Merge pull request #17289 from QuLogic/np119

Prepare for ragged array warnings in NumPy 1.19

Author: jklymak

lib/matplotlib/axes/_axes.py

@@ -3778,7 +3778,7 @@ def boxplot(self, x, notch=None, sym=None, vert=None, whis=None,
                         stats['med'] = med
 
         if conf_intervals is not None:
-            if np.shape(conf_intervals)[0] != len(bxpstats):
+            if len(conf_intervals) != len(bxpstats):
                 raise ValueError(
                     "'conf_intervals' and 'x' have different lengths")
             else:
@@ -6555,8 +6555,6 @@ def hist(self, x, bins=None, range=None, density=False, weights=None,
         if histtype == 'barstacked' and not stacked:
             stacked = True
 
-        # basic input validation
-        input_empty = np.size(x) == 0
         # Massage 'x' for processing.
         x = cbook._reshape_2D(x, 'x')
         nx = len(x)  # number of datasets
@@ -6581,9 +6579,13 @@ def hist(self, x, bins=None, range=None, density=False, weights=None,
         if len(w) != nx:
             raise ValueError('weights should have the same shape as x')
 
+        input_empty = True
         for xi, wi in zip(x, w):
-            if wi is not None and len(wi) != len(xi):
+            len_xi = len(xi)
+            if wi is not None and len(wi) != len_xi:
                 raise ValueError('weights should have the same shape as x')
+            if len_xi:
+                input_empty = False
 
         if color is None:
             color = [self._get_lines.get_next_color() for i in range(nx)]

lib/matplotlib/cbook/__init__.py

@@ -989,7 +989,12 @@ def _combine_masks(*args):
         else:
             if isinstance(x, np.ma.MaskedArray) and x.ndim > 1:
                 raise ValueError("Masked arrays must be 1-D")
-            x = np.asanyarray(x)
+            try:
+                x = np.asanyarray(x)
+            except (np.VisibleDeprecationWarning, ValueError):
+                # NumPy 1.19 raises a warning about ragged arrays, but we want
+                # to accept basically anything here.
+                x = np.asanyarray(x, dtype=object)
             if x.ndim == 1:
                 x = safe_masked_invalid(x)
                 seqlist[i] = True
@@ -1316,24 +1321,48 @@ def _reshape_2D(X, name):
     Use Fortran ordering to convert ndarrays and lists of iterables to lists of
     1D arrays.
 
-    Lists of iterables are converted by applying `np.asarray` to each of their
-    elements.  1D ndarrays are returned in a singleton list containing them.
-    2D ndarrays are converted to the list of their *columns*.
+    Lists of iterables are converted by applying `np.asanyarray` to each of
+    their elements.  1D ndarrays are returned in a singleton list containing
+    them.  2D ndarrays are converted to the list of their *columns*.
 
     *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(f'{name} must have 2 or fewer dimensions')
+
+    # Iterate over list of iterables.
     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.asanyarray(xi)
+        nd = np.ndim(xi)
+        if nd > 1:
+            raise ValueError(f'{name} must have 2 or fewer dimensions')
+        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):
@@ -1399,10 +1428,10 @@ def violin_stats(X, method, points=100, quantiles=None):
         quantiles = _reshape_2D(quantiles, "quantiles")
     # Else, mock quantiles if is none or empty
     else:
-        quantiles = [[]] * np.shape(X)[0]
+        quantiles = [[]] * len(X)
 
     # quantiles should has the same size as dataset
-    if np.shape(X)[:1] != np.shape(quantiles)[:1]:
+    if len(X) != len(quantiles):
         raise ValueError("List of violinplot statistics and quantiles values"
                          " must have the same length")
 
@@ -1577,8 +1606,15 @@ def index_of(y):
     try:
         return y.index.values, y.values
     except AttributeError:
+        pass
+    try:
         y = _check_1d(y)
+    except (np.VisibleDeprecationWarning, ValueError):
+        # NumPy 1.19 will warn on ragged input, and we can't actually use it.
+        pass
+    else:
         return np.arange(y.shape[0], dtype=float), y
+    raise ValueError('Input could not be cast to an at-least-1D NumPy array')
 
 
 def safe_first_element(obj):

lib/matplotlib/tests/test_backend_svg.py

@@ -191,7 +191,8 @@ def include(gid, obj):
             elif obj.axes is None:
                 return False
         if isinstance(obj, plt.Line2D):
-            if np.array(obj.get_data()).shape == (2, 1):
+            xdata, ydata = obj.get_data()
+            if len(xdata) == len(ydata) == 1:
                 return False
             elif not hasattr(obj, "axes") or obj.axes is None:
                 return False

lib/mpl_toolkits/mplot3d/art3d.py

@@ -257,7 +257,7 @@ def set_segments(self, segments):
         """
         Set 3D segments.
         """
-        self._segments3d = np.asanyarray(segments)
+        self._segments3d = segments
         LineCollection.set_segments(self, [])
 
     def do_3d_projection(self, renderer):