Corrfunc package

Corrfunc is a set of high-performance routines for computing clustering statistics on a distribution of points.

Corrfunc.read_text_file(filename, encoding='utf-8')[source]

Reads a file under python3 with encoding (default UTF-8). Also works under python2, without encoding. Uses the EAFP (https://docs.python.org/2/glossary.html#term-eafp) principle.

Corrfunc.which(program, mode=1, path=None)[source]

Mimics the Unix utility which. For python3.3+, shutil.which provides all of the required functionality. An implementation is provided in case shutil.which does not exist.

Parameters

  • program – (required) string Name of program (can be fully-qualified path as well)

  • mode – (optional) integer flag bits Permissions to check for in the executable Default: os.F_OK (file exists) | os.X_OK (executable file)

  • path – (optional) string A custom path list to check against. Implementation taken from shutil.py.

Returns

A fully qualified path to program as resolved by path or user environment. Returns None when program can not be resolved.

Corrfunc.write_text_file(filename, contents, encoding='utf-8')[source]

Writes a file under python3 with encoding (default UTF-8). Also works under python2, without encoding. Uses the EAFP (https://docs.python.org/2/glossary.html#term-eafp) principle.

Corrfunc.bases module

Corrfunc.bases.bao_bases(rmin, rmax, projfn, cosmo_base=None, ncont=2000, redshift=0.0, alpha_guess=1.0, dalpha=0.001, bias=1.0, k0=0.1, k1=10.0, k2=0.1, k3=0.001)[source]

Compute the 5-component BAO basis functions based on a cosmological model and linearized around the scale dilation parameter alpha.

Parameters

  • rmin (double) – Minimum r-value for basis functions

  • rmax (double) – Maximum r-value for basis functions

  • projfn (string, default=None) – Path to projection file if necessary

  • cosmo_base (nbodykit cosmology object, default=nbodykit.cosmology.Planck15) – Cosmology object for the BAO model.

  • ncont (int, default=2000) – Number of continuous r-values at which to write the basis function file

  • redshift (double, default=0.0) – Redshift at which to compute power spectrum

  • alpha_guess (double, default=1.0) – The alpha (scale dilation parameter) at which to compute the model (alpha=1.0 is no scale shift)

  • dalpha (double, default=0.001) – The change in alpha (scale dilation parameter) used to calculate the numerical partial derivative

  • bias (double, default=1.0) – The bias parameter by which to scale the model amplitude (bias=1.0 indicates no bias)

  • k0 (double, default=0.1) – The initial magnitude of the derivative term

  • k1 (double, default=1.0) – The initial magnitude of the s^2 nuisance parameter term

  • k2 (double, default=0.1) – The initial magnitude of the s nuisance parameter term

  • k3 (double, default=0.001) – The initial magnitude of the constant nuisance parameter term

Returns

bases – 2-d array of basis function values; first column is r-values

Return type

array-like, double

Corrfunc.bases.spline_bases(rmin, rmax, projfn, ncomponents, ncont=2000, order=3)[source]

Compute a set of spline basis functions for the given order.

Parameters

  • rmin (double) – Minimum r-value for basis functions

  • rmax (double) – Maximum r-value for basis functions

  • projfn (string, default=None) – Path to projection file if necessary

  • ncomponents (int) – Number of components (basis functions)

  • ncont (int, default=2000) – Number of continuous r-values at which to write the basis function file

  • order (int, default=3) – Order of spline to use; default is cubic spline

Returns

bases – 2-d array of basis function values; first column is r-values

Return type

array-like, double

Corrfunc.io module

Routines to read galaxy catalogs from disk. Updated with lognormal scripts!

Corrfunc.io.read_ascii_catalog(filename, return_dtype=None)[source]

Read a galaxy catalog from an ascii file.

Parameters

  • filename (string) – Filename containing the galaxy positions

  • return_dtype (numpy dtype for returned arrays. Default numpy.float) – Specifies the datatype for the returned arrays. Must be in {np.float, np.float32}

Returns

X, Y, Z – Returns the triplet of X/Y/Z positions as separate numpy arrays.

Return type

numpy arrays

Example

>>> from __future__ import print_function
>>> from os.path import dirname, abspath, join as pjoin
>>> import Corrfunc
>>> from Corrfunc.io import read_ascii_catalog
>>> filename = pjoin(dirname(abspath(Corrfunc.__file__)),
...                 "../mocks/tests/data/", "Mr19_mock_northonly.rdcz.dat")
>>> ra, dec, cz = read_ascii_catalog(filename)
>>> N = 20
>>> for r,d,c in zip(ra[0:N], dec[0:N], cz[0:]):
...     print("{0:10.5f} {1:10.5f} {2:10.5f}".format(r, d, c))
...     
178.45087   67.01112 19905.28514
178.83495   67.72519 19824.02285
179.50132   67.67628 19831.21553
182.75497   67.13004 19659.79825
186.29853   68.64099 20030.64412
186.32346   68.65879 19763.38137
187.36173   68.15151 19942.66996
187.20613   68.56189 19996.36607
185.56358   67.97724 19729.32308
183.27930   67.11318 19609.71345
183.86498   67.82823 19500.44130
184.07771   67.43429 19440.53790
185.13370   67.15382 19390.60304
189.15907   68.28252 19858.85853
190.12209   68.55062 20044.29744
193.65245   68.36878 19445.62469
194.93514   68.34870 19158.93155
180.36897   67.50058 18671.40780
179.63278   67.51318 18657.59191
180.75742   67.95530 18586.88913
Corrfunc.io.read_catalog(filebase=None, return_dtype=<Mock id='139780786855600'>)[source]

Reads a galaxy/randoms catalog and returns 3 XYZ arrays.

Parameters

  • filebase (string (optional)) – The fully qualified path to the file. If omitted, reads the theory galaxy catalog under ../theory/tests/data/

  • return_dtype (numpy dtype for returned arrays. Default numpy.float) – Specifies the datatype for the returned arrays. Must be in {np.float, np.float32}

Returns

  • x y z - Unpacked numpy arrays compatible with the installed

  • version of Corrfunc.

Note

If the filename is omitted, then first the fast-food file is searched for, and then the ascii file. End-users should always supply the full filename.

Corrfunc.io.read_fastfood_catalog(filename, return_dtype=None, need_header=None)[source]

Read a galaxy catalog from a fast-food binary file.

Parameters

  • filename (string) – Filename containing the galaxy positions

  • return_dtype (numpy dtype for returned arrays. Default numpy.float) – Specifies the datatype for the returned arrays. Must be in {np.float, np.float32}

  • need_header (boolean, default None.) – Returns the header found in the fast-food file in addition to the X/Y/Z arrays.

Returns

X, Y, Z – Returns the triplet of X/Y/Z positions as separate numpy arrays.

If need_header is set, then the header is also returned

Return type

numpy arrays

Example

>>> import numpy as np
>>> from os.path import dirname, abspath, join as pjoin
>>> import Corrfunc
>>> from Corrfunc.io import read_fastfood_catalog
>>> filename = pjoin(dirname(abspath(Corrfunc.__file__)),
...                  "../theory/tests/data/",
...                  "gals_Mr19.ff")
>>> X, Y, Z = read_fastfood_catalog(filename)
>>> N = 20
>>> for x,y,z in zip(X[0:N], Y[0:N], Z[0:]):
...     print("{0:10.5f} {1:10.5f} {2:10.5f}".format(x, y, z))
...     
419.94550    1.96340    0.01610
419.88272    1.79736    0.11960
0.32880   10.63620    4.16550
0.15314   10.68723    4.06529
0.46400    8.91150    6.97090
6.30690    9.77090    8.61080
5.87160    9.65870    9.29810
8.06210    0.42350    4.89410
11.92830    4.38660    4.54410
11.95543    4.32622    4.51485
11.65676    4.34665    4.53181
11.75739    4.26262    4.31666
11.81329    4.27530    4.49183
11.80406    4.54737    4.26824
12.61570    4.14470    3.70140
13.23640    4.34750    5.26450
13.19833    4.33196    5.29435
13.21249    4.35695    5.37418
13.06805    4.24275    5.35126
13.19693    4.37618    5.28772
Corrfunc.io.read_fortran_catalog(filename, return_dtype=None)[source]
Corrfunc.io.read_lognormal_catalog(n='2e-4')[source]

Corrfunc.utils module

A set of utility routines

Corrfunc.utils.compute_amps(ncomponents, nd1, nd2, nr1, nr2, dd, dr, rd, rr, trr)[source]

Compute the amplitude vector for the continuous correlation function from the component vectors (a.k.a. pair counts in a given basis).

Parameters

  • ncomponents (int) – Number of basis functions

  • nd1 (int) – Number of particles in data catalog 1

  • nd2 (int) – Number of particles in data catalog 2

  • nr1 (int) – Number of particles in random1

  • nr2 (int) – Number of particles in random2

  • dd (array-like, double) – Projection vector for data-data cross-correlation

  • dr (array-like, double) – Projection vector for data-random cross-correlation

  • rd (array-like, double) – Projection vector for random-data cross-correlation

  • rr (array-like, double) – Projection vector for random-random cross-correlation

  • trr (array-like, double) – Projection tensor for random-random cross-correlation

Returns

amps – Vector of amplitudes, with length ncomponents

Return type

array-like, double

Corrfunc.utils.compute_nbins(max_diff, binsize, refine_factor=1, max_nbins=None)[source]

Helper utility to find the number of bins for that satisfies the constraints of (binsize, refine_factor, and max_nbins).

Parameters

  • max_diff (double) – Max. difference (spatial or angular) to be spanned, (i.e., range of allowed domain values)

  • binsize (double) – Min. allowed binsize (spatial or angular)

  • refine_factor (integer, default 1) – How many times to refine the bins. The refinements occurs after nbins has already been determined (with refine_factor-1). Thus, the number of bins will be exactly higher by refine_factor compared to the base case of refine_factor=1

  • max_nbins (integer, default None) – Max number of allowed cells

Returns

nbins – Number of bins that satisfies the constraints of bin size >= binsize, the refinement factor and nbins <= max_nbins.

Return type

integer, >= 1

Example

>>> from Corrfunc.utils import compute_nbins
>>> max_diff = 180
>>> binsize = 10
>>> compute_nbins(max_diff, binsize)
18
>>> refine_factor=2
>>> max_nbins = 20
>>> compute_nbins(max_diff, binsize, refine_factor=refine_factor,
...              max_nbins=max_nbins)
20
Corrfunc.utils.convert_3d_counts_to_cf(ND1, ND2, NR1, NR2, D1D2, D1R2, D2R1, R1R2, estimator='LS')[source]

Converts raw pair counts to a correlation function.

Parameters

  • ND1 (integer) – Number of points in the first dataset

  • ND2 (integer) – Number of points in the second dataset

  • NR1 (integer) – Number of points in the randoms for first dataset

  • NR2 (integer) – Number of points in the randoms for second dataset

  • D1D2 (array-like, integer) – Pair-counts for the cross-correlation between D1 and D2

  • D1R2 (array-like, integer) – Pair-counts for the cross-correlation between D1 and R2

  • D2R1 (array-like, integer) – Pair-counts for the cross-correlation between D2 and R1

  • R1R2 (array-like, integer) – Pair-counts for the cross-correlation between R1 and R2

  • all of these pair-counts arrays (For) –

  • corresponding numpy (the) –

  • returned by the theory/mocks modules can also be passed (struct) –

  • estimator (string, default='LS' (Landy-Szalay)) – The kind of estimator to use for computing the correlation function. Currently, only supports Landy-Szalay

Returns

cf – The correlation function, calculated using the chosen estimator, is returned. NAN is returned for the bins where the RR count is 0.

Return type

A numpy array

Example

>>> from __future__ import print_function
>>> import numpy as np
>>> from Corrfunc.theory.DD import DD
>>> from Corrfunc.io import read_catalog
>>> from Corrfunc.utils import convert_3d_counts_to_cf
>>> X, Y, Z = read_catalog()
>>> N = len(X)
>>> boxsize = 420.0
>>> rand_N = 3*N
>>> seed = 42
>>> np.random.seed(seed)
>>> rand_X = np.random.uniform(0, boxsize, rand_N)
>>> rand_Y = np.random.uniform(0, boxsize, rand_N)
>>> rand_Z = np.random.uniform(0, boxsize, rand_N)
>>> nthreads = 2
>>> rmin = 0.1
>>> rmax = 15.0
>>> nbins = 10
>>> bins = np.linspace(rmin, rmax, nbins + 1)
>>> autocorr = 1
>>> DD_counts = DD(autocorr, nthreads, bins, X, Y, Z)
>>> autocorr = 0
>>> DR_counts = DD(autocorr, nthreads, bins,
...                X, Y, Z,
...                X2=rand_X, Y2=rand_Y, Z2=rand_Z)
>>> autocorr = 1
>>> RR_counts = DD(autocorr, nthreads, bins, rand_X, rand_Y, rand_Z)
>>> cf = convert_3d_counts_to_cf(N, N, rand_N, rand_N,
...                              DD_counts, DR_counts,
...                              DR_counts, RR_counts)
>>> for xi in cf: print("{0:10.6f}".format(xi))
...                    
22.769019
 3.612709
 1.621372
 1.000969
 0.691646
 0.511819
 0.398872
 0.318815
 0.255643
 0.207759
Corrfunc.utils.convert_rp_pi_counts_to_wp(ND1, ND2, NR1, NR2, D1D2, D1R2, D2R1, R1R2, nrpbins, pimax, dpi=1.0, estimator='LS')[source]

Converts raw pair counts to a correlation function.

Parameters

  • ND1 (integer) – Number of points in the first dataset

  • ND2 (integer) – Number of points in the second dataset

  • NR1 (integer) – Number of points in the randoms for first dataset

  • NR2 (integer) – Number of points in the randoms for second dataset

  • D1D2 (array-like, integer) – Pair-counts for the cross-correlation between D1 and D2

  • D1R2 (array-like, integer) – Pair-counts for the cross-correlation between D1 and R2

  • D2R1 (array-like, integer) – Pair-counts for the cross-correlation between D2 and R1

  • R1R2 (array-like, integer) – Pair-counts for the cross-correlation between R1 and R2

  • all of these pair-counts arrays (For) –

  • corresponding numpy (the) –

  • returned by the theory/mocks modules can also be passed (struct) –

  • nrpbins (integer) – Number of bins in rp

  • pimax (float) – Integration distance along the line of sight direction

  • dpi (float, default=1.0 Mpc/h) – Binsize in the line of sight direction

  • estimator (string, default='LS' (Landy-Szalay)) – The kind of estimator to use for computing the correlation function. Currently, only supports Landy-Szalay

Returns

wp – The projected correlation function, calculated using the chosen estimator, is returned. If any of the pi bins (in an rp bin) contains 0 for the RR counts, then NAN is returned for that rp bin.

Return type

A numpy array

Example

>>> from __future__ import print_function
>>> import numpy as np
>>> from Corrfunc.theory.DDrppi import DDrppi
>>> from Corrfunc.io import read_catalog
>>> from Corrfunc.utils import convert_rp_pi_counts_to_wp
>>> X, Y, Z = read_catalog()
>>> N = len(X)
>>> boxsize = 420.0
>>> rand_N = 3*N
>>> seed = 42
>>> np.random.seed(seed)
>>> rand_X = np.random.uniform(0, boxsize, rand_N)
>>> rand_Y = np.random.uniform(0, boxsize, rand_N)
>>> rand_Z = np.random.uniform(0, boxsize, rand_N)
>>> nthreads = 4
>>> pimax = 40.0
>>> nrpbins = 20
>>> rpmin = 0.1
>>> rpmax = 10.0
>>> bins = np.linspace(rpmin, rpmax, nrpbins + 1)
>>> autocorr = 1
>>> DD_counts = DDrppi(autocorr, nthreads, pimax, bins,
...                    X, Y, Z)
>>> autocorr = 0
>>> DR_counts = DDrppi(autocorr, nthreads, pimax, bins,
...                    X, Y, Z,
...                    X2=rand_X, Y2=rand_Y, Z2=rand_Z)
>>> autocorr = 1
>>> RR_counts = DDrppi(autocorr, nthreads, pimax, bins,
...                    rand_X, rand_Y, rand_Z)
>>> wp = convert_rp_pi_counts_to_wp(N, N, rand_N, rand_N,
...                                 DD_counts, DR_counts,
...                                 DR_counts, RR_counts,
...                                 nrpbins, pimax)
>>> for w in wp: print("{0:10.6f}".format(w))
...                    
187.592199
 83.059181
 53.200599
 40.389354
 33.356371
 29.045476
 26.088133
 23.628340
 21.703961
 20.153125
 18.724781
 17.433235
 16.287183
 15.443230
 14.436193
 13.592727
 12.921226
 12.330074
 11.696364
 11.208365
Corrfunc.utils.evaluate_xi(amps, rvals, proj_type, rbins=None, projfn=None, weights1=None, weights2=None, weight_type=None)[source]

Evaluate the correlation function for the given amplitudes and separation values.

Parameters

  • amps (array-like, double) – Vector of amplitudes, e.g. that returned by compute_amps

  • rvals (array-like, double) – Array of radial separation values at which to evaluate the correlation function

  • proj_type (string) – Projection method to use; currently supported methods are [‘tophat’, ‘piecewise’, ‘generalr’, ‘gaussian_kernel’, ‘gradient’]

  • rbins (array-like, double, default=None) – Bin edges; necessary for tophat or piecewise bases

  • projfn (string, default=None) – Path to projection file; necessary for proj_type=’generalr’

  • weights1 (array-like, double, default=None) – Weights/metadata for custom basis functions, for first set of imaginary galaxies

  • weights2 (array-like, double, default=None) – Weights/metadata for custom basis functions, for second set of imaginary galaxies

  • weight_type (string, default=None) – Name of weight function. If using ‘gradient’, must set weight_type="pair_product_gradient".

Returns

xi – Vector of xi values, same shape as rvals

Return type

array-like, double

Corrfunc.utils.fix_cz(cz)[source]

Multiplies the input array by speed of light, if the input values are too small.

Essentially, converts redshift into cz, if the user passed redshifts instead of cz.

Parameters

cz (array-like, reals) – An array containing [Speed of Light *] redshift values.

Returns

cz – Actual cz values, multiplying the input cz array by the Speed of Light, if redshift values were passed as input cz.

Return type

array-like

Corrfunc.utils.fix_ra_dec(ra, dec)[source]

Wraps input RA and DEC values into range expected by the extensions.

Parameters

  • RA (array-like, units must be degrees) – Right Ascension values (astronomical longitude)

  • DEC (array-like, units must be degrees) – Declination values (astronomical latitude)

Returns

Tuple (RA, DEC) – RA is wrapped into range [0.0, 360.0] Declination is wrapped into range [-90.0, 90.0]

Return type

array-like

A method to optimally partition spherical regions such that pairs of points within a certain angular separation, thetamax, can be quickly computed.

Generates the binning scheme used in Corrfunc.mocks.DDtheta_mocks for a spherical region in Right Ascension (RA), Declination (DEC) and a maximum angular separation.

For a given thetamax, regions on the sphere are divided into bands in DEC bands, with the width in DEC equal to thetamax. If link_in_ra is set, then these DEC bands are further sub-divided into RA cells.

Parameters

  • thetamax (double) – Max. angular separation of pairs. Expected to be in degrees unless input_in_degrees is set to False.

  • ra_limits (array of 2 doubles. Default [0.0, 2*pi]) – Range of Righ Ascension (longitude) for the spherical region

  • dec_limits (array of 2 doubles. Default [-pi/2, pi/2]) – Range of Declination (latitude) values for the spherical region

  • link_in_ra (Boolean. Default True) – Whether linking in RA is done (in addition to linking in DEC)

  • ra_refine_factor (integer, >= 1. Default 1) – Controls the sub-division of the RA cells. For a large number of particles, higher ra_refine_factor typically results in a faster runtime

  • dec_refine_factor (integer, >= 1. Default 1) – Controls the sub-division of the DEC cells. For a large number of particles, higher dec_refine_factor typically results in a faster runtime

  • max_ra_cells (integer, >= 1. Default 100) – The max. number of RA cells per DEC band.

  • max_dec_cells (integer >= 1. Default 200) – The max. number of total DEC bands

  • return_num_ra_cells (bool, default False) – Flag to return the number of RA cells per DEC band

  • input_in_degrees (Boolean. Default True) – Flag to show if the input quantities are in degrees. If set to False, all angle inputs will be taken to be in radians.

Returns

  • sphere_grid (A numpy compound array, shape (ncells, 2)) – A numpy compound array with fields dec_limit and ra_limit of size 2 each. These arrays contain the beginning and end of DEC and RA regions for the cell.

  • num_ra_cells (numpy array, returned if return_num_ra_cells is set) – A numpy array containing the number of RA cells per declination band

Note

If link_in_ra=False, then there is effectively one RA bin per DEC band. The ‘ra_limit’ field will show the range of allowed RA values.

See also

Corrfunc.mocks.DDtheta_mocks

Example

>>> from Corrfunc.utils import gridlink_sphere
>>> import numpy as np
>>> try:  # Backwards compatibility with old Numpy print formatting
...     np.set_printoptions(legacy='1.13')
... except TypeError:
...     pass
>>> thetamax=30
>>> grid = gridlink_sphere(thetamax)
>>> print(grid)  
[([-1.57079633, -1.04719755], [ 0.        ,  3.14159265])
 ([-1.57079633, -1.04719755], [ 3.14159265,  6.28318531])
 ([-1.04719755, -0.52359878], [ 0.        ,  3.14159265])
 ([-1.04719755, -0.52359878], [ 3.14159265,  6.28318531])
 ([-0.52359878,  0.        ], [ 0.        ,  1.25663706])
 ([-0.52359878,  0.        ], [ 1.25663706,  2.51327412])
 ([-0.52359878,  0.        ], [ 2.51327412,  3.76991118])
 ([-0.52359878,  0.        ], [ 3.76991118,  5.02654825])
 ([-0.52359878,  0.        ], [ 5.02654825,  6.28318531])
 ([ 0.        ,  0.52359878], [ 0.        ,  1.25663706])
 ([ 0.        ,  0.52359878], [ 1.25663706,  2.51327412])
 ([ 0.        ,  0.52359878], [ 2.51327412,  3.76991118])
 ([ 0.        ,  0.52359878], [ 3.76991118,  5.02654825])
 ([ 0.        ,  0.52359878], [ 5.02654825,  6.28318531])
 ([ 0.52359878,  1.04719755], [ 0.        ,  3.14159265])
 ([ 0.52359878,  1.04719755], [ 3.14159265,  6.28318531])
 ([ 1.04719755,  1.57079633], [ 0.        ,  3.14159265])
 ([ 1.04719755,  1.57079633], [ 3.14159265,  6.28318531])]
>>> grid = gridlink_sphere(60, dec_refine_factor=3, ra_refine_factor=2)
>>> print(grid)  
[([-1.57079633, -1.22173048], [ 0.        ,  1.57079633])
 ([-1.57079633, -1.22173048], [ 1.57079633,  3.14159265])
 ([-1.57079633, -1.22173048], [ 3.14159265,  4.71238898])
 ([-1.57079633, -1.22173048], [ 4.71238898,  6.28318531])
 ([-1.22173048, -0.87266463], [ 0.        ,  1.57079633])
 ([-1.22173048, -0.87266463], [ 1.57079633,  3.14159265])
 ([-1.22173048, -0.87266463], [ 3.14159265,  4.71238898])
 ([-1.22173048, -0.87266463], [ 4.71238898,  6.28318531])
 ([-0.87266463, -0.52359878], [ 0.        ,  1.57079633])
 ([-0.87266463, -0.52359878], [ 1.57079633,  3.14159265])
 ([-0.87266463, -0.52359878], [ 3.14159265,  4.71238898])
 ([-0.87266463, -0.52359878], [ 4.71238898,  6.28318531])
 ([-0.52359878, -0.17453293], [ 0.        ,  1.57079633])
 ([-0.52359878, -0.17453293], [ 1.57079633,  3.14159265])
 ([-0.52359878, -0.17453293], [ 3.14159265,  4.71238898])
 ([-0.52359878, -0.17453293], [ 4.71238898,  6.28318531])
 ([-0.17453293,  0.17453293], [ 0.        ,  1.57079633])
 ([-0.17453293,  0.17453293], [ 1.57079633,  3.14159265])
 ([-0.17453293,  0.17453293], [ 3.14159265,  4.71238898])
 ([-0.17453293,  0.17453293], [ 4.71238898,  6.28318531])
 ([ 0.17453293,  0.52359878], [ 0.        ,  1.57079633])
 ([ 0.17453293,  0.52359878], [ 1.57079633,  3.14159265])
 ([ 0.17453293,  0.52359878], [ 3.14159265,  4.71238898])
 ([ 0.17453293,  0.52359878], [ 4.71238898,  6.28318531])
 ([ 0.52359878,  0.87266463], [ 0.        ,  1.57079633])
 ([ 0.52359878,  0.87266463], [ 1.57079633,  3.14159265])
 ([ 0.52359878,  0.87266463], [ 3.14159265,  4.71238898])
 ([ 0.52359878,  0.87266463], [ 4.71238898,  6.28318531])
 ([ 0.87266463,  1.22173048], [ 0.        ,  1.57079633])
 ([ 0.87266463,  1.22173048], [ 1.57079633,  3.14159265])
 ([ 0.87266463,  1.22173048], [ 3.14159265,  4.71238898])
 ([ 0.87266463,  1.22173048], [ 4.71238898,  6.28318531])
 ([ 1.22173048,  1.57079633], [ 0.        ,  1.57079633])
 ([ 1.22173048,  1.57079633], [ 1.57079633,  3.14159265])
 ([ 1.22173048,  1.57079633], [ 3.14159265,  4.71238898])
 ([ 1.22173048,  1.57079633], [ 4.71238898,  6.28318531])]
Corrfunc.utils.return_file_with_rbins(rbins)[source]

Helper function to ensure that the binfile required by the Corrfunc extensions is a actually a string.

Checks if the input is a string and file; return if True. If not, and the input is an array, then a temporary file is created and the contents of rbins is written out.

Parameters

rbins (string or array-like) – Expected to be a string or an array containing the bins

Returns

binfile – If the input rbins was a valid filename, then returns the same string. If rbins was an array, then this function creates a temporary file with the contents of the rbins arrays. This temporary filename is returned

Return type

string, filename

Corrfunc.utils.translate_isa_string_to_enum(isa)[source]

Helper function to convert an user-supplied string to the underlying enum in the C-API. The extensions only have specific implementations for AVX512F, AVX, SSE42 and FALLBACK. Any other value will raise a ValueError.

Parameters

isa (string) – A string containing the desired instruction set. Valid values are [‘AVX512F’, ‘AVX’, ‘SSE42’, ‘FALLBACK’, ‘FASTEST’]

Returns

instruction_set – An integer corresponding to the desired instruction set, as used in the underlying C API. The enum used here should be defined exactly the same way as the enum in utils/defs.h.

Return type

integer

Corrfunc.utils.trr_analytic(rmin, rmax, nd, volume, ncomponents, proj_type, rbins=None, projfn=None)[source]

Compute the T_RR tensor analytically for a periodic box, for the given set of basis functions.

Parameters

  • rmin (double) – Minimum r-value for integration

  • rmax (double) – Maximum r-value for integration

  • nd (int) – Number of points in data catalog

  • volume (double) – Volume of data cube, in the same units as the r-values (cubed)

  • ncomponents (int) – Number of basis functions

  • proj_type (string) – Projection method to use; currently supported methods are [‘tophat’, ‘piecewise’, ‘generalr’, ‘gaussian_kernel’, ‘gradient’]

  • rbins (array-like, double, default=None) – Edges of r-bins for tophat or piecewise projections

  • projfn (string, default=None) – Path to projection file if necessary

Returns

trr – Tensor of T_RR values, with size (ncomponents, ncomponents)

Return type

array-like, double