mirror of
https://github.com/Richard-Sti/csiborgtools.git
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Overlap reader (#28)
* Edit output path * remove matching of many sims * Add a class skeleton * Add a setter for only nsim * Deletei mport * Simplify cross and delete combined halo catalogue * Simplify reading in catalogues * Add distance calculation * Add mass ratio * Clean up the submission script * Add the summer overlap * Docs * Add array copying * Add import * Add resampling mean
This commit is contained in:
Richard Stiskalek
GitHub
parent
8dea3da4de
commit
1c859dbdac
6 changed files with 473 additions and 402 deletions
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@ -16,10 +16,10 @@
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import numpy
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from scipy.ndimage import gaussian_filter
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from tqdm import (tqdm, trange)
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from datetime import datetime
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from astropy.coordinates import SkyCoord
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from numba import jit
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from gc import collect
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from ..read import (CombinedHaloCatalogue, concatenate_clumps, clumps_pos2cell) # noqa
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from ..read import (concatenate_clumps, clumps_pos2cell)
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def brute_spatial_separation(c1, c2, angular=False, N=None, verbose=False):
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@ -77,58 +77,118 @@ class RealisationsMatcher:
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neighbours in all remaining IC realisations that are within some mass
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range of it.
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Parameters
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----------
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cats : :py:class`csiborgtools.read.CombinedHaloCatalogue`
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Combined halo catalogue to search.
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"""
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_cats = None
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nmult : float or int, optional
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Multiple of the sum of pair initial Lagrangian patch sizes
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within which to return neighbours. By default 1.
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dlogmass : float, optional
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Tolerance on the absolute logarithmic mass difference of potential
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matches. By default 2.
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mass_kind : str, optional
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The mass kind whose similarity is to be checked. Must be a valid
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catalogue key. By default `totpartmass`, i.e. the total particle
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mass associated with a halo.
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overlapper_kwargs : dict, optional
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Keyword arguments passed to `ParticleOverlapper`.
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remove_nooverlap : bool, optional
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Whether to remove pairs with exactly zero overlap. By default `True`.
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def __init__(self, cats):
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self.cats = cats
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"""
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_nmult = None
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_dlogmass = None
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_mass_kind = None
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_overlapper = None
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_remove_nooverlap = None
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def __init__(self, nmult=1., dlogmass=2., mass_kind="totpartmass",
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overlapper_kwargs={}, remove_nooverlap=True):
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self.nmult = nmult
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self.dlogmass = dlogmass
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self.mass_kind = mass_kind
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self._overlapper = ParticleOverlap(**overlapper_kwargs)
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self.remove_nooverlap = remove_nooverlap
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@property
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def cats(self):
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def nmult(self):
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"""
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Combined catalogues.
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Multiple of the sum of pair initial Lagrangian patch sizes within which
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to return neighbours.
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Returns
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-------
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cats : :py:class`csiborgtools.read.CombinedHaloCatalogue`
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Combined halo catalogue.
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nmult : float
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"""
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return self._cats
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return self._nmult
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@cats.setter
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def cats(self, cats):
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"""
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Sets `cats`, ensures that this is a `CombinedHaloCatalogue` object.
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"""
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if not isinstance(cats, CombinedHaloCatalogue):
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raise TypeError("`cats` must be of type `CombinedHaloCatalogue`.")
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self._cats = cats
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@nmult.setter
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def nmult(self, nmult):
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"""Set `nmult`."""
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assert nmult > 0
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self._nmult = nmult
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def search_sim_indices(self, n_sim):
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@property
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def dlogmass(self):
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"""
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Return indices of all other IC realisations but of `n_sim`.
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Parameters
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----------
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n_sim : int
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IC realisation index of `self.cats` to be skipped.
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Tolerance on the absolute logarithmic mass difference of potential
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matches.
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Returns
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-------
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indxs : list of int
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The remaining IC indices.
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dlogmass : float
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"""
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return [i for i in range(self.cats.N) if i != n_sim]
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return self._dlogmass
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def _check_masskind(self, mass_kind):
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"""Check that `mass_kind` is a valid key."""
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if mass_kind not in self.cats[0].keys:
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raise ValueError("Invalid mass kind `{}`.".format(mass_kind))
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@dlogmass.setter
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def dlogmass(self, dlogmass):
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"""Set `dlogmass`."""
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assert dlogmass > 0
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self._dlogmass = dlogmass
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@property
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def mass_kind(self):
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"""
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The mass kind whose similarity is to be checked.
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Returns
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-------
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mass_kind : str
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"""
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return self._mass_kind
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@mass_kind.setter
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def mass_kind(self, mass_kind):
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"""Set `mass_kind`."""
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assert isinstance(mass_kind, str)
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self._mass_kind = mass_kind
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@property
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def remove_nooverlap(self):
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"""
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Whether to remove pairs with exactly zero overlap.
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Returns
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-------
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remove_nooverlap : bool
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"""
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return self._remove_nooverlap
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@remove_nooverlap.setter
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def remove_nooverlap(self, remove_nooverlap):
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"""Set `remove_nooverlap`."""
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assert isinstance(remove_nooverlap, bool)
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self._remove_nooverlap = remove_nooverlap
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@property
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def overlapper(self):
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"""
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The overlapper object.
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Returns
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-------
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overlapper : :py:class:`csiborgtools.match.ParticleOverlap`
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"""
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return self._overlapper
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@staticmethod
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def _cat2clump_mapping(cat_indxs, clump_indxs):
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@ -154,264 +214,111 @@ class RealisationsMatcher:
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mapping[ind2] = ind1
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return mapping
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def cross_knn_position_single(self, n_sim, nmult=1, dlogmass=None,
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mass_kind="totpartmass", overlap=False,
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overlapper_kwargs={}, select_initial=True,
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remove_nooverlap=True, fast_neighbours=False,
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verbose=True):
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def cross(self, nsim0, nsimx, cat0, catx, overlap=False, verbose=True):
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r"""
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Find all neighbours within a multiple of the sum of either the initial
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Lagrangian patch sizes (distance at :math:`z = 70`) or :math:`R_{200c}`
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(distance at :math:`z = 0`). Enforces that the neighbours' are similar
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in mass up to `dlogmass` dex and optionally calculates their overlap.
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Find all neighbours whose CM separation is less than `nmult` times the
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sum of their initial Lagrangian patch sizes. Enforces that the
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neighbours' are similar in mass up to `dlogmass` dex.
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Parameters
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----------
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n_sim : int
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Index of an IC realisation in `self.cats` whose halos' neighbours
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in the remaining simulations to search for.
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nmult : float or int, optional
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Multiple of the sum of pair Lagrangian patch sizes or
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:math:`R_{200c}` within which to return neighbours. By default 1.
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dlogmass : float, optional
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Tolerance on mass logarithmic mass difference. By default `None`.
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mass_kind : str, optional
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The mass kind whose similarity is to be checked. Must be a valid
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catalogue key. By default `totpartmass`, i.e. the total particle
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mass associated with a halo.
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nsim0, nsimx : int
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The reference and cross simulation IDs.
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cat0, catx: :py:class:`csiborgtools.read.HaloCatalogue`
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Halo catalogues corresponding to `nsim0` and `nsimx`, respectively.
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overlap : bool, optional
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Whether to calculate overlap between clumps in the initial
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snapshot. By default `False`. This operation is slow.
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overlapper_kwargs : dict, optional
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Keyword arguments passed to `ParticleOverlapper`.
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select_initial : bool, optional
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Whether to select nearest neighbour at the initial or final
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snapshot. By default `True`, i.e. at the initial snapshot.
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remove_nooverlap : bool, optional
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Whether to remove pairs with exactly zero overlap. By default
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`True`.
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fast_neighbours : bool, optional
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Whether to calculate neighbours within a fixed radius of each
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clump. Note that this will result in missing some matches. If
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`True` then `nmult` is a multiple of either the initial patch size
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of :math:`R_{200c}`.
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whether to calculate overlap between clumps in the initial
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snapshot. by default `false`. this operation is slow.
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verbose : bool, optional
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Iterator verbosity flag. By default `True`.
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iterator verbosity flag. by default `true`.
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Returns
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-------
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matches : composite array
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Array, indices are `(n_sims - 1, 5, n_halos, n_matches)`. The
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2nd axis is `index` of the neighbouring halo in its catalogue,
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`dist` is the 3D distance to the halo whose neighbours are
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searched, `dist0` is the separation of the initial CMs, and
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`overlap` is the overlap over the initial clumps, respectively.
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indxs : 1-dimensional array of shape `(nhalos, )`
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Indices of halos in the reference catalogue.
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match_indxs : 1-dimensional array of arrays
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Indices of halo counterparts in the cross catalogue.
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overlaps : 1-dimensional array of arrays
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Overlaps with the cross catalogue.
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"""
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self._check_masskind(mass_kind)
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# Halo properties of this simulation
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logmass = numpy.log10(self.cats[n_sim][mass_kind])
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pos = self.cats[n_sim].positions # Grav potential minimum
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pos0 = self.cats[n_sim].positions0 # CM positions
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if select_initial:
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R = self.cats[n_sim]["lagpatch"] # Initial Lagrangian patch size
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else:
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R = self.cats[n_sim]["r200"] # R200c at z = 0
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assert (nsim0 == cat0.paths.n_sim) & (nsimx == catx.paths.n_sim)
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if overlap:
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overlapper = ParticleOverlap(**overlapper_kwargs)
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if verbose:
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print("Loading initial clump particles for `n_sim = {}`."
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.format(n_sim), flush=True)
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# Grab a paths object. What it is set to is unimportant
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paths = self.cats[0].paths
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with open(paths.clump0_path(self.cats.n_sims[n_sim]), "rb") as f:
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clumps0 = numpy.load(f, allow_pickle=True)
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clumps_pos2cell(clumps0, overlapper)
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# Precalculate min and max cell along each axis
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mins0, maxs0 = get_clumplims(clumps0,
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ncells=overlapper.inv_clength,
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nshift=overlapper.nshift)
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cat2clumps0 = self._cat2clump_mapping(self.cats[n_sim]["index"],
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clumps0["ID"])
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matches = [None] * (self.cats.N - 1)
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# Verbose iterator
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# Query the KNN
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if verbose:
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iters = enumerate(tqdm(self.search_sim_indices(n_sim)))
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else:
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iters = enumerate(self.search_sim_indices(n_sim))
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iters = enumerate(self.search_sim_indices(n_sim))
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# Search for neighbours in the other simulations at z = 70
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for count, i in iters:
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print("{}: querying the KNN.".format(datetime.now()), flush=True)
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match_indxs = radius_neighbours(
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catx.knn0, cat0.positions0, radiusX=cat0["lagpatch"],
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radiusKNN=catx["lagpatch"], nmult=self.nmult, enforce_in32=True,
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verbose=verbose)
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# Remove neighbours whose mass is too large/small
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if self.dlogmass is not None:
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for j, indx in enumerate(match_indxs):
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# |log(M1 / M2)|
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p = self.mass_kind
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aratio = numpy.abs(numpy.log10(catx[p][indx] / cat0[p][j]))
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match_indxs[j] = match_indxs[j][aratio < self.dlogmass]
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# Initialise the array outside in case `overlap` is `False`
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cross = [numpy.asanyarray([], dtype=numpy.float32)] * match_indxs.size
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if overlap:
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if verbose:
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print("Querying the KNN for `n_sim = {}`.".format(n_sim),
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flush=True)
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# Query the KNN either fast (miss some) or slow (get all)
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if select_initial:
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if fast_neighbours:
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dist0, indxs = self.cats[i].radius_neigbours(
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pos0, R * nmult, select_initial=True)
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else:
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dist0, indxs = radius_neighbours(
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self.cats[i].knn0, pos0, radiusX=R,
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radiusKNN=self.cats[i]["lagpatch"], nmult=nmult,
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verbose=verbose)
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else:
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# Will switch dist0 <-> dist at the end
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if fast_neighbours:
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dist0, indxs = self.cats[i].radius_neigbours(
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pos, R * nmult, select_initial=False)
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else:
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dist0, indxs = radius_neighbours(
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self.cats[i].knn, pos, radiusX=R,
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radiusKNN=self.cats[i]["r200"], nmult=nmult,
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verbose=verbose)
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# Enforce int32 and float32
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for n in range(dist0.size):
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dist0[n] = dist0[n].astype(numpy.float32)
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indxs[n] = indxs[n].astype(numpy.int32)
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print("Loading the clump particles", flush=True)
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with open(cat0.paths.clump0_path(nsim0), "rb") as f:
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clumps0 = numpy.load(f, allow_pickle=True)
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with open(catx.paths.clump0_path(nsimx), 'rb') as f:
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clumpsx = numpy.load(f, allow_pickle=True)
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# Get rid of neighbors whose mass is too off
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if dlogmass is not None:
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for j, indx in enumerate(indxs):
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match_logmass = numpy.log10(self.cats[i][mass_kind][indx])
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mask = numpy.abs(match_logmass - logmass[j]) < dlogmass
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dist0[j] = dist0[j][mask]
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indxs[j] = indx[mask]
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# Convert 3D positions to particle IDs
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clumps_pos2cell(clumps0, self.overlapper)
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clumps_pos2cell(clumpsx, self.overlapper)
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# Find the distance at z = 0 (or z = 70 dep. on `search_initial``)
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dist = [numpy.asanyarray([], dtype=numpy.float32)] * dist0.size
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with_neigbours = numpy.where([ii.size > 0 for ii in indxs])[0]
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# Fill the pre-allocated array on positions with neighbours
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for k in with_neigbours:
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if select_initial:
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dist[k] = numpy.linalg.norm(
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pos[k] - self.cats[i].positions[indxs[k]], axis=1)
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else:
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dist[k] = numpy.linalg.norm(
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pos0[k] - self.cats[i].positions0[indxs[k]], axis=1)
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# Calculate the particle field
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if verbose:
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print("Creating and smoothing the crossed field.", flush=True)
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delta = self.overlapper.make_delta(concatenate_clumps(clumpsx),
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to_smooth=False)
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delta = self.overlapper.smooth_highres(delta)
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# Calculate the initial snapshot overlap
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cross = [numpy.asanyarray([], dtype=numpy.float32)] * dist0.size
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if overlap:
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if verbose:
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print("Loading initial clump particles for `n_sim = {}` "
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"to compare against `n_sim = {}`.".format(i, n_sim),
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flush=True)
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with open(paths.clump0_path(self.cats.n_sims[i]), 'rb') as f:
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clumpsx = numpy.load(f, allow_pickle=True)
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clumps_pos2cell(clumpsx, overlapper)
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# Min and max cells along each axis for each halo
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limkwargs = {"ncells": self.overlapper.inv_clength,
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"nshift": self.overlapper.nshift}
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mins0, maxs0 = get_clumplims(clumps0, **limkwargs)
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minsx, maxsx = get_clumplims(clumpsx, **limkwargs)
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# Calculate the particle field
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if verbose:
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print("Loading and smoothing the crossed total field.",
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flush=True)
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particles = concatenate_clumps(clumpsx)
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delta = overlapper.make_delta(particles, to_smooth=False)
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del particles; collect() # noqa - no longer needed
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delta = overlapper.smooth_highres(delta)
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if verbose:
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print("Smoothed up the field.", flush=True)
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# Precalculate min and max cell along each axis
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minsx, maxsx = get_clumplims(clumpsx,
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ncells=overlapper.inv_clength,
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nshift=overlapper.nshift)
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# Mapping from a catalogue halo index to the list of clumps
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cat2clumps0 = self._cat2clump_mapping(cat0["index"], clumps0["ID"])
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cat2clumpsx = self._cat2clump_mapping(catx["index"], clumpsx["ID"])
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cat2clumpsx = self._cat2clump_mapping(self.cats[i]["index"],
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clumpsx["ID"])
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# Loop only over halos that have neighbours
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for k in tqdm(with_neigbours) if verbose else with_neigbours:
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# Find which clump matches index of this halo from cat
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match0 = cat2clumps0[k]
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# Loop only over halos that have neighbours
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wneigbours = numpy.where([ii.size > 0 for ii in match_indxs])[0]
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for k in tqdm(wneigbours) if verbose else wneigbours:
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match0 = cat2clumps0[k] # Clump pos matching this halo
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# The clump, its mass and mins & maxs
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cl0 = clumps0["clump"][match0]
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mass0 = numpy.sum(cl0['M'])
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mins0_current, maxs0_current = mins0[match0], maxs0[match0]
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# Unpack this clum, its mamss and mins and maxs
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cl0 = clumps0["clump"][match0]
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mass0 = numpy.sum(cl0['M'])
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mins0_current, maxs0_current = mins0[match0], maxs0[match0]
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# Preallocate this array.
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crosses = numpy.full(indxs[k].size, numpy.nan,
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numpy.float32)
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# Array to store overlaps of this halo
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crosses = numpy.full(match_indxs[k].size, numpy.nan,
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numpy.float32)
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# Loop over matches of this halo from the other simulation
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for ii, ind in enumerate(match_indxs[k]):
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matchx = cat2clumpsx[ind] # Clump pos matching this halo
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clx = clumpsx["clump"][matchx]
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crosses[ii] = self.overlapper(
|
||||
cl0, clx, delta, mins0_current, maxs0_current,
|
||||
minsx[matchx], maxsx[matchx],
|
||||
mass1=mass0, mass2=numpy.sum(clx['M']))
|
||||
cross[k] = crosses
|
||||
|
||||
# Loop over the ones we cross-correlate with
|
||||
for ii, ind in enumerate(indxs[k]):
|
||||
# Again which cross clump to this index
|
||||
matchx = cat2clumpsx[ind]
|
||||
clx = clumpsx["clump"][matchx]
|
||||
crosses[ii] = overlapper(
|
||||
cl0, clx, delta, mins0_current, maxs0_current,
|
||||
minsx[matchx], maxsx[matchx],
|
||||
mass1=mass0, mass2=numpy.sum(clx['M']))
|
||||
# Optionally remove matches with exactly 0 overlap
|
||||
if self.remove_nooverlap:
|
||||
mask = cross[k] > 0
|
||||
match_indxs[k] = match_indxs[k][mask]
|
||||
cross[k] = cross[k][mask]
|
||||
|
||||
cross[k] = crosses
|
||||
# Optionally remove points whose overlap is exaclt zero
|
||||
if remove_nooverlap:
|
||||
mask = cross[k] > 0
|
||||
indxs[k] = indxs[k][mask]
|
||||
dist[k] = dist[k][mask]
|
||||
dist0[k] = dist0[k][mask]
|
||||
cross[k] = cross[k][mask]
|
||||
|
||||
# Append as a composite array. Flip dist order if not select_init
|
||||
if select_initial:
|
||||
matches[count] = numpy.asarray(
|
||||
[indxs, dist, dist0, cross], dtype=object)
|
||||
else:
|
||||
matches[count] = numpy.asarray(
|
||||
[indxs, dist0, dist, cross], dtype=object)
|
||||
|
||||
return numpy.asarray(matches, dtype=object)
|
||||
|
||||
def cross_knn_position_all(self, nmult=1, dlogmass=None,
|
||||
mass_kind="totpartmass", overlap=False,
|
||||
overlapper_kwargs={},
|
||||
select_initial=True, remove_nooverlap=True,
|
||||
verbose=True):
|
||||
r"""
|
||||
Find all counterparts of halos in all simulations listed in
|
||||
`self.cats`. See `self.cross_knn_position_single` for more details.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nmult : float or int, optional
|
||||
Multiple of the sum of pair Lagrangian patch sizes or
|
||||
:math:`R_{200c}` within which to return neighbours. By default 1.
|
||||
dlogmass : float, optional
|
||||
Tolerance on mass logarithmic mass difference. By default `None`.
|
||||
mass_kind : str, optional
|
||||
The mass kind whose similarity is to be checked. Must be a valid
|
||||
catalogue key. By default `totpartmass`, i.e. the total particle
|
||||
mass associated with a halo.
|
||||
overlap : bool, optional
|
||||
Whether to calculate overlap between clumps in the initial
|
||||
snapshot. By default `False`. Note that this operation is
|
||||
substantially slower.
|
||||
overlapper_kwargs : dict, optional
|
||||
Keyword arguments passed to `ParticleOverlapper`.
|
||||
select_initial : bool, optional
|
||||
Whether to select nearest neighbour at the initial or final
|
||||
snapshot. By default `True`, i.e. at the initial snapshot.
|
||||
remove_nooverlap : bool, optional
|
||||
Whether to remove pairs with exactly zero overlap. By default
|
||||
`True`.
|
||||
verbose : bool, optional
|
||||
Iterator verbosity flag. By default `True`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
matches : list of composite arrays
|
||||
List whose length is `n_sims`. For description of its elements see
|
||||
`self.cross_knn_position_single(...)`.
|
||||
"""
|
||||
N = self.cats.N # Number of catalogues
|
||||
matches = [None] * N
|
||||
# Loop over each catalogue
|
||||
for i in trange(N) if verbose else range(N):
|
||||
matches[i] = self.cross_knn_position_single(
|
||||
i, nmult, dlogmass, mass_kind=mass_kind, overlap=overlap,
|
||||
overlapper_kwargs=overlapper_kwargs,
|
||||
select_initial=select_initial,
|
||||
remove_nooverlap=remove_nooverlap, verbose=verbose)
|
||||
return matches
|
||||
return cat0["index"], match_indxs, cross
|
||||
|
||||
|
||||
###############################################################################
|
||||
|
|
@ -1002,7 +909,8 @@ def dist_percentile(dist, qs, distmax=0.075):
|
|||
return x
|
||||
|
||||
|
||||
def radius_neighbours(knn, X, radiusX, radiusKNN, nmult=1., verbose=True):
|
||||
def radius_neighbours(knn, X, radiusX, radiusKNN, nmult=1., enforce_in32=False,
|
||||
verbose=True):
|
||||
"""
|
||||
Find all neigbours of a trained KNN model whose center of mass separation
|
||||
is less than `nmult` times the sum of their respective radii.
|
||||
|
|
@ -1020,13 +928,14 @@ def radius_neighbours(knn, X, radiusX, radiusKNN, nmult=1., verbose=True):
|
|||
Patch radii corresponding to clumps used to train `knn`.
|
||||
nmult : float, optional
|
||||
Multiple of the sum of two radii below which to consider a match.
|
||||
enforce_int32 : bool, optional
|
||||
Whether to enforce 32-bit integer precision of the indices. By default
|
||||
`False`.
|
||||
verbose : bool, optional
|
||||
Verbosity flag.
|
||||
|
||||
Returns
|
||||
-------
|
||||
dists : 1-dimensional array `(n_samples,)` of arrays
|
||||
Distance from `X` to matches from `knn`.
|
||||
indxs : 1-dimensional array `(n_samples,)` of arrays
|
||||
Matches to `X` from `knn`.
|
||||
"""
|
||||
|
|
@ -1035,7 +944,6 @@ def radius_neighbours(knn, X, radiusX, radiusKNN, nmult=1., verbose=True):
|
|||
assert radiusKNN.size == knn.n_samples_fit_ # patchknn matches the knn?
|
||||
|
||||
nsamples = X.shape[0]
|
||||
dists = [None] * nsamples # Initiate lists
|
||||
indxs = [None] * nsamples
|
||||
patchknn_max = numpy.max(radiusKNN) # Maximum for completeness
|
||||
|
||||
|
|
@ -1046,9 +954,8 @@ def radius_neighbours(knn, X, radiusX, radiusKNN, nmult=1., verbose=True):
|
|||
# Note that `dist` and `indx` are wrapped in 1-element arrays
|
||||
# so we take the first item where appropriate
|
||||
mask = (dist[0] / (radiusX[i] + radiusKNN[indx[0]])) < nmult
|
||||
dists[i] = dist[0][mask]
|
||||
indxs[i] = indx[0][mask]
|
||||
if enforce_in32:
|
||||
indxs[i] = indxs[i].astype(numpy.int32)
|
||||
|
||||
dists = numpy.asarray(dists, dtype=object) # Turn into array of arrays
|
||||
indxs = numpy.asarray(indxs, dtype=object)
|
||||
return dists, indxs
|
||||
return numpy.asarray(indxs, dtype=object)
|
||||
|
|
|
|||
|
|
@ -14,9 +14,8 @@
|
|||
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
|
||||
|
||||
from .readsim import (CSiBORGPaths, ParticleReader, read_mmain, read_initcm, halfwidth_select) # noqa
|
||||
from .make_cat import (HaloCatalogue, CombinedHaloCatalogue, concatenate_clumps, # noqa
|
||||
clumps_pos2cell) # noqa
|
||||
from .make_cat import (HaloCatalogue, concatenate_clumps, clumps_pos2cell) # noqa
|
||||
from .readobs import (PlanckClusters, MCXCClusters, TwoMPPGalaxies, # noqa
|
||||
TwoMPPGroups, SDSS) # noqa
|
||||
from .outsim import (dump_split, combine_splits, make_ascii_powmes) # noqa
|
||||
from .summaries import PKReader # noqa
|
||||
from .summaries import (PKReader, OverlapReader, binned_resample_mean) # noqa
|
||||
|
|
|
|||
|
|
@ -17,10 +17,8 @@ Functions to read in the particle and clump files.
|
|||
"""
|
||||
import numpy
|
||||
from os.path import join
|
||||
from tqdm import trange
|
||||
from copy import deepcopy
|
||||
from sklearn.neighbors import NearestNeighbors
|
||||
from .readsim import (read_mmain, read_initcm)
|
||||
from .readsim import (CSiBORGPaths, read_mmain, read_initcm)
|
||||
from ..utils import (flip_cols, add_columns)
|
||||
from ..units import (BoxUnits, cartesian_to_radec)
|
||||
|
||||
|
|
@ -47,7 +45,11 @@ class HaloCatalogue:
|
|||
_positions = None
|
||||
_positions0 = None
|
||||
|
||||
def __init__(self, paths, min_m500=None, max_dist=None):
|
||||
def __init__(self, nsim, min_m500=None, max_dist=None):
|
||||
# Set up paths
|
||||
paths = CSiBORGPaths(n_sim=nsim)
|
||||
paths.n_snap = paths.get_maximum_snapshot()
|
||||
self._paths = paths
|
||||
self._box = BoxUnits(paths)
|
||||
min_m500 = 0 if min_m500 is None else min_m500
|
||||
max_dist = numpy.infty if max_dist is None else max_dist
|
||||
|
|
@ -378,99 +380,6 @@ class HaloCatalogue:
|
|||
return self._data[key]
|
||||
|
||||
|
||||
class CombinedHaloCatalogue:
|
||||
r"""
|
||||
A combined halo catalogue, containing `HaloCatalogue` for each IC
|
||||
realisation at the latest redshift.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
paths : py:class`csiborgtools.read.CSiBORGPaths`
|
||||
CSiBORG paths-handling object. Doest not have to have set set `n_sim`
|
||||
and `n_snap`.
|
||||
min_m500 : float, optional
|
||||
The minimum :math:`M_{rm 500c} / M_\odot` mass. By default no
|
||||
threshold.
|
||||
max_dist : float, optional
|
||||
The maximum comoving distance of a halo. By default no upper limit.
|
||||
verbose : bool, optional
|
||||
Verbosity flag for reading the catalogues.
|
||||
"""
|
||||
_n_sims = None
|
||||
_n_snaps = None
|
||||
_cats = None
|
||||
|
||||
def __init__(self, paths, min_m500=None, max_dist=None, verbose=True):
|
||||
# Read simulations and their maximum snapshots
|
||||
# NOTE later change this back to all simulations
|
||||
# self._n_sims = [7468, 7588, 8020, 8452, 8836]
|
||||
self._n_sims = [7468, 7588]
|
||||
# self._n_sims = paths.ic_ids
|
||||
n_snaps = [paths.get_maximum_snapshot(i) for i in self._n_sims]
|
||||
self._n_snaps = numpy.asanyarray(n_snaps)
|
||||
|
||||
cats = [None] * self.N
|
||||
for i in trange(self.N) if verbose else range(self.N):
|
||||
paths = deepcopy(paths)
|
||||
paths.set_info(self.n_sims[i], self.n_snaps[i])
|
||||
cats[i] = HaloCatalogue(paths, min_m500, max_dist)
|
||||
self._cats = cats
|
||||
|
||||
@property
|
||||
def N(self):
|
||||
"""
|
||||
Number of IC realisations in this combined catalogue.
|
||||
|
||||
Returns
|
||||
-------
|
||||
N : int
|
||||
Number of catalogues.
|
||||
"""
|
||||
return len(self.n_sims)
|
||||
|
||||
@property
|
||||
def n_sims(self):
|
||||
"""
|
||||
IC realisations CSiBORG identifiers.
|
||||
|
||||
Returns
|
||||
-------
|
||||
ids : 1-dimensional array
|
||||
Array of IDs.
|
||||
"""
|
||||
return self._n_sims
|
||||
|
||||
@property
|
||||
def n_snaps(self):
|
||||
"""
|
||||
Snapshot numbers corresponding to `self.n_sims`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
n_snaps : 1-dimensional array
|
||||
Array of snapshot numbers.
|
||||
"""
|
||||
return self._n_snaps
|
||||
|
||||
@property
|
||||
def cats(self):
|
||||
"""
|
||||
Catalogues associated with this object.
|
||||
|
||||
Returns
|
||||
-------
|
||||
cats : list of `HaloCatalogue`
|
||||
Catalogues.
|
||||
"""
|
||||
return self._cats
|
||||
|
||||
def __getitem__(self, n):
|
||||
if n > self.N:
|
||||
raise ValueError("Catalogue count is {}, requested catalogue {}."
|
||||
.format(self.N, n))
|
||||
return self.cats[n]
|
||||
|
||||
|
||||
def concatenate_clumps(clumps):
|
||||
"""
|
||||
Concatenate an array of clumps to a single array containing all particles.
|
||||
|
|
|
|||
|
|
@ -88,7 +88,8 @@ class CSiBORGPaths:
|
|||
self.to_new = to_new
|
||||
if n_sim is not None and n_snap is not None:
|
||||
self.set_info(n_sim, n_snap)
|
||||
# "/mnt/extraspace/rstiskalek/csiborg/initmatch/clump_cm_7468.npy"
|
||||
if n_sim is not None:
|
||||
self.n_sim = n_sim
|
||||
|
||||
@property
|
||||
def srcdir(self):
|
||||
|
|
|
|||
|
|
@ -166,3 +166,268 @@ class PKReader:
|
|||
k += 1
|
||||
|
||||
return ks, xpks
|
||||
|
||||
|
||||
class OverlapReader:
|
||||
"""
|
||||
TODO: docs
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, nsim0, nsimx, cat0, catx, fskel=None):
|
||||
if fskel is None:
|
||||
fskel = "/mnt/extraspace/rstiskalek/csiborg/overlap/"
|
||||
fskel += "cross_{}_{}.npz"
|
||||
self._data = numpy.load(fskel.format(nsim0, nsimx), allow_pickle=True)
|
||||
self._set_cats(nsim0, nsimx, cat0, catx)
|
||||
|
||||
@property
|
||||
def nsim0(self):
|
||||
"""
|
||||
The reference simulation ID.
|
||||
|
||||
Returns
|
||||
-------
|
||||
nsim0 : int
|
||||
"""
|
||||
return self._nsim0
|
||||
|
||||
@property
|
||||
def nsimx(self):
|
||||
"""
|
||||
The cross simulation ID.
|
||||
|
||||
Returns
|
||||
-------
|
||||
nsimx : int
|
||||
"""
|
||||
return self._nsimx
|
||||
|
||||
@property
|
||||
def cat0(self):
|
||||
"""
|
||||
The reference halo catalogue.
|
||||
|
||||
Returns
|
||||
-------
|
||||
cat0 : :py:class:`csiborgtools.read.HaloCatalogue`
|
||||
"""
|
||||
return self._cat0
|
||||
|
||||
@property
|
||||
def catx(self):
|
||||
"""
|
||||
The cross halo catalogue.
|
||||
|
||||
Returns
|
||||
-------
|
||||
catx : :py:class:`csiborgtools.read.HaloCatalogue`
|
||||
"""
|
||||
return self._catx
|
||||
|
||||
def _set_cats(self, nsim0, nsimx, cat0, catx):
|
||||
"""
|
||||
Set the simulation IDs and catalogues.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nsim0, nsimx : int
|
||||
The reference and cross simulation IDs.
|
||||
cat0, catx: :py:class:`csiborgtools.read.HaloCatalogue`
|
||||
Halo catalogues corresponding to `nsim0` and `nsimx`, respectively.
|
||||
|
||||
Returns
|
||||
-------
|
||||
None
|
||||
"""
|
||||
assert (nsim0 == cat0.paths.n_sim) & (nsimx == catx.paths.n_sim)
|
||||
self._nsim0 = nsim0
|
||||
self._nsimx = nsimx
|
||||
self._cat0 = cat0
|
||||
self._catx = catx
|
||||
|
||||
@property
|
||||
def indxs(self):
|
||||
"""
|
||||
Indices of halos from the reference catalogue.
|
||||
|
||||
Returns
|
||||
-------
|
||||
indxs : 1-dimensional array
|
||||
"""
|
||||
return self._data["indxs"]
|
||||
|
||||
@property
|
||||
def match_indxs(self):
|
||||
"""
|
||||
Indices of halos from the cross catalogue.
|
||||
|
||||
Returns
|
||||
-------
|
||||
match_indxs : array of 1-dimensional arrays of shape `(nhalos, )`
|
||||
"""
|
||||
return self._data["match_indxs"]
|
||||
|
||||
@property
|
||||
def overlap(self):
|
||||
"""
|
||||
Pair overlap of halos between the reference and cross simulations.
|
||||
|
||||
Returns
|
||||
-------
|
||||
ovelap : array of 1-dimensional arrays of shape `(nhalos, )`
|
||||
"""
|
||||
return self._data["cross"]
|
||||
|
||||
def dist(self, in_initial, norm=None):
|
||||
"""
|
||||
Final snapshot pair distances.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
in_initial : bool
|
||||
Whether to calculate separation in the initial or final snapshot.
|
||||
|
||||
Returns
|
||||
-------
|
||||
dist : array of 1-dimensional arrays of shape `(nhalos, )`
|
||||
"""
|
||||
assert norm is None or norm in ("r200", "ref_patch", "sum_patch")
|
||||
# Positions either in the initial or final snapshot
|
||||
if in_initial:
|
||||
pos0 = self.cat0.positions0
|
||||
posx = self.catx.positions0
|
||||
else:
|
||||
pos0 = self.cat0.positions
|
||||
posx = self.catx.positions
|
||||
|
||||
dist = [None] * self.indxs.size
|
||||
for n, ind in enumerate(self.match_indxs):
|
||||
dist[n] = numpy.linalg.norm(pos0[n, :] - posx[ind, :], axis=1)
|
||||
|
||||
# Normalisation
|
||||
if norm == "r200":
|
||||
dist[n] /= self.cat0["r200"][n]
|
||||
if norm == "ref_patch":
|
||||
dist[n] /= self.cat0["lagpatch"][n]
|
||||
if norm == "sum_patch":
|
||||
dist[n] /= (self.cat0["lagpatch"][n]
|
||||
+ self.catx["lagpatch"][ind])
|
||||
return numpy.array(dist, dtype=object)
|
||||
|
||||
def mass_ratio(self, mass_kind="totpartmass", in_log=True, in_abs=True):
|
||||
"""
|
||||
Pair mass ratio.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
mass_kind : str, optional
|
||||
The mass kind whose ratio is to be calculated. Must be a valid
|
||||
catalogue key. By default `totpartmass`, i.e. the total particle
|
||||
mass associated with a halo.
|
||||
in_log : bool, optional
|
||||
Whether to return logarithm of the ratio. By default `True`.
|
||||
in_abs : bool, optional
|
||||
Whether to return absolute value of the ratio. By default `True`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
ratio : array of 1-dimensional arrays of shape `(nhalos, )`
|
||||
"""
|
||||
mass0 = self.cat0[mass_kind]
|
||||
massx = self.catx[mass_kind]
|
||||
|
||||
ratio = [None] * self.indxs.size
|
||||
for n, ind in enumerate(self.match_indxs):
|
||||
ratio[n] = mass0[n] / massx[ind]
|
||||
if in_log:
|
||||
ratio[n] = numpy.log10(ratio[n])
|
||||
if in_abs:
|
||||
ratio[n] = numpy.abs(ratio[n])
|
||||
return numpy.array(ratio, dtype=object)
|
||||
|
||||
def summed_overlap(self):
|
||||
"""
|
||||
Summed overlap of each halo in the reference simulation with the cross
|
||||
simulation.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
None
|
||||
|
||||
Returns
|
||||
-------
|
||||
summed_overlap : 1-dimensional array of shape `(nhalos, )`
|
||||
"""
|
||||
return numpy.array([numpy.sum(cross) for cross in self._data["cross"]])
|
||||
|
||||
def copy_per_match(self, par):
|
||||
"""
|
||||
Make an array like `self.match_indxs` where each of its element is an
|
||||
equal value array of the pair clump property from the reference
|
||||
catalogue.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
par : str
|
||||
Property to be copied over.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : 1-dimensional array of shape `(nhalos, )`
|
||||
"""
|
||||
out = [None] * self.indxs.size
|
||||
for n, ind in enumerate(self.match_indxs):
|
||||
out[n] = numpy.ones(ind.size) * self.cat0[par][n]
|
||||
return numpy.array(out, dtype=object)
|
||||
|
||||
|
||||
def binned_resample_mean(x, y, prob, bins, nresample=50, seed=42):
|
||||
"""
|
||||
Calculate binned average of `y` by MC resampling. Each point is kept with
|
||||
probability `prob`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
x : 1-dimensional array
|
||||
Independent variable.
|
||||
y : 1-dimensional array
|
||||
Dependent variable.
|
||||
prob : 1-dimensional array
|
||||
Sample probability.
|
||||
bins : 1-dimensional array
|
||||
Bin edges to bin `x`.
|
||||
nresample : int, optional
|
||||
Number of MC resamples. By default 50.
|
||||
seed : int, optional
|
||||
Random seed.
|
||||
|
||||
Returns
|
||||
-------
|
||||
bin_centres : 1-dimensional array
|
||||
Bin centres.
|
||||
stat : 2-dimensional array
|
||||
Mean and its standard deviation from MC resampling.
|
||||
"""
|
||||
assert (x.ndim == 1) & (x.shape == y.shape == prob.shape)
|
||||
|
||||
gen = numpy.random.RandomState(seed)
|
||||
|
||||
loop_stat = numpy.full(nresample, numpy.nan) # Preallocate loop arr
|
||||
stat = numpy.full((bins.size - 1, 2), numpy.nan) # Preallocate output
|
||||
|
||||
for i in range(bins.size - 1):
|
||||
mask = (x > bins[i]) & (x <= bins[i + 1])
|
||||
nsamples = numpy.sum(mask)
|
||||
|
||||
loop_stat[:] = numpy.nan # Clear it
|
||||
for j in range(nresample):
|
||||
loop_stat[j] = numpy.mean(y[mask][gen.rand(nsamples) < prob[mask]])
|
||||
|
||||
stat[i, 0] = numpy.mean(loop_stat)
|
||||
stat[i, 1] = numpy.std(loop_stat)
|
||||
|
||||
bin_centres = (bins[1:] + bins[:-1]) / 2
|
||||
|
||||
return bin_centres, stat
|
||||
|
||||
|
|
@ -32,35 +32,25 @@ import utils
|
|||
parser = ArgumentParser()
|
||||
parser.add_argument("--nmult", type=float)
|
||||
parser.add_argument("--overlap", type=lambda x: bool(strtobool(x)))
|
||||
parser.add_argument("--select_initial", type=lambda x: bool(strtobool(x)))
|
||||
parser.add_argument("--fast_neighbours", type=lambda x: bool(strtobool(x)))
|
||||
args = parser.parse_args()
|
||||
|
||||
# File paths
|
||||
ic = 7468
|
||||
fperm = join(utils.dumpdir, "overlap", "cross_{}.npy")
|
||||
|
||||
paths = csiborgtools.read.CSiBORGPaths(to_new=False)
|
||||
paths.set_info(ic, paths.get_maximum_snapshot(ic))
|
||||
nsim0 = 7468
|
||||
nsimx = 7588
|
||||
fperm = join(utils.dumpdir, "overlap", "cross_{}_{}.npy")
|
||||
|
||||
print("{}: loading catalogues.".format(datetime.now()), flush=True)
|
||||
cat = csiborgtools.read.CombinedHaloCatalogue(paths)
|
||||
|
||||
|
||||
matcher = csiborgtools.match.RealisationsMatcher(cat)
|
||||
nsim0 = cat.n_sims[0]
|
||||
nsimx = cat.n_sims[1]
|
||||
cat0 = csiborgtools.read.HaloCatalogue(nsim0)
|
||||
catx = csiborgtools.read.HaloCatalogue(nsimx)
|
||||
|
||||
matcher = csiborgtools.match.RealisationsMatcher()
|
||||
print("{}: crossing the simulations.".format(datetime.now()), flush=True)
|
||||
|
||||
out = matcher.cross_knn_position_single(
|
||||
0, nmult=args.nmult, dlogmass=2., overlap=args.overlap,
|
||||
select_initial=args.select_initial, fast_neighbours=args.fast_neighbours)
|
||||
|
||||
indxs, match_indxs, cross = matcher.cross(
|
||||
nsim0, nsimx, cat0, catx, overlap=False)
|
||||
# Dump the result
|
||||
fout = fperm.format(nsim0)
|
||||
fout = fperm.format(nsim0, nsimx)
|
||||
print("Saving results to `{}`.".format(fout), flush=True)
|
||||
with open(fout, "wb") as f:
|
||||
numpy.save(fout, out)
|
||||
numpy.savez(fout, indxs=indxs, match_indxs=match_indxs, cross=cross)
|
||||
|
||||
print("All finished.", flush=True)
|
||||
|
|
|
|||
Loading…
Reference in a new issue