# synapses
synparams = []
for inloc in inlocs:
synparams.append(copy.deepcopy(inloc))
synparams[-1]['weight'] = w; synparams[-1]['E_r'] = 0.
synparams[-1]['tau1'] = t1; synparams[-1]['tau2'] = t2
# synparams_2 = []
# for inloc in inlocs_2:
# synparams_2.append(copy.deepcopy(inloc))
# synparams_2[-1]['weight'] = w; synparams_2[-1]['E_r'] = 0.
# synparams_2[-1]['tau1'] = t1; synparams_2[-1]['tau2'] = t2
# poisson spiketrains
import spikeTrainGenerator as s
spiketimes = []
for i in range(len(synparams)):
spiketimes.append({'ID': synparams[i]['ID'], 'spks': s.poissonTrain(rate,tmax)})
# spiketimes.append({'ID': synparams[i]['ID'], 'spks': []})
inlocs_IDs = np.array([inloc['ID'] for inloc in inlocs])
# spiketimes_2 = [copy.deepcopy(spiketimes[0])]
# for i, synpar in enumerate(synparams_2[1:]):
# spks = []
# for ID in synpar['IDs']:
# ind = np.where(inlocs_IDs == ID)[0][0]
# spks += spiketimes[ind]['spks']
# spiketimes_2.append({'ID': synpar['ID'], 'spks': np.sort(spks)})
# spiketimes[0]['spks'] = [0.125] + spiketimes[0]['spks']
# for i in range(len(synparams)):
# spiketimes.append({'ID': synparams[i]['ID'], 'spks': [i*100.+2.]})
# calc inloc conductances
# gs_point, es_point, gcalctype_point = greenstree_sim.calc_IP_conductances(inlocs)
# for key in gs_point.keys():
for N in range(2,len(inlocs_all)):
print '\n>>> simulation with ' + str(N) + ' inlocs'
inlocs = inlocs_all[:N]
IDs = IDs_all[:N]
NNs,_ = greenstree.get_nearest_neighbours(inlocs, add_leaves=False, separate=False, test=False)
# synapses
synparams = []
for inloc in inlocs:
synparams.append(copy.deepcopy(inloc))
synparams[-1]['weight'] = w; synparams[-1]['E_r'] = 0.
synparams[-1]['tau1'] = t1; synparams[-1]['tau2'] = t2
# poisson spiketrains
import spikeTrainGenerator as s
spiketimes = []
for i in range(len(synparams)):
spiketimes.append({'ID': synparams[i]['ID'], 'spks': np.array(s.poissonTrain(rate/N,tmax))})
# spiketimes.append({'ID': synparams[i]['ID'], 'spks': np.array([100.+100.*i])})
# spiketimes.append({'ID': synparams[i]['ID'], 'spks': np.array([])})
# spiketimes.append({'ID': synparams[i]['ID'], 'spks': np.linspace(0.,tmax, int(tmax/dt))})
inlocs_IDs = np.array([inloc['ID'] for inloc in inlocs])
# compute SGF
alphas, gammas, pairs, Ms = gfcalc.kernelSet_sparse(inlocs, FFT=False, kernelconstants=True, pprint=False)
# preprocessor
prep = neurM.preprocessor()
# C model no spikes
sgfM_ns, nK = prep.construct_C_model_hybrid(dt, inlocs, NNs, alphas, gammas, K)
sgfM_ns.add_recorder(ind_recloc)
print "<n_K> =", nK
nK_avg_list.append(nK)
# C model spikes
sgfM_s, _ = prep.construct_C_model_hybrid(dt, inlocs, NNs, alphas, gammas, K)
