def generateGao10e3(vpn):
''' Experiment 3 from Gao et al. (2010)
Gao, T., McCarthy, G., & Scholl, B. J. (2010).
The Wolfpack Effect Perception of Animacy Irresistibly
Influences Interactive Behavior.
Psychological science, 21(12), 1845-1853.
vpn - tuple of ints, each value gives the subject id
'''
offs=5.875; sz=(2*offs+Q.agentSize,2*offs+Q.agentSize)
quadrants=[EmptyMaze((1,1),dispSize=sz,pos=(offs,offs),lw2cwRatio=0),
EmptyMaze((1,1),dispSize=sz,pos=(-offs,offs),lw2cwRatio=0),
EmptyMaze((1,1),dispSize=sz,pos=(offs,-offs),lw2cwRatio=0),
EmptyMaze((1,1),dispSize=sz,pos=(-offs,-offs),lw2cwRatio=0)]
nrtrials=42;
os.chdir('..');os.chdir('input/')
for vp in vpn:
vpname='vp%03d' % vp;os.mkdir(vpname);os.chdir(vpname)
for trial in range(nrtrials):
if vp>300 and vp<400 and vp!=350: continue
trajectories=[]
for k in range(len(quadrants)):
traj=generateTrial(5,maze=quadrants[k], rejectionDistance=0.0)
trajectories.append(traj[:,2:,:])
fn='%strial%03d'% (vpname,trial);
np.save(fn,np.concatenate(trajectories,axis=1))
np.save('order%sb%d'% (vpname,0),np.random.permutation(nrtrials))
np.save('order%sb%d'% (vpname,1),np.random.permutation(nrtrials))
np.save('order%sb%d'% (vpname,2),np.random.permutation(nrtrials))
Q.save('SettingsTraj.pkl')
os.chdir('..')
def generateExperiment(vpn):
'''
vpn - tuple of ints, each value gives the subject id
'''
offs=5.875; sz=(2*offs+Q.agentSize,2*offs+Q.agentSize)
quadrants=[EmptyMaze((1,1),dispSize=sz,pos=(offs,offs),lw2cwRatio=0),
EmptyMaze((1,1),dispSize=sz,pos=(-offs,offs),lw2cwRatio=0),
EmptyMaze((1,1),dispSize=sz,pos=(offs,-offs),lw2cwRatio=0),
EmptyMaze((1,1),dispSize=sz,pos=(-offs,-offs),lw2cwRatio=0)]
nrtrials=42;
os.chdir('..');os.chdir('input/')
for vp in vpn:
vpname='vp%03d' % vp;os.mkdir(vpname);os.chdir(vpname)
for trial in range(nrtrials):
if vp>300 and vp<400 and vp!=350: continue
trajectories=[]
for k in range(len(quadrants)):
traj=generateTrial(5,maze=quadrants[k], rejectionDistance=0.0)
trajectories.append(traj[:,2:,:])
fn='%strial%03d'% (vpname,trial);
np.save(fn,np.concatenate(trajectories,axis=1))
np.save('order%sb%d'% (vpname,0),np.random.permutation(nrtrials))
np.save('order%sb%d'% (vpname,1),np.random.permutation(nrtrials))
np.save('order%sb%d'% (vpname,2),np.random.permutation(nrtrials))
Q.save('SettingsTraj.pkl')
os.chdir('..')
def generateGao10e4(vpn):
''' Experiment 4 from Gao et al. (2010)
Gao, T., McCarthy, G., & Scholl, B. J. (2010).
The Wolfpack Effect Perception of Animacy Irresistibly
Influences Interactive Behavior.
Psychological science, 21(12), 1845-1853.
vpn - tuple of ints, each value gives the subject id
'''
# gao10e4 settings
maze=EmptyMaze((1,1),dispSize=(18,18),lw2cwRatio=0)
Q.setTrialDur(8); nrtrials=90;
Q.setAspeed(5.1)
os.chdir('..');os.chdir('input/')
for vp in vpn:
vpname='vp%03d' % vp;os.mkdir(vpname);os.chdir(vpname)
for trial in range(nrtrials):
if vp>400 and vp<500: continue
trajectories=generateTrial(12,maze=maze, rejectionDistance=0.0)
fn='%strial%03d'% (vpname,trial);
np.save(fn,trajectories[:,2:,:])
np.save('order%sb0'% (vpname),np.random.permutation(nrtrials))
np.save('order%sb1'% (vpname),np.random.permutation(nrtrials))
np.save('order%sb2'% (vpname),np.random.permutation(nrtrials))
Q.save('SettingsTraj.pkl')
os.chdir('..')
def PFinit(vp,event,suf=''):
path,inpath,fp=initPath(vp,event)
if event>=0: N=[50,15,8,2][event]
else: N=1
dat={'N':N,'os':64,'rot':1,
'width':10,'hz':85.0,'SX':0.3,'SY':0.3,'ST':40}
np.save(inpath+'stackPF.npy',range(dat['N']+1))
Q.save(inpath+'PF%s.q'%suf)
f=open(inpath+'PF%s.pars'%suf,'w')
pickle.dump(dat,f)
f.close()
def __init__(self,of=None):
# ask subject the information
myDlg = gui.Dlg(title="Experiment zur Bewegungswahrnehmung",pos=Q.guiPos)
myDlg.addText('VP Infos')
myDlg.addField('Subject ID:',0)
myDlg.addField('Block:',0)
myDlg.addField('Alter:', 21) #age
myDlg.addField('Geschlecht (m/w):',choices=(u'weiblich',u'maennlich'))#gender
myDlg.addField(u'Händigkeit:',choices=('rechts','links'))# handedness
myDlg.addField(u'Dominantes Auge:',choices=('rechts','links')) #dominant eye
myDlg.addField(u'Sehschärfe: ',choices=('korrigiert','normal')) # acuity
# hours per week spent at screen
myDlg.addField(u'Wochenstunden vor dem Komputerbildschirm:', choices=('0','0-2','2-5','5-10','10-20','20-40','40+'))
# hours per week playing computer games
myDlg.addField(u'Wochenstunden Komputerspielen:', choices=('0','0-2','2-5','5-9','10-20','20+'))
myDlg.addField('Starte bei Trial:', 0) # starting trial, for debug only
myDlg.addField(u'Stimulus:',choices=('dart','eyes')) # shape of the stimulus
myDlg.show()#show dialog and wait for OK or Cancel
vpInfo = myDlg.data
if myDlg.OK:#then the user pressed OK
subinf = open(Q.outputPath+'vpinfo.res','a')
subinf.write('%d\t%d\t%d\t%s\t%s\t%s\t%s\t%s\t%s\t%d\t%s\n'% tuple(vpInfo))
subinf.close()
else:
print 'Experiment cancelled'
return
self.id=vpInfo[0]
self.block=vpInfo[1]
self.initTrial=vpInfo[-2]
self.isDart= vpInfo[-1] == 'dart'
# save settings, which we will use
Q.save(Q.inputPath+'vp%03d'%self.id+Q.delim+'SettingsExp.pkl')
if of==None: self.output = open(Q.outputPath+'vp%03d.res'%self.id,'a')
else: self.output = open(Q.outputPath+of,'a')
#init stuff
self.wind=Q.initDisplay()
# init text
fs=1 # font size
self.text1=visual.TextStim(self.wind,text='Error',wrapWidth=30,pos=[0,2])
self.text2=visual.TextStim(self.wind,text='Error',wrapWidth=30,pos=[0,0])
self.text3=visual.TextStim(self.wind, text='Error',wrapWidth=30,pos=[0,-10])
self.text1.setHeight(fs)
self.text2.setHeight(fs)
self.text3.setHeight(fs)
self.f=0
self.permut=np.load(Q.inputPath+'vp%03d'%self.id+Q.delim
+'ordervp%03db%d.npy'%(self.id,self.block))
if len(self.permut.shape)>1 and self.permut.shape[1]>1:
self.data=self.permut[:,1:]
self.permut=self.permut[:,0]
self.nrtrials=self.permut.size
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('..')
def generateMixedExperiment(vpn,trialstotal,blocks=4,condition=14,
dispSize=26,maze=None,probeTrials=False):
'''my work in progress, experiment with chatch trials'''
#os.chdir('..')
os.chdir(Q.inputPath)
mazes=[]
if probeTrials: bs=range(0,blocks+1)
else: bs=range(22,blocks+1)
print 'Generating Trajectories'
for vp in vpn:
vpname='vp%03d' % vp
#os.mkdir(vpname)
os.chdir(vpname)
Q.save('SettingsTraj.pkl')
for block in bs:
if block ==0: nrtrials=10
else: nrtrials=trialstotal
for trial in range(nrtrials):
if vp>1 and vp<10: continue
if trial >= nrtrials*0.9: rd=0.0
else: rd=3.0
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
fn='%sb%dtrial%03d'% (vpname,block,trial)
print fn
np.save(fn,trajectories)
while True:# check that more than 1 consecutive control trials do not occur
r=np.random.permutation(nrtrials)
r2=np.roll(np.random.permutation(nrtrials)>=nrtrials-0.1*nrtrials,1)
#r3=np.roll(np.random.permutation(50)>=45,2)
if not np.any(np.bitwise_and(r,r2)):
break
np.save('order%sb%d'% (vpname,block),r)
os.chdir('..')
os.chdir('..')
def __init__(self,vp=None):
''' inits variables and presents the intro dialog
vp - subject id, useful for replay functionality'''
# ask infos
myDlg = gui.Dlg(title="Experiment zur Bewegungswahrnehmung",pos=Q.guiPos)
myDlg.addText('VP Infos')
myDlg.addField('Subject ID:',201)# subject id
myDlg.addField('Block:',0) # block id
myDlg.addField('Alter:', 21) # age
myDlg.addField('Geschlecht (m/w):',choices=(u'weiblich',u'maennlich')) #gender
myDlg.addField(u'Händigkeit:',choices=('rechts','links'))# handedness
myDlg.addField(u'Dominantes Auge:',choices=('rechts','links'))# dominant eye
myDlg.addField(u'Sehschärfe: ',choices=('korrigiert','normal')) # visual acuity
# weekly hours spent on computer screen
myDlg.addField(u'Wochenstunden vor dem Komputerbildschirm:', choices=('0','0-2','2-5','5-10','10-20','20-40','40+'))
# weekly hours spent playing video games
myDlg.addField(u'Wochenstunden Komputerspielen:', choices=('0','0-2','2-5','5-9','10-20','20+'))
myDlg.addField('Starte bei Trial:', 0) # start trial id, for debug only
if vp is None:
myDlg.show()#show dialog and wait for OK or Cancel
vpInfo = myDlg.data
else: vpInfo=[vp,0,21,'','','','','','',0]
self.id=vpInfo[0]
self.block=vpInfo[1]
self.initTrial=vpInfo[-1]
self.scale=1#vpInfo[2]
try:#then the user pressed OK
subinf = open(Q.outputPath+'vpinfo.res','a')
subinf.write('%d\t%d\t%d\t%s\t%s\t%s\t%s\t%s\t%s\t%d\n'% tuple(vpInfo))
subinf.close()
except: print 'Experiment cancelled'
# save settings, which we will use
Q.save(Q.inputPath+'vp%03d'%self.id+Q.delim+'SettingsExp.pkl')
#init stuff
self.wind=Q.initDisplay()
self.mouse = event.Mouse(False,None,self.wind)
self.mouse.setVisible(False)
fcw=0.1; fch=0.8 #fixcross width and height
fclist=[ visual.ShapeStim(win=self.wind, pos=[0,0],fillColor='white',
vertices=((fcw,fch),(-fcw,fch),(-fcw,-fch),(fcw,-fch)),interpolate=False),
visual.ShapeStim(win=self.wind, pos=[0,0],fillColor='white',
vertices=((fch,fcw),(-fch,fcw),(-fch,-fcw),(fch,-fcw)),interpolate=False),
visual.Circle(win=self.wind, pos=[0,0],fillColor='black',radius=0.1,interpolate=False)]
self.fixcross=visual.BufferImageStim(self.wind,stim=fclist)
self.wind.flip(); self.wind.flip()
self.score=0
self.rt=0
# init text
fs=1 # font size
self.text1=visual.TextStim(self.wind,text='Error',wrapWidth=30,pos=[0,2])
self.text2=visual.TextStim(self.wind,text='Error',wrapWidth=30,pos=[0,0])
self.text3=visual.TextStim(self.wind, text='Error',wrapWidth=30,pos=[0,-10])
self.text1.setHeight(fs)
self.text2.setHeight(fs)
self.text3.setHeight(fs)
self.f=0
self.permut=np.load(Q.inputPath+'vp%03d'%self.id+Q.delim
+'ordervp%03db%d.npy'%(self.id,self.block))
if len(self.permut.shape)>1 and self.permut.shape[1]>1:
self.data=self.permut[:,1:]
self.permut=self.permut[:,0]
self.nrtrials=self.permut.size
