def collate_one(self, data0, shnum, psfnum, ccd):
from esutil.numpy_util import match
if self['verbose']:
print ' collating with catalog'
cat=read_cat(run=self['run'],
shnum=shnum,
psfnum=psfnum,
ccd=ccd,
verbose=self['verbose'])
extra=[('mag_auto_r','f8'),
('shnum','i2'),
('psfnum','i2'),
('ccd','i2'),
('sratio','f8')]
dt=data0.dtype.descr + extra
data=zeros(data0.size, dtype=dt)
for n in data0.dtype.names:
data[n] = data0[n]
mcat,mshear=match(cat['id'], data0['id'])
if mshear.size != data0.size:
raise ValueError("not all matched!")
data['mag_auto_r'][mshear] = cat['mag_auto_r'][mcat]
data['shnum'] = shnum
data['psfnum'] = psfnum
data['ccd'] = ccd
data['sratio'] = sqrt(1./data0['s2'])
return data
def read_exposure_data(dir,exposurename):
out_dtype=[('ccd','i1'),
('x','f4'),('y','f4'),
('psf_flags','i4'),
('e1','f4'),('e2','f4'),
('e1interp','f4'),('e2interp','f4')]
datalist=[]
for ccd in xrange(1,1+62):
psf_file = path_join(dir,'%s_%02d_psf.fits' % (exposurename,ccd))
psf=esutil.io.read(psf_file)
psfe1 = psf['shapelets'][:,3]*sqrt(2)
psfe2 = -psf['shapelets'][:,4]*sqrt(2)
shear_file = path_join(dir,'%s_%02d_shear.fits' % (exposurename,ccd))
shear=esutil.io.read(shear_file)
e1interp = shear['interp_psf_coeffs'][:,3]*sqrt(2)
e2interp = -shear['interp_psf_coeffs'][:,4]*sqrt(2)
mpsf,mshear=numpy_util.match(psf['id'], shear['id'])
tdata = numpy.zeros(mpsf.size, dtype=out_dtype)
tdata['ccd'] = ccd
tdata['x'] = psf['x'][mpsf]
tdata['y'] = psf['y'][mpsf]
tdata['e1'] = psfe1[mpsf]
tdata['e2'] = psfe2[mpsf]
tdata['psf_flags'] = psf['psf_flags'][mpsf]
tdata['e1interp'] = e1interp[mshear]
tdata['e2interp'] = e2interp[mshear]
datalist.append(tdata)
data = numpy_util.combine_arrlist(datalist)
return data
def match(self, tilename):
"""
match and write the output file
"""
from esutil import numpy_util as nu
scat_data = read_orig_scat(self.scat_name, tilename)
print("matching")
mdg, msc = nu.match(scat_data["coadd_objects_id"], self.ids)
print(" matched: %d/%d" % (mdg.size, scat_data.size))
if mdg.size == 0:
print(" skipping")
return
scat_data = scat_data[mdg]
add_dt = [("scinv_flags", "i4"), ("scinv", "f8", self.nz)]
newdata = nu.add_fields(scat_data, add_dt)
newdata["scinv"] = self.data["scinv"][msc, :]
newdata["scinv_flags"] = self.data["flags"][msc]
self._write_data(newdata, tilename)
def make_im3shape(config, info):
"""Make the im3shape catalog file
"""
# Setup the dtype:
dt = np.dtype([
('coadd_objects_id', 'i8'),
('e_1', 'f8'),
('e_2', 'f8'),
('nbc_m', 'f8'),
('nbc_c1', 'f8'),
('nbc_c2', 'f8'),
('w', 'f8'),
('error_flag', 'i4'),
('info_flag', 'i4'),
('snr_w', 'f4'),
('snr_r', 'f4'),
('flux_r', 'f4'),
('radius', 'f4'),
('is_bulge', 'i4'),
('mean_rgpp_rp', 'f4'),
('mean_psf_e1', 'f8'),
('mean_psf_e2', 'f8'),
('mean_psf_fwhm', 'f4'),
('ra_shift', 'f4'),
('dec_shift', 'f4'),
('chi2', 'f4'),
('likelihood', 'f4'),
('stamp_size', 'i4'),
('n_exposure', 'i4') ])
# Read the source catalog
imcat = fitsio.read(config['im3shape'])
# im3shape objects are sorted, but has some that are not in info catalog.
im, dim = numpy_util.match(imcat['coadd_objects_id'], info['COADD_OBJECTS_ID'])
#assert np.all(imcat['coadd_objects_id'] == sorted(imcat['coadd_objects_id']))
# Set reasonable defaults for float fields:
data = np.zeros(len(info), dtype=dt)
for col, t in dt.descr:
if 'f' in t:
data[col] = -9999
# The coadd_objects_id column can match for all rows
data['coadd_objects_id'] = info['COADD_OBJECTS_ID']
# Check that the overlap with imcat is correct
assert np.all(data['coadd_objects_id'][dim] == imcat['coadd_objects_id'][im])
# Default error_flag is NO_ATTEMPT
data['error_flag'] = 2**30
data['info_flag'] = 2**25
print 'unique error_flags in original catalog = ',np.unique(imcat['error_flag'])
print 'unique error_flags that match info = ',np.unique(imcat['error_flag'][im])
# Copy in the columns from the source catalog that keep the same name:
for col in ['nbc_m', 'nbc_c1', 'nbc_c2', 'w', 'error_flag', 'info_flag',
'radius', 'mean_rgpp_rp', 'mean_psf_fwhm', 'likelihood',
'stamp_size', 'n_exposure']:
data[col][dim] = imcat[col][im]
print 'unique error_flags in final = ',np.unique(data['error_flag'][dim])
# Some get a new name:
data['e_1'][dim] = imcat['e1'][im]
data['e_2'][dim] = imcat['e2'][im]
data['snr_w'][dim] = imcat['snr'][im]
data['snr_r'][dim] = imcat['round_snr'][im]
data['mean_psf_e1'][dim] = imcat['mean_psf_e1_sky'][im]
data['mean_psf_e2'][dim] = imcat['mean_psf_e2_sky'][im]
data['ra_shift'][dim] = imcat['ra_as'][im]
data['dec_shift'][dim] = imcat['dec_as'][im]
data['chi2'][dim] = imcat['chi2_pixel'][im]
# Do a calculation to get the flux from separate bulge/disc fluxes.
data['is_bulge'][dim] = imcat['bulge_flux'][im] != 0.
# Only one is non-zero:
bulge = imcat['bulge_flux'][im] != 0.
disc = imcat['disc_flux'][im] != 0.
print 'sum isbulge = ',np.sum(data['is_bulge'][dim])
print 'sum bulge_flux > 0. = ',np.sum(imcat['bulge_flux'][im] != 0.)
print 'sum disc_flux > 0. = ',np.sum(imcat['disc_flux'][im] != 0.)
assert np.all((imcat['bulge_flux'][im] != 0) != (imcat['disc_flux'][im] != 0))
data['flux_r'][dim] = imcat['mean_flux'][im] * (imcat['bulge_flux'][im]+imcat['disc_flux'][im])
# clip the weights
data['w'][dim] = np.clip(data['w'][dim], 0.0, 0.24**(-2.0))
# unblind
for tag in ['e_1','e_2']:
data[tag][dim] /= unblind.get_factor()
# Before writing out, make all the names upper case
data.dtype.names = tuple([name.upper() for name in data.dtype.names])
fitsio.write(config['release_im3shape'],data,clobber=True)
def make_ngmix(config, info):
"""Make the ngmix catalog file
"""
# Setup the dtype:
dt = np.dtype([
('coadd_objects_id', 'i8'),
('e_1', 'f8'),
('e_2', 'f8'),
('sens_avg', 'f8'),
('w', 'f8'),
('e_cov_1_1', 'f8'),
('e_cov_1_2', 'f8'),
('e_cov_2_1', 'f8'),
('e_cov_2_2', 'f8'),
('error_flag', 'i4'),
('snr_w', 'f4'),
('snr_r', 'f4'),
('flux_i', 'f4'),
('mag_i', 'f4'),
('t', 'f4'),
('t_err', 'f4'),
('t_r', 'f4'),
('snr_t', 'f4'),
('snr_t_r', 'f4'),
('log10_sb_i', 'f4'),
('mean_psf_e1', 'f8'),
('mean_psf_e2', 'f8'),
('mean_psf_t', 'f4'),
('sens_1', 'f4'),
('sens_2', 'f4'),
('arate', 'f4'),
('stamp_size', 'i4'),
('mask_frac', 'f4') ])
# Read the source catalogs
ngcat = fitsio.read(config['ngmix'])
# ngmix catalogs are not sorted, so need to use match function
# Also, ngmix has some object that are not in info catalog.
ng, dng = numpy_util.match(ngcat['coadd_objects_id'], info['COADD_OBJECTS_ID'])
# Set reasonable defaults for float fields:
data = np.zeros(len(info), dtype=dt)
for col, t in dt.descr:
if 'f' in t:
data[col] = -9999
# The coadd_objects_id column can match for all rows
data['coadd_objects_id'] = info['COADD_OBJECTS_ID']
# Check that the overlap with ngcat is correct
assert np.all(data['coadd_objects_id'][dng] == ngcat['coadd_objects_id'][ng])
# Copy in the columns from the source catalog that keep the same name:
for col in ['mask_frac']:
data[col][dng] = ngcat[col][ng]
# Some columns just need to drop the 'exp_' prefix:
for col in ['e_1', 'e_2', 'flux_i', 'mag_i', 'arate', 't', 't_err',
'e_cov_1_1', 'e_cov_1_2', 'e_cov_2_2']:
data[col][dng] = ngcat['exp_' + col][ng]
# Some need to be renamed:
data['snr_w'][dng] = ngcat['exp_s2n_w'][ng]
data['snr_t'][dng] = ngcat['exp_t_s2n'][ng]
data['sens_1'][dng] = ngcat['exp_e_sens_1'][ng]
data['sens_2'][dng] = ngcat['exp_e_sens_2'][ng]
data['stamp_size'][dng] = ngcat['box_size'][ng]
# Combine the flags we have so far:
print 'flags range from %d to %d'%(
np.min(ngcat['flags'][ngcat['flags']>0]),
np.max(ngcat['flags']))
print 'exp_flags range from %d to %d'%(
np.min(ngcat['exp_flags'][ngcat['exp_flags']>0]),
np.max(ngcat['exp_flags']))
data['error_flag'] = 2**30
data['error_flag'][dng] = ngcat['flags'][ng]
data['error_flag'][dng] |= ngcat['exp_flags'][ng]
# Calculate mean sensitivity
data['sens_avg'][dng] = (ngcat['exp_e_sens_1'][ng] + ngcat['exp_e_sens_2'][ng]) / 2.
# Calculate the recommended weight.
data['w'][dng] = 1.0/(2.0*0.22*0.22 + ngcat['exp_e_cov_1_1'][ng] + ngcat['exp_e_cov_2_2'][ng])
# Calculate log10(sb)
data['log10_sb_i'][dng] = np.log10(np.abs(ngcat['exp_flux_i'][ng]/ngcat['exp_t'][ng]))
# swap e1 signs
for tag in ['e_1', 'e_cov_1_2', 'e_cov_2_1']:
data[tag][dng] *= -1.0
data['e_cov_2_1'][dng] = data['e_cov_1_2'][dng]
# unblind
for tag in ['e_1','e_2']:
data[tag][dng] /= unblind.get_factor()
# Bring in the round columns from the s2n catalog
scat = fitsio.read(config['ngmix_s2n'])
sc, dsc = numpy_util.match(scat['id'], info['COADD_OBJECTS_ID'])
data['snr_r'][dsc] = scat['exp_s2n_r'][sc]
data['t_r'][dsc] = scat['exp_T_r'][sc]
data['snr_t_r'][dsc] = scat['exp_T_s2n_r'][sc]
# Check that round_flags are consisten with current error_flag
print 'round_flags range from %d to %d'%(
np.min(scat['round_flags'][scat['round_flags']>0]),
np.max(scat['round_flags']))
print 'exp_round_flags range from %d to %d'%(
np.min(scat['exp_round_flags'][scat['exp_round_flags']>0]),
np.max(scat['exp_round_flags'][scat['exp_round_flags']<2**30]))
assert np.all(data['error_flag'][dsc][ scat['round_flags'][sc] > 0 ] > 0)
assert np.all(data['error_flag'][dsc][ scat['exp_round_flags'][sc] == 2**30 ] > 0)
# Combine the round flags into the error_flag column
data['error_flag'][dsc] |= scat['round_flags'][sc]
data['error_flag'][dsc] |= scat['exp_round_flags'][sc]
# Bring in the mean psf information from the psf catalog
pcat = fitsio.read(config['ngmix_psfs'])
pc, dpc = numpy_util.match(pcat['id'], info['COADD_OBJECTS_ID'])
data['mean_psf_e1'][dpc] = pcat['psfrec_e'][pc,0]
data['mean_psf_e2'][dpc] = pcat['psfrec_e'][pc,1]
data['mean_psf_t'][dpc] = pcat['psfrec_T'][pc]
# swap e1 signs
for tag in ['mean_psf_e1']:
data[tag][dng] *= -1.0
# Before writing out, make all the names upper case
data.dtype.names = tuple([name.upper() for name in data.dtype.names])
fitsio.write(config['release_ngmix'],data,clobber=True)
phi_best_rrlyr = []
r0_best_rrlyr = []
F_best_rrlyr = []
table2 = np.genfromtxt('%s/table2.dat' % home_path, names='id, type, P, rA', usecols=(0, 1, 2, 11), dtype='u4, |S10, f8, f8')
# load light curve parameters of SDSS S82 RR Lyrae stars
true_params = np.genfromtxt('%s/table2.dat' % home_path, names='id, type, P, gA, g0, gE, gT, rA, r0, rE, rT, iA, i0, iE, iT, zA, z0, zE, zT', usecols=(0, 1, 2, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22), dtype='u4, |S10, f8, f8, f8, f8, u2, f8, f8, f8, u2, f8, f8, f8, u2, f8, f8, f8, u2')
ab = true_params['type'] == 'ab'
ab = true_params[ab]
# sort RRab star-templates by phi_08
phi_08 = np.genfromtxt('%s/objid_phi08_all.txt' % template_path, names='id, phi_08g, phi_08r, phi_08i, phi_08z', dtype='u4, f8, f8, f8, f8')
# match two lists using id
m1, m2 = match(ab['id'], phi_08['id'])
assert (m1.size == ab.size)
ab = ab[m1]
phi_08 = phi_08[m2]
# sort in ascending order, starting with g band
sI = np.argsort(phi_08, order=('phi_08g', 'phi_08r', 'phi_08i', 'phi_08z'))
ab = ab[sI]
# select 2 RRc star-templates
c = (true_params['id'] == 1342595) | (true_params['id'] == 3031571)
c = true_params[c]
# merge RRab and RRc star-templates (RRc need to go at the end otherwise the code will break)
true_params = np.concatenate((ab, c))
# load from table3 ra & dec
def make_im3shape(config, info):
"""Make the im3shape catalog file
"""
# Setup the dtype:
dt = np.dtype([('coadd_objects_id', 'i8'), ('e_1', 'f8'), ('e_2', 'f8'),
('nbc_m', 'f8'), ('nbc_c1', 'f8'), ('nbc_c2', 'f8'),
('w', 'f8'), ('error_flag', 'i4'), ('info_flag', 'i4'),
('snr_w', 'f4'), ('snr_r', 'f4'), ('flux_r', 'f4'),
('radius', 'f4'), ('is_bulge', 'i4'),
('mean_rgpp_rp', 'f4'), ('mean_psf_e1', 'f8'),
('mean_psf_e2', 'f8'), ('mean_psf_fwhm', 'f4'),
('ra_shift', 'f4'), ('dec_shift', 'f4'), ('chi2', 'f4'),
('likelihood', 'f4'), ('stamp_size', 'i4'),
('n_exposure', 'i4')])
# Read the source catalog
imcat = fitsio.read(config['im3shape'])
# im3shape objects are sorted, but has some that are not in info catalog.
im, dim = numpy_util.match(imcat['coadd_objects_id'],
info['COADD_OBJECTS_ID'])
#assert np.all(imcat['coadd_objects_id'] == sorted(imcat['coadd_objects_id']))
# Set reasonable defaults for float fields:
data = np.zeros(len(info), dtype=dt)
for col, t in dt.descr:
if 'f' in t:
data[col] = -9999
# The coadd_objects_id column can match for all rows
data['coadd_objects_id'] = info['COADD_OBJECTS_ID']
# Check that the overlap with imcat is correct
assert np.all(
data['coadd_objects_id'][dim] == imcat['coadd_objects_id'][im])
# Default error_flag is NO_ATTEMPT
data['error_flag'] = 2**30
data['info_flag'] = 2**25
print 'unique error_flags in original catalog = ', np.unique(
imcat['error_flag'])
print 'unique error_flags that match info = ', np.unique(
imcat['error_flag'][im])
# Copy in the columns from the source catalog that keep the same name:
for col in [
'nbc_m', 'nbc_c1', 'nbc_c2', 'w', 'error_flag', 'info_flag',
'radius', 'mean_rgpp_rp', 'mean_psf_fwhm', 'likelihood',
'stamp_size', 'n_exposure'
]:
data[col][dim] = imcat[col][im]
print 'unique error_flags in final = ', np.unique(data['error_flag'][dim])
# Some get a new name:
data['e_1'][dim] = imcat['e1'][im]
data['e_2'][dim] = imcat['e2'][im]
data['snr_w'][dim] = imcat['snr'][im]
data['snr_r'][dim] = imcat['round_snr'][im]
data['mean_psf_e1'][dim] = imcat['mean_psf_e1_sky'][im]
data['mean_psf_e2'][dim] = imcat['mean_psf_e2_sky'][im]
data['ra_shift'][dim] = imcat['ra_as'][im]
data['dec_shift'][dim] = imcat['dec_as'][im]
data['chi2'][dim] = imcat['chi2_pixel'][im]
# Do a calculation to get the flux from separate bulge/disc fluxes.
data['is_bulge'][dim] = imcat['bulge_flux'][im] != 0.
# Only one is non-zero:
bulge = imcat['bulge_flux'][im] != 0.
disc = imcat['disc_flux'][im] != 0.
print 'sum isbulge = ', np.sum(data['is_bulge'][dim])
print 'sum bulge_flux > 0. = ', np.sum(imcat['bulge_flux'][im] != 0.)
print 'sum disc_flux > 0. = ', np.sum(imcat['disc_flux'][im] != 0.)
assert np.all(
(imcat['bulge_flux'][im] != 0) != (imcat['disc_flux'][im] != 0))
data['flux_r'][dim] = imcat['mean_flux'][im] * (imcat['bulge_flux'][im] +
imcat['disc_flux'][im])
# clip the weights
data['w'][dim] = np.clip(data['w'][dim], 0.0, 0.24**(-2.0))
# unblind
for tag in ['e_1', 'e_2']:
data[tag][dim] /= unblind.get_factor()
# Before writing out, make all the names upper case
data.dtype.names = tuple([name.upper() for name in data.dtype.names])
fitsio.write(config['release_im3shape'], data, clobber=True)
def make_ngmix(config, info):
"""Make the ngmix catalog file
"""
# Setup the dtype:
dt = np.dtype([('coadd_objects_id', 'i8'), ('e_1', 'f8'), ('e_2', 'f8'),
('sens_avg', 'f8'), ('w', 'f8'), ('e_cov_1_1', 'f8'),
('e_cov_1_2', 'f8'),
('e_cov_2_1', 'f8'), ('e_cov_2_2', 'f8'),
('error_flag', 'i4'), ('snr_w', 'f4'), ('snr_r', 'f4'),
('flux_i', 'f4'), ('mag_i', 'f4'), ('t', 'f4'),
('t_err', 'f4'), ('t_r', 'f4'), ('snr_t', 'f4'),
('snr_t_r', 'f4'), ('log10_sb_i', 'f4'),
('mean_psf_e1', 'f8'), ('mean_psf_e2', 'f8'),
('mean_psf_t', 'f4'), ('sens_1', 'f4'), ('sens_2', 'f4'),
('arate', 'f4'), ('stamp_size', 'i4'), ('mask_frac', 'f4')])
# Read the source catalogs
ngcat = fitsio.read(config['ngmix'])
# ngmix catalogs are not sorted, so need to use match function
# Also, ngmix has some object that are not in info catalog.
ng, dng = numpy_util.match(ngcat['coadd_objects_id'],
info['COADD_OBJECTS_ID'])
# Set reasonable defaults for float fields:
data = np.zeros(len(info), dtype=dt)
for col, t in dt.descr:
if 'f' in t:
data[col] = -9999
# The coadd_objects_id column can match for all rows
data['coadd_objects_id'] = info['COADD_OBJECTS_ID']
# Check that the overlap with ngcat is correct
assert np.all(
data['coadd_objects_id'][dng] == ngcat['coadd_objects_id'][ng])
# Copy in the columns from the source catalog that keep the same name:
for col in ['mask_frac']:
data[col][dng] = ngcat[col][ng]
# Some columns just need to drop the 'exp_' prefix:
for col in [
'e_1', 'e_2', 'flux_i', 'mag_i', 'arate', 't', 't_err',
'e_cov_1_1', 'e_cov_1_2', 'e_cov_2_2'
]:
data[col][dng] = ngcat['exp_' + col][ng]
# Some need to be renamed:
data['snr_w'][dng] = ngcat['exp_s2n_w'][ng]
data['snr_t'][dng] = ngcat['exp_t_s2n'][ng]
data['sens_1'][dng] = ngcat['exp_e_sens_1'][ng]
data['sens_2'][dng] = ngcat['exp_e_sens_2'][ng]
data['stamp_size'][dng] = ngcat['box_size'][ng]
# Combine the flags we have so far:
print 'flags range from %d to %d' % (np.min(
ngcat['flags'][ngcat['flags'] > 0]), np.max(ngcat['flags']))
print 'exp_flags range from %d to %d' % (np.min(
ngcat['exp_flags'][ngcat['exp_flags'] > 0]), np.max(
ngcat['exp_flags']))
data['error_flag'] = 2**30
data['error_flag'][dng] = ngcat['flags'][ng]
data['error_flag'][dng] |= ngcat['exp_flags'][ng]
# Calculate mean sensitivity
data['sens_avg'][dng] = (ngcat['exp_e_sens_1'][ng] +
ngcat['exp_e_sens_2'][ng]) / 2.
# Calculate the recommended weight.
data['w'][dng] = 1.0 / (2.0 * 0.22 * 0.22 + ngcat['exp_e_cov_1_1'][ng] +
ngcat['exp_e_cov_2_2'][ng])
# Calculate log10(sb)
data['log10_sb_i'][dng] = np.log10(
np.abs(ngcat['exp_flux_i'][ng] / ngcat['exp_t'][ng]))
# swap e1 signs
for tag in ['e_1', 'e_cov_1_2', 'e_cov_2_1']:
data[tag][dng] *= -1.0
data['e_cov_2_1'][dng] = data['e_cov_1_2'][dng]
# unblind
for tag in ['e_1', 'e_2']:
data[tag][dng] /= unblind.get_factor()
# Bring in the round columns from the s2n catalog
scat = fitsio.read(config['ngmix_s2n'])
sc, dsc = numpy_util.match(scat['id'], info['COADD_OBJECTS_ID'])
data['snr_r'][dsc] = scat['exp_s2n_r'][sc]
data['t_r'][dsc] = scat['exp_T_r'][sc]
data['snr_t_r'][dsc] = scat['exp_T_s2n_r'][sc]
# Check that round_flags are consisten with current error_flag
print 'round_flags range from %d to %d' % (np.min(
scat['round_flags'][scat['round_flags'] > 0]),
np.max(scat['round_flags']))
print 'exp_round_flags range from %d to %d' % (
np.min(scat['exp_round_flags'][scat['exp_round_flags'] > 0]),
np.max(scat['exp_round_flags'][scat['exp_round_flags'] < 2**30]))
assert np.all(data['error_flag'][dsc][scat['round_flags'][sc] > 0] > 0)
assert np.all(
data['error_flag'][dsc][scat['exp_round_flags'][sc] == 2**30] > 0)
# Combine the round flags into the error_flag column
data['error_flag'][dsc] |= scat['round_flags'][sc]
data['error_flag'][dsc] |= scat['exp_round_flags'][sc]
# Bring in the mean psf information from the psf catalog
pcat = fitsio.read(config['ngmix_psfs'])
pc, dpc = numpy_util.match(pcat['id'], info['COADD_OBJECTS_ID'])
data['mean_psf_e1'][dpc] = pcat['psfrec_e'][pc, 0]
data['mean_psf_e2'][dpc] = pcat['psfrec_e'][pc, 1]
data['mean_psf_t'][dpc] = pcat['psfrec_T'][pc]
# swap e1 signs
for tag in ['mean_psf_e1']:
data[tag][dng] *= -1.0
# Before writing out, make all the names upper case
data.dtype.names = tuple([name.upper() for name in data.dtype.names])
fitsio.write(config['release_ngmix'], data, clobber=True)
#find objects within the searchRadius and whose noOfObs is atleast 3
angSepMask = sphdist(ra, dec, medRa, medDec) <= (searchRadius*60) #& (nObs>=3.0) -- not needed since this has been taken care of while calculating residuals and medians previously
global triand
#separate these entire rows from the original testH5file
#we donot need to check for galaxies now, since the parameters being referred to have already been checked
#searchFile = triand[angSepMask]
objInRadius = objID[angSepMask]
k = triand[triand.get_where_list('obj_id == o') for o in objInRadius]
#this might be stupid
searchFile = []
for o in objInRadius:
searchFile.append(triand[triand.get_where_list('obj_id == o')])
m1, m2 = match(triand.col('obj_id'), objInRadius)
m1, m2 = match_multi(triand.col('obj_id'), objInRadius)
print triand[m1]
print triand.col('obj_id')[m1]
print objInRadius[m2]
#putting unique object IDs in this file
uniqueSearchFile = np.unique(searchFile['obj_id'])
#file containing data for objects inside pixel
objInPixelFile =searchFile[pixelIndexForObj == pixelNo]
#i make a copy of the file, since i need the original values later
objInPixelFile2 = searchFile[pixelIndexForObj == pixelNo]
def get_matched_struct(self, pipe):
dt=[
('simid','i4'),
('ccd','i2'),
('id','i4'),
('flags','i4'),
('row','f8'),
('col','f8'),
('model','S4'),
('s2n_w','f8'),
('Ts2n','f8'),
('s2','f8'),
('sratio','f8'),
('objtype','S4'),
('g','f8',2),
('gcov','f8',(2,2)),
('gsens','f8',2),
('Tmean','f8'),
('weight','f8'),
('use1','i2'),
('use2','i2'),
('use3','i2'),
('use4','i2')]
# note this s2n min is just the limit used in the
# shear measurement code
wgal=pipe.get_gals(s2n_min=self.conf['shear_s2n_min'])
gals=pipe.cat[wgal]
output=zeros(gals.size, dtype=dt)
print wgal.size, pipe.shear_res.size
w,=where(gals['simid'] != pipe.shear_res['simid'])
if w.size != 0:
raise ValueError("gals and shear don't line up")
mcat,mshear=match(gals['simid'], pipe.shear_res['simid'])
if mshear.size != pipe.shear_res.size:
mess="""not all shear objects matched
by simid: %d %d""" % (pipe.shear_res.size,mshear.size)
print mess
# gets simid, row, col
copy_fields(gals, output)
output['ccd'] = pipe['ccd']
output['flags'][:] = pipe.shear_res['flags'][:]
output['s2n_w'][:] = pipe.shear_res['s2n_w'][:]
output['Ts2n'][:] = pipe.shear_res['Ts2n'][:]
output['s2'][:] = pipe.shear_res['s2'][:]
output['sratio'][:] = sqrt(1./output['s2'][:])
for i in xrange(output.size):
output['objtype'][i] = pipe.shear_res['model'][i]
output['g'][:,:] = pipe.shear_res['g'][:,:]
output['gcov'][:,:,:] = pipe.shear_res['gcov'][:,:,:]
output['gsens'][:,:] = pipe.shear_res['gsens'][:,:]
output['Tmean'][:] = pipe.shear_res['Tmean'][:]
output['model'][:] = pipe.shear_res['model'][:]
wts=stats.get_weights(pipe.shear_res['gcov'][:,:,:])
output['weight'][:] = wts[:]
return output