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, 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.
Fields in yt are denoted by a two-element tuple, of the form
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.
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.
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.
For a more programmatic method of accessing fields, you can utilize the
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
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())
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
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. 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.
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
concatenates all particle types.) For example, if I were to then query for the
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"])
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”
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.
pyne, and so on.) The default name for
particle fields, if they do not have a particle type affiliated with them, is
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
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,
aliased into an internal yt-name (for example, (
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 (see Unit Systems
for a guide to using the different unit systems in yt), 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.)
Recall that fields are formally accessed in two parts: (‘field type‘, ‘field name‘). Here we describe the different field types you will encounter:
pyneand 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
dzand 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 (see Unit Systems for more information).
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.
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)
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 () and tesla (), respectively, and numerically . However, if we examine the base units, we find that they do indeed have different dimensions:
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:
where 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.
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.
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
used to set and retrieve field parameter values for a given data object. In the
cases above, the field parameters are
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.
yt provides a way to compute gradients of spatial fields using the
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"))
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_magnitude". To see an example
of how to create and use these fields, see Complicated Derived Fields.
add_gradient_fields currently only supports Cartesian geometries!
Every particle will contain both a
that tracks the position and velocity (respectively) in code units.
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
cic) deposition of the
all particle type, you would
access the field
yt defines a few particular types of deposition internally, and creating new
ones can be done by modifying the files
yt/fields/particle_fields.py, although that is an advanced topic
somewhat outside the scope of this section. The default deposition types
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_massin a given mesh field and divides by the volume.
mass- this field takes the total sum of
particle_massin 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.
For gas particles from SPH simulations, each particle will typically carry
a field for the smoothing length
h, which is roughly equivalent to
rho are the particle mass and density
respectively. This can be useful for doing neighbour finding.
As a note, SPH fields are special cases of the “deposited” particle fields.
They contain an additional piece of information about what is being examined,
and any fields that are recognized as being identical to intrinsic yt fields
will be aliased. For example, in a Gadget dataset, the smoothed density of
Gas particles will be aliased to the mesh field
("gas", "density") so
that operations conducted on the mesh field
density (which are frequent
occurrences) will operate on the smoothed gas density from the SPH particles.
The special deposition types based on smoothing (
smoothed) are defined in
yt/geometry/particle_smooth.pyx, and they require non-local
operations defined on a variable number of neighbors. The default smoothing
type utilizes a cubic spline kernel and uses 64 nearest neighbors, providing a
volume-normalized smoothing. Other types are possible, and yt provides
functionality for many different types of non-local correlation between
particles. (For instance, a friends-of-friends grouper has been built on this
Every particle field on a smoothed particle type is the source for a smoothed
field; this is not always useful, but it errs on the side of extra fields,
rather than too few fields. (For instance, it may be unlikely that the
smoothed angular momentum field will be useful.) The naming scheme is an
extension of the scheme described in Deposited Particle Fields, and is
defined as such:
("deposit", "particletype_smoothed_fieldname"), where
fieldname is the name of the field being smoothed. For example, smoothed
Temperature of the
Gas particle type would be
"Gas_smoothed_Temperature"), which in most cases would be aliased to the
("gas", "temperature") for convenience.
Other smoothing kernels besides the cubic spline one are available through a
kernel_name of the method
Current available kernel names include:
quintic- spline kernels.
wendland6- Wendland kernels.
The added smoothed particle field can be accessed by
("deposit", "particletype_kernelname_smoothed_fieldname") (except for the
cubic spline kernel, which obeys the naming scheme given above).
One particularly useful field that can be created is that of the distance to the Nth-nearest neighbor. This field can then be used as input to smoothing operations, in the case when a particular particle type does not have an associated smoothing length or other length estimate.
yt defines this field as a plugin, and it can be added like so:
import yt from yt.fields.particle_fields import \ add_nearest_neighbor_field ds = yt.load("snapshot_033/snap_033.0.hdf5") fn, = add_nearest_neighbor_field("all", "particle_position", ds) dd = ds.all_data() print(dd[fn])
fn here is the “field name” that yt adds. It will be of the form
(ptype, nearest_neighbor_distance_NN) where
NN is the integer. By
default this is 64, but it can be supplied as the final argument to
add_nearest_neighbor_field. For the example above, it would be
This can then be used as input to the function
add_volume_weighted_smoothed_field, which can enable smoothing particle
types that would normally not be smoothed.