[statistics] improved scatter of displayunivariatedata.py
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@ -1,5 +1,6 @@
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.stats import gaussian_kde
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#rng = np.random.RandomState(981)
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#data = rng.randn(40, 1) + 4.0
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@ -10,58 +11,23 @@ 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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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.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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#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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@ -92,6 +58,44 @@ ax.annotate('median',
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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, 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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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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# 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.scatter(scatterpos+barwidth*x*(rng.rand(len(data))-0.5), 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+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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