Add gridspec to have equal aspect ratio and remove references of deleted

patniharshit · dstansby · commit 92b11961dd97 · 2017-04-12T11:51:51.000+01:00
files
diff --git a/doc/users/prev_whats_new/whats_new_1.2.rst b/doc/users/prev_whats_new/whats_new_1.2.rst
@@ -153,7 +153,7 @@ In addition to simply plotting the streamlines of the vector field,
 line widths of the streamlines to a separate parameter, such as the speed or
 local intensity of the vector field.
 
-.. plot:: gallery/images_contours_and_fields/streamplot_features.py
+.. plot:: mpl_examples/images_contours_and_fields/streamplot_demo.py
 
 
 New hist functionality
diff --git a/doc/users/screenshots.rst b/doc/users/screenshots.rst
@@ -71,7 +71,7 @@ a vector field. In addition to simply plotting the streamlines, it allows you
 to map the colors and/or line widths of streamlines to a separate parameter,
 such as the speed or local intensity of the vector field.
 
-.. plot:: gallery/images_contours_and_fields/streamplot_features.py
+.. plot:: mpl_examples/images_contours_and_fields/streamplot_demo.py
 
 This feature complements the :meth:`~matplotlib.pyplot.quiver` function for
 plotting vector fields. Thanks to Tom Flannaghan and Tony Yu for adding the
diff --git a/examples/images_contours_and_fields/streamplot_demo.py b/examples/images_contours_and_fields/streamplot_demo.py
@@ -2,39 +2,42 @@
 ==========
 Streamplot
 ==========
-A streamplot, or streamline plot, is used to display 2D vector fields. This
+
+A stream plot, or stream line plot, is used to display 2D vector fields. This
 example shows a few features of the stream plot function:
 
-    * Varying the color along a streamline.
-    * Varying the density of streamlines.
+    * Varying the color along a stream line.
+    * Varying the density of stream lines.
     * Varying the line width along a stream line.
-    * Controlling the start points of streamlines.
-    * Streamlines skipping masked regions and NaN values.
+    * Controlling the starting points of stream lines.
+    * Stream lines skipping masked regions and NaN values.
 """
 import numpy as np
 import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
 
 w = 3
 Y, X = np.mgrid[-w:w:100j, -w:w:100j]
 U = -1 - X**2 + Y
 V = 1 + X - Y**2
 speed = np.sqrt(U*U + V*V)
 
-fig = plt.figure()
+fig = plt.figure(figsize=(7, 9))
+gs = gridspec.GridSpec(nrows=3, ncols=2, height_ratios=[1, 1, 2])
 
 #  Varying density along a streamline
-ax0 = fig.add_subplot(321)
+ax0 = fig.add_subplot(gs[0, 0])
 ax0.streamplot(X, Y, U, V, density=[0.5, 1])
 ax0.set_title('Varying Density')
 
 # Varying color along a streamline
-ax1 = fig.add_subplot(322)
-strm = ax1.streamplot(X, Y, U, V, color=U, linewidth=2, cmap=plt.cm.autumn)
+ax1 = fig.add_subplot(gs[0, 1])
+strm = ax1.streamplot(X, Y, U, V, color=U, linewidth=2, cmap='autumn')
 fig.colorbar(strm.lines)
 ax1.set_title('Varying color')
 
 #  Varying line width along a streamline
-ax2 = fig.add_subplot(323)
+ax2 = fig.add_subplot(gs[1, 0])
 lw = 5*speed / speed.max()
 ax2.streamplot(X, Y, U, V, density=0.6, color='k', linewidth=lw)
 ax2.set_title('Varying Line Width')
@@ -45,16 +48,15 @@
 U, V = np.mgrid[-3:3:100j, 0:0:100j]
 seed_points = np.array([[-2, -1, 0, 1, 2, -1], [-2, -1,  0, 1, 2, 2]])
 
-ax3 = fig.add_subplot(324)
+ax3 = fig.add_subplot(gs[1, 1])
 strm = ax3.streamplot(X, Y, U, V, color=U, linewidth=2,
-                     cmap=plt.cm.autumn, start_points=seed_points.T)
+                     cmap='autumn', start_points=seed_points.T)
 fig.colorbar(strm.lines)
 ax3.set_title('Controlling Starting Points')
 
-# Displaying the starting points with red symbols.
+# Displaying the starting points with blue symbols.
 ax3.plot(seed_points[0], seed_points[1], 'bo')
-
-ax3.axis((-3, 3, -3, 3))
+ax3.axis((-w, w, -w, w))
 
 # Create a mask
 w = 3
@@ -68,12 +70,13 @@
 U[:20, :20] = np.nan
 U = np.ma.array(U, mask=mask)
 
-ax4 = fig.add_subplot(325)
+ax4 = fig.add_subplot(gs[2:, :])
 ax4.streamplot(X, Y, U, V, color='r')
-ax4.set_title('Streamline with Masking')
+ax4.set_title('Streamplot with Masking')
 
 ax4.imshow(~mask, extent=(-w, w, -w, w), alpha=0.5,
-          interpolation='nearest', cmap=plt.cm.gray, aspect='auto')
+          interpolation='nearest', cmap='gray', aspect='auto')
+ax4.set_aspect('equal')
 
 plt.tight_layout()
 plt.show()
