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Add Stokes_cov_stat to fits and compute again P_debiased in plots

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Thibault Barnouin
2025-08-08 11:45:11 +02:00
parent e639695618
commit f4effac343
4 changed files with 223 additions and 142 deletions
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27
package/lib/fits.py
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@@ -105,7 +105,9 @@ def get_obs_data(infiles, data_folder="", compute_flux=False):
return data_array, headers
def save_Stokes(Stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, header_stokes, data_mask, filename, data_folder="", return_hdul=False):
def save_Stokes(
Stokes, Stokes_cov, Stokes_cov_stat, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, header_stokes, data_mask, filename, data_folder="", return_hdul=False
):
"""
Save computed polarimetry parameters to a single fits file,
updating header accordingly.
@@ -116,8 +118,9 @@ def save_Stokes(Stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P,
Stokes parameters I, Q, U, V, Polarization degree and debieased,
its error propagated and assuming Poisson noise, Polarization angle,
its error propagated and assuming Poisson noise.
Stokes_cov : numpy.ndarray
Covariance matrix of the Stokes parameters I, Q, U.
Stokes_cov, Stokes_cov_stat : numpy.ndarray
Covariance matrix of the Stokes parameters I, Q, U, V and its statistical
uncertainties part.
headers : header list
Header of reference some keywords will be copied from (CRVAL, CDELT,
INSTRUME, PROPOSID, TARGNAME, ORIENTAT, EXPTOT).
@@ -135,14 +138,14 @@ def save_Stokes(Stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P,
----------
Return:
hdul : astropy.io.fits.hdu.hdulist.HDUList
HDUList containing the Stokes cube in the PrimaryHDU, then
P, s_P, PA, s_PA in this order. Headers have been updated to relevant
informations (WCS, orientation, data_type).
HDUList containing the Stokes cube in the PrimaryHDU, then Stokes_cov,
Stokes_cov_stat, P, s_P, PA, s_PA in this order. Headers have been updated
to relevant informations (WCS, orientation, data_type).
Only returned if return_hdul is True.
"""
# Create new WCS object given the modified images
new_wcs = WCS(header_stokes).celestial.deepcopy()
header = remove_stokes_axis_from_header(header_stokes).copy()
header = remove_stokes_axis_from_header(header_stokes).copy() if header_stokes["NAXIS"] > 2 else header_stokes.copy()
if data_mask.shape != (1, 1):
vertex = clean_ROI(data_mask)
@@ -176,6 +179,7 @@ def save_Stokes(Stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P,
if data_mask.shape != (1, 1):
Stokes = Stokes[:, vertex[2] : vertex[3], vertex[0] : vertex[1]]
Stokes_cov = Stokes_cov[:, :, vertex[2] : vertex[3], vertex[0] : vertex[1]]
Stokes_cov_stat = Stokes_cov_stat[:, :, vertex[2] : vertex[3], vertex[0] : vertex[1]]
P = P[vertex[2] : vertex[3], vertex[0] : vertex[1]]
debiased_P = debiased_P[vertex[2] : vertex[3], vertex[0] : vertex[1]]
s_P = s_P[vertex[2] : vertex[3], vertex[0] : vertex[1]]
@@ -192,7 +196,7 @@ def save_Stokes(Stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P,
# Add I_stokes as PrimaryHDU
header["datatype"] = ("STOKES", "type of data stored in the HDU")
Stokes[np.broadcast_to((1 - data_mask).astype(bool), Stokes.shape)] = 0.0
hdu_head = add_stokes_axis_to_header(header, 2)
hdu_head = add_stokes_axis_to_header(header, 0)
primary_hdu = fits.PrimaryHDU(data=Stokes, header=hdu_head)
primary_hdu.name = "STOKES"
hdul.append(primary_hdu)
@@ -200,6 +204,7 @@ def save_Stokes(Stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P,
# Add Stokes_cov, P, s_P, PA, s_PA to the HDUList
for data, name in [
[Stokes_cov, "STOKES_COV"],
[Stokes_cov_stat, "STOKES_COV_STAT"],
[P, "Pol_deg"],
[debiased_P, "Pol_deg_debiased"],
[s_P, "Pol_deg_err"],
@@ -211,9 +216,9 @@ def save_Stokes(Stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P,
]:
hdu_head = header.copy()
hdu_head["datatype"] = name
if name == "STOKES_COV":
hdu_head = add_stokes_axis_to_header(hdu_head, 2)
hdu_head = add_stokes_axis_to_header(hdu_head, 3)
if name[:10] == "STOKES_COV":
hdu_head = add_stokes_axis_to_header(hdu_head, 0)
hdu_head = add_stokes_axis_to_header(hdu_head, 0)
data[np.broadcast_to((1 - data_mask).astype(bool), data.shape)] = 0.0
else:
data[(1 - data_mask).astype(bool)] = 0.0