Tibeuleu/FOC_Reduction
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Change reduction to take into account transmittance, and try plotting FOC's FOV

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This commit is contained in:
Thibault Barnouin
2021-06-21 20:22:45 +02:00
parent d9c45870e6
commit 5493120a56
22 changed files with 124 additions and 49 deletions
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src/FOC_reduction.py
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@@ -8,20 +8,20 @@ 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
from lib.convex_hull import image_hull
def main():
##### User inputs
## Input and output locations
# globals()['data_folder'] = "../data/NGC1068_x274020/"
# infiles = ['x274020at.c0f.fits','x274020bt.c0f.fits','x274020ct.c0f.fits',
# 'x274020dt.c0f.fits','x274020et.c0f.fits','x274020ft.c0f.fits',
# 'x274020gt.c0f.fits','x274020ht.c0f.fits','x274020it.c0f.fits']
# globals()['plots_folder'] = "../plots/NGC1068_x274020/"
globals()['data_folder'] = "../data/NGC1068_x274020/"
infiles = ['x274020at.c0f.fits','x274020bt.c0f.fits','x274020ct.c0f.fits',
'x274020dt.c0f.fits','x274020et.c0f.fits','x274020ft.c0f.fits',
'x274020gt.c0f.fits','x274020ht.c0f.fits','x274020it.c0f.fits']
globals()['plots_folder'] = "../plots/NGC1068_x274020/"
# globals()['data_folder'] = "../data/NGC1068_x14w010/"
# infiles = ['x14w0101t_c0f.fits','x14w0102t_c0f.fits','x14w0103t_c0f.fits',
@@ -44,13 +44,13 @@ def main():
# infiles = ['x3mc0101m_c0f.fits','x3mc0102m_c0f.fits','x3mc0103m_c0f.fits']
# globals()['plots_folder'] = "../plots/3C109_x3mc010/"
globals()['data_folder'] = "../data/MKN463_x2rp030/"
infiles = ['x2rp0201t_c0f.fits', 'x2rp0202t_c0f.fits', 'x2rp0203t_c0f.fits',
'x2rp0204t_c0f.fits', 'x2rp0205t_c0f.fits', 'x2rp0206t_c0f.fits',
'x2rp0207t_c0f.fits', 'x2rp0301t_c0f.fits', 'x2rp0302t_c0f.fits',
'x2rp0303t_c0f.fits', 'x2rp0304t_c0f.fits', 'x2rp0305t_c0f.fits',
'x2rp0306t_c0f.fits', 'x2rp0307t_c0f.fits']
globals()['plots_folder'] = "../plots/MKN463_x2rp030/"
# globals()['data_folder'] = "../data/MKN463_x2rp030/"
# infiles = ['x2rp0201t_c0f.fits', 'x2rp0202t_c0f.fits', 'x2rp0203t_c0f.fits',
# 'x2rp0204t_c0f.fits', 'x2rp0205t_c0f.fits', 'x2rp0206t_c0f.fits',
# 'x2rp0207t_c0f.fits', 'x2rp0301t_c0f.fits', 'x2rp0302t_c0f.fits',
# 'x2rp0303t_c0f.fits', 'x2rp0304t_c0f.fits', 'x2rp0305t_c0f.fits',
# 'x2rp0306t_c0f.fits', 'x2rp0307t_c0f.fits']
# globals()['plots_folder'] = "../plots/MKN463_x2rp030/"
# globals()['data_folder'] = "../data/PG1630+377_x39510/"
# infiles = ['x3990201m_c0f.fits', 'x3990205m_c0f.fits', 'x3995101r_c0f.fits',
@@ -108,10 +108,10 @@ def main():
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 = 'MKN463_FOC' #target/intrument name
figtype = '_combine_FWHM020_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%.
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%.
step_vec = 1 #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted
##### Pipeline start
@@ -145,16 +145,26 @@ def main():
for data in data_array:
if (data < 0.).any():
print("ETAPE 5 : ", data)
# Rotate data to have North up
vertex = image_hull((1.-data_mask),step=5,null_val=0.,inside=True)
shape = np.array([vertex[1]-vertex[0],vertex[3]-vertex[2]])
rectangle = [vertex[2], vertex[0], shape[1], shape[0], 0., 'w']
# Rotate data to have North up
ref_header = copy.deepcopy(headers[0])
if rotate_data:
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[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:
if (data < 0.).any():
print("ETAPE 6 : ", data)
#Plot array for checking output
if display_data:
proj_plots.plot_obs(data_array, headers, vmin=data_array.min(), vmax=data_array.max(), savename=figname+"_center_"+align_center, plots_folder=plots_folder)
proj_plots.plot_obs(data_array, headers, vmin=data_array.min(), vmax=data_array.max(), rectangle =[rectangle,]*data_array.shape[0], savename=figname+"_center_"+align_center, plots_folder=plots_folder)
## Step 2:
# Compute Stokes I, Q, U with smoothed polarized images
@@ -166,8 +176,13 @@ def main():
## Step 3:
# Rotate images to have North up
ref_header = copy.deepcopy(headers[0])
if rotate_stokes:
ref_header = copy.deepcopy(headers[0])
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[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)
# 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)
@@ -178,11 +193,11 @@ 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), 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')
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')
return 0