import popgen.Plots as pplt import popgen.Estimate as est # reload(dta) import popgen.hypoxia.Utils as hutl a=hutl.load()['L'] d=a.xs('D',level='READ',axis=1) reload(pplt) print d dd=d.groupby(level=[0,1],axis=1).apply(lambda xx: utl.scanGenome(xx,f=lambda x:x.max(),winSize=500000,step=500000).iloc[:,0]) pplt.Manhattan(dd) # plt.savefig(utl.home+'L.coverage.png', format='png', dpi=100) L17=hutl.loadscores('L',17).max(1).rename('L17') L=hutl.loadscores('L',180).max(1) C=hutl.loadscores('C',180).max(1) H=hutl.loadscores('H',180).max(1) all=pd.concat([L,C,H],1);all.columns=['L','C','H'] # H=L.apply(lambda x: x.idxmax(),1).rename('h') all.std(1) all.apply(lambda x: utl.scanGenome(x,f=lambda x:x.mean(),winSize=5000,step=1000)[0]) reload(hutl) L.sort_values() scan=utl.scanGenome(all,f=lambda x:x.mean(),winSize=5000,step=1000)