def plot_psycurve_fit_and_data(bdata, plotColor): #Calculate the psychometric 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 psycurve fit estimate = behavioranalysis.psycurve_fit_from_bdata(bdata, plotFits=0) #Plot in log2 space(fit is already in this space) possibleValues = np.log2(possibleValues) #Plot the stuff ax = plt.gca() plt.hold(1) xRange = possibleValues[-1] - possibleValues[1] fitxval = np.linspace(possibleValues[0] - 0.1 * xRange, possibleValues[-1] + 0.1 * xRange, 40) fityvals = extrastats.psychfun(fitxval, *estimate) upperWhisker = ciHitsEachValue[1, :] - fractionHitsEachValue lowerWhisker = fractionHitsEachValue - ciHitsEachValue[0, :] (pline, pcaps, pbars) = plt.errorbar(possibleValues, fractionHitsEachValue, yerr=[lowerWhisker, upperWhisker], color='k', fmt=None) pdots = plt.plot(possibleValues, fractionHitsEachValue, 'o', mec='none', mfc='k', ms=8) setp(pcaps, color=plotColor) setp(pbars, color=plotColor) setp(pdots, markerfacecolor=plotColor) if np.unique(freqEachTrial)[0] < 1000: #If Hz ax.set_xticks(possibleValues) ax.set_xticklabels(np.unique(freqEachTrial)) plt.xlabel('Frequency (Hz)') else: ax.set_xticks(possibleValues) freqLabels = [ '{:.03}'.format(x) for x in np.unique(freqEachTrial) / 1000.0 ] ax.set_xticklabels(freqLabels) plt.xlabel('Frequency (kHz)') ax.plot(fitxval, fityvals, color=plotColor) plt.ylim([0, 1]) plt.ylabel('Fraction Rightward Trials') return estimate
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_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_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
salineSessions = ['20160412a', '20160414a', '20160416a'] animal = 'amod002' muscimolData = behavioranalysis.load_many_sessions(animal, muscimolSessions) salineData = behavioranalysis.load_many_sessions(animal, salineSessions) soundTypes = ['amp_mod', 'chords'] soundType = 'chords' trialsSoundTypeMusAmp = muscimolData['soundType']==muscimolData.labels['soundType'][soundType] rightTrialsMusAmp = (muscimolData['choice']==muscimolData.labels['choice']['right'])[trialsSoundTypeMusAmp] freqEachTrialMusAmp = muscimolData['targetFrequency'][trialsSoundTypeMusAmp] validMusAmp = muscimolData['valid'][trialsSoundTypeMusAmp] (possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue) = behavioranalysis.calculate_psychometric(rightTrialsMusAmp, freqEachTrialMusAmp, validMusAmp) M = pypsignifit.psignidata.BootstrapInference(zip(possibleValues, nHitsEachValue, nTrialsEachValue), sample=True, nafc=1, plotprm={"color": "r", "label": "Condition 0"}) 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) S = pypsignifit.psignidata.BootstrapInference(zip(possibleValues, nHitsEachValue, nTrialsEachValue), sample=True, nafc=1, plotprm={"color": "k", "label": "Condition 0"}) figure() # pypsignifit.psigniplot.plotPMF(B)
salineSessions = ['20160412a', '20160414a', '20160416a'] animal = 'amod002' muscimolData = behavioranalysis.load_many_sessions(animal, muscimolSessions) salineData = behavioranalysis.load_many_sessions(animal, salineSessions) soundTypes = ['amp_mod', 'chords'] soundType = 'chords' trialsSoundTypeMusAmp = muscimolData['soundType']==muscimolData.labels['soundType'][soundType] rightTrialsMusAmp = (muscimolData['choice']==muscimolData.labels['choice']['right'])[trialsSoundTypeMusAmp] freqEachTrialMusAmp = muscimolData['targetFrequency'][trialsSoundTypeMusAmp] validMusAmp = muscimolData['valid'][trialsSoundTypeMusAmp] (possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue) = behavioranalysis.calculate_psychometric(rightTrialsMusAmp, freqEachTrialMusAmp, validMusAmp) M = pypsignifit.psignidata.BootstrapInference(zip(possibleValues, nHitsEachValue, nTrialsEachValue), sample=True, nafc=1, plotprm={"color": "r", "label": "Condition 0"}) 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) S = pypsignifit.psignidata.BootstrapInference(zip(possibleValues, nHitsEachValue, nTrialsEachValue), sample=True, nafc=1, plotprm={"color": "k", "label": "Condition 0"}) figure() # pypsignifit.psigniplot.plotPMF(B)
muscimolData = behavioranalysis.load_many_sessions(animal, muscimolSessions) salineData = behavioranalysis.load_many_sessions(animal, salineSessions) soundTypes = ['amp_mod', 'chords'] soundType = 'chords' trialsSoundTypeMusAmp = muscimolData['soundType'] == muscimolData.labels[ 'soundType'][soundType] rightTrialsMusAmp = (muscimolData['choice'] == muscimolData.labels['choice'] ['right'])[trialsSoundTypeMusAmp] freqEachTrialMusAmp = muscimolData['targetFrequency'][trialsSoundTypeMusAmp] validMusAmp = muscimolData['valid'][trialsSoundTypeMusAmp] (possibleValues, fractionHitsEachValue, ciHitsEachValue, nTrialsEachValue, nHitsEachValue) = behavioranalysis.calculate_psychometric( rightTrialsMusAmp, freqEachTrialMusAmp, validMusAmp) M = pypsignifit.psignidata.BootstrapInference(zip(possibleValues, nHitsEachValue, nTrialsEachValue), sample=True, nafc=1, plotprm={ "color": "r", "label": "Condition 0" }) trialsSoundTypeSalAmp = salineData['soundType'] == salineData.labels[ 'soundType'][soundType] rightTrialsSalAmp = (salineData['choice'] == salineData.labels['choice'] ['right'])[trialsSoundTypeSalAmp]
animal, muscimolSessions) salineDataObjs, salineSoundTypes = behavioranalysis.load_behavior_sessions_sound_type( animal, salineSessions) #Testing with a single bdata object hold(1) bdata = salineDataObjs[0] bdata = muscimolDataObjs[0] #Calculate hit trials, freq each trial, valid - then calc psychometric rightTrials = bdata['choice'] == bdata.labels['choice']['right'] freqEachTrial = bdata['targetFrequency'] valid = bdata['valid'] (possibleValues, fractionHitsEachValue, ciHitsEachValue, nTrialsEachValue, nHitsEachValue) = behavioranalysis.calculate_psychometric( rightTrials, freqEachTrial, valid) extraplots.plot_psychometric(possibleValues, fractionHitsEachValue, ciHitsEachValue) #calculating the estimate without constraints estimate = extrastats.psychometric_fit(possibleValues, nTrialsEachValue, nHitsEachValue) #Calculating with constraints constraints = ('Uniform(8, 32)', 'Uniform(0,20)', 'Uniform(0,1)', 'Uniform(0,1)') estimate = extrastats.psychometric_fit(possibleValues, nTrialsEachValue, nHitsEachValue, constraints) #The x values over which to evaluate the fitted function
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
#Load the data objects for the muscimol and saline sessions muscimolDataObjs, muscimolSoundTypes = behavioranalysis.load_behavior_sessions_sound_type(animal, muscimolSessions) salineDataObjs, salineSoundTypes = behavioranalysis.load_behavior_sessions_sound_type(animal, salineSessions) #Testing with a single bdata object hold(1) bdata = salineDataObjs[0] bdata = muscimolDataObjs[0] #Calculate hit trials, freq each trial, valid - then calc psychometric rightTrials = bdata['choice']==bdata.labels['choice']['right'] freqEachTrial = bdata['targetFrequency'] valid = bdata['valid'] (possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue) = behavioranalysis.calculate_psychometric(rightTrials, freqEachTrial, valid) extraplots.plot_psychometric(possibleValues, fractionHitsEachValue, ciHitsEachValue) #calculating the estimate without constraints estimate = extrastats.psychometric_fit(possibleValues, nTrialsEachValue, nHitsEachValue) #Calculating with constraints constraints = ( 'Uniform(8, 32)','Uniform(0,20)' ,'Uniform(0,1)', 'Uniform(0,1)') estimate = extrastats.psychometric_fit(possibleValues, nTrialsEachValue, nHitsEachValue, constraints) #The x values over which to evaluate the fitted function xRange = possibleValues[-1]-possibleValues[1] fitxval = np.linspace(possibleValues[0]-0.1*xRange,possibleValues[-1]+0.1*xRange,40) #The fitted points
choiceRight = choice==bdata.labels['choice']['right'] choiceRightAmpMod = choiceRight[trialsAmpMod] choiceRightTones = choiceRight[trialsTones] targetFrequencyAmpMod = bdata['targetFrequency'][trialsAmpMod] targetFrequencyTones = bdata['targetFrequency'][trialsTones] valid = bdata['valid'] validAmpMod = valid[trialsAmpMod] validTones = valid[trialsTones] #Tones figure() (possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\ behavioranalysis.calculate_psychometric(choiceRightTones,targetFrequencyTones,validTones) (pline, pcaps, pbars, pdots) = extraplots.plot_psychometric(1e-3*possibleValues, fractionHitsEachValue, ciHitsEachValue, xTickPeriod=1) #Amp Mod figure() (possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\ behavioranalysis.calculate_psychometric(choiceRightAmpMod,targetFrequencyAmpMod,validAmpMod) (pline, pcaps, pbars, pdots) = extraplots.plot_psychometric(1e-3*possibleValues, fractionHitsEachValue,
def plot_psycurve_fit_and_data(bdata, plotColor): #Calculate the psychometric 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 psycurve fit estimate = behavioranalysis.psycurve_fit_from_bdata(bdata, plotFits=0) #Plot in log2 space(fit is already in this space) possibleValues = np.log2(possibleValues) #Plot the stuff ax = plt.gca() plt.hold(1) xRange = possibleValues[-1]-possibleValues[1] fitxval = np.linspace(possibleValues[0]-0.1*xRange,possibleValues[-1]+0.1*xRange,40) fityvals = extrastats.psychfun(fitxval, *estimate) upperWhisker = ciHitsEachValue[1,:]-fractionHitsEachValue lowerWhisker = fractionHitsEachValue-ciHitsEachValue[0,:] (pline, pcaps, pbars) = plt.errorbar(possibleValues, fractionHitsEachValue, yerr = [lowerWhisker, upperWhisker],color='k', fmt=None) pdots = plt.plot(possibleValues, fractionHitsEachValue, 'o',mec='none',mfc='k',ms=8) setp(pcaps, color=plotColor) setp(pbars, color=plotColor) setp(pdots, markerfacecolor=plotColor) if np.unique(freqEachTrial)[0]<1000: #If Hz ax.set_xticks(possibleValues) ax.set_xticklabels(np.unique(freqEachTrial)) plt.xlabel('Frequency (Hz)') else: ax.set_xticks(possibleValues) freqLabels = ['{:.03}'.format(x) for x in np.unique(freqEachTrial)/1000.0] ax.set_xticklabels(freqLabels) plt.xlabel('Frequency (kHz)') ax.plot(fitxval, fityvals, color=plotColor) plt.ylim([0, 1]) plt.ylabel('Fraction Rightward Trials') return estimate