"""
Construct  normal probability plot

"""
import math
import scipy.stats as ss
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

import prettytable
from random import seed
seed(100)
################
mu = 0.0
sigma = 1.0
x = np.linspace(mu - 5*sigma, mu + 5*sigma, 10)
norm_pdf_x= ss.norm.pdf(x,mu,sigma)
plt.plot(x,norm_pdf_x)
print("The Std. Dev. of normally Dist. x =", np.std(norm_pdf_x))
print()

######------Second Method-----######
"""
#####--- x Values are derived from the first program----####

# Plot between -10 and 10 with .001 steps.
#x_axis = np.arange(-10, 10, 0.001)
Mean = 1.0
SD = 1.
#plt.plot(x, ss.norm.pdf(x,Mean,SD))

"""

#####---- X Values Come From The First Program ----#########################
print("Type x =", type(x))
x_list = x.tolist()
print(type(x_list))
x_s = np.sort(x_list)
mean_x = np.mean(x_s)
std_x = np.std(x_s)
print("The Std. Dev. of sorted x =", std_x)
print()
print("The Standard Deviation of the Normal Data =", sigma)
print()
print("The mean of sorted x =",mean_x)
p = x_s/len(x_s)
#####----------Calculate the normal distribution of p----###
#pdf_x_s = ss.norm.pdf(x_s,mean_x,std_x)
#z = (x_s - mean_x)/std_x
#plt.plot(norm_pdf_x,z)
#plt.plot(p,z)
###########################################################
#from tabulate import tabulate
#print(tabulate([['Alice', 24], ['Bob', 19]], headers = ['Name', 'Age'], tablefmt='orgtable'))
############################################################
from prettytable import PrettyTable
t = PrettyTable(['i', 'x_sorted'])
print(t)

plt.show()