def likelihoodWithDifferentN(N=1000,s=0):
T=mkv.computePowerForSandSaveRealData((s,0.5),N=N,save=False)
CD,E=dta.precomputeCDandEmissionsFor(pd.DataFrame(cdi).T,N=N)
return computeLikelihoodReal((CD,E,T)).rename(N)
scan=pd.concat([utl.scanGenome(utl.zpvalgenome(s)).rename('win'),utl.scanGenomeSNP(utl.zpvalgenome(s)).rename('snp')],1)
pplt.Manhattan(scan)
pplt.GenomeChromosomewise(utl.zpvalgenome(utl.scanGenome(utl.zpvalgenome(s))))
pplt.GenomeChromosomewise(utl.zpvalgenome(utl.scanGenome(scores.abs())))
reload(utl)
pplt.GenomeChromosomewise(utl.scanGenomeSNP(utl.zpvalgenome2tail(s)))
scores.sort_values()
pplt.GenomeChromosomewise(utl.scanGenomeSNP(scores.abs(),lambda x: x[x>=x.quantile(0.5)].sum()))
df=pd.concat([scores,s],1);df=pd.concat([df,df.rank()],1,keys=['val','rank']).sort_values(('val','s'))
dfy=pd.concat([df,y],1).dropna()
dfy.sort_values(0)
i=df.index[-1];
cdi=cdAll.loc[i];print cdi.unstack('REP');pplt.plotSiteReal(cdi)
cdiun=cdi.unstack('REP')
CD,E=dta.precomputeCDandEmissionsFor(pd.DataFrame(cdi).T)
h=0.5
reload(mkv)
mkv.computeLikelihoodReal((CD, E, 0, 0.5))
likes=pd.concat(map(lambda x:mkv.computeLikelihoodReal((CD, E, x, 0.5)),S),keys=S).reset_index().iloc[:,[0,-1]].set_index('level_0')[0]
likes[0]
reload(pplt)
plt.figure(figsize=(6,3),dpi=150);plt.subplot(1,2,1);pd.DataFrame(likes).plot(ax=plt.gca());plt.subplot(1,2,2);pplt.plotSiteReal(cdi,ax=plt.gca());print cdi.unstack('REP')
res=res.reset_index().iloc[:,[0,3]];res=res.set_index(res.columns[0]).iloc[:,0]
NN=np.arange(100,1500,100)
def likelihoodWithDifferentN(N=1000,s=0):
T=mkv.computePowerForSandSaveRealData((s,0.5),N=N,save=False)
