API: suavespecific routines¶
Corrfunc.theory¶
Wrapper for all clustering statistic calculations on galaxies in a simulation volume.

Corrfunc.theory.
DDsmu
(autocorr, nthreads, binfile, mu_max, nmu_bins, X1, Y1, Z1, weights1=None, periodic=True, X2=None, Y2=None, Z2=None, weights2=None, verbose=False, boxsize=0.0, 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='fastest', weight_type=None, proj_type=None, ncomponents=None, projfn=None)[source] Calculate the 2D component values (e.g. paircounts for the tophat basis) corresponding to the redshiftspace correlation function, \(\xi(s, \mu)\) Pairs which are separated by less than the
s
bins (specified inbinfile
) in 3D, and less thans*mu_max
in the Zdimension 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 onweight_type
.To use the projection capability with suave, set the
proj_type
parameter for the desired basis functions, and setncomponents
andprojfn
accordingly.Note
This module only returns pair counts and not the actual correlation function \(\xi(s, \mu)\). See the utilities
Corrfunc.utils.convert_3d_counts_to_cf
for computing \(\xi(s, \mu)\) from the pair counts.New in version 2.1.0.
 Parameters
autocorr (boolean, required) – Boolean flag for auto/crosscorrelation. 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 forDDsmu_mocks
. The file should contain whitespace separated values of (smin, smax) specifying eachs
bin wanted. The bins need to be contiguous and sorted in increasing order (smallest bins come first).For arraylike input: A sequence of
s
values that provides the binedges. 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 doubleprecision 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 \(0 <= \cos(\theta_{LOS}) < \mu_{max}\) are counted (no equality).
nmu_bins (int) – The number of linear
mu
bins, with the bins ranging from from (0, \(\mu_{max}\))X1/Y1/Z1 (arraylike, 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 theweightavg
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.
X2/Y2/Z2 (arraylike, 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 (arraylike, 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
boxsize (double) – The sidelength of the cube in the cosmological simulation. Present to facilitate exact calculations for periodic wrapping. If boxsize is not supplied, then the wrapping is done based on the maximum difference within each dimension of the X/Y/Z arrays.
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 singleprecision,s
will suffer from numerical loss of precision and can not be trusted. If you need accurates
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 byfast_divide_and_NR_steps
of NewtonRaphson. Can improve runtime by ~1520% on older computers. Value of 0 uses the standard division operation.(xyz)bin_refine_factor (integer (default (2,2,1) typical values in [13])) – 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 [50300])) – 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 reordered inplace
New in version 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.
New in version 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 setisa
to, say,avx
andavx
is not available on the computer, then the code will revert to usingfallback
(even thoughsse42
might be available). Unless you are benchmarking the different instruction sets, you should always leaveisa
to the default value. And if you are benchmarking, then the string supplied here gets translated into anenum
for the instruction set defined inutils/defs.h
.weight_type (str, optional) – The type of pair weighting to apply. Options: “pair_product”, “pair_product_gradient”, None; Default: None.
proj_type (string (default None)) –
Projection method to use; currently supported methods are [‘tophat’, ‘piecewise’, ‘generalr’, ‘gaussian_kernel’, ‘gradient’]. If using ‘gradient’, must set
weight_type="pair_product_gradient"
.New in version suave.
ncomponents (int (default None)) –
Number of basis functions; necessary if projection method
proj_type
is defined.New in version suave.
projfn (string (default None)) –
File path of projection file; necessary for
proj_type='generalr'
.New in version suave.
 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 thebinfile
. There will be a total ofnmu_bins
ranging from [0,mu_max
) per spatial bin. Ifoutput_savg
is not set, thensavg
will be set to 0.0 for all bins; similarly forweight_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.v_proj (arraylike, double, optional) – Only returned if proj_type is not None. The projection vector, an array of length
ncomponents
.t_proj (arraylike, double, optional) – Only returned if proj_type is not None. The projection tensor, unrolled in the form of an array with length
ncomponents``*``ncomponents
.
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) >>> 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'])) ... 5.788530 8.249250 7.148213 0.1 230 1.000000 5.788530 8.249250 7.157218 0.2 236 1.000000 5.788530 8.249250 7.165338 0.3 208 1.000000 5.788530 8.249250 7.079905 0.4 252 1.000000 5.788530 8.249250 7.251661 0.5 184 1.000000 5.788530 8.249250 7.118536 0.6 222 1.000000 5.788530 8.249250 7.083466 0.7 238 1.000000 5.788530 8.249250 7.198184 0.8 170 1.000000 5.788530 8.249250 7.127409 0.9 208 1.000000 5.788530 8.249250 6.973090 1.0 206 1.000000 8.249250 11.756000 10.149183 0.1 592 1.000000 8.249250 11.756000 10.213009 0.2 634 1.000000 8.249250 11.756000 10.192220 0.3 532 1.000000 8.249250 11.756000 10.246931 0.4 544 1.000000 8.249250 11.756000 10.102675 0.5 530 1.000000 8.249250 11.756000 10.276180 0.6 644 1.000000 8.249250 11.756000 10.251264 0.7 666 1.000000 8.249250 11.756000 10.138399 0.8 680 1.000000 8.249250 11.756000 10.191916 0.9 566 1.000000 8.249250 11.756000 10.243229 1.0 608 1.000000 11.756000 16.753600 14.552776 0.1 1734 1.000000 11.756000 16.753600 14.579991 0.2 1806 1.000000 11.756000 16.753600 14.599611 0.3 1802 1.000000 11.756000 16.753600 14.471100 0.4 1820 1.000000 11.756000 16.753600 14.480192 0.5 1740 1.000000 11.756000 16.753600 14.493679 0.6 1746 1.000000 11.756000 16.753600 14.547713 0.7 1722 1.000000 11.756000 16.753600 14.465390 0.8 1750 1.000000 11.756000 16.753600 14.547465 0.9 1798 1.000000 11.756000 16.753600 14.440975 1.0 1828 1.000000 16.753600 23.875500 20.720406 0.1 5094 1.000000 16.753600 23.875500 20.735403 0.2 5004 1.000000 16.753600 23.875500 20.721069 0.3 5172 1.000000 16.753600 23.875500 20.723648 0.4 5014 1.000000 16.753600 23.875500 20.650621 0.5 5094 1.000000 16.753600 23.875500 20.688135 0.6 5076 1.000000 16.753600 23.875500 20.735691 0.7 4910 1.000000 16.753600 23.875500 20.714097 0.8 4864 1.000000 16.753600 23.875500 20.751836 0.9 4954 1.000000 16.753600 23.875500 20.721183 1.0 5070 1.000000
Corrfunc.mocks¶
Wrapper for all clustering statistic calculations on galaxies in a mock catalog.

Corrfunc.mocks.
DDsmu_mocks
(autocorr, cosmology, nthreads, mu_max, nmu_bins, binfile, RA1, DEC1, CZ1, weights1=None, RA2=None, DEC2=None, CZ2=None, weights2=None, is_comoving_dist=False, 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='fastest', weight_type=None, proj_type=None, ncomponents=None, projfn=None)[source] Calculate the 2D paircounts corresponding to the projected correlation function, \(\xi(s, \mu)\). The pairs are counted in bins of radial separation and cosine of angle to the lineofsight (LOS). The input positions are expected to be onsky coordinates. 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 onweight_type
.Returns a numpy structured array containing the pair counts for the specified bins.
To use the projection capability with suave, set the
proj_type
parameter for the desired basis functions, and setncomponents
andprojfn
accordingly.Note
This module only returns pair counts and not the actual correlation function \(\xi(s, \mu)\). See the utilities
Corrfunc.utils.convert_3d_counts_to_cf
for computing \(\xi(s, \mu)\) from the pair counts.New in version 2.1.0.
 Parameters
autocorr (boolean, required) – Boolean flag for auto/crosscorrelation. 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 comoving distance, based on your preferred cosmology. Setis_comoving_dist=True
, to indicate that the comoving distance conversion has already been done. Choices:
LasDamas cosmology. \(\Omega_m=0.25\), \(\Omega_\Lambda=0.75\)
Planck cosmology. \(\Omega_m=0.302\), \(\Omega_\Lambda=0.698\)
To setup a new cosmology, add an entry to the function,
init_cosmology
inROOT/utils/cosmology_params.c
and reinstall the entire package.nthreads (integer) – The number of OpenMP threads to use. Has no effect if OpenMP was not enabled during library compilation.
mu_max (double. Must be in range [0.0, 1.0]) –
A doubleprecision value for the maximum cosine of the angular separation from the line of sight (LOS). Here,
mu
is defined as the angle betweens
andl
. If \(v_1\) and \(v_2\) represent the vectors to each point constituting the pair, then \(s := v_1  v_2\) and \(l := 1/2 (v_1 + v_2)\).Note: Only pairs with \(0 <= \cos(\theta_{LOS}) < \mu_{max}\) are counted (no equality).
nmu_bins (int) – The number of linear
mu
bins, with the bins ranging from from (0, \(\mu_{max}\))binfile (string or an list/array of floats) –
For string input: filename specifying the
s
bins forDDsmu_mocks
. The file should contain whitespace separated values of (smin, smax) specifying eachs
bin wanted. The bins need to be contiguous and sorted in increasing order (smallest bins come first).For arraylike input: A sequence of
s
values that provides the binedges. 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 (arraylike, 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 (arraylike, 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 (arraylike, 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 thespeed of light
.If is_comoving_dist is set, then
CZ1
is interpreted as the comoving 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
orweights2
is specified, the other will be set to uniform weights.RA2 (arraylike, 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 (arraylike, 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 (arraylike, 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 thespeed of light
.If is_comoving_dist is set, then
CZ2
is interpreted as the comoving distance, rather than cz.Must be of same precision type as RA1/DEC1/CZ1.
weights2 (arraylike, 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 comoving distances. This flag allows arbitrary cosmologies to be used inCorrfunc
.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 singleprecision,savg
will suffer from numerical loss of precision and can not be trusted. If you need accuratesavg
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 byfast_divide_and_NR_steps
of NewtonRaphson. Can improve runtime by ~1520% on older computers. Value of 0 uses the standard division operation.(xyz)bin_refine_factor (integer, default is (2,2,1); typically within [13]) – 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 [50300]) – 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 reordered inplace
New in version 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.
New in version 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, caseinsensitive (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 setisa
to, say,avx
andavx
is not available on the computer, then the code will revert to usingfallback
(even thoughsse42
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 anenum
for the instruction set defined inutils/defs.h
.weight_type (string, optional (default None)) – The type of weighting to apply. One of [“pair_product”, “pair_product_gradient”, None].
proj_type (string (default None)) –
Projection method to use; currently supported methods are [‘tophat’, ‘piecewise’, ‘generalr’, ‘gaussian_kernel’, ‘gradient’]. If using ‘gradient’, must set
weight_type="pair_product_gradient"
.New in version suave.
ncomponents (int (default None)) –
Number of basis functions; necessary if projection method
proj_type
is defined.New in version suave.
projfn (string (default None)) –
Path to projection file; necessary for
proj_type='generalr'
.New in version suave.
 Returns
results (Numpy structured array) – A numpy structured array containing [smin, smax, savg, mumax, npairs, weightavg]. There are a total of
nmu_bins
inmu
for each separation bin specified in thebinfile
, withmumax
being the upper limit of themu
bin. Ifoutput_savg
is not set, thensavg
will be set to 0.0 for all bins; similarly forweightavg
.npairs
contains the number of pairs in that bin and can be used to compute the actual \(\xi(s, \mu)\) 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.v_proj (arraylike, double, optional) – Only returned if proj_type is not None. The projection vector, an array of length
ncomponents
.t_proj (arraylike, double, optional) – Only returned if proj_type is not None. The projection tensor, unrolled in the form of an array with length
ncomponents``*``ncomponents
.
Corrfunc.utils¶
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 (arraylike, double) – Projection vector for datadata crosscorrelation
dr (arraylike, double) – Projection vector for datarandom crosscorrelation
rd (arraylike, double) – Projection vector for randomdata crosscorrelation
rr (arraylike, double) – Projection vector for randomrandom crosscorrelation
trr (arraylike, double) – Projection tensor for randomrandom crosscorrelation
 Returns
amps – Vector of amplitudes, with length ncomponents
 Return type
arraylike, double

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 (arraylike, double) – Vector of amplitudes, e.g. that returned by compute_amps
rvals (arraylike, 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 (arraylike, 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 (arraylike, double, default=None) – Weights/metadata for custom basis functions, for first set of imaginary galaxies
weights2 (arraylike, 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
arraylike, double

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 rvalue for integration
rmax (double) – Maximum rvalue for integration
nd (int) – Number of points in data catalog
volume (double) – Volume of data cube, in the same units as the rvalues (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 (arraylike, double, default=None) – Edges of rbins 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
arraylike, double
Corrfunc.bases¶

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 5component BAO basis functions based on a cosmological model and linearized around the scale dilation parameter alpha.
 Parameters
rmin (double) – Minimum rvalue for basis functions
rmax (double) – Maximum rvalue 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 rvalues 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 – 2d array of basis function values; first column is rvalues
 Return type
arraylike, 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 rvalue for basis functions
rmax (double) – Maximum rvalue 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 rvalues at which to write the basis function file
order (int, default=3) – Order of spline to use; default is cubic spline
 Returns
bases – 2d array of basis function values; first column is rvalues
 Return type
arraylike, double