def plot_dynamics(behavData, winsize=40, fontsize=12, soundfreq=None):
'''
Plot performance in time for one session.
First argument is an object created by loadbehavior.BehaviorData (or subclasses)
'''
ax = plt.gca()
ax.cla()
lineWidth = 2
if not soundfreq:
possibleFreq = np.unique(behavData['targetFrequency'])
else:
possibleFreq = soundfreq
possibleColors = FREQCOLORS + ['k', 'm', 'c', 'b', 'r', 'g']
colorEachFreq = dict(zip(possibleFreq, possibleColors))
behavData.find_trials_each_block()
nBlocks = behavData.blocks['nBlocks']
trialsEachBlock = behavData.blocks['trialsEachBlock']
validEachBlock = trialsEachBlock & (
behavData['valid'][:, np.newaxis].astype(bool))
nValidEachBlock = np.sum(validEachBlock, axis=0)
lastValidEachBlock = np.cumsum(
nValidEachBlock) # Actually, these values correspond to lastIndex+1
firstValidEachBlock = np.concatenate(([0], lastValidEachBlock[:-1]))
rightChoice = behavData['choice'] == behavData.labels['choice']['right']
hPlots = []
plt.hold(True)
for indb in range(nBlocks):
trialsThisBlock = trialsEachBlock[:, indb]
validThisBlock = validEachBlock[:, indb]
for indf, thisFreq in enumerate(possibleFreq):
thisColor = colorEachFreq[thisFreq]
trialsThisFreq = (behavData['targetFrequency'] == thisFreq)
choiceVecThisFreq = np.ma.masked_array(rightChoice[validThisBlock])
choiceVecThisFreq.mask = ~trialsThisFreq[validThisBlock]
movAvChoice = extrafuncs.moving_average_masked(
choiceVecThisFreq, winsize)
hp, = plt.plot(range(firstValidEachBlock[indb],
lastValidEachBlock[indb]),
100 * movAvChoice,
lw=lineWidth,
color=thisColor)
hPlots.append(hp)
plt.ylim([-5, 105])
plt.axhline(50, color='0.5', ls='--')
plt.ylabel('% rightward', fontsize=fontsize)
plt.xlabel('Trial', fontsize=fontsize)
extraplots.set_ticks_fontsize(ax, fontsize)
plt.draw()
plt.show()
return hPlots
def plot_summary(behavData,fontsize=12,soundfreq=None):
'''
Show summary of performance.
First argument is an object created by loadbehavior.BehaviorData (or subclasses)
'''
correct = behavData['outcome']==behavData.labels['outcome']['correct']
early = behavData['outcome']==behavData.labels['outcome']['invalid']
#possibleFreq = np.unique(behavData['targetFrequency'])
if soundfreq is None:
possibleFreq = np.unique(behavData['targetFrequency'])
else:
possibleFreq = soundfreq
possibleBlockID = np.unique(behavData['currentBlock'])
trialsEachType = find_trials_each_type_each_block(behavData['targetFrequency'],possibleFreq,
behavData['currentBlock'],possibleBlockID)
validTrialsEachType = trialsEachType & behavData['valid'][:,np.newaxis,np.newaxis].astype(bool)
correctTrialsEachType = validTrialsEachType & correct[:,np.newaxis,np.newaxis]
nCorrectEachType = np.sum(correctTrialsEachType,axis=0)
nValidEachType = np.sum(validTrialsEachType,axis=0)
#perfEachType = np.where(nValidEachType>0, nCorrectEachType/nValidEachType.astype(float), np.nan)
perfEachType = nCorrectEachType/nValidEachType.astype(float)
# --- Plot results ---
itemsToPlot = nValidEachType.flatten()>0 #~np.isnan(perfEachType.flatten())
perfToPlot = perfEachType.flatten()[itemsToPlot] # Show only 2 freq for each block type
freqLabels = np.repeat(possibleFreq,len(possibleBlockID))[itemsToPlot]
nValidCounts = nValidEachType.flatten()[itemsToPlot]
xPos = [0,1,3,4][:len(perfToPlot)]
ax = plt.gca()
ax.set_xlim([-1,5])
ax.set_ylim([0,100])
plt.hold(True)
hline50 = plt.axhline(50,linestyle=':',color='k',zorder=-1)
hline75 = plt.axhline(75,linestyle=':',color='k',zorder=-1)
hbars = plt.bar(xPos,100*perfToPlot,align='center',fc=[0.8,0.8,0.8],ec='k')
for thispos,thistext in zip(xPos,nValidCounts):
plt.text(thispos,10,str(thistext),ha='center',fontsize=fontsize)
ax.set_ylabel('% correct',fontsize=fontsize)
ax.set_xticks(xPos)
ax.set_xticklabels(freqLabels/1000)
titleStr = '{0} [{1}] {2}\n'.format(behavData.session['subject'],behavData.session['date'],
behavData.session['hostname'])
titleStr += '{0} valid, {1:.0%} early'.format(sum(nValidCounts),np.mean(early))
ax.set_title(titleStr,fontweight='bold',fontsize=fontsize,y=0.95)
ax.set_xlabel('Frequency (kHz)',fontsize=fontsize)
extraplots.set_ticks_fontsize(ax,fontsize)
plt.draw()
plt.show()
def plot_dynamics(behavData,winsize=40,fontsize=12,soundfreq=None):
'''
Plot performance in time for one session.
First argument is an object created by loadbehavior.BehaviorData (or subclasses)
'''
ax = plt.gca()
ax.cla()
lineWidth = 2
if not soundfreq:
possibleFreq = np.unique(behavData['targetFrequency'])
else:
possibleFreq = soundfreq
possibleColors = FREQCOLORS + ['k','m','c', 'b','r','g']
colorEachFreq = dict(zip(possibleFreq,possibleColors))
behavData.find_trials_each_block()
nBlocks = behavData.blocks['nBlocks']
trialsEachBlock = behavData.blocks['trialsEachBlock']
validEachBlock = trialsEachBlock & (behavData['valid'][:,np.newaxis].astype(bool))
nValidEachBlock = np.sum(validEachBlock,axis=0)
lastValidEachBlock = np.cumsum(nValidEachBlock) # Actually, these values correspond to lastIndex+1
firstValidEachBlock = np.concatenate(([0],lastValidEachBlock[:-1]))
rightChoice = behavData['choice']==behavData.labels['choice']['right']
hPlots = []
plt.hold(True)
for indb in range(nBlocks):
trialsThisBlock = trialsEachBlock[:,indb]
validThisBlock = validEachBlock[:,indb]
for indf,thisFreq in enumerate(possibleFreq):
thisColor = colorEachFreq[thisFreq]
trialsThisFreq = (behavData['targetFrequency']==thisFreq)
choiceVecThisFreq = np.ma.masked_array(rightChoice[validThisBlock])
choiceVecThisFreq.mask = ~trialsThisFreq[validThisBlock]
movAvChoice = extrafuncs.moving_average_masked(choiceVecThisFreq,winsize)
hp, = plt.plot(range(firstValidEachBlock[indb],lastValidEachBlock[indb]),100*movAvChoice,
lw=lineWidth,color=thisColor)
hPlots.append(hp)
plt.ylim([-5,105])
plt.axhline(50,color='0.5',ls='--')
plt.ylabel('% rightward',fontsize=fontsize)
plt.xlabel('Trial',fontsize=fontsize)
extraplots.set_ticks_fontsize(ax,fontsize)
plt.draw()
plt.show()
return hPlots
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(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_dynamics(behavData,winsize=40,fontsize=12):
ax = plt.gca()
ax.cla()
lineWidth = 2
possibleFreq = np.unique(behavData['TargetFreq'])
possibleColors = ['b','r','g','k','m','c']
colorEachFreq = dict(zip(possibleFreq,possibleColors))
behavData.find_trials_each_block()
nBlocks = len(behavData.eachBlockID)
trialsEachBlock = behavData.trialsEachBlock
nValidEachBlock = np.sum(behavData.trialsEachBlock & (~behavData.early[:,np.newaxis]),axis=0)
lastValidEachBlock = np.cumsum(nValidEachBlock)
firstValidEachBlock = np.concatenate(([0],lastValidEachBlock[:-1]))
for indb in range(nBlocks):
trialsThisBlock = trialsEachBlock[:,indb]
freqsThisBlock = np.unique(behavData['TargetFreq'][trialsThisBlock])
validThisBlock = trialsThisBlock & (~behavData.early)
for indf,thisFreq in enumerate(freqsThisBlock):
thisColor = colorEachFreq[thisFreq]
trialsThisFreq = (behavData['TargetFreq']==thisFreq)
choiceVecThisFreq = np.ma.masked_array(behavData.rightChoice[validThisBlock])
choiceVecThisFreq.mask = ~trialsThisFreq[validThisBlock]
movAvChoice = extrafuncs.moving_average_masked(choiceVecThisFreq,winsize)
plt.plot(range(firstValidEachBlock[indb],lastValidEachBlock[indb]),100*movAvChoice,
lw=lineWidth,color=thisColor)
plt.hold(True)
plt.ylim([-5,105])
plt.axhline(50,color='0.5',ls='--')
plt.ylabel('% rightward',fontsize=fontsize)
plt.xlabel('Trial',fontsize=fontsize)
extraplots.set_ticks_fontsize(ax,fontsize)
plt.draw()
plt.show()
def plot_summary(behavData, fontsize=12, soundfreq=None):
'''
Show summary of performance.
First argument is an object created by loadbehavior.BehaviorData (or subclasses)
'''
correct = behavData['outcome'] == behavData.labels['outcome']['correct']
early = behavData['outcome'] == behavData.labels['outcome']['invalid']
#possibleFreq = np.unique(behavData['targetFrequency'])
if soundfreq is None:
possibleFreq = np.unique(behavData['targetFrequency'])
else:
possibleFreq = soundfreq
possibleBlockID = np.unique(behavData['currentBlock'])
trialsEachType = find_trials_each_type_each_block(
behavData['targetFrequency'], possibleFreq, behavData['currentBlock'],
possibleBlockID)
validTrialsEachType = trialsEachType & behavData[
'valid'][:, np.newaxis, np.newaxis].astype(bool)
correctTrialsEachType = validTrialsEachType & correct[:, np.newaxis,
np.newaxis]
nCorrectEachType = np.sum(correctTrialsEachType, axis=0)
nValidEachType = np.sum(validTrialsEachType, axis=0)
#perfEachType = np.where(nValidEachType>0, nCorrectEachType/nValidEachType.astype(float), np.nan)
perfEachType = nCorrectEachType / nValidEachType.astype(float)
# --- Plot results ---
itemsToPlot = nValidEachType.flatten(
) > 0 #~np.isnan(perfEachType.flatten())
perfToPlot = perfEachType.flatten()[
itemsToPlot] # Show only 2 freq for each block type
freqLabels = np.repeat(possibleFreq, len(possibleBlockID))[itemsToPlot]
nValidCounts = nValidEachType.flatten()[itemsToPlot]
xPos = [0, 1, 3, 4][:len(perfToPlot)]
ax = plt.gca()
ax.set_xlim([-1, 5])
ax.set_ylim([0, 100])
plt.hold(True)
hline50 = plt.axhline(50, linestyle=':', color='k', zorder=-1)
hline75 = plt.axhline(75, linestyle=':', color='k', zorder=-1)
hbars = plt.bar(xPos,
100 * perfToPlot,
align='center',
fc=[0.8, 0.8, 0.8],
ec='k')
for thispos, thistext in zip(xPos, nValidCounts):
plt.text(thispos, 10, str(thistext), ha='center', fontsize=fontsize)
ax.set_ylabel('% correct', fontsize=fontsize)
ax.set_xticks(xPos)
ax.set_xticklabels(freqLabels / 1000)
titleStr = '{0} [{1}] {2}\n'.format(behavData.session['subject'],
behavData.session['date'],
behavData.session['hostname'])
titleStr += '{0} valid, {1:.0%} early'.format(sum(nValidCounts),
np.mean(early))
ax.set_title(titleStr, fontweight='bold', fontsize=fontsize, y=0.95)
ax.set_xlabel('Frequency (kHz)', fontsize=fontsize)
extraplots.set_ticks_fontsize(ax, fontsize)
plt.draw()
plt.show()
def plot_summary(behavData,fontsize=12):
'''Show summary of performance.
Input is an object created by loadbehavior.ReversalBehaviorData()
'''
behavData.extract_event_times() # In case it hasn't
behavData.find_trials_each_type() # In case it hasn't
percentEarly = 100*sum(behavData.early)/float(behavData['nTrials'])
mostCommonBlock = np.argmax(np.bincount(behavData['CurrentBlock'].astype(int)))
if mostCommonBlock != 1:
# -- Blocks change from LowFreq to HighFreq
g1 = behavData.lowFreqs & behavData.leftReward
g2 = behavData.lowFreqs & behavData.rightReward
g3 = behavData.highFreqs & behavData.leftReward
g4 = behavData.highFreqs & behavData.rightReward
else:
# -- ExtremeFreq --
g1 = behavData.leftReward
g2 = zeros(len(behavData.leftReward)).astype(bool)
g3 = zeros(len(behavData.leftReward)).astype(bool)
g4 = behavData.rightReward
trialsEachCond = np.array([g1,g2,g3,g4])
validTrialsEachCond = trialsEachCond & (~behavData.early)
correctTrialsEachCond = validTrialsEachCond & behavData.correct
percentEarly = 100*sum(behavData.early)/float(len(behavData.early))
### WARNING: this gives a different number of valid trials than matlab!
### check if first empty trial is included or not
nCorrectEachCond = np.sum(correctTrialsEachCond,axis=1)
nValidEachCond = np.sum(validTrialsEachCond,axis=1)
perfEachCond = nCorrectEachCond/nValidEachCond.astype(float)
freqValues = [behavData['FreqLow'][-1],
behavData['FreqMid'][-1],
behavData['FreqMid'][-1],
behavData['FreqHigh'][-1]]
freqLabels = ['%0.1f'%(x/1000) for x in freqValues]
# --- Plot results ---
xPos = [0,1,3,4]
#plt.clf()
#ax = plt.axes()
ax = plt.gca()
ax.set_xlim([-1,5])
ax.set_ylim([0,100])
plt.hold(True)
hline50 = plt.axhline(50,linestyle=':',color='k',zorder=-1)
hline75 = plt.axhline(75,linestyle=':',color='k',zorder=-1)
hbars = plt.bar(xPos,100*perfEachCond,align='center',fc=[0.8,0.8,0.8],ec='k')
#htrials(indtype) = text(BarXpos(indtype),10,num2str(NtrialsEachCond(indc)));
for thispos,thistext in zip(xPos,nValidEachCond):
plt.text(thispos,10,str(thistext),ha='center',fontsize=fontsize)
ax.set_ylabel('% correct',fontsize=fontsize)
ax.set_xticks(xPos)
ax.set_xticklabels(freqLabels)
titleStr = '%s [%s] %s\n'%(behavData['Subject'],behavData['Date'],behavData['HostName'])
titleStr += '%d valid, %d%% early'%(sum(nValidEachCond),round(percentEarly))
ax.set_title(titleStr,fontweight='bold',fontsize=fontsize)
extraplots.set_ticks_fontsize(ax,fontsize)
#plt.gcf().set_size_inches(4,5)
plt.draw()
plt.show()
