def _calc_weight_reg(x, y, weights):
"""Apply a linear regression (IDL function) to fit the hole phase and error."""
reg = regress_noc(x, y, weights)
sig = cov2cor(reg.cov)[1]
hole_ph = reg.coeff
hole_ph_err = sig * np.sqrt(reg.MSE)
return hole_ph, hole_ph_err
def _normalize_all_obs(
bs_quantities,
v2_quantities,
cvis_arr,
cp_cov,
bs_v2_cov,
fluxes,
index_mask,
infos,
expert_plot=False,
save=False,
):
"""Normalize all observables by the appropriate factor proportional to
the averaged fluxes and the number of holes."""
bs_arr = bs_quantities["bs_arr"]
v2_arr = v2_quantities["v2_arr"]
v2 = v2_quantities["v2"]
v2_cov = v2_quantities["v2_cov"]
avar = v2_quantities["avar"]
err_avar = v2_quantities["err_avar"]
bs = bs_quantities["bs"]
bs_cov = bs_quantities["bs_cov"]
bs_var = bs_quantities["bs_var"]
n_holes = index_mask.n_holes
bs_arr_norm = bs_arr / np.mean(fluxes ** 3) * n_holes ** 3
v2_arr_norm = v2_arr / np.mean(fluxes ** 2) * n_holes ** 2
cvis_arr_norm = cvis_arr / np.mean(fluxes) * n_holes
v2_norm = (v2 / np.mean(fluxes ** 2)) * n_holes ** 2
v2_cov_norm = (v2_cov / np.mean(fluxes ** 4)) * n_holes ** 4
bs_norm = bs / np.mean(fluxes ** 3) * n_holes ** 3
avar_norm = avar / np.mean(fluxes ** 4) * n_holes ** 4
err_avar_norm = err_avar / np.mean(fluxes ** 4) * n_holes ** 4
try:
cp_cov_norm = cp_cov / np.mean(fluxes ** 6) * n_holes ** 6
except TypeError:
cp_cov_norm = None
bs_cov_norm = bs_cov / np.mean(fluxes ** 6) * n_holes ** 6
bs_v2_cov_norm = np.real(bs_v2_cov / np.mean(fluxes ** 5) * n_holes ** 5)
bs_var_norm = bs_var / np.mean(fluxes ** 6) * n_holes ** 6
if expert_plot:
plt.figure(figsize=(12, 6))
plt.title("DIAGNOSTIC PLOTS - V2 - %s" % infos.target)
plt.plot(v2_arr_norm[0], color="grey", alpha=0.2, label="V$^2$ dispersion")
plt.plot(v2_arr_norm.T, color="grey", alpha=0.2)
plt.plot(v2_norm, color="crimson", label="Raw V$^2$")
plt.grid(alpha=0.2)
plt.legend()
plt.xlabel("# baselines")
plt.ylabel("Raw visibilities")
plt.tight_layout()
# We compute the correlation matrix (to be used lated)
v2_cor = cov2cor(v2_cov)[0]
norm_quantities = {
"bs_arr": bs_arr_norm,
"v2_arr": v2_arr_norm,
"cvis_arr": cvis_arr_norm,
"bs": bs_norm,
"v2_cov": v2_cov_norm,
"bs_cov": bs_cov_norm,
"avar": avar_norm,
"err_avar": err_avar_norm,
"cp_cov": cp_cov_norm,
"bs_var": bs_var_norm,
"bs_v2_cov": bs_v2_cov_norm,
"v2_cor": v2_cor,
}
return v2_norm, norm_quantities
def _normalize_all_obs(bs_quantities, v2_quantities, cvis_arr, cp_cov,
bs_v2_cov, fluxes, index_mask, infos, expert_plot=False):
""" Normalize all observables by the appropriate factor proportional to
the averaged fluxes and the number of holes."""
bs_arr = bs_quantities['bs_arr']
v2_arr = v2_quantities['v2_arr']
v2 = v2_quantities['v2']
v2_cov = v2_quantities['v2_cov']
avar = v2_quantities['avar']
err_avar = v2_quantities['err_avar']
bs = bs_quantities['bs']
bs_cov = bs_quantities['bs_cov']
bs_var = bs_quantities['bs_var']
n_holes = index_mask.n_holes
bs_arr_norm = bs_arr / np.mean(fluxes ** 3) * n_holes ** 3
v2_arr_norm = v2_arr / np.mean(fluxes ** 2) * n_holes ** 2
cvis_arr_norm = cvis_arr / np.mean(fluxes) * n_holes
v2_norm = (v2 / np.mean(fluxes ** 2)) * n_holes ** 2
v2_cov_norm = (v2_cov / np.mean(fluxes ** 4)) * n_holes ** 4
bs_norm = bs / np.mean(fluxes ** 3) * n_holes ** 3
avar_norm = avar / np.mean(fluxes ** 4) * n_holes ** 4
err_avar_norm = err_avar / np.mean(fluxes ** 4) * n_holes ** 4
try:
cp_cov_norm = cp_cov / np.mean(fluxes ** 6) * n_holes ** 6
except TypeError:
cp_cov_norm = None
bs_cov_norm = bs_cov / np.mean(fluxes ** 6) * n_holes ** 6
bs_v2_cov_norm = np.real(bs_v2_cov / np.mean(fluxes ** 5) * n_holes ** 5)
bs_var_norm = bs_var / np.mean(fluxes ** 6) * n_holes ** 6
if expert_plot:
plt.figure(figsize=(5, 4))
plt.title('DIAGNOSTIC PLOTS - V2 - %s' % infos.target)
plt.plot(v2_arr_norm[0], color='grey', alpha=.2, label='V$^2$ dispersion')
plt.plot(v2_arr_norm.T, color='grey', alpha=.2)
plt.plot(v2_norm, color='crimson', label='Raw V$^2$')
plt.grid(alpha=.2)
plt.legend()
plt.xlabel('# baselines')
plt.ylabel('Raw visibilities')
plt.tight_layout()
# We compute the correlation matrix (to be used lated)
v2_cor = cov2cor(v2_cov)[0]
norm_quantities = {'bs_arr': bs_arr_norm, 'v2_arr': v2_arr_norm,
'cvis_arr': cvis_arr_norm, 'bs': bs_norm,
'v2_cov': v2_cov_norm, 'bs_cov': bs_cov_norm,
'avar': avar_norm, 'err_avar': err_avar_norm,
'cp_cov': cp_cov_norm, 'bs_var': bs_var_norm,
'bs_v2_cov': bs_v2_cov_norm, 'v2_cor': v2_cor
}
return v2_norm, norm_quantities