Tibeuleu/FOC_Reduction
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fix background estimation in get_error

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Tibeuleu
2022-11-25 16:40:36 +01:00
parent 1052784286
commit 93f43394e2
118 changed files with 43 additions and 38 deletions
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12
src/FOC_reduction.py
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@@ -123,8 +123,8 @@ def main():
# Initial crop
display_crop = False
# Error estimation
error_sub_shape = (80,80)
display_error = False
error_sub_shape = (15,15)
display_error = True
# Data binning
rebin = True
if rebin:
@@ -143,7 +143,7 @@ def main():
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 = 'NGC1068_FOC' #target/intrument name
figtype = '_combine_FWHM010' #additionnal informations
@@ -163,10 +163,12 @@ def main():
# Deconvolve data using Richardson-Lucy iterative algorithm with a gaussian PSF of given FWHM.
if deconvolve:
data_array = proj_red.deconvolve_array(data_array, headers, psf=psf, FWHM=psf_FWHM, scale=psf_scale, shape=psf_shape, iterations=iterations, algo=algo)
# Estimate error from data background, estimated from sub-image of desired sub_shape.
background = None
if px_scale.lower() not in ['full','integrate']:
#data_array, error_array, headers, background = proj_red.get_error(data_array, headers, error_array, sub_shape=error_sub_shape, display=display_error, savename=figname+"_errors", plots_folder=plots_folder, return_background=True)
data_array, error_array, headers, background = proj_red.get_error2(data_array, headers, error_array, display=display_error, savename=figname+"_errors", plots_folder=plots_folder, return_background=True)
data_array, error_array, headers, background = proj_red.get_error_hist(data_array, headers, error_array, display=display_error, savename=figname+"_errors", plots_folder=plots_folder, return_background=True)
# data_array, error_array, headers, background = proj_red.get_error(data_array, headers, error_array, display=display_error, savename=figname+"_errors", plots_folder=plots_folder, return_background=True)
# Align and rescale images with oversampling.
data_array, error_array, headers, data_mask = proj_red.align_data(data_array, headers, error_array=error_array, background=background, upsample_factor=10, ref_center=align_center, return_shifts=False)