8000 Add missing spaces after commas. · matplotlib/matplotlib@b547660 · GitHub
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anntzeranntzer
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Add miss 8000 ing spaces after commas.
... and various other small docstring fixes.
1 parent 9f1c730 commit b547660
  • axis.py
  • backend_bases.py
  • backend_tools.py
  • backends
  • cbook
  • collections.py
  • colorbar.py
  • colors.py
  • contour.py
  • dates.py
  • figure.py
  • image.py
  • legend.py
  • markers.py
  • offsetbox.py
  • patches.py
  • projections
  • pylab.py
  • pyplot.py
  • quiver.py
  • sankey.py
  • sphinxext
  • streamplot.py
  • table.py
  • tests
  • texmanager.py
  • text.py
  • ticker.py
  • transforms.py
  • tri
  • widgets.py
  • mpl_toolkits
  • tutorials

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    97 files changed

    +417
    -459
    lines changed

    ‎examples/axes_grid1/inset_locator_demo.py

    Lines changed: 3 additions & 3 deletions
    Original file line numberDiff line numberDiff line change
    @@ -55,9 +55,9 @@
    5555
    ax = fig.add_subplot(121)
    5656

    5757
    # We use the axes transform as bbox_transform. Therefore the bounding box
    58-
    # needs to be specified in axes coordinates ((0,0) is the lower left corner
    59-
    # of the axes, (1,1) is the upper right corner).
    60-
    # The bounding box (.2, .4, .6, .5) starts at (.2,.4) and ranges to (.8,.9)
    58+
    # needs to be specified in axes coordinates ((0, 0) is the lower left corner
    59+
    # of the axes, (1, 1) is the upper right corner).
    60+
    # The bounding box (.2, .4, .6, .5) starts at (.2, .4) and ranges to (.8, .9)
    6161
    # in those coordinates.
    6262
    # Inside of this bounding box an inset of half the bounding box' width and
    6363
    # three quarters of the bounding box' height is created. The lower left corner

    ‎examples/event_handling/trifinder_event_demo.py

    Lines changed: 1 addition & 1 deletion
    Original file line numberDiff line numberDiff line change
    @@ -54,7 +54,7 @@ def motion_notify(event):
    5454
    # Setup plot and callbacks.
    5555
    plt.subplot(111, aspect='equal')
    5656
    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)
    5959
    plt.gca().add_patch(polygon)
    6060
    plt.gcf().canvas.mpl_connect('motion_notify_event', motion_notify)

    ‎examples/event_handling/zoom_window.py

    Lines changed: 1 addition & 1 deletion
    Original file line numberDiff line numberDiff line change
    @@ -8,7 +8,7 @@
    88
    99
    If you click on a point in the first window, the z and y limits of the second
    1010
    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.
    1212
    1313
    Note the diameter of the circles in the scatter are defined in points**2, so
    1414
    their size is independent of the zoom.

    ‎examples/images_contours_and_fields/image_demo.py

    Lines changed: 4 additions & 4 deletions
    Original file line numberDiff line numberDiff line change
    @@ -78,9 +78,9 @@
    7878
    # achieving the look you want. Below we'll display the same (small) array,
    7979
    # interpolated with three different interpolation methods.
    8080
    #
    81-
    # 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,
    81+
    # 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,
    8484
    # the pixel center will have the same color as it does with nearest, but
    8585
    # other pixels will be interpolated between the neighboring pixels.
    8686
    #
    @@ -129,7 +129,7 @@
    129129

    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.
    132+
    # x[0, 0] in the upper left or lower right by using the origin parameter.
    133133
    # You can also control the default setting image.origin in your
    134134
    # :ref:`matplotlibrc file <customizing-with-matplotlibrc-files>`. For more on
    135135
    # this topic see the :doc:`complete guide on origin and extent

    ‎examples/images_contours_and_fields/irregulardatagrid.py

    Lines changed: 1 addition & 4 deletions
    Original file line numberDiff line numberDiff line change
    @@ -44,8 +44,7 @@
    4444
    xi = np.linspace(-2.1, 2.1, ngridx)
    4545
    yi = np.linspace(-2.1, 2.1, ngridy)
    4646

    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).
    4948
    triang = tri.Triangulation(x, y)
    5049
    interpolator = tri.LinearTriInterpolator(triang, z)
    5150
    Xi, Yi = np.meshgrid(xi, yi)
    @@ -56,7 +55,6 @@
    5655
    #from scipy.interpolate import griddata
    5756
    #zi = griddata((x, y), z, (xi[None,:], yi[:,None]), method='linear')
    5857

    59-
    6058
    ax1.contour(xi, yi, zi, levels=14, linewidths=0.5, colors='k')
    6159
    cntr1 = ax1.contourf(xi, yi, zi, levels=14, cmap="RdBu_r")
    6260

    @@ -66,7 +64,6 @@
    6664
    ax1.set_title('grid and contour (%d points, %d grid points)' %
    6765
    (npts, ngridx * ngridy))
    6866

    69-
    7067
    # ----------
    7168
    # Tricontour
    7269
    # ----------

    ‎examples/lines_bars_and_markers/psd_demo.py

    Original file line numberDiff line numberDiff line change
    @@ -45,7 +45,7 @@
    4545
    # s = 0.1*sin(2*pi*t) + cnse;
    4646
    #
    4747
    # subplot(211)
    48-
    # plot(t,s)
    48+
    # plot(t, s)
    4949
    # subplot(212)
    5050
    # psd(s, 512, 1/dt)
    5151
    #
    Original file line numberDiff line numberDiff line change
    @@ -19,7 +19,7 @@
    1919
    sizes = np.array([60, 80, 120])
    2020

    2121
    # calculate the points of the first pie marker
    22-
    # 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))
    2525
    xy1 = np.row_stack([[0, 0], np.column_stack([x1, y1])])
    Original file line numberDiff line numberDiff line change
    @@ -22,7 +22,7 @@ def set_pub():
    2222
    2323
    >>> set_pub()
    2424
    >>> subplot(111)
    25-
    >>> plot([1,2,3])
    25+
    >>> plot([1, 2, 3])
    2626
    >>> savefig('myfig')
    2727
    >>> rcdefaults() # restore the defaults
    2828
    Original file line numberDiff line numberDiff line change
    @@ -264,16 +264,16 @@ def drop_shadow_patches(ax):
    264264
    men_means = [20, 35, 30, 35, 27]
    265265

    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
    268268

    269269
    rects1 = ax.bar(ind, men_means, width, color='r', ec="w", lw=2)
    270270

    271271
    women_means = [25, 32, 34, 20, 25]
    272272
    rects2 = ax.bar(ind + width + 0.1, women_means, width,
    273273
    color='y', ec="w", lw=2)
    274274

    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)
    Original file line numberDiff line numberDiff line change
    @@ -11,7 +11,7 @@
    1111
    1212
    To set the linestyle of a line to be dashed, you can do::
    1313
    14-
    >>> line, = plt.plot([1,2,3])
    14+
    >>> line, = plt.plot([1, 2, 3])
    1515
    >>> plt.setp(line, linestyle='--')
    1616
    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::
    3333
    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)
    3737
    >>> lines = plt.plot(x, y1, x, y2)
    Original file line numberDiff line numberDiff line change
    @@ -16,7 +16,7 @@
    1616
    # Plot a sin curve using the x and y axes.
    1717
    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)')
    2020

    2121
    # 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)')
    3535

    3636
    # Make legend, set axes limits and labels
    3737
    ax.legend()
    Original file line numberDiff line numberDiff line change
    @@ -3,8 +3,8 @@
    33
    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
    +
    plotted on the plane ``z=c``.
    88
    """
    99

    1010
    from mpl_toolkits.mplot3d import axes3d
    Original file line numberDiff line numberDiff line change
    @@ -28,7 +28,8 @@ def polygon_under_graph(xlist, ylist):
    2828
    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 = []
    3334

    3435
    # Set up the x sequence
    Original file line numberDiff line numberDiff line change
    @@ -28,7 +28,7 @@
    2828
    # 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.
    3232

    3333
    import numpy as np
    3434
    import matplotlib.pyplot as plt
    Original file line numberDiff line numberDiff line change
    @@ -13,7 +13,7 @@
    1313

    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)
    1919

    Original file line numberDiff line numberDiff line change
    @@ -17,7 +17,7 @@
    1717

    1818
    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'))
    2222

    2323
    def on_draw(event):
    Original file line numberDiff line numberDiff line change
    @@ -74,7 +74,7 @@
    7474
    # This is actually more efficient because boxplot converts
    7575
    # a 2-D array into a list of vectors internally anyway.
    7676

    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)
    Original file line numberDiff line numberDiff line change
    @@ -11,7 +11,7 @@
    1111
    t = np.arange(0.0, 2.0, 0.01)
    1212
    s = np.sin(2*np.pi*t)
    1313

    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)
    1717
    plt.text(0.6, 0.6, r'$\mathcal{A}\mathrm{sin}(2 \omega t)$',
    Original file line numberDiff line numberDiff line change
    @@ -6,7 +6,7 @@
    66
    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()
    1212

    Original file line numberDiff line numberDiff line change
    @@ -12,11 +12,10 @@
    1212
    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
    Original file line numberDiff line numberDiff line change
    @@ -71,7 +71,7 @@
    7171
    # If linestyle is omitted, 'solid' is used
    7272
    # See :class:`matplotlib.collections.LineCollection` for more information
    7373

    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')
    Original file line numberDiff line numberDiff line change
    @@ -205,6 +205,6 @@ def bullseye_plot(ax, data, seg_bold=None, cmap=None, norm=None):
    205205

    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')
    209209

    210210
    plt.show()
    Original file line numberDiff line numberDiff line change
    @@ -82,8 +82,8 @@
    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)
    Original file line numberDiff line numberDiff line change
    @@ -42,8 +42,8 @@
    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

    Original file line numberDiff line numberDiff line change
    @@ -78,13 +78,13 @@
    7878

    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')
    Original file line numberDiff line numberDiff line change
    @@ -20,21 +20,21 @@
    2020
    # 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.
    Original file line numberDiff line numberDiff line change
    @@ -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
    Original file line numberDiff line numberDiff line change
    @@ -8,12 +8,11 @@
    88
    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
    1212
    point, and then aligned according to the bounding box of the rotated text.
    1313
    1414
    So if you specify left, bottom alignment, the bottom left of the
    15-
    bounding box of the rotated text will be at the x,y coordinate of the
    16-
    text.
    15+
    bounding box of the rotated text will be at the (x, y) coordinate of the text.
    1716
    1817
    But a picture is worth a thousand words!
    1918
    """