matplotlib · NelleV · Jan 22, 2017 · Jan 20, 2017 · Jan 20, 2017 · Jan 21, 2017
diff --git a/examples/color/color_cycle_default.py b/examples/color/color_cycle_default.py
@@ -1,10 +1,14 @@
 """
+====================================
+Colors in the default property cycle
+====================================
+
 Display the colors from the default prop_cycle.
 """
-
 import numpy as np
 import matplotlib.pyplot as plt
 
+
 prop_cycle = plt.rcParams['axes.prop_cycle']
 colors = prop_cycle.by_key()['color']
 

diff --git a/examples/color/color_cycle_demo.py b/examples/color/color_cycle_demo.py
@@ -1,30 +1,36 @@
 """
-Demo of custom property-cycle settings to control colors and such
-for multi-line plots.
+===================
+Styling with cycler
+===================
+
+Demo of custom property-cycle settings to control colors and other style
+properties for multi-line plots.
 
 This example demonstrates two different APIs:
 
-    1. Setting the default rc-parameter specifying the property cycle.
+    1. Setting the default rc parameter specifying the property cycle.
        This affects all subsequent axes (but not axes already created).
-    2. Setting the property cycle for a specific axes. This only
-       affects a single axes.
+    2. Setting the property cycle for a single pair of axes.
 """
 from cycler import cycler
 import numpy as np
 import matplotlib.pyplot as plt
 
+
 x = np.linspace(0, 2 * np.pi)
 offsets = np.linspace(0, 2*np.pi, 4, endpoint=False)
 # Create array with shifted-sine curve along each column
 yy = np.transpose([np.sin(x + phi) for phi in offsets])
 
+# 1. Setting prop cycle on default rc parameter
 plt.rc('lines', linewidth=4)
 plt.rc('axes', prop_cycle=(cycler('color', ['r', 'g', 'b', 'y']) +
                            cycler('linestyle', ['-', '--', ':', '-.'])))
 fig, (ax0, ax1) = plt.subplots(nrows=2)
 ax0.plot(yy)
 ax0.set_title('Set default color cycle to rgby')
 
+# 2. Define prop cycle for single set of axes
 ax1.set_prop_cycle(cycler('color', ['c', 'm', 'y', 'k']) +
                    cycler('lw', [1, 2, 3, 4]))
 ax1.plot(yy)

diff --git a/examples/color/colormaps_reference.py b/examples/color/colormaps_reference.py
@@ -1,4 +1,8 @@
 """
+==================
+Colormap reference
+==================
+
 Reference for colormaps included with Matplotlib.
 
 This reference example shows all colormaps included with Matplotlib. Note that
@@ -35,9 +39,9 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
+
 # Have colormaps separated into categories:
 # http://matplotlib.org/examples/color/colormaps_reference.html
-
 cmaps = [('Perceptually Uniform Sequential',
                             ['viridis', 'inferno', 'plasma', 'magma']),
          ('Sequential',     ['Blues', 'BuGn', 'BuPu',
@@ -65,7 +69,7 @@
 gradient = np.vstack((gradient, gradient))
 
 
-def plot_color_gradients(cmap_category, cmap_list):
+def plot_color_gradients(cmap_category, cmap_list, nrows):
     fig, axes = plt.subplots(nrows=nrows)
     fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99)
     axes[0].set_title(cmap_category + ' colormaps', fontsize=14)
@@ -81,7 +85,8 @@ def plot_color_gradients(cmap_category, cmap_list):
     for ax in axes:
         ax.set_axis_off()
 
+
 for cmap_category, cmap_list in cmaps:
-    plot_color_gradients(cmap_category, cmap_list)
+    plot_color_gradients(cmap_category, cmap_list, nrows)
 
 plt.show()
diff --git a/examples/color/named_colors.py b/examples/color/named_colors.py
@@ -1,44 +1,37 @@
 """
-Visualization of named colors.
+========================
+Visualizing named colors
+========================
 
 Simple plot example with the named colors and its visual representation.
 """
+from __future__ import division
 
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
-import six
-
-import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib import colors as mcolors
 
 
 colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)
 
-# Sort by hue, saturation, value and name.
+# Sort colors by hue, saturation, value and name.
 by_hsv = sorted((tuple(mcolors.rgb_to_hsv(mcolors.to_rgba(color)[:3])), name)
                 for name, color in colors.items())
-
-# Get the sorted color names.
 sorted_names = [name for hsv, name in by_hsv]
 
 n = len(sorted_names)
 ncols = 4
-nrows = int(np.ceil(1. * n / ncols))
+nrows = n // ncols + 1
 
 fig, ax = plt.subplots(figsize=(8, 5))
 
+# Get height and width
 X, Y = fig.get_dpi() * fig.get_size_inches()
-
-# row height
 h = Y / (nrows + 1)
-# col width
 w = X / ncols
 
 for i, name in enumerate(sorted_names):
     col = i % ncols
-    row = int(i / ncols)
+    row = i // ncols
     y = Y - (row * h) - h
 
     xi_line = w * (col + 0.05)
@@ -49,8 +42,8 @@
             horizontalalignment='left',
             verticalalignment='center')
 
-    ax.hlines(
-        y + h * 0.1, xi_line, xf_line, color=colors[name], linewidth=(h * 0.6))
+    ax.hlines(y + h * 0.1, xi_line, xf_line,
+              color=colors[name], linewidth=(h * 0.6))
 
 ax.set_xlim(0, X)
 ax.set_ylim(0, Y)

diff --git a/examples/pie_and_polar_charts/pie_demo_features.py b/examples/pie_and_polar_charts/pie_demo_features.py
@@ -1,4 +1,8 @@
 """
+===============
+Basic pie chart
+===============
+
 Demo of a basic pie chart plus a few additional features.
 
 In addition to the basic pie chart, this demo shows a few optional features:

diff --git a/examples/pie_and_polar_charts/polar_bar_demo.py b/examples/pie_and_polar_charts/polar_bar_demo.py
@@ -1,12 +1,18 @@
 """
+=======================
+Pie chart on polar axis
+=======================
+
 Demo of bar plot on a polar axis.
 """
 import numpy as np
 import matplotlib.pyplot as plt
 
+
 # Fixing random state for reproducibility
 np.random.seed(19680801)
 
+# Compute pie slices
 N = 20
 theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)
 radii = 10 * np.random.rand(N)

diff --git a/examples/pie_and_polar_charts/polar_scatter_demo.py b/examples/pie_and_polar_charts/polar_scatter_demo.py
@@ -1,4 +1,8 @@
 """
+==========================
+Scatter plot on polar axis
+==========================
+
 Demo of scatter plot on a polar axis.
 
 Size increases radially in this example and color increases with angle
@@ -7,18 +11,18 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
+
 # Fixing random state for reproducibility
 np.random.seed(19680801)
 
-
+# Compute areas and colors
 N = 150
 r = 2 * np.random.rand(N)
 theta = 2 * np.pi * np.random.rand(N)
 area = 200 * r**2
 colors = theta
 
 ax = plt.subplot(111, projection='polar')
-c = ax.scatter(theta, r, c=colors, s=area, cmap=plt.cm.hsv)
-c.set_alpha(0.75)
+c = ax.scatter(theta, r, c=colors, s=area, cmap='hsv', alpha=0.75)
 
 plt.show()
diff --git a/examples/style_sheets/plot_bmh.py b/examples/style_sheets/plot_bmh.py
@@ -18,7 +18,6 @@
 def plot_beta_hist(ax, a, b):
     ax.hist(beta(a, b, size=10000), histtype="stepfilled",
             bins=25, alpha=0.8, normed=True)
-    return ax
 
 
 fig, ax = plt.subplots()

diff --git a/examples/style_sheets/plot_fivethirtyeight.py b/examples/style_sheets/plot_fivethirtyeight.py
@@ -11,21 +11,22 @@
 from matplotlib import pyplot as plt
 import numpy as np
 
+plt.style.use('fivethirtyeight')
+
 x = np.linspace(0, 10)
 
 # Fixing random state for reproducibility
 np.random.seed(19680801)
 
 fig, ax = plt.subplots()
 
-with plt.style.context('fivethirtyeight'):
-    ax.plot(x, np.sin(x) + x + np.random.randn(50))
-    ax.plot(x, np.sin(x) + 0.5 * x + np.random.randn(50))
-    ax.plot(x, np.sin(x) + 2 * x + np.random.randn(50))
-    ax.plot(x, np.sin(x) - 0.5 * x + np.random.randn(50))
-    ax.plot(x, np.sin(x) - 2 * x + np.random.randn(50))
-    ax.plot(x, np.sin(x) + np.random.randn(50))
-    ax.set_title("'fivethirtyeight' style sheet")
+ax.plot(x, np.sin(x) + x + np.random.randn(50))
+ax.plot(x, np.sin(x) + 0.5 * x + np.random.randn(50))
+ax.plot(x, np.sin(x) + 2 * x + np.random.randn(50))
+ax.plot(x, np.sin(x) - 0.5 * x + np.random.randn(50))
+ax.plot(x, np.sin(x) - 2 * x + np.random.randn(50))
+ax.plot(x, np.sin(x) + np.random.randn(50))
+ax.set_title("'fivethirtyeight' style sheet")
 
 
 plt.show()