Tibeuleu/FOC_Reduction
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NGC1068 with 0.05" binning

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This commit is contained in:
Thibault Barnouin
2022-07-05 12:49:26 +02:00
parent cc4625ffd5
commit e1e1f8635e
9 changed files with 19 additions and 19 deletions
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38
src/FOC_reduction.py
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@@ -20,17 +20,17 @@ from astropy.wcs import WCS
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']
## psf_file = 'NGC1068_f253m00.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']
# psf_file = 'NGC1068_f253m00.fits'
globals()['plots_folder'] = "../plots/NGC1068_x274020/"
globals()['data_folder'] = "../data/IC5063_x3nl030/"
infiles = ['x3nl0301r_c0f.fits','x3nl0302r_c0f.fits','x3nl0303r_c0f.fits']
# psf_file = 'IC5063_f502m00.fits'
globals()['plots_folder'] = "../plots/IC5063_x3nl030/"
# globals()['data_folder'] = "../data/IC5063_x3nl030/"
# infiles = ['x3nl0301r_c0f.fits','x3nl0302r_c0f.fits','x3nl0303r_c0f.fits']
## psf_file = 'IC5063_f502m00.fits'
# globals()['plots_folder'] = "../plots/IC5063_x3nl030/"
# globals()['data_folder'] = "../data/NGC1068_x14w010/"
# infiles = ['x14w0101t_c0f.fits','x14w0102t_c0f.fits','x14w0103t_c0f.fits',
@@ -105,7 +105,7 @@ def main():
# Data binning
rebin = True
if rebin:
pxsize = 0.10
pxsize = 0.05
px_scale = 'arcsec' #pixel, arcsec or full
rebin_operation = 'sum' #sum or average
# Alignement
@@ -113,19 +113,19 @@ def main():
display_data = False
# Smoothing
smoothing_function = 'combine' #gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
smoothing_FWHM = 0.20 #If None, no smoothing is done
smoothing_FWHM = 0.10 #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
# Final crop
crop = False #Crop to desired ROI
final_display = False
final_display = True
# Polarization map output
figname = 'IC5063_FOC' #target/intrument name
figtype = '_combine_FWHM020' #additionnal informations
SNRp_cut = 3. #P measurments with SNR>3
SNRi_cut = 60. #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
figname = 'NGC1068_FOC' #target/intrument name
figtype = '_combine_FWHM010' #additionnal informations
SNRp_cut = 5. #P measurments with SNR>3
SNRi_cut = 50. #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
# if step_vec = 0 then all vectors are displayed at full length
@@ -146,7 +146,7 @@ def main():
data_mask = np.ones(data_array.shape[1:]).astype(bool)
alpha = headers[0]['orientat']
mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)], [np.sin(-alpha), np.cos(-alpha)]])
data_array, error_array, headers, data_mask = proj_red.rotate_data(data_array, error_array, data_mask, headers, -alpha)
data_array, error_array, data_mask, headers = proj_red.rotate_data(data_array, error_array, data_mask, headers, -alpha)
# Align and rescale images with oversampling.
data_array, error_array, headers, data_mask = proj_red.align_data(data_array, headers, error_array=error_array, upsample_factor=10, ref_center=align_center, return_shifts=False)
@@ -181,7 +181,7 @@ def main():
# Rotate images to have North up
if rotate_stokes:
alpha = headers[0]['orientat']
I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, data_mask = proj_red.rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, -alpha, 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, -alpha, 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)