import matplotlib
matplotlib.use('Agg')
import numpy
from pylab import *
import mytools
import pickle
import sys
import scipy.io
Is = [0.2+0.05*x for x in range(0,25)]
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]]
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]
theseMutValsAll = unpickledlist[2]
ispDef = 0 # Consider a local maximum above -35mV a spike only if after last spike the membrane potential came below -45mV
#ispDef = 1 # Consider every local maximum above -35mV a spike
variants = [[0,0,0],[2,3,0],[1,2,13],[3,0,1],[5,0,0]]
for icell in range(0,1):
theseCoeffsAll = theseCoeffsAllAll[icell]
unpicklefile = open('ifcurves_cs'+str(icell)+'_0_0_0.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
spTimesThisMutVal = unpickledlist[1+ispDef]
nSpikes_control = [sum([1 for x in spTimesThisMutVal[5][j] if x >= 500]) for j in range(0,len(Is))]
unpicklefile = open('steadystate_cs'+str(icell)+'_0_0_0.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
times_control = unpickledlist[1][5]
Vsoma_control = unpickledlist[2][5]
unpicklefile = open('DCshortthreshs_cs'+str(icell)+'_0_0_0.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
threshIs_control = unpickledlist[0][5]
styles = ['g-','g-','g-','g-','g-','g-','g-','g-','g-']
#cols = ['#00aaaa','#11cc44','#55ee00','#bbaa00','#ee6600','#ff0000', '#aa00aa','#772277','#333333']
cols = ['#666666','#012345','#aa00aa','#bbaa00','#ee6600','#ff0000', '#00aaaa','#772277','#00cc00']
col_control = '#2222ff'
counter = -1
close("all")
f, axarr = plt.subplots(2, len(variants))
#axarr.set_position([0.13, 0.1, 0.85, 0.67])
timesAll = []
VsomaAll = []
spikeFreqsAll = []
threshIsAll = []
for ivar in range(0,len(variants)):
igene = variants[ivar][0]
imut = variants[ivar][1]
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]]
iallmutval = variants[ivar][2]
unpicklefile = open('ifcurves_cs'+str(icell)+'_'+str(igene)+'_'+str(imut)+'_'+str(iallmutval)+'.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
ISIsThisMutVal = unpickledlist[0]
spTimesThisMutVal = unpickledlist[1+ispDef]
try:
unpicklefile = open('steadystate_cs'+str(icell)+'_'+str(igene)+'_'+str(imut)+'_'+str(iallmutval)+'.sav', 'r')
except:
print 'steadystate_cs'+str(icell)+'_'+str(igene)+'_'+str(imut)+'_'+str(iallmutval)+'.sav not found!'
unpicklefile = open('steadystate_cs'+str(icell)+'_0_0_0.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
timesThisVal = unpickledlist[1]
VsomaThisVal = unpickledlist[2]
unpicklefile = open('DCshortthreshs_cs'+str(icell)+'_'+str(igene)+'_'+str(imut)+'_'+str(iallmutval)+'.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
threshIsThisVal = unpickledlist[0]
mutText = ""
for imutvar in range(0,len(MT[igene][imut])):
if imutvar > 0 and imutvar%2==0:
mutText = mutText+"\n"
mutvars = allmutvars[iallmutval][imutvar]
mutvals = allmutvals[iallmutval][imutvar]
if type(mutvars) is str:
mutvars = [mutvars]
mutText = mutText + str(mutvars) + ": "
for kmutvar in range(0,len(mutvars)):
mutvar = mutvars[kmutvar]
if mutvar.find('offm') > -1 or mutvar.find('offh') > -1 or mutvar.find('ehcn') > -1:
if mutvals >= 0 and kmutvar==0:
mutText = mutText + "+" + str(mutvals) +" mV"
elif kmutvar==0:
mutText = mutText + str(mutvals) +" mV"
else:
if kmutvar==0:
mutText = mutText + "*" + str(mutvals)
if kmutvar < len(mutvars)-1:
mutText = mutText + ", "
mutText = mutText + " coeff = "+str(theseCoeffs[iallmutval])
iters = [0, 2, 5, 6, 8]
spikeFreqsThisVal = []
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))]
spikeFreqsThisVal.append([x/7.5 for x in nSpikes])
axarr[0,ivar].plot(Is, [x/7.5 for x in nSpikes], styles[iter],color=cols[iter])
axarr[1,ivar].plot(timesThisVal[iiter], VsomaThisVal[iiter], styles[iter],color=cols[iter])
spikeFreqs_control = [x/7.5 for x in nSpikes_control]
axarr[0,ivar].plot(Is, [x/7.5 for x in nSpikes_control], styles[iter],color=col_control)
axarr[1,ivar].plot(times_control, Vsoma_control, styles[iter],color=col_control)
#axarr.set_title('I-F curve')
axarr[0,ivar].set_ylim([0,25])
axarr[1,ivar].set_xlim([190,600])
#axarr.set_title('I-F curve')
axarr[0,ivar].set_ylim([0,25])
axarr[0,ivar].set_xlabel('I (nA)')
axarr[1,ivar].set_xlabel('t (ms)')
if ivar==0:
axarr[0,ivar].set_ylabel('F (Hz)')
axarr[0,ivar].set_ylabel('V_m (mV)')
timesAll.append([timesThisVal[i] for i in [0,1,3,4]])
VsomaAll.append([VsomaThisVal[i] for i in [0,1,3,4]])
spikeFreqsAll.append(spikeFreqsThisVal[:])
threshIsAll.append([threshIsThisVal[i] for i in [0,1,3,4]])
f.suptitle(mutText)
f.savefig("fig2_cs"+str(icell)+".eps")
scipy.io.savemat('fig2_curves.mat', {'Is': Is, 'spikeFreqs': spikeFreqsAll, 'times': timesAll, 'Vsoma': VsomaAll, 'threshIs': threshIsAll,
'spikeFreqs_control': spikeFreqs_control, 'times_control': times_control, 'Vsoma_control': Vsoma_control, 'threshI_control': threshIs_control,
'variants': variants})
picklelist = [Is, spikeFreqsAll, timesAll, VsomaAll, threshIsAll, spikeFreqs_control, times_control, Vsoma_control, threshIs_control, variants]
file = open('fig2_curves.sav', 'w')
pickle.dump(picklelist,file)
file.close()