Tibeuleu/FOC_Reduction
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propagate statistical error with single covariance matrix

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Thibault Barnouin
2025-04-15 16:42:13 +02:00
parent c41482af77
commit e4acb9755c
2 changed files with 37 additions and 32 deletions
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28
package/FOC_reduction.py
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@@ -41,12 +41,12 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
# Background estimation
error_sub_type = "scott" # sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51, 51))
subtract_error = 1.50
subtract_error = 0.50
display_bkg = True
# Data binning
pxsize = 0.10
pxscale = "arcsec" # pixel, arcsec or full
pxsize = 4
pxscale = "px" # pixel, arcsec or full
rebin_operation = "sum" # sum or average
# Alignement
@@ -59,8 +59,8 @@ 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 = 0.150 # 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
@@ -216,29 +216,27 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
# FWHM of FOC have been estimated at about 0.03" across 1500-5000 Angstrom band, which is about 2 detector pixels wide
# see Jedrzejewski, R.; Nota, A.; Hack, W. J., A Comparison Between FOC and WFPC2
# Bibcode : 1995chst.conf...10J
I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, coeff_stokes, sigma_flux = proj_red.compute_Stokes(
I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, s_IQU_stat = proj_red.compute_Stokes(
data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=transmitcorr
)
I_bkg, Q_bkg, U_bkg, S_cov_bkg, header_bkg, coeff_stokes, sigma_flux_bkg = proj_red.compute_Stokes(
I_bkg, Q_bkg, U_bkg, S_cov_bkg, header_bkg, s_IQU_stat_bkg = proj_red.compute_Stokes(
background, background_error, np.array(True).reshape(1, 1), headers, FWHM=None, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=False
)
# Step 3:
# Rotate images to have North up
if rotate_North:
I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes, sigma_flux = proj_red.rotate_Stokes(
I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes, sigma_flux=sigma_flux, SNRi_cut=None
I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes, s_IQU_stat = proj_red.rotate_Stokes(
I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes, s_IQU_stat=s_IQU_stat, SNRi_cut=None
)
I_bkg, Q_bkg, U_bkg, S_cov_bkg, data_mask_bkg, header_bkg, sigma_flux_bkg = proj_red.rotate_Stokes(
I_bkg, Q_bkg, U_bkg, S_cov_bkg, np.array(True).reshape(1, 1), header_bkg, sigma_flux=sigma_flux_bkg, SNRi_cut=None
I_bkg, Q_bkg, U_bkg, S_cov_bkg, data_mask_bkg, header_bkg, s_IQU_stat_bkg = proj_red.rotate_Stokes(
I_bkg, Q_bkg, U_bkg, S_cov_bkg, np.array(True).reshape(1, 1), header_bkg, s_IQU_stat=s_IQU_stat_bkg, SNRi_cut=None
)
# 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, coeff_stokes=coeff_stokes, sigma_flux=sigma_flux
)
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, s_IQU_stat=s_IQU_stat)
P_bkg, debiased_P_bkg, s_P_bkg, s_P_P_bkg, PA_bkg, s_PA_bkg, s_PA_P_bkg = proj_red.compute_pol(
I_bkg, Q_bkg, U_bkg, S_cov_bkg, header_bkg, coeff_stokes=coeff_stokes, sigma_flux=sigma_flux_bkg
I_bkg, Q_bkg, U_bkg, S_cov_bkg, header_bkg, s_IQU_stat=s_IQU_stat_bkg
)
# Step 4: