Tibeuleu/FOC_Reduction
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Save the raw total flux image as PrimaryHDU

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Thibault Barnouin
2025-03-14 14:30:30 +01:00
parent 749a08eae0
commit 4ac47f8e3d
2 changed files with 58 additions and 17 deletions
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34
package/FOC_reduction.py
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@@ -40,13 +40,13 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
display_crop = False
# Background estimation
error_sub_type = "scott" # sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51, 51))
subtract_error = 2.0
error_sub_type = "scott" # sqrt, sturges, rice, freedman-diaconis, scott (default) or shape (example (51, 51))
subtract_error = 3.0
display_bkg = True
# Data binning
pxsize = 0.05
pxscale = "arcsec" # pixel, arcsec or full
pxsize = 40
pxscale = "px" # pixel, arcsec or full
rebin_operation = "sum" # sum or average
# Alignement
@@ -59,15 +59,15 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
# Smoothing
smoothing_function = "combine" # gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
smoothing_FWHM = 0.075 # If None, no smoothing is done
smoothing_scale = "arcsec" # pixel or arcsec
smoothing_FWHM = 1.5 # If None, no smoothing is done
smoothing_scale = "px" # pixel or arcsec
# Rotation
rotate_North = True
# Polarization map output
P_cut = 5 # if >=1.0 cut on the signal-to-noise else cut on the confidence level in Q, U
SNRi_cut = 5.0 # I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
SNRi_cut = 1.0 # I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
flux_lim = None # lowest and highest flux displayed on plot, defaults to bkg and maximum in cut if None
scale_vec = 3
step_vec = 1 # plot all vectors in the array. if step_vec = 2, then every other vector will be plotted if step_vec = 0 then all vectors are displayed at full length
@@ -182,6 +182,14 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
norm=LogNorm(vmin=data_array[data_array > 0.0].min() * headers[0]["photflam"], vmax=data_array[data_array > 0.0].max() * headers[0]["photflam"]),
)
flux_data, flux_error, flux_mask, flux_head = (
deepcopy(data_array.sum(axis=0)),
deepcopy(np.sqrt(np.sum(error_array**2, axis=0))),
deepcopy(data_mask),
deepcopy(headers[0]),
)
flux_head["EXPTIME"] = np.sum([head["EXPTIME"] for head in headers])
# Rebin data to desired pixel size.
if (pxsize is not None) and not (pxsize == 1 and pxscale.lower() in ["px", "pixel", "pixels"]):
data_array, error_array, headers, Dxy, data_mask = proj_red.rebin_array(
@@ -233,6 +241,8 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
I_bkg, Q_bkg, U_bkg, S_cov_bkg, data_mask_bkg, header_bkg = proj_red.rotate_Stokes(
I_bkg, Q_bkg, U_bkg, S_cov_bkg, np.array(True).reshape(1, 1), header_bkg, SNRi_cut=None
)
flux_data, flux_error, flux_mask, flux_head = proj_red.rotate_data(np.array([flux_data]), np.array([flux_error]), flux_mask, [flux_head])
flux_data, flux_error, flux_head = flux_data[0], flux_error[0], flux_head[0]
# Compute polarimetric parameters (polarization degree and angle).
P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P = proj_red.compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes)
@@ -258,8 +268,10 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
figname,
data_folder=data_folder,
return_hdul=True,
flux_data=flux_data,
flux_head=flux_head,
)
outfiles.append("/".join([data_folder, Stokes_hdul[0].header["FILENAME"] + ".fits"]))
outfiles.append("/".join([data_folder, Stokes_hdul["I_STOKES"].header["FILENAME"] + ".fits"]))
# Step 5:
# crop to desired region of interest (roi)
@@ -269,15 +281,15 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
stokescrop.crop()
stokescrop.write_to("/".join([data_folder, figname + ".fits"]))
Stokes_hdul, header_stokes = stokescrop.hdul_crop, stokescrop.hdul_crop[0].header
outfiles.append("/".join([data_folder, Stokes_hdul[0].header["FILENAME"] + ".fits"]))
outfiles.append("/".join([data_folder, Stokes_hdul["I_STOKES"].header["FILENAME"] + ".fits"]))
data_mask = Stokes_hdul["data_mask"].data.astype(bool)
print(
"F_int({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(
header_stokes["PHOTPLAM"],
*sci_not(
Stokes_hdul[0].data[data_mask].sum() * header_stokes["PHOTFLAM"],
np.sqrt(Stokes_hdul[3].data[0, 0][data_mask].sum()) * header_stokes["PHOTFLAM"],
Stokes_hdul["I_STOKES"].data[data_mask].sum() * header_stokes["PHOTFLAM"],
np.sqrt(Stokes_hdul["IQU_COV_MATRIX"].data[0, 0][data_mask].sum()) * header_stokes["PHOTFLAM"],
2,
out=int,
),