# coding: utf-8

# # Commuting Cost Analysis in Helsinki Region
#
#
# Valeri Tsatsishvili
#

import numpy as np
import matplotlib.pyplot as plt

# Initialize: input all necessary information here

FuelCons = [7 ,12]                      # fuel consumption highway(min)  and town(max) L per 100km
FuelPrice = 1.45                        # how much 1L fuel costs
TotalExpCar = 1200                      #'how much do you spend on a car annually including regular bills and repairs/services
InsTaxCar = 700                         #'only fixed tax+insurance
PubTrCostInt = 2.20                     # Internal single ticket
PubTrCostReg = 4.20                     # single 2zone regional ticket
PubTrCostPer = 106.5/30                 # 30 day 2 zone regional ticket
DrvNowCostMin = 0.46                    # 'DriveNow price per min'
dist=list(range(1,51))                  # range of distances in km



# First, lets define couple of functions
FuelConsRange = max(FuelCons) - min(FuelCons)
Scaling = np.round(FuelConsRange/60,3)   #here I assume that average speed in Helsinki region can range between 10km/h up to 70km/h.

def EstFC(FuelCons,AvgSpeed,Scaling):
    # rough estimate of fuel consumption based on average speed
    FuelConsEst = max(FuelCons) - AvgSpeed*Scaling
    return FuelConsEst

def CostAnalysis(dist, AvgSpeed,FuelConsEst,FuelPrice,InsTaxCar,TotalExpCar, DrvNowCostMin):
    # estimate costs as a function of distance
    TrCostCar = []
    TrCostTaxi = []
    TrCostDrvNow =[]
    DrvNowCostHr= DrvNowCostMin*60
    for d in range(1,max(dist)+1):
        TrCostCar.append(TotalExpCar/365 + (FuelConsEst/100 * d)*FuelPrice)
        TrCostTaxi.append(5.90 + d*1.60 + InsTaxCar/365)
        TrCostDrvNow.append(d/AvgSpeed*DrvNowCostHr+InsTaxCar/365)
    return TrCostCar, TrCostTaxi,TrCostDrvNow


# In[] Visualization

get_ipython().magic('matplotlib notebook')
from IPython.core.pylabtools import figsize
figsize(10, 15)
plt.figure()
count = 0

# The script below iteratively estimates costs for disserent transports at different average speed and plot the results

for AvgSpeed in [20, 30, 40]:

    FuelConsEst = EstFC(FuelCons,AvgSpeed,Scaling)
    [TrCostCar, TrCostTaxi,TrCostDrvNow] = CostAnalysis(dist, AvgSpeed,FuelConsEst,FuelPrice,InsTaxCar,TotalExpCar, DrvNowCostMin)

    count = count + 1
    index = 310 + count
    plt.subplot(index)

    plt.plot(dist,TrCostCar,'b.-')
    plt.plot(dist,TrCostDrvNow,'g.-')
    plt.plot(dist,TrCostTaxi,'r.-')

    plt.plot(dist,InsTaxCar/365 + PubTrCostInt*np.ones(max(dist)),'c-.')
    plt.plot(dist,InsTaxCar/365 + PubTrCostReg*np.ones(max(dist)),'y-.')
    plt.plot(dist,InsTaxCar/365 + PubTrCostPer*np.ones(max(dist)),'k-.')

    # some formatting
    plt.title(('Aerage speed in km/h:', AvgSpeed))
    plt.xlabel('Distance km')
    plt.ylabel('Cost Eur')
    plt.legend(['Car','DrvNow','Taxi','PubTrInt','PubTrReg','PubTrPer'])
    plt.xticks(np.arange(0,51,5))
    plt.xlim(0,55)
    plt.ylim(0,30)
    plt.grid()