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Switch initial radius definition (#26)
* Add search for neighbours at z = 0 * Add initial snapshot KNN * Add initi search either z =0 or z = 70 * Add import * Add clumps_pos2cell * Add function argument * Add import * Add spherical overlap and speed up make_delta * Add clump limits calculation * Sped up make_delta * Add patch size conversion * Add patch sizes * Add patch size calculation * Force catalogues to be in float32 * Optimised script * Option to remove points with no overlap * Add spherical aproximate overlap * Fix subboxing bug * Remove print diagnostics * Add Lagrangian patch size * Add patch size documentation * Move when clumpsx converted to int * Edit docs * Remove spherical overlap * New Langrangian patch calculation
This commit is contained in:
Richard Stiskalek
GitHub
parent
912a38acfb
commit
beb811e84c
6 changed files with 426 additions and 142 deletions
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@ -13,6 +13,7 @@
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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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from .match import (brute_spatial_separation, RealisationsMatcher, cosine_similarity, ParticleOverlap) # noqa
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from .match import (brute_spatial_separation, RealisationsMatcher, cosine_similarity, # noqa
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ParticleOverlap, get_clumplims, lagpatch_size) # noqa
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from .num_density import (binned_counts, number_density) # noqa
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# from .correlation import (get_randoms_sphere, sphere_angular_tpcf) # noqa
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@ -14,11 +14,13 @@
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# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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import numpy
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from scipy.interpolate import interp1d
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from scipy.ndimage import gaussian_filter
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from tqdm import (tqdm, trange)
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from astropy.coordinates import SkyCoord
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from numba import jit
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from ..read import (CombinedHaloCatalogue, concatenate_clumps)
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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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def brute_spatial_separation(c1, c2, angular=False, N=None, verbose=False):
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@ -154,13 +156,14 @@ class RealisationsMatcher:
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return mapping
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def cross_knn_position_single(self, n_sim, nmult=5, dlogmass=None,
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mass_kind="totpartmass", init_dist=False,
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overlap=False, overlapper_kwargs={},
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verbose=True):
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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, verbose=True):
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r"""
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Find all neighbours within :math:`n_{\rm mult} R_{200c}` of halos in
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the `nsim`th simulation. Also enforces that the neighbours'
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:math:`\log M / M_\odot` be within `dlogmass` dex.
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Find all neighbours within a multiple of either :math:`R_{\rm init}`
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(distance at :math:`z = 70`) or :math:`R_{200c}` (distance at
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:math:`z = 0`) of halos in the `nsim`th simulation. 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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@ -168,45 +171,49 @@ class RealisationsMatcher:
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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 :math:`R_{200c}` within which to return neighbours. By
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default 5.
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Multiple of :math:`R_{\rm init}` or :math:`R_{200c}` within which
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to return neighbours. By default 5.
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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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init_dist : bool, optional
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Whether to calculate separation of the initial CMs. By default
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`False`.
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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`. Note that this operation is
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substantially slower.
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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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verbose : bool, optional
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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, 4, n_halos, n_matches)`. The
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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, all respectively.
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The latter two are calculated only if `init_dist` or `overlap` is
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`True`.
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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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"""
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self._check_masskind(mass_kind)
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# Radius, mass and positions of halos in `n_sim` IC realisation
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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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R = self.cats[n_sim]["r200"]
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pos = self.cats[n_sim].positions
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if init_dist:
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pos0 = self.cats[n_sim].positions0 # These are CM positions
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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]["patch_size"] # 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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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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@ -214,7 +221,11 @@ class RealisationsMatcher:
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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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overlapper = ParticleOverlap(**overlapper_kwargs)
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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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@ -225,28 +236,42 @@ class RealisationsMatcher:
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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
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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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dist, indxs = self.cats[i].radius_neigbours(pos, R * nmult)
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if select_initial:
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dist0, indxs = self.cats[i].radius_initial_neigbours(
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pos0, R * nmult)
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else:
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# Will switch dist0 <-> dist at the end
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dist0, indxs = self.cats[i].radius_neigbours(
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pos, R * nmult)
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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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# 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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dist[j] = dist[j][mask]
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dist0[j] = dist0[j][mask]
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indxs[j] = indx[mask]
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# Find distance to the between the initial CM
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dist0 = [numpy.asanyarray([], dtype=numpy.float64)] * dist.size
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if init_dist:
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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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dist0[k] = numpy.linalg.norm(
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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 initial snapshot overlap
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cross = [numpy.asanyarray([], dtype=numpy.float64)] * dist.size
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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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@ -254,6 +279,7 @@ class RealisationsMatcher:
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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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# Calculate the particle field
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if verbose:
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@ -261,38 +287,61 @@ class RealisationsMatcher:
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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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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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with_neigbours = numpy.where([ii.size > 0 for ii in indxs])[0]
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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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# Get the clump and pre-calculate its cell assignment
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# Unpack this clum and its mins and maxs
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cl0 = clumps0["clump"][match0]
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dint = numpy.full(indxs[k].size, numpy.nan, numpy.float64)
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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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# Loop over the ones we cross-correlate with
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for ii, ind in enumerate(indxs[k]):
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# Again which cross clump to this index
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matchx = cat2clumpsx[ind]
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dint[ii] = overlapper(cl0, clumpsx["clump"][matchx],
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delta)
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crosses[ii] = overlapper(
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cl0, clumpsx["clump"][matchx], delta,
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mins0_current, maxs0_current,
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minsx[matchx], maxsx[matchx])
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cross[k] = dint
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cross[k] = crosses
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# Optionally remove points whose overlap is exaclt zero
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if remove_nooverlap:
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mask = cross[k] > 0
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indxs[k] = indxs[k][mask]
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dist[k] = dist[k][mask]
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dist0[k] = dist0[k][mask]
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cross[k] = cross[k][mask]
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# Append as a composite array
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matches[count] = numpy.asarray(
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[indxs, dist, dist0, cross], dtype=object)
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# Append as a composite array. Flip dist order if not select_init
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if select_initial:
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matches[count] = numpy.asarray(
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[indxs, dist, dist0, cross], dtype=object)
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else:
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matches[count] = numpy.asarray(
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[indxs, dist0, dist, cross], dtype=object)
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return numpy.asarray(matches, dtype=object)
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def cross_knn_position_all(self, nmult=5, dlogmass=None,
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mass_kind="totpartmass", init_dist=False,
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overlap=False, overlapper_kwargs={},
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select_initial=True, remove_nooverlap=True,
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verbose=True):
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r"""
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Find all neighbours within :math:`n_{\rm mult} R_{200c}` of halos in
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@ -319,6 +368,12 @@ class RealisationsMatcher:
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substantially slower.
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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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verbose : bool, optional
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Iterator verbosity flag. By default `True`.
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@ -333,9 +388,11 @@ class RealisationsMatcher:
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# Loop over each catalogue
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for i in trange(N) if verbose else range(N):
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matches[i] = self.cross_knn_position_single(
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i, nmult, dlogmass, mass_kind=mass_kind, init_dist=init_dist,
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overlap=overlap, overlapper_kwargs=overlapper_kwargs,
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verbose=verbose)
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i, nmult, dlogmass, mass_kind=mass_kind,
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init_dist=init_dist, overlap=overlap,
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overlapper_kwargs=overlapper_kwargs,
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select_initial=select_initial,
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remove_nooverlap=remove_nooverlap, verbose=verbose)
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return matches
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@ -446,7 +503,9 @@ class ParticleOverlap:
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def pos2cell(self, pos):
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"""
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Convert position to cell number.
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Convert position to cell number. If `pos` is in
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`numpy.typecodes["AllInteger"]` assumes it to already be the cell
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number.
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Parameters
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----------
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@ -456,6 +515,9 @@ class ParticleOverlap:
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-------
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cells : 1-dimensional array
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"""
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# Check whether this is already the cell
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if pos.dtype.char in numpy.typecodes["AllInteger"]:
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return pos
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return numpy.floor(pos * self.inv_clength).astype(int)
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def smooth_highres(self, delta):
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@ -486,7 +548,8 @@ class ParticleOverlap:
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delta[start:end, start:end, start:end] = highres
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return delta
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def make_delta(self, clump, subbox=False, to_smooth=True):
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def make_delta(self, clump, mins=None, maxs=None, subbox=False,
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to_smooth=True):
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"""
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Calculate a NGP density field of a halo on a cubic grid.
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@ -494,6 +557,8 @@ class ParticleOverlap:
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----------
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clump: structurered arrays
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Clump structured array, keys must include `x`, `y`, `z` and `M`.
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mins, maxs : 1-dimensional arrays of shape `(3,)`
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Minimun and maximum cell numbers along each dimension.
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subbox : bool, optional
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Whether to calculate the density field on a grid strictly enclosing
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the clump.
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@ -504,30 +569,37 @@ class ParticleOverlap:
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-------
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delta : 3-dimensional array
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"""
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coords = ('x', 'y', 'z')
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xcell, ycell, zcell = (self.pos2cell(clump[p]) for p in coords)
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if subbox:
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# Shift the box so that each non-zero grid cell is 0th
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xcell -= max(numpy.min(xcell) - self.nshift, 0)
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ycell -= max(numpy.min(ycell) - self.nshift, 0)
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zcell -= max(numpy.min(zcell) - self.nshift, 0)
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cells = [self.pos2cell(clump[p]) for p in ('x', 'y', 'z')]
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ncells = max(*(numpy.max(p) + self.nshift
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for p in (xcell, ycell, zcell)))
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ncells += 1 # Bump up by one to get NUMBER of cells
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ncells = min(ncells, self.inv_clength)
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# Check that minima and maxima are integers
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if not (mins is None and maxs is None):
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assert mins.dtype.char in numpy.typecodes["AllInteger"]
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assert maxs.dtype.char in numpy.typecodes["AllInteger"]
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if subbox:
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# Minimum xcell, ycell and zcell of this clump
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if mins is None or maxs is None:
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mins = numpy.asanyarray(
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[max(numpy.min(cell) - self.nshift, 0) for cell in cells])
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maxs = numpy.asanyarray(
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[min(numpy.max(cell) + self.nshift, self.inv_clength)
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for cell in cells])
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ncells = numpy.max(maxs - mins) + 1 # To get the number of cells
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else:
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mins = (0, 0, 0,)
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ncells = self.inv_clength
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# Preallocate and fill the array
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delta = numpy.zeros((ncells,) * 3, dtype=numpy.float32)
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fill_delta(delta, xcell, ycell, zcell, clump['M'])
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fill_delta(delta, *cells, *mins, clump['M'])
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if to_smooth and self.smooth_scale is not None:
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gaussian_filter(delta, self.smooth_scale, output=delta)
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return delta
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def make_deltas(self, clump1, clump2):
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def make_deltas(self, clump1, clump2, mins1=None, maxs1=None,
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mins2=None, maxs2=None):
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"""
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Calculate a NGP density fields of two halos on a grid that encloses
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them both.
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@ -537,6 +609,12 @@ class ParticleOverlap:
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clump1, clump2 : structurered arrays
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Particle structured array of the two clumps. Keys must include `x`,
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`y`, `z` and `M`.
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mins1, maxs1 : 1-dimensional arrays of shape `(3,)`
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Minimun and maximum cell numbers along each dimension of `clump1`.
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Optional.
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mins2, maxs2 : 1-dimensional arrays of shape `(3,)`
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Minimun and maximum cell numbers along each dimension of `clump2`.
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Optional.
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||||
|
||||
Returns
|
||||
-------
|
||||
|
|
@ -545,39 +623,36 @@ class ParticleOverlap:
|
|||
cellmins : len-3 tuple
|
||||
Tuple of left-most cell ID in the full box.
|
||||
"""
|
||||
coords = ('x', 'y', 'z')
|
||||
xcell1, ycell1, zcell1 = (self.pos2cell(clump1[p]) for p in coords)
|
||||
xcell2, ycell2, zcell2 = (self.pos2cell(clump2[p]) for p in coords)
|
||||
xc1, yc1, zc1 = (self.pos2cell(clump1[p]) for p in ('x', 'y', 'z'))
|
||||
xc2, yc2, zc2 = (self.pos2cell(clump2[p]) for p in ('x', 'y', 'z'))
|
||||
|
||||
# Minimum cell number of the two halos along each dimension
|
||||
xmin = min(numpy.min(xcell1), numpy.min(xcell2)) - self.nshift
|
||||
ymin = min(numpy.min(ycell1), numpy.min(ycell2)) - self.nshift
|
||||
zmin = min(numpy.min(zcell1), numpy.min(zcell2)) - self.nshift
|
||||
xmin, ymin, zmin = max(xmin, 0), max(ymin, 0), max(zmin, 0)
|
||||
cellmins = (xmin, ymin, zmin)
|
||||
# Maximum cell number of the two halos along each dimension
|
||||
xmax = max(numpy.max(xcell1), numpy.max(xcell2))
|
||||
ymax = max(numpy.max(ycell1), numpy.max(ycell2))
|
||||
zmax = max(numpy.max(zcell1), numpy.max(zcell2))
|
||||
if any(obj is None for obj in (mins1, maxs1, mins2, maxs2)):
|
||||
# Minimum cell number of the two halos along each dimension
|
||||
xmin = min(numpy.min(xc1), numpy.min(xc2)) - self.nshift
|
||||
ymin = min(numpy.min(yc1), numpy.min(yc2)) - self.nshift
|
||||
zmin = min(numpy.min(zc1), numpy.min(zc2)) - self.nshift
|
||||
# Make sure shifting does not go beyond boundaries
|
||||
xmin, ymin, zmin = [max(px, 0) for px in (xmin, ymin, zmin)]
|
||||
|
||||
# Number of cells is the maximum + 1
|
||||
ncells = max(xmax - xmin, ymax - ymin, zmax - zmin) + self.nshift
|
||||
ncells += 1
|
||||
ncells = min(ncells, self.inv_clength)
|
||||
# Maximum cell number of the two halos along each dimension
|
||||
xmax = max(numpy.max(xc1), numpy.max(xc2)) + self.nshift
|
||||
ymax = max(numpy.max(yc1), numpy.max(yc2)) + self.nshift
|
||||
zmax = max(numpy.max(zc1), numpy.max(zc2)) + self.nshift
|
||||
# Make sure shifting does not go beyond boundaries
|
||||
xmax, ymax, zmax = [min(px, self.inv_clength - 1)
|
||||
for px in (xmax, ymax, zmax)]
|
||||
else:
|
||||
xmin, ymin, zmin = [min(mins1[i], mins2[i]) for i in range(3)]
|
||||
xmax, ymax, zmax = [max(maxs1[i], maxs2[i]) for i in range(3)]
|
||||
|
||||
# Shift the box so that the first non-zero grid cell is 0th
|
||||
xcell1 -= xmin
|
||||
xcell2 -= xmin
|
||||
ycell1 -= ymin
|
||||
ycell2 -= ymin
|
||||
zcell1 -= zmin
|
||||
zcell2 -= zmin
|
||||
cellmins = (xmin, ymin, zmin, ) # Cell minima
|
||||
ncells = max(xmax - xmin, ymax - ymin, zmax - zmin) + 1 # Num cells
|
||||
|
||||
# Preallocate and fill the array
|
||||
delta1 = numpy.zeros((ncells,)*3, dtype=numpy.float32)
|
||||
fill_delta(delta1, xcell1, ycell1, zcell1, clump1['M'])
|
||||
fill_delta(delta1, xc1, yc1, zc1, *cellmins, clump1['M'])
|
||||
delta2 = numpy.zeros((ncells,)*3, dtype=numpy.float32)
|
||||
fill_delta(delta2, xcell2, ycell2, zcell2, clump2['M'])
|
||||
fill_delta(delta2, xc2, yc2, zc2, *cellmins, clump2['M'])
|
||||
|
||||
if self.smooth_scale is not None:
|
||||
gaussian_filter(delta1, self.smooth_scale, output=delta1)
|
||||
|
|
@ -606,7 +681,8 @@ class ParticleOverlap:
|
|||
"""
|
||||
return _calculate_overlap(delta1, delta2, cellmins, delta2_full)
|
||||
|
||||
def __call__(self, clump1, clump2, delta2_full):
|
||||
def __call__(self, clump1, clump2, delta2_full, mins1=None, maxs1=None,
|
||||
mins2=None, maxs2=None):
|
||||
"""
|
||||
Calculate overlap between `clump1` and `clump2`. See
|
||||
`self.overlap(...)` and `self.make_deltas(...)` for further
|
||||
|
|
@ -622,17 +698,24 @@ class ParticleOverlap:
|
|||
delta2_full : 3-dimensional array
|
||||
Density field of the whole box calculated with particles assigned
|
||||
to halos at zero redshift.
|
||||
mins1, maxs1 : 1-dimensional arrays of shape `(3,)`
|
||||
Minimun and maximum cell numbers along each dimension of `clump1`.
|
||||
Optional.
|
||||
mins2, maxs2 : 1-dimensional arrays of shape `(3,)`
|
||||
Minimun and maximum cell numbers along each dimension of `clump2`.
|
||||
Optional.
|
||||
|
||||
Returns
|
||||
-------
|
||||
overlap : float
|
||||
"""
|
||||
delta1, delta2, cellmins = self.make_deltas(clump1, clump2)
|
||||
delta1, delta2, cellmins = self.make_deltas(
|
||||
clump1, clump2, mins1, maxs1, mins2, maxs2)
|
||||
return _calculate_overlap(delta1, delta2, cellmins, delta2_full)
|
||||
|
||||
|
||||
@jit(nopython=True)
|
||||
def fill_delta(delta, xcell, ycell, zcell, weights):
|
||||
def fill_delta(delta, xcell, ycell, zcell, xmin, ymin, zmin, weights):
|
||||
"""
|
||||
Fill array delta at the specified indices with their weights. This is a JIT
|
||||
implementation.
|
||||
|
|
@ -643,6 +726,8 @@ def fill_delta(delta, xcell, ycell, zcell, weights):
|
|||
Grid to be filled with weights.
|
||||
xcell, ycell, zcell : 1-dimensional arrays
|
||||
Indices where to assign `weights`.
|
||||
xmin, ymin, zmin : ints
|
||||
Minimum cell IDs of particles.
|
||||
weights : 1-dimensional arrays
|
||||
Particle mass.
|
||||
|
||||
|
|
@ -651,7 +736,43 @@ def fill_delta(delta, xcell, ycell, zcell, weights):
|
|||
None
|
||||
"""
|
||||
for i in range(xcell.size):
|
||||
delta[xcell[i], ycell[i], zcell[i]] += weights[i]
|
||||
delta[xcell[i] - xmin, ycell[i] - ymin, zcell[i] - zmin] += weights[i]
|
||||
|
||||
|
||||
def get_clumplims(clumps, ncells, nshift=None):
|
||||
"""
|
||||
Get the lower and upper limit of clumps' positions or cell numbers.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
clumps : array of arrays
|
||||
Array of clump structured arrays.
|
||||
ncells : int
|
||||
Number of grid cells of the box along a single dimension.
|
||||
nshift : int, optional
|
||||
Lower and upper shift of the clump limits.
|
||||
|
||||
Returns
|
||||
-------
|
||||
mins, maxs : 2-dimensional arrays of shape `(n_samples, 3)`
|
||||
The minimum and maximum along each axis.
|
||||
"""
|
||||
dtype = clumps[0][0]['x'].dtype # dtype of the first clump's 'x'
|
||||
# Check that for real positions we cannot apply nshift
|
||||
if nshift is not None and dtype.char not in numpy.typecodes["AllInteger"]:
|
||||
raise TypeError("`nshift` supported only positions are cells.")
|
||||
nshift = 0 if nshift is None else nshift # To simplify code below
|
||||
|
||||
nclumps = clumps.size
|
||||
mins = numpy.full((nclumps, 3), numpy.nan, dtype=dtype)
|
||||
maxs = numpy.full((nclumps, 3), numpy.nan, dtype=dtype)
|
||||
|
||||
for i, clump in enumerate(clumps):
|
||||
for j, p in enumerate(['x', 'y', 'z']):
|
||||
mins[i, j] = max(numpy.min(clump[0][p]) - nshift, 0)
|
||||
maxs[i, j] = min(numpy.max(clump[0][p]) + nshift, ncells - 1)
|
||||
|
||||
return mins, maxs
|
||||
|
||||
|
||||
@jit(nopython=True)
|
||||
|
|
@ -682,18 +803,20 @@ def _calculate_overlap(delta1, delta2, cellmins, delta2_full):
|
|||
weight = 0. # Weight to account for other halos
|
||||
count = 0 # Total number of pixels that are both non-zero
|
||||
|
||||
i0, j0, k0 = cellmins # Unpack things
|
||||
for i in range(imax):
|
||||
ii = cellmins[0] + i
|
||||
ii = i0 + i
|
||||
for j in range(jmax):
|
||||
jj = cellmins[1] + j
|
||||
jj = j0 + j
|
||||
for k in range(kmax):
|
||||
kk = cellmins[2] + k
|
||||
kk = k0 + k
|
||||
|
||||
cell1, cell2 = delta1[i, j, k], delta2[i, j, k]
|
||||
totmass += cell1 + cell2
|
||||
cell = cell1 + cell2
|
||||
totmass += cell
|
||||
# If both are zero then skip
|
||||
if cell1 > 0 and cell2 > 0:
|
||||
intersect += cell1 + cell2
|
||||
if cell1 * cell2 > 0:
|
||||
intersect += cell
|
||||
weight += cell2 / delta2_full[ii, jj, kk]
|
||||
count += 1
|
||||
|
||||
|
|
@ -701,3 +824,64 @@ def _calculate_overlap(delta1, delta2, cellmins, delta2_full):
|
|||
intersect *= 0.5
|
||||
weight = weight / count if count > 0 else 0.
|
||||
return weight * intersect / (totmass - intersect)
|
||||
|
||||
|
||||
def lagpatch_size(x, y, z, M, dr=0.0025, dqperc=1, minperc=75, defperc=95,
|
||||
rmax=0.075):
|
||||
"""
|
||||
Calculate an approximate Lagrangian patch size in the initial conditions.
|
||||
Returned as the first bin whose percentile drops by less than `dqperc` and
|
||||
is above `minperc`. Note that all distances must be in box units.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
x, y, z : 1-dimensional arrays
|
||||
Particle coordinates.
|
||||
M : 1-dimensional array
|
||||
Particle masses.
|
||||
dr : float, optional
|
||||
Separation spacing to evaluate q-th percentile change. Optional, by
|
||||
default 0.0025
|
||||
dqperc : int or float, optional
|
||||
Change of q-th percentile in a bin to find a threshold separation.
|
||||
Optional, by default 1.
|
||||
minperc : int or float, optional
|
||||
Minimum q-th percentile of separation to be considered a patch size.
|
||||
Optional, by default 75.
|
||||
defperc : int or float, optional
|
||||
Default q-th percentile if reduction by `minperc` is not satisfied in
|
||||
any bin. Optional. By default 95.
|
||||
rmax : float, optional
|
||||
The maximum allowed patch size. Optional, by default 0.075.
|
||||
|
||||
Returns
|
||||
-------
|
||||
size : float
|
||||
"""
|
||||
# CM along each dimension
|
||||
cmx, cmy, cmz = [numpy.average(p, weights=M) for p in (x, y, z)]
|
||||
# Particle distance from the CM
|
||||
sep = numpy.sqrt(numpy.square(x - cmx)
|
||||
+ numpy.square(y - cmy)
|
||||
+ numpy.square(z - cmz))
|
||||
|
||||
qs = numpy.linspace(0, 100, 100) # Percentile: where to evaluate
|
||||
per = numpy.percentile(sep, qs) # Percentile: evaluated
|
||||
sep2qs = interp1d(per, qs) # Separation to q-th percentile
|
||||
|
||||
# Evaluate in q-th percentile in separation bins
|
||||
sep_bin = numpy.arange(per[0], per[-1], dr)
|
||||
q_bin = sep2qs(sep_bin) # Evaluate for everyhing
|
||||
dq_bin = (q_bin[1:] - q_bin[:-1]) # Take the difference
|
||||
# Indices when q-th percentile changes below tolerance and is above limit
|
||||
k = numpy.where((dq_bin < dqperc) & (q_bin[1:] > minperc))[0]
|
||||
|
||||
if k.size == 0:
|
||||
return per[defperc] # Nothing found, so default percentile
|
||||
else:
|
||||
k = k[0] # Take the first one that satisfies the cut.
|
||||
|
||||
size = 0.5 * (sep_bin[k + 1] + sep_bin[k]) # Bin centre
|
||||
size = rmax if size > rmax else size # Enforce maximum size
|
||||
|
||||
return size
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@
|
|||
# 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
|
||||
from .make_cat import (HaloCatalogue, CombinedHaloCatalogue, 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
|
||||
|
|
|
|||
|
|
@ -43,6 +43,7 @@ class HaloCatalogue:
|
|||
_paths = None
|
||||
_data = None
|
||||
_knn = None
|
||||
_knn0 = None
|
||||
_positions = None
|
||||
_positions0 = None
|
||||
|
||||
|
|
@ -52,11 +53,15 @@ class HaloCatalogue:
|
|||
max_dist = numpy.infty if max_dist is None else max_dist
|
||||
self._paths = paths
|
||||
self._set_data(min_m500, max_dist)
|
||||
# Initialise the KNN
|
||||
# Initialise the KNN at z = 0 and at z = 70
|
||||
knn = NearestNeighbors()
|
||||
knn.fit(self.positions)
|
||||
self._knn = knn
|
||||
|
||||
knn0 = NearestNeighbors()
|
||||
knn0.fit(self.positions0)
|
||||
self._knn0 = knn0
|
||||
|
||||
@property
|
||||
def data(self):
|
||||
"""
|
||||
|
|
@ -180,11 +185,25 @@ class HaloCatalogue:
|
|||
# Pre-allocate the positions arrays
|
||||
self._positions = numpy.vstack(
|
||||
[data["peak_{}".format(p)] for p in ("x", "y", "z")]).T
|
||||
self._positions = self._positions.astype(numpy.float32)
|
||||
# And do the unit transform
|
||||
if initcm is not None:
|
||||
data = self.box.convert_from_boxunits(data, ["x0", "y0", "z0"])
|
||||
data = self.box.convert_from_boxunits(
|
||||
data, ["x0", "y0", "z0", "patch_size"])
|
||||
self._positions0 = numpy.vstack(
|
||||
[data["{}0".format(p)] for p in ("x", "y", "z")]).T
|
||||
self._positions0 = self._positions0.astype(numpy.float32)
|
||||
|
||||
# Convert all that is not an integer to float32
|
||||
names = list(data.dtype.names)
|
||||
formats = []
|
||||
for name in names:
|
||||
if data[name].dtype.char in numpy.typecodes["AllInteger"]:
|
||||
formats.append(numpy.int32)
|
||||
else:
|
||||
formats.append(numpy.float32)
|
||||
dtype = numpy.dtype({"names": names, "formats": formats})
|
||||
data = data.astype(dtype)
|
||||
|
||||
self._data = data
|
||||
|
||||
|
|
@ -238,10 +257,10 @@ class HaloCatalogue:
|
|||
raise ValueError(
|
||||
"Ordering of `initcat` and `clumps` is inconsistent.")
|
||||
|
||||
X = numpy.full((clumps.size, 3), numpy.nan)
|
||||
for i, p in enumerate(['x', 'y', 'z']):
|
||||
X = numpy.full((clumps.size, 4), numpy.nan)
|
||||
for i, p in enumerate(['x', 'y', 'z', "patch_size"]):
|
||||
X[:, i] = initcat[p]
|
||||
return add_columns(clumps, X, ["x0", "y0", "z0"])
|
||||
return add_columns(clumps, X, ["x0", "y0", "z0", "patch_size"])
|
||||
|
||||
@property
|
||||
def positions(self):
|
||||
|
|
@ -317,7 +336,8 @@ class HaloCatalogue:
|
|||
|
||||
def radius_neigbours(self, X, radius):
|
||||
"""
|
||||
Return sorted nearest neigbours within `radius` or `X`.
|
||||
Return sorted nearest neigbours within `radius` of `X` in the final
|
||||
snapshot.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
|
|
@ -341,6 +361,33 @@ class HaloCatalogue:
|
|||
# Query the KNN
|
||||
return self._knn.radius_neighbors(X, radius, sort_results=True)
|
||||
|
||||
def radius_initial_neigbours(self, X, radius):
|
||||
r"""
|
||||
Return sorted nearest neigbours within `radius` or `X` in the initial
|
||||
snapshot.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
X : 2-dimensional array
|
||||
Array of shape `(n_queries, 3)`, where the latter axis represents
|
||||
`x`, `y` and `z`.
|
||||
radius : float
|
||||
Limiting distance of neighbours.
|
||||
|
||||
Returns
|
||||
-------
|
||||
dist : list of 1-dimensional arrays
|
||||
List of length `n_queries` whose elements are arrays of distances
|
||||
to the nearest neighbours.
|
||||
knns : list of 1-dimensional arrays
|
||||
List of length `n_queries` whose elements are arrays of indices of
|
||||
nearest neighbours in this catalogue.
|
||||
"""
|
||||
if not (X.ndim == 2 and X.shape[1] == 3):
|
||||
raise TypeError("`X` must be an array of shape `(n_samples, 3)`.")
|
||||
# Query the KNN
|
||||
return self._knn0.radius_neighbors(X, radius, sort_results=True)
|
||||
|
||||
@property
|
||||
def keys(self):
|
||||
"""Catalogue keys."""
|
||||
|
|
@ -462,8 +509,15 @@ def concatenate_clumps(clumps):
|
|||
N = 0
|
||||
for clump, __ in clumps:
|
||||
N += clump.size
|
||||
# Infer dtype of positions
|
||||
if clumps[0][0]['x'].dtype.char in numpy.typecodes["AllInteger"]:
|
||||
posdtype = numpy.int32
|
||||
else:
|
||||
posdtype = numpy.float32
|
||||
|
||||
# Pre-allocate array
|
||||
dtype = {"names": ['x', 'y', 'z', "M"], "formats": [numpy.float32] * 4}
|
||||
dtype = {"names": ['x', 'y', 'z', 'M'],
|
||||
"formats": [posdtype] * 3 + [numpy.float32]}
|
||||
particles = numpy.full(N, numpy.nan, dtype)
|
||||
|
||||
# Fill it one clump by another
|
||||
|
|
@ -475,3 +529,41 @@ def concatenate_clumps(clumps):
|
|||
start = end
|
||||
|
||||
return particles
|
||||
|
||||
|
||||
def clumps_pos2cell(clumps, overlapper):
|
||||
"""
|
||||
Convert clump positions directly to cell IDs. Useful to speed up subsequent
|
||||
calculations. Overwrites the passed in arrays.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
clumps : array of arrays
|
||||
Array of clump structured arrays whose `x`, `y`, `z` keys will be
|
||||
converted.
|
||||
overlapper : py:class:`csiborgtools.match.ParticleOverlapper`
|
||||
`ParticleOverlapper` handling the cell assignment.
|
||||
|
||||
Returns
|
||||
-------
|
||||
None
|
||||
"""
|
||||
# Check if clumps are probably already in cells
|
||||
if any(clumps[0][0].dtype[p].char in numpy.typecodes["AllInteger"]
|
||||
for p in ('x', 'y', 'z')):
|
||||
raise ValueError("Positions appear to already be converted cells.")
|
||||
|
||||
# Get the new dtype that replaces float for int for positions
|
||||
names = clumps[0][0].dtype.names # Take the first one, doesn't matter
|
||||
formats = [descr[1] for descr in clumps[0][0].dtype.descr]
|
||||
|
||||
for i in range(len(names)):
|
||||
if names[i] in ('x', 'y', 'z'):
|
||||
formats[i] = numpy.int32
|
||||
dtype = numpy.dtype({"names": names, "formats": formats})
|
||||
|
||||
# Loop switch positions for cells IDs and change dtype
|
||||
for n in range(clumps.size):
|
||||
for p in ('x', 'y', 'z'):
|
||||
clumps[n][0][p] = overlapper.pos2cell(clumps[n][0][p])
|
||||
clumps[n][0] = clumps[n][0].astype(dtype)
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ from ..read import ParticleReader
|
|||
# Map of unit conversions
|
||||
CONV_NAME = {
|
||||
"length": ["peak_x", "peak_y", "peak_z", "Rs", "rmin", "rmax", "r200",
|
||||
"r500", "x0", "y0", "z0"],
|
||||
"r500", "x0", "y0", "z0", "patch_size"],
|
||||
"mass": ["mass_cl", "totpartmass", "m200", "m500", "mass_mmain"],
|
||||
"density": ["rho0"]
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue