matplotlib · jklymak · Jul 17, 2019 · Jun 1, 2019
diff --git a/examples/axes_grid1/inset_locator_demo.py b/examples/axes_grid1/inset_locator_demo.py
@@ -55,9 +55,9 @@
 ax = fig.add_subplot(121)
 
 # We use the axes transform as bbox_transform. Therefore the bounding box
-# needs to be specified in axes coordinates ((0,0) is the lower left corner
-# of the axes, (1,1) is the upper right corner).
-# The bounding box (.2, .4, .6, .5) starts at (.2,.4) and ranges to (.8,.9)
+# needs to be specified in axes coordinates ((0, 0) is the lower left corner
+# of the axes, (1, 1) is the upper right corner).
+# The bounding box (.2, .4, .6, .5) starts at (.2, .4) and ranges to (.8, .9)
 # in those coordinates.
 # Inside of this bounding box an inset of half the bounding box' width and
 # three quarters of the bounding box' height is created. The lower left corner

diff --git a/examples/event_handling/trifinder_event_demo.py b/examples/event_handling/trifinder_event_demo.py
@@ -54,7 +54,7 @@ def motion_notify(event):
 # Setup plot and callbacks.
 plt.subplot(111, aspect='equal')
 plt.triplot(triang, 'bo-')
-polygon = Polygon([[0, 0], [0, 0]], facecolor='y')  # dummy data for xs,ys
+polygon = Polygon([[0, 0], [0, 0]], facecolor='y')  # dummy data for (xs, ys)
 update_polygon(-1)
 plt.gca().add_patch(polygon)
 plt.gcf().canvas.mpl_connect('motion_notify_event', motion_notify)

diff --git a/examples/event_handling/zoom_window.py b/examples/event_handling/zoom_window.py
@@ -8,7 +8,7 @@
 
 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.
+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.

diff --git a/examples/images_contours_and_fields/image_demo.py b/examples/images_contours_and_fields/image_demo.py
@@ -78,9 +78,9 @@
 # achieving the look you want. Below we'll display the same (small) array,
 # interpolated with three different interpolation methods.
 #
-# The center of the pixel at A[i,j] is plotted at i+0.5, i+0.5.  If you
-# are using interpolation='nearest', the region bounded by (i,j) and
-# (i+1,j+1) will have the same color.  If you are using interpolation,
+# The center of the pixel at A[i, j] is plotted at (i+0.5, i+0.5).  If you
+# are using interpolation='nearest', the region bounded by (i, j) and
+# (i+1, j+1) will have the same color.  If you are using interpolation,
 # the pixel center will have the same color as it does with nearest, but
 # other pixels will be interpolated between the neighboring pixels.
 #
@@ -129,7 +129,7 @@
 
 ###############################################################################
 # You can specify whether images should be plotted with the array origin
-# x[0,0] in the upper left or lower right by using the origin parameter.
+# x[0, 0] in the upper left or lower right by using the origin parameter.
 # You can also control the default setting image.origin in your
 # :ref:`matplotlibrc file <customizing-with-matplotlibrc-files>`. For more on
 # this topic see the :doc:`complete guide on origin and extent

diff --git a/examples/images_contours_and_fields/irregulardatagrid.py b/examples/images_contours_and_fields/irregulardatagrid.py
@@ -44,8 +44,7 @@
 xi = np.linspace(-2.1, 2.1, ngridx)
 yi = np.linspace(-2.1, 2.1, ngridy)
 
-# Perform linear interpolation of the data (x,y)
-# on a grid defined by (xi,yi)
+# Linearly interpolate the data (x, y) on a grid defined by (xi, yi).
 triang = tri.Triangulation(x, y)
 interpolator = tri.LinearTriInterpolator(triang, z)
 Xi, Yi = np.meshgrid(xi, yi)
@@ -56,7 +55,6 @@
 #from scipy.interpolate import griddata
 #zi = griddata((x, y), z, (xi[None,:], yi[:,None]), method='linear')
 
-
 ax1.contour(xi, yi, zi, levels=14, linewidths=0.5, colors='k')
 cntr1 = ax1.contourf(xi, yi, zi, levels=14, cmap="RdBu_r")
 
@@ -66,7 +64,6 @@
 ax1.set_title('grid and contour (%d points, %d grid points)' %
               (npts, ngridx * ngridy))
 
-
 # ----------
 # Tricontour
 # ----------

diff --git a/examples/lines_bars_and_markers/psd_demo.py b/examples/lines_bars_and_markers/psd_demo.py
@@ -45,7 +45,7 @@
 #     s = 0.1*sin(2*pi*t) + cnse;
 #
 #     subplot(211)
-#     plot(t,s)
+#     plot(t, s)
 #     subplot(212)
 #     psd(s, 512, 1/dt)
 #

diff --git a/examples/lines_bars_and_markers/scatter_piecharts.py b/examples/lines_bars_and_markers/scatter_piecharts.py
@@ -19,7 +19,7 @@
 sizes = np.array([60, 80, 120])
 
 # calculate the points of the first pie marker
-# these are just the origin (0,0) + some points on a circle cos,sin
+# these are just the origin (0, 0) + some (cos, sin) points on a circle
 x1 = np.cos(2 * np.pi * np.linspace(0, r1))
 y1 = np.sin(2 * np.pi * np.linspace(0, r1))
 xy1 = np.row_stack([[0, 0], np.column_stack([x1, y1])])

diff --git a/examples/misc/customize_rc.py b/examples/misc/customize_rc.py
@@ -22,7 +22,7 @@ def set_pub():
 
     >>> set_pub()
     >>> subplot(111)
-    >>> plot([1,2,3])
+    >>> plot([1, 2, 3])
     >>> savefig('myfig')
     >>> rcdefaults()  # restore the defaults
 

diff --git a/examples/misc/demo_agg_filter.py b/examples/misc/demo_agg_filter.py
@@ -264,16 +264,16 @@ def drop_shadow_patches(ax):
     men_means = [20, 35, 30, 35, 27]
 
     ind = np.arange(N)  # the x locations for the groups
-    width = 0.35       # the width of the bars
+    width = 0.35  # the width of the bars
 
     rects1 = ax.bar(ind, men_means, width, color='r', ec="w", lw=2)
 
     women_means = [25, 32, 34, 20, 25]
     rects2 = ax.bar(ind + width + 0.1, women_means, width,
                     color='y', ec="w", lw=2)
 
-    # gauss = GaussianFilter(1.5, offsets=(1,1), )
-    gauss = DropShadowFilter(5, offsets=(1, 1), )
+    # gauss = GaussianFilter(1.5, offsets=(1, 1))
+    gauss = DropShadowFilter(5, offsets=(1, 1))
     shadow = FilteredArtistList(rects1 + rects2, gauss)
     ax.add_artist(shadow)
     shadow.set_zorder(rects1[0].get_zorder() - 0.1)

diff --git a/examples/misc/set_and_get.py b/examples/misc/set_and_get.py
@@ -11,7 +11,7 @@
 
 To set the linestyle of a line to be dashed, you can do::
 
-  >>> line, = plt.plot([1,2,3])
+  >>> line, = plt.plot([1, 2, 3])
   >>> plt.setp(line, linestyle='--')
 
 If you want to know the valid types of arguments, you can provide the
@@ -31,7 +31,7 @@
 the instances will be set.  e.g., suppose you have a list of two lines,
 the following will make both lines thicker and red::
 
-    >>> x = np.arange(0,1.0,0.01)
+    >>> x = np.arange(0, 1, 0.01)
     >>> y1 = np.sin(2*np.pi*x)
     >>> y2 = np.sin(4*np.pi*x)
     >>> lines = plt.plot(x, y1, x, y2)

diff --git a/examples/mplot3d/2dcollections3d.py b/examples/mplot3d/2dcollections3d.py
@@ -16,7 +16,7 @@
 # Plot a sin curve using the x and y axes.
 x = np.linspace(0, 1, 100)
 y = np.sin(x * 2 * np.pi) / 2 + 0.5
-ax.plot(x, y, zs=0, zdir='z', label='curve in (x,y)')
+ax.plot(x, y, zs=0, zdir='z', label='curve in (x, y)')
 
 # Plot scatterplot data (20 2D points per colour) on the x and z axes.
 colors = ('r', 'g', 'b', 'k')
@@ -30,8 +30,8 @@
 for c in colors:
     c_list.extend([c] * 20)
 # By using zdir='y', the y value of these points is fixed to the zs value 0
-# and the (x,y) points are plotted on the x and z axes.
-ax.scatter(x, y, zs=0, zdir='y', c=c_list, label='points in (x,z)')
+# and the (x, y) points are plotted on the x and z axes.
+ax.scatter(x, y, zs=0, zdir='y', c=c_list, label='points in (x, z)')
 
 # Make legend, set axes limits and labels
 ax.legend()

diff --git a/examples/mplot3d/contour3d.py b/examples/mplot3d/contour3d.py
@@ -3,8 +3,8 @@
 Demonstrates plotting contour (level) curves in 3D
 ==================================================
 
-This is like a contour plot in 2D except that the f(x,y)=c curve is plotted
-on the plane z=c.
+This is like a contour plot in 2D except that the ``f(x, y)=c`` curve is
+plotted on the plane ``z=c``.
 """
 
 from mpl_toolkits.mplot3d import axes3d

diff --git a/examples/mplot3d/polys3d.py b/examples/mplot3d/polys3d.py
@@ -28,7 +28,8 @@ def polygon_under_graph(xlist, ylist):
 fig = plt.figure()
 ax = fig.gca(projection='3d')
 
-# Make verts a list, verts[i] will be a list of (x,y) pairs defining polygon i
+# Make verts a list such that verts[i] is a list of (x, y) pairs defining
+# polygon i.
 verts = []
 
 # Set up the x sequence

diff --git a/examples/pie_and_polar_charts/pie_and_donut_labels.py b/examples/pie_and_polar_charts/pie_and_donut_labels.py
@@ -28,7 +28,7 @@
 # the pie. Here we use the axes coordinates ``(1, 0, 0.5, 1)`` together
 # with the location ``"center left"``; i.e.
 # the left central point of the legend will be at the left central point of the
-# bounding box, spanning from ``(1,0)`` to ``(1.5,1)`` in axes coordinates.
+# bounding box, spanning from ``(1, 0)`` to ``(1.5, 1)`` in axes coordinates.
 
 import numpy as np
 import matplotlib.pyplot as plt

diff --git a/examples/pyplots/annotation_polar.py b/examples/pyplots/annotation_polar.py
@@ -13,7 +13,7 @@
 
 fig = plt.figure()
 ax = fig.add_subplot(111, polar=True)
-r = np.arange(0,1,0.001)
+r = np.arange(0, 1, 0.001)
 theta = 2 * 2*np.pi * r
 line, = ax.plot(theta, r, color='#ee8d18', lw=3)
 

diff --git a/examples/pyplots/auto_subplots_adjust.py b/examples/pyplots/auto_subplots_adjust.py
@@ -17,7 +17,7 @@
 
 fig, ax = plt.subplots()
 ax.plot(range(10))
-ax.set_yticks((2,5,7))
+ax.set_yticks((2, 5, 7))
 labels = ax.set_yticklabels(('really, really, really', 'long', 'labels'))
 
 def on_draw(event):

diff --git a/examples/pyplots/boxplot_demo_pyplot.py b/examples/pyplots/boxplot_demo_pyplot.py
@@ -74,7 +74,7 @@
 # This is actually more efficient because boxplot converts
 # a 2-D array into a list of vectors internally anyway.
 
-data = [data, d2, d2[::2,0]]
+data = [data, d2, d2[::2, 0]]
 fig7, ax7 = plt.subplots()
 ax7.set_title('Multiple Samples with Different sizes')
 ax7.boxplot(data)

diff --git a/examples/pyplots/pyplot_mathtext.py b/examples/pyplots/pyplot_mathtext.py
@@ -11,7 +11,7 @@
 t = np.arange(0.0, 2.0, 0.01)
 s = np.sin(2*np.pi*t)
 
-plt.plot(t,s)
+plt.plot(t, s)
 plt.title(r'$\alpha_i > \beta_i$', fontsize=20)
 plt.text(1, -0.6, r'$\sum_{i=0}^\infty x_i$', fontsize=20)
 plt.text(0.6, 0.6, r'$\mathcal{A}\mathrm{sin}(2 \omega t)$',

diff --git a/examples/pyplots/pyplot_simple.py b/examples/pyplots/pyplot_simple.py
@@ -6,7 +6,7 @@
 A most simple plot, where a list of numbers is plotted against their index.
 """
 import matplotlib.pyplot as plt
-plt.plot([1,2,3,4])
+plt.plot([1, 2, 3, 4])
 plt.ylabel('some numbers')
 plt.show()
 

diff --git a/examples/shapes_and_collections/collections.py b/examples/shapes_and_collections/collections.py
@@ -12,11 +12,10 @@
 The third subplot will make regular polygons, with the same
 type of scaling and positioning as in the first two.
 
-The last subplot illustrates the use of "offsets=(xo,yo)",
+The last subplot illustrates the use of "offsets=(xo, yo)",
 that is, a single tuple instead of a list of tuples, to generate
 successively offset curves, with the offset given in data
 units.  This behavior is available only for the LineCollection.
-
 '''
 
 import matplotlib.pyplot as plt
@@ -53,7 +52,7 @@
 trans = fig.dpi_scale_trans + transforms.Affine2D().scale(1.0/72.0)
 col.set_transform(trans)  # the points to pixels transform
 # Note: the first argument to the collection initializer
-# must be a list of sequences of x,y tuples; we have only
+# must be a list of sequences of (x, y) tuples; we have only
 # one sequence, but we still have to put it in a list.
 ax1.add_collection(col, autolim=True)
 # autolim=True enables autoscaling.  For collections with

diff --git a/examples/shapes_and_collections/line_collection.py b/examples/shapes_and_collections/line_collection.py
@@ -71,7 +71,7 @@
 #          If linestyle is omitted, 'solid' is used
 # See :class:`matplotlib.collections.LineCollection` for more information
 
-# Make a sequence of x,y pairs
+# Make a sequence of (x, y) pairs.
 line_segments = LineCollection([np.column_stack([x, y]) for y in ys],
                                linewidths=(0.5, 1, 1.5, 2),
                                linestyles='solid')

diff --git a/examples/specialty_plots/leftventricle_bulleye.py b/examples/specialty_plots/leftventricle_bulleye.py
@@ -205,6 +205,6 @@ def bullseye_plot(ax, data, seg_bold=None, cmap=None, norm=None):
 
 bullseye_plot(ax[2], data, seg_bold=[3, 5, 6, 11, 12, 16],
               cmap=cmap3, norm=norm3)
-ax[2].set_title('Segments [3,5,6,11,12,16] in bold')
+ax[2].set_title('Segments [3, 5, 6, 11, 12, 16] in bold')
 
 plt.show()
diff --git a/examples/statistics/boxplot_demo.py b/examples/statistics/boxplot_demo.py
@@ -82,8 +82,8 @@
 # properties of the original sample, and a boxplot is one visual tool
 # to make this assessment
 
-random_dists = ['Normal(1,1)', ' Lognormal(1,1)', 'Exp(1)', 'Gumbel(6,4)',
-                'Triangular(2,9,11)']
+random_dists = ['Normal(1, 1)', 'Lognormal(1, 1)', 'Exp(1)', 'Gumbel(6, 4)',
+                'Triangular(2, 9, 11)']
 N = 500
 
 norm = np.random.normal(1, 1, N)

diff --git a/examples/subplots_axes_and_figures/broken_axis.py b/examples/subplots_axes_and_figures/broken_axis.py
@@ -42,8 +42,8 @@
 # This looks pretty good, and was fairly painless, but you can get that
 # cut-out diagonal lines look with just a bit more work. The important
 # thing to know here is that in axes coordinates, which are always
-# between 0-1, spine endpoints are at these locations (0,0), (0,1),
-# (1,0), and (1,1).  Thus, we just need to put the diagonals in the
+# between 0-1, spine endpoints are at these locations (0, 0), (0, 1),
+# (1, 0), and (1, 1).  Thus, we just need to put the diagonals in the
 # appropriate corners of each of our axes, and so long as we use the
 # right transform and disable clipping.
 

diff --git a/examples/subplots_axes_and_figures/subplots_demo.py b/examples/subplots_axes_and_figures/subplots_demo.py
@@ -78,13 +78,13 @@
 
 fig, axs = plt.subplots(2, 2)
 axs[0, 0].plot(x, y)
-axs[0, 0].set_title('Axis [0,0]')
+axs[0, 0].set_title('Axis [0, 0]')
 axs[0, 1].plot(x, y, 'tab:orange')
-axs[0, 1].set_title('Axis [0,1]')
+axs[0, 1].set_title('Axis [0, 1]')
 axs[1, 0].plot(x, -y, 'tab:green')
-axs[1, 0].set_title('Axis [1,0]')
+axs[1, 0].set_title('Axis [1, 0]')
 axs[1, 1].plot(x, -y, 'tab:red')
-axs[1, 1].set_title('Axis [1,1]')
+axs[1, 1].set_title('Axis [1, 1]')
 
 for ax in axs.flat:
     ax.set(xlabel='x-label', ylabel='y-label')

diff --git a/examples/text_labels_and_annotations/annotation_demo.py b/examples/text_labels_and_annotations/annotation_demo.py
@@ -20,21 +20,21 @@
 # Specifying text points and annotation points
 # --------------------------------------------
 #
-# You must specify an annotation point `xy=(x,y)` to annotate this point.
-# additionally, you may specify a text point `xytext=(x,y)` for the
+# You must specify an annotation point `xy=(x, y)` to annotate this point.
+# additionally, you may specify a text point `xytext=(x, y)` for the
 # location of the text for this annotation.  Optionally, you can
 # specify the coordinate system of `xy` and `xytext` with one of the
 # following strings for `xycoords` and `textcoords` (default is 'data')::
 #
-#   'figure points'   : points from the lower left corner of the figure
-#   'figure pixels'   : pixels from the lower left corner of the figure
-#   'figure fraction' : 0,0 is lower left of figure and 1,1 is upper, right
-#   'axes points'     : points from lower left corner of axes
-#   'axes pixels'     : pixels from lower left corner of axes
-#   'axes fraction'   : 0,0 is lower left of axes and 1,1 is upper right
-#   'offset points'   : Specify an offset (in points) from the xy value
-#   'offset pixels'   : Specify an offset (in pixels) from the xy value
-#   'data'            : use the axes data coordinate system
+#  'figure points'   : points from the lower left corner of the figure
+#  'figure pixels'   : pixels from the lower left corner of the figure
+#  'figure fraction' : (0, 0) is lower left of figure and (1, 1) is upper right
+#  'axes points'     : points from lower left corner of axes
+#  'axes pixels'     : pixels from lower left corner of axes
+#  'axes fraction'   : (0, 0) is lower left of axes and (1, 1) is upper right
+#  'offset points'   : Specify an offset (in points) from the xy value
+#  'offset pixels'   : Specify an offset (in pixels) from the xy value
+#  'data'            : use the axes data coordinate system
 #
 # Note: for physical coordinate systems (points or pixels) the origin is the
 # (bottom, left) of the figure or axes.

diff --git a/examples/text_labels_and_annotations/text_alignment.py b/examples/text_labels_and_annotations/text_alignment.py
@@ -3,9 +3,8 @@
 Precise text layout
 ===================
 
-You can precisely layout text in data or axes (0,1) coordinates.  This
-example shows you some of the alignment and rotation specifications for text
-layout.
+You can precisely layout text in data or axes coordinates.  This example shows
+you some of the alignment and rotation specifications for text layout.
 """
 
 import matplotlib.pyplot as plt

diff --git a/examples/text_labels_and_annotations/text_rotation.py b/examples/text_labels_and_annotations/text_rotation.py
@@ -8,12 +8,11 @@
 
 The text is aligned by its bounding box (the rectangular box that surrounds the
 ink rectangle).  The order of operations is rotation then alignment.
-Basically, the text is centered at your x,y location, rotated around this
+Basically, the text is centered at your (x, y) location, rotated around this
 point, and then aligned according to the bounding box of the rotated text.
 
 So if you specify left, bottom alignment, the bottom left of the
-bounding box of the rotated text will be at the x,y coordinate of the
-text.
+bounding box of the rotated text will be at the (x, y) coordinate of the text.
 
 But a picture is worth a thousand words!
 """