Fields in yt

Fields are spatially-dependent quantities associated with a parent dataset. Examples of fields are gas density, gas temperature, particle mass, etc. The fundamental way to query data in yt is to access a field, either in its raw form (by examining a data container) or a processed form (derived quantities, projections, aggregations, and so on). “Field” is something of a loaded word, as it can refer to quantities that are defined everywhere, which we refer to as “mesh” or “fluid” fields, or discrete points that populate the domain, traditionally thought of as “particle” fields. The word “particle” here is gradually falling out of favor, as these discrete fields can be any type of sparsely populated data.

What are fields?

Fields in yt are denoted by a two-element tuple, of the form (field_type, field_name). The first element, the “field type” is a category for a field. Possible field types used in yt include gas (for fluid mesh fields defined on a mesh) or io (for fields defined at particle locations). Field types can also correspond to distinct particle of fluid types in a single simulation. For example, a plasma physics simulation using the Particle in Cell method might have particle types corresponding to electrons and ions. See Field types known to yt below for more info about field types in yt.

The second element of field tuples, the field_name, denotes the specific field to select, given the field type. Possible field names include density, velocity_x or pressure — these three fields are examples of field names that might be used for a fluid defined on a mesh. Examples of particle fields include particle_mass particle_position or particle_velocity_x In general, particle field names are prefixed by particle_, which makes it easy to distinguish between a particle field or a mesh field when no field type is provided.

What fields are available?

We provide a full list of fields that yt recognizes by default at Field List. If you want to create additional custom derived fields, see Creating Derived Fields.

Every dataset has an attribute, ds.fields. This attribute possesses attributes itself, each of which is a “field type,” and each field type has as its attributes the fields themselves. When one of these is printed, it returns information about the field and things like units and so on. You can use this for tab-completing as well as easier access to information.

Additionally, if you have ipywidgets installed and are in a Jupyter environment, you can view the rich representation of the fields (including source code) by either typing ds.fields as the last item in a cell or by calling display(ds.fields). The resulting output will have tabs and source:

../_images/fields_ipywidget.png

As an example, you might browse the available fields like so:

print(dir(ds.fields))
print(dir(ds.fields.gas))
print(ds.fields.gas.density)

On an Enzo dataset, the result from the final command would look something like this::

Alias Field for "('enzo', 'Density')" (gas, density): (units: g/cm**3)

You can use this to easily explore available fields, particularly through tab-completion in Jupyter/IPython.

It’s also possible to iterate over the list of fields associated with each field type. For example, to print all of the 'gas' fields, one might do:

for field in ds.fields.gas:
    print(field)

You can also check if a given field is associated with a field type using standard python syntax:

# these examples evaluate to True for a dataset that has ('gas', 'density')
"density" in ds.fields.gas
("gas", "density") in ds.fields.gas
ds.fields.gas.density in ds.fields.gas

For a more programmatic method of accessing fields, you can utilize the ds.field_list, ds.derived_field_list and some accessor methods to gain information about fields. The full list of fields available for a dataset can be found as the attribute field_list for native, on-disk fields and derived_field_list for derived fields (derived_field_list is a superset of field_list). You can view these lists by examining a dataset like this:

ds = yt.load("my_data")
print(ds.field_list)
print(ds.derived_field_list)

By using the field_info() class, one can access information about a given field, like its default units or the source code for it.

ds = yt.load("my_data")
ds.index
print(ds.field_info["gas", "pressure"].get_units())
print(ds.field_info["gas", "pressure"].get_source())

Using fields to access data

Warning

These specific operations will load the entire field – which can be extremely memory intensive with large datasets! If you are looking to compute quantities, see Data Objects for methods for computing aggregates, averages, subsets, regriddings, etc.

The primary use of fields in yt is to access data from a dataset. For example, if I want to use a data object (see Data Objects for more detail about data objects) to access the ('gas', 'density') field, one can do any of the following:

ad = ds.all_data()

# just a field name
density = ad["density"]

# field tuple with no parentheses
density = ad["gas", "density"]

# full field tuple
density = ad[("gas", "density")]

# through the ds.fields object
density = ad[ds.fields.gas.density]

The first data access example is the simplest. In that example, the field type is inferred from the name of the field. However, yt will complain if there are multiple field names that could be meant by this simple string access. The next two examples use the field type explicitly, this might be necessary if there is more than one field type with a “density” field defined in the same dataset. The third example is slightly more verbose but is syntactically identical to the second example due to the way indexing works in the Python language.

The final example uses the ds.fields object described above. This way of accessing fields lends itself to interactive use, especially if you make heavy use of IPython’s tab completion features. Any of these ways of denoting the ('gas', 'density') field can be used when supplying a field name to a yt data object, analysis routines, or plotting and visualization function.

Accessing Fields without a Field Type

In previous versions of yt, there was a single mechanism of accessing fields on a data container – by their name, which was mandated to be a single string, and which often varied between different code frontends. yt 3.0 allows for datasets containing multiple different types of fluid fields, mesh fields, particles (with overlapping or disjoint lists of fields). However, to preserve backward compatibility and make interactive use simpler, yt will still accept field names given as a string and will try to infer the field type given a field name.

As an example, we may be in a situation where have multiple types of particles which possess the particle_position field. In the case where a data container, here called ad (short for “all data”) contains a field, we can specify which particular particle type we want to query:

print(ad["dark_matter", "particle_position"])
print(ad["stars", "particle_position"])
print(ad["black_holes", "particle_position"])

Each of these three fields may have different sizes. In order to enable falling back on asking only for a field by the name, yt will use the most recently requested field type for subsequent queries. (By default, if no field has been queried, it will look for the special field all, which concatenates all particle types.) For example, if I were to then query for the velocity:

print(ad["particle_velocity"])

it would select black_holes as the field type, since the last field accessed used that field type.

The same operations work for fluid and mesh fields. As an example, in some cosmology simulations, we may want to examine the mass of particles in a region versus the mass of gas. We can do so by examining the special “deposit” field types (described below) versus the gas fields:

print(ad["deposit", "dark_matter_density"] / ad["gas", "density"])

The deposit field type is a mesh field, so it will have the same shape as the gas density. If we weren’t using deposit, and instead directly querying a particle field, this wouldn’t work, as they are different shapes. This is the primary difference, in practice, between mesh and particle fields – they will be different shapes and so cannot be directly compared without translating one to the other, typically through a “deposition” or “smoothing” step.

How are fields implemented?

There are two classes of fields in yt. The first are those fields that exist external to yt, which are immutable and can be queried – most commonly, these are fields that exist on disk. These will often be returned in units that are not in a known, external unit system (except possibly by design, on the part of the code that wrote the data), and yt will take every effort possible to use the names by which they are referred to by the data producer. The default field type for mesh fields that are “on-disk” is the name of the code frontend. (For example, art, enzo, pyne, and so on.) The default name for particle fields, if they do not have a particle type affiliated with them, is io.

The second class of field is the “derived field.” These are fields that are functionally defined, either ab initio or as a transformation or combination of other fields. For example, when dealing with simulation codes, often the fields that are evolved and output to disk are not the fields that are the most relevant to researchers. Rather than examining the internal gas energy, it is more convenient to think of the temperature. By applying one or multiple functions to on-disk quantities, yt can construct new derived fields from them. Derived fields do not always have to relate to the data found on disk; special fields such as x, y, phi and dz all relate exclusively to the geometry of the mesh, and provide information about the mesh that can be used elsewhere for further transformations.

For more information, see Creating Derived Fields.

There is a third, borderline class of field in yt, as well. This is the “alias” type, where a field on disk (for example, (frontend, Density)) is aliased into an internal yt-name (for example, (gas, density)). The aliasing process allows universally-defined derived fields to take advantage of internal names, and it also provides an easy way to address what units something should be returned in. If an aliased field is requested (and aliased fields will always be lowercase, with underscores separating words) it will be returned in the units specified by the unit system of the database, whereas if the frontend-specific field is requested, it will not undergo any unit conversions from its natural units. (This rule is occasionally violated for fields which are mesh-dependent, specifically particle masses in some cosmology codes.)

Field types known to yt

Recall that fields are formally accessed in two parts: (‘field type’, ‘field name’). Here we describe the different field types you will encounter:

  • frontend-name – Mesh or fluid fields that exist on-disk default to having the name of the frontend as their type name (e.g., enzo, flash, pyne and so on). The units of these types are whatever units are designated by the source frontend when it writes the data.

  • index – This field type refers to characteristics of the mesh, whether that mesh is defined by the simulation or internally by an octree indexing of particle data. A few handy fields are x, y, z, theta, phi, radius, dx, dy, dz and so on. Default units are in CGS.

  • gas – This is the usual default for simulation frontends for fluid types. These fields are typically aliased to the frontend-specific mesh fields for grid-based codes or to the deposit fields for particle-based codes. Default units are in the unit system of the dataset.

  • particle type – These are particle fields that exist on-disk as written by individual frontends. If the frontend designates names for these particles (i.e. particle type) those names are the field types. Additionally, any particle unions or filters will be accessible as field types. Examples of particle types are Stars, DM, io, etc. Like the front-end specific mesh or fluid fields, the units of these fields are whatever was designated by the source frontend when written to disk.

  • io – If a data frontend does not have a set of multiple particle types, this is the default for all particles.

  • all – This is a special particle field type that represents a concatenation of all particle field types using Particle Unions.

  • deposit – This field type refers to the deposition of particles (discrete data) onto a mesh, typically to compute smoothing kernels, local density estimates, counts, and the like. See Deposited Particle Fields for more information.

While it is best to be explicit access fields by their full names (i.e. (‘field type’, ‘field name’)), yt provides an abbreviated interface for accessing common fields (i.e. ‘field name’). In the abbreviated case, yt will assume you want the last field type accessed. If you haven’t previously accessed a field type, it will default to field type = 'all' in the case of particle fields and field type = 'gas' in the case of mesh fields.

Field Plugins

Derived fields are organized via plugins. Inside yt are a number of field plugins, which take information about fields in a dataset and then construct derived fields on top of them. This allows them to take into account variations in naming system, units, data representations, and most importantly, allows only the fields that are relevant to be added. This system will be expanded in future versions to enable much deeper semantic awareness of the data types being analyzed by yt.

The field plugin system works in this order:

  • Available, inherent fields are identified by yt

  • The list of enabled field plugins is iterated over. Each is called, and new derived fields are added as relevant.

  • Any fields which are not available, or which throw errors, are discarded.

  • Remaining fields are added to the list of derived fields available for a dataset

  • Dependencies for every derived field are identified, to enable data preloading

Field plugins can be loaded dynamically, although at present this is not particularly useful. Plans for extending field plugins to dynamically load, to enable simple definition of common types (divergence, curl, etc), and to more verbosely describe available fields, have been put in place for future versions.

The field plugins currently available include:

  • Angular momentum fields for particles and fluids

  • Astrophysical fields, such as those related to cosmology

  • Vector fields for fluid fields, such as gradients and divergences

  • Particle vector fields

  • Magnetic field-related fields

  • Species fields, such as for chemistry species (yt can recognize the entire periodic table in field names and construct ionization fields as need be)

Field Labeling

By default yt formats field labels nicely for plots. To adjust the chosen format you can use the ds.set_field_label_format method like so:

ds = yt.load("my_data")
ds.set_field_label_format("ionization_label", "plus_minus")

The first argument accepts a format_property, or specific aspect of the labeling, and the second sets the corresponding value. Currently available format properties are

  • ionization_label: sets how the ionization state of ions are labeled. Available

    options are "plus_minus" and "roman_numeral"

Energy and Momemtum Fields

Fields in yt representing energy and momentum quantities follow a specific naming convention (as of yt-4.x). In hydrodynamic simulations, the relevant quantities are often energy per unit mass or volume, momentum, or momentum density To distinguish clearly between the different types of fields, the following naming convention is adhered to:

  • Energy per unit mass fields are named as specific_*_energy

  • Energy per unit volume fields are named as *_energy_density

  • Momentum fields should be named momentum_density_* for momentum per unit density, or momentum_* for momentum, where the * indicates one of three coordinate axes in any supported coordinate system.

For example, in the case of kinetic energy, the fields should be kinetic_energy_density and specific_kinetic_energy.

In versions of yt previous to v4.0.0, these conventions were not adopted, and so energy fields in particular could be ambiguous with respect to units. For example, the kinetic_energy field was actually kinetic energy per unit volume, whereas the thermal_energy field, usually defined by various frontends, was typically thermal energy per unit mass. The above scheme rectifies these problems, but for the time being the previous field names are mapped to the current field naming scheme with a deprecation warning. These aliases will be removed in yt v4.1.0. Following this, new fields will be created which have dimensions of energy, which will simply be named *_energy as above.

Magnetic Fields

Magnetic fields require special handling, because their dimensions are different in different systems of units, in particular between the CGS and MKS (SI) systems of units. Superficially, it would appear that they are in the same dimensions, since the units of the magnetic field in the CGS and MKS system are gauss (\(\rm{G}\)) and tesla (\(\rm{T}\)), respectively, and numerically \(1~\rm{G} = 10^{-4}~\rm{T}\). However, if we examine the base units, we find that they do indeed have different dimensions:

\[\begin{split}\rm{1~G = 1~\frac{\sqrt{g}}{\sqrt{cm}\cdot{s}}} \\ \rm{1~T = 1~\frac{kg}{A\cdot{s^2}}}\end{split}\]

It is easier to see the difference between the dimensionality of the magnetic field in the two systems in terms of the definition of the magnetic pressure:

\[\begin{split}p_B = \frac{B^2}{8\pi}~\rm{(cgs)} \\ p_B = \frac{B^2}{2\mu_0}~\rm{(MKS)}\end{split}\]

where \(\mu_0 = 4\pi \times 10^{-7}~\rm{N/A^2}\) is the vacuum permeability. yt automatically detects on a per-frontend basis what units the magnetic should be in, and allows conversion between different magnetic field units in the different unit systems as well. To determine how to set up special magnetic field handling when designing a new frontend, check out Creating Aliases for Magnetic Fields.

Species Fields

For many types of data, yt is able to detect different chemical elements and molecules within the dataset, as well as their abundances and ionization states. Examples include:

  • CO (Carbon monoxide)

  • Co (Cobalt)

  • OVI (Oxygen ionized five times)

  • H:math:^{2+} (Molecular Hydrogen ionized once)

  • H:math:^{-} (Hydrogen atom with an additional electron)

The naming scheme for the fields starts with prefixes in the form MM[_[mp][NN]]. MM is the molecule, defined as a concatenation of atomic symbols and numbers, with no spaces or underscores. The second sequence is only required if ionization states are present in the dataset, and is of the form p and m to indicate “plus” or “minus” respectively, followed by the number. If a given species has no ionization states given, the prefix is simply MM.

For the examples above, the prefixes would be:

  • CO

  • Co

  • O_p5

  • H2_p1

  • H_m1

The name El is used for electron fields, as it is unambiguous and will not be utilized elsewhere. Neutral ionic species (e.g. H I, O I) are represented as MM_p0. Additionally, the isotope of \(^2\).

Finally, in those frontends which are single-fluid, these fields for each species are defined:

  • MM[_[mp][NN]]_fraction

  • MM[_[mp][NN]]_number_density

  • MM[_[mp][NN]]_density

  • MM[_[mp][NN]]_mass

To refer to the number density of the entirety of a single atom or molecule (regardless of its ionization state), please use the MM_nuclei_density fields.

Many datasets do not have species defined, but there may be an underlying assumption of primordial abundances of H and He which are either fully ionized or fully neutral. This will also determine the value of the mean molecular weight of the gas, which will determine the value of the temperature if derived from another quantity like the pressure or thermal energy. To allow for these possibilities, there is a keyword argument default_species_fields which can be passed to load():

import yt

ds = yt.load(
    "GasSloshing/sloshing_nomag2_hdf5_plt_cnt_0150", default_species_fields="ionized"
)

By default, the value of this optional argument is None, which will not initialize any default species fields. If the default_species_fields argument is not set to None, then the following fields are defined:

  • H_nuclei_density

  • He_nuclei_density

More specifically, if default_species_fields="ionized", then these additional fields are defined:

  • H_p1_number_density (Ionized hydrogen: equal to the value of H_nuclei_density)

  • He_p2_number_density (Doubly ionized helium: equal to the value of He_nuclei_density)

  • El_number_density (Free electrons: assuming full ionization)

Whereas if default_species_fields="neutral", then these additional fields are defined:

  • H_p0_number_density (Neutral hydrogen: equal to the value of H_nuclei_density)

  • He_p0_number_density (Neutral helium: equal to the value of He_nuclei_density)

In this latter case, because the gas is neutral, El_number_density is not defined.

The mean_molecular_weight field will be constructed from the abundances of the elements in the dataset. If no element or molecule fields are defined, the value of this field is determined by the value of default_species_fields. If it is set to None or "ionized", the mean_molecular_weight field is set to \(\mu \approx 0.6\), whereas if default_species_fields is set to "neutral", then the mean_molecular_weight field is set to \(\mu \approx 1.14\). Some frontends do not directly store the gas temperature in their datasets, in which case it must be computed from the pressure and/or thermal energy as well as the mean molecular weight, so check this carefully!

Particle Fields

Naturally, particle fields contain properties of particles rather than grid cells. By examining the particle field in detail, you can see that each element of the field array represents a single particle, whereas in mesh fields each element represents a single mesh cell. This means that for the most part, operations cannot operate on both particle fields and mesh fields simultaneously in the same way, like filters (see Filtering your Dataset). However, many of the particle fields have corresponding mesh fields that can be populated by “depositing” the particle values onto a yt grid as described below.

Field Parameters

Certain fields require external information in order to be calculated. For example, the radius field has to be defined based on some point of reference and the radial velocity field needs to know the bulk velocity of the data object so that it can be subtracted. This information is passed into a field function by setting field parameters, which are user-specified data that can be associated with a data object. The set_field_parameter() and get_field_parameter() functions are used to set and retrieve field parameter values for a given data object. In the cases above, the field parameters are center and bulk_velocity respectively – the two most commonly used field parameters.

ds = yt.load("my_data")
ad = ds.all_data()

ad.set_field_parameter("wickets", 13)

print(ad.get_field_parameter("wickets"))

If a field parameter is not set, get_field_parameter will return None. Within a field function, these can then be retrieved and used in the same way.

def _wicket_density(field, data):
    n_wickets = data.get_field_parameter("wickets")
    if n_wickets is None:
        # use a default if unset
        n_wickets = 88
    return data["gas", "density"] * n_wickets

For a practical application of this, see Radial Velocity Profile.

Gradient Fields

yt provides a way to compute gradients of spatial fields using the add_gradient_fields() method. If you have a spatially-based field such as density or temperature, and want to calculate the gradient of that field, you can do it like so:

ds = yt.load("GasSloshing/sloshing_nomag2_hdf5_plt_cnt_0150")
grad_fields = ds.add_gradient_fields(("gas", "temperature"))

where the grad_fields list will now have a list of new field names that can be used in calculations, representing the 3 different components of the field and the magnitude of the gradient, e.g., "temperature_gradient_x", "temperature_gradient_y", "temperature_gradient_z", and "temperature_gradient_magnitude". To see an example of how to create and use these fields, see Complicated Derived Fields.

Note

add_gradient_fields currently only supports Cartesian geometries!

Relative Vector Fields

yt makes use of “relative” fields for certain vector fields, which are fields which have been defined relative to a particular origin in the space of that field. For example, relative particle positions can be specified relative to a center coordinate, and relative velocities can be specified relative to a bulk velocity. These origin points are specified by setting field parameters as detailed below (see Field Parameters for more information).

The relative fields which are currently supported for gas fields are:

  • ("gas", "relative_velocity_{xyz}"), defined by setting the "bulk_velocity" field parameter

  • ("gas", "relative_magnetic_field_{xyz}"), defined by setting the "bulk_magnetic_field" field parameter

For particle fields, for a given particle type ptype, the relative fields which are supported are:

  • (ptype, "relative_particle_position"), defined by setting the "center" field parameter

  • (ptype, "relative_particle_velocity"), defined by setting the "bulk_velocity" field parameter

  • (ptype, "relative_particle_position_{xyz}"), defined by setting the "center" field parameter

  • (ptype, "relative_particle_velocity_{xyz}"), defined by setting the "bulk_velocity" field parameter

These fields are in use when defining magnitude fields, line-of-sight fields, etc.. The "bulk_{}" field parameters are [0.0, 0.0, 0.0] by default, and the "center" field parameter depends on the data container in use.

There is currently no mechanism to create new relative fields, but one may be added at a later time.

Line of Sight Fields

In astrophysics applications, one often wants to know the component of a vector field along a given line of sight. If you are doing a projection of a vector field along an axis, or just want to obtain the values of a vector field component along an axis, you can use a line-of-sight field. For projections, this will be handled automatically:

prj = yt.ProjectionPlot(
    ds, "z", ("gas", "velocity_los"), weight_field=("gas", "density")
)

Which, because the axis is "z", will give you the same result if you had projected the "velocity_z" field. This also works for off-axis projections:

prj = yt.OffAxisProjectionPlot(
    ds, [0.1, -0.2, 0.3], ("gas", "velocity_los"), weight_field=("gas", "density")
)

This shows that the projection axis can be along a principle axis of the domain or an arbitrary off-axis 3-vector (which will be automatically normalized). If you want to examine a line-of-sight vector within a 3-D data object, set the "axis" field parameter:

dd = ds.all_data()
# Set to one of [0, 1, 2] for ["x", "y", "z"] axes
dd.set_field_parameter("axis", 1)
print(dd["gas", "magnetic_field_los"])
# Set to a three-vector for an off-axis component
dd.set_field_parameter("axis", [0.3, 0.4, -0.7])
print(dd["gas", "velocity_los"])

Warning

If you need to change the axis of the line of sight on the same data container (sphere, box, cylinder, or whatever), you will need to delete the field using del dd["velocity_los"] and re-generate it.

At this time, this functionality is enabled for the velocity and magnetic vector fields, ("gas", "velocity_los") and ("gas", "magnetic_field_los"). The following fields built into yt make use of these line-of-sight fields:

  • ("gas", "sz_kinetic") uses ("gas", "velocity_los")

  • ("gas", "rotation_measure") uses ("gas", "magnetic_field_los")

General Particle Fields

Every particle will contain both a particle_position and particle_velocity that tracks the position and velocity (respectively) in code units.

Deposited Particle Fields

In order to turn particle (discrete) fields into fields that are deposited in some regular, space-filling way (even if that space is empty, it is defined everywhere) yt provides mechanisms for depositing particles onto a mesh. These are in the special field-type space deposit, and are typically of the form ("deposit", "particletype_depositiontype") where depositiontype is the mechanism by which the field is deposited, and particletype is the particle type of the particles being deposited. If you are attempting to examine the cloud-in-cell (cic) deposition of the all particle type, you would access the field ("deposit", "all_cic").

yt defines a few particular types of deposition internally, and creating new ones can be done by modifying the files yt/geometry/particle_deposit.pyx and yt/fields/particle_fields.py, although that is an advanced topic somewhat outside the scope of this section. The default deposition types available are:

  • count - this field counts the total number of particles of a given type in a given mesh zone. Note that because, in general, the mesh for particle datasets is defined by the number of particles in a region, this may not be the most useful metric. This may be made more useful by depositing particle data onto an Arbitrary Grids Objects.

  • density - this field takes the total sum of particle_mass in a given mesh field and divides by the volume.

  • mass - this field takes the total sum of particle_mass in each mesh zone.

  • cic - this field performs cloud-in-cell interpolation (see Section 2.2 for more information) of the density of particles in a given mesh zone.

  • smoothed - this is a special deposition type. See discussion below for more information, in SPH Fields.

You can also directly use the add_deposited_particle_field() function defined on each dataset to depose any particle field onto the mesh like so:

import yt

ds = yt.load("output_00080/info_00080.txt")
fname = ds.add_deposited_particle_field(
    ("all", "particle_velocity_x"), method="nearest"
)

print(f"The velocity of the particles are (stored in {fname}")
print(ds.r[fname])

Note

In this example, we are using the returned field name as our input. You could also access it directly, but it might take a slightly different form than you expect – in this particular case, the field name will be ("deposit", "all_nn_velocity_x"), which has removed the prefix particle_ from the deposited name!

Possible deposition methods are:

  • simple_smooth - perform an SPH-like deposition of the field onto the mesh optionally accepting a kernel_name.

  • sum - sums the value of the particle field for all particles found in each cell.

  • std - computes the standard deviation of the value of the particle field for all particles found in each cell.

  • cic - performs cloud-in-cell interpolation (see Section 2.2 for more information) of the particle field on a given mesh zone.

  • weighted_mean - computes the mean of the particle field, weighted by the field passed into weight_field (by default, it uses the particle mass).

  • count - counts the number of particles in each cell.

  • nearest - assign to each cell the value of the closest particle.

In addition, the add_deposited_particle_field() function returns the name of the newly created field.

Deposited particle fields can be useful for visualizing particle data, including particles without defined smoothing lengths. See Additional Notes for Plotting Particle Data for more information.

Mesh Sampling Particle Fields

In order to turn mesh fields into discrete particle field, yt provides a mechanism to do sample mesh fields at particle locations. This operation is the inverse operation of Deposited Particle Fields: for each particle the cell containing the particle is found and the value of the field in the cell is assigned to the particle. This is for example useful when using tracer particles to have access to the Eulerian information for Lagrangian particles.

The particle fields are named (ptype, cell_ftype_fname) where ptype is the particle type onto which the deposition occurs, ftype is the mesh field type (e.g. gas) and fname is the field (e.g. temperature, density, …). You can directly use the add_mesh_sampling_particle_field() function defined on each dataset to impose a field onto the particles like so:

import yt

ds = yt.load("output_00080/info_00080.txt")
ds.add_mesh_sampling_particle_field(("gas", "temperature"), ptype="all")

print("The temperature at the location of the particles is")
print(ds.r["all", "cell_gas_temperature"])

For octree codes (e.g. RAMSES), you can trigger the build of an index so that the next sampling operations will be mush faster

import yt

ds = yt.load("output_00080/info_00080.txt")
ds.add_mesh_sampling_particle_field(("gas", "temperature"), ptype="all")

ad = ds.all_data()
ad[
    "all", "cell_index"
]  # Trigger the build of the index of the cell containing the particles
ad["all", "cell_gas_temperature"]  # This is now much faster

SPH Fields

See What’s New and Different in yt 4.0?.

In previous versions of yt, there were ways of computing the distance to the N-th nearest neighbor of a particle, as well as computing the nearest particle value on a mesh. Unfortunately, because of changes to the way that particles are regarded in yt, these are not currently available. We hope that this will be rectified in future versions and are tracking this in Issue 3301. You can read a bit more about the way yt now handles particles in the section How Particles are Indexed.

But! It is possible to compute the smoothed values from SPH particles on grids. For example, one can construct a covering grid that extends over the entire domain of a simulation, with resolution 256x256x256, and compute the gas density with this reasonable terse command:

import yt

ds = yt.load("snapshot_033/snap_033.0.hdf5")
cg = ds.r[::256j, ::256j, ::256j]
smoothed_values = cg["gas", "density"]

This will work for any smoothed field; any field that is under the "gas" field type will be a smoothed field in an SPH-based simulation. Here we have used the ds.r[] notation, as described in Slicing Syntax for Selecting Data for creating what’s called an “arbitrary grid” (YTArbitraryGrid). You can, of course, also supply left and right edges to make the grid take up a much smaller portion of the domain, as well, by supplying the arguments as detailed in Selecting Fixed Resolution Regions and supplying the bounds as the first and second elements in each element of the slice.