From d2b59cf05a1a07e2cdcbed445711c719c0561e50 Mon Sep 17 00:00:00 2001 From: Thibault Barnouin Date: Fri, 23 Feb 2024 16:42:13 +0100 Subject: [PATCH] update main script for pylsp --- src/FOC_reduction.py | 221 +++++++++++++++++++++---------------------- 1 file changed, 110 insertions(+), 111 deletions(-) diff --git a/src/FOC_reduction.py b/src/FOC_reduction.py index d73540c..8761d88 100755 --- a/src/FOC_reduction.py +++ b/src/FOC_reduction.py @@ -1,200 +1,199 @@ -#!/usr/bin/python3 -#-*- coding:utf-8 -*- +# !/usr/bin/python3 +# -*- coding:utf-8 -*- """ Main script where are progressively added the steps for the FOC pipeline reduction. """ -#Project libraries +# Project libraries import numpy as np from copy import deepcopy -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.deconvolve import from_file_psf +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.query import retrieve_products, path_exists, system from matplotlib.colors import LogNorm def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=0, interactive=0): ## Reduction parameters - # Deconvolution + # Deconvolution deconvolve = False if deconvolve: - psf = 'gaussian' #Can be user-defined as well - #psf = from_file_psf(data_folder+psf_file) + # 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 = 0.15 psf_scale = 'arcsec' - psf_shape=(25,25) + psf_shape = (25, 25) iterations = 5 - algo="richardson" - - # Initial crop + algo = "richardson" + + # Initial crop display_crop = False - - # Background estimation - error_sub_type = 'freedman-diaconis' #sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51,51)) + + # Background estimation + error_sub_type = 'freedman-diaconis' # sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51, 51)) subtract_error = 1.00 display_error = False - - # Data binning + + # Data binning rebin = True pxsize = 0.10 - px_scale = 'arcsec' #pixel, arcsec or full - rebin_operation = 'sum' #sum or average - - # Alignement - align_center = 'center' #If None will not align the images + px_scale = 'arcsec' # pixel, arcsec or full + rebin_operation = 'sum' # sum or average + + # Alignement + align_center = 'center' # If None will not align the images display_bkg = False display_align = False display_data = False - - # Smoothing - smoothing_function = 'combine' #gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine - smoothing_FWHM = 0.10 #If None, no smoothing is done - smoothing_scale = 'arcsec' #pixel or arcsec - - # Rotation - rotate_data = False #rotation to North convention can give erroneous results + + # Smoothing + smoothing_function = 'combine' # gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine + smoothing_FWHM = 0.10 # If None, no smoothing is done + smoothing_scale = 'arcsec' # pixel or arcsec + + # Rotation + rotate_data = False # rotation to North convention can give erroneous results rotate_stokes = True - - # Final crop - #crop = False #Crop to desired ROI - #interactive = False #Whether to output to intercative analysis tool - - # Polarization map output - SNRp_cut = 3. #P measurments with SNR>3 - SNRi_cut = 30. #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 + + # Final crop + # crop = False #Crop to desired ROI + # interactive = False #Whether to output to intercative analysis tool + + # Polarization map output + SNRp_cut = 3. # P measurments with SNR>3 + SNRi_cut = 30. # 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 vec_scale = 3 - 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 + 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 ##### Pipeline start ## Step 1: - # Get data from fits files and translate to flux in erg/cm²/s/Angstrom. + # Get data from fits files and translate to flux in erg/cm²/s/Angstrom. if not infiles is None: - prod = np.array([["/".join(filepath.split('/')[:-1]),filepath.split('/')[-1]] for filepath in infiles],dtype=str) + 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"))) + 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: - target, products = retrieve_products(target,proposal_id,output_dir=output_dir) + target, products = retrieve_products(target, proposal_id, output_dir=output_dir) prod = products.pop() for prods in products: - main(target=target,infiles=["/".join(pr) for pr in prods],output_dir=output_dir) + main(target=target, infiles=["/".join(pr) for pr in prods], output_dir=output_dir) data_folder = prod[0][0] try: - plots_folder = data_folder.replace("data","plots") - except: + 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)) infiles = [p[1] for p in prod] data_array, headers = proj_fits.get_obs_data(infiles, data_folder=data_folder, compute_flux=True) - figname = "_".join([target,"FOC"]) + figname = "_".join([target, "FOC"]) if rebin: - if not px_scale in ['full']: - figtype = "".join(["b","{0:.2f}".format(pxsize),px_scale]) #additionnal informations + if px_scale not in ['full']: + figtype = "".join(["b", "{0:.2f}".format(pxsize), px_scale]) # additionnal informations else: figtype = "full" - if not smoothing_FWHM is None: - figtype += "_"+"".join(["".join([s[0] for s in smoothing_function.split("_")]),"{0:.2f}".format(smoothing_FWHM),smoothing_scale]) #additionnal informations + if smoothing_FWHM is not None: + figtype += "_"+"".join(["".join([s[0] for s in smoothing_function.split("_")]), "{0:.2f}".format(smoothing_FWHM), smoothing_scale]) # additionnal informations if align_center is None: figtype += "_not_aligned" - # Crop data to remove outside blank margins. + # 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) - # Deconvolve data using Richardson-Lucy iterative algorithm with a gaussian PSF of given FWHM. + # 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. + # Estimate error from data background, estimated from sub-image of desired sub_shape. background = None - 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="_".join([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="_".join([figname, "errors"]), plots_folder=plots_folder, return_background=True) if display_bkg: - 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) + 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) - # Align and rescale images with oversampling. + # 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) if display_align: - 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) + 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) - # Rebin data to desired pixel size. + # Rebin data to desired pixel size. if rebin: 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 + + # Rotate data to have North up if rotate_data: data_mask = np.ones(data_array.shape[1:]).astype(bool) alpha = headers[0]['orientat'] data_array, error_array, data_mask, headers = proj_red.rotate_data(data_array, error_array, data_mask, headers, -alpha) - #Plot array for checking output - if display_data and px_scale.lower() not in ['full','integrate']: - 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) + # Plot array for checking output + if display_data and px_scale.lower() not in ['full', 'integrate']: + 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) - 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)]) + 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 = proj_red.compute_Stokes(data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function,transmitcorr=False) - 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) + # 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 = proj_red.compute_Stokes(data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=False) + 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) ## Step 3: - # Rotate images to have North up + # Rotate images to have North up if rotate_stokes: 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) - 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) + 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) - # Compute polarimetric parameters (polarisation degree and angle). + # Compute polarimetric parameters (polarisation 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, headers) 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) ## Step 4: - # Save image to FITS. - 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) + # Save image to FITS. + 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) data_mask = Stokes_test[-1].data.astype(bool) ## Step 5: - # crop to desired region of interest (roi) + # crop to desired region of interest (roi) if crop: figtype += "_crop" - stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_test),norm=LogNorm()) + stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_test), norm=LogNorm()) stokescrop.crop() - stokescrop.writeto("/".join([data_folder,"_".join([figname,figtype+".fits"])])) + stokescrop.writeto("/".join([data_folder, "_".join([figname, figtype+".fits"])])) Stokes_test, data_mask, headers = stokescrop.hdul_crop, stokescrop.data_mask, [dataset.header for dataset in stokescrop.hdul_crop] - 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} ±t {1:.1f} °".format(headers[0]['pa_int'],np.ceil(headers[0]['pa_int_err']*10.)/10.)) - # Background values - print("F_bkg({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(headers[0]['photplam'],*proj_plots.sci_not(I_bkg[0,0]*headers[0]['photflam'],np.sqrt(S_cov_bkg[0,0][0,0])*headers[0]['photflam'],2,out=int))) - print("P_bkg = {0:.1f} ± {1:.1f} %".format(debiased_P_bkg[0,0]*100.,np.ceil(s_P_bkg[0,0]*1000.)/10.)) - print("PA_bkg = {0:.1f} ± {1:.1f} °".format(PA_bkg[0,0],np.ceil(s_PA_bkg[0,0]*10.)/10.)) - # Plot polarisation map (Background is either total Flux, Polarization degree or Polarization degree error). - if px_scale.lower() not in ['full','integrate'] and not interactive: - 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) - 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') - 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') - 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') - 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') - 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') - 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') - 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') - 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') + 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} ±t {1:.1f} °".format(headers[0]['pa_int'], np.ceil(headers[0]['pa_int_err']*10.)/10.)) + # Background values + print("F_bkg({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(headers[0]['photplam'], *proj_plots.sci_not(I_bkg[0, 0]*headers[0]['photflam'], np.sqrt(S_cov_bkg[0, 0][0, 0])*headers[0]['photflam'], 2, out=int))) + print("P_bkg = {0:.1f} ± {1:.1f} %".format(debiased_P_bkg[0, 0]*100., np.ceil(s_P_bkg[0, 0]*1000.)/10.)) + print("PA_bkg = {0:.1f} ± {1:.1f} °".format(PA_bkg[0, 0], np.ceil(s_PA_bkg[0, 0]*10.)/10.)) + # Plot polarisation map (Background is either total Flux, Polarization degree or Polarization degree error). + if px_scale.lower() not in ['full', 'integrate'] and not interactive: + 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) + 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') + 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') + 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') + 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') + 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') + 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') + 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') + 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') elif not interactive: - 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') + 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') elif px_scale.lower() not in ['full', 'integrate']: pol_map = proj_plots.pol_map(Stokes_test, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim) @@ -205,18 +204,18 @@ if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description='Query MAST for target products') - parser.add_argument('-t','--target', metavar='targetname', required=False, + 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, + 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('-f','--files', metavar='path', required=False, nargs='*', + 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, + 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', metavar='crop_boolean', required=False, + parser.add_argument('-c', '--crop', metavar='crop_boolean', required=False, help='whether to crop the analysis region', type=int, default=0) - parser.add_argument('-i','--interactive', metavar='interactive_boolean', required=False, + parser.add_argument('-i', '--interactive', metavar='interactive_boolean', required=False, help='whether to output to the interactive analysis tool', type=int, default=0) args = parser.parse_args() exitcode = main(target=args.target, proposal_id=args.proposal_id, infiles=args.files, output_dir=args.output_dir, crop=args.crop, interactive=args.interactive) - print("Finished with ExitCode: ",exitcode) + print("Finished with ExitCode: ", exitcode)