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Multivar imshow#30597

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expose multivariate plotting functionality to top level functions ims…
…how, pcolor, pcolormesh, and Collection
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@trygvrad
trygvrad committedDec 1, 2025
commit50f55a8da12700287361a59dcad6b3c7be82b619
1 change: 1 addition & 0 deletionsdoc/api/colors_api.rst
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Expand Up@@ -55,6 +55,7 @@ Multivariate Colormaps
BivarColormap
SegmentedBivarColormap
BivarColormapFromImage
MultivarColormap

Other classes
-------------
Expand Down
53 changes: 39 additions & 14 deletionslib/matplotlib/axes/_axes.py
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Expand Up@@ -6150,6 +6150,7 @@ def imshow(self, X, cmap=None, norm=None, *, aspect=None,
- (M, N): an image with scalar data. The values are mapped to
colors using normalization and a colormap. See parameters *norm*,
*cmap*, *vmin*, *vmax*.
- (v, M, N): if coupled with a cmap that supports v scalars
- (M, N, 3): an image with RGB values (0-1 float or 0-255 int).
- (M, N, 4): an image with RGBA values (0-1 float or 0-255 int),
i.e. including transparency.
Expand All@@ -6159,15 +6160,16 @@ def imshow(self, X, cmap=None, norm=None, *, aspect=None,

Out-of-range RGB(A) values are clipped.

%(cmap_doc)s

%(multi_cmap_doc)s

This parameter is ignored if *X* is RGB(A).

%(norm_doc)s
%(multi_norm_doc)s

This parameter is ignored if *X* is RGB(A).

%(vmin_vmax_doc)s
%(multi_vmin_vmax_doc)s

This parameter is ignored if *X* is RGB(A).

Expand DownExpand Up@@ -6246,6 +6248,9 @@ def imshow(self, X, cmap=None, norm=None, *, aspect=None,
See :doc:`/gallery/images_contours_and_fields/image_antialiasing` for
a discussion of image antialiasing.

Only 'data' is available when using `~matplotlib.colors.BivarColormap`
or `~matplotlib.colors.MultivarColormap`
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what do you mean by data?

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'data' refers to the keyword argumentinterpolation_stage

        interpolation_stage : {'auto', 'data', 'rgba'}, default: 'auto'            Supported values:            - 'data': Interpolation is carried out on the data provided by the user              This is useful if interpolating between pixels during upsampling.            - 'rgba': The interpolation is carried out in RGBA-space after the              color-mapping has been applied. This is useful if downsampling and              combining pixels visually.            - 'auto': Select a suitable interpolation stage automatically. This uses              'rgba' when downsampling, or upsampling at a rate less than 3, and              'data' when upsampling at a higher rate.            See :doc:`/gallery/images_contours_and_fields/image_antialiasing` for            a discussion of image antialiasing.            Only 'data' is available when using `~matplotlib.colors.BivarColormap`            or `~matplotlib.colors.MultivarColormap`

I'm changing the last lines to:

            When using a `~matplotlib.colors.BivarColormap` or             `~matplotlib.colors.MultivarColormap`, 'data' is the only valid            interpolation_stage.

in an attempt to increase clarity


alpha : float or array-like, optional
The alpha blending value, between 0 (transparent) and 1 (opaque).
If *alpha* is an array, the alpha blending values are applied pixel
Expand DownExpand Up@@ -6351,6 +6356,7 @@ def imshow(self, X, cmap=None, norm=None, *, aspect=None,
if aspect is not None:
self.set_aspect(aspect)

X = mcolorizer._ensure_multivariate_data(X, im.norm.n_components)
im.set_data(X)
im.set_alpha(alpha)
if im.get_clip_path() is None:
Expand DownExpand Up@@ -6506,9 +6512,10 @@ def pcolor(self, *args, shading=None, alpha=None, norm=None, cmap=None,

Parameters
----------
C : 2D array-like
C : 2Dor 3Darray-like
The color-mapped values. Color-mapping is controlled by *cmap*,
*norm*, *vmin*, and *vmax*.
*norm*, *vmin*, and *vmax*. 3D arrays are supported only if the
cmap supports v channels, where v is the size along the first axis.

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I think (?, ?, ?) notation might help make this clearer?

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good idea. Does the following work for you?

        Parameters        ----------        C : 2D (I, J) or 3D (v, I, J) array-like            The color-mapped values.  Color-mapping is controlled by *cmap*,            *norm*, *vmin*, and *vmax*. 3D arrays are supported only if the            cmap supports v channels.        X, Y : array-like, optional            The coordinates of the corners of quadrilaterals of a pcolormesh::                (X[i+1, j], Y[i+1, j])       (X[i+1, j+1], Y[i+1, j+1])                                      ●╶───╴●                                      │     │                                      ●╶───╴●                    (X[i, j], Y[i, j])       (X[i, j+1], Y[i, j+1])            Note that the column index corresponds to the x-coordinate, and            the row index corresponds to y. For details, see the            :ref:`Notes <axes-pcolormesh-grid-orientation>` section below.            If ``shading='flat'`` the dimensions of *X* and *Y* should be one            greater than those of *C*, and the quadrilateral is colored due            to the value at ``C[i, j]``.  If *X*, *Y* and *C* have equal            dimensions, a warning will be raised and the last row and column            of *C* will be ignored.

I'm using uppercase I and J inC : 2D (I, J) or 3D (v, I, J) array-like as these refer to the length, while the lowercase versions are used further down for the corresponding indexes.

X, Y : array-like, optional
The coordinates of the corners of quadrilaterals of a pcolormesh::
Expand DownExpand Up@@ -6555,11 +6562,11 @@ def pcolor(self, *args, shading=None, alpha=None, norm=None, cmap=None,
See :doc:`/gallery/images_contours_and_fields/pcolormesh_grids`
for more description.

%(cmap_doc)s
%(multi_cmap_doc)s

%(norm_doc)s
%(multi_norm_doc)s

%(vmin_vmax_doc)s
%(multi_vmin_vmax_doc)s

%(colorizer_doc)s

Expand DownExpand Up@@ -6634,8 +6641,17 @@ def pcolor(self, *args, shading=None, alpha=None, norm=None, cmap=None,
if shading is None:
shading = mpl.rcParams['pcolor.shading']
shading = shading.lower()
X, Y, C, shading = self._pcolorargs('pcolor', *args, shading=shading,
kwargs=kwargs)

if colorizer is None:
cmap = mcolorizer._ensure_cmap(cmap, accept_multivariate=True)
C = mcolorizer._ensure_multivariate_data(args[-1], cmap.n_variates)
else:
C = mcolorizer._ensure_multivariate_data(args[-1],
colorizer.cmap.n_variates)
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Suggested change
ifcolorizerisNone:
cmap=mcolorizer._ensure_cmap(cmap,accept_multivariate=True)
C=mcolorizer._ensure_multivariate_data(args[-1],cmap.n_variates)
else:
C=mcolorizer._ensure_multivariate_data(args[-1],
colorizer.cmap.n_variates)
ifcolorizerisNone:
cmap=mcolorizer._ensure_cmap(cmap,accept_multivariate=True)
colorizer=
C=mcolorizer._ensure_multivariate_data(args[-1],
colorizer.cmap.n_variates)

is there a reason to not instantiate the colorizer here?

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I believe it was designed this way to ensure error handling was done in the same way as previously, but I was able to rework it to a cleaner state by doing

mcolorizer.ColorizingArtist._check_exclusionary_keywords(colorizer,                                                          vmin=vmin, vmax=vmax,                                                          norm=norm, cmap=cmap)

before the creation of the QuadMesh rather than

collection._check_exclusionary_keywords(colorizer, vmin=vmin, vmax=vmax)

after.


X, Y, C, shading = self._pcolorargs('pcolor', *args[:-1], C,
shading=shading, kwargs=kwargs)

linewidths = (0.25,)
if 'linewidth' in kwargs:
kwargs['linewidths'] = kwargs.pop('linewidth')
Expand DownExpand Up@@ -6710,6 +6726,7 @@ def pcolormesh(self, *args, alpha=None, norm=None, cmap=None, vmin=None,
- (M, N) or M*N: a mesh with scalar data. The values are mapped to
colors using normalization and a colormap. See parameters *norm*,
*cmap*, *vmin*, *vmax*.
- (v, M, N): if coupled with a cmap that supports v scalars
- (M, N, 3): an image with RGB values (0-1 float or 0-255 int).
- (M, N, 4): an image with RGBA values (0-1 float or 0-255 int),
i.e. including transparency.
Expand DownExpand Up@@ -6746,11 +6763,11 @@ def pcolormesh(self, *args, alpha=None, norm=None, cmap=None, vmin=None,
expanded as needed into the appropriate 2D arrays, making a
rectangular grid.

%(cmap_doc)s
%(multi_cmap_doc)s

%(norm_doc)s
%(multi_norm_doc)s

%(vmin_vmax_doc)s
%(multi_vmin_vmax_doc)s

%(colorizer_doc)s

Expand DownExpand Up@@ -6873,7 +6890,15 @@ def pcolormesh(self, *args, alpha=None, norm=None, cmap=None, vmin=None,
shading = mpl._val_or_rc(shading, 'pcolor.shading').lower()
kwargs.setdefault('edgecolors', 'none')

X, Y, C, shading = self._pcolorargs('pcolormesh', *args,
if colorizer is None:
cmap = mcolorizer._ensure_cmap(cmap, accept_multivariate=True)
C = mcolorizer._ensure_multivariate_data(args[-1], cmap.n_variates)
else:
C = mcolorizer._ensure_multivariate_data(args[-1],
colorizer.cmap.n_variates)

Comment on lines 6897 to 6899
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potentially turn into a helper function if it repeats?

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@trygvradtrygvradNov 30, 2025•
edited
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The following lines are repeated for pcolor and pcolormesh:

mcolorizer.ColorizingArtist._check_exclusionary_keywords(colorizer,vmin=vmin,vmax=vmax,norm=norm,cmap=cmap)ifcolorizerisNone:colorizer=mcolorizer.Colorizer(cmap=cmap,norm=norm)C=mcolorizer._ensure_multivariate_data(args[-1],colorizer.cmap.n_variates)

We could make this a helper function :)
If so, should it be part of the Axes class? and do you have a suggestion for a name?

EDIT: this is after modifying it in accordance with one of your comments above :)


X, Y, C, shading = self._pcolorargs('pcolormesh', *args[:-1], C,
shading=shading, kwargs=kwargs)
coords = np.stack([X, Y], axis=-1)

Expand Down
32 changes: 19 additions & 13 deletionslib/matplotlib/axes/_axes.pyi
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Expand Up@@ -12,7 +12,13 @@ from matplotlib.collections import (
QuadMesh,
)
from matplotlib.colorizer import Colorizer
from matplotlib.colors import Colormap, Normalize
from matplotlib.colors import (
Colormap,
BivarColormap,
MultivarColormap,
Norm,
Normalize,
)
from matplotlib.container import (
BarContainer, PieContainer, ErrorbarContainer, StemContainer)
from matplotlib.contour import ContourSet, QuadContourSet
Expand DownExpand Up@@ -500,14 +506,14 @@ class Axes(_AxesBase):
def imshow(
self,
X: ArrayLike | PIL.Image.Image,
cmap: str | Colormap | None = ...,
norm: str |Normalize | None = ...,
cmap: str | Colormap |BivarColormap | MultivarColormap |None = ...,
norm: str |Norm | None = ...,
*,
aspect: Literal["equal", "auto"] | float | None = ...,
interpolation: str | None = ...,
alpha: float | ArrayLike | None = ...,
vmin: float | None = ...,
vmax: float | None = ...,
vmin: float |tuple[float] |None = ...,
vmax: float |tuple[float] |None = ...,
colorizer: Colorizer | None = ...,
origin: Literal["upper", "lower"] | None = ...,
extent: tuple[float, float, float, float] | None = ...,
Expand All@@ -524,10 +530,10 @@ class Axes(_AxesBase):
*args: ArrayLike,
shading: Literal["flat", "nearest", "auto"] | None = ...,
alpha: float | None = ...,
norm: str |Normalize | None = ...,
cmap: str | Colormap | None = ...,
vmin: float | None = ...,
vmax: float | None = ...,
norm: str |Norm | None = ...,
cmap: str | Colormap |BivarColormap | MultivarColormap |None = ...,
vmin: float |tuple[float] |None = ...,
vmax: float |tuple[float] |None = ...,
colorizer: Colorizer | None = ...,
data=...,
**kwargs
Expand All@@ -536,10 +542,10 @@ class Axes(_AxesBase):
self,
*args: ArrayLike,
alpha: float | None = ...,
norm: str |Normalize | None = ...,
cmap: str | Colormap | None = ...,
vmin: float | None = ...,
vmax: float | None = ...,
norm: str |Norm | None = ...,
cmap: str | Colormap |BivarColormap | MultivarColormap |None = ...,
vmin: float |tuple[float] |None = ...,
vmax: float |tuple[float] |None = ...,
colorizer: Colorizer | None = ...,
shading: Literal["flat", "nearest", "gouraud", "auto"] | None = ...,
antialiased: bool = ...,
Expand Down
4 changes: 3 additions & 1 deletionlib/matplotlib/collections.py
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Expand Up@@ -2352,6 +2352,8 @@ def set_array(self, A):
h,w=height,width
ok_shapes= [(h,w,3), (h,w,4), (h,w), (h*w,)]
ifAisnotNone:
ifhasattr(self,'norm'):
A=mcolorizer._ensure_multivariate_data(A,self.norm.n_components)
shape=np.shape(A)
ifshapenotinok_shapes:
raiseValueError(
Expand DownExpand Up@@ -2609,7 +2611,7 @@ def _get_unmasked_polys(self):
mask= (mask[0:-1,0:-1]|mask[1:,1:]|mask[0:-1,1:]|mask[1:,0:-1])
arr=self.get_array()
ifarrisnotNone:
arr=np.ma.getmaskarray(arr)
arr=self._getmaskarray(arr)
ifarr.ndim==3:
# RGB(A) case
mask|=np.any(arr,axis=-1)
Expand Down
11 changes: 8 additions & 3 deletionslib/matplotlib/collections.pyi
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Expand Up@@ -7,7 +7,12 @@ from numpy.typing import ArrayLike, NDArray
from . import colorizer, transforms
from .backend_bases import MouseEvent
from .artist import Artist
from .colors import Normalize, Colormap
from .colors import (
Colormap,
BivarColormap,
MultivarColormap,
Norm,
)
from .lines import Line2D
from .path import Path
from .patches import Patch
Expand All@@ -29,8 +34,8 @@ class Collection(colorizer.ColorizingArtist):
antialiaseds: bool | Sequence[bool] | None = ...,
offsets: tuple[float, float] | Sequence[tuple[float, float]] | None = ...,
offset_transform: transforms.Transform | None = ...,
norm:Normalize | None = ...,
cmap: Colormap | None = ...,
norm:Norm | None = ...,
cmap: Colormap |BivarColormap | MultivarColormap |None = ...,
colorizer: colorizer.Colorizer | None = ...,
pickradius: float = ...,
hatch: str | None = ...,
Expand Down
12 changes: 12 additions & 0 deletionslib/matplotlib/colorizer.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -600,6 +600,18 @@ def get_array(self):
"""
return self._A

def _getmaskarray(self, A):
"""
Similar to np.ma.getmaskarray but also handles the case where
the data has multiple fields.

The return array always has the same shape as the input, and dtype bool
"""
mask = np.ma.getmaskarray(A)
if isinstance(self.norm, colors.MultiNorm):
mask = np.any(mask.view('bool').reshape((*A.shape, -1)), axis=-1)
return mask

def changed(self):
"""
Call this whenever the mappable is changed to notify all the
Expand Down
28 changes: 14 additions & 14 deletionslib/matplotlib/colorizer.pyi
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Expand Up@@ -9,13 +9,13 @@ class Colorizer:
callbacks: cbook.CallbackRegistry
def __init__(
self,
cmap: str | colors.Colormap | None = ...,
cmap: str | colors.Colormap |colors.BivarColormap | colors.MultivarColormap |None = ...,
norm: str | colors.Norm | None = ...,
) -> None: ...
@property
def norm(self) -> colors.Norm: ...
@norm.setter
def norm(self, norm: colors.Norm | str | None) -> None: ...
def norm(self, norm: colors.Norm | str |tuple[str] |None) -> None: ...
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tuple[str, ...]

def to_rgba(
self,
x: np.ndarray,
Expand All@@ -26,28 +26,28 @@ class Colorizer:
def autoscale(self, A: ArrayLike) -> None: ...
def autoscale_None(self, A: ArrayLike) -> None: ...
@property
def cmap(self) -> colors.Colormap: ...
def cmap(self) -> colors.Colormap | colors.BivarColormap | colors.MultivarColormap: ...
@cmap.setter
def cmap(self, cmap: colors.Colormap | str | None) -> None: ...
def cmap(self, cmap: colors.Colormap |colors.BivarColormap | colors.MultivarColormap |str | None) -> None: ...
def get_clim(self) -> tuple[float, float]: ...
def set_clim(self, vmin: float | tuple[float, float] | None = ..., vmax: float | None = ...) -> None: ...
def changed(self) -> None: ...
@property
def vmin(self) -> float | None: ...
def vmin(self) -> float |tuple[float] |None: ...
@vmin.setter
def vmin(self, value: float | None) -> None: ...
def vmin(self, value: float |tuple[float] |None) -> None: ...
@property
def vmax(self) -> float | None: ...
def vmax(self) -> float |tuple[float] |None: ...
@vmax.setter
def vmax(self, value: float | None) -> None: ...
def vmax(self, value: float |tuple[float] |None) -> None: ...
@property
def clip(self) -> bool: ...
def clip(self) -> bool | tuple[bool, ...]: ...
@clip.setter
def clip(self, value: bool) -> None: ...
def clip(self, value:ArrayLike |bool) -> None: ...


class _ColorizerInterface:
cmap: colors.Colormap
cmap: colors.Colormap | colors.BivarColormap | colors.MultivarColormap
colorbar: colorbar.Colorbar | None
callbacks: cbook.CallbackRegistry
def to_rgba(
Expand All@@ -60,8 +60,8 @@ class _ColorizerInterface:
def get_clim(self) -> tuple[float, float]: ...
def set_clim(self, vmin: float | tuple[float, float] | None = ..., vmax: float | None = ...) -> None: ...
def get_alpha(self) -> float | None: ...
def get_cmap(self) -> colors.Colormap: ...
def set_cmap(self, cmap: str | colors.Colormap) -> None: ...
def get_cmap(self) -> colors.Colormap | colors.BivarColormap | colors.MultivarColormap: ...
def set_cmap(self, cmap: str | colors.Colormap | colors.BivarColormap | colors.MultivarColormap) -> None: ...
@property
def norm(self) -> colors.Norm: ...
@norm.setter
Expand All@@ -75,7 +75,7 @@ class _ScalarMappable(_ColorizerInterface):
def __init__(
self,
norm: colors.Norm | None = ...,
cmap: str | colors.Colormap | None = ...,
cmap: str | colors.Colormap |colors.BivarColormap | colors.MultivarColormap |None = ...,
*,
colorizer: Colorizer | None = ...,
**kwargs
Expand Down
8 changes: 4 additions & 4 deletionslib/matplotlib/colors.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -1431,9 +1431,9 @@ def __init__(self, colormaps, combination_mode, name='multivariate colormap'):
Describe how colormaps are combined in sRGB space

- If 'sRGB_add' -> Mixing produces brighter colors
`sRGB = sum(colors)`
``sRGB = sum(colors)``
- If 'sRGB_sub' -> Mixing produces darker colors
`sRGB = 1 - sum(1 - colors)`
``sRGB = 1 - sum(1 - colors)``
name : str, optional
The name of the colormap family.
"""
Expand DownExpand Up@@ -1609,11 +1609,11 @@ def with_extremes(self, *, bad=None, under=None, over=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
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
If tuple, the'over' value of each component is set with the values
from the tuple.

Returns
Expand Down
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