8000 Add missing spaces after commas. · matplotlib/matplotlib@290dfb0 · GitHub
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Add missing spaces after commas.
... and various other small docstring fixes.
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‎examples/axes_grid1/inset_locator_demo.py

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@@ -55,9 +55,9 @@
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ax = fig.add_subplot(121)
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# We use the axes transform as bbox_transform. Therefore the bounding box
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# needs to be specified in axes coordinates ((0,0) is the lower left corner
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# of the axes, (1,1) is the upper right corner).
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# The bounding box (.2, .4, .6, .5) starts at (.2,.4) and ranges to (.8,.9)
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# needs to be specified in axes coordinates ((0, 0) is the lower left corner
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# of the axes, (1, 1) is the upper right corner).
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# The bounding box (.2, .4, .6, .5) starts at (.2, .4) and ranges to (.8, .9)
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# in those coordinates.
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# Inside of this bounding box an inset of half the bounding box' width and
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# three quarters of the bounding box' height is created. The lower left corner

‎examples/event_handling/trifinder_event_demo.py

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@@ -54,7 +54,7 @@ def motion_notify(event):
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# Setup plot and callbacks.
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plt.subplot(111, aspect='equal')
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plt.triplot(triang, 'bo-')
57-
polygon = Polygon([[0, 0], [0, 0]], facecolor='y') # dummy data for xs,ys
57+
polygon = Polygon([[0, 0], [0, 0]], facecolor='y') # dummy data for (xs, ys)
5858
update_polygon(-1)
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plt.gca().add_patch(polygon)
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plt.gcf().canvas.mpl_connect('motion_notify_event', motion_notify)

‎examples/event_handling/zoom_window.py

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@@ -8,7 +8,7 @@
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If you click on a point in the first window, the z and y limits of the second
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will be adjusted so that the center of the zoom in the second window will be
11-
the x,y coordinates of the clicked point.
11+
the (x, y) coordinates of the clicked point.
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Note the diameter of the circles in the scatter are defined in points**2, so
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their size is independent of the zoom.

‎examples/images_contours_and_fields/image_demo.py

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# achieving the look you want. Below we'll display the same (small) array,
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# interpolated with three different interpolation methods.
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#
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# The center of the pixel at A[i,j] is plotted at i+0.5, i+0.5. If you
82-
# are using interpolation='nearest', the region bounded by (i,j) and
83-
# (i+1,j+1) will have the same color. If you are using interpolation,
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# The center of the pixel at A[i, j] is plotted at (i+0.5, i+0.5). If you
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# are using interpolation='nearest', the region bounded by (i, j) and
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# (i+1, j+1) will have the same color. If you are using interpolation,
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# the pixel center will have the same color as it does with nearest, but
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# other pixels will be interpolated between the neighboring pixels.
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#
@@ -129,7 +129,7 @@
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130130
###############################################################################
131131
# You can specify whether images should be plotted with the array origin
132-
# x[0,0] in the upper left or lower right by using the origin parameter.
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# x[0, 0] in the upper left or lower right by using the origin parameter.
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# You can also control the default setting image.origin in your
134134
# :ref:`matplotlibrc file <customizing-with-matplotlibrc-files>`. For more on
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# this topic see the :doc:`complete guide on origin and extent

‎examples/images_contours_and_fields/irregulardatagrid.py

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@@ -44,8 +44,7 @@
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xi = np.linspace(-2.1, 2.1, ngridx)
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yi = np.linspace(-2.1, 2.1, ngridy)
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47-
# Perform linear interpolation of the data (x,y)
48-
# on a grid defined by (xi,yi)
47+
# Linearly interpolate the data (x, y) on a grid defined by (xi, yi).
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triang = tri.Triangulation(x, y)
5049
interpolator = tri.LinearTriInterpolator(triang, z)
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Xi, Yi = np.meshgrid(xi, yi)
@@ -56,7 +55,6 @@
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#from scipy.interpolate import griddata
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#zi = griddata((x, y), z, (xi[None,:], yi[:,None]), method='linear')
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59-
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ax1.contour(xi, yi, zi, levels=14, linewidths=0.5, colors='k')
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cntr1 = ax1.contourf(xi, yi, zi, levels=14, cmap="RdBu_r")
6260

@@ -66,7 +64,6 @@
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ax1.set_title('grid and contour (%d points, %d grid points)' %
6765
(npts, ngridx * ngridy))
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69-
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# ----------
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# Tricontour
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# ----------

‎examples/lines_bars_and_markers/psd_demo.py

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@@ -45,7 +45,7 @@
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# s = 0.1*sin(2*pi*t) + cnse;
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#
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# subplot(211)
48-
# plot(t,s)
48+
# plot(t, s)
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# subplot(212)
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# psd(s, 512, 1/dt)
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#

‎examples/lines_bars_and_markers/scatter_piecharts.py

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sizes = np.array([60, 80, 120])
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# calculate the points of the first pie marker
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# these are just the origin (0,0) + some points on a circle cos,sin
22+
# these are just the origin (0, 0) + some (cos, sin) points on a circle
2323
x1 = np.cos(2 * np.pi * np.linspace(0, r1))
2424
y1 = np.sin(2 * np.pi * np.linspace(0, r1))
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xy1 = np.row_stack([[0, 0], np.column_stack([x1, y1])])

‎examples/misc/customize_rc.py

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@@ -22,7 +22,7 @@ def set_pub():
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>>> set_pub()
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>>> subplot(111)
25-
>>> plot([1,2,3])
25+
>>> plot([1, 2, 3])
2626
>>> savefig('myfig')
2727
>>> rcdefaults() # restore the defaults
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‎examples/misc/demo_agg_filter.py

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@@ -264,16 +264,16 @@ def drop_shadow_patches(ax):
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men_means = [20, 35, 30, 35, 27]
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266266
ind = np.arange(N) # the x locations for the groups
267-
width = 0.35 # the width of the bars
267+
width = 0.35 # the width of the bars
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269269
rects1 = ax.bar(ind, men_means, width, color='r', ec="w", lw=2)
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women_means = [25, 32, 34, 20, 25]
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rects2 = ax.bar(ind + width + 0.1, women_means, width,
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color='y', ec="w", lw=2)
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275-
# gauss = GaussianFilter(1.5, offsets=(1,1), )
276-
gauss = DropShadowFilter(5, offsets=(1, 1), )
275+
# gauss = GaussianFilter(1.5, offsets=(1, 1))
276+
gauss = DropShadowFilter(5, offsets=(1, 1))
277277
shadow = FilteredArtistList(rects1 + rects2, gauss)
278278
ax.add_artist(shadow)
279279
shadow.set_zorder(rects1[0].get_zorder() - 0.1)

‎examples/misc/set_and_get.py

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@@ -11,7 +11,7 @@
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To set the linestyle of a line to be dashed, you can do::
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14-
>>> line, = plt.plot([1,2,3])
14+
>>> line, = plt.plot([1, 2, 3])
1515
>>> plt.setp(line, linestyle='--')
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1717
If you want to know the valid types of arguments, you can provide the
@@ -31,7 +31,7 @@
3131
the instances will be set. e.g., suppose you have a list of two lines,
3232
the following will make both lines thicker and red::
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34-
>>> x = np.arange(0,1.0,0.01)
34+
>>> x = np.arange(0, 1, 0.01)
3535
>>> y1 = np.sin(2*np.pi*x)
3636
>>> y2 = np.sin(4*np.pi*x)
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>>> lines = plt.plot(x, y1, x, y2)

‎examples/mplot3d/2dcollections3d.py

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@@ -16,7 +16,7 @@
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# Plot a sin curve using the x and y axes.
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x = np.linspace(0, 1, 100)
1818
y = np.sin(x * 2 * np.pi) / 2 + 0.5
19-
ax.plot(x, y, zs=0, zdir='z', label='curve in (x,y)')
19+
ax.plot(x, y, zs=0, zdir='z', label='curve in (x, y)')
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# Plot scatterplot data (20 2D points per colour) on the x and z axes.
2222
colors = ('r', 'g', 'b', 'k')
@@ -30,8 +30,8 @@
3030
for c in colors:
3131
c_list.extend([c] * 20)
3232
# By using zdir='y', the y value of these points is fixed to the zs value 0
33-
# and the (x,y) points are plotted on the x and z axes.
34-
ax.scatter(x, y, zs=0, zdir='y', c=c_list, label='points in (x,z)')
33+
# and the (x, y) points are plotted on the x and z axes.
34+
ax.scatter(x, y, zs=0, zdir='y', c=c_list, label='points in (x, z)')
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# Make legend, set axes limits and labels
3737
ax.legend()

‎examples/mplot3d/contour3d.py

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@@ -3,8 +3,8 @@
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Demonstrates plotting contour (level) curves in 3D
44
==================================================
55
6-
This is like a contour plot in 2D except that the f(x,y)=c curve is plotted
7-
on the plane z=c.
6+
This is like a contour plot in 2D except that the ``f(x, y)=c`` curve is
7+
plotted on the plane ``z=c``.
88
"""
99

1010
from mpl_toolkits.mplot3d import axes3d

‎examples/mplot3d/polys3d.py

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@@ -28,7 +28,8 @@ def polygon_under_graph(xlist, ylist):
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fig = plt.figure()
2929
ax = fig.gca(projection='3d')
3030

31-
# Make verts a list, verts[i] will be a list of (x,y) pairs defining polygon i
31+
# Make verts a list such that verts[i] is a list of (x, y) pairs defining
32+
# polygon i.
3233
verts = []
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3435
# Set up the x sequence

‎examples/pie_and_polar_charts/pie_and_donut_labels.py

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# the pie. Here we use the axes coordinates ``(1, 0, 0.5, 1)`` together
2929
# with the location ``"center left"``; i.e.
3030
# the left central point of the legend will be at the left central point of the
31-
# bounding box, spanning from ``(1,0)`` to ``(1.5,1)`` in axes coordinates.
31+
# bounding box, spanning from ``(1, 0)`` to ``(1.5, 1)`` in axes coordinates.
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3333
import numpy as np
3434
import matplotlib.pyplot as plt

‎examples/pyplots/annotation_polar.py

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@@ -13,7 +13,7 @@
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1414
fig = plt.figure()
1515
ax = fig.add_subplot(111, polar=True)
16-
r = np.arange(0,1,0.001)
16+
r = np.arange(0, 1, 0.001)
1717
theta = 2 * 2*np.pi * r
1818
line, = ax.plot(theta, r, color='#ee8d18', lw=3)
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‎examples/pyplots/auto_subplots_adjust.py

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fig, ax = plt.subplots()
1919
ax.plot(range(10))
20-
ax.set_yticks((2,5,7))
20+
ax.set_yticks((2, 5, 7))
2121
labels = ax.set_yticklabels(('really, really, really', 'long', 'labels'))
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2323
def on_draw(event):

‎examples/pyplots/boxplot_demo_pyplot.py

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# This is actually more efficient because boxplot converts
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# a 2-D array into a list of vectors internally anyway.
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77-
data = [data, d2, d2[::2,0]]
77+
data = [data, d2, d2[::2, 0]]
7878
fig7, ax7 = plt.subplots()
7979
ax7.set_title('Multiple Samples with Different sizes')
8080
ax7.boxplot(data)

‎examples/pyplots/pyplot_mathtext.py

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t = np.arange(0.0, 2.0, 0.01)
1212
s = np.sin(2*np.pi*t)
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14-
plt.plot(t,s)
14+
plt.plot(t, s)
1515
plt.title(r'$\alpha_i > \beta_i$', fontsize=20)
1616
plt.text(1, -0.6, r'$\sum_{i=0}^\infty x_i$', fontsize=20)
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plt.text(0.6, 0.6, r'$\mathcal{A}\mathrm{sin}(2 \omega t)$',

‎examples/pyplots/pyplot_simple.py

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A most simple plot, where a list of numbers is plotted against their index.
77
"""
88
import matplotlib.pyplot as plt
9-
plt.plot([1,2,3,4])
9+
plt.plot([1, 2, 3, 4])
1010
plt.ylabel('some numbers')
1111
plt.show()
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‎examples/pyplots/text_layout.py

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top = bottom + height
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1717
fig = plt.figure()
18-
ax = fig.add_axes([0,0,1,1])
18+
ax = fig.add_axes([0, 0, 1, 1])
1919

20-
# axes coordinates are 0,0 is bottom left and 1,1 is upper right
20+
# axes coordinates: (0, 0) is bottom left and (1, 1) is upper right
2121
p = patches.Rectangle(
2222
(left, bottom), width, height,
2323
fill=False, transform=ax.transAxes, clip_on=False

‎examples/shapes_and_collections/collections.py

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The third subplot will make regular polygons, with the same
1313
type of scaling and positioning as in the first two.
1414
15-
The last subplot illustrates the use of "offsets=(xo,yo)",
15+
The last subplot illustrates the use of "offsets=(xo, yo)",
1616
that is, a single tuple instead of a list of tuples, to generate
1717
successively offset curves, with the offset given in data
1818
units. This behavior is available only for the LineCollection.
19-
2019
'''
2120

2221
import matplotlib.pyplot as plt
@@ -53,7 +52,7 @@
5352
trans = fig.dpi_scale_trans + transforms.Affine2D().scale(1.0/72.0)
5453
col.set_transform(trans) # the points to pixels transform
5554
# Note: the first argument to the collection initializer
56-
# must be a list of sequences of x,y tuples; we have only
55+
# must be a list of sequences of (x, y) tuples; we have only
5756
# one sequence, but we still have to put it in a list.
5857
ax1.add_collection(col, autolim=True)
5958
# autolim=True enables autoscaling. For collections with

‎examples/shapes_and_collections/line_collection.py

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# If linestyle is omitted, 'solid' is used
7272
# See :class:`matplotlib.collections.LineCollection` for more information
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74-
# Make a sequence of x,y pairs
74+
# Make a sequence of (x, y) pairs.
7575
line_segments = LineCollection([np.column_stack([x, y]) for y in ys],
7676
linewidths=(0.5, 1, 1.5, 2),
7777
linestyles='solid')

‎examples/specialty_plots/leftventricle_bulleye.py

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@@ -205,6 +205,6 @@ def bullseye_plot(ax, data, seg_bold=None, cmap=None, norm=None):
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206206
bullseye_plot(ax[2], data, seg_bold=[3, 5, 6, 11, 12, 16],
207207
cmap=cmap3, norm=norm3)
208-
ax[2].set_title('Segments [3,5,6,11,12,16] in bold')
208+
ax[2].set_title('Segments [3, 5, 6, 11, 12, 16] in bold')
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210210
plt.show()

‎examples/statistics/boxplot_demo.py

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8282
# properties of the original sample, and a boxplot is one visual tool
8383
# to make this assessment
8484

85-
random_dists = ['Normal(1,1)', ' Lognormal(1,1)', 'Exp(1)', 'Gumbel(6,4)',
86-
'Triangular(2,9,11)']
85+
random_dists = ['Normal(1, 1)', 'Lognormal(1, 1)', 'Exp(1)', 'Gumbel(6, 4)',
86+
'Triangular(2, 9, 11)']
8787
N = 500
8888

8989
norm = np.random.normal(1, 1, N)

‎examples/subplots_axes_and_figures/broken_axis.py

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4242
# This looks pretty good, and was fairly painless, but you can get that
4343
# cut-out diagonal lines look with just a bit more work. The important
4444
# thing to know here is that in axes coordinates, which are always
45-
# between 0-1, spine endpoints are at these locations (0,0), (0,1),
46-
# (1,0), and (1,1). Thus, we just need to put the diagonals in the
45+
# between 0-1, spine endpoints are at these locations (0, 0), (0, 1),
46+
# (1, 0), and (1, 1). Thus, we just need to put the diagonals in the
4747
# appropriate corners of each of our axes, and so long as we use the
4848
# right transform and disable clipping.
4949

‎examples/subplots_axes_and_figures/subplots_demo.py

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7979
fig, axs = plt.subplots(2, 2)
8080
axs[0, 0].plot(x, y)
81-
axs[0, 0].set_title('Axis [0,0]')
81+
axs[0, 0].set_title('Axis [0, 0]')
8282
axs[0, 1].plot(x, y, 'tab:orange')
83-
axs[0, 1].set_title('Axis [0,1]')
83+
axs[0, 1].set_title('Axis [0, 1]')
8484
axs[1, 0].plot(x, -y, 'tab:green')
85-
axs[1, 0].set_title('Axis [1,0]')
85+
axs[1, 0].set_title('Axis [1, 0]')
8686
axs[1, 1].plot(x, -y, 'tab:red')
87-
axs[1, 1].set_title('Axis [1,1]')
87+
axs[1, 1].set_title('Axis [1, 1]')
8888

8989
for ax in axs.flat:
9090
ax.set(xlabel='x-label', ylabel='y-label')

‎examples/text_labels_and_annotations/annotation_demo.py

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# Specifying text points and annotation points
2121
# --------------------------------------------
2222
#
23-
# You must specify an annotation point `xy=(x,y)` to annotate this point.
24-
# additionally, you may specify a text point `xytext=(x,y)` for the
23+
# You must specify an annotation point `xy=(x, y)` to annotate this point.
24+
# additionally, you may specify a text point `xytext=(x, y)` for the
2525
# location of the text for this annotation. Optionally, you can
2626
# specify the coordinate system of `xy` and `xytext` with one of the
2727
# following strings for `xycoords` and `textcoords` (default is 'data')::
2828
#
29-
# 'figure points' : points from the lower left corner of the figure
30-
# 'figure pixels' : pixels from the lower left corner of the figure
31-
# 'figure fraction' : 0,0 is lower left of figure and 1,1 is upper, right
32-
# 'axes points' : points from lower left corner of axes
33-
# 'axes pixels' : pixels from lower left corner of axes
34-
# 'axes fraction' : 0,0 is lower left of axes and 1,1 is upper right
35-
# 'offset points' : Specify an offset (in points) from the xy value
36-
# 'offset pixels' : Specify an offset (in pixels) from the xy value
37-
# 'data' : use the axes data coordinate system
29+
# 'figure points' : points from the lower left corner of the figure
30+
# 'figure pixels' : pixels from the lower left corner of the figure
31+
# 'figure fraction' : (0, 0) is lower left of figure and (1, 1) is upper right
32+
# 'axes points' : points from lower left corner of axes
33+
# 'axes pixels' : pixels from lower left corner of axes
34+
# 'axes fraction' : (0, 0) is lower left of axes and (1, 1) is upper right
35+
# 'offset points' : Specify an offset (in points) from the xy value
36+
# 'offset pixels' : Specify an offset (in pixels) from the xy value
37+
# 'data' : use the axes data coordinate system
3838
#
3939
# Note: for physical coordinate systems (points or pixels) the origin is the
4040
# (bottom, left) of the figure or axes.

‎examples/text_labels_and_annotations/text_alignment.py

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@@ -3,9 +3,8 @@
33
Precise text layout
44
===================
55
6-
You can precisely layout text in data or axes (0,1) coordinates. This
7-
example shows you some of the alignment and rotation specifications for text
8-
layout.
6+
You can precisely layout text in data or axes coordinates. This example shows
7+
you some of the alignment and rotation specifications for text layout.
98
"""
109

1110
import matplotlib.pyplot as plt

‎examples/text_labels_and_annotations/text_rotation.py

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@@ -8,12 +8,11 @@
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99
The text is aligned by its bounding box (the rectangular box that surrounds the
1010
ink rectangle). The order of operations is rotation then alignment.
11-
Basically, the text is centered at your x,y location, rotated around this
11+
Basically, the text is centered at your (x, y) location, rotated around this
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point, and then aligned according to the bounding box of the rotated text.
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So if you specify left, bottom alignment, the bottom left of the
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bounding box of the rotated text will be at the x,y coordinate of the
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text.
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bounding box of the rotated text will be at the (x, y) coordinate of the text.
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But a picture is worth a thousand words!
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"""

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