Tibeuleu/FOC_Reduction
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Put I,Q,U into single Stokes matrix

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Thibault Barnouin
2025-08-05 18:56:45 +02:00
parent 8b4c7c38f2
commit 20280e7226
4 changed files with 148 additions and 255 deletions
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48
package/lib/fits.py
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@@ -105,17 +105,15 @@ def get_obs_data(infiles, data_folder="", compute_flux=False):
return data_array, headers
def save_Stokes(
I_stokes, Q_stokes, U_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, 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.
----------
Inputs:
I_stokes, Q_stokes, U_stokes, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P : numpy.ndarray
Images (2D float arrays) containing the computed polarimetric data :
Stokes parameters I, Q, U, Polarization degree and debieased,
Stokes, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P : numpy.ndarray
Stokes cube (3D float arrays) containing the computed polarimetric data :
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
@@ -137,7 +135,7 @@ def save_Stokes(
----------
Return:
hdul : astropy.io.fits.hdu.hdulist.HDUList
HDUList containing I_stokes in the PrimaryHDU, then Q_stokes, U_stokes,
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).
Only returned if return_hdul is True.
@@ -148,8 +146,8 @@ def save_Stokes(
if data_mask.shape != (1, 1):
vertex = clean_ROI(data_mask)
shape = vertex[1::2] - vertex[0::2]
new_wcs.array_shape = shape
new_wcs.wcs.crpix = np.array(new_wcs.wcs.crpix) - vertex[0::-2]
new_wcs.array_shape = (4, *shape)
new_wcs.wcs.crpix[1:] = np.array(new_wcs.wcs.crpix[1:]) - vertex[0::-2]
header = new_wcs.to_header()
header["TELESCOP"] = (header_stokes["TELESCOP"] if "TELESCOP" in list(header_stokes.keys()) else "HST", "telescope used to acquire data")
@@ -161,6 +159,7 @@ def save_Stokes(
header["EXPTIME"] = (header_stokes["EXPTIME"], "Total exposure time in sec")
header["PROPOSID"] = (header_stokes["PROPOSID"], "PEP proposal identifier for observation")
header["TARGNAME"] = (header_stokes["TARGNAME"], "Target name")
header["TARGET_NAME"] = (header_stokes["TARGNAME"], "Target name")
header["ORIENTAT"] = (header_stokes["ORIENTAT"], "Angle between North and the y-axis of the image")
header["FILENAME"] = (filename, "ORIGINAL FILENAME")
header["BKG_TYPE"] = (header_stokes["BKG_TYPE"], "Bkg estimation method used during reduction")
@@ -174,9 +173,8 @@ def save_Stokes(
# Crop Data to mask
if data_mask.shape != (1, 1):
I_stokes = I_stokes[vertex[2] : vertex[3], vertex[0] : vertex[1]]
Q_stokes = Q_stokes[vertex[2] : vertex[3], vertex[0] : vertex[1]]
U_stokes = U_stokes[vertex[2] : vertex[3], vertex[0] : vertex[1]]
Stokes = Stokes[:, vertex[2] : vertex[3], vertex[0] : vertex[1]]
Stokes_cov = Stokes_cov[:, :, 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]]
@@ -184,14 +182,6 @@ def save_Stokes(
PA = PA[vertex[2] : vertex[3], vertex[0] : vertex[1]]
s_PA = s_PA[vertex[2] : vertex[3], vertex[0] : vertex[1]]
s_PA_P = s_PA_P[vertex[2] : vertex[3], vertex[0] : vertex[1]]
new_Stokes_cov = np.zeros((*Stokes_cov.shape[:-2], *shape[::-1]))
for i in range(3):
for j in range(3):
Stokes_cov[i, j][(1 - data_mask).astype(bool)] = 0.0
new_Stokes_cov[i, j] = Stokes_cov[i, j][vertex[2] : vertex[3], vertex[0] : vertex[1]]
Stokes_cov = new_Stokes_cov
data_mask = data_mask[vertex[2] : vertex[3], vertex[0] : vertex[1]]
data_mask = data_mask.astype(float, copy=False)
@@ -199,17 +189,15 @@ def save_Stokes(
hdul = fits.HDUList([])
# Add I_stokes as PrimaryHDU
header["datatype"] = ("I_stokes", "type of data stored in the HDU")
I_stokes[(1 - data_mask).astype(bool)] = 0.0
primary_hdu = fits.PrimaryHDU(data=I_stokes, header=header)
primary_hdu.name = "I_stokes"
header["datatype"] = ("Stokes", "type of data stored in the HDU")
Stokes[np.broadcast_to((1 - data_mask).astype(bool), Stokes.shape)] = 0.0
primary_hdu = fits.PrimaryHDU(data=Stokes, header=header)
primary_hdu.name = "Stokes"
hdul.append(primary_hdu)
# Add Q, U, Stokes_cov, P, s_P, PA, s_PA to the HDUList
# Add Stokes_cov, P, s_P, PA, s_PA to the HDUList
for data, name in [
[Q_stokes, "Q_stokes"],
[U_stokes, "U_stokes"],
[Stokes_cov, "IQU_cov_matrix"],
[Stokes_cov, "STOKES_COV"],
[P, "Pol_deg"],
[debiased_P, "Pol_deg_debiased"],
[s_P, "Pol_deg_err"],
@@ -221,7 +209,9 @@ def save_Stokes(
]:
hdu_header = header.copy()
hdu_header["datatype"] = name
if not name == "IQU_cov_matrix":
if name == "STOKES_COV":
data[np.broadcast_to((1 - data_mask).astype(bool), data.shape)] = 0.0
else:
data[(1 - data_mask).astype(bool)] = 0.0
hdu = fits.ImageHDU(data=data, header=hdu_header)
hdu.name = name