swiftsimio 1.2.0

SWIFTsim (swift.dur.ac.uk) i/o routines for python.

SWIFTsimIO

The SWIFT astrophysical simulation code (http://swift.dur.ac.uk) is used widely. There exists many ways of reading the data from SWIFT, which outputs HDF5 files. These range from reading directly using h5py to using a complex system such as yt; however these either are unsatisfactory (e.g. a lack of unit information in reading HDF5), or too complex for most use-cases. This (thin) wrapper provides an object-oriented API to read (dynamically) data from SWIFT.

Requirements

This requires python3.6.0 or higher. No effort will be made to support python versions below this. Please update your systems.

Python packages

• h5py
• unyt

Usage

Example usage is shown below, which plots a density-temperature phase diagram, with density and temperature given in CGS units:

import swiftsimio as sw

# This loads all metadata but explicitly does _not_ read any particle data yet
data = sw.load("/path/to/swift/output")

import matplotlib.pyplot as plt

data.gas.density.convert_to_cgs()
data.gas.temperature.convert_to_cgs()

plt.loglog()

plt.scatter(
    data.gas.density,
    data.gas.temperature,
    s=1
)

plt.xlabel(fr"Gas density $\left[{data.gas.density.units.latex_repr}\right]$")
plt.ylabel(fr"Gas temperature $\left[{data.gas.temperature.units.latex_repr}\right]$")

plt.tight_layout()

plt.savefig("test_plot.png", dpi=300)

In the above it's important to note the following:

• All metadata is read in when the load function is called.
• Only the density and temperature (corresponding to the PartType0/Density and PartType0/Temperature) datasets are read in.
• That data is only read in once the convert_to_cgs method is called.
• convert_to_cgs converts data in-place; i.e. it returns None.
• The data is cached and not re-read in when plt.scatter is called.

Writing datasets

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.

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.

Ahead-of-time Masking

SWIFT snapshots contain cell metadata that allow us to spatially mask the data ahead of time. swiftsimio provides a number of objects that help with this. See the example below.

import swiftsimio as sw

# This creates and sets up the masking object.
mask = sw.mask("/path/to/swift/snapshot")

# This ahead-of-time creates a spatial mask based on the cell metadata.
mask.constrain_spatial([[0.2 * mask.metadata.boxsize[0], 0.7 * mask.metadata.boxsize[0]], None, None])

# Now, just for fun, we also constrain the density between 0.4 g/cm^3 and 0.8. This reads in
# the relevant data in the region, and tests it element-by-element.
density_units = mask.units.mass / mask.units.length**3
mask.constrain_mask("gas", "density", 0.4 * density_units, 0.8 * density_units)

# Now we can grab the actual data object. This includes the mask as a parameter.
data = sw.load("/Users/josh/Documents/swiftsim-add-anarchy/examples/SodShock_3D/sodShock_0001.hdf5", mask=mask)

When the attributes of this data object are accessed, transparently only the ones that belong to the masked region (in both density and spatial) are read. I.e. if I ask for the temperature of particles, it will recieve an array containing temperatures of particles that lie in the region [0.2, 0.7] and have a density between 0.4 and 0.8 g/cm^3.

User-defined particle types

It is now possible to add user-defined particle types that are not already present in the swiftsimio metadata. All you need to do is specify the three names (see below) and then the particle datasets that you have provided in SWIFT will be automatically read.

import swiftsimio as sw
import swiftsimio.metadata.particle as swp
from swiftsimio.objects import cosmo_factor, a

swp.particle_name_underscores[6] = "extratype"
swp.particle_name_class[6] = "Extratype"
swp.particle_name_text[6] = "Extratype"

data = sw.load(
    "extra_test.hdf5",
)

Previously, there was a generator function API that performed this task; this has now been removed (last version was v0.8.0).

Image Creation

swiftsimio provides some very basic visualisation software that is designed to create projections of the entire box along the z-axis, with x and y representing the final square. Note that it only currently supports square boxes. These are accelerated with numba, and we do not suggest using these routines unless you have it installed. Finally, you can use parallel versions of these functions (by default they run in serial) by passing the argument parallel=True.

You can do the following:

from swiftsimio import load
from swiftsimio.visualisation import project_gas

data = load("my_snapshot_0000.hdf5")
# This creates a grid that has units K / Mpc^2, and can be transformed like
# any other unyt quantity
temperature_map = project_gas(data, resolution=1024, project="temperature")

from matplotlib.pyplot import imsave
from matplotlib.colors import LogNorm

# There is huge dynamic range usually in temperature, so log normalize it before
# saving.
imsave("temperature_map.png", LogNorm()(temperature_map.value), cmap="twilight")

It's also possible to create videos in a fairly straightforward way:

from swiftsimio import load
from swiftsimio.visualisation import project_gas_pixel_grid

# project_gas_pixel_grid does not perform the unit calculation.

from p_tqdm import p_map

def load_and_make_image(number):
    filename = f"snapshot_{number:04d}.hdf5"
    data = load(filename)
    image = project_gas_pixel_grid(data, 1024, "temperature")

    return image

# Make frames in parallel (reading also parallel!)
frames = p_map(load_and_make_image, range(0, 120))

#... You can do your funcAnimation stuff here now you have the frames.

It is also possible to create slice plots, rather than projections.

from swiftsimio import load
from swiftsimio.visualisation import slice_gas

data = load("my_snapshot_0000.hdf5")
# This creates a grid that has units K / Mpc^3, and can be transformed like
# any other unyt quantity. The slice variable gives where we want to slice through
# as a function of the box-size, so here we slice through the centre of the box.
temperature_map = slice_gas(data, slice=0.5, resolution=1024, project="temperature")

from matplotlib.pyplot import imsave
from matplotlib.colors import LogNorm

# There is huge dynamic range usually in temperature, so log normalize it before
# saving.
imsave("temperature_map.png", LogNorm()(temperature_map.value), cmap="twilight")

Finally, we provide a wrapper of the ever-popular py-sphviewer for easy use with swiftsimio datasets. Particle datasets that do not contain smoothing lengths will have them generated through the use of the scipy cKDTree. You can get access to the objects through a sub-module as follows:

from swiftsimio import load
from swiftsimio.visualisation.sphviewer import SPHViewerWrapper

data = load("my_snapshot_0000.hdf5")

resolution = 2048

gas = SPHViewer(data.gas, smooth_over="masses")
gas_temp = SPHViewer(
    data.gas,
    smooth_over=data.gas.masses * data.gas.temperatures
)
dark_matter = SPHViewer(data.dark_matter, smooth_over="masses")

gas.quick_view(xsize=resolution, ysize=resolution, r="infinity")
gas_temp.quick_view(xsize=resolution, ysize=resolution, r="infinity")
dark_matter.quick_view(xsize=resolution, ysize=resolution, r="infinity")

plt.imsave("gas_image.png", gas.image)
plt.imsave("gas_temp.png", gas_temp.image / gas.image)
plt.imsave("dm_image.png", dark_matter.image)