def laser_tuning_curve(bdata, ephysData, gs):
plt.subplot(gs[3, 1])
freqEachTrial = bdata['currentFreq']
laserEachTrial = bdata['laserOn']
intEachTrial = bdata['currentIntensity']
eventOnsetTimes = ephysData['events']['stimOn']
spikeTimeStamps = ephysData['spikeTimes']
timeRange = [0.0, 0.1]
possiblefreqs = np.unique(freqEachTrial)
freqLabels = [round(x/1000, 1) for x in possiblefreqs]
possiblelaser = np.unique(laserEachTrial)
possibleInts = np.unique(intEachTrial)
laserTrialsEachCond = behavioranalysis.find_trials_each_combination(freqEachTrial, possiblefreqs, laserEachTrial, possiblelaser)
intTrialsEachCond = behavioranalysis.find_trials_each_combination(freqEachTrial, possiblefreqs, intEachTrial, possibleInts)
spikeTimesFromEventOnset, trialIndexForEachSpike, indexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(
spikeTimeStamps, eventOnsetTimes, timeRange)
for intInd, inten in enumerate(possibleInts):
line = '-'
if intInd == 0:
line = '--'
for indLaser in possiblelaser:
color = 'black'
if indLaser == 1:
color = 'blue'
laser = 'No Laser - '
if indLaser == 1:
laser = 'Laser - '
curveName = laser + str(inten) + ' dB'
trialsEachCond = laserTrialsEachCond[:,:,indLaser] & intTrialsEachCond[:,:,intInd]
try:
freq_avgs = spikesanalysis.avg_num_spikes_each_condition(trialsEachCond, indexLimitsEachTrial)
except:
behavIndexLimitsEachTrial = indexLimitsEachTrial[0:2,:-1]
freq_avgs = spikesanalysis.avg_num_spikes_each_condition(trialsEachCond, behavIndexLimitsEachTrial)
xpoints = [x for x in range(0, len(possiblefreqs))]
#plt.semilogx(possiblefreqs, freq_avgs, linestyle = line, color = color, label = curveName)
#plt.plot(xvalues, freq_avgs, linestyle = line, color = 'black', label = curveName, xlabels = possiblefreqs)
plt.plot(xpoints, freq_avgs, linestyle = line, color = color, marker = 'o', label = curveName)
plt.xticks(xpoints, freqLabels)
plt.hold(True)
plt.title('Frequency Tuning Curve - Laser Session')
plt.legend(fontsize = 'x-small')
plt.hold(False)
def laser_tuning_curve(bdata, ephysData, gs):
plt.subplot(gs[3, 1])
freqEachTrial = bdata['currentFreq']
laserEachTrial = bdata['laserOn']
intEachTrial = bdata['currentIntensity']
eventOnsetTimes = ephysData['events']['stimOn']
spikeTimeStamps = ephysData['spikeTimes']
timeRange = [0.0, 0.1]
possiblefreqs = np.unique(freqEachTrial)
freqLabels = [round(x/1000, 1) for x in possiblefreqs]
possiblelaser = np.unique(laserEachTrial)
possibleInts = np.unique(intEachTrial)
laserTrialsEachCond = behavioranalysis.find_trials_each_combination(freqEachTrial, possiblefreqs, laserEachTrial, possiblelaser)
intTrialsEachCond = behavioranalysis.find_trials_each_combination(freqEachTrial, possiblefreqs, intEachTrial, possibleInts)
spikeTimesFromEventOnset, trialIndexForEachSpike, indexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(
spikeTimeStamps, eventOnsetTimes, timeRange)
for intInd, inten in enumerate(possibleInts):
line = '-'
if intInd == 0:
line = '--'
for indLaser in possiblelaser:
color = 'black'
if indLaser == 1:
color = 'blue'
laser = 'No Laser - '
if indLaser == 1:
laser = 'Laser - '
curveName = laser + str(inten) + ' dB'
trialsEachCond = laserTrialsEachCond[:,:,indLaser] & intTrialsEachCond[:,:,intInd]
try:
freq_avgs = spikesanalysis.avg_num_spikes_each_condition(trialsEachCond, indexLimitsEachTrial)
except:
behavIndexLimitsEachTrial = indexLimitsEachTrial[0:2,:-1]
freq_avgs = spikesanalysis.avg_num_spikes_each_condition(trialsEachCond, behavIndexLimitsEachTrial)
xpoints = [x for x in range(0, len(possiblefreqs))]
#plt.semilogx(possiblefreqs, freq_avgs, linestyle = line, color = color, label = curveName)
#plt.plot(xvalues, freq_avgs, linestyle = line, color = 'black', label = curveName, xlabels = possiblefreqs)
plt.plot(xpoints, freq_avgs, linestyle = line, color = color, marker = 'o', label = curveName)
plt.xticks(xpoints, freqLabels)
plt.hold(True)
plt.title('Frequency Tuning Curve - Laser Session')
plt.legend(fontsize = 'x-small')
plt.hold(False)
def tuning_curve(bdata, ephysData, gs):
plt.subplot(gs[3, 0])
freqEachTrial = bdata['currentFreq']
intEachTrial = bdata['currentIntensity']
eventOnsetTimes = ephysData['events']['stimOn']
spikeTimeStamps = ephysData['spikeTimes']
timeRange = [0.0, 0.1]
possiblefreqs = np.unique(freqEachTrial)
freqLabels = [round(x/1000, 1) for x in possiblefreqs]
possibleInts = np.unique(intEachTrial)
intTrialsEachCond = behavioranalysis.find_trials_each_combination(freqEachTrial, possiblefreqs, intEachTrial, possibleInts)
spikeTimesFromEventOnset, trialIndexForEachSpike, indexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(
spikeTimeStamps, eventOnsetTimes, timeRange)
for intInd, inten in enumerate(possibleInts):
line = '-'
if intInd == 0 and len(possibleInts) > 1:
line = '--'
curveName = str(inten) + ' dB'
trialsEachCond = intTrialsEachCond[:,:,intInd]
try:
freq_avgs = spikesanalysis.avg_num_spikes_each_condition(trialsEachCond, indexLimitsEachTrial)
except:
behavIndexLimitsEachTrial = indexLimitsEachTrial[0:2,:-1]
freq_avgs = spikesanalysis.avg_num_spikes_each_condition(trialsEachCond, behavIndexLimitsEachTrial)
xpoints = [x for x in range(0, len(possiblefreqs))]
xpointticks = [x for x in range(1, len(possiblefreqs), 2)]
freqticks = [freqLabels[x] for x in range(1, len(freqLabels), 2)]
#plt.semilogx(possiblefreqs, freq_avgs, linestyle = line, color = 'black', label = curveName)
#plt.plot(log(possiblefreqs), freq_avgs, linestyle = line, color = 'black', label = curveName, xlabels = possiblefreqs)
plt.plot(xpoints, freq_avgs, linestyle = line, color = 'black', marker = 'o', label = curveName)
plt.xticks(xpointticks, freqticks)
plt.hold(True)
xlabel = 'Frequency (kHz)'
ylabel = 'Firing Rate (spikes/s)'
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title('Frequency Tuning Curve')
plt.legend(fontsize = 'x-small', loc = 'upper left')
plt.hold(False)
possibleIntensity = np.array(possibleIntensity)
#--------------- THIS PART PRODUCES THE ARRAY TO PLOT ---------------
timeRange = [0, 6]
(spikeTimesFromEventOnset, trialIndexForEachSpike,
indexLimitsEachTrial) = spikesanalysis.eventlocked_spiketimes(
spikeTimestamps, eventOnsetTimes, timeRange)
CASE = 1
if CASE == 0: #Freq/intensity
trialsEachCond = behavioranalysis.find_trials_each_combination(
intensityEachTrial, possibleIntensity, freqEachTrial, possibleFreq)
elif CASE == 1: #Only a single sorting variable, frequency
trialsEachCond = behavioranalysis.find_trials_each_type(
freqEachTrial, possibleFreq)
avgSpikesArray = spikesanalysis.avg_num_spikes_each_condition(
indexLimitsEachTrial, trialsEachCond)
#--------------- THIS PART PLOTS THE ARRAY ---------------
plt.clf()
if CASE == 0:
#for F/I data, the spike array is more understandable if we flip it upside down
#to make the higher intensities on the top of the plot
avgSpikesArray = np.flipud(avgSpikesArray)
plt.imshow(avgSpikesArray, interpolation='none', cmap='Blues')
ax = plt.gca()
ax.set_xticks(possibleFreq)
ax.set_xticklabels(possibleFreq)
ax.set_xlabel('Freq')
ax.set_yticks(possibleIntensity)
ax.set_yticklabels(possibleIntensity[::-1])
ax.set_ylabel('Intensity')
