Merge pull request matplotlib#24619 from greglucas/pcolormesh-rgba

ENH: Allow RGB(A) arrays for pcolormesh

Author: ksunden

doc/users/next_whats_new/rgba_pcolormesh.rst

@@ -0,0 +1,16 @@
+``pcolormesh`` accepts RGB(A) colors
+------------------------------------
+
+The `~.Axes.pcolormesh` method can now handle explicit colors
+specified with RGB(A) values. To specify colors, the array must be 3D
+with a shape of ``(M, N, [3, 4])``.
+
+.. plot::
+    :include-source: true
+
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    colors = np.linspace(0, 1, 90).reshape((5, 6, 3))
+    plt.pcolormesh(colors)
+    plt.show()

lib/matplotlib/axes/_axes.py

@@ -5679,7 +5679,7 @@ def _pcolorargs(self, funcname, *args, shading='auto', **kwargs):
 
         if len(args) == 1:
             C = np.asanyarray(args[0])
-            nrows, ncols = C.shape
+            nrows, ncols = C.shape[:2]
             if shading in ['gouraud', 'nearest']:
                 X, Y = np.meshgrid(np.arange(ncols), np.arange(nrows))
             else:
@@ -5708,7 +5708,7 @@ def _pcolorargs(self, funcname, *args, shading='auto', **kwargs):
                     X = X.data  # strip mask as downstream doesn't like it...
                 if isinstance(Y, np.ma.core.MaskedArray):
                     Y = Y.data
-            nrows, ncols = C.shape
+            nrows, ncols = C.shape[:2]
         else:
             raise _api.nargs_error(funcname, takes="1 or 3", given=len(args))
 
@@ -6045,9 +6045,18 @@ def pcolormesh(self, *args, alpha=None, norm=None, cmap=None, vmin=None,
 
         Parameters
         ----------
-        C : 2D array-like
-            The color-mapped values.  Color-mapping is controlled by *cmap*,
-            *norm*, *vmin*, and *vmax*.
+        C : array-like
+            The mesh data. Supported array shapes are:
+
+            - (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*.
+            - (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.
+
+            The first two dimensions (M, N) define the rows and columns of
+            the mesh data.
 
         X, Y : array-like, optional
             The coordinates of the corners of quadrilaterals of a pcolormesh::
@@ -6207,8 +6216,9 @@ def pcolormesh(self, *args, alpha=None, norm=None, cmap=None, vmin=None,
         X, Y, C, shading = self._pcolorargs('pcolormesh', *args,
                                             shading=shading, kwargs=kwargs)
         coords = np.stack([X, Y], axis=-1)
-        # convert to one dimensional array
-        C = C.ravel()
+        # convert to one dimensional array, except for 3D RGB(A) arrays
+        if C.ndim != 3:
+            C = C.ravel()
 
         kwargs.setdefault('snap', mpl.rcParams['pcolormesh.snap'])
 
@@ -6384,14 +6394,10 @@ def pcolorfast(self, *args, alpha=None, norm=None, cmap=None, vmin=None,
         if style == "quadmesh":
             # data point in each cell is value at lower left corner
             coords = np.stack([x, y], axis=-1)
-            if np.ndim(C) == 2:
-                qm_kwargs = {"array": np.ma.ravel(C)}
-            elif np.ndim(C) == 3:
-                qm_kwargs = {"color": np.ma.reshape(C, (-1, C.shape[-1]))}
-            else:
+            if np.ndim(C) not in {2, 3}:
                 raise ValueError("C must be 2D or 3D")
             collection = mcoll.QuadMesh(
-                coords, **qm_kwargs,
+                coords, array=C,
                 alpha=alpha, cmap=cmap, norm=norm,
                 antialiased=False, edgecolors="none")
             self.add_collection(collection, autolim=False)

lib/matplotlib/collections.py

@@ -1955,7 +1955,16 @@ def set_array(self, A):
 
         Parameters
         ----------
-        A : (M, N) array-like or M*N array-like
+        A : array-like
+            The mesh data. Supported array shapes are:
+
+            - (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*.
+            - (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.
+
             If the values are provided as a 2D grid, the shape must match the
             coordinates grid. If the values are 1D, they are reshaped to 2D.
             M, N follow from the coordinates grid, where the coordinates grid
@@ -1976,11 +1985,19 @@ def set_array(self, A):
             if len(shape) == 1:
                 if shape[0] != (h*w):
                     faulty_data = True
-            elif shape != (h, w):
-                if np.prod(shape) == (h * w):
+            elif shape[:2] != (h, w):
+                if np.prod(shape[:2]) == (h * w):
                     misshapen_data = True
                 else:
                     faulty_data = True
+            elif len(shape) == 3 and shape[2] not in {3, 4}:
+                # 3D data must be RGB(A) (h, w, [3,4])
+                # the (h, w) check is taken care of above
+                raise ValueError(
+                    f"For X ({width}) and Y ({height}) with "
+                    f"{self._shading} shading, the expected shape of "
+                    f"A with RGB(A) colors is ({h}, {w}, [3 or 4]), not "
+                    f"{A.shape}")
 
             if misshapen_data:
                 raise ValueError(

lib/matplotlib/tests/test_axes.py

@@ -1298,6 +1298,17 @@ def test_pcolormesh_alpha():
     ax4.pcolormesh(Qx, Qy, Z, cmap=cmap, shading='gouraud', zorder=1)
 
 
+@pytest.mark.parametrize("dims,alpha", [(3, 1), (4, 0.5)])
+@check_figures_equal(extensions=["png"])
+def test_pcolormesh_rgba(fig_test, fig_ref, dims, alpha):
+    ax = fig_test.subplots()
+    c = np.ones((5, 6, dims), dtype=float) / 2
+    ax.pcolormesh(c)
+
+    ax = fig_ref.subplots()
+    ax.pcolormesh(c[..., 0], cmap="gray", vmin=0, vmax=1, alpha=alpha)
+
+
 @image_comparison(['pcolormesh_datetime_axis.png'], style='mpl20')
 def test_pcolormesh_datetime_axis():
     # Remove this line when this test image is regenerated.

lib/matplotlib/tests/test_collections.py

@@ -830,6 +830,24 @@ def test_quadmesh_set_array_validation():
                        r"are incompatible with X \(11\) and/or Y \(8\)"):
         coll.set_array(z.ravel())
 
+    # RGB(A) tests
+    z = np.ones((9, 6, 3))  # RGB with wrong X/Y dims
+    with pytest.raises(TypeError, match=r"Dimensions of A \(9, 6, 3\) "
+                       r"are incompatible with X \(11\) and/or Y \(8\)"):
+        coll.set_array(z)
+
+    z = np.ones((9, 6, 4))  # RGBA with wrong X/Y dims
+    with pytest.raises(TypeError, match=r"Dimensions of A \(9, 6, 4\) "
+                       r"are incompatible with X \(11\) and/or Y \(8\)"):
+        coll.set_array(z)
+
+    z = np.ones((7, 10, 2))  # Right X/Y dims, bad 3rd dim
+    with pytest.raises(ValueError, match=r"For X \(11\) and Y \(8\) with "
+                       r"flat shading, the expected shape of "
+                       r"A with RGB\(A\) colors is \(7, 10, \[3 or 4\]\), "
+                       r"not \(7, 10, 2\)"):
+        coll.set_array(z)
+
     x = np.arange(10)
     y = np.arange(7)
     z = np.random.random((7, 10))
@@ -1048,6 +1066,9 @@ def test_array_wrong_dimensions():
     pc = plt.pcolormesh(z)
     pc.set_array(z)  # 2D is OK for Quadmesh
     pc.update_scalarmappable()
+    # 3D RGB is OK as well
+    z = np.arange(36).reshape(3, 4, 3)
+    pc.set_array(z)
 
 
 def test_get_segments():