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
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 muscimol_plot_sound_type(animal, muscimolSessions, salineSessions, soundType, fontsize=12):
'''
DEPRECATED - this is a bad function
Plot the average psycurve for muscimol sessions in red on top of the average saline psycurve in black.
Psycurves are limited by the sound type.
'''
muscimolData = behavioranalysis.load_many_sessions(animal, muscimolSessions)
trialsSoundTypeMus = muscimolData['soundType']==muscimolData.labels['soundType'][soundType]
rightTrialsMus = (muscimolData['choice']==muscimolData.labels['choice']['right'])[trialsSoundTypeMus]
freqEachTrialMus = muscimolData['targetFrequency'][trialsSoundTypeMus]
validMus = muscimolData['valid'][trialsSoundTypeMus]
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
calculate_psychometric(rightTrialsMus,freqEachTrialMus,validMus)
(plineM, pcapsM, pbarsM, pdotsM) = 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)
setp(plineM, color='r')
setp(pcapsM, color='r')
setp(pbarsM, color='r')
setp(pdotsM, markerfacecolor='r')
salineData = behavioranalysis.load_many_sessions(animal, salineSessions)
trialsSoundTypeSal = salineData['soundType']==salineData.labels['soundType'][soundType]
rightTrialsSal = (salineData['choice']==salineData.labels['choice']['right'])[trialsSoundTypeSal]
freqEachTrialSal = salineData['targetFrequency'][trialsSoundTypeSal]
validSal = salineData['valid'][trialsSoundTypeSal]
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
calculate_psychometric(rightTrialsSal,freqEachTrialSal,validSal)
(plineS, pcapsS, pbarsS, pdotsS) = extraplots.plot_psychometric(1e-3*possibleValues,fractionHitsEachValue,
ciHitsEachValue,xTickPeriod=1)
title("{}, {}".format(animal, soundType))
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 plot_multiple_psycurves(bdataList, colorsList, fontsize=12):
'''
Nick 2016-05-20
Plots psychometric curves for bdata objects in different colors on the same plot
Args:
bdataList (list of jaratoolbox.loadbehavior.LoadBehaviorData objects): list of bdata to plot
colorsList (list of str): list of color strings the same length as bdataList ('k', 'b', etc.)
fontsize (int): Size of the label font for the figure
'''
plt.hold(1)
for ind, bdata in enumerate(bdataList):
rightTrials = bdata['choice']==bdata.labels['choice']['right']
freqEachTrial = bdata['targetFrequency']
valid = bdata['valid']
(possibleValues,
fractionHitsEachValue,
ciHitsEachValue,
nTrialsEachValue,
nHitsEachValue)= calculate_psychometric(rightTrials,
freqEachTrial,
valid)
(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=colorsList[ind])
plt.setp(pcaps, color=colorsList[ind])
plt.setp(pbars, color=colorsList[ind])
plt.setp(pdots, markerfacecolor=colorsList[ind])
def plot_frequency_psycurve(bdata,fontsize=12):
'''
Show psychometric curve (for frequency)
'''
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 = find_trials_each_type(targetFrequency,possibleFreq)
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
calculate_psychometric(choiceRight,targetFrequency,valid)
(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)
return (pline, pcaps, pbars, pdots)
def plot_frequency_psycurve_soundtype(bdata, soundType, fontsize=12):
'''
Show psychometric curve (for any arbitrary sound type)
'''
trialsSoundType = bdata['soundType']==bdata.labels['soundType'][soundType]
choice = bdata['choice']
choiceRight = choice==bdata.labels['choice']['right']
choiceRightSoundType = choiceRight[trialsSoundType]
targetFrequencySoundType = bdata['targetFrequency'][trialsSoundType]
valid = bdata['valid']
validSoundType = valid[trialsSoundType]
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
behavioranalysis.calculate_psychometric(choiceRightSoundType,targetFrequencySoundType,validSoundType)
(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)
return (pline, pcaps, pbars, pdots)
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 plot_frequency_psycurve_soundtype(bdata, soundType, fontsize=12):
'''
Show psychometric curve (for any arbitrary sound type)
'''
trialsSoundType = bdata['soundType']==bdata.labels['soundType'][soundType]
choice = bdata['choice']
choiceRight = choice==bdata.labels['choice']['right']
choiceRightSoundType = choiceRight[trialsSoundType]
targetFrequencySoundType = bdata['targetFrequency'][trialsSoundType]
valid = bdata['valid']
validSoundType = valid[trialsSoundType]
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
behavioranalysis.calculate_psychometric(choiceRightSoundType,targetFrequencySoundType,validSoundType)
(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)
return (pline, pcaps, pbars, pdots)
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,
pdots) = extraplots.plot_psychometric(1e-3 * possibleValues,
fractionHitsEachValue,
ciHitsEachValue,
xTickPeriod=1,
xscale='linear')
(plineL, pcapsL, pbarsL,
pdotsL) = extraplots.plot_psychometric(1e-3 * possibleValuesL,
fractionHitsEachValueL,
ciHitsEachValueL,
xTickPeriod=1,
xscale='linear')
plt.setp([pline, plineL], lw=3)
plt.setp([plineL, pcapsL, pbarsL, pdotsL], color=laserColor)
plt.setp(pdotsL, mfc=laserColor)
#plt.setp([plineL,pcapsL,pbarsL,pdotsL],visible=False)
#plt.setp([pline,pcaps,pbars,pdots],visible=False)
validCorrect = outcome[valid.astype(bool)]==bdata.labels['outcome']['correct']
nValid = np.sum(valid)
fractionValid = np.mean(valid)
fractionCorrect = np.mean(validCorrect)
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
behavioranalysis.calculate_psychometric(choiceRight,targetFrequency,valid)
nValidAll.append(nValid)
fractionValidAll.append(fractionValid)
fractionCorrectAll.append(fractionCorrect)
plt.subplot(nSubjects//2+1,2,inds+1)
# -- plot things here --
(plineC, pcapsC, pbarsC, pdotsC) = extraplots.plot_psychometric(possibleValues,
fractionHitsEachValue,
ciHitsEachValue)
#plt.ylabel('{0}'.format(animalList[inds]))
plt.ylabel('Trials rightward (%)')
#plt.xlim([-0.1,0.8])
plt.xlabel('Frequency (Hz)')
#if inds==0:
plt.title('[{0}] Delay-to-go = {1:0.2}s'.format(animalList[inds],float(delayToGoSignal)))
plt.draw()
plt.show()
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])
ax.set_xticks([3000,7000,16000])
ax.set_xticks(np.arange(1000,40000,1000),minor=True)
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()
'''
validControl = valid & ~laserTrials
validLaser = valid & laserTrials
# -- Calculate and plot psychometric points --
(possibleValuesControl,fractionHitsEachValueControl,ciHitsEachValueControl,
nTrialsEachValueControl,nHitsEachValueControl)=\
behavioranalysis.calculate_psychometric(choiceRight,targetFrequency,validControl)
(possibleValuesLaser,fractionHitsEachValueLaser,ciHitsEachValueLaser,
nTrialsEachValueLaser,nHitsEachValueLaser)=\
behavioranalysis.calculate_psychometric(choiceRight,targetFrequency,validLaser)
plt.clf()
plt.hold(True)
(plineC, pcapsC, pbarsC,
pdotsC) = extraplots.plot_psychometric(possibleValuesControl,
fractionHitsEachValueControl,
ciHitsEachValueControl)
(plineL, pcapsL, pbarsL,
pdotsL) = extraplots.plot_psychometric(possibleValuesLaser,
fractionHitsEachValueLaser,
ciHitsEachValueLaser)
plt.setp([plineL, pcapsL, pbarsL], color='g')
plt.setp(pdotsL, mfc='g', mec='g')
plt.hold(False)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Rightward choice (%)')
plt.title('{0} - {1}'.format(subject, session))
plt.legend([plineC, plineL], ['Control', 'Laser'], loc='upper left')
plt.show()
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)
show()
# -- Save figure --
outputDir = '/tmp/' #figparams.figuresDir
#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)
#plt.legend(bbox_to_anchor=(1, 1), bbox_transform=plt.gcf().transFigure)
plt.show()
margins(0.2)
plt.subplots_adjust(bottom=0.2)
title(animal)
ylabel('Average Percent Correct')
show()
#### Need to start moving towards loading the data already split by sound type and then using generic fxns from behavioranalysis and extraplots
animal = 'amod002'
dataObjs, dataSoundTypes = behavioranalysis.load_behavior_sessions_sound_type(animal, ['20160421a'])
for bdata in dataObjs
bdata = dataObjs[0]
hitTrials = bdata['choice']==bdata.labels['choice']['right']
paramValueEachTrial = bdata['targetFrequency']
valid = bdata['valid']
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue) = behavioranalysis.calculate_psychometric(hitTrials, paramValueEachTrial, valid)
clf()
extraplots.plot_psychometric(possibleValues,fractionHitsEachValue,ciHitsEachValue)
