# nmonte realisations
for nd, spectra in enumerate(data.data):
for nm in xrange(nmonte):
buffer[nd][nm] = np.random.normal(spectra.flux, spectra.ferr)
# allocate space for results as we need to complete all
# computations before getting answers.
chisq = np.empty((nfreq,nmonte,len(cond)))
# outer loop over frequency so SVD need only to be done once per
# frequency
for nf, f in enumerate(fs):
grid.period = 1/f
# generate the matrix
A = doppler.genmat(grid, data, ntdiv)
# Carry out full SVD, returning smallest matrices possible
u, s, v = np.linalg.svd(A,full_matrices=False)
# replace u and v by their transposes
v = np.transpose(v)
u = np.transpose(u)
smax = s[0]
# Go through each value of the conditioning numbers to compute maximum
# number of SVDs to use
mok = 0
for nc, c in enumerate(cond):
# select the highest singular values with a method
parser.add_argument('data', help='data file')
parser.add_argument('svd', help='name of the SVD output file')
# optional
parser.add_argument('-n', dest='ntdiv', type=int,
default=11, help='spectrum sub-division factor')
# OK, done with arguments.
args = parser.parse_args()
# load grid and data
grid = doppler.Grid.rfits(doppler.afits(args.grid))
data = doppler.Data.rfits(doppler.afits(args.data))
# generate the matrix
A = doppler.genmat(grid, data, args.ntdiv)
# Carry out full SVD, returning smallest matrices possible
u, s, v = np.linalg.svd(A,full_matrices=False)
ng = grid.data.shape[0]
# Matrix v is the one we want. It should have dimensions (ng*ng) x (ng*ng) where
# N is number along each side of the grids. Need to re-shape
v = np.reshape(v, (ng*ng,ng,ng))
# now write to FITS
head = fits.Header()
head['SOURCE'] = 'svd.py'
hdu = fits.PrimaryHDU(data=v, header=head)
hdu.writeto(doppler.afits(args.svd))
