57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
#!/usr/bin/env python
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.stats import norm
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## Data goes here for now --foley
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data = np.array([1, 1.1, 0.9, 1, 1, 0.9, 0.9])
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lowerbound = 0.9
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upperbound = 1.0
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mean = data.mean()
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stddev = data.std(ddof=1)
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# Delta Degrees of Freedom: ddof=0 for population, ddof=1 for sample std dev
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prob = norm.cdf(upperbound, mean, stddev) - norm.cdf(lowerbound, mean, stddev)
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#print("probability: %f", prob)
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info = -np.emath.log2(prob)
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#print("information content: %f bits", info)
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## place text on plot: https://matplotlib.org/3.3.4/gallery/recipes/placing_text_boxes.html
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fig, ax = plt.subplots()
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textstr = '\n'.join((
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r'$n=%d$' % (len(data)),
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r'$\mu=%.2f$' % (mean, ),
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r'$P=%.2f$' % (prob, ),
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r'$I=%.2f$' % (info, )))
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# these are matplotlib.patch.Patch properties
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props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
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# place a text box in upper left in axes coords
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ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=14,
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verticalalignment='top', bbox=props)
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x = np.linspace(mean-3*stddev, mean+3*stddev, 500)
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y = norm.pdf(x, mean, stddev)
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plt.axvline(x=mean, color="green", linestyle="dashed", label="mean")
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plt.axvline(lowerbound, color="red")
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plt.axvline(upperbound, color="red")
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plt.plot(x, y, 'b-', label='Normal distribution')
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#yt = scipy.stats.t.pdf(x, len(data)-1, mean, stddev)
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#plt.plot(x, yt, 'g-', label='T Distribution')
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coloredregion = (x >= lowerbound) & ( x <= upperbound ) #select x values
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plt.fill_between(x, 0, y, where=coloredregion, color="grey", alpha=0.5, label="Design range")
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plt.xlabel('X')
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plt.ylabel('Probability density')
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plt.legend()
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plt.grid(True)
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top = plt.ylim()[1]
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plt.show()
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# annotate values on X after drawing the graphs
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