Avoid deprecations in examples.

QuLogic · QuLogic · commit c4197dfa22ae · 2018-02-06T02:07:11.000-05:00
diff --git a/examples/images_contours_and_fields/image_transparency_blend.py b/examples/images_contours_and_fields/image_transparency_blend.py
@@ -48,7 +48,8 @@ def normal_pdf(x, mean, var):
 weights = weights_high + weights_low
 
 # We'll also create a grey background into which the pixels will fade
-greys = np.ones(weights.shape + (3,)) * 70
+greys = np.empty(weights.shape + (3,), dtype=np.uint8)
+greys.fill(70)
 
 # First we'll plot these blobs using only ``imshow``.
 vmax = np.abs(weights).max()
diff --git a/examples/showcase/bachelors_degrees_by_gender.py b/examples/showcase/bachelors_degrees_by_gender.py
@@ -10,12 +10,14 @@
 as an alternative to a conventional legend.
 """
 
+import numpy as np
 import matplotlib.pyplot as plt
-from matplotlib.mlab import csv2rec
 from matplotlib.cbook import get_sample_data
 
-with get_sample_data('percent_bachelors_degrees_women_usa.csv') as fname:
-    gender_degree_data = csv2rec(fname)
+
+fname = get_sample_data('percent_bachelors_degrees_women_usa.csv',
+                        asfileobj=False)
+gender_degree_data = np.genfromtxt(fname, delimiter=',', names=True)
 
 # These are the colors that will be used in the plot
 color_sequence = ['#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c',
@@ -59,8 +61,8 @@
 
 # Remove the tick marks; they are unnecessary with the tick lines we just
 # plotted.
-plt.tick_params(axis='both', which='both', bottom='off', top='off',
-                labelbottom='on', left='off', right='off', labelleft='on')
+plt.tick_params(axis='both', which='both', bottom=False, top=False,
+                labelbottom=True, left=False, right=False, labelleft=True)
 
 # Now that the plot is prepared, it's time to actually plot the data!
 # Note that I plotted the majors in order of the highest % in the final year.
@@ -80,9 +82,9 @@
 
 for rank, column in enumerate(majors):
     # Plot each line separately with its own color.
-    column_rec_name = column.replace('\n', '_').replace(' ', '_').lower()
+    column_rec_name = column.replace('\n', '_').replace(' ', '_')
 
-    line = plt.plot(gender_degree_data.year,
+    line = plt.plot(gender_degree_data['Year'],
                     gender_degree_data[column_rec_name],
                     lw=2.5,
                     color=color_sequence[rank])
diff --git a/examples/statistics/hist.py b/examples/statistics/hist.py
@@ -63,7 +63,7 @@
     thispatch.set_facecolor(color)
 
 # We can also normalize our inputs by the total number of counts
-axs[1].hist(x, bins=n_bins, normed=True)
+axs[1].hist(x, bins=n_bins, density=True)
 
 # Now we format the y-axis to display percentage
 axs[1].yaxis.set_major_formatter(PercentFormatter(xmax=1))
diff --git a/examples/subplots_axes_and_figures/broken_axis.py b/examples/subplots_axes_and_figures/broken_axis.py
@@ -36,7 +36,7 @@
 ax.spines['bottom'].set_visible(False)
 ax2.spines['top'].set_visible(False)
 ax.xaxis.tick_top()
-ax.tick_params(labeltop='off')  # don't put tick labels at the top
+ax.tick_params(labeltop=False)  # don't put tick labels at the top
 ax2.xaxis.tick_bottom()
 
 # This looks pretty good, and was fairly painless, but you can get that
diff --git a/examples/ticks_and_spines/date_index_formatter.py b/examples/ticks_and_spines/date_index_formatter.py
@@ -12,15 +12,19 @@
 """
 
 from __future__ import print_function
+
 import numpy as np
-from matplotlib.mlab import csv2rec
+
 import matplotlib.pyplot as plt
 import matplotlib.cbook as cbook
+from matplotlib.dates import bytespdate2num, num2date
 from matplotlib.ticker import Formatter
 
+
 datafile = cbook.get_sample_data('msft.csv', asfileobj=False)
 print('loading %s' % datafile)
-r = csv2rec(datafile)[-40:]
+r = np.genfromtxt(datafile, delimiter=',', names=True,
+                  converters={0: bytespdate2num('%d-%b-%y')})[-40:]
 
 
 class MyFormatter(Formatter):
@@ -34,12 +38,12 @@ def __call__(self, x, pos=0):
         if ind >= len(self.dates) or ind < 0:
             return ''
 
-        return self.dates[ind].strftime(self.fmt)
+        return num2date(self.dates[ind]).strftime(self.fmt)
 
-formatter = MyFormatter(r.date)
+formatter = MyFormatter(r['Date'])
 
 fig, ax = plt.subplots()
 ax.xaxis.set_major_formatter(formatter)
-ax.plot(np.arange(len(r)), r.close, 'o-')
+ax.plot(np.arange(len(r)), r['Close'], 'o-')
 fig.autofmt_xdate()
 plt.show()
diff --git a/tutorials/introductory/pyplot.py b/tutorials/introductory/pyplot.py
@@ -333,7 +333,7 @@ def f(t):
 x = mu + sigma * np.random.randn(10000)
 
 # the histogram of the data
-n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)
+n, bins, patches = plt.hist(x, 50, density=1, facecolor='g', alpha=0.75)
 
 
 plt.xlabel('Smarts')
