from ISM.Extinction import *
from ISM.Extinction.Nicer import *
from ISM.FITS.FITS import MakeEmptyFitsDim
# Select a target cloud
ra0 = HMS2RAD( 15, 39, 42.0)
de0 = DMS2RAD( -7, 10, 0.0)
box = 60.0*ARCMIN_TO_RADIAN # map size in radians
pix = 0.5*ARCMIN_TO_RADIAN # pixel size
fwhm = 3.0*ARCMIN_TO_RADIAN # fwhm of the Av map
npix = int(box/pix) # map size as number of pixels
# Read 2Mass stars for a reference field (OFF field)
coo, mag, dmag = read_2mass_www(ra0, de0+box, box_size=0.5*box, filename='OFF.2Mass')
# Read 2Mass stars for the ON field
COO, MAG, DMAG = read_2mass_www(ra0, de0 , box_size=box, filename='ON.2Mass')
# Make a template FITS image for the extinction map and the error map
F = MakeEmptyFitsDim(ra0, de0, 0.5*ARCMIN_TO_RADIAN, npix, npix)
dF = MakeEmptyFitsDim(ra0, de0, 0.5*ARCMIN_TO_RADIAN, npix, npix)
# Choose extinction curve
EX_AV = get_AX_AV(['J', 'H', 'Ks'], Rv=3.1)
# Calculate extinction and save the results to FITS files
NICER_with_OpenCL(F, COO, MAG, DMAG, mag, dmag, EX_AV, FWHM=3.0*ARCMIN_TO_RADIAN, CLIP=3.0, GPU=0, dF=dF)
F.writeto( 'test_Av.fits' , overwrite=True)
dF.writeto('test_dAv.fits', overwrite=True)
for i in range(1000):
K = 4.0 * log(2.0) / (clip(2.0 + 1.0 * randn(), 2.0, 99)**2.0)
G[0].data += 98.0 * rand() * exp(-K * ((J - N * rand())**2.0 +
(I - M * rand())**2.0))
G[0].data = clip(G[0].data, 1.0, +100.0)
G[0].data[0:3, :] = 0.0
G[0].data[-3:, :] = 0.0
G[0].data[:, 0:3] = 0.0
G[0].data[:, -3:] = 0.0
if (1): # rotate the input image
rot = 30 * DEGREE_TO_RADIAN
G[0].header['CD1_1'] = G[0].header['CDELT1'] * cos(rot)
G[0].header['CD1_2'] = -G[0].header['CDELT2'] * sin(rot)
G[0].header['CD2_1'] = G[0].header['CDELT1'] * sin(rot)
G[0].header['CD2_2'] = G[0].header['CDELT2'] * cos(rot)
G.writeto('g.fits', overwrite=True)
# Run montage.reproject
A[0].header.totextfile('ref.header', overwrite=True)
montage.reproject('g.fits',
'A.fits',
'ref.header',
exact_size=True,
factor=factor)
A = pyfits.open('A.fits')
# Run the OpenCL routine
Reproject(G, B, GPU=GPU, cstep=cstep, shrink=1.0)
# ignore the borders .... note that pixel values may be very small for the last pixels
# on the edges of the area covered by the input data
J, I = indices((N, M), np.float32)
for i in range(30):
K = 4.0 * log(2.0) / (clip(2.0 + 1.0 * randn(), 2.0, 99)**2.0)
G[0].data += 100.0 * rand() * exp(-K * ((J - N * rand())**2.0 +
(I - M * rand())**2.0))
G[0].data[0:3, :] = 0.0
G[0].data[-3:, :] = 0.0
G[0].data[:, 0:3] = 0.0
G[0].data[:, -3:] = 0.0
if (1): # rotate the input image
rot = 30 * DEGREE_TO_RADIAN
G[0].header['CD1_1'] = G[0].header['CDELT1'] * cos(rot)
G[0].header['CD1_2'] = -G[0].header['CDELT2'] * sin(rot)
G[0].header['CD2_1'] = G[0].header['CDELT1'] * sin(rot)
G[0].header['CD2_2'] = G[0].header['CDELT2'] * cos(rot)
G.writeto('g.fits', overwrite=True)
# Run montage.reproject
if (
N < 10000
): # laptop tmp directory was running out of space for larger images...
time.sleep(
SLEEP
) # sleep before and after Montage, to avoid (reduce) CPU throttling
t0 = time.time()
A[0].header.totextfile('ref.header', overwrite=True)
montage.reproject('g.fits',
'A.fits',
'ref.header',
exact_size=True,
factor=factor)