import json
import numpy as np
import matplotlib.pyplot as plt

colorList = json.load(open('color/config.json','r'))["color"]

import csv

data_pasg = {}
data_temp = {}

with open('data/passenger.csv', 'r') as f:
    reader = csv.reader(f)
    header = next(reader)
    data = [row for row in reader]

data = np.array(data).astype(float).T
data[0]=data[0].astype(int)

xList=data[1]+data[2]+data[3]
yList=np.full(np.shape(data[6])[0],0)
for i in range(1,np.shape(data[6])[0]):
    yList[i]=data[6][i]-data[6][i-1]


print(yList)

plt.scatter(xList[1:],yList[1:],color=colorList[0])

from scipy.optimize import curve_fit
def linear(x,k,b):
    return k*x+b
valk,valb = curve_fit(linear,xList,yList)[0]
residuals = yList - linear(xList,valk,valb)
ss_res = np.sum(residuals**2)
ss_tot = np.sum((yList-np.mean(yList))**2)
r_squared = 1 - (ss_res / ss_tot)
print("R-squared:", r_squared)

plt.plot(np.arange(0,2.5e6,1e5),linear(np.arange(0,2.5e6,1e5),valk,valb),color=colorList[1])

plt.xlabel('Passenger')
plt.ylabel('Local Population Decline')
plt.title('Relation between Passenger and Local Population Decline')
plt.text( 60000, 110, "k = %f\nb = %f\nR^2 = %f"%(valk,valb,r_squared),color=colorList[1])
plt.savefig('result/passenger-and-locals-relation.png')
plt.show()