Various examples updates.

- don't import out of pyplot or numpy, import as plt/np.
- move some examples to use subplots() instead of add_subplot.
- set random seed for some examples.
- fix some docstrings.

Author: anntzer

examples/api/font_file.py

@@ -15,7 +15,8 @@
 """
 
 import os
-from matplotlib import font_manager as fm, pyplot as plt, rcParams
+from matplotlib import font_manager as fm, rcParams
+import matplotlib.pyplot as plt
 
 fig, ax = plt.subplots()
 

examples/api/power_norm.py

@@ -7,7 +7,7 @@
 
 """
 
-from matplotlib import pyplot as plt
+import matplotlib.pyplot as plt
 import matplotlib.colors as mcolors
 import numpy as np
 from numpy.random import multivariate_normal

examples/axes_grid1/demo_colorbar_with_axes_divider.py

@@ -4,27 +4,23 @@
 ===============================
 
 """
+
 import matplotlib.pyplot as plt
 from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable
-
 from mpl_toolkits.axes_grid1.colorbar import colorbar
-# from matplotlib.pyplot import colorbar
 
-fig = plt.figure(1, figsize=(6, 3))
+fig, (ax1, ax2) = plt.subplots(1, 2)
 fig.subplots_adjust(wspace=0.5)
 
-ax1 = fig.add_subplot(121)
 im1 = ax1.imshow([[1, 2], [3, 4]])
-
 ax1_divider = make_axes_locatable(ax1)
 cax1 = ax1_divider.append_axes("right", size="7%", pad="2%")
 cb1 = colorbar(im1, cax=cax1)
 
-ax2 = fig.add_subplot(122)
 im2 = ax2.imshow([[1, 2], [3, 4]])
-
 ax2_divider = make_axes_locatable(ax2)
 cax2 = ax2_divider.append_axes("top", size="7%", pad="2%")
 cb2 = colorbar(im2, cax=cax2, orientation="horizontal")
 cax2.xaxis.set_ticks_position("top")
+
 plt.show()

examples/event_handling/lasso_demo.py

@@ -10,13 +10,12 @@
 This is currently a proof-of-concept implementation (though it is
 usable as is).  There will be some refinement of the API.
 """
-from matplotlib.widgets import Lasso
-from matplotlib.collections import RegularPolyCollection
-from matplotlib import colors as mcolors, path
 
+from matplotlib import colors as mcolors, path
+from matplotlib.collections import RegularPolyCollection
 import matplotlib.pyplot as plt
-from numpy import nonzero
-from numpy.random import rand
+from matplotlib.widgets import Lasso
+import numpy as np
 
 
 class Datum(object):
@@ -77,9 +76,12 @@ def onpress(self, event):
         # acquire a lock on the widget drawing
         self.canvas.widgetlock(self.lasso)
 
+
 if __name__ == '__main__':
 
-    data = [Datum(*xy) for xy in rand(100, 2)]
+    np.random.seed(19680801)
+
+    data = [Datum(*xy) for xy in np.random.rand(100, 2)]
     ax = plt.axes(xlim=(0, 1), ylim=(0, 1), autoscale_on=False)
     ax.set_title('Lasso points using left mouse button')
 

examples/event_handling/pick_event_demo2.py

@@ -29,11 +29,10 @@ def onpick(event):
     if not N:
         return True
 
-    figi = plt.figure()
-    for subplotnum, dataind in enumerate(event.ind):
-        ax = figi.add_subplot(N, 1, subplotnum + 1)
+    figi, axs = plt.subplots(N, squeeze=False)
+    for ax, dataind in zip(axs.flat, event.ind):
         ax.plot(X[dataind])
-        ax.text(0.05, 0.9, 'mu=%1.3f\nsigma=%1.3f' % (xs[dataind], ys[dataind]),
+        ax.text(.05, .9, 'mu=%1.3f\nsigma=%1.3f' % (xs[dataind], ys[dataind]),
                 transform=ax.transAxes, va='top')
         ax.set_ylim(-0.5, 1.5)
     figi.show()

examples/event_handling/zoom_window.py

@@ -6,27 +6,27 @@
 This example shows how to connect events in one window, for example, a mouse
 press, to another figure window.
 
-If you click on a point in the first window, the z and y limits of the
-second will be adjusted so that the center of the zoom in the second
-window will be the x,y coordinates of the clicked point.
+If you click on a point in the first window, the z and y limits of the second
+will be adjusted so that the center of the zoom in the second window will be
+the x,y coordinates of the clicked point.
 
-Note the diameter of the circles in the scatter are defined in
-points**2, so their size is independent of the zoom
+Note the diameter of the circles in the scatter are defined in points**2, so
+their size is independent of the zoom.
 """
-from matplotlib.pyplot import figure, show
+
+import matplotlib.pyplot as plt
 import numpy as np
-figsrc = figure()
-figzoom = figure()
-
-axsrc = figsrc.add_subplot(111, xlim=(0, 1), ylim=(0, 1), autoscale_on=False)
-axzoom = figzoom.add_subplot(111, xlim=(0.45, 0.55), ylim=(0.4, .6),
-                             autoscale_on=False)
-axsrc.set_title('Click to zoom')
-axzoom.set_title('zoom window')
+
+figsrc, axsrc = plt.subplots()
+figzoom, axzoom = plt.subplots()
+axsrc.set(xlim=(0, 1), ylim=(0, 1), autoscale_on=False,
+          title='Click to zoom')
+axzoom.set(xlim=(0.45, 0.55), ylim=(0.4, 0.6), autoscale_on=False,
+           title='Zoom window')
+
 x, y, s, c = np.random.rand(4, 200)
 s *= 200
 
-
 axsrc.scatter(x, y, s, c)
 axzoom.scatter(x, y, s, c)
 
@@ -40,4 +40,4 @@ def onpress(event):
     figzoom.canvas.draw()
 
 figsrc.canvas.mpl_connect('button_press_event', onpress)
-show()
+plt.show()

examples/images_contours_and_fields/multi_image.py

@@ -6,7 +6,8 @@
 Make a set of images with a single colormap, norm, and colorbar.
 """
 
-from matplotlib import colors, pyplot as plt
+from matplotlib import colors
+import matplotlib.pyplot as plt
 import numpy as np
 
 np.random.seed(19680801)

examples/lines_bars_and_markers/eventplot_demo.py

@@ -32,18 +32,15 @@
 lineoffsets1 = np.array([-15, -3, 1, 1.5, 6, 10])
 linelengths1 = [5, 2, 1, 1, 3, 1.5]
 
-fig = plt.figure()
+fig, axs = plt.subplots(2, 2)
 
 # create a horizontal plot
-ax1 = fig.add_subplot(221)
-ax1.eventplot(data1, colors=colors1, lineoffsets=lineoffsets1,
-              linelengths=linelengths1)
-
+axs[0, 0].eventplot(data1, colors=colors1, lineoffsets=lineoffsets1,
+                    linelengths=linelengths1)
 
 # create a vertical plot
-ax2 = fig.add_subplot(223)
-ax2.eventplot(data1, colors=colors1, lineoffsets=lineoffsets1,
-              linelengths=linelengths1, orientation='vertical')
+axs[1, 0].eventplot(data1, colors=colors1, lineoffsets=lineoffsets1,
+                    linelengths=linelengths1, orientation='vertical')
 
 # create another set of random data.
 # the gamma distribution is only used fo aesthetic purposes
@@ -57,14 +54,12 @@
 linelengths2 = 1
 
 # create a horizontal plot
-ax1 = fig.add_subplot(222)
-ax1.eventplot(data2, colors=colors2, lineoffsets=lineoffsets2,
-              linelengths=linelengths2)
+axs[0, 1].eventplot(data2, colors=colors2, lineoffsets=lineoffsets2,
+                    linelengths=linelengths2)
 
 
 # create a vertical plot
-ax2 = fig.add_subplot(224)
-ax2.eventplot(data2, colors=colors2, lineoffsets=lineoffsets2,
-              linelengths=linelengths2, orientation='vertical')
+axs[1, 1].eventplot(data2, colors=colors2, lineoffsets=lineoffsets2,
+                    linelengths=linelengths2, orientation='vertical')
 
 plt.show()

examples/lines_bars_and_markers/gradient_bar.py

@@ -5,8 +5,9 @@
 
 """
 import matplotlib.pyplot as plt
-from numpy import arange
-from numpy.random import rand
+import numpy as np
+
+np.random.seed(19680801)
 
 
 def gbar(ax, x, y, width=0.5, bottom=0):
@@ -17,20 +18,19 @@ def gbar(ax, x, y, width=0.5, bottom=0):
                   extent=(left, right, bottom, top), alpha=1)
 
 
-fig = plt.figure()
-
 xmin, xmax = xlim = 0, 10
 ymin, ymax = ylim = 0, 1
-ax = fig.add_subplot(111, xlim=xlim, ylim=ylim,
-                     autoscale_on=False)
-X = [[.6, .6], [.7, .7]]
 
+fig, ax = plt.subplots()
+ax.set(xlim=xlim, ylim=ylim, autoscale_on=False)
+
+X = [[.6, .6], [.7, .7]]
 ax.imshow(X, interpolation='bicubic', cmap=plt.cm.copper,
           extent=(xmin, xmax, ymin, ymax), alpha=1)
 
 N = 10
-x = arange(N) + 0.25
-y = rand(N)
+x = np.arange(N) + 0.25
+y = np.random.rand(N)
 gbar(ax, x, y, width=0.7)
 ax.set_aspect('auto')
 plt.show()

examples/lines_bars_and_markers/interp_demo.py

@@ -5,13 +5,13 @@
 
 """
 import matplotlib.pyplot as plt
-from numpy import pi, sin, linspace
+import numpy as np
 from matplotlib.mlab import stineman_interp
 
-x = linspace(0, 2*pi, 20)
-y = sin(x)
+x = np.linspace(0, 2*np.pi, 20)
+y = np.sin(x)
 yp = None
-xi = linspace(x[0], x[-1], 100)
+xi = np.linspace(x[0], x[-1], 100)
 yi = stineman_interp(xi, x, y, yp)
 
 fig, ax = plt.subplots()