def plot_bandwidth_report(cell,
type='normal',
bandTimeRange=[0.2, 1.0],
bandBaseRange=[-1.0, -0.2]):
plt.clf()
bandIndex = int(cell['bestBandSession'])
if bandIndex is None:
print 'No bandwidth session given'
return
#create cell object for loading data
cellObj = ephyscore.Cell(cell)
#change dimensions of report to add laser trials if they exist
if len(cellObj.get_session_inds('laserPulse')) > 0:
laser = True
gs = gridspec.GridSpec(13, 6)
else:
laser = False
gs = gridspec.GridSpec(9, 6)
offset = 4 * laser
gs.update(left=0.15, right=0.85, top=0.96, wspace=0.7, hspace=1.0)
tetrode = int(cell['tetrode'])
cluster = int(cell['cluster'])
#load bandwidth ephys and behaviour data
bandEphysData, bandBData = cellObj.load_by_index(bandIndex)
bandEventOnsetTimes = ephysanalysis.get_sound_onset_times(
bandEphysData, 'bandwidth')
bandSpikeTimestamps = bandEphysData['spikeTimes']
timeRange = [-0.2, 1.5]
bandEachTrial = bandBData['currentBand']
numBands = np.unique(bandEachTrial)
#change the trial type that the bandwidth session is split by so we can use this report for Arch-inactivation experiments
#also changes the colours to be more thematically appropriate! (in Anna's opinion)
if type == 'laser':
secondSort = bandBData['laserTrial']
secondSortLabels = ['no laser', 'laser']
colours = ['k', '#c4a000']
errorColours = ['0.5', '#fce94f']
gaussFitCol = 'gaussFit'
tuningR2Col = 'tuningFitR2'
elif type == 'normal':
secondSort = bandBData['currentAmp']
secondSortLabels = [
'{} dB'.format(amp) for amp in np.unique(secondSort)
]
colours = ['#4e9a06', '#5c3566']
errorColours = ['#8ae234', '#ad7fa8']
gaussFitCol = 'gaussFit'
tuningR2Col = 'tuningFitR2'
charfreq = str(np.unique(bandBData['charFreq'])[0] / 1000)
modrate = str(np.unique(bandBData['modRate'])[0])
numBands = np.unique(bandEachTrial)
# -- plot rasters of the bandwidth trials --
rasterColours = [
np.tile([colours[0], errorColours[0]],
len(numBands) / 2 + 1),
np.tile([colours[1], errorColours[1]],
len(numBands) / 2 + 1)
]
plot_separated_rasters(gs, [0, 3],
5 + offset,
bandEachTrial,
secondSort,
bandSpikeTimestamps,
bandEventOnsetTimes,
colours=rasterColours,
titles=secondSortLabels,
plotHeight=2)
# -- plot bandwidth tuning curves --
plt.subplot(gs[5 + offset:, 3:])
tuningDict = ephysanalysis.calculate_tuning_curve_inputs(
bandSpikeTimestamps,
bandEventOnsetTimes,
bandEachTrial,
secondSort,
bandTimeRange,
baseRange=bandBaseRange,
info='plotting')
plot_tuning_curve(tuningDict['responseArray'],
tuningDict['errorArray'],
numBands,
tuningDict['baselineSpikeRate'],
linecolours=colours,
errorcolours=errorColours)
# load tuning ephys and behaviour data
tuningEphysData, tuningBData = cellObj.load('tuningCurve')
tuningEventOnsetTimes = ephysanalysis.get_sound_onset_times(
tuningEphysData, 'tuningCurve')
tuningSpikeTimestamps = tuningEphysData['spikeTimes']
# -- plot frequency tuning at intensity used in bandwidth trial with gaussian fit --
# high amp bandwidth trials used to select appropriate frequency
maxAmp = max(np.unique(bandBData['currentAmp']))
if maxAmp < 1:
maxAmp = 66.0 #HARDCODED dB VALUE FOR SESSIONS DONE BEFORE NOISE CALIBRATION
# find tone intensity that corresponds to tone sessions in bandwidth trial
toneInt = maxAmp - 15.0 #HARDCODED DIFFERENCE IN TONE AND NOISE AMP BASED ON OSCILLOSCOPE READINGS FROM RIG 2
freqEachTrial = tuningBData['currentFreq']
plt.subplot(gs[2 + offset:4 + offset, 0:3])
plot_tuning_fitted_gaussian(tuningSpikeTimestamps,
tuningEventOnsetTimes,
tuningBData,
toneInt,
cell[gaussFitCol],
cell[tuningR2Col],
timeRange=cell['tuningTimeRange'])
# -- plot frequency tuning raster --
plt.subplot(gs[0 + offset:2 + offset, 0:3])
freqLabels = ["%.1f" % freq for freq in np.unique(freqEachTrial) / 1000.0]
plot_sorted_raster(tuningSpikeTimestamps,
tuningEventOnsetTimes,
freqEachTrial,
timeRange=[-0.2, 0.6],
labels=freqLabels)
plt.title('Frequency Tuning Raster')
# -- plot AM PSTH --
amEphysData, amBData = cellObj.load('AM')
amEventOnsetTimes = ephysanalysis.get_sound_onset_times(amEphysData, 'AM')
amSpikeTimestamps = amEphysData['spikeTimes']
rateEachTrial = amBData['currentFreq']
timeRange = [-0.2, 1.5]
colourList = ['b', 'g', 'y', 'orange', 'r']
plt.subplot(gs[2 + offset:4 + offset, 3:])
plot_sorted_psth(amSpikeTimestamps,
amEventOnsetTimes,
rateEachTrial,
timeRange=[-0.2, 0.8],
binsize=25,
colorEachCond=colourList)
plt.xlabel('Time from sound onset (sec)')
plt.ylabel('Firing rate (Hz)')
plt.title('AM PSTH')
# -- plot AM raster --
plt.subplot(gs[0 + offset:2 + offset, 3:])
rateLabels = ["%.0f" % rate for rate in np.unique(rateEachTrial)]
plot_sorted_raster(amSpikeTimestamps,
amEventOnsetTimes,
rateEachTrial,
timeRange=[-0.2, 0.8],
labels=rateLabels,
colorEachCond=colourList)
plt.xlabel('Time from sound onset (sec)')
plt.ylabel('Modulation Rate (Hz)')
plt.title('AM Raster')
# -- plot laser pulse and laser train data (if available) --
if laser:
# -- plot laser pulse raster --
laserEphysData, noBehav = cellObj.load('laserPulse')
laserEventOnsetTimes = laserEphysData['events']['laserOn']
laserSpikeTimestamps = laserEphysData['spikeTimes']
timeRange = [-0.1, 0.4]
plt.subplot(gs[0:2, 0:3])
laserSpikeTimesFromEventOnset, trialIndexForEachSpike, laserIndexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(
laserSpikeTimestamps, laserEventOnsetTimes, timeRange)
pRaster, hcond, zline = extraplots.raster_plot(
laserSpikeTimesFromEventOnset, laserIndexLimitsEachTrial,
timeRange)
plt.xlabel('Time from laser onset (sec)')
plt.title('Laser Pulse Raster')
# -- plot laser pulse psth --
plt.subplot(gs[2:4, 0:3])
binsize = 10 / 1000.0
spikeTimesFromEventOnset, trialIndexForEachSpike, indexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(
laserSpikeTimestamps, laserEventOnsetTimes,
[timeRange[0] - binsize, timeRange[1]])
binEdges = np.around(np.arange(timeRange[0] - binsize,
timeRange[1] + 2 * binsize, binsize),
decimals=2)
spikeCountMat = spikesanalysis.spiketimes_to_spikecounts(
spikeTimesFromEventOnset, indexLimitsEachTrial, binEdges)
pPSTH = extraplots.plot_psth(spikeCountMat / binsize, 1, binEdges[:-1])
plt.xlim(timeRange)
plt.xlabel('Time from laser onset (sec)')
plt.ylabel('Firing Rate (Hz)')
plt.title('Laser Pulse PSTH')
# -- didn't record laser trains for some earlier sessions --
if len(cellObj.get_session_inds('laserTrain')) > 0:
# -- plot laser train raster --
laserTrainEphysData, noBehav = cellObj.load('laserTrain')
laserTrainEventOnsetTimes = laserTrainEphysData['events'][
'laserOn']
laserTrainSpikeTimestamps = laserTrainEphysData['spikeTimes']
laserTrainEventOnsetTimes = spikesanalysis.minimum_event_onset_diff(
laserTrainEventOnsetTimes, 0.5)
timeRange = [-0.2, 1.0]
plt.subplot(gs[0:2, 3:])
spikeTimesFromEventOnset, trialIndexForEachSpike, indexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(
laserTrainSpikeTimestamps, laserTrainEventOnsetTimes,
timeRange)
pRaster, hcond, zline = extraplots.raster_plot(
spikeTimesFromEventOnset, indexLimitsEachTrial, timeRange)
plt.xlabel('Time from laser onset (sec)')
plt.title('Laser Train Raster')
# -- plot laser train psth --
plt.subplot(gs[2:4, 3:])
binsize = 10 / 1000.0
spikeTimesFromEventOnset, trialIndexForEachSpike, indexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(
laserTrainSpikeTimestamps, laserTrainEventOnsetTimes,
[timeRange[0] - binsize, timeRange[1]])
binEdges = np.around(np.arange(timeRange[0] - binsize,
timeRange[1] + 2 * binsize,
binsize),
decimals=2)
spikeCountMat = spikesanalysis.spiketimes_to_spikecounts(
spikeTimesFromEventOnset, indexLimitsEachTrial, binEdges)
pPSTH = extraplots.plot_psth(spikeCountMat / binsize, 1,
binEdges[:-1])
plt.xlim(timeRange)
plt.xlabel('Time from laser onset (sec)')
plt.ylabel('Firing Rate (Hz)')
plt.title('Laser Train PSTH')
# -- show cluster analysis --
#tsThisCluster, wavesThisCluster, recordingNumber = celldatabase.load_all_spikedata(cell)
# -- Plot ISI histogram --
plt.subplot(gs[4 + offset, 0:2])
spikesorting.plot_isi_loghist(bandSpikeTimestamps)
# -- Plot waveforms --
plt.subplot(gs[4 + offset, 2:4])
spikesorting.plot_waveforms(bandEphysData['samples'])
# -- Plot events in time --
plt.subplot(gs[4 + offset, 4:6])
spikesorting.plot_events_in_time(bandSpikeTimestamps)
title = '{0}, {1}, {2}um, Tetrode {3}, Cluster {4}, {5}kHz, {6}Hz modulation'.format(
cell['subject'], cell['date'], cell['depth'], tetrode, cluster,
charfreq, modrate)
plt.suptitle(title)
fig_path = '/home/jarauser/Pictures/cell reports'
fig_name = '{0}_{1}_{2}um_TT{3}Cluster{4}.png'.format(
cell['subject'], cell['date'], cell['depth'], tetrode, cluster)
full_fig_path = os.path.join(fig_path, fig_name)
fig = plt.gcf()
fig.set_size_inches(20, 25)
fig.savefig(full_fig_path, format='png', bbox_inches='tight')
def plot_harmonics_summary(cell, harmIndex, bandIndex):
plt.clf()
gs = gridspec.GridSpec(3, 2)
gs.update(left=0.15, right=0.85, top=0.95, wspace=0.2, hspace=0.3)
tetrode = int(cell['tetrode'])
cluster = int(cell['cluster'])
#create cell object for loading data
cellObj = ephyscore.Cell(cell)
#load harmonics ephys and behaviour data
harmEphysData, harmBData = cellObj.load('harmonics')
harmEventOnsetTimes = ephysanalysis.get_sound_onset_times(
harmEphysData, 'harmonics')
harmSpikeTimestamps = harmEphysData['spikeTimes']
timeRange = [-0.2, 1.5]
bandEachTrial = harmBData['currentBand']
secondSort = harmBData['harmTrialType']
secondSortLabels = ['noise', 'harmonics']
numBands = np.unique(bandEachTrial)
numSec = np.unique(secondSort)
#plot rasters of the noise and harmonics trials
colours = [
np.tile(['#4e9a06', '#8ae234'],
len(numBands) / 2 + 1),
np.tile(['#5c3566', '#ad7fa8'],
len(numBands) / 2 + 1)
]
plot_separated_rasters(gs, [0, 1],
1,
bandEachTrial,
secondSort,
harmSpikeTimestamps,
harmEventOnsetTimes,
titles=secondSortLabels,
duplicate=True,
colours=colours)
#plot a response curve of the noise and harmonics trials
plt.subplot(gs[1:, 1:])
timeRange = [0, 1.0]
spikeArray, errorArray, baselineSpikeRate = ephysanalysis.calculate_tuning_curve_inputs(
harmSpikeTimestamps, harmEventOnsetTimes, bandEachTrial, secondSort,
timeRange)
spikeArray[0, 0] = spikeArray[0, 1]
errorArray[0, 0] = errorArray[0, 1]
plot_tuning_curve(spikeArray,
errorArray,
numBands,
baselineSpikeRate,
legend=True,
labels=['noise', 'harmonics'],
linecolours=['#4e9a06', '#5c3566'],
errorcolours=['#8ae234', '#ad7fa8'])
#load frequency tuning data and plot frequency tuning raster
plt.subplot(gs[0, 0])
tuningEphysData, tuningBData = cellObj.load('tuningCurve')
tuningEventOnsetTimes = ephysanalysis.get_sound_onset_times(
tuningEphysData, 'tuningCurve')
tuningSpikeTimestamps = tuningEphysData['spikeTimes']
freqEachTrial = tuningBData['currentFreq']
labels = ['%.1f' % f for f in np.unique(freqEachTrial) / 1000]
plot_sorted_raster(tuningSpikeTimestamps,
tuningEventOnsetTimes,
freqEachTrial,
timeRange=[-0.2, 0.6],
labels=labels)
#load bandwidth tuning data and plot bandwidth tuning curve
plt.subplot(gs[0, 1])
bandEphysData, bandBData = cellObj.load('bandwidth')
bandEventOnsetTimes = ephysanalysis.get_sound_onset_times(
bandEphysData, 'bandwidth')
bandSpikeTimestamps = bandEphysData['spikeTimes']
timeRange = [-0.2, 1.5]
bandEachTrial = bandBData['currentBand']
secondSort = bandBData['currentAmp']
numBands = np.unique(bandEachTrial)
numSec = np.unique(secondSort)
spikeArray, errorArray, baselineSpikeRate = ephysanalysis.calculate_tuning_curve_inputs(
bandSpikeTimestamps, bandEventOnsetTimes, bandEachTrial, secondSort,
timeRange)
plot_tuning_curve(spikeArray[:, -1].reshape(len(numBands), 1),
errorArray[:, -1].reshape(len(numBands), 1),
numBands,
baselineSpikeRate,
linecolours=['k'],
errorcolours=['0.5'])
#save report
charfreq = str(np.unique(harmBData['charFreq'])[0] / 1000)
modrate = str(np.unique(harmBData['modRate'])[0])
plt.suptitle(
'{0}, {1}, {2}um, Tetrode {3}, Cluster {4}, {5}kHz, {6}Hz modulation'.
format(cell['subject'], cell['date'], cell['depth'], tetrode, cluster,
charfreq, modrate))
fig_path = '/home/jarauser/Pictures/cell reports'
fig_name = 'harmonics_report_{0}_{1}_{2}um_TT{3}Cluster{4}.png'.format(
cell['subject'], cell['date'], int(cell['depth']), tetrode, cluster)
full_fig_path = os.path.join(fig_path, fig_name)
fig = plt.gcf()
fig.set_size_inches(14, 10)
fig.savefig(full_fig_path, format='png', bbox_inches='tight')
def plot_freq_bandwidth_tuning(cell, bandIndex=None):
plt.clf()
if bandIndex is None:
print 'No bandwidth session given'
return
gs = gridspec.GridSpec(3, 2)
gs.update(left=0.15, right=0.85, top=0.95, wspace=0.2, hspace=0.3)
tetrode = int(cell['tetrode'])
cluster = int(cell['cluster'])
#create cell object for loading data
cellObj = ephyscore.Cell(cell)
#load bandwidth data
bandEphysData, bandBData = cellObj.load('bandwidth')
bandEventOnsetTimes = ephysanalysis.get_sound_onset_times(
bandEphysData, 'bandwidth')
bandSpikeTimestamps = bandEphysData['spikeTimes']
bandEachTrial = bandBData['currentBand']
secondSort = bandBData['currentAmp']
numBands = np.unique(bandEachTrial)
numSec = np.unique(secondSort)
#plot rasters of the bandwidth trials
colours = [
np.tile(['#4e9a06', '#8ae234'],
len(numBands) / 2 + 1),
np.tile(['#5c3566', '#ad7fa8'],
len(numBands) / 2 + 1)
]
plot_separated_rasters(gs, [0, 1],
1,
bandEachTrial,
secondSort,
bandSpikeTimestamps,
bandEventOnsetTimes,
colours=colours)
#plot bandwidth tuning curve
plt.subplot(gs[1:, 1])
timeRange = [0.0, 1.0]
baseRange = [-1.1, -0.1]
spikeArray, errorArray, baselineSpikeRate = ephysanalysis.calculate_tuning_curve_inputs(
bandSpikeTimestamps, bandEventOnsetTimes, bandEachTrial, secondSort,
timeRange, baseRange)
plot_tuning_curve(spikeArray,
errorArray,
numBands,
baselineSpikeRate,
linecolours=['#4e9a06', '#5c3566'],
errorcolours=['#8ae234', '#ad7fa8'])
#load frequency tuning data and plot frequency tuning raster
plt.subplot(gs[0, 0])
tuningEphysData, tuningBData = cellObj.load('tuningCurve')
tuningEventOnsetTimes = ephysanalysis.get_sound_onset_times(
tuningEphysData, 'tuningCurve')
tuningSpikeTimestamps = tuningEphysData['spikeTimes']
freqEachTrial = tuningBData['currentFreq']
labels = ['%.1f' % f for f in np.unique(freqEachTrial) / 1000]
plot_sorted_raster(tuningSpikeTimestamps,
tuningEventOnsetTimes,
freqEachTrial,
timeRange=[-0.2, 0.6],
labels=labels)
#plot frequency tuning and fitted gaussian
plt.subplot(gs[0, 1])
plot_tuning_fitted_gaussian(tuningSpikeTimestamps,
tuningEventOnsetTimes,
tuningBData,
55,
cell['gaussFreqFit'],
cell['tuningFitR2'],
timeRange=cell['bestFreqTuningWindow'])
plt.ylabel('Firing Rate (spk/s)')
plt.xlabel('Frequency (kHz)')
#save report
charfreq = str(np.unique(bandBData['charFreq'])[0] / 1000)
modrate = str(np.unique(bandBData['modRate'])[0])
plt.suptitle(
'{0}, {1}, {2}um, Tetrode {3}, Cluster {4}, {5}kHz, {6}Hz modulation'.
format(cell['subject'], cell['date'], int(cell['depth']), tetrode,
cluster, charfreq, modrate))
fig_path = '/home/jarauser/Pictures/cell reports'
fig_name = 'bandwidth_tuning_report_short_{0}_{1}_{2}um_TT{3}Cluster{4}.png'.format(
cell['subject'], cell['date'], int(cell['depth']), tetrode, cluster)
full_fig_path = os.path.join(fig_path, fig_name)
fig = plt.gcf()
fig.set_size_inches(14, 10)
fig.savefig(full_fig_path, format='png', bbox_inches='tight')
def plot_one_int_bandwidth_summary(cell, bandIndex, intIndex=-1):
plt.clf()
gs = gridspec.GridSpec(1, 3)
gs.update(left=0.15, right=0.85, top=0.90, wspace=0.2, hspace=0.5)
tetrode = int(cell['tetrode'])
cluster = int(cell['cluster'])
#create cell object for loading data
cellObj = ephyscore.Cell(cell)
#load bandwidth data
bandEphysData, bandBData = cellObj.load_by_index(bandIndex)
bandEventOnsetTimes = ephysanalysis.get_sound_onset_times(
bandEphysData, 'bandwidth')
bandSpikeTimestamps = bandEphysData['spikeTimes']
bandEachTrial = bandBData['currentBand']
secondSort = bandBData['currentAmp']
numBands = np.unique(bandEachTrial)
numSec = np.unique(secondSort)
timeRange = [-0.2, 1.5]
#plot raster of the high amp bandwidth trials
plt.subplot(gs[0, 0])
colours = np.tile(['#5c3566', '#ad7fa8'], len(numBands) / 2 + 1)
trialsEachCond = behavioranalysis.find_trials_each_combination(
bandEachTrial, numBands, secondSort, numSec)
spikeTimesFromEventOnset, trialIndexForEachSpike, indexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(
bandSpikeTimestamps, bandEventOnsetTimes, timeRange)
trialsOneAmp = trialsEachCond[:, :, intIndex]
firstSortLabels = ['{}'.format(band) for band in numBands]
pRaster, hcond, zline = extraplots.raster_plot(spikeTimesFromEventOnset,
indexLimitsEachTrial,
timeRange,
trialsEachCond=trialsOneAmp,
labels=firstSortLabels,
colorEachCond=colours)
plt.setp(pRaster, ms=3)
plt.ylabel('Bandwidth (octaves)')
plt.xlabel('Time from sound onset (s)')
#plot bandwidth tuning curves
plt.subplot(gs[0, 2])
sustainedTimeRange = [0.2, 1.0]
tuningDict = ephysanalysis.calculate_tuning_curve_inputs(
bandSpikeTimestamps,
bandEventOnsetTimes,
secondSort,
bandEachTrial,
sustainedTimeRange,
info='plotting')
plot_tuning_curve(tuningDict['responseArray'][intIndex, :].reshape([7, 1]),
tuningDict['errorArray'][intIndex, :].reshape([7, 1]),
numBands,
tuningDict['baselineSpikeRate'],
linecolours=['#5c3566'],
errorcolours=['#ad7fa8'])
plt.title('Average sustained response (200-1000 ms)', fontsize=10)
plt.subplot(gs[0, 1])
onsetTimeRange = [0.0, 0.05]
tuningDict2 = ephysanalysis.calculate_tuning_curve_inputs(
bandSpikeTimestamps,
bandEventOnsetTimes,
secondSort,
bandEachTrial,
onsetTimeRange,
info='plotting')
plot_tuning_curve(tuningDict2['responseArray'][intIndex, :].reshape([7,
1]),
tuningDict2['errorArray'][intIndex, :].reshape([7, 1]),
numBands,
tuningDict2['baselineSpikeRate'],
linecolours=['#5c3566'],
errorcolours=['#ad7fa8'])
plt.title('Average onset response (0-50 ms)', fontsize=10)
#save report
charfreq = str(np.unique(bandBData['charFreq'])[0] / 1000)
modrate = str(np.unique(bandBData['modRate'])[0])
plt.suptitle(
'{0}, {1}, {2}um, Tetrode {3}, Cluster {4}, {5}kHz, {6}Hz modulation'.
format(cell['subject'], cell['date'], int(cell['depth']), tetrode,
cluster, charfreq, modrate))
fig_path = '/home/jarauser/Pictures/cell reports'
fig_name = 'bandwidth_high_amp_response_{0}_{1}_{2}um_TT{3}Cluster{4}.png'.format(
cell['subject'], cell['date'], int(cell['depth']), tetrode, cluster)
full_fig_path = os.path.join(fig_path, fig_name)
fig = plt.gcf()
fig.set_size_inches(14, 3)
fig.savefig(full_fig_path, format='png', bbox_inches='tight')
def plot_laser_bandwidth_summary(cell, bandIndex):
plt.clf()
gs = gridspec.GridSpec(2, 3)
gs.update(left=0.15, right=0.85, top=0.90, wspace=0.2, hspace=0.3)
tetrode = int(cell['tetrode'])
cluster = int(cell['cluster'])
#create cell object for loading data
cellObj = ephyscore.Cell(cell)
#load bandwidth data
bandEphysData, bandBData = cellObj.load_by_index(bandIndex)
bandEventOnsetTimes = ephysanalysis.get_sound_onset_times(
bandEphysData, 'bandwidth')
bandSpikeTimestamps = bandEphysData['spikeTimes']
bandEachTrial = bandBData['currentBand']
secondSort = bandBData['laserTrial']
numBands = np.unique(bandEachTrial)
numSec = np.unique(secondSort)
timeRange = [-0.5, 1.7]
colours = [['k', '0.5'], ['#c4a000', '#fce94f']]
#plot raster of the high amp bandwidth trials
plt.subplot(gs[0, 0])
rasterColours = [
np.tile(colours[0],
len(numBands) / 2 + 1),
np.tile(colours[1],
len(numBands) / 2 + 1)
]
titles = ['No laser', 'Laser']
plot_separated_rasters(gs, [0, 1],
0,
bandEachTrial,
secondSort,
bandSpikeTimestamps,
bandEventOnsetTimes,
titles=titles,
colours=rasterColours,
timeRange=timeRange)
plt.ylabel('Bandwidth (octaves)')
plt.xlabel('Time from sound onset (s)')
#plot bandwidth tuning curves
plt.subplot(gs[0:, 2])
sustainedTimeRange = [0.2, 1.0]
tuningDict = ephysanalysis.calculate_tuning_curve_inputs(
bandSpikeTimestamps,
bandEventOnsetTimes,
bandEachTrial,
secondSort,
sustainedTimeRange,
info='plotting')
plot_tuning_curve(tuningDict['responseArray'],
tuningDict['errorArray'],
numBands,
tuningDict['baselineSpikeRate'],
linecolours=['k', '#c4a000'],
errorcolours=['0.5', '#fce94f'])
plt.title('Average sustained response (200-1000 ms)', fontsize=10)
plt.subplot(gs[0:, 1])
onsetTimeRange = [0.0, 0.05]
tuningDict2 = ephysanalysis.calculate_tuning_curve_inputs(
bandSpikeTimestamps,
bandEventOnsetTimes,
bandEachTrial,
secondSort,
onsetTimeRange,
info='plotting')
plot_tuning_curve(tuningDict2['responseArray'],
tuningDict2['errorArray'],
numBands,
tuningDict2['baselineSpikeRate'],
linecolours=['k', '#c4a000'],
errorcolours=['0.5', '#fce94f'])
plt.title('Average onset response (0-50 ms)', fontsize=10)
#save report
charfreq = str(np.unique(bandBData['charFreq'])[0] / 1000)
modrate = str(np.unique(bandBData['modRate'])[0])
plt.suptitle(
'{0}, {1}, {2}um, Tetrode {3}, Cluster {4}, {5}kHz, {6}Hz modulation'.
format(cell['subject'], cell['date'], int(cell['depth']), tetrode,
cluster, charfreq, modrate))
fig_path = '/home/jarauser/Pictures/cell reports'
fig_name = 'bandwidth_laser_inactivation_{0}_{1}_{2}um_TT{3}Cluster{4}.png'.format(
cell['subject'], cell['date'], int(cell['depth']), tetrode, cluster)
full_fig_path = os.path.join(fig_path, fig_name)
fig = plt.gcf()
fig.set_size_inches(14, 6)
fig.savefig(full_fig_path, format='png', bbox_inches='tight')
def plot_bandwidth_report(cell, bandIndex, type='normal'):
plt.clf()
if bandIndex is None:
print 'No bandwidth session given'
return
#create cell object for loading data
cellObj = ephyscore.Cell(cell)
#change dimensions of report to add laser trials if they exist
if len(cellObj.get_session_inds('laserPulse'))>0:
laser = True
gs = gridspec.GridSpec(13, 6)
else:
laser = False
gs = gridspec.GridSpec(9, 6)
offset = 4*laser
gs.update(left=0.15, right=0.85, top = 0.96, wspace=0.7, hspace=1.0)
tetrode=int(cell['tetrode'])
cluster=int(cell['cluster'])
#load bandwidth ephys and behaviour data
bandEphysData, bandBData = cellObj.load_by_index(bandIndex)
bandEventOnsetTimes = ephysanalysis.get_sound_onset_times(bandEphysData, 'bandwidth')
bandSpikeTimestamps = bandEphysData['spikeTimes']
timeRange = [-0.2, 1.5]
bandEachTrial = bandBData['currentBand']
numBands = np.unique(bandEachTrial)
#change the trial type that the bandwidth session is split by so we can use this report for Arch-inactivation experiments
#also changes the colours to be more thematically appropriate! (in Anna's opinion)
if type=='laser':
secondSort = bandBData['laserTrial']
secondSortLabels = ['no laser','laser']
colours = ['k', '#c4a000']
errorColours = ['0.5', '#fce94f']
gaussFitCol = 'gaussFit'
tuningR2Col = 'tuningFitR2'
elif type=='normal':
secondSort = bandBData['currentAmp']
secondSortLabels = ['{} dB'.format(amp) for amp in np.unique(secondSort)]
colours = ['#4e9a06','#5c3566']
errorColours = ['#8ae234','#ad7fa8']
gaussFitCol = 'gaussFit'
tuningR2Col = 'tuningFitR2'
charfreq = str(np.unique(bandBData['charFreq'])[0]/1000)
modrate = str(np.unique(bandBData['modRate'])[0])
numBands = np.unique(bandEachTrial)
# -- plot rasters of the bandwidth trials --
rasterColours = [np.tile([colours[0],errorColours[0]],len(numBands)/2+1), np.tile([colours[1],errorColours[1]],len(numBands)/2+1)]
plot_separated_rasters(gs, [0,3], 5+offset, bandEachTrial, secondSort, bandSpikeTimestamps, bandEventOnsetTimes, colours=rasterColours, titles=secondSortLabels, plotHeight=2)
# -- plot bandwidth tuning curves --
plt.subplot(gs[5+offset:, 3:])
timeRange = [0.2, 1.0]# if type=='normal' else [0.1, 1.1]
baseRange = [-1.1, -0.3]
tuningDict = ephysanalysis.calculate_tuning_curve_inputs(bandSpikeTimestamps, bandEventOnsetTimes, bandEachTrial, secondSort, timeRange, info='plotting')
plot_tuning_curve(tuningDict['responseArray'], tuningDict['errorArray'], numBands, tuningDict['baselineSpikeRate'], linecolours=colours, errorcolours=errorColours)
# load tuning ephys and behaviour data
tuningEphysData, tuningBData = cellObj.load('tuningCurve')
tuningEventOnsetTimes = ephysanalysis.get_sound_onset_times(tuningEphysData,'tuningCurve')
tuningSpikeTimestamps = tuningEphysData['spikeTimes']
# -- plot frequency tuning at intensity used in bandwidth trial with gaussian fit --
# high amp bandwidth trials used to select appropriate frequency
maxAmp = max(np.unique(bandBData['currentAmp']))
if maxAmp < 1:
maxAmp = 66.0 #HARDCODED dB VALUE FOR SESSIONS DONE BEFORE NOISE CALIBRATION
# find tone intensity that corresponds to tone sessions in bandwidth trial
toneInt = maxAmp - 15.0 #HARDCODED DIFFERENCE IN TONE AND NOISE AMP BASED ON OSCILLOSCOPE READINGS FROM RIG 2
freqEachTrial = tuningBData['currentFreq']
plt.subplot(gs[2+offset:4+offset, 0:3])
plot_tuning_fitted_gaussian(tuningSpikeTimestamps, tuningEventOnsetTimes, tuningBData, toneInt, cell[gaussFitCol], cell[tuningR2Col], timeRange=cell['tuningTimeRange'])
# -- plot frequency tuning raster --
plt.subplot(gs[0+offset:2+offset, 0:3])
freqLabels = ["%.1f" % freq for freq in np.unique(freqEachTrial)/1000.0]
plot_sorted_raster(tuningSpikeTimestamps, tuningEventOnsetTimes, freqEachTrial, timeRange=[-0.2,0.6], labels=freqLabels)
plt.title('Frequency Tuning Raster')
# -- plot AM PSTH --
amEphysData, amBData = cellObj.load('AM')
amEventOnsetTimes = ephysanalysis.get_sound_onset_times(amEphysData, 'AM')
amSpikeTimestamps = amEphysData['spikeTimes']
rateEachTrial = amBData['currentFreq']
timeRange = [-0.2, 1.5]
colourList = ['b', 'g', 'y', 'orange', 'r']
plt.subplot(gs[2+offset:4+offset, 3:])
plot_sorted_psth(amSpikeTimestamps, amEventOnsetTimes, rateEachTrial, timeRange = [-0.2, 0.8], binsize = 25, colorEachCond = colourList)
plt.xlabel('Time from sound onset (sec)')
plt.ylabel('Firing rate (Hz)')
plt.title('AM PSTH')
# -- plot AM raster --
plt.subplot(gs[0+offset:2+offset, 3:])
rateLabels = ["%.0f" % rate for rate in np.unique(rateEachTrial)]
plot_sorted_raster(amSpikeTimestamps, amEventOnsetTimes, rateEachTrial, timeRange=[-0.2, 0.8], labels=rateLabels, colorEachCond=colourList)
plt.xlabel('Time from sound onset (sec)')
plt.ylabel('Modulation Rate (Hz)')
plt.title('AM Raster')
# -- plot laser pulse and laser train data (if available) --
if laser:
# -- plot laser pulse raster --
laserEphysData, noBehav = cellObj.load('laserPulse')
laserEventOnsetTimes = laserEphysData['events']['laserOn']
laserSpikeTimestamps = laserEphysData['spikeTimes']
timeRange = [-0.1, 0.4]
plt.subplot(gs[0:2, 0:3])
laserSpikeTimesFromEventOnset, trialIndexForEachSpike, laserIndexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(
laserSpikeTimestamps, laserEventOnsetTimes, timeRange)
pRaster, hcond, zline = extraplots.raster_plot(laserSpikeTimesFromEventOnset,laserIndexLimitsEachTrial,timeRange)
plt.xlabel('Time from laser onset (sec)')
plt.title('Laser Pulse Raster')
# -- plot laser pulse psth --
plt.subplot(gs[2:4, 0:3])
binsize = 10/1000.0
spikeTimesFromEventOnset, trialIndexForEachSpike, indexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(laserSpikeTimestamps,
laserEventOnsetTimes,
[timeRange[0]-binsize,
timeRange[1]])
binEdges = np.around(np.arange(timeRange[0]-binsize, timeRange[1]+2*binsize, binsize), decimals=2)
spikeCountMat = spikesanalysis.spiketimes_to_spikecounts(spikeTimesFromEventOnset, indexLimitsEachTrial, binEdges)
pPSTH = extraplots.plot_psth(spikeCountMat/binsize, 1, binEdges[:-1])
plt.xlim(timeRange)
plt.xlabel('Time from laser onset (sec)')
plt.ylabel('Firing Rate (Hz)')
plt.title('Laser Pulse PSTH')
# -- didn't record laser trains for some earlier sessions --
if len(cellObj.get_session_inds('laserTrain')) > 0:
# -- plot laser train raster --
laserTrainEphysData, noBehav = cellObj.load('laserTrain')
laserTrainEventOnsetTimes = laserTrainEphysData['events']['laserOn']
laserTrainSpikeTimestamps = laserTrainEphysData['spikeTimes']
laserTrainEventOnsetTimes = spikesanalysis.minimum_event_onset_diff(laserTrainEventOnsetTimes, 0.5)
timeRange = [-0.2, 1.0]
plt.subplot(gs[0:2, 3:])
spikeTimesFromEventOnset, trialIndexForEachSpike, indexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(laserTrainSpikeTimestamps,
laserTrainEventOnsetTimes,
timeRange)
pRaster, hcond, zline = extraplots.raster_plot(spikeTimesFromEventOnset,indexLimitsEachTrial,timeRange)
plt.xlabel('Time from laser onset (sec)')
plt.title('Laser Train Raster')
# -- plot laser train psth --
plt.subplot(gs[2:4, 3:])
binsize = 10/1000.0
spikeTimesFromEventOnset, trialIndexForEachSpike, indexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(laserTrainSpikeTimestamps,
laserTrainEventOnsetTimes,
[timeRange[0]-binsize,
timeRange[1]])
binEdges = np.around(np.arange(timeRange[0]-binsize, timeRange[1]+2*binsize, binsize), decimals=2)
spikeCountMat = spikesanalysis.spiketimes_to_spikecounts(spikeTimesFromEventOnset, indexLimitsEachTrial, binEdges)
pPSTH = extraplots.plot_psth(spikeCountMat/binsize, 1, binEdges[:-1])
plt.xlim(timeRange)
plt.xlabel('Time from laser onset (sec)')
plt.ylabel('Firing Rate (Hz)')
plt.title('Laser Train PSTH')
# -- show cluster analysis --
#tsThisCluster, wavesThisCluster, recordingNumber = celldatabase.load_all_spikedata(cell)
# -- Plot ISI histogram --
plt.subplot(gs[4+offset, 0:2])
spikesorting.plot_isi_loghist(bandSpikeTimestamps)
# -- Plot waveforms --
plt.subplot(gs[4+offset, 2:4])
spikesorting.plot_waveforms(bandEphysData['samples'])
# -- Plot events in time --
plt.subplot(gs[4+offset, 4:6])
spikesorting.plot_events_in_time(bandSpikeTimestamps)
title = '{0}, {1}, {2}um, Tetrode {3}, Cluster {4}, {5}kHz, {6}Hz modulation'.format(cell['subject'],
cell['date'],
cell['depth'],
tetrode,
cluster,
charfreq,
modrate)
plt.suptitle(title)
fig_path = '/home/jarauser/Pictures/cell reports'
fig_name = '{0}_{1}_{2}um_TT{3}Cluster{4}.png'.format(cell['subject'], cell['date'], cell['depth'], tetrode, cluster)
full_fig_path = os.path.join(fig_path, fig_name)
fig = plt.gcf()
fig.set_size_inches(20, 25)
fig.savefig(full_fig_path, format = 'png', bbox_inches='tight')
def plot_freq_bandwidth_tuning(cell, bandIndex=None):
plt.clf()
if bandIndex is None:
print 'No bandwidth session given'
return
gs = gridspec.GridSpec(3, 2)
gs.update(left=0.15, right=0.85, top = 0.95, wspace=0.2, hspace=0.3)
tetrode=int(cell['tetrode'])
cluster=int(cell['cluster'])
#create cell object for loading data
cellObj = ephyscore.Cell(cell)
#load bandwidth data
bandEphysData, bandBData = cellObj.load('bandwidth')
bandEventOnsetTimes = ephysanalysis.get_sound_onset_times(bandEphysData, 'bandwidth')
bandSpikeTimestamps = bandEphysData['spikeTimes']
bandEachTrial = bandBData['currentBand']
secondSort = bandBData['currentAmp']
numBands = np.unique(bandEachTrial)
numSec = np.unique(secondSort)
#plot rasters of the bandwidth trials
colours = [np.tile(['#4e9a06','#8ae234'],len(numBands)/2+1), np.tile(['#5c3566','#ad7fa8'],len(numBands)/2+1)]
plot_separated_rasters(gs, [0,1], 1, bandEachTrial, secondSort, bandSpikeTimestamps, bandEventOnsetTimes, colours=colours)
#plot bandwidth tuning curve
plt.subplot(gs[1:,1])
timeRange = [0.0, 1.0]
baseRange = [-1.1, -0.1]
spikeArray, errorArray, baselineSpikeRate = ephysanalysis.calculate_tuning_curve_inputs(bandSpikeTimestamps, bandEventOnsetTimes, bandEachTrial, secondSort, timeRange, baseRange)
plot_tuning_curve(spikeArray, errorArray, numBands, baselineSpikeRate, linecolours=['#4e9a06','#5c3566'], errorcolours=['#8ae234','#ad7fa8'])
#load frequency tuning data and plot frequency tuning raster
plt.subplot(gs[0, 0])
tuningEphysData, tuningBData = cellObj.load('tuningCurve')
tuningEventOnsetTimes = ephysanalysis.get_sound_onset_times(tuningEphysData, 'tuningCurve')
tuningSpikeTimestamps = tuningEphysData['spikeTimes']
freqEachTrial = tuningBData['currentFreq']
labels = ['%.1f' % f for f in np.unique(freqEachTrial)/1000]
plot_sorted_raster(tuningSpikeTimestamps, tuningEventOnsetTimes, freqEachTrial, timeRange=[-0.2,0.6], labels=labels)
#plot frequency tuning and fitted gaussian
plt.subplot(gs[0,1])
plot_tuning_fitted_gaussian(tuningSpikeTimestamps, tuningEventOnsetTimes, tuningBData, 55, cell['gaussFreqFit'], cell['tuningFitR2'], timeRange=cell['bestFreqTuningWindow'])
plt.ylabel('Firing Rate (spk/s)')
plt.xlabel('Frequency (kHz)')
#save report
charfreq = str(np.unique(bandBData['charFreq'])[0]/1000)
modrate = str(np.unique(bandBData['modRate'])[0])
plt.suptitle('{0}, {1}, {2}um, Tetrode {3}, Cluster {4}, {5}kHz, {6}Hz modulation'.format(cell['subject'],
cell['date'],
int(cell['depth']),
tetrode,
cluster,
charfreq,
modrate))
fig_path = '/home/jarauser/Pictures/cell reports'
fig_name = 'bandwidth_tuning_report_short_{0}_{1}_{2}um_TT{3}Cluster{4}.png'.format(cell['subject'], cell['date'], int(cell['depth']), tetrode, cluster)
full_fig_path = os.path.join(fig_path, fig_name)
fig = plt.gcf()
fig.set_size_inches(14, 10)
fig.savefig(full_fig_path, format = 'png', bbox_inches='tight')
def plot_harmonics_summary(cell, harmIndex, bandIndex):
plt.clf()
gs = gridspec.GridSpec(3, 2)
gs.update(left=0.15, right=0.85, top = 0.95, wspace=0.2, hspace=0.3)
tetrode=int(cell['tetrode'])
cluster=int(cell['cluster'])
#create cell object for loading data
cellObj = ephyscore.Cell(cell)
#load harmonics ephys and behaviour data
harmEphysData, harmBData = cellObj.load('harmonics')
harmEventOnsetTimes = ephysanalysis.get_sound_onset_times(harmEphysData, 'harmonics')
harmSpikeTimestamps = harmEphysData['spikeTimes']
timeRange = [-0.2, 1.5]
bandEachTrial = harmBData['currentBand']
secondSort = harmBData['harmTrialType']
secondSortLabels = ['noise','harmonics']
numBands = np.unique(bandEachTrial)
numSec = np.unique(secondSort)
#plot rasters of the noise and harmonics trials
colours = [np.tile(['#4e9a06','#8ae234'],len(numBands)/2+1), np.tile(['#5c3566','#ad7fa8'],len(numBands)/2+1)]
plot_separated_rasters(gs, [0,1], 1, bandEachTrial, secondSort, harmSpikeTimestamps, harmEventOnsetTimes, titles=secondSortLabels, duplicate=True, colours=colours)
#plot a response curve of the noise and harmonics trials
plt.subplot(gs[1:, 1:])
timeRange = [0,1.0]
spikeArray, errorArray, baselineSpikeRate = ephysanalysis.calculate_tuning_curve_inputs(harmSpikeTimestamps, harmEventOnsetTimes, bandEachTrial, secondSort, timeRange)
spikeArray[0,0]=spikeArray[0,1]
errorArray[0,0]=errorArray[0,1]
plot_tuning_curve(spikeArray, errorArray, numBands, baselineSpikeRate, legend=True, labels=['noise','harmonics'], linecolours=['#4e9a06','#5c3566'], errorcolours=['#8ae234','#ad7fa8'])
#load frequency tuning data and plot frequency tuning raster
plt.subplot(gs[0, 0])
tuningEphysData, tuningBData = cellObj.load('tuningCurve')
tuningEventOnsetTimes = ephysanalysis.get_sound_onset_times(tuningEphysData, 'tuningCurve')
tuningSpikeTimestamps = tuningEphysData['spikeTimes']
freqEachTrial = tuningBData['currentFreq']
labels = ['%.1f' % f for f in np.unique(freqEachTrial)/1000]
plot_sorted_raster(tuningSpikeTimestamps, tuningEventOnsetTimes, freqEachTrial, timeRange=[-0.2,0.6], labels=labels)
#load bandwidth tuning data and plot bandwidth tuning curve
plt.subplot(gs[0,1])
bandEphysData, bandBData = cellObj.load('bandwidth')
bandEventOnsetTimes = ephysanalysis.get_sound_onset_times(bandEphysData, 'bandwidth')
bandSpikeTimestamps = bandEphysData['spikeTimes']
timeRange = [-0.2, 1.5]
bandEachTrial = bandBData['currentBand']
secondSort = bandBData['currentAmp']
numBands = np.unique(bandEachTrial)
numSec = np.unique(secondSort)
spikeArray, errorArray, baselineSpikeRate = ephysanalysis.calculate_tuning_curve_inputs(bandSpikeTimestamps, bandEventOnsetTimes, bandEachTrial, secondSort, timeRange)
plot_tuning_curve(spikeArray[:,-1].reshape(len(numBands),1), errorArray[:,-1].reshape(len(numBands),1), numBands, baselineSpikeRate, linecolours=['k'], errorcolours=['0.5'])
#save report
charfreq = str(np.unique(harmBData['charFreq'])[0]/1000)
modrate = str(np.unique(harmBData['modRate'])[0])
plt.suptitle('{0}, {1}, {2}um, Tetrode {3}, Cluster {4}, {5}kHz, {6}Hz modulation'.format(cell['subject'],
cell['date'],
cell['depth'],
tetrode,
cluster,
charfreq,
modrate))
fig_path = '/home/jarauser/Pictures/cell reports'
fig_name = 'harmonics_report_{0}_{1}_{2}um_TT{3}Cluster{4}.png'.format(cell['subject'], cell['date'], int(cell['depth']), tetrode, cluster)
full_fig_path = os.path.join(fig_path, fig_name)
fig = plt.gcf()
fig.set_size_inches(14, 10)
fig.savefig(full_fig_path, format = 'png', bbox_inches='tight')
def plot_laser_bandwidth_summary(cell, bandIndex):
plt.clf()
gs = gridspec.GridSpec(2,3)
gs.update(left=0.15, right=0.85, top = 0.90, wspace=0.2, hspace=0.3)
tetrode=int(cell['tetrode'])
cluster=int(cell['cluster'])
#create cell object for loading data
cellObj = ephyscore.Cell(cell)
#load bandwidth data
bandEphysData, bandBData = cellObj.load_by_index(bandIndex)
bandEventOnsetTimes = ephysanalysis.get_sound_onset_times(bandEphysData, 'bandwidth')
bandSpikeTimestamps = bandEphysData['spikeTimes']
bandEachTrial = bandBData['currentBand']
secondSort = bandBData['laserTrial']
numBands = np.unique(bandEachTrial)
numSec = np.unique(secondSort)
timeRange = [-0.5, 1.7]
colours = [['k','0.5'], ['#c4a000','#fce94f']]
#plot raster of the high amp bandwidth trials
plt.subplot(gs[0,0])
rasterColours = [np.tile(colours[0],len(numBands)/2+1),np.tile(colours[1],len(numBands)/2+1)]
titles = ['No laser', 'Laser']
plot_separated_rasters(gs, [0,1], 0, bandEachTrial, secondSort, bandSpikeTimestamps, bandEventOnsetTimes, titles=titles, colours=rasterColours, timeRange=timeRange)
plt.ylabel('Bandwidth (octaves)')
plt.xlabel('Time from sound onset (s)')
#plot bandwidth tuning curves
plt.subplot(gs[0:,2])
sustainedTimeRange = [0.2,1.0]
tuningDict = ephysanalysis.calculate_tuning_curve_inputs(bandSpikeTimestamps, bandEventOnsetTimes, bandEachTrial, secondSort, sustainedTimeRange, info='plotting')
plot_tuning_curve(tuningDict['responseArray'], tuningDict['errorArray'], numBands, tuningDict['baselineSpikeRate'], linecolours=['k','#c4a000'], errorcolours=['0.5','#fce94f'])
plt.title('Average sustained response (200-1000 ms)', fontsize=10)
plt.subplot(gs[0:,1])
onsetTimeRange = [0.0, 0.05]
tuningDict2 = ephysanalysis.calculate_tuning_curve_inputs(bandSpikeTimestamps, bandEventOnsetTimes, bandEachTrial, secondSort, onsetTimeRange, info='plotting')
plot_tuning_curve(tuningDict2['responseArray'], tuningDict2['errorArray'], numBands, tuningDict2['baselineSpikeRate'], linecolours=['k','#c4a000'], errorcolours=['0.5','#fce94f'])
plt.title('Average onset response (0-50 ms)', fontsize=10)
#save report
charfreq = str(np.unique(bandBData['charFreq'])[0]/1000)
modrate = str(np.unique(bandBData['modRate'])[0])
plt.suptitle('{0}, {1}, {2}um, Tetrode {3}, Cluster {4}, {5}kHz, {6}Hz modulation'.format(cell['subject'],
cell['date'],
int(cell['depth']),
tetrode,
cluster,
charfreq,
modrate))
fig_path = '/home/jarauser/Pictures/cell reports'
fig_name = 'bandwidth_laser_inactivation_{0}_{1}_{2}um_TT{3}Cluster{4}.png'.format(cell['subject'], cell['date'], int(cell['depth']), tetrode, cluster)
full_fig_path = os.path.join(fig_path, fig_name)
fig = plt.gcf()
fig.set_size_inches(14, 6)
fig.savefig(full_fig_path, format = 'png', bbox_inches='tight')
def plot_one_int_bandwidth_summary(cell, bandIndex, intIndex=-1):
plt.clf()
gs = gridspec.GridSpec(1,3)
gs.update(left=0.15, right=0.85, top = 0.90, wspace=0.2, hspace=0.5)
tetrode=int(cell['tetrode'])
cluster=int(cell['cluster'])
#create cell object for loading data
cellObj = ephyscore.Cell(cell)
#load bandwidth data
bandEphysData, bandBData = cellObj.load_by_index(bandIndex)
bandEventOnsetTimes = ephysanalysis.get_sound_onset_times(bandEphysData, 'bandwidth')
bandSpikeTimestamps = bandEphysData['spikeTimes']
bandEachTrial = bandBData['currentBand']
secondSort = bandBData['currentAmp']
numBands = np.unique(bandEachTrial)
numSec = np.unique(secondSort)
timeRange = [-0.2, 1.5]
#plot raster of the high amp bandwidth trials
plt.subplot(gs[0,0])
colours = np.tile(['#5c3566','#ad7fa8'],len(numBands)/2+1)
trialsEachCond = behavioranalysis.find_trials_each_combination(bandEachTrial,
numBands,
secondSort,
numSec)
spikeTimesFromEventOnset, trialIndexForEachSpike, indexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(
bandSpikeTimestamps,
bandEventOnsetTimes,
timeRange)
trialsOneAmp = trialsEachCond[:, :, intIndex]
firstSortLabels = ['{}'.format(band) for band in numBands]
pRaster, hcond, zline = extraplots.raster_plot(spikeTimesFromEventOnset,
indexLimitsEachTrial,
timeRange,
trialsEachCond=trialsOneAmp,
labels=firstSortLabels,
colorEachCond = colours)
plt.setp(pRaster, ms=3)
plt.ylabel('Bandwidth (octaves)')
plt.xlabel('Time from sound onset (s)')
#plot bandwidth tuning curves
plt.subplot(gs[0,2])
sustainedTimeRange = [0.2,1.0]
tuningDict = ephysanalysis.calculate_tuning_curve_inputs(bandSpikeTimestamps, bandEventOnsetTimes, secondSort, bandEachTrial, sustainedTimeRange, info='plotting')
plot_tuning_curve(tuningDict['responseArray'][intIndex,:].reshape([7,1]), tuningDict['errorArray'][intIndex,:].reshape([7,1]), numBands, tuningDict['baselineSpikeRate'], linecolours=['#5c3566'], errorcolours=['#ad7fa8'])
plt.title('Average sustained response (200-1000 ms)', fontsize=10)
plt.subplot(gs[0,1])
onsetTimeRange = [0.0, 0.05]
tuningDict2 = ephysanalysis.calculate_tuning_curve_inputs(bandSpikeTimestamps, bandEventOnsetTimes, secondSort, bandEachTrial, onsetTimeRange, info='plotting')
plot_tuning_curve(tuningDict2['responseArray'][intIndex,:].reshape([7,1]), tuningDict2['errorArray'][intIndex,:].reshape([7,1]), numBands, tuningDict2['baselineSpikeRate'], linecolours=['#5c3566'], errorcolours=['#ad7fa8'])
plt.title('Average onset response (0-50 ms)', fontsize=10)
#save report
charfreq = str(np.unique(bandBData['charFreq'])[0]/1000)
modrate = str(np.unique(bandBData['modRate'])[0])
plt.suptitle('{0}, {1}, {2}um, Tetrode {3}, Cluster {4}, {5}kHz, {6}Hz modulation'.format(cell['subject'],
cell['date'],
int(cell['depth']),
tetrode,
cluster,
charfreq,
modrate))
fig_path = '/home/jarauser/Pictures/cell reports'
fig_name = 'bandwidth_high_amp_response_{0}_{1}_{2}um_TT{3}Cluster{4}.png'.format(cell['subject'], cell['date'], int(cell['depth']), tetrode, cluster)
full_fig_path = os.path.join(fig_path, fig_name)
fig = plt.gcf()
fig.set_size_inches(14, 3)
fig.savefig(full_fig_path, format = 'png', bbox_inches='tight')