diff --git a/package/FOC_reduction.py b/package/FOC_reduction.py index dc1c192..afc0b0b 100755 --- a/package/FOC_reduction.py +++ b/package/FOC_reduction.py @@ -5,26 +5,19 @@ Main script where are progressively added the steps for the FOC pipeline reducti """ # 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 lib.fits as proj_fits # Functions to handle fits files +import lib.plots as proj_plots # Functions for plotting data +import lib.reduction as proj_red # Functions used in reduction pipeline 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 +from lib.utils import princ_angle, sci_not +from matplotlib.colors import LogNorm - - - -def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir="./data", crop=False, interactive=False): +def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=False, interactive=False): # Reduction parameters # Deconvolution deconvolve = False @@ -42,10 +35,8 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir= 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 @@ -55,7 +46,6 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir= # Alignement align_center = "center" # If None will not align the images - display_align = False display_data = False @@ -64,7 +54,7 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir= # 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_FWHM = 0.10 # If None, no smoothing is done smoothing_scale = "arcsec" # pixel or arcsec # Rotation @@ -84,47 +74,37 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir= # 3. Use the same alignment as the routine # 4. Skip the rebinning step # 5. Calulate the Stokes parameters without smoothing - optimal_binning = True + optimal_binning = False 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 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>") - - data_array, headers = proj_fits.get_obs_data(infiles, data_folder=data_folder, compute_flux=True) + 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)[0]) + data_folder = prod[0][0] 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)) + 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"]) figtype = "" @@ -133,65 +113,129 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir= 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} - + from lib.background import subtract_bkg + + 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_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_array, error_array, headers = proj_red.crop_array( + data_array, headers, step=5, null_val=0.0, 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, 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) + _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) + _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) - + _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) - + _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) + _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"], @@ -208,66 +252,196 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir= # 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) + _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) + 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.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} + 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_array, error_array, headers = proj_red.crop_array( + data_array, headers, step=5, null_val=0.0, 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) + 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) + 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) - + 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'])) + proj_plots.plot_obs( + data_array, + headers, + savename="_".join([figname, str(align_center)]), + plots_folder=plots_folder, + norm=LogNorm( + vmin=data_array[data_array > 0.0].min() * headers[0]["photflam"], vmax=data_array[data_array > 0.0].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) + 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 @@ -288,13 +462,29 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir= ) # 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'])) + 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 > 0.0].min() * headers[0]["photflam"], vmax=data_array[data_array > 0.0].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)]) + 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 @@ -303,16 +493,28 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir= # 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) + 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) + 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) @@ -321,8 +523,24 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir= # 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) + 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: @@ -331,11 +549,11 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir= figname += "_crop" stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_hdul), norm=LogNorm()) stokescrop.crop() - stokescrop.write_to("/".join([data_folder, figname+".fits"])) + 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) + 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"], @@ -352,55 +570,161 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir= # 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) + 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) + 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.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') - + 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("-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) \ No newline at end of file + 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("Written to: ", exitcode) diff --git a/package/lib/plots.py b/package/lib/plots.py index 2581c1f..276535d 100755 --- a/package/lib/plots.py +++ b/package/lib/plots.py @@ -406,7 +406,7 @@ def polarization_map( plt.rcdefaults() ratiox = max(int(stkI.shape[1]/(stkI.shape[0])),1) ratioy = max(int((stkI.shape[0])/stkI.shape[1]),1) - fig, ax = plt.subplots(figsize=(6*ratiox, 6*ratioy), layout="compressed", subplot_kw=dict(projection=wcs)) + fig, ax = plt.subplots(figsize=(7*ratiox, 7*ratioy), layout="compressed", subplot_kw=dict(projection=wcs)) ax.set(aspect="equal", fc="k", ylim=[-stkI.shape[0]*0.10,stkI.shape[0]*1.15]) # fig.subplots_adjust(hspace=0, wspace=0, left=0.102, right=1.02) @@ -531,8 +531,8 @@ def polarization_map( ax.transAxes, "E", "N", - length=-0.05, - fontsize=0.02, + length=-0.07, + fontsize=0.03, loc=1, aspect_ratio=-(stkI.shape[1]/(stkI.shape[0]*1.25)), sep_y=0.01, @@ -736,7 +736,7 @@ class align_maps(object): length=-0.08, fontsize=0.03, loc=1, - aspect_ratio=-(self.map_data.shape[1]/self.map_data.shape[0]), + aspect_ratio=-(self.map_ax.get_xbound()[1]-self.map_ax.get_xbound()[0])/(self.map_ax.get_ybound()[1]-self.map_ax.get_ybound()[0]), sep_y=0.01, sep_x=0.01, angle=-self.map_header["orientat"], @@ -788,13 +788,13 @@ class align_maps(object): ) if "ORIENTAT" in list(self.other_header.keys()): north_dir2 = AnchoredDirectionArrows( - self.map_ax.transAxes, + self.other_ax.transAxes, "E", "N", length=-0.08, fontsize=0.03, loc=1, - aspect_ratio=-(self.other_data.shape[1]/self.other_data.shape[0]), + aspect_ratio=-(self.other_ax.get_xbound()[1]-self.other_ax.get_xbound()[0])/(self.other_ax.get_ybound()[1]-self.other_ax.get_ybound()[0]), sep_y=0.01, sep_x=0.01, angle=-self.other_header["orientat"], @@ -1338,7 +1338,9 @@ class overplot_pol(align_maps): pol[SNRi < SNRi_cut] = np.nan plt.rcParams.update({"font.size": 16}) - self.fig_overplot, self.ax_overplot = plt.subplots(figsize=(11, 10), subplot_kw=dict(projection=self.other_wcs)) + ratiox = max(int(stkI.shape[1]/stkI.shape[0]),1) + ratioy = max(int(stkI.shape[0]/stkI.shape[1]),1) + self.fig_overplot, self.ax_overplot = plt.subplots(figsize=(10*ratiox, 10*ratioy), subplot_kw=dict(projection=self.other_wcs)) self.fig_overplot.subplots_adjust(hspace=0, wspace=0, bottom=0.1, left=0.1, top=0.80, right=1.02) self.ax_overplot.set_xlabel(label="Right Ascension (J2000)") @@ -1393,11 +1395,12 @@ class overplot_pol(align_maps): ) # Display full size polarization vectors + px_scale = self.wcs_UV.wcs.get_cdelt()[0]/self.other_wcs.wcs.get_cdelt()[0] if scale_vec is None: - self.scale_vec = 2.0 + self.scale_vec = 2.0*px_scale pol[np.isfinite(pol)] = 1.0 / 2.0 else: - self.scale_vec = scale_vec + self.scale_vec = scale_vec*px_scale step_vec = 1 self.X, self.Y = np.meshgrid(np.arange(stkI.shape[1]), np.arange(stkI.shape[0])) self.U, self.V = pol * np.cos(np.pi / 2.0 + pang * np.pi / 180.0), pol * np.sin(np.pi / 2.0 + pang * np.pi / 180.0) @@ -1414,8 +1417,8 @@ class overplot_pol(align_maps): headwidth=0.0, headlength=0.0, headaxislength=0.0, - width=0.5, - linewidth=0.75, + width=0.5*px_scale, + linewidth=0.3*px_scale, color="white", edgecolor="black", transform=self.ax_overplot.get_transform(self.wcs_UV), @@ -1454,7 +1457,7 @@ class overplot_pol(align_maps): length=-0.08, fontsize=0.03, loc=1, - aspect_ratio=-(stkI.shape[1]/stkI.shape[0]), + aspect_ratio=-(self.ax_overplot.get_xbound()[1]-self.ax_overplot.get_xbound()[0])/(self.ax_overplot.get_ybound()[1]-self.ax_overplot.get_ybound()[0]), sep_y=0.01, sep_x=0.01, angle=-self.Stokes_UV[0].header["orientat"], diff --git a/package/lib/reduction.py b/package/lib/reduction.py index aaf58d0..5cdc229 100755 --- a/package/lib/reduction.py +++ b/package/lib/reduction.py @@ -217,9 +217,9 @@ def bin_ndarray(ndarray, new_shape, operation="sum"): elif operation.lower() in ["mean", "average", "avg"]: ndarray = ndarray.mean(-1 * (i + 1)) else: - row_comp = np.mat(get_row_compressor(ndarray.shape[0], new_shape[0], operation)) - col_comp = np.mat(get_column_compressor(ndarray.shape[1], new_shape[1], operation)) - ndarray = np.array(row_comp * np.mat(ndarray) * col_comp) + row_comp = np.asmatrix(get_row_compressor(ndarray.shape[0], new_shape[0], operation)) + col_comp = np.asmatrix(get_column_compressor(ndarray.shape[1], new_shape[1], operation)) + ndarray = np.array(row_comp * np.asmatrix(ndarray) * col_comp) return ndarray