# -*- coding: utf-8 -*- """ Created on Tue Nov 12 15:51:47 2013 @author: guille """ import numpy as np import mlds ## definition of stimulus values stim = np.linspace(0.0, 1.0, 11) ## generating quadruples quads, indxq, invordq = mlds.generate_quadruples(stim) ## generating triads triads, indxt, invordt = mlds.generate_triads(stim) # EOF
def simulateobserver(func, sigma, stim, nblocks=1, gamma=1):
""" Simulate an observer responding according to a function
"""
## Simulation triads experiment
triads, indxt, order = mlds.generate_triads(stim)
## where to save the results
filename = str(uuid.uuid4()) + '.csv'
rfl = open( filename, 'wb')
writer = csv.writer(rfl, delimiter=' ')
writer.writerow(['Trial', 'Response', 's1', 's2', 's3', 'invord', 'i1', 'i2', 'i3'])
# going through every trial
for b in range(nblocks):
for t in range( len(triads) ):
# slants of current triad
if order[t]==1:
s1 = triads[t][2]
s2 = triads[t][1]
s3 = triads[t][0]
else:
s1 = triads[t][0]
s2 = triads[t][1]
s3 = triads[t][2]
# get mean response
v1 = func(s1, gamma)
v2 = func(s2, gamma)
v3 = func(s3, gamma)
# responses from cue
# random.gauss accepts negative sigmas, it probably gets
# the absolute number. np.random.gauss, instead, doesnt,
# gives an error.. This could be problematic for unconstrained
# optimization routines that take sigma as parameter
f1 = random.gauss( v1, sigma)
f2 = random.gauss( v2, sigma)
# if we used the same value of f2, apparently introduces correlation and scale noise estimation (sigma) is lower
#f2b = f2
# we should then sample twice for the value of stimulus 2, so variances are as predicted.
f2b = random.gauss( v2, sigma)
f3 = random.gauss( v3, sigma)
# decision variable
delta = (f3 - f2b) - (f2-f1)
#delta = (v3 - v2) - (v2 -v1) + random.gauss(0, sigma)
if delta>0:
response=1
elif delta<0:
response=0
else:
response=None
print "None"
if order[t]==1:
response = int(not response)
writer.writerow([t, response, "%.2f" % triads[t][0], "%.2f" % triads[t][1], "%.2f" % triads[t][2], order[t], indxt[t][0]+1, indxt[t][1]+1, indxt[t][2]+1])
rfl.close()
return filename
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 12 15:51:47 2013
@author: guille
"""
import numpy as np
# appends directory where mlds.py is, and imports
import sys
sys.path.append("../")
import mlds
## definition of stimulus values
stim = np.linspace(0.0, 1.0, 11)
## generating quadruples
quads, indx, topbot = mlds.generate_quadruples(stim)
## generating triads
triads, indxt, topbot = mlds.generate_triads(stim)
# EOF
