77 lines
2.3 KiB
Python
77 lines
2.3 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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import matplotlib.ticker as ticker
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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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def kerneldensity(data, xmin, xmax, sigma=1.0) :
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dx = 0.05*sigma
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xg = np.arange(-4.0*sigma, 4.0*sigma + 0.5*dx, dx)
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gauss = np.exp(-0.5*xg*xg/sigma/sigma)/np.sqrt(2.0*np.pi)/sigma
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ng = len(gauss)//2
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x = np.arange(xmin, xmax+0.5*dx, dx)
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kd = np.zeros(len(x))
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for xd in data:
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inx = int((xd-xmin)/dx)
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k0 = inx-ng
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k1 = inx+ng+1
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g0 = 0
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g1 = len(gauss)
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if inx < ng:
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k0 = 0
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g0 = ng-inx
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if inx >= len(kd)-ng:
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k1 = len(kd)
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g1 = len(gauss)-(inx+ng-len(kd)+1)
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kd[k0:k1] += gauss[g0:g1]
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kd /= len(data)
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return kd, x
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fig = plt.figure()
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spec = gridspec.GridSpec(nrows=2, ncols=2, wspace=0.35, hspace=0.3,
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**adjust_fs(fig, left=5.5, top=0.2, bottom=2.7))
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ax = fig.add_subplot(spec[0, 0])
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ax.set_xlim(-3.2, 3.2)
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ax.xaxis.set_major_locator(ticker.MultipleLocator(1.0))
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ax.xaxis.set_major_formatter(ticker.NullFormatter())
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ax.set_ylabel('p(x)')
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ax.set_ylim(0.0, 0.49)
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ax.yaxis.set_major_locator(ticker.MultipleLocator(0.2))
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if mpl_major > 1:
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ax.hist(r, np.arange(-4.1, 4, 0.4), density=True, zorder=-5, **fsC)
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else:
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ax.hist(r, np.arange(-4.1, 4, 0.4), normed=True, zorder=-5, **fsC)
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ax = fig.add_subplot(spec[1, 0])
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ax.set_xlabel('x')
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ax.set_xlim(-3.2, 3.2)
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ax.xaxis.set_major_locator(ticker.MultipleLocator(1.0))
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ax.set_ylabel('p(x)')
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ax.set_ylim(0.0, 0.49)
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ax.yaxis.set_major_locator(ticker.MultipleLocator(0.2))
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if mpl_major > 1:
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ax.hist(r, np.arange(-4.3, 4, 0.4), density=True, zorder=-5, **fsC)
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else:
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ax.hist(r, np.arange(-4.3, 4, 0.4), normed=True, zorder=-5, **fsC)
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ax = fig.add_subplot(spec[:, 1])
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ax.set_xlabel('x')
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ax.set_xlim(-3.2, 3.2)
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ax.xaxis.set_major_locator(ticker.MultipleLocator(1.0))
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ax.set_ylabel('Prob. density p(x)')
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ax.set_ylim(0.0, 0.49)
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ax.set_yticks(np.arange(0.0, 0.41, 0.1))
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kd, xx = kerneldensity(r, -3.2, 3.2, 0.2)
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ax.fill_between(xx, 0.0, kd, zorder=-5, **fsDs)
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ax.plot(xx, kd, '-', zorder=-1, **lsB)
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fig.savefig('kerneldensity.pdf')
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