def extract_cols(self, data):
"""
get the actual columns we want to write
"""
from esutil.numpy_util import copy_fields
dt = [(self["ra_col"], "f8"), (self["dec_col"], "f8"), (self["e1_col"], "f8"), (self["e2_col"], "f8")]
if self["shear_style"] == "lensfit":
dt += [(self["e1sens_col"], "f8"), (self["e2sens_col"], "f8")]
dt += [("weight", "f8")]
if self["scinv_style"] == "interp":
nzl = data[self["scinv_col"]].shape[1]
dt += [(self["scinv_col"], "f8", nzl)]
elif self["scinv_style"] == "interp-summed":
nzl = self.scinv_data.size
dt += [("scinv", "f8", nzl)]
else:
dt += [(self["z_col"], "f8")]
newdata = numpy.zeros(data.size, dtype=dt)
copy_fields(data, newdata)
if self["scinv_style"] == "interp-summed":
newdata["scinv"] = self.scinv_data[newaxis, :]
var = 2 * self["shapenoise"] ** 2 + data[self["e_cov_11_col"]] + data[self["e_cov_22_col"]]
weight = 1.0 / var
newdata["weight"] = weight
return newdata
def add_scinv_to_raw(self, fname, clobber=True):
"""
Read in the raw file and write out a new file with just ra,dec and the
mean scinv as a function of lens redshift
"""
import lensing
outfile = self.scinv_name(fname)
if os.path.exists(outfile) and not clobber:
stdout.write("file exists, skipping\n")
return
if self.type != 'dr7pofz':
raise ValueError("only support dr4cc2 for now")
data = self.read_raw(fname)
zsvals = self.dr7pofz_zvals()
zsmax = zsvals.max()
zsmin = zsvals.min()
scalc = lensing.tools.ScinvCalculator(zsmin, zsmax)
output = self.scinv_array(scalc.n_zlens, data.size)
stdout.write("Copying in common columns\n")
stdout.flush()
numpy_util.copy_fields(data, output)
for i in range(data.size):
if ((i+1) % 10000) == 0:
stdout.write("%d/%d\n" % ((i+1), data.size))
stdout.flush()
pz = data['pz'][i]
output['mean_scinv'][i] = scalc.calc_mean_sigmacritinv(zsvals, pz)
hdr = {'zlvals': list(scalc.zlvals)}
stdout.write("Writing scinv file: %s\n" % outfile)
esutil.sfile.write(output, outfile, header=hdr)
stdout.write("done\n")
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
