Corrfunc 1.1.0

Pre-requisites

1. make >= 3.80

2. OpenMP capable compiler like icc, gcc or clang >= 3.7. If not available, please disable USE_OMP option option in theory.options and mocks.options. You might need to ask your sys-admin for system-wide installs of the compiler; if you prefer to install your own then conda install gcc (MAC/linux) or (sudo) port install gcc5 (on MAC) should work. Note ``gcc`` on macports defaults to ``gcc48`` and the portfile is currently broken on ``El Capitan``.

3. gsl. Use either conda install -c https://conda.anaconda.org/asmeurer gsl (MAC/linux) or (sudo) port install gsl (MAC) to install gsl if necessary.

4. python >= 2.6 or python>=3.4 for compiling the C extensions.

5. numpy>=1.7 for compiling the C extensions.

If python and/or numpy are not available, then the C extensions will not be compiled.

Default compiler on MAC is set to ``clang``, if you want to specify a different compiler, you will have to call ``make CC=yourcompiler``

Preferred Method

$ git clone https://github.com/manodeep/Corrfunc/
$ make
$ make install
$ python setup.py install (--user)
$ make tests

Assuming you have gcc in your PATH, make and make install should compile and install the C libraries + python extensions within the source directory. If you would like to install the python C extensions in your environment, then python setup.py install (--user) should be sufficient.

Alternative

The python package is directly installable via pip install Corrfunc. However, in that case you will lose the ability to recompile the code according to your needs. Not recommended unless you are desperate (i.e., email me if you are having install issues).

Installation notes

If compilation went smoothly, please run make tests to ensure the code is working correctly. Depending on the hardware and compilation options, the tests might take more than a few minutes. Note that the tests are exhaustive and not traditional unit tests.

While I have tried to ensure that the package compiles and runs out of the box, cross-platform compatibility turns out to be incredibly hard. If you run into any issues during compilation and you have all of the pre-requisites, please see the FAQ or email me. Also, feel free to create a new issue with the Installation label.

Clustering Measures on a Cosmological box

All codes that work on cosmological boxes with co-moving positions are located in the xi_theory directory. The various clustering measures are:

1. xi_of_r – Measures auto/cross-correlations between two boxes. The boxes do not need to be cubes.

2. xi – Measures 3-d auto-correlation in a cubic cosmological box. Assumes PERIODIC boundary conditions.

3. wp – Measures auto 2-d point projected correlation function in a cubic cosmological box. Assumes PERIODIC boundary conditions.

4. xi_rp_pi – Measures the auto/cross correlation function between two boxes. The boxes do not need to be cubes.

5. vpf – Measures the void probability function + counts-in-cells.

Clustering measures on a Mock

All codes that work on mock catalogs (RA, DEC, CZ) are located in the xi_mocks directory. The various clustering measures are:

1. DDrppi – The standard auto/cross correlation between two data sets. The outputs, DD, DR and RR can be combined using wprp to produce the Landy-Szalay estimator for \(w_p(r_p)\).

2. wtheta – Computes angular correlation function between two data sets. The outputs from DDtheta_mocks need to be combined with wtheta to get the full \(\omega(\theta)\)

3. vpf – Computes the void probability function on mocks.

Mocks

1. LINK_IN_DEC – creates binning in declination for mocks. Please check that for your desired binning in \(r_p\)/\(\theta\), this binning does not produce incorrect results (due to numerical precision).

2. LINK_IN_RA – creates binning in RA once binning in DEC has been enabled. Same numerical issues as LINK_IN_DEC

3. FAST_DIVIDE – Divisions are slow but required \(DD(r_p,\pi)\). This Makefile option (in mocks.options) replaces the divisions to a reciprocal followed by a Newton-Raphson. The code will run ~20% faster at the expense of some numerical precision. Please check that the loss of precision is not important for your use-case. Also, note that the mocks tests for \(DD(r_p, \pi)\) will fail if you enable FAST_DIVIDE.

Using the command-line interface

Navigate to the correct directory. Make sure that the options, set in either theory.options or mocks.options in the root directory are what you want. If not, edit those two files (and possibly common.mk), and recompile. Then, you can use the command-line executables in each individual subdirectory corresponding to the clustering measure you are interested in. For example, if you want to compute the full 3-D correlation function, \xi(r), then navigate to xi_theory/xi and run the executable xi. If you run executables without any arguments, the message will you tell you all the required arguments.

Calling from C

Look under the xi_theory/examples/run_correlations.c and xi_mocks/examples/run_correlations_mocks.c to see examples of calling the C API directly. If you run the executables, run_correlations and run_correlations_mocks, the output will also show how to call the command-line interface for the various clustering measures.

Calling from Python

If all went well, the codes can be directly called from python. Please see Corrfunc/call_correlation_functions.py and Corrfunc/call_correlation_functions_mocks.py for examples on how to use the Python interface. Here are a few examples:

from __future__ import print_function
import os.path as path
import numpy as np
import Corrfunc
from Corrfunc._countpairs import countpairs_wp as wp

# Setup the problem for wp
boxsize = 500.0
pimax = 40.0
nthreads = 4

# Create a fake data-set.
Npts = 100000
x = np.float32(np.random.random(Npts))
y = np.float32(np.random.random(Npts))
z = np.float32(np.random.random(Npts))
x *= boxsize
y *= boxsize
z *= boxsize

# Use a file with histogram bins, containing Nbins pairs of (rmin rmax)
binfile = path.join(path.dirname(path.abspath(Corrfunc.__file__)), "../xi_theory/tests/", "bins")

# Call wp
wp_results = wp(boxsize, pimax, nthreads, binfile, x, y, z)

# Print the results
print("###########################################")
print("##   rmin       rmax        wp       npairs")
print("###########################################")
for wp in wp_results:
    print("{0:10.4f} {1:10.4f} {2:12.6f} {3:8d}"
          .format(wp[0], wp[1], wp[3], wp[4]))