#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Python wrapper around the C extension for the pair counter in
``theory/DDrppi/``. This wrapper is in :py:mod:`Corrfunc.theory.DDrppi`
"""
from __future__ import (division, print_function, absolute_import,
unicode_literals)
__author__ = ('Manodeep Sinha')
__all__ = ('DDrppi', )
[docs]
def DDrppi(autocorr, nthreads, pimax, binfile, X1, Y1, Z1, weights1=None,
periodic=True, boxsize=None,
X2=None, Y2=None, Z2=None, weights2=None,
verbose=False, output_rpavg=False,
xbin_refine_factor=2, ybin_refine_factor=2,
zbin_refine_factor=1, max_cells_per_dim=100,
copy_particles=True, enable_min_sep_opt=True,
c_api_timer=False, isa=r'fastest', weight_type=None):
"""
Calculate the 3-D pair-counts corresponding to the real-space correlation
function, :math:`\\xi(r_p, \\pi)` or :math:`\\wp(r_p)`. Pairs which are
separated by less than the ``rp`` bins (specified in ``binfile``) in the
X-Y plane, and less than ``pimax`` in the Z-dimension are
counted.
If ``weights`` are provided, the mean pair weight is stored in the
``"weightavg"`` field of the results array. The raw pair counts in the
``"npairs"`` field are not weighted. The weighting scheme depends on
``weight_type``.
.. note:: that this module only returns pair counts and not the actual
correlation function :math:`\\xi(r_p, \\pi)` or :math:`wp(r_p)`. See the
utilities :py:mod:`Corrfunc.utils.convert_3d_counts_to_cf` and
:py:mod:`Corrfunc.utils.convert_rp_pi_counts_to_wp` for computing
:math:`\\xi(r_p, \\pi)` and :math:`wp(r_p)` respectively from the
pair counts.
Parameters
-----------
autocorr: boolean, required
Boolean flag for auto/cross-correlation. If autocorr is set to 1,
then the second set of particle positions are not required.
nthreads: integer
The number of OpenMP threads to use. Has no effect if OpenMP was not
enabled during library compilation.
pimax: double
A double-precision value for the maximum separation along
the Z-dimension.
Distances along the :math:``\\pi`` direction are binned with unit
depth. For instance, if ``pimax=40``, then 40 bins will be created
along the ``pi`` direction.
Note: Only pairs with ``0 <= dz < pimax`` are counted (no equality).
binfile: string or an list/array of floats
For string input: filename specifying the ``rp`` bins for
``DDrppi``. The file should contain white-space separated values
of (rpmin, rpmax) for each ``rp`` wanted. The bins need to be
contiguous and sorted in increasing order (smallest bins come first).
For array-like input: A sequence of ``rp`` values that provides the
bin-edges. For example,
``np.logspace(np.log10(0.1), np.log10(10.0), 15)`` is a valid
input specifying **14** (logarithmic) bins between 0.1 and 10.0. This
array does not need to be sorted.
X1/Y1/Z1: array-like, real (float/double)
The array of X/Y/Z positions for the first set of points.
Calculations are done in the precision of the supplied arrays.
weights1: array_like, real (float/double), optional
A scalar, or an array of weights of shape (n_weights, n_positions) or
(n_positions,). ``weight_type`` specifies how these weights are used;
results are returned in the ``weightavg`` field. If only one of
weights1 and weights2 is specified, the other will be set to uniform
weights.
periodic: boolean
Boolean flag to indicate periodic boundary conditions.
boxsize: double or 3-tuple of double, required if ``periodic=True``
The (X,Y,Z) side lengths of the spatial domain. Present to facilitate
exact calculations for periodic wrapping. A scalar ``boxsize`` will
be broadcast to a 3-tuple. If the boxsize in a dimension is 0., then
then that dimension's wrap is done based on the extent of the particle
distribution. If the boxsize in a dimension is -1., then periodicity
is disabled for that dimension.
.. versionchanged:: 2.4.0
Required if ``periodic=True``.
.. versionchanged:: 2.5.0
Accepts a 3-tuple of side lengths.
X2/Y2/Z2: array-like, real (float/double)
Array of XYZ positions for the second set of points. *Must* be the same
precision as the X1/Y1/Z1 arrays. Only required when ``autocorr==0``.
weights2: array-like, real (float/double), optional
Same as weights1, but for the second set of positions
verbose: boolean (default false)
Boolean flag to control output of informational messages
output_rpavg: boolean (default false)
Boolean flag to output the average ``rp`` for each bin. Code will
run slower if you set this flag.
Note: If you are calculating in single-precision, ``rpavg`` will
suffer from numerical loss of precision and can not be trusted. If
you need accurate ``rpavg`` values, then pass in double precision
arrays for the particle positions.
(xyz)bin_refine_factor: integer, default is (2,2,1); typically within [1-3]
Controls the refinement on the cell sizes. Can have up to a 20% impact
on runtime.
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 ``rpmax`` 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
enable_min_sep_opt: boolean (default true)
Boolean flag to allow optimizations based on min. separation between
pairs of cells. Here to allow for comparison studies.
.. 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 (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``.
weight_type: string, optional. Default: None.
The type of weighting to apply. One of ["pair_product", None].
Returns
--------
results: Numpy structured array
A numpy structured array containing [rpmin, rpmax, rpavg, pimax,
npairs, weightavg] for each radial bin specified in the ``binfile``.
If ``output_rpavg`` is not set, then ``rpavg`` will be set to 0.0 for
all bins; similarly for ``weightavg``. ``npairs`` contains the number
of pairs in that bin and can be used to compute :math:`\\xi(r_p, \\pi)`
by combining with (DR, RR) counts.
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 numpy as np
>>> from os.path import dirname, abspath, join as pjoin
>>> import Corrfunc
>>> from Corrfunc.theory.DDrppi import DDrppi
>>> binfile = pjoin(dirname(abspath(Corrfunc.__file__)),
... "../theory/tests/", "bins")
>>> N = 10000
>>> boxsize = 420.0
>>> nthreads = 4
>>> autocorr = 1
>>> pimax = 40.0
>>> seed = 42
>>> np.random.seed(seed)
>>> X = np.random.uniform(0, boxsize, N)
>>> Y = np.random.uniform(0, boxsize, N)
>>> Z = np.random.uniform(0, boxsize, N)
>>> weights = np.ones_like(X)
>>> results = DDrppi(autocorr, nthreads, pimax, binfile,
... X, Y, Z, weights1=weights, weight_type='pair_product',
... output_rpavg=True, boxsize=boxsize, periodic=True)
>>> for r in results[519:]: print("{0:10.6f} {1:10.6f} {2:10.6f} {3:10.1f}"
... " {4:10d} {5:10.6f}".format(r['rmin'], r['rmax'],
... r['rpavg'], r['pimax'], r['npairs'], r['weightavg']))
... # doctest: +NORMALIZE_WHITESPACE
11.756000 16.753600 14.388268 40.0 1142 1.000000
16.753600 23.875500 20.451822 1.0 2602 1.000000
16.753600 23.875500 20.603847 2.0 2366 1.000000
16.753600 23.875500 20.526435 3.0 2426 1.000000
16.753600 23.875500 20.478537 4.0 2460 1.000000
16.753600 23.875500 20.462300 5.0 2528 1.000000
16.753600 23.875500 20.535332 6.0 2524 1.000000
16.753600 23.875500 20.445645 7.0 2420 1.000000
16.753600 23.875500 20.476452 8.0 2358 1.000000
16.753600 23.875500 20.422480 9.0 2508 1.000000
16.753600 23.875500 20.466759 10.0 2474 1.000000
16.753600 23.875500 20.486209 11.0 2402 1.000000
16.753600 23.875500 20.371804 12.0 2418 1.000000
16.753600 23.875500 20.655139 13.0 2382 1.000000
16.753600 23.875500 20.563170 14.0 2420 1.000000
16.753600 23.875500 20.530697 15.0 2452 1.000000
16.753600 23.875500 20.578696 16.0 2378 1.000000
16.753600 23.875500 20.475367 17.0 2342 1.000000
16.753600 23.875500 20.537281 18.0 2498 1.000000
16.753600 23.875500 20.528432 19.0 2506 1.000000
16.753600 23.875500 20.509429 20.0 2498 1.000000
16.753600 23.875500 20.512102 21.0 2546 1.000000
16.753600 23.875500 20.476031 22.0 2436 1.000000
16.753600 23.875500 20.437518 23.0 2350 1.000000
16.753600 23.875500 20.558304 24.0 2466 1.000000
16.753600 23.875500 20.532998 25.0 2476 1.000000
16.753600 23.875500 20.570077 26.0 2352 1.000000
16.753600 23.875500 20.532271 27.0 2370 1.000000
16.753600 23.875500 20.512475 28.0 2516 1.000000
16.753600 23.875500 20.484714 29.0 2456 1.000000
16.753600 23.875500 20.603416 30.0 2386 1.000000
16.753600 23.875500 20.505218 31.0 2480 1.000000
16.753600 23.875500 20.484996 32.0 2532 1.000000
16.753600 23.875500 20.515608 33.0 2548 1.000000
16.753600 23.875500 20.489220 34.0 2530 1.000000
16.753600 23.875500 20.494801 35.0 2384 1.000000
16.753600 23.875500 20.481582 36.0 2360 1.000000
16.753600 23.875500 20.369323 37.0 2544 1.000000
16.753600 23.875500 20.450425 38.0 2460 1.000000
16.753600 23.875500 20.587416 39.0 2396 1.000000
16.753600 23.875500 20.504153 40.0 2492 1.000000
"""
try:
from Corrfunc._countpairs import countpairs_rp_pi as DDrppi_extn
except ImportError:
msg = "Could not import the C extension for the 3-D "\
"real-space pair counter."
raise ImportError(msg)
import numpy as np
from Corrfunc.utils import translate_isa_string_to_enum,\
return_file_with_rbins, convert_to_native_endian,\
sys_pipes, process_weights
from future.utils import bytes_to_native_str
if not autocorr:
if X2 is None or Y2 is None or Z2 is None:
msg = "Must pass valid arrays for X2/Y2/Z2 for "\
"computing cross-correlation"
raise ValueError(msg)
else:
# TODO: is this needed?
X2 = np.empty(1)
Y2 = np.empty(1)
Z2 = np.empty(1)
if periodic and boxsize is None:
raise ValueError("Must specify a boxsize if periodic=True")
if boxsize is not None:
boxsize = np.atleast_1d(boxsize)
if len(boxsize) == 1:
boxsize = (boxsize[0], boxsize[0], boxsize[0])
boxsize = tuple(boxsize)
weights1, weights2 = process_weights(weights1, weights2, X1, X2, weight_type, autocorr)
# Ensure all input arrays are native endian
X1, Y1, Z1, weights1, X2, Y2, Z2, weights2 = [
convert_to_native_endian(arr, warn=True) for arr in
[X1, Y1, Z1, weights1, X2, Y2, Z2, weights2]]
# Passing None parameters breaks the parsing code, so avoid this
kwargs = {}
for k in ['weights1', 'weights2', 'weight_type',
'X2', 'Y2', 'Z2', 'boxsize']:
v = locals()[k]
if v is not None:
kwargs[k] = v
integer_isa = translate_isa_string_to_enum(isa)
rbinfile, delete_after_use = return_file_with_rbins(binfile)
with sys_pipes():
extn_results = DDrppi_extn(autocorr, nthreads,
pimax, rbinfile,
X1, Y1, Z1,
periodic=periodic,
verbose=verbose,
output_rpavg=output_rpavg,
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,
enable_min_sep_opt=enable_min_sep_opt,
c_api_timer=c_api_timer,
isa=integer_isa, **kwargs)
if extn_results is None:
msg = "RuntimeError occurred"
raise RuntimeError(msg)
else:
extn_results, api_time = extn_results
if delete_after_use:
import os
os.remove(rbinfile)
results_dtype = np.dtype([(bytes_to_native_str(b'rmin'), np.float64),
(bytes_to_native_str(b'rmax'), np.float64),
(bytes_to_native_str(b'rpavg'), np.float64),
(bytes_to_native_str(b'pimax'), np.float64),
(bytes_to_native_str(b'npairs'), np.uint64),
(bytes_to_native_str(b'weightavg'), np.float64),
])
results = np.array(extn_results, dtype=results_dtype)
if not c_api_timer:
return results
else:
return results, api_time
if __name__ == '__main__':
import doctest
doctest.testmod()
