Reading Catalogs for Corrfunc

All of the Corrfunc routines require some sort of position arrays, X/Y/Z, as input. These arrays are expected to be 1-D arrays of type np.array. If you already have have the required numpy arrays, then you can just pass them straight to Corrfunc. If you need to read the arrays in from disk, then read on. For the command-line interface, the input files can only be in ASCII or fast-food format (for description of fast-food binaries, see Fast-food binary format).

• Fast-food binary format

Reading from ASCII files

This is the most straight forward way – you need an ASCII file with columns X/Y/Z (white-space separated).

Using numpy.genfromtxt

import numpy as np
fname = "myfile_containing_xyz_columns.dat"

# For double precision calculations
dtype = np.float64  ## change to np.float32 for single precision

X, Y, Z = np.genfromtxt(fname, dtype=dtype, unpack=True)

Note

Corrfunc.read_catalog uses this exact code-snippet to read in ASCII files in python.

Reading from fast-food files

If you are using the command-line interface, then the code will have to read the arrays from files. While Corrfunc natively supports both ASCII and fast-food formats (for description of fast-food binaries, see Fast-food binary format), the following python utility is intended to read both these types of files.

Using utility: Corrfunc.io.read_catalog

Corrfunc.io.read_catalog can directly read ASCII files or fast-food binary files.

from Corrfunc.io import read_catalog

# Read the standard theory catalog (on a box)
# supplied with Corrfunc
X, Y, Z = read_catalog()

# Read some other format -> have to specify
# filename
fname = "myfile_containing_xyz_columns.dat"
X, Y, Z = read_catalog(fname)