40 lines
1.0 KiB
Python
40 lines
1.0 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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# normal distribution:
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x = np.arange( -4.0, 4.0, 0.01 )
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g = np.exp(-0.5*x*x)/np.sqrt(2.0*np.pi)
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r = np.random.randn( 100 )
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plt.xkcd()
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fig = plt.figure( figsize=(6,4) )
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ax = fig.add_subplot( 1, 2, 1 )
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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_xlabel( 'x' )
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ax.set_ylabel( 'Frequency' )
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#ax.set_ylim( 0.0, 0.46 )
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#ax.set_yticks( np.arange( 0.0, 0.45, 0.1 ) )
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ax.hist(r, 5, color='#CC0000')
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ax.hist(r, 20, color='#FFCC00')
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ax = fig.add_subplot( 1, 2, 2 )
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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_xlabel( 'x' )
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ax.set_ylabel( 'Probability density p(x)' )
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#ax.set_ylim( 0.0, 0.46 )
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#ax.set_yticks( np.arange( 0.0, 0.45, 0.1 ) )
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ax.hist(r, 5, normed=True, color='#CC0000')
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ax.hist(r, 20, normed=True, color='#FFCC00')
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plt.tight_layout()
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fig.savefig( 'pdfhistogram.pdf' )
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#plt.show()
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