Tibeuleu/FOC_Reduction
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allow to play with background estimation

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Tibeuleu
2023-02-10 17:01:25 +01:00
parent 22758be11a
commit 001759556d
6 changed files with 57 additions and 35 deletions
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31
src/FOC_reduction.py
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@@ -25,10 +25,10 @@ globals()['infiles'] = ['x274020at_c0f.fits','x274020bt_c0f.fits','x274020ct_c0f
#psf_file = 'NGC1068_f253m00.fits'
globals()['plots_folder'] = "../plots/NGC1068_x274020/"
#globals()['data_folder'] = "../data/IC5063_x3nl030/"
#globals()['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/"
globals()['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/"
#globals()['infiles'] = ['x14w0101t_c0f.fits','x14w0102t_c0f.fits','x14w0103t_c0f.fits',
@@ -131,8 +131,8 @@ def main():
display_crop = False
# Error estimation
error_sub_type = 'freedman-diaconis' #sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (15,15))
subtract_error = True
display_error = False
subtract_error = 1.25
display_error = True
# Data binning
rebin = True
pxsize = 0.10
@@ -152,10 +152,10 @@ def main():
crop = False #Crop to desired ROI
final_display = True
# Polarization map output
figname = 'NGC1068_FOC' #target/intrument name
figname = 'IC5063_FOC' #target/intrument name
figtype = '_c_020' #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%.
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%.
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
@@ -181,8 +181,8 @@ def main():
# Rebin data to desired pixel size.
if rebin:
data_array, error_array, headers, Dxy, data_mask = proj_red.rebin_array(data_array, error_array, headers, pxsize=pxsize, scale=px_scale, operation=rebin_operation, data_mask=data_mask)
# Rotate data to have North up
# Rotate data to have North up
if rotate_data:
data_mask = np.ones(data_array.shape[1:]).astype(bool)
alpha = headers[0]['orientat']
@@ -194,7 +194,10 @@ def main():
shape = np.array([vertex[1]-vertex[0],vertex[3]-vertex[2]])
rectangle = [vertex[2], vertex[0], shape[1], shape[0], 0., 'g']
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)
proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array>0.].min(), vmax=data_array[data_array>0.].max(), rectangle =[rectangle,]*data_array.shape[0], savename=figname+"_center_"+align_center, plots_folder=plots_folder)
background = np.array([np.array(bkg).reshape(1,1) for bkg in background])
background_error = np.array([np.array(np.sqrt((bkg-background[np.array([h['filtnam1']==head['filtnam1'] for h in headers],dtype=bool)].mean())**2/np.sum([h['filtnam1']==head['filtnam1'] for h in headers]))).reshape(1,1) for bkg,head in zip(background,headers)])
## Step 2:
# Compute Stokes I, Q, U with smoothed polarized images
@@ -202,8 +205,8 @@ 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, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function,transmitcorr=True)
I_bkg, Q_bkg, U_bkg, S_cov_bkg = proj_red.compute_Stokes(background, background_error, np.array(True).reshape(1,1), headers, FWHM=None, scale=smoothing_scale, smoothing=smoothing_function,transmitcorr=True)
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,transmitcorr=False)
I_bkg, Q_bkg, U_bkg, S_cov_bkg = proj_red.compute_Stokes(background, background_error, np.array(True).reshape(1,1), headers, FWHM=None, scale=smoothing_scale, smoothing=smoothing_function,transmitcorr=False)
## Step 3:
# Rotate images to have North up