reg=reg[['CHROM','start','end','name','score']] i=reg.loc[reg.score.idxmax()] print 'track name="Hypoxia Selected" description="{} regions" color=255,0,0'.format(i.CHROM) print 'browser position {}:{}-{}'.format(i.CHROM,i.start,i.end) print reg.to_csv(sep='\t',index=None,header=None) L.loc['3R'].loc[5663533] cd.loc['3R'].loc[5663533] o=scan[scan.C>scan.C.quantile(0.9999)] pplt.Manhattan(scan,Outliers=o);plt.suptitle('Outlieres of Normaxia(C) is shown in red.');plt.savefig(utl.home+'C.png',dpi=100) hutl.loadscores('L',180).loc[d.index[-10:]] hutl.loadss('L',180).loc[d.index[-10:]] o=scan[scan.H>scan.H.quantile(0.9995)] pplt.Manhattan(scan,Outliers=o);plt.suptitle('Outlieres of Hypoeroxia(H) is shown in red.');plt.savefig(utl.home+'H.png',dpi=100) dall=all+1 xp=utl.scanGenome(pd.concat([(dall.L/dall.C).rename('XPL'),(dall.H/dall.C).rename('XPH')],1).applymap(lambda x: np.log(1+x)),f=lambda x:x[x>x.quantile(0.5)].mean(),winSize=5000,step=1000) o=xp[xp.XPL>xp.XPL.quantile(0.999)];pplt.Manhattan(xp,Outliers=o);plt.suptitle('XP-CLEAR');plt.savefig(utl.home+'XPClear2.png',dpi=100) dominace=hutl.loadscores('L',180).idxmax(1).rename('h') x0=hutl.load()['L'][4].groupby(level=0,axis=1).apply(lambda x: x[x.name].C/x[x.name].D) x0=(hutl.load()['L'][4].xs('C',level='READ',axis=1).sum(1)/hutl.load()['L'][4].xs('D',level='READ',axis=1).sum(1)).rename('x0') x17=(hutl.load()['L'][17].xs('C',level='READ',axis=1).sum(1)/hutl.load()['L'][17].xs('D',level='READ',axis=1).sum(1)).rename('x17') xt=(hutl.load()['L'][180].xs('C',level='READ',axis=1).sum(1)/hutl.load()['L'][180].xs('D',level='READ',axis=1).sum(1)).rename('xt') cd=hutl.load()['L'] a=L[L.index.get_level_values('CHROM')=='3R'] reload(pplt) b=a[a>20].rename('score')#.iloc[:150]