def func_to_optimize(parameters, saveFig="", deleteFiles=True, rankID=0):
A = run_model(parameters, saveFig, deleteFiles, rankID)
timesAll = A[0]
timeCoursesAll = A[1]
mydict = {}
if len(saveFig) > 0:
close("all")
f, axarr = subplots(len(OBJECTIVE_DATA), 1)
objs = []
imeas = [0, 5] # cAMP and PKAc traces
for iobjective in range(0, len(OBJECTIVE_DATA)):
objs_thisobj = []
times = timesAll[0]
timeCourses = mytools.interpolate(times[0],
timeCoursesAll[0][imeas[iobjective]],
OBJECTIVE_ts)
mydict['f' + str(iobjective)] = sum(
abs(array(timeCourses) - array(OBJECTIVE_DATA[iobjective]))) / max(
array(OBJECTIVE_DATA[iobjective]))
if len(saveFig) > 0:
axarr[iobjective].plot(OBJECTIVE_ts_highres,
OBJECTIVE_DATA_highres[iobjective], 'b--')
axarr[iobjective].plot(OBJECTIVE_ts, OBJECTIVE_DATA[iobjective],
'bx')
axarr[iobjective].plot(times[0],
timeCoursesAll[0][imeas[iobjective]], 'r--')
axarr[iobjective].plot(OBJECTIVE_ts, timeCourses, 'rx')
#axarr[iobjective].text(times[0],Ylims[1]*0.2+Ylims[0]*0.8,str(objs[iobjective]),fontsize=5)
#axarr[iobjective].set_ylabel(Objective_titles[iobjective],fontsize=5)
for tick in axarr[iobjective].xaxis.get_major_ticks(
) + axarr[iobjective].yaxis.get_major_ticks():
tick.label.set_fontsize(3.5)
Ylims = axarr[iobjective].get_ylim()
if iobjective == 0:
axarr[0].text(OBJECTIVE_ts[0],
Ylims[1] * 0.8 + Ylims[0] * 0.2,
str(parameters),
color='#0000FF',
fontsize=5)
else:
axarr[1].text(OBJECTIVE_ts[0],
Ylims[1] * 0.8 + Ylims[0] * 0.2,
str(mydict),
color='#0000FF',
fontsize=5)
if len(saveFig) > 0:
picklelist = A
file = open(saveFig + '_data.sav', 'w')
pickle.dump(picklelist, file)
file.close()
f.savefig(saveFig + '_obj.eps')
return mydict
Cadend3 = np.array(h.cadend3)
SKdend3 = np.array(h.skdend3)
spikes = mytools.spike_times(times, Vsoma, -35, -45)
CasomaupThisIter.append(max(Casoma))
SKsomaupThisIter.append(max(SKsoma))
VdendupThisIter.append(max(Vdend))
Vdend2upThisIter.append(max(Vdend2))
Vdend3upThisIter.append(max(Vdend3))
Cadend3upThisIter.append(max(Cadend3))
SKdend3upThisIter.append(max(SKdend3))
spikesupThisIter.append(spikes[:])
times_tcshort = [2900 + x for x in range(0, 301)]
Vdend3tcupThisIter.append(
mytools.interpolate(times, Vdend3, times_tcshort))
Cadend3tcupThisIter.append(
mytools.interpolate(times, Cadend3, times_tcshort))
SKdend3tcupThisIter.append(
mytools.interpolate(times, SKdend3, times_tcshort))
if epspdts[idt] in epspdts_savetimecourses:
times_tc = [2680 + x for x in range(0, 641)]
Vsoma_tc = mytools.interpolate(
times, Vsoma, times_tc)
Casoma_tc = mytools.interpolate(
times, Casoma, times_tc)
SKsoma_tc = mytools.interpolate(
times, SKsoma, times_tc)
Vdend_tc = mytools.interpolate(
times, Vdend, times_tc)
if len(sys.argv) > 5 and (sys.argv[1].find("mpi") > -1
or sys.argv[2].find("mpi") > -1):
argv_i_flux = 4
if len(sys.argv) > argv_i_flux:
cAMP_flux = float(sys.argv[argv_i_flux])
A = scipy.io.loadmat(
'cAMP_withdiss_test_tstop22000_tol1e-08_onset800.0_n1_freq1.0_dur16000.0_flux'
+ str(cAMP_flux) + '.mat')
if A['times'].shape[1] == 1:
A['times'] = A['times'].T
OBJECTIVE_ts = [1000 + 1000 * i for i in range(0, 21)]
OBJECTIVE_DATA = [
mytools.interpolate(A['times'][0], A['tcDATA'][0] + A['tcDATA'][1],
OBJECTIVE_ts), #Time course of cAMP
mytools.interpolate(A['times'][0], A['tcDATA'][5], OBJECTIVE_ts)
] #Time course of PKAc
OBJECTIVE_ts_highres = [1000 + 100 * i for i in range(0, 201)]
OBJECTIVE_DATA_highres = [
mytools.interpolate(A['times'][0], A['tcDATA'][0] + A['tcDATA'][1],
OBJECTIVE_ts_highres), #Time course of cAMP
mytools.interpolate(A['times'][0], A['tcDATA'][5], OBJECTIVE_ts_highres)
] #Time course of PKAc
mesh_input_file = open('mesh_general.out', 'r')
mesh_firstline = mesh_input_file.readline()
mesh_secondline = mesh_input_file.readline()
mesh_values = mesh_secondline.split()
my_volume = float(mesh_values[-2]) * 1e-15 #litres
mesh_input_file.close()
mySquareAmpsAll = unpickledlist[0]
mySynAmpsAll = unpickledlist[1]
squareAmps = [
mySquareAmpsAll[i][0][-1] for i in range(0, len(mySquareAmpsAll))
]
synAmps = [mySynAmpsAll[i][0][-1] for i in range(0, len(mySynAmpsAll))]
squareDurs = [5, 5, 5]
unpicklefile = open('control_cs' + str(icell) + '.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
Is = [0.7, 0.75, 0.8, 0.85]
Is_control = unpickledlist[19]
spikfreqs_control = mytools.interpolate(Is_control, unpickledlist[0], Is)
timesc_control = unpickledlist[1]
Vsomac_control = unpickledlist[2]
VDerivc_control = unpickledlist[3]
VDcoeff_control = unpickledlist[4][0]
counter = -1
for igene in range(0, len(MT)):
#for igene in range(0,1):
theseCoeffsGene = []
for imut in range(0, len(MT[igene])):
#for imut in range(0,1):
theseCoeffsMut = []
nVals = len(MT[igene][imut]) * [0]
thesemutvars = []
for imutvar in range(0, len(MT[igene][imut])):
"noisy_icell" + str(icell) + "_n" + str(stimN) + "_" + str(stimfreq) +
"_neckLen" + str(neckLen) + "_neckDiam" + str(neckDiam) + "_Nsyn" +
str(Nsyn) + "_Ninputs" + str(Ninputs) + dendtree + spinelocations +
"_Econ" + str(Econ) + '_wNMDA' + str(weight_factor_NMDA) + "_rateE" +
str(rateE) + "_Npulses" + str(Npulses) + "_ISI" + str(ISIpairing) +
"_dtpulses" + str(dtpulses) + "_pulseamp" + str(pulseamp) + "_seed" +
str(rdSeed) + ".mat", {
'tstart_samp':
tstart_samp,
'dt_samp':
dt_samp,
'Nsamp':
Nsamp,
'DATA': [
mytools.interpolate(
array(h.time), array(h.iNMDAs_stim[isyn]),
[tstart_samp + i * dt_samp for i in range(0, Nsamp)])
for isyn in range(0, len(h.iNMDAs_stim))
],
'stimdistances':
stimdistances,
'stimcomps':
stimcomps,
'stimsecs':
stimsecs,
'stimxs':
stimxs,
'vsoma':
array(h.voltage),
'times':
array(h.time)
for ifile in range(0,len(filenames_nrn)):
filename_nrn = filenames_nrn[ifile]
assert exists(filename_nrn), filename_nrn+' does not exist'
DATANRNs.append(scipy.io.loadmat(filename_nrn))
print "loaded "+filename_nrn
mydraw(axarr[4],['PKCt'],DATANRNs,[[],[3,1],[1,3]],['#000000','#808080','#808080']) ############# E ################# (PKCt)
mydraw(axarr[5],['PKCp'],DATANRNs,[[],[3,1],[1,3]],['#000000','#808080','#808080']) ############# F ################# (PKCp)
GluR2_S880_tcs = mydraw(axarr[6],['GluR2_S880','GluR2_S880_PP2A','GluR2_memb_S880','GluR2_memb_S880_PP2A'],DATANRNs,[[],[3,1],[1,3]],['#000000','#808080','#808080']) ############# G ################# (GluR2 S880 time course)
########## H ############
if True:
A = scipy.io.loadmat('cAMP_withdiss_test_tstop22000_tol1e-08_onset800.0_n1_freq1.0_dur16000.0_flux0.64.mat')
if A['times'].shape[1] == 1:
A['times'] = A['times'].T
OBJECTIVE_ts = [1000+1000*i for i in range(0,18)]
OBJECTIVE_DATA = [mytools.interpolate(A['times'][0],A['tcDATA'][0]+A['tcDATA'][1],OBJECTIVE_ts), #Time course of cAMP
mytools.interpolate(A['times'][0],A['tcDATA'][5],OBJECTIVE_ts)] #Time course of PKAc
ks = [0.4e9, 1.0e9, 1.6e9, 2.2e9, 2.8e9]
fs = []
polygon = Polygon(array([[1000,17000,17000,1000],[0,0,100,100]]).T, True)
p = PatchCollection([polygon], cmap=matplotlib.cm.jet)
p.set_facecolor('#E0E0E0')
p.set_edgecolor('none')
axarr[7].add_collection(p)
mydashes = [[2,2],[1,3],[1,1],[1,3,2,2],[3,1]]
for ik in range(0,len(ks)):
unpicklefile = open('run_cAMP_withdiss_flux0.64_k'+str(ks[ik])+'_data.sav','r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()