def automated_analysis(folder, contains, vmSuffix, synapseSuffix,
averageSynapseName, outName):
vmNames = []
# scan_directory(folder, vmNames, vmSuffix)
scan_directory2(folder, vmNames, vmSuffix, contains)
synNames = []
# scan_directory(folder, synNames, synapseSuffix)
scan_directory2(folder, synNames, synapseSuffix, contains)
#===========================================================================
# first, determine avg. ongoing nr. of synapses per cell type
# later subtracted from total nr. to give evoked nr. of synapses per type
#===========================================================================
averageSynsPerCellType = load_average_synapse_file(averageSynapseName)
vmData = {}
for n in range(len(vmNames)):
fname = vmNames[n]
print 'Loading traces from file %s' % fname
data_ = np.loadtxt(fname, skiprows=1, unpack=True)
vmData[fname] = data_
print 'Loading %d synapse activation files...' % len(synNames)
synapseData = {}
for synTrialFile in synNames:
synapseData[synTrialFile] = scp.read_complete_synapse_activation_file(
synTrialFile)
# for synchronyWindow in range(25,0,-1):
## for synchronyWindow in range(2,0,-2):
## for synchronyWindow in range(12,0,-2):
## for synchronyWindowOffset in range(0,22-synchronyWindow,2):
## for synchronyWindowOffset in range(0,4,2):
## for synchronyWindowOffset in range(8,22-synchronyWindow,2):
# for synchronyWindowOffset in range(0,26-synchronyWindow,1):
# if synchronyWindow%2 == 0 and synchronyWindowOffset%2 == 0:
# continue
# tmpOutName = outName + '_window_' + str(synchronyWindow) + '_offset_' + str(synchronyWindowOffset)
# print '****************************************'
# print 'Analyzing synaptic inputs'
# print 'synchrony window: %dms' % synchronyWindow
# print 'window offset: %dms' % synchronyWindowOffset
# print 'output base name: %s' % tmpOutName
# print '****************************************'
# synaptic_input_window_analysis(vmData, synapseData, averageSynsPerCellType, tmpOutName, synchronyWindow, synchronyWindowOffset)
synchronyWindow = 50
synchronyWindowOffset = 0
tmpOutName = outName + '_window_' + str(
synchronyWindow) + '_offset_' + str(synchronyWindowOffset)
print '****************************************'
print 'Analyzing synaptic inputs'
print 'synchrony window: %dms' % synchronyWindow
print 'window offset: %dms' % synchronyWindowOffset
print 'output base name: %s' % tmpOutName
print '****************************************'
synaptic_input_window_analysis(vmData, synapseData, averageSynsPerCellType,
tmpOutName, synchronyWindow,
synchronyWindowOffset)
def automated_analysis(folder, contains, vmSuffix, synapseSuffix, outName):
vmNames = []
# scan_directory(folder, vmNames, vmSuffix)
scan_directory2(folder, vmNames, vmSuffix, contains)
synNames = []
# scan_directory(folder, synNames, synapseSuffix)
scan_directory2(folder, synNames, synapseSuffix, contains)
print 'Loading %d synapse activation files...' % len(synNames)
synapseData = {}
for synTrialFile in synNames:
synapseData[synTrialFile] = scp.read_complete_synapse_activation_file(
synTrialFile)
synaptic_input_PCA(vmNames, synapseData, outName)
def spike_time_correlation_synaptic_input(folder, contains, vmSuffix,
synapseSuffix, averageSynapseName,
outName):
'''
relative contributions of different
presynaptic cell types as a function
of spike time after stimulus
'''
excTypes = ('L2', 'L34', 'L4py', 'L4sp', 'L4ss', 'L5st', 'L5tt',\
'L6cc', 'L6ccinv', 'L6ct', 'VPM')
inhTypes = ('L1','L23Trans','L45Sym','L45Peak','L56Trans',\
'SymLocal1','SymLocal2','SymLocal3','SymLocal4','SymLocal5','SymLocal6')
cellTypeColorMap = {'L2': 'dodgerblue', 'L34': 'blue', 'L4py': 'palegreen',\
'L4sp': 'green', 'L4ss': 'lime', 'L5st': 'yellow', 'L5tt': 'orange',\
'L6cc': 'indigo', 'L6ccinv': 'violet', 'L6ct': 'magenta', 'VPM': 'black',\
'INH': 'grey', 'EXC': 'red'}
plotTypes = ('L2', 'L34', 'L4py', 'L4sp', 'L4ss', 'L5st', 'L5tt',\
'L6cc', 'L6ccinv', 'L6ct', 'VPM', 'EXC', 'INH')
vmNames = []
# scan_directory(folder, vmNames, vmSuffix)
scan_directory2(folder, vmNames, vmSuffix, contains)
synNames = []
# scan_directory(folder, synNames, synapseSuffix)
scan_directory2(folder, synNames, synapseSuffix, contains)
#===========================================================================
# first, determine avg. ongoing nr. of synapses per cell type
# later subtracted from total nr. to give evoked nr. of synapses per type
#===========================================================================
averageSynsPerCellType = load_average_synapse_file(averageSynapseName)
vmData = {}
for n in range(len(vmNames)):
fname = vmNames[n]
print 'Loading traces from file %s' % fname
data_ = np.loadtxt(fname, skiprows=1, unpack=True)
vmData[fname] = data_
print 'Loading %d synapse activation files...' % len(synNames)
synapseData = {}
for synTrialFile in synNames:
synapseData[synTrialFile] = scp.read_complete_synapse_activation_file(
synTrialFile)
print 'Computing reverse correlation of synaptic input from %d files' % len(
synNames)
# vmNames = vmData.keys()
# synNames = synapseData.keys()
# look at spikes in early phase (0-25ms)
# and late phase (25-50ms) separately
tOffset = 245.0
# tStim = 245.0
tStim = 253.0 # after VPM activation only
tStimWindow = 50.0
# earlyWindow = 25.0
earlyWindow = 17.0 # after VPM activation only: 25-8
binWidth = 1.0
spikeTrialSyns = {}
spikeTrialSynsProx = {}
spikeTrialSynsDistal = {}
spikeTrialSynsEarly = {}
spikeTrialSynsEarlyProx = {}
spikeTrialSynsEarlyDistal = {}
# contains bins (i.e. int) of spike times at resolution binWidth (in ms)
spikeTimesEarly = []
spikeTrialSynsLate = {}
spikeTrialSynsLateProx = {}
spikeTrialSynsLateDistal = {}
for cellType in plotTypes:
spikeTrialSyns[cellType] = []
spikeTrialSynsProx[cellType] = []
spikeTrialSynsDistal[cellType] = []
spikeTrialSynsEarly[cellType] = []
spikeTrialSynsEarlyProx[cellType] = []
spikeTrialSynsEarlyDistal[cellType] = []
spikeTrialSynsLate[cellType] = []
spikeTrialSynsLateProx[cellType] = []
spikeTrialSynsLateDistal[cellType] = []
data = []
trialSpikeTimes = [[] for j in range(len(vmNames))]
trialWithSpikes = {}
for n in range(len(vmNames)):
fname = vmNames[n]
# pathName = fname[:fname.rfind('/')]
# print 'pathName = %s' % pathName
print 'Analyzing traces in file %s' % fname
# data_ = np.loadtxt(fname, skiprows=1, unpack=True)
data_ = vmData[fname]
data.append(data_)
t = data_[0]
trialWithSpikes[n] = []
for i in range(1, len(data_)):
trialSpikeTimes[n].append([])
v = data_[i]
trialSpikeTimes_ = sca.simple_spike_detection(t, v)
trialWithSpikes_ = False
for tSpike in trialSpikeTimes_:
if tSpike >= tStim and tSpike < tStim + earlyWindow:
trialSpikeTimes[n][-1].append(tSpike - tStim)
trialWithSpikes_ = True
if trialWithSpikes_:
tSpikeMin = np.min(trialSpikeTimes[n][-1])
spikeTimesEarly.append(int(tSpikeMin / binWidth))
trialWithSpikes[n].append(trialWithSpikes_)
allTrialSpikeTimes = [
tSpike for traceFile in trialSpikeTimes for trace in traceFile
for tSpike in trace
]
totalMeanSpikeTime = np.mean(allTrialSpikeTimes)
print 'total mean spike time = %.1fms' % totalMeanSpikeTime
for n in range(len(vmNames)):
nrSpikeTrials = 0
nrNoSpikeTrials = 0
earlyProxSyns = 0
for i in range(1, len(data[n])):
# for i in range(1,10):
print 'Computing reverse correlation for spikes in trial %d of %d\r' % (
i, len(data[n]) - 1),
sys.stdout.flush()
trialNr = i - 1
synTrialStr = 'simulation_run%04d_synapses.csv' % trialNr
synTrialFile = ''
tmpVmName = vmNames[n]
for name in synNames:
if synTrialStr in name and os.path.split(
tmpVmName)[0] == os.path.split(name)[0]:
synTrialFile = name
break
if synTrialFile == '':
errstr = 'Could not find synapse activation file for trial nr. %d' % trialNr
raise RuntimeError(errstr)
# activeSyns = scp.read_complete_synapse_activation_file(synTrialFile)
activeSyns = synapseData[synTrialFile]
synapseTimes = {}
synapseTimesProx = {}
synapseTimesDistal = {}
for excType in excTypes:
synapseTimes[excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimesProx[excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimesDistal[excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimes['EXC'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimesProx['EXC'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimesDistal['EXC'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimes['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
synapseTimesProx['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
synapseTimesDistal['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
for synType in activeSyns.keys():
preCellType = synType.split('_')[0]
for excType in excTypes:
if excType == preCellType:
for syn in activeSyns[synType]:
somaDist = syn[1]
structure = syn[4]
synTimes = syn[5]
synapseTimes[excType][structure].extend(synTimes)
synapseTimes[excType]['Total'].extend(synTimes)
synapseTimes['EXC'][structure].extend(synTimes)
synapseTimes['EXC']['Total'].extend(synTimes)
if somaDist < 500.0:
synapseTimesProx[excType][structure].extend(
synTimes)
synapseTimesProx[excType]['Total'].extend(
synTimes)
synapseTimesProx['EXC'][structure].extend(
synTimes)
synapseTimesProx['EXC']['Total'].extend(
synTimes)
else:
synapseTimesDistal[excType][structure].extend(
synTimes)
synapseTimesDistal[excType]['Total'].extend(
synTimes)
synapseTimesDistal['EXC'][structure].extend(
synTimes)
synapseTimesDistal['EXC']['Total'].extend(
synTimes)
for inhType in inhTypes:
if inhType == preCellType:
for syn in activeSyns[synType]:
somaDist = syn[1]
structure = syn[4]
synTimes = syn[5]
synapseTimes['INH'][structure].extend(synTimes)
synapseTimes['INH']['Total'].extend(synTimes)
if somaDist < 500.0:
synapseTimesProx['INH'][structure].extend(
synTimes)
synapseTimesProx['INH']['Total'].extend(
synTimes)
else:
synapseTimesDistal['INH'][structure].extend(
synTimes)
synapseTimesDistal['INH']['Total'].extend(
synTimes)
if not trialWithSpikes[n][trialNr]:
nrNoSpikeTrials += 1
continue
elif trialWithSpikes[n][trialNr]:
nrSpikeTrials += 1
tSpikeReference = np.min(trialSpikeTimes[n][trialNr])
for cellType in plotTypes:
spikeTrialSyns[cellType].append(0)
spikeTrialSynsProx[cellType].append(0)
spikeTrialSynsDistal[cellType].append(0)
spikeTrialSynsEarly[cellType].append(0)
spikeTrialSynsEarlyProx[cellType].append(0)
spikeTrialSynsEarlyDistal[cellType].append(0)
spikeTrialSynsLate[cellType].append(0)
spikeTrialSynsLateProx[cellType].append(0)
spikeTrialSynsLateDistal[cellType].append(0)
synTimes = synapseTimes[cellType]['Total']
synTimesProx = synapseTimesProx[cellType]['Total']
synTimesDistal = synapseTimesDistal[cellType]['Total']
for tSyn in synTimesProx:
# 8.0: VPM offset (tOffset vs. tStim)
if tOffset <= tSyn < tOffset + tSpikeReference + 8.0:
spikeTrialSynsEarlyProx[cellType][-1] += 1
earlyProxSyns += 1
print ''
print 'Nr of trials with spike: %d' % nrSpikeTrials
print 'Nr of trials without spike: %d' % nrNoSpikeTrials
print 'earlyProxSyns = %d' % earlyProxSyns
print 'mean spike time = %.1fms' % np.mean(
[tSpike for trace in trialSpikeTimes[n] for tSpike in trace])
cellTypeTrialContributions = {}
for cellType in plotTypes:
cellTypeTrialContributions[cellType] = [
[] for i in range(int(earlyWindow))
]
for i in range(len(spikeTimesEarly)):
evokedSyn = {}
# totalNr = spikeTrialSynsEarlyProx['EXC'][i] - (spikeTimesEarly[i]+9.0)*averageSynsPerCellType['EXC']
# if not totalNr:
# continue
# for cellType in excTypes:
# # 9.0 = 1.0 + tStim-tOffset
# evokedSyn_ = spikeTrialSynsEarlyProx[cellType][i] - (spikeTimesEarly[i]+9.0)*averageSynsPerCellType[cellType]
# evokedSyn[cellType] = np.max([0.0, evokedSyn_])
# cellTypeTrialContributions[cellType][spikeTimesEarly[i]].append(evokedSyn[cellType]/totalNr)
norm = 0.0
for cellType in excTypes:
# 9.0 = 1.0 + tStim-tOffset
evokedSyn_ = spikeTrialSynsEarlyProx[cellType][i] - (
spikeTimesEarly[i] + 9.0) * averageSynsPerCellType[cellType]
evokedSyn[cellType] = np.max([0.0, evokedSyn_])
norm += evokedSyn[cellType]
for cellType in excTypes:
cellTypeTrialContributions[cellType][spikeTimesEarly[i]].append(
evokedSyn[cellType] / norm)
if not outName.endswith('.csv'):
outName += '.csv'
with open(outName, 'w') as outFile:
header = 'spike time\tnr. of spikes'
for cellType in excTypes:
header += '\t'
header += cellType
header += ' AVG\t'
header += cellType
header += ' STD'
header += '\n'
outFile.write(header)
for i in range(int(earlyWindow)):
line = str(i + 0.5)
line += '\t'
line += str(len(cellTypeTrialContributions['VPM'][i]))
for cellType in excTypes:
avg = np.mean(cellTypeTrialContributions[cellType][i])
std = np.std(cellTypeTrialContributions[cellType][i])
line += '\t'
line += str(avg)
line += '\t'
line += str(std)
line += '\n'
outFile.write(line)
def reverse_correlation_synaptic_input(folder, contains, vmSuffix,
synapseSuffix):
'''
load all traces, compute spike times
and create raster plots
'''
excTypes = ('L2', 'L34', 'L4py', 'L4sp', 'L4ss', 'L5st', 'L5tt',\
'L6cc', 'L6ccinv', 'L6ct', 'VPM')
inhTypes = ('L1','L23Trans','L45Sym','L45Peak','L56Trans',\
'SymLocal1','SymLocal2','SymLocal3','SymLocal4','SymLocal5','SymLocal6')
cellTypeColorMap = {'L2': 'dodgerblue', 'L34': 'blue', 'L4py': 'palegreen',\
'L4sp': 'green', 'L4ss': 'lime', 'L5st': 'yellow', 'L5tt': 'orange',\
'L6cc': 'indigo', 'L6ccinv': 'violet', 'L6ct': 'magenta', 'VPM': 'black',\
'INH': 'grey', 'EXC': 'red'}
plotTypes = ('L2', 'L34', 'L4py', 'L4sp', 'L4ss', 'L5st', 'L5tt',\
'L6cc', 'L6ccinv', 'L6ct', 'VPM', 'EXC', 'INH')
vmNames = []
# scan_directory(folder, vmNames, vmSuffix)
scan_directory2(folder, vmNames, vmSuffix, contains)
synNames = []
# scan_directory(folder, synNames, synapseSuffix)
scan_directory2(folder, synNames, synapseSuffix, contains)
print 'Computing reverse correlation of synaptic input from %d files' % len(
synNames)
# look at spikes in early phase (0-20ms)
# and late phase (20-50ms) separately
reverseTimes = {}
reverseTimesProx = {}
reverseTimesDistal = {}
reverseTimesEarly = {}
reverseTimesEarlyProx = {}
reverseTimesEarlyDistal = {}
reverseTimesLate = {}
reverseTimesLateProx = {}
reverseTimesLateDistal = {}
for cellType in plotTypes:
reverseTimes[cellType] = []
reverseTimesProx[cellType] = []
reverseTimesDistal[cellType] = []
reverseTimesEarly[cellType] = []
reverseTimesEarlyProx[cellType] = []
reverseTimesEarlyDistal[cellType] = []
reverseTimesLate[cellType] = []
reverseTimesLateProx[cellType] = []
reverseTimesLateDistal[cellType] = []
tOffset = 100.0
tStim = 245.0
tStimWindow = 50.0
earlyWindow = 25.0
correlationWindow = 50.0
binWidth = 1.0
for n in range(len(vmNames)):
fname = vmNames[n]
pathName = fname[:fname.rfind('/')]
print 'pathName = %s' % pathName
print 'Loading traces from file %s' % fname
data = np.loadtxt(fname, skiprows=1, unpack=True)
t = data[0]
for i in range(1, len(data)):
# for i in range(1,10):
print 'Computing reverse correlation for spikes in trial %d of %d\r' % (
i, len(data) - 1),
sys.stdout.flush()
trialNr = i - 1
synTrialStr = 'simulation_run%04d_synapses.csv' % trialNr
synTrialFile = ''
for name in synNames:
if synTrialStr in name and pathName in name:
synTrialFile = name
break
if synTrialFile == '':
errstr = 'Could not find synapse activation file for trial nr. %d' % trialNr
raise RuntimeError(errstr)
activeSyns = scp.read_complete_synapse_activation_file(
synTrialFile)
synapseTimes = {}
synapseTimesProx = {}
synapseTimesDistal = {}
for excType in excTypes:
synapseTimes[excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimesProx[excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimesDistal[excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimes['EXC'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimesProx['EXC'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimesDistal['EXC'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimes['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
synapseTimesProx['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
synapseTimesDistal['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
for synType in activeSyns.keys():
preCellType = synType.split('_')[0]
for excType in excTypes:
if excType == preCellType:
for syn in activeSyns[synType]:
somaDist = syn[1]
structure = syn[4]
synTimes = syn[5]
synapseTimes[excType][structure].extend(synTimes)
synapseTimes[excType]['Total'].extend(synTimes)
synapseTimes['EXC'][structure].extend(synTimes)
synapseTimes['EXC']['Total'].extend(synTimes)
if somaDist < 500.0:
synapseTimesProx[excType][structure].extend(
synTimes)
synapseTimesProx[excType]['Total'].extend(
synTimes)
synapseTimesProx['EXC'][structure].extend(
synTimes)
synapseTimesProx['EXC']['Total'].extend(
synTimes)
else:
synapseTimesDistal[excType][structure].extend(
synTimes)
synapseTimesDistal[excType]['Total'].extend(
synTimes)
synapseTimesDistal['EXC'][structure].extend(
synTimes)
synapseTimesDistal['EXC']['Total'].extend(
synTimes)
for inhType in inhTypes:
if inhType == preCellType:
for syn in activeSyns[synType]:
somaDist = syn[1]
structure = syn[4]
synTimes = syn[5]
synapseTimes['INH'][structure].extend(synTimes)
synapseTimes['INH']['Total'].extend(synTimes)
if somaDist < 500.0:
synapseTimesProx['INH'][structure].extend(
synTimes)
synapseTimesProx['INH']['Total'].extend(
synTimes)
else:
synapseTimesDistal['INH'][structure].extend(
synTimes)
synapseTimesDistal['INH']['Total'].extend(
synTimes)
v = data[i]
trialSpikeTimes = sca.simple_spike_detection(t, v)
for tSpike in trialSpikeTimes:
#evoked
if tSpike <= tStim or tSpike > tStim + tStimWindow:
#ongoing
#if tSpike < tOffset or tSpike > tStim:
continue
for cellType in plotTypes:
reverseTimes[cellType].append([])
reverseTimesProx[cellType].append([])
reverseTimesDistal[cellType].append([])
if tStim <= tSpike < tStim + earlyWindow:
reverseTimesEarly[cellType].append([])
reverseTimesEarlyProx[cellType].append([])
reverseTimesEarlyDistal[cellType].append([])
else:
reverseTimesLate[cellType].append([])
reverseTimesLateProx[cellType].append([])
reverseTimesLateDistal[cellType].append([])
synTimes = synapseTimes[cellType]['Total']
synTimesProx = synapseTimesProx[cellType]['Total']
synTimesDistal = synapseTimesDistal[cellType]['Total']
for tSyn in synTimes:
# for tSyn in synTimesDistal:
if tSpike - correlationWindow <= tSyn < tSpike:
reverseTimes[cellType][-1].append(tSyn - tSpike)
if tStim <= tSpike < tStim + earlyWindow:
reverseTimesEarly[cellType][-1].append(tSyn -
tSpike)
else:
reverseTimesLate[cellType][-1].append(tSyn -
tSpike)
for tSyn in synTimesProx:
if tSpike - correlationWindow <= tSyn < tSpike:
reverseTimesProx[cellType][-1].append(tSyn -
tSpike)
if tStim <= tSpike < tStim + earlyWindow:
reverseTimesEarlyProx[cellType][-1].append(
tSyn - tSpike)
else:
reverseTimesLateProx[cellType][-1].append(
tSyn - tSpike)
for tSyn in synTimesDistal:
if tSpike - correlationWindow <= tSyn < tSpike:
reverseTimesDistal[cellType][-1].append(tSyn -
tSpike)
if tStim <= tSpike < tStim + earlyWindow:
reverseTimesEarlyDistal[cellType][-1].append(
tSyn - tSpike)
else:
reverseTimesLateDistal[cellType][-1].append(
tSyn - tSpike)
#fig.add_subplot(2,1,1)
#spikes = [i for time in trialSpikeTimes]
#ax.append(plt.plot(trialSpikeTimes, spikes, 'k|'))
print ''
ax = []
fig = plt.figure(1)
for cellType in plotTypes:
hist, bins = sca.PSTH_from_spike_times(reverseTimes[cellType],
binWidth, -correlationWindow,
0.0)
offset = 0.5 * (bins[1] - bins[0])
fig.add_subplot(3, 1, 1)
ax.append(
plt.plot(bins[:-1] + offset,
hist,
color=cellTypeColorMap[cellType],
label=cellType,
linewidth=2))
hist, bins = sca.PSTH_from_spike_times(reverseTimesEarly[cellType],
binWidth, -correlationWindow,
0.0)
offset = 0.5 * (bins[1] - bins[0])
fig.add_subplot(3, 1, 2)
ax.append(
plt.plot(bins[:-1] + offset,
hist,
color=cellTypeColorMap[cellType],
label=cellType,
linewidth=2))
hist, bins = sca.PSTH_from_spike_times(reverseTimesLate[cellType],
binWidth, -correlationWindow,
0.0)
offset = 0.5 * (bins[1] - bins[0])
fig.add_subplot(3, 1, 3)
ax.append(
plt.plot(bins[:-1] + offset,
hist,
color=cellTypeColorMap[cellType],
label=cellType,
linewidth=2))
#fig.add_subplot(3,1,1)
plt.xlabel('t [ms]')
plt.ylabel('All spikes')
plt.xlim([-correlationWindow, 0.0])
#fig.add_subplot(3,1,2)
#plt.xlabel('t [ms]')
#plt.ylabel('Early spikes')
#plt.xlim([-correlationWindow, 0.0])
#fig.add_subplot(3,1,3)
#plt.xlabel('t [ms]')
#plt.ylabel('Late spikes')
#plt.xlim([-correlationWindow, 0.0])
fig = plt.figure(2)
for cellType in plotTypes:
hist, bins = sca.PSTH_from_spike_times(reverseTimesProx[cellType],
binWidth, -correlationWindow,
0.0)
offset = 0.5 * (bins[1] - bins[0])
fig.add_subplot(3, 1, 1)
ax.append(
plt.plot(bins[:-1] + offset,
hist,
color=cellTypeColorMap[cellType],
label=cellType,
linewidth=2))
hist, bins = sca.PSTH_from_spike_times(reverseTimesEarlyProx[cellType],
binWidth, -correlationWindow,
0.0)
offset = 0.5 * (bins[1] - bins[0])
fig.add_subplot(3, 1, 2)
ax.append(
plt.plot(bins[:-1] + offset,
hist,
color=cellTypeColorMap[cellType],
label=cellType,
linewidth=2))
hist, bins = sca.PSTH_from_spike_times(reverseTimesLateProx[cellType],
binWidth, -correlationWindow,
0.0)
offset = 0.5 * (bins[1] - bins[0])
fig.add_subplot(3, 1, 3)
ax.append(
plt.plot(bins[:-1] + offset,
hist,
color=cellTypeColorMap[cellType],
label=cellType,
linewidth=2))
plt.xlabel('t [ms]')
plt.ylabel('All spikes (proximal syns)')
plt.xlim([-correlationWindow, 0.0])
fig = plt.figure(3)
for cellType in plotTypes:
hist, bins = sca.PSTH_from_spike_times(reverseTimesDistal[cellType],
binWidth, -correlationWindow,
0.0)
offset = 0.5 * (bins[1] - bins[0])
fig.add_subplot(3, 1, 1)
ax.append(
plt.plot(bins[:-1] + offset,
hist,
color=cellTypeColorMap[cellType],
label=cellType,
linewidth=2))
hist, bins = sca.PSTH_from_spike_times(
reverseTimesEarlyDistal[cellType], binWidth, -correlationWindow,
0.0)
offset = 0.5 * (bins[1] - bins[0])
fig.add_subplot(3, 1, 2)
ax.append(
plt.plot(bins[:-1] + offset,
hist,
color=cellTypeColorMap[cellType],
label=cellType,
linewidth=2))
hist, bins = sca.PSTH_from_spike_times(
reverseTimesLateDistal[cellType], binWidth, -correlationWindow,
0.0)
offset = 0.5 * (bins[1] - bins[0])
fig.add_subplot(3, 1, 3)
ax.append(
plt.plot(bins[:-1] + offset,
hist,
color=cellTypeColorMap[cellType],
label=cellType,
linewidth=2))
plt.xlabel('t [ms]')
plt.ylabel('All spikes (distal syns)')
plt.xlim([-correlationWindow, 0.0])
# outName = fname[:-4]
# outName += '_spike_raster_plot.pdf'
# plt.savefig(outName)
plt.show()
def create_active_synapse_histogram_spike_no_spike(folder, suffix, vmSuffix,
contains, outName):
'''
load all traces, compute spike times
and create raster plots
'''
excTypes = ('L2', 'L34', 'L4py', 'L4sp', 'L4ss', 'L5st', 'L5tt',\
'L6cc', 'L6ccinv', 'L6ct', 'VPM')
inhTypes = ('L1','L23Trans','L45Sym','L45Peak','L56Trans',\
'SymLocal1','SymLocal2','SymLocal3','SymLocal4','SymLocal5','SymLocal6')
plotTypes = ('L2', 'L34', 'L4py', 'L4sp', 'L4ss', 'L5st', 'L5tt',\
'L6cc', 'L6ccinv', 'L6ct', 'VPM', 'EXC', 'INH')
vmNames = []
scan_directory2(folder, vmNames, vmSuffix, contains)
synNames = []
scan_directory2(folder, synNames, suffix, contains)
nrOfFiles = len(synNames)
print 'Creating active synapse plots from %d files' % nrOfFiles
synData = {}
for fname in synNames:
activeSyns = scp.read_complete_synapse_activation_file(fname)
synData[fname] = activeSyns
synapseTimes = {}
spikeTrialSyns = {}
noSpikeTrialSyns = {}
for excType in excTypes:
synapseTimes[excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
spikeTrialSyns[excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
noSpikeTrialSyns[excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimes['EXC'] = {'ApicalDendrite': [], 'Dendrite': [], 'Total': []}
synapseTimes['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
spikeTrialSyns['EXC'] = {'ApicalDendrite': [], 'Dendrite': [], 'Total': []}
spikeTrialSyns['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
noSpikeTrialSyns['EXC'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
noSpikeTrialSyns['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
tStim = 253.0
earlyWindow = 17.0
trials = []
trialSpikeTimes = [[] for j in range(len(vmNames))]
trialWithSpikes = {}
for n in range(len(vmNames)):
fname = vmNames[n]
print 'Loading spike times from file %s' % fname
trialWithSpikes[n] = []
trialSpikeTimes_ = scp.read_spike_times_file(fname)
nrOfTrials = len(trialSpikeTimes_.keys())
trials.append(nrOfTrials)
for trial in trialSpikeTimes_.keys():
trialSpikeTimes[n].append([])
trialWithSpikes_ = False
for tSpike in trialSpikeTimes_[trial]:
if tSpike >= tStim and tSpike < tStim + earlyWindow:
trialSpikeTimes[n][-1].append(tSpike - tStim)
trialWithSpikes_ = True
trialWithSpikes[n].append(trialWithSpikes_)
for n in range(len(vmNames)):
nrSpikeTrials = 0
nrNoSpikeTrials = 0
earlyProxSyns = 0
for trialNr in range(trials[n]):
print 'Counting active synapses in trial %d of %d\r' % (
trialNr + 1, trials[n]),
sys.stdout.flush()
synTrialStr = 'simulation_run%04d_synapses.csv' % trialNr
synTrialFile = ''
tmpVmName = vmNames[n]
for name in synNames:
if synTrialStr in name and os.path.split(
tmpVmName)[0] == os.path.split(name)[0]:
synTrialFile = name
break
if synTrialFile == '':
errstr = 'Could not find synapse activation file for trial nr. %d' % trialNr
raise RuntimeError(errstr)
activeSyns = synData[synTrialFile]
for excType in excTypes:
synapseTimes[excType]['ApicalDendrite'].append([])
synapseTimes[excType]['Dendrite'].append([])
synapseTimes[excType]['Total'].append([])
if trialWithSpikes[n][trialNr]:
spikeTrialSyns[excType]['ApicalDendrite'].append([])
spikeTrialSyns[excType]['Dendrite'].append([])
spikeTrialSyns[excType]['Total'].append([])
else:
noSpikeTrialSyns[excType]['ApicalDendrite'].append([])
noSpikeTrialSyns[excType]['Dendrite'].append([])
noSpikeTrialSyns[excType]['Total'].append([])
synapseTimes['EXC']['ApicalDendrite'].append([])
synapseTimes['EXC']['Dendrite'].append([])
synapseTimes['EXC']['Total'].append([])
synapseTimes['INH']['ApicalDendrite'].append([])
synapseTimes['INH']['Dendrite'].append([])
synapseTimes['INH']['Soma'].append([])
synapseTimes['INH']['Total'].append([])
if trialWithSpikes[n][trialNr]:
spikeTrialSyns['EXC']['ApicalDendrite'].append([])
spikeTrialSyns['EXC']['Dendrite'].append([])
spikeTrialSyns['EXC']['Total'].append([])
spikeTrialSyns['INH']['ApicalDendrite'].append([])
spikeTrialSyns['INH']['Dendrite'].append([])
spikeTrialSyns['INH']['Soma'].append([])
spikeTrialSyns['INH']['Total'].append([])
else:
noSpikeTrialSyns['EXC']['ApicalDendrite'].append([])
noSpikeTrialSyns['EXC']['Dendrite'].append([])
noSpikeTrialSyns['EXC']['Total'].append([])
noSpikeTrialSyns['INH']['ApicalDendrite'].append([])
noSpikeTrialSyns['INH']['Dendrite'].append([])
noSpikeTrialSyns['INH']['Soma'].append([])
noSpikeTrialSyns['INH']['Total'].append([])
for synType in activeSyns.keys():
preCellType = synType.split('_')[0]
for excType in excTypes:
if excType == preCellType:
for syn in activeSyns[synType]:
somaDist = syn[1]
structure = syn[4]
synTimes = syn[5]
if somaDist < 500.0:
synapseTimes[excType][structure][-1].extend(
synTimes)
synapseTimes[excType]['Total'][-1].extend(
synTimes)
synapseTimes['EXC'][structure][-1].extend(
synTimes)
synapseTimes['EXC']['Total'][-1].extend(
synTimes)
if trialWithSpikes[n][trialNr]:
spikeTrialSyns[excType][structure][
-1].extend(synTimes)
spikeTrialSyns[excType]['Total'][
-1].extend(synTimes)
spikeTrialSyns['EXC'][structure][
-1].extend(synTimes)
spikeTrialSyns['EXC']['Total'][-1].extend(
synTimes)
else:
noSpikeTrialSyns[excType][structure][
-1].extend(synTimes)
noSpikeTrialSyns[excType]['Total'][
-1].extend(synTimes)
noSpikeTrialSyns['EXC'][structure][
-1].extend(synTimes)
noSpikeTrialSyns['EXC']['Total'][
-1].extend(synTimes)
for inhType in inhTypes:
if inhType == preCellType:
for syn in activeSyns[synType]:
somaDist = syn[1]
structure = syn[4]
synTimes = syn[5]
if somaDist < 500.0:
synapseTimes['INH'][structure][-1].extend(
synTimes)
synapseTimes['INH']['Total'][-1].extend(
synTimes)
if trialWithSpikes[n][trialNr]:
spikeTrialSyns['INH'][structure][
-1].extend(synTimes)
spikeTrialSyns['INH']['Total'][-1].extend(
synTimes)
else:
noSpikeTrialSyns['INH'][structure][
-1].extend(synTimes)
noSpikeTrialSyns['INH']['Total'][
-1].extend(synTimes)
print ''
tOffset = 100.0
tPlotBegin = 220.0
tPlotBeginWindow = 50.0
binWidth = 1.0
maxCount = 0
synapseHistograms = {}
spikeTrialHistograms = {}
noSpikeTrialHistograms = {}
for cellType in synapseTimes.keys():
synapseHistograms[cellType] = {}
spikeTrialHistograms[cellType] = {}
noSpikeTrialHistograms[cellType] = {}
for structure in synapseTimes[cellType].keys():
synTimes = synapseTimes[cellType][structure]
hist, bins = sca.PSTH_from_spike_times(
synTimes, binWidth, tOffset, tPlotBegin + tPlotBeginWindow)
synapseHistograms[cellType][structure] = hist, bins
spikeTrialSynTimes = spikeTrialSyns[cellType][structure]
hist2, bins2 = sca.PSTH_from_spike_times(
spikeTrialSynTimes, binWidth, tOffset,
tPlotBegin + tPlotBeginWindow)
spikeTrialHistograms[cellType][structure] = hist2, bins2
noSpikeTrialSynTimes = noSpikeTrialSyns[cellType][structure]
hist3, bins3 = sca.PSTH_from_spike_times(
noSpikeTrialSynTimes, binWidth, tOffset,
tPlotBegin + tPlotBeginWindow)
noSpikeTrialHistograms[cellType][structure] = hist3, bins3
tableOutName = outName + '_all_trials.csv'
with open(tableOutName, 'w') as outputTable:
hist, bins = synapseHistograms['EXC']['Total']
header = 'Bin start\tbin end\tbin center'
for cellType in plotTypes:
header += '\t'
header += cellType
header += '\n'
outputTable.write(header)
for i in range(len(bins) - 1):
line = str(bins[i])
line += '\t'
line += str(bins[i + 1])
line += '\t'
line += str(0.5 * (bins[i] + bins[i + 1]))
for cellType in plotTypes:
line += '\t'
line += str(synapseHistograms[cellType]['Total'][0][i])
line += '\n'
outputTable.write(line)
tableOutName2 = outName + '_spike_trials.csv'
with open(tableOutName2, 'w') as outputTable:
hist, bins = spikeTrialHistograms['EXC']['Total']
header = 'Bin start\tbin end\tbin center'
for cellType in plotTypes:
header += '\t'
header += cellType
header += '\n'
outputTable.write(header)
for i in range(len(bins) - 1):
line = str(bins[i])
line += '\t'
line += str(bins[i + 1])
line += '\t'
line += str(0.5 * (bins[i] + bins[i + 1]))
for cellType in plotTypes:
line += '\t'
line += str(spikeTrialHistograms[cellType]['Total'][0][i])
line += '\n'
outputTable.write(line)
tableOutName3 = outName + '_no_spike_trials.csv'
with open(tableOutName3, 'w') as outputTable:
hist, bins = noSpikeTrialHistograms['EXC']['Total']
header = 'Bin start\tbin end\tbin center'
for cellType in plotTypes:
header += '\t'
header += cellType
header += '\n'
outputTable.write(header)
for i in range(len(bins) - 1):
line = str(bins[i])
line += '\t'
line += str(bins[i + 1])
line += '\t'
line += str(0.5 * (bins[i] + bins[i + 1]))
for cellType in plotTypes:
line += '\t'
line += str(noSpikeTrialHistograms[cellType]['Total'][0][i])
line += '\n'
outputTable.write(line)
def create_active_synapse_histogram(folder, suffix, contains, outName):
'''
load all traces, compute spike times
and create raster plots
'''
excTypes = ('L2', 'L34', 'L4py', 'L4sp', 'L4ss', 'L5st', 'L5tt',\
'L6cc', 'L6ccinv', 'L6ct', 'VPM')
inhTypes = ('L1','L23Trans','L45Sym','L45Peak','L56Trans',\
'SymLocal1','SymLocal2','SymLocal3','SymLocal4','SymLocal5','SymLocal6')
plotTypes = ('INH', 'L2', 'L34', 'L4py', 'L4sp', 'L4ss', 'L5st', 'L5tt',\
'L6cc', 'L6ccinv', 'L6ct', 'VPM')
columns = ('A1','A2','A3','A4','Alpha','B1','B2','B3','B4','Beta',\
'C1','C2','C3','C4','D1','D2','D3','D4','Delta','E1','E2','E3','E4','Gamma')
fnames = []
# scan_directory(folder, fnames, suffix)
scan_directory2(folder, fnames, suffix, contains)
nrOfFiles = len(fnames)
print 'Creating active synapse plots from %d files' % nrOfFiles
synapseTimes = {}
synapseTimesProximal = {}
synapseTimesDistal = {}
for col in columns:
synapseTimes[col] = {}
synapseTimesProximal[col] = {}
synapseTimesDistal[col] = {}
for excType in excTypes:
synapseTimes[col][excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimesProximal[col][excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimesDistal[col][excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimes[col]['EXC'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimes[col]['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
synapseTimesProximal[col]['EXC'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimesProximal[col]['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
synapseTimesDistal[col]['EXC'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimesDistal[col]['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
for n in range(len(fnames)):
fname = fnames[n]
print 'Loading synapse activation times from file %s (file %d of %d)\r' % (
fname, n + 1, nrOfFiles),
sys.stdout.flush()
activeSyns = scp.read_complete_synapse_activation_file(fname)
for col in columns:
for excType in excTypes:
synapseTimes[col][excType]['ApicalDendrite'].append([])
synapseTimes[col][excType]['Dendrite'].append([])
synapseTimes[col][excType]['Total'].append([])
synapseTimesProximal[col][excType]['ApicalDendrite'].append([])
synapseTimesProximal[col][excType]['Dendrite'].append([])
synapseTimesProximal[col][excType]['Total'].append([])
synapseTimesDistal[col][excType]['ApicalDendrite'].append([])
synapseTimesDistal[col][excType]['Dendrite'].append([])
synapseTimesDistal[col][excType]['Total'].append([])
synapseTimes[col]['EXC']['ApicalDendrite'].append([])
synapseTimes[col]['EXC']['Dendrite'].append([])
synapseTimes[col]['EXC']['Total'].append([])
synapseTimesProximal[col]['EXC']['ApicalDendrite'].append([])
synapseTimesProximal[col]['EXC']['Dendrite'].append([])
synapseTimesProximal[col]['EXC']['Total'].append([])
synapseTimesDistal[col]['EXC']['ApicalDendrite'].append([])
synapseTimesDistal[col]['EXC']['Dendrite'].append([])
synapseTimesDistal[col]['EXC']['Total'].append([])
synapseTimes[col]['INH']['ApicalDendrite'].append([])
synapseTimes[col]['INH']['Dendrite'].append([])
synapseTimes[col]['INH']['Soma'].append([])
synapseTimes[col]['INH']['Total'].append([])
synapseTimesProximal[col]['INH']['ApicalDendrite'].append([])
synapseTimesProximal[col]['INH']['Dendrite'].append([])
synapseTimesProximal[col]['INH']['Soma'].append([])
synapseTimesProximal[col]['INH']['Total'].append([])
synapseTimesDistal[col]['INH']['ApicalDendrite'].append([])
synapseTimesDistal[col]['INH']['Dendrite'].append([])
synapseTimesDistal[col]['INH']['Soma'].append([])
synapseTimesDistal[col]['INH']['Total'].append([])
for synType in activeSyns.keys():
preCellType = synType.split('_')[0]
preCol = synType.split('_')[1]
for col in columns:
if col == preCol:
for excType in excTypes:
if excType == preCellType:
for syn in activeSyns[synType]:
somaDist = syn[1]
structure = syn[4]
synTimes = syn[5]
synapseTimes[col][excType][structure][
n].extend(synTimes)
synapseTimes[col][excType]['Total'][n].extend(
synTimes)
synapseTimes[col]['EXC'][structure][n].extend(
synTimes)
synapseTimes[col]['EXC']['Total'][n].extend(
synTimes)
if somaDist < 500.0:
synapseTimesProximal[col][excType][
structure][n].extend(synTimes)
synapseTimesProximal[col][excType][
'Total'][n].extend(synTimes)
synapseTimesProximal[col]['EXC'][
structure][n].extend(synTimes)
synapseTimesProximal[col]['EXC']['Total'][
n].extend(synTimes)
if somaDist >= 500.0:
synapseTimesDistal[col][excType][
structure][n].extend(synTimes)
synapseTimesDistal[col][excType]['Total'][
n].extend(synTimes)
synapseTimesDistal[col]['EXC'][structure][
n].extend(synTimes)
synapseTimesDistal[col]['EXC']['Total'][
n].extend(synTimes)
for inhType in inhTypes:
if inhType == preCellType:
for syn in activeSyns[synType]:
somaDist = syn[1]
structure = syn[4]
synTimes = syn[5]
synapseTimes[col]['INH'][structure][n].extend(
synTimes)
synapseTimes[col]['INH']['Total'][n].extend(
synTimes)
if somaDist < 500.0:
synapseTimesProximal[col]['INH'][
structure][n].extend(synTimes)
synapseTimesProximal[col]['INH']['Total'][
n].extend(synTimes)
if somaDist >= 500.0:
synapseTimesDistal[col]['INH'][structure][
n].extend(synTimes)
synapseTimesDistal[col]['INH']['Total'][
n].extend(synTimes)
print ''
tOffset = 100.0
tOngoing = 120.0
tOngoingWindow = 100.0
tStim = 245.0
tStimWindow = 25.0
binWidth = 1.0
maxCount = 0
synapseHistogramsEvoked = {}
synapseHistogramsOngoing = {}
synapseHistogramsEvokedProximal = {}
synapseHistogramsOngoingProximal = {}
synapseHistogramsEvokedDistal = {}
synapseHistogramsOngoingDistal = {}
for col in columns:
synapseHistogramsEvoked[col] = {}
synapseHistogramsOngoing[col] = {}
synapseHistogramsEvokedProximal[col] = {}
synapseHistogramsOngoingProximal[col] = {}
synapseHistogramsEvokedDistal[col] = {}
synapseHistogramsOngoingDistal[col] = {}
for cellType in synapseTimes[col].keys():
synapseHistogramsEvoked[col][cellType] = {}
synapseHistogramsOngoing[col][cellType] = {}
synapseHistogramsEvokedProximal[col][cellType] = {}
synapseHistogramsOngoingProximal[col][cellType] = {}
synapseHistogramsEvokedDistal[col][cellType] = {}
synapseHistogramsOngoingDistal[col][cellType] = {}
for structure in synapseTimes[col][cellType].keys():
synTimes1 = synapseTimes[col][cellType][structure]
hist1, bins1 = sca.PSTH_from_spike_times(
synTimes1, binWidth, tStim, tStim + tStimWindow)
synapseHistogramsEvoked[col][cellType][structure] = np.sum(
hist1)
hist2, bins2 = sca.PSTH_from_spike_times(
synTimes1, binWidth, tOngoing, tOngoing + tOngoingWindow)
synapseHistogramsOngoing[col][cellType][structure] = np.sum(
hist2)
synTimes2 = synapseTimesProximal[col][cellType][structure]
hist3, bins3 = sca.PSTH_from_spike_times(
synTimes2, binWidth, tStim, tStim + tStimWindow)
synapseHistogramsEvokedProximal[col][cellType][
structure] = np.sum(hist3)
hist4, bins4 = sca.PSTH_from_spike_times(
synTimes2, binWidth, tOngoing, tOngoing + tOngoingWindow)
synapseHistogramsOngoingProximal[col][cellType][
structure] = np.sum(hist4)
synTimes3 = synapseTimesDistal[col][cellType][structure]
hist5, bins5 = sca.PSTH_from_spike_times(
synTimes3, binWidth, tStim, tStim + tStimWindow)
synapseHistogramsEvokedDistal[col][cellType][
structure] = np.sum(hist5)
hist6, bins6 = sca.PSTH_from_spike_times(
synTimes3, binWidth, tOngoing, tOngoing + tOngoingWindow)
synapseHistogramsOngoingDistal[col][cellType][
structure] = np.sum(hist6)
ongoingOutName = outName + '_ongoing_syn.csv'
with open(ongoingOutName, 'w') as outputTable:
header = 'Column\tcell type\tnr. of active synapses (100ms)\n'
outputTable.write(header)
for cellType in plotTypes:
for col in columns:
line = col
line += '\t'
line += cellType
line += '\t'
line += str(synapseHistogramsOngoing[col][cellType]['Total'])
line += '\n'
outputTable.write(line)
writeHeader = True
evokedOutName = outName + '_total_evoked_syn.csv'
with open(evokedOutName, 'w') as outputTable:
header = 'Column\tcell type\tnr. of active synapses (25ms post-stimulus)\n'
outputTable.write(header)
for cellType in plotTypes:
for col in columns:
line = col
line += '\t'
line += cellType
line += '\t'
line += str(synapseHistogramsEvoked[col][cellType]['Total'])
line += '\n'
outputTable.write(line)
ongoingProximalOutName = outName + '_ongoing_proximal_syn.csv'
with open(ongoingProximalOutName, 'w') as outputTable:
header = 'Column\tcell type\tnr. of active synapses (100ms)\n'
outputTable.write(header)
for cellType in plotTypes:
for col in columns:
line = col
line += '\t'
line += cellType
line += '\t'
line += str(
synapseHistogramsOngoingProximal[col][cellType]['Total'])
line += '\n'
outputTable.write(line)
writeHeader = True
evokedProximalOutName = outName + '_total_evoked_proximal_syn.csv'
with open(evokedProximalOutName, 'w') as outputTable:
header = 'Column\tcell type\tnr. of active synapses (25ms post-stimulus)\n'
outputTable.write(header)
for cellType in plotTypes:
for col in columns:
line = col
line += '\t'
line += cellType
line += '\t'
line += str(
synapseHistogramsEvokedProximal[col][cellType]['Total'])
line += '\n'
outputTable.write(line)
ongoingDistalOutName = outName + '_ongoing_distal_syn.csv'
with open(ongoingDistalOutName, 'w') as outputTable:
header = 'Column\tcell type\tnr. of active synapses (100ms)\n'
outputTable.write(header)
for cellType in plotTypes:
for col in columns:
line = col
line += '\t'
line += cellType
line += '\t'
line += str(
synapseHistogramsOngoingDistal[col][cellType]['Total'])
line += '\n'
outputTable.write(line)
writeHeader = True
evokedDistalOutName = outName + '_total_evoked_distal_syn.csv'
with open(evokedDistalOutName, 'w') as outputTable:
header = 'Column\tcell type\tnr. of active synapses (25ms post-stimulus)\n'
outputTable.write(header)
for cellType in plotTypes:
for col in columns:
line = col
line += '\t'
line += cellType
line += '\t'
line += str(
synapseHistogramsEvokedDistal[col][cellType]['Total'])
line += '\n'
outputTable.write(line)
def subcellular_distribution_active_synapses(folder, contains, synapseSuffix, outName):
'''
load all synapse times from all trials
and compute average subcellular histograms
of active synapses at 50 micron resolution
'''
synNames = []
# scan_directory(folder, synNames, synapseSuffix)
scan_directory2(folder, synNames, synapseSuffix, contains)
print 'Loading %d synapse activation files...' % len(synNames)
synapseData = {}
for synTrialFile in synNames:
synapseData[synTrialFile] = scp.read_complete_synapse_activation_file(synTrialFile)
# all separately
excTypes = ('L2', 'L34', 'L4py', 'L4sp', 'L4ss', 'L5st', 'L5tt',\
'L6cc', 'L6ccinv', 'L6ct', 'VPM')
inhTypes = ('L1','L23Trans','L45Sym','L45Peak','L56Trans',\
'SymLocal1','SymLocal2','SymLocal3','SymLocal4','SymLocal5','SymLocal6')
cellTypeColorMap = {'L2': 'dodgerblue', 'L34': 'blue', 'L4py': 'palegreen',\
'L4sp': 'green', 'L4ss': 'lime', 'L5st': 'yellow', 'L5tt': 'orange',\
'L6cc': 'indigo', 'L6ccinv': 'violet', 'L6ct': 'magenta', 'VPM': 'black',\
'INH': 'grey', 'EXC': 'red'}
# all separately
#plotTypes = ('L2', 'L34', 'L4py', 'L4sp', 'L4ss', 'L5st', 'L5tt',\
#'L6cc', 'L6ccinv', 'L6ct', 'VPM', 'EXC', 'INH')
# only EXC/INH
plotTypes = ('EXC', 'INH')
synFileNames = synapseData.keys()
tOffset = 245.0
tStim = 253.0 # after VPM activation only
offsetWindow50 = 42.0
bins = [i*50 for i in range(33)] # 0-1600 microns
synapseHistograms = {}
for cellType in plotTypes:
synapseHistograms[cellType] = []
for n in range(len(synFileNames)):
print 'Computing subcellular histogram of synapses in trial %d of %d\r' % (n+1, len(synFileNames)),
synTrialFile = synFileNames[n]
activeSyns = synapseData[synTrialFile]
synapseDistances = {}
for excType in excTypes:
synapseDistances[excType] = []
synapseDistances['EXC'] = []
synapseDistances['INH'] = []
for synType in activeSyns.keys():
preCellType = synType.split('_')[0]
for excType in excTypes:
if excType == preCellType:
for syn in activeSyns[synType]:
somaDist = syn[1]
synTimes = syn[5]
for tSyn in synTimes:
# spike trial 0-50ms all synapses mode
if tStim <= tSyn < tStim + offsetWindow50:
synapseDistances[excType].append(somaDist)
synapseDistances['EXC'].append(somaDist)
break
for inhType in inhTypes:
if inhType == preCellType:
for syn in activeSyns[synType]:
somaDist = syn[1]
synTimes = syn[5]
for tSyn in synTimes:
# spike trial 0-50ms all synapses mode
if tStim <= tSyn < tStim + offsetWindow50:
synapseDistances['INH'].append(somaDist)
break
for cellType in plotTypes:
hist, _ = np.histogram(synapseDistances[cellType], bins=bins)
synapseHistograms[cellType].append(hist)
print ''
histAvg, histStd = {}, {}
for cellType in plotTypes:
histAvg[cellType] = np.mean(synapseHistograms[cellType], axis=0)
histStd[cellType] = np.std(synapseHistograms[cellType], axis=0)
histName = ''
if not outName.endswith('.csv'):
histName = outName + '_subcellular_synapse_distribution.csv'
else:
histName = outName[:-4] + '_subcellular_synapse_distribution.csv'
with open(histName, 'w') as outFile:
header = '#bin begin (microns)\tbin end'
for cellType in plotTypes:
header += '\t'
header += cellType
header += ' AVG'
header += '\t'
header += cellType
header += ' STD'
header += '\n'
outFile.write(header)
for i in range(len(bins)-1):
line = str(bins[i])
line += '\t'
line += str(bins[i+1])
for cellType in plotTypes:
line += '\t'
line += str(histAvg[cellType][i])
line += '\t'
line += str(histStd[cellType][i])
line += '\n'
outFile.write(line)