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 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 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 __init__(self, gazeData,phase=1,wind=None,eyes=1):
from ETData import interpRange
self.gazeData=gazeData;self.eyes=eyes
self.wind=wind;self.phase=phase;self.behsel=None
g=self.gazeData.getGaze()
self.trialDur=g.shape[0]/self.gazeData.hz*1000
step=1000/float(Q.refreshRate)
tcur=np.arange(0,self.trialDur-3*step,step)
self.t=tcur
step=1000/float(gazeData.hz)
t=np.arange(0,g.shape[0]*step,step)
self.gaze=np.array((interpRange(t,g[:,1],tcur),
interpRange(t,g[:,2],tcur)))
self.pos=np.ones((self.gaze.shape[0],1,2))*np.nan
try:
if type(self.wind)==type(None):
self.wind=Q.initDisplay()
self.cond=1
self.gazeDataRefresh=gazeData.hz
clrs=np.ones((self.cond,3))
self.elem=visual.ElementArrayStim(self.wind,fieldShape='sqr',
nElements=self.cond,sizes=Q.agentSize,rgbs=clrs,
elementMask='circle',elementTex=None)
except:
self.wind.close()
raise
def PFparallel(vp,event,suf=''):
''' please run PFinit() first
suf - output name suffix
'''
path,inpath,fp=initPath(vp,event)
E=np.load(inpath+'DG%s.npy'%suf)[:,:,:,:2]
print E.shape
stack=np.load(inpath+'stackPF.npy').tolist()
f=open(inpath+'PF%s.pars'%suf,'r');dat=pickle.load(f);f.close()
N=dat['N']
wind=Q.initDisplay()
elem=visual.ElementArrayStim(wind,fieldShape='sqr',
nElements=E.shape[1], sizes=Q.agentSize,
elementMask=RING,elementTex=None,colors='white')
while len(stack):
jobid=stack.pop(0)
np.save(inpath+'stackPF.npy',stack)
PFextract(E,[jobid,N],wind=wind, elem=elem,inpath=inpath,suf=suf)
loaded=False
while not loaded:
try:
stack=np.load(inpath+'stackPF.npy').tolist()
loaded=True
except IOError:
print 'IOError'
core.wait(1)
wind.close()
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 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 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 traj2movie(traj,width=5,outsize=64,elem=None,wind=None,rot=2,
hz=85.0,SX=0.3,SY=0.3,ST=20):
''' extracts window at position 0,0 of width WIDTH deg
from trajectories and subsamples to OUTSIZExOUTSIZE pixels
HZ - trajectory sampling frequency
ROT - int number of rotations to output or float angle in radians
SX,SY,ST - standard deviation of gaussian filter in deg,deg,ms
'''
if type(wind)==type(None):
close=True; wind=Q.initDisplay()
else: close=False
if type(elem)==type(None):
elem=visual.ElementArrayStim(wind,fieldShape='sqr',
nElements=traj.shape[1], sizes=Q.agentSize,
elementMask=RING,elementTex=None,colors='white')
try:
sig=[ST/1000.0*hz]
sig.append(deg2pix(SX,wind.monitor))
sig.append(deg2pix(SY,wind.monitor))
w=int(np.round(deg2pix(width,wind.monitor)/2.0))
D=np.zeros((traj.shape[0],outsize,outsize,rot),dtype=np.uint8)
Ims=[]
for f in range(0,traj.shape[0]):
Im=position2image(traj[f,:,:],wind=wind)
cx=int(Im.size[0]/2.0);cy=int(Im.size[1]/2.0)
Im=Im.crop(np.int32((cx-1.5*w,cy-1.5*w,cx+1.5*w,cy+1.5*w)))
Im=np.asarray(Im,dtype=np.float32)
Ims.append(Im)
Ims=np.array(Ims)
if np.any(np.array(sig)!=0):Ims=gaussian_filter(Ims,sig)
if np.any(Ims>255): print 'warning, too large'
if np.any(Ims<0): print 'warning, too small'
Ims=np.uint8(np.round(Ims))
for f in range(Ims.shape[0]):
Im=Image.fromarray(np.array(Ims[f,:,:]))
bb=int(Im.size[0]/2.0)
I=Im.crop((bb-w,bb-w,bb+w,bb+w))
I=np.asarray(I.resize((outsize,outsize),Image.ANTIALIAS))
D[f,:,:,0]=I
for r in range(1,rot):
I2=Im.rotate(90/float(rot)*r)
I2=I2.crop((bb-w,bb-w,bb+w,bb+w))
I2=np.asarray(I2.resize((outsize,outsize),Image.ANTIALIAS))
D[f,:,:,r]=I2
if close: wind.close()
return D
except:
if close: wind.close()
raise
def PFextract(E,part=[0,1],wind=None,elem=None,inpath='',suf=''):
"""
part[0] - current part
part[1] - total number of parts in the paralel computation
wind - psychopy window, elem - psychopy ElementArrayStim
inpath - input path
suf - output name suffix
"""
f=open(inpath+'PF%s.pars'%suf,'r');dat=pickle.load(f);f.close()
inc=E.shape[0]/part[1]
start=part[0]*inc
ende=min((part[0]+1)*inc,E.shape[0])
print start,ende,E.shape
os=dat['os'];rot=dat['rot']
phis=np.load(inpath+'phi%s.npy'%suf)
D=np.zeros((ende-start,E.shape[1],os,os,rot),dtype=np.uint8)
try:
if type(wind)==type(None):
close=True; wind=Q.initDisplay()
else: close=False
if elem==None:
elem=visual.ElementArrayStim(wind,fieldShape='sqr',
nElements=E.shape[1], sizes=Q.agentSize,
elementMask=RING,elementTex=None,colors='white')
for i in range(ende-start):
phi=phis[i+start]# rotate clockwise by phi
R=np.array([[np.cos(phi),np.sin(phi)],
[-np.sin(phi),np.cos(phi)]])
temp=np.copy(E[i+start,:,:,:])
for a in range(14):temp[:,a,:]=R.dot(temp[:,a,:].T).T
D[i,:,:,:,:]=traj2movie(temp,outsize=os,
elem=elem,wind=wind,rot=rot,width=dat['width'],
hz=dat['hz'],SX=dat['SX'],SY=dat['SY'],ST=dat['ST'])
#from matustools.matusplotlib import ndarray2gif
#outt=np.float32(D[i,:,:,:,0].T)
#outt-= np.min(outt)
#outt/= np.max(outt)
#ndarray2gif('test%d'%i,outt)
#if i==3: bla
if close: wind.close()
PF=np.rollaxis(D,1,5)
if not oss.path.exists(inpath+'PF%s/'%suf):
oss.makedirs(inpath+'PF%s/'%suf)
if len(part)==2: np.save(inpath+'PF%s/PF%03d.npy'%(suf,part[0]),PF)
else: np.save('PF.npy',PF)
except:
if close: wind.close()
raise
def __init__(self,gazeData,maze=None,wind=None,
highlightChase=False,phase=1,eyes=1,coderid=0):
self.wind=wind
self.phase=phase
try: self.cond=gazeData.oldtraj.shape[1]
except AttributeError: self.cond=0
self.pos=[]
self.eyes=eyes
self.behsel=None
# determine common time intervals
g=gazeData.getGaze(phase)
ts=max(g[0,0], gazeData.fs[0,1])
te=min(g[-1,0],gazeData.fs[-1,1])
self.t=np.linspace(ts,te,int(round((te-ts)*Q.refreshRate/1000.0)))
# put data together
g=gazeData.getGaze(phase,hz=self.t)
if self.cond: tr=gazeData.getTraj(hz=self.t)
else: tr=np.zeros((g.shape[0],0,2))*np.nan
if eyes==1: g=g[:,[7,8]];g=np.array(g,ndmin=3)
else: g=np.array([g[:,[1,2]],g[:,[4,5]]])
g=np.rollaxis(g,0,2)
self.pos=np.concatenate([tr,g],axis=1)
try:
if type(self.wind)==type(None):
self.wind=Q.initDisplay()
#if gazeData!=None:
self.cond+=1
if eyes==2: self.cond+=1
clrs=np.ones((self.cond,3))
clrs[-1,[0,1,2]]=-1
if eyes==2: clrs[-2,[0,1,2]]=-1
if highlightChase: clrs[0,[0,2]]=0; clrs[1,[0,2]]=-1
self.elem=visual.ElementArrayStim(self.wind,fieldShape='sqr',
nElements=self.cond,sizes=Q.agentSize,interpolate=False,
colorSpace='rgb',elementMask='circle',elementTex=None)
self.elem.setColors(clrs)
if type(maze)!=type(None):
self.maze=maze
self.maze.draw(wind)
except:
self.wind.close()
raise
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 position2image(positions,elem=None,wind=None):
'''transforms vector of agent positions to display snapshot
output format is HxW matrix of light intensity values (uint8)
'''
if type(wind)==type(None):
close=True; wind=Q.initDisplay()
else: close=False
if type(elem)==type(None):
elem=visual.ElementArrayStim(wind,fieldShape='sqr',
nElements=positions.shape[0], sizes=Q.agentSize,
elementMask=RING,elementTex=None,colors='white')
try:
elem.setXYs(positions)
elem.draw()
wind.getMovieFrame(buffer='back')
ret=wind.movieFrames[0]
wind.movieFrames=[]
pyglet.gl.glClear(pyglet.gl.GL_COLOR_BUFFER_BIT | pyglet.gl.GL_DEPTH_BUFFER_BIT)
wind._defDepth=0.0
if close: wind.close()
return grayscale(ret)# make grey, convert to npy
except:
if close: wind.close()
raise
def getWind(self):
'''returns the window handle '''
try: return self.wind
except AttributeError:
self.wind=Q.initDisplay()
return self.wind
def __init__(self,gazeData,**kwargs):
wind = kwargs.get('wind',None)
if wind is None: wind = Q.initDisplay(sz=(1280,1100))
Trajectory.__init__(self,gazeData,wind=wind,**kwargs)
self.gazeData=gazeData
self.mouse = event.Mouse(True,None,self.wind)
self.coderid = kwargs.get('coderid',0)
try:
indic=['Velocity','Acceleration','Saccade','Fixation','OL Pursuit','CL Pursuit','HEV','Tracking']
self.lim=([0,450],[-42000,42000],[0,1],[0,1],[0,1],[0,1],[0,1],[0,1])# limit of y axis
self.span=(0.9,0.9,0.6,0.6,0.6,0.6,0.6,0.6)# height of the window taken by graph
self.offset=(0.1,0.1,0.2,0.2,0.2,0.2,0.2,0.2)
fhandles=[self.gazeData.getVelocity,self.gazeData.getAcceleration,
self.gazeData.getSaccades, self.gazeData.getFixations,
self.gazeData.getOLP,self.gazeData.getCLP,
self.gazeData.getHEV,self.gazeData.getTracking]
self.ws=30; self.sws=150.0 # selection window size
ga=[7.8337, 18.7095,-13.3941+5,13.3941+5] # graph area
self.ga=ga
mid=ga[0]+(ga[1]-ga[0])/2.0
inc=(ga[3]-ga[2])/float(len(indic));self.inc=inc
self.spar=(0,-9.4,2) #parameters for selection tool, posx, posy, height
self.apar=(0,-12.7,4.5) # parameters for agent selection tool
frame=[visual.Line(self.wind,(ga[0],ga[3]),(ga[0],ga[2]),lineWidth=4.0),
visual.Line(self.wind,(-ga[1],ga[3]),(ga[1],ga[3]),lineWidth=4.0),
visual.Line(self.wind,(mid,ga[3]),(mid,ga[2]),lineWidth=2.0),
visual.Line(self.wind,(ga[1],ga[3]),(ga[1],ga[2]),lineWidth=4.0),
visual.Line(self.wind, (-ga[1],ga[2]),(ga[1],ga[2]),lineWidth=4.0),
visual.Line(self.wind, (-ga[1],ga[2]),(-ga[1],ga[3]),lineWidth=4.0),
visual.Rect(self.wind, width=ga[1]*2,height=self.spar[2], pos=(self.spar[0],self.spar[1]),lineWidth=4.0),
visual.Rect(self.wind, width=ga[1]*2,height=self.apar[2], pos=(self.apar[0],self.apar[1]),lineWidth=4.0),
visual.Line(self.wind,(0,self.spar[1]+self.spar[2]/2.0),(0,self.apar[1]-self.apar[2]/2.0),lineWidth=2.0)
]
self.seltoolrect=frame[6]
self.atoolrect=frame[7]
self.graphs=[]
self.selrects=[];self.sacrects=[]; self.arects=[]
for i in range(15): self.selrects.append(visual.Rect(self.wind,
height=self.spar[2],width=1,fillColor='red',opacity=0.5,lineColor='red'))
for i in range(20): self.sacrects.append(visual.Rect(self.wind,
height=self.spar[2]+self.apar[2],width=2,fillColor='blue',opacity=0.5,lineColor='blue'))
for i in range(30): self.arects.append(visual.Rect(self.wind,
height=self.apar[2],width=2,fillColor='green',opacity=0.5,lineColor='blue'))
for f in range(len(indic)):
frame.append(visual.Line(self.wind,(ga[0],ga[3]-(f+1)*inc),
(ga[1],ga[3]-(f+1)*inc),lineWidth=4.0))
frame.append(visual.TextStim(self.wind,indic[f],
pos=(ga[0]+0.1,ga[3]-0.1-f*inc),
alignHoriz='left',alignVert='top',height=0.5))
self.graphs.append(visual.ShapeStim(self.wind,
closeShape=False,lineWidth=2.0))
self.graphs[f].setAutoDraw(True)
self.frame=visual.BufferImageStim(self.wind,stim=frame)
self.tmsg=visual.TextStim(self.wind,color=(0.5,0.5,0.5),pos=(-13,-7.8))
self.msg= visual.TextStim(self.wind,color=(0.5,0.5,0.5),
pos=(0,-7.8),text=' ',wrapWidth=20)
self.msg.setAutoDraw(True)
self.gData=[]
for g in range(len(indic)):
yOld=fhandles[g](self.phase,hz=self.t)
self.gData.append(yOld)
self.sev=[]
scale=Q.refreshRate/self.gazeData.hz
for gs in self.gazeData.sev:
s=int(np.round(scale*gs[0]))
e=min(len(self.t)-1,max(s+1, int(np.round(scale*gs[1]))))
self.sev.append([s,e,gs[0],gs[1]])
for gs in self.gazeData.bev:
s=int(np.round(scale*gs[0]))
e=min(len(self.t)-1,max(s+1, int(np.round(scale*gs[1]))))
self.sev.append([s,e,gs[0],gs[1]])
self.selected=[[]]
try:
for tr in self.gazeData.track:
s=int(np.round(scale*tr[0]))
e=min(len(self.t)-1,max(s+1, int(np.round(scale*tr[1]))))
self.selected[0].append([self.t[s],s,tr[0],self.t[e],e,tr[1],tr[2],[],False])
for a in tr[3]:
s=int(np.round(scale*a[0]))
e=min(len(self.t)-1,max(s+1, int(np.round(scale*a[1]))))
self.selected[0][-1][7].append([self.t[s],s,a[0],self.t[e],e,a[1],[-1,-1,-1]])
except AttributeError: print 'Tracking events not available'
#self.selected=[Coder.loadSelection(self.gazeData.vp,
# self.gazeData.block,self.gazeData.trial,prefix= 'track/coder1/')]
self.pos[:,:,0]-=6 # shift agents locations on the screen
self.pos[:,:,1]+=5
self.wind.flip()
self.released=False # mouse key flag
self.save=False # flag for save selection tool data
except:
self.wind.close()
raise
## OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
## THE SOFTWARE.
from psychopy import visual, core, event,gui
from psychopy.misc import pix2deg
import numpy as np
import pylab as plt
import random, os
from Settings import Q
from Constants import *
# these routines were used to correct detection response where
# the subject clicked incorrect agent because a distractor overlapped
# or crossed with a target.
wind=Q.initDisplay()
# some constants
CHASEE=0
CHASER=1
X=0;Y=1;PHI=2
def exportFrame(trajectories,f,a1,a2,fn):
nrframes=trajectories.shape[0]
cond=trajectories.shape[1]
clrs=np.ones((cond,3))
clrs[CHASEE]=(0,1,0)
clrs[CHASER]=(1,0,0)
if a1>1:
clrs[a1]=(0,0,1)
else:
clrs[a2]=(0,0,1)
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