900 lines
No EOL
30 KiB
Python
900 lines
No EOL
30 KiB
Python
#!/usr/bin/env python
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import inspect
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import PySimpleGUIWeb as sg
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import numpy as np
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from matplotlib.backends.backend_tkagg import FigureCanvasAgg
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import matplotlib.figure
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import io
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import numpy as np
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import matplotlib.pyplot as plt
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"""
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Demonstrates one way of embedding Matplotlib figures into a PySimpleGUIWeb window.
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Basic steps are:
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* Create a window with an Image Element
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* Get a selection from user from list of available figures to be drawn
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* Create figure using Matplotlib
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* Update Image Element with generated plot
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Each plotting function was copied directly from Matplot examples page
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Copyright 2020 PySimpleGUI.org
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"""
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def PyplotSimple():
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import numpy as np
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import matplotlib.pyplot as plt
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# evenly sampled time .2 intervals
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t = np.arange(0., 5., 0.2) # go from 0 to 5 using .2 intervals
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# red dashes, blue squares and green triangles
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plt.plot(t, t, 'r--', t, t ** 2, 'bs', t, t ** 3, 'g^')
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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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plt.style.use('ggplot')
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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fig, axes = plt.subplots(ncols=2, nrows=2)
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ax1, ax2, ax3, ax4 = axes.ravel()
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# scatter plot (Note: `plt.scatter` doesn't use default colors)
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x, y = np.random.normal(size=(2, 200))
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ax1.plot(x, y, 'o')
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# sinusoidal lines with colors from default color cycle
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L = 2 * np.pi
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x = np.linspace(0, L)
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ncolors = len(plt.rcParams['axes.prop_cycle'])
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shift = np.linspace(0, L, ncolors, endpoint=False)
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for s in shift:
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ax2.plot(x, np.sin(x + s), '-')
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ax2.margins(0)
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# bar graphs
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x = np.arange(5)
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y1, y2 = np.random.randint(1, 25, size=(2, 5))
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width = 0.25
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ax3.bar(x, y1, width)
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ax3.bar(x + width, y2, width,
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color=list(plt.rcParams['axes.prop_cycle'])[2]['color'])
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ax3.set_xticks(x + width)
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ax3.set_xticklabels(['a', 'b', 'c', 'd', 'e'])
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# circles with colors from default color cycle
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for i, color in enumerate(plt.rcParams['axes.prop_cycle']):
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xy = np.random.normal(size=2)
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ax4.add_patch(plt.Circle(xy, radius=0.3, color=color['color']))
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ax4.axis('equal')
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ax4.margins(0)
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fig = plt.gcf() # get the figure to show
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return fig
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def PyplotBoxPlot():
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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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# fake up some data
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spread = np.random.rand(50) * 100
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center = np.ones(25) * 50
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flier_high = np.random.rand(10) * 100 + 100
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flier_low = np.random.rand(10) * -100
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data = np.concatenate((spread, center, flier_high, flier_low), 0)
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fig1, ax1 = plt.subplots()
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ax1.set_title('Basic Plot')
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ax1.boxplot(data)
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return fig1
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def PyplotRadarChart():
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.path import Path
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from matplotlib.spines import Spine
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from matplotlib.projections.polar import PolarAxes
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from matplotlib.projections import register_projection
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def radar_factory(num_vars, frame='circle'):
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"""Create a radar chart with `num_vars` axes.
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This function creates a RadarAxes projection and registers it.
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Parameters
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----------
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num_vars : int
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Number of variables for radar chart.
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frame : {'circle' | 'polygon'}
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Shape of frame surrounding axes.
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"""
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# calculate evenly-spaced axis angles
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theta = np.linspace(0, 2 * np.pi, num_vars, endpoint=False)
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def draw_poly_patch(self):
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# rotate theta such that the first axis is at the top
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verts = unit_poly_verts(theta + np.pi / 2)
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return plt.Polygon(verts, closed=True, edgecolor='k')
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def draw_circle_patch(self):
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# unit circle centered on (0.5, 0.5)
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return plt.Circle((0.5, 0.5), 0.5)
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patch_dict = {'polygon': draw_poly_patch, 'circle': draw_circle_patch}
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if frame not in patch_dict:
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raise ValueError('unknown value for `frame`: %s' % frame)
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class RadarAxes(PolarAxes):
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name = 'radar'
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# use 1 line segment to connect specified points
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RESOLUTION = 1
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# define draw_frame method
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draw_patch = patch_dict[frame]
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def __init__(self, *args, **kwargs):
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super(RadarAxes, self).__init__(*args, **kwargs)
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# rotate plot such that the first axis is at the top
|
|
self.set_theta_zero_location('N')
|
|
|
|
def fill(self, *args, **kwargs):
|
|
"""Override fill so that line is closed by default"""
|
|
closed = kwargs.pop('closed', True)
|
|
return super(RadarAxes, self).fill(closed=closed, *args, **kwargs)
|
|
|
|
def plot(self, *args, **kwargs):
|
|
"""Override plot so that line is closed by default"""
|
|
lines = super(RadarAxes, self).plot(*args, **kwargs)
|
|
for line in lines:
|
|
self._close_line(line)
|
|
|
|
def _close_line(self, line):
|
|
x, y = line.get_data()
|
|
# FIXME: markers at x[0], y[0] get doubled-up
|
|
if x[0] != x[-1]:
|
|
x = np.concatenate((x, [x[0]]))
|
|
y = np.concatenate((y, [y[0]]))
|
|
line.set_data(x, y)
|
|
|
|
def set_varlabels(self, labels):
|
|
self.set_thetagrids(np.degrees(theta), labels)
|
|
|
|
def _gen_axes_patch(self):
|
|
return self.draw_patch()
|
|
|
|
def _gen_axes_spines(self):
|
|
if frame == 'circle':
|
|
return PolarAxes._gen_axes_spines(self)
|
|
# The following is a hack to get the spines (i.e. the axes frame)
|
|
# to draw correctly for a polygon frame.
|
|
|
|
# spine_type must be 'left', 'right', 'top', 'bottom', or `circle`.
|
|
spine_type = 'circle'
|
|
verts = unit_poly_verts(theta + np.pi / 2)
|
|
# close off polygon by repeating first vertex
|
|
verts.append(verts[0])
|
|
path = Path(verts)
|
|
|
|
spine = Spine(self, spine_type, path)
|
|
spine.set_transform(self.transAxes)
|
|
return {'polar': spine}
|
|
|
|
register_projection(RadarAxes)
|
|
return theta
|
|
|
|
def unit_poly_verts(theta):
|
|
"""Return vertices of polygon for subplot axes.
|
|
|
|
This polygon is circumscribed by a unit circle centered at (0.5, 0.5)
|
|
"""
|
|
x0, y0, r = [0.5] * 3
|
|
verts = [(r * np.cos(t) + x0, r * np.sin(t) + y0) for t in theta]
|
|
return verts
|
|
|
|
def example_data():
|
|
# The following data is from the Denver Aerosol Sources and Health study.
|
|
# See doi:10.1016/j.atmosenv.2008.12.017
|
|
#
|
|
# The data are pollution source profile estimates for five modeled
|
|
# pollution sources (e.g., cars, wood-burning, etc) that emit 7-9 chemical
|
|
# species. The radar charts are experimented with here to see if we can
|
|
# nicely visualize how the modeled source profiles change across four
|
|
# scenarios:
|
|
# 1) No gas-phase species present, just seven particulate counts on
|
|
# Sulfate
|
|
# Nitrate
|
|
# Elemental Carbon (EC)
|
|
# Organic Carbon fraction 1 (OC)
|
|
# Organic Carbon fraction 2 (OC2)
|
|
# Organic Carbon fraction 3 (OC3)
|
|
# Pyrolized Organic Carbon (OP)
|
|
# 2)Inclusion of gas-phase specie carbon monoxide (CO)
|
|
# 3)Inclusion of gas-phase specie ozone (O3).
|
|
# 4)Inclusion of both gas-phase species is present...
|
|
data = [
|
|
['Sulfate', 'Nitrate', 'EC', 'OC1', 'OC2', 'OC3', 'OP', 'CO', 'O3'],
|
|
('Basecase', [
|
|
[0.88, 0.01, 0.03, 0.03, 0.00, 0.06, 0.01, 0.00, 0.00],
|
|
[0.07, 0.95, 0.04, 0.05, 0.00, 0.02, 0.01, 0.00, 0.00],
|
|
[0.01, 0.02, 0.85, 0.19, 0.05, 0.10, 0.00, 0.00, 0.00],
|
|
[0.02, 0.01, 0.07, 0.01, 0.21, 0.12, 0.98, 0.00, 0.00],
|
|
[0.01, 0.01, 0.02, 0.71, 0.74, 0.70, 0.00, 0.00, 0.00]]),
|
|
('With CO', [
|
|
[0.88, 0.02, 0.02, 0.02, 0.00, 0.05, 0.00, 0.05, 0.00],
|
|
[0.08, 0.94, 0.04, 0.02, 0.00, 0.01, 0.12, 0.04, 0.00],
|
|
[0.01, 0.01, 0.79, 0.10, 0.00, 0.05, 0.00, 0.31, 0.00],
|
|
[0.00, 0.02, 0.03, 0.38, 0.31, 0.31, 0.00, 0.59, 0.00],
|
|
[0.02, 0.02, 0.11, 0.47, 0.69, 0.58, 0.88, 0.00, 0.00]]),
|
|
('With O3', [
|
|
[0.89, 0.01, 0.07, 0.00, 0.00, 0.05, 0.00, 0.00, 0.03],
|
|
[0.07, 0.95, 0.05, 0.04, 0.00, 0.02, 0.12, 0.00, 0.00],
|
|
[0.01, 0.02, 0.86, 0.27, 0.16, 0.19, 0.00, 0.00, 0.00],
|
|
[0.01, 0.03, 0.00, 0.32, 0.29, 0.27, 0.00, 0.00, 0.95],
|
|
[0.02, 0.00, 0.03, 0.37, 0.56, 0.47, 0.87, 0.00, 0.00]]),
|
|
('CO & O3', [
|
|
[0.87, 0.01, 0.08, 0.00, 0.00, 0.04, 0.00, 0.00, 0.01],
|
|
[0.09, 0.95, 0.02, 0.03, 0.00, 0.01, 0.13, 0.06, 0.00],
|
|
[0.01, 0.02, 0.71, 0.24, 0.13, 0.16, 0.00, 0.50, 0.00],
|
|
[0.01, 0.03, 0.00, 0.28, 0.24, 0.23, 0.00, 0.44, 0.88],
|
|
[0.02, 0.00, 0.18, 0.45, 0.64, 0.55, 0.86, 0.00, 0.16]])
|
|
]
|
|
return data
|
|
|
|
N = 9
|
|
theta = radar_factory(N, frame='polygon')
|
|
|
|
data = example_data()
|
|
spoke_labels = data.pop(0)
|
|
|
|
fig, axes = plt.subplots(figsize=(9, 9), nrows=2, ncols=2,
|
|
subplot_kw=dict(projection='radar'))
|
|
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
|
|
|
|
colors = ['b', 'r', 'g', 'm', 'y']
|
|
# Plot the four cases from the example data on separate axes
|
|
for ax, (title, case_data) in zip(axes.flatten(), data):
|
|
ax.set_rgrids([0.2, 0.4, 0.6, 0.8])
|
|
ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),
|
|
horizontalalignment='center', verticalalignment='center')
|
|
for d, color in zip(case_data, colors):
|
|
ax.plot(theta, d, color=color)
|
|
ax.fill(theta, d, facecolor=color, alpha=0.25)
|
|
ax.set_varlabels(spoke_labels)
|
|
|
|
# add legend relative to top-left plot
|
|
ax = axes[0, 0]
|
|
labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5')
|
|
legend = ax.legend(labels, loc=(0.9, .95),
|
|
labelspacing=0.1, fontsize='small')
|
|
|
|
fig.text(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios',
|
|
horizontalalignment='center', color='black', weight='bold',
|
|
size='large')
|
|
return fig
|
|
|
|
|
|
def DifferentScales():
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
|
|
# Create some mock data
|
|
t = np.arange(0.01, 10.0, 0.01)
|
|
data1 = np.exp(t)
|
|
data2 = np.sin(2 * np.pi * t)
|
|
|
|
fig, ax1 = plt.subplots()
|
|
|
|
color = 'tab:red'
|
|
ax1.set_xlabel('time (s)')
|
|
ax1.set_ylabel('exp', color=color)
|
|
ax1.plot(t, data1, color=color)
|
|
ax1.tick_params(axis='y', labelcolor=color)
|
|
|
|
ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis
|
|
|
|
color = 'tab:blue'
|
|
ax2.set_ylabel('sin', color=color) # we already handled the x-label with ax1
|
|
ax2.plot(t, data2, color=color)
|
|
ax2.tick_params(axis='y', labelcolor=color)
|
|
|
|
fig.tight_layout() # otherwise the right y-label is slightly clipped
|
|
return fig
|
|
|
|
|
|
def ExploringNormalizations():
|
|
import matplotlib.pyplot as plt
|
|
import matplotlib.colors as mcolors
|
|
import numpy as np
|
|
from numpy.random import multivariate_normal
|
|
|
|
data = np.vstack([
|
|
multivariate_normal([10, 10], [[3, 2], [2, 3]], size=100000),
|
|
multivariate_normal([30, 20], [[2, 3], [1, 3]], size=1000)
|
|
])
|
|
|
|
gammas = [0.8, 0.5, 0.3]
|
|
|
|
fig, axes = plt.subplots(nrows=2, ncols=2)
|
|
|
|
axes[0, 0].set_title('Linear normalization')
|
|
axes[0, 0].hist2d(data[:, 0], data[:, 1], bins=100)
|
|
|
|
for ax, gamma in zip(axes.flat[1:], gammas):
|
|
ax.set_title(r'Power law $(\gamma=%1.1f)$' % gamma)
|
|
ax.hist2d(data[:, 0], data[:, 1],
|
|
bins=100, norm=mcolors.PowerNorm(gamma))
|
|
|
|
fig.tight_layout()
|
|
return fig
|
|
|
|
|
|
def PyplotFormatstr():
|
|
def f(t):
|
|
return np.exp(-t) * np.cos(2 * np.pi * t)
|
|
|
|
t1 = np.arange(0.0, 5.0, 0.1)
|
|
t2 = np.arange(0.0, 5.0, 0.02)
|
|
|
|
plt.figure(1)
|
|
plt.subplot(211)
|
|
plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')
|
|
|
|
plt.subplot(212)
|
|
plt.plot(t2, np.cos(2 * np.pi * t2), 'r--')
|
|
fig = plt.gcf() # get the figure to show
|
|
return fig
|
|
|
|
|
|
def UnicodeMinus():
|
|
import numpy as np
|
|
import matplotlib
|
|
import matplotlib.pyplot as plt
|
|
|
|
# Fixing random state for reproducibility
|
|
np.random.seed(19680801)
|
|
|
|
matplotlib.rcParams['axes.unicode_minus'] = False
|
|
fig, ax = plt.subplots()
|
|
ax.plot(10 * np.random.randn(100), 10 * np.random.randn(100), 'o')
|
|
ax.set_title('Using hyphen instead of Unicode minus')
|
|
return fig
|
|
|
|
|
|
def Subplot3d():
|
|
from mpl_toolkits.mplot3d.axes3d import Axes3D
|
|
from matplotlib import cm
|
|
# from matplotlib.ticker import LinearLocator, FixedLocator, FormatStrFormatter
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
|
|
fig = plt.figure()
|
|
|
|
ax = fig.add_subplot(1, 2, 1, projection='3d')
|
|
X = np.arange(-5, 5, 0.25)
|
|
Y = np.arange(-5, 5, 0.25)
|
|
X, Y = np.meshgrid(X, Y)
|
|
R = np.sqrt(X ** 2 + Y ** 2)
|
|
Z = np.sin(R)
|
|
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet,
|
|
linewidth=0, antialiased=False)
|
|
ax.set_zlim3d(-1.01, 1.01)
|
|
|
|
# ax.w_zaxis.set_major_locator(LinearLocator(10))
|
|
# ax.w_zaxis.set_major_formatter(FormatStrFormatter('%.03f'))
|
|
|
|
fig.colorbar(surf, shrink=0.5, aspect=5)
|
|
|
|
from mpl_toolkits.mplot3d.axes3d import get_test_data
|
|
ax = fig.add_subplot(1, 2, 2, projection='3d')
|
|
X, Y, Z = get_test_data(0.05)
|
|
ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)
|
|
return fig
|
|
|
|
|
|
def PyplotScales():
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
|
|
from matplotlib.ticker import NullFormatter # useful for `logit` scale
|
|
|
|
# Fixing random state for reproducibility
|
|
np.random.seed(19680801)
|
|
|
|
# make up some data in the interval ]0, 1[
|
|
y = np.random.normal(loc=0.5, scale=0.4, size=1000)
|
|
y = y[(y > 0) & (y < 1)]
|
|
y.sort()
|
|
x = np.arange(len(y))
|
|
|
|
# plot with various axes scales
|
|
plt.figure(1)
|
|
|
|
# linear
|
|
plt.subplot(221)
|
|
plt.plot(x, y)
|
|
plt.yscale('linear')
|
|
plt.title('linear')
|
|
plt.grid(True)
|
|
|
|
# log
|
|
plt.subplot(222)
|
|
plt.plot(x, y)
|
|
plt.yscale('log')
|
|
plt.title('log')
|
|
plt.grid(True)
|
|
|
|
# symmetric log
|
|
plt.subplot(223)
|
|
plt.plot(x, y - y.mean())
|
|
plt.yscale('symlog', linthreshy=0.01)
|
|
plt.title('symlog')
|
|
plt.grid(True)
|
|
|
|
# logit
|
|
plt.subplot(224)
|
|
plt.plot(x, y)
|
|
plt.yscale('logit')
|
|
plt.title('logit')
|
|
plt.grid(True)
|
|
# Format the minor tick labels of the y-axis into empty strings with
|
|
# `NullFormatter`, to avoid cumbering the axis with too many labels.
|
|
plt.gca().yaxis.set_minor_formatter(NullFormatter())
|
|
# Adjust the subplot layout, because the logit one may take more space
|
|
# than usual, due to y-tick labels like "1 - 10^{-3}"
|
|
plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,
|
|
wspace=0.35)
|
|
return plt.gcf()
|
|
|
|
|
|
def AxesGrid():
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
from mpl_toolkits.axes_grid1.axes_rgb import RGBAxes
|
|
|
|
def get_demo_image():
|
|
# prepare image
|
|
delta = 0.5
|
|
|
|
extent = (-3, 4, -4, 3)
|
|
x = np.arange(-3.0, 4.001, delta)
|
|
y = np.arange(-4.0, 3.001, delta)
|
|
X, Y = np.meshgrid(x, y)
|
|
Z1 = np.exp(-X ** 2 - Y ** 2)
|
|
Z2 = np.exp(-(X - 1) ** 2 - (Y - 1) ** 2)
|
|
Z = (Z1 - Z2) * 2
|
|
|
|
return Z, extent
|
|
|
|
def get_rgb():
|
|
Z, extent = get_demo_image()
|
|
|
|
Z[Z < 0] = 0.
|
|
Z = Z / Z.max()
|
|
|
|
R = Z[:13, :13]
|
|
G = Z[2:, 2:]
|
|
B = Z[:13, 2:]
|
|
|
|
return R, G, B
|
|
|
|
fig = plt.figure(1)
|
|
ax = RGBAxes(fig, [0.1, 0.1, 0.8, 0.8])
|
|
|
|
r, g, b = get_rgb()
|
|
kwargs = dict(origin="lower", interpolation="nearest")
|
|
ax.imshow_rgb(r, g, b, **kwargs)
|
|
|
|
ax.RGB.set_xlim(0., 9.5)
|
|
ax.RGB.set_ylim(0.9, 10.6)
|
|
|
|
plt.draw()
|
|
return plt.gcf()
|
|
|
|
|
|
# ---------------------------- Beginning of PySimpleGUI program ----------------------------
|
|
|
|
# ---------------------------- draw_figure (the helper function) ----------------------------
|
|
def draw_figure(fig, element):
|
|
"""
|
|
Draws the previously created "figure" in the supplied Image Element
|
|
|
|
:param fig: a Matplotlib figure
|
|
:param element: an Image Element
|
|
:return: The figure canvas
|
|
"""
|
|
|
|
plt.close('all') # erases previously drawn plots
|
|
canv = FigureCanvasAgg(fig)
|
|
buf = io.BytesIO()
|
|
canv.print_figure(buf, format='png')
|
|
if buf is None:
|
|
return None
|
|
buf.seek(0)
|
|
element.update(data=buf.read())
|
|
return canv
|
|
|
|
|
|
# -------------------------------- GUI Starts Here -------------------------------#
|
|
def main():
|
|
|
|
fig_dict = {'Pyplot Simple': PyplotSimple, 'Pyplot Formatstr': PyplotFormatstr, 'PyPlot Three': Subplot3d,
|
|
'Unicode Minus': UnicodeMinus, 'Pyplot Scales': PyplotScales, 'Axes Grid': AxesGrid,
|
|
'Exploring Normalizations': ExploringNormalizations, 'Different Scales': DifferentScales,
|
|
'Pyplot Box Plot': PyplotBoxPlot, 'Pyplot ggplot Style Sheet': PyplotGGPlotSytleSheet,
|
|
'Pyplot Line Poly Collection': PyplotLinePolyCollection, 'Pyplot Line Styles': PyplotLineStyles,
|
|
'Pyplot Scatter With Legend': PyplotScatterWithLegend, 'Artist Customized Box Plots': PyplotArtistBoxPlots,
|
|
'Artist Customized Box Plots 2': ArtistBoxplot2, 'Pyplot Histogram': PyplotHistogram}
|
|
|
|
# define the layout
|
|
|
|
col_listbox = [[sg.Listbox(values=fig_dict.keys(), enable_events=True, size=(25, len(fig_dict.keys())), key='-LISTBOX-')],
|
|
[sg.Exit(size=(5, 2))]]
|
|
|
|
layout = [[sg.Text('Matplotlib Plot Browser', font=('current 25'))],
|
|
[sg.Text('Choose a plot from the list to see the plot and the source code used to make it.')],
|
|
[sg.Column(col_listbox, element_justification='c'), sg.Image(key='-IMAGE-'),
|
|
sg.MLine(size=(40, 20), font='Courier 12', key='-MULTILINE-')], ]
|
|
|
|
# create the window
|
|
window = sg.Window('Embedding Matplotlib In PySimpleGUIWeb', layout)
|
|
|
|
while True: # The event loop
|
|
event, values = window.read()
|
|
# print(event, values) # helps greatly when debugging
|
|
if event in (sg.WIN_CLOSED, 'Exit'): # if user closed window or clicked Exit button
|
|
break
|
|
|
|
choice = values['-LISTBOX-'][0] # get first listbox item chosen (returned as a list)
|
|
the_plotting_func = fig_dict[choice] # get function to call from the dictionary
|
|
window['-MULTILINE-'].update(inspect.getsource(the_plotting_func)) # show source code to function in multiline
|
|
draw_figure(the_plotting_func(), window['-IMAGE-']) # draw the figure
|
|
window.close()
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main() |