rem stringlengths 0 322k | add stringlengths 0 2.05M | context stringlengths 8 228k |
|---|---|---|
summary = '''<h2><a name="pop_%d">Dataset %d</a></h2>\n''' \ % (popIdx, popIdx) summary += '''<h3>Log file (LD and other statistics): <a href="pop_%d/pop_%d.log">pop_%d.log</a></h3>\n''' \ % (popIdx, popIdx, popIdx) | summary = '''\ <h2><a name="pop_%d">Dataset %d</a></h2> <h3>Log file (LD and other statistics): <a href="pop_%d/pop_%d.log">pop_%d.log</a></h3> ''' % (popIdx, popIdx, popIdx, popIdx, popIdx) | def processOnePopulation(dataDir, numChrom, numLoci, markerType, DSLafter, DSLdist, initSize, meanInitAllele, burnin, introGen, minAlleleFreq, maxAlleleFreq, fitness, mlSelModel, numSubPop, finalSize, noMigrGen, mixingGen, popSizeFunc, migrModel, mu, mi, rec, dryrun, popIdx): ''' this function organize all previous fun... |
if hasRPy: (suc,ldres) = plotLD(pop, epsFile, jpgFile) if suc > 0 : summary += """<p>D' measures on two chromosomes with/without DSL at the last gen: <a href="pop_%d/LD_%d.eps">LD.eps</a></p>\n""" % (popIdx, popIdx) if suc > 1 : summary += '''<img src="pop_%d/LD_%d.jpg" width=800, height=600>''' % (popIdx, popIdx) res... | (suc,ldres) = plotLD(pop, epsFile, jpgFile) if suc > 0 : summary += """<p>D' measures on two chromosomes with/without DSL at the last gen: <a href="pop_%d/LD_%d.eps">LD.eps</a></p>\n""" % (popIdx, popIdx) if suc > 1 : summary += '''<img src="pop_%d/LD_%d.jpg" width=800, height=600>''' % (popIdx, popIdx) result.extend(... | def processOnePopulation(dataDir, numChrom, numLoci, markerType, DSLafter, DSLdist, initSize, meanInitAllele, burnin, introGen, minAlleleFreq, maxAlleleFreq, fitness, mlSelModel, numSubPop, finalSize, noMigrGen, mixingGen, popSizeFunc, migrModel, mu, mi, rec, dryrun, popIdx): ''' this function organize all previous fun... |
result.append(res) | def processOnePopulation(dataDir, numChrom, numLoci, markerType, DSLafter, DSLdist, initSize, meanInitAllele, burnin, introGen, minAlleleFreq, maxAlleleFreq, fitness, mlSelModel, numSubPop, finalSize, noMigrGen, mixingGen, popSizeFunc, migrModel, mu, mi, rec, dryrun, popIdx): ''' this function organize all previous fun... | |
if allParam[-3] != '': for i in range(len(allParam[3])): summary.write('<th>TDT%d</th>'%(i+1)) | if len(allParam[-8]) > 0: for p in allParam[-8]: summary.write('<th>%s:TDT</th>'%p) | def writeReport(content, allParam, results): ''' write a HTML file. The parts for each population has been written but we need a summary table. ''' print "Writing a report (saved in summary.htm )" try: summary = open(outputDir + "/summary.htm", 'w') except: raise exceptions.IOError("Can not open a summary file : " + ou... |
b = runScriptInteractively(filename = sys.argv[1]) | b = runScriptInteractively(locals=locals(), filename = sys.argv[1]) | def __getattr__(self, key): return getattr(self.file, key) |
SWIG = 'swig -O -templatereduce -shadow -python -c++ -keyword -nodefaultctor -w-503,-312,-511,-362,-383,-384,-389,-315,-525' | SWIG = 'swig -O -templatereduce -shadow -python -outdir src -c++ -keyword -nodefaultctor -w-503,-312,-511,-362,-383,-384,-389,-315,-525' | def swig_version(): ''' get the version of swig ''' fout = os.popen('swig -version') # try: version = re.match('SWIG Version\s*(\d+).(\d+).(\d+).*', fout.readlines()[1]).groups() except: print 'Can not obtain swig version, please install swig' sys.exit(1) return map(int, version) |
SWIG = 'swig -shadow -c++ -python -keyword -w-312,-401,-503,-511,-362,-383,-384,-389,-315,-525' | SWIG = 'swig -shadow -c++ -python -outdir src -keyword -w-312,-401,-503,-511,-362,-383,-384,-389,-315,-525' | def swig_version(): ''' get the version of swig ''' fout = os.popen('swig -version') # try: version = re.match('SWIG Version\s*(\d+).(\d+).(\d+).*', fout.readlines()[1]).groups() except: print 'Can not obtain swig version, please install swig' sys.exit(1) return map(int, version) |
'Tsting counting number of male' | 'Testing counting number of male' | def testNumOfMale(self): 'Tsting counting number of male' pop = population(subPop=[200, 800]) for i in range(100): pop.individual(i,0).setSex(Male) pop.individual(i,1).setSex(Male) for i in range(100,200): pop.individual(i,0).setSex(Female) for i in range(100,800): pop.individual(i,1).setSex(Female) Stat(pop, numOfMale... |
'Tsting counting number of affected individuals' | 'Testing counting number of affected individuals' | def testNumOfAffected(self): 'Tsting counting number of affected individuals' pop = population(subPop=[200, 800]) for i in range(100): pop.individual(i,0).setAffected(True) pop.individual(i,1).setAffected(True) for i in range(100,200): pop.individual(i,0).setAffected(False) for i in range(100,800): pop.individual(i,1).... |
penFun = customPene(penePara) | penFun = custom(penePara) | def drawSamples(pop, peneFunc, penePara, numSample, saveFormat, dataDir, reAnalyzeOnly ): ''' get samples of different type using a penetrance function, and save samples in dataDir in saveFormat pop: population peneFunc: penetrance function name, can be recessive1 etc penePara: parameter of the penetrance function num... |
print "\nList script", proc_jobs[0] script = getScript(proc_jobs[0], options) | print "\nList pbs_script", proc_jobs[0] pbs_script = getScript(proc_jobs[0], options) | def allJobs(): ''' list all jobs ''' names = [] for job in alljobs: names.append(job['name']) return names |
print >> pbs, script | print >> pbs, pbs_script | def allJobs(): ''' list all jobs ''' names = [] for job in alljobs: names.append(job['name']) return names |
print script if '$' in script: | print pbs_script if '$' in pbs_script: | def allJobs(): ''' list all jobs ''' names = [] for job in alljobs: names.append(job['name']) return names |
print 'Warning: symbol $ exists in the script, indicating unsubstituted variables' print for line in script.split(): | print 'Warning: symbol $ exists in the pbs_script, indicating unsubstituted variables' print for line in pbs_script.split(): | def allJobs(): ''' list all jobs ''' names = [] for job in alljobs: names.append(job['name']) return names |
script = getScript(job, options) | pbs_script = getScript(job, options) | def allJobs(): ''' list all jobs ''' names = [] for job in alljobs: names.append(job['name']) return names |
if '$' in script and not force: print script print print 'Warning: symbol $ exists in the script, indicating unsubstituted variables' print for line in script.split(): | if '$' in pbs_script and not force: print pbs_script print print 'Warning: symbol $ exists in the pbs_script, indicating unsubstituted variables' print for line in pbs_script.split(): | def allJobs(): ''' list all jobs ''' names = [] for job in alljobs: names.append(job['name']) return names |
print 'Please check your script, if there is no problem, please use option -f (--force)' | print 'Please check your pbs_script, if there is no problem, please use option -f (--force)' | def allJobs(): ''' list all jobs ''' names = [] for job in alljobs: names.append(job['name']) return names |
os.system('qsub %s.pbs' % job) | def allJobs(): ''' list all jobs ''' names = [] for job in alljobs: names.append(job['name']) return names | |
def TDT(DSL, dataDir, data, epsFile, jpgFile): | def TDT(DSL, cutoff, dataDir, data, epsFile, jpgFile): | def TDT(DSL, dataDir, data, epsFile, jpgFile): ''' use TDT method to analyze the results. Has to have rpy installed ''' if not hasRPy: return (0,[]) # write a batch file and call gh allPvalue = [] print "Applying TDT method to affected sibpairs " for ch in range(numChrom): inputfile = dataDir+data+ "_%d" % ch if not os... |
xlab="chromosome", ylab="-log10 p-value", type='l', axes=False) | xlab="chromosome", ylab="-log10 p-value", type='l', axes=False, ylim=[0.01, 5]) | def TDT(DSL, dataDir, data, epsFile, jpgFile): ''' use TDT method to analyze the results. Has to have rpy installed ''' if not hasRPy: return (0,[]) # write a batch file and call gh allPvalue = [] print "Applying TDT method to affected sibpairs " for ch in range(numChrom): inputfile = dataDir+data+ "_%d" % ch if not os... |
r.abline( h = -math.log10(0.05)) | r.abline( h = cutoff ) | def TDT(DSL, dataDir, data, epsFile, jpgFile): ''' use TDT method to analyze the results. Has to have rpy installed ''' if not hasRPy: return (0,[]) # write a batch file and call gh allPvalue = [] print "Applying TDT method to affected sibpairs " for ch in range(numChrom): inputfile = dataDir+data+ "_%d" % ch if not os... |
def Linkage(DSL, dataDir, data, epsFile, jpgFile): | def Linkage(DSL, cutoff, dataDir, data, epsFile, jpgFile): | def Linkage(DSL, dataDir, data, epsFile, jpgFile): ''' use Linkage method to analyze the results. Has to have rpy installed ''' if not hasRPy: return (0,[]) # write a batch file and call gh allPvalue = [] print "Applying Linkage (LOD) method to affected sibpairs " for ch in range(numChrom): inputfile = dataDir+data+ "_... |
r.plot(allPvalue, main="-log10(P-value) for each marker (TDT)", | r.plot(allPvalue, main="-log10(P-value) for each marker (LOD)", ylim=[0.01,5], | def Linkage(DSL, dataDir, data, epsFile, jpgFile): ''' use Linkage method to analyze the results. Has to have rpy installed ''' if not hasRPy: return (0,[]) # write a batch file and call gh allPvalue = [] print "Applying Linkage (LOD) method to affected sibpairs " for ch in range(numChrom): inputfile = dataDir+data+ "_... |
r.abline( h = -math.log10(0.05)) | r.abline( h = cutoff ) | def Linkage(DSL, dataDir, data, epsFile, jpgFile): ''' use Linkage method to analyze the results. Has to have rpy installed ''' if not hasRPy: return (0,[]) # write a batch file and call gh allPvalue = [] print "Applying Linkage (LOD) method to affected sibpairs " for ch in range(numChrom): inputfile = dataDir+data+ "_... |
(suc,res) = TDT(pop.dvars().DSL, penDir, "/Linkage/Aff", penDir + "/TDT.eps", penDir + "/TDT.jpg") | (suc,res) = TDT(pop.dvars().DSL, -math.log10(0.05/pop.totNumLoci()), penDir, "/Linkage/Aff", penDir + "/TDT.eps", penDir + "/TDT.jpg") | def processOnePopulation(dataDir, numChrom, numLoci, markerType, DSLafter, DSLdist, initSize, meanInitAllele, burnin, introGen, minAlleleFreq, maxAlleleFreq, fitness, mlSelModel, numSubPop, finalSize, noMigrGen, mixingGen, popSizeFunc, migrModel, mu, mi, rec, peneFunc, penePara, N, numSample, dryrun, popIdx): ''' this... |
(suc,res) = Linkage(pop.dvars().DSL, penDir, "/Linkage/Aff", penDir + "/LOD.eps", penDir + "/LOD.jpg") | (suc,res) = Linkage(pop.dvars().DSL, -math.log10(0.05/pop.totNumLoci()), penDir, "/Linkage/Aff", penDir + "/LOD.eps", penDir + "/LOD.jpg") | def processOnePopulation(dataDir, numChrom, numLoci, markerType, DSLafter, DSLdist, initSize, meanInitAllele, burnin, introGen, minAlleleFreq, maxAlleleFreq, fitness, mlSelModel, numSubPop, finalSize, noMigrGen, mixingGen, popSizeFunc, migrModel, mu, mi, rec, peneFunc, penePara, N, numSample, dryrun, popIdx): ''' this... |
DSL, allele frequency at DSL, -log10 p-values (TDT method and Linkage method) at all relevant DSL. </p> | DSL, allele frequency at DSL, -log10 p-values (TDT method and Linkage method, + for exceeds and - for less than cutoff value -log10(pvalue/total number of loci) ) at all relevant DSL. Other statistics include K (population prevalence), Ks (sibling recurrance risk), Ls (lambda_s, sibling recurrance ratio), P11 (P(NN | a... | def writeReport(content, allParam, results): ''' write a HTML file. The parts for each population has been written but we need a summary table. ''' print "Writing a report (saved in summary.htm )" try: summary = open(outputDir + "/summary.htm", 'w') except: raise exceptions.IOError("Can not open a summary file : " + ou... |
summary.write('<td>%.5g</td>' % res['Fst']) summary.write('<td>%.5g</td>' % res['AvgHet']) summary.write('<td>%.5g/%.5g</td>' % (res['DpDSL'], res['DpNon'])) summary.write('<td>%.5g/%.5g</td>' % (res['DDSL'], res['DNon'])) for i in range(len(allParam[3])): | summary.write('<td>%.2g</td>' % res['Fst']) summary.write('<td>%.2g</td>' % res['AvgHet']) summary.write('<td>%.2g</td>' % res['DpDSL']) summary.write('<td>%.2g</td>' % res['DDSL']) summary.write('<td>%.2g</td>' % res['DpNon']) summary.write('<td>%.2g</td>' % res['DNon']) for i in range(len(allParam[3])): | def writeReport(content, allParam, results): ''' write a HTML file. The parts for each population has been written but we need a summary table. ''' print "Writing a report (saved in summary.htm )" try: summary = open(outputDir + "/summary.htm", 'w') except: raise exceptions.IOError("Can not open a summary file : " + ou... |
for met in ['TDT', 'LOD']: for p in allParam[-9]: | for p in allParam[-9]: summary.write('<td>%.2g</td>' % res[p+'_K']) summary.write('<td>%.2g</td>' % res[p+'_Ks']) summary.write('<td>%.2g</td>' % res[p+'_Ls']) summary.write('<td>' + ','.join( ['%.2g'%x for x in res[p+'_P11'] ]) + '</td>') summary.write('<td>' + ','.join( ['%.2g'%x for x in res[p+'_P12'] ]) + '</td>') ... | def writeReport(content, allParam, results): ''' write a HTML file. The parts for each population has been written but we need a summary table. ''' print "Writing a report (saved in summary.htm )" try: summary = open(outputDir + "/summary.htm", 'w') except: raise exceptions.IOError("Can not open a summary file : " + ou... |
plusMinus = '<td>' for p in res[met+'_'+p+'_'+str(num)]: if p > -math.log10(0.01/400.): | plusMinus = '' for pvalue in res[met+'_'+p+'_'+str(num)]: if pvalue > -math.log10(0.05/(allParam[0]*allParam[1])): | def writeReport(content, allParam, results): ''' write a HTML file. The parts for each population has been written but we need a summary table. ''' print "Writing a report (saved in summary.htm )" try: summary = open(outputDir + "/summary.htm", 'w') except: raise exceptions.IOError("Can not open a summary file : " + ou... |
summary.write(plusMinus+'</td>') | summary.write('<td>'+plusMinus+'</td>') | def writeReport(content, allParam, results): ''' write a HTML file. The parts for each population has been written but we need a summary table. ''' print "Writing a report (saved in summary.htm )" try: summary = open(outputDir + "/summary.htm", 'w') except: raise exceptions.IOError("Can not open a summary file : " + ou... |
if len(traj) < max(2, minMutAge): | if True in [len(t) < max(2, minMutAge) for t in traj]: | def FreqTrajectoryMultiStochWithSubPop( curGen, numLoci, freq, NtFunc, fitness, minMutAge, maxMutAge, mode = 'uneven', ploidy=2, restartIfFail=True): ''' Simulate frequency trajectory with subpopulation structure, migration is currently ignored. The essential part of this script is to simulate the trajectory of each su... |
print 'Info:' | print "len: ", [len(t) for t in traj] | def FreqTrajectoryMultiStochWithSubPop( curGen, numLoci, freq, NtFunc, fitness, minMutAge, maxMutAge, mode = 'uneven', ploidy=2, restartIfFail=True): ''' Simulate frequency trajectory with subpopulation structure, migration is currently ignored. The essential part of this script is to simulate the trajectory of each su... |
if len(traj) == 0: | if len(traj) == 1: | def FreqTrajectoryMultiStochWithSubPop( curGen, numLoci, freq, NtFunc, fitness, minMutAge, maxMutAge, mode = 'uneven', ploidy=2, restartIfFail=True): ''' Simulate frequency trajectory with subpopulation structure, migration is currently ignored. The essential part of this script is to simulate the trajectory of each su... |
if maxMutAge == 0: maxMutAge = endGen | def FreqTrajectoryMultiStochWithSubPop( curGen, numLoci, freq, NtFunc, fitness, minMutAge, maxMutAge, mode = 'uneven', ploidy=2, restartIfFail=True): ''' Simulate frequency trajectory with subpopulation structure, migration is currently ignored. The essential part of this script is to simulate the trajectory of each su... | |
introFree, selFreeIntensity, initSize, endingSize, growthModel, | initSize, endingSize, growthModel, | def simuComplexDisease(numChrom, numLoci, markerType, DSLafter, DSLdistTmp, introFree, selFreeIntensity, initSize, endingSize, growthModel, burninGen, splitGen, mixingGen, endingGen, numSubPop, migrModel, migrRate, alleleDistInSubPop, curAlleleFreqTmp, minMutAge, maxMutAge, fitnessTmp, mlSelModelTmp, mutaRate, recRate,... |
introFree, selFreeIntensity, initSize, endingSize, growthModel, | initSize, endingSize, growthModel, | def saveAncestors(gen): if gen >= endingGen - savedGen: return savedGen else: return 1 |
options['command'] = 'qsub' | def readConfigFile(): ''' read variables from configuration file ''' config = os.path.join(os.environ['HOME'], '.simuCluster') tmp = {} res = {} if os.path.isfile(config): execfile(config, tmp, res) return res | |
assert (simu.dvars(0).haploFreq['0-1']['2-3'] - 0.05) < 0.01 assert (simu.dvars(0).haploFreq['0-1']['3-4'] - 0.05) < 0.01 assert (simu.dvars(0).haploFreq['0-1']['4-5'] - 0.25) < 0.01 assert (simu.dvars(0).haploFreq['0-1']['5-6'] - 0.05) < 0.01 | assert (simu.dvars(0).haploFreq['2-3']['1-2'] - 0.05) < 0.01 assert (simu.dvars(0).haploFreq['3-4']['1-2'] - 0.05) < 0.01 assert (simu.dvars(0).haploFreq['4-5']['1-2'] - 0.25) < 0.01 assert (simu.dvars(0).haploFreq['5-6']['1-2'] - 0.05) < 0.01 def testRecRates(self): ' see if we actually recombine at this rate ' pop =... | def testRecRate(self): ' see if we actually recombine at this rate ' pop = population(10000, loci=[2,3,2]) InitByValue(pop, value=[1]*7+[2]*7) simu = simulator(pop, randomMating()) simu.step( [ stat( haploFreq = [[0,1], [2,3], [3,4], [4,5], [5,6]]), recombinator(rate = 0.1) ] ) # the supposed proportions are 1-1: 0.5-r... |
def testRecombine(self): | def testRecProportion(self): | def testRecombine(self): ' verify table 4 of H&C 3nd edition P49 ' N = 10000 r = 0.1 genoDad = [[1,1],[1,1],[1,1],[1,1],[1,2],[1,2],[1,2],[2,1],[2,1],[2,2]] genoMom = [[1,1],[1,2],[2,1],[2,2],[1,2],[2,1],[2,2],[2,1],[2,2],[2,2]] prop = [ [1, 0, 0, 0], [.5, .5, 0, 0], [.5, 0, 0.5, 0], [0.5-r/2, r/2, r/2, 0.5-r/2], [0, ... |
pass | r = 0.1 N = 100 pop = population(size=N, loci=[2,5], sexChrom=True) InitByValue(pop, indRange=[0,N/2-1], sex=[Male]*(N/2), atPloidy=0, value=[1]*7) InitByValue(pop, indRange=[0,N/2-1], sex=[Male]*(N/2), atPloidy=1, value=[3]*7) InitByValue(pop, indRange=[N/2,N-1], sex=[Female]*(N/2), value=[1]*7+[2]*7) simu = simulato... | def testNoMaleRec(self): ' male chromosome is not supposed to recombine ' # create such an situation |
selection = maSelector( loci=range(numDSL), fitness=selCoef, wildtype=[1] ) | selection = maSelector( loci=range(numDSL), fitness=selCoef, wildtype=[0] ) | def simuCDCV( numDSL, initSpec, selModel, selModelAllDSL, selCoef, mutaModel, maxAllele, mutaRate, initSize, finalSize, burnin, noMigrGen, mixingGen, growth, numSubPop, migrModel, migrRate, update, dispPlot, saveAt, savePop, resume, resumeAtGen, name, dryrun): ''' parameters are self-expanary. See help info for detaile... |
sel.append( maSelector(locus=d, fitness=[1,1,1-selCoef[d]], wildtype=[1])) | sel.append( maSelector(locus=d, fitness=[1,1,1-selCoef[d]], wildtype=[0])) | def simuCDCV( numDSL, initSpec, selModel, selModelAllDSL, selCoef, mutaModel, maxAllele, mutaRate, initSize, finalSize, burnin, noMigrGen, mixingGen, growth, numSubPop, migrModel, migrRate, update, dispPlot, saveAt, savePop, resume, resumeAtGen, name, dryrun): ''' parameters are self-expanary. See help info for detaile... |
sel.append( maSelector(locus=d, fitness=[1,1-selCoef[d]/2.,1-selCoef[d]], wildtype=[1])) | sel.append( maSelector(locus=d, fitness=[1,1-selCoef[d]/2.,1-selCoef[d]], wildtype=[0])) | def simuCDCV( numDSL, initSpec, selModel, selModelAllDSL, selCoef, mutaModel, maxAllele, mutaRate, initSize, finalSize, burnin, noMigrGen, mixingGen, growth, numSubPop, migrModel, migrRate, update, dispPlot, saveAt, savePop, resume, resumeAtGen, name, dryrun): ''' parameters are self-expanary. See help info for detaile... |
pyEval(r"'%d\t%d\n' % (gen, popSize)", step=50), | pyEval(r"'%d\t%d\t%f\n' % (gen, popSize, alleleFreq[0][0])", step=50), | def simuCDCV( numDSL, initSpec, selModel, selModelAllDSL, selCoef, mutaModel, maxAllele, mutaRate, initSize, finalSize, burnin, noMigrGen, mixingGen, growth, numSubPop, migrModel, migrRate, update, dispPlot, saveAt, savePop, resume, resumeAtGen, name, dryrun): ''' parameters are self-expanary. See help info for detaile... |
if j not in proc_jobs and n in all_jobs: | if j not in proc_jobs and j in all_jobs: | def allJobs(): ''' list all jobs ''' names = [] for job in alljobs: names.append(job['name']) return names |
def testBinormialSelection(self): | def testBinomialSelection(self): | def testBinormialSelection(self): 'Testing binomialSelection mating scheme (FIXME: imcomplete)' simu = simulator(population(10, loci=[1], ploidy=1), binomialSelection()) |
return with_mode(NO_CONVERSION, r.do_call)('rbind', mat) | return r.do_call('rbind', mat) | def rmatrix(mat): ''' VERY IMPORTANT convert a Python 2d list to r matrix format that can be passed to functions like image directly. ''' return with_mode(NO_CONVERSION, r.do_call)('rbind', mat) |
self.mfrow = mfrow | self.mfrow = [int(x) for x in mfrow] | def __init__(self, nplot, update, title, xlab, ylab, axes, lty, col, mfrow, plotType, saveAs, leaveOpen, dev='', width=0, height=0): """ initialization function of base properties (layout etc) of all plotters """ # save parameters self.nplot = nplot self.axes = axes if lty==[]: self.lty = range(1, nplot+1) else: self.l... |
self.mfrow[0] = int(math.ceil(math.sqrt( self.nplot ))) self.mfrow[1] = int(math.ceil(self.nplot/float(self.mfrow[0]) )) | self.mfrow[0] = int(ceil(sqrt( self.nplot))) self.mfrow[1] = int(ceil(self.nplot/float(self.mfrow[0]))) | def layout(self): # calculate layout if self.mfrow != [1,1] and self.mfrow[0]*self.mfrow[1] < self.nplot: raise ValueError("mfrow is not enough to hold " + str(self.nplot) + " figures") |
w = 7 * r.par("csi") * 2.54 | w = 7 * with_mode(BASIC_CONVERSION, r.par)("csi") * 2.54 | def layout(self): # calculate layout if self.mfrow != [1,1] and self.mfrow[0]*self.mfrow[1] < self.nplot: raise ValueError("mfrow is not enough to hold " + str(self.nplot) + " figures") |
levels = r.seq(lim[0], lim[1], length=level) | levels = with_mode(BASIC_CONVERSION, r.seq)(lim[0], lim[1], length=level) | def colorBar(self, level, lim): " draw a color bar to the right of the plot" mar = [0]*4 mar[0] = [5, 1, 4, 2] self.color = r.rainbow(level, start=.7, end=.1) r.par(mar=mar) levels = r.seq(lim[0], lim[1], length=level) r.plot_new() r.plot_window(xlim = [0, 1], ylim = lim, xaxs = "i", yaxs = "i") r.rect(0, levels[:(len... |
xlim=self.xlim, z= r.t(r.matrix( self.data[rep].flatData(),axes=self.axes, byrow=True, ncol=len(self.data[rep].data[0]))),xlab=self.xlab, | xlim=self.xlim, z= r.t(r.matrix(self.data[rep].flatData(), byrow=True, ncol=len(self.data[rep].data[0]))), xlab=self.xlab, axes=self.axes, | def plot(self, pop, expr): gen = pop.gen() rep = pop.rep() data = pop.evaluate(expr) _data = data # now start! if type(_data) == type(0) or type(_data) == type(0.): _data = [_data] |
def getScript(name): | def getScript(name, options): | def getScript(name): ''' return the script for simulation 'name' ''' if not globals().has_key('script'): print 'Vairable script is not defined' sys.exit(0) # for job in alljobs: # a dictionary if job['name'] == name: s = script print alljobs, job for k,v in job.items(): # subsitute in script if '"' in 'v': s = re.sub(r... |
print alljobs, job for k,v in job.items(): | for k,v in (job.items() + options.items()): | def getScript(name): ''' return the script for simulation 'name' ''' if not globals().has_key('script'): print 'Vairable script is not defined' sys.exit(0) # for job in alljobs: # a dictionary if job['name'] == name: s = script print alljobs, job for k,v in job.items(): # subsitute in script if '"' in 'v': s = re.sub(r... |
if '"' in 'v': s = re.sub(r'\$%s\s' % k, "'%s' " % v, s) s = re.sub(r'\$\{%s\}' % k, "'%s' " % v, s) | if True in [x in str(v) for x in [' ', '*', ',', '[', ']']]: if '"' in str(v): quote = '"' else: quote = "'" s = re.sub(r'\$%s(\W)' % k, r"%s%s%s\1" % (quote, str(v), quote), s) s = re.sub(r'\$\{%s\}' % k, "%s%s%s" % (quote, str(v), quote), s) | def getScript(name): ''' return the script for simulation 'name' ''' if not globals().has_key('script'): print 'Vairable script is not defined' sys.exit(0) # for job in alljobs: # a dictionary if job['name'] == name: s = script print alljobs, job for k,v in job.items(): # subsitute in script if '"' in 'v': s = re.sub(r... |
s = re.sub(r'\$%s\s' % k, '"%s" ' % v, s) s = re.sub(r'\$\{%s\}' % k, '"%s" ' % v, s) for k, v in options.items(): if '"' in 'v': s = re.sub(r'\$%s\s' % k, "'%s' " % v, s) s = re.sub(r'\$\{%s\}' % k, "'%s' " % v, s) else: s = re.sub(r'\$%s\s' % k, '"%s" ' % v, s) s = re.sub(r'\$\{%s\}' % k, '"%s" ' % v, s) | s = re.sub(r'\$%s(\W)' % k, r"%s\1" % str(v), s) s = re.sub(r'\$\{%s\}' % k, str(v), s) | def getScript(name): ''' return the script for simulation 'name' ''' if not globals().has_key('script'): print 'Vairable script is not defined' sys.exit(0) # for job in alljobs: # a dictionary if job['name'] == name: s = script print alljobs, job for k,v in job.items(): # subsitute in script if '"' in 'v': s = re.sub(r... |
options = {} | options = {'time':96} | def allJobs(): ''' list all jobs ''' names = [] for job in alljobs: names.append(job['name']) return names |
print alljobs | def allJobs(): ''' list all jobs ''' names = [] for job in alljobs: names.append(job['name']) return names | |
print getScript(proc_jobs[0]) | print getScript(proc_jobs[0], options) | def allJobs(): ''' list all jobs ''' names = [] for job in alljobs: names.append(job['name']) return names |
print >> pbs, getScript(job) | print >> pbs, getScript(job, options) | def allJobs(): ''' list all jobs ''' names = [] for job in alljobs: names.append(job['name']) return names |
os.system('qsub ' + job + '.pbs') | print "Submitting job via 'qsub %s.pbs'" % job os.system('qsub %s.pbs' % job) | def allJobs(): ''' list all jobs ''' names = [] for job in alljobs: names.append(job['name']) return names |
'simuUtil', 'simuSciPy', 'simuMatPlt', 'simuRPy', 'simuViewPop' ], | 'simuPOP_ba', 'simuPOP_baop', 'simuUtil', 'simuSciPy', 'simuMatPlt', 'simuRPy', 'simuViewPop' ], | def buildStaticLibrary(sourceFiles, libName, libDir, compiler): '''Build libraries to be linked to simuPOP modules''' # get a c compiler print 'Creating library', libName comp = new_compiler(compiler=compiler, verbose=True) objFiles = comp.compile(sourceFiles, include_dirs=['.']) comp.create_static_lib(objFiles, libNam... |
self.assertUnqqual([x.allele(0,0) for x in pop.individuals(0)], [int(x) for x in pop.indInfo('a', 0, False)]) | def testRearrange(self): 'Test if info and genotype are migrated with individuals' if alleleType() == 'binary': return #TurnOnDebug(DBG_POPULATION) pop = population(subPop=[4, 6], loci=[1], infoFields=['a','b']) pop.arrGenotype(True)[:] = range(20) pop.arrIndInfo(True)[:] = range(20) self.assertEqual([x.allele(0,0) for... | |
self.assertEqual(simu.population(i), simu.population(i)) | self.assertEqual(simu.population(i), simu1.population(i)) | def testClone(self): 'Testing cloning of simulator' pop = population(subPop=[10,4], loci=[2,5]) simu = simulator(pop, randomMating(), rep = 3) simu.evolve( preOps = [initByFreq([0.3, .7])], ops = [stat(alleleFreq=range(pop.totNumLoci()))], end = 10 ) simu1 = simu.clone() for i in range(3): self.assertEqual(simu.populat... |
define_macros = [ ('SIMUPOP_MODULE', '"simuPOP_std"')] + serial_macro, | define_macros = [ ('SIMUPOP_MODULE', 'simuPOP_std')] + serial_macro, | def addCarrayEntry(file): ' add a line at the wrap file for carray type definition' shutil.copy(file, file+'tmp') ofile = open(file, 'w') ifile = open(file+'tmp', 'r') for line in ifile.readlines(): if line.find('static PyMethodDef SwigMethods[] = ') != -1: ofile.write('''static PyMethodDef SwigMethods[] = { /* add car... |
define_macros = [ ('SIMUPOP_MODULE', '"simuPOP_op"'), ('OPTIMIZED', None)] + serial_macro, | define_macros = [ ('SIMUPOP_MODULE', 'simuPOP_op'), ('OPTIMIZED', None)] + serial_macro, | def addCarrayEntry(file): ' add a line at the wrap file for carray type definition' shutil.copy(file, file+'tmp') ofile = open(file, 'w') ifile = open(file+'tmp', 'r') for line in ifile.readlines(): if line.find('static PyMethodDef SwigMethods[] = ') != -1: ofile.write('''static PyMethodDef SwigMethods[] = { /* add car... |
define_macros = [ ('SIMUPOP_MODULE', '"simuPOP_la"'), ('LONGALLELE', None) ] + serial_macro, | define_macros = [ ('SIMUPOP_MODULE', 'simuPOP_la'), ('LONGALLELE', None) ] + serial_macro, | def addCarrayEntry(file): ' add a line at the wrap file for carray type definition' shutil.copy(file, file+'tmp') ofile = open(file, 'w') ifile = open(file+'tmp', 'r') for line in ifile.readlines(): if line.find('static PyMethodDef SwigMethods[] = ') != -1: ofile.write('''static PyMethodDef SwigMethods[] = { /* add car... |
define_macros = [ ('SIMUPOP_MODULE', '"simuPOP_laop"'), ('LONGALLELE', None), ('OPTIMIZED', None) ] + serial_macro, | define_macros = [ ('SIMUPOP_MODULE', 'simuPOP_laop'), ('LONGALLELE', None), ('OPTIMIZED', None) ] + serial_macro, | def addCarrayEntry(file): ' add a line at the wrap file for carray type definition' shutil.copy(file, file+'tmp') ofile = open(file, 'w') ifile = open(file+'tmp', 'r') for line in ifile.readlines(): if line.find('static PyMethodDef SwigMethods[] = ') != -1: ofile.write('''static PyMethodDef SwigMethods[] = { /* add car... |
for a in range(2,len(alleleNum[i])): | for a in range(1,len(alleleNum[i])): | def simuCDCV(numDSL, initSpec, selModel, selModelAllDSL, selCoef, mutaModel, maxAllele, mutaRate, initSize, finalSize, burnin, noMigrGen, mixingGen, growth, numSubPop, migrModel, migrRate, update, dispPlot, saveAt, savePop, resume, resumeAtGen, name, dryrun): ''' parameters are self-expanary. See help info for detailed... |
ancNum += num[al-2] | ancNum += num[al-1] | def getStats(v, highest): # the following are statistics for each DSL perc = [] # percentage numAllele = [] # number of alleles effNumAllele = [] # effective number of alleles overallFreq = [] # size of disease percMostCommon = []... |
MODU_INFO[modu]['libraries'] = ['libboost_serialization-mgw-mt-s-1_33_1', 'libboost_iostreams-mgw-mt-s-1_33_1', 'stdc++'] | MODU_INFO[modu]['libraries'] = ['libboost_serialization-%s' % TOOLSET, 'libboost_iostreams-%s' % TOOLSET] | def swig_version(): ''' get the version of swig ''' fout = os.popen('swig -version') # try: version = re.match('SWIG Version\s*(\d+).(\d+).(\d+).*', fout.readlines()[1]).groups() except: print 'Can not obtain swig version, please install swig' sys.exit(1) return map(int, version) |
MODU_INFO[modu]['libraries'] = ['boost_serialization-%s' % TOOLSET, 'boost_iostreams-%s' % TOOLSET, 'stdc++'] | MODU_INFO[modu]['libraries'] = ['boost_serialization', 'boost_iostreams', 'stdc++'] | def swig_version(): ''' get the version of swig ''' fout = os.popen('swig -version') # try: version = re.match('SWIG Version\s*(\d+).(\d+).(\d+).*', fout.readlines()[1]).groups() except: print 'Can not obtain swig version, please install swig' sys.exit(1) return map(int, version) |
self.assertEqual(simu.pop(0).indInfo('info1'), tuple([6.0]*10)) self.assertEqual(simu.pop(0).indInfo('info2'), tuple([12.]*10)) | self.assertEqual(simu.population(0).indInfo('info1'), tuple([6.0]*10)) self.assertEqual(simu.population(0).indInfo('info2'), tuple([12.]*10)) | def indFunc1(ind, param): 'do something to info2' ind.setInfo(ind.info('info2')+param[1], 'info2') return True |
MODU_INFO[modu]['src'].extend(GSL_FILES + SERIAL_FILES + IOSTREAMS_FILES) | def buildStaticLibrary(sourceFiles, libName, libDir, compiler): '''Build libraries to be linked to simuPOP modules''' # get a c compiler print 'Creating library', libName comp = new_compiler(compiler=compiler, verbose=True) objFiles = comp.compile(sourceFiles, include_dirs=['.']) comp.create_static_lib(objFiles, libNam... | |
if modu in src: | if '_'+modu in src: | def buildStaticLibrary(sourceFiles, libName, libDir, compiler): '''Build libraries to be linked to simuPOP modules''' # get a c compiler print 'Creating library', libName comp = new_compiler(compiler=compiler, verbose=True) objFiles = comp.compile(sourceFiles, include_dirs=['.']) comp.create_static_lib(objFiles, libNam... |
toCtrDist = [abs(pop.locusDist(x)-numLoci/2) for x in DSL] | toCtrDist = [abs(pop.locusPos(x)-numLoci/2) for x in DSL] | def outputStatistics(pop, args): ''' this function will be working with a pyOperator to output statistics. Many parameters will be passed, packed as a tuple. We need to output 1. LD (D') from a central marker to all others on a non-DSL chromosome, at burnin and before and after migration 2. LD (D') from a central DSL ... |
lociDist = [] | lociPos = [] | def simuComplexDisease( numChrom, numLoci, markerType, DSLafter, DSLdist, initSize, meanInitAllele, burnin, introGen, minAlleleFreq, maxAlleleFreq, fitness, mlSelModel, numSubPop, finalSize, noMigrGen, mixingGen, popSizeFunc, migrModel, mu, mi, rec, dryrun, logFile): ''' run a simulation of complex disease with given p... |
lociDist.append([]) | lociPos.append([]) | def simuComplexDisease( numChrom, numLoci, markerType, DSLafter, DSLdist, initSize, meanInitAllele, burnin, introGen, minAlleleFreq, maxAlleleFreq, fitness, mlSelModel, numSubPop, finalSize, noMigrGen, mixingGen, popSizeFunc, migrModel, mu, mi, rec, dryrun, logFile): ''' run a simulation of complex disease with given p... |
lociDist[ch].append(loc+1) lociDist[ch].append(loc+1 + DSLdist[j]) | lociPos[ch].append(loc+1) lociPos[ch].append(loc+1 + DSLdist[j]) | def simuComplexDisease( numChrom, numLoci, markerType, DSLafter, DSLdist, initSize, meanInitAllele, burnin, introGen, minAlleleFreq, maxAlleleFreq, fitness, mlSelModel, numSubPop, finalSize, noMigrGen, mixingGen, popSizeFunc, migrModel, mu, mi, rec, dryrun, logFile): ''' run a simulation of complex disease with given p... |
lociDist[ch].append(loc+1) | lociPos[ch].append(loc+1) | def simuComplexDisease( numChrom, numLoci, markerType, DSLafter, DSLdist, initSize, meanInitAllele, burnin, introGen, minAlleleFreq, maxAlleleFreq, fitness, mlSelModel, numSubPop, finalSize, noMigrGen, mixingGen, popSizeFunc, migrModel, mu, mi, rec, dryrun, logFile): ''' run a simulation of complex disease with given p... |
loci = loci, maxAllele = maxAle, lociDist = lociDist) | loci = loci, maxAllele = maxAle, lociPos = lociPos) | def last_two(gen): if gen >= endGen -2: return 2 else: return 1 |
dist.append(pop.locusDist(ld[0])) | dist.append(pop.locusPos(ld[0])) | def plotLD(pop, epsFile, jpgFile): ''' plot LD values in R and convert to jpg if possible ''' # return max LD res = {} # dist: distance (location) of marker # ldprime: D' value dist = [] ldprime = [] # D' ldvalue = [] # D for ld in pop.dvars().ctrDSLLD: if ld[1] == pop.dvars().ctrChromDSL: dist.append(pop.locusDist(ld[... |
dist.append(pop.locusDist(ld[1])) | dist.append(pop.locusPos(ld[1])) | def plotLD(pop, epsFile, jpgFile): ''' plot LD values in R and convert to jpg if possible ''' # return max LD res = {} # dist: distance (location) of marker # ldprime: D' value dist = [] ldprime = [] # D' ldvalue = [] # D for ld in pop.dvars().ctrDSLLD: if ld[1] == pop.dvars().ctrChromDSL: dist.append(pop.locusDist(ld[... |
r.abline( v = pop.locusDist(pop.dvars().ctrChromDSL), lty=3 ) r.axis( 1, [pop.locusDist(pop.dvars().ctrChromDSL)], ['DSL']) | r.abline( v = pop.locusPos(pop.dvars().ctrChromDSL), lty=3 ) r.axis( 1, [pop.locusPos(pop.dvars().ctrChromDSL)], ['DSL']) | def plotLD(pop, epsFile, jpgFile): ''' plot LD values in R and convert to jpg if possible ''' # return max LD res = {} # dist: distance (location) of marker # ldprime: D' value dist = [] ldprime = [] # D' ldvalue = [] # D for ld in pop.dvars().ctrDSLLD: if ld[1] == pop.dvars().ctrChromDSL: dist.append(pop.locusDist(ld[... |
r.abline( v = pop.locusDist(pop.chromBegin(pop.dvars().noDSLChrom)+pop.dvars().numLoci/2), lty=3 ) | r.abline( v = pop.locusPos(pop.chromBegin(pop.dvars().noDSLChrom)+pop.dvars().numLoci/2), lty=3 ) | def plotLD(pop, epsFile, jpgFile): ''' plot LD values in R and convert to jpg if possible ''' # return max LD res = {} # dist: distance (location) of marker # ldprime: D' value dist = [] ldprime = [] # D' ldvalue = [] # D for ld in pop.dvars().ctrDSLLD: if ld[1] == pop.dvars().ctrChromDSL: dist.append(pop.locusDist(ld[... |
summary.write('<th>K</th><th>Ks</th><th>Ks/K</th>') summary.write('<th>P11</th><th>P12</th><th>P22</th>') summary.write("<th>F'</th>") | def writeReport(content, allParam, numChrom, numLoci, DSLafter, peneFunc, numSample, results): ''' write a HTML file. The parts for each population has been written but we need a summary table. ''' print "Writing a report (saved in summary.htm )" try: summary = open(outputDir + "/summary.htm", 'w') except: raise except... | |
noDSLChrom = [pop.numLoci(x)==numLoci for x in range(pop.numChrom())].index(True) | try: noDSLChrom = [pop.numLoci(x)==numLoci for x in range(pop.numChrom())].index(True) except: noDSLChrom = -1 | def outputStatistics(pop, args): ''' this function will be working with a pyOperator to output statistics. Many parameters will be passed, packed as a tuple. We need to output 1. LD (D') from a central marker to all others on a non-DSL chromosome, at burnin and before and after migration 2. LD (D') from a central DSL ... |
i = pop.chromBegin( noDSLChrom) noDSLLD = [ [i+x, i+numLoci/2] for x in range(numLoci/2)] + \ [ [i + numLoci/2, i+x] for x in range(numLoci/2+1, numLoci)] | if noDSLChrom > -1: i = pop.chromBegin( noDSLChrom) noDSLLD = [ [i+x, i+numLoci/2] for x in range(numLoci/2)] + \ [ [i + numLoci/2, i+x] for x in range(numLoci/2+1, numLoci)] else: noDSLLD = [] | def outputStatistics(pop, args): ''' this function will be working with a pyOperator to output statistics. Many parameters will be passed, packed as a tuple. We need to output 1. LD (D') from a central marker to all others on a non-DSL chromosome, at burnin and before and after migration 2. LD (D') from a central DSL ... |
print >> output, "\n\nD between a center marker %d (chrom %d) and surrounding markers at gen %d" \ % (pop.chromBegin(noDSLChrom)+numLoci/2, noDSLChrom, gen) for ld in noDSLLD: print >> output, '%.4f ' % pop.dvars().LD[ld[0]][ld[1]], | if noDSLChrom > -1 : print >> output, "\n\nD between a center marker %d (chrom %d) and surrounding markers at gen %d" \ % (pop.chromBegin(noDSLChrom)+numLoci/2, noDSLChrom, gen) for ld in noDSLLD: print >> output, '%.4f ' % pop.dvars().LD[ld[0]][ld[1]], | def outputStatistics(pop, args): ''' this function will be working with a pyOperator to output statistics. Many parameters will be passed, packed as a tuple. We need to output 1. LD (D') from a central marker to all others on a non-DSL chromosome, at burnin and before and after migration 2. LD (D') from a central DSL ... |
print >> output, "\n\nD' between a center marker %d (chrom %d) and surrounding markers at gen %d" \ % (pop.chromBegin(noDSLChrom)+numLoci/2, noDSLChrom, gen) for ld in noDSLLD: print >> output, '%.4f ' % pop.dvars().LD_prime[ld[0]][ld[1]], | if noDSLChrom > -1: print >> output, "\n\nD' between a center marker %d (chrom %d) and surrounding markers at gen %d" \ % (pop.chromBegin(noDSLChrom)+numLoci/2, noDSLChrom, gen) for ld in noDSLLD: print >> output, '%.4f ' % pop.dvars().LD_prime[ld[0]][ld[1]], | def outputStatistics(pop, args): ''' this function will be working with a pyOperator to output statistics. Many parameters will be passed, packed as a tuple. We need to output 1. LD (D') from a central marker to all others on a non-DSL chromosome, at burnin and before and after migration 2. LD (D') from a central DSL ... |
for ld in pop.dvars().noDSLLD: if ld[1] == pop.chromBegin(pop.dvars().noDSLChrom) + numLoci/2: dist.append(pop.locusDist(ld[0])) else: dist.append(pop.locusDist(ld[1])) ldprime.append(pop.dvars().LD_prime[ld[0]][ld[1]]) ldvalue.append(pop.dvars().LD[ld[0]][ld[1]]) res['DpNon'] = max(ldprime) res['DNon'] = max(ldvalue) ... | if pop.dvars().noDSLChrom > -1: for ld in pop.dvars().noDSLLD: if ld[1] == pop.chromBegin(pop.dvars().noDSLChrom) + numLoci/2: dist.append(pop.locusDist(ld[0])) else: dist.append(pop.locusDist(ld[1])) ldprime.append(pop.dvars().LD_prime[ld[0]][ld[1]]) ldvalue.append(pop.dvars().LD[ld[0]][ld[1]]) res['DpNon'] = max(ldpr... | def plotLD(pop, epsFile, jpgFile): ''' plot LD values in R and convert to jpg if possible ''' # return max LD res = {} # dist: distance (location) of marker # ldprime: D' value dist = [] ldprime = [] # D' ldvalue = [] # D for ld in pop.dvars().ctrDSLLD: if ld[1] == pop.dvars().ctrChromDSL: dist.append(pop.locusDist(ld[... |
def TDT(DSL, cutoff, dataDir, data, epsFile, jpgFile): | def TDT(geneHunter, DSL, cutoff, dataDir, data, epsFile, jpgFile): | def TDT(DSL, cutoff, dataDir, data, epsFile, jpgFile): ''' use TDT method to analyze the results. Has to have rpy installed ''' if not hasRPy: return (0,[]) # write a batch file and call gh allPvalue = [] print "Applying TDT method to affected sibpairs " for ch in range(numChrom): inputfile = dataDir+data+ "_%d" % ch i... |
if not hasRPy: | if not hasRPy or geneHunter in ['', 'none']: | def TDT(DSL, cutoff, dataDir, data, epsFile, jpgFile): ''' use TDT method to analyze the results. Has to have rpy installed ''' if not hasRPy: return (0,[]) # write a batch file and call gh allPvalue = [] print "Applying TDT method to affected sibpairs " for ch in range(numChrom): inputfile = dataDir+data+ "_%d" % ch i... |
def Linkage(DSL, cutoff, dataDir, data, epsFile, jpgFile): | def Linkage(geneHunter, DSL, cutoff, dataDir, data, epsFile, jpgFile): | def Linkage(DSL, cutoff, dataDir, data, epsFile, jpgFile): ''' use Linkage method to analyze the results. Has to have rpy installed ''' if not hasRPy: return (0,[]) # write a batch file and call gh allPvalue = [] print "Applying Linkage (LOD) method to affected sibpairs " for ch in range(numChrom): inputfile = dataDir+... |
if not hasRPy: | if not hasRPy or geneHunter in ['', 'none']: | def Linkage(DSL, cutoff, dataDir, data, epsFile, jpgFile): ''' use Linkage method to analyze the results. Has to have rpy installed ''' if not hasRPy: return (0,[]) # write a batch file and call gh allPvalue = [] print "Applying Linkage (LOD) method to affected sibpairs " for ch in range(numChrom): inputfile = dataDir+... |
mixingGen, popSizeFunc, migrModel, mu, mi, rec, peneFunc, penePara, N, numSample, dryrun, popIdx): | mixingGen, popSizeFunc, migrModel, mu, mi, rec, peneFunc, penePara, N, numSample, geneHunter, dryrun, popIdx): | def processOnePopulation(dataDir, numChrom, numLoci, markerType, DSLafter, DSLdist, initSize, meanInitAllele, burnin, introGen, minAlleleFreq, maxAlleleFreq, fitness, mlSelModel, numSubPop, finalSize, noMigrGen, mixingGen, popSizeFunc, migrModel, mu, mi, rec, peneFunc, penePara, N, numSample, dryrun, popIdx): ''' this... |
(suc,res) = TDT(pop.dvars().DSL, -math.log10(0.05/pop.totNumLoci()), | (suc,res) = TDT(geneHunter, pop.dvars().DSL, -math.log10(0.05/pop.totNumLoci()), | def processOnePopulation(dataDir, numChrom, numLoci, markerType, DSLafter, DSLdist, initSize, meanInitAllele, burnin, introGen, minAlleleFreq, maxAlleleFreq, fitness, mlSelModel, numSubPop, finalSize, noMigrGen, mixingGen, popSizeFunc, migrModel, mu, mi, rec, peneFunc, penePara, N, numSample, dryrun, popIdx): ''' this... |
(suc,res) = Linkage(pop.dvars().DSL, -math.log10(0.05/pop.totNumLoci()), | (suc,res) = Linkage(geneHunter, pop.dvars().DSL, -math.log10(0.05/pop.totNumLoci()), | def processOnePopulation(dataDir, numChrom, numLoci, markerType, DSLafter, DSLdist, initSize, meanInitAllele, burnin, introGen, minAlleleFreq, maxAlleleFreq, fitness, mlSelModel, numSubPop, finalSize, noMigrGen, mixingGen, popSizeFunc, migrModel, mu, mi, rec, peneFunc, penePara, N, numSample, dryrun, popIdx): ''' this... |
dryrun, popIdx) | geneHunter, dryrun, popIdx) | def writeReport(content, allParam, numChrom, numLoci, DSLafter, peneFunc, numSample, results): ''' write a HTML file. The parts for each population has been written but we need a summary table. ''' print "Writing a report (saved in summary.htm )" try: summary = open(outputDir + "/summary.htm", 'w') except: raise except... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.