import matplotlib
matplotlib.use('Agg')
import numpy
from pylab import *
import mytools
import pickle
import sys
import scipy.io
from os.path import exists
f,axarr = subplots(1,3)
for i in range(0,3):
axarr[i].set_position([0.1+0.3*i,0.5,0.23,0.45])
f.text(0.065+0.3*i, 0.94, chr(ord('A')+i), fontsize=22)
Is = unique([0.34+0.0025*x for x in range(0,11)]+[0.35+0.05*x for x in range(0,22)])
styles = ['g-','g-','g-','g-','g-','g-','g-','g-','g-']
cols = ['#666666','#012345','#aa00aa','#bbaa00','#ee6600','#ff0000', '#00aaaa','#772277','#00cc00']
col_control = '#2222ff'
ispDef = 0 # Consider a local maximum above -35mV a spike only if after last spike the membrane potential came below -45mV
icell = 0
icomb = 0
combs_all = [ [[0,5,1], [1,2,15], [2,4,7], [3,1,0], [5,0,0], [8,5,0], [13,2,0]],
[[0,5,0], [1,3,0], [2,5,1], [3,1,1], [6,3,0], [8,3,0], [12,1,1], [13,5,0]],
[[0,5,1], [1,2,15], [2,4,7], [3,1,0], [5,0,0], [8,5,0], [13,3,0]],
[[0,5,0], [1,3,0], [2,5,1], [3,1,1], [6,3,0], [8,3,0], [12,1,1], [13,5,0]],
[[0,5,1], [1,2,15], [2,4,7], [3,1,0], [5,0,0], [8,5,0], [13,2,0]],
[[0,5,0], [1,3,0], [2,5,1], [3,1,1], [6,3,0], [8,3,0], [12,1,1], [13,5,0]],
[[0,5,1], [1,2,15], [2,4,7], [3,1,1], [5,0,0], [8,5,0], [13,5,0]],
[[0,5,0], [1,3,0], [2,5,1], [3,0,1], [6,3,0], [8,3,0], [12,1,1], [13,4,0]],
[[0,5,1], [1,2,15], [2,4,7], [3,1,1], [5,0,0], [8,5,0], [13,1,0]],
[[0,5,0], [1,3,0], [2,5,1], [3,0,1], [6,3,0], [8,3,0], [12,1,1], [13,5,0]],
[[0,5,1], [1,2,15], [2,4,7], [3,1,1], [5,0,0], [8,5,0], [13,5,0]],
[[0,5,0], [1,3,0], [2,5,1], [3,0,1], [6,3,0], [8,3,0], [12,1,1], [13,3,0]],
[[0,5,1], [1,2,15], [2,4,7], [3,1,1], [5,0,0], [8,5,0], [13,0,0]],
[[0,5,0], [1,3,0], [2,5,1], [3,0,1], [6,3,0], [8,3,0], [12,1,1], [13,5,0]] ]
lensToStart = [150.0, 300.0, 450.0, 600.0, 650.0]
startdist = int(lensToStart[1])
currCoeff = 1.1
unpicklefile = open('ifcurvesmut_cs'+str(icell)+'_comb'+str(icomb)+'.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
ISIsThisMutVal = unpickledlist[0]
spTimesThisMutVal = unpickledlist[1+ispDef]
Is_control = [0.35+0.05*x for x in range(0,22)]
unpicklefile = open('ifcurvesmut_cs'+str(icell)+'_0_0_0.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
spTimesThisMutVal0 = unpickledlist[1+ispDef]
nSpikes_control = [sum([1 for x in spTimesThisMutVal0[5][j] if x >= 500]) for j in range(0,len(Is_control))]
somaticIs = [-0.1, -0.09, -0.08, -0.07, -0.06, -0.05, -0.04, -0.03, -0.02, -0.01, 0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1]
synconductances = unique([0.000005, 0.00001, 0.000015, 0.00002, 0.000025, 0.00003, 0.000035, 0.00004, 0.000045, 0.00005, 0.000055, 0.0000025, 0.0000075, 0.0000125, 0.0000175, 0.0000225, 0.0000275, 0.0000325, 0.0000375, 0.0000425, 0.0000475, 0.0000525])
thrs = [[0.00011, 0.00012, inf],[0.00011, 0.00012, inf],[0.00011, 0.00012, inf],[0.00011, 0.00012, inf],[0.00011, 0.00012, inf],[0.00011, 0.00012, inf],[0,1,2,3,4,inf]]
iters = [0, 2, 5, 6, 8]
for iiter in range(0,len(iters)):
iter = iters[iiter]
if iter==5:
continue
nSpikes = [sum([1 for x in spTimesThisMutVal[iiter][j] if x >= 500]) for j in range(0,len(Is))]
axarr[0].plot(Is, [x/7.5 for x in nSpikes], styles[iter],color=cols[iter],linewidth=1)
axarr[0].plot(Is_control, [x/7.5 for x in nSpikes_control], styles[iter],color=col_control,linewidth=1)
if exists('PPIcoeffs300_cs'+str(icell)+'_0.sav'):
print ' opening PPIcoeffs300_cs'+str(icell)+'_0.sav and PPIcoeffs_complement_300_cs'+str(icell)+'_0.sav'
unpicklefile = open('PPIcoeffs300_cs'+str(icell)+'_0.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
unpicklefile = open('PPIcoeffs_complement_300_cs'+str(icell)+'_0.sav', 'r')
unpickledlist2 = pickle.load(unpicklefile)
unpicklefile.close()
PPIcoeffs_control_Hay = unpickledlist[1][4]
print str(unpickledlist[1][4])
ISIs_control_Hay = unpickledlist[2]
for iISI in range(0,len(unpickledlist2[2])):
ind = next((i for i,x in enumerate(ISIs_control_Hay) if unpickledlist2[2][iISI] < x ))
#print "unpickledlist2[2][iISI]="+str(unpickledlist2[2][iISI])+", ISIs_Hay="+str(ISIs_Hay)+", ind="+str(ind)
ISIs_control_Hay.insert(ind,unpickledlist2[2][iISI])
PPIcoeffs_control_Hay.insert(ind,unpickledlist2[1][4][iISI])
else:
print ' PPIcoeffs300_cs'+str(icell)+'_0.sav not found'
unpicklefile = open('PPIcoeffs'+str(startdist)+'_cs'+str(icell)+'_comb'+str(icomb)+'.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
PPIcoeffs_Hay_thismutval = [x[:] for x in unpickledlist[1]]
#ISIs_Hay = unpickledlist[2][:]
ISIs_Hay = unique([4*x for x in range(0,25)]+[20*x for x in range(0,26)])
iters = [0, 2, 6, 8]
for iiter in range(0,len(iters)):
iter = iters[iiter]
if iter==5:
continue
axarr[1].plot(ISIs_Hay,[x[2]*currCoeff for x in PPIcoeffs_Hay_thismutval[iiter]],color=cols[iter],linewidth=1.0)
axarr[1].plot(ISIs_control_Hay,[x[2]*currCoeff for x in PPIcoeffs_control_Hay],color=col_control,linewidth=1.0)
iters = [0, 2, 5, 6, 8, -1]
coding_outputs_thismut = []
coding_outputs_control = []
for isynconductance in range(0,len(synconductances)):
coding_outputs_thiscond = []
coding_outputs_control_thiscond = []
synconductance = synconductances[isynconductance]
for iI in range(0,len(somaticIs)):
unpicklefile = open('codings_nonprop'+str(synconductance)+'_cs'+str(icell)+'_comb'+str(icomb)+'_somaticI'+str(somaticIs[iI])+'.sav', 'r') #maybe codings/codings_nonprop'+str(synconductance)+'_cs'+str(icell)+'_comb'+str(icomb)+'.sav'
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
coding_outputs = unpickledlist[2]
myfigs = [[],[],[],[],[]]
myfigs_control = []
for iplot in range(0,7):
for iiter in range(0,len(iters)):
iter = iters[iiter]
if iter == -1:
myfigs_control.append([next((i for i,x in enumerate(thrs[iplot]) if x >= coding_outputs[iiter][j][iplot])) for j in range(0,128)])
continue # don't add the control anyway to myfigs
else:
myfigs[iiter].append([next((i for i,x in enumerate(thrs[iplot]) if x >= coding_outputs[iiter][j][iplot])) for j in range(0,128)])
coding_outputs_thiscond.append(myfigs[:])
if iter == -1:
coding_outputs_control_thiscond.append(myfigs_control[:])
coding_outputs_thismut.append(coding_outputs_thiscond[:])
if iter == -1:
coding_outputs_control.append(coding_outputs_control_thiscond[:])
print "Analyzing controls..."
Npatterns_control = []
for icond in range(0,len(synconductances)):
Npatterns_control.append(mean([len(unique([sum([(4**iplot)*coding_outputs_control[icond][iI][iplot][j] for iplot in range(0,7)]) for j in range(0,128)])) for iI in range(0,len(somaticIs))]))
for iiter in range(0,len(iters)-1):
iter = iters[iiter]
if iter == 5:
continue
Npatterns = []
for icond in range(0,len(synconductances)):
Npatterns.append(mean([len(unique([sum([(4**iplot)*coding_outputs_thismut[icond][iI][iiter][iplot][j] for iplot in range(0,7)]) for j in range(0,128)])) for iI in range(0,len(somaticIs))]))
#print str(Npatterns)
print "iiter="+str(iiter)+", Npatterns="+str(mean(Npatterns))+" +- "+str(std(Npatterns))
axarr[2].plot(1e6*synconductances,Npatterns,'b-',color=cols[iter])
axarr[2].plot(1e6*synconductances,Npatterns_control,'b-',color=col_control)
axarr[0].set_xlabel('$I$ (nA)',fontsize=9)
axarr[0].set_xticks([0.4,0.8,1.2])
axarr[0].set_ylabel('$f$ (Hz)',fontsize=9)
axarr[0].set_yticks([0,10,20])
axarr[1].set_xlabel('ISI (ms)',fontsize=9)
axarr[1].set_xticks([0,100,200,300,400,500])
axarr[1].set_ylabel('PPI factor',fontsize=9)
axarr[1].set_yticks([0,5,10])
axarr[2].set_xlabel('Single-synapse conductance (pS)',fontsize=9)
axarr[2].set_xticks([0,20,40,60])
axarr[2].set_ylabel('output diversity',fontsize=9)
axarr[2].set_yticks([0,10,20])
for i in range(0,3):
for tick in axarr[i].yaxis.get_major_ticks()+axarr[i].xaxis.get_major_ticks():
tick.label.set_fontsize(6)
f.savefig("figcomb.eps")
