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)
    for row in reader:
        data_pasg[row[0]] = np.array(row[1:],dtype=float)

with open('data/temperature.csv', 'r') as f:
    reader = csv.reader(f)
    header = next(reader)
    for row in reader:
        data_temp[row[0]] = np.array(row[1:],dtype=float)

xList = np.array([])
yList = np.array([])
for year in range(2014,2024):
    plt.scatter(data_pasg[str(year)][0], data_temp[str(year)][2], color=colorList[0]) 
    xList = np.append(xList,data_pasg[str(year)][0])
    yList = np.append(yList,data_temp[str(year)][2])

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(0,80000,1000),linear(np.arange(0,80000,1000),valk,valb),color=colorList[1])

plt.xlabel('Passenger')
plt.ylabel('SnowFall')
plt.title('Relation between Passenger and SnowFall')
plt.text( 60000, 110, "k = %f\nb = %f\nR^2 = %f"%(valk,valb,r_squared),color=colorList[1])
# plt.show()
plt.savefig('result/relation-plot0.png')