pyread-eagle 1.0.2

Pure-python port of J. Helly's read_eagle.

Read routines for Peano-Hilbert key sorted Eagle snapshots

Pure-python port of J. Helly's read_eagle.

Quickstart

If you're used to using the original read_eagle, all you should need to do is change import statements from import read_eagle to import pyread_eagle as read_eagle, or similar (from pyread_eagle import EagleSnapshot, etc.). Existing scripts should work seamlessly with this change.

Installation

Install from pip with:

• 'pip install pyread_eagle'

Install from github:

• Download via web UI, or 'git clone https://github.com/kyleaoman/pyread_eagle.git'
• Install dependencies if necessary (see 'setup.py'), some may be found in other repositories by kyleaoman.
• Global install (Linux):
  • cd to directory with 'setup.py'
  • run 'sudo pip install -e .' (-e installs via symlink, so pulling repository will do a 'live' update of the installation)
• User install (Linux):
  • cd to directory with 'setup.py'
  • ensure '~/lib/python3.7/site-packages' or similar is on your PYTHONPATH (e.g. 'echo $PYTHONPATH'), if not, add it (perhaps in .bash_profile or similar)
  • run 'pip install --prefix ~ -e .' (-e installs via symlink, so pulling repository will do a 'live' update of the installation)
• cd to a directory outside the module and launch python; you should be able to do 'from pyread_eagle import *'

Alternately, pip can retrieve and install the version on github directly:

• 'pip3 install git+https://github.com/kyleaoman/pyread_eagle.git'

Documentation

The documentation below is adapted from the original read_eagle readme file.

These routines can be used if Gadget was run with the -DEAGLE_SORT_OUTPUT flag enabled. They provide a quick way to read in spatial regions without having to read all the data or even open all of the files.

It works by splitting the simulation volume into a grid and ensuring that particles in the same grid cell are stored consecutively in the snapshot files. Some extra datasets are added to the snapshot which specify which grid cells are stored in which files and the location of the start of each cell in the file.

The procedure to read in data is as follows:

• Open the snapshot by specifying the name of one file
• Flag the grid cells to be read in by calling select_region one or more times.
• Call read_dataset once for each quantity to be read, specifying which particle type to read and the hdf5 dataset name.

You can then either close/delete the snapshot object or call clear_selection to read in a different region.

Any of the datasets in the PartTypeX groups can be read in. The code makes no assumptions about what datasets are present in the snapshot.

Unlike the original read_eagle, this version is written directly in python and therefore does not depend on having a properly configured C compiler or hdf5 libraries, all that is needed is python, numpy and h5py.

Python2 is not supported. I think pyread_eagle will run in python2.7, but since python2 is deprecated as of Jan 2020, I have not and have no intention to test this.

Simple example usage

from pyread_eagle import EagleSnapshot

snap = EagleSnapshot("./snap_020.0.hdf5")
snap.select_region(4.0, 5.0, 2.0, 3.0, 3.0, 4.0)
xyz = snap.read_dataset(0, "Coordinates")
ids = snap.read_dataset(0, "ParticleIDs")
del snap

Speed

pyread_eagle is comparable in speed to read_eagle. The actual read_dataset calls vary depending on the region (how contiguous it is in the dataset) between very roughly 2x faster and 5x slower than the C version. Given the limited flexibility of the h5py API, I believe it will be difficult to speed this up further.

Error handling

If a routine fails it will raise an exception. Because of differences in the structure of the code relative to read_eagle, not all the same sanity checks are made, not all the same errors can arise, and therefore not all the same error messages may appear. I have also chosen to be more lax and not try to catch every edge case by hand, rather letting failures happen and be reported organically by the underlying python libraries. Please report any errors whose messages are obscure, and of course any bugs.

Random sampling

Note that the output of pyread_eagle will differ from the original version when the sampling rate is set < 1.0. In the original version, a subsample was drawn by comparing a pseudo-random number to the sampling rate for each particle. This resulted in a subsample of approximately the requested fraction of particles. Since the seed was set explicitly, the results were reproducible. Since pyread_eagle uses the numpy PRNG, the output cannot match what would be obtained using the C PRNG. Instead, a random sample of consistent length is drawn (the seed is still set explicitly, so results remain reproducible run-to-run), equal to floor(number of particle * sampling rate), evaluated file-by-file. So, the overall total number of particles times the sampling rate will not always be equal to the length of the output array, due to rounding errors (the largest possible difference is the number of files in the snapshot).

Description of the routines and parameters

Open snapshot

snap = pyread_eagle.EagleSnapshot(fname, verbose=False)

This opens the snapshot which contains the specified file. Closing explicitly is not necessary: in python the snapshot will be automatically deallocated once there are no more references to it.

Parameters

• fname: name of any one file in the snapshot
• verbose: if True, print extra messages during function calls (default: False)

Return value An instance of the EagleSnapshot class

Close snapshot

del snap snap.close()

Deallocates memory associated with the snap object. Not strictly necessary - will happen automatically when the snap variable goes out of scope. Attempting to call methods of a closed snapshot raises an EagleSnapshotClosedException.

Region selection

snap.select_region(xmin, xmax, ymin, ymax, zmin, zmax)

All grid cells overlapping the specified region are flagged to be read in by subsequent read_dataset calls. You can call select_region multiple times to make oddly shaped or disjoint selections.

If selected regions overlap or the same region is selected multiple times particles in these regions will still only be read in once.

Parameters

• xmin: the minimum x coordinate of the region to read
• xmax: the maximum x coordinate of the region to read
• ymin: the minimum y coordinate of the region to read
• ymax: the maximum y coordinate of the region to read
• zmin: the minimum z coordinate of the region to read
• zmax: the maximum z coordinate of the region to read

snap.select_grid_cells(ixmin, ixmax, iymin, iymax, izmin, izmax)

All grid cells in the specified range of grid coordinates are flagged to be read in by subsequent read_dataset calls. You can call select_grid_cells multiple times to make oddly shaped or disjoint selections.

The coordinates ixmin, ixmax etc are integer coordinates in the hash grid, starting from zero. The maximum coordinate is (2**hashbits)-1. The value of hashbits is stored in snap.hashbits.

If selected regions overlap or the same region is selected multiple times particles in these regions will still only be read in once.

Parameters

• ixmin: the minimum x coordinate of the region to read
• ixmax: the maximum x coordinate of the region to read
• iymin: the minimum y coordinate of the region to read
• iymax: the maximum y coordinate of the region to read
• izmin: the minimum z coordinate of the region to read
• izmax: the maximum z coordinate of the region to read

snap.select_rotated_region(centre, xvec, yvec, zvec, length)

Selects a box-shaped region rotated relative to the simulation box axes. The [xyz]vec parameters should be a set of mutually orthogonal unit vectors defining the axes of the box to be selected.

Parameters

• centre: coordinate centre of the region to select, shape (3, )
• xvec: selection box 'x' unit vector in simulation box coordinates, shape (3, )
• yvec: selection box 'y' unit vector in simulation box coordinates, shape (3, )
• zvec: selection box 'z' unit vector in simulation box coordinates, shape (3, )
• length: selection box lengths (not half-length) along each axis, shape (3, )

Count particles

n = snap.count_particles(itype)

This returns the number of particles of the specified type which will be read by the next read_dataset call. Note that only whole grid cells can be read so some particles outside the selected region may be read in. These are included in the count.

In python this routine is not usually needed, but it is included for compatibility with codes formerly using the read_eagle python wrapper.

Parameters

• itype: which particle type to count (integer, 0-5)

Return value The number of particles to be read in

Get particle locations

file_index, file_offset = snap.get_particle_locations(itype)

This returns two arrays which each have one element for each selected particle. file_index contains the index of the file each particle is in. file_offset contains the position in the file, numbering from zero.

Parameters

• itype: which particle type to count (integer, 0-5)

Return value file_index - integer array with index of the file containing each particle file_offset - integer array with position of each particle in its file

Read datasets

data = snap.read_dataset(itype, name)

This reads in the specified dataset for all particles of type itype in the selected region(s). Use repeated calls to read in multiple datasets.

The type of array you get back reflects the type of the dataset in the file.

Parameters

• itype: which particle type to read (integer, 0-5)
• name: the HDF5 name of the dataset, relative to the PartTypeX group

Return value The contents of the dataset for the selected particles

data = snap.read_extra_dataset(itype, name, basename)

This is the same as read_dataset, except that it reads from a set of "auxiliary files" comprised of the same number of parts as the snapshot and which contain additional datasets with the same sorting, distribution across files, etc. as the datasets in the main snapshot files.

Parameters

• itype: which particle type to read (integer, 0-5)
• name: the HDF5 name of the dataset, relative to the PartTypeX group
• basename: name (including path, if necessary) of the auxiliary files, omitting the .X.hdf5 portion

Clear selection

snap.clear_selection()

This clears the flags which specify which grid cells should be read in on the next read_dataset() call. If you've already read in a region and you want to read a different region you should call clear_selection() before calling select_region() again.

Split selection

snap.split_selection(ThisTask, NTask)

This is for use in MPI programs to allow parallel I/O. When running under MPI all read_eagle routines are collective - they must be called on all processes.

The procedure to read a region in parallel is as follows:

1. All processes open the snapshot
2. All processes select the SAME region
3. Call split_selection. This causes each processor to select a subset of the originally selected particles.
4. Call read_dataset() on all processors to read the required quantities

This results in all of the particles in the specified region being read in exactly once with the data spread across the MPI processes.

I have not made any attempt to use pyread_eagle with e.g. mpi4py or other MPI-ish implementations in python. If you succeed in this, please get in touch!

Parameters

• ThisTask: rank of this process in an MPI program
• NTask: number of processes in an MPI program

Set sampling rate

snap.set_sampling_rate(rate)

If rate is set < 1 before reading datasets, a random subsample of particles will be read, with the fraction of particles read set by the rate. See the note on random subsamples (above).

Parameters

• rate: fraction of particles to be read (rate >= 1 for all particles)

List datasets

dsets = snap.datasets(itype)

Returns a list of the available datasets for a given particle type. This can be iterated to read all datasets, for instance.

Parameters

• itype: which particle type to read (integer, 0-5)