-
Notifications
You must be signed in to change notification settings - Fork 8
/
ps_fig2_equate.py
142 lines (125 loc) · 6.9 KB
/
ps_fig2_equate.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
139
140
141
142
#make Figure 3
import json
import bisect
import pylab as pb
import scipy.stats as stats
import numpy as np
import pickle
import matplotlib.pyplot as plt # Matplotlib's pyplot: MATLAB-like syntax
print "loading data"
data = json.load(open('data_by_cookie.json'))
# basics
print "organising data"
players = data.keys()
num_attempts = [len(data[player]) for player in players]
attempt_numbers = [sorted(data[player].keys()) for player in players]
scores = [[data[player][attempt][0] for attempt in attempt_number] for attempt_number, player in zip(attempt_numbers, players)]
maxscore_method=1
if maxscore_method:
#this defines max score as max on any play
maxscores = pb.array([max(score) for score in scores])
else:
#this defines max score as max on 20th play
maxscores_notarray=[]
for score in scores:
if len(score)>19:
maxscores_notarray.append(score[19])
else:
maxscores_notarray.append(0)
maxscores = pb.array(maxscores_notarray)
# rows: players
# columns: attempts
first_20_scores = pb.zeros((len(scores),20))
for i,attempts in enumerate(scores):
for j,score in enumerate(attempts[:20]):
first_20_scores[i,j] = score
def mean_score_per_attempt(num_attempts, num_attempt_limit, maxscores, first_20_scores):
#create mask, so we only include people who play more than some number of games
played_more_than = pb.array(num_attempts) > num_attempt_limit
# this mask means we only look at people who got roughly equivalent scores on plays 1 and 2
equate_mask1bot=pb.array(first_20_scores[:,0]>7500)
equate_mask1top=pb.array(first_20_scores[:,0]<12500)
equate_mask2bot=pb.array(first_20_scores[:,1]>7500)
equate_mask2top=pb.array(first_20_scores[:,1]<12500)
equate_mask3bot=pb.array(first_20_scores[:,2]>7500)
equate_mask3top=pb.array(first_20_scores[:,2]<12500)
#can't figure out the all function to do this elegantly
#equate_mask=equate_mask1bot & equate_mask1top & equate_mask2bot & equate_mask2top & equate_mask3bot & equate_mask3top
equate_mask=equate_mask1bot & equate_mask1top
#eligible_mask = equate_mask & played_more_than
eligible_mask = played_more_than
print str(sum(eligible_mask))+' players in analysis'
#now rank everyone according to their top score
#this calculates the boundaries
percentiles = [stats.scoreatpercentile(maxscores[eligible_mask], per) for per in range(20,120,20)]
#this assigns to percentiles (actually quintile) groups
#NB assignment is to ALL players, but based on boundaries of only those who are eligible
#non-eligible players get excluded later (because we reuse the eligible mask)
player_percentiles = [bisect.bisect(percentiles, maxscore) for maxscore in maxscores]
#this sets up some holding variables for the means
mean_score_at_attempt_i_for_percentile_j = pb.zeros((20,5))
count_at_attempt_i_for_percentile_j = pb.zeros((20,5))
std_at_attempt_i_for_percentile_j = pb.zeros((20,5))
# -------------
for j in range(5):
print "looking at percentile group %s"%j
# this second mask chooses only those players whose max score is in the jth percentile
scores_in_this_percentile = pb.array(player_percentiles) == j
# we AND these together to get the mask
#mask = played_more_than & scores_in_this_percentile & equate_mask
percentile_mask = scores_in_this_percentile & eligible_mask
# Then add up all the scores that satisfy the mask (everyone in this percentile who played more than 19 times)
# and we divide them by the number of people maskwho satisfy the mask
## NOTE THAT WE ASSUME THAT NOONE SCORES ZERO!
mean_score_at_attempt_i_for_percentile_j[:,j] = pb.sum(first_20_scores[percentile_mask ,:],0) / sum(first_20_scores[percentile_mask ,:] > 0, 0)
#
#tom added this
count_at_attempt_i_for_percentile_j[:,j] = sum(first_20_scores[percentile_mask ,:] > 0, 0)
# the square route of the average squared difference from the mean
std_at_attempt_i_for_percentile_j[:,j] = np.sqrt(sum(((first_20_scores[percentile_mask ,:]-mean_score_at_attempt_i_for_percentile_j[:,j])**2)*(first_20_scores[percentile_mask,:] > 0),0)/count_at_attempt_i_for_percentile_j[:,j])
pickle.dump(count_at_attempt_i_for_percentile_j, open('save_count_at_attempt_i_for_percentile_j.p', 'wb'))
pickle.dump(std_at_attempt_i_for_percentile_j, open('save_std_at_attempt_i_for_percentile_j.p', 'wb'))
return mean_score_at_attempt_i_for_percentile_j
## we still don't know why these don't look like they should
#pb.plot(mean_score_per_attempt(num_attempts, 19, maxscores, first_20_scores), 'b')
#pb.plot(mean_score_per_attempt(num_attempts, 3, maxscores, first_20_scores), 'r')
#pb.plot(mean_score_per_attempt(num_attempts, 8, maxscores, first_20_scores), 'g')
#pb.plot(mean_score_per_attempt(num_attempts, 15, maxscores, first_20_scores), 'k')
#pb.xlabel('Attempt number')
#pb.ylabel('Average score')
##pb.show()
#pb.savefig('mike_debug.png')
makefig=1
if makefig:
print "making figure"
s=mean_score_per_attempt(num_attempts, 19, maxscores, first_20_scores)
count_at_attempt_i_for_percentile_j=pickle.load(open('save_count_at_attempt_i_for_percentile_j.p', 'rb'))
std_at_attempt_i_for_percentile_j=pickle.load(open('save_std_at_attempt_i_for_percentile_j.p', 'rb'))
stderr=std_at_attempt_i_for_percentile_j/np.sqrt(count_at_attempt_i_for_percentile_j)
vals=[(1,20),(21,40),(41,60),(61,80),(81,100)]
markervals=['o','s','v','^','D']
for i in range(4,-1,-1):
labelname='Percentiles %i to %i' %(vals[i])
pb.errorbar(range(1,21),s[:,i],yerr=stderr[:,i],marker=markervals[i],color='k',label=labelname)
#errorbar(range(1,21), s[:,i],yerr=scores_var[:,i],marker=markervals[i],color='k',label=labelname)
plt.legend(loc=4)
plt.xlabel('Attempt number')
plt.ylabel('Average score')
plt.xlim([0.5, 20.5])
plt.ylim([-7000,49000])
plt.savefig('StaffordFig2equate.png', dpi=300, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0.1)
generatepaperfigs=0
if generatepaperfigs:
plt.savefig('../psycscience/StaffordFig2equate.png', dpi=300, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0.1)
#tell me the n
countdata=pickle.load(open('save_count_at_attempt_i_for_percentile_j.p','rb'))
print sum(countdata,0)/20
plt.clf()
plt.plot(countdata)
plt.savefig('counts.png', dpi=300, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0.1)