diff --git a/code/Coord.py b/code/Coord.py index 2ad2a4d..c36572f 100644 --- a/code/Coord.py +++ b/code/Coord.py @@ -689,9 +689,49 @@ def createIdealObserver(vpnr=999,N=5000,rseed=10): g=(D[n,mdp,0,:]+D[n,mdp,1,:])/2. D[n,:,:,0]-=g[0] D[n,:,:,1]-=g[1] - np.save(path+'E1/DG.npy',D) - + np.save(path+'E1/DG.npy',D) +########################################################### +def codingComparison(): + res=[]; + for vp in [2,1]: + initVP(vpl=vp,evl=0) + si1=np.load(path+'sicoder1.npy').tolist() + si2=np.load(path+'sicoder2.npy').tolist() + sel=[] + for i in range(len(si1)): + for j in range(i+1,len(si1)): + if si1[i][0]==si1[j][0] and si1[i][-1]==si1[j][-1] and si1[i][-2]==si1[j][-2]: + sel.append(j) + print sel + j=-1 + for a in si1: + j+=1 + if j in sel: continue + for b in si2: + if a[0]==b[0] and a[-1]==b[-1] and a[-2]==b[-2]: res.append([a[14],b[14]]) + print vp,len(res),len(si1),len(si2) + def analyze(dat): + tp=np.logical_and(dat[:,0],dat[:,1]).sum() + fn=np.logical_and(~dat[:,0],dat[:,1]).sum() + tn=np.logical_and(~dat[:,0],~dat[:,1]).sum() + fp=np.logical_and(dat[:,0],~dat[:,1]).sum() + acc=(tn+tp)/float(dat.shape[0]) + sens=tp/float(tp+fn) + spec=tn/float(fp+tn) + corr=(tp*tn-fp*fn)/np.sqrt((tp+fp)*(tp+fn)*(tn+fp)*(tn+fn)) + return [acc,sens,spec,corr] + #np.save(path+'ress',res) + out=[];res=np.array(res) + for dat in [res>0,res==0,res==1]: out.append(analyze(dat)) + res=res[np.logical_or(res[:,1]==0,res[:,1]==1),:] + out.append(analyze(res==1)) + np.save(path+'codingComparison',out) + from matustools.matusplotlib import ndarray2latextable + print ndarray2latextable(np.array(out)) + if __name__ == '__main__': + codingComparison() + bla for event in range(0,3)+range(96,100): for vpl in range(1,5): initVP(vpl=vpl,evl=event) diff --git a/code/ETData.py b/code/ETData.py index 3bcf489..3f72cad 100644 --- a/code/ETData.py +++ b/code/ETData.py @@ -324,7 +324,8 @@ def __init__(self,dat,calib,t0,info,recTime="0:0:0",fs=None, self.t0=[t0[1]-t0[0],t0[2]-t0[0]] self.fs=fs self.msgs=msgs - if self.fs==None or self.fs.size==0: self.fs= self.computeFs() + if self.fs==None or self.fs.size==0: + self.fs= self.computeFs() #fsa=self.computeFs() #m=min(self.fs.shape[0],fsa.shape[0]) #print np.round(np.max(np.abs(self.fs[:m,:2]-fsa[:m,:])),1), self.t0[1]-self.t0[0] @@ -353,7 +354,7 @@ def computeFs(self): f.close() dur=self.t0[1]-self.t0[0] inc=1000/monhz - N=min(int(round((dur)/inc)), int(Q.trialDur*monhz)+1) + N=min(int(round((dur)/inc)), int(Qexp.trialDur*monhz)+1) return np.array([range(N),np.linspace(inc/2.0,dur-inc,N)]).T ####################################################### # helper functions for drift correction and extraction of basic events @@ -495,7 +496,7 @@ def extractBasicEvents(self): dat=self.gaze # add two columns with binocular gaze point if self.focus==BINOCULAR: - print self.gaze.shape + #print self.gaze.shape gazep=np.array([dat[:,[1,4]].mean(1),dat[:,[2,5]].mean(1)]).T temp=dat[np.isnan(dat[:,1]),:] if temp.size>0: gazep[np.isnan(dat[:,1]),:]=temp[:,[4,5]] diff --git a/code/FiguresMoviesTables.py b/code/FiguresMoviesTables.py index 6e9c859..0d5c09c 100644 --- a/code/FiguresMoviesTables.py +++ b/code/FiguresMoviesTables.py @@ -27,6 +27,7 @@ from matustools.matusplotlib import * from Coord import initVP import os +from scipy.optimize import fmin LINEWIDTH=5.74 # peerj line width in inches DPI=300 @@ -45,32 +46,12 @@ ('Distance to Saccade Target in Degrees', 'Motion Saliency'), ('Time to Saccade Onset in Seconds', 'Contrast Saliency'), ('Time to Saccade Onset in Seconds', 'Motion Saliency')), - ('Pixel/buttonPress','Time in seconds','Distance in degrees','S',-15,30,72), + ('Pixel/buttonPress','Time to button press in seconds','Distance in degrees','S',-15,30,72), ('Coord/trackEv','ES CS1 ... CS9 ... CS19','S'), ('Coord/trackVel',('Time in seconds','Velocity in deg/s',['S1','S2','S3','S4'])), ('Coord/trackPFrail',('Start','Middle','End'),'S',-15), ('S',-15,'CS1','ES') ) -FIG2=(('behdata.png',('Reaktionszeit','Wahrscheinlichkeit' ), - ('Versuchsperson','Reaktionszeit'), - ('Versuchsperson','Reaktionszeit'), - ('Versuchsperson','Genauigkeit'), - ('Versuchsperson','Genauigkeit')), - ('bdtime.png',('Trial','Genauigkeit',['VP1','VP2','VP3','VP4'])), - ('Coord/agdens',('Entfernung zum Sakkadenziel in Grad','Ringedichte',['VP1','VP2','VP3','VP4','RZ']), - ('Zeit zum Sakkadenanfang in Ms','Ringedichte'), - ('Entfernung zum Sakkadenziel in Grad',u'Bewegungsänderungen'), - ('Zeit zum Sakkadenanfang in Ms','Bewegungsändereungen'), - ('Entfernung zum Sakkadenziel in Grad', 'Kontrastsalienz'), - ('Entfernung zum Sakkadenziel in Grad', 'Bewegungssalienz'), - ('Zeit zum Sakkadenanfang in Ms', 'Kontrastsalienz'), - ('Zeit zum Sakkadenanfang in Ms', 'Bewegungssalienz')), - ('Pixel/buttonPress','Zeit in Sek','Entfernung in Grad','VP',-20,35,75), - ('Coord/trackEv','ES AS1 ... AS9 ... AS19','VP'), - ('Coord/trackVel',('Zeit in Sek','Geschwindigkeit in Grad/Sek',['VP1','VP2','VP3','VP4'])), - ('Coord/trackPFrail',('Anfang','Mitte','Ende'),'VP',-20), - ('VP',-20,'AS1','ES') - ) inpath = os.getcwd().rstrip('code')+'evaluation'+os.path.sep figpath = os.getcwd().rstrip('code')+'figures'+os.path.sep @@ -382,8 +363,56 @@ def tabLatent(ev,pcs=5): path=inpath+'vp%03d/E%d/X/'%(vp,ev) dat.append(np.load(path+'latent.npy')[:pcs]*100) return ndarray2latextable(np.array(dat),decim=1) - -def pcAddition(): + +def plotPC1rail(): + T=68;P=64 + t=np.linspace(-0.8,0.8,T);p=np.linspace(-5,5,P) + tm=np.repeat(t[np.newaxis,:],P,axis=0) + pm=np.repeat(p[:,np.newaxis],T,axis=1) + fig=figure(size=3,aspect=0.7) + fig.tight_layout() + bnds=[(1,None),(None,0),(None,None)];est=[] + for ev in [0,1]: + for vp in range(1,5): + fn=inpath+'vp%03d/E%d/'%(vp,ev)+'X/coeff.npy' + pc=_getPC(np.load(fn),0) + if pc.mean()>=0.4: pc=1-pc + inc=0 + D= pc.T[:,(31+inc):(33+inc),:].mean(1) + D/=D.sum(); + subplot(2,4,ev*4+vp) + plt.pcolor(p,t,D.T,cmap='gray') + # below we set the initial guess + x0=np.array((5,-12,0.2)) + xopt=fmin(func=_fun,x0=x0,args=(D,t,p,False)) + est.append(xopt.tolist()) + plt.plot(xopt[0]-xopt[1]*t,t,'r',lw=1,alpha=0.4) + plt.grid(True,'both'); + plt.xlim([p[0],p[-1]]);plt.ylim([t[0],t[-1]]); + ax=plt.gca();ax.set_axisbelow(False) + ax.set_xticks([-4,-2,0,2,4]) + ax.set_yticks(np.linspace(-0.8,0.8,5)) + if not ev: ax.set_xticklabels([]) + if vp>1: ax.set_yticklabels([]) + else: + ax.set_yticklabels(np.linspace(-0.8,0.8,5)) + plt.ylabel(['ES','CS1'][ev]+'\nTime to saccade in sec.') + #if i==1: plt.text(2,-1,FIG[3][2],size=8) + #if i==1: plt.xlabel(FIG[3][2]) + #else: plt.ylabel('subject %d'%(i+1)) + if not ev: plt.title(FIG[3][3]+str(vp)) + print FIG[2][1][0] + plt.subplot(2,4,6);plt.text(-9,-1.15,FIG[2][1][0]) + plt.subplots_adjust(wspace=-1) + plt.savefig(figpath+'Pixel'+os.path.sep+'pcSacRail', + dpi=DPI,bbox_inches='tight') + est=np.array(est) + print est.ndim, est + if est.ndim>2: est=np.squeeze(est) + est[:,1]*=(t[-1]-t[0])/0.8 + print ndarray2latextable(est.T,decim=2) + +def pcAddition(MOVIE=True): #BP S1 out=[] for vp in [1,0]: @@ -403,8 +432,23 @@ def pcAddition(): out[-1].append(pc1) out[-1].append(1-pc2) out[-1].append((pc1+pc2)/2.) - plotGifGrid(out,fn=figpath+'Pixel/pcAddition'+FMT,bcgclr=1, + if MOVIE: + plotGifGrid(out,fn=figpath+'Pixel/pcAddition'+FMT,bcgclr=1,snapshot=2, plottime=True,text=[['A',20,12,-10],['B',20,84,-10]]) + bla + print out[0][0].shape + cols=5;fs=np.linspace(0,out[0][0].shape[0]-1,cols) + ps=np.arange(out[0][0].shape[1]) + for i in range(len(out)): + for j in range(len(out[0])): + for fi in range(cols): + plt.subplot(3,cols,j*cols+fi+1) + plt.pcolor(ps,ps,out[i][j][fs[fi],:,:],cmap='gray') + #plt.grid() + plt.savefig(figpath+'Pixel'+os.path.sep+'pcAddition', + dpi=DPI,bbox_inches='tight') + + def plotScore(vp,event,pcs=5,scs=3): @@ -444,13 +488,56 @@ def plotScore(vp,event,pcs=5,scs=3): #print h,i,ns[i],ns[i]/bd,ns[i]%bd, s R[1:,s[0]:s[0]+64,s[1]:s[1]+64]= _getPC(np.float32(pf),h) ndarray2gif(figpath+'Pixel/scoreVp%de%d'%(vp,event)+FMT, - np.uint8(R*255),duration=0.1,plottime=True) + np.uint8(R*255),duration=0.1,plottime=True,snapshot=True) +def plotPCvp(vp,ev,t=1,pcs=5): + '''t - 0=start,1=middle,2=end ''' + plt.close() + path=inpath+'vp%03d/E%d/'%(vp,ev) + coeff=np.load(path+'X/coeff.npy') + for h in range(pcs): + ax=plt.subplot(1,pcs,h+1) + ax.set_xticks([]);ax.set_yticks([]); + ax.spines['bottom'].set_visible(False) + ax.spines['left'].set_visible(False) + pc=_getPC(coeff,h) + if pc.mean()>=0.4: pc=1-pc + pc=pc[[0,pc.shape[0]/2,pc.shape[0]][t],:,:] + plt.imshow(pc,cmap='gray') + plt.grid(False); + plt.title('PC'+str(h+1)) + plt.savefig(figpath+'Pixel/PCvp%dev%d'%(vp,ev),dpi=DPI,bbox_inches='tight') + + + + +def _fun(x,D=None,t=None,p=None,verbose=False): + ''' objective function for fitting lines to rail diagrams''' + T=t.size;P=p.size + tm=np.repeat(t[np.newaxis,:],P,axis=0) + pm=np.repeat(p[:,np.newaxis],T,axis=1) + nrlines=len(x)/3; p1=np.nan; + if nrlines==1: p0,v0,s0=tuple(x) + elif nrlines==2: p0,v0,s0,p1,v1,s1=tuple(x) + else: raise ValueError + out=np.ones((P,T)) + dist=np.abs(pm+v0*tm-p0)/np.sqrt(1+v0**2)/s0 + out=np.maximum(1-np.power(dist,3),0)/float(nrlines) + if nrlines==2: + dist=np.abs(pm+v1*tm-p1)/np.sqrt(1+v1**2)/s1 + out+=np.maximum(1-np.power(dist,3),0)/float(nrlines) + out/=out.sum() + if D is None: return out + fout=-np.corrcoef(D.flatten(),out.flatten())[0,1]# np.linalg.norm(D-out)**2 + if verbose: print 'p0=%.2f, v=%.2f, p1=%.2f, s=%.2f, f=%f'%(p0,v,p1,s1,s2,fout) + return fout ######################################################### # button press -def plotBTmean(MAX=16): +def plotBTavg(MAX=16,MOVIE=True): from matustools.matusplotlib import plotGifGrid - dat=[] + dat=[];T=68;P=64;est=[] + t=np.linspace(-0.8,0,T);p=np.linspace(-5,5,P) + figure(size=3,aspect=1) for vp in range(1,5): dat.append([]) for event in range(-6,0): @@ -458,6 +545,48 @@ def plotBTmean(MAX=16): d=np.squeeze(np.load(fn)) #print np.max(d.mean(axis=0)),np.min(d.mean(axis=0)) dat[-1].append(d.mean(axis=0)/float(MAX)) + + inc=0#[-2,2,0,0,0,0][i+1] + D= (d.mean(axis=0)/float(MAX)) + D=np.rollaxis(D,1) + D=D[:,(31+inc):(33+inc),:].mean(1);D/=D.sum(); + j=vp-1; i=event+6 + subplot(6,4,i*4+j+1) + plt.pcolor(p,t,D.T,cmap='gray') + # below we set the initial guess + if i==3: + if vp==1: x0=np.array((0.5,-12,0.3)) + else: x0=np.array((3,-12,0.2,-0.5,-12,0.2)) + xopt=fmin(func=_fun,x0=x0,args=(D,t,p,False)) + est.append(xopt.tolist()) + plt.plot(xopt[0]-xopt[1]*t,t,'r',lw=1,alpha=0.4) + if vp!=1: plt.plot(xopt[3]-xopt[4]*t,t,'r',lw=1,alpha=0.4) + else:est[-1].extend([np.nan]*3) + plt.grid(True,'both'); + plt.xlim([p[0],p[-1]]);plt.ylim([t[0],t[-1]]); + ax=plt.gca();ax.set_axisbelow(False) + ax.set_xticks([-4,-2,0,2,4]) + ytck=np.linspace(-0.8,0,5)[1:] + ax.set_yticks(ytck) + if j>0: ax.set_yticklabels([]) + else: ax.set_yticklabels(ytck-[0.5,0.4,0.3,0.2,0.1,0.05][i]) + #if i==1: plt.text(2,-1,FIG[3][2],size=8) + if i<5: ax.set_xticklabels([]) + #else: plt.ylabel('subject %d'%(i+1)) + #if i==0: plt.title(str(m[j]*2+2)) + # + if i==0: plt.title(FIG[3][3]+str(j+1)) + plt.subplot(6,4,5) + plt.text(-9,-0.5,FIG[3][1],rotation='vertical') + plt.subplot(6,4,22) + plt.text(-2,-1.2,FIG[3][2]) + plt.subplots_adjust(wspace=-1) + plt.savefig(figpath+FIG[3][0]+'avg',dpi=DPI,bbox_inches='tight') + est=np.array(est) + print est.ndim, est + if est.ndim>2: est=np.squeeze(est) + print ndarray2latextable(est,decim=2) + if not MOVIE: return dat lbl=[] #for i in range(4):lbl.append([FIG[6][1][3]+str(i+1),20,32+i*72,-15]) for i in range(4):lbl.append([FIG[3][3]+str(i+1),20,32+i*72,FIG[3][4]]) @@ -467,28 +596,11 @@ def plotBTmean(MAX=16): text=lbl,plottime=True) return dat -def plotBTpt(vpn=range(1,5),pcaEv=97): +def plotBTpc(vpn=range(1,5),pcaEv=97): ''' vpn - list with subject ids pcaEv - id of the catch-up saccade, 0=exploration saccade, 97 - 200 ms before button press''' - from scipy.optimize import fmin - def fun(x,D=None,verbose=False): - nrlines=len(x)/3; p1=np.nan;s=0.15 - if nrlines==1: p0,v0,s0=tuple(x) - elif nrlines==2: p0,v0,s0,p1,v1,s1=tuple(x) - else: raise ValueError - out=np.ones((P,T)) - dist=np.abs(pm+v0*tm-p0)/np.sqrt(1+v0**2)/s0 - out=np.maximum(1-np.power(dist,3),0)/float(nrlines) - if nrlines==2: - dist=np.abs(pm+v1*tm-p1)/np.sqrt(1+v1**2)/s1 - out+=np.maximum(1-np.power(dist,3),0)/float(nrlines) - out/=out.sum() - if D is None: return out - fout=-np.corrcoef(D.flatten(),out.flatten())[0,1]# np.linalg.norm(D-out)**2 - if verbose: print 'p0=%.2f, v=%.2f, p1=%.2f, s=%.2f, f=%f'%(p0,v,p1,s,fout) - return fout - #dat=plotBTmean() + #dat=plotBTavg(MOVIE=False) T=68#dat[0][0].shape[-1] P=64#dat[0][0].shape[0] t=np.linspace(-0.8,0,T);p=np.linspace(-5,5,P) @@ -496,59 +608,56 @@ def fun(x,D=None,verbose=False): pm=np.repeat(p[:,np.newaxis],T,axis=1) rows=len(vpn) #cols=len(dat[0]) - fig=figure(size=3,aspect=0.35) + fig=figure(size=2,aspect=0.65) fig.tight_layout() #m=[-251,-201,-151,-101,-51,-1] bnds=[(1,None),(None,0),(None,None)] est=[] - for i in range(-1,rows): + for i in range(rows+2): #for k in [1]: #est.append([]) - j=4# 200 ms - fn=inpath+'vp%03d/E%d/'%(vpn[max(0,i)],pcaEv)+'X/coeff.npy' - if i==0: pc=(_getPC(np.load(fn),0)+_getPC(np.load(fn),1))/2. - elif i==-1: pc=(_getPC(np.load(fn),0)-_getPC(np.load(fn),1)+1)/2. + #j=4# 200 ms + fn=inpath+'vp%03d/E%d/'%(vpn[max(0,i-2)],pcaEv)+'X/coeff.npy' + if i==1: pc=(_getPC(np.load(fn),0)+_getPC(np.load(fn),1))/2. + elif i==0: pc=(_getPC(np.load(fn),0)-_getPC(np.load(fn),1)+1)/2. else: pc=_getPC(np.load(fn),0) if pc.mean()>=0.4: pc=1-pc - inc=[-2,2,0,0,0,0][i+1] + inc=[-2,2,0,0,0,0][i] D= pc.T[:,(31+inc):(33+inc),:].mean(1) - #else: D=dat[i][j][31:33,:,:].mean(0) - D/=D.sum() - #print i,k,D.shape,D.sum() - - subplot(1,5,i+2) + D/=D.sum(); + subplot(2,3,i+1) plt.pcolor(p,t,D.T,cmap='gray') # below we set the initial guess - if vpn[i]==999: x0=np.array((3,-12,0.1,7,-12,0.1)) - elif vpn[max(0,i)]==1: - if i==-1: x0=np.array((1,-12,0.3,-2,-12,0.1)) - else:x0=np.array((3,-12,0.1,0,-12,0.1)) + if vpn[max(0,i-2)]==999: x0=np.array((3,-12,0.1,7,-12,0.1)) + elif i==0: x0=np.array((1,-12,0.3,-2,-12,0.1)) + elif i==1:x0=np.array((3,-12,0.1,0,-12,0.1)) + elif i==2: x0=np.array((0,-12,0.1)) else: x0=np.array((3,-12,0.1,-2,-12,0.1)) - xopt=fmin(func=fun,x0=x0,args=(D,False)) + xopt=fmin(func=_fun,x0=x0,args=(D,t,p,False)) est.append(xopt.tolist()) plt.plot(xopt[0]-xopt[1]*t,t,'r',lw=1,alpha=0.4) - #if vpn[i]!=1: - plt.plot(xopt[3]-xopt[4]*t,t,'r',lw=1,alpha=0.4) - #else:est[-1].extend([np.nan]*3) + if x0.size==6: plt.plot(xopt[3]-xopt[4]*t,t,'r',lw=1,alpha=0.4) + elif x0.size==3:est[-1].extend([np.nan]*3) + else: raise ValueError plt.grid(True,'both'); plt.xlim([p[0],p[-1]]);plt.ylim([t[0],t[-1]]); ax=plt.gca();ax.set_axisbelow(False) ax.set_xticks([-4,-2,0,2,4]) ax.set_yticks(np.linspace(-0.8,0,5)) - if i in set((0,1,2,3)): ax.set_yticklabels([]) + if i%3!=0: ax.set_yticklabels([]) else: ax.set_yticklabels(np.linspace(-1,-0.2,5)) - plt.ylabel(FIG[3][1]) - #if i==1: plt.text(2,-1,FIG[3][2],size=8) - if i==1: plt.xlabel(FIG[3][2]) + if i<3: ax.set_xticklabels([]) + if i==0: plt.text(-9,-0.2,FIG[3][1],size=8,rotation='vertical') + if i==4: plt.xlabel(FIG[3][2]) #else: plt.ylabel('subject %d'%(i+1)) #if i==0: plt.title(str(m[j]*2+2)) - plt.text(1,-0.75,FIG[3][3]+str(vpn[max(i,0)])+['a','b',''][min(i+1,2)],color='w') + plt.text(2.1,-0.75,FIG[3][3]+str(vpn[max(i-2,0)])+['a','b',''][min(i,2)],color='w') plt.subplots_adjust(wspace=-1) plt.savefig(figpath+FIG[3][0]+'%d'%pcaEv, dpi=DPI,bbox_inches='tight') est=np.array(est) - print est.ndim, est + print est.shape,est if est.ndim>2: est=np.squeeze(est) print ndarray2latextable(est,decim=2) @@ -663,7 +772,7 @@ def plotTrack(MOVIE=True): if h==2:FFs[vp][[0,2,1][g]]=temp lbl=[] for i in range(4):lbl.append([FIG[6][2]+str(i+1),20,65+i*137,FIG[6][3]]) - for i in range(3):lbl.append([['1','2','>2'][i],20,-10,85+i*135]) + for i in range(3):lbl.append([['1','2','>2'][i],20,-10,85+i*135]) if MOVIE: plotGifGrid(Fs,fn=figpath+'Coord/'+['trackPFforw', 'trackPFback','trackPFmid'][g]+FMT, bcgclr=1,plottime=True,text=lbl,P=129,F=85) @@ -697,11 +806,33 @@ def plotTrack(MOVIE=True): ax.set_xticks([-5,-2.5,0,2.5,5]) plt.savefig(figpath+FIG[6][0],dpi=DPI,bbox_inches='tight') + for vp in range(D.shape[0]): + for h in range(1,D.shape[1]): + denom=[0.1,0.05,0.05][h-1] + d=D[vp,h,0,60:68,:,:].mean(0)/denom + d[d>1]=1 + d[np.isnan(d)]=0 # happens when no samples are available + T=d.shape[1];P=d.shape[0] + print vp,h ,np.min(d),np.max(d) + ax=subplot(4,3,h+3*vp) + t=np.linspace(0,T*1/85.,T) + if not g:plt.ylabel(FIG[6][2]+str(vp+1),size=18) + if not vp:plt.title(['1','2','>2'][h-1],size=18) + p=np.linspace(-5,5,P) + plt.pcolor(p,t,d.T,cmap='gray') + plt.xlim([p[0],p[-1]]);plt.ylim([t[0],t[-1]]) + if h>1: ax.set_yticklabels([]) + if vp<3: ax.set_xticklabels([]) + ax.set_yticks([0,0.250,0.500,0.750,1.]) + ax.set_axisbelow(False);plt.grid(True) + ax.set_xticks([[-5],[]][h>1]+[-2.5,0,2.5,5]) + plt.savefig(figpath+'Coord/trackPFforw',dpi=DPI,bbox_inches='tight') + ############################# # SVM def svmPlotExtrRep(event=0,plot=True,suf=''): from Pixel import initPath - plt.close() + if plot: plt.close() P=32;F=34 dat=[] for vp in range(1,5): @@ -734,19 +865,68 @@ def svmPlotExtrema(): out[vp].extend([dat[vp][1],dat[vp][5]]) path,inpath,figpath=initPath(1,0) plotGifGrid(out,fn=figpath+'svmExtrema'+FMT,bcgclr=0.5,F=34,P=32, - duration=0.2,plottime=True,snapshot=True) - + duration=0.2,plottime=True,snapshot=True) + +def svmComparison(): + from Pixel import initPath + figure(size=3,aspect=0.35) + svm=svmPlotExtrRep(0,plot=False) + for vp in range(1,5): + est=np.zeros((2,3)) + ax=subplot(1,4,vp) + for ev in [0,1]: + path,inpath,figpath=initPath(vp,ev) + coeff=np.load(inpath+'X/coeff.npy') + pc=_getPC(coeff,0) + if pc.mean()>=0.4: pc=1-pc + t=np.linspace(-0.4,0.4,pc.shape[0]) + p=np.linspace(-5,5,pc.shape[1]) + D= pc.T[:,31:33,:].mean(1) + D/=D.sum(); + # below we set the initial guess + x0=np.array((0,-12,0.1)) + xopt=fmin(func=_fun,x0=x0,args=(D,t,p,False)) + est[ev,:]=xopt + plt.plot(xopt[0]-xopt[1]*t,t,['g','m'][ev],lw=1,alpha=1) + + #plt.plot(est[:,0].mean()-est[:,1].mean()*t,t,'r',lw=1,alpha=0.4) + dat=svm[vp-1][1][15:17,:,:].mean(0) + t=np.linspace(-0.4,0.4,dat.shape[1]) + p=np.linspace(-5,5,dat.shape[0]) + print dat.shape + plt.xlim([p[0],p[-1]]);plt.ylim([t[0],t[-1]]); + plt.pcolor(p,t,dat.T,cmap='gray'); + ax.set_axisbelow(False);plt.grid(True); + ax.set_yticks([-0.4,-0.2,0,0.2,0.4]) + if vp>1: ax.set_yticklabels([]) + plt.text(-3.9,0.21,'S'+str(vp),color='w',fontsize=16) + leg=plt.legend(['ES','CS1'],loc=4,frameon=True) + box=leg.get_frame() + box.set_linewidth(0.) + box.set_facecolor([0.9]*3) + plt.subplot(1,4,1);plt.text(8,-0.57,FIG[2][1][0],fontsize=8) + plt.ylabel('Time to Saccade in Sec.') + plt.savefig(figpath+'svmComparison',dpi=DPI,bbox_inches='tight') + if __name__=='__main__': + #plotScore(999,1,pcs=4,scs=0) + #plotTrack(MOVIE=False) + #plotPCvp(999,1) + plotBTpc() + bla + + # to create figures run plotBehData() plotAnalysis(event=0) plotAnalysis(event=1) plotEvStats() - plotBTpt() + plotBTpc() plotVel() - plotTrack() + plotTrack(MOVIE=False) + svmComparison() from ReplayData import compareCoding compareCoding(vp=2,block=18,cids=[0,1,2,4]) @@ -756,8 +936,9 @@ def svmPlotExtrema(): plotCoeff(97) plotCoeff(0) plotCoeff(1) + plotTrack() svmPlotExtrema() - plotScore(999,1,pcs=5,scs=0) + plotScore(999,1,pcs=4,scs=0) from ReplayData import replayTrial #note the following trials will be displayed but not saved as movies @@ -771,6 +952,8 @@ def svmPlotExtrema(): tabLatent(0,pcs=5) tabLatent(1,pcs=5) tabLatent(97,pcs=5) + + diff --git a/code/Pixel.py b/code/Pixel.py index aec9b85..0d5a850 100644 --- a/code/Pixel.py +++ b/code/Pixel.py @@ -377,7 +377,7 @@ def SsimilSvm(vp,ev,beta,fn,suf=''): S=np.exp(-np.exp(beta)*S/5000.) f=open(pth+'E%d/svm%s/svm%d.in'%(e1,suf,int(beta*10)),'w') for row in range(n1+n2): - s='%d 0:%d'%(int(row0: - dist=np.linalg.norm(g[b[1]+1,[7,8]]-g[b[0]-1,[7,8]]) - D.append([b[1]-b[0],dist]) - D=np.array(D) + import sys + saveTrackedAgs(vp=2,coderid=4) + bla + vp=int(sys.argv[1]) + coder=int(sys.argv[2]) + saveETinfo(vp=vp,coderid=coder,suf='coder%d'%coder) + #print np.int32(np.isnan(data[0].gaze[:1000,1])) #data[0].extractBasicEvents() diff --git a/code/ReplayData.py b/code/ReplayData.py index 566a55d..ada6c16 100644 --- a/code/ReplayData.py +++ b/code/ReplayData.py @@ -573,7 +573,7 @@ def replayTrial(vp,block,trial,tlag=0,coderid=0): R.play(tlag=tlag) def replayBlock(vp,block,trial,tlag=0,coderid=0,exportAlgo=False): - ''' replays the remaining block from the chase experiment starting at + ''' replays the remaining block from the chase experiment starting at vp - subject id block - block id trial - trial id on which the replay will start @@ -641,16 +641,20 @@ def compareCoding(vp,block,cids=[0,1,2]): plt.savefig(PATH+'comparison'+os.path.sep+'vp%db%d.jpg'%(vp,block),format='jpg',dpi=100,bbox_inches='tight') #plt.show() -def missingTEfiles(): +def missingTEfiles(vp=1): ''' checks for missing coding files''' - vp=1 for b in range(1,22): for t in range(40): - for c in range(2): + coded=[0,0,0] + for c in [1,2,4]: try: - dat=Coder.loadSelection(vp,b,t,coder=c+1) + dat=Coder.loadSelection(vp,b,t,coder=c) except IOError: - print vp,b,t,c+1 + coded[[7,0,1,7,2][c]]=1 + if sum(coded)>0 and sum(coded)<3: print vp,b,t,coded + + + if __name__ == '__main__': RH=0 # set right handed (1) or left handed (0) layout diff --git a/code/Settings.py b/code/Settings.py index 2091a85..e5214b5 100644 --- a/code/Settings.py +++ b/code/Settings.py @@ -84,7 +84,7 @@ def __init__(self,monitor,os,trialDur,refreshRate,agentSize,phiRange, else: self.delim='/' path = getcwd() path = path.rstrip('code') - self.inputPath=path+"trajData"+self.delim + self.inputPath=path+"input"+self.delim self.outputPath=path+"behData"+self.delim self.stimPath=path+"stimuli"+self.delim self.agentRadius=self.agentSize/2.0 @@ -249,7 +249,7 @@ def load(filepath): 'fullscr': True} matusdesktop ={'monitor' : eizo, - 'refreshRate': 60, # [hz] + 'refreshRate': 60 , # [hz] 'os': LINUX, # Linux or Windows 'phiRange': [120,0*2], # in degrees [0-360] 'agentSize': 1, # in degrees of visial angle @@ -259,15 +259,13 @@ def load(filepath): 'agentCLR': 1, # [1 -1] 'mouseoverCLR': 0.5, # [1 -1] 'selectedCLR': -0.5, # [1 -1] - 'trialDur': 120, # in seconds + 'trialDur': 30, # in seconds 'aSpeed': 14.5, # in degrees of visual angle per second 'guiPos': (200,400), # in pixels 'winPos': (0,0), # in pixels 'fullscr': False} -Q=Settings(**tobiilab) - Q=Settings(**matusdesktop) -Qexp=Settings(**tobiilab) +Qexp=Settings(**eyelinklab) diff --git a/evaluation/coding/coder1/vp001b21tr38.trc b/evaluation/coding/coder1/vp001b21tr38.trc deleted file mode 100644 index 199fbc5..0000000 --- a/evaluation/coding/coder1/vp001b21tr38.trc +++ /dev/null @@ -1,3 +0,0 @@ -658 49 327 1220 91 609 1 658 49 327 1220 91 609 0 -2222 166 1108 2556 191 1272 11 2222 166 1108 2556 191 1272 0 -2792 209 1395 5845 500 3333 0 1 2797 209 1395 5845 437 3333 2797 209 1395 5845 437 3333 1 diff --git a/evaluation/coding/coder1/vp001b9tr13.trc b/evaluation/coding/coder1/vp001b9tr13.trc deleted file mode 100644 index 58c8562..0000000 --- a/evaluation/coding/coder1/vp001b9tr13.trc +++ /dev/null @@ -1,6 +0,0 @@ -579 43 289 899 67 445 11 579 43 289 899 67 445 0 -1500 112 746 1940 145 968 9 1500 112 746 1940 145 968 0 -2862 214 1429 3729 279 1858 3 10 2862 214 1429 3729 279 1858 2862 214 1429 3729 279 1858 0 -4410 330 2203 4517 338 2255 9 4410 330 2203 4517 338 2255 0 -5986 448 2984 6987 523 3486 8 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