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jklymakjklymak
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New doc gallery for plot_types
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‎doc/_templates/layout.html

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<ul>
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<li><a href="{{ pathto('users/installing') }}">Installation</a></li>
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<li><a href="{{ pathto('contents') }}">Documentation</a></li>
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<li><a href="{{ pathto('plot_types/index') }}">Plot Types</a></li>
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<li><a href="{{ pathto('gallery/index') }}">Examples</a></li>
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<li><a href="{{ pathto('tutorials/index') }}">Tutorials</a></li>
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<li><a href="{{ pathto('devel/index') }}">Contributing</a></li>

‎doc/conf.py

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# Sphinx gallery configuration
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sphinx_gallery_conf = {
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'examples_dirs': ['../examples', '../tutorials'],
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'examples_dirs': ['../examples', '../tutorials', '../plot_types'],
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'filename_pattern': '^((?!sgskip).)*$',
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'gallery_dirs': ['gallery', 'tutorials'],
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'gallery_dirs': ['gallery', 'tutorials', 'plot_types'],
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'doc_module': ('matplotlib', 'mpl_toolkits'),
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'reference_url': {
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'matplotlib': None,

‎lib/matplotlib/mpl-data/stylelib/cheatsheet_gallery.mplstyle

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# from http://www.huyng.com/posts/sane-color-scheme-for-matplotlib/
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patch.linewidth: 0.5
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patch.facecolor: 348ABD # blue
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patch.edgecolor: EEEEEE
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patch.antialiased: True
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font.size: 0.0
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axes.facecolor: white
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axes.edgecolor: black
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axes.linewidth: 1.0
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axes.grid: True
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axes.titlesize: 0
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axes.labelsize: 0
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axes.labelcolor: 555555
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axes.axisbelow: True # grid/ticks are below elements (e.g., lines, text)
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xtick.color: 555555
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xtick.direction: out
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ytick.color: 555555
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ytick.direction: out
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grid.color: b0b0b0
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grid.linewidth: 0.7
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grid.linestyle: - # solid line
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figure.facecolor: white
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figure.figsize: 2, 2
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figure.subplot.left: 0.01
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figure.subplot.right: 0.99
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figure.subplot.bottom: 0.01
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figure.subplot.top: 0.99
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‎plot_types/basic_plots/A_basic/README.rst

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.. _basic_plots:
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Basic
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-----
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Basic plot types, usually x versus y.

‎plot_types/basic_plots/A_basic/a_plot.py

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"""
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======================
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plot([X], Y, [fmt]...)
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======================
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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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# make data
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X = np.linspace(0, 10, 100)
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Y = 4 + 2 * np.sin(2 * X)
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# plot
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with plt.style.context('cheatsheet_gallery'):
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fig, ax = plt.subplots()
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ax.plot(X, Y, color="C1", linewidth=2.0)
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ax.set_xlim(0, 8), ax.set_xticks(np.arange(1, 8))
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ax.set_ylim(0, 8), ax.set_yticks(np.arange(1, 8))
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plt.show()

‎plot_types/basic_plots/A_basic/b_scatter.py

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"""
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==================
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scatter(X, Y, ...)
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==================
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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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# make the data
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np.random.seed(3)
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X = 4 + np.random.normal(0, 1.25, 24)
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Y = 4 + np.random.normal(0, 1.25, len(X))
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# plot
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with plt.style.context('cheatsheet_gallery'):
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fig, ax = plt.subplots()
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ax.scatter(X, Y, 20, zorder=10,
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edgecolor="none", facecolor="C1", linewidth=0.25)
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ax.set_xlim(0, 8), ax.set_xticks(np.arange(1, 8))
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ax.set_ylim(0, 8), ax.set_yticks(np.arange(1, 8))
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plt.show()

‎plot_types/basic_plots/A_basic/c_bar.py

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"""
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======================
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bar[h](x, height, ...)
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======================
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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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# make data:
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np.random.seed(3)
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X = 0.5 + np.arange(8)
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Y = np.random.uniform(2, 7, len(X))
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# plot
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with plt.style.context('cheatsheet_gallery'):
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fig, ax = plt.subplots()
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ax.bar(X, Y, bottom=0, width=1, edgecolor="white",
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facecolor="C1", linewidth=0.7)
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ax.set_xlim(0, 8), ax.set_xticks(np.arange(1, 8))
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ax.set_ylim(0, 8), ax.set_yticks(np.arange(1, 8))
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plt.show()

‎plot_types/basic_plots/A_basic/d_stem.py

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"""
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=================
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stem([x], y, ...)
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=================
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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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# make data
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np.random.seed(3)
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X = 0.5 + np.arange(8)
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Y = np.random.uniform(2, 7, len(X))
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# plot
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with plt.style.context('cheatsheet_gallery'):
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fig, ax = plt.subplots()
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ax.stem(X, Y, bottom=0, linefmt="C1-", markerfmt="C1d")
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ax.set_xlim(0, 8), ax.set_xticks(np.arange(1, 8))
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ax.set_ylim(0, 8), ax.set_yticks(np.arange(1, 8))
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plt.show()

‎plot_types/basic_plots/A_basic/e_step.py

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"""
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=====================
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step(x, y, where=...)
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=====================
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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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# make data
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np.random.seed(3)
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X = 0.5 + np.arange(8)
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Y = np.random.uniform(2, 7, len(X))
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# plot
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with plt.style.context('cheatsheet_gallery'):
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fig, ax = plt.subplots()
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ax.step(X, Y, color="C1", linewidth=2.5)
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ax.set_xlim(0, 8), ax.set_xticks(np.arange(1, 8))
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ax.set_ylim(0, 8), ax.set_yticks(np.arange(1, 8))
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plt.show()

‎plot_types/basic_plots/A_basic/f_pie.py

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"""
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====================
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pie(X, explode, ...)
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====================
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"""
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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import numpy as np
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# make data
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X = [1, 2, 3, 4]
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colors = np.zeros((len(X), 4))
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colors[:] = mpl.colors.to_rgba("C1")
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colors[:, 3] = np.linspace(0.25, 0.75, len(X))
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# plot
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with plt.style.context('cheatsheet_gallery'):
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fig, ax = plt.subplots()
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ax.pie(X, colors=["white"]*len(X), radius=3, center=(4, 4),
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wedgeprops={"linewidth": 1, "edgecolor": "white"}, frame=True)
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ax.pie(X, colors=colors, radius=3, center=(4, 4),
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wedgeprops={"linewidth": 1, "edgecolor": "white"}, frame=True)
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ax.set_xlim(0, 8), ax.set_xticks(np.arange(1, 8))
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ax.set_ylim(0, 8), ax.set_yticks(np.arange(1, 8))
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plt.show()

‎plot_types/basic_plots/A_basic/g_fill_between.py

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"""
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=================================
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fill[_between][x](X, Y1, Y2, ...)
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=================================
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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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# make data
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np.random.seed(1)
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X = np.linspace(0, 8, 16)
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Y1 = 3 + 4*X/8 + np.random.uniform(0.0, 0.5, len(X))
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Y2 = 1 + 2*X/8 + np.random.uniform(0.0, 0.5, len(X))
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# plot
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with plt.style.context('cheatsheet_gallery'):
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fig, ax = plt.subplots()
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ax.fill_between(X, Y1, Y2, color="C1", alpha=.5, linewidth=0)
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ax.plot(X, (Y1+Y2)/2, color="C1", linewidth=2.5)
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ax.set_xlim(0, 8), ax.set_xticks(np.arange(1, 8))
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ax.set_ylim(0, 8), ax.set_yticks(np.arange(1, 8))
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plt.show()

‎plot_types/basic_plots/B_arrays/README.rst

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.. _stats_plots:
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Specialized statistics plots
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----------------------------
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Matplotlib has some specialized plots for statistical analysis.

‎plot_types/basic_plots/B_arrays/a_imshow.py

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"""
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=======================
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imshow(Z, [cmap=], ...)
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=======================
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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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# make data
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X, Y = np.meshgrid(np.linspace(-3, 3, 256), np.linspace(-3, 3, 256))
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Z = (1 - X/2. + X**5 + Y**3) * np.exp(-X**2 - Y**2)
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Z = Z - Z.min()
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Z = Z[::16, ::16]
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# plot
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with plt.style.context('cheatsheet_gallery'):
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fig, ax = plt.subplots()
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ax.imshow(Z, extent=[0, 8, 0, 8], interpolation="nearest",
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cmap=plt.get_cmap('Oranges'), vmin=0, vmax=1.6)
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ax.set_xlim(0, 8), ax.set_xticks([])
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ax.set_ylim(0, 8), ax.set_yticks([])
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plt.show()

‎plot_types/basic_plots/B_arrays/b_pcolormesh.py

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"""
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===================================
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pcolormesh([X, Y], Z, [cmap=], ...)
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===================================
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`.axes.pcolormesh` is more flexible than `.axes.imshow` in that the
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x and y vectors need not be equally spaced (indeed they can be skewed).
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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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# make data:
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np.random.seed(3)
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Z = np.random.uniform(0.1, 1.0, (8, 8))
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x = np.array([0, 0.5, 1.5, 3, 5.2, 6.3, 7.2, 7.5, 8])
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y = 1.2**np.arange(0, 9)
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# plot
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with plt.style.context('cheatsheet_gallery'):
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fig, ax = plt.subplots()
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# plot:
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ax.pcolormesh(x, y, Z, cmap=plt.get_cmap('Oranges'), vmin=0, vmax=1.1)
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ax.set_ylim(1, np.max(y))
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ax.set_xlim(np.min(x), np.max(x))
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plt.show()

‎plot_types/basic_plots/B_arrays/c_contourf.py

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"""
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=====================================
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contour[f]([X, Y], Z, [levels=], ...)
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=====================================
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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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# make data
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X, Y = np.meshgrid(np.linspace(-3, 3, 256), np.linspace(-3, 3, 256))
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Z = (1 - X/2. + X**5 + Y**3)*np.exp(-X**2-Y**2)
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Z = Z - Z.min()
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levs = np.linspace(np.min(Z), np.max(Z), 7)
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# plot
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with plt.style.context('cheatsheet_gallery'):
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fig, ax = plt.subplots()
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plt.contourf(X, Y, Z, levels=levs, cmap=plt.get_cmap('Oranges'))
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plt.contour(X, Y, Z, levels=levs, colors="white", linewidths=0.5)
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plt.show()

‎plot_types/basic_plots/B_arrays/d_quiver.py

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"""
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=========================
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quiver([X, Y], U, V, ...)
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=========================
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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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# make data
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T = np.linspace(0, 2*np.pi, 8)
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X, Y = 4 + 1 * np.cos(T), 4 + 1 * np.sin(T)
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U, V = 1.5 * np.cos(T), 1.5 * np.sin(T)
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# plot
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with plt.style.context('cheatsheet_gallery'):
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fig, ax = plt.subplots()
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plt.quiver(X, Y, U, V, color="C1", angles='xy',
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scale_units='xy', scale=0.5, width=.05)
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ax.set_xlim(0, 8), ax.set_xticks(np.arange(1, 8))
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ax.set_ylim(0, 8), ax.set_yticks(np.arange(1, 8))
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plt.show()

‎plot_types/basic_plots/B_arrays/e_streamplot.py

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"""
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========================
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streamplot([X, Y], U, V)
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========================
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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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# make a stream function:
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X, Y = np.meshgrid(np.linspace(-3, 3, 256), np.linspace(-3, 3, 256))
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Z = (1 - X/2. + X**5 + Y**3) * np.exp(-X**2 - Y**2)
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Z = Z - Z.min()
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# make U and V out of the streamfunction:
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V = np.diff(Z[1:, :], axis=1)
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U = -np.diff(Z[:, 1:], axis=0)
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# plot:
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with plt.style.context('cheatsheet_gallery'):
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fig, ax = plt.subplots()
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# contour stream function
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ax.contour(X, Y, Z, colors='C0', alpha=0.5, zorder=1, linewidths=3)
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# plot stream plot
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ax.streamplot(X[1:, 1:], Y[1:, 1:], U, V, color='C1', zorder=2)
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plt.show()

‎plot_types/basic_plots/C_stats/README.rst

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.. _stats_plots:
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Specialized statistics plots
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============================
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Matplotlib has some specialized plots for statistical analysis.

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