Tibeuleu/FOC_Reduction
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Add error propagation from uncertainties on the polarizer axis

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Thibault Barnouin
2021-06-23 13:37:54 +02:00
parent 5493120a56
commit 65aa4e2b2e
22 changed files with 125 additions and 268 deletions
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27
src/FOC_reduction.py
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@@ -109,9 +109,9 @@ def main():
rotate_data = False #rotation to North convention can give erroneous results
# Polarization map output
figname = 'NGC1068_FOC' #target/intrument name
figtype = '_3_combine_FWHM020' #additionnal informations
SNRp_cut = 30 #P measurments with SNR>3
SNRi_cut = 300 #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
figtype = '_2_combine_FWHM020_rot' #additionnal informations
SNRp_cut = 20 #P measurments with SNR>3
SNRi_cut = 130 #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
##### Pipeline start
@@ -156,7 +156,7 @@ def main():
alpha = ref_header['orientat']
mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)],
[np.sin(-alpha), np.cos(-alpha)]])
rectangle[0:2] = np.dot(mrot, np.asarray(rectangle[0:2]))
rectangle[0:2] = np.dot(mrot, np.asarray(rectangle[0:2]))+np.array(data_array.shape[1:])/2
rectangle[4] = alpha
data_array, error_array, data_mask, headers = proj_red.rotate_data(data_array, error_array, data_mask, headers, -ref_header['orientat'])
for data in data_array:
@@ -172,7 +172,7 @@ 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 = 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, pol_flux = 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
@@ -181,11 +181,11 @@ def main():
alpha = ref_header['orientat']
mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)],
[np.sin(-alpha), np.cos(-alpha)]])
rectangle[0:2] = np.dot(mrot, np.asarray(rectangle[0:2]))
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, data_mask, headers = proj_red.rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, -ref_header['orientat'], SNRi_cut=None)
I_stokes, Q_stokes, U_stokes, Stokes_cov, pol_flux, data_mask, headers = proj_red.rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, pol_flux, 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, 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, pol_flux, headers)
## Step 4:
# Save image to FITS.
@@ -193,11 +193,12 @@ def main():
## Step 5:
# Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error).
proj_plots.polarization_map(copy.deepcopy(Stokes_test), rectangle, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype, plots_folder=plots_folder, display=None)
proj_plots.polarization_map(copy.deepcopy(Stokes_test), rectangle, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P", plots_folder=plots_folder, display='Pol_deg')
proj_plots.polarization_map(copy.deepcopy(Stokes_test), rectangle, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P_err", plots_folder=plots_folder, display='Pol_deg_err')
proj_plots.polarization_map(copy.deepcopy(Stokes_test), rectangle, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRi", plots_folder=plots_folder, display='SNRi')
proj_plots.polarization_map(copy.deepcopy(Stokes_test), rectangle, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRp", plots_folder=plots_folder, display='SNRp')
proj_plots.polarization_map(copy.deepcopy(Stokes_test), rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype, plots_folder=plots_folder, display=None)
proj_plots.polarization_map(copy.deepcopy(Stokes_test), rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P_flux", plots_folder=plots_folder, display='Pol_Flux')
proj_plots.polarization_map(copy.deepcopy(Stokes_test), rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P", plots_folder=plots_folder, display='Pol_deg')
proj_plots.polarization_map(copy.deepcopy(Stokes_test), rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P_err", plots_folder=plots_folder, display='Pol_deg_err')
proj_plots.polarization_map(copy.deepcopy(Stokes_test), rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRi", plots_folder=plots_folder, display='SNRi')
proj_plots.polarization_map(copy.deepcopy(Stokes_test), rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRp", plots_folder=plots_folder, display='SNRp')
return 0