103 lines
3.8 KiB
Python
103 lines
3.8 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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from scipy.stats import gaussian_kde
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from plotstyle import *
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#rng = np.random.RandomState(981)
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#data = rng.randn(40, 1) + 4.0
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rng = np.random.RandomState(1981)
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data = rng.gamma(1.0, 1.5, 40) + 1.0
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data = data[data<7.5]
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barwidth = 0.8
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scatterpos = 1.0
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barpos = 2.5
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boxpos = 4.0
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fig = plt.figure(figsize=cm_size(figure_width, 1.1*figure_height))
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spec = gridspec.GridSpec(nrows=1, ncols=2, width_ratios=[3, 1], wspace=0.1,
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**adjust_fs(fig, left=4.0))
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ax = fig.add_subplot(spec[0, 0], label='1')
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wh = ax.boxplot( data, positions=[boxpos], widths=[barwidth], whis=100.0, patch_artist=True)
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mediancolor = 'k'
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wh['medians'][0].set_linewidth(4)
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wh['medians'][0].set_color(mediancolor)
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wh['whiskers'][0].set_linewidth(2)
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wh['whiskers'][1].set_linewidth(2)
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wh['whiskers'][0].set_linestyle('-')
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wh['whiskers'][1].set_linestyle('-')
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whiskercolor = 'k'
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wh['whiskers'][0].set_color(whiskercolor)
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wh['whiskers'][1].set_color(whiskercolor)
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wh['caps'][0].set_color(whiskercolor)
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wh['caps'][1].set_color(whiskercolor)
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wh['boxes'][0].set_facecolor('#99ff00')
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ax.set_xlim(0.0, 4.8)
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ax.set_ylim( 0.0, 8.0)
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ax.set_xticklabels([])
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ax = fig.add_subplot(spec[0, 0], label='2')
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ax.set_xlim(0.0, 4.8)
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ax.set_xticks([scatterpos, barpos, boxpos])
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ax.set_xticklabels(['(1) data', '(2) bar\n plot', '(3) box-\nwhisker'], fontsize='medium')
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ax.set_ylabel('x')
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ax.set_ylim( 0.0, 8.0)
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# annotate box whisker:
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ax.annotate('maximum',
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xy=(boxpos, 6.5), xycoords='data',
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xytext=(boxpos-1*barwidth, 7.6), textcoords='data', ha='left',
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arrowprops=dict(arrowstyle="->", relpos=(1.0,0.5),
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connectionstyle="angle3,angleA=0,angleB=120") )
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ax.annotate('3. quartile',
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xy=(boxpos-0.3*barwidth, 3.7), xycoords='data',
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xytext=(boxpos-0.1*barwidth, 5.5), textcoords='data', ha='right',
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arrowprops=dict(arrowstyle="->", relpos=(0.4,0.0),
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connectionstyle="angle3,angleA=0,angleB=120") )
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ax.annotate('median',
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xy=(boxpos+0.6*barwidth, 2.2), xycoords='data',
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xytext=(boxpos+0.1*barwidth, 4.2), textcoords='data', ha='left',
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arrowprops=dict(arrowstyle="->", relpos=(0.8,0.0),
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connectionstyle="angle3,angleA=-60,angleB=20") )
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# scatter data points according to their density:
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kernel = gaussian_kde(data)
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x = kernel(data)
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x /= np.max(x)
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ax.plot(scatterpos+barwidth*x*(rng.rand(len(data))-0.5), data, **psA)
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barmean = np.mean(data)
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barstd = np.std(data)
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ew = 0.2
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if mpl_major > 1:
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ax.bar([barpos], [barmean], barwidth, **fsC)
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else:
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ax.bar([barpos-0.5*barwidth], [barmean], barwidth, **fsC)
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ax.plot([barpos, barpos], [barmean-barstd, barmean+barstd], **lsMarker)
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ax.plot([barpos-0.5*ew, barpos+0.5*ew], [barmean-barstd, barmean-barstd], **lsMarker)
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ax.plot([barpos-0.5*ew, barpos+0.5*ew], [barmean+barstd, barmean+barstd], **lsMarker)
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ax.annotate('mean',
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xy=(barpos-0.4*barwidth, 2.7), xycoords='data',
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xytext=(barpos-1*barwidth, 5.5), textcoords='data', ha='left',
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arrowprops=dict(arrowstyle="->", relpos=(1.0,0.5),
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connectionstyle="angle3,angleA=0,angleB=120") )
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ax.annotate('mean plus\nstd. dev.',
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xy=(barpos+0.05*barwidth, 4.2), xycoords='data',
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xytext=(barpos-1*barwidth, 7.0), textcoords='data', ha='left',
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arrowprops=dict(arrowstyle="->", relpos=(0.5,0.0),
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connectionstyle="angle3,angleA=-60,angleB=80") )
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ax = fig.add_subplot(spec[0, 1])
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ax.set_yticklabels([])
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ax.set_ylim( 0.0, 8.0)
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ax.set_xticks(np.arange(0.0, 0.4, 0.1))
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ax.set_xlabel('(4) pdf')
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bw = 0.75
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bins = np.arange(0, 8.0+bw, bw)
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h, b = np.histogram(data, bins)
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ax.barh(b[:-1], h/bw/np.sum(h), bw, **fsB)
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fig.subplots_adjust(top=0.9, bottom=0.2)
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plt.savefig('displayunivariatedata.pdf')
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