def plotQuantile(df, kde):
import Util as utl
quantiles = np.sort(np.append(np.linspace(0.0, 1, 1000)[:-1], np.linspace(0.999, 1, 10)))
qq = pd.concat([utl.getQantilePvalues(df.COMALE, kde, quantiles=quantiles),
utl.getQantilePvalues(df.COMALENC, kde, quantiles=quantiles)], axis=1);
qq.columns = ['data', 'null'];
QQPval(qq, fname=utl.paperFiguresPath + 'qq.pdf')
def real():
G = pd.read_pickle(utl.outpath + 'real/real.replicates.uptoF59.maxLikelihoods.regularized.LowCovRemoved.df');
G = G.s * (G.alt - G.null)
R = pd.read_pickle(utl.outpath + 'real/real.replicates.uptoF59.df');
F=pd.read_pickle(utl.outpath+'real/negativeControl.Simulations.maxLikelihoods.regularized.df');F=F.s*(F.alt-F.null)
kde=utl.getDensity(F,width=100)
q = np.sort(np.append(np.linspace(0.0, 1, 100)[:-1], np.linspace(0.999, 1, 1000)))
qq=pd.concat([utl.getQantilePvalues(G,kde,quantiles=q),utl.getQantilePvalues(F,kde,quantiles=q)],axis=1);qq.columns=['data','null'];
pplt.QQPval(qq, fname=utl.paperFiguresPath + 'qq.pdf')
reload(pplt)
def Simulation():
a=pd.read_pickle('{}ROC/{}.df'.format(utl.outpath, 'COMALE'));a=a.s*(a.alt-a.null);
pos=a.loc[(0.1,'COMALE',0.1,1,0)];neg=a.loc[(0.005,'COMALE',0.0,-1,0)]
F=pd.read_pickle(utl.outpath+'real/negativeControl.Simulations.maxLikelihoods.regularized.df').loc[0];F=F.s*(F.alt-F.null)
q=np.linspace(0,1,1200)
kde=utl.getDensity(F,width=50)
qq=pd.concat([utl.getQantilePvalues(pos,kde,quantiles=q),utl.getQantilePvalues(neg,kde,quantiles=q)],axis=1);qq.columns=['data','null'];
pplt.QQPval(qq)
plt.savefig(utl.paperFiguresPath + 'qqsim.pdf')
