Tibeuleu/FOC_Reduction
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Create analysis tool

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This commit is contained in:
Thibault Barnouin
2022-04-06 15:45:14 +02:00
parent c4311bbb4b
commit f6df92a1c2
15 changed files with 652 additions and 184 deletions
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49
src/lib/fits.py
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@@ -118,10 +118,11 @@ def save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P,
exp_tot = np.array([header['exptime'] for header in headers]).sum()
new_wcs = wcs.WCS(ref_header).deepcopy()
vertex = clean_ROI(data_mask)
shape = vertex[1::2]-vertex[0::2]
new_wcs.array_shape = shape
new_wcs.wcs.crpix = np.array(new_wcs.wcs.crpix) - vertex[0::-2]
if data_mask.shape != (1,1):
vertex = clean_ROI(data_mask)
shape = vertex[1::2]-vertex[0::2]
new_wcs.array_shape = shape
new_wcs.wcs.crpix = np.array(new_wcs.wcs.crpix) - vertex[0::-2]
header = new_wcs.to_header()
header['instrume'] = (ref_header['instrume'], 'Instrument from which data is reduced')
@@ -138,25 +139,27 @@ def save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P,
header['PA_int_err'] = (ref_header['PA_int_err'], 'Integrated polarization angle error')
#Crop Data to mask
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]]
new_Stokes_cov = np.zeros((3,3,shape[0],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]]
Stokes_cov = new_Stokes_cov
data_mask = data_mask[vertex[0]:vertex[1],vertex[2]:vertex[3]].astype(float, copy=False)
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]]
new_Stokes_cov = np.zeros((3,3,shape[0],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]]
Stokes_cov = new_Stokes_cov
data_mask = data_mask[vertex[0]:vertex[1],vertex[2]:vertex[3]]
data_mask = data_mask.astype(float, copy=False)
#Create HDUList object
hdul = fits.HDUList([])