yt.frontends.tipsy.data_structures module

class yt.frontends.tipsy.data_structures.TipsyDataset(filename=None, *args, **kwargs)

Bases: yt.frontends.sph.data_structures.SPHDataset

add_deposited_particle_field(deposit_field, method, kernel_name='cubic', weight_field=None)

Add a new deposited particle field

Creates a new deposited field based on the particle deposit_field.

Parameters

• deposit_field (tuple) – The field name tuple of the particle field the deposited field will be created from. This must be a field name tuple so yt can appropriately infer the correct particle type.

• method (string) – This is the “method name” which will be looked up in the particle_deposit namespace as methodname_deposit. Current methods include simple_smooth, sum, std, cic, weighted_mean, nearest and count.

• kernel_name (string, default 'cubic') – This is the name of the smoothing kernel to use. It is only used for the simple_smooth method and is otherwise ignored. Current supported kernel names include cubic, quartic, quintic, wendland2, wendland4, and wendland6.

• weight_field ((field_type, field_name) or None) – Weighting field name for deposition method weighted_mean. If None, use the particle mass.

Returns

Return type

The field name tuple for the newly created field.

add_field(name, function, sampling_type, *, force_override=False, **kwargs)

Dataset-specific call to add_field

Add a new field, along with supplemental metadata, to the list of available fields. This respects a number of arguments, all of which are passed on to the constructor for DerivedField.

Parameters

• name (str) – is the name of the field.

• function (callable) – A function handle that defines the field. Should accept arguments (field, data)

• sampling_type (str) – “cell” or “particle” or “local”

• force_override (bool) – If False (default), an error will be raised if a field of the same name already exists.

• units (str) – A plain text string encoding the unit. Powers must be in python syntax (** instead of ^).

• take_log (bool) – Describes whether the field should be logged

• validators (list) – A list of FieldValidator objects

• vector_field (bool) – Describes the dimensionality of the field. Currently unused.

• display_name (str) – A name used in the plots

• force_override – Whether to override an existing derived field. Does not work with on-disk fields.

add_gradient_fields(fields=None)

Add gradient fields.

Creates four new grid-based fields that represent the components of the gradient of an existing field, plus an extra field for the magnitude of the gradient. The gradient is computed using second-order centered differences.

Parameters

fields (str or tuple(str, str), or a list of the previous) – Label(s) for at least one field. Can either represent a tuple (<field type>, <field fname>) or simply the field name. Warning: several field types may match the provided field name, in which case the first one discovered internally is used.

Returns

Return type

A list of field name tuples for the newly created fields.

Raises

• YTFieldNotParsable – If fields are not parsable to yt field keys.

• YTFieldNotFound : – If at least one field can not be identified.

Examples

>>> grad_fields = ds.add_gradient_fields(("gas", "density"))
>>> print(grad_fields)
... [
...     ("gas", "density_gradient_x"),
...     ("gas", "density_gradient_y"),
...     ("gas", "density_gradient_z"),
...     ("gas", "density_gradient_magnitude"),
... ]

Note that the above example assumes ds.geometry == ‘cartesian’. In general, the function will create gradient components along the axes of the dataset coordinate system. For instance, with cylindrical data, one gets ‘density_gradient_<r,theta,z>’

add_mesh_sampling_particle_field(sample_field, ptype='all')

Add a new mesh sampling particle field

Creates a new particle field which has the value of the deposit_field at the location of each particle of type ptype.

Parameters

• sample_field (tuple) – The field name tuple of the mesh field to be deposited onto the particles. This must be a field name tuple so yt can appropriately infer the correct particle type.

• ptype (string, default 'all') – The particle type onto which the deposition will occur.

Returns

Return type

The field name tuple for the newly created field.

Examples

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

>>> print("The density at the location of the particle is:")
... print(ds.r["all", "cell_gas_density"])
The density at the location of the particle is:
[9.33886124e-30 1.22174333e-28 1.20402333e-28 ... 2.77410331e-30
 8.79467609e-31 3.50665136e-30] g/cm**3

>>> len(ds.r["all", "cell_gas_density"]) == len(ds.r["all", "particle_ones"])
True

add_particle_filter(filter)

Add particle filter to the dataset.

Add filter to the dataset and set up relevant derived_field. It will also add any filtered_type that the filter depends on.

add_particle_union(union)

all_data(find_max=False, **kwargs)

all_data is a wrapper to the Region object for creating a region which covers the entire simulation domain.

property arr

Converts an array into a yt.units.yt_array.YTArray

The returned YTArray will be dimensionless by default, but can be cast to arbitrary units using the units keyword argument.

Parameters

• input_array (Iterable) – A tuple, list, or array to attach units to

• units (String unit specification, unit symbol or astropy object) – The units of the array. Powers must be specified using python syntax (cm**3, not cm^3).

• input_units (Deprecated in favor of 'units') –

• dtype (string or NumPy dtype object) – The dtype of the returned array data

Examples

>>> import yt
>>> import numpy as np
>>> ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
>>> a = ds.arr([1, 2, 3], "cm")
>>> b = ds.arr([4, 5, 6], "m")
>>> a + b
YTArray([ 401.,  502.,  603.]) cm
>>> b + a
YTArray([ 4.01,  5.02,  6.03]) m

Arrays returned by this function know about the dataset’s unit system

>>> a = ds.arr(np.ones(5), "code_length")
>>> a.in_units("Mpccm/h")
YTArray([ 1.00010449,  1.00010449,  1.00010449,  1.00010449,
         1.00010449]) Mpc

property backup_filename

property basename

box(left_edge, right_edge, **kwargs)

box is a wrapper to the Region object for creating a region without having to specify a center value. It assumes the center is the midpoint between the left_edge and right_edge.

Keyword arguments are passed to the initializer of the YTRegion object (e.g. ds.region).

property checksum

Computes md5 sum of a dataset.

Note: Currently this property is unable to determine a complete set of files that are a part of a given dataset. As a first approximation, the checksum of parameter_file is calculated. In case parameter_file is a directory, checksum of all files inside the directory is calculated.

close()

conversion_factors: Optional[Dict[str, float]] = None

coordinates = None

create_field_info()

default_field = ('gas', 'density')

default_fluid_type = 'gas'

default_kernel_name = 'cubic'

default_units = {'length_unit': 'cm', 'magnetic_unit': 'gauss', 'mass_unit': 'g', 'temperature_unit': 'K', 'time_unit': 's', 'velocity_unit': 'cm/s'}

define_unit(symbol, value, tex_repr=None, offset=None, prefixable=False)

Define a new unit and add it to the dataset’s unit registry.

Parameters

• symbol (string) – The symbol for the new unit.

• value (tuple or YTQuantity) – The definition of the new unit in terms of some other units. For example, one would define a new “mph” unit with (1.0, “mile/hr”)

• tex_repr (string, optional) – The LaTeX representation of the new unit. If one is not supplied, it will be generated automatically based on the symbol string.

• offset (float, optional) – The default offset for the unit. If not set, an offset of 0 is assumed.

• prefixable (bool, optional) – Whether or not the new unit can use SI prefixes. Default: False

Examples

>>> ds.define_unit("mph", (1.0, "mile/hr"))
>>> two_weeks = YTQuantity(14.0, "days")
>>> ds.define_unit("fortnight", two_weeks)

property derived_field_list

property directory

domain_center = None

domain_dimensions = None

domain_left_edge = None

domain_offset = array([0, 0, 0])

domain_right_edge = None

domain_width = None

property field_list

field_units: Optional[Dict[Union[Tuple[str, str], str], unyt.unit_object.Unit]] = None

property fields

fields_detected = False

property filename

filter_bbox = False

find_field_values_at_point(fields, coords)

Returns the values [field1, field2,…] of the fields at the given coordinates. Returns a list of field values in the same order as the input fields.

find_field_values_at_points(fields, coords)

Returns the values [field1, field2,…] of the fields at the given [(x1, y1, z2), (x2, y2, z2),…] points. Returns a list of field values in the same order as the input fields.

find_max(field, source=None, to_array=True)

Returns (value, location) of the maximum of a given field.

This is a wrapper around _find_extremum

find_min(field, source=None, to_array=True)

Returns (value, location) for the minimum of a given field.

This is a wrapper around _find_extremum

fluid_types: Tuple[str, …] = ('gas', 'deposit', 'index')

force_periodicity(val=True)

Override box periodicity to (True, True, True). Use ds.force_periodicty(False) to use the actual box periodicity.

property fullpath

geometry: yt.geometry.geometry_enum.Geometry = 'cartesian'

get_smallest_appropriate_unit(v, quantity='distance', return_quantity=False)

Returns the largest whole unit smaller than the YTQuantity passed to it as a string.

The quantity keyword can be equal to distance or time. In the case of distance, the units are: ‘Mpc’, ‘kpc’, ‘pc’, ‘au’, ‘rsun’, ‘km’, etc. For time, the units are: ‘Myr’, ‘kyr’, ‘yr’, ‘day’, ‘hr’, ‘s’, ‘ms’, etc.

If return_quantity is set to True, it finds the largest YTQuantity object with a whole unit and a power of ten as the coefficient, and it returns this YTQuantity.

get_unit_from_registry(unit_str)

Creates a unit object matching the string expression, using this dataset’s unit registry.

Parameters

unit_str (str) – string that we can parse for a sympy Expr.

has_key(key)

Checks units, parameters, and conversion factors. Returns a boolean.

property index

property ires_factor

known_filters: Optional[Dict[str, yt.data_objects.particle_filters.ParticleFilter]] = None

property max_level

property min_level

property num_neighbors

property parameter_filename

property particle_fields_by_type

property particle_type_counts

particle_types: Tuple[str, …] = ('io',)

particle_types_raw: Optional[Tuple[str, …]] = ('io',)

particle_unions: Optional[Dict[str, yt.data_objects.unions.ParticleUnion]] = None

property particles_exist

property periodicity

print_key_parameters()

print_stats()

property quan

Converts an scalar into a yt.units.yt_array.YTQuantity

The returned YTQuantity will be dimensionless by default, but can be cast to arbitrary units using the units keyword argument.

Parameters

• input_scalar (an integer or floating point scalar) – The scalar to attach units to

• units (String unit specification, unit symbol or astropy object) – The units of the quantity. Powers must be specified using python syntax (cm**3, not cm^3).

• input_units (Deprecated in favor of 'units') –

• dtype (string or NumPy dtype object) – The dtype of the array data.

Examples

>>> import yt
>>> ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")

>>> a = ds.quan(1, "cm")
>>> b = ds.quan(2, "m")
>>> a + b
201.0 cm
>>> b + a
2.01 m

Quantities created this way automatically know about the unit system of the dataset.

>>> a = ds.quan(5, "code_length")
>>> a.in_cgs()
1.543e+25 cm

relative_refinement(l0, l1)

set_code_units()

set_field_label_format(format_property, value)

Set format properties for how fields will be written out. Accepts

format_property : string indicating what property to set value: the value to set for that format_property

set_units()

Creates the unit registry for this dataset.

setup_cosmology()

If this dataset is cosmological, add a cosmology object.

setup_deprecated_fields()

property sph_smoothing_style

storage_filename = None

property unique_identifier

property units

property use_sph_normalization

class yt.frontends.tipsy.data_structures.TipsyFile(ds, io, filename, file_id, range=None)

Bases: yt.data_objects.static_output.ParticleFile

count(selector)

end: Optional[int] = None

file_id: int

filename: str

select(selector)

start: Optional[int] = None

total_particles: Optional[DefaultDict[str, int]] = None