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Commit 3ce3835

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QuLogicQuLogic
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Merge branch 'v2.0.0-doc' into v2.0.x
2 parents 6061a43 + 68d2327 commit 3ce3835
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‎doc/contents.rst

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@@ -8,6 +8,8 @@ Overview
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:Release: |version|
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:Date: |today|
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Download `PDF <Matplotlib.pdf>`_
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.. toctree::
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:maxdepth: 2

‎doc/devel/testing.rst

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@@ -86,7 +86,7 @@ a colon, e.g., (this is assuming the test is installed)::
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If you want to run the full test suite, but want to save wall time try
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running the tests in parallel::
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python tests.py --nocapture --nose-verbose --processes=5 --process-timeout=300
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python tests.py --nocapture --verbose --processes=5 --process-timeout=300
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An alternative implementation that does not look at command line

‎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/misc/multiprocess.py

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for ii in range(10):
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pl.plot()
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time.sleep(0.5)
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raw_input('press Enter...')
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try:
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input = raw_input
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except NameError:
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pass
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input('press Enter...')
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pl.plot(finished=True)
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if __name__ == '__main__':

‎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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# 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 matplotlib.pyplot as plt
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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/pylab_examples/quiver_demo.py

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'''
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Demonstration of quiver and quiverkey functions. This is using the
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new version coming from the code in quiver.py.
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========================================================
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Demonstration of advanced quiver and quiverkey functions
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========================================================
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Known problem: the plot autoscaling does not take into account
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the arrows, so those on the boundaries are often out of the picture.
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This is *not* an easy problem to solve in a perfectly general way.
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The workaround is to manually expand the axes.
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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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U = np.cos(X)
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V = np.sin(Y)
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19-
# 1
20-
plt.figure()
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Q = plt.quiver(U, V)
22-
qk = plt.quiverkey(Q, 0.5, 0.98, 2, r'$2 \frac{m}{s}$', labelpos='W',
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fontproperties={'weight': 'bold'})
24-
l, r, b, t = plt.axis()
25-
dx, dy = r - l, t - b
26-
plt.axis([l - 0.05*dx, r + 0.05*dx, b - 0.05*dy, t + 0.05*dy])
27-
28-
plt.title('Minimal arguments, no kwargs')
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30-
# 2
3119
plt.figure()
20+
plt.title('Arrows scale with plot width, not view')
3221
Q = plt.quiver(X, Y, U, V, units='width')
33-
qk = plt.quiverkey(Q, 0.9, 0.95, 2, r'$2 \frac{m}{s}$',
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labelpos='E',
35-
coordinates='figure',
36-
fontproperties={'weight': 'bold'})
37-
plt.axis([-1, 7, -1, 7])
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plt.title('scales with plot width, not view')
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qk = plt.quiverkey(Q, 0.9, 0.9, 2, r'$2 \frac{m}{s}$', labelpos='E',
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coordinates='figure')
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40-
# 3
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plt.figure()
42-
Q = plt.quiver(X[::3, ::3], Y[::3, ::3], U[::3, ::3], V[::3, ::3],
43-
pivot='mid', color='r', units='inches')
44-
qk = plt.quiverkey(Q, 0.5, 0.03, 1, r'$1 \frac{m}{s}$',
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fontproperties={'weight': 'bold'})
46-
plt.plot(X[::3, ::3], Y[::3, ::3], 'k.')
47-
plt.axis([-1, 7, -1, 7])
4826
plt.title("pivot='mid'; every third arrow; units='inches'")
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Q = plt.quiver(X[::3, ::3], Y[::3, ::3], U[::3, ::3], V[::3, ::3],
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pivot='mid', units='inches')
29+
qk = plt.quiverkey(Q, 0.9, 0.9, 1, r'$1 \frac{m}{s}$', labelpos='E',
30+
coordinates='figure')
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plt.scatter(X[::3, ::3], Y[::3, ::3], color='r', s=5)
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# 4
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plt.figure()
34+
plt.title("pivot='tip'; scales with x view")
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M = np.hypot(U, V)
53-
Q = plt.quiver(X, Y, U, V, M,
54-
units='x',
55-
pivot='tip',
56-
width=0.022,
36+
Q = plt.quiver(X, Y, U, V, M, units='x', pivot='tip', width=0.022,
5737
scale=1 / 0.15)
58-
qk = plt.quiverkey(Q, 0.9, 1.05, 1, r'$1 \frac{m}{s}$',
59-
labelpos='E',
60-
fontproperties={'weight': 'bold'})
61-
plt.plot(X, Y, 'k.', markersize=2)
62-
plt.axis([-1, 7, -1, 7])
63-
plt.title("scales with x view; pivot='tip'")
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65-
# 5
66-
plt.figure()
67-
Q = plt.quiver(X[::3, ::3], Y[::3, ::3], U[::3, ::3], V[::3, ::3],
68-
color='r', units='x',
69-
linewidths=(0.5,), edgecolors=('k'), headaxislength=5)
70-
qk = plt.quiverkey(Q, 0.5, 0.03, 1, r'$1 \frac{m}{s}$',
71-
fontproperties={'weight': 'bold'})
72-
plt.axis([-1, 7, -1, 7])
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plt.title("triangular head; scale with x view; black edges")
74-
75-
# 6
76-
plt.figure()
77-
M = np.zeros(U.shape, dtype='bool')
78-
XMaskStart = U.shape[0]//3
79-
YMaskStart = U.shape[1]//3
80-
XMaskStop = 2*U.shape[0]//3
81-
YMaskStop = 2*U.shape[1]//3
82-
83-
M[XMaskStart:XMaskStop,
84-
YMaskStart:YMaskStop] = True
85-
U = ma.masked_array(U, mask=M)
86-
V = ma.masked_array(V, mask=M)
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Q = plt.quiver(U, V)
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qk = plt.quiverkey(Q, 0.5, 0.98, 2, r'$2 \frac{m}{s}$', labelpos='W',
89-
fontproperties={'weight': 'bold'})
90-
l, r, b, t = plt.axis()
91-
dx, dy = r - l, t - b
92-
plt.axis([l - 0.05 * dx, r + 0.05 * dx, b - 0.05 * dy, t + 0.05 * dy])
93-
plt.title('Minimal arguments, no kwargs - masked values')
94-
38+
qk = plt.quiverkey(Q, 0.9, 0.9, 1, r'$1 \frac{m}{s}$', labelpos='E',
39+
coordinates='figure')
40+
plt.scatter(X, Y, color='k', s=5)
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plt.show()

‎examples/pylab_examples/quiver_simple_demo.py

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'''
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==================================================
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A simple example of a quiver plot with a quiverkey
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==================================================
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'''
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import matplotlib.pyplot as plt
7+
import numpy as np
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9+
X = np.arange(-10, 10, 1)
10+
Y = np.arange(-10, 10, 1)
11+
U, V = np.meshgrid(X, Y)
12+
13+
fig, ax = plt.subplots()
14+
q = ax.quiver(X, Y, U, V)
15+
ax.quiverkey(q, X=0.3, Y=1.1, U=10,
16+
label='Quiver key, length = 10', labelpos='E')
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18+
plt.show()

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