import itertools
import Starfish
from Starfish import emulator
from Starfish.grid_tools import HDF5Interface
from Starfish.emulator import PCAGrid, Gprior, Glnprior, Emulator
from Starfish.covariance import Sigma
import os
if args.create:
myHDF5 = HDF5Interface()
my_pca = PCAGrid.create(myHDF5)
my_pca.write()
if args.plot == "reconstruct":
my_HDF5 = HDF5Interface()
my_pca = PCAGrid.open()
recon_fluxes = my_pca.reconstruct_all()
# we need to apply the same normalization to the synthetic fluxes that we
# used for the reconstruction
fluxes = np.empty((my_pca.M, my_pca.npix))
for i, spec in enumerate(my_HDF5.fluxes):
fluxes[i, :] = spec
# Normalize all of the fluxes to an average value of 1
# In order to remove uninteresting correlations
fluxes = fluxes / np.average(fluxes, axis=1)[np.newaxis].T
data = zip(my_HDF5.grid_points, fluxes, recon_fluxes)
import Starfish
from Starfish import emulator
from Starfish.grid_tools import HDF5Interface
from Starfish.emulator import PCAGrid, Gprior, Glnprior, Emulator
from Starfish.covariance import Sigma
import os
if args.create:
myHDF5 = HDF5Interface()
my_pca = PCAGrid.create(myHDF5)
my_pca.write()
if args.plot == "reconstruct":
my_HDF5 = HDF5Interface()
my_pca = PCAGrid.open()
recon_fluxes = my_pca.reconstruct_all()
# we need to apply the same normalization to the synthetic fluxes that we
# used for the reconstruction
fluxes = np.empty((my_pca.M, my_pca.npix))
for i, spec in enumerate(my_HDF5.fluxes):
fluxes[i,:] = spec
# Normalize all of the fluxes to an average value of 1
# In order to remove uninteresting correlations
fluxes = fluxes/np.average(fluxes, axis=1)[np.newaxis].T
data = zip(my_HDF5.grid_points, fluxes, recon_fluxes)
