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Plots nicely, shows important info in text box including Info

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Joseph Timothy Foley
2025-12-12 14:10:43 +00:00
parent 49fb7ed3fb
commit 3f0c5c084c
1 changed files with 38 additions and 10 deletions
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48
normdist.py
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@@ -1,9 +1,9 @@
#!/usr/bin/env python #!/usr/bin/env python
# From https://www.geeksforgeeks.org/python/how-to-plot-a-normal-distribution-with-matplotlib-in-python/
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import scipy.stats from scipy.stats import norm
## Data goes here for now --foley
data = np.array([1, 1.1, 0.9, 1, 1, 0.9, 0.9]) data = np.array([1, 1.1, 0.9, 1, 1, 0.9, 0.9])
lowerbound = 0.9 lowerbound = 0.9
upperbound = 1.0 upperbound = 1.0
@@ -11,18 +11,46 @@ upperbound = 1.0
mean = data.mean() mean = data.mean()
stddev = data.std(ddof=1) stddev = data.std(ddof=1)
# Delta Degrees of Freedom: ddof=0 for population, ddof=1 for sample std dev # Delta Degrees of Freedom: ddof=0 for population, ddof=1 for sample std dev
x = np.linspace(mean-3*stddev, mean+3*stddev, 1000) prob = norm.cdf(upperbound, mean, stddev) - norm.cdf(lowerbound, mean, stddev)
y = scipy.stats.norm.pdf(x, mean, stddev) #print("probability: %f", prob)
plt.plot(x, y, 'b-', label='Normal Distribution') info = -np.emath.log2(prob)
#print("information content: %f bits", info)
## place text on plot: https://matplotlib.org/3.3.4/gallery/recipes/placing_text_boxes.html
fig, ax = plt.subplots()
textstr = '\n'.join((
r'$n=%d$' % (len(data)),
r'$\mu=%.2f$' % (mean, ),
r'$P=%.2f$' % (prob, ),
r'$I=%.2f$' % (info, )))
# these are matplotlib.patch.Patch properties
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
# place a text box in upper left in axes coords
ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
x = np.linspace(mean-3*stddev, mean+3*stddev, 500)
y = norm.pdf(x, mean, stddev)
plt.axvline(x=mean, color="green", linestyle="dashed", label="mean")
plt.axvline(lowerbound, color="red")
plt.axvline(upperbound, color="red")
plt.plot(x, y, 'b-', label='Normal distribution')
#yt = scipy.stats.t.pdf(x, len(data)-1, mean, stddev) #yt = scipy.stats.t.pdf(x, len(data)-1, mean, stddev)
#plt.plot(x, yt, 'g-', label='T Distribution') #plt.plot(x, yt, 'g-', label='T Distribution')
coloredregion = (x >= lowerbound) & ( x <= upperbound ) #select x values coloredregion = (x >= lowerbound) & ( x <= upperbound ) #select x values
plt.vlines([lowerbound, upperbound], 0, plt.ylim()[1], color="red")#ylim returns [bot, top] plt.fill_between(x, 0, y, where=coloredregion, color="grey", alpha=0.5, label="Design range")
plt.fill_between(x, 0, y, where=coloredregion, color="grey", alpha=0.5, label="Design Range")
plt.xlabel('X') plt.xlabel('X')
plt.ylabel('Probability Density') plt.ylabel('Probability density')
plt.legend() plt.legend()
plt.grid(True) plt.grid(True)
plt.show()
top = plt.ylim()[1]
plt.show()
# annotate values on X after drawing the graphs