forked from wichmann-lab/python-psignifit
-
Notifications
You must be signed in to change notification settings - Fork 0
/
stats_manual.py
25 lines (22 loc) · 935 Bytes
/
stats_manual.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
import numpy as np
import psignifit as ps
data = np.array([[0.0010, 45.0000, 90.0000],
[0.0015, 50.0000, 90.0000],
[0.0020, 44.0000, 90.0000],
[0.0025, 44.0000, 90.0000],
[0.0030, 52.0000, 90.0000],
[0.0035, 53.0000, 90.0000],
[0.0040, 62.0000, 90.0000],
[0.0045, 64.0000, 90.0000],
[0.0050, 76.0000, 90.0000],
[0.0060, 79.0000, 90.0000],
[0.0070, 88.0000, 90.0000],
[0.0080, 90.0000, 90.0000],
[0.0100, 90.0000, 90.0000]])
options = dict()
options['sigmoidName'] = 'norm' # choose a cumulative Gauss as the sigmoid
options['experiment_type'] = '2AFC'
# options['fixedPars'] = np.nan*np.ones(5)
# options['fixedPars'][2] = 0.01
# options['fixedPars'][3] = 0.5
fit = ps.psignifit(data, sigmoid='norm', experiment_type='2AFC')