def compute_latencies(folder, suffix, contains, outName):
'''
load all traces, compute spike times
and create raster plots
'''
fnames = []
scan_directory2(folder, fnames, suffix, contains)
print 'Computing latencies from %d files' % len(fnames)
tOffset = 100.0
tStim = 245.0
tWindow = 25.0
tStop = 345.0
allLatencies = []
nTrials = 0
for n in range(len(fnames)):
fname = fnames[n]
print 'Loading spike times from file %s' % fname
spikeTimes = scp.read_spike_times_file(fname)
for trial in spikeTimes.keys():
nTrials += 1
for tSpike in spikeTimes[trial]:
if tStim < tSpike <= tStim + tWindow:
allLatencies.append(tSpike - tStim)
break
if not outName.endswith('.csv'):
outName += '.csv'
with open(outName, 'w') as outFile:
line = '# Latencies in %d trials\n' % nTrials
for latency in allLatencies:
line += str(latency)
line += '\n'
outFile.write(line)
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 synaptic_input_PCA(vmNames, synData, outName):
'''
calculate PCA of temporal synaptic input patterns for all trials
with/without spikes to identify "synaptic pattern dimension" with highest variance
Parameterization: Per trial, E/I syn. proximal/distal in 1ms bins (0-25ms)
--> 4*25-dimensional space
'''
excTypes = ('L2', 'L34', 'L4py', 'L4sp', 'L4ss', 'L5st', 'L5tt',\
'L6cc', 'L6ccinv', 'L6ct', 'VPM')
inhTypes = ('L1','L23Trans','L45Sym','L45Peak','L56Trans',\
'SymLocal1','SymLocal2','SymLocal3','SymLocal4','SymLocal5','SymLocal6')
# Generic excitatory synapse analysis
plotTypes = ('EXC', 'INH')
spikeTrialSynsEarlyProx = {}
spikeTrialSynsEarlyDistal = {}
noSpikeTrialSynsEarlyProx = {}
noSpikeTrialSynsEarlyDistal = {}
for cellType in plotTypes:
spikeTrialSynsEarlyProx[cellType] = []
spikeTrialSynsEarlyDistal[cellType] = []
noSpikeTrialSynsEarlyProx[cellType] = []
noSpikeTrialSynsEarlyDistal[cellType] = []
tOffset = 245.0
tStim = 253.0 # after VPM activation only
tStimWindow = 50.0
earlySynWindow = 25.0
earlyWindow = 17.0 # after VPM activation only: 25-8
binWidth = 1.0
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_)
synNames = synData.keys()
for n in range(len(vmNames)):
nrSpikeTrials = 0
nrNoSpikeTrials = 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]
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]
if somaDist < 500.0:
synapseTimesProx['EXC']['Total'].extend(
synTimes)
else:
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]
if somaDist < 500.0:
synapseTimesProx['INH']['Total'].extend(
synTimes)
else:
synapseTimesDistal['INH']['Total'].extend(
synTimes)
if not trialWithSpikes[n][trialNr]:
nrNoSpikeTrials += 1
for cellType in plotTypes:
synTimesProx = synapseTimesProx[cellType]['Total']
synTimesDistal = synapseTimesDistal[cellType]['Total']
tSynProxTmpList = []
tSynDistalTmpList = []
for tSyn in synTimesProx:
if tOffset <= tSyn < tOffset + earlySynWindow:
tSynProxTmpList.append(tSyn - tOffset)
for tSyn in synTimesDistal:
if tOffset <= tSyn < tOffset + earlySynWindow:
tSynDistalTmpList.append(tSyn - tOffset)
proxHist, tmpBins = np.histogram(tSynProxTmpList,
bins=range(26))
distalHist, tmpBins = np.histogram(tSynDistalTmpList,
bins=range(26))
noSpikeTrialSynsEarlyProx[cellType].append(proxHist)
noSpikeTrialSynsEarlyDistal[cellType].append(distalHist)
elif trialWithSpikes[n][trialNr]:
nrSpikeTrials += 1
tSpikeReference = np.min(trialSpikeTimes[n][trialNr])
for cellType in plotTypes:
synTimesProx = synapseTimesProx[cellType]['Total']
synTimesDistal = synapseTimesDistal[cellType]['Total']
tSynProxTmpList = []
tSynDistalTmpList = []
for tSyn in synTimesProx:
if tOffset <= tSyn < tOffset + earlySynWindow:
tSynProxTmpList.append(tSyn - tOffset)
for tSyn in synTimesDistal:
if tOffset <= tSyn < tOffset + earlySynWindow:
tSynDistalTmpList.append(tSyn - tOffset)
proxHist, tmpBins = np.histogram(tSynProxTmpList,
bins=range(26))
distalHist, tmpBins = np.histogram(tSynDistalTmpList,
bins=range(26))
spikeTrialSynsEarlyProx[cellType].append(proxHist)
spikeTrialSynsEarlyDistal[cellType].append(distalHist)
print ''
print 'Nr of trials with spike: %d' % nrSpikeTrials
print 'Nr of trials without spike: %d' % nrNoSpikeTrials
print 'mean spike time = %.1fms' % np.mean(
[tSpike for trace in trialSpikeTimes[n] for tSpike in trace])
trialSpikeList = []
totalHistList = []
totalSpikeTrials = len(spikeTrialSynsEarlyProx['EXC'])
totalNoSpikeTrials = len(noSpikeTrialSynsEarlyProx['EXC'])
# concatenate E/I/prox/distal histograms in the order:
# prox. E/prox. I/distal E/distal I
for i in range(totalSpikeTrials):
trialSpikeList.append(1)
trialHist = []
trialHist.extend(spikeTrialSynsEarlyProx['EXC'][i])
trialHist.extend(spikeTrialSynsEarlyProx['INH'][i])
trialHist.extend(spikeTrialSynsEarlyDistal['EXC'][i])
trialHist.extend(spikeTrialSynsEarlyDistal['INH'][i])
totalHistList.append(trialHist)
for i in range(totalNoSpikeTrials):
trialSpikeList.append(0)
trialHist = []
trialHist.extend(noSpikeTrialSynsEarlyProx['EXC'][i])
trialHist.extend(noSpikeTrialSynsEarlyProx['INH'][i])
trialHist.extend(noSpikeTrialSynsEarlyDistal['EXC'][i])
trialHist.extend(noSpikeTrialSynsEarlyDistal['INH'][i])
totalHistList.append(trialHist)
allData = np.array(totalHistList)
dataMean = np.mean(allData)
allData = allData - dataMean
eigenvectors, eigenvals, V = np.linalg.svd(allData.T, full_matrices=False)
projectedData = np.dot(allData, eigenvectors).transpose()
PC1LoadVec = eigenvectors.transpose()[0]
proxELoad = PC1LoadVec[:25]
proxILoad = PC1LoadVec[25:50]
distalELoad = PC1LoadVec[50:75]
distalILoad = PC1LoadVec[75:]
PC2LoadVec = eigenvectors.transpose()[1]
proxELoad2 = PC2LoadVec[:25]
proxILoad2 = PC2LoadVec[25:50]
distalELoad2 = PC2LoadVec[50:75]
distalILoad2 = PC2LoadVec[75:]
with open(outName + '_PC1_PC2_Load.csv', 'w') as outFile1:
header = 'time (ms)\tPC1 E prox load\tPC1 I prox load\tPC1 E distal load\tPC1 I distal load\t'
header += 'PC2 E prox load\tPC2 I prox load\tPC2 E distal load\tPC2 I distal load\n'
outFile1.write(header)
for i in range(25):
line = str(i + 0.5)
line += '\t'
line += str(proxELoad[i])
line += '\t'
line += str(proxILoad[i])
line += '\t'
line += str(distalELoad[i])
line += '\t'
line += str(distalILoad[i])
line += '\t'
line += str(proxELoad2[i])
line += '\t'
line += str(proxILoad2[i])
line += '\t'
line += str(distalELoad2[i])
line += '\t'
line += str(distalILoad2[i])
line += '\n'
outFile1.write(line)
with open(outName + '_PC1_PC2.csv', 'w') as outFile2:
header = 'spike trial 1/0\tPC1\tPC2\n'
outFile2.write(header)
for i in range(len(projectedData[0])):
line = str(trialSpikeList[i])
line += '\t'
line += str(projectedData[0][i])
line += '\t'
line += str(projectedData[1][i])
line += '\n'
outFile2.write(line)
def iso_probability_plot(folder, suffix, whisker, outName):
fnames = []
scan_directory(folder, fnames, suffix)
synNrTimingFilenames = {}
for fname in fnames:
basePath = fname.split('/')[-3]
nSyn = int(basePath.split('_')[1])
synTiming = float(basePath.split('_')[5])
if not synNrTimingFilenames.has_key(nSyn):
synNrTimingFilenames[nSyn] = {}
synNrTimingFilenames[nSyn][synTiming] = fname
tBegin = 8.0
if whisker == 'PW':
window = 7.0
elif whisker == 'SW':
window = 9.0
elif whisker == 'E2':
window = 17.0
synNrTimingProbs = {}
offset = 245.0
for nSyn in synNrTimingFilenames.keys():
synNrTimingProbs[nSyn] = {}
for synTiming in synNrTimingFilenames[nSyn].keys():
fname = synNrTimingFilenames[nSyn][synTiming]
print 'Analyzing spike times in file %s' % fname
trialSpikeTimes = scp.read_spike_times_file(fname)
nrOfTrials = len(trialSpikeTimes.keys())
spikeTrials = 0.0
for trial in trialSpikeTimes.keys():
for tSpike in trialSpikeTimes[trial]:
if tBegin <= tSpike - offset < tBegin + window:
spikeTrials += 1
break
spikeProb = spikeTrials / nrOfTrials
synNrTimingProbs[nSyn][synTiming] = spikeProb
print 'Window: %.1f - %.1f ms: spike prob = %.2f' % (
tBegin, tBegin + window, spikeProb)
numbers = np.array(range(25, 350, 25), dtype=np.float64)
timings = np.array(range(2, 25, 1), dtype=np.float64)
#numberTimingMesh = np.meshgrid(numbers,timings)
#spikeProbMesh = np.zeros_like(numberTimingMesh[0])
#for i in range(len(spikeProbMesh)-1):
#for j in range(len(spikeProbMesh[i])-1):
#number = int(numberTimingMesh[0][i][j])
#timing = numberTimingMesh[1][i][j]
#try:
#spikeProbMesh[i][j] = synNrTimingProbs[number][timing]
#except KeyError:
#spikeProbMesh[i][j] = 0.0
numberTimingMesh = np.meshgrid(timings, numbers)
spikeProbMesh = np.zeros_like(numberTimingMesh[0])
for i in range(len(spikeProbMesh) - 1):
for j in range(len(spikeProbMesh[i]) - 1):
timing = numberTimingMesh[0][i][j]
number = int(numberTimingMesh[1][i][j])
try:
spikeProbMesh[i][j] = synNrTimingProbs[number][timing]
except KeyError:
spikeProbMesh[i][j] = 0.0
plt.figure(1)
if whisker == 'PW':
isocontours = [0.02, 0.04, 0.08, 0.15, 0.18, 0.3, 0.6, 0.9]
elif whisker == 'SW' or whisker == 'E2':
isocontours = [0.01, 0.02, 0.04, 0.08, 0.15, 0.3, 0.6, 0.9]
CS = plt.contour(numberTimingMesh[0], numberTimingMesh[1], spikeProbMesh,
isocontours)
plt.clabel(CS, inline=1, fontsize=10, inline_spacing=-15)
titleStr = 'Reduced SPD model - iso-probability contours for %s touch' % whisker
plt.title(titleStr)
plt.xlabel('Synapse timing (median; ms)')
plt.ylabel('Synapse number')
plt.xlim(2.0, 24.0)
plt.ylim(0, 325)
plt.xticks(np.arange(2, 24, 1.0), [str(i) for i in range(2, 24, 1)])
if whisker == 'PW':
plt.yticks(np.arange(25, 325, 25.0),
[str(0.76 * i)
for i in range(25, 325, 25)]) # PW: 76% syn <500 microns
elif whisker == 'SW':
plt.yticks(np.arange(25, 325, 25.0),
[str(0.69 * i)
for i in range(25, 325, 25)]) # SW: 69% syn <500 microns
elif whisker == 'E2':
plt.yticks(np.arange(25, 325, 25.0),
[str(0.68 * i)
for i in range(25, 325, 25)]) # E2: 68% syn <500 microns
cbar = plt.colorbar()
cbar.set_label('Spike prob')
plt.savefig(outName + '_isocontours.pdf')
def PSTH_timing_number_analysis(folder, suffix, tBegin, window, outName):
fnames = []
scan_directory(folder, fnames, suffix)
synNrTimingFilenames = {}
for fname in fnames:
basePath = fname.split('/')[1]
nSyn = int(basePath.split('_')[1])
synTiming = float(basePath.split('_')[5])
if not synNrTimingFilenames.has_key(nSyn):
synNrTimingFilenames[nSyn] = {}
synNrTimingFilenames[nSyn][synTiming] = fname
#synNrTimingProbs = {}
#binWidth = 5.0
#offset = 145.0 # 245 - 100
#beginBin = int((offset+tBegin)/binWidth)
#endBin = beginBin + int(window/binWidth) + 1
#for nSyn in synNrTimingFilenames.keys():
# synNrTimingProbs[nSyn] = {}
# for synTiming in synNrTimingFilenames[nSyn].keys():
# fname = synNrTimingFilenames[nSyn][synTiming]
# data = np.loadtxt(fname, skiprows=1, unpack=True)
# PSTH = data[2]
# spikeProb = np.sum(PSTH[beginBin:endBin])
# synNrTimingProbs[nSyn][synTiming] = spikeProb
#synNrTimingProbs = {}
#offset = 245.0
#for nSyn in synNrTimingFilenames.keys():
# synNrTimingProbs[nSyn] = {}
# for synTiming in synNrTimingFilenames[nSyn].keys():
# fname = synNrTimingFilenames[nSyn][synTiming]
# print 'Analyzing traces in file %s' % fname
# vmData = np.loadtxt(fname, skiprows=1, unpack=True)
# t = vmData[0]
# nrOfTrials = len(vmData) - 1
# spikeTrials = 0.0
# for i in range(nrOfTrials):
# v = vmData[i+1]
# trialSpikeTimes = sca.simple_spike_detection(t, v)
# for tSpike in trialSpikeTimes:
# if tBegin <= tSpike - offset < tBegin + window:
# spikeTrials += 1
# break
# spikeProb = spikeTrials/nrOfTrials
# synNrTimingProbs[nSyn][synTiming] = spikeProb
# print 'Window: %.1f - %.1f ms: spike prob = %.2f' % (tBegin, tBegin+window, spikeProb)
synNrTimingProbs = {}
offset = 245.0
for nSyn in synNrTimingFilenames.keys():
synNrTimingProbs[nSyn] = {}
for synTiming in synNrTimingFilenames[nSyn].keys():
fname = synNrTimingFilenames[nSyn][synTiming]
print 'Analyzing spike times in file %s' % fname
trialSpikeTimes = scp.read_spike_times_file(fname)
nrOfTrials = len(trialSpikeTimes.keys())
spikeTrials = 0.0
for trial in trialSpikeTimes.keys():
for tSpike in trialSpikeTimes[trial]:
if tBegin <= tSpike - offset < tBegin + window:
spikeTrials += 1
break
spikeProb = spikeTrials / nrOfTrials
synNrTimingProbs[nSyn][synTiming] = spikeProb
print 'Window: %.1f - %.1f ms: spike prob = %.2f' % (
tBegin, tBegin + window, spikeProb)
numbers = np.array(range(25, 350, 25), dtype=np.float64)
timings = np.array(range(2, 25, 1), dtype=np.float64)
#timings = np.array(range(2,11,1), dtype=np.float64)
numberTimingMesh = np.meshgrid(numbers, timings)
spikeProbMesh = np.zeros_like(numberTimingMesh[0])
for i in range(len(spikeProbMesh) - 1):
for j in range(len(spikeProbMesh[i]) - 1):
number = int(numberTimingMesh[0][i][j])
timing = numberTimingMesh[1][i][j]
try:
spikeProbMesh[i][j] = synNrTimingProbs[number][timing]
except KeyError:
spikeProbMesh[i][j] = 0.0
## for SuW use 0.5 as max:
#if spikeProbMesh[i][j] > 0.5:
#spikeProbMesh[i][j] = 0.5
#with open(outName+'_grid_probs.csv', 'w') as outFile:
#synNumbers = synNrTimingProbs.keys()
#synNumbers.sort()
#synTimings = synNrTimingProbs[synNumbers[0]].keys()
#synTimings.sort()
#header = '#syn nr.\\timing'
#for synTiming in synTimings:
#header += '\t'
#header += '%.1f' % synTiming
#header += '\n'
#outFile.write(header)
#for synNr in synNumbers:
##line = str(synNr)
##line = str(0.76*synNr) # PW
#line = str(0.69*synNr) # SuW
#for synTiming in synTimings:
#line += '\t'
#try:
#line += str(synNrTimingProbs[synNr][synTiming])
#except KeyError:
#line += '-1.0'
#line += '\n'
#outFile.write(line)
# PW
# L3, L4ss/sp, L5tt, L6cc, VPM, L6cc+VPM
#numberMeans = 1/0.76*np.array([74.4, 89.9, 62.2, 54.8, 54.6, 109.4])
#numberSTD = 1/0.76*np.array([14.3, 24.8, 18.2, 12.5, 14.1, 17.7])
#timingMeans = np.array([16.0, 20.3, 14.9, 7.0, 2.8, 4.1])
#timingSTD = np.array([2.8, 2.3, 3.3, 3.6, 0.7, 0.6])
# SuW
# L3, L4ss/sp, L5tt, L6cc, VPM
#numberMeans = 1/0.69*np.array([24.4, 23.0, 40.0, 37.7, 6.1])
#numberSTD = 1/0.69*np.array([15.1, 22.2, 19.9, 12.2, 6.8])
#timingMeans = np.array([20.5, 21.0, 18.2, 10.1, 8.1])
#timingSTD = np.array([5.2, 6.0, 4.2, 5.1, 7.2])
# E2
# L3/4, L5tt, L6cc, VPM
numberMeans = 1 / 0.68 * np.array([2.2, 9.0, 19.1, 0.1])
numberSTD = 1 / 0.68 * np.array([9.6, 11.8, 10.2, 1.4])
timingMeans = np.array([21.6, 20.8, 14.1, 18.3])
timingSTD = np.array([5.3, 5.1, 7.0, 11.7])
plt.figure(1)
plt.errorbar(numberMeans,
timingMeans,
xerr=numberSTD,
yerr=timingSTD,
fmt='ro')
#plt.pcolormesh(numberTimingMesh[0], numberTimingMesh[1], spikeProbMesh, cmap='hot')
# PW
#isocontours = [0.02, 0.04, 0.08, 0.15, 0.18, 0.3, 0.6, 0.9]
# SuW
isocontours = [0.01, 0.02, 0.04, 0.08, 0.15, 0.3, 0.6, 0.9]
CS = plt.contour(numberTimingMesh[0], numberTimingMesh[1], spikeProbMesh,
isocontours)
plt.clabel(CS, inline=1, fontsize=10, inline_spacing=-15)
plt.title('Simplest default with labels')
plt.xlabel('Synapse number')
plt.ylabel('Synapse timing (median; ms)')
plt.xlim(0, 325)
plt.ylim(2.0, 24.0)
#plt.xticks(np.arange(25,325,25.0), [str(0.76*i) for i in range(25,325,25)]) # PW: 76% syn <500 microns
#plt.xticks(np.arange(25,325,25.0), [str(0.69*i) for i in range(25,325,25)]) # SuW: 69% syn <500 microns
plt.xticks(np.arange(25, 325, 25.0),
[str(0.68 * i)
for i in range(25, 325, 25)]) # SuW: 68% syn <500 microns
#plt.yticks(np.arange(2,23,1.0), [str(i) for i in range(2,23,1)])
plt.yticks(np.arange(2, 24, 1.0), [str(i) for i in range(2, 24, 1)])
cbar = plt.colorbar()
cbar.set_label('Spike prob')
plt.savefig(outName + '_isocontours.pdf')
def PSTH_timing_number_analysis(folder, suffix, tBegin, window, outName):
fnames = []
scan_directory(folder, fnames, suffix)
synNrTimingFilenames = {}
for fname in fnames:
basePath = fname.split('/')[1]
nSyn = int(basePath.split('_')[1])
synTiming = float(basePath.split('_')[5])
if not synNrTimingFilenames.has_key(nSyn):
synNrTimingFilenames[nSyn] = {}
synNrTimingFilenames[nSyn][synTiming] = fname
#synNrTimingProbs = {}
#binWidth = 5.0
#offset = 145.0 # 245 - 100
#beginBin = int((offset+tBegin)/binWidth)
#endBin = beginBin + int(window/binWidth) + 1
#for nSyn in synNrTimingFilenames.keys():
#synNrTimingProbs[nSyn] = {}
#for synTiming in synNrTimingFilenames[nSyn].keys():
#fname = synNrTimingFilenames[nSyn][synTiming]
#data = np.loadtxt(fname, skiprows=1, unpack=True)
#PSTH = data[2]
#spikeProb = np.sum(PSTH[beginBin:endBin])
#synNrTimingProbs[nSyn][synTiming] = spikeProb
#synNrTimingProbs = {}
#offset = 245.0
#for nSyn in synNrTimingFilenames.keys():
# synNrTimingProbs[nSyn] = {}
# for synTiming in synNrTimingFilenames[nSyn].keys():
# fname = synNrTimingFilenames[nSyn][synTiming]
# print 'Analyzing traces in file %s' % fname
# vmData = np.loadtxt(fname, skiprows=1, unpack=True)
# t = vmData[0]
# nrOfTrials = len(vmData) - 1
# spikeTrials = 0.0
# for i in range(nrOfTrials):
# v = vmData[i+1]
# trialSpikeTimes = sca.simple_spike_detection(t, v)
# for tSpike in trialSpikeTimes:
# if tBegin <= tSpike - offset < tBegin + window:
# spikeTrials += 1
# break
# spikeProb = spikeTrials/nrOfTrials
# synNrTimingProbs[nSyn][synTiming] = spikeProb
# print 'Window: %.1f - %.1f ms: spike prob = %.2f' % (tBegin, tBegin+window, spikeProb)
synNrTimingProbs = {}
offset = 245.0
for nSyn in synNrTimingFilenames.keys():
synNrTimingProbs[nSyn] = {}
for synTiming in synNrTimingFilenames[nSyn].keys():
fname = synNrTimingFilenames[nSyn][synTiming]
print 'Analyzing spike times in file %s' % fname
trialSpikeTimes = scp.read_spike_times_file(fname)
nrOfTrials = len(trialSpikeTimes.keys())
spikeTrials = 0.0
for trial in trialSpikeTimes.keys():
for tSpike in trialSpikeTimes[trial]:
if tBegin <= tSpike - offset < tBegin + window:
spikeTrials += 1
break
spikeProb = spikeTrials / nrOfTrials
synNrTimingProbs[nSyn][synTiming] = spikeProb
print 'Window: %.1f - %.1f ms: spike prob = %.2f' % (
tBegin, tBegin + window, spikeProb)
numbers = np.array(range(50, 400, 25), dtype=np.float64)
timings = np.array(range(2, 11, 1), dtype=np.float64)
numberTimingMesh = np.meshgrid(numbers, timings)
spikeProbMesh = np.zeros_like(numberTimingMesh[0])
for i in range(len(spikeProbMesh) - 1):
for j in range(len(spikeProbMesh[i]) - 1):
number = int(numberTimingMesh[0][i][j])
timing = numberTimingMesh[1][i][j]
spikeProbMesh[i][j] = synNrTimingProbs[number][timing]
with open(outName + '_grid_probs.csv', 'w') as outFile:
synNumbers = synNrTimingProbs.keys()
synNumbers.sort()
synTimings = synNrTimingProbs[synNumbers[0]].keys()
synTimings.sort()
header = '#syn nr.\\timing'
for synTiming in synTimings:
header += '\t'
header += '%.1f' % synTiming
header += '\n'
outFile.write(header)
for synNr in synNumbers:
line = str(synNr)
for synTiming in synTimings:
line += '\t'
line += str(synNrTimingProbs[synNr][synTiming])
line += '\n'
outFile.write(line)
plt.figure(1)
plt.pcolormesh(numberTimingMesh[0],
numberTimingMesh[1],
spikeProbMesh,
cmap='hot')
plt.xlabel('Synapse number')
plt.ylabel('Synapse timing (median; ms)')
plt.xlim(50, 350)
plt.ylim(2.0, 10.0)
plt.xticks(np.arange(62.5, 362.5, 25.0),
[str(0.76 * i)
for i in range(50, 350, 25)]) # PW: 76% syn <500 microns
plt.yticks(np.arange(2.5, 10.5, 1.0), [str(i) for i in range(2, 10, 1)])
cbar = plt.colorbar()
cbar.set_label('Spike prob')
plt.savefig(outName + '_heatmap.pdf')