Add ability to scale BBox with just x or y values

Author: dstansby

lib/matplotlib/transforms.py

@@ -902,11 +902,51 @@ def update_from_path(self, path, ignore=None, updatex=True, updatey=True):
                 self._points[:, 1] = points[:, 1]
                 self._minpos[1] = minpos[1]
 
+    def update_from_data_x(self, x, ignore=None):
+        """
+        Update the x-bounds of the `Bbox` based on the passed in data. After
+        updating, the bounds will have positive *width*, and *x0* will be the
+        minimal value.
+
+        Parameters
+        ----------
+        x : ndarray
+            Array of x-values.
+
+        ignore : bool, optional
+           - When ``True``, ignore the existing bounds of the `Bbox`.
+           - When ``False``, include the existing bounds of the `Bbox`.
+           - When ``None``, use the last value passed to :meth:`ignore`.
+        """
+        x = np.ravel(x)
+        self.update_from_data_xy(np.column_stack([x, np.ones(x.size)]),
+                                 ignore=ignore, updatey=False)
+
+    def update_from_data_y(self, y, ignore=None):
+        """
+        Update the y-bounds of the `Bbox` based on the passed in data. After
+        updating, the bounds will have positive *height*, and *y0* will be the
+        minimal value.
+
+        Parameters
+        ----------
+        y : ndarray
+            Array of y-values.
+
+        ignore : bool, optional
+           - When ``True``, ignore the existing bounds of the `Bbox`.
+           - When ``False``, include the existing bounds of the `Bbox`.
+           - When ``None``, use the last value passed to :meth:`ignore`.
+        """
+        y = np.array(y).ravel()
+        self.update_from_data_xy(np.column_stack([np.ones(y.size), y]),
+                                 ignore=ignore, updatex=False)
+
     def update_from_data_xy(self, xy, ignore=None, updatex=True, updatey=True):
         """
-        Update the bounds of the `Bbox` based on the passed in
-        data.  After updating, the bounds will have positive *width*
-        and *height*; *x0* and *y0* will be the minimal values.
+        Update the bounds of the `Bbox` based on the passed in data. After
+        updating, the bounds will have positive *width* and *height*;
+        *x0* and *y0* will be the minimal values.
 
         Parameters
         ----------

lib/mpl_toolkits/mplot3d/axes3d.py

@@ -107,6 +107,7 @@ def __init__(
         self.xy_viewLim = Bbox.unit()
         self.zz_viewLim = Bbox.unit()
         self.xy_dataLim = Bbox.unit()
+        # z-limits are encoded in the x-component of the Bbox, y is un-used
         self.zz_dataLim = Bbox.unit()
 
         # inhibit autoscale_view until the axes are defined
@@ -643,14 +644,14 @@ def autoscale(self, enable=True, axis='both', tight=None):
     def auto_scale_xyz(self, X, Y, Z=None, had_data=None):
         # This updates the bounding boxes as to keep a record as to what the
         # minimum sized rectangular volume holds the data.
-        X = np.reshape(X, -1)
-        Y = np.reshape(Y, -1)
-        self.xy_dataLim.update_from_data_xy(
-            np.column_stack([X, Y]), not had_data)
+        if np.shape(X) == np.shape(Y):
+            self.xy_dataLim.update_from_data_xy(
+                np.column_stack([np.ravel(X), np.ravel(Y)]), not had_data)
+        else:
+            self.xy_dataLim.update_from_data_x(X, not had_data)
+            self.xy_dataLim.update_from_data_y(Y, not had_data)
         if Z is not None:
-            Z = np.reshape(Z, -1)
-            self.zz_dataLim.update_from_data_xy(
-                np.column_stack([Z, Z]), not had_data)
+            self.zz_dataLim.update_from_data_x(Z, not had_data)
         # Let autoscale_view figure out how to use this data.
         self.autoscale_view()
 

lib/mpl_toolkits/tests/test_mplot3d.py

@@ -198,6 +198,17 @@ def test_tricontour():
     ax.tricontourf(x, y, z)
 
 
+def test_contour3d_1d_input():
+    # Check that 1D sequences of different length for {x, y} doesn't error
+    fig = plt.figure()
+    ax = fig.add_subplot(projection='3d')
+    nx, ny = 30, 20
+    x = np.linspace(-10, 10, nx)
+    y = np.linspace(-10, 10, ny)
+    z = np.random.randint(0, 2, [ny, nx])
+    ax.contour(x, y, z, [0.5])
+
+
 @mpl3d_image_comparison(['lines3d.png'])
 def test_lines3d():
     fig = plt.figure()