Tibeuleu/FOC_Reduction
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Change display, margin handling and redo all reductions

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Thibault Barnouin
2021-06-17 22:00:52 +02:00
parent 8c0ab1fad1
commit 44a060e2ae
402 changed files with 176 additions and 2601 deletions
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src/FOC_reduction.py
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@@ -8,6 +8,7 @@ Main script where are progressively added the steps for the FOC pipeline reducti
import sys
import numpy as np
import copy
import matplotlib.pyplot as plt
import lib.fits as proj_fits #Functions to handle fits files
import lib.reduction as proj_red #Functions used in reduction pipeline
import lib.plots as proj_plots #Functions for plotting data
@@ -85,6 +86,8 @@ def main():
psf_scale = 'arcsec'
psf_shape=(9,9)
iterations = 10
# Cropping
display_crop = False
# Error estimation
error_sub_shape = (75,75)
display_error = False
@@ -98,17 +101,17 @@ def main():
align_center = 'image' #If None will align image to image center
display_data = False
# Smoothing
smoothing_function = 'gaussian' #gaussian_after, gaussian or combine
smoothing_FWHM = 0.10 #If None, no smoothing is done
smoothing_function = 'combine' #gaussian_after, gaussian or combine
smoothing_FWHM = 0.20 #If None, no smoothing is done
smoothing_scale = 'arcsec' #pixel or arcsec
# Rotation
rotate_stokes = True #rotation to North convention can give erroneous results
rotate_data = False #rotation to North convention can give erroneous results
# Polarization map output
figname = 'NGC1068_FOC' #target/intrument name
figtype = '_gaussian_FWHM010_rot' #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%.
figtype = '_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
@@ -119,7 +122,7 @@ def main():
if (data < 0.).any():
print("ETAPE 1 : ", data)
# Crop data to remove outside blank margins.
data_array, error_array = proj_red.crop_array(data_array, step=5, null_val=0., inside=True)
data_array, error_array = proj_red.crop_array(data_array, headers, step=5, null_val=0., inside=True, display=display_crop, savename=figname, plots_folder=plots_folder)
for data in data_array:
if (data < 0.).any():
print("ETAPE 2 : ", data)
@@ -138,14 +141,14 @@ def main():
if (data < 0.).any():
print("ETAPE 4 : ", data)
#Align and rescale images with oversampling.
data_array, error_array = proj_red.align_data(data_array, error_array, upsample_factor=int(Dxy.min()), ref_center=align_center, return_shifts=False)
data_array, error_array, 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)
# Rotate data to have North up
ref_header = copy.deepcopy(headers[0])
if rotate_data:
data_array, error_array, headers = proj_red.rotate_data(data_array, error_array, headers, -ref_header['orientat'])
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:
if (data < 0.).any():
print("ETAPE 6 : ", data)
@@ -159,13 +162,13 @@ 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, 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
if rotate_stokes:
ref_header = copy.deepcopy(headers[0])
I_stokes, Q_stokes, U_stokes, Stokes_cov, headers = proj_red.rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, -ref_header['orientat'], SNRi_cut=None)
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)
# 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)
@@ -175,11 +178,11 @@ 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), 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), 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), 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), 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), 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), data_mask, 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), data_mask, 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), data_mask, 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), data_mask, 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), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRp", plots_folder=plots_folder, display='SNRp')
return 0