Tibeuleu/FOC_Reduction
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add gaussian fitting for better background estimation

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Tibeuleu
2023-01-27 17:52:52 +01:00
parent c0083c97b6
commit 47c4dfd2a0
5 changed files with 197 additions and 44 deletions
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13
src/FOC_reduction.py
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@@ -131,28 +131,29 @@ 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 = False
display_error = False
# Data binning
rebin = True
pxsize = 0.10
px_scale = 'arcsec' #pixel, arcsec or full
pxsize = 10
px_scale = 'pixel' #pixel, arcsec or full
rebin_operation = 'sum' #sum or average
# Alignement
align_center = 'image' #If None will align image to image center
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 = None #If None, no smoothing is done
smoothing_scale = 'arcsec' #pixel or arcsec
# Rotation
rotate_stokes = True
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 = '_c_FWHM020' #additionnal informations
figtype = '_bin10px' #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
@@ -173,7 +174,7 @@ def main():
# 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_type=error_sub_type, 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, sub_type=error_sub_type, subtract_error=subtract_error, 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)