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]
