def get_optical_psf(expid, aos=False):
# set up objects. make sure I get the right mesh
digestor = Digestor()
PSF_Evaluator = Moment_Evaluator()
mesh_name = "Science-20121120s1-v20i2_All"
PSF_Interpolator = Mesh_Interpolator(mesh_name=mesh_name, directory=mesh_directory)
# This will be our main wavefront
WF = DECAM_Model_Wavefront(PSF_Interpolator=PSF_Interpolator)
# load up data
expid_path = "{0:08d}/{1:08d}".format(expid - expid % 1000, expid)
data_directory = base_directory + expid_path
files = sorted(glob(data_directory + "/*{0}".format("_selpsfcat.fits")))
# load up all the data from an exposure. Unfortunately, pandas is stupid and
# can't handle the vignet format, so we don't load those up
# note that you CAN load them up by passing "do_exclude=True", which then
# returns a second variable containing the vignets and aperture fluxes and
# errors
# data has certain columns removed, needed for processing.
# unfortunately I need full_data's vignettes and other info for later steps
# TODO optimize this cuz this is clearly wasteful
# I'm loading an HDUlist in 2 places, but overhauling the digestor to load it once would be a challenge
data = digestor.digest_fits(files[0], do_exclude=False)
metaHDUList = [fits.open(files[0])] # list of HDULists #META
for file in files[1:]:
tmpData = digestor.digest_fits(file, do_exclude=False)
data = data.append(tmpData)
metaHDUList.append(fits.open(file))
fit_i = jamierod_results.loc[expid]
misalignment = {
"z04d": fit_i["z04d"],
"z04x": fit_i["z04x"],
"z04y": fit_i["z04y"],
"z05d": fit_i["z05d"],
"z05x": fit_i["z05x"],
"z05y": fit_i["z05y"],
"z06d": fit_i["z06d"],
"z06x": fit_i["z06x"],
"z06y": fit_i["z06y"],
"z07d": fit_i["z07d"],
"z07x": fit_i["z07x"],
"z07y": fit_i["z07y"],
"z08d": fit_i["z08d"],
"z08x": fit_i["z08x"],
"z08y": fit_i["z08y"],
"z09d": fit_i["z09d"],
"z09x": fit_i["z09x"],
"z09y": fit_i["z09y"],
"z10d": fit_i["z10d"],
"z10x": fit_i["z10x"],
"z10y": fit_i["z10y"],
"rzero": fit_i["rzero"],
}
# print(misalignment['rzero'])
# rzero needs to be adjusted to be smaller than the stars!
x = 0.3 / 0.14 # 4
misalignment["rzero"] = 1 / (1 / misalignment["rzero"] - x)
# print(misalignment['rzero'])
# print(.14*x )
data["rzero"] = misalignment["rzero"]
optPSFStamps, model = WF.draw_psf(data, misalignment=misalignment)
# optPSFStamps is a numpy data cube
# full_data is data frame including vignettes of the stars
return optPSFStamps, metaHDUList
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
