def test_pymask_grid(example_oifits):
pa_prior = [30, 50]
sep_prior = [100, 200]
cr_prior = [100, 300]
param_pymask = {
"pa_prior": pa_prior,
"sep_prior": sep_prior,
"cr_prior": cr_prior,
}
fit = amical.pymask_grid(str(example_oifits), **param_pymask)
assert isinstance(fit, dict)
# Pymask proposes to add some extra_error on the CP. This allows to take
# into account a possibly understimated uncertainties on the data. Indeed,
# some bias due to mismatch between the calibrator and the science spectral type,
# or some systematic temporal effect could produce additional errors not properly
# retrieved by the covariance matrix.
# In addition, we can also add some scaling parameter (`err_scale`) on the CP
# uncertainties to deal with the number of independant closure phases (N(N-1)(N-2)/6)
# compare to the dependant one ((N-1)(N-2)/2). If you considere the full CP set (35 for
# a 7 holes mask), you possibly over-use your data, so you have to scale
# your uncertainties by the factor of additional CP, which is sqrt(N/3).
# ** Note that if you used only a subset of CP (by selecting one common hole to
# save the oifits, see amical.save for details), this additional `err_scale` is unusable.
fit2 = amical.pymask_grid(inputdata, **param_pymask)
param_mcmc = {'niters': 800,
'walkers': 100,
'initial_guess': [146, 47, 244],
'burn_in': 100}
fit3 = amical.pymask_mcmc(inputdata, **param_pymask, **param_mcmc)
cr_pymask = amical.pymask_cr_limit(inputdata, nsim=500, ncore=12, smax=250,
nsep=100, cmax=5000, nth=30, ncrat=60)
if use_candid & use_pymask:
plt.figure()
plt.plot(cr_candid['r'], cr_candid['cr_limit'],
label='CANDID', alpha=.5, lw=3)
def test_pymask(filepath):
fit1 = amical.pymask_grid(str(filepath))
assert isinstance(fit1, dict)
fit2 = amical.pymask_grid([filepath])
assert isinstance(fit2, dict)
def test_pymask(example_oifits):
fit1 = amical.pymask_grid(str(example_oifits))
assert isinstance(fit1, dict)
fit2 = amical.pymask_grid([example_oifits])
assert isinstance(fit2, dict)