Loading Data

This section contains information on how to load data into yt, as well as some important caveats about different data formats.

Sample Data

The yt community has provided a large number of sample datasets, which are accessible from https://yt-project.org/data/ . yt also provides a helper function, yt.load_sample, that can load from a set of sample datasets. The quickstart notebooks in this documentation utilize this.

The files are, in general, named identically to their listings on the data catalog page. For instance, you can load IsolatedGalaxy by executing:

import yt

ds = yt.load_sample("IsolatedGalaxy")

To find a list of all available datasets, you can call load_sample without any arguments, and it will return a list of the names that can be supplied:

import yt

yt.load_sample()

This will return a list of possible filenames; more information can be accessed on the data catalog.

Archived Data

If your data is stored as a (compressed) tar file, you can access the contained dataset directly without extracting the tar file. This can be achieved using the load_archive function:

import yt

ds = yt.load_archive("IsolatedGalaxy.tar.gz", "IsolatedGalaxy/galaxy0030/galaxy0030")

The first argument is the path to the archive file, the second one is the path to the file to load in the archive. Subsequent arguments are passed to yt.load.

The functionality requires the package ratarmount to be installed. Under the hood, yt will mount the archive as a (read-only) filesystem. Note that this requires the entire archive to be read once to compute the location of each file in the archive; subsequent accesses will be much faster. All archive formats supported by ratarmount should be loadable, provided the dependencies are installed; this includes tar, tar.gz and tar.bz2`` formats.

Simple HDF5 Data

Note

This wrapper takes advantage of the functionality described in Loading Data via Functions but the basics of setting up function handlers, guessing fields, etc, are handled by yt.

Using the function yt.loaders.load_hdf5_file(), you can load a generic set of fields from an HDF5 file and have a fully-operational yt dataset. For instance, in the yt sample data repository, we have the UniGrid Data dataset (~1.6GB). This dataset includes the file turb_vels.h5 with this structure:

$ h5ls -r h5ls -r ./UnigridData/turb_vels.h5

/                        Group
/Bx                      Dataset {256, 256, 256}
/By                      Dataset {256, 256, 256}
/Bz                      Dataset {256, 256, 256}
/Density                 Dataset {256, 256, 256}
/MagneticEnergy          Dataset {256, 256, 256}
/Temperature             Dataset {256, 256, 256}
/turb_x-velocity         Dataset {256, 256, 256}
/turb_y-velocity         Dataset {256, 256, 256}
/turb_z-velocity         Dataset {256, 256, 256}
/x-velocity              Dataset {256, 256, 256}
/y-velocity              Dataset {256, 256, 256}
/z-velocity              Dataset {256, 256, 256}

In versions of yt prior to 4.1, these could be loaded into memory individually and then accessed en masse by the yt.loaders.load_uniform_grid() function. Introduced in version 4.1, however, was the ability to provide the filename and then allow yt to identify the available fields and even subset them into chunks to preserve memory. Only those requested fields will be loaded at the time of the request, and they will be subset into chunks to avoid over-allocating for reduction operations.

To use the auto-loader, call load_hdf5_file() with the name of the file. Optionally, you can specify the root node of the file to probe for fields – for instance, if all of the fields are stored under /grid (as they are in output from the ytdata frontend). You can also provide the expected bounding box, which will otherwise default to 0..1 in all dimensions, the names of fields to make available (by default yt will probe for them) and the number of chunks to subdivide the file into. If the number of chunks is not specified it defaults to trying to keep the size of each individual chunk no more than $64^3$ zones.

To load the above file, we would use the function as follows:

import yt

ds = yt.load_hdf5_file("UnigridData/turb_vels.h5")

At this point, we now have a dataset that we can do all of our normal operations on, and all of the known yt derived fields will be available.

AMRVAC Data

To load data to yt, simply use

import yt
ds = yt.load("output0010.dat")

Dataset geometry & periodicity

Starting from AMRVAC 2.2, and datfile format 5, a geometry flag (e.g. “Cartesian_2.5D”, “Polar_2D”, “Cylindrical_1.5D”…) was added to the datfile header. yt will fall back to a cartesian mesh if the geometry flag is not found. For older datfiles however it is possible to provide it externally with the geometry_override parameter.

# examples
ds = yt.load("output0010.dat", geometry_override="polar")
ds = yt.load("output0010.dat", geometry_override="cartesian")

Note that geometry_override has priority over any geometry flag present in recent datfiles, which means it can be used to force r VS theta 2D plots in polar geometries (for example), but this may produce unpredictable behaviour and comes with no guarantee.

A ndim-long periodic boolean array was also added to improve compatibility with yt. See http://amrvac.org/md_doc_fileformat.html for details.

Auto-setup for derived fields

Yt will attempt to mimic the way AMRVAC internally defines kinetic energy, pressure, and sound speed. To see a complete list of fields that are defined after loading, one can simply type

print(ds.derived_field_list)

Note that for adiabatic (magneto-)hydrodynamics, i.e. (m)hd_energy = False in AMRVAC, additional input data is required in order to setup some of these fields. This is done by passing the corresponding parfile(s) at load time

# example using a single parfile
ds = yt.load("output0010.dat", parfiles="amrvac.par")

# ... or using multiple parfiles
ds = yt.load("output0010.dat", parfiles=["amrvac.par", "modifier.par"])

In case more than one parfile is passed, yt will create a single namelist by replicating AMRVAC’s rules (see “Using multiple par files” http://amrvac.org/md_doc_commandline.html).

Unit System

AMRVAC only supports dimensionless fields and as such, no unit system is ever attached to any given dataset. yt however defines physical quantities and give them units. As is customary in yt, the default unit system is cgs, e.g. lengths are read as “cm” unless specified otherwise.

The user has two ways to control displayed units, through unit_system ("cgs", "mks" or "code") and units_override. Example:

units_override = dict(length_unit=(100.0, "au"), mass_unit=yt.units.mass_sun)
ds = yt.load("output0010.dat", units_override=units_override, unit_system="mks")

To ensure consistency with normalisations as used in AMRVAC we only allow overriding a maximum of three units. Allowed unit combinations at the moment are

{numberdensity_unit, temperature_unit, length_unit}
{mass_unit, temperature_unit, length_unit}
{mass_unit, time_unit, length_unit}
{numberdensity_unit, velocity_unit, length_unit}
{mass_unit, velocity_unit, length_unit}

Appropriate errors are thrown for other combinations.

Partially supported and unsupported features

• a maximum of 100 dust species can be read by yt at the moment. If your application needs this limit increased, please report an issue https://github.com/yt-project/yt/issues

• particle data: currently not supported (but might come later)

• staggered grids (AMRVAC 2.2 and later): yt logs a warning if you load staggered datasets, but the flag is currently ignored.

• “stretched grids” are being implemented in yt, but are not yet fully-supported. (Previous versions of this file suggested they would “never” be supported, which we hope to prove incorrect once we finish implementing stretched grids in AMR. At present, stretched grids are only supported on a single level of refinement.)

Note

Ghost cells exist in .dat files but never read by yt.

ART Data

ART data has been supported in the past by Christopher Moody and is currently cared for by Kenza Arraki. Please contact the yt-dev mailing list if you are interested in using yt for ART data, or if you are interested in assisting with development of yt to work with ART data.

To load an ART dataset you can use the yt.load command and provide it the gas mesh file. It will search for and attempt to find the complementary dark matter and stellar particle header and data files. However, your simulations may not follow the same naming convention.

import yt

ds = yt.load("D9p_500/10MpcBox_HartGal_csf_a0.500.d")

It will search for and attempt to find the complementary dark matter and stellar particle header and data files. However, your simulations may not follow the same naming convention.

For example, the single snapshot given in the sample data has a series of files that look like this:

10MpcBox_HartGal_csf_a0.500.d  #Gas mesh
PMcrda0.500.DAT                #Particle header
PMcrs0a0.500.DAT               #Particle data (positions,velocities)
stars_a0.500.dat               #Stellar data (metallicities, ages, etc.)

The ART frontend tries to find the associated files matching the above, but if that fails you can specify file_particle_header, file_particle_data, and file_particle_stars, in addition to specifying the gas mesh. Note that the pta0.500.dat or pt.dat file containing particle time steps is not loaded by yt.

You also have the option of gridding particles and assigning them onto the meshes. This process is in beta, and for the time being, it’s probably best to leave do_grid_particles=False as the default.

To speed up the loading of an ART file, you have a few options. You can turn off the particles entirely by setting discover_particles=False. You can also only grid octs up to a certain level, limit_level=5, which is useful when debugging by artificially creating a ‘smaller’ dataset to work with.

Finally, when stellar ages are computed we ‘spread’ the ages evenly within a smoothing window. By default this is turned on and set to 10Myr. To turn this off you can set spread=False, and you can tweak the age smoothing window by specifying the window in seconds, spread=1.0e7*365*24*3600.

There is currently preliminary support for dark matter only ART data. To load a dataset use the yt.load command and provide it the particle data file. It will search for the complementary particle header file.

import yt

ds = yt.load("PMcrs0a0.500.DAT")

Important: This should not be used for loading just the dark matter data for a ‘regular’ hydrodynamical data set as the units and IO are different!

ARTIO Data

ARTIO data has a well-specified internal parameter system and has few free parameters. However, for optimization purposes, the parameter that provides the most guidance to yt as to how to manage ARTIO data is max_range. This governs the maximum number of space-filling curve cells that will be used in a single “chunk” of data read from disk. For small datasets, setting this number very large will enable more data to be loaded into memory at any given time; for very large datasets, this parameter can be left alone safely. By default it is set to 1024; it can in principle be set as high as the total number of SFC cells.

To load ARTIO data, you can specify a command such as this:

ds = load("./A11QR1/s11Qzm1h2_a1.0000.art")

Athena Data

Athena 4.x VTK data is supported and cared for by John ZuHone. Both uniform grid and SMR datasets are supported.

Note

yt also recognizes Fargo3D data written to VTK files as Athena data, but support for Fargo3D data is preliminary.

Loading Athena datasets is slightly different depending on whether your dataset came from a serial or a parallel run. If the data came from a serial run or you have joined the VTK files together using the Athena tool join_vtk, you can load the data like this:

import yt

ds = yt.load("kh.0010.vtk")

The filename corresponds to the file on SMR level 0, whereas if there are multiple levels the corresponding files will be picked up automatically, assuming they are laid out in lev* subdirectories under the directory where the base file is located.

For parallel datasets, yt assumes that they are laid out in directories named id*, one for each processor number, each with lev* subdirectories for additional refinement levels. To load this data, call load with the base file in the id0 directory:

import yt

ds = yt.load("id0/kh.0010.vtk")

which will pick up all of the files in the different id* directories for the entire dataset.

The default unit system in yt is cgs (“Gaussian”) units, but Athena data is not normally stored in these units, so the code unit system is the default unit system for Athena data. This means that answers to field queries from data objects and plots of data will be expressed in code units. Note that the default conversions from these units will still be in terms of cgs units, e.g. 1 code_length equals 1 cm, and so on. If you would like to provided different conversions, you may supply conversions for length, time, and mass to load using the units_override functionality:

import yt

units_override = {
    "length_unit": (1.0, "Mpc"),
    "time_unit": (1.0, "Myr"),
    "mass_unit": (1.0e14, "Msun"),
}

ds = yt.load("id0/cluster_merger.0250.vtk", units_override=units_override)

This means that the yt fields, e.g. ("gas","density"), ("gas","velocity_x"), ("gas","magnetic_field_x"), will be in cgs units (or whatever unit system was specified), but the Athena fields, e.g., ("athena","density"), ("athena","velocity_x"), ("athena","cell_centered_B_x"), will be in code units.

The default normalization for various magnetic-related quantities such as magnetic pressure, Alfven speed, etc., as well as the conversion between magnetic code units and other units, is Gaussian/CGS, meaning that factors of \(4\pi\) or \(\sqrt{4\pi}\) will appear in these quantities, e.g. \(p_B = B^2/8\pi\). To use the Lorentz-Heaviside normalization instead, in which the factors of \(4\pi\) are dropped (\(p_B = B^2/2), for example), set ``magnetic_normalization="lorentz_heaviside"`\) in the call to yt.load:

ds = yt.load(
    "id0/cluster_merger.0250.vtk",
    units_override=units_override,
    magnetic_normalization="lorentz_heaviside",
)

Some 3D Athena outputs may have large grids (especially parallel datasets subsequently joined with the join_vtk script), and may benefit from being subdivided into “virtual grids”. For this purpose, one can pass in the nprocs parameter:

import yt

ds = yt.load("sloshing.0000.vtk", nprocs=8)

which will subdivide each original grid into nprocs grids. Note that this parameter is independent of the number of MPI tasks assigned to analyze the data set in parallel (see Parallel Computation With yt), and ideally should be (much) larger than this.

Note

Virtual grids are only supported (and really only necessary) for 3D data.

Alternative values for the following simulation parameters may be specified using a parameters dict, accepting the following keys:

• gamma: ratio of specific heats, Type: Float. If not specified, \(\gamma = 5/3\) is assumed.

• geometry: Geometry type, currently accepts "cartesian" or "cylindrical". Default is "cartesian".

• periodicity: Is the domain periodic? Type: Tuple of boolean values corresponding to each dimension. Defaults to True in all directions.

• mu: mean molecular weight, Type: Float. If not specified, \(\mu = 0.6\) (for a fully ionized primordial plasma) is assumed.

import yt

parameters = {
    "gamma": 4.0 / 3.0,
    "geometry": "cylindrical",
    "periodicity": (False, False, False),
}

ds = yt.load("relativistic_jet_0000.vtk", parameters=parameters)

Caveats

• yt primarily works with primitive variables. If the Athena dataset contains conservative variables, the yt primitive fields will be generated from the conserved variables on disk.

• Special relativistic datasets may be loaded, but at this time not all of their fields are fully supported. In particular, the relationships between quantities such as pressure and thermal energy will be incorrect, as it is currently assumed that their relationship is that of an ideal a \(\gamma\)-law equation of state. This will be rectified in a future release.

• Domains may be visualized assuming periodicity.

• Particle list data is currently unsupported.

Athena++ Data

Athena++ HDF5 data is supported and cared for by John ZuHone. Uniform-grid, SMR, and AMR datasets in cartesian coordinates are fully supported. Support for curvilinear coordinates and/or non-constant grid cell sizes exists, but is preliminary.

The default unit system in yt is cgs (“Gaussian”) units, but Athena++ data is not normally stored in these units, so the code unit system is the default unit system for Athena++ data. This means that answers to field queries from data objects and plots of data will be expressed in code units. Note that the default conversions from these units will still be in terms of cgs units, e.g. 1 code_length equals 1 cm, and so on. If you would like to provided different conversions, you may supply conversions for length, time, and mass to load using the units_override functionality:

import yt

units_override = {
    "length_unit": (1.0, "Mpc"),
    "time_unit": (1.0, "Myr"),
    "mass_unit": (1.0e14, "Msun"),
}

ds = yt.load("AM06/AM06.out1.00400.athdf", units_override=units_override)

This means that the yt fields, e.g. ("gas","density"), ("gas","velocity_x"), ("gas","magnetic_field_x"), will be in cgs units (or whatever unit system was specified), but the Athena fields, e.g., ("athena_pp","density"), ("athena_pp","vel1"), ("athena_pp","Bcc1"), will be in code units.

The default normalization for various magnetic-related quantities such as magnetic pressure, Alfven speed, etc., as well as the conversion between magnetic code units and other units, is Gaussian/CGS, meaning that factors of \(4\pi\) or \(\sqrt{4\pi}\) will appear in these quantities, e.g. \(p_B = B^2/8\pi\). To use the Lorentz-Heaviside normalization instead, in which the factors of \(4\pi\) are dropped (\(p_B = B^2/2), for example), set ``magnetic_normalization="lorentz_heaviside"`\) in the call to yt.load:

ds = yt.load(
    "AM06/AM06.out1.00400.athdf",
    units_override=units_override,
    magnetic_normalization="lorentz_heaviside",
)

Alternative values for the following simulation parameters may be specified using a parameters dict, accepting the following keys:

• gamma: ratio of specific heats, Type: Float. If not specified, \(\gamma = 5/3\) is assumed.

• geometry: Geometry type, currently accepts "cartesian" or "cylindrical". Default is "cartesian".

• periodicity: Is the domain periodic? Type: Tuple of boolean values corresponding to each dimension. Defaults to True in all directions.

• mu: mean molecular weight, Type: Float. If not specified, \(\mu = 0.6\) (for a fully ionized primordial plasma) is assumed.

Caveats

• yt primarily works with primitive variables. If the Athena++ dataset contains conservative variables, the yt primitive fields will be generated from the conserved variables on disk.

• Special relativistic datasets may be loaded, but at this time not all of their fields are fully supported. In particular, the relationships between quantities such as pressure and thermal energy will be incorrect, as it is currently assumed that their relationship is that of an ideal \(\gamma\)-law equation of state. This will be rectified in a future release.

• Domains may be visualized assuming periodicity.

AMReX / BoxLib Data

AMReX and BoxLib share a frontend (currently named boxlib), since the file format nearly identical. yt has been tested with AMReX/BoxLib data generated by Orion, Nyx, Maestro, Castro, IAMR, and WarpX. Currently it is cared for by a combination of Andrew Myers, Matthew Turk, and Mike Zingale.

To load an AMReX/BoxLib dataset, you can use the yt.load command on the plotfile directory name. In general, you must also have the inputs file in the base directory, but Maestro, Castro, Nyx, and WarpX will get all the necessary parameter information from the job_info file in the plotfile directory. For instance, if you were in a directory with the following files:

inputs
pltgmlcs5600/
pltgmlcs5600/Header
pltgmlcs5600/Level_0
pltgmlcs5600/Level_0/Cell_H
pltgmlcs5600/Level_1
pltgmlcs5600/Level_1/Cell_H
pltgmlcs5600/Level_2
pltgmlcs5600/Level_2/Cell_H
pltgmlcs5600/Level_3
pltgmlcs5600/Level_3/Cell_H
pltgmlcs5600/Level_4
pltgmlcs5600/Level_4/Cell_H

You would feed it the filename pltgmlcs5600:

import yt

ds = yt.load("pltgmlcs5600")

For Maestro, Castro, Nyx, and WarpX, you would not need the inputs file, and you would have a job_info file in the plotfile directory.

Caveats

• yt does not read the Maestro base state (although you can have Maestro map it to a full Cartesian state variable before writing the plotfile to get around this). E-mail the dev list if you need this support.

• yt supports AMReX/BoxLib particle data stored in the standard format used by Nyx and WarpX, and optionally Castro. It currently does not support the ASCII particle data used by Maestro and Castro.

• For Maestro, yt aliases either “tfromp” or “tfromh to” temperature depending on the value of the use_tfromp runtime parameter.

• For Maestro, some velocity fields like velocity_magnitude or mach_number will always use the on-disk value, and not have yt derive it, due to the complex interplay of the base state velocity.

Viewing raw fields in WarpX

Most AMReX/BoxLib codes output cell-centered data. If the underlying discretization is not cell-centered, then fields are typically averaged to cell centers before they are written to plot files for visualization. WarpX, however, has the option to output the raw (i.e., not averaged to cell centers) data as well. If you run your WarpX simulation with warpx.plot_raw_fields = 1 in your inputs file, then you should get an additional raw_fields subdirectory inside your plot file. When you load this dataset, yt will have additional on-disk fields defined, with the “raw” field type:

import yt

ds = yt.load("Laser/plt00015/")
print(ds.field_list)

The raw fields in WarpX are nodal in at least one direction. We define a field to be “nodal” in a given direction if the field data is defined at the “low” and “high” sides of the cell in that direction, rather than at the cell center. Instead of returning one field value per cell selected, nodal fields return a number of values, depending on their centering. This centering is marked by a nodal_flag that describes whether the fields is nodal in each dimension. nodal_flag = [0, 0, 0] means that the field is cell-centered, while nodal_flag = [0, 0, 1] means that the field is nodal in the z direction and cell centered in the others, i.e. it is defined on the z faces of each cell. nodal_flag = [1, 1, 0] would mean that the field is centered in the z direction, but nodal in the other two, i.e. it lives on the four cell edges that are normal to the z direction.

ds.index
ad = ds.all_data()
print(ds.field_info[("raw", "Ex")].nodal_flag)
print(ad["raw", "Ex"].shape)
print(ds.field_info[("raw", "Bx")].nodal_flag)
print(ad["raw", "Bx"].shape)
print(ds.field_info["raw", "Bx"].nodal_flag)
print(ad["raw", "Bx"].shape)

Here, the field ('raw', 'Ex') is nodal in two directions, so four values per cell are returned, corresponding to the four edges in each cell on which the variable is defined. ('raw', 'Bx') is nodal in one direction, so two values are returned per cell. The standard, averaged-to-cell-centers fields are still available.

Currently, slices and data selection are implemented for nodal fields. Projections, volume rendering, and many of the analysis modules will not work.

Pluto Data

Support for Pluto AMR data is provided through the Chombo frontend, which is currently maintained by Andrew Myers. Pluto output files that don’t use the Chombo HDF5 format are currently not supported. To load a Pluto dataset, you can use the yt.load command on the *.hdf5 files. For example, the KelvinHelmholtz sample dataset is a directory that contains the following files:

data.0004.hdf5
pluto.ini

To load it, you can navigate into that directory and do:

import yt

ds = yt.load("data.0004.hdf5")

The pluto.ini file must also be present alongside the HDF5 file. By default, all of the Pluto fields will be in code units.

Enzo Data

Enzo data is fully supported and cared for by Matthew Turk. To load an Enzo dataset, you can use the yt.load command and provide it the dataset name. This would be the name of the output file, and it contains no extension. For instance, if you have the following files:

DD0010/
DD0010/data0010
DD0010/data0010.index
DD0010/data0010.cpu0000
DD0010/data0010.cpu0001
DD0010/data0010.cpu0002
DD0010/data0010.cpu0003

You would feed the load command the filename DD0010/data0010 as mentioned.

import yt

ds = yt.load("DD0010/data0010")

Caveats

• There are no major caveats for Enzo usage

• Units should be correct, if you utilize standard unit-setting routines. yt will notify you if it cannot determine the units, although this notification will be passive.

• 2D and 1D data are supported, but the extraneous dimensions are set to be of length 1.0 in “code length” which may produce strange results for volume quantities.

Enzo MHDCT data

The electric and magnetic fields for Enzo MHDCT simulations are defined on cell faces, unlike other Enzo fields which are defined at cell centers. In yt, we call face-centered fields like this “nodal”. We define a field to be nodal in a given direction if the field data is defined at the “low” and “high” sides of the cell in that direction, rather than at the cell center. Instead of returning one field value per cell selected, nodal fields return a number of values, depending on their centering. This centering is marked by a nodal_flag that describes whether the fields is nodal in each dimension. nodal_flag = [0, 0, 0] means that the field is cell-centered, while nodal_flag = [0, 0, 1] means that the field is nodal in the z direction and cell centered in the others, i.e. it is defined on the z faces of each cell. nodal_flag = [1, 1, 0] would mean that the field is centered in the z direction, but nodal in the other two, i.e. it lives on the four cell edges that are normal to the z direction.

ds.index
ad = ds.all_data()
print(ds.field_info[("enzo", "Ex")].nodal_flag)
print(ad["enzo", "Ex"].shape)
print(ds.field_info[("enzo", "BxF")].nodal_flag)
print(ad["enzo", "Bx"].shape)
print(ds.field_info[("enzo", "Bx")].nodal_flag)
print(ad["enzo", "Bx"].shape)

Here, the field ('enzo', 'Ex') is nodal in two directions, so four values per cell are returned, corresponding to the four edges in each cell on which the variable is defined. ('enzo', 'BxF') is nodal in one direction, so two values are returned per cell. The standard, non-nodal field ('enzo', 'Bx') is also available.

Currently, slices and data selection are implemented for nodal fields. Projections, volume rendering, and many of the analysis modules will not work.

Enzo-E Data

Enzo-E outputs have three types of files.

hello-0200/
hello-0200/hello-0200.block_list
hello-0200/hello-0200.file_list
hello-0200/hello-0200.hello-c0020-p0000.h5

To load Enzo-E data into yt, provide the block list file:

import yt

ds = yt.load("hello-0200/hello-0200.block_list")

Mesh and particle fields are fully supported for 1, 2, and 3D datasets. Enzo-E supports arbitrary particle types defined by the user. The available particle types will be known as soon as the dataset index is created.

ds = yt.load("ENZOP_DD0140/ENZOP_DD0140.block_list")
ds.index
print(ds.particle_types)
print(ds.particle_type_counts)
print(ds.r["dark", "particle_position"])

Exodus II Data

Note

To load Exodus II data, you need to have the netcdf4 python interface installed.

Exodus II is a file format for Finite Element datasets that is used by the MOOSE framework for file IO. Support for this format (and for unstructured mesh data in general) is a new feature as of yt 3.3, so while we aim to fully support it, we also expect there to be some buggy features at present. Currently, yt can visualize quads, hexes, triangles, and tetrahedral element types at first order. Additionally, there is experimental support for the high-order visualization of 20-node hex elements. Development of more high-order visualization capability is a work in progress.

To load an Exodus II dataset, you can use the yt.load command on the Exodus II file:

import yt

ds = yt.load("MOOSE_sample_data/out.e-s010", step=0)

Because Exodus II datasets can have multiple steps (which can correspond to time steps, Picard iterations, non-linear solve iterations, etc…), you can also specify a step argument when you load an Exodus II data that defines the index at which to look when you read data from the file. Omitting this argument is the same as passing in 0, and setting step=-1 selects the last time output in the file.

You can access the connectivity information directly by doing:

import yt

ds = yt.load("MOOSE_sample_data/out.e-s010", step=-1)
print(ds.index.meshes[0].connectivity_coords)
print(ds.index.meshes[0].connectivity_indices)
print(ds.index.meshes[1].connectivity_coords)
print(ds.index.meshes[1].connectivity_indices)

This particular dataset has two meshes in it, both of which are made of 8-node hexes. yt uses a field name convention to access these different meshes in plots and data objects. To see all the fields found in a particular dataset, you can do:

import yt

ds = yt.load("MOOSE_sample_data/out.e-s010")
print(ds.field_list)

This will give you a list of field names like ('connect1', 'diffused') and ('connect2', 'convected'). Here, fields labelled with 'connect1' correspond to the first mesh, and those with 'connect2' to the second, and so on. To grab the value of the 'convected' variable at all the nodes in the first mesh, for example, you would do:

import yt

ds = yt.load("MOOSE_sample_data/out.e-s010")
ad = ds.all_data()  # geometric selection, this just grabs everything
print(ad["connect1", "convected"])

In this dataset, ('connect1', 'convected') is nodal field, meaning that the field values are defined at the vertices of the elements. If we examine the shape of the returned array:

import yt

ds = yt.load("MOOSE_sample_data/out.e-s010")
ad = ds.all_data()
print(ad["connect1", "convected"].shape)

we see that this mesh has 12480 8-node hexahedral elements, and that we get 8 field values for each element. To get the vertex positions at which these field values are defined, we can do, for instance:

import yt

ds = yt.load("MOOSE_sample_data/out.e-s010")
ad = ds.all_data()
print(ad["connect1", "vertex_x"])

If we instead look at an element-centered field, like ('connect1', 'conv_indicator'), we get:

import yt

ds = yt.load("MOOSE_sample_data/out.e-s010")
ad = ds.all_data()
print(ad["connect1", "conv_indicator"].shape)

we instead get only one field value per element.

For information about visualizing unstructured mesh data, including Exodus II datasets, please see Unstructured Mesh Slices and Unstructured Mesh Rendering.

Displacement Fields

Finite element codes often solve for the displacement of each vertex from its original position as a node variable, rather than updating the actual vertex positions with time. For analysis and visualization, it is often useful to turn these displacements on or off, and to be able to scale them arbitrarily to emphasize certain features of the solution. To allow this, if yt detects displacement fields in an Exodus II dataset (using the convention that they will be named disp_x, disp_y, etc…), it will optionally add these to the mesh vertex positions for the purposes of visualization. Displacement fields can be controlled when a dataset is loaded by passing in an optional dictionary to the yt.load command. This feature is turned off by default, meaning that a dataset loaded as

import yt

ds = yt.load("MOOSE_sample_data/mps_out.e")

will not include the displacements in the vertex positions. The displacements can be turned on separately for each mesh in the file by passing in a tuple of (scale, offset) pairs for the meshes you want to enable displacements for. For example, the following code snippet turns displacements on for the second mesh, but not the first:

import yt

ds = yt.load(
    "MOOSE_sample_data/mps_out.e",
    step=10,
    displacements={"connect2": (1.0, [0.0, 0.0, 0.0])},
)

The displacements can also be scaled by an arbitrary factor before they are added in to the vertex positions. The following code turns on displacements for both connect1 and connect2, scaling the former by a factor of 5.0 and the later by a factor of 10.0:

import yt

ds = yt.load(
    "MOOSE_sample_data/mps_out.e",
    step=10,
    displacements={
        "connect1": (5.0, [0.0, 0.0, 0.0]),
        "connect2": (10.0, [0.0, 0.0, 0.0]),
    },
)

Finally, we can also apply an arbitrary offset to the mesh vertices after the scale factor is applied. For example, the following code scales all displacements in the second mesh by a factor of 5.0, and then shifts each vertex in the mesh by 1.0 unit in the z-direction:

import yt

ds = yt.load(
    "MOOSE_sample_data/mps_out.e",
    step=10,
    displacements={"connect2": (5.0, [0.0, 0.0, 1.0])},
)

FITS Data

FITS data is mostly supported and cared for by John ZuHone. In order to read FITS data, AstroPy must be installed. FITS data cubes can be loaded in the same way by yt as other datasets. yt can read FITS image files that have the following (case-insensitive) suffixes:

• fits

• fts

• fits.gz

• fts.gz

yt can currently read two kinds of FITS files: FITS image files and FITS binary table files containing positions, times, and energies of X-ray events. These are described in more detail below.

Types of FITS Datasets Supported by yt

yt FITS Data Standard

yt has facilities for creating 2 and 3-dimensional FITS images from derived, fixed-resolution data products from other datasets. These include images produced from slices, projections, and 3D covering grids. The resulting FITS images are fully-describing in that unit, parameter, and coordinate information is passed from the original dataset. These can be created via the FITSImageData class and its subclasses. For information about how to use these special classes, see Writing FITS Images.

Once you have produced a FITS file in this fashion, you can load it using yt and it will be detected as a YTFITSDataset object, and it can be analyzed in the same way as any other dataset in yt.

Astronomical Image Data

These files are one of three types:

• Generic two-dimensional FITS images in sky coordinates

• Three or four-dimensional “spectral cubes”

• Chandra event files

These FITS images typically are in celestial or galactic coordinates, and for 3D spectral cubes the third axis is typically in velocity, wavelength, or frequency units. For these datasets, since yt does not yet recognize non-spatial axes, the coordinates are in units of the image pixels. The coordinates of these pixels in the WCS coordinate systems will be available in separate fields.

Often, the aspect ratio of 3D spectral cubes can be far from unity. Because yt sets the pixel scale as the code_length, certain visualizations (such as volume renderings) may look extended or distended in ways that are undesirable. To adjust the width in code_length of the spectral axis, set spectral_factor equal to a constant which gives the desired scaling, or set it to "auto" to make the width the same as the largest axis in the sky plane:

ds = yt.load("m33_hi.fits.gz", spectral_factor=0.1)

For 4D spectral cubes, the fourth axis is assumed to be composed of different fields altogether (e.g., Stokes parameters for radio data).

Chandra X-ray event data, which is in tabular form, will be loaded as particle fields in yt, but a grid will be constructed from the WCS information in the FITS header. There is a helper function, setup_counts_fields, which may be used to make deposited image fields from the event data for different energy bands (for an example see xray_fits).

Generic FITS Images

If the FITS file contains images but does not have adequate header information to fall into one of the above categories, yt will still load the data, but the resulting field and/or coordinate information will necessarily be incomplete. Field names may not be descriptive, and units may be incorrect. To get the full use out of yt for FITS files, make sure that the file is sufficiently self-descripting to fall into one of the above categories.

Making the Most of yt for FITS Data

yt will load data without WCS information and/or some missing header keywords, but the resulting field and/or coordinate information will necessarily be incomplete. For example, field names may not be descriptive, and units will not be correct. To get the full use out of yt for FITS files, make sure that for each image HDU the following standard header keywords have sensible values:

• CDELTx: The pixel width in along axis x

• CRVALx: The coordinate value at the reference position along axis x

• CRPIXx: The reference pixel along axis x

• CTYPEx: The projection type of axis x

• CUNITx: The units of the coordinate along axis x

• BTYPE: The type of the image, this will be used as the field name

• BUNIT: The units of the image

FITS header keywords can easily be updated using AstroPy. For example, to set the BTYPE and BUNIT keywords:

from astropy.io import fits

f = fits.open("xray_flux_image.fits", mode="update")
f[0].header["BUNIT"] = "cts/s/pixel"
f[0].header["BTYPE"] = "flux"
f.flush()
f.close()

FITS Data Decomposition

Though a FITS image is composed of a single array in the FITS file, upon being loaded into yt it is automatically decomposed into grids:

import yt

ds = yt.load("m33_hi.fits")
ds.print_stats()

level  # grids         # cells     # cells^3
----------------------------------------------
  0       512          981940800       994
----------------------------------------------
          512          981940800

For 3D spectral-cube data, the decomposition into grids will be done along the spectral axis since this will speed up many common operations for this particular type of dataset.

yt will generate its own domain decomposition, but the number of grids can be set manually by passing the nprocs parameter to the load call:

ds = yt.load("m33_hi.fits", nprocs=64)

Fields in FITS Datasets

Multiple fields can be included in a FITS dataset in several different ways. The first way, and the simplest, is if more than one image HDU is contained within the same file. The field names will be determined by the value of BTYPE in the header, and the field units will be determined by the value of BUNIT. The second way is if a dataset has a fourth axis, with each slice along this axis corresponding to a different field. In this case, the field names will be determined by the value of the CTYPE4 keyword and the index of the slice. So, for example, if BTYPE = "intensity" and CTYPE4 = "stokes", then the fields will be named "intensity_stokes_1", "intensity_stokes_2", and so on.

The third way is if auxiliary files are included along with the main file, like so:

ds = yt.load("flux.fits", auxiliary_files=["temp.fits", "metal.fits"])

The image blocks in each of these files will be loaded as a separate field, provided they have the same dimensions as the image blocks in the main file.

Additionally, fields corresponding to the WCS coordinates will be generated based on the corresponding CTYPEx keywords. When queried, these fields will be generated from the pixel coordinates in the file using the WCS transformations provided by AstroPy.

Note

Each FITS image from a single dataset, whether from one file or from one of multiple files, must have the same dimensions and WCS information as the first image in the primary file. If this is not the case, yt will raise a warning and will not load this field.

Additional Options

The following are additional options that may be passed to the load command when analyzing FITS data:

nan_mask

FITS image data may include NaNs. If you wish to mask this data out, you may supply a nan_mask parameter, which may either be a single floating-point number (applies to all fields) or a Python dictionary containing different mask values for different fields:

# passing a single float for all images
ds = yt.load("m33_hi.fits", nan_mask=0.0)

# passing a dict
ds = yt.load("m33_hi.fits", nan_mask={"intensity": -1.0, "temperature": 0.0})

suppress_astropy_warnings

Generally, AstroPy may generate a lot of warnings about individual FITS files, many of which you may want to ignore. If you want to see these warnings, set suppress_astropy_warnings = False.

Miscellaneous Tools for Use with FITS Data

A number of tools have been prepared for use with FITS data that enhance yt’s visualization and analysis capabilities for this particular type of data. These are included in the yt.frontends.fits.misc module, and can be imported like so:

from yt.frontends.fits.misc import PlotWindowWCS, ds9_region, setup_counts_fields

setup_counts_fields

This function can be used to create image fields from X-ray counts data in different energy bands:

ebounds = [(0.1, 2.0), (2.0, 5.0)]  # Energies are in keV
setup_counts_fields(ds, ebounds)

which would make two fields, "counts_0.1-2.0" and "counts_2.0-5.0", and add them to the field registry for the dataset ds.

ds9_region

This function takes a ds9 region and creates a “cut region” data container from it, that can be used to select the cells in the FITS dataset that fall within the region. To use this functionality, the regions package must be installed.

ds = yt.load("m33_hi.fits")
circle_region = ds9_region(ds, "circle.reg")
print(circle_region.quantities.extrema("flux"))

PlotWindowWCS

This class takes a on-axis SlicePlot or ProjectionPlot of FITS data and adds celestial coordinates to the plot axes. To use it, a version of AstroPy >= 1.3 must be installed.

wcs_slc = PlotWindowWCS(slc)
wcs_slc.show()  # for Jupyter notebooks
wcs_slc.save()

WCSAxes is still in an experimental state, but as its functionality improves it will be utilized more here.

create_spectral_slabs

Note

The following functionality requires the spectral-cube library to be installed.

If you have a spectral intensity dataset of some sort, and would like to extract emission in particular slabs along the spectral axis of a certain width, create_spectral_slabs can be used to generate a dataset with these slabs as different fields. In this example, we use it to extract individual lines from an intensity cube:

slab_centers = {
    "13CN": (218.03117, "GHz"),
    "CH3CH2CHO": (218.284256, "GHz"),
    "CH3NH2": (218.40956, "GHz"),
}
slab_width = (0.05, "GHz")
ds = create_spectral_slabs(
    "intensity_cube.fits", slab_centers, slab_width, nan_mask=0.0
)

All keyword arguments to create_spectral_slabs are passed on to load when creating the dataset (see Additional Options above). In the returned dataset, the different slabs will be different fields, with the field names taken from the keys in slab_centers. The WCS coordinates on the spectral axis are reset so that the center of the domain along this axis is zero, and the left and right edges of the domain along this axis are \(\pm\) 0.5*slab_width.

Examples of Using FITS Data

The following Jupyter notebooks show examples of working with FITS data in yt, which we recommend you look at in the following order:

• radio_cubes

• xray_fits

• Writing FITS Images

FLASH Data

FLASH HDF5 data is mostly supported and cared for by John ZuHone. To load a FLASH dataset, you can use the yt.load command and provide it the file name of a plot file, checkpoint file, or particle file. Particle files require special handling depending on the situation, the main issue being that they typically lack grid information. The first case is when you have a plotfile and a particle file that you would like to load together. In the simplest case, this occurs automatically. For instance, if you were in a directory with the following files:

radio_halo_1kpc_hdf5_plt_cnt_0100 # plotfile
radio_halo_1kpc_hdf5_part_0100 # particle file

where the plotfile and the particle file were created at the same time (therefore having particle data consistent with the grid structure of the former). Notice also that the prefix "radio_halo_1kpc_" and the file number 100 are the same. In this special case, the particle file will be loaded automatically when yt.load is called on the plotfile. This also works when loading a number of files in a time series.

If the two files do not have the same prefix and number, but they nevertheless have the same grid structure and are at the same simulation time, the particle data may be loaded with the particle_filename optional argument to yt.load:

import yt

ds = yt.load(
    "radio_halo_1kpc_hdf5_plt_cnt_0100",
    particle_filename="radio_halo_1kpc_hdf5_part_0100",
)

However, if you don’t have a corresponding plotfile for a particle file, but would still like to load the particle data, you can still call yt.load on the file. However, the grid information will not be available, and the particle data will be loaded in a fashion similar to other particle-based datasets in yt.

Mean Molecular Weight and Number Density Fields

The way the mean molecular weight and number density fields are defined depends on what type of simulation you are running. If you are running a simulation without species and a \(\gamma\)-law equation of state, then the mean molecular weight is defined using the eos_singleSpeciesA parameter in the FLASH dataset. If you have multiple species and your dataset contains the FLASH field "abar", then this is used as the mean molecular weight. In either case, the number density field is calculated using this weight.

If you are running a FLASH simulation where the fields "sumy" and "ye" are present, Then the mean molecular weight is the inverse of "sumy", and the fields "El_number_density", "ion_number_density", and "number_density" are defined using the following mathematical definitions:

• "El_number_density" \(n_e = N_AY_e\rho\)

• "ion_number_density" \(n_i = N_A\rho/\bar{A}\)

• "number_density" \(n = n_e + n_i\)

where \(n_e\) and \(n_i\) are the electron and ion number densities, \(\rho\) is the mass density, \(Y_e\) is the electron number per baryon, \(\bar{A}\) is the mean molecular weight, and \(N_A\) is Avogadro’s number.

Caveats

• Please be careful that the units are correctly utilized; yt assumes cgs by default, but conversion to other unit systems is also possible.

Gadget Data

Note

For more information about how yt indexes and reads particle data, set the section How Particles are Indexed.

yt has support for reading Gadget data in both raw binary and HDF5 formats. It is able to access the particles as it would any other particle dataset, and it can apply smoothing kernels to the data to produce both quantitative analysis and visualization. See SPH Particle Data for more details and gadget-notebook for a detailed example of loading, analyzing, and visualizing a Gadget dataset. An example which makes use of a Gadget snapshot from the OWLS project can be found at owls-notebook.

Note

If you are loading a multi-file dataset with Gadget, you can either supply the zeroth file to the load command or the directory containing all of the files. For instance, to load the zeroth file: yt.load("snapshot_061.0.hdf5") . To give just the directory, if you have all of your snapshot_000.* files in a directory called snapshot_000, do: yt.load("/path/to/snapshot_000").

Gadget data in HDF5 format can be loaded with the load command:

import yt

ds = yt.load("snapshot_061.hdf5")

Gadget data in raw binary format can also be loaded with the load command. This is supported for snapshots created with the SnapFormat parameter set to 1 or 2.

import yt

ds = yt.load("snapshot_061")

Units and Bounding Boxes

There are two additional pieces of information that may be needed. If your simulation is cosmological, yt can often guess the bounding box and the units of the simulation. However, for isolated simulations and for cosmological simulations with non-standard units, these must be supplied by the user. For example, if a length unit of 1.0 corresponds to a kiloparsec, you can supply this in the constructor. yt can accept units such as Mpc, kpc, cm, Mpccm/h and so on. In particular, note that Mpc/h and Mpccm/h (cm for comoving here) are usable unit definitions.

yt will attempt to use units for mass, length, time, and magnetic as supplied in the argument unit_base. The bounding_box argument is a list of two-item tuples or lists that describe the left and right extents of the particles. In this example we load a dataset with a custom bounding box and units.

bbox = [[-600.0, 600.0], [-600.0, 600.0], [-600.0, 600.0]]
unit_base = {
    "length": (1.0, "kpc"),
    "velocity": (1.0, "km/s"),
    "mass": (1.0, "Msun"),
}

ds = yt.load("snap_004", unit_base=unit_base, bounding_box=bbox)

Warning

If a bounding_box argument is supplied and the original dataset has periodic boundaries, it will no longer have periodic boundaries after the bounding box is applied.

In addition, you can use UnitLength_in_cm, UnitVelocity_in_cm_per_s, UnitMass_in_g, and UnitMagneticField_in_gauss as keys for the unit_base dictionary. These name come from the names used in the Gadget runtime parameter file. This example will initialize a dataset with the same units as the example above:

unit_base = {
    "UnitLength_in_cm": 3.09e21,
    "UnitVelocity_in_cm_per_s": 1e5,
    "UnitMass_in_g": 1.989e33,
}

ds = yt.load("snap_004", unit_base=unit_base, bounding_box=bbox)

Field Specifications

Binary Gadget outputs often have additional fields or particle types that are non-standard from the default Gadget distribution format. These can be specified in the call to GadgetDataset by either supplying one of the sets of field specifications as a string or by supplying a field specification itself. As an example, yt has built-in definitions for default (the default), agora_unlv, group0000, and magneticum_box2_hr. They can be used like this:

ds = yt.load("snap_100", field_spec="group0000")

Field specifications must be tuples, and must be of this format:

default = (
    "Coordinates",
    "Velocities",
    "ParticleIDs",
    "Mass",
    ("InternalEnergy", "Gas"),
    ("Density", "Gas"),
    ("SmoothingLength", "Gas"),
)

This is the default specification used by the Gadget frontend. It means that the fields are, in order, Coordinates, Velocities, ParticleIDs, Mass, and the fields InternalEnergy, Density and SmoothingLength only for Gas particles. So for example, if you have defined a Metallicity field for the particle type Halo, which comes right after ParticleIDs in the file, you could define it like this:

import yt

my_field_def = (
    "Coordinates",
    "Velocities",
    "ParticleIDs",
    ("Metallicity", "Halo"),
    "Mass",
    ("InternalEnergy", "Gas"),
    ("Density", "Gas"),
    ("SmoothingLength", "Gas"),
)

ds = yt.load("snap_100", field_spec=my_field_def)

To save time, you can utilize the plugins file for yt and use it to add items to the dictionary where these definitions are stored. You could do this like so:

import yt
from yt.frontends.gadget.definitions import gadget_field_specs

gadget_field_specs["my_field_def"] = my_field_def

ds = yt.load("snap_100", field_spec="my_field_def")

Please also feel free to issue a pull request with any new field specifications, as we’re happy to include them in the main distribution!

Magneticum halos downloaded using the SIMCUT method from the Cosmological Web Portal can be loaded using the "magneticum_box2_hr" value for the field_spec argumemt. However, this is strictly only true for halos downloaded after May 14, 2021, since before then the halos had the following signature (with the "StellarAge" field for the "Bndry" particles missing):

magneticum_box2_hr = (
    "Coordinates",
    "Velocities",
    "ParticleIDs",
    "Mass",
    ("InternalEnergy", "Gas"),
    ("Density", "Gas"),
    ("SmoothingLength", "Gas"),
    ("ColdFraction", "Gas"),
    ("Temperature", "Gas"),
    ("StellarAge", "Stars"),
    "Potential",
    ("InitialMass", "Stars"),
    ("ElevenMetalMasses", ("Gas", "Stars")),
    ("StarFormationRate", "Gas"),
    ("TrueMass", "Bndry"),
    ("AccretionRate", "Bndry"),
)

and before November 20, 2020, the field specification had the "ParticleIDs" and "Mass" fields swapped:

magneticum_box2_hr = (
    "Coordinates",
    "Velocities",
    "Mass",
    "ParticleIDs",
    ("InternalEnergy", "Gas"),
    ("Density", "Gas"),
    ("SmoothingLength", "Gas"),
    ("ColdFraction", "Gas"),
    ("Temperature", "Gas"),
    ("StellarAge", "Stars"),
    "Potential",
    ("InitialMass", "Stars"),
    ("ElevenMetalMasses", ("Gas", "Stars")),
    ("StarFormationRate", "Gas"),
    ("TrueMass", "Bndry"),
    ("AccretionRate", "Bndry"),
)

In general, to determine what fields are in your Gadget binary file, it may be useful to inspect them with the g3read code first.

Gadget Species Fields

Gas and star particles in Gadget binary and HDF5 files can have fields corresponding to different species fractions or masses. The following field definitions are supported, in the sense that they are automatically detected and will be used to construct species fractions, densities, and number densities after the manner specified in Species Fields. For Gadget binary files, the following fields (as specified in the field_spec argument) are supported:

• "ElevenMetalMasses": 11 mass fields: He, C, Ca, O, N, Ne, Mg, S, Si, Fe, Ej

• "FourMetalFractions": 4 fraction fields: C, O, Si, Fe

For Gadget HDF5 files, the fields "MetalMasses" or "Mass Of Metals" are supported, with the number of species determined by the size of the dataset’s second dimension in the file. Four different numbers of species in these fields are supported, corresponding to the following species:

• 7, corresponding to C, N, O, Mg, Si, Fe, Ej

• 8, corresponding to He, C, O, Mg, S, Si, Fe, Ej

• 11, corresponding to He, C, Ca, O, N, Ne, Mg, S, Si, Fe, Ej

• 15, corresponding to He, C, Ca, O, N, Ne, Mg, S, Si, Fe, Na, Al, Ar, Ni, Ej

Two points should be noted about the above: the “Ej” species corresponds to the remaining mass of elements heavier than hydrogen and not enumerated, and in the case of 8, 11, and 15 species, hydrogen is assumed to be the remaining mass fraction.

Finally, for Gadget HDF5 files, element fields which are of the form "X_fraction" are also suppoted, and correspond to the mass fraction of element X.

Long Particle IDs

Some Gadget binary files use 64-bit integers for particle IDs. To use these, simply set long_ids=True when loading the dataset:

import yt

ds = yt.load("snap_100", long_ids=True)

This is needed, for example, for Magneticum halos downloaded using the SIMCUT method from the Cosmological Web Portal

Particle Type Definitions

In some cases, research groups add new particle types or re-order them. You can supply alternate particle types by using the keyword ptype_spec to the GadgetDataset call. The default for Gadget binary data is:

("Gas", "Halo", "Disk", "Bulge", "Stars", "Bndry")

You can specify alternate names, but note that this may cause problems with the field specification if none of the names match old names.

Header Specification

If you have modified the header in your Gadget binary file, you can specify an alternate header specification with the keyword header_spec. This can either be a list of strings corresponding to individual header types known to yt, or it can be a combination of strings and header specifications. The default header specification (found in yt/frontends/sph/definitions.py) is:

default = (
    ("Npart", 6, "i"),
    ("Massarr", 6, "d"),
    ("Time", 1, "d"),
    ("Redshift", 1, "d"),
    ("FlagSfr", 1, "i"),
    ("FlagFeedback", 1, "i"),
    ("Nall", 6, "i"),
    ("FlagCooling", 1, "i"),
    ("NumFiles", 1, "i"),
    ("BoxSize", 1, "d"),
    ("Omega0", 1, "d"),
    ("OmegaLambda", 1, "d"),
    ("HubbleParam", 1, "d"),
    ("FlagAge", 1, "i"),
    ("FlagMEtals", 1, "i"),
    ("NallHW", 6, "i"),
    ("unused", 16, "i"),
)

These items will all be accessible inside the object ds.parameters, which is a dictionary. You can add combinations of new items, specified in the same way, or alternately other types of headers. The other string keys defined are pad32, pad64, pad128, and pad256 each of which corresponds to an empty padding in bytes. For example, if you have an additional 256 bytes of padding at the end, you can specify this with:

header_spec = "default+pad256"

Note that a single string like this means a single header block. To specify multiple header blocks, use a list of strings instead:

header_spec = ["default", "pad256"]

This can then be supplied to the constructor. Note that you can also define header items manually, for instance with:

from yt.frontends.gadget.definitions import gadget_header_specs

gadget_header_specs["custom"] = (("some_value", 8, "d"), ("another_value", 1, "i"))
header_spec = "default+custom"

The letters correspond to data types from the Python struct module. Please feel free to submit alternate header types to the main yt repository.

Specifying Units

If you are running a cosmology simulation, yt will be able to guess the units with some reliability. However, if you are not and you do not specify a dataset, yt will not be able to and will use the defaults of length being 1.0 Mpc/h (comoving), velocity being in cm/s, and mass being in 10^10 Msun/h. You can specify alternate units by supplying the unit_base keyword argument of this form:

unit_base = {"length": (1.0, "cm"), "mass": (1.0, "g"), "time": (1.0, "s")}

yt will utilize length, mass and time to set up all other units.

SWIFT Data

Note

For more information about how yt indexes and reads particle data, set the section How Particles are Indexed.

yt has support for reading in SWIFT data from the HDF5 file format. It is able to access all particles and fields which are stored on-disk and it is also able to generate derived fields, i.e, linear momentum from on-disk fields.

It is also possible to smooth the data onto a grid or an octree. This interpolation can be done using an SPH kernel using either the scatter or gather approach. The SWIFT frontend is supported and cared for by Ashley Kelly.

SWIFT data in HDF5 format can be loaded with the load command:

import yt

ds = yt.load("EAGLE_6/eagle_0005.hdf5")

Arepo Data

Note

For more information about how yt indexes and reads discrete data, set the section How Particles are Indexed.

Arepo data is currently treated as SPH data. The gas cells have smoothing lengths assigned using the following prescription for a given gas cell \(i\):

\[h_{\rm sml} = \alpha\left(\frac{3}{4\pi}\frac{m_i}{\rho_i}\right)^{1/3}\]

where \(\alpha\) is a constant factor. By default, \(\alpha = 2\). In practice, smoothing lengths are only used for creating slices and projections, and this value of \(\alpha\) works well for this purpose. However, this value can be changed when loading an Arepo dataset by setting the smoothing_factor parameter:

import yt

ds = yt.load("snapshot_100.hdf5", smoothing_factor=1.5)

Currently, only Arepo HDF5 snapshots are supported.

If the “GFM” metal fields are present in your dataset, they will be loaded in and aliased to the appropriate species fields in the "GFM_Metals" field on-disk. For more information, see the Illustris TNG documentation.

If passive scalar fields are present in your dataset, they will be loaded in and aliased to fields with the naming convention "PassiveScalars_XX" where XX is the number of the passive scalar array, e.g. "00", "01", etc.

HDF5 snapshots will be detected as Arepo data if they have the "GFM_Metals" field present, or if they have a "Config" group in the header. If neither of these are the case, and your snapshot is Arepo data, you can fix this with the following:

import h5py

with h5py.File(saved_filename, "r+") as f:
    f.create_group("Config")
    f["/Config"].attrs["VORONOI"] = 1

GAMER Data

GAMER HDF5 data is supported and cared for by Hsi-Yu Schive and John ZuHone. Datasets using hydrodynamics, particles, magnetohydrodynamics, wave dark matter, and special relativistic hydrodynamics are supported. You can load the data like this:

import yt

ds = yt.load("InteractingJets/jet_000002")

For simulations without units (i.e., OPT__UNIT = 0), you can supply conversions for length, time, and mass to load using the units_override functionality:

import yt

code_units = {
    "length_unit": (1.0, "kpc"),
    "time_unit": (3.08567758096e13, "s"),
    "mass_unit": (1.4690033e36, "g"),
}
ds = yt.load("InteractingJets/jet_000002", units_override=code_units)

Particle data are supported and are always stored in the same file as the grid data.

For special relativistic simulations, both the gamma-law and Taub-Mathews EOSes are supported, and the following fields are defined:

• ("gas", "density"): Comoving rest-mass density \(\rho\)

• ("gas", "frame_density"): Coordinate-frame density \(D = \gamma\rho\)

• ("gas", "gamma"): Ratio of specific heats \(\Gamma\)

• ("gas", "four_velocity_[txyz]"): Four-velocity fields \(U_t, U_x, U_y, U_z\)

• ("gas", "lorentz_factor"): Lorentz factor \(\gamma = \sqrt{1+U_iU^i/c^2}\) (where \(i\) runs over the spatial indices)

These, and other fields following them (3-velocity, energy densities, etc.) are computed in the same manner as in the GAMER-SR paper to avoid catastrophic cancellations.

Caveats

• GAMER data in raw binary format (i.e., OPT__OUTPUT_TOTAL = "C-binary") is not supported.

Generic AMR Data

See Loading Generic Array Data and load_amr_grids() for more detail.

Note

It is now possible to load data using only functions, rather than using the fully-in-memory method presented here. For more information and examples, see Loading Data via Functions.

It is possible to create native yt dataset from Python’s dictionary that describes set of rectangular patches of data of possibly varying resolution.

import yt

grid_data = [
    dict(
        left_edge=[0.0, 0.0, 0.0],
        right_edge=[1.0, 1.0, 1.0],
        level=0,
        dimensions=[32, 32, 32],
    ),
    dict(
        left_edge=[0.25, 0.25, 0.25],
        right_edge=[0.75, 0.75, 0.75],
        level=1,
        dimensions=[32, 32, 32],
    ),
]

for g in grid_data:
    g["density"] = np.random.random(g["dimensions"]) * 2 ** g["level"]

ds = yt.load_amr_grids(grid_data, [32, 32, 32], 1.0)

Note

yt only supports a block structure where the grid edges on the n-th refinement level are aligned with the cell edges on the n-1-th level.

Particle fields are supported by adding 1-dimensional arrays to each grid’s dict:

for g in grid_data:
    g["particle_position_x"] = np.random.random(size=100000)

Caveats

• Some functions may behave oddly, and parallelism will be disappointing or non-existent in most cases.

• No consistency checks are performed on the index

• Data must already reside in memory.

• Consistency between particle positions and grids is not checked; load_amr_grids assumes that particle positions associated with one grid are not bounded within another grid at a higher level, so this must be ensured by the user prior to loading the grid data.

Generic Array Data

See Loading Generic Array Data and load_uniform_grid() for more detail.

Even if your data is not strictly related to fields commonly used in astrophysical codes or your code is not supported yet, you can still feed it to yt to use its advanced visualization and analysis facilities. The only requirement is that your data can be represented as one or more uniform, three dimensional numpy arrays. Assuming that you have your data in arr, the following code:

import yt

data = dict(Density=arr)
bbox = np.array([[-1.5, 1.5], [-1.5, 1.5], [1.5, 1.5]])
ds = yt.load_uniform_grid(data, arr.shape, 3.08e24, bbox=bbox, nprocs=12)

will create yt-native dataset ds that will treat your array as density field in cubic domain of 3 Mpc edge size (3 * 3.08e24 cm) and simultaneously divide the domain into 12 chunks, so that you can take advantage of the underlying parallelism.

Particle fields are added as one-dimensional arrays in a similar manner as the three-dimensional grid fields:

import yt

data = dict(
    Density=dens,
    particle_position_x=posx_arr,
    particle_position_y=posy_arr,
    particle_position_z=posz_arr,
)
bbox = np.array([[-1.5, 1.5], [-1.5, 1.5], [1.5, 1.5]])
ds = yt.load_uniform_grid(data, arr.shape, 3.08e24, bbox=bbox, nprocs=12)

where in this example the particle position fields have been assigned. If no particle fields are supplied, then the number of particles is assumed to be zero.

Caveats

• Particles may be difficult to integrate.

• Data must already reside in memory.

Semi-Structured Grid Data

Note

With the release of yt-4.1, functionality has been added to allow loading “stretched” grids that are operated on in a more efficient way. This is done via the load_uniform_grid() operation, supplying the cell_widths argument. Using the hexahedral mesh is no longer suggested for situations where the mesh can be adequately described with three arrays of cell widths.

See Stretched Grid Data for more information.

See Loading Generic Array Data, hexahedral_connectivity(), load_hexahedral_mesh() for more detail.

In addition to uniform grids as described above, you can load in data with non-uniform spacing between datapoints. To load this type of data, you must first specify a hexahedral mesh, a mesh of six-sided cells, on which it will live. You define this by specifying the x,y, and z locations of the corners of the hexahedral cells. The following code:

import numpy

import yt

xgrid = numpy.array([-1, -0.65, 0, 0.65, 1])
ygrid = numpy.array([-1, 0, 1])
zgrid = numpy.array([-1, -0.447, 0.447, 1])

coordinates, connectivity = yt.hexahedral_connectivity(xgrid, ygrid, zgrid)

will define the (x,y,z) coordinates of the hexahedral cells and information about that cell’s neighbors such that the cell corners will be a grid of points constructed as the Cartesian product of xgrid, ygrid, and zgrid.

Then, to load your data, which should be defined on the interiors of the hexahedral cells, and thus should have the shape, (len(xgrid)-1, len(ygrid)-1, len(zgrid)-1), you can use the following code:

bbox = numpy.array(
    [
        [numpy.min(xgrid), numpy.max(xgrid)],
        [numpy.min(ygrid), numpy.max(ygrid)],
        [numpy.min(zgrid), numpy.max(zgrid)],
    ]
)
data = {"density": arr}
ds = yt.load_hexahedral_mesh(data, conn, coords, 1.0, bbox=bbox)

to load your data into the dataset ds as described above, where we have assumed your data is stored in the three-dimensional array arr.

Caveats

• Integration is not implemented.

• Some functions may behave oddly or not work at all.

• Data must already reside in memory.

Stretched Grid Data

Warning

API consistency for loading stretched grids is not guaranteed until at least yt 4.2! There may be changes in between then and now, as this is a preliminary feature.

With version 4.1, yt has the ability to specify cell widths for grids. This allows situations where a grid has a functional form for cell widths, or where widths are provided in advance.

Note

At present, support is available for a single grid with varying cell-widths, loaded through the stream handler. Future versions of yt will have more complete and flexible support!

To load a stretched grid, you use the standard (and now rather-poorly named) load_uniform_grid function, but supplying a cell_widths argument. This argument should be a list of three arrays, corresponding to the first, second and third index-direction cell widths. (For instance, in a “standard” cartesian dataset, this would be x, y, z.)

This script, demonstrates loading a simple “random” dataset with a random set of cell-widths.

import yt
import numpy as np

N = 8

data = {"density": np.random.random((N, N, N))}

cell_widths = []
for i in range(3):
    widths = np.random.random(N)
    widths /= widths.sum()  # Normalize to span 0 .. 1.
    cell_widths.append(widths)

ds = yt.load_uniform_grid(
    data,
    [N, N, N],
    bbox=np.array([[0.0, 1.0], [0.0, 1.0], [0.0, 1.0]]),
    cell_widths=cell_widths,
)

This can be modified to load data from a file, as well as to use more (or fewer) cells.

Unstructured Grid Data

See Loading Generic Array Data, load_unstructured_mesh() for more detail.

In addition to the above grid types, you can also load data stored on unstructured meshes. This type of mesh is used, for example, in many finite element calculations. Currently, hexahedral and tetrahedral mesh elements are supported.

To load an unstructured mesh, you need to specify the following. First, you need to have a coordinates array, which should be an (L, 3) array that stores the (x, y, z) positions of all of the vertices in the mesh. Second, you need to specify a connectivity array, which describes how those vertices are connected into mesh elements. The connectivity array should be (N, M), where N is the number of elements and M is the connectivity length, i.e. the number of vertices per element. Finally, you must also specify a data dictionary, where the keys should be the names of the fields and the values should be numpy arrays that contain the field data. These arrays can either supply the cell-averaged data for each element, in which case they would be (N, 1), or they can have node-centered data, in which case they would also be (N, M).

Here is an example of how to load an in-memory, unstructured mesh dataset:

import numpy as np

import yt

coords = np.array([[0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 1.0]], dtype=np.float64)

connect = np.array([[0, 1, 3], [1, 2, 3]], dtype=np.int64)

data = {}
data["connect1", "test"] = np.array(
    [[0.0, 1.0, 3.0], [1.0, 2.0, 3.0]], dtype=np.float64
)

Here, we have made up a simple, 2D unstructured mesh dataset consisting of two triangles and one node-centered data field. This data can be loaded as an in-memory dataset as follows:

ds = yt.load_unstructured_mesh(connect, coords, data)

The in-memory dataset can then be visualized as usual, e.g.:

sl = yt.SlicePlot(ds, "z", ("connect1", "test"))
sl.annotate_mesh_lines()

Note that load_unstructured_mesh can take either a single mesh or a list of meshes. To load multiple meshes, you can do:

import numpy as np

import yt

coordsMulti = np.array(
    [[0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 1.0]], dtype=np.float64
)

connect1 = np.array(
    [
        [0, 1, 3],
    ],
    dtype=np.int64,
)
connect2 = np.array(
    [
        [1, 2, 3],
    ],
    dtype=np.int64,
)

data1 = {}
data2 = {}
data1["connect1", "test"] = np.array(
    [
        [0.0, 1.0, 3.0],
    ],
    dtype=np.float64,
)
data2["connect2", "test"] = np.array(
    [
        [1.0, 2.0, 3.0],
    ],
    dtype=np.float64,
)

connectList = [connect1, connect2]
dataList = [data1, data2]

ds = yt.load_unstructured_mesh(connectList, coordsMulti, dataList)

# only plot the first mesh
sl = yt.SlicePlot(ds, "z", ("connect1", "test"))

# only plot the second
sl = yt.SlicePlot(ds, "z", ("connect2", "test"))

# plot both
sl = yt.SlicePlot(ds, "z", ("all", "test"))

Note that you must respect the field naming convention that fields on the first mesh will have the type connect1, fields on the second will have connect2, etc…

Caveats

• Integration is not implemented.

• Some functions may behave oddly or not work at all.

• Data must already reside in memory.

Generic Particle Data

Note

For more information about how yt indexes and reads particle data, set the section How Particles are Indexed.

See Loading Generic Particle Data and load_particles() for more detail.

You can also load generic particle data using the same stream functionality discussed above to load in-memory grid data. For example, if your particle positions and masses are stored in positions and masses, a vertically-stacked array of particle x,y, and z positions, and a 1D array of particle masses respectively, you would load them like this:

import yt

data = dict(particle_position=positions, particle_mass=masses)
ds = yt.load_particles(data)

You can also load data using 1D x, y, and z position arrays:

import yt

data = dict(
    particle_position_x=posx,
    particle_position_y=posy,
    particle_position_z=posz,
    particle_mass=masses,
)
ds = yt.load_particles(data)

The load_particles function also accepts the following keyword parameters:

length_unit

The units used for particle positions.

mass_unit

The units of the particle masses.

time_unit

The units used to represent times. This is optional and is only used if your data contains a creation_time field or a particle_velocity field.

velocity_unit

The units used to represent velocities. This is optional and is only used if you supply a velocity field. If this is not supplied, it is inferred from the length and time units.

bbox

The bounding box for the particle positions.

Adding Smoothing Lengths for Non-SPH Particles

A novel use of the load_particles function is to facilitate SPH visualization of non-SPH particles. See the example below:

import yt

# Load dataset and center on the dense region
ds = yt.load("FIRE_M12i_ref11/snapshot_600.hdf5")
_, center = ds.find_max(("PartType0", "density"))

# Reload DM particles into a stream dataset
ad = ds.all_data()
pt = "PartType1"
fields = ["particle_mass"] + [f"particle_position_{ax}" for ax in "xyz"]
data = {field: ad[pt, field] for field in fields}
ds_dm = yt.load_particles(data, data_source=ad)

# Generate the missing SPH fields
ds_dm.add_sph_fields()

# Make the SPH projection plot
p = yt.ProjectionPlot(ds_dm, "z", ("io", "density"), center=center, width=(1, "Mpc"))
p.set_unit(("io", "density"), "Msun/kpc**2")
p.show()

Here we see two new things. First, load_particles accepts a data_source argument to infer parameters like code units, which could be tedious to provide otherwise. Second, the returned StreamParticleDataset has an add_sph_fields() method, to create the smoothing_length and density fields required for SPH visualization to work.

Gizmo Data

Note

For more information about how yt indexes and reads particle data, set the section How Particles are Indexed.

Gizmo datasets, including FIRE outputs, can be loaded into yt in the usual manner. Like other SPH data formats, yt loads Gizmo data as particle fields and then uses smoothing kernels to deposit those fields to an underlying grid structure as spatial fields as described in Gadget Data. To load Gizmo datasets using the standard HDF5 output format:

import yt
ds = yt.load("snapshot_600.hdf5")

Because the Gizmo output format is similar to the Gadget format, yt may load Gizmo datasets as Gadget depending on the circumstances, but this should not pose a problem in most situations. FIRE outputs will be loaded accordingly due to the number of metallicity fields found (11 or 17).

If ("PartType0", "MagneticField") is present in the output, it would be loaded and aliased to ("PartType0", "particle_magnetic_field"). The corresponding component field like ("PartType0", "particle_magnetic_field_x") would be added automatically.

Note that ("PartType4", "StellarFormationTime") field has different meanings depending on whether it is a cosmological simulation. For cosmological runs this is the scale factor at the redshift when the star particle formed. For non-cosmological runs it is the time when the star particle formed. (See the GIZMO User Guide) For this reason, ("PartType4", "StellarFormationTime") is loaded as a dimensionless field. We defined two related fields ("PartType4", "creation_time"), and ("PartType4", "age") with physical units for your convenience.

For Gizmo outputs written as raw binary outputs, you may have to specify a bounding box, field specification, and units as are done for standard Gadget outputs. See Gadget Data for more information.

Halo Catalog Data

Note

For more information about how yt indexes and reads particle data, set the section How Particles are Indexed.

yt has support for reading halo catalogs produced by the AdaptaHOP, Amiga Halo Finder (AHF), Rockstar and the inline FOF/SUBFIND halo finders of Gadget and OWLS. The halo catalogs are treated as particle datasets where each particle represents a single halo. For example, this means that the "particle_mass" field refers to the mass of the halos. For Gadget FOF/SUBFIND catalogs, the member particles for a given halo can be accessed by creating halo data containers. See Halo Data Containers for more information.

If you have access to both the halo catalog and the simulation snapshot from the same redshift, additional analysis can be performed for each halo using Halo Analysis. The resulting product can be reloaded in a similar manner to the other halo catalogs shown here.

AdataHOP

AdaptaHOP halo catalogs are loaded by providing the path to the tree_bricksXXX file. As the halo catalog does not contain all the information about the simulation (for example the cosmological parameters), you also need to pass the parent dataset for it to load correctly. Some fields of note available from AdaptaHOP are:

Rockstar field	yt field name
halo id	particle_identifier
halo mass	particle_mass
virial mass	virial_mass
virial radius	virial_radius
virial temperature	virial_temperature
halo position	particle_position_(x,y,z)
halo velocity	particle_velocity_(x,y,z)

Numerous other AdataHOP fields exist. To see them, check the field list by typing ds.field_list for a dataset loaded as ds. Like all other datasets, fields must be accessed through Data Objects.

import yt

parent_ds = yt.load("output_00080/info_00080.txt")
ds = yt.load("output_00080_halos/tree_bricks080", parent_ds=parent_ds)
ad = ds.all_data()
# halo masses
print(ad["halos", "particle_mass"])
# halo radii
print(ad["halos", "virial_radius"])

Halo Data Containers

Halo member particles are accessed by creating halo data containers with the the halo id and the type of the particles. Scalar values for halos can be accessed in the same way. Halos also have mass, position, velocity, and member ids attributes.

halo = ds.halo(1, ptype="io")
# member particles for this halo
print(halo.member_ids)
# masses of the halo particles
print(halo["io", "particle_mass"])
# halo mass
print(halo.mass)

In addition, the halo container contains a sphere container. This is the smallest sphere that contains all the halos’ particles

halo = ds.halo(1, ptype="io")
sp = halo.sphere
# Density in halo
sp["gas", "density"]
# Entropy in halo
sp["gas", "entropy"]

Amiga Halo Finder

Amiga Halo Finder (AHF) halo catalogs are loaded by providing the path to the .parameter files. The corresponding .log and .AHF_halos files must exist for data loading to succeed. The field type for all fields is “halos”. Some fields of note available from AHF are:

AHF field	yt field name
ID	particle_identifier
Mvir	particle_mass
Rvir	virial_radius
(X,Y,Z)c	particle_position_(x,y,z)
V(X,Y,Z)c	particle_velocity_(x,y,z)

Numerous other AHF fields exist. To see them, check the field list by typing ds.field_list for a dataset loaded as ds. Like all other datasets, fields must be accessed through Data Objects.

import yt

ds = yt.load("ahf_halos/snap_N64L16_135.parameter", hubble_constant=0.7)
ad = ds.all_data()
# halo masses
print(ad["halos", "particle_mass"])
# halo radii
print(ad["halos", "virial_radius"])

Note

Currently the dimensionless Hubble parameter that yt needs is not provided in AHF outputs. So users need to provide the hubble_constant (default to 1.0) while loading datasets, as shown above.

Rockstar

Rockstar halo catalogs are loaded by providing the path to one of the .bin files. In the case where multiple files were produced, one need only provide the path to a single one of them. The field type for all fields is “halos”. Some fields of note available from Rockstar are:

Rockstar field	yt field name
halo id	particle_identifier
virial mass	particle_mass
virial radius	virial_radius
halo position	particle_position_(x,y,z)
halo velocity	particle_velocity_(x,y,z)

Numerous other Rockstar fields exist. To see them, check the field list by typing ds.field_list for a dataset loaded as ds. Like all other datasets, fields must be accessed through Data Objects.

import yt

ds = yt.load("rockstar_halos/halos_0.0.bin")
ad = ds.all_data()
# halo masses
print(ad["halos", "particle_mass"])
# halo radii
print(ad["halos", "virial_radius"])

Gadget FOF/SUBFIND

Gadget FOF/SUBFIND halo catalogs work in the same way as those created by Rockstar, except there are two field types: FOF for friend-of-friends groups and Subhalo for halos found with the SUBFIND substructure finder. Also like Rockstar, there are a number of fields specific to these halo catalogs.

FOF/SUBFIND field	yt field name
halo id	particle_identifier
halo mass	particle_mass
halo position	particle_position_(x,y,z)
halo velocity	particle_velocity_(x,y,z)
num. of particles	particle_number
num. of subhalos	subhalo_number (FOF only)

Many other fields exist, especially for SUBFIND subhalos. Check the field list by typing ds.field_list for a dataset loaded as ds. Like all other datasets, fields must be accessed through Data Objects.

import yt

ds = yt.load("gadget_fof_halos/groups_042/fof_subhalo_tab_042.0.hdf5")
ad = ds.all_data()
# The halo mass
print(ad["Group", "particle_mass"])
print(ad["Subhalo", "particle_mass"])
# Halo ID
print(ad["Group", "particle_identifier"])
print(ad["Subhalo", "particle_identifier"])
# positions
print(ad["Group", "particle_position_x"])
# velocities
print(ad["Group", "particle_velocity_x"])

Multidimensional fields can be accessed through the field name followed by an underscore and the index.

# x component of the spin
print(ad["Subhalo", "SubhaloSpin_0"])

Halo Data Containers

Halo member particles are accessed by creating halo data containers with the type of halo (“Group” or “Subhalo”) and the halo id. Scalar values for halos can be accessed in the same way. Halos also have mass, position, and velocity attributes.

halo = ds.halo("Group", 0)
# member particles for this halo
print(halo["member_ids"])
# halo virial radius
print(halo["Group_R_Crit200"])
# halo mass
print(halo.mass)

Subhalos containers can be created using either their absolute ids or their subhalo ids.

# first subhalo of the first halo
subhalo = ds.halo("Subhalo", (0, 0))
# this subhalo's absolute id
print(subhalo.group_identifier)
# member particles
print(subhalo["member_ids"])

OWLS FOF/SUBFIND

OWLS halo catalogs have a very similar structure to regular Gadget halo catalogs. The two field types are FOF and SUBFIND. See Gadget FOF/SUBFIND for more information. At this time, halo member particles cannot be loaded.

import yt

ds = yt.load("owls_fof_halos/groups_008/group_008.0.hdf5")
ad = ds.all_data()
# The halo mass
print(ad["FOF", "particle_mass"])

YTHaloCatalog

These are catalogs produced by the analysis discussed in Halo Analysis. In the case where multiple files were produced, one need only provide the path to a single one of them. The field type for all fields is “halos”. The fields available here are similar to other catalogs. Any addition Quantities will also be accessible as fields.

HaloCatalog field	yt field name
halo id	particle_identifier
virial mass	particle_mass
virial radius	virial_radius
halo position	particle_position_(x,y,z)
halo velocity	particle_velocity_(x,y,z)

import yt

ds = yt.load("tiny_fof_halos/DD0046/DD0046.0.h5")
ad = ds.all_data()
# The halo mass
print(ad["halos", "particle_mass"])

Halo Data Containers

Halo particles can be accessed by creating halo data containers with the type of halo (“halos”) and the halo id and then querying the “member_ids” field. Halo containers have mass, radius, position, and velocity attributes. Additional fields for which there will be one value per halo can be accessed in the same manner as conventional data containers.

halo = ds.halo("halos", 0)
# particles for this halo
print(halo["member_ids"])
# halo properties
print(halo.mass, halo.radius, halo.position, halo.velocity)

openPMD Data

openPMD is an open source meta-standard and naming scheme for mesh based data and particle data. It does not actually define a file format.

HDF5-containers respecting the minimal set of meta information from versions 1.0.0 and 1.0.1 of the standard are compatible. Support for the ED-PIC extension is not available. Mesh data in cartesian coordinates and particle data can be read by this frontend.

To load the first in-file iteration of a openPMD datasets using the standard HDF5 output format:

import yt

ds = yt.load("example-3d/hdf5/data00000100.h5")

If you operate on large files, you may want to modify the virtual chunking behaviour through open_pmd_virtual_gridsize. The supplied value is an estimate of the size of a single read request for each particle attribute/mesh (in Byte).

import yt

ds = yt.load("example-3d/hdf5/data00000100.h5", open_pmd_virtual_gridsize=10e4)
sp = yt.SlicePlot(ds, "x", ("openPMD", "rho"))
sp.show()

Particle data is fully supported:

import yt

ds = yt.load("example-3d/hdf5/data00000100.h5")
ad = f.all_data()
ppp = yt.ParticlePhasePlot(
    ad,
    ("all", "particle_position_y"),
    ("all", "particle_momentum_y"),
    ("all", "particle_weighting"),
)
ppp.show()

Caveats

• 1D, 2D and 3D data is compatible, but lower dimensional data might yield strange results since it gets padded and treated as 3D. Extraneous dimensions are set to be of length 1.0m and have a width of one cell.

• The frontend has hardcoded logic for renaming the openPMD position of particles to positionCoarse

PyNE Data

PyNE is an open source nuclear engineering toolkit maintained by the PyNE development team (pyne-dev@googlegroups.com). PyNE meshes utilize the Mesh-Oriented datABase (MOAB) and can be Cartesian or tetrahedral. In addition to field data, pyne meshes store pyne Material objects which provide a rich set of capabilities for nuclear engineering tasks. PyNE Cartesian (Hex8) meshes are supported by yt.

To create a pyne mesh:

from pyne.mesh import Mesh

num_divisions = 50
coords = linspace(-1, 1, num_divisions)
m = Mesh(structured=True, structured_coords=[coords, coords, coords])

Field data can then be added:

from pyne.mesh import iMeshTag

m.neutron_flux = IMeshTag()
# neutron_flux_data is a list or numpy array of size num_divisions^3
m.neutron_flux[:] = neutron_flux_data

Any field data or material data on the mesh can then be viewed just like any other yt dataset!

import yt

pf = yt.frontends.moab.data_structures.PyneMoabHex8Dataset(m)
s = yt.SlicePlot(pf, "z", "neutron_flux")
s.display()

RAMSES Data

In yt-4.x, RAMSES data is fully supported. If you are interested in taking a development or stewardship role, please contact the yt-dev mailing list. To load a RAMSES dataset, you can use the yt.load command and provide it the info*.txt filename. For instance, if you were in a directory with the following files:

output_00007
output_00007/amr_00007.out00001
output_00007/grav_00007.out00001
output_00007/hydro_00007.out00001
output_00007/info_00007.txt
output_00007/part_00007.out00001

You would feed it the filename output_00007/info_00007.txt:

import yt

ds = yt.load("output_00007/info_00007.txt")

yt will attempt to guess the fields in the file. For more control over the hydro fields or the particle fields, see Arguments passed to the load function.

yt also support the new way particles are handled introduced after version stable_17_09 (the version introduced after the 2017 Ramses User Meeting). In this case, the file part_file_descriptor.txt containing the different fields in the particle files will be read. If you use a custom version of RAMSES, make sure this file is up-to-date and reflects the true layout of the particles.

yt supports outputs made by the mainline RAMSES code as well as the RAMSES-RT fork. Files produces by RAMSES-RT are recognized as such based on the presence of a info_rt_*.txt file in the output directory.

Note

for backward compatibility, particles from the part_XXXXX.outYYYYY files have the particle type io by default (including dark matter, stars, tracer particles, …). Sink particles have the particle type sink.

Arguments passed to the load function

It is possible to provide extra arguments to the load function when loading RAMSES datasets. Here is a list of the ones specific to RAMSES:

fields

A list of fields to read from the hydro files. For example, in a pure hydro simulation with an extra custom field named my-awesome-field, one would specify the fields argument following this example:

import yt

fields = [
    "Density",
    "x-velocity",
    "y-velocity",
    "z-velocity",
    "Pressure",
    "my-awesome-field",
]
ds = yt.load("output_00123/info_00123.txt", fields=fields)
"my-awesome-field" in ds.field_list  # is True

extra_particle_fields

A list of tuples describing extra particles fields to read in. By default, yt will try to detect as many fields as possible, assuming the extra ones to be double precision floats. This argument is useful if you have extra fields besides the particle mass, position, and velocity fields that yt cannot detect automatically. For example, for a dataset containing two extra particle integer fields named family and info, one would do:

import yt

extra_fields = [("family", "I"), ("info", "I")]
ds = yt.load("output_00001/info_00001.txt", extra_particle_fields=extra_fields)
# ('all', 'family') and ('all', 'info') now in ds.field_list

The format of the extra_particle_fields argument is as follows: [('field_name_1', 'type_1'), ..., ('field_name_n', 'type_n')] where the second element of the tuple follows the python struct format convention. Note that if extra_particle_fields is defined, yt will not assume that the particle_birth_time and particle_metallicity fields are present in the dataset. If these fields are present, they must be explicitly enumerated in the extra_particle_fields argument.

cosmological

Force yt to consider a simulation to be cosmological or not. This may be useful for some specific simulations e.g. that run down to negative redshifts.

bbox

The subbox to load. yt will only read CPUs intersecting with the subbox. This is especially useful for large simulations or zoom-in simulations, where you don’t want to have access to data outside of a small region of interest. This argument will prevent yt from loading AMR files outside the subbox and will hence spare memory and time. For example, one could use

import yt

# Only load a small cube of size (0.1)**3
bbox = [[0.0, 0.0, 0.0], [0.1, 0.1, 0.1]]
ds = yt.load("output_00001/info_00001.txt", bbox=bbox)

# See the note below for the following examples
ds.right_edge == [1, 1, 1]  # is True

ad = ds.all_data()
ad["all", "particle_position_x"].max() > 0.1  # _may_ be True

bb = ds.box(left_edge=bbox[0], right_edge=bbox[1])
bb["all", "particle_position_x"].max() < 0.1  # is True

Note

When using the bbox argument, yt will read all the CPUs intersecting with the subbox. However it may also read some data outside the selected region. This is due to the fact that domains have a complicated shape when using Hilbert ordering. Internally, yt will hence assume the loaded dataset covers the entire simulation. If you only want the data from the selected region, you may want to use ds.box(...).

Note

The bbox feature is only available for datasets using Hilbert ordering.

max_level, max_level_convention

This will set the deepest level to be read from file. Both arguments have to be set, where the convention can be either “ramses” or “yt”.

In the “ramses” convention, levels go from 1 (the root grid) to levelmax, such that the finest cells have a size of boxsize/2**levelmax. In the “yt” convention, levels are numbered from 0 (the coarsest uniform grid at RAMSES’ levelmin) to max_level, such that the finest cells are 2**max_level smaller than the coarsest.

import yt

# Assuming RAMSES' levelmin=6, i.e. the structure is full
# down to levelmin=6
ds_all = yt.load("output_00080/info_00080.txt")
ds_yt = yt.load("output_00080/info_00080.txt", max_level=2, max_level_convention="yt")
ds_ramses = yt.load(
    "output_00080/info_00080.txt",
    max_level=8,
    max_level_convention="ramses",
)

any(ds_all.r["index", "grid_level"] > 2)  # True
all(ds_yt.r["index", "grid_level"] <= 2)  # True
all(ds_ramses.r["index", "grid_level"] <= 2)  # True

Adding custom particle fields

There are three way to make yt detect all the particle fields. For example, if you wish to make yt detect the birth time and metallicity of your particles, use one of these methods

1. yt.load method. Whenever loading a dataset, add the extra particle fields as a keyword argument to the yt.load call.

   import yt
   
   epf = [("particle_birth_time", "d"), ("particle_metallicity", "d")]
   ds = yt.load("dataset", extra_particle_fields=epf)
   
   ("io", "particle_birth_time") in ds.derived_field_list  # is True
   ("io", "particle_metallicity") in ds.derived_field_list  # is True
   

2. yt config method. If you don’t want to pass the arguments for each call of yt.load, you can add in your configuration

   [ramses-particles]
   fields = """
      particle_position_x, d
      particle_position_y, d
      particle_position_z, d
      particle_velocity_x, d
      particle_velocity_y, d
      particle_velocity_z, d
      particle_mass, d
      particle_identifier, i
      particle_refinement_level, I
      particle_birth_time, d
      particle_metallicity, d
   """
   

   Each line should contain the name of the field and its data type (d for double precision, f for single precision, i for integer and l for long integer). You can also configure the auto detected fields for fluid types by adding a section ramses-hydro, ramses-grav or ramses-rt in the config file. For example, if you customized your gravity files so that they contain the potential, the potential in the previous timestep and the x, y and z accelerations, you can use :

   [ramses-grav]
   fields = [ "Potential", "Potential-old", "x-acceleration", "y-acceleration", "z-acceleration" ]
   

3. New RAMSES way. Recent versions of RAMSES automatically write in their output an hydro_file_descriptor.txt file that gives information about which field is where. If you wish, you can simply create such a file in the folder containing the info_xxxxx.txt file

   # version:  1
   # ivar, variable_name, variable_type
    1, position_x, d
    2, position_y, d
    3, position_z, d
    4, velocity_x, d
    5, velocity_y, d
    6, velocity_z, d
    7, mass, d
    8, identity, i
    9, levelp, i
   10, birth_time, d
   11, metallicity, d
   

   It is important to note that this file should not end with an empty line (but in this case with 11, metallicity, d).

Note

The kind (i, d, I, …) of the field follow the python convention.

Customizing the particle type association

In versions of RAMSES more recent than December 2017, particles carry along a family array. The value of this array gives the kind of the particle, e.g. 1 for dark matter. It is possible to customize the association between particle type and family by customizing the yt config (see The Configuration), adding

[ramses-families]
gas_tracer = 100
star_tracer = 101
dm = 0
star = 1

Particle ages and formation times

For non-cosmological simulations, particle ages are stored in physical units on disk. To access the birth time for the particles, use the particle_birth_time field. The time recorded in this field is relative to the beginning of the simulation. Particles that were present in the initial conditions will have negative values for particle_birth_time.

For cosmological simulations that include star particles, RAMSES stores particle formation times as conformal times. To access the formation time field data in conformal units use the conformal_birth_time field. This will return the formation times of particles in the simulation in conformal units as a dimensionless array. To access the formation time in physical units, use the particle_birth_time field. Finally, to access the ages of star particles in your simulation, use the star_age field. Note that this field is defined for all particle types but will only make sense for star particles.

For simulations conducted in Newtownian coordinates, with no cosmology or comoving expansion, the time is equal to zero at the beginning of the simulation. That means that particles present in the initial conditions may have negative birth times. This can happen, for example, in idealized isolated galaxy simulations, where star particles are included in the initial conditions. For simulations conducted in cosmological comoving units, the time is equal to zero at the big bang, and all particles should have positive values for the particle_birth_time field.

To help clarify the above discussion, the following table describes the meaning of the various particle formation time and age fields:

Simulation type	Field name	Description
cosmological	conformal_birth_time	Formation time in conformal units (dimensionless)
any	particle_birth_time	The time relative to the beginning of the simulation when the particle was formed. For non-cosmological simulations, this field will have positive values for particles formed during the simulation and negative for particles of finite age in the initial conditions. For cosmological simulations this is the time the particle formed relative to the big bang, therefore the value of this field should be between 0 and 13.7 Gyr.
any	star_age	Age of the particle. Only physically meaningful for stars and particles that formed dynamically during the simulation.

RAMSES datasets produced by a version of the code newer than November 2017 contain the metadata necessary for yt to automatically distinguish between star particles and other particle types. If you are working with a dataset produced by a version of RAMSES older than November 2017, yt will only automatically recognize a single particle io. It may be convenient to define a particle filter in your scripts to distinguish between particles present in the initial conditions and particles that formed dynamically during the simulation by filtering particles with "conformal_birth_time" values equal to zero and not equal to zero. An example particle filter definition for dynamically formed stars might look like this:

@yt.particle_filter(requires=["conformal_birth_time"], filtered_type="io")
def stars(pfilter, data):
    filter = data[pfilter.filtered_type, "conformal_birth_time"] != 0
    return filter

For a cosmological simulation, this filter will distinguish between stars and dark matter particles.

SPH Particle Data

Note

For more information about how yt indexes and reads particle data, set the section How Particles are Indexed.

For all of the SPH frontends, yt uses cython-based SPH smoothing onto an in-memory octree to create deposited mesh fields from individual SPH particle fields.

This uses a standard M4 smoothing kernel and the smoothing_length field to calculate SPH sums, filling in the mesh fields. This gives you the ability to both track individual particles (useful for tasks like following contiguous clouds of gas that would be require a clump finder in grid data) as well as doing standard grid-based analysis (i.e. slices, projections, and profiles).

The smoothing_length variable is also useful for determining which particles can interact with each other, since particles more distant than twice the smoothing length do not typically see each other in SPH simulations. By changing the value of the smoothing_length and then re-depositing particles onto the grid, you can also effectively mimic what your data would look like at lower resolution.

Tipsy Data

Note

For more information about how yt indexes and reads particle data, set the section How Particles are Indexed.

See tipsy-notebook and SPH Particle Data for more details.

yt also supports loading Tipsy data. Many of its characteristics are similar to how Gadget data is loaded.

ds = load("./halo1e11_run1.00400")

Specifying Tipsy Cosmological Parameters and Setting Default Units

Cosmological parameters can be specified to Tipsy to enable computation of default units. For example do the following, to load a Tipsy dataset whose path is stored in the variable my_filename with specified cosmology parameters:

cosmology_parameters = {
    "current_redshift": 0.0,
    "omega_lambda": 0.728,
    "omega_matter": 0.272,
    "hubble_constant": 0.702,
}

ds = yt.load(my_filename, cosmology_parameters=cosmology_parameters)

If you wish to set the unit system directly, you can do so by using the unit_base keyword in the load statement.

import yt

ds = yt.load(filename, unit_base={"length", (1.0, "Mpc")})

See the documentation for the TipsyDataset class for more information.

Loading Cosmological Simulations

If you are not using a parameter file (i.e. non-Gasoline users), then you must use keyword cosmology_parameters when loading your data set to indicate to yt that it is a cosmological data set. If you do not wish to set any non-default cosmological parameters, you may pass an empty dictionary.

import yt

ds = yt.load(filename, cosmology_parameters={})

CfRadial Data

Cf/Radial is a CF compliant netCDF convention for radial data from radar and lidar platforms that supports both airborne and ground-based sensors. Because of its CF-compliance, CfRadial will allow researchers familiar with CF to read the data into a wide variety of analysis tools, models etc. For more see: [CfRadialDoc.v1.4.20160801.pdf](https://github.com/NCAR/CfRadial/blob/d4562a995d0589cea41f4f6a4165728077c9fc9b/docs/CfRadialDoc.v1.4.20160801.pdf)

yt provides support for loading cartesian-gridded CfRadial netcdf-4 files as well as polar coordinate Cfradial netcdf-4 files. When loading a standard CfRadial dataset in polar coordinates, yt will first build a sample on a cartesian grid (see Gridding Behavior). To load a CfRadial data file:

import yt

ds = yt.load("CfRadialGrid/grid1.nc")

Gridding Behavior

When you load a CfRadial dataset in polar coordinates (elevation, azimuth and range), yt will first build a sample by mapping the data onto a cartesian grid using the Python-ARM Radar Toolkit (pyart). Grid points are found by interpolation of all data points within a specified radius of influence. This data, now in x, y, z coordinate domain is then saved as a new dataset and subsequent loads of the original native CfRadial dataset will use the gridded file. Mapping the data from spherical to Cartesian coordinates is useful for 3D volume rendering the data using yt.

See the documentation for the CFRadialDataset class for a description of how to adjust the gridding parameters and storage of the gridded file.