#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
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

mean = data.mean()
stddev = data.std(ddof=1)
# Delta Degrees of Freedom: ddof=0 for population, ddof=1 for sample std dev
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'$I=%.2f$ bits' % (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.fill_between(x, 0, y, where=coloredregion, color="grey", alpha=0.5, label="Design range")
plt.xlabel('X')
plt.ylabel('Probability density')
plt.legend()
plt.grid(True)

top = plt.ylim()[1]


plt.show()
# annotate values on X after drawing the graphs