def plot_fitted_psycurve(bdata, color='k', linestyle=None):
rightTrials = bdata['choice'] == bdata.labels['choice']['right']
freqEachTrial = bdata['targetFrequency']
valid = bdata['valid']
(possibleValues, fractionHitsEachValue, ciHitsEachValue, nTrialsEachValue,
nHitsEachValue) = behavioranalysis.calculate_psychometric(
rightTrials, freqEachTrial, valid)
pline, pcaps, pbars, pdots = extraplots.plot_psychometric(
possibleValues, fractionHitsEachValue, ciHitsEachValue)
setp(pline, color='w')
setp(pcaps, color=color)
setp(pbars, color=color)
setp(pdots, markerfacecolor=color)
estimate = extrastats.psychometric_fit(possibleValues, nTrialsEachValue,
nHitsEachValue)
yvals = nHitsEachValue.astype(float) / nTrialsEachValue
xvals = possibleValues
xRange = xvals[-1] - xvals[0]
fitxval = np.linspace(xvals[0] - 0.1 * xRange, xvals[-1] + 0.1 * xRange,
40)
fityval = extrastats.psychfun(fitxval, *estimate)
hfit = plot(fitxval, 100 * fityval, '-', linewidth=2, color=color)
return (estimate, (pline, pcaps, pbars, pdots, hfit))
def psycurveLinear(bdata):
from jaratoolbox import extraplots
fontsize=FONTSIZE
if 'targetPercentage' in bdata.viewkeys():
targetPercentage = bdata['targetPercentage']
else:
targetPercentage = bdata['targetFrequency'] # I used name 'frequency' initially
choiceRight = bdata['choice']==bdata.labels['choice']['right']
valid=bdata['valid']& (bdata['choice']!=bdata.labels['choice']['none'])
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
behavioranalysis.calculate_psychometric(choiceRight,targetPercentage,valid)
(pline, pcaps, pbars, pdots) = extraplots.plot_psychometric(1e-3*possibleValues,fractionHitsEachValue,
ciHitsEachValue,xTickPeriod=1, xscale='linear')
plt.xlabel('Stimulus',fontsize=fontsize)
plt.ylabel('Rightward trials (%)',fontsize=fontsize)
extraplots.set_ticks_fontsize(plt.gca(),fontsize)
plt.gcf().set_size_inches([8,6])
def calculate_psychometric_and_estimate(bdata, fullDataPath):
'''
Calculates psychometric from bdata and estimates fit
'''
targetFrequency = bdata['targetFrequency']
choice=bdata['choice']
valid=bdata['valid'] & (choice!=bdata.labels['choice']['none'])
choiceRight = choice==bdata.labels['choice']['right']
possibleFreq = np.unique(targetFrequency)
nFreq = len(possibleFreq)
(possibleValues,
fractionHitsEachValue,
ciHitsEachValue,
nTrialsEachValue,
nHitsEachValue) = behavioranalysis.calculate_psychometric(choiceRight,
targetFrequency,
valid)
#Calculate the estimate for the psychometric function
logPossibleValues = np.log2(possibleValues)
lowerFreqConstraint = logPossibleValues[1]
upperFreqConstraint = logPossibleValues[-2]
maxFreq = max(logPossibleValues)
minFreq = min(logPossibleValues)
constraints = ( 'Uniform({}, {})'.format(lowerFreqConstraint, upperFreqConstraint),
'Uniform(0,5)' ,
'Uniform(0,1)',
'Uniform(0,1)')
estimate = extrastats.psychometric_fit(logPossibleValues,
nTrialsEachValue,
nHitsEachValue,
constraints)
np.savez(fullDataPath,
possibleValues=possibleValues,
fractionHitsEachValue=fractionHitsEachValue,
ciHitsEachValue=ciHitsEachValue,
nTrialsEachValue=nTrialsEachValue,
nHitsEachValue=nHitsEachValue,
logPossibleValues=logPossibleValues,
estimate=estimate,
script=scriptFullPath)
print 'Saved results to {}'.format(fullDataPath)
def test(self):
hits = bdata['outcome']==bdata.labels['outcome']['correct']
freqEachTrial = bdata['targetFrequency']
valid = bdata['valid']
(possibleValues,
fractionHitsEachValue,
ciHitsEachValue,
nTrialsEachValue,
nHitsEachValue) = behavioranalysis.calculate_psychometric(hits,
freqEachTrial,
valid)
#Get the percent correct for the low and high freqs and test against the threshol
lowPerCorr = 100*fractionHitsEachValue[0]
highPerCorr = 100*fractionHitsEachValue[-1]
print 'Low Freq Percent Correct: {}'.format(lowPerCorr)
print 'High Freq Percent Correct: {}'.format(highPerCorr)
print 'Threshold: {}'.format(self.threshold)
return ((lowPerCorr>self.threshold) & (highPerCorr>self.threshold))
subplot(1, len(animalsNames), inda + 1)
for indset, sessions in enumerate(allSessions[:2]):
bdata = behavioranalysis.load_many_sessions(animalName, sessions)
targetFrequency = bdata['targetFrequency']
choice = bdata['choice']
valid = bdata['valid'] & (choice != bdata.labels['choice']['none'])
intensities = bdata['targetIntensity']
choiceRight = choice == bdata.labels['choice']['right']
possibleFreq = np.unique(targetFrequency)
nFreq = len(possibleFreq)
trialsEachFreq = behavioranalysis.find_trials_each_type(
targetFrequency, possibleFreq)
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
behavioranalysis.calculate_psychometric(choiceRight,targetFrequency,valid)
hold(True)
(pline, pcaps, pbars,
pdots) = extraplots.plot_psychometric(possibleValues,
fractionHitsEachValue,
ciHitsEachValue)
setp(pdots, ms=6, mec='k', mew=2, mfc=markerFaceColors[indset])
plotHandles.append(pdots[0])
xlabel('Frequency (Hz)', fontsize=fontSize)
ylabel('Rightward choice (%)', fontsize=fontSize)
legend(plotHandles, ['pre', 'post'],
numpoints=1,
loc='upper left',
fontsize=fontSize - 2)
title(animalName)
targetFrequency = bdata['targetFrequency']
choice = bdata['choice']
# valid=bdata['valid']& (choice!=bdata.labels['choice']['none'])
laser = bdata['laserOn'] == 1
noLaser = bdata['laserOn'] == 0
choiceRight = choice == bdata.labels['choice']['right']
possibleFreq = np.unique(targetFrequency)
nFreq = len(possibleFreq)
trialsEachFreq = behavioranalysis.find_trials_each_type(
targetFrequency, possibleFreq)
#noLaser first
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
behavioranalysis.calculate_psychometric(choiceRight,targetFrequency,noLaser)
(pline, pcaps, pbars,
pdots) = extraplots.plot_psychometric(1e-3 * possibleValues,
fractionHitsEachValue,
ciHitsEachValue,
xTickPeriod=1)
plt.xlabel('Frequency (kHz)', fontsize=fontsize)
plt.ylabel('Rightward trials (%)', fontsize=fontsize)
extraplots.set_ticks_fontsize(plt.gca(), fontsize)
plt.setp(pline, color='k')
plt.setp(pcaps, color='k')
plt.setp(pbars, color='k')
plt.setp(pdots, markerfacecolor='k')
plt.hold(1)
#bfile = loadbehavior.path_to_behavior_data(subject,paradigm,session)
#bdata = loadbehavior.BehaviorData(bfile,readmode='full')
bdata = behavioranalysis.load_many_sessions(subjects, sessions)
correct = bdata['outcome'] == bdata.labels['outcome']['correct']
targetPercentage = bdata[
'targetFrequency'] # I used name 'frequency' initially
choiceRight = bdata['choice'] == bdata.labels['choice']['right']
valid = bdata['valid'].astype(bool) & (bdata['choice'] !=
bdata.labels['choice']['none'])
laserTrials = bdata['laserTrial'] == bdata.labels['laserTrial']['yes']
validLaser = valid & laserTrials
validNoLaser = valid & ~laserTrials
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
behavioranalysis.calculate_psychometric(choiceRight,targetPercentage,validNoLaser)
(possibleValuesL,fractionHitsEachValueL,ciHitsEachValueL,nTrialsEachValueL,nHitsEachValueL)=\
behavioranalysis.calculate_psychometric(choiceRight,targetPercentage,validLaser)
nNoLaser = np.sum(validNoLaser)
nLaser = np.sum(validLaser)
fractionCorrectNoLaser = np.mean(correct[validNoLaser])
fractionCorrectLaser = np.mean(correct[validLaser])
print('Correct: NoLaser({})={:0.1%} Laser({})={:0.1%}'.format(
nNoLaser, fractionCorrectNoLaser, nLaser, fractionCorrectLaser))
#plt.subplot(4,2,inds+1)
#plt.title('{0} [{1}]'.format(subject,session))
plt.hold(1)
(pline, pcaps, pbars,
def plot_ave_psycurve_reward_change(animal, sessions):
FREQCOLORS = [colorpalette.TangoPalette['Chameleon3'],
colorpalette.TangoPalette['ScarletRed1'],
colorpalette.TangoPalette['SkyBlue2']]
allBehavDataThisAnimal = behavioranalysis.load_many_sessions(animal,sessions)
targetFrequency = allBehavDataThisAnimal['targetFrequency']
choice=allBehavDataThisAnimal['choice']
valid=allBehavDataThisAnimal['valid']& (choice!=allBehavDataThisAnimal.labels['choice']['none'])
choiceRight = choice==allBehavDataThisAnimal.labels['choice']['right']
currentBlock = allBehavDataThisAnimal['currentBlock']
blockTypes = [allBehavDataThisAnimal.labels['currentBlock']['same_reward'],allBehavDataThisAnimal.labels['currentBlock']['more_left'],allBehavDataThisAnimal.labels['currentBlock']['more_right']]
#blockTypes = [allBehavDataThisAnimal.labels['currentBlock']['more_left'],allBehavDataThisAnimal.labels['currentBlock']['more_right']]
blockLabels = ['same_reward','more_left','more_right']
#blockLabels = ['more_left','more_right']
trialsEachType = behavioranalysis.find_trials_each_type(currentBlock,blockTypes)
nFreqs = len(np.unique(targetFrequency))
print '{}, freqs: {}'.format(animal, str(np.unique(targetFrequency)))
#print trialsEachType
nBlocks = len(blockTypes)
#thisAnimalPos = inda
#ax1=plt.subplot(gs[thisAnimalPos])
#plt.clf()
fontsize = 12
allPline = []
blockLegends = []
fractionHitsEachValueAllBlocks = np.empty((nBlocks,nFreqs))
for blockType in range(nBlocks):
if np.any(trialsEachType[:,blockType]):
targetFrequencyThisBlock = targetFrequency[trialsEachType[:,blockType]]
validThisBlock = valid[trialsEachType[:,blockType]]
choiceRightThisBlock = choiceRight[trialsEachType[:,blockType]]
#currentBlockValue = currentBlock[trialsEachBlock[0,block]]
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
behavioranalysis.calculate_psychometric(choiceRightThisBlock,targetFrequencyThisBlock,validThisBlock)
fractionHitsEachValueAllBlocks[blockType,:] = fractionHitsEachValue
(pline, pcaps, pbars, pdots) = extraplots.plot_psychometric(1e-3*possibleValues,fractionHitsEachValue,
ciHitsEachValue,xTickPeriod=1)
plt.setp((pline, pcaps, pbars), color=FREQCOLORS[blockType])
plt.setp(pdots, mfc=FREQCOLORS[blockType], mec=FREQCOLORS[blockType])
allPline.append(pline)
blockLegends.append(blockLabels[blockType])
if blockType == nBlocks-1:
plt.xlabel('Frequency (kHz)',fontsize=fontsize)
plt.ylabel('Rightward trials (%)',fontsize=fontsize)
extraplots.set_ticks_fontsize(plt.gca(),fontsize)
legend = plt.legend(allPline,blockLegends,loc=2)
# Add the legend manually to the current Axes.
ax = plt.gca().add_artist(legend)
#plt.hold(True)
#plt.legend(bbox_to_anchor=(1, 1), bbox_transform=plt.gcf().transFigure)
#plt.show()
plt.title('%s_%sto%s'%(animal,sessions[0],sessions[-1]))
return fractionHitsEachValueAllBlocks
inds += 1
bdata = allBehavData
'''
valid = bdata['valid']
choice = bdata['choice']
print sum(valid)
#Is this the correct thing to get to calculate psychometrics?
choiceRight = choice==bdata.labels['choice']['right']
targetFreq = bdata['targetFrequency']
noLaserTrials = valid&(bdata['trialType']==0)
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
behavioranalysis.calculate_psychometric(choiceRight, targetFreq, noLaserTrials)
(pline, pcaps, pbars, pdots) = extraplots.plot_psychometric(1e-3*possibleValues,fractionHitsEachValue,ciHitsEachValue,xTickPeriod=1)
allPlines.append(pline)
ax = pline.axes
extraplots.boxoff(ax)
extraplots.set_ticks_fontsize(ax, fontsize)
#plt.title(subject+' - '+sessions[0]+'-'+sessions[-1], fontsize=fontsize)
plt.xlabel('Frequency (kHz)',fontsize=fontsize)
plt.ylabel('Rightward trials (%)',fontsize=fontsize)
extraplots.set_ticks_fontsize(plt.gca(),fontsize)
plt.setp(pline, color='k')
plt.setp(pcaps, color='k')
def plot_ave_photostim_psycurve_by_trialtype(animal,
sessions,
trialLimit=None):
'''
Arguments:
animal is a string of the animal name you want to plot.
sessions is a list of strings of the behavior session names you want to plot.
trialLimit is an optional parameter, should be a list of integers giving the beginning and end of trials numbers you want to plot.
'''
FREQCOLORS = [
colorpalette.TangoPalette['Chameleon3'],
colorpalette.TangoPalette['ScarletRed1'],
colorpalette.TangoPalette['SkyBlue2'], 'g', 'm', 'k'
]
allBehavDataThisAnimal = behavioranalysis.load_many_sessions(
animal, sessions)
targetFrequency = allBehavDataThisAnimal['targetFrequency']
choice = allBehavDataThisAnimal['choice']
valid = allBehavDataThisAnimal['valid'] & (
choice != allBehavDataThisAnimal.labels['choice']['none'])
if trialLimit:
trialSelector = np.zeros(len(valid), dtype=bool)
trialSelector[trialLimit[0]:trialLimit[1]] = True
else:
trialSelector = np.ones(len(valid), dtype=bool)
valid = (valid & trialSelector)
#print sum(trialSelector), sum(valid)
choiceRight = choice == allBehavDataThisAnimal.labels['choice']['right']
trialType = allBehavDataThisAnimal['trialType']
stimTypes = [
allBehavDataThisAnimal.labels['trialType']['no_laser'],
allBehavDataThisAnimal.labels['trialType']['laser_left'],
allBehavDataThisAnimal.labels['trialType']['laser_right']
]
stimLabels = ['no_laser', 'laser_left', 'laser_right']
trialsEachType = behavioranalysis.find_trials_each_type(
trialType, stimTypes)
#trialsEachType=np.vstack(( ( (trialType==0) | (trialType==2) ),trialType==1, np.zeros(len(trialType),dtype=bool) )).T ###This is a hack when percentLaserTrials were sum of both sides and just did one side stim
#print trialsEachType
nBlocks = len(stimTypes)
#thisAnimalPos = inda
#ax1=plt.subplot(gs[thisAnimalPos,0])
#ax1=plt.subplot(gs[thisAnimalPos])
#plt.figure()
fontsize = 8
allPline = []
curveLegends = []
for stimType in range(nBlocks):
if np.any(trialsEachType[:, stimType]):
targetFrequencyThisBlock = targetFrequency[
trialsEachType[:, stimType]]
validThisBlock = valid[trialsEachType[:, stimType]]
#print len(validThisBlock), sum(validThisBlock)
choiceRightThisBlock = choiceRight[trialsEachType[:, stimType]]
numValidTrialThisBlock = sum(validThisBlock)
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
behavioranalysis.calculate_psychometric(choiceRightThisBlock,targetFrequencyThisBlock,validThisBlock)
(pline, pcaps, pbars,
pdots) = extraplots.plot_psychometric(1e-3 * possibleValues,
fractionHitsEachValue,
ciHitsEachValue,
xTickPeriod=1)
plt.setp((pline, pcaps, pbars), color=FREQCOLORS[stimType])
plt.hold(True)
plt.setp(pdots, mfc=FREQCOLORS[stimType], mec=FREQCOLORS[stimType])
plt.hold(True)
plt.annotate('%s: %s trials' %
(stimLabels[stimType], numValidTrialThisBlock),
xy=(0.2, 0.35 + stimType / 10.0),
fontsize=fontsize,
xycoords='axes fraction')
allPline.append(pline)
curveLegends.append(stimLabels[stimType])
#plt.hold(True)
plt.xlabel('Frequency (kHz)', fontsize=fontsize)
plt.ylabel('Rightward trials (%)', fontsize=fontsize)
extraplots.set_ticks_fontsize(plt.gca(), fontsize)
legend = plt.legend(allPline,
curveLegends,
loc='best',
labelspacing=0.2,
prop={'size': 4})
# Add the legend manually to the current Axes.
#ax = plt.gca().add_artist(legend)
plt.gca().add_artist(legend)
#plt.legend(bbox_to_anchor=(1, 1), bbox_transform=plt.gcf().transFigure)
#plt.show()
if len(sessions) == 1:
plt.title('%s_%s' % (animal, sessions[0]), fontsize=fontsize)
else:
plt.title('%s_%sto%s' % (animal, sessions[0], sessions[-1]),
fontsize=fontsize)
plt.show()
'''
def plot_fitted_psycurve(bdata, color='k', linestyle=None):
rightTrials = bdata['choice']==bdata.labels['choice']['right']
freqEachTrial = bdata['targetFrequency']
valid = bdata['valid']
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue) = behavioranalysis.calculate_psychometric(rightTrials, freqEachTrial, valid)
pline, pcaps, pbars, pdots = extraplots.plot_psychometric(possibleValues, fractionHitsEachValue, ciHitsEachValue)
setp(pline, color='w')
setp(pcaps, color=color)
setp(pbars, color=color)
setp(pdots, markerfacecolor=color)
estimate = extrastats.psychometric_fit(possibleValues, nTrialsEachValue, nHitsEachValue)
yvals = nHitsEachValue.astype(float)/nTrialsEachValue
xvals = possibleValues
xRange = xvals[-1]-xvals[0]
fitxval = np.linspace(xvals[0]-0.1*xRange,xvals[-1]+0.1*xRange,40)
fityval = extrastats.psychfun(fitxval,*estimate)
hfit = plot(fitxval,100*fityval,'-',linewidth=2, color=color)
return (estimate, (pline, pcaps, pbars, pdots, hfit))
nValid = bdata['nValid'][-1]
fractionCorrect = nReward / float(nValid)
targetFrequency = bdata['targetFrequency']
possibleTarget = unique(targetFrequency)
cueFrequency = bdata['cueFrequency']
possibleCue = unique(cueFrequency)
trialsEachCue = behavioranalysis.find_trials_each_type(cueFrequency,
possibleCue)
choiceRight = bdata['choice'] == (bdata.labels['choice']['right'])
colorEachCond = ['g', 'r']
clf()
hold(True)
for indcue, cueFreq in enumerate(possibleCue):
(possibleValues,fractionHitsEachValue,ciHitsEachValue,
nTrialsEachValue,nHitsEachValue)=\
behavioranalysis.calculate_psychometric(choiceRight,
targetFrequency,
trialsEachCue[:,indcue])
(pline, pcaps, pbars,
pdots) = extraplots.plot_psychometric(possibleValues,
fractionHitsEachValue,
ciHitsEachValue)
setp([pline, pcaps, pbars], color=colorEachCond[indcue])
setp(pdots, mec=colorEachCond[indcue], mfc=colorEachCond[indcue])
xlabel('Target frequency')
ylabel('% rightward')
show()
valid = bdata['valid']
choice = bdata['choice']
#Is this the correct thing to get to calculate psychometrics?
choiceRight = choice==bdata.labels['choice']['right']
targetFreq = bdata['targetFrequency']
possibleInt = np.unique(targetIntensity)
trialsEachInt = behavioranalysis.find_trials_each_type(targetIntensity,possibleInt)
trialsInds = {}
allPlines = []
for indInt, inten in enumerate(possibleInt):
trialsInds[inten] = valid&(bdata['targetIntensity']==inten)
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
behavioranalysis.calculate_psychometric(choiceRight, targetFreq, trialsInds[inten])
(pline, pcaps, pbars, pdots) = extraplots.plot_psychometric(1e-3*possibleValues,fractionHitsEachValue,ciHitsEachValue,xTickPeriod=1)
allPlines.append(pline)
plt.title(subject+' - '+session, fontsize=fontsize)
plt.xlabel('Frequency (kHz)',fontsize=fontsize)
plt.ylabel('Rightward trials (%)',fontsize=fontsize)
extraplots.set_ticks_fontsize(plt.gca(),fontsize)
plt.setp(pline, color=colors[indInt])
plt.setp(pcaps, color=colors[indInt])
plt.setp(pbars, color=colors[indInt])
plt.setp(pdots, markerfacecolor=colors[indInt])
legend = plt.legend(allPlines, possibleInt)
choice = bdata['choice']
#Is this the correct thing to get to calculate psychometrics?
choiceRight = choice == bdata.labels['choice']['right']
targetFreq = bdata['targetFrequency']
possibleInt = np.unique(targetIntensity)
trialsEachInt = behavioranalysis.find_trials_each_type(
targetIntensity, possibleInt)
trialsInds = {}
allPlines = []
for indInt, inten in enumerate(possibleInt):
trialsInds[inten] = valid & (bdata['targetIntensity'] == inten)
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
behavioranalysis.calculate_psychometric(choiceRight, targetFreq, trialsInds[inten])
(pline, pcaps, pbars,
pdots) = extraplots.plot_psychometric(1e-3 * possibleValues,
fractionHitsEachValue,
ciHitsEachValue,
xTickPeriod=1)
allPlines.append(pline)
plt.title(subject + ' - ' + session, fontsize=fontsize)
plt.xlabel('Frequency (kHz)', fontsize=fontsize)
plt.ylabel('Rightward trials (%)', fontsize=fontsize)
extraplots.set_ticks_fontsize(plt.gca(), fontsize)
plt.setp(pline, color=colors[indInt])
plt.setp(pcaps, color=colors[indInt])
def plot_ave_psycurve_reward_change(animal, sessions):
FREQCOLORS = [
'0.3', colorpalette.TangoPalette['Orange2'],
colorpalette.TangoPalette['SkyBlue2']
]
allBehavDataThisAnimal = behavioranalysis.load_many_sessions(
animal, sessions)
targetFrequency = allBehavDataThisAnimal['targetFrequency']
choice = allBehavDataThisAnimal['choice']
valid = allBehavDataThisAnimal['valid'] & (
choice != allBehavDataThisAnimal.labels['choice']['none'])
choiceRight = choice == allBehavDataThisAnimal.labels['choice']['right']
currentBlock = allBehavDataThisAnimal['currentBlock']
blockTypes = [
allBehavDataThisAnimal.labels['currentBlock']['same_reward'],
allBehavDataThisAnimal.labels['currentBlock']['more_left'],
allBehavDataThisAnimal.labels['currentBlock']['more_right']
]
#blockTypes = [allBehavDataThisAnimal.labels['currentBlock']['more_left'],allBehavDataThisAnimal.labels['currentBlock']['more_right']]
blockLabels = ['same_reward', 'more_left', 'more_right']
#blockLabels = ['more_left','more_right']
trialsEachType = behavioranalysis.find_trials_each_type(
currentBlock, blockTypes)
nFreqs = len(np.unique(targetFrequency))
#print trialsEachType
nBlocks = len(blockTypes)
#thisAnimalPos = inda
#ax1=plt.subplot(gs[thisAnimalPos])
#plt.clf()
fontsize = 12
allPline = []
blockLegends = []
fractionHitsEachValueAllBlocks = np.empty((nBlocks, nFreqs))
for blockType in range(nBlocks):
if np.any(trialsEachType[:, blockType]):
targetFrequencyThisBlock = targetFrequency[
trialsEachType[:, blockType]]
validThisBlock = valid[trialsEachType[:, blockType]]
choiceRightThisBlock = choiceRight[trialsEachType[:, blockType]]
#currentBlockValue = currentBlock[trialsEachBlock[0,block]]
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
behavioranalysis.calculate_psychometric(choiceRightThisBlock,targetFrequencyThisBlock,validThisBlock)
logPossibleValues = np.log2(possibleValues)
lowerFreqConstraint = logPossibleValues[1]
upperFreqConstraint = logPossibleValues[-2]
maxFreq = max(logPossibleValues)
minFreq = min(logPossibleValues)
constraints = ('Uniform({}, {})'.format(lowerFreqConstraint,
upperFreqConstraint),
'Uniform(0,5)', 'Uniform(0,1)', 'Uniform(0,1)')
estimate = extrastats.psychometric_fit(logPossibleValues,
nTrialsEachValue,
nHitsEachValue, constraints)
fractionHitsEachValueAllBlocks[
blockType, :] = fractionHitsEachValue
upperWhisker = ciHitsEachValue[1, :] - fractionHitsEachValue
lowerWhisker = fractionHitsEachValue - ciHitsEachValue[0, :]
xRange = logPossibleValues[-1] - logPossibleValues[1]
fitxvals = np.linspace(logPossibleValues[0] - 0.1 * xRange,
logPossibleValues[-1] + 0.1 * xRange, 40)
fityvals = extrastats.psychfun(fitxvals, *estimate)
# (pline, pcaps, pbars, pdots) = extraplots.plot_psychometric(1e-3*possibleValues,fractionHitsEachValue, ciHitsEachValue,xTickPeriod=1)
ax = plt.gca()
ax.hold(True)
(pline, pcaps, pbars) = ax.errorbar(
logPossibleValues,
100 * fractionHitsEachValue,
yerr=[100 * lowerWhisker, 100 * upperWhisker],
ecolor=FREQCOLORS[blockType],
fmt=None,
clip_on=False)
pdots = ax.plot(logPossibleValues,
100 * fractionHitsEachValue,
'o',
ms=6,
mec='None',
mfc=FREQCOLORS[blockType],
clip_on=False)
if blockType == 0:
pfit = ax.plot(fitxvals,
100 * fityvals,
color=FREQCOLORS[blockType],
lw=2,
clip_on=False,
linestyle='--')
else:
pfit = ax.plot(fitxvals,
100 * fityvals,
color=FREQCOLORS[blockType],
lw=2,
clip_on=False)
# plt.setp((pline, pcaps, pbars), color=FREQCOLORS[blockType])
# plt.setp((pline, pbars), color=FREQCOLORS[blockType])
# plt.setp(pdots, mfc=FREQCOLORS[blockType], mec=FREQCOLORS[blockType])
allPline.append(pline)
blockLegends.append(blockLabels[blockType])
if blockType == nBlocks - 1:
plt.xlabel('Frequency (kHz)', fontsize=fontsize)
plt.ylabel('Rightward trials (%)', fontsize=fontsize)
extraplots.set_ticks_fontsize(plt.gca(), fontsize)
ax.set_xticks(logPossibleValues)
tickLabels = [''] * len(possibleValues)
tickLabels[0] = 6.2
tickLabels[-1] = 19.2
ax.set_xticklabels(tickLabels)
ax.axhline(y=50, linestyle='-', color='0.7')
extraplots.boxoff(ax)
# legend = plt.legend(allPline,blockLegends,loc=2) #Add the legend manually to the current Axes.
# ax = plt.gca().add_artist(legend)
#plt.hold(True)
#plt.legend(bbox_to_anchor=(1, 1), bbox_transform=plt.gcf().transFigure)
#plt.show()
# plt.title('%s %s-%s'%(animal,sessions[0],sessions[-1]))
return fractionHitsEachValueAllBlocks
sys.exit()
behavFile = loadbehavior.path_to_behavior_data(animalName,experimenter,paradigm,session)
bdata = loadbehavior.FlexCategBehaviorData(behavFile)
targetFrequency = bdata['targetFrequency']
choice=bdata['choice']
valid=bdata['valid']& (choice!=bdata.labels['choice']['none'])
choiceRight = choice==bdata.labels['choice']['right']
possibleFreq = np.unique(targetFrequency)
nFreq = len(possibleFreq)
trialsEachFreq = behavioranalysis.find_trials_each_type(targetFrequency,possibleFreq)
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
behavioranalysis.calculate_psychometric(choiceRight,targetFrequency,valid)
(pline, pcaps, pbars, pdots) = extraplots.plot_psychometric(possibleValues,fractionHitsEachValue,ciHitsEachValue)
'''
upperWhisker = ciHitsEachValue[1,:]-fractionHitsEachValue
lowerWhisker = fractionHitsEachValue-ciHitsEachValue[0,:]
(pline, pcaps, pbars) = errorbar(possibleValues, 100*fractionHitsEachValue,
yerr = [100*lowerWhisker, 100*upperWhisker],color='k')
plot(possibleValues, 100*fractionHitsEachValue, 'o',mec='none',mfc='k',ms=8)
setp(pline,lw=2)
axhline(y=50, color = '0.5',ls='--')
ax=gca()
ax.set_xscale('log')
#ax.set_xticks([3000,5000,7000,10000,14000,20000,40000])
trialsEachType = behavioranalysis.find_trials_each_type(currentBlock,blockTypes)
#print trialsEachType
nBlocks = len(blockTypes)
thisAnimalPos = inda
ax1=plt.subplot(gs[thisAnimalPos])
fontsize = 12
allPline = []
legendLabels = []
for blockType in range(nBlocks):
targetFrequencyThisBlock = targetFrequency[trialsEachType[:,blockType]]
validThisBlock = valid[trialsEachType[:,blockType]]
choiceRightThisBlock = choiceRight[trialsEachType[:,blockType]]
#currentBlockValue = currentBlock[trialsEachBlock[0,block]]
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
behavioranalysis.calculate_psychometric(choiceRightThisBlock,targetFrequencyThisBlock,validThisBlock)
(pline, pcaps, pbars, pdots) = extraplots.plot_psychometric(1e-3*possibleValues,fractionHitsEachValue,
ciHitsEachValue,xTickPeriod=1)
plt.setp((pline, pcaps, pbars), color=FREQCOLORS[blockType])
plt.setp(pdots, mfc=FREQCOLORS[blockType], mec=FREQCOLORS[blockType])
allPline.append(pline)
if blockType == nBlocks-1:
plt.xlabel('Frequency (kHz)',fontsize=fontsize)
plt.ylabel('Rightward trials (%)',fontsize=fontsize)
extraplots.set_ticks_fontsize(plt.gca(),fontsize)
legend = plt.legend(allPline,blockLabels,loc=2)
# Add the legend manually to the current Axes.
ax = plt.gca().add_artist(legend)
#plt.hold(True)
psycurveDict['possibleFreqs'] = possibleValues
logPossibleValues = np.log2(possibleValues)
psycurveDict['logPossibleFreqs'] = logPossibleValues
psycurveDict['fractionHitsEachBlockEachFreq'] = np.empty((nBlocks,nFreqs))
psycurveDict['ciHitsEachBlockEachFreq'] = np.empty((nBlocks,2,nFreqs))
psycurveDict['nTrialsEachBlockEachFreq'] = np.empty((nBlocks,nFreqs))
psycurveDict['nHitsEachBlockEachFreq'] = np.empty((nBlocks,nFreqs))
psycurveDict['fitxval'] = np.empty((nBlocks,40))
psycurveDict['fityval'] = np.empty((nBlocks,40))
for blockType in range(nBlocks):
if np.any(trialsEachType[:,blockType]):
targetFrequencyThisBlock = targetFrequency[trialsEachType[:,blockType]]
validThisBlock = valid[trialsEachType[:,blockType]]
choiceRightThisBlock = choiceRight[trialsEachType[:,blockType]]
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue) = behavioranalysis.calculate_psychometric(choiceRightThisBlock,targetFrequencyThisBlock,validThisBlock)
psycurveDict['fractionHitsEachBlockEachFreq'][blockType,:] = fractionHitsEachValue
psycurveDict['ciHitsEachBlockEachFreq'][blockType,:,:] = ciHitsEachValue
psycurveDict['nTrialsEachBlockEachFreq'][blockType,:] = nTrialsEachValue
psycurveDict['nHitsEachBlockEachFreq'][blockType,:] = nHitsEachValue
# -- Fit psycurve and store fitted values -- #
lowerFreqConstraint = logPossibleValues[0]
upperFreqConstraint = logPossibleValues[-1]
constraints = ( 'Uniform({}, {})'.format(lowerFreqConstraint, upperFreqConstraint),
'Uniform(0,5)' ,
'Uniform(0,1)',
'Uniform(0,1)')
data = np.c_[logPossibleValues,nHitsEachValue,nTrialsEachValue]
# linear core 'ab': (x-a)/b; logistic sigmoid: 1/(1+np.exp(-(xval-alpha)/beta))
psyCurveInference = psi.BootstrapInference(data,sample=False, sigmoid='logistic', core='ab',priors=constraints, nafc=1)
