56 lines
1.8 KiB
Python
56 lines
1.8 KiB
Python
import numpy as np
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import scipy.stats as st
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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from plotstyle import colors, cm_size, show_spines
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def boltzmann(x, x0, k):
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return 1.0/(1.0+np.exp(-k*(x-x0)))
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if __name__ == "__main__":
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n = 50
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xmin = -18.0
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xmax = 18.0
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x0 = 2.0
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k = 0.25
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sigma = 0.08
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rng = np.random.RandomState(15281)
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x = np.linspace(xmin, xmax, n)
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y = boltzmann(x, x0, k) + sigma*rng.randn(len(x))
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xx = np.linspace(xmin, xmax, 200)
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yy = boltzmann(xx, x0, k)
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fig = plt.figure(figsize=cm_size(16.0, 6.0))
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spec = gridspec.GridSpec(nrows=1, ncols=2,
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left=0.10, bottom=0.23, right=0.97, top=0.96, wspace=0.4)
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ax1 = fig.add_subplot(spec[0, 0])
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show_spines(ax1, 'lb')
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ax1.plot(xx, yy, colors['red'], lw=2)
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ax1.scatter(x, y, c=colors['blue'], edgecolor='white', s=50)
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ax1.set_xlabel('Hair deflection / nm')
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ax1.set_ylabel('Open probability')
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ax1.set_xlim(-20, 20)
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ax1.set_ylim(-0.2, 1.17)
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ax1.set_xticks(np.arange(-20.0, 21.0, 10.0))
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ax1.set_yticks(np.arange(-0.2, 1.1, 0.2))
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ax2 = fig.add_subplot(spec[0, 1])
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show_spines(ax2, 'lb')
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xg = np.linspace(-1.0, 1.01, 200)
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yg = st.norm.pdf(xg, 0.0, sigma)
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ax2.plot(xg, yg, colors['red'], lw=2)
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bw = 0.05
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h, b = np.histogram(y-boltzmann(x, x0, k), np.arange(-1.0, 1.01, bw))
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ax2.bar(b[:-1], h/np.sum(h)/(b[1]-b[0]), fc=colors['yellow'], width=0.9*bw, align='edge')
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ax2.set_xlabel('residuals')
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ax2.set_ylabel('pdf')
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ax2.set_xlim(-0.3, 0.3)
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#ax2.set_ylim(0, 370)
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#ax2.set_xticks([0, 0.005, 0.01])
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#ax2.set_xticklabels(['0', '0.005', '0.01'])
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#ax2.set_yticks(np.arange(0, 351, 100))
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#ax2.set_yticklabels([])
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fig.savefig("staticnonlinearity.pdf")
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plt.close()
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