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98 lines
3.1 KiB
Python
98 lines
3.1 KiB
Python
# Copyright (C) 2022 Richard Stiskalek
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# This program is free software; you can redistribute it and/or modify it
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# under the terms of the GNU General Public License as published by the
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# Free Software Foundation; either version 3 of the License, or (at your
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# option) any later version.
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#
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# This program is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
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# Public License for more details.
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#
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# You should have received a copy of the GNU General Public License along
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# with this program; if not, write to the Free Software Foundation, Inc.,
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# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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"""
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Scripts to read in observation.
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"""
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import numpy
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from astropy.io import fits
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from ..utils import (add_columns, cols_to_structured)
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def read_planck2015(fpath, dist_cosmo, max_comdist=None):
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r"""
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Read the Planck 2nd Sunyaev-Zeldovich source catalogue [1]. The following
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is performed:
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- removes clusters without a redshift estimate,
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- calculates the comoving distance with the provided cosmology.
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- Converts `MSZ` from units of :math:`1e14 M_\odot` to :math:`M_\odot`
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Parameters
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----------
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fpath : str
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Path to the source catalogue.
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dist_cosmo : `astropy.cosmology` object
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The cosmology to calculate cluster comoving distance from redshift.
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max_comdist : float, optional
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Maximum comoving distance threshold in units of :math:`\mathrm{MPc}`.
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By default `None` and no threshold is applied.
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References
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----------
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[1] https://heasarc.gsfc.nasa.gov/W3Browse/all/plancksz2.html
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Returns
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-------
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out : `astropy.io.fits.FITS_rec`
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The catalogue structured array.
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"""
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data = fits.open(fpath)[1].data
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# Convert FITS to a structured array
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out = numpy.full(data.size, numpy.nan, dtype=data.dtype.descr)
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for name in out.dtype.names:
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out[name] = data[name]
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# Take only clusters with redshifts
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out = out[out["REDSHIFT"] >= 0]
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# Add comoving distance
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dist = dist_cosmo.comoving_distance(out["REDSHIFT"]).value
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out = add_columns(out, dist, "COMDIST")
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# Convert masses
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for par in ("MSZ", "MSZ_ERR_UP", "MSZ_ERR_LOW"):
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out[par] *= 1e14
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# Distance threshold
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if max_comdist is not None:
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out = out[out["COMDIST"] < max_comdist]
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return out
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def read_2mpp(fpath):
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"""
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Read in the 2M++ galaxy redshift catalogue [1], with the catalogue at [2].
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Removes fake galaxies used to fill the zone of avoidance.
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Parameters
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----------
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fpath : str
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File path to the catalogue.
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Returns
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-------
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out : structured array
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The catalogue.
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"""
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# Read the catalogue and select non-fake galaxies
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cat = numpy.genfromtxt(fpath, delimiter="|", )
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cat = cat[cat[:, 12] == 0, :]
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F64 = numpy.float64
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cols = [("RA", F64), ("DEC", F64), ("Ksmag", F64)]
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out = cols_to_structured(cat.shape[0], cols)
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out["RA"] = cat[:, 1] - 180
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out["DEC"] = cat[:, 2]
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out["Ksmag"] = cat[:, 5]
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return out
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