def stamp_collector(expid, Nmax=0, rzero=None, snr_max=400, snr_min=90):
results = np.load(jamierod_dir + '/params/params_{0:08d}.npy'.format(expid)).item()
keys = sorted([key for key in results.keys() if ('error' not in key and 'fix' not in key and 'limit' not in key)])
misalignment = {key: results[key] for key in keys}
params = {'expid': expid,
'misalignment': misalignment,
# jamie's params:
'mesh_directory': '/nfs/slac/g/ki/ki22/roodman/ComboMeshesv20',
'mesh_name': 'Science-20121120s1-v20i2_All', }
params_default = {'snr_key': 'SNR_WIN',
'snr_max': snr_max,
'snr_min': snr_min,
'data_name': '_selpsfcat.fits',
'num_bins': 4
}
params_default.update(param_default_kils(expid))
params_default.update(params)
params = params_default
fits_files = glob(params['data_directory'] + '/*{0}'.format(params['data_name']))
dig = Digestor()
model, data_stamps = dig.digest_fits(fits_files[0], do_exclude=True)
data_stamps = data_stamps['VIGNET']
for fits_file in fits_files[1:]:
model_i, data_stamps_i = dig.digest_fits(fits_file, do_exclude=True)
model = model.append(model_i, ignore_index=True)
data_stamps = np.vstack((data_stamps, data_stamps_i['VIGNET']))
if params['snr_key'] in model.columns:
conds = ((model[params['snr_key']] > params['snr_min']) &
(model[params['snr_key']] < params['snr_max']))
data_stamps = data_stamps[conds.values]
model = model[conds]
if Nmax > 0:
# cut out Nmax objects
rows = np.random.choice(len(model),
Nmax, replace=False)
model = model.iloc[rows]
data_stamps = data_stamps[rows]
# set data_stamps to be float64 like the model stamps
data_stamps = data_stamps.astype(np.float64)
if type(rzero) == type(None):
model['rzero'] = results['rzero']
else:
model['rzero'] = rzero
# get the PSF_Interpolator
PSF_Interpolator = Mesh_Interpolator(mesh_name=params['mesh_name'],
directory=params['mesh_directory'])
WF = DECAM_Model_Wavefront(PSF_Interpolator=PSF_Interpolator,
num_bins=params['num_bins'],
model=model)
def plane(rzero,
z04d, z04x, z04y,
z05d, z05x, z05y,
z06d, z06x, z06y,
z07d, z07x, z07y,
z08d, z08x, z08y,
z09d, z09x, z09y,
z10d, z10x, z10y,
z11d, z11x, z11y,
dz, dx, dy, xt, yt,
e0, e1, e2,
delta1, delta2,
zeta1, zeta2, **kwargs):
wf_misalignment = {'z04d': z04d, 'z04x': z04x, 'z04y': z04y,
'z05d': z05d, 'z05x': z05x, 'z05y': z05y,
'z06d': z06d, 'z06x': z06x, 'z06y': z06y,
'z07d': z07d, 'z07x': z07x, 'z07y': z07y,
'z08d': z08d, 'z08x': z08x, 'z08y': z08y,
'z09d': z09d, 'z09x': z09x, 'z09y': z09y,
'z10d': z10d, 'z10x': z10x, 'z10y': z10y,
'z11d': z11d, 'z11x': z11x, 'z11y': z11y,
'dz': dz, 'dx': dx, 'dy': dy, 'xt': xt, 'yt': yt}
return wf_misalignment
wf_misalignment = plane(**misalignment)
# get evaluated PSFs
stamps, eval_data = WF.draw_psf(WF.data, force_interpolation=True,
misalignment=wf_misalignment)
# apply shear to stamps directly!
evaluated_psfs = WF.evaluate_psf(stamps, misalignment=wf_misalignment)
# make sure evaluated_psfs has the right index
evaluated_psfs.index = eval_data.index
# combine the results from PSF_Evaluator with your input data
combined_df = evaluated_psfs.combine_first(eval_data)
# TODO: Deal with constant jitter terms! Currently the results are off and seem to be off related to the overall magnitude...
## # add dc factors
## # combined_df['e0'] += results['e0']
## # combined_df['e1'] += results['e1']
## # combined_df['e2'] += results['e2']
## # combined_df['delta1'] += results['delta1']
## # combined_df['delta2'] += results['delta2']
## # combined_df['zeta1'] += results['zeta1']
## # combined_df['zeta2'] += results['zeta2']
## e1 = results['e1'] * 5 # empirical correction?
## e2 = results['e2'] * 5
## Mx = combined_df['Mx'].values
## My = combined_df['My'].values
## esq = e1 * e1 + e2 * e2
## if esq < 1e-8:
## A = 1 + esq / 8 + e1 * (0.5 + esq * 3 / 16)
## B = 1 + esq / 8 - e1 * (0.5 + esq * 3 / 16)
## C = e2 * (0.5 + esq * 3 / 16)
## else:
## temp = np.sqrt(1 - esq)
## cc = np.sqrt(0.5 * (1 + 1 / temp))
## temp = cc * (1 - temp) / esq
## C = temp * e2
## temp *= e1
## A = cc + temp
## B = cc - temp
## matrix = np.array([[A, C], [C, B]])
## matrix /= A * B - C * C
## offsetsx = -Mx * (A + C - 1)
## offsetsy = -My * (C + B - 1)
## # apply transformation
## stamps_transformed = []
## for stamp, offsetx, offsety in zip(stamps, offsetsx, offsetsy):
## stamps_transformed_i = affine_transform(stamp, matrix, (offsetx, offsety))
## stamps_transformed.append(stamps_transformed_i)
## stamps_transformed = np.array(stamps_transformed)
## evaluated_psfs = WF.evaluate_psf(stamps_transformed, misalignment=wf_misalignment)
## # make sure evaluated_psfs has the right index
## evaluated_psfs.index = eval_data.index
## # combine the results from PSF_Evaluator with your input data
## combined_df_trans = evaluated_psfs.combine_first(eval_data)
## e1 = combined_df['e1']
## e2 = combined_df['e2']
## e1_t = combined_df_trans['e1']
## e2_t = combined_df_trans['e2']
## print((e1 - e1_t) / (results['e1']))
## print((e2 - e2_t) / (results['e2']))
return combined_df, stamps, data_stamps
