matplotlib · tacaswell · Nov 22, 2021 · May 23, 2021
diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
@@ -504,9 +504,7 @@ def _create_lookup_table(N, data, gamma=1.0):
         adata = np.array(data)
     except Exception as err:
         raise TypeError("data must be convertible to an array") from err
-    shape = adata.shape
-    if len(shape) != 2 or shape[1] != 3:
-        raise ValueError("data must be nx3 format")
+    _api.check_shape((None, 3), data=adata)
 
     x = adata[:, 0]
     y0 = adata[:, 1]

diff --git a/lib/matplotlib/transforms.py b/lib/matplotlib/transforms.py
@@ -1656,11 +1656,10 @@ def transform_angles(self, angles, pts, radians=False, pushoff=1e-5):
             raise NotImplementedError('Only defined in 2D')
         angles = np.asarray(angles)
         pts = np.asarray(pts)
-        if angles.ndim != 1 or angles.shape[0] != pts.shape[0]:
-            raise ValueError("'angles' must be a column vector and have same "
-                             "number of rows as 'pts'")
-        if pts.shape[1] != 2:
-            raise ValueError("'pts' must be array with 2 columns for x, y")
+        _api.check_shape((None, 2), pts=pts)
+        _api.check_shape((None,), angles=angles)
+        if len(angles) != len(pts):
+            raise ValueError("There must be as many 'angles' as 'pts'")
         # Convert to radians if desired
         if not radians:
             angles = np.deg2rad(angles)

diff --git a/lib/matplotlib/tri/triinterpolate.py b/lib/matplotlib/tri/triinterpolate.py
@@ -1406,8 +1406,7 @@ def _safe_inv22_vectorized(M):
 
     *M* : array of (2, 2) matrices to inverse, shape (n, 2, 2)
     """
-    assert M.ndim == 3
-    assert M.shape[-2:] == (2, 2)
+    _api.check_shape((None, 2, 2), M=M)
     M_inv = np.empty_like(M)
     prod1 = M[:, 0, 0]*M[:, 1, 1]
     delta = prod1 - M[:, 0, 1]*M[:, 1, 0]
@@ -1441,8 +1440,7 @@ def _pseudo_inv22sym_vectorized(M):
 
     *M* : array of (2, 2) matrices to inverse, shape (n, 2, 2)
     """
-    assert M.ndim == 3
-    assert M.shape[-2:] == (2, 2)
+    _api.check_shape((None, 2, 2), M=M)
     M_inv = np.empty_like(M)
     prod1 = M[:, 0, 0]*M[:, 1, 1]
     delta = prod1 - M[:, 0, 1]*M[:, 1, 0]

diff --git a/lib/matplotlib/widgets.py b/lib/matplotlib/widgets.py
@@ -893,11 +893,8 @@ def set_val(self, val):
         ----------
         val : tuple or array-like of float
         """
-        val = np.sort(np.asanyarray(val))
-        if val.shape != (2,):
-            raise ValueError(
-                f"val must have shape (2,) but has shape {val.shape}"
-            )
+        val = np.sort(val)
+        _api.check_shape((2,), val=val)
         val[0] = self._min_in_bounds(val[0])
         val[1] = self._max_in_bounds(val[1])
         xy = self.poly.xy

diff --git a/lib/mpl_toolkits/mplot3d/axes3d.py b/lib/mpl_toolkits/mplot3d/axes3d.py
@@ -343,13 +343,8 @@ def set_box_aspect(self, aspect, *, zoom=1):
         if aspect is None:
             aspect = np.asarray((4, 4, 3), dtype=float)
         else:
-            orig_aspect = aspect
             aspect = np.asarray(aspect, dtype=float)
-            if aspect.shape != (3,):
-                raise ValueError(
-                    "You must pass a 3-tuple that can be cast to floats. "
-                    f"You passed {orig_aspect!r}"
-                )
+            _api.check_shape((3,), aspect=aspect)
         # default scale tuned to match the mpl32 appearance.
         aspect *= 1.8294640721620434 * zoom / np.linalg.norm(aspect)