def set_averages(self):
nsh=len(files.SHNUMS)
dt=[('s2n','f8'),
('g1true','f8'),
('g2true','f8'),
('g1','f8'),('g1_err','f8'),
('g2','f8'),('g2_err','f8'),
('g1sens','f8'),('g1sens_err','f8'),
('g2sens','f8'),('g2sens_err','f8')]
out=zeros(nsh, dtype=dt)
for i,shnum in enumerate(files.SHNUMS):
data=self.datadict[shnum]
shres=stats.get_mean_shear_shstruct(data)
wts=stats.get_weights(data['gcov'])
out['s2n'][shnum-1],err = wmom(data[self.s2n_field], wts)
out['g1true'][shnum-1] = cluster_step.sh1exp[shnum]
out['g2true'][shnum-1] = cluster_step.sh2exp[shnum]
for n in shres:
out[n][shnum-1] = shres[n]
self.avg=out
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
