def generateGao09e1(vpn):
''' Experiment 1 from Gao et al. (2009)
Gao, T., Newman, G. E., & Scholl, B. J. (2009).
The psychophysics of chasing: A case study in
the perception of animacy.
Cognitive psychology, 59(2), 154-179.
vpn - tuple of ints, each value gives the subject id
'''
# gao09e1 settings
# TODO move settings to Settings.py
nrtrials=15
maze=EmptyMaze((1,1),dispSize=(32,24),lw2cwRatio=0)
chs=[0,60,120,180,240,300]
Q.setTrialDur(10);Q.phiRange=(120,120)
Q.setpDirChange([5.9,5.9,5.9])
block=0
#os.chdir('..')
os.chdir('..')
os.chdir('input/')
for vp in vpn:
vpname='vp%03d' % vp
os.mkdir(vpname)
os.chdir(vpname)
i=0
r=np.zeros((2*6*nrtrials,2))
r[:,0]=np.random.permutation(2*6*nrtrials)
for cond in range(6):
for trial in range(nrtrials):
Q.phiRange=(Q.phiRange[0],chs[cond])
trajectories=None
while trajectories ==None:
trajectories=generateTrial(5,maze=maze,
rejectionDistance=5.0)
#target present trial
r[i,1]=cond
fn='gao09e1%sb%dtrial%03d'% (vpname,block,i);
np.save(fn,trajectories[:,:-1,:]);i+=1
#target absent trial
r[i,1]=cond+6
fn='gao09e1%sb%dtrial%03d'% (vpname,block,i);
np.save(fn,trajectories[:,1:,:]);i+=1
np.save('gao09e1order%sb%d'% (vpname,block),r)
Q.save('SettingsTraj.pkl')
os.chdir('..')
os.chdir('..')
def generateBabyExperiment(vpn,nrtrials=10,blocks=1,conditions=[6,8],rd=0,pdch=None,
dispSize=29,maze=None):
'''my work in progress, baby experiment'''
#os.chdir('..')
if not pdch is None: Q.setpDirChange(pdch)
os.chdir(Q.inputPath)
mazes=[]
Q.nrframes+= Q.refreshRate *5
print 'Generating Trajectories'
for vp in vpn:
vpname='vp%03d' % vp
os.mkdir(vpname)
os.chdir(vpname)
r=[]
phase=[0,1,1,2]
for i in range((len(conditions)*nrtrials-len(phase))/2):
if np.random.rand()>0.5: phase.extend([1,2])
else: phase.extend([2,1])
print 'phase', phase
for block in range(blocks):
i=0
for condition in conditions:
for trial in range(nrtrials):
if condition==conditions[0]:
if np.random.rand()>0.5: r.extend([trial, trial+nrtrials])
else: r.extend([trial+nrtrials,trial])
trajectories=None
while trajectories ==None:
trajectories=generateTrial(condition,
maze=EmptyMaze((1,1),dispSize=(dispSize,dispSize)),rejectionDistance=rd)
#fn='%str%03dcond%02d'% (vpname,trial,conditions[order[trial]])
#fn = 'trial%03d' % trial
trajectories=trajectories[(Q.refreshRate*5):]
#print trajectories.shape
fn='%sb%dtrial%03d'% (vpname,block,i)
i+=1
print fn
np.save(fn,trajectories)
#r=np.random.permutation(nrtrials*len(conditions))
r=np.array(r)
print r
np.save('order%sb%d'% (vpname,block),r)
np.save('phase%sb%d'% (vpname,block),phase)
Q.save('SettingsTraj.pkl')
os.chdir('..')
os.chdir('..')
