The OpenCosmo Python Toolkit provides utilities for reading, writing and manipulating data from cosmological simulations produced by the Cosmolgical Physics and Advanced Computing (CPAC) group at Argonne National Laboratory. It can be used to work with smaller quantities data retrieved with the CosmoExplorer, as well as the much larget datasets these queries draw from. The OpenCosmo toolkit integrates with standard tools such as AstroPy, and allows you to manipulate data in a fully-consistent cosmological context.

Installation

The OpenCosmo library is available for Python 3.11 and later and can be installed with pip:

pip install opencosmo

There's a good chance the default version of Python on your system is less than 3.11. Whether or not this is the case, we recommend installing opencosmo into a virtual environment. If you're using Conda, you can create a new environment with Python 3.11 and install opencosmo into it like so:

conda create -n opencosmo_env python=3.11
conda activate opencosmo_env
pip install opencosmo

This will create a new environment called opencosmo with Python 3.11 and install the opencosmo package into it with all necessary dependencies. If you plan to use opencosmo in a Jupyter notebook, you can install the ipykernel package to make the environment available as a kernel:

conda install ipykernel
python -m ipykernel install --user --name=opencosmo

Be sure you have run the "activate" command shown above before running the ipykernel command.

Getting Started

To get started, download the "haloproperites.hdf5" from the OpenCosmo Google Drive. This file contains properties of dark-matter halos from a small hydrodynamical simulation run with HACC. You can easily read the data with the open command:

import opencosmo as oc

dataset = oc.read("haloproperties.hdf5")
print(dataset)

OpenCosmo Dataset (length=237441)
Cosmology: FlatLambdaCDM(name=None, H0=<Quantity 67.66 km / (Mpc s)>, Om0=0.3096446816186967, Tcmb0=<Quantity 0. K>, Neff=3.04, m_nu=None, Ob0=0.04897468161869667)
First 10 rows:
block fof_halo_1D_vel_disp fof_halo_center_x ... sod_halo_sfr unique_tag
             km / s               Mpc        ... solMass / yr
int32       float32             float32      ...   float32      int64
----- -------------------- ----------------- ... ------------ ----------
    0            32.088795         1.4680439 ...       -101.0      21674
    0             41.14525        0.19616994 ...       -101.0      44144
    0             73.82962         1.5071135 ...    3.1447952      48226
    0             31.17231         0.7526525 ...       -101.0      58472
    0            23.038841         5.3246417 ...       -101.0      60550
    0            37.071426         0.5153746 ...       -101.0     537760
    0            26.203058         2.1734374 ...       -101.0     542858
    0              78.7636         2.1477687 ...          0.0     548994
    0             37.12636         6.9660196 ...       -101.0     571540
    0             58.09235          6.072006 ...    1.5439711     576648

The read function returns a Dataset object, which holds the raw data as well as information about the simulation. You can easily access the data and cosmology as Astropy objects:

dataset.data
dataset.cosmology

The first will return an astropy table of the data, with all associated units already applied. The second will return the astropy cosmology object that represents the cosmology the simulation was run with.

Basic Querying

Although you can access data directly, opencosmo provides tools for querying and transforming the data in a fully cosmology-aware context. For example, suppose we wanted to plot the concentration-mass relationship for the halos in our simulation above a certain mass. One way to perform this would be as follows:

dataset = dataset
    .filter(oc.col("fof_halo_mass") > 1e13)
    .take(1000, at="random")
    .select(("fof_halo_mass", "sod_halo_cdelta"))

print(dataset)

OpenCosmo Dataset (length=1000)
Cosmology: FlatLambdaCDM(name=None, H0=<Quantity 67.66 km / (Mpc s)>, Om0=0.3096446816186967, Tcmb0=<Quantity 0. K>, Neff=3.04, m_nu=None, Ob0=0.04897468161869667)
First 10 rows:
 fof_halo_mass   sod_halo_cdelta
    solMass
    float32          float32
---------------- ---------------
11220446000000.0       4.5797048
17266723000000.0       7.4097505
51242150000000.0       1.8738283
70097712000000.0       4.2764015
51028305000000.0        2.678151
11960567000000.0       3.9594727
15276915000000.0        5.793542
16002001000000.0       2.4318497
47030307000000.0       3.7146702
15839942000000.0        3.245569

We could then plot the data, or perform further transformations. This is cool on its own, but the real power of opencosmo comes from its ability to work with different data types. Go ahead and download the "haloparticles" file from the OpenCosmo Google Drive and try the following:

import opencosmo as oc

data = oc.open_linked_files("haloproperties.hdf5", "haloparticles.hdf5")

This will return a data collection that will allow you to query and transform the data as before, but will associate the halos with their particles.

data = data
    .filter(oc.col("fof_halo_mass") > 1e13)
    .take(1000, at="random")

for halo_properties, halo_particles in data.objects(["dm_particles", "star_particles"]):
    # do work

In each iteration, "halo properties" will be a dictionary containing the properties of the halo, while "halo_particles" will be a dictionary of datasets, one for the dark matter particles and one for the star particles. Because these are just like the dataset object we saw eariler, we can further query and transform the particles as needed for our analysis. For more details on how to use the library, check out the full documentation.

Testing

To run tests, first download the test data from Google Drive. Set environment variable OPENCOSMO_DATA_PATH to the path where the data is stored. Then run the tests with pytest:

export OPENCOSMO_DATA_PATH=/path/to/data
# From the repository root
pytest --ignore test/parallel 

Although opencosmo does support multi-core processing via MPI, the default installation does not include the necessary dependencies to work in an MPI environment. If you need these capabilities, check out the guide in our documentation.

Contributing

We welcome bug reports and feature requests from the community. If you would like to contribute to the project, please check out the contributing guide for more information.