Source code for Corrfunc.theory.DDsmu

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

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

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

__author__ = ('Manodeep Sinha', 'Nick Hand')
__all__ = ('DDsmu', )


[docs]def DDsmu(autocorr, nthreads, binfile, mu_max, nmu_bins, X1, Y1, Z1, weights1=None, periodic=True, boxsize=None, X2=None, Y2=None, Z2=None, weights2=None, verbose=False, output_savg=False, fast_divide_and_NR_steps=0, 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 2-D pair-counts corresponding to the redshift-space correlation function, :math:`\\xi(s, \mu)` Pairs which are separated by less than the ``s`` bins (specified in ``binfile``) in 3-D, and less than ``s*mu_max`` 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:: This module only returns pair counts and not the actual correlation function :math:`\\xi(s, \mu)`. See the utilities :py:mod:`Corrfunc.utils.convert_3d_counts_to_cf` for computing :math:`\\xi(s, \mu)` from the pair counts. .. versionadded:: 2.1.0 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 ``s`` bins for ``DDsmu_mocks``. The file should contain white-space separated values of (smin, smax) specifying each ``s`` bin wanted. The bins need to be contiguous and sorted in increasing order (smallest bins come first). For array-like input: A sequence of ``s`` 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. mu_max: double. Must be in range (0.0, 1.0] A double-precision value for the maximum cosine of the angular separation from the line of sight (LOS). Here, LOS is taken to be along the Z direction. Note: Only pairs with :math:`0 <= \cos(\\theta_{LOS}) < \mu_{max}` are counted (no equality). nmu_bins: int The number of linear ``mu`` bins, with the bins ranging from from (0, :math:`\mu_{max}`) 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_savg : boolean (default false) Boolean flag to output the average ``s`` for each bin. Code will run slower if you set this flag. Also, note, if you are calculating in single-precision, ``s`` will suffer from numerical loss of precision and can not be trusted. If you need accurate ``s`` values, then pass in double precision arrays for the particle positions. fast_divide_and_NR_steps: integer (default 0) Replaces the division in ``AVX`` implementation with an approximate reciprocal, followed by ``fast_divide_and_NR_steps`` of Newton-Raphson. Can improve runtime by ~15-20% on older computers. Value of 0 uses the standard division operation. (xyz)bin_refine_factor: integer (default (2,2,1) typical values in [1-3]) Controls the refinement on the cell sizes. Can have up to a 20% impact on runtime. max_cells_per_dim: integer (default 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 : str, optional The type of pair weighting to apply. Options: "pair_product", None; Default: None. Returns -------- results : A python list A python list containing ``nmu_bins`` of [smin, smax, savg, mu_max, npairs, weightavg] for each spatial bin specified in the ``binfile``. There will be a total of ``nmu_bins`` ranging from [0, ``mu_max``) *per* spatial bin. If ``output_savg`` is not set, then ``savg`` will be set to 0.0 for all bins; similarly for ``weight_avg``. ``npairs`` contains the number of pairs in that bin. 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.DDsmu import DDsmu >>> binfile = pjoin(dirname(abspath(Corrfunc.__file__)), ... "../theory/tests/", "bins") >>> N = 10000 >>> boxsize = 420.0 >>> nthreads = 4 >>> autocorr = 1 >>> mu_max = 1.0 >>> seed = 42 >>> nmu_bins = 10 >>> 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 = DDsmu(autocorr, nthreads, binfile, mu_max, nmu_bins, ... X, Y, Z, weights1=weights, weight_type='pair_product', ... output_savg=True, boxsize=boxsize, periodic=True) >>> for r in results[100:]: print("{0:10.6f} {1:10.6f} {2:10.6f} {3:10.1f}" ... " {4:10d} {5:10.6f}".format(r['smin'], r['smax'], ... r['savg'], r['mu_max'], r['npairs'], r['weightavg'])) ... # doctest: +NORMALIZE_WHITESPACE 5.788530 8.249250 7.149762 0.1 230 1.000000 5.788530 8.249250 7.158884 0.2 236 1.000000 5.788530 8.249250 7.153403 0.3 210 1.000000 5.788530 8.249250 7.091504 0.4 254 1.000000 5.788530 8.249250 7.216417 0.5 182 1.000000 5.788530 8.249250 7.120980 0.6 222 1.000000 5.788530 8.249250 7.086361 0.7 238 1.000000 5.788530 8.249250 7.199075 0.8 170 1.000000 5.788530 8.249250 7.128768 0.9 208 1.000000 5.788530 8.249250 6.973382 1.0 206 1.000000 8.249250 11.756000 10.147488 0.1 590 1.000000 8.249250 11.756000 10.216417 0.2 634 1.000000 8.249250 11.756000 10.195979 0.3 532 1.000000 8.249250 11.756000 10.248775 0.4 544 1.000000 8.249250 11.756000 10.091439 0.5 530 1.000000 8.249250 11.756000 10.282170 0.6 642 1.000000 8.249250 11.756000 10.245368 0.7 666 1.000000 8.249250 11.756000 10.139694 0.8 680 1.000000 8.249250 11.756000 10.190839 0.9 566 1.000000 8.249250 11.756000 10.241730 1.0 606 1.000000 11.756000 16.753600 14.553911 0.1 1736 1.000000 11.756000 16.753600 14.576144 0.2 1800 1.000000 11.756000 16.753600 14.595632 0.3 1798 1.000000 11.756000 16.753600 14.477071 0.4 1820 1.000000 11.756000 16.753600 14.479887 0.5 1740 1.000000 11.756000 16.753600 14.492835 0.6 1748 1.000000 11.756000 16.753600 14.546800 0.7 1720 1.000000 11.756000 16.753600 14.467235 0.8 1750 1.000000 11.756000 16.753600 14.541123 0.9 1798 1.000000 11.756000 16.753600 14.445188 1.0 1826 1.000000 16.753600 23.875500 20.722545 0.1 5088 1.000000 16.753600 23.875500 20.730212 0.2 5000 1.000000 16.753600 23.875500 20.717056 0.3 5166 1.000000 16.753600 23.875500 20.727119 0.4 5014 1.000000 16.753600 23.875500 20.654365 0.5 5094 1.000000 16.753600 23.875500 20.695877 0.6 5082 1.000000 16.753600 23.875500 20.729774 0.7 4900 1.000000 16.753600 23.875500 20.718821 0.8 4874 1.000000 16.753600 23.875500 20.750061 0.9 4946 1.000000 16.753600 23.875500 20.723266 1.0 5066 1.000000 """ try: from Corrfunc._countpairs import countpairs_s_mu as DDsmu_extn except ImportError: msg = "Could not import the C extension for the 3-D "\ "redshift-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 # Check if mu_max is scalar if not np.isscalar(mu_max): msg = "The parameter `mu_max` = {0}, has size = {1}. "\ "The code is expecting a scalar quantity (and not "\ "not a list, array)".format(mu_max, np.size(mu_max)) raise TypeError(msg) # Check that mu_max is within (0.0, 1.0] if mu_max <= 0.0 or mu_max > 1.0: msg = "The parameter `mu_max` = {0}, is the max. of cosine of an "\ "angle and should be within (0.0, 1.0]".format(mu_max) raise ValueError(msg) 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) sbinfile, delete_after_use = return_file_with_rbins(binfile) with sys_pipes(): extn_results = DDsmu_extn(autocorr, nthreads, sbinfile, mu_max, nmu_bins, X1, Y1, Z1, periodic=periodic, verbose=verbose, output_savg=output_savg, fast_divide_and_NR_steps=fast_divide_and_NR_steps, 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(sbinfile) results_dtype = np.dtype([(bytes_to_native_str(b'smin'), np.float64), (bytes_to_native_str(b'smax'), np.float64), (bytes_to_native_str(b'savg'), np.float64), (bytes_to_native_str(b'mu_max'), 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()