import popgen.Run.TimeSeries.RealData.Utils as rutl import scipy as sc import popgen.Plots as pplt import popgen.Run.TimeSeries.RealData.Utils as rutl import popgen.Run.TimeSeries.RealData.Data as dta import popgen.TimeSeries.Markov as mkv S=np.arange(-1,1,0.05).round(2);chroms=['2L','2R','3L','3R','X'] pd.read_pickle('/home/arya/out/real/HMM1x/h5.000000E-01.df').loc[i] scores = utl.getEuChromatin(rutl.loadScores(skipHetChroms=True)).loc[chroms].rename('score') cdAll=utl.getEuChromatin(pd.read_pickle('/home/arya/out/real/CD.F59.df').loc[chroms]) freq=lambda x:x.xs('C',level='READ',axis=1).sum(1)/x.xs('D',level='READ',axis=1).sum(1) s=estimateS(cdAll.groupby(axis=1,level='GEN').apply(freq)[[0,37,59]]) x=pd.read_pickle('/home/arya/out/real/HMM1x/h5.000000E-01.df').loc[chroms,0.5] pplt.Manhattan(utl.zpvalgenome(utl.scanGenome(utl.zpvalgenome2tail(s)))) (x.s*(x.alt-x.null)).hist(bins=100) D=cdAll.xs('D',axis=1,level='READ') d=D.median(1).rename('d') f=lambda x:(x.alt-x.null) pplt.Manhattan(utl.scanGenome(x2p(f(x)))) x2p=lambda X2: -pd.Series(1 - sc.stats.chi2.cdf(X2, 1),index=X2.index).apply(np.log) y=(f(pd.read_pickle('/home/arya/out/real/HMM/h5.000000E-01.df')[0.5]).loc[chroms].rename('y')*pd.read_pickle('/home/arya/out/real/HMM/h5.000000E-01.df')[0.5].s).dropna() y.sort_values() y=utl.zpvalgenome(pd.read_pickle('/home/arya/out/real/HMM/h5.000000E-01.df')[0.5].s.loc[chroms]) i=utl.getEuChromatin(y.sort_values()).index[-20] pplt.GenomeChromosomewise(utl.scanGenome(utl.zpvalgenome(y.abs()))) pplt.GenomeChromosomewise(utl.scanGenome(utl.zpvalgenome(s))) scan=pd.concat([utl.scanGenome(utl.zpvalgenome(s)).rename('win'),utl.scanGenomeSNP(utl.zpvalgenome(s)).rename('snp')],1)