112 lines
3.9 KiB
Python
112 lines
3.9 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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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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xpos = 0.08
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ypos = 0.15
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width = 0.65
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height = 0.8
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plt.xkcd()
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fig = plt.figure( figsize=(6,3.4) )
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ax = fig.add_axes([xpos, ypos, width, height])
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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ax.yaxis.set_ticks_position('left')
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ax.xaxis.set_ticks_position('bottom')
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ax.set_xticklabels([])
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ax.set_xlim(0.0, 4.8)
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ax.set_ylabel('x')
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ax.set_ylim( 0.0, 8.0)
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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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ax.set_xticks([scatterpos, barpos, boxpos])
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ax.set_xticklabels(['(1) data', '(2) bar\n plot', '(3) box-\nwhisker'])
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ax.scatter(scatterpos-0.5*barwidth+rng.rand(len(data)), data, s=50)
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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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ax.bar([barpos-0.5*barwidth], [barmean], barwidth, color='#FFCC00')
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eargs = {'color': 'k', 'lw': 2}
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ax.plot([barpos, barpos], [barmean-barstd, barmean+barstd], **eargs)
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ax.plot([barpos-0.5*ew, barpos+0.5*ew], [barmean-barstd, barmean-barstd], **eargs)
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ax.plot([barpos-0.5*ew, barpos+0.5*ew], [barmean+barstd, barmean+barstd], **eargs)
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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_axes([xpos, ypos, width, height])
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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ax.spines['left'].set_visible(False)
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ax.spines['bottom'].set_visible(False)
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ax.xaxis.set_ticks_position('none')
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ax.yaxis.set_ticks_position('none')
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ax.set_xticklabels([])
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ax.set_yticklabels([])
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wh = ax.boxplot( data, positions=[boxpos], widths=[barwidth], whis=100.0, patch_artist=True)
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wh['medians'][0].set_linewidth(4)
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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.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-1.3*barwidth, 5.5), textcoords='data', ha='left',
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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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ax = fig.add_axes([xpos+width+0.03, ypos, 0.98-(xpos+width+0.03), height])
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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ax.xaxis.set_ticks_position('bottom')
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ax.yaxis.set_ticks_position('left')
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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) p(x)')
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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, color='#CC0000')
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plt.savefig('displayunivariatedata.pdf')
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#plt.show()
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