotlib/image.pyi
+++ b/lib/matplotlib/image.pyi
@@ -11,7 +11,7 @@ import matplotlib.artist as martist
from matplotlib.axes import Axes
from matplotlib import cm
from matplotlib.backend_bases import RendererBase, MouseEvent
-from matplotlib.colors import Colormap, Normalize
+from matplotlib.colors import Colormap, BivarColormap, MultivarColormap, Normalize
from matplotlib.figure import Figure
from matplotlib.transforms import Affine2D, BboxBase, Bbox, Transform
@@ -58,14 +58,14 @@ def composite_images(
images: Sequence[_ImageBase], renderer: RendererBase, magnification: float = ...
) -> tuple[np.ndarray, float, float]: ...
-class _ImageBase(martist.Artist, cm.ScalarMappable):
+class _ImageBase(martist.Artist, cm.VectorMappable):
zorder: float
origin: Literal["upper", "lower"]
axes: Axes
def __init__(
self,
ax: Axes,
- cmap: str | Colormap | None = ...,
+ cmap: str | Colormap | BivarColormap | MultivarColormap | None = ...,
norm: str | Normalize | None = ...,
interpolation: str | None = ...,
origin: Literal["upper", "lower"] | None = ...,
@@ -104,7 +104,7 @@ class AxesImage(_ImageBase):
self,
ax: Axes,
*,
- cmap: str | Colormap | None = ...,
+ cmap: str | Colormap | BivarColormap | MultivarColormap | None = ...,
norm: str | Normalize | None = ...,
interpolation: str | None = ...,
origin: Literal["upper", "lower"] | None = ...,
@@ -158,7 +158,7 @@ class FigureImage(_ImageBase):
self,
fig: Figure,
*,
- cmap: str | Colormap | None = ...,
+ cmap: str | Colormap | BivarColormap | MultivarColormap | None = ...,
norm: str | Normalize | None = ...,
offsetx: int = ...,
offsety: int = ...,
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/pyplot.py b/lib/matplotlib/pyplot.py
index d54a25056175..2ad6b63c1e08 100644
--- a/lib/matplotlib/pyplot.py
+++ b/lib/matplotlib/pyplot.py
@@ -76,7 +76,8 @@
from matplotlib.scale import get_scale_names # noqa: F401
from matplotlib.cm import _colormaps
-from matplotlib.colors import _color_sequences, Colormap
+from matplotlib.colors import _color_sequences, Colormap, \
+ BivarColormap, MultivarColormap
import numpy as np
@@ -97,7 +98,7 @@
from matplotlib.axis import Tick
from matplotlib.axes._base import _AxesBase
from matplotlib.backend_bases import RendererBase, Event
- from matplotlib.cm import ScalarMappable
+ from matplotlib.cm import VectorMappable
from matplotlib.contour import ContourSet, QuadContourSet
from matplotlib.collections import (
Collection,
@@ -2514,7 +2515,7 @@ def _get_pyplot_commands() -> list[str]:
@_copy_docstring_and_deprecators(Figure.colorbar)
def colorbar(
- mappable: ScalarMappable | None = None,
+ mappable: VectorMappable | None = None,
cax: matplotlib.axes.Axes | None = None,
ax: matplotlib.axes.Axes | Iterable[matplotlib.axes.Axes] | None = None,
**kwargs
@@ -2538,7 +2539,7 @@ def clim(vmin: float | None = None, vmax: float | None = None) -> None:
will be used for color scaling.
If you want to set the clim of multiple images, use
- `~.ScalarMappable.set_clim` on every image, for example::
+ `~.VectorMappable.set_clim` on every image, for example::
for im in gca().get_images():
im.set_clim(0, 0.5)
@@ -2574,10 +2575,14 @@ def get_cmap(name: Colormap | str | None = None, lut: int | None = None) -> Colo
if isinstance(name, Colormap):
return name
_api.check_in_list(sorted(_colormaps), name=name)
+
+ # A `cm.ColormapRegistry` can contain Colormap, BivarColormap or MultivarColormap
+ # objects, but `_colormaps` only contains Colormap objects.
+ # We therefore need to use ignore[arg-type, return-value]
if lut is None:
- return _colormaps[name]
+ return _colormaps[name] # type: ignore[return-value]
else:
- return _colormaps[name].resampled(lut)
+ return _colormaps[name].resampled(lut) # type: ignore[arg-type, return-value]
def set_cmap(cmap: Colormap | str) -> None:
@@ -2757,7 +2762,7 @@ def gca() -> Axes:
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
@_copy_docstring_and_deprecators(Figure._gci)
-def gci() -> ScalarMappable | None:
+def gci() -> VectorMappable | None:
return gcf()._gci()
@@ -3556,7 +3561,7 @@ def hlines(
@_copy_docstring_and_deprecators(Axes.imshow)
def imshow(
X: ArrayLike | PIL.Image.Image,
- cmap: str | Colormap | None = None,
+ cmap: str | Colormap | BivarColormap | MultivarColormap | None = None,
norm: str | Normalize | None = None,
*,
aspect: Literal["equal", "auto"] | float | None = None,
@@ -3674,7 +3679,7 @@ def pcolor(
shading: Literal["flat", "nearest", "auto"] | None = None,
alpha: float | None = None,
norm: str | Normalize | None = None,
- cmap: str | Colormap | None = None,
+ cmap: str | Colormap | BivarColormap | MultivarColormap | None = None,
vmin: float | None = None,
vmax: float | None = None,
data=None,
@@ -3701,7 +3706,7 @@ def pcolormesh(
*args: ArrayLike,
alpha: float | None = None,
norm: str | Normalize | None = None,
- cmap: str | Colormap | None = None,
+ cmap: str | Colormap | BivarColormap | MultivarColormap | None = None,
vmin: float | None = None,
vmax: float | None = None,
shading: Literal["flat", "nearest", "gouraud", "auto"] | None = None,
@@ -4017,7 +4022,7 @@ def spy(
origin=origin,
**kwargs,
)
- if isinstance(__ret, cm.ScalarMappable):
+ if isinstance(__ret, cm.VectorMappable):
sci(__ret)
return __ret
@@ -4345,7 +4350,7 @@ def xcorr(
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
@_copy_docstring_and_deprecators(Axes._sci)
-def sci(im: ScalarMappable) -> None:
+def sci(im: VectorMappable) -> None:
gca()._sci(im)
diff --git a/lib/matplotlib/streamplot.py b/lib/matplotlib/streamplot.py
index 84f99732c709..e367a2072079 100644
--- a/lib/matplotlib/streamplot.py
+++ b/lib/matplotlib/streamplot.py
@@ -232,8 +232,8 @@ def streamplot(axes, x, y, u, v, density=1, linewidth=None, color=None,
lc.sticky_edges.y[:] = [grid.y_origin, grid.y_origin + grid.height]
if use_multicolor_lines:
lc.set_array(np.ma.hstack(line_colors))
- lc.set_cmap(cmap)
- lc.set_norm(norm)
+ lc.colorizer.cmap = cmap
+ lc.colorizer.norm = norm
axes.add_collection(lc)
ac = mcollections.PatchCollection(arrows)
diff --git a/lib/matplotlib/tests/baseline_images/test_multivariate_axes/bivar_cmap_from_image.png b/lib/matplotlib/tests/baseline_images/test_multivariate_axes/bivar_cmap_from_image.png
new file mode 100644
index 000000000000..7395b33ab35d
Binary files /dev/null and b/lib/matplotlib/tests/baseline_images/test_multivariate_axes/bivar_cmap_from_image.png differ
diff --git a/lib/matplotlib/tests/baseline_images/test_multivariate_axes/bivariate_cmap_call.png b/lib/matplotlib/tests/baseline_images/test_multivariate_axes/bivariate_cmap_call.png
new file mode 100644
index 000000000000..117b856c0a32
Binary files /dev/null and b/lib/matplotlib/tests/baseline_images/test_multivariate_axes/bivariate_cmap_call.png differ
diff --git a/lib/matplotlib/tests/baseline_images/test_multivariate_axes/bivariate_cmap_shapes.png b/lib/matplotlib/tests/baseline_images/test_multivariate_axes/bivariate_cmap_shapes.png
new file mode 100644
index 000000000000..1cc1b7558ede
Binary files /dev/null and b/lib/matplotlib/tests/baseline_images/test_multivariate_axes/bivariate_cmap_shapes.png differ
diff --git a/lib/matplotlib/tests/baseline_images/test_multivariate_axes/bivariate_visualizations.png b/lib/matplotlib/tests/baseline_images/test_multivariate_axes/bivariate_visualizations.png
new file mode 100644
index 000000000000..d53e76b97b91
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diff --git a/lib/matplotlib/tests/baseline_images/test_multivariate_axes/multivar_cmap_call.png b/lib/matplotlib/tests/baseline_images/test_multivariate_axes/multivar_cmap_call.png
new file mode 100644
index 000000000000..2a3b6edd1142
Binary files /dev/null and b/lib/matplotlib/tests/baseline_images/test_multivariate_axes/multivar_cmap_call.png differ
diff --git a/lib/matplotlib/tests/baseline_images/test_multivariate_axes/multivariate_figimage.png b/lib/matplotlib/tests/baseline_images/test_multivariate_axes/multivariate_figimage.png
new file mode 100644
index 000000000000..8e10bddaa5ed
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diff --git a/lib/matplotlib/tests/baseline_images/test_multivariate_axes/multivariate_imshow_alpha.png b/lib/matplotlib/tests/baseline_images/test_multivariate_axes/multivariate_imshow_alpha.png
new file mode 100644
index 000000000000..b37ce878dec1
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diff --git a/lib/matplotlib/tests/baseline_images/test_multivariate_axes/multivariate_imshow_norm.png b/lib/matplotlib/tests/baseline_images/test_multivariate_axes/multivariate_imshow_norm.png
new file mode 100644
index 000000000000..2fe279670721
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diff --git a/lib/matplotlib/tests/baseline_images/test_multivariate_axes/multivariate_pcolormesh_alpha.png b/lib/matplotlib/tests/baseline_images/test_multivariate_axes/multivariate_pcolormesh_alpha.png
new file mode 100644
index 000000000000..580ea19bd233
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diff --git a/lib/matplotlib/tests/baseline_images/test_multivariate_axes/multivariate_pcolormesh_norm.png b/lib/matplotlib/tests/baseline_images/test_multivariate_axes/multivariate_pcolormesh_norm.png
new file mode 100644
index 000000000000..c00f5a862618
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diff --git a/lib/matplotlib/tests/baseline_images/test_multivariate_axes/multivariate_visualizations.png b/lib/matplotlib/tests/baseline_images/test_multivariate_axes/multivariate_visualizations.png
new file mode 100644
index 000000000000..791d4ff23492
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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..22d5bb1b9513 100644
--- a/lib/matplotlib/tests/meson.build
+++ b/lib/matplotlib/tests/meson.build
@@ -57,6 +57,8 @@ python_sources = [
'test_marker.py',
'test_mathtext.py',
'test_matplotlib.py',
+ 'test_multivariate_axes.py',
+ 'test_multivariate_colormaps.py',
'test_mlab.py',
'test_offsetbox.py',
'test_patches.py',
diff --git a/lib/matplotlib/tests/test_image.py b/lib/matplotlib/tests/test_image.py
index dfacfccb3e0e..9ec9ae356d7c 100644
--- a/lib/matplotlib/tests/test_image.py
+++ b/lib/matplotlib/tests/test_image.py
@@ -1123,11 +1123,13 @@ def test_image_cursor_formatting():
# Create a dummy image to be able to call format_cursor_data
im = ax.imshow(np.zeros((4, 4)))
- data = np.ma.masked_array([0], mask=[True])
+ # im.get_cursor_data(event) provides single values,
+ # we therefore also test with single values
+ data = np.ma.masked_array(0, mask=[True])
assert im.format_cursor_data(data) == '[]'
- data = np.ma.masked_array([0], mask=[False])
- assert im.format_cursor_data(data) == '[0]'
+ data = np.ma.masked_array(0, mask=[False])
+ assert im.format_cursor_data(data) == '[0.0]'
data = np.nan
assert im.format_cursor_data(data) == '[nan]'
diff --git a/lib/matplotlib/tests/test_multivariate_axes.py b/lib/matplotlib/tests/test_multivariate_axes.py
new file mode 100644
index 000000000000..70426715afcf
--- /dev/null
+++ b/lib/matplotlib/tests/test_multivariate_axes.py
@@ -0,0 +1,585 @@
+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.colors as mcolors
+import matplotlib as mpl
+import pytest
+import re
+
+
+@image_comparison(["bivariate_visualizations.png"])
+def test_bivariate_visualizations():
+ x_0 = np.arange(25, dtype='float32').reshape(5, 5) % 5
+ x_1 = np.arange(25, dtype='float32').reshape(5, 5).T % 5
+
+ fig, axes = plt.subplots(1, 6, figsize=(10, 2))
+
+ axes[0].imshow((x_0, x_1), cmap='BiPeak', interpolation='nearest')
+ axes[1].matshow((x_0, x_1), cmap='BiPeak')
+ axes[2].pcolor((x_0, x_1), cmap='BiPeak')
+ axes[3].pcolormesh((x_0, x_1), cmap='BiPeak')
+
+ x = np.arange(5)
+ y = np.arange(5)
+ X, Y = np.meshgrid(x, y)
+ axes[4].pcolormesh(X, Y, (x_0, x_1), cmap='BiPeak')
+
+ patches = [
+ mpl.patches.Wedge((.3, .7), .1, 0, 360), # Full circle
+ mpl.patches.Wedge((.7, .8), .2, 0, 360, width=0.05), # Full ring
+ mpl.patches.Wedge((.8, .3), .2, 0, 45), # Full sector
+ mpl.patches.Wedge((.8, .3), .2, 22.5, 90, width=0.10), # Ring sector
+ ]
+ colors_0 = np.arange(len(patches)) // 2
+ colors_1 = np.arange(len(patches)) % 2
+ p = mpl.collections.PatchCollection(patches, cmap='BiPeak', alpha=0.5)
+ p.set_array((colors_0, colors_1))
+ axes[5].add_collection(p)
+
+ remove_ticks_and_titles(fig)
+
+
+@image_comparison(["multivariate_visualizations.png"])
+def test_multivariate_visualizations():
+ x_0 = np.arange(25, dtype='float32').reshape(5, 5) % 5
+ x_1 = np.arange(25, dtype='float32').reshape(5, 5).T % 5
+ x_2 = np.arange(25, dtype='float32').reshape(5, 5) % 6
+
+ fig, axes = plt.subplots(1, 6, figsize=(10, 2))
+
+ axes[0].imshow((x_0, x_1, x_2), cmap='3VarAddA', interpolation='nearest')
+ axes[1].matshow((x_0, x_1, x_2), cmap='3VarAddA')
+ axes[2].pcolor((x_0, x_1, x_2), cmap='3VarAddA')
+ axes[3].pcolormesh((x_0, x_1, x_2), cmap='3VarAddA')
+
+ x = np.arange(5)
+ y = np.arange(5)
+ X, Y = np.meshgrid(x, y)
+ axes[4].pcolormesh(X, Y, (x_0, x_1, x_2), cmap='3VarAddA')
+
+ patches = [
+ mpl.patches.Wedge((.3, .7), .1, 0, 360), # Full circle
+ mpl.patches.Wedge((.7, .8), .2, 0, 360, width=0.05), # Full ring
+ mpl.patches.Wedge((.8, .3), .2, 0, 45), # Full sector
+ mpl.patches.Wedge((.8, .3), .2, 22.5, 90, width=0.10), # Ring sector
+ ]
+ colors_0 = np.arange(len(patches)) // 2
+ colors_1 = np.arange(len(patches)) % 2
+ colors_2 = np.arange(len(patches)) % 3
+ p = mpl.collections.PatchCollection(patches, cmap='3VarAddA', alpha=0.5)
+ p.set_array((colors_0, colors_1, colors_2))
+ axes[5].add_collection(p)
+
+ remove_ticks_and_titles(fig)
+
+
+@image_comparison(["multivariate_pcolormesh_alpha.png"])
+def test_multivariate_pcolormesh_alpha():
+ """
+ Check that the the alpha keyword works for pcolormesh
+
+ This test covers all plotting modes that use the same pipeline
+ (inherit from Collection).
+ """
+ x_0 = np.arange(25, dtype='float32').reshape(5, 5) % 5
+ x_1 = np.arange(25, dtype='float32').reshape(5, 5).T % 5
+ x_2 = np.arange(25, dtype='float32').reshape(5, 5) % 6
+
+ fig, axes = plt.subplots(2, 3)
+
+ axes[0, 0].pcolormesh(x_1, alpha=0.5)
+ axes[0, 1].pcolormesh((x_0, x_1), cmap='BiPeak', alpha=0.5)
+ axes[0, 2].pcolormesh((x_0, x_1, x_2), cmap='3VarAddA', alpha=0.5)
+
+ al = np.arange(25, dtype='float32').reshape(5, 5)[::-1].T % 6 / 5
+
+ axes[1, 0].pcolormesh(x_1, alpha=al)
+ axes[1, 1].pcolormesh((x_0, x_1), cmap='BiPeak', alpha=al)
+ axes[1, 2].pcolormesh((x_0, x_1, x_2), cmap='3VarAddA', alpha=al)
+
+ remove_ticks_and_titles(fig)
+
+
+@image_comparison(["multivariate_pcolormesh_norm.png"])
+def test_multivariate_pcolormesh_norm():
+ """
+ Test vmin, vmax and norm
+ Norm is checked via a LogNorm, as this converts
+ A LogNorm converts the input to a masked array, masking for X <= 0
+ By using a LogNorm, this functionality is also tested.
+
+ This test covers all plotting modes that use the same pipeline
+ (inherit from Collection).
+ """
+ x_0 = np.arange(25, dtype='float32').reshape(5, 5) % 5
+ x_1 = np.arange(25, dtype='float32').reshape(5, 5).T % 5
+ x_2 = np.arange(25, dtype='float32').reshape(5, 5) % 6
+
+ fig, axes = plt.subplots(3, 5)
+
+ axes[0, 0].pcolormesh(x_1)
+ axes[0, 1].pcolormesh((x_0, x_1), cmap='BiPeak')
+ axes[0, 2].pcolormesh((x_0, x_1, x_2), cmap='3VarAddA')
+ axes[0, 3].pcolormesh((x_0, x_1), cmap='BiPeak')
+ axes[0, 4].pcolormesh((x_0, x_1, x_2), cmap='3VarAddA')
+
+ vmin = 1
+ vmax = 3
+ axes[1, 0].pcolormesh(x_1, vmin=vmin, vmax=vmax)
+ axes[1, 1].pcolormesh((x_0, x_1), cmap='BiPeak', vmin=vmin, vmax=vmax)
+ axes[1, 2].pcolormesh((x_0, x_1, x_2), cmap='3VarAddA',
+ vmin=vmin, vmax=vmax)
+ axes[1, 3].pcolormesh((x_0, x_1), cmap='BiPeak',
+ vmin=(None, vmin), vmax=(None, vmax))
+ axes[1, 4].pcolormesh((x_0, x_1, x_2), cmap='3VarAddA',
+ vmin=(None, vmin, None), vmax=(None, vmax, None))
+
+ n = mcolors.LogNorm(vmin=1, vmax=5)
+ axes[2, 0].pcolormesh(x_1, norm=n)
+ axes[2, 1].pcolormesh((x_0, x_1), cmap='BiPeak', norm=(n, n))
+ axes[2, 2].pcolormesh((x_0, x_1, x_2), cmap='3VarAddA', norm=(n, n, n))
+ axes[2, 3].pcolormesh((x_0, x_1), cmap='BiPeak', norm=(None, n))
+ axes[2, 4].pcolormesh((x_0, x_1, x_2), cmap='3VarAddA',
+ norm=(None, n, None))
+
+ remove_ticks_and_titles(fig)
+
+
+@image_comparison(["multivariate_imshow_alpha.png"])
+def test_multivariate_imshow_alpha():
+ """
+ Check that the the alpha keyword works for pcolormesh
+ """
+ x_0 = np.arange(25, dtype='float32').reshape(5, 5) % 5
+ x_1 = np.arange(25, dtype='float32').reshape(5, 5).T % 5
+ x_2 = np.arange(25, dtype='float32').reshape(5, 5) % 6
+
+ fig, axes = plt.subplots(2, 3)
+
+ # interpolation='nearest' to reduce size of baseline image
+ axes[0, 0].imshow(x_1, interpolation='nearest', alpha=0.5)
+ axes[0, 1].imshow((x_0, x_1), interpolation='nearest', cmap='BiPeak', alpha=0.5)
+ axes[0, 2].imshow((x_0, x_1, x_2), interpolation='nearest',
+ cmap='3VarAddA', alpha=0.5)
+
+ al = np.arange(25, dtype='float32').reshape(5, 5)[::-1].T % 6 / 5
+
+ axes[1, 0].imshow(x_1, interpolation='nearest', alpha=al)
+ axes[1, 1].imshow((x_0, x_1), interpolation='nearest', cmap='BiPeak', alpha=al)
+ axes[1, 2].imshow((x_0, x_1, x_2), interpolation='nearest',
+ cmap='3VarAddA', alpha=al)
+
+ remove_ticks_and_titles(fig)
+
+
+@image_comparison(["multivariate_imshow_norm.png"])
+def test_multivariate_imshow_norm():
+ """
+ Test vmin, vmax and norm
+ Norm is checked via a LogNorm.
+ A LogNorm converts the input to a masked array, masking for X <= 0
+ By using a LogNorm, this functionality is also tested.
+ """
+ x_0 = np.arange(25, dtype='float32').reshape(5, 5) % 5
+ x_1 = np.arange(25, dtype='float32').reshape(5, 5).T % 5
+ x_2 = np.arange(25, dtype='float32').reshape(5, 5) % 6
+
+ fig, axes = plt.subplots(3, 5, dpi=10)
+
+ # interpolation='nearest' to reduce size of baseline image and
+ # removes ambiguity when using masked array (from LogNorm)
+ axes[0, 0].imshow(x_1, interpolation='nearest')
+ axes[0, 1].imshow((x_0, x_1), interpolation='nearest', cmap='BiPeak')
+ axes[0, 2].imshow((x_0, x_1, x_2), interpolation='nearest', cmap='3VarAddA')
+ axes[0, 3].imshow((x_0, x_1), interpolation='nearest', cmap='BiPeak')
+ axes[0, 4].imshow((x_0, x_1, x_2), interpolation='nearest', cmap='3VarAddA')
+
+ vmin = 1
+ vmax = 3
+ axes[1, 0].imshow(x_1, interpolation='nearest', vmin=vmin, vmax=vmax)
+ axes[1, 1].imshow((x_0, x_1), interpolation='nearest', cmap='BiPeak',
+ vmin=vmin, vmax=vmax)
+ axes[1, 2].imshow((x_0, x_1, x_2), interpolation='nearest', cmap='3VarAddA',
+ vmin=vmin, vmax=vmax)
+ axes[1, 3].imshow((x_0, x_1), interpolation='nearest', cmap='BiPeak',
+ vmin=(None, vmin), vmax=(None, vmax))
+ axes[1, 4].imshow((x_0, x_1, x_2), interpolation='nearest', cmap='3VarAddA',
+ vmin=(None, vmin, None), vmax=(None, vmax, None))
+
+ n = mcolors.LogNorm(vmin=1, vmax=5)
+ axes[2, 0].imshow(x_1, interpolation='nearest', norm=n)
+ axes[2, 1].imshow((x_0, x_1), interpolation='nearest', cmap='BiPeak', norm=(n, n))
+ axes[2, 2].imshow((x_0, x_1, x_2), interpolation='nearest', cmap='3VarAddA',
+ norm=(n, n, n))
+ axes[2, 3].imshow((x_0, x_1), interpolation='nearest', cmap='BiPeak',
+ norm=(None, n))
+ axes[2, 4].imshow((x_0, x_1, x_2), interpolation='nearest', cmap='3VarAddA',
+ norm=(None, n, None))
+
+ remove_ticks_and_titles(fig)
+
+
+@image_comparison(["bivariate_cmap_shapes.png"])
+def test_bivariate_cmap_shapes():
+ x_0 = np.arange(100, dtype='float32').reshape(10, 10) % 10
+ x_1 = np.arange(100, dtype='float32').reshape(10, 10).T % 10
+
+ fig, axes = plt.subplots(1, 4, figsize=(10, 2))
+
+ # shape = square
+ axes[0].imshow((x_0, x_1), cmap='BiPeak', vmin=1, vmax=8, interpolation='nearest')
+ # shape = cone
+ axes[1].imshow((x_0, x_1), cmap='BiCone', vmin=0.5, vmax=8.5,
+ interpolation='nearest')
+
+ # shape = ignore
+ cmap = mpl.bivar_colormaps['BiCone']
+ cmap = cmap.with_extremes(shape='ignore')
+ axes[2].imshow((x_0, x_1), cmap=cmap, vmin=1, vmax=8, interpolation='nearest')
+
+ # shape = circleignore
+ cmap = mpl.bivar_colormaps['BiCone']
+ cmap = cmap.with_extremes(shape='circleignore')
+ axes[3].imshow((x_0, x_1), cmap=cmap, vmin=0.5, vmax=8.5, interpolation='nearest')
+ remove_ticks_and_titles(fig)
+
+
+@image_comparison(["multivariate_figimage.png"])
+def test_multivariate_figimage():
+ fig = plt.figure(figsize=(2, 2), dpi=100)
+ x, y = np.ix_(np.arange(100) / 100.0, np.arange(100) / 100)
+ z = np.sin(x**2 + y**2 - x*y)
+ c = np.sin(20*x**2 + 50*y**2)
+ img = np.stack((z, c))
+
+ fig.figimage(img, xo=0, yo=0, origin='lower', cmap='BiPeak')
+ fig.figimage(img[:, ::-1, :], xo=0, yo=100, origin='lower', cmap='BiPeak')
+ fig.figimage(img[:, :, ::-1], xo=100, yo=0, origin='lower', cmap='BiPeak')
+ fig.figimage(img[:, ::-1, ::-1], xo=100, yo=100, origin='lower', cmap='BiPeak')
+
+
+@image_comparison(["multivar_cmap_call.png"])
+def test_multivar_cmap_call():
+ """
+ This evaluates manual calls to a bivariate colormap
+ The figure exists because implementing an image comparison
+ is easier than anumeraical comparisons for mulitdimensional arrays
+ """
+ x_0 = np.arange(100, dtype='float32').reshape(10, 10) % 10
+ x_1 = np.arange(100, dtype='float32').reshape(10, 10).T % 10
+
+ fig, axes = plt.subplots(1, 5, figsize=(10, 2))
+
+ cmap = mpl.multivar_colormaps['2VarAddA']
+
+ # call with 0D
+ axes[0].scatter(3, 6, c=[cmap((0.5, 0.5))])
+
+ # call with 1D
+ im = cmap((x_0[0]/9, x_1[::-1, 0]/9))
+ axes[0].scatter(np.arange(10), np.arange(10), c=im)
+
+ # call with 2D
+ cmap = mpl.multivar_colormaps['2VarSubA']
+ im = cmap((x_0/9, x_1/9))
+ axes[1].imshow(im, interpolation='nearest')
+
+ # call with 3D array
+ im = cmap(((x_0/9, x_0/9),
+ (x_1/9, x_1/9)))
+ axes[2].imshow(im.reshape((20, 10, 4)), interpolation='nearest')
+
+ # call with constant alpha, and data of type int
+ im = cmap((x_0.astype('int')*25, x_1.astype('int')*25), alpha=0.5)
+ axes[3].imshow(im, interpolation='nearest')
+
+ # call with variable alpha
+ im = cmap((x_0/9, x_1/9), alpha=(x_0/9)**2, bytes=True)
+ axes[4].imshow(im, interpolation='nearest')
+
+ # call with wrong shape
+ with pytest.raises(ValueError, match="must have a first dimension 2"):
+ cmap((0, 0, 0))
+
+ # call with wrong shape of alpha
+ with pytest.raises(ValueError, match=r"shape \(2,\) does not match "
+ r"that of X\[0\] \(\)"):
+ cmap((0, 0), alpha=(1, 1))
+ remove_ticks_and_titles(fig)
+
+
+def test_multivar_cmap_call_tuple():
+ 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)
+
+
+@image_comparison(["bivariate_cmap_call.png"])
+def test_bivariate_cmap_call():
+ """
+ This evaluates manual calls to a bivariate colormap
+ The figure exists because implementing an image comparison
+ is easier than anumeraical comparisons for mulitdimensional arrays
+ """
+ x_0 = np.arange(100, dtype='float32').reshape(10, 10) % 10
+ x_1 = np.arange(100, dtype='float32').reshape(10, 10).T % 10
+
+ fig, axes = plt.subplots(1, 5, figsize=(10, 2))
+
+ cmap = mpl.bivar_colormaps['BiCone']
+
+ # call with 1D
+ im = cmap((x_0[0]/9, x_1[::-1, 0]/9))
+ axes[0].scatter(np.arange(10), np.arange(10), c=im)
+
+ # call with 2D
+ im = cmap((x_0/9, x_1/9))
+ axes[1].imshow(im, interpolation='nearest')
+
+ # call with 3D array
+ im = cmap(((x_0/9, x_0/9),
+ (x_1/9, x_1/9)))
+ axes[2].imshow(im.reshape((20, 10, 4)), interpolation='nearest')
+
+ cmap = mpl.bivar_colormaps['BiOrangeBlue']
+ # call with constant alpha, and data of type int
+ im = cmap((x_0.astype('int')*25, x_1.astype('int')*25), alpha=0.5)
+ axes[3].imshow(im, interpolation='nearest')
+
+ # call with variable alpha
+ im = cmap((x_0/9, x_1/9), alpha=(x_0/9)**2, bytes=True)
+ axes[4].imshow(im, interpolation='nearest')
+
+ # call with wrong shape
+ with pytest.raises(ValueError, match="must have a first dimension 2"):
+ cmap((0, 0, 0))
+
+ # call with wrong shape of alpha
+ with pytest.raises(ValueError, match=r"shape \(2,\) does not match "
+ r"that of X\[0\] \(\)"):
+ cmap((0, 0), alpha=(1, 1))
+
+ remove_ticks_and_titles(fig)
+
+
+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)
+
+
+@image_comparison(["bivar_cmap_from_image.png"])
+def test_bivar_cmap_from_image():
+ """
+ This tests the creation and use of a bivariate colormap
+ generated from an image
+ """
+ # create bivariate colormap
+ im = np.ones((10, 12, 4))
+ im[:, :, 0] = np.arange(10)[:, np.newaxis]/10
+ im[:, :, 1] = np.arange(12)[np.newaxis, :]/12
+ fig, axes = plt.subplots(1, 2)
+ axes[0].imshow(im, interpolation='nearest')
+
+ # use bivariate colormap
+ data_0 = np.arange(12).reshape((3, 4))
+ data_1 = np.arange(12).reshape((4, 3)).T
+ cmap = mpl.colors.BivarColormapFromImage(im)
+ axes[1].imshow((data_0, data_1), cmap=cmap,
+ interpolation='nearest')
+
+ remove_ticks_and_titles(fig)
+
+
+def test_wrong_multivar_clim_shape():
+ fig, ax = plt.subplots()
+ im = np.arange(24).reshape((2, 3, 4))
+ with pytest.raises(ValueError, match="Unable to map the input for vmin"):
+ ax.imshow(im, cmap='BiPeak', vmin=(None, None, None))
+ with pytest.raises(ValueError, match="Unable to map the input for vmax"):
+ ax.imshow(im, cmap='BiPeak', vmax=(None, None, None))
+
+
+def test_wrong_multivar_norm_shape():
+ fig, ax = plt.subplots()
+ im = np.arange(24).reshape((2, 3, 4))
+ with pytest.raises(ValueError, match="Unable to map the input for norm"):
+ ax.imshow(im, cmap='BiPeak', norm=(None, None, None))
+
+
+def test_wrong_multivar_data_shape():
+ fig, ax = plt.subplots()
+ im = np.arange(12).reshape((1, 3, 4))
+ with pytest.raises(ValueError, match="The data must contain complex numbers, or"):
+ ax.imshow(im, cmap='BiPeak')
+ im = np.arange(12).reshape((3, 4))
+ with pytest.raises(ValueError, match="The data must contain complex numbers, or"):
+ ax.imshow(im, cmap='BiPeak')
+
+
+def test_missing_multivar_cmap_imshow():
+ fig, ax = plt.subplots()
+ im = np.arange(200).reshape((2, 10, 10))
+ with pytest.raises(TypeError,
+ match=("a valid colormap must be explicitly declared"
+ + ", for example cmap='BiOrangeBlue'")):
+ ax.imshow(im)
+ im = np.arange(300).reshape((3, 10, 10))
+ with pytest.raises(TypeError,
+ match=("multivariate colormap must be explicitly declared"
+ + ", for example cmap='3VarAddA")):
+ ax.imshow(im)
+ im = np.arange(1000).reshape((10, 10, 10))
+ with pytest.raises(TypeError,
+ match=re.escape("Invalid shape (10, 10, 10) for image data")):
+ ax.imshow(im)
+
+
+def test_setting_A_on_ScalarMappable():
+ # correct use
+ vm = mpl.cm.ScalarMappable(cmap='3VarAddA')
+ data = np.arange(3*25).reshape((3, 5, 5))
+ vm.set_array(data)
+
+ # attempting to set wrong shape of data
+ with pytest.raises(ValueError, match=re.escape(
+ " must have a first dimension 3 or be of"
+ )):
+ data = np.arange(2*25).reshape((2, 5, 5))
+ vm.set_array(data)
+
+
+def test_setting_norm_on_ScalarMappable():
+ # correct use
+ vm = mpl.cm.ScalarMappable(cmap='3VarAddA')
+ vm.set_norm('linear')
+ vm.set_norm(['linear', 'log', 'asinh'])
+
+ # attempting to set wrong shape of norm
+ with pytest.raises(ValueError, match=re.escape(
+ "Unable to map the input for norm (('None', 'None')) to 3 variables"
+ )):
+ vm.set_norm(('None', 'None'))
+
+
+def test_setting_clim_on_ScalarMappable():
+ # correct use
+ vm = mpl.cm.ScalarMappable(cmap='3VarAddA')
+ vm.set_clim(0, 1)
+ vm.set_clim([0, 0, 0], [1, 2, 3])
+ # attempting to set wrong shape of vmin/vmax
+ with pytest.raises(ValueError, match=re.escape(
+ "Unable to map the input for vmin ([0, 0]) to 3 variables"
+ )):
+ vm.set_clim(vmin=[0, 0])
+ with pytest.raises(ValueError, match=re.escape(
+ "Unable to map the input for vmax ([1, 2]) to 3 variables"
+ )):
+ vm.set_clim(vmax=[1, 2])
+
+
+def test_bivar_eq():
+ """
+ Tests equality between bivariate colormaps
+ """
+ cmap_0 = mpl.bivar_colormaps['BiOrangeBlue']
+
+ cmap_1 = mpl.bivar_colormaps['BiOrangeBlue']
+ 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['BiPeak']
+ assert (cmap_0 == cmap_1) is False
+
+ cmap_1 = mpl.bivar_colormaps['BiOrangeBlue']
+ cmap_1 = cmap_1.with_extremes(bad='k')
+ assert (cmap_0 == cmap_1) is False
+
+ cmap_1 = mpl.bivar_colormaps['BiOrangeBlue']
+ cmap_1 = cmap_1.with_extremes(outside='k')
+ assert (cmap_0 == cmap_1) is False
+
+ cmap_1 = mpl.bivar_colormaps['BiOrangeBlue']
+ cmap_1 = cmap_1.with_extremes(shape='circle')
+ assert (cmap_0 == cmap_1) is False
+
+
+def test_cmap_error():
+ data = np.ones((2, 4, 4))
+ for call in (plt.imshow, plt.pcolor, plt.pcolormesh):
+ with pytest.raises(ValueError, match=re.escape(
+ "See matplotlib.bivar_colormaps() and matplotlib.multivar_colormaps()"
+ " for bivariate and multivariate colormaps."
+ )):
+ call(data, cmap='not_a_cmap')
+
+ with pytest.raises(ValueError, match=re.escape(
+ "See matplotlib.bivar_colormaps() and matplotlib.multivar_colormaps()"
+ " for bivariate and multivariate colormaps."
+ )):
+ mpl.collections.PatchCollection([], cmap='not_a_cmap')
+
+
+def test_artist_format_cursor_data_multivar():
+
+ X = np.zeros((3, 3))
+ X[0, 0] = 0.9
+ X[0, 1] = 0.99
+ X[0, 2] = 0.999
+ X[1, 0] = -1
+ X[1, 1] = 0
+ X[1, 2] = 1
+ X[2, 0] = 0.09
+ X[2, 1] = 0.009
+ X[2, 2] = 0.0009
+
+ labels_list = [
+ "[0.9, 0.0]",
+ "[1., 0.0]",
+ "[1., 0.0]",
+ "[-1.0, 0.0]",
+ "[0.0, 0.0]",
+ "[1.0, 0.0]",
+ "[0.09, 0.0]",
+ "[0.009, 0.0]",
+ "[0.0009, 0.0]",
+ ]
+
+ pos = [[0, 0], [1, 0], [2, 0],
+ [0, 1], [1, 1], [2, 1],
+ [0, 2], [1, 2], [2, 2]]
+
+ from matplotlib.backend_bases import MouseEvent
+
+ for cmap in ['BiOrangeBlue', '2VarAddA']:
+ fig, ax = plt.subplots()
+ norm = mpl.colors.BoundaryNorm(np.linspace(-1, 1, 20), 256)
+ data = (X, np.zeros(X.shape))
+ im = ax.imshow(data, cmap=cmap, norm=(norm, None))
+
+ for v, text in zip(pos, labels_list):
+ xdisp, ydisp = ax.transData.transform(v)
+ event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
+ assert im.format_cursor_data(im.get_cursor_data(event)) == text
+
+
+def test_multivariate_safe_masked_invalid():
+ from matplotlib import cbook
+ dt = np.dtype('float32, float32').newbyteorder('>')
+ x = np.zeros(2, dtype=dt)
+ x['f0'][0] = np.nan
+ xm = cbook.safe_masked_invalid(x)
+ assert (xm['f0'].mask == (True, False)).all()
+ assert (xm['f1'].mask == (False, False)).all()
+ assert '
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_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
+
+
+def test_vectormappable():
+ vm = mpl.cm.ScalarMappable(cmap='2VarAddA')
+ X = (np.linspace(0, 1, 3)[:, np.newaxis] * np.ones((3, 3)),
+ np.linspace(0, 1, 3)[np.newaxis, :] * np.ones((3, 3)))
+ res = np.array([[[0., 0., 0., 1.],
+ [0.38551765, 0.24977647, 0.13414118, 1.],
+ [0.813, 0.423, 0.105, 1.]],
+ [[0.17331765, 0.30909412, 0.42372941, 1.],
+ [0.55883529, 0.55887059, 0.55787059, 1.],
+ [0.98631765, 0.73209412, 0.52872941, 1.]],
+ [[0.187, 0.577, 0.895, 1.],
+ [0.57251765, 0.82677647, 1., 1.],
+ [1., 1., 1., 1.]]])
+ assert_allclose(vm.to_rgba(X), res)
+ vm = mpl.cm.ScalarMappable(cmap='BiCone')
+ res = np.array([[[0.30631738, 0.74203125, 0.89724219, 1.],
+ [0.11054785, 0.78253223, 0.67161328, 1.],
+ [0.48632812, 0.75336328, 0.39531641, 1.]],
+ [[0.61757812, 0.65564453, 0.99548438, 1.],
+ [0.99269824, 0.97310156, 0.96819922, 1.],
+ [0.78989453, 0.65757422, 0.21412500, 1.]],
+ [[0.83592969, 0.55910547, 0.91921094, 1.],
+ [0.96039453, 0.50968750, 0.66529297, 1.],
+ [0.95374219, 0.54730859, 0.35664844, 1.]]])
+ assert_allclose(vm.to_rgba(X), res)
+
+ X_complex128 = np.empty(X[0].shape, dtype=np.complex128)
+ X_complex128[:, :] = X[0]+X[1]*1j
+ assert_allclose(vm.to_rgba(X_complex128), res)
+ X_complex64 = np.empty(X[0].shape, dtype=np.complex64)
+ X_complex64[:, :] = X[0]+X[1]*1j
+ assert_allclose(vm.to_rgba(X_complex64), res)
+
+ dt = np.dtype('float32, float64')
+ X_dt = np.empty(X[0].shape, dtype=dt)
+ X_dt['f0'] = X[0]
+ X_dt['f1'] = X[1]
+ assert_allclose(vm.to_rgba(X_dt), res)
+
+ # test norm=False
+ assert_allclose(vm.to_rgba(X, norm=False), res)
+
+ # test set_array
+ dt = np.dtype('str, float64')
+ X_dt = np.empty(X[0].shape, dtype=dt)
+ X_dt['f1'] = X[1]
+
+ with pytest.raises(TypeError, match='cannot be converted to a sequence of float'):
+ vm.set_array(X_dt)
+
+ # get clim
+ vm = mpl.cm.ScalarMappable(cmap='BiCone')
+ vm.set_array(X)
+ assert_allclose(vm.get_clim(), ([0, 0], [1, 1]))
diff --git a/lib/matplotlib/tri/_tripcolor.py b/lib/matplotlib/tri/_tripcolor.py
index 1ac6c48a2d7c..4ebdd81e9854 100644
--- a/lib/matplotlib/tri/_tripcolor.py
+++ b/lib/matplotlib/tri/_tripcolor.py
@@ -135,7 +135,7 @@ def tripcolor(ax, *args, alpha=1.0, norm=None, cmap=None, vmin=None,
collection = PolyCollection(verts, alpha=alpha, array=colors,
cmap=cmap, norm=norm, **kwargs)
- collection._scale_norm(norm, vmin, vmax)
+ collection.colorizer._scale_norm(norm, vmin, vmax, collection.get_array())
ax.grid(False)
minx = tri.x.min()
diff --git a/lib/matplotlib/typing.py b/lib/matplotlib/typing.py
index b70b4cc264dc..2dd80c05b892 100644
--- a/lib/matplotlib/typing.py
+++ b/lib/matplotlib/typing.py
@@ -11,9 +11,9 @@
"""
from collections.abc import Hashable, Sequence
import pathlib
-from typing import Any, Literal, TypeAlias, TypeVar
+from typing import Any, Literal, TypeAlias, TypeVar, Union
-from . import path
+from . import path, colors
from ._enums import JoinStyle, CapStyle
from .markers import MarkerStyle
@@ -34,6 +34,7 @@
RGBAColourType: TypeAlias = RGBAColorType
ColourType: TypeAlias = ColorType
+ColorMapType = Union[colors.Colormap, colors.BivarColormap, colors.MultivarColormap]
LineStyleType: TypeAlias = str | tuple[float, Sequence[float]]
DrawStyleType: TypeAlias = Literal["default", "steps", "steps-pre", "steps-mid",
"steps-post"]
diff --git a/lib/mpl_toolkits/mplot3d/tests/test_axes3d.py b/lib/mpl_toolkits/mplot3d/tests/test_axes3d.py
index c31398fb8260..f95fe161b3ed 100644
--- a/lib/mpl_toolkits/mplot3d/tests/test_axes3d.py
+++ b/lib/mpl_toolkits/mplot3d/tests/test_axes3d.py
@@ -2103,7 +2103,7 @@ def test_scalarmap_update(fig_test, fig_ref):
# force a draw
fig_test.canvas.draw()
# mark it as "stale"
- sc_test.changed()
+ sc_test.colorizer.changed()
# ref
ax_ref = fig_ref.add_subplot(111, projection='3d')
diff --git a/tools/boilerplate.py b/tools/boilerplate.py
index d4f8a01d0493..56286aeb81ce 100644
--- a/tools/boilerplate.py
+++ b/tools/boilerplate.py
@@ -305,7 +305,7 @@ def boilerplate_gen():
'hist2d': 'sci(__ret[-1])',
'imshow': 'sci(__ret)',
'spy': (
- 'if isinstance(__ret, cm.ScalarMappable):\n'
+ 'if isinstance(__ret, cm.VectorMappable):\n'
' sci(__ret)'
),
'quiver': 'sci(__ret)',