def saveCCFPositionsAsArray(self, appendEntry=True):
for pid in self.probes_to_analyze:
f = os.path.join(self.dataDir,
'UnitAndTipCCFPositions_probe' + pid + '.npy')
d = np.array([
self.units[pid][u]['ccf']
for u in probeSync.getOrderedUnits(self.units[pid])
]).astype(float)
if appendEntry:
d = np.concatenate(
(d, self.probeCCF[pid]['entry'][None, :3])).astype(
float) # add probe entry point
d /= 25 # 25 um per ccf voxel
d += 1 # for ImageGui
rf = os.path.join(self.dataDir,
'VisualResponsiveness_probe' + pid + '.npy')
r = np.array([
self.units[pid][u]['peakMeanVisualResponse']
for u in probeSync.getOrderedUnits(self.units[pid])
]).astype(float)
if appendEntry:
r = np.append(r, np.median(r)).astype(
float) # add probe entry point
np.save(f, d)
np.save(rf, r)
def plotLFPNoise(obj, agarChRange=None, timeRange=[0, 10]):
for pid in obj.probes_to_analyze:
t = obj.lfp[pid]['time']
d = obj.lfp[pid]['data']
i = (t >= t[0] + timeRange[0]) & (t <= t[0] + timeRange[1])
if agarChRange is None:
d = d[i]
else:
agar = np.median(d[i, agarChRange[0]:agarChRange[1]], axis=1)
d = d[i] - agar[:, None]
peakChan = [
obj.units[pid][u]['position'][1] / 10
for u in probeSync.getOrderedUnits(obj.units[pid])
]
unitCount, bins = np.histogram(peakChan, range(d.shape[1] + 1))
fig = plt.figure()
ax = plt.subplot(1, 1, 1)
ax2 = ax.twinx()
ax2.plot(unitCount, 'b')
ax2.set_ylabel('Unit Count', color='b')
ax.plot(d.std(axis=0), 'k')
ax.set_xlabel('Channel')
ax.set_ylabel('Noise')
def all_unit_summary(obj, probesToAnalyze=None, name_tag = ''):
plt.close('all')
if probesToAnalyze is None:
probesToAnalyze = obj.probes_to_analyze
for pid in probesToAnalyze:
multipageObj = PdfPages(os.path.join(obj.dataDir, 'SummaryPlots_' + pid + name_tag + '.pdf'))
try:
orderedUnits = probeSync.getOrderedUnits(obj.units[pid])
for u in orderedUnits:
plot_unit_summary(obj, pid, u, multipageObj)
plot_unit_behavior_summary(obj, pid, u, multipageObj)
finally:
multipageObj.close()
def getUnitsByArea(self, area, cortex=True):
pids = []
us = []
for pid in self.probes_to_analyze:
for u in probeSync.getOrderedUnits(self.units[pid]):
if cortex:
if self.units[pid][u]['inCortex'] and area==self.probeCCF[pid]['ISIRegion']:
pids.append(pid)
us.append(u)
else:
if area==self.units[pid][u]['ccfRegion']:
pids.append(pid)
us.append(u)
return np.array(pids), np.array(us)
selectedTrials = (b.hit | b.miss)&(~b.ignore)
changeTimes = b.frameAppearTimes[np.array(b.trials['change_frame'][selectedTrials]).astype(int)+1] #add one to correct for change frame indexing problem
image_flash_times = b.frameAppearTimes[np.array(b.core_data['visual_stimuli']['frame'])]
image_id = np.array(b.core_data['visual_stimuli']['image_name'])
preChangeIndices = np.searchsorted(image_flash_times, changeTimes)-1
preChangeTimes = image_flash_times[preChangeIndices]
preChangeIDs = image_id[preChangeIndices]
changeIDs = image_id[preChangeIndices+1] #double check to make sure this worked
regionsOfInterest = ['VISam', 'VISpm', 'VISp', 'VISl', 'VISal', 'VISrl']
preTime = 0.25
postTime = 0.5
for pid in b.probes_to_analyze:
for u in probeSync.getOrderedUnits(b.units[pid]):
spikes = b.units[pid][u]['times']
#exclude cells that fire below 1Hz during task
if np.sum(spikes<3600)<3600:
continue
#check if RF
if rfFilter:
rfmat = summaryPlots.plot_rf(b, spikes, plot=False, returnMat=True)
if rfmat[1].max() < 99.9:
continue
changesdfs = []
presdfs = []
for im in np.unique(preChangeIDs):