Aug 7, 2024 · Aug 7, 2024 · Jan 6, 2025 · Jan 6, 2025 · Feb 3, 2025 · Feb 5, 2025
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
            # If provided, _pcolorargs will check that X, Y and C have the same shape.
            # Before this check, we need to convert C from shape (K, N, M), where K is
            # the number of variates, to (N, M) with a data type with K fields.
            data = mcolorizer._ensure_multivariate_data(colorizer_obj.norm.n_input,
 args[-1])
            data = mcolorizer._ensure_multivariate_data(args[-1],
 colorizer_obj.norm.n_input)
            args = (*args[:-1], data)

        X, Y, C, shading = self._pcolorargs('pcolor', *args, shading=shading,
            # If provided, _pcolorargs will check that X, Y and C have the same shape.
            # Before this check, we need to convert C from shape (K, N, M), where K is
            # the number of variates, to (N, M) with a data type with K fields.
            data = mcolorizer._ensure_multivariate_data(colorizer_obj.norm.n_input,
 args[-1])
            data = mcolorizer._ensure_multivariate_data(args[-1],
 colorizer_obj.norm.n_input)
            args = (*args[:-1], data)

        X, Y, C, shading = self._pcolorargs('pcolormesh', *args,
diff --git a/lib/matplotlib/colorizer.py b/lib/matplotlib/colorizer.py
            self._A = None
            return

        A = _ensure_multivariate_data(self.norm.n_input, A)
        A = _ensure_multivariate_data(A,self.norm.n_input)

        A = cbook.safe_masked_invalid(A, copy=True)
        if not np.can_cast(A.dtype, float, "same_kind"):
    return cm.colormaps[cmap_name]


 def _ensure_multivariate_data(n_input, data):
 def _ensure_multivariate_data(data, n_input):
    """
    Ensure that the data has dtype with n_input.
    Input data of shape (n_input, n, m) is converted to an array of shape
diff --git a/lib/matplotlib/image.py b/lib/matplotlib/image.py
        Check validity of image-like input *A* and normalize it to a format suitable for
        Image subclasses.
        """
        A = mcolorizer._ensure_multivariate_data(n_input, A)
        A = mcolorizer._ensure_multivariate_data(A, n_input)
        A = cbook.safe_masked_invalid(A, copy=True)
        if n_input == 1:
            if A.dtype != np.uint8 and not np.can_cast(A.dtype, float, "same_kind"):
Original file line number	Diff line number	Diff line change
Expand Up		@@ -6208,8 +6208,8 @@ def pcolor(self, *args, shading=None, alpha=None, norm=None, cmap=None,
		# If provided, _pcolorargs will check that X, Y and C have the same shape.
		# Before this check, we need to convert C from shape (K, N, M), where K is
		# the number of variates, to (N, M) with a data type with K fields.
		data = mcolorizer._ensure_multivariate_data(colorizer_obj.norm.n_input,
		args[-1])
		data = mcolorizer._ensure_multivariate_data(args[-1],
		colorizer_obj.norm.n_input)
		args = (*args[:-1], data)

		X, Y, C, shading = self._pcolorargs('pcolor', *args, shading=shading,
Expand DownExpand Up		@@ -6465,8 +6465,8 @@ def pcolormesh(self, *args, alpha=None, norm=None, cmap=None, vmin=None,
		# If provided, _pcolorargs will check that X, Y and C have the same shape.
		# Before this check, we need to convert C from shape (K, N, M), where K is
		# the number of variates, to (N, M) with a data type with K fields.
		data = mcolorizer._ensure_multivariate_data(colorizer_obj.norm.n_input,
		args[-1])
		data = mcolorizer._ensure_multivariate_data(args[-1],
		colorizer_obj.norm.n_input)
		args = (*args[:-1], data)

		X, Y, C, shading = self._pcolorargs('pcolormesh', *args,
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -582,7 +582,7 @@ def set_array(self, A):
		self._A = None
		return

		A = _ensure_multivariate_data(self.norm.n_input, A)
		A = _ensure_multivariate_data(A,self.norm.n_input)

		A = cbook.safe_masked_invalid(A, copy=True)
		if not np.can_cast(A.dtype, float, "same_kind"):
Expand DownExpand Up		@@ -862,7 +862,7 @@ def _ensure_cmap(cmap, accept_multivariate=False):
		return cm.colormaps[cmap_name]


		def _ensure_multivariate_data(n_input, data):
		def _ensure_multivariate_data(data, n_input):
		"""
		Ensure that the data has dtype with n_input.
		Input data of shape (n_input, n, m) is converted to an array of shape
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -646,7 +646,7 @@ def _normalize_image_array(A, n_input=1):
		Check validity of image-like input A and normalize it to a format suitable for
		Image subclasses.
		"""
		A = mcolorizer._ensure_multivariate_data(n_input, A)
		A = mcolorizer._ensure_multivariate_data(A, n_input)
		A = cbook.safe_masked_invalid(A, copy=True)
		if n_input == 1:
		if A.dtype != np.uint8 and not np.can_cast(A.dtype, float, "same_kind"):
Expand Down