kKAb9B9s

1from flask.globals import g
2import pandas as pd
3import numpy as np
4import json 
5import os
6
7from flask import Flask, Blueprint, jsonify, request
8from collections import defaultdict
9
10api = Blueprint('api',__name__)
11
12def get_month_name(month_number):
13    months = {
14        1: "January",
15        2: "February",
16        3: "March",
17        4: "April",
18        5: "May",
19        6: "June",
20        7: "July",
21        8: "August",
22        9: "September",
23        10: "October",
24        11: "November",
25        12: "December"
26    }
27    return months[month_number]
28
29def calculate(sub_df, index):
30    if index == 'LF':
31        total_seats = sub_df['seats'].sum()
32        total_paid  = sub_df['passengers_paid'].sum()
33        load_factor = total_paid / total_seats * 100
34        
35        if total_seats == 0:
36            return 0
37        else: 
38            return load_factor
39    else:
40        return float(sub_df[index].sum()) 
41
42def filter_df(request):
43
44    fd = get_flight_data() #fd = flight_data
45
46    input_data = request.get_json()
47    input_filters = input_data['filters']
48
49    from_year = input_data.get('from_year', 2011)
50    to_year = input_data.get('to_year', 2021)
51
52    # Subset flight data to period between start_year and end_year
53    fd = fd[(fd['year'] >= from_year) & (fd['year'] <= to_year)]
54
55    if len(input_filters) >= 1:    
56        for key, [value] in input_filters:
57            # Subset dataframe for each filter requested by user
58            fd = fd[fd[key] == value]
59
60    return input_data, fd
61
62def get_flight_data():
63    filename = os.path.join(os.getcwd(), 'datasets', 'flight_log.csv')
64    return pd.read_csv(filename)
65
66@api.route('/heatmap', methods=['POST'])
67def heatmap():
68    input_data, fd = filter_df(request)
69    index = input_data.get('index', 'lf') # If user does not provide an index, we calculate the load factor
70
71    output_data = defaultdict(dict)
72
73    for year_month, sub_df in fd.groupby(["year", "month"]):
74        year, month = year_month # Unpack year and month from tuple index
75        month_name = get_month_name(month)
76        #output_data[year]["year"] = int(year)
77        output_data[year][month_name] = calculate(sub_df, index)
78
79    return jsonify(
80        {
81            'request' : input_data, 
82            'response' : output_data.items()
83        }
84    )
85
86@api.route('/line', methods=['POST'])
87def line():
88    input_data, fd = filter_df(request)
89
90
91