warned=False; if args.verbose>1: sys.stderr.write(" Iteration %i.\n"%(i)); viterbi = {} noChange=0; viterbiCat =np.zeros(allDataCat.shape[0]); curTot=0; curNumCNVs=0; for chr in chrOrder: #3. Calculate Viterbi path given data if args.verbose>2: sys.stderr.write(" i=%i; Calculating Viterbi path for %s.\n"%(i,chr)); framelogprob = model._compute_log_likelihood(allData[chr]) #sys.stderr.write("framelogprob dim: "+str(framelogprob.shape)+"\n"); framelogprob[:,cnvsToStateIs[args.ploidy]] = np.subtract(framelogprob[:,cnvsToStateIs[args.ploidy]], args.standardPrior); #add log(prior) if args.scalePDF>0: framelogprob = np.subtract(framelogprob,statePDFMaxima) #### This requires some explanation. See Note 1 below. logprob, viterbi[chr] = model._do_viterbi_pass(framelogprob); curLen = len(viterbi[chr]); #4. For each non-standard state, calculate the mean in that state and add a state with a mean representing that ploidy changeStart=-1 viterbi[chr] = np.insert(viterbi[chr],[0,curLen],[normalState,normalState]); # add initial and terminal normalStates so that telomeres in CNV will be detected. for j in range(1,len(viterbi[chr])): if viterbi[chr][j]!=viterbi[chr][j-1]:#there was a change if changeStart==-1: if viterbi[chr][j]==normalState: raise Exception("new state is normal ploidy state"); changeStart=j; else: #from changeStart to j-1 #calculate the means of this region localMean = np.mean(allData[chr][changeStart:j,:],axis=0); #figure out the local CN as the local means divided by the global means, rounded to the nearest logical ploidy meanRatio = np.divide(localMean,meanNormal);