F[-1].append(fit)
# plot fit and save estimates
for f in F:
rtmc=[]
plt.figure(figsize=(16,12))
for vp in range(4):
d=rts[vp,rejs[vp,:]==1]
d=d[d>0]
plt.subplot(2,2,vp+1)
x=np.linspace(1,30,31)
plt.hist(d,bins=x,facecolor='w')
plt.ylim([0,140])
plt.xlim([0,30])
w=f[vp].extract()
varc=np.square(w['rtsc'])
vard=np.square(w['rtsd'])
rtmc.append([np.exp(w['rtmc']+varc/2.),
np.exp(w['rtmd']+vard/2.),
#np.sqrt((np.exp(varc)-1)*np.exp(2*w['rtmc']+varc)),
#np.sqrt((np.exp(vard)-1)*np.exp(2*w['rtmd']+vard)),
w['csrate'],w['dsrate'],
w['rtmc'],w['rtsc']])
plt.plot(x-0.5,d.size*lognorm(mu=w['rtmc'].mean(),
sigma=w['rtsc'].mean()).pdf(x-0.5))
rtmc=np.array(rtmc,ndmin=2)
np.save(os.getcwd().rstrip('code')+'evaluation/rtmc',rtmc)
np.save(os.getcwd().rstrip('code')+'evaluation/rejs',rejs)
np.save(os.getcwd().rstrip('code')+'evaluation/rts',rts)
np.save(os.getcwd().rstrip('code')+'evaluation/acc',acc)
def plotBehData():
plt.close()
from matustools.matustats import lognorm
rtmc=np.load(inpath+'rtmc.npy')
rejs=np.load(inpath+'rejs.npy')
rts=np.load(inpath+'rts.npy')
acc=np.load(inpath+'acc.npy')
figure(size=3,aspect=0.6)
ax = plt.subplot2grid((2,3), (0, 0), colspan=2)
#subplot(3,1,1)
formatAxes(ax)
#ax=plt.gca()
vp=0
d=rts[vp,rejs[vp,:]==1]
d=d[d>0]
x=np.linspace(1,30,30)
hist(d,bins=x,normed=True)
ax.plot(x-0.5,lognorm(mu=rtmc[vp,-2,:].mean(),
sigma=rtmc[vp,-1,:].mean()).pdf(x-0.5),'k')
plt.xlabel(FIG[0][1][0])
plt.ylabel(FIG[0][1][1])
subplotAnnotate(loc='ne',nr=0)
#plt.subplot(2,2,i+1);i+=1
for k in range(4):
subplot(2,3,[4,5,3,6][k])
plt.xlabel(FIG[0][2+k][0])
#else:plt.gca().set_xticklabels([])
#if k==0:
# print FIG[0][1+k]
plt.ylabel(FIG[0][2+k][1])
#elif k>1:
if k==1 or k==0: plt.ylim([6,20])
else: plt.ylim([0.75,1])
#if k==2:plt.ylabel(FIG[0][2+k][1])
#if k%2==1: plt.gca().set_yticklabels([])
errorbar(rtmc[:,k,:].T,x=range(1,5))
subplotAnnotate(loc='ne',nr=k+1)
plt.subplots_adjust(wspace=0.01,hspace=-0.2)
plt.savefig(figpath+FIG[0][0],dpi=DPI,bbox_inches='tight')
figure(size=2,aspect=0.4)
for vp in range(4):
sel= ~np.isnan(rts[vp,:])
d=np.int32(acc[vp,sel]==1)
d=np.reshape(d,(d.size/10.,10))
y=d.mean(1)
x=np.arange(y.size)*10
plt.plot(x+(vp-1.5)*1,y+(vp-1.5)*0.01,'+',ms=5,mew=1)
plt.ylim([0.2,1.05])
plt.gca().set_xticks(range(0,y.size*10,40))
plt.grid(True,axis='x')
plt.xlim([-4,250])
plt.xlabel(FIG[1][1][0])
plt.ylabel(FIG[1][1][1])
leg=plt.legend(FIG[1][1][2],loc=0,numpoints=1,frameon=True,
ncol=4)#,fontsize='small',labelspacing=0)
box=leg.get_frame()
box.set_linewidth(0.)
box.set_facecolor([0.9]*3)
plt.savefig(figpath+FIG[1][0],dpi=DPI,bbox_inches='tight')
plt.close('all')
