fix smoothing, move background computation to library file
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@@ -18,12 +18,12 @@ from astropy.wcs import WCS
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##### User inputs
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## Input and output locations
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#globals()['data_folder'] = "../data/NGC1068_x274020/"
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#globals()['infiles'] = ['x274020at_c0f.fits','x274020bt_c0f.fits','x274020ct_c0f.fits',
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# 'x274020dt_c0f.fits','x274020et_c0f.fits','x274020ft_c0f.fits',
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# 'x274020gt_c0f.fits','x274020ht_c0f.fits','x274020it_c0f.fits']
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##psf_file = 'NGC1068_f253m00.fits'
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#globals()['plots_folder'] = "../plots/NGC1068_x274020/"
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globals()['data_folder'] = "../data/NGC1068_x274020/"
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globals()['infiles'] = ['x274020at_c0f.fits','x274020bt_c0f.fits','x274020ct_c0f.fits',
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'x274020dt_c0f.fits','x274020et_c0f.fits','x274020ft_c0f.fits',
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'x274020gt_c0f.fits','x274020ht_c0f.fits','x274020it_c0f.fits']
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#psf_file = 'NGC1068_f253m00.fits'
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globals()['plots_folder'] = "../plots/NGC1068_x274020/"
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#globals()['data_folder'] = "../data/IC5063_x3nl030/"
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#globals()['infiles'] = ['x3nl0301r_c0f.fits','x3nl0302r_c0f.fits','x3nl0303r_c0f.fits']
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@@ -80,12 +80,12 @@ from astropy.wcs import WCS
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#globals()['infiles'] = ['x3nl0201r_c0f.fits','x3nl0202r_c0f.fits','x3nl0203r_c0f.fits']
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#globals()['plots_folder'] = "../plots/MKN78_x3nl020/"
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globals()['data_folder'] = "../data/MRK231_x4qr010/"
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globals()['infiles'] = ['x4qr010ar_c0f.fits', 'x4qr010br_c0f.fits', 'x4qr010dr_c0f.fits',
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'x4qr010er_c0f.fits', 'x4qr010gr_c0f.fits', 'x4qr010hr_c0f.fits',
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'x4qr010jr_c0f.fits', 'x4qr010kr_c0f.fits', 'x4qr0104r_c0f.fits',
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'x4qr0105r_c0f.fits', 'x4qr0107r_c0f.fits', 'x4qr0108r_c0f.fits']
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globals()['plots_folder'] = "../plots/MRK231_x4qr010/"
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#globals()['data_folder'] = "../data/MRK231_x4qr010/"
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#globals()['infiles'] = ['x4qr010ar_c0f.fits', 'x4qr010br_c0f.fits', 'x4qr010dr_c0f.fits',
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# 'x4qr010er_c0f.fits', 'x4qr010gr_c0f.fits', 'x4qr010hr_c0f.fits',
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# 'x4qr010jr_c0f.fits', 'x4qr010kr_c0f.fits', 'x4qr0104r_c0f.fits',
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# 'x4qr0105r_c0f.fits', 'x4qr0107r_c0f.fits', 'x4qr0108r_c0f.fits']
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#globals()['plots_folder'] = "../plots/MRK231_x4qr010/"
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#globals()['data_folder'] = "../data/3C273_x0u20/"
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#globals()['infiles'] = ['x0u20101t_c0f.fits','x0u20102t_c0f.fits','x0u20103t_c0f.fits',
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@@ -130,30 +130,29 @@ def main():
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# Initial crop
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display_crop = False
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# Error estimation
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error_sub_shape = (15,15)
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error_sub_type = 'freedman-diaconis' #sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (15,15))
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display_error = False
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# Data binning
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rebin = True
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if rebin:
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pxsize = 0.05
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px_scale = 'arcsec' #pixel, arcsec or full
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rebin_operation = 'sum' #sum or average
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pxsize = 0.10
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px_scale = 'arcsec' #pixel, arcsec or full
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rebin_operation = 'sum' #sum or average
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# Alignement
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align_center = 'image' #If None will align image to image center
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display_data = False
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# Smoothing
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smoothing_function = 'combine' #gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
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smoothing_FWHM = 0.10 #If None, no smoothing is done
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smoothing_FWHM = 0.20 #If None, no smoothing is done
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smoothing_scale = 'arcsec' #pixel or arcsec
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# Rotation
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rotate_stokes = True #rotation to North convention can give erroneous results
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rotate_stokes = True
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rotate_data = False #rotation to North convention can give erroneous results
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# Final crop
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crop = False #Crop to desired ROI
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final_display = True
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final_display = False
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# Polarization map output
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figname = 'MRK231_FOC' #target/intrument name
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figtype = '_combine_FWHM010' #additionnal informations
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figname = 'NGC1068_FOC' #target/intrument name
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figtype = '_c_FWHM020' #additionnal informations
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SNRp_cut = 5. #P measurments with SNR>3
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SNRi_cut = 50. #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
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step_vec = 1 #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted
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@@ -174,8 +173,7 @@ def main():
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# Estimate error from data background, estimated from sub-image of desired sub_shape.
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background = None
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if px_scale.lower() not in ['full','integrate']:
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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)
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# 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)
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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)
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# Align and rescale images with oversampling.
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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)
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