modify files to comply with pep8 format
This commit is contained in:
@@ -1,4 +1,4 @@
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# !/usr/bin/python3
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#!/usr/bin/python3
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# -*- coding:utf-8 -*-
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"""
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Main script where are progressively added the steps for the FOC pipeline reduction.
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@@ -15,8 +15,8 @@ from matplotlib.colors import LogNorm
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def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=0, interactive=0):
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## Reduction parameters
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# Deconvolution
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# Reduction parameters
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# Deconvolution
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deconvolve = False
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if deconvolve:
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# from lib.deconvolve import from_file_psf
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@@ -28,38 +28,38 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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iterations = 5
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algo = "richardson"
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# Initial crop
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# Initial crop
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display_crop = False
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# Background estimation
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# Background estimation
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error_sub_type = 'freedman-diaconis' # sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51, 51))
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subtract_error = 1.00
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display_error = False
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# Data binning
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# Data binning
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rebin = True
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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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# Alignement
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align_center = 'center' # If None will not align the images
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display_bkg = False
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display_align = False
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display_data = False
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# Smoothing
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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_scale = 'arcsec' # pixel or arcsec
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# Rotation
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# Rotation
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rotate_data = False # rotation to North convention can give erroneous results
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rotate_stokes = True
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# Final crop
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# crop = False #Crop to desired ROI
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# interactive = False #Whether to output to intercative analysis tool
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# Final crop
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crop = False # Crop to desired ROI
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interactive = False # Whether to output to intercative analysis tool
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# Polarization map output
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SNRp_cut = 3. # P measurments with SNR>3
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@@ -68,10 +68,10 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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vec_scale = 3
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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
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##### Pipeline start
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## Step 1:
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# Pipeline start
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# Step 1:
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# Get data from fits files and translate to flux in erg/cm²/s/Angstrom.
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if not infiles is None:
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if infiles is not None:
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prod = np.array([["/".join(filepath.split('/')[:-1]), filepath.split('/')[-1]] for filepath in infiles], dtype=str)
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obs_dir = "/".join(infiles[0].split("/")[:-1])
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if not path_exists(obs_dir):
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@@ -100,12 +100,14 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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else:
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figtype = "full"
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if smoothing_FWHM is not None:
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figtype += "_"+"".join(["".join([s[0] for s in smoothing_function.split("_")]), "{0:.2f}".format(smoothing_FWHM), smoothing_scale]) # additionnal informations
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figtype += "_"+"".join(["".join([s[0] for s in smoothing_function.split("_")]),
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"{0:.2f}".format(smoothing_FWHM), smoothing_scale]) # additionnal informations
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if align_center is None:
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figtype += "_not_aligned"
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# Crop data to remove outside blank margins.
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data_array, error_array, headers = proj_red.crop_array(data_array, headers, step=5, null_val=0., inside=True, display=display_crop, savename=figname, plots_folder=plots_folder)
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data_array, error_array, headers = proj_red.crop_array(data_array, headers, step=5, null_val=0.,
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inside=True, display=display_crop, savename=figname, plots_folder=plots_folder)
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# Deconvolve data using Richardson-Lucy iterative algorithm with a gaussian PSF of given FWHM.
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if deconvolve:
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@@ -119,16 +121,16 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array > 0.].min()*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam'], savename="_".join([figname, "bkg"]), plots_folder=plots_folder)
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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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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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if display_align:
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proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array > 0.].min()*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam'], savename="_".join([figname, str(align_center)]), plots_folder=plots_folder)
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# Rebin data to desired pixel size.
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if rebin:
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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)
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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)
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# Rotate data to have North up
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# Rotate data to have North up
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if rotate_data:
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data_mask = np.ones(data_array.shape[1:]).astype(bool)
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alpha = headers[0]['orientat']
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@@ -139,34 +141,34 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array > 0.].min()*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam'], savename="_".join([figname, "rebin"]), plots_folder=plots_folder)
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background = np.array([np.array(bkg).reshape(1, 1) for bkg in background])
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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)])
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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)])
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## Step 2:
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# Compute Stokes I, Q, U with smoothed polarized images
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# SMOOTHING DISCUSSION :
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# FWHM of FOC have been estimated at about 0.03" across 1500-5000 Angstrom band, which is about 2 detector pixels wide
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# see Jedrzejewski, R.; Nota, A.; Hack, W. J., A Comparison Between FOC and WFPC2
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# Bibcode : 1995chst.conf...10J
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# Step 2:
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# Compute Stokes I, Q, U with smoothed polarized images
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# SMOOTHING DISCUSSION :
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# FWHM of FOC have been estimated at about 0.03" across 1500-5000 Angstrom band, which is about 2 detector pixels wide
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# see Jedrzejewski, R.; Nota, A.; Hack, W. J., A Comparison Between FOC and WFPC2
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# Bibcode : 1995chst.conf...10J
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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)
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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)
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## Step 3:
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# Rotate images to have North up
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# Step 3:
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# Rotate images to have North up
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if rotate_stokes:
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I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers = proj_red.rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, SNRi_cut=None)
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I_bkg, Q_bkg, U_bkg, S_cov_bkg, _, _ = proj_red.rotate_Stokes(I_bkg, Q_bkg, U_bkg, S_cov_bkg, np.array(True).reshape(1, 1), headers, SNRi_cut=None)
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# Compute polarimetric parameters (polarisation degree and angle).
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# Compute polarimetric parameters (polarisation degree and angle).
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P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P = proj_red.compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers)
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P_bkg, debiased_P_bkg, s_P_bkg, s_P_P_bkg, PA_bkg, s_PA_bkg, s_PA_P_bkg = proj_red.compute_pol(I_bkg, Q_bkg, U_bkg, S_cov_bkg, headers)
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## Step 4:
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# Save image to FITS.
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# Step 4:
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# Save image to FITS.
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Stokes_test = proj_fits.save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, headers, data_mask, "_".join([figname, figtype]), data_folder=data_folder, return_hdul=True)
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data_mask = Stokes_test[-1].data.astype(bool)
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## Step 5:
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# crop to desired region of interest (roi)
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# Step 5:
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# crop to desired region of interest (roi)
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if crop:
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figtype += "_crop"
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stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_test), norm=LogNorm())
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@@ -183,19 +185,29 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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print("PA_bkg = {0:.1f} ± {1:.1f} °".format(PA_bkg[0, 0], np.ceil(s_PA_bkg[0, 0]*10.)/10.))
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# Plot polarisation map (Background is either total Flux, Polarization degree or Polarization degree error).
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if px_scale.lower() not in ['full', 'integrate'] and not interactive:
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname, figtype]), plots_folder=plots_folder)
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname, figtype, "I"]), plots_folder=plots_folder, display='Intensity')
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname, figtype, "P_flux"]), plots_folder=plots_folder, display='Pol_Flux')
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname, figtype, "P"]), plots_folder=plots_folder, display='Pol_deg')
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname, figtype, "PA"]), plots_folder=plots_folder, display='Pol_ang')
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname, figtype, "I_err"]), plots_folder=plots_folder, display='I_err')
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname, figtype, "P_err"]), plots_folder=plots_folder, display='Pol_deg_err')
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname, figtype, "SNRi"]), plots_folder=plots_folder, display='SNRi')
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname, figtype, "SNRp"]), plots_folder=plots_folder, display='SNRp')
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim,
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step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname, figtype]), plots_folder=plots_folder)
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
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vec_scale=vec_scale, savename="_".join([figname, figtype, "I"]), plots_folder=plots_folder, display='Intensity')
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
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vec_scale=vec_scale, savename="_".join([figname, figtype, "P_flux"]), plots_folder=plots_folder, display='Pol_Flux')
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
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vec_scale=vec_scale, savename="_".join([figname, figtype, "P"]), plots_folder=plots_folder, display='Pol_deg')
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
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vec_scale=vec_scale, savename="_".join([figname, figtype, "PA"]), plots_folder=plots_folder, display='Pol_ang')
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
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vec_scale=vec_scale, savename="_".join([figname, figtype, "I_err"]), plots_folder=plots_folder, display='I_err')
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
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vec_scale=vec_scale, savename="_".join([figname, figtype, "P_err"]), plots_folder=plots_folder, display='Pol_deg_err')
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
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vec_scale=vec_scale, savename="_".join([figname, figtype, "SNRi"]), plots_folder=plots_folder, display='SNRi')
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
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vec_scale=vec_scale, savename="_".join([figname, figtype, "SNRp"]), plots_folder=plots_folder, display='SNRp')
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elif not interactive:
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename="_".join([figname, figtype]), plots_folder=plots_folder, display='integrate')
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut,
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savename="_".join([figname, figtype]), plots_folder=plots_folder, display='integrate')
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elif px_scale.lower() not in ['full', 'integrate']:
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pol_map = proj_plots.pol_map(Stokes_test, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim)
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proj_plots.pol_map(Stokes_test, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim)
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return 0
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@@ -204,18 +216,15 @@ if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description='Query MAST for target products')
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parser.add_argument('-t', '--target', metavar='targetname', required=False,
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help='the name of the target', type=str, default=None)
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parser.add_argument('-p', '--proposal_id', metavar='proposal_id', required=False,
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help='the proposal id of the data products', type=int, default=None)
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parser.add_argument('-f', '--files', metavar='path', required=False, nargs='*',
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help='the full or relative path to the data products', default=None)
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parser.add_argument('-t', '--target', metavar='targetname', required=False, help='the name of the target', type=str, default=None)
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parser.add_argument('-p', '--proposal_id', metavar='proposal_id', required=False, help='the proposal id of the data products', type=int, default=None)
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parser.add_argument('-f', '--files', metavar='path', required=False, nargs='*', help='the full or relative path to the data products', default=None)
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parser.add_argument('-o', '--output_dir', metavar='directory_path', required=False,
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help='output directory path for the data products', type=str, default="./data")
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parser.add_argument('-c', '--crop', metavar='crop_boolean', required=False,
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help='whether to crop the analysis region', type=int, default=0)
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parser.add_argument('-c', '--crop', metavar='crop_boolean', required=False, help='whether to crop the analysis region', type=int, default=0)
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parser.add_argument('-i', '--interactive', metavar='interactive_boolean', required=False,
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help='whether to output to the interactive analysis tool', type=int, default=0)
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args = parser.parse_args()
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exitcode = main(target=args.target, proposal_id=args.proposal_id, infiles=args.files, output_dir=args.output_dir, crop=args.crop, interactive=args.interactive)
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exitcode = main(target=args.target, proposal_id=args.proposal_id, infiles=args.files,
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output_dir=args.output_dir, crop=args.crop, interactive=args.interactive)
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print("Finished with ExitCode: ", exitcode)
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