def setup():
from jaratest.nick.database import ephysinterface
reload(ephysinterface)
global interface
interface = ephysinterface.EphysInterface('pinp003', '2015-06-24', 'nick',
'laser_tuning_curve')
global spikeTimestamps
global events
global eventOnsetTimes
global bdata
spikeTimestamps = interface.loader.get_session_spikes('15-31-48',
6).timestamps
events = interface.loader.get_session_events('15-31-48')
eventOnsetTimes = interface.loader.get_event_onset_times(events)
bdata = interface.loader.get_session_behavior('a')
def plot_bandwidth_report(mouse, date, site, siteName):
sessions = site.get_session_ephys_filenames()
behavFilename = site.get_session_behav_filenames()
ei = ephysinterface.EphysInterface(mouse, date, '', 'bandwidth_am')
bdata = ei.loader.get_session_behavior(behavFilename[3][-4:-3])
charfreq = str(np.unique(bdata['charFreq'])[0] / 1000)
modrate = str(np.unique(bdata['modRate'])[0])
ei2 = ephysinterface.EphysInterface(mouse, date, '', 'am_tuning_curve')
bdata2 = ei2.loader.get_session_behavior(behavFilename[1][-4:-3])
bdata3 = ei2.loader.get_session_behavior(behavFilename[2][-4:-3])
currentFreq = bdata2['currentFreq']
currentBand = bdata['currentBand']
currentAmp = bdata['currentAmp']
currentInt = bdata2['currentIntensity']
currentRate = bdata3['currentFreq']
#for tetrode in site.tetrodes:
for tetrode in [2]:
oneTT = sitefuncs.cluster_site(site, siteName, tetrode)
dataSpikes = ei.loader.get_session_spikes(sessions[3], tetrode)
dataSpikes2 = ei2.loader.get_session_spikes(sessions[1], tetrode)
#clusters = np.unique(dataSpikes.clusters)
clusters = [8]
for cluster in clusters:
plt.clf()
# -- plot bandwidth rasters --
eventData = ei.loader.get_session_events(sessions[3])
spikeData = ei.loader.get_session_spikes(sessions[3],
tetrode,
cluster=cluster)
eventOnsetTimes = ei.loader.get_event_onset_times(eventData)
spikeTimestamps = spikeData.timestamps
timeRange = [-0.2, 1.5]
numBands = np.unique(currentBand)
numAmps = np.unique(currentAmp)
firstSortLabels = [
'{}'.format(band) for band in np.unique(currentBand)
]
secondSortLabels = [
'Amplitude: {}'.format(amp) for amp in np.unique(currentAmp)
]
trialsEachCond = behavioranalysis.find_trials_each_combination(
currentBand, numBands, currentAmp, numAmps)
spikeTimesFromEventOnset, trialIndexForEachSpike, indexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(
spikeTimestamps, eventOnsetTimes, timeRange)
for ind, secondArrayVal in enumerate(numAmps):
plt.subplot2grid((12, 15), (5 * ind, 0), rowspan=4, colspan=7)
trialsThisSecondVal = trialsEachCond[:, :, ind]
pRaster, hcond, zline = extraplots.raster_plot(
spikeTimesFromEventOnset,
indexLimitsEachTrial,
timeRange,
trialsEachCond=trialsThisSecondVal,
labels=firstSortLabels)
plt.setp(pRaster, ms=4)
plt.title(secondSortLabels[ind])
plt.ylabel('bandwidth (octaves)')
if ind == len(np.unique(currentAmp)) - 1:
plt.xlabel("Time from sound onset (sec)")
# -- plot Yashar plots for bandwidth data --
plt.subplot2grid((12, 15), (10, 0), rowspan=2, colspan=3)
band_select_plot(spikeTimestamps,
eventOnsetTimes,
currentAmp,
currentBand, [0.0, 1.0],
title='bandwidth selectivity')
plt.subplot2grid((12, 15), (10, 3), rowspan=2, colspan=3)
band_select_plot(spikeTimestamps,
eventOnsetTimes,
currentAmp,
currentBand, [0.2, 1.0],
title='first 200ms excluded')
# -- plot frequency tuning heat map --
plt.subplot2grid((12, 15), (5, 7), rowspan=4, colspan=4)
eventData = ei2.loader.get_session_events(sessions[1])
spikeData = ei2.loader.get_session_spikes(sessions[1],
tetrode,
cluster=cluster)
eventOnsetTimes = ei2.loader.get_event_onset_times(eventData)
spikeTimestamps = spikeData.timestamps
dataplotter.two_axis_heatmap(
spikeTimestamps=spikeTimestamps,
eventOnsetTimes=eventOnsetTimes,
firstSortArray=currentInt,
secondSortArray=currentFreq,
firstSortLabels=[
"%.1f" % inten for inten in np.unique(currentInt)
],
secondSortLabels=[
"%.1f" % freq for freq in np.unique(currentFreq) / 1000.0
],
xlabel='Frequency (kHz)',
ylabel='Intensity (dB SPL)',
plotTitle='Frequency Tuning Curve',
flipFirstAxis=True,
flipSecondAxis=False,
timeRange=[0, 0.1])
plt.ylabel('Intensity (dB SPL)')
plt.xlabel('Frequency (kHz)')
plt.title('Frequency Tuning Curve')
# -- plot frequency tuning raster --
plt.subplot2grid((12, 15), (0, 7), rowspan=4, colspan=4)
freqLabels = [
"%.1f" % freq for freq in np.unique(currentFreq) / 1000.0
]
dataplotter.plot_raster(spikeTimestamps,
eventOnsetTimes,
sortArray=currentFreq,
timeRange=[-0.1, 0.5],
labels=freqLabels)
plt.xlabel('Time from sound onset (sec)')
plt.ylabel('Frequency (kHz)')
plt.title('Frequency Tuning Raster')
# -- plot AM PSTH --
eventData = ei2.loader.get_session_events(sessions[2])
spikeData = ei2.loader.get_session_spikes(sessions[2],
tetrode,
cluster=cluster)
eventOnsetTimes = ei2.loader.get_event_onset_times(eventData)
spikeTimestamps = spikeData.timestamps
timeRange = [-0.2, 1.5]
spikeTimesFromEventOnset, trialIndexForEachSpike, indexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(
spikeTimestamps, eventOnsetTimes, timeRange)
colourList = ['b', 'g', 'y', 'orange', 'r']
numRates = np.unique(currentRate)
trialsEachCond = behavioranalysis.find_trials_each_type(
currentRate, numRates)
binEdges = np.around(np.arange(-0.2, 0.85, 0.05), decimals=2)
spikeCountMat = spikesanalysis.spiketimes_to_spikecounts(
spikeTimesFromEventOnset, indexLimitsEachTrial, binEdges)
plt.subplot2grid((12, 15), (5, 11), rowspan=4, colspan=4)
pPSTH = extraplots.plot_psth(spikeCountMat / 0.05,
1,
binEdges[:-1],
trialsEachCond,
colorEachCond=colourList)
plt.setp(pPSTH)
plt.xlabel('Time from sound onset (sec)')
plt.ylabel('Firing rate (Hz)')
plt.title('AM PSTH')
# -- plot AM raster --
plt.subplot2grid((12, 15), (0, 11), rowspan=4, colspan=4)
rateLabels = ["%.1f" % rate for rate in np.unique(currentRate)]
dataplotter.plot_raster(spikeTimestamps,
eventOnsetTimes,
sortArray=currentRate,
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')
# -- show cluster analysis --
tsThisCluster = oneTT.timestamps[oneTT.clusters == cluster]
wavesThisCluster = oneTT.samples[oneTT.clusters == cluster]
# -- Plot ISI histogram --
plt.subplot2grid((12, 15), (10, 6), rowspan=2, colspan=3)
spikesorting.plot_isi_loghist(tsThisCluster)
plt.ylabel('c%d' % cluster, rotation=0, va='center', ha='center')
plt.xlabel('')
# -- Plot waveforms --
plt.subplot2grid((12, 15), (10, 9), rowspan=2, colspan=3)
spikesorting.plot_waveforms(wavesThisCluster)
# -- Plot projections --
plt.subplot2grid((12, 15), (10, 12), rowspan=1, colspan=3)
spikesorting.plot_projections(wavesThisCluster)
# -- Plot events in time --
plt.subplot2grid((12, 15), (11, 12), rowspan=1, colspan=3)
spikesorting.plot_events_in_time(tsThisCluster)
plt.subplots_adjust(wspace=1.5)
plt.suptitle(
'{0}, {1}, {2}, Tetrode {3}, Cluster {4}, {5}kHz, {6}Hz modulation'
.format(mouse, date, siteName, tetrode, cluster, charfreq,
modrate))
fig_path = oneTT.clustersDir
fig_name = 'TT{0}Cluster{1}.png'.format(tetrode, cluster)
full_fig_path = os.path.join(fig_path, fig_name)
fig = plt.gcf()
fig.set_size_inches(24, 12)
fig.savefig(full_fig_path, format='png', bbox_inches='tight')
from jaratest.nick.database import ephysinterface
reload(ephysinterface)
#Some sessions from 2015-06-24 for reference.
# d1site1 = day1.add_site(depth = 3543, tetrodes = [6])
# d1site1.add_session('15-22-29', None, sessionTypes['nb'])
# d1site1.add_session('15-27-37', None, sessionTypes['lt'])
# d1site1.add_session('15-31-48', 'a', sessionTypes['tc'])
# d1site1.add_session('15-45-22', 'b', sessionTypes['bf'])
interface = ephysinterface.EphysInterface('pinp003', '2015-06-24', 'nick', 'laser_tuning_curve')
#Plotting session rasters seems to work when providing an ind
# interface.plot_session_raster(-1, 6)
# interface.plot_session_raster('19-16-41', 6, replace=1)
#interface.plot_array_raster(-1)
#This is working as expected.
# interface.plot_sorted_tuning_raster('15-31-48', 6, 'a')
# interface.plot_session_tc_heatmap('15-31-48', 6, 'a')
# interface.plot_array_freq_tuning('15-31-48', 'a')
interface.flip_tetrode_tuning('15-31-48', 'a')
from jaratest.nick.database import ephysinterface
from jaratest.nick.database import dataplotter
ei = ephysinterface.EphysInterface('pinp013', '2016-05-25', 'nick',
'am_tuning_curve')
bdata = ei.loader.get_session_behavior('a')
plotTitle = ei.loader.get_session_filename(
site1.get_mouse_relative_ephys_filenames()[1])
#Why is this giving an error
eventData = ei.loader.get_session_events(
site1.get_mouse_relative_ephys_filenames()[1])
eventData = ei.loader.get_session_events('14-41-52')
spikeData = ei.loader.get_session_spikes('14-41-52', 1)
cluster = 5
spikeTimestamps = spikeData.timestamps[spikeData.clusters == cluster]
eventOnsetTimes = ei.loader.get_event_onset_times(eventData)
freqEachTrial = bdata['currentFreq']
intensityEachTrial = bdata['currentIntensity']
possibleFreq = np.unique(freqEachTrial)
possibleIntensity = np.unique(intensityEachTrial)
xlabel = 'Frequency (kHz)'
ylabel = 'Intensity (dB SPL)'
freqLabels = ["%.1f" % freq for freq in possibleFreq / 1000.0]
intenLabels = ["%.1f" % inten for inten in possibleIntensity]