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Lagrangian patch + HMF calculation (#65)
* Rename lagpatch * Fix old bug * Fix small bug * Add number of cells calculation * Fix a small bug * Rename column * Move file * Small changes * Edit style * Add plot script * Add delta2ncells * Add HMF calculation * Move definition around * Add HMF plot * pep8 * Update HMF plotting routine * Small edit
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Richard Stiskalek
GitHub
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commit
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13 changed files with 376 additions and 132 deletions
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@ -12,6 +12,7 @@
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# You should have received a copy of the GNU General Public License along
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# with this program; if not, write to the Free Software Foundation, Inc.,
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# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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from .halo import Clump, Halo, dist_centmass # noqa
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from .halo import (Clump, Halo, delta2ncells, dist_centmass, # noqa
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number_counts)
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from .haloprofile import NFWPosterior, NFWProfile # noqa
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from .utils import split_jobs # noqa
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@ -15,9 +15,10 @@
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"""A clump object."""
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from abc import ABC
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from numba import jit
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import numpy
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from numba import jit
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class BaseStructure(ABC):
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"""
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@ -337,3 +338,51 @@ def dist_centmass(clump):
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cmx, cmy, cmz = [numpy.average(xi, weights=mass) for xi in (x, y, z)]
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dist = ((x - cmx)**2 + (y - cmy)**2 + (z - cmz)**2)**0.5
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return dist, [cmx, cmy, cmz]
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@jit(nopython=True)
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def delta2ncells(delta):
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"""
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Calculate the number of cells in `delta` that are non-zero.
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Parameters
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----------
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delta : 3-dimensional array
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Halo density field.
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Returns
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-------
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ncells : int
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Number of non-zero cells.
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"""
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tot = 0
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imax, jmax, kmax = delta.shape
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for i in range(imax):
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for j in range(jmax):
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for k in range(kmax):
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if delta[i, j, k] > 0:
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tot += 1
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return tot
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@jit(nopython=True)
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def number_counts(x, bin_edges):
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"""
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Calculate counts of samples in bins.
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Parameters
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----------
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x : 1-dimensional array
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Samples' values.
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bin_edges : 1-dimensional array
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Bin edges.
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Returns
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-------
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counts : 1-dimensional array
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Bin counts.
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"""
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out = numpy.full(bin_edges.size - 1, numpy.nan, dtype=numpy.float32)
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for i in range(bin_edges.size - 1):
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out[i] = numpy.sum((x >= bin_edges[i]) & (x < bin_edges[i + 1]))
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return out
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@ -16,5 +16,4 @@ from .match import (ParticleOverlap, RealisationsMatcher, # noqa
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calculate_overlap, calculate_overlap_indxs,
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cosine_similarity)
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from .nearest_neighbour import find_neighbour # noqa
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from .num_density import binned_counts, number_density # noqa
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from .utils import concatenate_parts # noqa
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@ -163,7 +163,7 @@ class RealisationsMatcher:
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print(f"{datetime.now()}: querying the KNN.", flush=True)
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match_indxs = radius_neighbours(
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catx.knn(in_initial=True), cat0.position(in_initial=True),
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radiusX=cat0["lagpatch"], radiusKNN=catx["lagpatch"],
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radiusX=cat0["lagpatch_size"], radiusKNN=catx["lagpatch_size"],
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nmult=self.nmult, enforce_int32=True, verbose=verbose)
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# We next remove neighbours whose mass is too large/small.
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@ -419,7 +419,7 @@ class ParticleOverlap:
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delta : 3-dimensional array
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"""
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nshift = read_nshift(smooth_kwargs)
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cells = self.pos2cell(pos)
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cells = pos2cell(pos, BOX_SIZE)
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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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@ -432,10 +432,10 @@ class ParticleOverlap:
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ncells = 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 = BOX_SIZE
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ncells = (BOX_SIZE, ) * 3
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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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delta = numpy.zeros(ncells, dtype=numpy.float32)
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fill_delta(delta, cells[:, 0], cells[:, 1], cells[:, 2], *mins, mass)
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if smooth_kwargs is not None:
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gaussian_filter(delta, output=delta, **smooth_kwargs)
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@ -1,116 +0,0 @@
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# Copyright (C) 2022 Richard Stiskalek
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# This program is free software; you can redistribute it and/or modify it
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# under the terms of the GNU General Public License as published by the
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# Free Software Foundation; either version 3 of the License, or (at your
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# option) any later version.
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#
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# This program is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
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# Public License for more details.
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#
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# You should have received a copy of the GNU General Public License along
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# with this program; if not, write to the Free Software Foundation, Inc.,
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# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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"""
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Calculation of number density functions.
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"""
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import numpy
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def binned_counts(x, bins):
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"""
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Calculate number of samples in bins.
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Parameters
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----------
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x : 1-dimensional array
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Samples' values.
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bins : 1-dimensional array
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Bin edges of shape `(n_edges, )`.
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Returns
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-------
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centres : 1-dimensional array
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Bin centres of shape `(n_edges - 1, )`.
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counts : 1-dimensional array
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Bin counts of shape `(n_edges - 1, )`.
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"""
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if not isinstance(bins, numpy.ndarray) and bins.ndim == 1:
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raise TypeError("`bins` must a 1-dimensional array.")
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n_bins = bins.size
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# Bin centres
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centres = numpy.asarray(
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[0.5 * (bins[i + 1] + bins[i]) for i in range(n_bins - 1)])
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# Bin counts
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out = numpy.full(n_bins - 1, numpy.nan, dtype=int)
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for i in range(n_bins - 1):
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out[i] = numpy.sum((x >= bins[i]) & (x < bins[i + 1]))
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return centres, out
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def number_density(data, feat, bins, max_dist, to_log10, return_counts=False):
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"""
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Calculate volume-limited number density of a feature `feat` from array
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`data`, normalised also by the bin width.
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Parameters
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----------
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data : structured array
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Input array of halos.
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feat : str
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Parameter whose number density to calculate.
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bins : 1-dimensional array
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Bin edges. Note that if `to_log10` then the edges must be specified
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in the logarithmic space, not linear.
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max_dist : float
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Maximum radial distance of the volume limited sample.
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to_log10 : bool
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Whether to take a logarithm of base 10 of the feature. If so, then the
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bins must also be logarithmic.
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return_counts : bool, optional
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Whether to also return number counts in each bin. By default `False`.
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Returns
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-------
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centres : 1-dimensional array
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Bin centres of shape `(n_edges - 1, )`. If `to_log10` then converts the
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bin centres back to linear space.
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nd : 1-dimensional array
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Number density of shape `(n_edges - 1, )`.
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nd_err : 1-dimensional array
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Poissonian uncertainty of `nd` of shape `(n_edges - 1, )`.
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counts: 1-dimensional array, optional
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Counts in each bin of shape `(n_edges - 1, )`. Returned only if
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`return_counts`.
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"""
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# Extract the param and optionally convert to log10
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x = data[feat]
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x = numpy.log10(x) if to_log10 else x
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# Get only things within distance from the origin
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rdist = (data["peak_x"]**2 + data["peak_y"]**2 + data["peak_z"]**2)**0.5
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x = x[rdist < max_dist]
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# Make sure bins equally spaced
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dbins = numpy.diff(bins)
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dbin = dbins[0]
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if not numpy.alltrue(dbins == dbin):
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raise ValueError("Bins must be equally spaced. Currently `{}`."
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.format(bins))
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# Encompassed volume around the origin
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volume = 4 * numpy.pi / 3 * max_dist**3
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# Poissonian statistics
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bin_centres, counts = binned_counts(x, bins)
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nd = counts / volume / dbin
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nd_err = counts**0.5 / volume / dbin
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# Convert bins to linear space if log10
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if to_log10:
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bin_centres = 10**bin_centres
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out = (bin_centres, nd, nd_err)
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if return_counts:
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out += counts
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return out
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@ -27,7 +27,7 @@ from .readsim import ParticleReader
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CONV_NAME = {
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"length": ["x", "y", "z", "peak_x", "peak_y", "peak_z", "Rs", "rmin",
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"rmax", "r200c", "r500c", "r200m", "x0", "y0", "z0",
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"lagpatch"],
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"lagpatch_size"],
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"velocity": ["vx", "vy", "vz"],
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"mass": ["mass_cl", "totpartmass", "m200c", "m500c", "mass_mmain", "M",
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"m200m"],
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@ -540,7 +540,7 @@ class HaloCatalogue(BaseCSiBORG):
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if not rawdata:
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if with_lagpatch:
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self._data = self._data[numpy.isfinite(self['lagpatch'])]
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self._data = self._data[numpy.isfinite(self["lagpatch_size"])]
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# Flip positions and convert from code units to cMpc. Convert M too
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flip_cols(self._data, "x", "z")
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for p in ("x", "y", "z"):
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@ -551,7 +551,7 @@ class HaloCatalogue(BaseCSiBORG):
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self._data = self.box.convert_from_box(self._data, names)
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if load_initial:
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names = ["x0", "y0", "z0", "lagpatch"]
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names = ["x0", "y0", "z0", "lagpatch_size"]
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self._data = self.box.convert_from_box(self._data, names)
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if bounds is not None:
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@ -276,10 +276,10 @@ class PairOverlap:
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if norm_kind == "r200c":
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norm = self.cat0("r200c")
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if norm_kind == "ref_patch":
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norm = self.cat0("lagpatch")
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norm = self.cat0("lagpatch_size")
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if norm_kind == "sum_patch":
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patch0 = self.cat0("lagpatch")
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patchx = self.catx("lagpatch")
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patch0 = self.cat0("lagpatch_size")
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patchx = self.catx("lagpatch_size")
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norm = [None] * len(self)
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for i, ind in enumerate(self["match_indxs"]):
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norm[i] = patch0[i] + patchx[ind]
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@ -387,6 +387,28 @@ class Paths:
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fname = f"{kind}_{MAS}_{str(nsim).zfill(5)}_grid{grid}.npy"
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return join(fdir, fname)
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def halo_counts(self, simname, nsim):
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"""
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Path to the files containing the binned halo counts.
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Parameters
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----------
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simname : str
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Simulation name. Must be `csiborg` or `quijote`.
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nsim : int
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IC realisation index.
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Returns
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-------
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path : str
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"""
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fdir = join(self.postdir, "HMF")
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if not isdir(fdir):
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makedirs(fdir)
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warn(f"Created directory `{fdir}`.", UserWarning, stacklevel=1)
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fname = f"halo_counts_{simname}_{str(nsim).zfill(5)}.npz"
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return join(fdir, fname)
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def cross_nearest(self, simname, run, nsim=None, nobs=None):
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"""
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Path to the files containing distance from a halo in a reference
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