def plot_psycurve_fit_and_data_nohigh(bdata, plotFits=True):
'''
This version actually works. Calculates things in log2(freq) space.
'''
rightTrials = bdata['choice']==bdata.labels['choice']['right']
freqEachTrial = bdata['targetFrequency']
valid = bdata['valid']
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue) = behavioranalysis.calculate_psychometric(rightTrials, freqEachTrial, valid)
plot(np.log2(possibleValues), fractionHitsEachValue, 'bo')
hold(1)
possibleFreq = np.log2(np.unique(freqEachTrial))
lowerFreqConstraint = possibleFreq[1]
upperFreqConstraint = possibleFreq[-2]
maxFreq = max(possibleFreq)
minFreq = min(possibleFreq)
constraints = ( 'Uniform({}, {})'.format(lowerFreqConstraint, upperFreqConstraint), 'Uniform(0,5)' ,'Uniform(0,1)', 'Uniform(0,1)')
estimate = extrastats.psychometric_fit(possibleFreq[:-1], nTrialsEachValue[:-1], nHitsEachValue[:-1], constraints)
xRange = possibleFreq[-1]-possibleFreq[1]
fitxval = np.linspace(possibleFreq[0]-0.1*xRange,possibleFreq[-1]+0.1*xRange,40)
fityvals = extrastats.psychfun(fitxval, *estimate)
plot(fitxval, fityvals)
ylim([0, 1])
return estimate
def plot_psycurve_fit_and_data(bdata, plotFits=True):
rightTrials = bdata['choice']==bdata.labels['choice']['right']
freqEachTrial = bdata['targetFrequency']
valid = bdata['valid']
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue) = behavioranalysis.calculate_psychometric(rightTrials, freqEachTrial, valid)
#Calculate the lower and upper freq in the bdata to set the correct constraints
# possibleFreq = np.unique(freqEachTrial)
possibleFreq = np.log2(np.unique(freqEachTrial))
maxFreq = max(possibleFreq)
minFreq = min(possibleFreq)
#Calculating with constraints
# constraints = ( 'Uniform({}, {})'.format(minFreq, maxFreq),'Uniform(0,20)' ,'Uniform(0,1)', 'Uniform(0,1)')
constraints = ( 'Uniform({}, {})'.format(minFreq, maxFreq), 'Uniform(0,10)' ,'Uniform(0,0.2)', 'Uniform(0,0.2)')
estimate = extrastats.psychometric_fit(possibleValues, nTrialsEachValue, nHitsEachValue, constraints)
if plotFits:
xRange = possibleFreq[-1]-possibleFreq[1]
fitxval = np.linspace(possibleFreq[0]-0.1*xRange,possibleFreq[-1]+0.1*xRange,40)
#The fitted points
fityvals = extrastats.psychfun(fitxval, estimate[0], estimate[1], estimate[2], estimate[3])
plot(fitxval, fityvals)
# hold(1)
# extraplots.plot_psychometric(possibleFreq, fractionHitsEachValue, ciHitsEachValue)
plot(np.log2(possibleFreq), fractionHitsEachValue, 'bo')
return estimate
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 psycurve_fit_from_bdata(bdata, plotFits=True):
'''
Nick 2016-05-20
Calculates psychometric curve fits in log2(freq) space.
Args:
bdata (jaratoolbox.loadbehavior.BehaviorData object): The bdata object to fit
plotFits (bool): whether or not to plot the data and fitted curve to the current axis
Returns:
estimate (list of float): The parameter estimates for the psychometric function (alpha, beta, gamma, lambda - see jaratoolbox.extrastats.psychometric_fit)
'''
rightTrials = bdata['choice'] == bdata.labels['choice']['right']
freqEachTrial = bdata['targetFrequency']
valid = bdata['valid']
(possibleValues, fractionHitsEachValue, ciHitsEachValue, nTrialsEachValue,
nHitsEachValue) = calculate_psychometric(rightTrials, freqEachTrial,
valid)
possibleFreq = np.log2(np.unique(freqEachTrial))
print 'FIXME: Arbitrary constraints for alpha pos!!'
lowerFreqConstraint = possibleFreq[1]
upperFreqConstraint = possibleFreq[-2]
# lowerFreqConstraint = possibleFreq[0]
# upperFreqConstraint = possibleFreq[-1]
maxFreq = max(possibleFreq)
minFreq = min(possibleFreq)
constraints = ('Uniform({}, {})'.format(lowerFreqConstraint,
upperFreqConstraint),
'Uniform(0,5)', 'Uniform(0,0.5)', 'Uniform(0,0.5)')
# constraints = ( 'unconstrained', 'Uniform(0,2)' ,'Uniform(0,1)', 'Uniform(0,1)')
estimate = extrastats.psychometric_fit(possibleFreq, nTrialsEachValue,
nHitsEachValue, constraints)
if plotFits:
ax = plt.gca()
xRange = possibleFreq[-1] - possibleFreq[1]
fitxval = np.linspace(possibleFreq[0] - 0.1 * xRange,
possibleFreq[-1] + 0.1 * xRange, 40)
fityvals = extrastats.psychfun(fitxval, *estimate)
ax.plot(possibleFreq, fractionHitsEachValue, 'bo')
plt.hold(1)
ax.plot(fitxval, fityvals)
plt.ylim([0, 1])
return estimate
def psycurve_fit_from_bdata(bdata, plotFits=True):
'''
Nick 2016-05-20
Calculates psychometric curve fits in log2(freq) space.
Args:
bdata (jaratoolbox.loadbehavior.BehaviorData object): The bdata object to fit
plotFits (bool): whether or not to plot the data and fitted curve to the current axis
Returns:
estimate (list of float): The parameter estimates for the psychometric function (alpha, beta, gamma, lambda - see jaratoolbox.extrastats.psychometric_fit)
'''
rightTrials = bdata['choice']==bdata.labels['choice']['right']
freqEachTrial = bdata['targetFrequency']
valid = bdata['valid']
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue) = calculate_psychometric(rightTrials, freqEachTrial, valid)
possibleFreq = np.log2(np.unique(freqEachTrial))
print 'FIXME: Arbitrary constraints for alpha pos!!'
lowerFreqConstraint = possibleFreq[1]
upperFreqConstraint = possibleFreq[-2]
# lowerFreqConstraint = possibleFreq[0]
# upperFreqConstraint = possibleFreq[-1]
maxFreq = max(possibleFreq)
minFreq = min(possibleFreq)
constraints = ( 'Uniform({}, {})'.format(lowerFreqConstraint, upperFreqConstraint), 'Uniform(0,5)' ,'Uniform(0,0.5)', 'Uniform(0,0.5)')
# constraints = ( 'unconstrained', 'Uniform(0,2)' ,'Uniform(0,1)', 'Uniform(0,1)')
estimate = extrastats.psychometric_fit(possibleFreq, nTrialsEachValue, nHitsEachValue, constraints)
if plotFits:
ax = plt.gca()
xRange = possibleFreq[-1]-possibleFreq[1]
fitxval = np.linspace(possibleFreq[0]-0.1*xRange,possibleFreq[-1]+0.1*xRange,40)
fityvals = extrastats.psychfun(fitxval, *estimate)
ax.plot(possibleFreq, fractionHitsEachValue, 'bo')
plt.hold(1)
ax.plot(fitxval, fityvals)
plt.ylim([0, 1])
return estimate
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 plot_psycurve_fit_and_data_nohigh(bdata, plotFits=True):
'''
This version actually works. Calculates things in log2(freq) space.
'''
rightTrials = bdata['choice'] == bdata.labels['choice']['right']
freqEachTrial = bdata['targetFrequency']
valid = bdata['valid']
(possibleValues, fractionHitsEachValue, ciHitsEachValue, nTrialsEachValue,
nHitsEachValue) = behavioranalysis.calculate_psychometric(
rightTrials, freqEachTrial, valid)
plot(np.log2(possibleValues), fractionHitsEachValue, 'bo')
hold(1)
possibleFreq = np.log2(np.unique(freqEachTrial))
lowerFreqConstraint = possibleFreq[1]
upperFreqConstraint = possibleFreq[-2]
maxFreq = max(possibleFreq)
minFreq = min(possibleFreq)
constraints = ('Uniform({}, {})'.format(lowerFreqConstraint,
upperFreqConstraint),
'Uniform(0,5)', 'Uniform(0,1)', 'Uniform(0,1)')
estimate = extrastats.psychometric_fit(possibleFreq[:-1],
nTrialsEachValue[:-1],
nHitsEachValue[:-1], constraints)
xRange = possibleFreq[-1] - possibleFreq[1]
fitxval = np.linspace(possibleFreq[0] - 0.1 * xRange,
possibleFreq[-1] + 0.1 * xRange, 40)
fityvals = extrastats.psychfun(fitxval, *estimate)
plot(fitxval, fityvals)
ylim([0, 1])
return estimate
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_psycurve_fit_and_data(bdata, plotFits=True):
rightTrials = bdata['choice'] == bdata.labels['choice']['right']
freqEachTrial = bdata['targetFrequency']
valid = bdata['valid']
(possibleValues, fractionHitsEachValue, ciHitsEachValue, nTrialsEachValue,
nHitsEachValue) = behavioranalysis.calculate_psychometric(
rightTrials, freqEachTrial, valid)
#Calculate the lower and upper freq in the bdata to set the correct constraints
# possibleFreq = np.unique(freqEachTrial)
possibleFreq = np.log2(np.unique(freqEachTrial))
maxFreq = max(possibleFreq)
minFreq = min(possibleFreq)
#Calculating with constraints
# constraints = ( 'Uniform({}, {})'.format(minFreq, maxFreq),'Uniform(0,20)' ,'Uniform(0,1)', 'Uniform(0,1)')
constraints = ('Uniform({}, {})'.format(minFreq, maxFreq), 'Uniform(0,10)',
'Uniform(0,0.2)', 'Uniform(0,0.2)')
estimate = extrastats.psychometric_fit(possibleValues, nTrialsEachValue,
nHitsEachValue, constraints)
if plotFits:
xRange = possibleFreq[-1] - possibleFreq[1]
fitxval = np.linspace(possibleFreq[0] - 0.1 * xRange,
possibleFreq[-1] + 0.1 * xRange, 40)
#The fitted points
fityvals = extrastats.psychfun(fitxval, estimate[0], estimate[1],
estimate[2], estimate[3])
plot(fitxval, fityvals)
# hold(1)
# extraplots.plot_psychometric(possibleFreq, fractionHitsEachValue, ciHitsEachValue)
plot(np.log2(possibleFreq), fractionHitsEachValue, 'bo')
return estimate
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
figure() # pypsignifit.psigniplot.plotPMF(B) pypsignifit.plotMultiplePMFs(M, S) # constraints = ( 'Uniform(0, 100)','Uniform(0,20)' ,'Uniform(0,20)', 'Uniform(0,100)') estimate = extrastats.psychometric_fit(possibleValues, nTrialsEachValue, nHitsEachValue, constraints) # clf() # pypsignifit.psigniplot.plotPMF(session) # show() from extracellpy import behavioranalysis as exbehav figure() exbehav.plot_psychcurve_fit(possibleValues, nTrialsEachValue, nHitsEachValue, estimate) show()
figure()
# pypsignifit.psigniplot.plotPMF(B)
pypsignifit.plotMultiplePMFs(M, S)
from jaratoolbox.extrastats import psychometric_fit
trialsSoundTypeSalAmp = salineData['soundType']==salineData.labels['soundType'][soundType]
rightTrialsSalAmp = (salineData['choice']==salineData.labels['choice']['right'])[trialsSoundTypeSalAmp]
freqEachTrialSalAmp = salineData['targetFrequency'][trialsSoundTypeSalAmp]
validSalAmp = salineData['valid'][trialsSoundTypeSalAmp]
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue) = behavioranalysis.calculate_psychometric(rightTrialsSalAmp, freqEachTrialSalAmp, validSalAmp)
estimate = psychometric_fit(possibleValues, nHitsEachValue, nTrialsEachValue)
#functions stolen from santiago
def weibull(xval,alpha,beta):
'''Weibull function
alpha: bias
beta: related to slope
NOTE: this function isnot symmetric
'''
return 1 - np.exp(-pow(xval/alpha,beta))
plt.clf()
#(pline, pcaps, pbars, pdots) = extraplots.plot_psychometric(possibleValues,fractionHitsEachValue,ciHitsEachValue)
#plt.setp(pdots,ms=6,mec='k',mew=2,mfc='k')
plt.plot(logPossibleValues, fractionHitsEachValue, 'o')
plt.xlabel('Frequency (log2(Hz))')
plt.ylabel('Rightward choice (%)')
plt.show()
# -- Calculate and plot psychometric fit --
#constraints = None
constraints = [
'Uniform(10,15)', 'Uniform(0,5)', 'Uniform(0,1)', 'Uniform(0,1)'
]
curveParams = extrastats.psychometric_fit(logPossibleValues, nTrialsEachValue,
nHitsEachValue, constraints)
print 'Psychometric parameters (bias, slope, upper, lower):'
print curveParams
plt.hold(True)
xValues = logPossibleValues
xRange = xValues[-1] - xValues[1]
fitxval = np.linspace(xValues[0] - 0.1 * xRange, xValues[-1] + 0.1 * xRange,
40)
fityval = extrastats.psychfun(fitxval, *curveParams)
hfit = plt.plot(fitxval, fityval, '-', linewidth=2, color='k')
#(hp,hfit) = extraplots.plot_psychometric_fit(possibleValues,nTrialsEachValue,
# nHitsEachValue,curveParams)