Tibeuleu/FOC_Reduction
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general improvements to pipeline

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Tibeuleu
2023-01-31 17:39:25 +01:00
parent 5c0d08a44b
commit fc80bafc66
4 changed files with 35 additions and 26 deletions
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20
src/FOC_reduction.py
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@@ -131,19 +131,19 @@ 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
subtract_error = True
display_error = False
# Data binning
rebin = True
pxsize = 10
px_scale = 'pixel' #pixel, arcsec or full
pxsize = 0.10
px_scale = 'arcsec' #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 = None #If None, no smoothing is done
smoothing_function = 'weighted_gaussian' #gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
smoothing_FWHM = 0.20 #If None, no smoothing is done
smoothing_scale = 'arcsec' #pixel or arcsec
# Rotation
rotate_stokes = True
@@ -153,7 +153,7 @@ def main():
final_display = True
# Polarization map output
figname = 'NGC1068_FOC' #target/intrument name
figtype = '_bin10px' #additionnal informations
figtype = '_wg_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%.
step_vec = 1 #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted
@@ -173,16 +173,13 @@ 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, subtract_error=subtract_error, 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)
# Rebin data to desired pixel size.
if rebin:
if px_scale.lower() in ['full','integrate']:
data_array, error_array, headers = proj_red.get_error(data_array, headers, error_array, data_mask, sub_shape=error_sub_shape, display=display_error, savename=figname+"_errors", plots_folder=plots_folder)
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
@@ -229,6 +226,9 @@ def main():
stokescrop.writeto(data_folder+figname+figtype+".fits")
Stokes_test, data_mask = stokescrop.hdul_crop, stokescrop.data_mask
print("F_int({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(headers[0]['photplam'],*proj_plots.sci_not(Stokes_test[0].data[data_mask].sum()*headers[0]['photflam'],np.sqrt(Stokes_test[3].data[0,0][data_mask].sum())*headers[0]['photflam'],2,out=int)))
print("P_int = {0:.1f} ± {1:.1f} %".format(headers[0]['p_int']*100.,np.ceil(headers[0]['p_int_err']*1000.)/10.))
print("PA_int = {0:.1f} ± {1:.1f} °".format(headers[0]['pa_int'],np.ceil(headers[0]['pa_int_err']*10.)/10.))
# Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error).
if px_scale.lower() not in ['full','integrate'] and final_display:
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype, plots_folder=plots_folder)