def genRMS(comm,sd,mi,k_neighbors):
#get and distribute pws and strains
pws = None
if comm.rank == 0:
pws = sd.getPathways()
strain_list = mi.getStrains()
pws = comm.bcast(pws)
strain_list = comm.bcast(strain_list)
for cstrain in strain_list:
logging.info('Starting strain [%s]' % cstrain)
mypws = [pw for i,pw in enumerate(pws) if i%comm.size == comm.rank]
alleles = mi.getNominalAlleles(cstrain)
indexes = ["%s_%s" % (pw,allele) for pw,allele in itertools.product(mypws,alleles)]
samples = mi.getSampleIDs(cstrain)
#preallocate results dataframe
results = pandas.DataFrame(np.empty((len(indexes), len(samples)), dtype=float), index=indexes, columns=samples)
for pw in mypws:
#partition samples by strain/allele
samples = partitionSamplesByAllele( alleles, mi, cstrain)
#generate pw srts for all samples partitioned by strain/allele
srts = getSRTSByAllele(alleles,pw,samples)
for allele_base in alleles:
for allele_compare in alleles:
r_index = "%s_%s" % (pw,allele_compare)
#list of samples with comparison allele
compare_list = samples[allele_compare]
for age, samp in samples[allele_base]:
samp_compare = kNearest(compare_list,samp_name, samp_age, k_neighbors)
comp_exp = srts[allele_compare].loc[:,samp_compare]
rt = dirac.getRT(comp_exp)
results[samp][r_index] = dirac.getRMS(srts[allele_base][samp],rt)
comm.barrier()
return results
def getRMS(self, rt, srt):
return dirac.getRMS( srt, rt )
def getRMS(self, rt, srt):
return dirac.getRMS(srt, rt)