better output from analysis plots
This commit is contained in:
@@ -22,10 +22,10 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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# from lib.deconvolve import from_file_psf
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psf = 'gaussian' # Can be user-defined as well
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# psf = from_file_psf(data_folder+psf_file)
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psf_FWHM = 0.15
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psf_FWHM = 0.015
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psf_scale = 'arcsec'
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psf_shape = (25, 25)
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iterations = 5
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psf_shape = (11, 11)
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iterations = 3
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algo = "richardson"
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# Initial crop
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@@ -33,7 +33,8 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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# Background estimation
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error_sub_type = 'freedman-diaconis' # sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51, 51))
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subtract_error = 1.00
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subtract_error = 1.20
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display_bkg = False
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display_error = False
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# Data binning
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@@ -44,13 +45,12 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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# Alignement
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align_center = 'center' # If None will not align the images
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display_bkg = False
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display_align = False
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display_data = False
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# Smoothing
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smoothing_function = 'combine' # gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
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smoothing_FWHM = 0.10 # If None, no smoothing is done
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smoothing_FWHM = 0.2 # If None, no smoothing is done
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smoothing_scale = 'arcsec' # pixel or arcsec
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# Rotation
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@@ -58,8 +58,8 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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rotate_stokes = True
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# Final crop
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crop = False # Crop to desired ROI
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interactive = False # Whether to output to intercative analysis tool
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crop = True # Crop to desired ROI
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interactive = True # Whether to output to intercative analysis tool
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# Polarization map output
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SNRp_cut = 3. # P measurments with SNR>3
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@@ -90,7 +90,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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plots_folder = "."
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if not path_exists(plots_folder):
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system("mkdir -p {0:s} ".format(plots_folder))
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infiles = [p[1] for p in prod]
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infiles = [p[1] for p in prod] # if p[1] not in ['x2rp0202t_c0f.fits', 'x2rp0302t_c0f.fits']]
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data_array, headers = proj_fits.get_obs_data(infiles, data_folder=data_folder, compute_flux=True)
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figname = "_".join([target, "FOC"])
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@@ -102,6 +102,8 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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if smoothing_FWHM is not None:
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figtype += "_"+"".join(["".join([s[0] for s in smoothing_function.split("_")]),
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"{0:.2f}".format(smoothing_FWHM), smoothing_scale]) # additionnal informations
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if deconvolve:
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figtype += "_deconv"
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if align_center is None:
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figtype += "_not_aligned"
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@@ -115,20 +117,25 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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# Estimate error from data background, estimated from sub-image of desired sub_shape.
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background = None
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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)
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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([
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figname, "errors"]), plots_folder=plots_folder, return_background=True)
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if display_bkg:
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proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array > 0.].min()*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam'], savename="_".join([figname, "bkg"]), plots_folder=plots_folder)
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proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array > 0.].min(
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)*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam'], savename="_".join([figname, "bkg"]), plots_folder=plots_folder)
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# Align and rescale images with oversampling.
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data_array, error_array, headers, data_mask = proj_red.align_data( data_array, headers, error_array=error_array, background=background, upsample_factor=10, ref_center=align_center, return_shifts=False)
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data_array, error_array, headers, data_mask = proj_red.align_data(
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data_array, headers, error_array=error_array, background=background, upsample_factor=10, ref_center=align_center, return_shifts=False)
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if display_align:
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proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array > 0.].min()*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam'], savename="_".join([figname, str(align_center)]), plots_folder=plots_folder)
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proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array > 0.].min(
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)*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam'], savename="_".join([figname, str(align_center)]), plots_folder=plots_folder)
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# Rebin data to desired pixel size.
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if rebin:
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data_array, error_array, headers, Dxy, data_mask = proj_red.rebin_array( data_array, error_array, headers, pxsize=pxsize, scale=px_scale, operation=rebin_operation, data_mask=data_mask)
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data_array, error_array, headers, Dxy, data_mask = proj_red.rebin_array(
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data_array, error_array, headers, pxsize=pxsize, scale=px_scale, operation=rebin_operation, data_mask=data_mask)
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# Rotate data to have North up
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if rotate_data:
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@@ -138,10 +145,12 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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# Plot array for checking output
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if display_data and px_scale.lower() not in ['full', 'integrate']:
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proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array > 0.].min()*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam'], savename="_".join([figname, "rebin"]), plots_folder=plots_folder)
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proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array > 0.].min(
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)*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam'], savename="_".join([figname, "rebin"]), plots_folder=plots_folder)
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background = np.array([np.array(bkg).reshape(1, 1) for bkg in background])
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background_error = np.array([np.array(np.sqrt((bkg-background[np.array([h['filtnam1'] == head['filtnam1'] for h in headers], dtype=bool)].mean()) ** 2/np.sum([h['filtnam1'] == head['filtnam1'] for h in headers]))).reshape(1, 1) for bkg, head in zip(background, headers)])
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background_error = np.array([np.array(np.sqrt((bkg-background[np.array([h['filtnam1'] == head['filtnam1'] for h in headers], dtype=bool)].mean())
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** 2/np.sum([h['filtnam1'] == head['filtnam1'] for h in headers]))).reshape(1, 1) for bkg, head in zip(background, headers)])
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# Step 2:
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# Compute Stokes I, Q, U with smoothed polarized images
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@@ -149,13 +158,16 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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# FWHM of FOC have been estimated at about 0.03" across 1500-5000 Angstrom band, which is about 2 detector pixels wide
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# see Jedrzejewski, R.; Nota, A.; Hack, W. J., A Comparison Between FOC and WFPC2
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# Bibcode : 1995chst.conf...10J
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I_stokes, Q_stokes, U_stokes, Stokes_cov = proj_red.compute_Stokes(data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=False)
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I_bkg, Q_bkg, U_bkg, S_cov_bkg = proj_red.compute_Stokes(background, background_error, np.array(True).reshape(1, 1), headers, FWHM=None, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=False)
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I_stokes, Q_stokes, U_stokes, Stokes_cov = proj_red.compute_Stokes(
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data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=False)
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I_bkg, Q_bkg, U_bkg, S_cov_bkg = proj_red.compute_Stokes(background, background_error, np.array(True).reshape(
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1, 1), headers, FWHM=None, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=False)
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# Step 3:
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# Rotate images to have North up
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if rotate_stokes:
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I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers = proj_red.rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, SNRi_cut=None)
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I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers = proj_red.rotate_Stokes(
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I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, SNRi_cut=None)
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I_bkg, Q_bkg, U_bkg, S_cov_bkg, _, _ = proj_red.rotate_Stokes(I_bkg, Q_bkg, U_bkg, S_cov_bkg, np.array(True).reshape(1, 1), headers, SNRi_cut=None)
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# Compute polarimetric parameters (polarisation degree and angle).
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@@ -164,7 +176,8 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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# Step 4:
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# Save image to FITS.
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Stokes_test = proj_fits.save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, headers, data_mask, "_".join([figname, figtype]), data_folder=data_folder, return_hdul=True)
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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,
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headers, data_mask, "_".join([figname, figtype]), data_folder=data_folder, return_hdul=True)
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data_mask = Stokes_test[-1].data.astype(bool)
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# Step 5:
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@@ -176,11 +189,13 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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stokescrop.writeto("/".join([data_folder, "_".join([figname, figtype+".fits"])]))
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Stokes_test, data_mask, headers = stokescrop.hdul_crop, stokescrop.data_mask, [dataset.header for dataset in stokescrop.hdul_crop]
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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)))
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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(
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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)))
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print("P_int = {0:.1f} ± {1:.1f} %".format(headers[0]['p_int']*100., np.ceil(headers[0]['p_int_err']*1000.)/10.))
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print("PA_int = {0:.1f} ±t {1:.1f} °".format(headers[0]['pa_int'], np.ceil(headers[0]['pa_int_err']*10.)/10.))
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# Background values
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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)))
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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(
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I_bkg[0, 0]*headers[0]['photflam'], np.sqrt(S_cov_bkg[0, 0][0, 0])*headers[0]['photflam'], 2, out=int)))
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print("P_bkg = {0:.1f} ± {1:.1f} %".format(debiased_P_bkg[0, 0]*100., np.ceil(s_P_bkg[0, 0]*1000.)/10.))
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print("PA_bkg = {0:.1f} ± {1:.1f} °".format(PA_bkg[0, 0], np.ceil(s_PA_bkg[0, 0]*10.)/10.))
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# Plot polarisation map (Background is either total Flux, Polarization degree or Polarization degree error).
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@@ -1,23 +1,22 @@
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#!/usr/bin/env python
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from lib.reduction import align_data, crop_array, princ_angle
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# !/usr/bin/env python
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from lib.background import gauss, bin_centers
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from lib.deconvolve import zeropad
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from lib.reduction import align_data
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from lib.plots import princ_angle
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from matplotlib.colors import LogNorm
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from os.path import join as path_join
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from os import walk as path_walk
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from astropy.io import fits
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from astropy.wcs import WCS
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from re import compile as regcompile, IGNORECASE
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from scipy.ndimage import shift
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from scipy.optimize import curve_fit
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import numpy as np
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import matplotlib.pyplot as plt
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root_dir = path_join('/home/t.barnouin/Documents/Thesis/HST')
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root_dir_K = path_join(root_dir,'Kishimoto','output')
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root_dir_S = path_join(root_dir,'FOC_Reduction','output')
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root_dir_data_S = path_join(root_dir,'FOC_Reduction','data','NGC1068','5144')
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root_dir_plot_S = path_join(root_dir,'FOC_Reduction','plots','NGC1068','5144','notaligned')
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root_dir_K = path_join(root_dir, 'Kishimoto', 'output')
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root_dir_S = path_join(root_dir, 'FOC_Reduction', 'output')
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root_dir_data_S = path_join(root_dir, 'FOC_Reduction', 'data', 'NGC1068', '5144')
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root_dir_plot_S = path_join(root_dir, 'FOC_Reduction', 'plots', 'NGC1068', '5144', 'notaligned')
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filename_S = "NGC1068_FOC_b10.00pixel_not_aligned.fits"
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plt.rcParams.update({'font.size': 15})
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@@ -26,14 +25,14 @@ SNRp_cut = 3.
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data_K = {}
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data_S = {}
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for d,i in zip(['I','Q','U','P','PA','sI','sQ','sU','sP','sPA'],[0,1,2,5,8,(3,0,0),(3,1,1),(3,2,2),6,9]):
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data_K[d] = np.loadtxt(path_join(root_dir_K,d+'.txt'))
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with fits.open(path_join(root_dir_data_S,filename_S)) as f:
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for d, i in zip(['I', 'Q', 'U', 'P', 'PA', 'sI', 'sQ', 'sU', 'sP', 'sPA'], [0, 1, 2, 5, 8, (3, 0, 0), (3, 1, 1), (3, 2, 2), 6, 9]):
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data_K[d] = np.loadtxt(path_join(root_dir_K, d+'.txt'))
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with fits.open(path_join(root_dir_data_S, filename_S)) as f:
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if not type(i) is int:
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data_S[d] = np.sqrt(f[i[0]].data[i[1],i[2]])
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data_S[d] = np.sqrt(f[i[0]].data[i[1], i[2]])
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else:
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data_S[d] = f[i].data
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if i==0:
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if i == 0:
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header = f[i].header
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wcs = WCS(header)
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convert_flux = header['photflam']
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@@ -41,64 +40,67 @@ convert_flux = header['photflam']
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bkg_S = np.median(data_S['I'])/3
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bkg_K = np.median(data_K['I'])/3
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#zeropad data to get same size of array
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# zeropad data to get same size of array
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shape = data_S['I'].shape
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for d in data_K:
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data_K[d] = zeropad(data_K[d],shape)
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data_K[d] = zeropad(data_K[d], shape)
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#shift array to get same information in same pixel
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data_arr, error_ar, heads, data_msk, shifts, shifts_err = align_data(np.array([data_S['I'],data_K['I']]), [header, header], error_array=np.array([data_S['sI'],data_K['sI']]), background=np.array([bkg_S,bkg_K]), upsample_factor=10., ref_center='center', return_shifts=True)
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# shift array to get same information in same pixel
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data_arr, error_ar, heads, data_msk, shifts, shifts_err = align_data(np.array([data_S['I'], data_K['I']]), [header, header], error_array=np.array(
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[data_S['sI'], data_K['sI']]), background=np.array([bkg_S, bkg_K]), upsample_factor=10., ref_center='center', return_shifts=True)
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for d in data_K:
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data_K[d] = shift(data_K[d],shifts[1],order=1,cval=0.)
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data_K[d] = shift(data_K[d], shifts[1], order=1, cval=0.)
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#compute pol components from shifted array
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# compute pol components from shifted array
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for d in [data_S, data_K]:
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for i in d:
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d[i][np.isnan(d[i])] = 0.
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d['P'] = np.where(np.logical_and(np.isfinite(d['I']),d['I']>0.),np.sqrt(d['Q']**2+d['U']**2)/d['I'],0.)
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d['sP'] = np.where(np.logical_and(np.isfinite(d['I']),d['I']>0.),np.sqrt((d['Q']**2*d['sQ']**2+d['U']**2*d['sU']**2)/(d['Q']**2+d['U']**2)+((d['Q']/d['I'])**2+(d['U']/d['I'])**2)*d['sI']**2)/d['I'],0.)
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d['d_P'] = np.where(np.logical_and(np.isfinite(d['P']),np.isfinite(d['sP'])),np.sqrt(d['P']**2-d['sP']**2),0.)
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d['PA'] = 0.5*np.arctan2(d['U'],d['Q'])+np.pi
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d['P'] = np.where(np.logical_and(np.isfinite(d['I']), d['I'] > 0.), np.sqrt(d['Q']**2+d['U']**2)/d['I'], 0.)
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d['sP'] = np.where(np.logical_and(np.isfinite(d['I']), d['I'] > 0.), np.sqrt((d['Q']**2*d['sQ']**2+d['U']**2*d['sU']**2) /
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(d['Q']**2+d['U']**2)+((d['Q']/d['I'])**2+(d['U']/d['I'])**2)*d['sI']**2)/d['I'], 0.)
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d['d_P'] = np.where(np.logical_and(np.isfinite(d['P']), np.isfinite(d['sP'])), np.sqrt(d['P']**2-d['sP']**2), 0.)
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d['PA'] = 0.5*np.arctan2(d['U'], d['Q'])+np.pi
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d['SNRp'] = np.zeros(d['d_P'].shape)
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d['SNRp'][d['sP']>0.] = d['d_P'][d['sP']>0.]/d['sP'][d['sP']>0.]
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d['SNRp'][d['sP'] > 0.] = d['d_P'][d['sP'] > 0.]/d['sP'][d['sP'] > 0.]
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d['SNRi'] = np.zeros(d['I'].shape)
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d['SNRi'][d['sI']>0.] = d['I'][d['sI']>0.]/d['sI'][d['sI']>0.]
|
||||
d['mask'] = np.logical_and(d['SNRi']>SNRi_cut,d['SNRp']>SNRp_cut)
|
||||
data_S['mask'], data_K['mask'] = np.logical_and(data_S['mask'],data_K['mask']), np.logical_and(data_S['mask'],data_K['mask'])
|
||||
d['SNRi'][d['sI'] > 0.] = d['I'][d['sI'] > 0.]/d['sI'][d['sI'] > 0.]
|
||||
d['mask'] = np.logical_and(d['SNRi'] > SNRi_cut, d['SNRp'] > SNRp_cut)
|
||||
data_S['mask'], data_K['mask'] = np.logical_and(data_S['mask'], data_K['mask']), np.logical_and(data_S['mask'], data_K['mask'])
|
||||
|
||||
|
||||
#####
|
||||
###Compute histogram of measured polarization in cut
|
||||
#####
|
||||
bins=int(data_S['mask'].sum()/5)
|
||||
bin_size=1./bins
|
||||
mod_p = np.linspace(0.,1.,300)
|
||||
#
|
||||
# Compute histogram of measured polarization in cut
|
||||
#
|
||||
bins = int(data_S['mask'].sum()/5)
|
||||
bin_size = 1./bins
|
||||
mod_p = np.linspace(0., 1., 300)
|
||||
for d in [data_S, data_K]:
|
||||
d['hist'], d['bin_edges'] = np.histogram(d['d_P'][d['mask']],bins=bins,range=(0.,1.))
|
||||
d['hist'], d['bin_edges'] = np.histogram(d['d_P'][d['mask']], bins=bins, range=(0., 1.))
|
||||
d['binning'] = bin_centers(d['bin_edges'])
|
||||
peak, bins_fwhm = d['binning'][np.argmax(d['hist'])], d['binning'][d['hist']>d['hist'].max()/2.]
|
||||
peak, bins_fwhm = d['binning'][np.argmax(d['hist'])], d['binning'][d['hist'] > d['hist'].max()/2.]
|
||||
fwhm = bins_fwhm[1]-bins_fwhm[0]
|
||||
p0 = [d['hist'].max(), peak, fwhm]
|
||||
try:
|
||||
popt, pcov = curve_fit(gauss, d['binning'], d['hist'], p0=p0)
|
||||
except RuntimeError:
|
||||
popt = p0
|
||||
d['hist_chi2'] = np.sum((d['hist']-gauss(d['binning'],*popt))**2)/d['hist'].size
|
||||
d['hist_chi2'] = np.sum((d['hist']-gauss(d['binning'], *popt))**2)/d['hist'].size
|
||||
d['hist_popt'] = popt
|
||||
|
||||
fig_p, ax_p = plt.subplots(num="Polarization degree histogram",figsize=(10,6),constrained_layout=True)
|
||||
ax_p.errorbar(data_S['binning'],data_S['hist'],xerr=bin_size/2.,fmt='b.',ecolor='b',label='P through this pipeline')
|
||||
ax_p.plot(mod_p,gauss(mod_p,*data_S['hist_popt']),'b--',label='mean = {1:.2f}, stdev = {2:.2f}'.format(*data_S['hist_popt']))
|
||||
ax_p.errorbar(data_K['binning'],data_K['hist'],xerr=bin_size/2.,fmt='r.',ecolor='r',label="P through Kishimoto's pipeline")
|
||||
ax_p.plot(mod_p,gauss(mod_p,*data_K['hist_popt']),'r--',label='mean = {1:.2f}, stdev = {2:.2f}'.format(*data_K['hist_popt']))
|
||||
ax_p.set(xlabel="Polarization degree",ylabel="Counts",title="Histogram of polarization degree computed in the cut for both pipelines.")
|
||||
ax_p.legend()
|
||||
fig_p.savefig(path_join(root_dir_plot_S,"NGC1068_K_pol_deg.png"),bbox_inches="tight",dpi=300)
|
||||
|
||||
#####
|
||||
###Compute angular difference between the maps in cut
|
||||
#####
|
||||
dtheta = np.where(data_S['mask'], 0.5*np.arctan((np.sin(2*data_S['PA'])*np.cos(2*data_K['PA'])-np.cos(2*data_S['PA'])*np.cos(2*data_K['PA']))/(np.cos(2*data_S['PA'])*np.cos(2*data_K['PA'])+np.cos(2*data_S['PA'])*np.sin(2*data_K['PA']))),np.nan)
|
||||
fig_p, ax_p = plt.subplots(num="Polarization degree histogram", figsize=(10, 6), constrained_layout=True)
|
||||
ax_p.errorbar(data_S['binning'], data_S['hist'], xerr=bin_size/2., fmt='b.', ecolor='b', label='P through this pipeline')
|
||||
ax_p.plot(mod_p, gauss(mod_p, *data_S['hist_popt']), 'b--', label='mean = {1:.2f}, stdev = {2:.2f}'.format(*data_S['hist_popt']))
|
||||
ax_p.errorbar(data_K['binning'], data_K['hist'], xerr=bin_size/2., fmt='r.', ecolor='r', label="P through Kishimoto's pipeline")
|
||||
ax_p.plot(mod_p, gauss(mod_p, *data_K['hist_popt']), 'r--', label='mean = {1:.2f}, stdev = {2:.2f}'.format(*data_K['hist_popt']))
|
||||
ax_p.set(xlabel="Polarization degree", ylabel="Counts", title="Histogram of polarization degree computed in the cut for both pipelines.")
|
||||
ax_p.legend()
|
||||
fig_p.savefig(path_join(root_dir_plot_S, "NGC1068_K_pol_deg.png"), bbox_inches="tight", dpi=300)
|
||||
|
||||
#
|
||||
# Compute angular difference between the maps in cut
|
||||
#
|
||||
dtheta = np.where(data_S['mask'], 0.5*np.arctan((np.sin(2*data_S['PA'])*np.cos(2*data_K['PA'])-np.cos(2*data_S['PA']) *
|
||||
np.cos(2*data_K['PA']))/(np.cos(2*data_S['PA'])*np.cos(2*data_K['PA'])+np.cos(2*data_S['PA'])*np.sin(2*data_K['PA']))), np.nan)
|
||||
fig_pa = plt.figure(num="Polarization degree alignement")
|
||||
ax_pa = fig_pa.add_subplot(111, projection=wcs)
|
||||
cbar_ax_pa = fig_pa.add_axes([0.88, 0.12, 0.01, 0.75])
|
||||
@@ -107,11 +109,11 @@ im_pa = ax_pa.imshow(np.cos(2*dtheta), vmin=-1., vmax=1., origin='lower', cmap='
|
||||
cbar_pa = plt.colorbar(im_pa, cax=cbar_ax_pa, label=r"$\zeta = \cos\left( 2 \cdot \delta\theta_P \right)$")
|
||||
ax_pa.coords[0].set_axislabel('Right Ascension (J2000)')
|
||||
ax_pa.coords[1].set_axislabel('Declination (J2000)')
|
||||
fig_pa.savefig(path_join(root_dir_plot_S,"NGC1068_K_pol_ang.png"),bbox_inches="tight",dpi=300)
|
||||
fig_pa.savefig(path_join(root_dir_plot_S, "NGC1068_K_pol_ang.png"), bbox_inches="tight", dpi=300)
|
||||
|
||||
#####
|
||||
###Compute power uncertainty difference between the maps in cut
|
||||
#####
|
||||
#
|
||||
# Compute power uncertainty difference between the maps in cut
|
||||
#
|
||||
eta = np.where(data_S['mask'], np.abs(data_K['d_P']-data_S['d_P'])/np.sqrt(data_S['sP']**2+data_K['sP']**2)/2., np.nan)
|
||||
fig_dif_p = plt.figure(num="Polarization power difference ratio")
|
||||
ax_dif_p = fig_dif_p.add_subplot(111, projection=wcs)
|
||||
@@ -121,54 +123,56 @@ im_dif_p = ax_dif_p.imshow(eta, vmin=0., vmax=2., origin='lower', cmap='bwr_r',
|
||||
cbar_dif_p = plt.colorbar(im_dif_p, cax=cbar_ax_dif_p, label=r"$\eta = \frac{2 \left|P^K-P^S\right|}{\sqrt{{\sigma^K_P}^2+{\sigma^S_P}^2}}$")
|
||||
ax_dif_p.coords[0].set_axislabel('Right Ascension (J2000)')
|
||||
ax_dif_p.coords[1].set_axislabel('Declination (J2000)')
|
||||
fig_dif_p.savefig(path_join(root_dir_plot_S,"NGC1068_K_pol_diff.png"),bbox_inches="tight",dpi=300)
|
||||
fig_dif_p.savefig(path_join(root_dir_plot_S, "NGC1068_K_pol_diff.png"), bbox_inches="tight", dpi=300)
|
||||
|
||||
#####
|
||||
###Compute angle uncertainty difference between the maps in cut
|
||||
#####
|
||||
#
|
||||
# Compute angle uncertainty difference between the maps in cut
|
||||
#
|
||||
eta = np.where(data_S['mask'], np.abs(data_K['PA']-data_S['PA'])/np.sqrt(data_S['sPA']**2+data_K['sPA']**2)/2., np.nan)
|
||||
fig_dif_pa = plt.figure(num="Polarization angle difference ratio")
|
||||
ax_dif_pa = fig_dif_pa.add_subplot(111, projection=wcs)
|
||||
cbar_ax_dif_pa = fig_dif_pa.add_axes([0.88, 0.12, 0.01, 0.75])
|
||||
ax_dif_pa.set_title(r"Degree of difference $\eta$ of the polarization from the 2 pipelines in the cut")
|
||||
im_dif_pa = ax_dif_pa.imshow(eta, vmin=0., vmax=2., origin='lower', cmap='bwr_r', label=r"$\eta$ between this pipeline and Kishimoto's")
|
||||
cbar_dif_pa = plt.colorbar(im_dif_pa, cax=cbar_ax_dif_pa, label=r"$\eta = \frac{2 \left|\theta_P^K-\theta_P^S\right|}{\sqrt{{\sigma^K_{\theta_P}}^2+{\sigma^S_{\theta_P}}^2}}$")
|
||||
cbar_dif_pa = plt.colorbar(im_dif_pa, cax=cbar_ax_dif_pa,
|
||||
label=r"$\eta = \frac{2 \left|\theta_P^K-\theta_P^S\right|}{\sqrt{{\sigma^K_{\theta_P}}^2+{\sigma^S_{\theta_P}}^2}}$")
|
||||
ax_dif_pa.coords[0].set_axislabel('Right Ascension (J2000)')
|
||||
ax_dif_pa.coords[1].set_axislabel('Declination (J2000)')
|
||||
fig_dif_pa.savefig(path_join(root_dir_plot_S,"NGC1068_K_polang_diff.png"),bbox_inches="tight",dpi=300)
|
||||
fig_dif_pa.savefig(path_join(root_dir_plot_S, "NGC1068_K_polang_diff.png"), bbox_inches="tight", dpi=300)
|
||||
|
||||
#####
|
||||
###display both polarization maps to check consistency
|
||||
#####
|
||||
#plt.rcParams.update({'font.size': 15})
|
||||
fig = plt.figure(num="Polarization maps comparison",figsize=(10,10))
|
||||
# display both polarization maps to check consistency
|
||||
# plt.rcParams.update({'font.size': 15})
|
||||
fig = plt.figure(num="Polarization maps comparison", figsize=(10, 10))
|
||||
ax = fig.add_subplot(111, projection=wcs)
|
||||
fig.subplots_adjust(right=0.85)
|
||||
cbar_ax = fig.add_axes([0.88, 0.12, 0.01, 0.75])
|
||||
|
||||
for d in [data_S, data_K]:
|
||||
d['X'], d['Y'] = np.meshgrid(np.arange(d['I'].shape[1]), np.arange(d['I'].shape[0]))
|
||||
d['xy_U'], d['xy_V'] = np.where(d['mask'],d['d_P']*np.cos(np.pi/2.+d['PA']), np.nan), np.where(d['mask'],d['d_P']*np.sin(np.pi/2.+d['PA']), np.nan)
|
||||
d['xy_U'], d['xy_V'] = np.where(d['mask'], d['d_P']*np.cos(np.pi/2.+d['PA']), np.nan), np.where(d['mask'], d['d_P']*np.sin(np.pi/2.+d['PA']), np.nan)
|
||||
|
||||
im0 = ax.imshow(data_S['I']*convert_flux,norm=LogNorm(data_S['I'][data_S['I']>0].min()*convert_flux,data_S['I'][data_S['I']>0].max()*convert_flux),origin='lower',cmap='gray',label=r"$I_{STOKES}$ through this pipeline")
|
||||
quiv0 = ax.quiver(data_S['X'],data_S['Y'],data_S['xy_U'],data_S['xy_V'],units='xy',angles='uv',scale=0.5,scale_units='xy',pivot='mid',headwidth=0.,headlength=0.,headaxislength=0.,width=0.2,color='b',alpha=0.75, label="PA through this pipeline")
|
||||
quiv1 = ax.quiver(data_K['X'],data_K['Y'],data_K['xy_U'],data_K['xy_V'],units='xy',angles='uv',scale=0.5,scale_units='xy',pivot='mid',headwidth=0.,headlength=0.,headaxislength=0.,width=0.1,color='r',alpha=0.75, label="PA through Kishimoto's pipeline")
|
||||
im0 = ax.imshow(data_S['I']*convert_flux, norm=LogNorm(data_S['I'][data_S['I'] > 0].min()*convert_flux, data_S['I']
|
||||
[data_S['I'] > 0].max()*convert_flux), origin='lower', cmap='gray', label=r"$I_{STOKES}$ through this pipeline")
|
||||
quiv0 = ax.quiver(data_S['X'], data_S['Y'], data_S['xy_U'], data_S['xy_V'], units='xy', angles='uv', scale=0.5, scale_units='xy',
|
||||
pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.2, color='b', alpha=0.75, label="PA through this pipeline")
|
||||
quiv1 = ax.quiver(data_K['X'], data_K['Y'], data_K['xy_U'], data_K['xy_V'], units='xy', angles='uv', scale=0.5, scale_units='xy',
|
||||
pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.1, color='r', alpha=0.75, label="PA through Kishimoto's pipeline")
|
||||
|
||||
ax.set_title(r"$SNR_P \geq$ "+str(SNRi_cut)+r"$\; & \; SNR_I \geq $"+str(SNRp_cut))
|
||||
#ax.coords.grid(True, color='white', ls='dotted', alpha=0.5)
|
||||
# ax.coords.grid(True, color='white', ls='dotted', alpha=0.5)
|
||||
ax.coords[0].set_axislabel('Right Ascension (J2000)')
|
||||
ax.coords[0].set_axislabel_position('b')
|
||||
ax.coords[0].set_ticklabel_position('b')
|
||||
ax.coords[1].set_axislabel('Declination (J2000)')
|
||||
ax.coords[1].set_axislabel_position('l')
|
||||
ax.coords[1].set_ticklabel_position('l')
|
||||
#ax.axis('equal')
|
||||
# ax.axis('equal')
|
||||
|
||||
cbar = plt.colorbar(im0, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
|
||||
ax.legend(loc='upper right')
|
||||
fig.savefig(path_join(root_dir_plot_S,"NGC1068_K_comparison.png"),bbox_inches="tight",dpi=300)
|
||||
fig.savefig(path_join(root_dir_plot_S, "NGC1068_K_comparison.png"), bbox_inches="tight", dpi=300)
|
||||
|
||||
#compute integrated polarization parameters on a specific cut
|
||||
# compute integrated polarization parameters on a specific cut
|
||||
for d in [data_S, data_K]:
|
||||
d['I_dil'] = np.sum(d['I'][d['mask']])
|
||||
d['sI_dil'] = np.sqrt(np.sum(d['sI'][d['mask']]**2))
|
||||
@@ -178,28 +182,33 @@ for d in [data_S, data_K]:
|
||||
d['sU_dil'] = np.sqrt(np.sum(d['sU'][d['mask']]**2))
|
||||
|
||||
d['P_dil'] = np.sqrt(d['Q_dil']**2+d['U_dil']**2)/d['I_dil']
|
||||
d['sP_dil'] = np.sqrt((d['Q_dil']**2*d['sQ_dil']**2+d['U_dil']**2*d['sU_dil']**2)/(d['Q_dil']**2+d['U_dil']**2)+((d['Q_dil']/d['I_dil'])**2+(d['U_dil']/d['I_dil'])**2)*d['sI_dil']**2)/d['I_dil']
|
||||
d['sP_dil'] = np.sqrt((d['Q_dil']**2*d['sQ_dil']**2+d['U_dil']**2*d['sU_dil']**2)/(d['Q_dil']**2+d['U_dil']**2) +
|
||||
((d['Q_dil']/d['I_dil'])**2+(d['U_dil']/d['I_dil'])**2)*d['sI_dil']**2)/d['I_dil']
|
||||
d['d_P_dil'] = np.sqrt(d['P_dil']**2-d['sP_dil']**2)
|
||||
d['PA_dil'] = princ_angle((90./np.pi)*np.arctan2(d['U_dil'],d['Q_dil']))
|
||||
d['PA_dil'] = princ_angle((90./np.pi)*np.arctan2(d['U_dil'], d['Q_dil']))
|
||||
d['sPA_dil'] = princ_angle((90./(np.pi*(d['Q_dil']**2+d['U_dil']**2)))*np.sqrt(d['Q_dil']**2*d['sU_dil']**2+d['U_dil']**2*d['sU_dil']**2))
|
||||
print('From this pipeline :\n', "P = {0:.2f} ± {1:.2f} %\n".format(data_S['d_P_dil']*100.,data_S['sP_dil']*100.), "PA = {0:.2f} ± {1:.2f} °".format(data_S['PA_dil'],data_S['sPA_dil']))
|
||||
print("From Kishimoto's pipeline :\n", "P = {0:.2f} ± {1:.2f} %\n".format(data_K['d_P_dil']*100.,data_K['sP_dil']*100.), "PA = {0:.2f} ± {1:.2f} °".format(data_K['PA_dil'],data_K['sPA_dil']))
|
||||
print('From this pipeline :\n', "P = {0:.2f} ± {1:.2f} %\n".format(
|
||||
data_S['d_P_dil']*100., data_S['sP_dil']*100.), "PA = {0:.2f} ± {1:.2f} °".format(data_S['PA_dil'], data_S['sPA_dil']))
|
||||
print("From Kishimoto's pipeline :\n", "P = {0:.2f} ± {1:.2f} %\n".format(
|
||||
data_K['d_P_dil']*100., data_K['sP_dil']*100.), "PA = {0:.2f} ± {1:.2f} °".format(data_K['PA_dil'], data_K['sPA_dil']))
|
||||
|
||||
#compare different types of error
|
||||
print("This pipeline : average sI/I={0:.2f} ; sQ/Q={1:.2f} ; sU/U={2:.2f} ; sP/P={3:.2f}".format(np.mean(data_S['sI'][data_S['mask']]/data_S['I'][data_S['mask']]),np.mean(data_S['sQ'][data_S['mask']]/data_S['Q'][data_S['mask']]),np.mean(data_S['sU'][data_S['mask']]/data_S['U'][data_S['mask']]),np.mean(data_S['sP'][data_S['mask']]/data_S['P'][data_S['mask']])))
|
||||
print("Kishimoto's pipeline : average sI/I={0:.2f} ; sQ/Q={1:.2f} ; sU/U={2:.2f} ; sP/P={3:.2f}".format(np.mean(data_K['sI'][data_S['mask']]/data_K['I'][data_S['mask']]),np.mean(data_K['sQ'][data_S['mask']]/data_K['Q'][data_S['mask']]),np.mean(data_K['sU'][data_S['mask']]/data_K['U'][data_S['mask']]),np.mean(data_K['sP'][data_S['mask']]/data_K['P'][data_S['mask']])))
|
||||
for d,i in zip(['I','Q','U','P','PA','sI','sQ','sU','sP','sPA'],[0,1,2,5,8,(3,0,0),(3,1,1),(3,2,2),6,9]):
|
||||
data_K[d] = np.loadtxt(path_join(root_dir_K,d+'.txt'))
|
||||
with fits.open(path_join(root_dir_data_S,filename_S)) as f:
|
||||
# compare different types of error
|
||||
print("This pipeline : average sI/I={0:.2f} ; sQ/Q={1:.2f} ; sU/U={2:.2f} ; sP/P={3:.2f}".format(np.mean(data_S['sI'][data_S['mask']]/data_S['I'][data_S['mask']]), np.mean(
|
||||
data_S['sQ'][data_S['mask']]/data_S['Q'][data_S['mask']]), np.mean(data_S['sU'][data_S['mask']]/data_S['U'][data_S['mask']]), np.mean(data_S['sP'][data_S['mask']]/data_S['P'][data_S['mask']])))
|
||||
print("Kishimoto's pipeline : average sI/I={0:.2f} ; sQ/Q={1:.2f} ; sU/U={2:.2f} ; sP/P={3:.2f}".format(np.mean(data_K['sI'][data_S['mask']]/data_K['I'][data_S['mask']]), np.mean(
|
||||
data_K['sQ'][data_S['mask']]/data_K['Q'][data_S['mask']]), np.mean(data_K['sU'][data_S['mask']]/data_K['U'][data_S['mask']]), np.mean(data_K['sP'][data_S['mask']]/data_K['P'][data_S['mask']])))
|
||||
for d, i in zip(['I', 'Q', 'U', 'P', 'PA', 'sI', 'sQ', 'sU', 'sP', 'sPA'], [0, 1, 2, 5, 8, (3, 0, 0), (3, 1, 1), (3, 2, 2), 6, 9]):
|
||||
data_K[d] = np.loadtxt(path_join(root_dir_K, d+'.txt'))
|
||||
with fits.open(path_join(root_dir_data_S, filename_S)) as f:
|
||||
if not type(i) is int:
|
||||
data_S[d] = np.sqrt(f[i[0]].data[i[1],i[2]])
|
||||
data_S[d] = np.sqrt(f[i[0]].data[i[1], i[2]])
|
||||
else:
|
||||
data_S[d] = f[i].data
|
||||
if i==0:
|
||||
if i == 0:
|
||||
header = f[i].header
|
||||
|
||||
#from Kishimoto's pipeline : IQU_dir, IQU_shift, IQU_stat, IQU_trans
|
||||
#from my pipeline : raw_bg, raw_flat, raw_psf, raw_shift, raw_wav, IQU_dir
|
||||
# but errors from my pipeline are propagated all along, how to compare then ?
|
||||
# from Kishimoto's pipeline : IQU_dir, IQU_shift, IQU_stat, IQU_trans
|
||||
# from my pipeline : raw_bg, raw_flat, raw_psf, raw_shift, raw_wav, IQU_dir
|
||||
# but errors from my pipeline are propagated all along, how to compare then ?
|
||||
|
||||
plt.show()
|
||||
plt.show()
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
#!/usr/bin/python3
|
||||
#-*- coding:utf-8 -*-
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Library function for simplified fits handling.
|
||||
|
||||
|
||||
@@ -146,7 +146,7 @@ def plot_obs(data_array, headers, shape=None, vmin=None, vmax=None, rectangle=No
|
||||
# im = axe.imshow(convert*data, vmin=vmin, vmax=vmax, origin='lower', cmap='gray')
|
||||
data[data*convert < vmin*10.] = vmin*10./convert
|
||||
im = axe.imshow(convert*data, norm=LogNorm(vmin, vmax), origin='lower', cmap='gray')
|
||||
if not (rectangle is None):
|
||||
if rectangle is not None:
|
||||
x, y, width, height, angle, color = rectangle[i]
|
||||
axe.add_patch(Rectangle((x, y), width, height, angle=angle,
|
||||
edgecolor=color, fill=False))
|
||||
@@ -519,7 +519,7 @@ class align_maps(object):
|
||||
|
||||
self.map_convert, self.map_unit = (float(self.map_header['photflam']), r"$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$") if "PHOTFLAM" in list(
|
||||
self.map_header.keys()) else (1., self.map_header['bunit'] if 'BUNIT' in list(self.map_header.keys()) else "Arbitray Units")
|
||||
self.other_convert, self.other_unit = (float(self.other_map[0].header['photflam']), r"$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$") if "PHOTFLAM" in list(
|
||||
self.other_convert, self.other_unit = (float(self.other_header['photflam']), r"$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$") if "PHOTFLAM" in list(
|
||||
self.other_header.keys()) else (1., self.other_header['bunit'] if 'BUNIT' in list(self.other_header.keys()) else "Arbitray Units")
|
||||
self.map_observer = "/".join([self.map_header['telescop'], self.map_header['instrume']]
|
||||
) if "INSTRUME" in list(self.map_header.keys()) else self.map_header['telescop']
|
||||
@@ -1003,7 +1003,7 @@ class overplot_pol(align_maps):
|
||||
|
||||
# Display Stokes I as contours
|
||||
if levels is None:
|
||||
levels = np.logspace(np.log(3)/np.log(10), 2., 5)/100.*np.max(stkI[stkI > 0.])*self.map_convert
|
||||
levels = np.array([2., 5., 10., 20., 90.])/100.*np.max(stkI[stkI > 0.])*self.map_convert
|
||||
cont_stkI = self.ax_overplot.contour(stkI*self.map_convert, levels=levels, colors='grey', alpha=0.75,
|
||||
transform=self.ax_overplot.get_transform(self.wcs_UV))
|
||||
# self.ax_overplot.clabel(cont_stkI, inline=True, fontsize=5)
|
||||
@@ -1027,7 +1027,7 @@ class overplot_pol(align_maps):
|
||||
self.cr_other, = self.ax_overplot.plot(*(self.other_wcs.celestial.wcs.crpix-(1., 1.)), 'g+')
|
||||
|
||||
if "PHOTPLAM" in list(self.other_header.keys()):
|
||||
self.legend_title = r"{0:s} image at $\lambda$ = {1:.0f} $\AA$".format(self.other_map_observer, float(self.other_header['photplam']))
|
||||
self.legend_title = r"{0:s} image at $\lambda$ = {1:.0f} $\AA$".format(self.other_observer, float(self.other_header['photplam']))
|
||||
elif "CRVAL3" in list(self.other_header.keys()):
|
||||
self.legend_title = "{0:s} image at {1:.2f} GHz".format(self.other_observer, float(self.other_header['crval3'])*1e-9)
|
||||
else:
|
||||
@@ -1884,9 +1884,7 @@ class pol_map(object):
|
||||
def submit_save(expression):
|
||||
ax_text_save.set(visible=False)
|
||||
if expression != '':
|
||||
plt.rcParams.update({'font.size': 15})
|
||||
save_fig = plt.figure(figsize=(15, 15))
|
||||
save_ax = save_fig.add_subplot(111, projection=self.wcs)
|
||||
save_fig, save_ax = plt.subplots(figsize=(12, 10), layout='tight', subplot_kw=dict(projection=self.wcs))
|
||||
self.ax_cosmetics(ax=save_ax)
|
||||
self.display(fig=save_fig, ax=save_ax)
|
||||
self.pol_vector(fig=save_fig, ax=save_ax)
|
||||
@@ -1901,7 +1899,6 @@ class pol_map(object):
|
||||
ax_vec_sc.set(visible=True)
|
||||
ax_save.set(visible=True)
|
||||
ax_dump.set(visible=True)
|
||||
plt.rcParams.update({'font.size': 10})
|
||||
self.fig.canvas.draw_idle()
|
||||
|
||||
text_save.on_submit(submit_save)
|
||||
@@ -2104,7 +2101,7 @@ class pol_map(object):
|
||||
else:
|
||||
vmin, vmax = flux_lim
|
||||
norm = LogNorm(vmin, vmax)
|
||||
label = r"$F_{\lambda} \cdot P$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]"
|
||||
label = r"$P \cdot F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]"
|
||||
elif self.display_selection.lower() in ['pol_deg']:
|
||||
self.data = self.P*100.
|
||||
vmin, vmax = 0., np.max(self.data[self.P > self.s_P])
|
||||
@@ -2139,7 +2136,9 @@ class pol_map(object):
|
||||
self.im = ax.imshow(self.data, norm=norm, aspect='equal', cmap='inferno')
|
||||
else:
|
||||
self.im = ax.imshow(self.data, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno')
|
||||
plt.rcParams.update({'font.size': 14})
|
||||
self.cbar = fig.colorbar(self.im, ax=ax, aspect=50, shrink=0.75, pad=0.025, label=label)
|
||||
plt.rcParams.update({'font.size': 10})
|
||||
fig.canvas.draw_idle()
|
||||
return self.im
|
||||
else:
|
||||
@@ -2149,8 +2148,11 @@ class pol_map(object):
|
||||
im = ax.imshow(self.data, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno')
|
||||
ax.set_xlim(0, self.data.shape[1])
|
||||
ax.set_ylim(0, self.data.shape[0])
|
||||
plt.colorbar(im, pad=0.025, aspect=80, label=label)
|
||||
plt.rcParams.update({'font.size': 14})
|
||||
fig.colorbar(im, ax=ax, aspect=50, shrink=0.75, pad=0.025, label=label)
|
||||
plt.rcParams.update({'font.size': 10})
|
||||
fig.canvas.draw_idle()
|
||||
return im
|
||||
|
||||
def pol_vector(self, fig=None, ax=None):
|
||||
P_cut = np.ones(self.P.shape)*np.nan
|
||||
|
||||
@@ -8,16 +8,18 @@ Stokes_UV = fits.open("./data/MRK463E/5960/MRK463E_FOC_b0.05arcsec_c0.10arcsec.f
|
||||
Stokes_IR = fits.open("./data/MRK463E/WFPC2/IR_rot_crop.fits")
|
||||
Stokes_Xr = fits.open("./data/MRK463E/Chandra/4913/primary/acisf04913N004_cntr_img2.fits")
|
||||
|
||||
levels = np.geomspace(1., 99., 10)
|
||||
levels = np.geomspace(1., 99., 7)
|
||||
|
||||
# A = overplot_chandra(Stokes_UV, Stokes_Xr)
|
||||
# A.plot(levels=levels, SNRp_cut=3.0, SNRi_cut=20.0, zoom=1, savename='./plots/MRK463E/Chandra_overplot.pdf')
|
||||
|
||||
B = overplot_chandra(Stokes_UV, Stokes_Xr, norm=LogNorm())
|
||||
B.plot(levels=levels, SNRp_cut=3.0, SNRi_cut=20.0, zoom=1, savename='./plots/MRK463E/Chandra_overplot_forced.pdf')
|
||||
B.plot(levels=levels, SNRp_cut=3.0, SNRi_cut=30.0, vec_scale=3, zoom=1, savename='./plots/MRK463E/Chandra_overplot_forced.pdf')
|
||||
B.write_to(path1="./data/MRK463E/FOC_data_Chandra.fits", path2="./data/MRK463E/Chandra_data.fits", suffix="aligned")
|
||||
|
||||
# C = overplot_pol(Stokes_UV, Stokes_IR)
|
||||
# C.plot(SNRp_cut=3.0, SNRi_cut=20.0, savename='./plots/MRK463E/IR_overplot.pdf')
|
||||
|
||||
D = overplot_pol(Stokes_UV, Stokes_IR, norm=LogNorm())
|
||||
D.plot(SNRp_cut=3.0, SNRi_cut=30.0, vec_scale=2, norm=LogNorm(1e-18, 1e-15), savename='./plots/MRK463E/IR_overplot_forced.pdf')
|
||||
D.plot(SNRp_cut=3.0, SNRi_cut=30.0, vec_scale=3, norm=LogNorm(1e-18, 1e-15), savename='./plots/MRK463E/IR_overplot_forced.pdf')
|
||||
D.write_to(path1="./data/MRK463E/FOC_data_WFPC.fits", path2="./data/MRK463E/WFPC_data.fits", suffix="aligned")
|
||||
|
||||
Reference in New Issue
Block a user