Added more plots
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MikeTheWatchGuy
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23bdd2664c
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1554679c20
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@ -35,6 +35,329 @@ def PyplotSimple():
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fig = plt.gcf() # get the figure to show
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return fig
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def PyplotHistogram():
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"""
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=============================================================
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Demo of the histogram (hist) function with multiple data sets
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=============================================================
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Plot histogram with multiple sample sets and demonstrate:
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* Use of legend with multiple sample sets
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* Stacked bars
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* Step curve with no fill
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* Data sets of different sample sizes
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Selecting different bin counts and sizes can significantly affect the
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shape of a histogram. The Astropy docs have a great section on how to
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select these parameters:
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http://docs.astropy.org/en/stable/visualization/histogram.html
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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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np.random.seed(0)
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n_bins = 10
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x = np.random.randn(1000, 3)
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fig, axes = plt.subplots(nrows=2, ncols=2)
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ax0, ax1, ax2, ax3 = axes.flatten()
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colors = ['red', 'tan', 'lime']
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ax0.hist(x, n_bins, normed=1, histtype='bar', color=colors, label=colors)
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ax0.legend(prop={'size': 10})
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ax0.set_title('bars with legend')
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ax1.hist(x, n_bins, normed=1, histtype='bar', stacked=True)
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ax1.set_title('stacked bar')
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ax2.hist(x, n_bins, histtype='step', stacked=True, fill=False)
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ax2.set_title('stack step (unfilled)')
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# Make a multiple-histogram of data-sets with different length.
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x_multi = [np.random.randn(n) for n in [10000, 5000, 2000]]
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ax3.hist(x_multi, n_bins, histtype='bar')
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ax3.set_title('different sample sizes')
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fig.tight_layout()
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return fig
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def PyplotArtistBoxPlots():
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"""
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=========================================
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Demo of artist customization in box plots
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=========================================
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This example demonstrates how to use the various kwargs
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to fully customize box plots. The first figure demonstrates
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how to remove and add individual components (note that the
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mean is the only value not shown by default). The second
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figure demonstrates how the styles of the artists can
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be customized. It also demonstrates how to set the limit
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of the whiskers to specific percentiles (lower right axes)
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A good general reference on boxplots and their history can be found
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here: http://vita.had.co.nz/papers/boxplots.pdf
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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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# fake data
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np.random.seed(937)
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data = np.random.lognormal(size=(37, 4), mean=1.5, sigma=1.75)
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labels = list('ABCD')
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fs = 10 # fontsize
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# demonstrate how to toggle the display of different elements:
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fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(6, 6), sharey=True)
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axes[0, 0].boxplot(data, labels=labels)
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axes[0, 0].set_title('Default', fontsize=fs)
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axes[0, 1].boxplot(data, labels=labels, showmeans=True)
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axes[0, 1].set_title('showmeans=True', fontsize=fs)
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axes[0, 2].boxplot(data, labels=labels, showmeans=True, meanline=True)
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axes[0, 2].set_title('showmeans=True,\nmeanline=True', fontsize=fs)
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axes[1, 0].boxplot(data, labels=labels, showbox=False, showcaps=False)
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tufte_title = 'Tufte Style \n(showbox=False,\nshowcaps=False)'
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axes[1, 0].set_title(tufte_title, fontsize=fs)
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axes[1, 1].boxplot(data, labels=labels, notch=True, bootstrap=10000)
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axes[1, 1].set_title('notch=True,\nbootstrap=10000', fontsize=fs)
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axes[1, 2].boxplot(data, labels=labels, showfliers=False)
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axes[1, 2].set_title('showfliers=False', fontsize=fs)
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for ax in axes.flatten():
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ax.set_yscale('log')
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ax.set_yticklabels([])
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fig.subplots_adjust(hspace=0.4)
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return fig
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def ArtistBoxplot2():
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# fake data
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np.random.seed(937)
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data = np.random.lognormal(size=(37, 4), mean=1.5, sigma=1.75)
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labels = list('ABCD')
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fs = 10 # fontsize
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# demonstrate how to customize the display different elements:
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boxprops = dict(linestyle='--', linewidth=3, color='darkgoldenrod')
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flierprops = dict(marker='o', markerfacecolor='green', markersize=12,
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linestyle='none')
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medianprops = dict(linestyle='-.', linewidth=2.5, color='firebrick')
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meanpointprops = dict(marker='D', markeredgecolor='black',
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markerfacecolor='firebrick')
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meanlineprops = dict(linestyle='--', linewidth=2.5, color='purple')
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fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(6, 6), sharey=True)
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axes[0, 0].boxplot(data, boxprops=boxprops)
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axes[0, 0].set_title('Custom boxprops', fontsize=fs)
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axes[0, 1].boxplot(data, flierprops=flierprops, medianprops=medianprops)
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axes[0, 1].set_title('Custom medianprops\nand flierprops', fontsize=fs)
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axes[0, 2].boxplot(data, whis='range')
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axes[0, 2].set_title('whis="range"', fontsize=fs)
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axes[1, 0].boxplot(data, meanprops=meanpointprops, meanline=False,
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showmeans=True)
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axes[1, 0].set_title('Custom mean\nas point', fontsize=fs)
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axes[1, 1].boxplot(data, meanprops=meanlineprops, meanline=True,
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showmeans=True)
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axes[1, 1].set_title('Custom mean\nas line', fontsize=fs)
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axes[1, 2].boxplot(data, whis=[15, 85])
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axes[1, 2].set_title('whis=[15, 85]\n#percentiles', fontsize=fs)
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for ax in axes.flatten():
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ax.set_yscale('log')
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ax.set_yticklabels([])
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fig.suptitle("I never said they'd be pretty")
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fig.subplots_adjust(hspace=0.4)
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return fig
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def PyplotScatterWithLegend():
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import matplotlib.pyplot as plt
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from numpy.random import rand
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fig, ax = plt.subplots()
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for color in ['red', 'green', 'blue']:
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n = 750
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x, y = rand(2, n)
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scale = 200.0 * rand(n)
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ax.scatter(x, y, c=color, s=scale, label=color,
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alpha=0.3, edgecolors='none')
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ax.legend()
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ax.grid(True)
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return fig
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def PyplotLineStyles():
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"""
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==========
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Linestyles
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==========
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This examples showcases different linestyles copying those of Tikz/PGF.
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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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from collections import OrderedDict
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from matplotlib.transforms import blended_transform_factory
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linestyles = OrderedDict(
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[('solid', (0, ())),
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('loosely dotted', (0, (1, 10))),
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('dotted', (0, (1, 5))),
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('densely dotted', (0, (1, 1))),
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('loosely dashed', (0, (5, 10))),
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('dashed', (0, (5, 5))),
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('densely dashed', (0, (5, 1))),
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('loosely dashdotted', (0, (3, 10, 1, 10))),
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('dashdotted', (0, (3, 5, 1, 5))),
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('densely dashdotted', (0, (3, 1, 1, 1))),
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('loosely dashdotdotted', (0, (3, 10, 1, 10, 1, 10))),
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('dashdotdotted', (0, (3, 5, 1, 5, 1, 5))),
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('densely dashdotdotted', (0, (3, 1, 1, 1, 1, 1)))])
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plt.figure(figsize=(10, 6))
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ax = plt.subplot(1, 1, 1)
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X, Y = np.linspace(0, 100, 10), np.zeros(10)
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for i, (name, linestyle) in enumerate(linestyles.items()):
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ax.plot(X, Y + i, linestyle=linestyle, linewidth=1.5, color='black')
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ax.set_ylim(-0.5, len(linestyles) - 0.5)
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plt.yticks(np.arange(len(linestyles)), linestyles.keys())
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plt.xticks([])
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# For each line style, add a text annotation with a small offset from
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# the reference point (0 in Axes coords, y tick value in Data coords).
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reference_transform = blended_transform_factory(ax.transAxes, ax.transData)
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for i, (name, linestyle) in enumerate(linestyles.items()):
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ax.annotate(str(linestyle), xy=(0.0, i), xycoords=reference_transform,
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xytext=(-6, -12), textcoords='offset points', color="blue",
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fontsize=8, ha="right", family="monospace")
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plt.tight_layout()
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return plt.gcf()
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def PyplotLinePolyCollection():
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import matplotlib.pyplot as plt
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from matplotlib import collections, colors, transforms
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import numpy as np
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nverts = 50
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npts = 100
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# Make some spirals
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r = np.arange(nverts)
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theta = np.linspace(0, 2 * np.pi, nverts)
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xx = r * np.sin(theta)
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yy = r * np.cos(theta)
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spiral = np.column_stack([xx, yy])
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# Fixing random state for reproducibility
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rs = np.random.RandomState(19680801)
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# Make some offsets
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xyo = rs.randn(npts, 2)
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# Make a list of colors cycling through the default series.
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colors = [colors.to_rgba(c)
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for c in plt.rcParams['axes.prop_cycle'].by_key()['color']]
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fig, axes = plt.subplots(2, 2)
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fig.subplots_adjust(top=0.92, left=0.07, right=0.97,
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hspace=0.3, wspace=0.3)
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((ax1, ax2), (ax3, ax4)) = axes # unpack the axes
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col = collections.LineCollection([spiral], offsets=xyo,
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transOffset=ax1.transData)
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trans = fig.dpi_scale_trans + transforms.Affine2D().scale(1.0 / 72.0)
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col.set_transform(trans) # the points to pixels transform
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# Note: the first argument to the collection initializer
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# must be a list of sequences of x,y tuples; we have only
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# one sequence, but we still have to put it in a list.
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ax1.add_collection(col, autolim=True)
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# autolim=True enables autoscaling. For collections with
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# offsets like this, it is neither efficient nor accurate,
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# but it is good enough to generate a plot that you can use
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# as a starting point. If you know beforehand the range of
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# x and y that you want to show, it is better to set them
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# explicitly, leave out the autolim kwarg (or set it to False),
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# and omit the 'ax1.autoscale_view()' call below.
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# Make a transform for the line segments such that their size is
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# given in points:
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col.set_color(colors)
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ax1.autoscale_view() # See comment above, after ax1.add_collection.
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ax1.set_title('LineCollection using offsets')
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# The same data as above, but fill the curves.
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col = collections.PolyCollection([spiral], offsets=xyo,
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transOffset=ax2.transData)
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trans = transforms.Affine2D().scale(fig.dpi / 72.0)
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col.set_transform(trans) # the points to pixels transform
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ax2.add_collection(col, autolim=True)
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col.set_color(colors)
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ax2.autoscale_view()
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ax2.set_title('PolyCollection using offsets')
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# 7-sided regular polygons
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col = collections.RegularPolyCollection(
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7, sizes=np.abs(xx) * 10.0, offsets=xyo, transOffset=ax3.transData)
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trans = transforms.Affine2D().scale(fig.dpi / 72.0)
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col.set_transform(trans) # the points to pixels transform
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ax3.add_collection(col, autolim=True)
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col.set_color(colors)
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ax3.autoscale_view()
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ax3.set_title('RegularPolyCollection using offsets')
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# Simulate a series of ocean current profiles, successively
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# offset by 0.1 m/s so that they form what is sometimes called
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# a "waterfall" plot or a "stagger" plot.
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nverts = 60
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ncurves = 20
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offs = (0.1, 0.0)
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yy = np.linspace(0, 2 * np.pi, nverts)
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ym = np.max(yy)
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xx = (0.2 + (ym - yy) / ym) ** 2 * np.cos(yy - 0.4) * 0.5
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segs = []
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for i in range(ncurves):
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xxx = xx + 0.02 * rs.randn(nverts)
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curve = np.column_stack([xxx, yy * 100])
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segs.append(curve)
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col = collections.LineCollection(segs, offsets=offs)
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ax4.add_collection(col, autolim=True)
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col.set_color(colors)
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ax4.autoscale_view()
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ax4.set_title('Successive data offsets')
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ax4.set_xlabel('Zonal velocity component (m/s)')
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ax4.set_ylabel('Depth (m)')
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# Reverse the y-axis so depth increases downward
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ax4.set_ylim(ax4.get_ylim()[::-1])
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return fig
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def PyplotGGPlotSytleSheet():
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import numpy as np
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import matplotlib.pyplot as plt
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@ -529,13 +852,16 @@ def draw_figure(canvas, figure, loc=(0, 0)):
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fig_dict = {'Pyplot Simple':PyplotSimple, 'Pyplot Formatstr':PyplotFormatstr,'PyPlot Three':Subplot3d,
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'Unicode Minus': UnicodeMinus, 'Pyplot Scales' : PyplotScales, 'Axes Grid' : AxesGrid,
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'Exploring Normalizations' : ExploringNormalizations, 'Different Scales' : DifferentScales,
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'Pyplot Box Plot' : PyplotBoxPlot, 'Pyplot ggplot Style Sheet' : PyplotGGPlotSytleSheet}
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'Pyplot Box Plot' : PyplotBoxPlot, 'Pyplot ggplot Style Sheet' : PyplotGGPlotSytleSheet,
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'Pyplot Line Poly Collection' : PyplotLinePolyCollection, 'Pyplot Line Styles' : PyplotLineStyles,
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'Pyplot Scatter With Legend' :PyplotScatterWithLegend, 'Artist Customized Box Plots' : PyplotArtistBoxPlots,
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'Artist Customized Box Plots 2' : ArtistBoxplot2, 'Pyplot Histogram' : PyplotHistogram}
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figure_w, figure_h = 640,480
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figure_w, figure_h = 650, 650
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canvas_elem = g.Canvas(size=(figure_w, figure_h)) # get the canvas we'll be drawing on
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# define the form layout
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listbox_values = [key for key in fig_dict.keys()]
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col_listbox = [[g.Listbox(values=listbox_values,size=(25,len(listbox_values)), key='func')],
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col_listbox = [[g.Listbox(values=listbox_values,size=(28,len(listbox_values)), key='func')],
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[g.T(' '), g.ReadFormButton('Plot', size=(5,2)), g.Exit(size=(5,2))]]
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layout = [[g.Text('Matplotlib Plot Test', font=('current 18'))],
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