Tibeuleu/FOC_Reduction
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save raw flux in fits file and display

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fix rebase display on main
This commit is contained in:
Thibault Barnouin
2025-03-19 16:38:36 +01:00
parent e3a3abdb0d
commit fb63b389e1
4 changed files with 204 additions and 110 deletions
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37
package/FOC_reduction.py
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@@ -20,6 +20,7 @@ import lib.reduction as proj_red # Functions used in reduction pipeline
import numpy as np
from lib.utils import princ_angle, sci_not
from matplotlib.colors import LogNorm
from astropy.wcs import WCS
def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=False, interactive=False):
@@ -40,9 +41,9 @@ 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, freedman-diaconis, scott (default) or shape (example (51, 51))
error_sub_type = "scott" # auto, sqrt, sturges, rice, freedman-diaconis, scott (default) or shape (example (51, 51))
subtract_error = 3.0
display_bkg = True
display_bkg = False
# Data binning
pxsize = 4
@@ -63,6 +64,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
smoothing_scale = "px" # pixel or arcsec
# Rotation
rotate_data = False
rotate_North = True
# Polarization map output
@@ -142,7 +144,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
)
# Rotate data to have same orientation
rotate_data = np.unique([np.round(float(head["ORIENTAT"]), 3) for head in headers]).size != 1
rotate_data = rotate_data or np.unique([np.round(float(head["ORIENTAT"]), 3) for head in headers]).size != 1
if rotate_data:
ang = np.mean([head["ORIENTAT"] for head in headers])
for head in headers:
@@ -158,7 +160,6 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
vmin=data_array[data_array > 0.0].min() * headers[0]["photflam"], vmax=data_array[data_array > 0.0].max() * headers[0]["photflam"]
),
)
# Align and rescale images with oversampling.
data_array, error_array, headers, data_mask, shifts, error_shifts = proj_red.align_data(
data_array,
@@ -243,6 +244,8 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
)
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]
elif not rotate_data:
figname += "_noROT"
# 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)
@@ -250,7 +253,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
# Step 4:
# Save image to FITS.
figname = "_".join([figname, figtype]) if figtype != "" else figname
savename = "_".join([figname, figtype]) if figtype != "" else figname
Stokes_hdul = proj_fits.save_Stokes(
I_stokes,
Q_stokes,
@@ -265,18 +268,18 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
s_PA_P,
header_stokes,
data_mask,
figname,
savename,
data_folder=data_folder,
return_hdul=True,
flux_data=flux_data,
flux_data=np.array([flux_data, flux_error, flux_mask]),
flux_head=flux_head,
)
outfiles.append("/".join([data_folder, Stokes_hdul["I_STOKES"].header["FILENAME"] + ".fits"]))
outfiles.append("/".join([data_folder, Stokes_hdul[0].header["FILENAME"] + ".fits"]))
# Step 5:
# crop to desired region of interest (roi)
if crop:
figname += "_crop"
savename += "_crop"
stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_hdul), norm=LogNorm())
stokescrop.crop()
stokescrop.write_to("/".join([data_folder, figname + ".fits"]))
@@ -286,10 +289,10 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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"],
flux_head["PHOTPLAM"],
*sci_not(
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"],
flux_data[flux_mask].sum() * flux_head["PHOTFLAM"],
np.sqrt(np.sum(flux_error[flux_mask] ** 2)) * flux_head["PHOTFLAM"],
2,
out=int,
),
@@ -315,12 +318,12 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
flux_lim=flux_lim,
step_vec=step_vec,
scale_vec=scale_vec,
savename="_".join([figname]),
savename=figname,
plots_folder=plots_folder,
)
for figtype, figsuffix in zip(
["Intensity", "Pol_flux", "Pol_deg", "Pol_ang", "I_err", "P_err", "SNRi", "SNRp", "confp"],
["I", "P_flux", "P", "PA", "I_err", "P_err", "SNRi", "SNRp", "confP"],
["FluxDensity", "Intensity", "Pol_flux", "Pol_deg", "Pol_ang", "I_err", "P_err", "SNRi", "SNRp", "confp"],
["F", "I", "P_flux", "P", "PA", "I_err", "P_err", "SNRi", "SNRp", "confP"],
):
try:
proj_plots.polarization_map(
@@ -331,7 +334,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
flux_lim=flux_lim,
step_vec=step_vec,
scale_vec=scale_vec,
savename="_".join([figname, figsuffix]),
savename="_".join([savename, figsuffix]),
plots_folder=plots_folder,
display=figtype,
)
@@ -339,7 +342,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
pass
elif not interactive:
proj_plots.polarization_map(
deepcopy(Stokes_hdul), data_mask, P_cut=P_cut, SNRi_cut=SNRi_cut, savename=figname, plots_folder=plots_folder, display="integrate"
deepcopy(Stokes_hdul), data_mask, P_cut=P_cut, SNRi_cut=SNRi_cut, savename=savename, plots_folder=plots_folder, display="integrate"
)
elif pxscale.lower() not in ["full", "integrate"]:
proj_plots.pol_map(Stokes_hdul, P_cut=P_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, scale_vec=scale_vec, flux_lim=flux_lim)