32 lines
839 B
Python
32 lines
839 B
Python
import numpy as np
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import matplotlib.pyplot as plt
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from plotstyle import *
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# normal distribution:
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rng = np.random.RandomState(6281)
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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 = rng.randn(100)
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fig, (ax1, ax2) = plt.subplots(1, 2)
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ax1.set_xlabel('x')
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ax1.set_xlim(-3.2, 3.2)
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ax1.set_xticks(np.arange(-3.0, 3.1, 1.0))
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ax1.set_ylabel('Frequency')
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ax1.set_yticks(np.arange(0.0, 41.0, 10.0))
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ax1.hist(r, 5, zorder=-10, **fsB)
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ax1.hist(r, 20, zorder=-5, **fsC)
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ax2.set_xlabel('x')
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ax2.set_xlim(-3.2, 3.2)
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ax2.set_xticks(np.arange(-3.0, 3.1, 1.0))
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ax2.set_ylabel('Probab. density p(x)')
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ax2.set_ylim(0.0, 0.44)
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ax2.set_yticks(np.arange(0.0, 0.41, 0.1))
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ax2.plot(x, g, zorder=-1, **lsA)
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ax2.hist(r, 5, normed=True, zorder=-10, **fsB)
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ax2.hist(r, 20, normed=True, zorder=-5, **fsC)
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fig.savefig('pdfhistogram.pdf')
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