8000 Fix several minor typos in examples. · matplotlib/matplotlib@96e6e50 · GitHub
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Fix several minor typos in examples.
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‎doc/mpl_toolkits/axes_grid/figures/simple_axisartist1.py

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ax.axis["bottom", "top", "right"].set_visible(False)
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# make an new axis along the first axis axis (x-axis) which pass
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# throught y=0.
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# through y=0.
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ax.axis["y=0"] = ax.new_floating_axis(nth_coord=0, value=0,
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axis_direction="bottom")
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ax.axis["y=0"].toggle(all=True)

‎doc/mpl_toolkits/axes_grid/figures/simple_axisline2.py

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@@ -4,7 +4,7 @@
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fig = plt.figure(1, (4,3))
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# a subplot with two additiona axis, "xzero" and "yzero". "xzero" is
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# a subplot with two additional axis, "xzero" and "yzero". "xzero" is
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# y=0 line, and "yzero" is x=0 line.
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ax = SubplotZero(fig, 1, 1, 1)
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fig.add_subplot(ax)

‎doc/users/plotting/examples/custom_boxstyle02.py

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@@ -3,7 +3,7 @@
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import matplotlib.pyplot as plt
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# we may derive from matplotlib.patches.BoxStyle._Base class.
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# You need to overide transmute method in this case.
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# You need to override transmute method in this case.
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class MyStyle(BoxStyle._Base):
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"""

‎examples/api/date_demo.py

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@@ -31,7 +31,7 @@
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datafile = cbook.get_sample_data('goog.npy')
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try:
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# Python3 cannot load python2 .npy files with datetime(object) arrays
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# unless the encoding is set to bytes. Hovever this option was
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# unless the encoding is set to bytes. However this option was
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# not added until numpy 1.10 so this example will only work with
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# python 2 or with numpy 1.10 and later.
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r = np.load(datafile, encoding='bytes').view(np.recarray)

‎examples/api/font_file.py

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@@ -7,7 +7,7 @@
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Although it is usually not a good idea to explicitly point to a single
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ttf file for a font instance, you can do so using the
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font_manager.FontProperties fname argument (for a more flexible
10-
solution, see the font_fmaily_rc.py and fonts_demo.py examples).
10+
solution, see the font_family_rc.py and fonts_demo.py examples).
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"""
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import sys
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import os

‎examples/api/histogram_path_demo.py

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@@ -8,7 +8,7 @@
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the faster method of using PolyCollections, were implemented before we
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had proper paths with moveto/lineto, closepoly etc in mpl. Now that
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we have them, we can draw collections of regularly shaped objects with
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homogeous properties more efficiently with a PathCollection. This
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homogeneous properties more efficiently with a PathCollection. This
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example makes a histogram -- its more work to set up the vertex arrays
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at the outset, but it should be much faster for large numbers of
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objects

‎examples/api/radar_chart.py

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@@ -135,7 +135,7 @@ def example_data():
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# Pyrolized Organic Carbon (OP)
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# 2)Inclusion of gas-phase specie carbon monoxide (CO)
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# 3)Inclusion of gas-phase specie ozone (O3).
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# 4)Inclusion of both gas-phase speciesis present...
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# 4)Inclusion of both gas-phase species is present...
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data = [
140140
['Sulfate', 'Nitrate', 'EC', 'OC1', 'OC2', 'OC3', 'OP', 'CO', 'O3'],
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('Basecase', [

‎examples/api/unicode_minus.py

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@@ -3,12 +3,12 @@
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Unicode minus
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=============
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6-
You can use the proper typesetting unicode minus (see
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http://en.wikipedia.org/wiki/Plus_sign#Plus_sign) or the ASCII hypen
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You can use the proper typesetting Unicode minus (see
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https://en.wikipedia.org/wiki/Plus_sign#Plus_sign) or the ASCII hyphen
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for minus, which some people prefer. The matplotlibrc param
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axes.unicode_minus controls the default behavior.
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The default is to use the unicode minus
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The default is to use the Unicode minus.
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"""
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import numpy as np
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import matplotlib
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matplotlib.rcParams['axes.unicode_minus'] = False
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fig, ax = plt.subplots()
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ax.plot(10*np.random.randn(100), 10*np.random.randn(100), 'o')
20-
ax.set_title('Using hypen instead of unicode minus')
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ax.set_title('Using hyphen instead of Unicode minus')
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plt.show()

‎examples/axes_grid/demo_curvelinear_grid.py

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@@ -113,7 +113,7 @@ def curvelinear_test2(fig):
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# A parasite axes with given transform
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ax2 = ParasiteAxesAuxTrans(ax1, tr, "equal")
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# note that ax2.transData == tr + ax1.transData
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# Anthing you draw in ax2 will match the ticks and grids of ax1.
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# Anything you draw in ax2 will match the ticks and grids of ax1.
117117
ax1.parasites.append(ax2)
118118
intp = cbook.simple_linear_interpolation
119119
ax2.plot(intp(np.array([0, 30]), 50),

‎examples/axes_grid/demo_parasite_axes2.py

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to plot multiple different values onto one single plot. Notice how in this
66
example, par1 and par2 are both calling twinx meaning both are tied directly to
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the x-axis. From there, each of those two axis can behave separately from the
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each other, meaning they can take on seperate values from themselves as well as
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each other, meaning they can take on separate values from themselves as well as
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the x-axis.
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"""
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from mpl_toolkits.axes_grid1 import host_subplot

‎examples/event_handling/README.txt

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@@ -5,7 +5,7 @@ matplotlib supports event handling with a GUI neutral event model. So
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you can connect to matplotlib events w/o knowledge of what user
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interface matplotlib will ultimately be plugged in to. This has two
77
advantages: the code you write will be more portable, and matplotlib
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events are aware of things like data coordinate space and whih axes
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events are aware of things like data coordinate space and which axes
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the event occurs in so you don't have to mess with low level
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transformation details to go from canvas space to data space. Object
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picking examples are also included.

‎examples/event_handling/legend_picking.py

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"""
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Enable picking on the legend to toggle the legended line on and off
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Enable picking on the legend to toggle the original line on and off
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"""
44
import numpy as np
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import matplotlib.pyplot as plt

‎examples/event_handling/viewlims.py

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@@ -17,7 +17,7 @@ def __call__(self, ax):
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# A class that will regenerate a fractal set as we zoom in, so that you
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# can actually see the increasing detail. A box in the left panel will show
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# the area to which we are zoomed.
20-
class MandlebrotDisplay(object):
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class MandelbrotDisplay(object):
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def __init__(self, h=500, w=500, niter=50, radius=2., power=2):
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self.height = h
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self.width = w
@@ -57,7 +57,7 @@ def ax_update(self, ax):
5757
im.set_extent((xstart, xend, ystart, yend))
5858
ax.figure.canvas.draw_idle()
5959

60-
md = MandlebrotDisplay()
60+
md = MandelbrotDisplay()
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Z = md(-2., 0.5, -1.25, 1.25)
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fig1, (ax1, ax2) = plt.subplots(1, 2)

‎examples/misc/ftface_props.py

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"""
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This is a demo script to show you how to use all the properties of an
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FT2Font object. These describe global font properties. For
5-
individual character metrices, use the Glyp object, as returned by
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individual character metrics, use the Glyph object, as returned by
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load_char
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"""
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import matplotlib
@@ -18,7 +18,7 @@
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print('Num faces :', font.num_faces) # number of faces in file
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print('Num glyphs :', font.num_glyphs) # number of glyphs in the face
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print('Family name :', font.family_name) # face family name
21-
print('Syle name :', font.style_name) # face syle name
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print('Style name :', font.style_name) # face style name
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print('PS name :', font.postscript_name) # the postscript name
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print('Num fixed :', font.num_fixed_sizes) # number of embedded bitmap in face
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‎examples/misc/sample_data_demo.py

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"""
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Grab mpl data from the ~/.matplotlib/sample_data cache if it exists, else
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fetch it from github and cache it
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fetch it from GitHub and cache it
44
"""
55
from __future__ import print_function
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import matplotlib.cbook as cbook

‎examples/misc/svg_filter_line.py

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"""
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Demonstrate SVG filtering effects which might be used with mpl.
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4-
Note that the filtering effects are only effective if your svg rederer
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Note that the filtering effects are only effective if your svg renderer
55
support it.
66
"""
77

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7878
for l in [l1, l2]:
7979
# pick up the svg element with given id
8080
shadow = xmlid[l.get_label() + "_shadow"]
81-
# apply shdow filter
81+
# apply shadow filter
8282
shadow.set("filter", 'url(#dropshadow)')
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fn = "svg_filter_line.svg"

‎examples/misc/svg_filter_pie.py

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Demonstrate SVG filtering effects which might be used with mpl.
33
The pie chart drawing code is borrowed from pie_demo.py
44
5-
Note that the filtering effects are only effective if your svg rederer
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Note that the filtering effects are only effective if your svg renderer
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support it.
77
"""
88

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5151

5252
# filter definition for shadow using a gaussian blur
53-
# and lighteneing effect.
54-
# The lightnening filter is copied from http://www.w3.org/TR/SVG/filters.html
53+
# and lightening effect.
54+
# The lightening filter is copied from http://www.w3.org/TR/SVG/filters.html
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5656
# I tested it with Inkscape and Firefox3. "Gaussian blur" is supported
57-
# in both, but the lightnening effect only in the inkscape. Also note
58-
# that, inkscape's exporting also may not support it.
57+
# in both, but the lightening effect only in the Inkscape. Also note
58+
# that, Inkscape's exporting also may not support it.
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6060
filter_def = """
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<defs xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink'>

‎examples/pylab_examples/accented_text.py

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1-
"""
1+
r"""
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matplotlib supports accented characters via TeX mathtext
33
44
The following accents are provided: \hat, \breve, \grave, \bar,

‎examples/pylab_examples/centered_ticklabels.py

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fh = cbook.get_sample_data('aapl.npy.gz')
2323
try:
2424
# Python3 cannot load python2 .npy files with datetime(object) arrays
25-
# unless the encoding is set to bytes. Hovever this option was
25+
# unless the encoding is set to bytes. However this option was
2626
# not added until numpy 1.10 so this example will only work with
2727
# python 2 or with numpy 1.10 and later.
2828
r = np.load(fh, encoding='bytes')

‎examples/pylab_examples/demo_agg_filter.py

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@@ -265,7 +265,7 @@ def drop_shadow_line(ax):
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266266

267267
def drop_shadow_patches(ax):
268-
# copyed from barchart_demo.py
268+
# Copied from barchart_demo.py
269269
N = 5
270270
menMeans = (20, 35, 30, 35, 27)
271271

‎examples/pylab_examples/errorbar_subsample.py

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ax.set_title('only every 5th errorbar')
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2727

28-
fig.suptitle('Errorbar subsampling for better visualibility')
28+
fig.suptitle('Errorbar subsampling for better appearance')
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3030
plt.show()

‎examples/pylab_examples/leftventricle_bulleye.py

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@@ -62,7 +62,7 @@ def bullseye_plot(ax, data, segBold=None, cmap=None, norm=None):
6262
theta_i = i*60*np.pi/180
6363
ax.plot([theta_i, theta_i], [r[1], 1], '-k', lw=linewidth)
6464

65-
# Create the bounds for the segmentss 13-16
65+
# Create the bounds for the segments 13-16
6666
for i in range(4):
6767
theta_i = i*90*np.pi/180 - 45*np.pi/180
6868
ax.plot([theta_i, theta_i], [r[0], r[1]], '-k', lw=linewidth)

‎examples/pylab_examples/pythonic_matplotlib.py

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show commands. These hide a lot of complexity from you that you don't
99
need to see in normal figure creation, like instantiating DPI
1010
instances, managing the bounding boxes of the figure elements,
11-
creating and reaslizing GUI windows and embedding figures in them.
11+
creating and realizing GUI windows and embedding figures in them.
1212
1313
1414
If you are an application developer and want to embed matplotlib in
@@ -19,7 +19,7 @@
1919
2020
If you are a web application developer, you may want to use the
2121
example in webapp_demo.py, which shows how to use the backend agg
22-
figure canvase directly, with none of the globals (current figure,
22+
figure canvas directly, with none of the globals (current figure,
2323
current axes) that are present in the pyplot interface. Note that
2424
there is no reason why the pyplot interface won't work for web
2525
application developers, however.

‎examples/pylab_examples/scatter_demo2.py

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1212
datafile = cbook.get_sample_data('goog.npy')
1313
try:
1414
# Python3 cannot load python2 .npy files with datetime(object) arrays
15-
# unless the encoding is set to bytes. Hovever this option was
15+
# unless the encoding is set to bytes. However this option was
1616
# not added until numpy 1.10 so this example will only work with
1717
# python 2 or with numpy 1.10 and later
1818
price_data = np.load(datafile, encoding='bytes').view(np.recarray)

‎examples/pylab_examples/scatter_masked.py

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55
r0 = 0.6
66
x = 0.9*np.random.rand(N)
77
y = 0.9*np.random.rand(N)
8-
area = np.pi*(10 * np.random.rand(N))**2 # 0 to 10 point radiuses
8+
area = np.pi*(10 * np.random.rand(N))**2 # 0 to 10 point radii
99
c = np.sqrt(area)
1010
r = np.sqrt(x*x + y*y)
1111
area1 = np.ma.masked_where(r < r0, area)

‎examples/pylab_examples/system_monitor.py

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5555
try:
5656
# make sure that the GUI framework has a chance to run its event loop
5757
# and clear any GUI events. This needs to be in a try/except block
58-
# because the default implemenation of this method is to raise
58+
# because the default implementation of this method is to raise
5959
# NotImplementedError
6060
fig.canvas.flush_events()
6161
except NotImplementedError:

‎examples/pylab_examples/tricontour_smooth_delaunay.py

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113113
plt.title("Filtering a Delaunay mesh\n" +
114114
"(application to high-resolution tricontouring)")
115115

116-
# 1) plot of the refined (computed) data countours:
116+
# 1) plot of the refined (computed) data contours:
117117
plt.tricontour(tri_refi, z_test_refi, levels=levels, cmap=cmap,
118118
linewidths=[2.0, 0.5, 1.0, 0.5])
119-
# 2) plot of the expected (analytical) data countours (dashed):
119+
# 2) plot of the expected (analytical) data contours (dashed):
120120
if plot_expected:
121121
plt.tricontour(tri_refi, z_expected, levels=levels, cmap=cmap,
122122
linestyles='--')

‎examples/pylab_examples/webapp_demo.py

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3636
# make a translucent scatter collection
3737
x = np.random.rand(100)
3838
y = np.random.rand(100)
39-
area = np.pi * (10 * np.random.rand(100)) ** 2 # 0 to 10 point radiuses
39+
area = np.pi * (10 * np.random.rand(100)) ** 2 # 0 to 10 point radii
4040
c = ax.scatter(x, y, area)
4141
c.set_alpha(0.5)
4242

‎examples/shapes_and_collections/scatter_demo.py

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x = np.random.rand(N)
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y = np.random.rand(N)
1111
colors = np.random.rand(N)
12-
area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radiuses
12+
area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radii
1313

1414
plt.scatter(x, y, s=area, c=colors, alpha=0.5)
1515
plt.show()

‎examples/specialty_plots/topographic_hillshading.py

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# If you need topographically accurate vertical exaggeration, or you don't want
2525
# to guess at what *vert_exag* should be, you'll need to specify the cellsize
2626
# of the grid (i.e. the *dx* and *dy* parameters). Otherwise, any *vert_exag*
27-
# value you specify will be realitive to the grid spacing of your input data
27+
# value you specify will be relative to the grid spacing of your input data
2828
# (in other words, *dx* and *dy* default to 1.0, and *vert_exag* is calculated
2929
# relative to those parameters). Similarly, *dx* and *dy* are assumed to be in
3030
# the same units as your input z-values. Therefore, we'll need to convert the

‎examples/statistics/histogram_demo_cumulative.py

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4949
fig, ax = plt.subplots(figsize=(8, 4))
5050

51-
# plot the cumulative histograme
51+
# plot the cumulative histogram
5252
n, bins, patches = ax.hist(x, n_bins, normed=1, histtype='step',
5353
cumulative=True, label='Empirical')
5454

‎examples/statistics/violinplot_demo.py

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sample. Rather than showing counts of data points that fall into bins
99
or order statistics, violin plots use kernel density estimation (KDE) to
1010
compute an empirical distribution of the sample. That computation
11-
is controlled by several parameters. This example demostrates how to
11+
is controlled by several parameters. This example demonstrates how to
1212
modify the number of points at which the KDE is evaluated (``points``)
1313
and how to modify the band-width of the KDE (``bw_method``).
1414

‎examples/units/units_sample.py

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11
"""
22
plot using a variety of cm vs inches conversions. The example shows
3-
how default unit instrospection works (ax1), how various keywords can
3+
how default unit introspection works (ax1), how various keywords can
44
be used to set the x and y units to override the defaults (ax2, ax3,
55
ax4) and how one can set the xlimits using scalars (ax3, current units
66
assumed) or units (conversions applied to get the numbers to current

‎examples/user_interfaces/README.wx

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1717
http://agni.phys.iit.edu/~kmcivor/wxmpl/
1818

19-
Each of these approachs has different benefits and drawbacks, so I
19+
Each of these approaches has different benefits and drawbacks, so I
2020
encourage you to evaluate each of them and select the one that best
2121
meets your needs.

‎examples/user_interfaces/embedding_in_tk_canvas.py

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@@ -27,7 +27,7 @@ def draw_figure(canvas, figure, loc=(0, 0)):
2727
# Position: convert from top-left anchor to center anchor
2828
canvas.create_image(loc[0] + figure_w/2, loc[1] + figure_h/2, image=photo)
2929

30-
# Unfortunatly, there's no accessor for the pointer to the native renderer
30+
# Unfortunately, there's no accessor for the pointer to the native renderer
3131
tkagg.blit(photo, figure_canvas_agg.get_renderer()._renderer, colormode=2)
3232

3333
# Return a handle which contains a reference to the photo object

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