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Merge pull request #11524 from tacaswell/doc_more_backports
Doc more backports
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‎examples/color/color_by_yvalue.py

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fig, ax = plt.subplots()
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ax.plot(t, smiddle, t, slower, t, supper)
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plt.show()
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#############################################################################
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#
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# ------------
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#
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# References
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# """"""""""
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#
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# The use of the following functions, methods, classes and modules is shown
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# in this example:
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import matplotlib
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matplotlib.axes.Axes.plot
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matplotlib.pyplot.plot

‎examples/color/color_cycle_default.py

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Colors in the default property cycle
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====================================
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Display the colors from the default prop_cycle.
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Display the colors from the default prop_cycle, which is obtained from the
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:ref:`rc parameters<sphx_glr_tutorials_introductory_customizing.py>`.
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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fig.suptitle('Colors in the default prop_cycle', fontsize='large')
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plt.show()
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#############################################################################
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#
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# ------------
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#
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# References
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# """"""""""
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#
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# The use of the following functions, methods, classes and modules is shown
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# in this example:
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import matplotlib
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matplotlib.axes.Axes.axhline
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matplotlib.axes.Axes.axvline
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matplotlib.pyplot.axhline
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matplotlib.pyplot.axvline
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matplotlib.axes.Axes.set_facecolor
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matplotlib.figure.Figure.suptitle

‎examples/color/color_cycler.py

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This example demonstrates two different APIs:
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1. Setting the default rc parameter specifying the property cycle.
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This affects all subsequent axes (but not axes already created).
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1. Setting the default
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:ref:`rc parameter<sphx_glr_tutorials_introductory_customizing.py>`
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specifying the property cycle. This affects all subsequent axes
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(but not axes already created).
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2. Setting the property cycle for a single pair of axes.
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"""
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from cycler import cycler
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# Tweak spacing between subplots to prevent labels from overlapping
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fig.subplots_adjust(hspace=0.3)
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plt.show()
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#############################################################################
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#
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# ------------
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#
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# References
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# """"""""""
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#
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# The use of the following functions, methods, classes and modules is shown
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# in this example:
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import matplotlib
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matplotlib.axes.Axes.plot
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matplotlib.axes.Axes.set_prop_cycle

‎examples/color/color_demo.py

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Color Demo
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==========
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matplotlib gives you 5 ways to specify colors,
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Matplotlib gives you 8 ways to specify colors,
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1) as a single letter string, ala MATLAB
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1) an RGB or RGBA tuple of float values in ``[0, 1]`` (e.g. ``(0.1, 0.2, 0.5)``
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or ``(0.1, 0.2, 0.5, 0.3)``). RGBA is short for Red, Green, Blue, Alpha;
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2) a hex RGB or RGBA string (e.g., ``'#0F0F0F'`` or ``'#0F0F0F0F'``);
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3) a string representation of a float value in ``[0, 1]`` inclusive for gray
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level (e.g., ``'0.5'``);
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4) a single letter string, i.e. one of
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``{'b', 'g', 'r', 'c', 'm', 'y', 'k', 'w'}``;
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5) a X11/CSS4 ("html") color name, e.g. ``"blue"``;
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6) a name from the `xkcd color survey <https://xkcd.com/color/rgb/>`__,
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prefixed with ``'xkcd:'`` (e.g., ``'xkcd:sky blue'``);
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7) a "Cn" color spec, i.e. `'C'` followed by a single digit, which is an index
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into the default property cycle
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(``matplotlib.rcParams['axes.prop_cycle']``); the indexing occurs at artist
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creation time and defaults to black if the cycle does not include color.
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8) one of ``{'tab:blue', 'tab:orange', 'tab:green',
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'tab:red', 'tab:purple', 'tab:brown', 'tab:pink',
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'tab:gray', 'tab:olive', 'tab:cyan'}`` which are the Tableau Colors from the
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'tab10' categorical palette (which is the default color cycle);
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2) as an html style hex string or html color name
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For more information on colors in matplotlib see
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3) as an R,G,B tuple, where R,G,B, range from 0-1
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4) as a string representing a floating point number
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from 0 to 1, corresponding to shades of gray.
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5) as a special color "Cn", where n is a number 0-9 specifying the
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nth color in the currently active color cycle.
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See help(colors) for more info.
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* the :ref:`sphx_glr_tutorials_colors_colors.py` tutorial;
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* the `matplotlib.colors` API;
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* the :ref:`sphx_glr_gallery_color_named_colors.py` example.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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t = np.arange(0.0, 2.0, 0.01)
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t = np.linspace(0.0, 2.0, 201)
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s = np.sin(2 * np.pi * t)
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fig, ax = plt.subplots(facecolor='darkslategray')
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ax.plot(t, s, 'C1')
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ax.set_xlabel('time (s)', color='C1')
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ax.set_ylabel('voltage (mV)', color='0.5') # grayscale color
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ax.set_title('About as silly as it gets, folks', color='#afeeee')
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# 1) RGB tuple:
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fig, ax = plt.subplots(facecolor=(.18, .31, .31))
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# 2) hex string:
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ax.set_facecolor('#eafff5')
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# 3) gray level string:
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ax.set_title('Voltage vs. time chart', color='0.7')
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# 4) single letter color string
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ax.set_xlabel('time (s)', color='c')
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# 5) a named color:
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ax.set_ylabel('voltage (mV)', color='peachpuff')
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# 6) a named xkcd color:
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ax.plot(t, s, 'xkcd:crimson')
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# 7) Cn notation:
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ax.plot(t, .7*s, color='C4', linestyle='--')
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# 8) tab notation:
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ax.tick_params(labelcolor='tab:orange')
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plt.show()
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#############################################################################
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#
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# ------------
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#
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# References
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# """"""""""
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#
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# The use of the following functions, methods, classes and modules is shown
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# in this example:
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import matplotlib
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matplotlib.colors
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matplotlib.axes.Axes.plot
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matplotlib.axes.Axes.set_facecolor
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matplotlib.axes.Axes.set_title
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matplotlib.axes.Axes.set_xlabel
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matplotlib.axes.Axes.set_ylabel
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matplotlib.axes.Axes.tick_params

‎examples/color/colorbar_basics.py

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Colorbar
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========
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Use colorbar by specifying the mappable object (here
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the imshow returned object) and the axes to attach the colorbar to.
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Use `~.figure.Figure.colorbar` by specifying the mappable object (here
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the `~.matplotlib.image.AxesImage` returned by `~.axes.Axes.imshow`)
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and the axes to attach the colorbar to.
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"""
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import numpy as np
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fig.colorbar(neg, ax=ax2)
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plt.show()
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#############################################################################
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#
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# ------------
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#
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# References
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# """"""""""
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#
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# The use of the following functions, methods, classes and modules is shown
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# in this example:
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import matplotlib
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matplotlib.axes.Axes.imshow
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matplotlib.pyplot.imshow
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matplotlib.figure.Figure.colorbar
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matplotlib.pyplot.colorbar

‎examples/color/colormap_reference.py

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plot_color_gradients(cmap_category, cmap_list, nrows)
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plt.show()
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#############################################################################
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#
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# ------------
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#
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# References
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# """"""""""
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#
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# The use of the following functions, methods, classes and modules is shown
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# in this example:
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import matplotlib
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matplotlib.colors
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matplotlib.axes.Axes.imshow
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matplotlib.figure.Figure.text
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matplotlib.axes.Axes.set_axis_off

‎examples/color/named_colors.py

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========================
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Simple plot example with the named colors and its visual representation.
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For more information on colors in matplotlib see
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* the :ref:`sphx_glr_tutorials_colors_colors.py` tutorial;
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* the `matplotlib.colors` API;
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* the :ref:`sphx_glr_gallery_color_color_demo.py`.
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"""
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from __future__ import division
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top=1, bottom=0,
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hspace=0, wspace=0)
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plt.show()
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#############################################################################
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#
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# ------------
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#
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# References
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# """"""""""
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#
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# The use of the following functions, methods, classes and modules is shown
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# in this example:
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import matplotlib
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matplotlib.colors
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matplotlib.colors.rgb_to_hsv
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matplotlib.colors.to_rgba
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matplotlib.figure.Figure.get_size_inches
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matplotlib.figure.Figure.subplots_adjust
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matplotlib.axes.Axes.text
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matplotlib.axes.Axes.hlines

‎examples/images_contours_and_fields/affine_image.py

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Affine transform of an image
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============================
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Prepending an affine transformation (:class:`~.transforms.Affine2D`)
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to the :ref:`data transform <data-coords>`
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of an image allows to manipulate the image's shape and orientation.
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This is an example of the concept of
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:ref:`transform chaining <transformation-pipeline>`.
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For the backends that support draw_image with optional affine
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transform (e.g., agg, ps backend), the image of the output should
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have its boundary match the dashed yellow rectangle.
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rotate_deg(30).skew_deg(30, 15).scale(-1, .5).translate(.5, -1))
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plt.show()
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#############################################################################
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#
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# ------------
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#
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# References
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# """"""""""
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#
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# The use of the following functions, methods and classes is shown
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# in this example:
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import matplotlib
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matplotlib.axes.Axes.imshow
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matplotlib.pyplot.imshow
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matplotlib.transforms.Affine2D

‎examples/images_contours_and_fields/barb_demo.py

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data = np.array(data, dtype=[('x', np.float32), ('y', np.float32),
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('u', np.float32), ('v', np.float32)])
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fig1, axs1 = plt.subplots(nrows=2, ncols=2)
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# Default parameters, uniform grid
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ax = plt.subplot(2, 2, 1)
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ax.barbs(X, Y, U, V)
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axs1[0, 0].barbs(X, Y, U, V)
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# Arbitrary set of vectors, make them longer and change the pivot point
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# (point around which they're rotated) to be the middle
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ax = plt.subplot(2, 2, 2)
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ax.barbs(data['x'], data['y'], data['u'], data['v'], length=8, pivot='middle')
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axs1[0, 1].barbs(data['x'], data['y'], data['u'], data['v'], length=8, pivot='middle')
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# Showing colormapping with uniform grid. Fill the circle for an empty barb,
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# don't round the values, and change some of the size parameters
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ax = plt.subplot(2, 2, 3)
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ax.barbs(X, Y, U, V, np.sqrt(U * U + V * V), fill_empty=True, rounding=False,
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sizes=dict(emptybarb=0.25, spacing=0.2, height=0.3))
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axs1[1, 0].barbs(X, Y, U, V, np.sqrt(U * U + V * V), fill_empty=True, rounding=False,
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sizes=dict(emptybarb=0.25, spacing=0.2, height=0.3))
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# Change colors as well as the increments for parts of the barbs
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ax = plt.subplot(2, 2, 4)
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ax.barbs(data['x'], data['y'], data['u'], data['v'], flagcolor='r',
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barbcolor=['b', 'g'],
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barb_increments=dict(half=10, full=20, flag=100), flip_barb=True)
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axs1[1, 1].barbs(data['x'], data['y'], data['u'], data['v'], flagcolor='r',
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barbcolor=['b', 'g'], flip_barb=True,
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barb_increments=dict(half=10, full=20, flag=100))
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# Masked arrays are also supported
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masked_u = np.ma.masked_array(data['u'])
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# Identical plot to panel 2 in the first figure, but with the point at
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# (0.5, 0.25) missing (masked)
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fig2 = plt.figure()
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ax = fig2.add_subplot(1, 1, 1)
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ax.barbs(data['x'], data['y'], masked_u, data['v'], length=8, pivot='middle')
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fig2, ax2 = plt.subplots()
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ax2.barbs(data['x'], data['y'], masked_u, data['v'], length=8, pivot='middle')
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plt.show()
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#############################################################################
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#
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# ------------
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#
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# References
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# """"""""""
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#
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# The use of the following functions, methods and classes is shown
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# in this example:
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import matplotlib
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matplotlib.axes.Axes.barbs
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matplotlib.pyplot.barbs

‎examples/images_contours_and_fields/barcode_demo.py

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Barcode Demo
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============
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This demo shows how to produce a one-dimensional image, or "bar code".
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"""
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import matplotlib.pyplot as plt
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fig = plt.figure()
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# a vertical barcode -- this is broken at present
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ax = fig.add_axes([0.1, 0.3, 0.1, 0.6], **axprops)
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ax.imshow(x.reshape((-1, 1)), **barprops)
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# a vertical barcode
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ax1 = fig.add_axes([0.1, 0.3, 0.1, 0.6], **axprops)
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ax1.imshow(x.reshape((-1, 1)), **barprops)
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# a horizontal barcode
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ax = fig.add_axes([0.3, 0.1, 0.6, 0.1], **axprops)
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ax.imshow(x.reshape((1, -1)), **barprops)
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ax2 = fig.add_axes([0.3, 0.1, 0.6, 0.1], **axprops)
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ax2.imshow(x.reshape((1, -1)), **barprops)
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plt.show()
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#############################################################################
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#
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# ------------
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#
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# References
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# """"""""""
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#
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# The use of the following functions, methods and classes is shown
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# in this example:
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import matplotlib
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matplotlib.axes.Axes.imshow
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matplotlib.pyplot.imshow

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