-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathplot_ridership.py
139 lines (120 loc) · 6.81 KB
/
plot_ridership.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
import pandas as pd
from functools import reduce
import cv2
import seaborn as sns
import numpy as np
colors = {'Bus': 'deepskyblue', 'TrolleyBus': 'orange', 'Tram': 'rebeccapurple', 'Metro': 'limegreen',
'Taxi': 'mediumseagreen',
'Monorail': 'crimson', 'Train': 'orangered', 'Boat': 'gold', 'Cable Car': 'blue', 'Air': 'mediumorchid',
'Short': 'steelblue', 'Long': 'chocolate', 'Niche': 'lightseagreen'}
def map_city(city):
line_0 = list(map(lambda x: x.replace('\'', ''), city[0].split('#')[0].split('\',\'')))
categories = line_0[1].split(',')
if len(city)==2:
line_0 = list(map(lambda x: int(x[0]) + int(x[1]), zip(line_0[0].split(','), city[1].replace('\'', '').split(','))))
line_0[-1] //= 2
line_0[-2] //= 2
else:
line_0 = list(map(int, line_0.split(',')))
return line_0, categories
def map_categories(categories):
return [int(x in categories) for x in ['archipelago', 'layered', 'singlebodied', 'flat', 'bumpy', 'valley', 'canal', 'international', 'european']]
def categorize(transport):
if transport in ['Bus', 'TrolleyBus', 'Tram', 'Boat']:
return 'Short'
if transport in ['Metro', 'Monorail', 'Train']:
return 'Long'
if transport in ['Cable Car', 'Air', 'Taxi']:
return 'Niche'
def process_data(data):
cities = list(map(lambda x: x.split('\n'), data.split('\n\n')))
data, categories = zip(*list(map(map_city, cities)))
categories = list(map(map_categories, categories))
data = pd.merge(pd.DataFrame(np.array(data),
columns=['Bus', 'TrolleyBus', 'Tram', 'Metro', 'Train', 'Boat', 'Air', 'Monorail',
'Cable Car', 'Taxi', 'Total', 'Population', 'Tiles']).reset_index(),
pd.DataFrame(np.array(categories),
columns=['archipelago', 'layered', 'singlebodied', 'flat', 'bumpy', 'valley', 'canal',
'international', 'european']).reset_index(),
left_on='index', right_on='index')
data = data.drop('index', axis=1).drop('Population', axis=1).drop('Tiles', axis=1)
return data
def plot(cities):
sets = {'All': cities,
'Archipelago': cities[cities['archipelago'] == 1],
'Layered': cities[cities['layered'] == 1],
'Single-bodied': cities[cities['singlebodied'] == 1],
'Flat': cities[cities['flat'] == 1],
'Bumpy': cities[cities['bumpy'] == 1],
'Valley': cities[cities['valley'] == 1],
'Canal': cities[cities['canal'] == 1],
'International': cities[cities['international'] == 1],
'European': cities[cities['european'] == 1]
}
for plot_type in ['individual', 'categories']:
for key in sets.keys():
selected = sets[key].copy()[['Bus', 'TrolleyBus', 'Tram', 'Metro', 'Train',
'Boat', 'Air', 'Monorail', 'Cable Car', 'Taxi', 'Total']]
cities_percent = selected.copy()
for transport in cities_percent.columns:
if transport != 'Total':
cities_percent[transport] = cities_percent[transport] / cities_percent['Total'] * 100
cities_percent['Total'] = cities_percent['Total'] / cities_percent['Total'] * 100
cities_square = cities_percent.copy()
cities_square.drop('Total', axis=1, inplace=True)
for transport in cities_square.columns:
cities_square[transport] = cities_square[transport].map(lambda x: x ** 2)
cities_square['Total'] = reduce(pd.Series.add,
[cities_square[transport] for transport in cities_square.columns])
for transport in cities_square.columns:
cities_square[transport] = cities_square[transport] / cities_square[
'Total'] * 100 # .map(lambda x: "%.3g" % x)
ncities = len(cities_square.index)
out = pd.concat([selected.sum(), cities_percent.mean()], axis=1)
out.columns = ['total_riders', 'mean_rider_percentage']
total = out['total_riders']['Total']
out = out.drop('Total')
out['Category'] = out.index
out['Category'] = out['Category'].map(categorize)
out['Transport'] = out.index
for col in ['total_riders', 'mean_rider_percentage']:
out = out.sort_values(col, ascending=False)
import matplotlib.pyplot as plt
sns.set(font_scale=1.5)
sns.set_style("whitegrid")
bar, ax = plt.subplots(figsize=(10, 6))
if plot_type == 'individual':
ax = sns.barplot(x=col, y='Transport', data=out,
ci=None, palette=colors, orient='h')
elif plot_type == 'categories':
pd.pivot_table(out, index='Category', columns='Transport', values=col).loc[
['Niche', 'Long', 'Short']].plot.barh(stacked=True, ax=ax, legend=False, color=colors)
if col != 'Total Riders':
ax.set_title(f"{key} Cities (riders average {int(total / ncities)}, {ncities} cities)", fontsize=20)
ax.set_xlabel(col)
ax.set_ylabel("Transport")
if plot_type == 'individual':
for rect in ax.patches:
ax.text(rect.get_width(), rect.get_y() + rect.get_height() / 2, "%.1f%%" % rect.get_width())
else:
ax.set_title(f"{key} Cities (total riders {int(total)}, {ncities} cities)", fontsize=20)
ax.set_xlabel(col)
ax.set_ylabel("Transport")
if plot_type == 'individual':
for rect in ax.patches:
ax.text(rect.get_width(), rect.get_y() + rect.get_height() / 2, int(rect.get_width()))
bar.savefig(f"files/results/{plot_type}_{col}_{key.replace('-', '').lower()}.jpg")
def combine_plots():
for plot_type in ['individual', 'categories']:
for col in ['total_riders', 'mean_rider_percentage']:
images = [['archipelago', 'canal', 'valley'], ['singlebodied', 'layered', 'international'],
['flat', 'bumpy', 'european']]
images = list(map(lambda y: list(map(lambda x: cv2.imread(f'files/results/{plot_type}_{col}_{x}.jpg'), y)), images))
cv2.imwrite(f'files/results/{plot_type}_{col}.jpg',
np.concatenate(list(map(lambda x: np.concatenate(x, axis=1), images)), axis=0))
if __name__ == '__main__':
with open('files/data.txt', 'r') as f:
data = f.read()
cities = process_data(data)
plot(cities)
combine_plots()