// \$Id: analyze_active.hoc,v 1.1 2007/04/19 17:01:11 ted Exp ted \$ // based on analyze_spiketuft.hoc // analyses results // monx can't detect v at 0 or 1 end // so we must use Vector record() to determine peak at origin of tuft objref vorigin vorigin = new Vector() // this is used by procs plotresults() and plotvmaxrvp() apic[2] vorigin.record(&v(0)) objref areavec, normareavec, vpeakvec, resultvec totalarea = 0 proc preanalysis() { areavec = new Vector() forsec tuft for (x,0) { areavec.append(area(x)) } // areavec.printf() totalarea = areavec.sum() normareavec = new Vector() normareavec = areavec.c.div(totalarea) } // calculate totalarea and contents of areavec and normareavec just once, before doing any simulations preanalysis() // for diagnosis and development LOWPCTTHRESH = 10 /* Modified from implementation in processpeakdata.hoc \$o1 is vpvec \$2 is LOWPCTTHRESH In implementation from processpeakdata.hoc, \$o3 is temprvec which returns results */ objref si, normareacusum, svpvec, snormareavec wmedian = -1 // three nonsense values wpctlo = -1 wpcthi = -1 // proc percentiles() { local ii, wmedian, wpctlo, wpcthi proc percentiles() { local ii si = new Vector(normareavec.size()) \$o1.sortindex(si) // the elements of si sort \$o1 (which is vpvec) in numerical order normareacusum = new Vector(normareavec.size()) // set up snormareavec here--we'll use it later for the distribution graph snormareavec = new Vector() snormareavec.index(normareavec, si) normareacusum = snormareavec.c // snormareavec and normareacusum hold the normalized areas // in order of increasing peak v for ii=1,normareavec.size()-1 normareacusum.x[ii] += normareacusum.x[ii-1] // normareacusum.printf() // now normareacusum holds the cusum of the normalized areas svpvec = \$o1.c.sort() // we're going to use the sorted vpvec repeatedly // so might as well sort it once and for all // find the index of the first element of normareacusum // whose value is > 0.5 ii = normareacusum.indwhere(">", 0.5) wmedian = svpvec.x[ii-1] // we want the immediately preceding value ii = normareacusum.indwhere(">", \$2/100) wpctlo = svpvec.x[ii-1] ii = normareacusum.indwhere(">", (100-\$2)/100) wpcthi = svpvec.x[ii-1] /* \$o3.x[MEDIAN] = wmedian \$o3.x[PCTLO] = wpctlo \$o3.x[PCTHI] = wpcthi */ print "wmedian ", wmedian, " wpctlo ", wpctlo, " wpcthi ", wpcthi } ////////////////////////////////////// proc analyze() { local ii, num, wmean, wvar, wstdev localobj tmpvec vpeakvec = new Vector() forsec tuft for (x,0) { vpeakvec.append(vmax_monx(x)) } vpeakvec.sub(v_init) // so amplitude is relative to resting potential // weighted mean tmpvec = vpeakvec.c() tmpvec.mul(areavec) wmean = tmpvec.sum()/totalarea // weighted variance tmpvec = vpeakvec.c() tmpvec.sub(wmean) for ii=0,tmpvec.size()-1 tmpvec.x[ii]*=tmpvec.x[ii] tmpvec.mul(areavec) num = tmpvec.size() wvar = (tmpvec.sum()/totalarea)*(num/(num-1)) // weighted stdev wstdev = sqrt(wvar) printf("Mean %4.3f Min %4.3f Max %4.3f Var %5.4f StDev %5.4f\n", \ wmean, vpeakvec.min(), vpeakvec.max(), wvar, wstdev) resultvec = new Vector() resultvec.append(wmean) resultvec.append(vpeakvec.min()) resultvec.append(vpeakvec.max()) resultvec.append(wvar) resultvec.append(wstdev) percentiles(vpeakvec, LOWPCTTHRESH) }