-
Notifications
You must be signed in to change notification settings - Fork 0
/
games_stats.py
50 lines (35 loc) · 1.27 KB
/
games_stats.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
# Used to provide insights into data for games
import numpy as np
import matplotlib.pyplot as plt
import games_data
gamedata = games_data.GamesData('users', 'games', 'ledger.json', 'users_conv.json', use_recent=True,
min_games_played=1)
print("Loading games")
games_by_user = np.genfromtxt('users_conv.json', dtype=float, delimiter=',')
print(games_by_user.shape)
print("Collecting sums")
sums = games_by_user.sum(axis=0)
print(sums.shape)
print("Sorting sums")
sums_sort = np.argsort(sums)
# sums = sums[sums_sort]
print("Collecting means")
means = games_by_user.mean(axis=0)
print(means.shape)
print("Sorting means by sums order")
means = means[sums_sort]
plt.title("Playtime by game")
plt.subplot(121)
plt.plot(sums, 'b-')
plt.subplot(122)
plt.plot(means, 'r.')
plt.show()
print("Normalizing data by percent total playtime per user (g = g / tot)")
sums_per_person = np.sum(games_by_user, axis=1)
sums_per_person = sums_per_person.reshape((sums_per_person.shape[0], 1))
norm_games = np.divide(games_by_user, sums_per_person)
norm_test = np.sum(norm_games, axis=1)
print(norm_test)
plt.title('Average percent playtime per person by game')
plt.plot(np.sort(np.mean(norm_games, axis=0)), 'b-')
plt.show()