def analyseNetwork(self, simName, simType=None, nPlotMax=10):
figDir = simName + "/figures/"
if (not os.path.exists(figDir)):
os.makedirs(figDir)
if (simType is None):
simType = self.simType
if (simType == "Straub2016LTS"):
preType = "LTS"
self.setupExpDataDict()
elif (simType == "Straub2016FS"):
preType = "FSN"
self.setupExpDataDict()
elif (simType == "Chuhma2011"):
preType = "SPN"
self.setupExpDataDict()
elif (simType == "Szydlowski2013"):
preType = "FSN"
else:
print("Unknown simType : " + simType)
exit(-1)
print("Analysing data in " + simName)
voltFile = simName + "/" + simType + "-network-stimulation-current.txt"
# Read data from file
data = np.genfromtxt(voltFile, delimiter=",")
assert (data[0, 0] == -1) # First column should be time
time = data[0, 1:] * 1e-3
current = dict()
for rows in data[1:, :]:
cID = int(rows[0])
current[cID] = rows[1:] * 1e-9
# Data in time, current now
# Read the network info
networkFile = simName + "/network-pruned-synapses.hdf5"
self.snuddaLoad = SnuddaLoad(networkFile)
self.data = self.snuddaLoad.data
recordedNeurons = [x for x in current]
# Group the neurons by type
if (simType == "Chuhma2011"):
neuronTypeList = ["dSPN", "iSPN", "FSN", "ChIN"]
elif (simType == "Straub2016FS" or simType == "Straub2016LTS"):
neuronTypeList = ["dSPN", "iSPN", "ChIN"]
elif (simType == "Szydlowski2013"):
neuronTypeList = ["LTS"]
else:
print("simulate: Unknown simType: " + simType)
exit(-1)
neuronPlotList = []
minTimeIdx = np.where(time > self.tInj)[0][0]
maxTimeIdx = np.where(time > self.tInj + self.tWindow)[0][0]
for nt in neuronTypeList:
IDList = [
x for x in current if self.data["neurons"][x]["type"] == nt
]
maxIdx = [np.argmax(np.abs(current[x][minTimeIdx:maxTimeIdx] \
-current[x][minTimeIdx])) + minTimeIdx \
for x in IDList]
neuronPlotList.append((IDList, maxIdx))
matplotlib.rcParams.update({'font.size': 22})
for plotID, maxIdx in neuronPlotList:
if (len(plotID) == 0):
continue
plotType = self.data["neurons"][plotID[0]]["type"]
figName = figDir + "/" + simType + "-" + plotType + "-current-traces.pdf"
figNameHist = figDir + "/" + simType + "-" + plotType + "-current-histogram.pdf"
goodMax = []
plt.figure()
peakAmp = []
peakTime = []
voltCurve = []
for pID, mIdx in zip(plotID, maxIdx):
tIdx = np.where(
np.logical_and(time > self.tInj,
time < self.tInj + self.tWindow))[0]
curAmp = current[pID][tIdx] - current[pID][tIdx[0] - 1]
maxAmp = current[pID][mIdx] - current[pID][tIdx[0] - 1]
if (mIdx < minTimeIdx or mIdx > maxTimeIdx
or abs(maxAmp) < 1e-12):
# No peaks
continue
goodMax.append(maxAmp * 1e9)
peakAmp.append(maxAmp * 1e9)
peakTime.append((time[mIdx] - time[tIdx[0]]) * 1e3)
voltCurve.append(
((time[tIdx] - time[tIdx[0]]) * 1e3, curAmp * 1e9))
# Pick which curves to plot
sortIdx = np.argsort(peakAmp)
if (len(sortIdx) < nPlotMax):
keepIdx = sortIdx
else:
keepIdx = [sortIdx[int(np.round(x))] for x in \
np.linspace(0,len(sortIdx)-1,nPlotMax)]
for x in keepIdx:
plt.plot(voltCurve[x][0], voltCurve[x][1], 'k-')
plt.scatter(peakTime, peakAmp, marker=".", c="blue", s=100)
nType = self.data["neurons"][plotID[0]]["type"]
if ((simType, nType) in self.expDataDict):
expData = self.expDataDict[(simType, nType)]
t = self.tWindow * 1e3 * (
1 + 0.03 * np.random.rand(expData.shape[0]))
plt.scatter(t, -expData, marker=".", c="red", s=100)
if (self.plotExpTrace and (simType, nType) in self.expTraceDict):
data = self.expTraceDict[(simType, nType)]
tExp = data[:, 0]
vExp = data[:, 1:]
tIdx = np.where(tExp < self.tWindow * 1e3)[0]
plt.plot(tExp[tIdx], vExp[tIdx, :], c="red")
plt.title(
self.neuronName(preType) + " to " + self.neuronName(plotType))
plt.xlabel("Time (ms)")
plt.ylabel("Current (nA)")
# Remove part of the frame
plt.gca().spines["right"].set_visible(False)
plt.gca().spines["top"].set_visible(False)
plt.tight_layout()
plt.ion()
plt.show()
plt.savefig(figName, dpi=300)
# Also plot histogram
plt.figure()
plt.hist(goodMax)
plt.xlabel("Current (nA)")
plt.title(
self.neuronName(preType) + " to " + self.neuronName(plotType))
# Remove part of the frame
plt.gca().spines["right"].set_visible(False)
plt.gca().spines["top"].set_visible(False)
plt.tight_layout()
plt.ion()
plt.show()
plt.savefig(figNameHist, dpi=300)
import pdb
pdb.set_trace()