Python package for generating 3D intensity maps and power spectra.

This is code for research, so it may break and change often. Nevertheless, questions, suggestions, and feedback are always welcome (tony.y.li@gmail.com).

Line intensity mapping code. Produces:

• A 3D data cube, with brightness temperature defined (in uK) for each cell. See below for more info on data cube format.
• Spherically averaged spatial power spectrum

Required Python modules:

• NumPy, tested on v1.7.1. Used for handling and manipulating arrays.
• AstroPy, tested on v0.3. Used for calculations that depend on cosmology (submodules used: astropy.cosmology, astropy.constants, astropy.units).

All parameters should be specified in a single parameter file. The executable mapit.py takes the parameter file as an argument, generates mock intensity mapping data, and writes the data cube and other outputs to the location specified. For an example, navigate to the example folder and execute on the sample parameter file, like so:

$ cd example
$ ../mapit.py example.cfg

• Parameter file: Text file containing all parameter values. More details on format and parameter descriptions to come.

• Halo lightcone : This is a NumPy *.npz file containing named arrays of all dark matter halo properties in a "lightcone" volume. The file location should be provided in the parameter file, in the lightcone_path field.

Each array should have the same length (the total number of halos). Currently, the following arrays are expected:

• 'x' : x-component of halo position [Mpc/h]
• 'y' : y-component of halo position [Mpc/h]
• 'z' : z-component of halo position [Mpc/h]
• 'ra' : right ascension [deg]
• 'dec' : declination [deg]
• 'm' : halo mass [Msun/h]
• 'pid' : parent halo ID (-1 for central halos)
• 'zcos' : cosmological redshift (i.e. without redshift space distortions)
• 'zlos' : line-of-sight redshift (i.e. with redshift space distortions)

The data cube is saved as a NumPy NPZ file and is loaded like so:

import numpy as np
data = np.load("/path/to/data/cube.npz")
x, y, z, t = [ data[k] for k in ('x', 'y', 'z', 't') ]

x, y, and z are 1D numpy arrays. x and y are in arcmin, while z is in GHz.

t is a 3D numpy array with units of uK.