diff --git a/package/FOC_reduction.py b/package/FOC_reduction.py index e1ae8da..2694e36 100755 --- a/package/FOC_reduction.py +++ b/package/FOC_reduction.py @@ -23,6 +23,7 @@ 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 @@ -90,8 +91,8 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir= # Step 1: # Get data from fits files and translate to flux in erg/cm²/s/Angstrom. - if data_dir is None: + outfiles = [] 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]) @@ -104,7 +105,7 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, 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, crop=crop, interactive=interactive) + 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] @@ -139,6 +140,7 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir= 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"]) @@ -201,29 +203,29 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir= 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(princ_angle(_PA_bkg[0, 0]), princ_angle(np.ceil(_s_PA_bkg[0, 0]*10.)/10.))) # Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error). - if px_scale.lower() not in ['full', 'integrate'] and not interactive: + if pxscale.lower() not in ['full', 'integrate'] and not interactive: proj_plots.polarization_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]), plots_folder=plots_folder, **options) + step_vec=step_vec, vec_scale=scale_vec, savename="_".join([figname]), plots_folder=plots_folder, **options) proj_plots.polarization_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, "I"]), plots_folder=plots_folder, display='Intensity', **options) + vec_scale=scale_vec, savename="_".join([figname, "I"]), plots_folder=plots_folder, display='Intensity', **options) proj_plots.polarization_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, "P_flux"]), plots_folder=plots_folder, display='Pol_Flux', **options) + vec_scale=scale_vec, savename="_".join([figname, "P_flux"]), plots_folder=plots_folder, display='Pol_Flux', **options) proj_plots.polarization_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, "P"]), plots_folder=plots_folder, display='Pol_deg', **options) + vec_scale=scale_vec, savename="_".join([figname, "P"]), plots_folder=plots_folder, display='Pol_deg', **options) proj_plots.polarization_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, "PA"]), plots_folder=plots_folder, display='Pol_ang', **options) + vec_scale=scale_vec, savename="_".join([figname, "PA"]), plots_folder=plots_folder, display='Pol_ang', **options) proj_plots.polarization_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, "I_err"]), plots_folder=plots_folder, display='I_err', **options) + vec_scale=scale_vec, savename="_".join([figname, "I_err"]), plots_folder=plots_folder, display='I_err', **options) proj_plots.polarization_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, "P_err"]), plots_folder=plots_folder, display='Pol_deg_err', **options) + vec_scale=scale_vec, savename="_".join([figname, "P_err"]), plots_folder=plots_folder, display='Pol_deg_err', **options) proj_plots.polarization_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, "SNRi"]), plots_folder=plots_folder, display='SNRi', **options) + vec_scale=scale_vec, savename="_".join([figname, "SNRi"]), plots_folder=plots_folder, display='SNRi', **options) proj_plots.polarization_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, "SNRp"]), plots_folder=plots_folder, display='SNRp', **options) + vec_scale=scale_vec, savename="_".join([figname, "SNRp"]), plots_folder=plots_folder, display='SNRp', **options) elif not interactive: proj_plots.polarization_map(deepcopy(_Stokes_test), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=figname, plots_folder=plots_folder, display='integrate', **options) - elif px_scale.lower() not in ['full', 'integrate']: + elif pxscale.lower() not in ['full', 'integrate']: proj_plots.pol_map(_Stokes_test, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim) else: @@ -251,18 +253,30 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir= 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 rebin: + 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=px_scale, 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 North up + # 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: - 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) + 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 px_scale.lower() not in ['full', 'integrate']: + 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'])) @@ -283,7 +297,7 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir= # Step 3: # Rotate images to have North up - if rotate_stokes: + if rotate_North: 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) @@ -295,21 +309,23 @@ 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_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, + 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, headers, 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_test), norm=LogNorm()) + stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_hdul), norm=LogNorm()) stokescrop.crop() stokescrop.write_to("/".join([data_folder, figname+".fits"])) - Stokes_test, headers = stokescrop.hdul_crop, [dataset.header for dataset in stokescrop.hdul_crop] + Stokes_hdul, headers = 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_test['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(headers[0]['photplam'], *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))) + Stokes_hdul[0].data[data_mask].sum()*headers[0]['photflam'], np.sqrt(Stokes_hdul[3].data[0, 0][data_mask].sum())*headers[0]['photflam'], 2, out=int))) print("P_int = {0:.1f} ± {1:.1f} %".format(headers[0]['p_int']*100., np.ceil(headers[0]['p_int_err']*1000.)/10.)) print("PA_int = {0:.1f} ± {1:.1f} °".format(princ_angle(headers[0]['pa_int']), princ_angle(np.ceil(headers[0]['pa_int_err']*10.)/10.))) # Background values @@ -318,30 +334,30 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir= 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(princ_angle(PA_bkg[0, 0]), princ_angle(np.ceil(s_PA_bkg[0, 0]*10.)/10.))) # Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error). - if px_scale.lower() not in ['full', 'integrate'] and not interactive: - proj_plots.polarization_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]), plots_folder=plots_folder, **options) - proj_plots.polarization_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, "I"]), plots_folder=plots_folder, display='Intensity', **options) - proj_plots.polarization_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, "P_flux"]), plots_folder=plots_folder, display='Pol_Flux', **options) - proj_plots.polarization_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, "P"]), plots_folder=plots_folder, display='Pol_deg', **options) - proj_plots.polarization_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, "PA"]), plots_folder=plots_folder, display='Pol_ang', **options) - proj_plots.polarization_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, "I_err"]), plots_folder=plots_folder, display='I_err', **options) - proj_plots.polarization_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, "P_err"]), plots_folder=plots_folder, display='Pol_deg_err', **options) - proj_plots.polarization_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, "SNRi"]), plots_folder=plots_folder, display='SNRi', **options) - proj_plots.polarization_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, "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_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, + 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 px_scale.lower() not in ['full', 'integrate']: - proj_plots.pol_map(Stokes_test, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim) + 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 @@ -350,7 +366,6 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir= 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) diff --git a/package/lib/plots.py b/package/lib/plots.py index ae31ef5..0add57b 100755 --- a/package/lib/plots.py +++ b/package/lib/plots.py @@ -99,7 +99,7 @@ def adaptive_binning(I_stokes, Q_stokes, U_stokes, Stokes_cov): return bin_map, bin_num -def plot_quiver(ax, stkI, stkQ, stkU, stk_cov, poldata, pangdata, step_vec=1., vec_scale=2., optimal_binning=False): +def plot_quiver(ax, stkI, stkQ, stkU, stk_cov, poldata, pangdata, step_vec=1., scale_vec=2., optimal_binning=False): if optimal_binning: bin_map, bin_num = adaptive_binning(stkI, stkQ, stkU, stk_cov) @@ -120,14 +120,14 @@ def plot_quiver(ax, stkI, stkQ, stkU, stk_cov, poldata, pangdata, step_vec=1., v pangdata_err = (1 / (2. *(bin_Q**2 + bin_U**2))) * \ np.sqrt(bin_U**2 * bin_cov[1,1] + bin_Q**2 * bin_cov[2,2] - 2. * bin_Q * bin_U * bin_cov[1,2]) - ax.quiver(y_center, x_center, poldata * np.cos(np.pi/2.+pangdata), poldata * np.sin(np.pi/2.+pangdata), units='xy', angles='uv', scale=1./vec_scale, scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.1, linewidth=0.5, color='white', edgecolor='white') - ax.quiver(y_center, x_center, poldata * np.cos(np.pi/2.+pangdata+pangdata_err), poldata * np.sin(np.pi/2.+pangdata+pangdata_err), units='xy', angles='uv', scale=1./vec_scale, scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.1, linewidth=0.5, color='black', edgecolor='black', ls='dashed') - ax.quiver(y_center, x_center, poldata * np.cos(np.pi/2.+pangdata-pangdata_err), poldata * np.sin(np.pi/2.+pangdata-pangdata_err), units='xy', angles='uv', scale=1./vec_scale, scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.1, linewidth=0.5, color='black', edgecolor='black', ls='dashed') + ax.quiver(y_center, x_center, poldata * np.cos(np.pi/2.+pangdata), poldata * np.sin(np.pi/2.+pangdata), units='xy', angles='uv', scale=1./scale_vec, scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.1, linewidth=0.5, color='white', edgecolor='white') + ax.quiver(y_center, x_center, poldata * np.cos(np.pi/2.+pangdata+pangdata_err), poldata * np.sin(np.pi/2.+pangdata+pangdata_err), units='xy', angles='uv', scale=1./scale_vec, scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.1, linewidth=0.5, color='black', edgecolor='black', ls='dashed') + ax.quiver(y_center, x_center, poldata * np.cos(np.pi/2.+pangdata-pangdata_err), poldata * np.sin(np.pi/2.+pangdata-pangdata_err), units='xy', angles='uv', scale=1./scale_vec, scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.1, linewidth=0.5, color='black', edgecolor='black', ls='dashed') else: X, Y = np.meshgrid(np.arange(stkI.shape[1]), np.arange(stkI.shape[0])) U, V = poldata*np.cos(np.pi/2.+pangdata*np.pi/180.), poldata*np.sin(np.pi/2.+pangdata*np.pi/180.) - ax.quiver(X[::step_vec, ::step_vec], Y[::step_vec, ::step_vec], U[::step_vec, ::step_vec], V[::step_vec, ::step_vec], units='xy', angles='uv', scale=1./vec_scale, scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.5, linewidth=0.75, color='w', edgecolor='k') + ax.quiver(X[::step_vec, ::step_vec], Y[::step_vec, ::step_vec], U[::step_vec, ::step_vec], V[::step_vec, ::step_vec], units='xy', angles='uv', scale=1./scale_vec, scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.5, linewidth=0.75, color='w', edgecolor='k') def plot_obs(data_array, headers, rectangle=None, savename=None, plots_folder="", **kwargs): @@ -545,14 +545,10 @@ def polarization_map( if step_vec == 0: poldata[np.isfinite(poldata)] = 1.0 / 2.0 step_vec = 1 - vec_scale = 2. - # X, Y = np.meshgrid(np.arange(stkI.shape[1]), np.arange(stkI.shape[0])) - # U, V = poldata*np.cos(np.pi/2.+pangdata*np.pi/180.), poldata*np.sin(np.pi/2.+pangdata*np.pi/180.) - # ax.quiver(X[::step_vec, ::step_vec], Y[::step_vec, ::step_vec], U[::step_vec, ::step_vec], V[::step_vec, ::step_vec], units='xy', angles='uv', - # scale=1./vec_scale, scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.5, linewidth=0.75, color='w', edgecolor='k') - plot_quiver(ax, stkI, stkQ, stkU, stk_cov, poldata, pangdata, step_vec=step_vec, vec_scale=vec_scale, optimal_binning=optimal_binning) - pol_sc = AnchoredSizeBar(ax.transData, vec_scale, r"$P$= 100 %", 4, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w') + scale_vec = 2. + plot_quiver(ax, stkI, stkQ, stkU, stk_cov, poldata, pangdata, step_vec=step_vec, scale_vec=scale_vec, optimal_binning=optimal_binning) + pol_sc = AnchoredSizeBar(ax.transData, scale_vec, r"$P$= 100 %", 4, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w') ax.add_artist(pol_sc) ax.add_artist(px_sc)