def plotSurf():
from scipy import interpolate
a=pd.read_pickle(utl.outpath+'real/real.maxLikelihoods.df')
idx=(a.s.abs()*a.h.abs()*(a.alt-a.null)).sort_values().index[-1]
R=pd.DataFrame(pd.read_pickle(utl.outpath+'real/real.replicates.df').loc[idx]).T
SH=dta.getSH()
ARGS=[(R,)+sh for sh in SH]
likelihoods=pd.concat(map(mkv.computeLikelihoodReal,ARGS),axis=1);likelihoods.columns.names=['s','h']
fig = plt.figure()
ax = fig.gca(projection='3d')
df=pd.concat([pd.Series(z[1].loc[z[0]].values,index=z[1].loc[z[0]].index,name=z[0]) for z in b.groupby(level=0)],axis=1)
Z=df.values
# Z[Z==Z.min()]=-1e3
X=np.tile(df.index.values[:,None],Z.shape[1])
Y=np.tile(df.columns.values[:,None],Z.shape[0]).T
Z.min()
Z.max()
nn = 401;
xi = np.linspace(-1.0, 2.0, 10);
yi = np.linspace(-0.5, 0.5, nn);
f = interpolate.interp2d(X,Y,Z,kind='cubic')
zi = f(xi, yi)
[xi, yi] = np.meshgrid(xi, yi);
# surf = ax.plot_surface(X, Y, Z, cmap=mpl.cm.autumn)
surf = ax.plot_surface(xi, yi, zi, cmap=mpl.cm.autumn)
fig.colorbar(surf, shrink=0.5, aspect=5)
# surf(xi, yi, zi, 'LineStyle', 'none', 'FaceColor', 'interp')
plt.show()
def SNPscan(R,regAlpha,numProcess):
reload(dta)
SH=dta.getSH(sparse=True)
ARGS=[(R,)+sh for sh in SH]
print pd.DataFrame(SH)
print R
if numProcess==1:
likelihoods=pd.concat(map(mkv.computeLikelihoodReal,ARGS),axis=1);likelihoods.columns.names=['s','h']
else:
pool=Pool(numProcess)
likelihoods=pd.concat(pool.map(mkv.computeLikelihoodReal,ARGS),axis=1);likelihoods.columns.names=['s','h']
pool.terminate()
del ARGS
gc.collect()
likelihoods=mkv.maxLikelihood(likelihoods,regAlpha=regAlpha)
gc.collect()
return likelihoods
