def analyze_batch(batchLabel, batchdatadir=batchdatadir):
params, data = batch_utils.load_batch(batchLabel,
batchdatadir=batchdatadir)
batch = (batchLabel, params, data)
batch_analysis.plot_batch_raster(batch,
timeRange=[100, 1000],
markerSize=0.5,
orderInverse=False)
batch_analysis.plot_vtraces(batch, timerange=[100, 1000])
def plot_batch_ind_conn(batchLabel,
batchdatadir=batchdatadir,
includePre=['all'],
includePost=['all'],
feature='strength',
orderBy='gid',
figSize=(10, 10),
groupBy='pop',
groupByIntervalPre=None,
groupByIntervalPost=None,
graphType='matrix',
synOrConn='syn',
synMech=None,
connsFile=None,
tagsFile=None,
clim=None,
fontSize=12,
saveData=None,
showFig=False,
save=True,
outputdir='batch_figs'):
"""Plots individual connectivity plots for each parameter combination."""
from netpyne import specs
if type(batchLabel) == str:
params, data = batch_utils.load_batch(batchLabel,
batchdatadir=batchdatadir)
elif type(batchLabel) == tuple:
batchLabel, params, data = batchLabel
else:
raise Exception()
simLabels = data.keys()
for simLabel in simLabels:
print('Plotting sim: ' + simLabel)
datum = data[simLabel]
cfg = specs.SimConfig(datum['simConfig'])
cfg.createNEURONObj = False
sim.initialize() # create network object and set cfg and net params
sim.loadAll('', data=datum, instantiate=False)
sim.setSimCfg(cfg)
try:
print('Cells created: ' + str(len(sim.net.allCells)))
except:
print('Alternate sim loading...')
sim.net.createPops()
sim.net.createCells()
sim.setupRecording()
sim.gatherData()
sim.allSimData = datum['simData']
features = [
'weight', 'delay', 'numConns', 'probability', 'strength',
'convergence', 'divergence'
]
for feature in features:
if save:
saveFig = batchdatadir + '/' + batchLabel + '/' + 'connFig_' + feature + simLabel + '.png'
else:
saveFig = None
sim.analysis.plotConn(includePre=includePre,
includePost=includePost,
feature=feature,
orderBy=orderBy,
figSize=figSize,
groupBy=groupBy,
groupByIntervalPre=groupByIntervalPre,
groupByIntervalPost=groupByIntervalPost,
graphType=graphType,
synOrConn=synOrConn,
synMech=synMech,
connsFile=connsFile,
tagsFile=tagsFile,
clim=clim,
fontSize=fontSize,
saveData=saveData,
saveFig=saveFig,
showFig=showFig)
def plot_batch_ind_stats(batchLabel,
batchdatadir=batchdatadir,
include=['allCells', 'eachPop'],
statDataIn={},
timeRange=None,
graphType='boxplot',
stats=['rate', 'isicv'],
bins=50,
popColors=[],
histlogy=False,
histlogx=False,
histmin=0.0,
density=False,
includeRate0=False,
legendLabels=None,
normfit=False,
histShading=True,
xlim=None,
dpi=100,
figSize=(6, 8),
fontSize=12,
saveData=None,
showFig=True,
save=True,
outputdir='batch_figs'):
"""Plots individual connectivity plots for each parameter combination."""
from netpyne import specs
if type(batchLabel) == str:
params, data = batch_utils.load_batch(batchLabel,
batchdatadir=batchdatadir)
elif type(batchLabel) == tuple:
batchLabel, params, data = batchLabel
else:
raise Exception()
simLabels = data.keys()
for simLabel in simLabels:
print('Plotting sim: ' + simLabel)
datum = data[simLabel]
cfg = specs.SimConfig(datum['simConfig'])
cfg.createNEURONObj = False
sim.initialize() # create network object and set cfg and net params
sim.loadAll('', data=datum, instantiate=False)
sim.setSimCfg(cfg)
try:
print('Cells created: ' + str(len(sim.net.allCells)))
except:
print('Alternate sim loading...')
sim.net.createPops()
sim.net.createCells()
sim.setupRecording()
sim.gatherData()
sim.allSimData = datum['simData']
if save:
saveFig = batchdatadir + '/' + batchLabel + '/' + 'statFig_' + simLabel
else:
saveFig = None
sim.analysis.plotSpikeStats(include=include,
statDataIn=statDataIn,
timeRange=timeRange,
graphType=graphType,
stats=stats,
bins=bins,
popColors=popColors,
histlogy=histlogy,
histlogx=histlogx,
histmin=histmin,
density=density,
includeRate0=includeRate0,
legendLabels=legendLabels,
normfit=normfit,
histShading=histShading,
xlim=xlim,
dpi=dpi,
figSize=figSize,
fontSize=fontSize,
saveData=saveData,
saveFig=saveFig,
showFig=showFig)
def plot_batch_ind_raster(batchLabel,
batchdatadir=batchdatadir,
include=['allCells'],
timeRange=None,
maxSpikes=1e8,
orderBy='gid',
orderInverse=False,
labels='legend',
popRates=False,
spikeHist=None,
spikeHistBin=5,
syncLines=False,
figSize=(10, 8),
saveData=None,
showFig=False,
save=True,
outputdir='batch_figs'):
"""Plots individual raster plots for each parameter combination."""
from netpyne import specs
if type(batchLabel) == str:
params, data = batch_utils.load_batch(batchLabel,
batchdatadir=batchdatadir)
elif type(batchLabel) == tuple:
batchLabel, params, data = batchLabel
else:
raise Exception()
simLabels = data.keys()
for simLabel in simLabels:
print('Plotting sim: ' + simLabel)
datum = data[simLabel]
cfg = specs.SimConfig(datum['simConfig'])
cfg.createNEURONObj = False
sim.initialize() # create network object and set cfg and net params
sim.loadAll('', data=datum, instantiate=False)
sim.setSimCfg(cfg)
try:
print('Cells created: ' + str(len(sim.net.allCells)))
except:
print('Alternate sim loading...')
sim.net.createPops()
sim.net.createCells()
sim.setupRecording()
sim.gatherData()
sim.allSimData = datum['simData']
if save:
saveFig = batchdatadir + '/' + batchLabel + '/' + 'rasterFig' + simLabel + '.png'
else:
saveFig = None
sim.analysis.plotRaster(include=include,
timeRange=timeRange,
maxSpikes=maxSpikes,
orderBy=orderBy,
orderInverse=orderInverse,
labels=labels,
popRates=popRates,
spikeHist=spikeHist,
spikeHistBin=spikeHistBin,
syncLines=syncLines,
figSize=figSize,
saveData=saveData,
saveFig=saveFig,
showFig=showFig)
# n = int(T * fs) # total number of samples
# t = np.linspace(0, T, n, endpoint=False)
# # "Noisy" data. We want to recover the 1.2 Hz signal from this.
# data = np.sin(1.2*2*np.pi*t) + 1.5*np.cos(9*2*np.pi*t) + 0.5*np.sin(12.0*2*np.pi*t)
# Using our own experimental data
exptrace = 2 # Trace number to filter (0 to 9)
expdata = batch_utils.import_excel(
"/u/graham/projects/eee/data/srdjan_20171017/B_73_004.xlsx")
t = np.array(expdata.index.tolist())
data = np.array(expdata.iloc[:, trace].tolist())
# Using our own sim data
simtrace = 6 # Trace number to filter (0 to 9)
batchname = 'glutAmp'
params, data = batch_utils.load_batch(batchname)
simdata = batch_analysis.get_vtraces(params,
data,
cellID=0,
section="soma",
stable=50)
data = simdata['yarray'][simtrace, :]
t = simdata['xvector']
# Filter the data, and plot both the original and filtered signals.
y = butter_lowpass_filter(data, cutoff, fs, order)
plt.subplot(2, 1, 2)
plt.plot(t, data, 'b-', label='data')
plt.plot(t, y, 'g-', linewidth=2, label='filtered data')
plt.xlabel('Time [sec]')