#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Python wrapper around the C extension for the counts-in-cells
for positions on the sky. Corresponding C codes are in ``mocks/vpf_mocks/``
while the python wrapper is in :py:mod:`Corrfunc.mocks.vpf_mocks`
"""
from __future__ import (division, print_function, absolute_import,
unicode_literals)
__author__ = ('Manodeep Sinha')
__all__ = ('vpf_mocks', )
[docs]
def vpf_mocks(rmax, nbins, nspheres, numpN,
threshold_ngb, centers_file, cosmology,
RA, DEC, CZ,
RAND_RA, RAND_DEC, RAND_CZ,
verbose=False, is_comoving_dist=False,
xbin_refine_factor=1, ybin_refine_factor=1,
zbin_refine_factor=1, max_cells_per_dim=100,
copy_particles=True, c_api_timer=False, isa=r'fastest'):
"""
Function to compute the counts-in-cells on points on the sky. Suitable
for mock catalogs and observed galaxies.
Returns a numpy structured array containing the probability of a
sphere of radius up to ``rmax`` containing ``0--numpN-1`` galaxies.
Parameters
----------
rmax : double
Maximum radius of the sphere to place on the particles
nbins : integer
Number of bins in the counts-in-cells. Radius of first shell
is rmax/nbins
nspheres : integer (>= 0)
Number of random spheres to place within the particle distribution.
For a small number of spheres, the error is larger in the measured
pN's.
numpN : integer (>= 1)
Governs how many unique pN's are to returned. If ``numpN`` is set to 1,
then only the vpf (p0) is returned. For ``numpN=2``, p0 and p1 are
returned.
More explicitly, the columns in the results look like the following:
====== ==========================
numpN Columns in output
====== ==========================
1 p0
2 p0 p1
3 p0 p1 p2
4 p0 p1 p2 p3
====== ==========================
and so on...
Note: ``p0`` is the vpf
threshold_ngb : integer
Minimum number of random points needed in a ``rmax`` sphere such that
it is considered to be entirely within the mock footprint. The
command-line version, ``mocks/vpf/vpf_mocks.c``, assumes that the
minimum number of randoms can be at most a 1-sigma deviation from
the expected random number density.
centers_file : string, filename
A file containing random sphere centers. If the file does not exist,
then a list of random centers will be written out. In that case, the
randoms arrays, ``RAND_RA``, ``RAND_DEC`` and ``RAND_CZ`` are used to
check that the sphere is entirely within the footprint. If the file
does exist but either ``rmax`` is too small or there are not enough
centers then the file will be overwritten.
Note: If the centers file has to be written, the code will take
significantly longer to finish. However, subsequent runs can re-use
that centers file and will be faster.
cosmology : integer, required
Integer choice for setting cosmology. Valid values are 1->LasDamas
cosmology and 2->Planck cosmology. If you need arbitrary cosmology,
easiest way is to convert the ``CZ`` values into co-moving distance,
based on your preferred cosmology. Set ``is_comoving_dist=True``, to
indicate that the co-moving distance conversion has already been done.
Choices:
1. LasDamas cosmology. :math:`\\Omega_m=0.25`, :math:`\\Omega_\\Lambda=0.75`
2. Planck cosmology. :math:`\\Omega_m=0.302`, :math:`\\Omega_\\Lambda=0.698`
To setup a new cosmology, add an entry to the function,
``init_cosmology`` in ``ROOT/utils/cosmology_params.c`` and re-install
the entire package.
RA : array-like, real (float/double)
The array of Right Ascensions for the first set of points. RA's
are expected to be in [0.0, 360.0], but the code will try to fix cases
where the RA's are in [-180, 180.0]. For peace of mind, always supply
RA's in [0.0, 360.0].
Calculations are done in the precision of the supplied arrays.
DEC : array-like, real (float/double)
Array of Declinations for the first set of points. DEC's are expected
to be in the [-90.0, 90.0], but the code will try to fix cases where
the DEC's are in [0.0, 180.0]. Again, for peace of mind, always supply
DEC's in [-90.0, 90.0].
Must be of same precision type as RA.
CZ : array-like, real (float/double)
Array of (Speed Of Light * Redshift) values for the first set of
points. Code will try to detect cases where ``redshifts`` have been
passed and multiply the entire array with the ``speed of light``.
If ``is_comoving_dist`` is set, then ``CZ`` is interpreted as the
co-moving distance, rather than (Speed Of Light * Redshift).
RAND_RA : array-like, real (float/double)
The array of Right Ascensions for the randoms. RA's are expected to be
in [0.0, 360.0], but the code will try to fix cases where the RA's are
in [-180, 180.0]. For peace of mind, always supply RA's in
[0.0, 360.0].
Must be of same precision type as RA/DEC/CZ.
RAND_DEC : array-like, real (float/double)
Array of Declinations for the randoms. DEC's are expected to be in the
[-90.0, 90.0], but the code will try to fix cases where the DEC's are
in [0.0, 180.0]. Again, for peace of mind, always supply DEC's in
[-90.0, 90.0].
Must be of same precision type as RA/DEC/CZ.
RAND_CZ : array-like, real (float/double)
Array of (Speed Of Light * Redshift) values for the randoms. Code
will try to detect cases where ``redshifts`` have been
passed and multiply the entire array with the ``speed of light``.
If ``is_comoving_dist`` is set, then ``CZ2`` is interpreted as the
co-moving distance, rather than ``(Speed Of Light * Redshift)``.
Note: RAND_RA, RAND_DEC and RAND_CZ are only used when the
``centers_file`` needs to be written out. In that case, the
RAND_RA, RAND_DEC, and RAND_CZ are used as random centers.
verbose : boolean (default false)
Boolean flag to control output of informational messages
is_comoving_dist : boolean (default false)
Boolean flag to indicate that ``cz`` values have already been
converted into co-moving distances. This flag allows arbitrary
cosmologies to be used in ``Corrfunc``.
(xyz)bin_refine_factor : integer, default is (1, 1, 1); typically in [1-2]
Controls the refinement on the cell sizes. Higher numbers might have
a negative impact on runtime.
Note: Since the counts in spheres calculation is symmetric
in all 3 dimensions, the defaults are different from the clustering
routines.
max_cells_per_dim : integer, default is 100, typical values in [50-300]
Controls the maximum number of cells per dimension. Total number of
cells can be up to (max_cells_per_dim)^3. Only increase if ``rmax`` is
too small relative to the boxsize (and increasing helps the runtime).
copy_particles: boolean (default True)
Boolean flag to make a copy of the particle positions
If set to False, the particles will be re-ordered in-place
.. versionadded:: 2.3.0
c_api_timer : boolean (default false)
Boolean flag to measure actual time spent in the C libraries. Here
to allow for benchmarking and scaling studies.
isa: string, case-insensitive (default ``fastest``)
Controls the runtime dispatch for the instruction set to use. Options
are: [``fastest``, ``avx512f``, ``avx``, ``sse42``, ``fallback``]
Setting isa to ``fastest`` will pick the fastest available instruction
set on the current computer. However, if you set ``isa`` to, say,
``avx`` and ``avx`` is not available on the computer, then the code
will revert to using ``fallback`` (even though ``sse42`` might be
available).
Unless you are benchmarking the different instruction sets, you should
always leave ``isa`` to the default value. And if you *are*
benchmarking, then the string supplied here gets translated into an
``enum`` for the instruction set defined in ``utils/defs.h``.
Returns
--------
results : Numpy structured array
A numpy structured array containing [rmax, pN[numpN]] with ``nbins``
elements. Each row contains the maximum radius of the sphere and the
``numpN`` elements in the ``pN`` array. Each element of this array
contains the probability that a sphere of radius ``rmax`` contains
*exactly* ``N`` galaxies. For example, pN[0] (p0, the void probibility
function) is the probability that a sphere of radius ``rmax`` contains
0 galaxies.
api_time : float, optional
Only returned if ``c_api_timer`` is set. ``api_time`` measures only
the time spent within the C library and ignores all python overhead.
Example
--------
>>> from __future__ import print_function
>>> import math
>>> from os.path import dirname, abspath, join as pjoin
>>> import numpy as np
>>> import Corrfunc
>>> from Corrfunc.mocks.vpf_mocks import vpf_mocks
>>> rmax = 10.0
>>> nbins = 10
>>> numbins_to_print = nbins
>>> nspheres = 10000
>>> numpN = 6
>>> threshold_ngb = 1 # does not matter since we have the centers
>>> cosmology = 1 # LasDamas cosmology
>>> centers_file = pjoin(dirname(abspath(Corrfunc.__file__)),
... "../mocks/tests/data/",
... "Mr19_centers_xyz_forVPF_rmax_10Mpc.txt")
>>> N = 1000000
>>> boxsize = 420.0
>>> seed = 42
>>> np.random.seed(seed)
>>> X = np.random.uniform(-0.5*boxsize, 0.5*boxsize, N)
>>> Y = np.random.uniform(-0.5*boxsize, 0.5*boxsize, N)
>>> Z = np.random.uniform(-0.5*boxsize, 0.5*boxsize, N)
>>> CZ = np.sqrt(X*X + Y*Y + Z*Z)
>>> inv_cz = 1.0/CZ
>>> X *= inv_cz
>>> Y *= inv_cz
>>> Z *= inv_cz
>>> DEC = 90.0 - np.arccos(Z)*180.0/math.pi
>>> RA = (np.arctan2(Y, X)*180.0/math.pi) + 180.0
>>> results = vpf_mocks(rmax, nbins, nspheres, numpN, threshold_ngb,
... centers_file, cosmology,
... RA, DEC, CZ,
... RA, DEC, CZ,
... is_comoving_dist=True)
>>> for r in results:
... print("{0:10.1f} ".format(r[0]), end="")
... # doctest: +NORMALIZE_WHITESPACE
... for pn in r[1]:
... print("{0:10.3f} ".format(pn), end="")
... # doctest: +NORMALIZE_WHITESPACE
... print("") # doctest: +NORMALIZE_WHITESPACE
1.0 0.999 0.001 0.000 0.000 0.000 0.000
2.0 0.992 0.007 0.001 0.000 0.000 0.000
3.0 0.982 0.009 0.005 0.002 0.001 0.000
4.0 0.975 0.006 0.006 0.005 0.003 0.003
5.0 0.971 0.004 0.003 0.003 0.004 0.003
6.0 0.967 0.003 0.003 0.001 0.003 0.002
7.0 0.962 0.004 0.002 0.003 0.002 0.001
8.0 0.958 0.004 0.002 0.003 0.001 0.002
9.0 0.953 0.003 0.003 0.002 0.003 0.001
10.0 0.950 0.003 0.002 0.002 0.001 0.002
"""
try:
from Corrfunc._countpairs_mocks import countspheres_vpf_mocks\
as vpf_extn
except ImportError:
msg = "Could not import the C extension for the Counts-in-Cells "\
" (vpf)"
raise ImportError(msg)
import numpy as np
from future.utils import bytes_to_native_str
from Corrfunc.utils import translate_isa_string_to_enum,\
convert_to_native_endian, sys_pipes
# Ensure all input arrays are native endian
RA, DEC, CZ, RAND_RA, RAND_DEC, RAND_CZ = [
convert_to_native_endian(arr, warn=True) for arr in
[RA, DEC, CZ, RAND_RA, RAND_DEC, RAND_CZ]]
integer_isa = translate_isa_string_to_enum(isa)
with sys_pipes():
extn_results = vpf_extn(rmax, nbins, nspheres, numpN,
threshold_ngb, centers_file,
cosmology,
RA, DEC, CZ,
RAND_RA, RAND_DEC, RAND_CZ,
verbose=verbose,
is_comoving_dist=is_comoving_dist,
xbin_refine_factor=xbin_refine_factor,
ybin_refine_factor=ybin_refine_factor,
zbin_refine_factor=zbin_refine_factor,
max_cells_per_dim=max_cells_per_dim,
copy_particles=copy_particles,
c_api_timer=c_api_timer,
isa=integer_isa)
if extn_results is None:
msg = "RuntimeError occurred"
raise RuntimeError(msg)
else:
extn_results, api_time = extn_results
results_dtype = np.dtype([(bytes_to_native_str(b'rmax'), np.float64),
(bytes_to_native_str(b'pN'),
(np.float64, numpN))])
nbin = len(extn_results)
results = np.zeros(nbin, dtype=results_dtype)
for ii, r in enumerate(extn_results):
results['rmax'][ii] = r[0]
if numpN == 1:
results['pN'] = r[1]
else:
for j in range(numpN):
results['pN'][ii][j] = r[1 + j]
if not c_api_timer:
return results
else:
return results, api_time
if __name__ == '__main__':
import doctest
doctest.testmod()