#plot each staircase
pylab.subplot(121)
colors = 'brgkcmbrgkcm'
lines, names = [], []
for fileN, thisStair in enumerate(allIntensities):
#lines.extend(pylab.plot(thisStair))
#names = files[fileN]
pylab.plot(thisStair, label=files[fileN])
#pylab.legend()
#get combined data
combinedInten, combinedResp, combinedN = \
data.functionFromStaircase(allIntensities, allResponses, 5)
#fit curve - in this case using a Weibull function
fit = data.FitFunction('weibullTAFC',combinedInten, combinedResp, \
guess=[0.2, 0.5])
smoothInt = pylab.arange(min(combinedInten), max(combinedInten), 0.001)
smoothResp = fit.eval(smoothInt)
thresh = fit.inverse(0.8)
print thresh
#plot curve
pylab.subplot(122)
pylab.plot(smoothInt, smoothResp, '-')
pylab.plot([thresh, thresh], [0, 0.8], '--')pylab.plot([0, thresh],\
[0.8,0.8],'--')
pylab.title('threshold = %0.3f' % (thresh))
#plot points
pylab.plot(combinedInten, combinedResp, 'o')
pylab.ylim([0, 1])
def fitData(stairs, percent):
allIntensities, allResponses = [], []
for s in stairs.staircases:
allIntensities.append(s.intensities)
allResponses.append(s.data)
for s in stairs.staircases:
print "Mean for condition ", s.condition['label'], "=", average(
s.reversalIntensities), "std=", std(s.reversalIntensities)
#plot each condition
pylab.subplot(221)
#for stairNumber, thisStair in enumerate(allIntensities):
pylab.plot(allIntensities[0],
'o-',
label=stairs.staircases[0].condition['label'])
pylab.xlabel('Trials')
pylab.ylabel('Speed [cm/s]')
pylab.legend()
# Get combined data
combinedInten, combinedResp, combinedN = data.functionFromStaircase(
allIntensities, allResponses, 10)
# Fit curve - in this case using a Weibull function
fit = data.FitFunction('weibullTAFC',
combinedInten,
combinedResp,
guess=None)
#fit = data.FitCumNormal(combinedInten,combinedResp)
intensitiesDomainInterp = pylab.arange(min(allIntensities[0]),
max(allIntensities[0]), 0.01)
smoothResponses = fit.eval(intensitiesDomainInterp)
thresh = fit.inverse(percent)
#Plot fitted curve
pylab.subplot(222)
pylab.axis(xmin=min(allIntensities[0]), xmax=max(allIntensities[0]))
pylab.xlabel('Speed [cm/s]')
pylab.ylabel('Probability')
pylab.plot(intensitiesDomainInterp, smoothResponses, '-')
pylab.plot([thresh, thresh], [0, percent], '--')
pylab.plot([0, thresh], [percent, percent], '--')
pylab.title('Threshold at ' + str(percent) + '= %0.3f' % (thresh))
# Plot points
pylab.plot(combinedInten, combinedResp, 'o')
pylab.ylim([0, 1])
# SECOND CONDITION, the plots are in a second row, under
pylab.subplot(223)
#for stairNumber, thisStair in enumerate(allIntensities):
pylab.plot(allIntensities[1],
'o-',
label=stairs.staircases[1].condition['label'])
pylab.xlabel('Trials')
pylab.ylabel('Speed [cm/s]')
pylab.legend()
# Get combined data
combinedInten, combinedResp, combinedN = data.functionFromStaircase(
allIntensities[1], allResponses[1], 10)
#fit curve - in this case using a Weibull function
#fit = data.FitFunction('weibullTAFC',combinedInten, combinedResp, guess=None)
fit = data.FitCumNormal(combinedInten, combinedResp)
intensitiesDomainInterp = pylab.arange(min(allIntensities[1]),
max(allIntensities[1]), 0.01)
smoothResponses = fit.eval(intensitiesDomainInterp)
thresh = fit.inverse(percent)
#print "Threshold at " + str(percent) +"% with Cumulative Normal= ",thresh
#Plot fitted curve
pylab.subplot(224)
pylab.axis(xmin=min(allIntensities[1]), xmax=max(allIntensities[1]))
pylab.xlabel('Speed [cm/s]')
pylab.ylabel('Probability')
pylab.plot(intensitiesDomainInterp, smoothResponses, '-')
pylab.plot([thresh, thresh], [0, percent], '--')
pylab.plot([0, thresh], [percent, percent], '--')
pylab.title('Threshold at ' + str(percent) + '= %0.3f' % (thresh))
# Plot points
pylab.plot(combinedInten, combinedResp, 'o')
pylab.ylim([0, 1])
pylab.show()