Source code for Corrfunc.theory.DD

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
Python wrapper around the C extension for the pair counter in
``theory/DD/``. This wrapper is in :py:mod:`Corrfunc.theory.DD`
"""

from __future__ import (division, print_function, absolute_import,
                        unicode_literals)

__author__ = ('Manodeep Sinha')
__all__ = ('DD', )


[docs]def DD(autocorr, nthreads, binfile, X1, Y1, Z1, weights1=None, periodic=True, boxsize=None, X2=None, Y2=None, Z2=None, weights2=None, verbose=False, output_ravg=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)`. 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:: This module only returns pair counts and not the actual correlation function :math:`\\xi(r)`. See :py:mod:`Corrfunc.utils.convert_3d_counts_to_cf` for computing :math:`\\xi(r)` from the pair counts returned. 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. binfile: string or an list/array of floats For string input: filename specifying the ``r`` bins for ``DD``. The file should contain white-space separated values of (rmin, rmax) for each ``r`` wanted. The bins need to be contiguous and sorted in increasing order (smallest bins come first). For array-like input: A sequence of ``r`` 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, required if ``periodic=True`` The side-length of the cube in the cosmological simulation. Present to facilitate exact calculations for periodic wrapping. If boxsize is 0., then the wrapping is done based on the maximum difference within each dimension of the X/Y/Z arrays. .. versionchanged:: 2.4.0 Required if ``periodic=True``. 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_ravg: boolean (default false) Boolean flag to output the average ``r`` for each bin. Code will run slower if you set this flag. Note: If you are calculating in single-precision, ``ravg`` will suffer from numerical loss of precision and can not be trusted. If you need accurate ``ravg`` 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 ``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 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 [rmin, rmax, ravg, npairs, weightavg] for each radial bin specified in the ``binfile``. If ``output_ravg`` is not set, then ``ravg`` 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 the actual :math:`\\xi(r)` 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.DD import DD >>> binfile = pjoin(dirname(abspath(Corrfunc.__file__)), ... "../theory/tests/", "bins") >>> N = 10000 >>> boxsize = 420.0 >>> nthreads = 4 >>> autocorr = 1 >>> 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 = DD(autocorr, nthreads, binfile, X, Y, Z, weights1=weights, ... weight_type='pair_product', output_ravg=True, ... boxsize=boxsize, periodic=True) >>> for r in results: print("{0:10.6f} {1:10.6f} {2:10.6f} {3:10d} {4:10.6f}". ... format(r['rmin'], r['rmax'], r['ravg'], ... r['npairs'], r['weightavg'])) # doctest: +NORMALIZE_WHITESPACE 0.167536 0.238755 0.000000 0 0.000000 0.238755 0.340251 0.000000 0 0.000000 0.340251 0.484892 0.000000 0 0.000000 0.484892 0.691021 0.000000 0 0.000000 0.691021 0.984777 0.945372 2 1.000000 0.984777 1.403410 1.340525 10 1.000000 1.403410 2.000000 1.732968 36 1.000000 2.000000 2.850200 2.549059 52 1.000000 2.850200 4.061840 3.559061 210 1.000000 4.061840 5.788530 4.996275 670 1.000000 5.788530 8.249250 7.124926 2156 1.000000 8.249250 11.756000 10.201393 5990 1.000000 11.756000 16.753600 14.517498 17736 1.000000 16.753600 23.875500 20.716714 50230 1.000000 """ try: from Corrfunc._countpairs import countpairs as DD_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) if periodic and boxsize is None: raise ValueError("Must specify a boxsize if periodic=True") 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 = DD_extn(autocorr, nthreads, rbinfile, X1, Y1, Z1, periodic=periodic, verbose=verbose, output_ravg=output_ravg, 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'ravg'), 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()