import numpy as np

import pandas as panda

import matplotlib.pyplot as plt

from scipy.stats import norm

import statistics

import random

df = 1

def readAll():

    dt = panda.read_csv(

    'cardio_train.csv',

    header=0

    )

    dt = dt.loc[dt.cardio == 1]

    return dt

#===========================================================================================

def CleanData(df):

    q25,q75 = np.percentile(df.ap_hi,[25,75])

    IQR = q75 - q25

    df = df.loc[df.ap_hi >= (q25 - 1.5 * IQR)]

    df = df.loc[df.ap_hi <= (q75 + 1.5 * IQR)]

    return df

#===========================================================================================

def ChangeSample(p): 

    df = panda.read_csv(

         'CleanPressure.csv',

         header=0, 

         skiprows=lambda i: i>0 and random.random() > p

    )

    df = df.loc[df.cardio == 1]

    return df

#===========================================================================================

def GenerateSamples(p):

    SamplesArr = []

    df = ChangeSample(p)   #=== Sample Of Eleven Peobles ===#

    sampleSize = df.cardio.count()

    for i in range(0,1000):

        df = ChangeSample(p)

        SamplesArr.append(ChangeSample(p).ap_hi.mean())

    return SamplesArr,sampleSize

#===========================================================================================

# These Lines Just To Clean The Data 

# df = readAll()

# df = CleanData(df)

# df.to_csv('CleanPressure.csv',index=False)

#===========================================================================================

p = 0.0002999

FirstSampleArr, FirstSampleSize = GenerateSamples(p)

plt.hist(FirstSampleArr, color="red")

plt.title("Data Distribution for means from sample size = " + str(FirstSampleSize))

plt.xlabel("Mean")

plt.ylabel("Freq")

plt.show()

p = 0.0005998

SecondSampleArr, SecondSampleSize = GenerateSamples(p)

plt.hist(SecondSampleArr, color="green")

plt.title("Data Distribution for means from sample size = " + str(SecondSampleSize))

plt.xlabel("Mean")

plt.ylabel("Freq")

plt.show()

p = 0.0011996

ThirdSampleArr, ThirdSampleSize = GenerateSamples(p)

plt.hist(ThirdSampleArr, color="blue")

plt.title("Data Distribution for means from sample size = " + str(ThirdSampleSize))

plt.xlabel("Mean")

plt.ylabel("Freq")

plt.show()

# From Previous Results We Can Notice That the population is almost normal

plt.hist(FirstSampleArr, color="red")

plt.hist(SecondSampleArr, color="green")

plt.hist(ThirdSampleArr, color="blue")

plt.xlabel("Mean")

plt.ylabel("Freq")

plt.show()

sampleMin = np.min(ThirdSampleArr)

sampleMax = np.max(ThirdSampleArr)

sampleMean = np.mean(ThirdSampleArr)

sampleSD = np.std(ThirdSampleArr)

sampleSize = ThirdSampleSize

n = ThirdSampleSize

x_axis = np.arange(sampleMin, sampleMax, 1)

print("Sample Mean = Population Mean = " + str(sampleMean))

print("Sample Standard Deviation = " + str(sampleSD))

plt.plot(x_axis, norm.pdf(x_axis,sampleMean,sampleSD))

plt.xlabel("Mean")

plt.ylabel("Freq")

plt.show()

# Assuming 95% Level Of Confidence ===> Z = 1.96

z = 1.96

MaxError = z * sampleSD / np.sqrt(n)

transformed = (sampleMax - sampleMin) / 2

x_axis = np.arange(-1 * transformed, transformed, 1)

plt.plot(x_axis, norm.pdf(x_axis,0,sampleSD))

plt.plot([z,z],[0,0.09])

plt.plot([-1 * z,-1 * z],[0,0.09])

plt.show()

print("Maximum Error Estimated For Sample Of Size " + str(n) + " = " + str(MaxError))

LeftX = MaxError + sampleMean

RightX = sampleMean - MaxError

if LeftX > RightX:

    A = LeftX

    LeftX = RightX

    RightX = A

print("The Population Mean Is Between [" + str(LeftX) + "," + str(RightX) + "] With Confidence Of 95%")