def test_weibull():
#fit to the fake data
fit = data.FitWeibull(contrasts, responses, display=0, expectedMin=0.5)
#check threshold is close (maybe not identical because not same function)
assert thresh-fit.inverse(0.75)<0.01
#check that inverse works too
modResps = fit.eval(contrasts)
invs = fit.inverse(modResps)
assert numpy.allclose(contrasts,invs), contrasts-invs#inverse should match the forwards function
#do a plot to check fits look right
if PLOTTING:
plotFit(modResps, thresh, 'Weibull (thresh=%.2f, params=%s)' %(fit.inverse(0.75), fit.params))
def test_weibull():
#fit to the fake data
fit = data.FitWeibull(contrasts, responses, display=0, expectedMin=0.5)
#check threshold is close (maybe not identical because not same function)
assert thresh - fit.inverse(0.75) < 0.001
#check that inverse works too
modelResponses = fit.eval(contrasts)
invs = fit.inverse(modelResponses)
assert numpy.allclose(
contrasts,
invs), contrasts - invs #inverse should match the forwards function
#do a plot to check fits look right
if PLOTTING:
pylab.figure(1)
pylab.plot(contrasts, responses, 'o')
pylab.plot(contrasts, fit.eval(contrasts))
pylab.plot([0, thresh], [0.75, 0.75], '--b') #horiz
pylab.plot([thresh, thresh], [0., 0.75], '--b') #vert
pylab.title('Fitting Weibull (thresh=%.2f)' % (fit.inverse(0.75)))
for fileN, thisStair in enumerate(allIntensities):
# lines.extend(pylab.plot(thisStair)) # uncomment for a legend for files
# names = files[fileN]
pylab.plot(thisStair, label=files[fileN])
# pylab.legend()
# get combined data
i, r, n = data.functionFromStaircase(allIntensities,
allResponses,
bins='unique')
combinedInten, combinedResp, combinedN = i, r, n
combinedN = pylab.array(combinedN) # convert to array so we can do maths
# fit curve
fit = data.FitWeibull(combinedInten,
combinedResp,
expectedMin=expectedMin,
sems=1.0 / combinedN)
smoothInt = pylab.arange(min(combinedInten), max(combinedInten), 0.001)
smoothResp = fit.eval(smoothInt)
thresh = fit.inverse(threshVal)
print(thresh)
# plot curve
pylab.subplot(122)
pylab.plot(smoothInt, smoothResp, 'k-')
pylab.plot([thresh, thresh], [0, threshVal], 'k--') # vertical dashed line
pylab.plot([0, thresh], [threshVal, threshVal],
'k--') # horizontal dashed line
pylab.title('threshold (%.2f) = %0.3f' % (threshVal, thresh))
# plot points
#plot each staircase
pylab.subplot(121)
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, 'unique')
#fit curve
guess= [num.average(combinedInten), num.average(combinedInten)/5]
fit = data.FitWeibull(combinedInten, combinedResp, guess=guess, expectedMin=0.0)
smoothInt = num.arange(min(combinedInten), max(combinedInten), 0.001)
smoothResp = fit.eval(smoothInt)
thresh = fit.inverse(0.5)
print thresh
#plot curve
pylab.subplot(122)
pylab.plot(smoothInt, smoothResp, '-')
pylab.plot([thresh, thresh],[0,0.5],'--'); pylab.plot([0, thresh],[0.5,0.5],'--')
pylab.title('threshold = %0.3f' %(thresh))
#plot points
pylab.plot(combinedInten, combinedResp, 'o')
pylab.ylim([0,1])
pylab.show()
staircase.importData( toStaircase(noiseEachTrial,descendingPsycho), np.array(corrEachTrial) )
printStaircase(staircase, briefTrialUpdate=False, printInternalVal=True, alsoLog=False)
#Fit and plot data
descendingPsycho = False
fit = None
intensityForCurveFitting = outOfStaircase(staircase.intensities,staircase,descendingPsycho)
#print('intensityForCurveFitting=',intensityForCurveFitting)
if descendingPsycho:
intensityForCurveFitting = 100-staircase.intensities #because fitWeibull assumes curve is ascending
#convert from list of trials to probabilities
combinedInten, combinedResp, combinedN = \
data.functionFromStaircase(intensityForCurveFitting, staircase.data, bins='unique')
print('combinedInten=',combinedInten,'combinedResp=',combinedResp)
try:
fit = data.FitWeibull(combinedInten, combinedResp, expectedMin=0, sems = 1.0/len(staircase.intensities))
print('fit=',fit)
except:
print("Fit failed.")
plotDataAndPsychometricCurve(staircase,fit,descendingPsycho,threshVal=0.75)
pylab.show() #must call this to actually show plot
testNoise = True
if testNoise: #Test noise
myWin = visual.Window()
proportnNoise = 0.9
noiseFieldWidthPix = 200
bgColor = [-.7,-.7,-.7]
(noise,allFieldCoords,numNoiseDots) = createNoise(proportnNoise,myWin,noiseFieldWidthPix, bgColor)
print("allFieldCoords[0:3]=",allFieldCoords[0:3])
from psychopy import data
import numpy as np
#my data
x = np.array([5.0, 10.0, 20.0, 50.0, 70.0, 80.0, 95.0])
y = np.array([0.4, 0.4, 0.4, 0.57, 1.0, 1.0, 1.0]) #good
expectedMin = 0
try:
data.FitWeibull(x, y, expectedMin=expectedMin)
print('Fit succeeded on my 1st dataset')
except:
print('Fit failed on my 1st dataset')
#my data
x = np.array([5.0, 10.0, 20.0, 50.0, 70.0, 80.0, 95.0])
y = np.array([0.0, 0.0, 0.0, 0.57, 1.0, 1.0,
1.0]) #bad FIT FAILS. Doesnt seem to like zeros
expectedMin = 0
try:
data.FitWeibull(x, y, expectedMin=expectedMin)
print('Fit succeeded on my 2nd dataset')
except:
print('Fit failed on my 2nd dataset')
#JWP JND_staircase_exp data
x = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 12.0, 20.0])
y = np.array([0.4, 0.63, 0.8, 0.92, 1.0, 1.0, 1.0])
expectedMin = 0
try:
data.FitWeibull(x, y, expectedMin=expectedMin)
printStaircase(staircase, descendingPsycho, briefTrialUpdate=True, printInternalVal=True, alsoLog=False)
#print('staircase.quantile=',round(staircase.quantile(),2),' sd=',round(staircase.sd(),2))
threshNoise = round(staircase.quantile(),3)
if descendingPsycho:
threshNoise = 100- threshNoise
threshNoise = max( 0, threshNoise ) #e.g. ff get all trials wrong, posterior peaks at a very negative number
msg= 'Staircase estimate of threshold = ' + str(threshNoise) + ' with sd=' + str(round(staircase.sd(),2))
logging.info(msg); print(msg)
myWin.close()
#Fit and plot data
fit = None
try:
intensityForCurveFitting = staircase.intensities
if descendingPsycho:
intensityForCurveFitting = 100-staircase.intensities #because fitWeibull assumes curve is ascending
fit = data.FitWeibull(intensityForCurveFitting, staircase.data, expectedMin=1/26., sems = 1.0/len(staircase.intensities))
except:
print("Fit failed.")
plotDataAndPsychometricCurve(staircase,fit,descendingPsycho,threshCriterion)
#save figure to file
pylab.savefig(fileName+'.pdf')
print('The plot has been saved, as '+fileName+'.pdf')
pylab.show() #must call this to actually show plot
else: #not staircase
noisePercent = defaultNoiseLevel
phasesMsg = 'Experiment will have '+str(trials.nTotal)+' trials. Letters will be drawn with superposed noise of' + "{:.2%}".format(defaultNoiseLevel)
print(phasesMsg); logging.info(phasesMsg)
#myWin= openMyStimWindow(); myWin.flip(); myWin.flip();myWin.flip();myWin.flip()
nDoneMain =0
while nDoneMain < trials.nTotal and expStop==False:
def run_quest_procedure(self, n_trials=10, start=20, start_sd=10, maxvalue=40, beta=3.5, delta=0.01, gamma=0.01, grain=0.01):
print("RUNNING QUEST\n" \
"-------------\n")
startVal=start
startValSd=start_sd
pThreshold=0.75
nTrials=n_trials
minVal=0
maxVal=maxvalue
beta=beta
delta=delta
gamma=gamma
grain=grain
method='quantile'
conditions = {
'startVal': startVal, 'startValSd': startValSd,
'minVal': 0, 'maxVal': maxVal, 'pThreshold': 0.75, 'gamma': gamma, 'delta': delta,
'grain': grain, 'method': method, 'nTrials': n_trials,
}
self.conditions = [
dict({'label': 'staircase_1'}.items() + conditions.items()),
dict({'label': 'staircase_2'}.items() + conditions.items()),
]
self.quest = data.MultiStairHandler(stairType='quest', method='random', conditions=self.conditions)
t1 = self.quest.staircases[0].mean()
t2 = self.quest.staircases[1].mean()
sd1 = self.quest.staircases[0].sd()
sd2 = self.quest.staircases[1].sd()
beta1 = contrib.quest.QuestObject.beta(self.quest.staircases[0])
beta1 = contrib.quest.QuestObject.beta(self.quest.staircases[1])
t_mean = (t1 + t2) / 2
beta_mean = (beta1 + beta2) / 2
fit = data.FitWeibull(t_mean, beta_mean, sems=1/n_trials)
for n, stim_level in enumerate(self.quest):
print("Calibration trial {0} / {1}\n" \
"Stimulation level = {2}\n" \
"-----------------------------".format(n + 1, n_trials, stim_level))
response = None
# Repeat if subject doesn't respond
while response == None:
trial = ConditioningTrial(self, n, True, max_voltage=stim_level)
response = trial.run()
# Add the response
if response:
print("Stimulation detected")
else:
print("Stimulation not detected")
if n >= self.config['quest_settings']['n_ignored']:
self.quest.addResponse(int(response))
p0 = fit.inverse(0)
p25 = fit.inverse(.25)
p50 = fit.inverse(.50)
p75 = fit.inverse(.75)
print("1% detection probability level = {0}\n"
"25% detection probability level = {1}\n"
"50% detection probability level = {2}\n"
"75% detection probability level = {3}\n".format(self.quest.quantile(0.01), self.quest.quantile(.25), self.quest.quantile(.5), self.quest.quantile(.75)))
return p0, p25, p50, p75
quantilePctNoise = 100-staircase.quantile()
#print('staircase quantile (median)=','{:.4f}'.format(staircase.quantile()), end='') #gets the median. Prints as floating point with 4 digits of precision
print('Median of posterior distribution according to QUEST, percent noise=','{:.4f}'.format(quantilePctNoise))
if plotFakeDataInstead: #plot standard fake data instead.
intensities = np.array([5.00, 20.00, 20.00, 20.00, 50.00, 50.00, 50.00, 5.00, 80.00, 80.00, 80.00, 5.00, 95.00, 95.00, 95.00, 74.56, 75.89, 76.94, 77.72, 78.43, 79.05, 79.68, 80.28, 80.87, 81.53, 82.22, 83.05, 84.01, 85.04, 86.09, 87.02, 87.80, 88.47, 89.03, 89.49, 89.88, 90.20, 90.49, 90.74, 90.96, 91.16, 89.97, 90.12, 90.24, 90.37, 90.48, 90.59, 90.69, 90.79, 90.10, 90.18, 90.26, 90.33, 90.40, 90.47, 90.54, 90.60, 90.66, 90.72, 90.28, 90.33, 90.37, 90.42, 90.06, 89.73
]) #debug, example data
corrEachTrial= np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1
]) #debug, example data
staircase.importData( toStaircase(intensities,descendingPsycho), corrEachTrial)
intensities = outOfStaircase( staircase.intensities, staircase, descendingPsycho ) #inverse log, 100-
responses = staircase.data
expectedMin = 1.0/26
#fit curve
fit = None
try:
intensityForCurveFitting = intensities
if descendingPsycho:
intensityForCurveFitting = 100-intensities #because fitWeibull assumes curve is ascending
fit = data.FitWeibull(intensityForCurveFitting, responses, expectedMin=expectedMin, sems = 1.0/len(intensityForCurveFitting))
except:
print("Fit failed.")
plotDataAndPsychometricCurve(staircase,fit,descendingPsycho,threshCriterion)
#save figure to file
outputFile = 'test_staircase_plot' # os.path.join(dataDir, 'test_staircase_plot')
pylab.savefig(outputFile + '.pdf')
pylab.savefig(outputFile + '.jpg')
pylab.show() #must call this to actually show plot
#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
fit = data.FitWeibull(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, 'k-')
pylab.plot([thresh, thresh], [0, 0.8], 'k--')
pylab.plot([0, thresh], [0.8, 0.8], 'k--')
pylab.title('threshold = %0.3f' % (thresh))
#plot points
pylab.plot(combinedInten, combinedResp, 'ko')
pylab.ylim([0, 1])
