Put I,Q,U into single Stokes matrix
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@@ -42,11 +42,11 @@ 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 = "scott" # sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51, 51))
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subtract_error = 0.50
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display_bkg = True
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display_bkg = False
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# Data binning
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pxsize = 4
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pxscale = "px" # pixel, arcsec or full
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pxsize = 0.10
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pxscale = "arcsec" # pixel, arcsec or full
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rebin_operation = "sum" # sum or average
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# Alignement
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@@ -59,17 +59,17 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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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 = 1.5 # If None, no smoothing is done
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smoothing_scale = "px" # pixel or arcsec
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smoothing_FWHM = 0.15 # 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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rotate_North = True
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# Polarization map output
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P_cut = 3 # if >=1.0 cut on the signal-to-noise else cut on the confidence level in Q, U
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SNRi_cut = 1.0 # I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
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SNRi_cut = 10.0 # I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
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flux_lim = None # lowest and highest flux displayed on plot, defaults to bkg and maximum in cut if None
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scale_vec = 5
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scale_vec = 3
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step_vec = 1 # plot all vectors in the array. if step_vec = 2, then every other vector will be plotted if step_vec = 0 then all vectors are displayed at full length
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# Pipeline start
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@@ -197,68 +197,30 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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norm=LogNorm(vmin=data_array[data_array > 0.0].min() * headers[0]["photflam"], vmax=data_array[data_array > 0.0].max() * headers[0]["photflam"]),
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)
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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(
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[
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np.array(
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np.sqrt(
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(bkg - background[np.array([h["filtnam1"] == head["filtnam1"] for h in headers], dtype=bool)].mean()) ** 2
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/ np.sum([h["filtnam1"] == head["filtnam1"] for h in headers])
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)
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).reshape(1, 1)
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for bkg, head in zip(background, headers)
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]
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)
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# Step 2:
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# Compute Stokes I, Q, U with smoothed polarized images
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# SMOOTHING DISCUSSION :
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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, header_stokes, s_IQU_stat = proj_red.compute_Stokes(
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Stokes, Stokes_cov, header_stokes, s_IQU_stat = 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=transmitcorr
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)
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I_bkg, Q_bkg, U_bkg, S_cov_bkg, header_bkg, s_IQU_stat_bkg = proj_red.compute_Stokes(
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background, background_error, np.array(True).reshape(1, 1), headers, FWHM=None, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=False
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)
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# Step 3:
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# Rotate images to have North up
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if rotate_North:
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I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes, s_IQU_stat = proj_red.rotate_Stokes(
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I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes, s_IQU_stat=s_IQU_stat, SNRi_cut=None
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)
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I_bkg, Q_bkg, U_bkg, S_cov_bkg, data_mask_bkg, header_bkg, s_IQU_stat_bkg = proj_red.rotate_Stokes(
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I_bkg, Q_bkg, U_bkg, S_cov_bkg, np.array(True).reshape(1, 1), header_bkg, s_IQU_stat=s_IQU_stat_bkg, SNRi_cut=None
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Stokes, Stokes_cov, data_mask, header_stokes, s_IQU_stat = proj_red.rotate_Stokes(
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Stokes, Stokes_cov, data_mask, header_stokes, s_IQU_stat=s_IQU_stat, SNRi_cut=None
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)
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# Compute polarimetric parameters (polarization degree and angle).
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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, s_IQU_stat=s_IQU_stat)
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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(
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I_bkg, Q_bkg, U_bkg, S_cov_bkg, header_bkg, s_IQU_stat=s_IQU_stat_bkg
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)
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P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P = proj_red.compute_pol(Stokes, Stokes_cov, header_stokes, s_IQU_stat=s_IQU_stat)
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# Step 4:
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# Save image to FITS.
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figname = "_".join([figname, figtype]) if figtype != "" else figname
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Stokes_hdul = proj_fits.save_Stokes(
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I_stokes,
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Q_stokes,
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U_stokes,
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Stokes_cov,
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P,
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debiased_P,
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s_P,
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s_P_P,
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PA,
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s_PA,
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s_PA_P,
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header_stokes,
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data_mask,
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figname,
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data_folder=data_folder,
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return_hdul=True,
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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
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)
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outfiles.append("/".join([data_folder, Stokes_hdul[0].header["FILENAME"] + ".fits"]))
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@@ -277,8 +239,8 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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"F_int({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(
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header_stokes["PHOTPLAM"],
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*sci_not(
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Stokes_hdul[0].data[data_mask].sum() * header_stokes["PHOTFLAM"],
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np.sqrt(Stokes_hdul[3].data[0, 0][data_mask].sum()) * header_stokes["PHOTFLAM"],
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Stokes_hdul["STOKES"].data[0][data_mask].sum() * header_stokes["PHOTFLAM"],
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np.sqrt(Stokes_hdul["STOKES_COV"].data[0, 0][data_mask].sum()) * header_stokes["PHOTFLAM"],
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2,
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out=int,
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),
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@@ -286,14 +248,6 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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)
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print("P_int = {0:.1f} ± {1:.1f} %".format(header_stokes["p_int"] * 100.0, np.ceil(header_stokes["sP_int"] * 1000.0) / 10.0))
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print("PA_int = {0:.1f} ± {1:.1f} °".format(princ_angle(header_stokes["pa_int"]), princ_angle(np.ceil(header_stokes["sPA_int"] * 10.0) / 10.0)))
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# Background values
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print(
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"F_bkg({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(
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header_stokes["photplam"], *sci_not(I_bkg[0, 0] * header_stokes["photflam"], np.sqrt(S_cov_bkg[0, 0][0, 0]) * header_stokes["photflam"], 2, out=int)
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)
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)
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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))
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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)))
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# Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error).
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if pxscale.lower() not in ["full", "integrate"] and not interactive:
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proj_plots.polarization_map(
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