46 lines
1.5 KiB
Python
46 lines
1.5 KiB
Python
import json
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import numpy as np
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import matplotlib.pyplot as plt
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colorList = json.load(open('color/config.json','r'))["color"]
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import csv
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with open('data/passenger.csv', 'r') as f:
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reader = csv.reader(f)
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header = next(reader)
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data = [row for row in reader]
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data = np.array(data).astype(float).T
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data[0]=data[0].astype(int)
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xList=data[1]+data[2]+data[3]
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yList=data[5]
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plt.scatter(xList[:6],yList[:6],color=colorList[0])
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from scipy.optimize import curve_fit
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def linear(x,k,b):
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return k*x+b
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valk,valb = curve_fit(linear,xList[:6],yList[:6])[0]
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residuals = yList[:6] - linear(xList[:6],valk,valb)
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ss_res = np.sum(residuals**2)
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ss_tot = np.sum((yList[:6]-np.mean(yList[:6]))**2)
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r_squared = 1 - (ss_res / ss_tot)
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print("Before 2020: k:%f, b:%f, R-squared:%f" % (valk,valb,r_squared))
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plt.plot(np.arange(0,2000000,1000),linear(np.arange(0,2000000,1000),valk,valb),color=colorList[2],label='before 2020')
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plt.scatter(xList[6:],yList[6:],color=colorList[1])
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valk,valb = curve_fit(linear,xList[6:],yList[6:])[0]
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residuals = yList[6:] - linear(xList[6:],valk,valb)
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ss_res = np.sum(residuals**2)
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ss_tot = np.sum((yList[6:]-np.mean(yList[6:]))**2)
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r_squared = 1 - (ss_res / ss_tot)
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print("2020 and after: k:%f, b:%f, R-squared:%f" % (valk,valb,r_squared))
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plt.plot(np.arange(0,2000000,1000),linear(np.arange(0,2000000,1000),valk,valb),color=colorList[3],label='2020 and after')
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plt.xlabel('Total Passengers')
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plt.ylabel('Total Revenue')
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plt.legend()
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plt.title('Passenger-Revenue Relation')
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plt.savefig('result/passenger-and-revenue-relation.png',dpi=1024)
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plt.show() |