First Steps
To follow this tutorial, download the “haloproperites.hdf5” and “haloparticles.hdf5” files from the OpenCosmo Google Drive and set the environment variable. 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 function:
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 directly 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)
.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.
Data Collections
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.
structures = data
.filter(oc.col("fof_halo_mass") > 1e13)
.take(1000, at="random")
for halo in structures.halos(["dm_particles", "star_particles"]):
halo_mass = halo["halo_properties"]["fof_halo_mass"]
dm_particles = halo["dm_particles"]
star_particles = halo["star_particles"]
# do_work
In each iteration, the “halo” object will be a dictionary containing a “halo_properties” dictionary as well as two opencosmo.Dataset objects, one containing the dark matter particles associated with the halos and the other containing the star particles. Because these are standard datasets, you can perform further transformaions on them as is useful for your analysis.