From 3f0c5c084c93e4ae8a47f62b16e85500cb3790a5 Mon Sep 17 00:00:00 2001
From: Joseph Timothy Foley <foley@ru.is>
Date: Fri, 12 Dec 2025 14:10:43 +0000
Subject: [PATCH] Plots nicely, shows important info in text box including Info

---
 normdist.py | 48 ++++++++++++++++++++++++++++++++++++++----------
 1 file changed, 38 insertions(+), 10 deletions(-)

diff --git a/normdist.py b/normdist.py
index f59a256..e4b5d1e 100644
--- a/normdist.py
+++ b/normdist.py
@@ -1,9 +1,9 @@
 #!/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 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])
 lowerbound = 0.9
 upperbound = 1.0
@@ -11,18 +11,46 @@ upperbound = 1.0
 mean = data.mean()
 stddev = data.std(ddof=1)
 # Delta Degrees of Freedom: ddof=0 for population, ddof=1 for sample std dev
-x = np.linspace(mean-3*stddev, mean+3*stddev, 1000)
-y = scipy.stats.norm.pdf(x, mean, stddev)
-plt.plot(x, y, 'b-', label='Normal Distribution')
+prob = norm.cdf(upperbound, mean, stddev) - norm.cdf(lowerbound, mean, stddev)
+#print("probability: %f", prob)
+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)
 #plt.plot(x, yt, 'g-', label='T Distribution')
 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.ylabel('Probability Density')
+plt.ylabel('Probability density')
 plt.legend()
 plt.grid(True)
-plt.show()
+
+top = plt.ylim()[1]
+
+
+plt.show()
+# annotate values on X after drawing the graphs
+
+
+