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from tools.accommodations.apis import Accommodations
from tools.flights.apis import Flights
from tools.restaurants.apis import Restaurants
from tools.rank.apis import Rank
from tools.filter.apis import Filter
from tools.googleDistanceMatrix.apis import GoogleDistanceMatrix
import pandas as pd
hotel = Accommodations()
flight = Flights()
flight.load_db()
restaurant = Restaurants()
rank = Rank()
filter = Filter()
distanceMatrix = GoogleDistanceMatrix()
def estimate_budget(data, mode):
"""
Estimate the budget based on the mode (lowest, highest, average) for flight, hotel, or restaurant data.
"""
if mode == "lowest":
return min(data)
elif mode == "highest":
return max(data)
elif mode == "average":
# filter the nan values
data = [x for x in data if str(x) != 'nan']
return sum(data) / len(data)
def budget_calc(org, dest, days, date:list , people_number=None, local_constraint = None):
"""
Calculate the estimated budget for all three modes: lowest, highest, average.
grain: city, state
"""
if days == 3:
grain = "city"
elif days in [5,7]:
grain = "state"
if grain not in ["city", "state"]:
raise ValueError("grain must be one of city, state")
# Multipliers based on days
multipliers = {
3: {"flight": 2, "hotel": 3, "restaurant": 9},
5: {"flight": 3, "hotel": 5, "restaurant": 15},
7: {"flight": 4, "hotel": 7, "restaurant": 21}
}
if grain == "city":
hotel_data = hotel.run(dest)
restaurant_data = restaurant.run(dest)
flight_data = flight.data[(flight.data["DestCityName"] == dest) & (flight.data["OriginCityName"] == org)]
elif grain == "state":
city_set = open('/home/user/app/database/background/citySet_with_states.txt').read().strip().split('\n')
all_hotel_data = []
all_restaurant_data = []
all_flight_data = []
for city in city_set:
if dest == city.split('\t')[1]:
candidate_city = city.split('\t')[0]
# Fetch data for the current city
current_hotel_data = hotel.run(candidate_city)
current_restaurant_data = restaurant.run(candidate_city)
current_flight_data = flight.data[(flight.data["DestCityName"] == candidate_city) & (flight.data["OriginCityName"] == org)]
# Append the dataframes to the lists
all_hotel_data.append(current_hotel_data)
all_restaurant_data.append(current_restaurant_data)
all_flight_data.append(current_flight_data)
# Use concat to combine all dataframes in the lists
hotel_data = pd.concat(all_hotel_data, axis=0)
restaurant_data = pd.concat(all_restaurant_data, axis=0)
flight_data = pd.concat(all_flight_data, axis=0)
# flight_data should be in the range of supported date
flight_data = flight_data[flight_data['FlightDate'].isin(date)]
if people_number:
hotel_data = hotel_data[hotel_data['maximum occupancy'] >= people_number]
if local_constraint:
if local_constraint['transportation'] == 'no self-driving':
if grain == "city":
if len(flight_data[flight_data['FlightDate'] == date[0]]) < 2:
raise ValueError("No flight data available for the given constraints.")
elif grain == "state":
if len(flight_data[flight_data['FlightDate'] == date[0]]) < 10:
raise ValueError("No flight data available for the given constraints.")
elif local_constraint['transportation'] == 'no flight':
if len(flight_data[flight_data['FlightDate'] == date[0]]) < 2 or flight_data.iloc[0]['Distance'] > 800:
raise ValueError("Impossible")
# if local_constraint['flgiht time']:
# if local_constraint['flgiht time'] == 'morning':
# flight_data = flight_data[flight_data['DepTime'] < '12:00']
# elif local_constraint['flgiht time'] == 'afternoon':
# flight_data = flight_data[(flight_data['DepTime'] >= '12:00') & (flight_data['DepTime'] < '18:00')]
# elif local_constraint['flgiht time'] == 'evening':
# flight_data = flight_data[flight_data['DepTime'] >= '18:00']
if local_constraint['room type']:
if local_constraint['room type'] == 'shared room':
hotel_data = hotel_data[hotel_data['room type'] == 'Shared room']
elif local_constraint['room type'] == 'not shared room':
hotel_data = hotel_data[(hotel_data['room type'] == 'Private room') | (hotel_data['room type'] == 'Entire home/apt')]
elif local_constraint['room type'] == 'private room':
hotel_data = hotel_data[hotel_data['room type'] == 'Private room']
elif local_constraint['room type'] == 'entire room':
hotel_data = hotel_data[hotel_data['room type'] == 'Entire home/apt']
if days == 3:
if len(hotel_data) < 3:
raise ValueError("No hotel data available for the given constraints.")
elif days == 5:
if len(hotel_data) < 5:
raise ValueError("No hotel data available for the given constraints.")
elif days == 7:
if len(hotel_data) < 7:
raise ValueError("No hotel data available for the given constraints.")
if local_constraint['house rule']:
if local_constraint['house rule'] == 'parties':
# the house rule should not contain 'parties'
hotel_data = hotel_data[~hotel_data['house_rules'].str.contains('No parties')]
elif local_constraint['house rule'] == 'smoking':
hotel_data = hotel_data[~hotel_data['house_rules'].str.contains('No smoking')]
elif local_constraint['house rule'] == 'children under 10':
hotel_data = hotel_data[~hotel_data['house_rules'].str.contains('No children under 10')]
elif local_constraint['house rule'] == 'pets':
hotel_data = hotel_data[~hotel_data['house_rules'].str.contains('No pets')]
elif local_constraint['house rule'] == 'visitors':
hotel_data = hotel_data[~hotel_data['house_rules'].str.contains('No visitors')]
if days == 3:
if len(hotel_data) < 3:
raise ValueError("No hotel data available for the given constraints.")
elif days == 5:
if len(hotel_data) < 5:
raise ValueError("No hotel data available for the given constraints.")
elif days == 7:
if len(hotel_data) < 7:
raise ValueError("No hotel data available for the given constraints.")
if local_constraint['cuisine']:
# judge whether the cuisine is in the cuisine list
restaurant_data = restaurant_data[restaurant_data['Cuisines'].str.contains('|'.join(local_constraint['cuisine']))]
if days == 3:
if len(restaurant_data) < 3:
raise ValueError("No restaurant data available for the given constraints.")
elif days == 5:
if len(restaurant_data) < 5:
raise ValueError("No restaurant data available for the given constraints.")
elif days == 7:
if len(restaurant_data) < 7:
raise ValueError("No restaurant data available for the given constraints.")
# hotel_data = filter.run(hotel_data, local_constraint)
# restaurant_data = filter.run(restaurant_data, local_constraint)
# flight_data = filter.run(flight_data, local_constraint)
# Calculate budgets for all three modes
budgets = {}
for mode in ["lowest", "highest", "average"]:
if local_constraint and local_constraint['transportation'] == 'self driving':
flight_budget = eval(distanceMatrix.run(org, dest)['cost'].replace("$","")) * multipliers[days]["flight"]
else:
flight_budget = estimate_budget(flight_data["Price"].tolist(), mode) * multipliers[days]["flight"]
hotel_budget = estimate_budget(hotel_data["price"].tolist(), mode) * multipliers[days]["hotel"]
restaurant_budget = estimate_budget(restaurant_data["Average Cost"].tolist(), mode) * multipliers[days]["restaurant"]
total_budget = flight_budget + hotel_budget + restaurant_budget
budgets[mode] = total_budget
return budgets
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