8000 DOC: Spell out args & kwargs in tutorials. · matplotlib/matplotlib@203f8a0 · GitHub
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DOC: Spell out args & kwargs in tutorials.
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‎tutorials/intermediate/constrainedlayout_guide.py

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@@ -79,7 +79,8 @@ def example_plot(ax, fontsize=12, hide_labels=False):
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# To prevent this, the location of axes needs to be adjusted. For
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# subplots, this can be done by adjusting the subplot params
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# (:ref:`howto-subplots-adjust`). However, specifying your figure with the
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# ``constrained_layout=True`` kwarg will do the adjusting automatically.
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# ``constrained_layout=True`` keyword argument will do the adjusting
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# automatically.
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fig, ax = plt.subplots(constrained_layout=True)
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example_plot(ax, fontsize=24)
@@ -112,7 +113,7 @@ def example_plot(ax, fontsize=12, hide_labels=False):
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#
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# .. note::
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#
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# For the `~.axes.Axes.pcolormesh` kwargs (``pc_kwargs``) we use a
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# For the `~.axes.Axes.pcolormesh` keyword arguments (``pc_kwargs``) we use a
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# dictionary. Below we will assign one colorbar to a number of axes each
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# containing a `~.cm.ScalarMappable`; specifying the norm and colormap
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# ensures the colorbar is accurate for all the axes.

‎tutorials/intermediate/imshow_extent.py

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*origin* and *extent* in `~.Axes.imshow`
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========================================
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:meth:`~.Axes.imshow` allows you to render an image (either a 2D array
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which will be color-mapped (based on *norm* and *cmap*) or a 3D RGB(A)
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array which will be used as-is) to a rectangular region in data space.
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The orientation of the image in the final rendering is controlled by
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the *origin* and *extent* kwargs (and attributes on the resulting
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`.AxesImage` instance) and the data limits of the axes.
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The *extent* kwarg controls the bounding box in data coordinates that
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the image will fill specified as ``(left, right, bottom, top)`` in
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**data coordinates**, the *origin* kwarg controls how the image fills
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that bounding box, and the orientation in the final rendered image is
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also affected by the axes limits.
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:meth:`~.Axes.imshow` allows you to render an image (either a 2D array which
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will be color-mapped (based on *norm* and *cmap*) or a 3D RGB(A) array which
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will be used as-is) to a rectangular region in data space. The orientation of
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the image in the final rendering is controlled by the *origin* and *extent*
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keyword arguments (and attributes on the resulting `.AxesImage` instance) and
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the data limits of the axes.
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The *extent* keyword arguments controls the bounding box in data coordinates
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that the image will fill specified as ``(left, right, bottom, top)`` in **data
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coordinates**, the *origin* keyword argument controls how the image fills that
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bounding box, and the orientation in the final rendered image is also affected
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by the axes limits.
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.. hint:: Most of the code below is used for adding labels and informative
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text to the plots. The described effects of *origin* and *extent* can be

‎tutorials/introductory/lifecycle.py

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@@ -169,14 +169,14 @@
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###############################################################################
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# We can also adjust the size of this plot using the :func:`pyplot.subplots`
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# function. We can do this with the ``figsize`` kwarg.
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# function. We can do this with the *figsize* keyword argument.
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#
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# .. note::
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#
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# While indexing in NumPy follows the form (row, column), the figsize
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# kwarg follows the form (width, height). This follows conventions in
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# visualization, which unfortunately are different from those of linear
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# algebra.
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# While indexing in NumPy follows the form (row, column), the *figsize*
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# keyword argument follows the form (width, height). This follows
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# conventions in visualization, which unfortunately are different from those
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# of linear algebra.
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fig, ax = plt.subplots(figsize=(8, 4))
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ax.barh(group_names, group_data)
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def currency(x, pos):
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"""The two args are the value and tick position"""
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"""The two arguments are the value and tick position"""
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if x >= 1e6:
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s = '${:1.1f}M'.format(x*1e-6)
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else:

‎tutorials/introductory/pyplot.py

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# antialiased, etc; see `matplotlib.lines.Line2D`. There are
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# several ways to set line properties
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#
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# * Use keyword args::
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# * Use keyword arguments::
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#
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# plt.plot(x, y, linewidth=2.0)
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#
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# MATLAB-style string/value pairs::
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#
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# lines = plt.plot(x1, y1, x2, y2)
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# # use keyword args
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# # use keyword arguments
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# plt.setp(lines, color='r', linewidth=2.0)
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# # or MATLAB style string value pairs
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# plt.setp(lines, 'color', 'r', 'linewidth', 2.0)

‎tutorials/introductory/usage.py

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@@ -223,7 +223,7 @@ def my_plotter(ax, data1, data2, param_dict):
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The y data
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param_dict : dict
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Dictionary of kwargs to pass to ax.plot
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Dictionary of keyword arguments to pass to ax.plot
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Returns
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-------

‎tutorials/text/text_intro.py

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# *position*, via which we can manually specify the label positions. Here we
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# put the xlabel to the far left of the axis. Note, that the y-coordinate of
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# this position has no effect - to adjust the y-position we need to use the
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# *labelpad* kwarg.
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# *labelpad* keyword argument.
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fig, ax = plt.subplots(figsize=(5, 3))
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fig.subplots_adjust(bottom=0.15, left=0.2)
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##############################################################################
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# All the labelling in this tutorial can be changed by manipulating the
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# `matplotlib.font_manager.FontProperties` method, or by named kwargs to
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# `~matplotlib.axes.Axes.set_xlabel`
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# `matplotlib.font_manager.FontProperties` method, or by named keyword
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# arguments to `~matplotlib.axes.Axes.set_xlabel`
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from matplotlib.font_manager import FontProperties
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‎tutorials/text/text_props.py

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#
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# font.sans-serif: Source Han Sans TW, Arial, sans-serif
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#
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# To control the font used on per-artist basis use the ``'name'``,
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# ``'fontname'`` or ``'fontproperties'`` kwargs documented :doc:`above
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# To control the font used on per-artist basis use the *name*, *fontname* or
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# *fontproperties* keyword arguments documented :doc:`above
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# </tutorials/text/text_props>`.
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#
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#

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