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')
pyfits.Column(name='true_hlr', format='D', array=true_hlr),
pyfits.Column(name='ra', format='D', array=coadd_cat['ALPHAWIN_J2000']),
pyfits.Column(name='dec', format='D', array=coadd_cat['DELTAWIN_J2000']),
pyfits.Column(name='mag', format='D', array=coadd_cat['MAG_AUTO']),
pyfits.Column(name='flux', format='D', array=coadd_cat['FLUX_AUTO']),
pyfits.Column(name='nexp', format='I', array=nexp),
pyfits.Column(name='mean_psf_g1', format='D', array=psf_g1),
pyfits.Column(name='mean_psf_g2', format='D', array=psf_g2),
pyfits.Column(name='mean_psf_fwhm', format='D', array=psf_fwhm),
pyfits.Column(name='mean_wcs_g1', format='D', array=wcs_g1),
pyfits.Column(name='mean_wcs_g2', format='D', array=wcs_g2),
pyfits.Column(name='mean_wcs_scale', format='D', array=wcs_scale),
pyfits.Column(name='mean_wcs_theta', format='D', array=wcs_theta),
]
coldefs = pyfits.ColDefs(columns)
table = pyfits.new_table(coldefs)
table.writeto(out_truth_file, clobber=True)
# Also, it turns out to be useful to have the coadd catalog in this directory as well:
# Update: now we make our own, which means the truth catalog isn't correct anymore (need to
# match up objects by ra,dec).
#shutil.copy2(coaddcat_file, out_dir)
# Do the final processing on each single epoch image and write them to disk.
for image_num in range(1,nimages):
im = images[image_num]
file = image_path[image_num]
print 'Finalize ',file
# Get the catalog name.
cat_file = file.replace('.fits.fz','_cat.fits')
pyfits.Column(name='GAL_HDU', format='J', array=range(0, n_gals)))
columns.append(
pyfits.Column(name='PSF_HDU', format='J', array=range(0, n_gals)))
columns.append(
pyfits.Column(name='PIXEL_SCALE',
format='D',
array=[pixel_scale] * n_gals))
columns.append(
pyfits.Column(name='NOISE_MEAN', format='D', array=[0] * n_gals))
columns.append(
pyfits.Column(name='NOISE_VARIANCE',
format='D',
array=[noise_var] * n_gals))
# create table
hdu_table = pyfits.new_table(columns)
# save all catalogs
hdu_table.writeto(filename_rgc, clobber=True)
logger.info('saved real galaxy catalog %s' % filename_rgc)
# if in debug mode, save some plots
# if config['args'].debug : SavePreviewRGC(config,filename_rgc)
def SavePreviewRGC(config, filename_rgc, n_gals_preview=10):
"""
Function for eyeballing the contents of the created mock RGC catalogs.
Arguments
---------
config config dict
# get the columns of the catalog
columns = []
columns.append( pyfits.Column( name='IDENT' ,format='J' ,array=range(0,n_gals) ))
columns.append( pyfits.Column( name='MAG' ,format='D' ,array=[MAG] * n_gals ))
columns.append( pyfits.Column( name='BAND' ,format='5A' ,array=[BAND] * n_gals ))
columns.append( pyfits.Column( name='WEIGHT' ,format='D' ,array=[WEIGHT] * n_gals ))
columns.append( pyfits.Column( name='GAL_FILENAME' ,format='23A',array=[filename_gals]*n_gals))
columns.append( pyfits.Column( name='PSF_FILENAME' ,format='27A',array=[filename_psfs]*n_gals))
columns.append( pyfits.Column( name='GAL_HDU' ,format='J' ,array=range(0,n_gals) ))
columns.append( pyfits.Column( name='PSF_HDU' ,format='J' ,array=range(0,n_gals) ))
columns.append( pyfits.Column( name='PIXEL_SCALE' ,format='D' ,array=[pixel_scale] * n_gals))
columns.append( pyfits.Column( name='NOISE_MEAN' ,format='D' ,array=[0] * n_gals ))
columns.append( pyfits.Column( name='NOISE_VARIANCE',format='D' ,array=[noise_var] * n_gals ))
# create table
hdu_table = pyfits.new_table(columns)
# save all catalogs
hdu_table.writeto(filename_rgc,clobber=True)
logger.info('saved real galaxy catalog %s' % filename_rgc)
# if in debug mode, save some plots
# if config['args'].debug : SavePreviewRGC(config,filename_rgc)
def SavePreviewRGC(config,filename_rgc,n_gals_preview=10):
"""
Function for eyeballing the contents of the created mock RGC catalogs.
Arguments
---------
config config dict
filename_rgc filename of the newly created real galaxy catalog fits
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='number', format='K', array=range(n_obj) ) )
cols.append( pyfits.Column(name='ncutout', format='K', array=cat['ncutout'] ) )
cols.append( pyfits.Column(name='id', format='K', array=cat['id'] ) )
cols.append( pyfits.Column(name='box_size', format='K', array=cat['box_size'] ) )
cols.append( pyfits.Column(name='file_id', format='%dK' % MAX_NCUTOUTS , array=numpy.array([[1]]*n_obj)) )
cols.append( pyfits.Column(name='start_row', format='%dK' % MAX_NCUTOUTS,
array=numpy.array(cat['start_row'])) )
cols.append( pyfits.Column(name='orig_row', format='%dD' % MAX_NCUTOUTS, array=numpy.array([[1]]*n_obj) ) )
cols.append( pyfits.Column(name='orig_col', format='%dD' % MAX_NCUTOUTS, array=numpy.array([[1]]*n_obj) ) )
cols.append( pyfits.Column(name='orig_start_row', format='%dK' % MAX_NCUTOUTS,
array=numpy.array([[1]]*n_obj) ) )
cols.append( pyfits.Column(name='orig_start_col', format='%dK' % MAX_NCUTOUTS,
array=numpy.array([[1]]*n_obj) ) )
cols.append( pyfits.Column(name='dudrow', format='%dD'% MAX_NCUTOUTS,
array=numpy.array(cat['dudrow']) ) )
cols.append( pyfits.Column(name='dudcol', format='%dD'% MAX_NCUTOUTS,
array=numpy.array(cat['dudcol']) ) )
cols.append( pyfits.Column(name='dvdrow', format='%dD'% MAX_NCUTOUTS,
array=numpy.array(cat['dvdrow']) ) )
cols.append( pyfits.Column(name='dvdcol', format='%dD'% MAX_NCUTOUTS,
array=numpy.array(cat['dvdcol']) ) )
cols.append( pyfits.Column(name='cutout_row', format='%dD'% MAX_NCUTOUTS,
array=numpy.array(cat['row0']) ) )
cols.append( pyfits.Column(name='cutout_col', format='%dD'% 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)
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)
