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 # Background estimation
error_sub_type = "scott" # sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51, 51)) 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 display_bkg = True
# Data binning # Data binning
pxsize = 0.10 pxsize = 4
pxscale = "arcsec" # pixel, arcsec or full pxscale = "px" # pixel, arcsec or full
rebin_operation = "sum" # sum or average rebin_operation = "sum" # sum or average
# Alignement # Alignement
@@ -59,8 +59,8 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
# Smoothing # Smoothing
smoothing_function = "combine" # gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine smoothing_function = "combine" # gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
smoothing_FWHM = 0.150 # If None, no smoothing is done smoothing_FWHM = 1.5 # If None, no smoothing is done
smoothing_scale = "arcsec" # pixel or arcsec smoothing_scale = "px" # pixel or arcsec
# Rotation # Rotation
rotate_North = True 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 # 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 # see Jedrzejewski, R.; Nota, A.; Hack, W. J., A Comparison Between FOC and WFPC2
# Bibcode : 1995chst.conf...10J # 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 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 background, background_error, np.array(True).reshape(1, 1), headers, FWHM=None, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=False
) )
# Step 3: # Step 3:
# Rotate images to have North up # Rotate images to have North up
if rotate_North: 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, s_IQU_stat = 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=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, 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, sigma_flux=sigma_flux_bkg, SNRi_cut=None 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). # 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( 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)
I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, coeff_stokes=coeff_stokes, sigma_flux=sigma_flux
)
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( 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: # Step 4:

41
package/lib/reduction.py
View File

@@ -1462,10 +1462,10 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale
header_stokes["PA_int"] = (PA_diluted, "Integrated polarization angle") header_stokes["PA_int"] = (PA_diluted, "Integrated polarization angle")
header_stokes["sPA_int"] = (np.ceil(PA_diluted_err * 10.0) / 10.0, "Integrated polarization angle error") header_stokes["sPA_int"] = (np.ceil(PA_diluted_err * 10.0) / 10.0, "Integrated polarization angle error")
return I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, coeff_stokes, sigma_flux return I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, s_IQU_stat
def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, coeff_stokes=None, sigma_flux=None): def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, s_IQU_stat=None):
""" """
Compute the polarization degree (in %) and angle (in deg) and their Compute the polarization degree (in %) and angle (in deg) and their
respective errors from given Stokes parameters. respective errors from given Stokes parameters.
@@ -1548,20 +1548,19 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, coeff_s
s_P_P = np.ones(I_stokes.shape) * fmax s_P_P = np.ones(I_stokes.shape) * fmax
s_PA_P = np.ones(I_stokes.shape) * fmax s_PA_P = np.ones(I_stokes.shape) * fmax
maskP = np.logical_and(mask, P > 0.0) maskP = np.logical_and(mask, P > 0.0)
if coeff_stokes is not None and sigma_flux is not None: if s_IQU_stat is not None:
s_P_P[maskP] = ( s_P_P[maskP] = (
P[maskP] P[maskP]
/ I_stokes[maskP] / I_stokes[maskP]
* np.sqrt( * np.sqrt(
np.sum( s_IQU_stat[0, 0][maskP]
[ - 2.0 / (I_stokes[maskP] * P[maskP] ** 2) * (Q_stokes[maskP] * s_IQU_stat[0, 1][maskP] + U_stokes[maskP] * s_IQU_stat[0, 2][maskP])
((coeff_stokes[1, i] * Q_stokes[maskP] + coeff_stokes[2, i] * U_stokes[maskP]) / (I_stokes[maskP] * P[maskP] ** 2) - coeff_stokes[0, i]) + 1.0
** 2 / (I_stokes[maskP] ** 2 * P[maskP] ** 4)
* sigma_flux[i][maskP] ** 2 * (
for i in range(sigma_flux.shape[0]) Q_stokes[maskP] ** 2 * s_IQU_stat[1, 1][maskP]
], + U_stokes[maskP] ** 2 * s_IQU_stat[2, 2][maskP] * Q_stokes[maskP] * U_stokes[maskP] * s_IQU_stat[1, 2][maskP]
axis=0, )
)[0]
) )
) )
else: else:
@@ -1588,7 +1587,7 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, coeff_s
return P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P return P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P
def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes, sigma_flux=None, SNRi_cut=None): def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes, s_IQU_stat=None, SNRi_cut=None):
""" """
Use scipy.ndimage.rotate to rotate I_stokes to an angle, and a rotation Use scipy.ndimage.rotate to rotate I_stokes to an angle, and a rotation
matrix to rotate Q, U of a given angle in degrees and update header matrix to rotate Q, U of a given angle in degrees and update header
@@ -1681,8 +1680,16 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_st
new_I_stokes[i, j], new_Q_stokes[i, j], new_U_stokes[i, j] = np.dot(mrot, np.array([new_I_stokes[i, j], new_Q_stokes[i, j], new_U_stokes[i, j]])).T new_I_stokes[i, j], new_Q_stokes[i, j], new_U_stokes[i, j] = np.dot(mrot, np.array([new_I_stokes[i, j], new_Q_stokes[i, j], new_U_stokes[i, j]])).T
new_Stokes_cov[:, :, i, j] = np.dot(mrot, np.dot(new_Stokes_cov[:, :, i, j], mrot.T)) new_Stokes_cov[:, :, i, j] = np.dot(mrot, np.dot(new_Stokes_cov[:, :, i, j], mrot.T))
if sigma_flux is not None: if s_IQU_stat is not None:
new_sigma_flux = sc_rotate(zeropad(sigma_flux, (sigma_flux.shape[0], *shape)), ang, order=1, reshape=False, cval=0.0) s_IQU_stat = zeropad(s_IQU_stat, [*s_IQU_stat.shape[:-2], *shape])
new_s_IQU_stat = np.zeros((*s_IQU_stat.shape[:-2], *shape))
for i in range(3):
for j in range(3):
new_s_IQU_stat[i, j] = sc_rotate(s_IQU_stat[i, j], ang, order=1, reshape=False, cval=0.0)
new_s_IQU_stat[i, i] = np.abs(new_s_IQU_stat[i, i])
for i in range(shape[0]):
for j in range(shape[1]):
new_s_IQU_stat[:, :, i, j] = np.dot(mrot, np.dot(new_s_IQU_stat[:, :, i, j], mrot.T))
# Update headers to new angle # Update headers to new angle
mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)], [np.sin(-alpha), np.cos(-alpha)]]) mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)], [np.sin(-alpha), np.cos(-alpha)]])
@@ -1737,8 +1744,8 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_st
new_header_stokes["PA_int"] = (PA_diluted, "Integrated polarization angle") new_header_stokes["PA_int"] = (PA_diluted, "Integrated polarization angle")
new_header_stokes["sPA_int"] = (np.ceil(PA_diluted_err * 10.0) / 10.0, "Integrated polarization angle error") new_header_stokes["sPA_int"] = (np.ceil(PA_diluted_err * 10.0) / 10.0, "Integrated polarization angle error")
if sigma_flux is not None: if s_IQU_stat is not None:
return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_data_mask, new_header_stokes, new_sigma_flux return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_data_mask, new_header_stokes, new_s_IQU_stat
else: else:
return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_data_mask, new_header_stokes return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_data_mask, new_header_stokes