from dateutil.parser import parse
import geopy.distance
import json
from datetime import datetime

RPI_dict = {'2Q2022': 163.9, '1Q2022': 159.5, '4Q2021': 155.7, '3Q2021': 150.6, '2Q2021': 146.4, '1Q2021': 142.2, '4Q2020': 138.1, '3Q2020': 133.9, '2Q2020': 131.9, '1Q2020': 131.5, '4Q2019': 131.5, '3Q2019': 130.9, '2Q2019': 130.8, '1Q2019': 131, '4Q2018': 131.4, '3Q2018': 131.6, '2Q2018': 131.7, '1Q2018': 131.6, '4Q2017': 132.6, '3Q2017': 132.8, '2Q2017': 133.7, '1Q2017': 133.9}

def is_date(string, fuzzy=False):
    """
    Return whether the string can be interpreted as a date.

    :param string: str, string to check for date
    :param fuzzy: bool, ignore unknown tokens in string if True
    """
    try: 
        parse(string, fuzzy=fuzzy)
        return True

    except ValueError:
        return False

def distance_to_mrt(lat, long, location):
    coord_mrt = tuple(location)
    coord_house = (lat, long)
    distance_km = geopy.distance.distance(coord_mrt, coord_house).km
    #print(distance_km)
    return distance_km


def nearest_mrt(lat, long, mrt_name, mrt_loc):
    count = 0
    distance_km = 100
    for mrt in mrt_name:
        distance_cal = distance_to_mrt(lat, long, mrt_loc[count])
        if distance_cal < distance_km:
            nearest_mr = mrt
            distance_km = distance_cal
        count += 1
    return distance_km, nearest_mr

def price_adj(price, qrtr_transaction):
    RPI = float(RPI_dict[qrtr_transaction])
    resale_price_adj = price*(133.9/RPI)       #133.9 is the CPI at 1Q2017, which is our baseline
    return resale_price_adj