# yt.frontends.athena_pp.data_structures module¶

Data structures for Athena.

class yt.frontends.athena_pp.data_structures.AthenaPPDataset(filename, dataset_type='athena_pp', storage_filename=None, parameters=None, units_override=None, unit_system='code')[source]
add_deposited_particle_field(deposit_field, method, kernel_name='cubic', weight_field='particle_mass')

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, mesh_id, and nearest. 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 (string, default 'particle_mass') – Weighting field name for deposition method weighted_mean. The field name tuple for the newly created field.
add_field(name, function=None, sampling_type=None, **kwargs)

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) 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 particle_type (bool) – Is this a particle (1D) field? vector_field (bool) – Describes the dimensionality of the field. Currently unused. display_name (str) – A name used in the plots force_override (bool) – Whether to override an existing derived field. Does not work with on-disk fields.
add_gradient_fields(input_field)

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. Currently only supported in Cartesian geometries. The gradient is computed using second-order centered differences.

Parameters: input_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 field type. A list of field name tuples for the newly created fields.

Examples

>>> grad_fields = ds.add_gradient_fields(("gas","temperature"))

add_particle_filter(filter)

Add particle filter to the dataset.

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

add_particle_union(union)
add_smoothed_particle_field(smooth_field, method='volume_weighted', nneighbors=64, kernel_name='cubic')

Add a new smoothed particle field

Creates a new smoothed field based on the particle smooth_field.

Parameters: smooth_field (tuple) – The field name tuple of the particle field the smoothed field will be created from. This must be a field name tuple so yt can appropriately infer the correct particle type. method (string, default 'volume_weighted') – The particle smoothing method to use. Can only be ‘volume_weighted’ for now. nneighbors (int, default 64) – The number of neighbors to examine during the process. kernel_name (string, default cubic) – This is the name of the smoothing kernel to use. Current supported kernel names include cubic, quartic, quintic, wendland2, wendland4, and wendland6. The field name tuple for the newly created field.
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.

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 input_units keyword argument.

Parameters: input_array (Iterable) – A tuple, list, or array to attach units to input_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). dtype (string or NumPy dtype object) – The dtype of the returned array data

Examples

>>> import yt
>>> import numpy as np
>>> 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

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.

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()
coordinates = None
create_field_info()
default_field = ('gas', 'density')
default_fluid_type = 'gas'
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)

derived_field_list
domain_center = None
domain_dimensions = None
domain_left_edge = None
domain_right_edge = None
domain_width = None
field_list
field_units = None
fields
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)

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

find_min(field)

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

fluid_types = ('gas', 'deposit', 'index')
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.
h
has_key(key)

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

hierarchy
hub_upload()
index
ires_factor
known_filters = None
max_level
particle_fields_by_type
particle_type_counts
particle_types = ('io',)
particle_types_raw = ('io',)
particle_unions = None
particles_exist
print_key_parameters()
print_stats()
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 input_units keyword argument.

Parameters: input_scalar (an integer or floating point scalar) – The scalar to attach units to input_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). dtype (string or NumPy dtype object) – The dtype of the array data.

Examples

>>> import yt

>>> 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_deprecated_fields()
storage_filename = None
class yt.frontends.athena_pp.data_structures.AthenaPPGrid(id, index, level)[source]
OverlappingSiblings = None
apply_units(arr, units)
argmax(field, axis=None)

Return the values at which the field is maximized.

This will, in a parallel-aware fashion, find the maximum value and then return to you the values at that maximum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field

Parameters: field (string or tuple field name) – The field to maximize. axis (string or list of strings, optional) – If supplied, the fields to sample along; if not supplied, defaults to the coordinate fields. This can be the name of the coordinate fields (i.e., ‘x’, ‘y’, ‘z’) or a list of fields, but cannot be 0, 1, 2. A list of YTQuantities as specified by the axis argument.

Examples

>>> temp_at_max_rho = reg.argmax("density", axis="temperature")
>>> max_rho_xyz = reg.argmax("density")
>>> t_mrho, v_mrho = reg.argmax("density", axis=["temperature",
...                 "velocity_magnitude"])
>>> x, y, z = reg.argmax("density")

argmin(field, axis=None)

Return the values at which the field is minimized.

This will, in a parallel-aware fashion, find the minimum value and then return to you the values at that minimum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field

Parameters: field (string or tuple field name) – The field to minimize. axis (string or list of strings, optional) – If supplied, the fields to sample along; if not supplied, defaults to the coordinate fields. This can be the name of the coordinate fields (i.e., ‘x’, ‘y’, ‘z’) or a list of fields, but cannot be 0, 1, 2. A list of YTQuantities as specified by the axis argument.

Examples

>>> temp_at_min_rho = reg.argmin("density", axis="temperature")
>>> min_rho_xyz = reg.argmin("density")
>>> t_mrho, v_mrho = reg.argmin("density", axis=["temperature",
...                 "velocity_magnitude"])
>>> x, y, z = reg.argmin("density")

blocks
child_index_mask

Generates self.child_index_mask, which is -1 where there is no child, and otherwise has the ID of the grid that resides there.

child_indices
child_mask

Generates self.child_mask, which is zero where child grids exist (and thus, where higher resolution data is available).

chunks(fields, chunking_style, **kwargs)
clear_data()

Clear out the following things: child_mask, child_indices, all fields, all field parameters.

clone()

Clone a data object.

This will make a duplicate of a data object; note that the field_parameters may not necessarily be deeply-copied. If you modify the field parameters in-place, it may or may not be shared between the objects, depending on the type of object that that particular field parameter is.

Notes

One use case for this is to have multiple identical data objects that are being chunked over in different orders.

Examples

>>> ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
>>> sp = ds.sphere("c", 0.1)
>>> sp_clone = sp.clone()
>>> sp["density"]
>>> print sp.field_data.keys()
[("gas", "density")]
>>> print sp_clone.field_data.keys()
[]

comm = None
convert(datatype)

This will attempt to convert a given unit to cgs from code units. It either returns the multiplicative factor or throws a KeyError.

count(selector)
count_particles(selector, x, y, z)
deposit(positions, fields=None, method=None, kernel_name='cubic')
fcoords
fcoords_vertex
fwidth
get_data(fields=None)
get_dependencies(fields)
get_field_parameter(name, default=None)

This is typically only used by derived field functions, but it returns parameters used to generate fields.

get_global_startindex()

Return the integer starting index for each dimension at the current level.

get_position(index)

Returns center position of an index.

get_vertex_centered_data(fields, smoothed=True, no_ghost=False)
has_field_parameter(name)

Checks if a field parameter is set.

has_key(key)

Checks if a data field already exists.

icoords
index
integrate(field, weight=None, axis=None)

Compute the integral (projection) of a field along an axis.

This projects a field along an axis.

Parameters: field (string or tuple field name) – The field to project. weight (string or tuple field name) – The field to weight the projection by axis (string) – The axis to project along. YTProjection

Examples

>>> column_density = reg.integrate("density", axis="z")

ires
keys()
max(field, axis=None)

Compute the maximum of a field, optionally along an axis.

This will, in a parallel-aware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘mip’ for maximum intensity. If the max has already been requested, it will use the cached extrema value.

Parameters: field (string or tuple field name) – The field to maximize. axis (string, optional) – If supplied, the axis to project the maximum along. Either a scalar or a YTProjection.

Examples

>>> max_temp = reg.max("temperature")
>>> max_temp_proj = reg.max("temperature", axis="x")

max_level
mean(field, axis=None, weight=None)

Compute the mean of a field, optionally along an axis, with a weight.

This will, in a parallel-aware fashion, compute the mean of the given field. If an axis is supplied, it will return a projection, where the weight is also supplied. By default the weight field will be “ones” or “particle_ones”, depending on the field being averaged, resulting in an unweighted average.

Parameters: field (string or tuple field name) – The field to average. axis (string, optional) – If supplied, the axis to compute the mean along (i.e., to project along) weight (string, optional) – The field to use as a weight. Scalar or YTProjection.

Examples

>>> avg_rho = reg.mean("density", weight="cell_volume")
>>> rho_weighted_T = reg.mean("temperature", axis="y", weight="density")

min(field, axis=None)

Compute the minimum of a field.

This will, in a parallel-aware fashion, compute the minimum of the given field. Supplying an axis is not currently supported. If the max has already been requested, it will use the cached extrema value.

Parameters: field (string or tuple field name) – The field to minimize. axis (string, optional) – If supplied, the axis to compute the minimum along. Scalar.

Examples

>>> min_temp = reg.min("temperature")

min_level
particle_operation(*args, **kwargs)
partition_index_2d(axis)
partition_index_3d(ds, padding=0.0, rank_ratio=1)
partition_index_3d_bisection_list()

Returns an array that is used to drive _partition_index_3d_bisection, below.

partition_region_3d(left_edge, right_edge, padding=0.0, rank_ratio=1)

Given a region, it subdivides it into smaller regions for parallel analysis.

pf
profile(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field='cell_mass', accumulation=False, fractional=False, deposition='ngp')

Create a 1, 2, or 3D profile object from this data_source.

The dimensionality of the profile object is chosen by the number of fields given in the bin_fields argument. This simply calls yt.data_objects.profiles.create_profile().

Parameters: bin_fields (list of strings) – List of the binning fields for profiling. fields (list of strings) – The fields to be profiled. n_bins (int or list of ints) – The number of bins in each dimension. If None, 64 bins for each bin are used for each bin field. Default: 64. extrema (dict of min, max tuples) – Minimum and maximum values of the bin_fields for the profiles. The keys correspond to the field names. Defaults to the extrema of the bin_fields of the dataset. If a units dict is provided, extrema are understood to be in the units specified in the dictionary. logs (dict of boolean values) – Whether or not to log the bin_fields for the profiles. The keys correspond to the field names. Defaults to the take_log attribute of the field. units (dict of strings) – The units of the fields in the profiles, including the bin_fields. weight_field (str or tuple field identifier) – The weight field for computing weighted average for the profile values. If None, the profile values are sums of the data in each bin. accumulation (bool or list of bools) – If True, the profile values for a bin n are the cumulative sum of all the values from bin 0 to n. If -True, the sum is reversed so that the value for bin n is the cumulative sum from bin N (total bins) to n. If the profile is 2D or 3D, a list of values can be given to control the summation in each dimension independently. Default: False. fractional (If True the profile values are divided by the sum of all) – the profile data such that the profile represents a probability distribution function. deposition (Controls the type of deposition used for ParticlePhasePlots.) – Valid choices are ‘ngp’ and ‘cic’. Default is ‘ngp’. This parameter is ignored the if the input fields are not of particle type.

Examples

Create a 1d profile. Access bin field from profile.x and field data from profile[<field_name>].

>>> ds = load("DD0046/DD0046")
...                          [("gas", "temperature"),
...                          ("gas", "velocity_x")])
>>> print (profile.x)
>>> print (profile["gas", "temperature"])
>>> plot = profile.plot()

ptp(field)

Compute the range of values (maximum - minimum) of a field.

This will, in a parallel-aware fashion, compute the “peak-to-peak” of the given field.

Parameters: field (string or tuple field name) – The field to average. Scalar

Examples

>>> rho_range = reg.ptp("density")

retrieve_ghost_zones(n_zones, fields, all_levels=False, smoothed=False)
save_as_dataset(filename=None, fields=None)

Export a data object to a reloadable yt dataset.

This function will take a data object and output a dataset containing either the fields presently existing or fields given in the fields list. The resulting dataset can be reloaded as a yt dataset.

Parameters: filename (str, optional) – The name of the file to be written. If None, the name will be a combination of the original dataset and the type of data container. fields (list of string or tuple field names, optional) – If this is supplied, it is the list of fields to be saved to disk. If not supplied, all the fields that have been queried will be saved. filename – The name of the file that has been created. str

Examples

>>> import yt
>>> sp = ds.sphere(ds.domain_center, (10, "Mpc"))
>>> fn = sp.save_as_dataset(fields=["density", "temperature"])
>>> # the original data container is available as the data attribute
>>> print (sds.data["density"])
[  4.46237613e-32   4.86830178e-32   4.46335118e-32 ...,   6.43956165e-30
3.57339907e-30   2.83150720e-30] g/cm**3
[  1.00000000e+00   1.00000000e+00   1.00000000e+00 ...,   4.40108359e+04
4.54380547e+04   4.72560117e+04] K

save_object(name, filename=None)

Save an object. If filename is supplied, it will be stored in a shelve file of that name. Otherwise, it will be stored via yt.data_objects.api.GridIndex.save_object().

select(selector, source, dest, offset)
select_blocks(selector)
select_fcoords(dobj)
select_fwidth(dobj)
select_icoords(dobj)
select_ires(dobj)
select_particles(selector, x, y, z)
select_tcoords(dobj)
selector
set_field_parameter(name, val)

Here we set up dictionaries that get passed up and down and ultimately to derived fields.

shape
smooth(*args, **kwargs)
std(field, weight=None)

Compute the variance of a field.

This will, in a parallel-ware fashion, compute the variance of the given field.

Parameters: field (string or tuple field name) – The field to calculate the variance of weight (string or tuple field name) – The field to weight the variance calculation by. Defaults to unweighted if unset. Scalar
sum(field, axis=None)

Compute the sum of a field, optionally along an axis.

This will, in a parallel-aware fashion, compute the sum of the given field. If an axis is specified, it will return a projection (using method type “sum”, which does not take into account path length) along that axis.

Parameters: field (string or tuple field name) – The field to sum. axis (string, optional) – If supplied, the axis to sum along. Either a scalar or a YTProjection.

Examples

>>> total_vol = reg.sum("cell_volume")
>>> cell_count = reg.sum("ones", axis="x")

tiles
to_dataframe(fields=None)

Export a data object to a pandas DataFrame.

This function will take a data object and construct from it and optionally a list of fields a pandas DataFrame object. If pandas is not importable, this will raise ImportError.

Parameters: fields (list of strings or tuple field names, default None) – If this is supplied, it is the list of fields to be exported into the data frame. If not supplied, whatever fields presently exist will be used. df – The data contained in the object. DataFrame

Examples

>>> dd = ds.all_data()
>>> df1 = dd.to_dataframe(["density", "temperature"])
>>> dd["velocity_magnitude"]
>>> df2 = dd.to_dataframe()

to_glue(fields, label='yt', data_collection=None)

Takes specific fields in the container and exports them to Glue (http://www.glueviz.org) for interactive analysis. Optionally add a label. If you are already within the Glue environment, you can pass a data_collection object, otherwise Glue will be started.

write_out(filename, fields=None, format='%0.16e')

Write out the YTDataContainer object in a text file.

This function will take a data object and produce a tab delimited text file containing the fields presently existing and the fields given in the fields list.

Parameters: filename (String) – The name of the file to write to. fields (List of string, Default = None) – If this is supplied, these fields will be added to the list of fields to be saved to disk. If not supplied, whatever fields presently exist will be used. format (String, Default = "%0.16e") – Format of numbers to be written in the file. ValueError – Raised when there is no existing field. YTException – Raised when field_type of supplied fields is inconsistent with the field_type of existing fields.

Examples

>>> ds = fake_particle_ds()
>>> sp = ds.sphere(ds.domain_center, 0.25)
>>> sp.write_out("sphere_1.txt")
>>> sp.write_out("sphere_2.txt", fields=["cell_volume"])

class yt.frontends.athena_pp.data_structures.AthenaPPHierarchy(ds, dataset_type='athena_pp')[source]
clear_all_data()

This routine clears all the data currently being held onto by the grids and the data io handler.

comm = None
convert(unit)
float_type = 'float64'
get_data(node, name)

Return the dataset with a given name located at node in the datafile.

get_dependencies(fields)
get_levels()
get_smallest_dx()

Returns (in code units) the smallest cell size in the simulation.

grid

alias of AthenaPPGrid

grid_corners
load_object(name)

Load and return and object from the data_file using the Pickle protocol, under the name name on the node /Objects.

lock_grids_to_parents()

This function locks grid edges to their parents.

This is useful in cases where the grid structure may be somewhat irregular, or where setting the left and right edges is a lossy process. It is designed to correct situations where left/right edges may be set slightly incorrectly, resulting in discontinuities in images and the like.

parameters
partition_index_2d(axis)
partition_index_3d(ds, padding=0.0, rank_ratio=1)
partition_index_3d_bisection_list()

Returns an array that is used to drive _partition_index_3d_bisection, below.

partition_region_3d(left_edge, right_edge, padding=0.0, rank_ratio=1)

Given a region, it subdivides it into smaller regions for parallel analysis.

print_stats()

Prints out (stdout) relevant information about the simulation

save_data(array, node, name, set_attr=None, force=False, passthrough=False)

Arbitrary numpy data will be saved to the region in the datafile described by node and name. If data file does not exist, it throws no error and simply does not save.

save_object(obj, name)

Save an object (obj) to the data_file using the Pickle protocol, under the name name on the node /Objects.

select_grids(level)

Returns an array of grids at level.

class yt.frontends.athena_pp.data_structures.AthenaPPLogarithmicIndex(ds, dataset_type='athena_pp')[source]
comm = None
convert(unit)
get_data(node, name)

Return the dataset with a given name located at node in the datafile.

get_dependencies(fields)
get_smallest_dx()

Returns (in code units) the smallest cell size in the simulation.

load_object(name)

Load and return and object from the data_file using the Pickle protocol, under the name name on the node /Objects.

partition_index_2d(axis)
partition_index_3d(ds, padding=0.0, rank_ratio=1)
partition_index_3d_bisection_list()

Returns an array that is used to drive _partition_index_3d_bisection, below.

partition_region_3d(left_edge, right_edge, padding=0.0, rank_ratio=1)

Given a region, it subdivides it into smaller regions for parallel analysis.

save_data(array, node, name, set_attr=None, force=False, passthrough=False)

Arbitrary numpy data will be saved to the region in the datafile described by node and name. If data file does not exist, it throws no error and simply does not save.

save_object(obj, name)

Save an object (obj) to the data_file using the Pickle protocol, under the name name on the node /Objects.

class yt.frontends.athena_pp.data_structures.AthenaPPLogarithmicMesh(mesh_id, filename, connectivity_indices, connectivity_coords, index, blocks, dims)[source]
apply_units(arr, units)
argmax(field, axis=None)

Return the values at which the field is maximized.

This will, in a parallel-aware fashion, find the maximum value and then return to you the values at that maximum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field

Parameters: field (string or tuple field name) – The field to maximize. axis (string or list of strings, optional) – If supplied, the fields to sample along; if not supplied, defaults to the coordinate fields. This can be the name of the coordinate fields (i.e., ‘x’, ‘y’, ‘z’) or a list of fields, but cannot be 0, 1, 2. A list of YTQuantities as specified by the axis argument.

Examples

>>> temp_at_max_rho = reg.argmax("density", axis="temperature")
>>> max_rho_xyz = reg.argmax("density")
>>> t_mrho, v_mrho = reg.argmax("density", axis=["temperature",
...                 "velocity_magnitude"])
>>> x, y, z = reg.argmax("density")

argmin(field, axis=None)

Return the values at which the field is minimized.

This will, in a parallel-aware fashion, find the minimum value and then return to you the values at that minimum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field

Parameters: field (string or tuple field name) – The field to minimize. axis (string or list of strings, optional) – If supplied, the fields to sample along; if not supplied, defaults to the coordinate fields. This can be the name of the coordinate fields (i.e., ‘x’, ‘y’, ‘z’) or a list of fields, but cannot be 0, 1, 2. A list of YTQuantities as specified by the axis argument.

Examples

>>> temp_at_min_rho = reg.argmin("density", axis="temperature")
>>> min_rho_xyz = reg.argmin("density")
>>> t_mrho, v_mrho = reg.argmin("density", axis=["temperature",
...                 "velocity_magnitude"])
>>> x, y, z = reg.argmin("density")

blocks
chunks(fields, chunking_style, **kwargs)
clear_data()

Clears out all data from the YTDataContainer instance, freeing memory.

clone()

Clone a data object.

This will make a duplicate of a data object; note that the field_parameters may not necessarily be deeply-copied. If you modify the field parameters in-place, it may or may not be shared between the objects, depending on the type of object that that particular field parameter is.

Notes

One use case for this is to have multiple identical data objects that are being chunked over in different orders.

Examples

>>> ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
>>> sp = ds.sphere("c", 0.1)
>>> sp_clone = sp.clone()
>>> sp["density"]
>>> print sp.field_data.keys()
[("gas", "density")]
>>> print sp_clone.field_data.keys()
[]

comm = None
convert(datatype)

This will attempt to convert a given unit to cgs from code units. It either returns the multiplicative factor or throws a KeyError.

count(selector)
count_particles(selector, x, y, z)
deposit(positions, fields=None, method=None, kernel_name='cubic')
fcoords
fcoords_vertex
fwidth
get_data(fields=None)
get_dependencies(fields)
get_field_parameter(name, default=None)

This is typically only used by derived field functions, but it returns parameters used to generate fields.

get_global_startindex()

Return the integer starting index for each dimension at the current level.

has_field_parameter(name)

Checks if a field parameter is set.

has_key(key)

Checks if a data field already exists.

icoords
index
integrate(field, weight=None, axis=None)

Compute the integral (projection) of a field along an axis.

This projects a field along an axis.

Parameters: field (string or tuple field name) – The field to project. weight (string or tuple field name) – The field to weight the projection by axis (string) – The axis to project along. YTProjection

Examples

>>> column_density = reg.integrate("density", axis="z")

ires
keys()
max(field, axis=None)

Compute the maximum of a field, optionally along an axis.

This will, in a parallel-aware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘mip’ for maximum intensity. If the max has already been requested, it will use the cached extrema value.

Parameters: field (string or tuple field name) – The field to maximize. axis (string, optional) – If supplied, the axis to project the maximum along. Either a scalar or a YTProjection.

Examples

>>> max_temp = reg.max("temperature")
>>> max_temp_proj = reg.max("temperature", axis="x")

max_level
mean(field, axis=None, weight=None)

Compute the mean of a field, optionally along an axis, with a weight.

This will, in a parallel-aware fashion, compute the mean of the given field. If an axis is supplied, it will return a projection, where the weight is also supplied. By default the weight field will be “ones” or “particle_ones”, depending on the field being averaged, resulting in an unweighted average.

Parameters: field (string or tuple field name) – The field to average. axis (string, optional) – If supplied, the axis to compute the mean along (i.e., to project along) weight (string, optional) – The field to use as a weight. Scalar or YTProjection.

Examples

>>> avg_rho = reg.mean("density", weight="cell_volume")
>>> rho_weighted_T = reg.mean("temperature", axis="y", weight="density")

min(field, axis=None)

Compute the minimum of a field.

This will, in a parallel-aware fashion, compute the minimum of the given field. Supplying an axis is not currently supported. If the max has already been requested, it will use the cached extrema value.

Parameters: field (string or tuple field name) – The field to minimize. axis (string, optional) – If supplied, the axis to compute the minimum along. Scalar.

Examples

>>> min_temp = reg.min("temperature")

min_level
partition_index_2d(axis)
partition_index_3d(ds, padding=0.0, rank_ratio=1)
partition_index_3d_bisection_list()

Returns an array that is used to drive _partition_index_3d_bisection, below.

partition_region_3d(left_edge, right_edge, padding=0.0, rank_ratio=1)

Given a region, it subdivides it into smaller regions for parallel analysis.

pf
profile(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field='cell_mass', accumulation=False, fractional=False, deposition='ngp')

Create a 1, 2, or 3D profile object from this data_source.

The dimensionality of the profile object is chosen by the number of fields given in the bin_fields argument. This simply calls yt.data_objects.profiles.create_profile().

Parameters: bin_fields (list of strings) – List of the binning fields for profiling. fields (list of strings) – The fields to be profiled. n_bins (int or list of ints) – The number of bins in each dimension. If None, 64 bins for each bin are used for each bin field. Default: 64. extrema (dict of min, max tuples) – Minimum and maximum values of the bin_fields for the profiles. The keys correspond to the field names. Defaults to the extrema of the bin_fields of the dataset. If a units dict is provided, extrema are understood to be in the units specified in the dictionary. logs (dict of boolean values) – Whether or not to log the bin_fields for the profiles. The keys correspond to the field names. Defaults to the take_log attribute of the field. units (dict of strings) – The units of the fields in the profiles, including the bin_fields. weight_field (str or tuple field identifier) – The weight field for computing weighted average for the profile values. If None, the profile values are sums of the data in each bin. accumulation (bool or list of bools) – If True, the profile values for a bin n are the cumulative sum of all the values from bin 0 to n. If -True, the sum is reversed so that the value for bin n is the cumulative sum from bin N (total bins) to n. If the profile is 2D or 3D, a list of values can be given to control the summation in each dimension independently. Default: False. fractional (If True the profile values are divided by the sum of all) – the profile data such that the profile represents a probability distribution function. deposition (Controls the type of deposition used for ParticlePhasePlots.) – Valid choices are ‘ngp’ and ‘cic’. Default is ‘ngp’. This parameter is ignored the if the input fields are not of particle type.

Examples

Create a 1d profile. Access bin field from profile.x and field data from profile[<field_name>].

>>> ds = load("DD0046/DD0046")
...                          [("gas", "temperature"),
...                          ("gas", "velocity_x")])
>>> print (profile.x)
>>> print (profile["gas", "temperature"])
>>> plot = profile.plot()

ptp(field)

Compute the range of values (maximum - minimum) of a field.

This will, in a parallel-aware fashion, compute the “peak-to-peak” of the given field.

Parameters: field (string or tuple field name) – The field to average. Scalar

Examples

>>> rho_range = reg.ptp("density")

save_as_dataset(filename=None, fields=None)

Export a data object to a reloadable yt dataset.

This function will take a data object and output a dataset containing either the fields presently existing or fields given in the fields list. The resulting dataset can be reloaded as a yt dataset.

Parameters: filename (str, optional) – The name of the file to be written. If None, the name will be a combination of the original dataset and the type of data container. fields (list of string or tuple field names, optional) – If this is supplied, it is the list of fields to be saved to disk. If not supplied, all the fields that have been queried will be saved. filename – The name of the file that has been created. str

Examples

>>> import yt
>>> sp = ds.sphere(ds.domain_center, (10, "Mpc"))
>>> fn = sp.save_as_dataset(fields=["density", "temperature"])
>>> # the original data container is available as the data attribute
>>> print (sds.data["density"])
[  4.46237613e-32   4.86830178e-32   4.46335118e-32 ...,   6.43956165e-30
3.57339907e-30   2.83150720e-30] g/cm**3
[  1.00000000e+00   1.00000000e+00   1.00000000e+00 ...,   4.40108359e+04
4.54380547e+04   4.72560117e+04] K

save_object(name, filename=None)

Save an object. If filename is supplied, it will be stored in a shelve file of that name. Otherwise, it will be stored via yt.data_objects.api.GridIndex.save_object().

select(selector, source, dest, offset)
select_blocks(selector)
select_fcoords(dobj=None)
select_fcoords_vertex(dobj=None)
select_fwidth(dobj)
select_icoords(dobj)
select_ires(dobj)
select_particles(selector, x, y, z)
select_tcoords(dobj)
selector
set_field_parameter(name, val)

Here we set up dictionaries that get passed up and down and ultimately to derived fields.

shape
std(field, weight=None)

Compute the variance of a field.

This will, in a parallel-ware fashion, compute the variance of the given field.

Parameters: field (string or tuple field name) – The field to calculate the variance of weight (string or tuple field name) – The field to weight the variance calculation by. Defaults to unweighted if unset. Scalar
sum(field, axis=None)

Compute the sum of a field, optionally along an axis.

This will, in a parallel-aware fashion, compute the sum of the given field. If an axis is specified, it will return a projection (using method type “sum”, which does not take into account path length) along that axis.

Parameters: field (string or tuple field name) – The field to sum. axis (string, optional) – If supplied, the axis to sum along. Either a scalar or a YTProjection.

Examples

>>> total_vol = reg.sum("cell_volume")
>>> cell_count = reg.sum("ones", axis="x")

tiles
to_dataframe(fields=None)

Export a data object to a pandas DataFrame.

This function will take a data object and construct from it and optionally a list of fields a pandas DataFrame object. If pandas is not importable, this will raise ImportError.

Parameters: fields (list of strings or tuple field names, default None) – If this is supplied, it is the list of fields to be exported into the data frame. If not supplied, whatever fields presently exist will be used. df – The data contained in the object. DataFrame

Examples

>>> dd = ds.all_data()
>>> df1 = dd.to_dataframe(["density", "temperature"])
>>> dd["velocity_magnitude"]
>>> df2 = dd.to_dataframe()

to_glue(fields, label='yt', data_collection=None)

Takes specific fields in the container and exports them to Glue (http://www.glueviz.org) for interactive analysis. Optionally add a label. If you are already within the Glue environment, you can pass a data_collection object, otherwise Glue will be started.

write_out(filename, fields=None, format='%0.16e')

Write out the YTDataContainer object in a text file.

This function will take a data object and produce a tab delimited text file containing the fields presently existing and the fields given in the fields list.

Parameters: filename (String) – The name of the file to write to. fields (List of string, Default = None) – If this is supplied, these fields will be added to the list of fields to be saved to disk. If not supplied, whatever fields presently exist will be used. format (String, Default = "%0.16e") – Format of numbers to be written in the file. ValueError – Raised when there is no existing field. YTException – Raised when field_type of supplied fields is inconsistent with the field_type of existing fields.

Examples

>>> ds = fake_particle_ds()
>>> sp = ds.sphere(ds.domain_center, 0.25)
>>> sp.write_out("sphere_1.txt")
>>> sp.write_out("sphere_2.txt", fields=["cell_volume"])