def benchmark_parameter_constraints(posbound, fluxbound, groupsize, seed=0):
def recipe():
return scopesim_groups(N1d=1,
border=300,
group_size=groupsize,
group_radius=12,
jitter=13,
magnitude=lambda N: np.random.normal(21, 2, N),
custom_subpixel_psf=epsf,
seed=seed)
cfg.bounds = {
'x_0': (posbound, posbound),
'y_0': (posbound, posbound),
'flux_0': (fluxbound, fluxbound)
}
img, tab = read_or_generate_image(f'1x{groupsize}_groups_seed{seed}', cfg,
recipe)
session = Session(cfg, image=img, input_table=tab)
session.fitter = TRFLSQFitter()
session.epsf = epsf
session.determine_psf_parameters()
n_calls, time = timeit.Timer(lambda: session.do_astrometry()).autorange()
return {'runtime': time / n_calls}
group_radius=1,
jitter=15,
magnitude=lambda N: [20.5] * N,
custom_subpixel_psf=epsf,
seed=10)
cfg.fithshape = 199
cfg.bounds = {'x_0': (0.3, 0.3), 'y_0': (0.3, 0.3), 'flux_0': (10000, 10000)}
cfg.niters = 1
img, tab = read_or_generate_image(f'169x1_group', cfg, recipe)
trialsession = Session(cfg, image=img, input_table=tab)
trialsession.fitter = TRFLSQFitter()
trialsession.epsf = epsf
trialsession.determine_psf_parameters()
trialsession.do_astrometry()
# %%
plot_image_with_source_and_measured(img, tab, trialsession.tables.result_table)
pass
# %%
plot_xy_deviation(trialsession.tables.result_table)
pass
# %% [markdown]
# # visualizing the grouper
# %%
rng = np.random.default_rng(seed=11)