Rewrite _reshape_2D to not use ragged ndarrays.

QuLogic · QuLogic · commit e842b1725628 · 2020-05-05T23:53:00.000-04:00
This is a raising a deprecation warning in NumPy 1.19, may go away some
time later, and is not strictly necessary for the implementation.
diff --git a/lib/matplotlib/cbook/__init__.py b/lib/matplotlib/cbook/__init__.py
@@ -1322,18 +1322,42 @@ def _reshape_2D(X, name):
 
     *name* is used to generate the error message for invalid inputs.
     """
-    # Iterate over columns for ndarrays, over rows otherwise.
-    X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))
+    # Iterate over columns for ndarrays.
+    if isinstance(X, np.ndarray):
+        X = X.T
+
+        if len(X) == 0:
+            return [[]]
+        elif X.ndim == 1 and np.ndim(X[0]) == 0:
+            # 1D array of scalars: directly return it.
+            return [X]
+        elif X.ndim in [1, 2]:
+            # 2D array, or 1D array of iterables: flatten them first.
+            return [np.reshape(x, -1) for x in X]
+        else:
+            raise ValueError("{} must have 2 or fewer dimensions".format(name))
+
+    # Iterate over rows for non-ndarrays.
     if len(X) == 0:
         return [[]]
-    elif X.ndim == 1 and np.ndim(X[0]) == 0:
+
+    result = []
+    is_1d = True
+    for xi in X:
+        xi = np.asarray(xi)
+        nd = np.ndim(xi)
+        if nd > 1:
+            raise ValueError("{} must have 2 or fewer dimensions".format(name))
+        elif nd == 1 and len(xi) != 1:
+            is_1d = False
+        result.append(xi.reshape(-1))
+
+    if is_1d:
         # 1D array of scalars: directly return it.
-        return [X]
-    elif X.ndim in [1, 2]:
-        # 2D array, or 1D array of iterables: flatten them first.
-        return [np.reshape(x, -1) for x in X]
+        return [np.reshape(result, -1)]
     else:
-        raise ValueError("{} must have 2 or fewer dimensions".format(name))
+        # 2D array, or 1D array of iterables: use flattened version.
+        return result
 
 
 def violin_stats(X, method, points=100, quantiles=None):
