stimulus = np.sin(2 * np.pi * frequency *
t) * (1 + contrast * np.sin(2 * np.pi * contrastf * t))
#kernel parameters
kernelparams = {'sigma': 0.001, 'lenfactor': 8, 'resolution': t_delta}
#create kernel
kernel, kerneltime = helpers.spike_gauss_kernel(**kernelparams)
convolvedspklist = np.zeros([t.shape[0],
ntrials]) #initialized list of convolved spikes
spiketrains = np.zeros([t.shape[0],
ntrials]) #initialized list of spike trains
for i in range(ntrials):
#run the model for the given stimulus and get spike times
spiketimes, spikeISI, meanspkfr = helpers.stimulus_ISI_calculator(
cellparams, stimulus, tlength=tlength)
convolvedspikes, spikearray = helpers.convolved_spikes(
spiketimes, stimulus, t, kernel)
convolvedspklist[:, i] = convolvedspikes
spiketrains[:, i] = spikearray
peristimulustimehist = np.mean(convolvedspklist, axis=1)
fig, (axp, axr, axs) = plt.subplots(3, 1, sharex=True)
axp.plot(t[t > 0.1] * 1000, peristimulustimehist[t > 0.1])
axp.set_title('Peristimulus time histogram')
axp.set_ylabel('Spiking frequency [Hz]')
axr.plot(t[t > 0.1] * 1000,
print("File exists and is readable")
continue
#Amplitude modulation
baselinefs, initialfs, steadyfs = helpers.amplitude_modulation(
cellparams, EODf, **ampmodinputs)
#Histogram
frequency = EODf #Electric organ discharge frequency in Hz, used for stimulus
t_delta = cellparams["deltat"] #time step in seconds
t = np.arange(0, tlength, t_delta)
stimulus = np.sin(2 * np.pi * frequency *
t) #simple sine wave for the hist
__, spikeISI, __ = helpers.stimulus_ISI_calculator(cellparams,
stimulus,
tlength=tlength)
spikeISI = spikeISI * EODf
ISIhist, ISIbins = np.histogram(spikeISI, bins=np.arange(0, 20.2, 0.2))
datarate = {
'baselinefs': baselinefs,
'initialfs': initialfs,
'steadyfs': steadyfs,
'contrasts': ampmodinputs['contrasts']
}
datahist = {'ISIhist': ISIhist, 'ISIbins': ISIbins[1:]}
dataISI = {'spikeISI': spikeISI}
prdf = pd.DataFrame(datarate)
phistdf = pd.DataFrame(datahist)
tstart = 0.15 #the time point to start plotting (in seconds)
tstop = 0.2 #the time point to stop plotting (in seconds)
cell_idx = 0 #index of the cell of interest.
#Run from here on, if you break and want to re-see what is up.
checkertotal = 1 #this to keep looking further to cells after breaking.
while checkertotal == 1:
cell, EODf, cellparams = helpers.parameters_dictionary_reformatting(
cell_idx, parameters)
frequency = EODf #Electric organ discharge frequency in Hz, used for stimulus
t_delta = cellparams["deltat"] #time step in seconds
t = np.arange(0, tlength, t_delta)
stimulus = np.sin(2 * np.pi * frequency * t)
spikevals = helpers.stimulus_ISI_calculator(
cellparams, stimulus, tlength=tlength) #spikevals is spiketimes,
#spikeISI, meanspkfr
ISIlist.append(spikevals[1])
meanspkfrs.append(spikevals[2]) #mean spike firing rate per second
helpers.stimulus_ISI_plotter(cell,
t,
EODf,
stimulus,
*spikevals,
tstart=tstart,
tstop=tstop)
checkerplot = 0
while checkerplot == 0:
a = input('press enter to continue, write esc to quit \n')