def run_corr2(x, y, g1, g2, k):
from galsim import pyfits
import os
# Use fits binary table for faster I/O. (Converting to/from strings is slow.)
assert x.shape == y.shape
assert x.shape == g1.shape
assert x.shape == g2.shape
assert x.shape == k.shape
x_col = pyfits.Column(name='x', format='1D', array=x.flatten() )
y_col = pyfits.Column(name='y', format='1D', array=y.flatten() )
g1_col = pyfits.Column(name='g1', format='1D', array=g1.flatten() )
g2_col = pyfits.Column(name='g2', format='1D', array=g2.flatten() )
k_col = pyfits.Column(name='k', format='1D', array=k.flatten() )
cols = pyfits.ColDefs([x_col, y_col, g1_col, g2_col, k_col])
table = pyfits.new_table(cols)
phdu = pyfits.PrimaryHDU()
hdus = pyfits.HDUList([phdu,table])
hdus.writeto('temp.fits',clobber=True)
subprocess.Popen(['corr2','corr2.params',
'e2_file_name=temp.e2', 'k2_file_name=temp.k2',
'min_sep=%f'%min_sep,'max_sep=%f'%max_sep,'nbins=%f'%nbins]).wait()
subprocess.Popen(['corr2','corr2.params',
'file_name2=temp.fits', 'ke_file_name=temp.ke',
'min_sep=%f'%min_sep,'max_sep=%f'%max_sep,'nbins=%f'%nbins]).wait()
os.remove('temp.fits')
a fits file) into images that can be used for the SBProfile tests.
The Maple program is saved in the same directory as moffat_pixel.mw.
And the output that it produces is saved as moffat_pixel.dat.
This program converts that into a fits file called moffat_pixel.fits.
"""
import numpy
from galsim import pyfits
import os
for input_file in ["moffat_pixel.dat", "moffat_pixel_distorted.dat"]:
output_file = input_file.split('.')[0] + '.fits'
print input_file, output_file
nx = 61
ny = 61
fin = open(input_file, 'r')
vals = map(float, fin.readlines())
array = numpy.array(vals).reshape(nx, ny).transpose()
hdus = pyfits.HDUList()
hdu = pyfits.PrimaryHDU(array)
hdus.append(hdu)
if os.path.isfile(output_file):
os.remove(output_file)
hdus.writeto(output_file)
noise_sigma = min_flux/1000. print 'add noise with sigma = ',noise_sigma noise = galsim.GaussianNoise(ud, sigma = noise_sigma) # For the coadd image, we just need to add noise and write the file to disk. coadd_im = images[0] coadd_im.addNoise(noise) print 'Added noise to coadd image' coadd_file = image_path[0] print 'Original coadd file = ',coadd_file # We will build a new hdulist for the new file and copy what we need from the old one. # Also, we write this in uncompressed form and then fpack it to make sure that the # final result is funpack-able. hdu_list = pyfits.open(coadd_file) new_hdu_list = pyfits.HDUList() # Copy the primary hdu #new_hdu_list.append(hdu_list[0]) assert coadd_hdu == 1 coadd_im.write(hdu_list=new_hdu_list) # copy over the header item SEXMGZPT new_hdu_list[0].header['SEXMGZPT'] = hdu_list[coadd_hdu].header['SEXMGZPT'] # Next is the weight image assert coadd_wt_hdu == 2 coadd_wt_im = galsim.fits.read(hdu_list=hdu_list[coadd_wt_hdu], compression='rice') coadd_wt_im *= (1./noise_sigma**2) / coadd_wt_im.array.mean() print 'coadd_wt_im.mean = ',coadd_wt_im.array.mean(),' should = ',1./noise_sigma**2 coadd_wt_im.write(hdu_list=new_hdu_list)
def write_meds(file_name, obj_list, clobber=True):
"""
@brief Writes the galaxy, weights, segmaps images to a MEDS file.
Arguments:
----------
@param file_name: Name of meds file to be written
@param obj_list: List of MultiExposureObjects
@param clobber Setting `clobber=True` when `file_name` is given will silently overwrite
existing files. (Default `clobber = True`.)
"""
import numpy
import sys
from galsim import pyfits
# initialise the catalog
cat = {}
cat['ncutout'] = []
cat['box_size'] = []
cat['start_row'] = []
cat['id'] = []
cat['dudrow'] = []
cat['dudcol'] = []
cat['dvdrow'] = []
cat['dvdcol'] = []
cat['row0'] = []
cat['col0'] = []
# initialise the image vectors
vec = {}
vec['image'] = []
vec['seg'] = []
vec['weight'] = []
# initialise the image vector index
n_vec = 0
# get number of objects
n_obj = len(obj_list)
# loop over objects
for obj in obj_list:
# initialise the start indices for each image
start_rows = numpy.ones(MAX_NCUTOUTS) * EMPTY_START_INDEX
dudrow = numpy.ones(MAX_NCUTOUTS) * EMPTY_JAC_diag
dudcol = numpy.ones(MAX_NCUTOUTS) * EMPTY_JAC_offdiag
dvdrow = numpy.ones(MAX_NCUTOUTS) * EMPTY_JAC_offdiag
dvdcol = numpy.ones(MAX_NCUTOUTS) * EMPTY_JAC_diag
row0 = numpy.ones(MAX_NCUTOUTS) * EMPTY_SHIFT
col0 = numpy.ones(MAX_NCUTOUTS) * EMPTY_SHIFT
# get the number of cutouts (exposures)
n_cutout = obj.n_cutouts
# append the catalog for this object
cat['ncutout'].append(n_cutout)
cat['box_size'].append(obj.box_size)
cat['id'].append(obj.id)
# loop over cutouts
for i in range(n_cutout):
# assign the start row to the end of image vector
start_rows[i] = n_vec
# update n_vec to point to the end of image vector
n_vec += len(obj.images[i].array.flatten())
# append the image vectors
vec['image'].append(obj.images[i].array.flatten())
vec['seg'].append(obj.segs[i].array.flatten())
vec['weight'].append(obj.weights[i].array.flatten())
# append the Jacobian
dudrow[i] = obj.wcs[i].dudx
dudcol[i] = obj.wcs[i].dudy
dvdrow[i] = obj.wcs[i].dvdx
dvdcol[i] = obj.wcs[i].dvdy
row0[i] = obj.wcs[i].origin.x
col0[i] = obj.wcs[i].origin.y
# check if we are running out of memory
if sys.getsizeof(vec) > MAX_MEMORY:
raise MemoryError(
'Running out of memory > %1.0fGB ' % MAX_MEMORY / 1.e9 +
'- you can increase the limit by changing MAX_MEMORY')
# update the start rows fields in the catalog
cat['start_row'].append(start_rows)
# add lists of Jacobians
cat['dudrow'].append(dudrow)
cat['dudcol'].append(dudcol)
cat['dvdrow'].append(dvdrow)
cat['dvdcol'].append(dvdcol)
cat['row0'].append(row0)
cat['col0'].append(col0)
# concatenate list to one big vector
vec['image'] = numpy.concatenate(vec['image'])
vec['seg'] = numpy.concatenate(vec['seg'])
vec['weight'] = numpy.concatenate(vec['weight'])
# get the primary HDU
primary = pyfits.PrimaryHDU()
# second hdu is the object_data
cols = []
cols.append(
pyfits.Column(name='ncutout', format='i4', array=cat['ncutout']))
cols.append(pyfits.Column(name='id', format='i4', array=cat['id']))
cols.append(
pyfits.Column(name='box_size', format='i4', array=cat['box_size']))
cols.append(pyfits.Column(name='file_id', format='i4', array=[1] * n_obj))
cols.append(
pyfits.Column(name='start_row',
format='%di4' % MAX_NCUTOUTS,
array=numpy.array(cat['start_row'])))
cols.append(pyfits.Column(name='orig_row', format='f8', array=[1] * n_obj))
cols.append(pyfits.Column(name='orig_col', format='f8', array=[1] * n_obj))
cols.append(
pyfits.Column(name='orig_start_row', format='i4', array=[1] * n_obj))
cols.append(
pyfits.Column(name='orig_start_col', format='i4', array=[1] * n_obj))
cols.append(
pyfits.Column(name='dudrow',
format='%df8' % MAX_NCUTOUTS,
array=numpy.array(cat['dudrow'])))
cols.append(
pyfits.Column(name='dudcol',
format='%df8' % MAX_NCUTOUTS,
array=numpy.array(cat['dudcol'])))
cols.append(
pyfits.Column(name='dvdrow',
format='%df8' % MAX_NCUTOUTS,
array=numpy.array(cat['dvdrow'])))
cols.append(
pyfits.Column(name='dvdcol',
format='%df8' % MAX_NCUTOUTS,
array=numpy.array(cat['dvdcol'])))
cols.append(
pyfits.Column(name='cutout_row',
format='%df8' % MAX_NCUTOUTS,
array=numpy.array(cat['row0'])))
cols.append(
pyfits.Column(name='cutout_col',
format='%df8' % MAX_NCUTOUTS,
array=numpy.array(cat['col0'])))
object_data = pyfits.new_table(pyfits.ColDefs(cols))
object_data.update_ext_name('object_data')
# third hdu is image_info
cols = []
cols.append(
pyfits.Column(name='image_path',
format='A256',
array=['generated_by_galsim']))
cols.append(
pyfits.Column(name='sky_path',
format='A256',
array=['generated_by_galsim']))
cols.append(
pyfits.Column(name='seg_path',
format='A256',
array=['generated_by_galsim']))
image_info = pyfits.new_table(pyfits.ColDefs(cols))
image_info.update_ext_name('image_info')
# fourth hdu is metadata
cols = []
cols.append(
pyfits.Column(name='cat_file',
format='A256',
array=['generated_by_galsim']))
cols.append(
pyfits.Column(name='coadd_file',
format='A256',
array=['generated_by_galsim']))
cols.append(pyfits.Column(name='coadd_hdu', format='A1', array=['x']))
cols.append(pyfits.Column(name='coadd_seg_hdu', format='A1', array=['x']))
cols.append(
pyfits.Column(name='coadd_srclist',
format='A256',
array=['generated_by_galsim']))
cols.append(pyfits.Column(name='coadd_wt_hdu', format='A1', array=['x']))
cols.append(
pyfits.Column(name='coaddcat_file',
format='A256',
array=['generated_by_galsim']))
cols.append(
pyfits.Column(name='coaddseg_file',
format='A256',
array=['generated_by_galsim']))
cols.append(
pyfits.Column(name='cutout_file',
format='A256',
array=['generated_by_galsim']))
cols.append(pyfits.Column(name='max_boxsize', format='A3', array=['x']))
cols.append(pyfits.Column(name='medsconf', format='A3', array=['x']))
cols.append(pyfits.Column(name='min_boxsize', format='A2', array=['x']))
cols.append(pyfits.Column(name='se_badpix_hdu', format='A1', array=['x']))
cols.append(pyfits.Column(name='se_hdu', format='A1', array=['x']))
cols.append(pyfits.Column(name='se_wt_hdu', format='A1', array=['x']))
cols.append(pyfits.Column(name='seg_hdu', format='A1', array=['x']))
cols.append(pyfits.Column(name='sky_hdu', format='A1', array=['x']))
metadata = pyfits.new_table(pyfits.ColDefs(cols))
metadata.update_ext_name('metadata')
# rest of HDUs are image vectors
image_cutouts = pyfits.ImageHDU(vec['image'], name='image_cutouts')
weight_cutouts = pyfits.ImageHDU(vec['weight'], name='weight_cutouts')
seg_cutouts = pyfits.ImageHDU(vec['seg'], name='seg_cutouts')
# write all
hdu_list = pyfits.HDUList([
primary, object_data, image_info, metadata, image_cutouts,
weight_cutouts, seg_cutouts
])
hdu_list.writeto(file_name, clobber=clobber)