Merge remote-tracking branch 'matplotlib/v2.x'

examples/api/collections_demo.py

@@ -41,6 +41,8 @@
           for c in plt.rcParams['axes.prop_cycle'].by_key()['color']]
 
 fig, axes = plt.subplots(2, 2)
+fig.subplots_adjust(top=0.92, left=0.07, right=0.97,
+                    hspace=0.3, wspace=0.3)
 ((ax1, ax2), (ax3, ax4)) = axes  # unpack the axes
 
 

examples/images_contours_and_fields/interpolation_none_vs_nearest.py

@@ -54,6 +54,9 @@
 # example, the 'Nearest_vs_none-pdf.pdf' has been pre-converted into
 #'Nearest_vs_none-pdf.png' at 80 dpi.  We simply need to load and
 # display it.
+# The conversion is done with:
+#  gs -dNOPAUSE -dBATCH -dDOINTERPOLATE -sDEVICE=pngalpha \
+#     -sOutputFile=None_vs_nearest-pdf.png -r80 None_vs_nearest-pdf.pdf
 pdf_im_path = cbook.get_sample_data('None_vs_nearest-pdf.png')
 pdf_im = plt.imread(pdf_im_path)
 fig2 = plt.figure(figsize=[8.0, 7.5])

examples/pylab_examples/hexbin_demo.py

@@ -17,19 +17,20 @@
 ymin = y.min()
 ymax = y.max()
 
-plt.subplots_adjust(hspace=0.5)
-plt.subplot(121)
-plt.hexbin(x, y, cmap=plt.cm.YlOrRd_r)
-plt.axis([xmin, xmax, ymin, ymax])
-plt.title("Hexagon binning")
-cb = plt.colorbar()
+fig, axs = plt.subplots(ncols=2, sharey=True, figsize=(7, 4))
+fig.subplots_adjust(hspace=0.5, left=0.07, right=0.93)
+ax = axs[0]
+hb = ax.hexbin(x, y, gridsize=50, cmap='inferno')
+ax.axis([xmin, xmax, ymin, ymax])
+ax.set_title("Hexagon binning")
+cb = fig.colorbar(hb, ax=ax)
 cb.set_label('counts')
 
-plt.subplot(122)
-plt.hexbin(x, y, bins='log', cmap=plt.cm.YlOrRd_r)
-plt.axis([xmin, xmax, ymin, ymax])
-plt.title("With a log color scale")
-cb = plt.colorbar()
+ax = axs[1]
+hb = ax.hexbin(x, y, gridsize=50, bins='log', cmap='inferno')
+ax.axis([xmin, xmax, ymin, ymax])
+ax.set_title("With a log color scale")
+cb = fig.colorbar(hb, ax=ax)
 cb.set_label('log10(N)')
 
 plt.show()

examples/pylab_examples/image_nonuniform.py

@@ -1,25 +1,30 @@
 '''
-This illustrates the NonUniformImage class, which still needs
-an axes method interface; either a separate interface, or a
-generalization of imshow.
+This illustrates the NonUniformImage class.  It is not
+available via an Axes method but it is easily added to an
+Axes instance as shown here.
 '''
 
-from matplotlib.pyplot import figure, show
 import numpy as np
+import matplotlib.pyplot as plt
 from matplotlib.image import NonUniformImage
 from matplotlib import cm
 
 interp = 'nearest'
 
+# Linear x array for cell centers:
 x = np.linspace(-4, 4, 9)
+
+# Highly nonlinear x array:
 x2 = x**3
+
 y = np.linspace(-4, 4, 9)
-#print('Size %d points' % (len(x) * len(y)))
+
 z = np.sqrt(x[np.newaxis, :]**2 + y[:, np.newaxis]**2)
 
-fig = figure()
-fig.suptitle('NonUniformImage class')
-ax = fig.add_subplot(221)
+fig, axs = plt.subplots(nrows=2, ncols=2)
+fig.subplots_adjust(bottom=0.07, hspace=0.3)
+fig.suptitle('NonUniformImage class', fontsize='large')
+ax = axs[0, 0]
 im = NonUniformImage(ax, interpolation=interp, extent=(-4, 4, -4, 4),
                      cmap=cm.Purples)
 im.set_data(x, y, z)
@@ -28,7 +33,7 @@
 ax.set_ylim(-4, 4)
 ax.set_title(interp)
 
-ax = fig.add_subplot(222)
+ax = axs[0, 1]
 im = NonUniformImage(ax, interpolation=interp, extent=(-64, 64, -4, 4),
                      cmap=cm.Purples)
 im.set_data(x2, y, z)
@@ -39,7 +44,7 @@
 
 interp = 'bilinear'
 
-ax = fig.add_subplot(223)
+ax = axs[1, 0]
 im = NonUniformImage(ax, interpolation=interp, extent=(-4, 4, -4, 4),
                      cmap=cm.Purples)
 im.set_data(x, y, z)
@@ -48,7 +53,7 @@
 ax.set_ylim(-4, 4)
 ax.set_title(interp)
 
-ax = fig.add_subplot(224)
+ax = axs[1, 1]
 im = NonUniformImage(ax, interpolation=interp, extent=(-64, 64, -4, 4),
                      cmap=cm.Purples)
 im.set_data(x2, y, z)
@@ -57,4 +62,4 @@
 ax.set_ylim(-4, 4)
 ax.set_title(interp)
 
-show()
+plt.show()

examples/pylab_examples/image_origin.py

@@ -7,19 +7,14 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-x = np.arange(100.0)
-x.shape = (10, 10)
+x = np.arange(120)
+x.shape = (10, 12)
 
 interp = 'bilinear'
-#interp = 'nearest'
-lim = -2, 11, -2, 6
-plt.subplot(211, facecolor='g')
-plt.title('blue should be up')
-plt.imshow(x, origin='upper', interpolation=interp)
-#plt.axis(lim)
+fig, axs = plt.subplots(nrows=2, sharex=True, figsize=(3, 5))
+axs[0].set_title('blue should be up')
+axs[0].imshow(x, origin='upper', interpolation=interp)
 
-plt.subplot(212, facecolor='y')
-plt.title('blue should be down')
-plt.imshow(x, origin='lower', interpolation=interp)
-#plt.axis(lim)
+axs[1].set_title('blue should be down')
+axs[1].imshow(x, origin='lower', interpolation=interp)
 plt.show()

examples/scales/scales.py

@@ -3,15 +3,18 @@
 """
 import numpy as np
 import matplotlib.pyplot as plt
+from matplotlib.ticker import NullFormatter
 
+np.random.seed(1)
 # make up some data in the interval ]0, 1[
 y = np.random.normal(loc=0.5, scale=0.4, size=1000)
 y = y[(y > 0) & (y < 1)]
 y.sort()
 x = np.arange(len(y))
 
 # plot with various axes scales
-fig, axs = plt.subplots(2, 2)
+fig, axs = plt.subplots(2, 2, sharex=True)
+fig.subplots_adjust(left=0.08, right=0.98, wspace=0.3)
 
 # linear
 ax = axs[0, 0]
@@ -30,18 +33,19 @@
 
 
 # symmetric log
-ax = axs[1, 0]
+ax = axs[1, 1]
 ax.plot(x, y - y.mean())
-ax.set_yscale('symlog', linthreshy=0.05)
+ax.set_yscale('symlog', linthreshy=0.02)
 ax.set_title('symlog')
 ax.grid(True)
 
 # logit
-ax = axs[1, 1]
+ax = axs[1, 0]
 ax.plot(x, y)
 ax.set_yscale('logit')
 ax.set_title('logit')
 ax.grid(True)
+ax.yaxis.set_minor_formatter(NullFormatter())
 
 
 plt.show()

examples/specialty_plots/hinton_demo.py

@@ -17,7 +17,7 @@ def hinton(matrix, max_weight=None, ax=None):
     ax = ax if ax is not None else plt.gca()
 
     if not max_weight:
-        max_weight = 2**np.ceil(np.log(np.abs(matrix).max())/np.log(2))
+        max_weight = 2 ** np.ceil(np.log(np.abs(matrix).max()) / np.log(2))
 
     ax.patch.set_facecolor('gray')
     ax.set_aspect('equal', 'box')
@@ -26,7 +26,7 @@ def hinton(matrix, max_weight=None, ax=None):
 
     for (x, y), w in np.ndenumerate(matrix):
         color = 'white' if w > 0 else 'black'
-        size = np.sqrt(np.abs(w))
+        size = np.sqrt(np.abs(w) / max_weight)
         rect = plt.Rectangle([x - size / 2, y - size / 2], size, size,
                              facecolor=color, edgecolor=color)
         ax.add_patch(rect)

tests.py

@@ -50,7 +50,7 @@ def run(extra_args):
     if '--no-network' in sys.argv:
         from matplotlib.testing import disable_internet
         disable_internet.turn_off_internet()
-        extra_args.extend(['--eval-attr="not network"'])
+        extra_args.extend(['-a', '!network'])
         sys.argv.remove('--no-network')
 
     run(extra_args)