Tibeuleu/FOC_Reduction
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correction on error propagation in compute_Stokes

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Thibault Barnouin
2022-03-17 11:40:00 +01:00
parent 4c8533ab94
commit 44c96dc8a2
75 changed files with 47 additions and 35 deletions
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src/FOC_reduction.py
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@@ -103,7 +103,7 @@ def main():
rebin = True
if rebin:
pxsize = 0.10
px_scale = 'arcsec' #pixel or arcsec
px_scale = 'arcsec' #pixel, arcsec or full
rebin_operation = 'sum' #sum or average
# Alignement
align_center = 'image' #If None will align image to image center
@@ -117,10 +117,11 @@ def main():
rotate_data = False #rotation to North convention can give erroneous results
# Polarization map output
figname = 'IC5063_FOC' #target/intrument name
figtype = '_combine_FWHM020' #additionnal informations
SNRp_cut = 7. #P measurments with SNR>3
SNRi_cut = 180. #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
figtype = '_combine_FWHM020_pol' #additionnal informations
SNRp_cut = 3. #P measurments with SNR>3
SNRi_cut = 70. #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
step_vec = 0 #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted
# if step_vec = 0 then all vectors are displayed at full length
##### Pipeline start
## Step 1:
@@ -150,12 +151,17 @@ def main():
if (data < 0.).any():
print("ETAPE 4 : ", data)
# Align and rescale images with oversampling.
data_array, error_array, headers, data_mask = proj_red.align_data(data_array, headers, error_array, upsample_factor=int(Dxy.min()), ref_center=align_center, return_shifts=False)
data_mask = np.zeros(data_array.shape[1:]).astype(bool)
if px_scale.lower() not in ['full','integrate']:
data_array, error_array, headers, data_mask = proj_red.align_data(data_array, headers, error_array, upsample_factor=int(Dxy.min()), ref_center=align_center, return_shifts=False)
for data in data_array:
if (data < 0.).any():
print("ETAPE 5 : ", data)
vertex = image_hull((1.-data_mask),step=5,null_val=0.,inside=True)
if px_scale.lower() not in ['full','integrate']:
vertex = image_hull((1.-data_mask),step=5,null_val=0.,inside=True)
else:
vertex = np.array([0.,0.,data_array.shape[2],data_array.shape[2]])
shape = np.array([vertex[1]-vertex[0],vertex[3]-vertex[2]])
rectangle = [vertex[2], vertex[0], shape[1], shape[0], 0., 'w']
@@ -206,13 +212,16 @@ def main():
Stokes_test = proj_fits.save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, headers, figname+figtype, data_folder=data_folder, return_hdul=True)
# Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error).
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, 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(deepcopy(Stokes_test), data_mask, 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(deepcopy(Stokes_test), data_mask, 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(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_I_err", plots_folder=plots_folder, display='I_err')
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, 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(deepcopy(Stokes_test), data_mask, 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(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRp", plots_folder=plots_folder, display='SNRp')
if px_scale.lower() not in ['full','integrate']:
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, 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(deepcopy(Stokes_test), data_mask, 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(deepcopy(Stokes_test), data_mask, 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(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_I_err", plots_folder=plots_folder, display='I_err')
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, 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(deepcopy(Stokes_test), data_mask, 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(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRp", plots_folder=plots_folder, display='SNRp')
else:
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype, plots_folder=plots_folder, display='default')
return 0