Tibeuleu/FOC_Reduction
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remove axis error computation on polarization components

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Thibault Barnouin
2022-03-23 15:09:46 +01:00
parent b0075ff6fc
commit 0d8048240d
10 changed files with 14 additions and 58 deletions
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src/FOC_reduction.py
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@@ -105,7 +105,7 @@ def main():
align_center = 'image' #If None will align image to image center
display_data = False
# Smoothing
smoothing_function = 'combine' #gaussian_after, gaussian or combine
smoothing_function = 'gaussian' #gaussian_after, gaussian or combine
smoothing_FWHM = 0.20 #If None, no smoothing is done
smoothing_scale = 'arcsec' #pixel or arcsec
# Rotation
@@ -113,7 +113,7 @@ def main():
rotate_data = False #rotation to North convention can give erroneous results
# Polarization map output
figname = 'NGC1068_FOC' #target/intrument name
figtype = '_combine_FWHM020_waeP' #additionnal informations
figtype = '_combine_FWHM020_wae' #additionnal informations
SNRp_cut = 3. #P measurments with SNR>3
SNRi_cut = 30. #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
step_vec = 1 #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted
@@ -182,7 +182,7 @@ def main():
# 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, dP_dtheta, dPA_dtheta = proj_red.compute_Stokes(data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function)
I_stokes, Q_stokes, U_stokes, Stokes_cov = proj_red.compute_Stokes(data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function)
## Step 3:
# Rotate images to have North up
@@ -193,9 +193,9 @@ def main():
[np.sin(-alpha), np.cos(-alpha)]])
rectangle[0:2] = np.dot(mrot, np.asarray(rectangle[0:2]))+np.array(data_array.shape[1:])/2
rectangle[4] = alpha
I_stokes, Q_stokes, U_stokes, Stokes_cov, dP_dtheta, dPA_dtheta, headers, data_mask = proj_red.rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, dP_dtheta, dPA_dtheta, data_mask, headers, -ref_header['orientat'], SNRi_cut=None)
I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, data_mask = proj_red.rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, -ref_header['orientat'], 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, dP_dtheta, dPA_dtheta, headers)
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, headers)
## Step 4:
# crop to desired region of interest (roi)