8000 Merge pull request #7900 from naoyak/mep12-pie-polar · matplotlib/matplotlib@60cd171 · GitHub
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NelleVNelleV
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Merge pull request #7900 from naoyak/mep12-pie-polar
DOC MEP12: pie/polar and color examples + style sheets fix
2 parents dcba98d + eecad0e commit 60cd171

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‎examples/color/color_cycle_default.py

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"""
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====================================
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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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"""
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import numpy as np
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import matplotlib.pyplot as plt
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prop_cycle = plt.rcParams['axes.prop_cycle']
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colors = prop_cycle.by_key()['color']
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‎examples/color/color_cycle_demo.py

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"""
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Demo of custom property-cycle settings to control colors and such
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for multi-line plots.
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===================
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Styling with cycler
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===================
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Demo of custom property-cycle settings to control colors and other style
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properties for multi-line plots.
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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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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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2. Setting the property cycle for a specific axes. This only
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affects a single axes.
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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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import numpy as np
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import matplotlib.pyplot as plt
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x = np.linspace(0, 2 * np.pi)
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offsets = np.linspace(0, 2*np.pi, 4, endpoint=False)
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# Create array with shifted-sine curve along each column
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yy = np.transpose([np.sin(x + phi) for phi in offsets])
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# 1. Setting prop cycle on default rc parameter
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plt.rc('lines', linewidth=4)
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plt.rc('axes', prop_cycle=(cycler('color', ['r', 'g', 'b', 'y']) +
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cycler('linestyle', ['-', '--', ':', '-.'])))
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fig, (ax0, ax1) = plt.subplots(nrows=2)
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ax0.plot(yy)
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ax0.set_title('Set default color cycle to rgby')
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# 2. Define prop cycle for single set of axes
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ax1.set_prop_cycle(cycler('color', ['c', 'm', 'y', 'k']) +
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cycler('lw', [1, 2, 3, 4]))
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ax1.plot(yy)

‎examples/color/colormaps_reference.py

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"""
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==================
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Colormap reference
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==================
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Reference for colormaps included with Matplotlib.
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This reference example shows all colormaps included with Matplotlib. Note that
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import numpy as np
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import matplotlib.pyplot as plt
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# Have colormaps separated into categories:
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# http://matplotlib.org/examples/color/colormaps_reference.html
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cmaps = [('Perceptually Uniform Sequential',
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['viridis', 'inferno', 'plasma', 'magma']),
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('Sequential', ['Blues', 'BuGn', 'BuPu',
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gradient = np.vstack((gradient, gradient))
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def plot_color_gradients(cmap_category, cmap_list):
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def plot_color_gradients(cmap_category, cmap_list, nrows):
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fig, axes = plt.subplots(nrows=nrows)
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fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99)
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axes[0].set_title(cmap_category + ' colormaps', fontsize=14)
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for ax in axes:
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ax.set_axis_off()
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for cmap_category, cmap_list in cmaps:
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plot_color_gradients(cmap_category, cmap_list)
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plot_color_gradients(cmap_category, cmap_list, nrows)
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plt.show()

‎examples/color/named_colors.py

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"""
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Visualization of named colors.
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========================
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Visualizing named colors
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========================
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Simple plot example with the named colors and its visual representation.
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"""
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from __future__ import division
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from __future__ import (absolute_import, division, print_function,
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unicode_literals)
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import six
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib import colors as mcolors
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colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)
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# Sort by hue, saturation, value and name.
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# Sort colors by hue, saturation, value and name.
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by_hsv = sorted((tuple(mcolors.rgb_to_hsv(mcolors.to_rgba(color)[:3])), name)
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for name, color in colors.items())
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# Get the sorted color names.
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sorted_names = [name for hsv, name in by_hsv]
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n = len(sorted_names)
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ncols = 4
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nrows = int(np.ceil(1. * n / ncols))
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nrows = n // ncols + 1
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fig, ax = plt.subplots(figsize=(8, 5))
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# Get height and width
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X, Y = fig.get_dpi() * fig.get_size_inches()
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# row height
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h = Y / (nrows + 1)
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# col width
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w = X / ncols
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for i, name in enumerate(sorted_names):
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col = i % ncols
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row = int(i / ncols)
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row = i // ncols
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y = Y - (row * h) - h
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xi_line = w * (col + 0.05)
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horizontalalignment='left',
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verticalalignment='center')
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ax.hlines(
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y + h * 0.1, xi_line, xf_line, color=colors[name], linewidth=(h * 0.6))
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ax.hlines(y + h * 0.1, xi_line, xf_line,
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color=colors[name], linewidth=(h * 0.6))
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ax.set_xlim(0, X)
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ax.set_ylim(0, Y)

‎examples/pie_and_polar_charts/pie_demo_features.py

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"""
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===============
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Basic pie chart
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===============
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Demo of a basic pie chart plus a few additional features.
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In addition to the basic pie chart, this demo shows a few optional features:

‎examples/pie_and_polar_charts/polar_bar_demo.py

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"""
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=======================
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Pie chart on polar axis
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=======================
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Demo of bar plot on a polar axis.
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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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# Fixing random state for reproducibility
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np.random.seed(19680801)
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# Compute pie slices
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N = 20
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theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)
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radii = 10 * np.random.rand(N)

‎examples/pie_and_polar_charts/polar_scatter_demo.py

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"""
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==========================
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Scatter plot on polar axis
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==========================
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Demo of scatter plot on a polar axis.
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Size increases radially in this example and color increases with angle
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import numpy as np
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import matplotlib.pyplot as plt
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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# Compute areas and colors
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N = 150
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r = 2 * np.random.rand(N)
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theta = 2 * np.pi * np.random.rand(N)
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area = 200 * r**2
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colors = theta
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ax = plt.subplot(111, projection='polar')
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c = ax.scatter(theta, r, c=colors, s=area, cmap=plt.cm.hsv)
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c.set_alpha(0.75)
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c = ax.scatter(theta, r, c=colors, s=area, cmap='hsv', alpha=0.75)
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plt.show()

‎examples/style_sheets/plot_bmh.py

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def plot_beta_hist(ax, a, b):
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ax.hist(beta(a, b, size=10000), histtype="stepfilled",
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bins=25, alpha=0.8, normed=True)
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return ax
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fig, ax = plt.subplots()

‎examples/style_sheets/plot_fivethirtyeight.py

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plt.style.use('fivethirtyeight')
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from matplotlib import pyplot as plt
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import numpy as np
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x = np.linspace(0, 10)
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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fig, ax = plt.subplots()
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with plt.style.context('fivethirtyeight'):
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ax.plot(x, np.sin(x) + x + np.random.randn(50))
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ax.plot(x, np.sin(x) + 0.5 * x + np.random.randn(50))
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ax.plot(x, np.sin(x) + 2 * x + np.random.randn(50))
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ax.plot(x, np.sin(x) - 0.5 * x + np.random.randn(50))
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ax.plot(x, np.sin(x) - 2 * x + np.random.randn(50))
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ax.plot(x, np.sin(x) + np.random.randn(50))
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ax.set_title("'fivethirtyeight' style sheet")
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ax.plot(x, np.sin(x) + x + np.random.randn(50))
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ax.plot(x, np.sin(x) + 0.5 * x + np.random.randn(50))
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ax.plot(x, np.sin(x) + 2 * x + np.random.randn(50))
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ax.plot(x, np.sin(x) - 0.5 * x + np.random.randn(50))
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ax.plot(x, np.sin(x) - 2 * x + np.random.randn(50))
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ax.plot(x, np.sin(x) + np.random.randn(50))
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ax.set_title("'fivethirtyeight' style sheet")
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plt.show()

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