Creating Initial Conditions

Writing datasets that are valid for consumption for cosmological codes can be difficult, especially when considering how to best use units. SWIFT uses a different set of internal units (specified in your parameter file) that does not necessarily need to be the same set of units that initial conditions are specified in. Nevertheless, it is important to ensure that units in the initial conditions are all consistent with each other. To facilitate this, we use unyt arrays. The below example generates randomly placed gas particles with uniform densities.

The functionality to create initial conditions is available through the swiftsimio.writer sub-module, and the top-level swiftsimio.Writer object.

Note that the properties that swiftsimio requires in the initial conditions are the only ones that are actually read by SWIFT; other fields will be left un-read and as such should not be included in initial conditions files.

A current known issue is that due to inconsistencies with the initial conditions and simulation snapshots, swiftsimio is not actually able to read the inititial conditions that it produces. We are aiming to fix this in an upcoming release.

Example

from swiftsimio import Writer
from swiftsimio.units import cosmo_units

import unyt
import numpy as np

# Box is 100 Mpc
boxsize = 100 * unyt.Mpc

# Generate object. cosmo_units corresponds to default Gadget-oid units
# of 10^10 Msun, Mpc, and km/s
x = Writer(cosmo_units, boxsize)

# 32^3 particles.
n_p = 32**3

# Randomly spaced coordinates from 0, 100 Mpc in each direction
x.gas.coordinates = np.random.rand(n_p, 3) * (100 * unyt.Mpc)

# Random velocities from 0 to 1 km/s
x.gas.velocities = np.random.rand(n_p, 3) * (unyt.km / unyt.s)

# Generate uniform masses as 10^6 solar masses for each particle
x.gas.masses = np.ones(n_p, dtype=float) * (1e6 * unyt.msun)

# Generate internal energy corresponding to 10^4 K
x.gas.internal_energy = (
    np.ones(n_p, dtype=float) * (1e4 * unyt.kb * unyt.K) / (1e6 * unyt.msun)
)

# Generate initial guess for smoothing lengths based on MIPS
x.gas.generate_smoothing_lengths(boxsize=boxsize, dimension=3)

# If IDs are not present, this automatically generates
x.write("test.hdf5")

Then, running h5glance on the resulting test.hdf5 produces:

test.hdf5
├Header
│ └5 attributes:
│   ├BoxSize: 100.0
│   ├Dimension: array [int64: 1]
│   ├Flag_Entropy_ICs: 0
│   ├NumPart_Total: array [int64: 6]
│   └NumPart_Total_HighWord: array [int64: 6]
├PartType0
│ ├Coordinates  [float64: 32768 × 3]
│ ├InternalEnergy       [float64: 32768]
│ ├Masses       [float64: 32768]
│ ├ParticleIDs  [float64: 32768]
│ ├SmoothingLength      [float64: 32768]
│ └Velocities   [float64: 32768 × 3]
└Units
└5 attributes:
    ├Unit current in cgs (U_I): array [float64: 1]
    ├Unit length in cgs (U_L): array [float64: 1]
    ├Unit mass in cgs (U_M): array [float64: 1]
    ├Unit temperature in cgs (U_T): array [float64: 1]
    └Unit time in cgs (U_t): array [float64: 1]

Note you do need to be careful that your choice of unit system does not allow values over 2^31, i.e. you need to ensure that your provided values (with units) when written to the file are safe to be interpreted as (single-precision) floats. The only exception to this is coordinates which are stored in double precision.