Source code for Corrfunc.mocks.DDrppi_mocks

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

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

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

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


[docs]def DDrppi_mocks(autocorr, cosmology, nthreads, pimax, binfile, RA1, DEC1, CZ1, weights1=None, RA2=None, DEC2=None, CZ2=None, weights2=None, is_comoving_dist=False, verbose=False, output_rpavg=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 projected correlation function, :math:`\\xi(r_p, \pi)`. 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. The input positions are expected to be on-sky co-ordinates. This module is suitable for calculating correlation functions for mock catalogs. If ``weights`` are provided, the resulting pair counts are weighted. The weighting scheme depends on ``weight_type``. Returns a numpy structured array containing the pair counts for the specified bins. .. 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. 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. 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. 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_mocks``. 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. RA1 : 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. DEC1 : 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 RA1. CZ1 : 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 ``CZ1`` is interpreted as the co-moving distance, rather than `cz`. 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. RA2 : array-like, real (float/double) The array of Right Ascensions for the second 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]. Must be of same precision type as RA1/DEC1/CZ1. DEC2 : array-like, real (float/double) Array of Declinations for the second 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 RA1/DEC1/CZ1. CZ2 : array-like, real (float/double) Array of (Speed Of Light * Redshift) values for the second 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 ``CZ2`` is interpreted as the co-moving distance, rather than `cz`. Must be of same precision type as RA1/DEC1/CZ1. weights2 : array-like, real (float/double), optional Same as weights1, but for the second set of positions 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``. 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. If you are calculating in single-precision, ``rpavg`` will suffer 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. 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 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, case-insensitive (default ``fastest``) Controls the runtime dispatch for the instruction set to use. Possible 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_ravg`` 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 the actual :math:`\\xi(r_p, \pi)` or :math:`wp(rp)` 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.mocks.DDrppi_mocks import DDrppi_mocks >>> import math >>> binfile = pjoin(dirname(abspath(Corrfunc.__file__)), ... "../mocks/tests/", "bins") >>> N = 100000 >>> 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) >>> weights = np.ones_like(X) >>> 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 >>> autocorr = 1 >>> cosmology = 1 >>> nthreads = 2 >>> pimax = 40.0 >>> results = DDrppi_mocks(autocorr, cosmology, nthreads, ... pimax, binfile, RA, DEC, CZ, ... weights1=weights, weight_type='pair_product', ... output_rpavg=True, is_comoving_dist=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 11.359969 16.852277 14.285169 40.0 104850 1.000000 16.852277 25.000000 21.181246 1.0 274144 1.000000 16.852277 25.000000 21.190844 2.0 272876 1.000000 16.852277 25.000000 21.183321 3.0 272294 1.000000 16.852277 25.000000 21.188486 4.0 272506 1.000000 16.852277 25.000000 21.170832 5.0 272100 1.000000 16.852277 25.000000 21.165379 6.0 271788 1.000000 16.852277 25.000000 21.175246 7.0 270040 1.000000 16.852277 25.000000 21.187417 8.0 269492 1.000000 16.852277 25.000000 21.172066 9.0 269682 1.000000 16.852277 25.000000 21.182460 10.0 268266 1.000000 16.852277 25.000000 21.170594 11.0 268744 1.000000 16.852277 25.000000 21.178608 12.0 266820 1.000000 16.852277 25.000000 21.187184 13.0 266510 1.000000 16.852277 25.000000 21.184937 14.0 265484 1.000000 16.852277 25.000000 21.180184 15.0 265258 1.000000 16.852277 25.000000 21.191504 16.0 262952 1.000000 16.852277 25.000000 21.187746 17.0 262602 1.000000 16.852277 25.000000 21.189778 18.0 260206 1.000000 16.852277 25.000000 21.188882 19.0 259410 1.000000 16.852277 25.000000 21.185684 20.0 256806 1.000000 16.852277 25.000000 21.194036 21.0 255574 1.000000 16.852277 25.000000 21.184115 22.0 255406 1.000000 16.852277 25.000000 21.178255 23.0 252394 1.000000 16.852277 25.000000 21.184644 24.0 252220 1.000000 16.852277 25.000000 21.187020 25.0 251668 1.000000 16.852277 25.000000 21.183827 26.0 249648 1.000000 16.852277 25.000000 21.183121 27.0 247160 1.000000 16.852277 25.000000 21.180872 28.0 246238 1.000000 16.852277 25.000000 21.185251 29.0 246030 1.000000 16.852277 25.000000 21.183488 30.0 242124 1.000000 16.852277 25.000000 21.194538 31.0 242426 1.000000 16.852277 25.000000 21.190702 32.0 239778 1.000000 16.852277 25.000000 21.188985 33.0 239046 1.000000 16.852277 25.000000 21.187092 34.0 237640 1.000000 16.852277 25.000000 21.185515 35.0 236256 1.000000 16.852277 25.000000 21.190278 36.0 233536 1.000000 16.852277 25.000000 21.183240 37.0 233274 1.000000 16.852277 25.000000 21.183796 38.0 231628 1.000000 16.852277 25.000000 21.200668 39.0 230378 1.000000 16.852277 25.000000 21.181153 40.0 229006 1.000000 """ try: from Corrfunc._countpairs_mocks import countpairs_rp_pi_mocks as\ DDrppi_extn except ImportError: msg = "Could not import the C extension for the on-sky"\ "pair counter." raise ImportError(msg) import numpy as np from Corrfunc.utils import translate_isa_string_to_enum, fix_ra_dec,\ return_file_with_rbins, convert_to_native_endian,\ sys_pipes, process_weights from future.utils import bytes_to_native_str if not autocorr: if RA2 is None or DEC2 is None or CZ2 is None: msg = "Must pass valid arrays for RA2/DEC2/CZ2 for "\ "computing cross-correlation" raise ValueError(msg) else: RA2 = np.empty(1) DEC2 = np.empty(1) CZ2 = np.empty(1) weights1, weights2 = process_weights(weights1, weights2, RA1, RA2, weight_type, autocorr) # Ensure all input arrays are native endian RA1, DEC1, CZ1, weights1, RA2, DEC2, CZ2, weights2 = [ convert_to_native_endian(arr, warn=True) for arr in [RA1, DEC1, CZ1, weights1, RA2, DEC2, CZ2, weights2]] fix_ra_dec(RA1, DEC1) if autocorr == 0: fix_ra_dec(RA2, DEC2) # Passing None parameters breaks the parsing code, so avoid this kwargs = {} for k in ['weights1', 'weights2', 'weight_type', 'RA2', 'DEC2', 'CZ2']: 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, cosmology, nthreads, pimax, rbinfile, RA1, DEC1, CZ1, is_comoving_dist=is_comoving_dist, verbose=verbose, output_rpavg=output_rpavg, 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(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()