#!/usr/bin/python # -*- coding:utf-8 -*- """ Main script where are progressively added the steps for the FOC pipeline reduction. """ # Project libraries from copy import deepcopy import os from os import system from os.path import exists as path_exists from matplotlib.colors import LogNorm import numpy as np from lib.background import subtract_bkg import lib.fits as proj_fits # Functions to handle fits files import lib.reduction as proj_red # Functions used in reduction pipeline import lib.plots as proj_plots # Functions for plotting data from lib.utils import sci_not, princ_angle def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir="./data", crop=False, interactive=False): # Reduction parameters # Deconvolution deconvolve = False if deconvolve: # from lib.deconvolve import from_file_psf psf = "gaussian" # Can be user-defined as well # psf = from_file_psf(data_folder+psf_file) psf_FWHM = 3.1 psf_scale = "px" psf_shape = None # (151, 151) iterations = 1 algo = "conjgrad" # Initial crop display_crop = False # Background estimation error_sub_type = "freedman-diaconis" # sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51, 51)) subtract_error = 1.0 display_bkg = False # Data binning pxsize = 0.05 pxscale = "arcsec" # pixel, arcsec or full rebin_operation = "sum" # sum or average # Alignement align_center = "center" # If None will not align the images display_align = False display_data = False # Transmittance correction transmitcorr = True # Smoothing smoothing_function = "combine" # gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine smoothing_FWHM = 0.1 # If None, no smoothing is done smoothing_scale = "arcsec" # pixel or arcsec # Rotation rotate_North = True # Polarization map output SNRp_cut = 3.0 # P measurments with SNR>3 SNRi_cut = 1.0 # I measurments with SNR>30, which implies an uncertainty in P of 4.7%. flux_lim = None # lowest and highest flux displayed on plot, defaults to bkg and maximum in cut if None scale_vec = 5 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 # Adaptive binning # in order to perfrom optimal binning, there are several steps to follow: # 1. Load the data again and preserve the full images # 2. Skip the cropping step but use the same error and background estimation # 3. Use the same alignment as the routine # 4. Skip the rebinning step # 5. Calulate the Stokes parameters without smoothing optimal_binning = True optimize = False # Pipeline start # Step 1: # Get data from fits files and translate to flux in erg/cm²/s/Angstrom. outfiles = [] if data_dir is None: if infiles is not None: prod = np.array([["/".join(filepath.split('/')[:-1]), filepath.split('/')[-1]] for filepath in infiles], dtype=str) obs_dir = "/".join(infiles[0].split("/")[:-1]) if not path_exists(obs_dir): system("mkdir -p {0:s} {1:s}".format(obs_dir, obs_dir.replace("data", "plots"))) if target is None: target = input("Target name:\n>") else: from lib.query import retrieve_products target, products = retrieve_products(target, proposal_id, output_dir=output_dir) prod = products.pop() for prods in products: outfiles.append(main(target=target, infiles=["/".join(pr) for pr in prods], output_dir=output_dir, crop=crop, interactive=interactive)) data_folder = prod[0][0] infiles = [p[1] for p in prod] data_array, headers = proj_fits.get_obs_data(infiles, data_folder=data_folder, compute_flux=True) else: infiles = [f for f in os.listdir(data_dir) if f.endswith('.fits') and f.startswith('x')] data_folder = data_dir if target is None: target = input("Target name:\n>") data_array, headers = proj_fits.get_obs_data(infiles, data_folder=data_folder, compute_flux=True) try: plots_folder = data_folder.replace("data", "plots") except ValueError: plots_folder = "." if not path_exists(plots_folder): system("mkdir -p {0:s} ".format(plots_folder)) figname = "_".join([target, "FOC"]) figtype = "" if (pxsize is not None) and not (pxsize == 1 and pxscale.lower() in ["px", "pixel", "pixels"]): if pxscale not in ["full"]: figtype = "".join(["b", "{0:.2f}".format(pxsize), pxscale]) # additionnal informations else: figtype = "full" if smoothing_FWHM is not None and smoothing_scale is not None: smoothstr = "".join([*[s[0] for s in smoothing_function.split("_")], "{0:.2f}".format(smoothing_FWHM), smoothing_scale]) figtype = "_".join([figtype, smoothstr] if figtype != "" else [smoothstr]) if deconvolve: figtype = "_".join([figtype, "deconv"] if figtype != "" else ["deconv"]) if align_center is None: figtype = "_".join([figtype, "not_aligned"] if figtype != "" else ["not_aligned"]) if optimal_binning: options = {'optimize': optimize, 'optimal_binning': True} # Step 1: Load the data again and preserve the full images _data_array, _headers = deepcopy(data_array), deepcopy(headers) # Preserve full images _data_mask = np.ones(_data_array[0].shape, dtype=bool) # Step 2: Skip the cropping step but use the same error and background estimation (I don't understand why this is wrong) 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) data_mask = np.ones(data_array[0].shape, dtype=bool) background = None _, _, _, background, error_bkg = proj_red.get_error(data_array, headers, error_array, data_mask=data_mask, sub_type=error_sub_type, subtract_error=subtract_error, display=display_bkg, savename="_".join([figname, "errors"]), plots_folder=plots_folder, return_background=True) # _background is the same as background, but for the optimal binning _background = None _data_array, _error_array, _, = proj_red.get_error(_data_array, _headers, error_array=None, data_mask=_data_mask, sub_type=error_sub_type, subtract_error=False, display=display_bkg, savename="_".join([figname, "errors"]), plots_folder=plots_folder, return_background=False) _error_bkg = np.ones_like(_data_array) * error_bkg[:, 0, 0, np.newaxis, np.newaxis] _data_array, _error_array, _background, _ = subtract_bkg(_data_array, _error_array, _data_mask, background, _error_bkg) # Step 3: Align and rescale images with oversampling. (has to disable croping in align_data function) _data_array, _error_array, _headers, _, shifts, error_shifts = proj_red.align_data(_data_array, _headers, error_array=_error_array, background=_background, upsample_factor=10, ref_center=align_center, return_shifts=True, optimal_binning=True) print("Image shifts: {} \nShifts uncertainty: {}".format(shifts, error_shifts)) _data_mask = np.ones(_data_array[0].shape, dtype=bool) # Step 4: Compute Stokes I, Q, U _background = np.array([np.array(bkg).reshape(1, 1) for bkg in _background]) _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)]) _I_stokes, _Q_stokes, _U_stokes, _Stokes_cov, _header_stokes = proj_red.compute_Stokes(_data_array, _error_array, _data_mask, _headers, FWHM=None, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=transmitcorr) _I_bkg, _Q_bkg, _U_bkg, _S_cov_bkg, _header_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) # Step 5: Compute polarimetric parameters (polarization degree and angle). _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, _header_stokes) _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, _header_bkg) # Step 6: Save image to FITS. figname = "_".join([figname, figtype]) if figtype != "" else figname _Stokes_hdul = 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, _header_stokes, _data_mask, figname, data_folder=data_folder, return_hdul=True) # Step 6: _data_mask = _Stokes_hdul['data_mask'].data.astype(bool) print( "F_int({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format( _header_stokes["PHOTPLAM"], *sci_not( _Stokes_hdul[0].data[_data_mask].sum() * _header_stokes["PHOTFLAM"], np.sqrt(_Stokes_hdul[3].data[0, 0][_data_mask].sum()) * _header_stokes["PHOTFLAM"], 2, out=int, ), ) ) print("P_int = {0:.1f} ± {1:.1f} %".format(_header_stokes["p_int"] * 100.0, np.ceil(_header_stokes["sP_int"] * 1000.0) / 10.0)) print("PA_int = {0:.1f} ± {1:.1f} °".format(princ_angle(_header_stokes["pa_int"]), princ_angle(np.ceil(_header_stokes["sPA_int"] * 10.0) / 10.0))) # Background values print( "F_bkg({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format( _header_stokes["PHOTFLAM"], *sci_not(_I_bkg[0, 0] * _header_stokes["PHOTFLAM"], np.sqrt(_S_cov_bkg[0, 0][0, 0]) * _header_stokes["PHOTFLAM"], 2, out=int) ) ) print("P_bkg = {0:.1f} ± {1:.1f} %".format(_debiased_P_bkg[0, 0] * 100.0, np.ceil(_s_P_bkg[0, 0] * 1000.0) / 10.0)) print("PA_bkg = {0:.1f} ± {1:.1f} °".format(princ_angle(_PA_bkg[0, 0]), princ_angle(np.ceil(_s_PA_bkg[0, 0] * 10.0) / 10.0))) # Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error). if pxscale.lower() not in ['full', 'integrate'] and not interactive: proj_plots.polarization_map(deepcopy(_Stokes_hdul), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=scale_vec, savename="_".join([figname]), plots_folder=plots_folder, **options) proj_plots.polarization_map(deepcopy(_Stokes_hdul), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=scale_vec, savename="_".join([figname, "I"]), plots_folder=plots_folder, display='Intensity', **options) proj_plots.polarization_map(deepcopy(_Stokes_hdul), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=scale_vec, savename="_".join([figname, "P_flux"]), plots_folder=plots_folder, display='Pol_Flux', **options) proj_plots.polarization_map(deepcopy(_Stokes_hdul), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=scale_vec, savename="_".join([figname, "P"]), plots_folder=plots_folder, display='Pol_deg', **options) proj_plots.polarization_map(deepcopy(_Stokes_hdul), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=scale_vec, savename="_".join([figname, "PA"]), plots_folder=plots_folder, display='Pol_ang', **options) proj_plots.polarization_map(deepcopy(_Stokes_hdul), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=scale_vec, savename="_".join([figname, "I_err"]), plots_folder=plots_folder, display='I_err', **options) proj_plots.polarization_map(deepcopy(_Stokes_hdul), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=scale_vec, savename="_".join([figname, "P_err"]), plots_folder=plots_folder, display='Pol_deg_err', **options) proj_plots.polarization_map(deepcopy(_Stokes_hdul), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=scale_vec, savename="_".join([figname, "SNRi"]), plots_folder=plots_folder, display='SNRi', **options) proj_plots.polarization_map(deepcopy(_Stokes_hdul), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=scale_vec, savename="_".join([figname, "SNRp"]), plots_folder=plots_folder, display='SNRp', **options) elif not interactive: proj_plots.polarization_map(deepcopy(_Stokes_hdul), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=figname, plots_folder=plots_folder, display='integrate', **options) elif pxscale.lower() not in ['full', 'integrate']: proj_plots.pol_map(_Stokes_hdul, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim) else: options = {'optimize': optimize, 'optimal_binning': False} # Crop data to remove outside blank margins. 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) data_mask = np.ones(data_array[0].shape, dtype=bool) # 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 data_array, error_array, headers, background, error_bkg = proj_red.get_error(data_array, headers, error_array, data_mask=data_mask, sub_type=error_sub_type, subtract_error=subtract_error, display=display_bkg, savename="_".join([figname, "errors"]), plots_folder=plots_folder, return_background=True) # Align and rescale images with oversampling. data_array, error_array, headers, data_mask, shifts, error_shifts = proj_red.align_data( data_array, headers, error_array=error_array, background=background, upsample_factor=10, ref_center=align_center, return_shifts=True) if display_align: print("Image shifts: {} \nShifts uncertainty: {}".format(shifts, error_shifts)) proj_plots.plot_obs(data_array, headers, savename="_".join([figname, str(align_center)]), plots_folder=plots_folder, norm=LogNorm( vmin=data_array[data_array > 0.].min()*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam'])) # Rebin data to desired pixel size. if (pxsize is not None) and not (pxsize == 1 and pxscale.lower() in ["px", "pixel", "pixels"]): data_array, error_array, headers, Dxy, data_mask = proj_red.rebin_array( data_array, error_array, headers, pxsize=pxsize, scale=pxscale, operation=rebin_operation, data_mask=data_mask) # Rotate data to have same orientation rotate_data = np.unique([np.round(float(head["ORIENTAT"]), 3) for head in headers]).size != 1 if rotate_data: ang = np.mean([head["ORIENTAT"] for head in headers]) for head in headers: head["ORIENTAT"] -= ang data_array, error_array, data_mask, headers = proj_red.rotate_data(data_array, error_array, data_mask, headers) if display_data: proj_plots.plot_obs( data_array, headers, savename="_".join([figname, "rotate_data"]), plots_folder=plots_folder, norm=LogNorm( vmin=data_array[data_array > 0.0].min() * headers[0]["photflam"], vmax=data_array[data_array > 0.0].max() * headers[0]["photflam"] ), ) # Plot array for checking output if display_data and pxscale.lower() not in ['full', 'integrate']: proj_plots.plot_obs(data_array, headers, savename="_".join([figname, "rebin"]), plots_folder=plots_folder, norm=LogNorm( vmin=data_array[data_array > 0.].min()*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam'])) background = np.array([np.array(bkg).reshape(1, 1) for bkg in background]) 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)]) # Step 2: # Compute Stokes I, Q, U with smoothed polarized images # SMOOTHING DISCUSSION : # FWHM of FOC have been estimated at about 0.03" across 1500-5000 Angstrom band, which is about 2 detector pixels wide # see Jedrzejewski, R.; Nota, A.; Hack, W. J., A Comparison Between FOC and WFPC2 # Bibcode : 1995chst.conf...10J I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes = proj_red.compute_Stokes( data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=transmitcorr) I_bkg, Q_bkg, U_bkg, S_cov_bkg, header_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) # Step 3: # Rotate images to have North up if rotate_North: I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes = proj_red.rotate_Stokes( I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes, SNRi_cut=None) I_bkg, Q_bkg, U_bkg, S_cov_bkg, data_mask_bkg, header_bkg = proj_red.rotate_Stokes(I_bkg, Q_bkg, U_bkg, S_cov_bkg, np.array(True).reshape(1, 1), header_bkg, SNRi_cut=None) # Compute polarimetric parameters (polarization degree and angle). 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, header_stokes) 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, header_bkg) # Step 4: # Save image to FITS. figname = "_".join([figname, figtype]) if figtype != "" else figname Stokes_hdul = 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, header_stokes, data_mask, figname, data_folder=data_folder, return_hdul=True) outfiles.append("/".join([data_folder, Stokes_hdul[0].header["FILENAME"] + ".fits"])) # Step 5: # crop to desired region of interest (roi) if crop: figname += "_crop" stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_hdul), norm=LogNorm()) stokescrop.crop() stokescrop.write_to("/".join([data_folder, figname+".fits"])) Stokes_hdul, header_stokes = stokescrop.hdul_crop, [dataset.header for dataset in stokescrop.hdul_crop] outfiles.append("/".join([data_folder, Stokes_hdul[0].header["FILENAME"] + ".fits"])) data_mask = Stokes_hdul['data_mask'].data.astype(bool) print( "F_int({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format( header_stokes["PHOTPLAM"], *sci_not( Stokes_hdul[0].data[data_mask].sum() * header_stokes["PHOTFLAM"], np.sqrt(Stokes_hdul[3].data[0, 0][data_mask].sum()) * header_stokes["PHOTFLAM"], 2, out=int, ), ) ) print("P_int = {0:.1f} ± {1:.1f} %".format(header_stokes["p_int"] * 100.0, np.ceil(header_stokes["sP_int"] * 1000.0) / 10.0)) print("PA_int = {0:.1f} ± {1:.1f} °".format(princ_angle(header_stokes["pa_int"]), princ_angle(np.ceil(header_stokes["sPA_int"] * 10.0) / 10.0))) # Background values print( "F_bkg({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format( header_stokes["PHOTPLAM"], *sci_not(I_bkg[0, 0] * header_stokes["PHOTPLAM"], np.sqrt(S_cov_bkg[0, 0][0, 0]) * header_stokes["PHOTPLAM"], 2, out=int) ) ) print("P_bkg = {0:.1f} ± {1:.1f} %".format(debiased_P_bkg[0, 0] * 100.0, np.ceil(s_P_bkg[0, 0] * 1000.0) / 10.0)) print("PA_bkg = {0:.1f} ± {1:.1f} °".format(princ_angle(PA_bkg[0, 0]), princ_angle(np.ceil(s_PA_bkg[0, 0] * 10.0) / 10.0))) # Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error). if pxscale.lower() not in ['full', 'integrate'] and not interactive: proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, scale_vec=scale_vec, savename="_".join([figname]), plots_folder=plots_folder, **options) proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, scale_vec=scale_vec, savename="_".join([figname, "I"]), plots_folder=plots_folder, display='Intensity', **options) proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, scale_vece=scale_vec, savename="_".join([figname, "P_flux"]), plots_folder=plots_folder, display='Pol_Flux', **options) proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, scale_vec=scale_vec, savename="_".join([figname, "P"]), plots_folder=plots_folder, display='Pol_deg', **options) proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, scale_vec=scale_vec, savename="_".join([figname, "PA"]), plots_folder=plots_folder, display='Pol_ang', **options) proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, scale_vec=scale_vec, savename="_".join([figname, "I_err"]), plots_folder=plots_folder, display='I_err', **options) proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, scale_vec=scale_vec, savename="_".join([figname, "P_err"]), plots_folder=plots_folder, display='Pol_deg_err', **options) proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, scale_vec=scale_vec, savename="_".join([figname, "SNRi"]), plots_folder=plots_folder, display='SNRi', **options) proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, scale_vec=scale_vec, savename="_".join([figname, "SNRp"]), plots_folder=plots_folder, display='SNRp', **options) elif not interactive: proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=figname, plots_folder=plots_folder, display='integrate', **options) elif pxscale.lower() not in ['full', 'integrate']: proj_plots.pol_map(Stokes_hdul, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim) return outfiles if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description='Query MAST for target products') parser.add_argument('-t', '--target', metavar='targetname', required=False, help='the name of the target', type=str, default=None) parser.add_argument('-p', '--proposal_id', metavar='proposal_id', required=False, help='the proposal id of the data products', type=int, default=None) parser.add_argument('-d', '--data_dir', metavar='directory_path', required=False, help='directory path to the data products', type=str, default=None) parser.add_argument('-f', '--files', metavar='path', required=False, nargs='*', help='the full or relative path to the data products', default=None) parser.add_argument('-o', '--output_dir', metavar='directory_path', required=False, help='output directory path for the data products', type=str, default="./data") parser.add_argument('-c', '--crop', action='store_true', required=False, help='whether to crop the analysis region') parser.add_argument('-i', '--interactive', action='store_true', required=False, help='whether to output to the interactive analysis tool') args = parser.parse_args() exitcode = main(target=args.target, proposal_id=args.proposal_id, data_dir=args.data_dir, infiles=args.files, output_dir=args.output_dir, crop=args.crop, interactive=args.interactive) print("Finished with ExitCode: ", exitcode)