file.py

#   Import Pandas
import pandas as pd
#   Import PuLP modeler functions
from pulp import *
#   Math functions for distance calculation
import math
#   Networkx to get connected components and subtours
import networkx as nx
#   Matplotlib for debugging
import matplotlib.pyplot as plt
#   Visually see loops progression
from tqdm import tqdm
#   To measure the optimizataion time
import time


class TSP():
    cities = None
    santa = None
    variables_dict = None
    x = None
    path = None
    sec_constraints = 0
    execution_time = 0

    def __init__(self, population_lower_bound=10e5 * 3):
        cities = pd.read_csv("worldcities.csv")
        self.cities = cities.loc[cities["population"] >= population_lower_bound][
            ["lat", "city", "lng", "population"]].reset_index()
        #   Add Santa's house
        santa_df = pd.DataFrame([["Santa's House", 66.550331132, 25.886996452, 10000]],
                                columns=["city", "lat", "lng", "population"])

        #   Add other cities with smaller population to improve the World coverage
        reykjavik = cities.loc[(cities["city_ascii"] == "Reykjavik") & (cities["country"] == "Iceland")]
        algiers = cities.loc[(cities["city_ascii"] == "Algiers") & (cities["country"] == "Algeria")]
        brazzaville = cities.loc[(cities["city_ascii"] == "Brazzaville")]
        dublin = cities.loc[(cities["city"] == "Dublin") & (cities["country"] == "Ireland")]
        guatemala_city = cities.loc[(cities["city_ascii"] == "Guatemala City")]
        ulaanbaatar = cities.loc[(cities["city_ascii"] == "Ulaanbaatar")]

        self.cities = pd.concat(
            [self.cities, santa_df, dublin, reykjavik, algiers,
             brazzaville, guatemala_city, ulaanbaatar]).reset_index()
        return