save raw flux in fits file and display
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@@ -20,6 +20,7 @@ import lib.reduction as proj_red # Functions used in reduction pipeline
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import numpy as np
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from lib.utils import princ_angle, sci_not
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from matplotlib.colors import LogNorm
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from astropy.wcs import WCS
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def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=False, interactive=False):
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@@ -40,12 +41,12 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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display_crop = False
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# Background estimation
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error_sub_type = "scott" # sqrt, sturges, rice, freedman-diaconis, scott (default) or shape (example (51, 51))
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error_sub_type = "scott" # auto, sqrt, sturges, rice, freedman-diaconis, scott (default) or shape (example (51, 51))
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subtract_error = 3.0
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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 = 40
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pxsize = 4
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pxscale = "px" # pixel, arcsec or full
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rebin_operation = "sum" # sum or average
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@@ -63,10 +64,11 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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smoothing_scale = "px" # pixel or arcsec
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# Rotation
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rotate_data = False
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rotate_North = True
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# Polarization map output
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P_cut = 5 # if >=1.0 cut on the signal-to-noise else cut on the confidence level in Q, U
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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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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 = 3
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@@ -142,7 +144,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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)
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# Rotate data to have same orientation
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rotate_data = np.unique([np.round(float(head["ORIENTAT"]), 3) for head in headers]).size != 1
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rotate_data = rotate_data or np.unique([np.round(float(head["ORIENTAT"]), 3) for head in headers]).size != 1
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if rotate_data:
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ang = np.mean([head["ORIENTAT"] for head in headers])
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for head in headers:
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@@ -158,7 +160,6 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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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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)
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# Align and rescale images with oversampling.
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data_array, error_array, headers, data_mask, shifts, error_shifts = proj_red.align_data(
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data_array,
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@@ -243,6 +244,8 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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)
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flux_data, flux_error, flux_mask, flux_head = proj_red.rotate_data(np.array([flux_data]), np.array([flux_error]), flux_mask, [flux_head])
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flux_data, flux_error, flux_head = flux_data[0], flux_error[0], flux_head[0]
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elif not rotate_data:
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figname += "_noROT"
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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)
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@@ -250,7 +253,7 @@ 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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figname = "_".join([figname, figtype]) if figtype != "" else figname
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savename = "_".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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@@ -265,18 +268,18 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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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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savename,
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data_folder=data_folder,
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return_hdul=True,
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flux_data=flux_data,
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flux_data=np.array([flux_data, flux_error, flux_mask]),
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flux_head=flux_head,
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)
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outfiles.append("/".join([data_folder, Stokes_hdul["I_STOKES"].header["FILENAME"] + ".fits"]))
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outfiles.append("/".join([data_folder, Stokes_hdul[0].header["FILENAME"] + ".fits"]))
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# Step 5:
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# crop to desired region of interest (roi)
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if crop:
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figname += "_crop"
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savename += "_crop"
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stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_hdul), norm=LogNorm())
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stokescrop.crop()
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stokescrop.write_to("/".join([data_folder, figname + ".fits"]))
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@@ -286,10 +289,10 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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data_mask = Stokes_hdul["data_mask"].data.astype(bool)
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print(
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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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flux_head["PHOTPLAM"],
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*sci_not(
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Stokes_hdul["I_STOKES"].data[data_mask].sum() * header_stokes["PHOTFLAM"],
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np.sqrt(Stokes_hdul["IQU_COV_MATRIX"].data[0, 0][data_mask].sum()) * header_stokes["PHOTFLAM"],
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flux_data[flux_mask].sum() * flux_head["PHOTFLAM"],
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np.sqrt(np.sum(flux_error[flux_mask] ** 2)) * flux_head["PHOTFLAM"],
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2,
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out=int,
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),
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@@ -315,12 +318,12 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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flux_lim=flux_lim,
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step_vec=step_vec,
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scale_vec=scale_vec,
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savename="_".join([figname]),
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savename=figname,
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plots_folder=plots_folder,
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)
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for figtype, figsuffix in zip(
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["Intensity", "Pol_flux", "Pol_deg", "Pol_ang", "I_err", "P_err", "SNRi", "SNRp", "confp"],
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["I", "P_flux", "P", "PA", "I_err", "P_err", "SNRi", "SNRp", "confP"],
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["FluxDensity", "Intensity", "Pol_flux", "Pol_deg", "Pol_ang", "I_err", "P_err", "SNRi", "SNRp", "confp"],
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["F", "I", "P_flux", "P", "PA", "I_err", "P_err", "SNRi", "SNRp", "confP"],
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):
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try:
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proj_plots.polarization_map(
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@@ -331,7 +334,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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flux_lim=flux_lim,
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step_vec=step_vec,
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scale_vec=scale_vec,
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savename="_".join([figname, figsuffix]),
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savename="_".join([savename, figsuffix]),
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plots_folder=plots_folder,
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display=figtype,
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)
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@@ -339,7 +342,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
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pass
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elif not interactive:
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proj_plots.polarization_map(
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deepcopy(Stokes_hdul), data_mask, P_cut=P_cut, SNRi_cut=SNRi_cut, savename=figname, plots_folder=plots_folder, display="integrate"
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deepcopy(Stokes_hdul), data_mask, P_cut=P_cut, SNRi_cut=SNRi_cut, savename=savename, plots_folder=plots_folder, display="integrate"
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)
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elif pxscale.lower() not in ["full", "integrate"]:
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proj_plots.pol_map(Stokes_hdul, P_cut=P_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, scale_vec=scale_vec, flux_lim=flux_lim)
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