import matplotlib
matplotlib.use('Agg')
import numpy
from pylab import *
import mytools
import pickle
import time
import sys
import random
from setparams import *
from os.path import exists
v0 = -80
ca0 = 0.0001
#proximalpoints = [100,100,100,200,200,200,300,300,300,400,400,400]
#distalpoints = [600,750,900,600,750,900,600,750,900,600,750,900]
proximalpoints = [200] #,200]
distalpoints = [600] #,900]
BACdt = 5.0
fs = 8
tstop = 5000.0
#epspdts = [0.25*x for x in range(-80,81)]
#epspdts = [0.5*x for x in range(-40,41)]
#epspdts = [2.0*x for x in range(-10,-5)]+range(-10,11)+[2.0*x for x in range(6,11)]
#epspdts = [10.0*x for x in range(-10,-8)]+[4.0*x for x in range(-20,-10)]+[2.0*x for x in range(-20,-10)]+[2.0*x for x in range(-10,11)]+[2.0*x for x in range(11,21)]+[4.0*x for x in range(11,21)]+[10.0*x for x in range(9,11)]
#epspdts_savetimecourses = [-100,-80,-60,-40,-30,-20,-10,0,10,20,30,40,60,80,100]
epspdts = [10.0*x for x in range(-10,-8)]+[4.0*x for x in range(-20,-10)]+[4.0*x for x in range(-10,-5)]+[4.0*x for x in range(-5,6)]+[4.0*x for x in range(6,11)]+[4.0*x for x in range(11,21)]+[10.0*x for x in range(9,11)]
#epspdts_savetimecourses = [-40,-20,-12,-4,0,4,12,20,40]
epspdts_savetimecourses = [-100,-80,-60,-32,0,32,60,80,100]
Is_st2 = 1.32
st2coeff = 0.40 #Somatic 5ms pulse
st2coeff_down = 1.35 #Somatic 5ms pulse
st1coeff = 0.9 #Proximal apical 200ms pulse
syn1coeff = 0.25 #Synaptic epsp-like input
import mutation_stuff
MT = mutation_stuff.getMT()
defVals = mutation_stuff.getdefvals()
keyList = defVals.keys()
for idefval in range(0,len(keyList)):
if type(defVals[keyList[idefval]]) is not list:
defVals[keyList[idefval]] = [defVals[keyList[idefval]], defVals[keyList[idefval]]] #make the dictionary values [somatic, apical]
updatedVars = ['somatic','apical','basal'] # the possible classes of segments that defVals may apply to
whichDefVal = [0,1,0] # use the defVal[0] for somatic and basal segments and defVal[1] for apical segments
unpicklefile = open('scalings_cs.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
theseCoeffsAllAll = unpickledlist[0]
theseMutValsAllAll = unpickledlist[2]
paramdicts = []
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 1.0, 'S_gCa_HVAbar_Ca_HVA': 1.0}) # 1 spike per burst, control
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 1.6}) # 1-2 spikes per burst
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 2.2}) # 2-3 spikes per burst
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 2.2, 'S_gCa_HVAbar_Ca_HVA': 0.9}) # 3-4 spikes per burst
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 2.2, 'S_gCa_HVAbar_Ca_HVA': 0.625}) # 3-5 spikes per burst
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 2.2, 'S_gCa_HVAbar_Ca_HVA': 0.5}) # 4-6 spikes per burst
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 2.2, 'S_gCa_HVAbar_Ca_HVA': 0.3}) # 5-9 spikes per burst
VsomaupAllAll = []
VsomadownAllAll = []
VdendupAllAll = []
VdenddownAllAll = []
rateCoeffs = [[1.1],[0.7]]
counter = -1
icell = 0
idist = 0
ext2a = '_syn1coeff0.25'
ext2b = '_syn1coeff0.5'
if not exists('updownresponsemean20ms_noisy_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+ext2a+'_controls.sav'):
exit('updownresponsemean20ms_noisy_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+ext2a+'_controls.sav not found')
unpicklefile = open('updownresponsemean20ms_noisy_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+ext2a+'_controls.sav','r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
spikes_control = unpickledlist[0]
Vdend3b_control = unpickledlist[1]
Cadend3b_control = unpickledlist[2]
SKdend3b_control = unpickledlist[3]
Vdend3b2_control = unpickledlist[4]
Cadend3b2_control = unpickledlist[5]
SKdend3b2_control = unpickledlist[6]
print 'updownresponsemean20ms_noisy_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+ext2a+'_controls.sav loaded!'
if True:
theseCoeffsAll = theseCoeffsAllAll[icell]
spikesAll = []
for idist in range(0,1):#len(proximalpoints)):
counter = counter + 1
proximalpoint = proximalpoints[idist]
distalpoint = distalpoints[idist]
fixedpoint = 700
paramdict = paramdicts[icell]
styles = ['b-','b-']
##cols = ['#00aaaa','#11cc44','#55ee00','#bbaa00','#ee6600','#ff0000', '#aa00aa','#772277','#333333']
#cols = ['#444444','#012345','#aa00aa','#bbaa00','#ee6600','#ff0000', '#009999','#772277','#00cc00']
cols = ['#666666','#012345','#aa00aa','#bbaa00','#ee6600','#ff0000', '#00aaaa','#772277','#00cc00']
col_control = '#2222ff'
coeffCoeffs = [[0.25,0],[0.125,0],[0.5,0],[0.5,1.0/3],[0.5,2.0/3],[0.5,1.0],[-0.25,0],[-0.125,0],[-0.5,0]]
lw = 0.5
mutcounter = -1
for igene in range(0,len(MT)):
for imut in range(0,len(MT[igene])):
nVals = len(MT[igene][imut])*[0]
thesemutvars = []
theseCoeffs = theseCoeffsAll[igene][imut]
for imutvar in range(0,len(MT[igene][imut])):
thesemutvars.append(MT[igene][imut][imutvar][0])
if type(MT[igene][imut][imutvar][1]) is int or type(MT[igene][imut][imutvar][1]) is float:
MT[igene][imut][imutvar][1] = [MT[igene][imut][imutvar][1]]
nVals[imutvar] = len(MT[igene][imut][imutvar][1])
cumprodnVals = cumprod(nVals)
allmutvars = cumprodnVals[len(MT[igene][imut])-1]*[thesemutvars]
allmutvals = []
for iallmutval in range(0,cumprodnVals[len(MT[igene][imut])-1]):
allmutvals.append([0]*len(thesemutvars))
for iallmutval in range(0,cumprodnVals[len(MT[igene][imut])-1]):
for imutvar in range(0,len(MT[igene][imut])):
if imutvar==0:
allmutvals[iallmutval][imutvar] = MT[igene][imut][imutvar][1][iallmutval%nVals[imutvar]]
else:
allmutvals[iallmutval][imutvar] = MT[igene][imut][imutvar][1][(iallmutval/cumprodnVals[imutvar-1])%nVals[imutvar]]
for iallmutval in range(0,cumprodnVals[len(MT[igene][imut])-1]):
mutcounter = mutcounter + 1
if len(sys.argv) > 1 and int(float(sys.argv[1])) != mutcounter: # and (igene!=0 or imut!=0 or iallmutval!=0): #If 0-0-0, go a bit further anyway to load the control data
continue
spikesThisMutVal = []
for icoeff in range(0,len(rateCoeffs[0])):
Vdend3_all = []
Cadend3_all = []
SKdend3_all = []
Vdend3_2_all = []
Cadend3_2_all = []
SKdend3_2_all = []
Vdend3_control = []
for iup in range(0,2):
Vdend3 = []
Cadend3 = []
SKdend3 = []
Vdend3_2 = []
Cadend3_2 = []
SKdend3_2 = []
if iup==0:
ext = 'up2'
ext2 = ext2a
else:
ext = 'down'
ext2 = ext2b
close("all")
#if not exists('updownresponsemean20ms_noisy'+ext+'_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+ext2+'_0_0_0_-1_'+str(rateCoeffs[iup][icoeff])+'.sav'):
# print 'updownresponsemean20ms_noisy'+ext+'_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+ext2+'_0_0_0_-1_'+str(rateCoeffs[iup][icoeff])+'.sav does not exist'
# continue
#unpicklefile = open('updownresponsemean20ms_noisy'+ext+'_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+ext2+'_0_0_0_-1_'+str(rateCoeffs[iup][icoeff])+'.sav','r')
#print 'updownresponsemean20ms_noisy'+ext+'_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+ext2+'_0_0_0_-1_'+str(rateCoeffs[iup][icoeff])+'.sav loaded'
#unpickledlist = pickle.load(unpicklefile)
#unpicklefile.close()
#spikes = unpickledlist[0]
#Vdend3b = unpickledlist[1]
#Vdend3_control.append([mean(Vdend3b[idt]) for idt in range(0,len(epspdts))])
for iter in [0,2,6,8]:
Vdendmaxs_thisIter = []
Vdendlens_thisIter = []
if not exists('updownresponsemean20ms_noisy'+ext+'_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+ext2+'_'+str(igene)+'_'+str(imut)+'_'+str(iallmutval)+'_'+str(iter)+'_'+str(rateCoeffs[iup][icoeff])+'.sav'):
print 'updownresponsemean20ms_noisy'+ext+'_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+ext2+'_'+str(igene)+'_'+str(imut)+'_'+str(iallmutval)+'_'+str(iter)+'_'+str(rateCoeffs[iup][icoeff])+'.sav does not exist'
continue
unpicklefile = open('updownresponsemean20ms_noisy'+ext+'_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+ext2+'_'+str(igene)+'_'+str(imut)+'_'+str(iallmutval)+'_'+str(iter)+'_'+str(rateCoeffs[iup][icoeff])+'.sav','r')
print 'updownresponsemean20ms_noisy'+ext+'_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+ext2+'_'+str(igene)+'_'+str(imut)+'_'+str(iallmutval)+'_'+str(iter)+'_'+str(rateCoeffs[iup][icoeff])+'.sav loaded'
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
spikes = unpickledlist[0]
Vdend3b = unpickledlist[1]
Cadend3b = unpickledlist[2]
SKdend3b = unpickledlist[3]
Vdend3b2 = unpickledlist[4]
Cadend3b2 = unpickledlist[5]
SKdend3b2 = unpickledlist[6]
Vdend3.append([mean(Vdend3b[idt]) for idt in range(0,len(epspdts))])
Cadend3.append([mean(Cadend3b[idt]) for idt in range(0,len(epspdts))])
SKdend3.append([mean(SKdend3b[idt]) for idt in range(0,len(epspdts))])
Vdend3_2.append([mean(Vdend3b2[idt]) for idt in range(0,len(epspdts))])
Cadend3_2.append([mean(Cadend3b2[idt]) for idt in range(0,len(epspdts))])
SKdend3_2.append([mean(SKdend3b2[idt]) for idt in range(0,len(epspdts))])
Vdend3_all.append(Vdend3[:])
Cadend3_all.append(Cadend3[:])
SKdend3_all.append(SKdend3[:])
Vdend3_2_all.append(Vdend3_2[:])
Cadend3_2_all.append(Cadend3_2[:])
SKdend3_2_all.append(SKdend3_2[:])
close("all")
f,axarr = subplots(1,1)
f2,axs = subplots(1,2)
iters = [0,2,6,8]
for iiter in range(0,len(iters)):
iter = iters[iiter]
axarr.plot(epspdts, Vdend3_all[0][iiter],'b-',color=cols[iter])
axarr.plot(epspdts, Vdend3_all[1][iiter],'b--',color=cols[iter])
#axarr.plot(epspdts, Vdend3_control[0],'b-',color=col_control)
#axarr.plot(epspdts, Vdend3_control[1],'b--',color=col_control)
axarr.plot(epspdts, [mean(Vdend3b_control[0][idt]) for idt in range(0,len(epspdts))],'b-',color=col_control)
axarr.plot(epspdts, [mean(Vdend3b_control[1][idt]) for idt in range(0,len(epspdts))],'b--',color=col_control)
axarr.set_ylim([-70,-25])
f.savefig('updownresponsemean20ms_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+ext2+'_'+str(igene)+'_'+str(imut)+'_'+str(iallmutval)+'.eps')
close("all")
f,axarr = subplots(1,1)
axarr.set_position([0.12,0.06,0.5,0.88])
f.text(0.04, 0.9, 'D', fontsize=27)
iters = [0,2,6,8]
for iiter in range(0,len(iters)):
iter = iters[iiter]
axarr.plot(epspdts, Vdend3_2_all[0][iiter],'b-',color=cols[iter])
axarr.plot(epspdts, Vdend3_2_all[1][iiter],'b--',color=cols[iter])
axarr.plot(epspdts, [mean(Vdend3b2_control[0][idt]) for idt in range(0,len(epspdts))],'b-',color=col_control)
axarr.plot(epspdts, [mean(Vdend3b2_control[1][idt]) for idt in range(0,len(epspdts))],'b--',color=col_control)
axarr.set_ylim([-70,-25])
for tick in axarr.xaxis.get_major_ticks() + axarr.yaxis.get_major_ticks():
tick.label.set_fontsize(fs)
axarr.set_xlabel('ISI (ms)',fontsize=10)
axarr.set_ylabel('max $V_m$ (dend) (mV)',fontsize=10)
f.savefig('updownresponsemean20ms_2_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+ext2+'_'+str(igene)+'_'+str(imut)+'_'+str(iallmutval)+'.eps')
Nsamp = 126
#Nsamp = 3
dy = 0
axs[0].set_position([0.12,0.06,0.37,0.72])
axs[1].set_position([0.55,0.06,0.37,0.72])
axnew = f2.add_axes([0.17,0.85,0.75,0.09])
for iup in range(0,2):
if iup==0:
ext = 'up2'
ext2 = ext2a
syn1coeff = 0.25
rateCoeff = 1.1
arrow1yplus = [0,-1,0,0,0,0,5,0,0] #red
arrow2yplus = [0,3,5,5,0,0,0,0,0] #black
else:
ext = 'down'
ext2 = ext2b
syn1coeff = 0.5
rateCoeff = 0.7
dy = -8
arrow1yplus = [-3,-3,-3,-3,-3,0,7,0,-3]
arrow2yplus = [0,3,12,12,0,0,0,0,0]
for iiter in range(0,len(iters)+1):
if iiter < len(iters):
iter = iters[iiter]
addition = str(igene)+'_'+str(imut)+'_'+str(iallmutval)+'_'+str(iter)+'_'
mycol = cols[iter]
else:
iter = -1
addition = '0_0_0_-1_'
mycol = col_control
Vdend3tc_all = []
for rdSeed in range(1,Nsamp):
if not exists('updownresponsetimecourse_noisy'+ext+'_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+'_syn1coeff'+str(syn1coeff)+'_'+addition+str(rateCoeff)+'_seed'+str(rdSeed)+'.sav'):
print 'updownresponsetimecourse_noisy'+ext+'_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+'_syn1coeff'+str(syn1coeff)+'_'+addition+str(rateCoeff)+'_seed'+str(rdSeed)+'.sav does not exists'
continue
unpicklefile = open('updownresponsetimecourse_noisy'+ext+'_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+'_syn1coeff'+str(syn1coeff)+'_'+addition+str(rateCoeff)+'_seed'+str(rdSeed)+'.sav','r')
print 'updownresponsetimecourse_noisy'+ext+'_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+'_syn1coeff'+str(syn1coeff)+'_'+addition+str(rateCoeff)+'_seed'+str(rdSeed)+'.sav loaded'
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
epspdts_savetimecourses = unpickledlist[2]
Vdend3tc = []
for idt in range(0,len(epspdts_savetimecourses)):
Vdend3 = unpickledlist[1][idt][6]
Cadend3 = unpickledlist[1][idt][7]
SKdend3 = unpickledlist[1][idt][8]
Vdend3tc.append(Vdend3[:])
if idt == 7 and iup == 1 and iter == -1:
axnew.plot(times,Vdend3,styles[iup], color='#AAAAFF',linewidth=0.5)
times = unpickledlist[1][idt][0]
Vdend3tc_all.append(Vdend3tc[:])
if len(Vdend3tc_all) > 0:
for idt in range(0,len(epspdts_savetimecourses)):
axs[iup].plot(times, [mean([Vdend3tc_all[isamp][idt][it] for isamp in range(0,len(Vdend3tc_all))])+idt*50 for it in range(0,len(Vdend3tc_all[0][idt]))],styles[iup], color=mycol,linewidth=0.5)
if idt == 7 and iup == 1 and iter == -1:
axnew.plot(times, [mean([Vdend3tc_all[isamp][idt][it] for isamp in range(0,len(Vdend3tc_all))]) for it in range(0,len(Vdend3tc_all[0][idt]))],styles[iup], color=mycol,linewidth=1)
axnew.text(2900,-50,"ISI="+str(epspdts_savetimecourses[idt])+" ms", fontsize=8)
#axs[iup].set_ylabel(str(epspdts[iy])+" ms", fontsize=8)
for iy in range(0,len(epspdts_savetimecourses)):
axs[iup].text(2900,iy*50-50,"ISI="+str(epspdts_savetimecourses[iy])+" ms", fontsize=8)
if iup == 1 and iy == 6:
mytools.drawarrow(axs[iup],[3000+epspdts_savetimecourses[iy]]*2, [-25+iy*50+dy+arrow1yplus[iy], -42+iy*50+dy+arrow1yplus[iy]],prc=0.8,lc='#FF0000')
else:
mytools.drawarrow(axs[iup],[3000+epspdts_savetimecourses[iy]]*2, [-75+iy*50+dy+arrow1yplus[iy], -58+iy*50+dy+arrow1yplus[iy]],prc=0.8,lc='#FF0000')
mytools.drawarrow(axs[iup],[3000]*2, [-35+iy*50+dy+arrow2yplus[iy], -52+iy*50+dy+arrow2yplus[iy]],prc=0.8,lc='#000000')
axs[iup].set_xlim([2880,3220])
for tick in axs[iup].xaxis.get_major_ticks() + axs[iup].yaxis.get_major_ticks():
tick.label.set_fontsize(fs)
axs[iup].set_ylim([-82,-66+len(epspdts_savetimecourses)*50])
axs[iup].set_xticks([])
axs[iup].set_yticks([])
axs[iup].plot([3150,3150],[355,380],'k-',linewidth=2)
axs[iup].plot([3150,3200],[355,355],'k-',linewidth=2)
axs[iup].text(3102,367,'25 mV',fontsize=8)
axs[iup].text(3160,360,'50 ms',fontsize=8)
axnew.set_xlim([2880,3220])
for tick in axnew.xaxis.get_major_ticks() + axnew.yaxis.get_major_ticks():
tick.label.set_fontsize(fs)
axnew.set_ylim([-75,-29])
axnew.set_yticks([-80,-60,-40])
axnew.set_ylabel('$V_m$ (dend)\n(mV)',fontsize=10)
axnew.set_xticks([2900,2950,3000,3050,3100,3150,3200])
axnew.set_xticklabels(['-100','-50','0','+50','+100','+150','+200'])
axnew.set_xlabel('$t$ (ms)',fontsize=10)
mytools.drawarrow(axnew,[3000+epspdts_savetimecourses[7]]*2, [-75+dy+arrow1yplus[7], -58+dy+arrow1yplus[7]],prc=0.8,lc='#FF0000')
mytools.drawarrow(axnew,[3000]*2, [-35+dy+arrow2yplus[7], -52+dy+arrow2yplus[7]],prc=0.8,lc='#000000')
for i in range(0,2):
f2.text(0.04, 0.9, 'A', fontsize=27)
f2.text(0.07, 0.74, 'B', fontsize=27)
f2.text(0.51, 0.74, 'C', fontsize=27)
f2.savefig('updownresponsetimecourses_noisys_cs'+str(icell)+'_dist'+str(proximalpoints[idist])+'_'+str(distalpoints[idist])+'_'+str(igene)+'_'+str(imut)+'_'+str(iallmutval)+'.eps')