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.
The OpenCosmo library is available for Python 3.11 and up on Linux and MacOS (and Windows via WSL). It can be installed easily with pip:
pip install opencosmoThere'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 and install opencosmo into it automatically:
conda create -n opencosmo_env conda-forge::opencosmo
conda activate opencosmo_envor if you already have a virtual environment to use:
conda install conda-forge::opencosmoIf you plan to use opencosmo in a Jupyter notebook, you can install the ipykernel package to make the environment available as a kernel:
pip install ipykernel # can also be installed with conda
python -m ipykernel install --user --name=opencosmoBe sure you have run the "activate" command shown above before running the ipykernel command.
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 open the data with the open command:
import opencosmo as oc
dataset = oc.open("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 open 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.cosmologyThe 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.
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("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 in data.halos():
halo_properties = halo["halo_properties"]
dm_particles = halo["dm_particles"]
star_particles = halo["star_particles"]In each iteration, "halo properties" will be a dictionary containing the properties of the halo (such as its total mass), while "dm_particles" and "star_particles" will be OpenCosmo datasets containing the dark matter and stars associated with the halo, respectively. 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.
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.
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.