62 lines
1.6 KiB
Python
Executable File
62 lines
1.6 KiB
Python
Executable File
#!/usr/bin/env python
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from matplotlib import pyplot as plt
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import numpy as np
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import sys
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def moyal(x, μ, σ):
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N = (1)/(np.sqrt(2 * np.pi) * σ)
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return N * np.exp(- 0.5 * ((x - μ)/σ + np.exp( - (x - μ)/σ)))
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# plt.figure()
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plt.figure(figsize=(3.5, 2.5))
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plt.rcParams['font.size'] = 8
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# useful coordinates
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# y_min = -0.0086 # y min axes
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# y_max = 0.1895 # y max axes
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# me = -0.22 # mode
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# f_me = 0.1806 # f(mode)
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# h_f_me = f_me/2 # falf f(mode)
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# x_m = -1.5867 # x₋
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# x_p = 2.4330 # x₊
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# prepare plot
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x, y = np.loadtxt(sys.stdin, unpack=True)
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# plt.title('Landau distribution', loc='right')
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# plt.xlim(-10, 10)
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# plt.ylim(y_min, y_max)
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# draw the lines
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# plt.plot([-10, me], [f_me, f_me], color='gray')
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# plt.plot([me, me], [f_me, y_min], color='gray')
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# plt.plot([-10, x_p], [h_f_me, h_f_me], color='gray')
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# plt.plot([x_m, x_m], [y_min, h_f_me], color='gray')
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# plt.plot([x_p, x_p], [y_min, h_f_me], color='gray')
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# draw the function
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plt.plot(x, y, color='#92182b')
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# draw the notes
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# s = 0.012
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# S = 0.2
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# plt.annotate('$f(m_e)$', [-10 + S, f_me - s])
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# plt.annotate('$f(m_e)/2$', [-10 + S, h_f_me - s])
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# plt.annotate('$x_-$', [x_m + S, y_min + s/2])
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# plt.annotate('$x_+$', [x_p + S, y_min + s/2])
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# plt.annotate('$m_e$', [me + S, y_min + s/2])
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# do Moyal plot
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μ = -0.22278298
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σ = 1.1191486
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x = np.arange(-10,30, 0.01)
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plt.plot(x, moyal(x, μ, σ), color='gray')
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# save figure
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plt.tight_layout()
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plt.show()
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# plt.savefig('notes/images/1-notes.pdf')
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# plt.savefig('slides/images/both-pdf.pdf', transparent=True)
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