Tibeuleu/FOC_Reduction
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move alignement before rebinning, before background computation

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Thibault Barnouin
2022-04-12 17:17:34 +02:00
parent 7bbd2bc2e8
commit 3770a78940
52 changed files with 269 additions and 187 deletions
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src/lib/fits.py
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@@ -13,6 +13,7 @@ import numpy as np
from astropy.io import fits
from astropy import wcs
from lib.convex_hull import image_hull, clean_ROI
import matplotlib.pyplot as plt
def get_obs_data(infiles, data_folder="", compute_flux=False):
@@ -140,25 +141,25 @@ def save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P,
#Crop Data to mask
if data_mask.shape != (1,1):
I_stokes = I_stokes[vertex[0]:vertex[1],vertex[2]:vertex[3]]
Q_stokes = Q_stokes[vertex[0]:vertex[1],vertex[2]:vertex[3]]
U_stokes = U_stokes[vertex[0]:vertex[1],vertex[2]:vertex[3]]
P = P[vertex[0]:vertex[1],vertex[2]:vertex[3]]
debiased_P = debiased_P[vertex[0]:vertex[1],vertex[2]:vertex[3]]
s_P = s_P[vertex[0]:vertex[1],vertex[2]:vertex[3]]
s_P_P = s_P_P[vertex[0]:vertex[1],vertex[2]:vertex[3]]
PA = PA[vertex[0]:vertex[1],vertex[2]:vertex[3]]
s_PA = s_PA[vertex[0]:vertex[1],vertex[2]:vertex[3]]
s_PA_P = s_PA_P[vertex[0]:vertex[1],vertex[2]:vertex[3]]
I_stokes = I_stokes[vertex[2]:vertex[3],vertex[0]:vertex[1]]
Q_stokes = Q_stokes[vertex[2]:vertex[3],vertex[0]:vertex[1]]
U_stokes = U_stokes[vertex[2]:vertex[3],vertex[0]:vertex[1]]
P = P[vertex[2]:vertex[3],vertex[0]:vertex[1]]
debiased_P = debiased_P[vertex[2]:vertex[3],vertex[0]:vertex[1]]
s_P = s_P[vertex[2]:vertex[3],vertex[0]:vertex[1]]
s_P_P = s_P_P[vertex[2]:vertex[3],vertex[0]:vertex[1]]
PA = PA[vertex[2]:vertex[3],vertex[0]:vertex[1]]
s_PA = s_PA[vertex[2]:vertex[3],vertex[0]:vertex[1]]
s_PA_P = s_PA_P[vertex[2]:vertex[3],vertex[0]:vertex[1]]
new_Stokes_cov = np.zeros((3,3,shape[0],shape[1]))
new_Stokes_cov = np.zeros((*Stokes_cov.shape[:-2],*shape[::-1]))
for i in range(3):
for j in range(3):
Stokes_cov[i,j][(1-data_mask).astype(bool)] = 0.
new_Stokes_cov[i,j] = Stokes_cov[i,j][vertex[0]:vertex[1],vertex[2]:vertex[3]]
new_Stokes_cov[i,j] = Stokes_cov[i,j][vertex[2]:vertex[3],vertex[0]:vertex[1]]
Stokes_cov = new_Stokes_cov
data_mask = data_mask[vertex[0]:vertex[1],vertex[2]:vertex[3]]
data_mask = data_mask[vertex[2]:vertex[3],vertex[0]:vertex[1]]
data_mask = data_mask.astype(float, copy=False)
#Create HDUList object
@@ -166,6 +167,7 @@ def save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P,
#Add I_stokes as PrimaryHDU
header['datatype'] = ('I_stokes', 'type of data stored in the HDU')
I_stokes[(1-data_mask).astype(bool)] = 0.
primary_hdu = fits.PrimaryHDU(data=I_stokes, header=header)
hdul.append(primary_hdu)
@@ -178,6 +180,8 @@ def save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P,
[data_mask, 'Data_mask']]:
hdu_header = header.copy()
hdu_header['datatype'] = name
if not name == 'IQU_cov_matrix':
data[(1-data_mask).astype(bool)] = 0.
hdu = fits.ImageHDU(data=data,header=hdu_header)
hdul.append(hdu)