yt.data_objects.construction_data_containers module¶

class
yt.data_objects.construction_data_containers.
LevelState
[source]¶ Bases:
object

base_dx
= None¶

current_dims
= None¶

current_dx
= None¶

current_level
= None¶

data_source
= None¶

dds
= None¶

domain_left_edge
= None¶

domain_right_edge
= None¶

domain_width
= None¶

fields
= None¶

global_startindex
= None¶

left_edge
= None¶

old_global_startindex
= None¶

right_edge
= None¶


class
yt.data_objects.construction_data_containers.
YTArbitraryGrid
(left_edge, right_edge, dims, ds=None, field_parameters=None)[source]¶ Bases:
yt.data_objects.construction_data_containers.YTCoveringGrid
A 3D region with arbitrary bounds and dimensions.
In contrast to the Covering Grid, this object accepts a left edge, a right edge, and dimensions. This allows it to be used for creating 3D particle deposition fields that are independent of the underlying mesh, whether that is ytgenerated or from the simulation data. For example, arbitrary boxes around particles can be drawn and particle deposition fields can be created. This object will refuse to generate any fluid fields.
 Parameters
left_edge (array_like) – The left edge of the region to be extracted
right_edge (array_like) – The left edge of the region to be extracted
dims (array_like) – Number of cells along each axis of resulting grid.
Examples
>>> obj = ds.arbitrary_grid( ... [0.0, 0.0, 0.0], [0.99, 0.99, 0.99], dims=[128, 128, 128] ... )

property
LeftEdge
¶

property
RightEdge
¶

apply_units
(arr, units)¶

argmax
(field, axis=None)¶ Return the values at which the field is maximized.
This will, in a parallelaware 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.
 Returns
 Return type
A list of YTQuantities as specified by the axis argument.
Examples
>>> temp_at_max_rho = reg.argmax( ... ("gas", "density"), axis=("gas", "temperature") ... ) >>> max_rho_xyz = reg.argmax(("gas", "density")) >>> t_mrho, v_mrho = reg.argmax( ... ("gas", "density"), ... axis=[("gas", "temperature"), ("gas", "velocity_magnitude")], ... ) >>> x, y, z = reg.argmax(("gas", "density"))

argmin
(field, axis=None)¶ Return the values at which the field is minimized.
This will, in a parallelaware 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.
 Returns
 Return type
A list of YTQuantities as specified by the axis argument.
Examples
>>> temp_at_min_rho = reg.argmin( ... ("gas", "density"), axis=("gas", "temperature") ... ) >>> min_rho_xyz = reg.argmin(("gas", "density")) >>> t_mrho, v_mrho = reg.argmin( ... ("gas", "density"), ... axis=[("gas", "temperature"), ("gas", "velocity_magnitude")], ... ) >>> x, y, z = reg.argmin(("gas", "density"))

property
blocks
¶

calculate_isocontour_flux
(field, value, field_x, field_y, field_z, fluxing_field=None)¶ This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and calculates the flux over those contours.
This function will conduct marching cubes on all the cells in a given data container (gridbygrid), and then for each identified triangular segment of an isocontour in a given cell, calculate the gradient (i.e., normal) in the isocontoured field, interpolate the local value of the “fluxing” field, the area of the triangle, and then return:
area * local_flux_value * (n dot v)
Where area, local_value, and the vector v are interpolated at the barycenter (weighted by the vertex values) of the triangle. Note that this specifically allows for the field fluxing across the surface to be different from the field being contoured. If the fluxing_field is not specified, it is assumed to be 1.0 everywhere, and the raw flux with no localweighting is returned.
Additionally, the returned flux is defined as flux into the surface, not flux out of the surface.
 Parameters
field (string) – Any field that can be obtained in a data object. This is the field which will be isocontoured and used as the “local_value” in the flux equation.
value (float) – The value at which the isocontour should be calculated.
field_x (string) – The xcomponent field
field_y (string) – The ycomponent field
field_z (string) – The zcomponent field
fluxing_field (string, optional) – The field whose passage over the surface is of interest. If not specified, assumed to be 1.0 everywhere.
 Returns
flux – The summed flux. Note that it is not currently scaled; this is simply the codeunit area times the fields.
 Return type
Examples
This will create a data object, find a nice value in the center, and calculate the metal flux over it.
>>> dd = ds.all_data() >>> rho = dd.quantities["WeightedAverageQuantity"]( ... ("gas", "density"), weight=("gas", "cell_mass") ... ) >>> flux = dd.calculate_isocontour_flux( ... ("gas", "density"), ... rho, ... ("gas", "velocity_x"), ... ("gas", "velocity_y"), ... ("gas", "velocity_z"), ... ("gas", "metallicity"), ... )

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 deeplycopied. If you modify the field parameters inplace, 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[("gas", "density")] >>> print(sp.field_data.keys()) [("gas", "density")] >>> print(sp_clone.field_data.keys()) []

comm
= None¶

create_firefly_object
(datadir=None, fields_to_include=None, fields_units=None, default_decimation_factor=100, velocity_units='km/s', coordinate_units='kpc', show_unused_fields=0, *, JSONdir=None, match_any_particle_types=True, **kwargs)¶ This function links a region of data stored in a yt dataset to the Python frontend API for [Firefly](http://github.com/ageller/Firefly), a browserbased particle visualization tool.
 Parameters
datadir (string) – Path to where any .json files should be saved. If a relative path will assume relative to ${HOME}. A value of None will default to ${HOME}/Data.
fields_to_include (array_like of strings or field tuples) – A list of fields that you want to include in your Firefly visualization for onthefly filtering and colormapping.
default_decimation_factor (integer) – The factor by which you want to decimate each particle group by (e.g. if there are 1e7 total particles in your simulation you might want to set this to 100 at first). Randomly samples your data like shuffled_data[::decimation_factor] so as to not overtax a system. This is adjustable on a per particle group basis by changing the returned reader’s reader.particleGroup[i].decimation_factor before calling reader.writeToDisk().
velocity_units (string) – The units that the velocity should be converted to in order to show streamlines in Firefly. Defaults to km/s.
coordinate_units (string) – The units that the coordinates should be converted to. Defaults to kpc.
show_unused_fields (boolean) – A flag to optionally print the fields that are available, in the dataset but were not explicitly requested to be tracked.
match_any_particle_types (boolean) – If True, when any of the fields_to_include match multiple particle groups then the field will be added for all matching particle groups. If False, an error is raised when encountering an ambiguous field. Default is True.
additional keyword arguments are passed to (Any) –
firefly.data_reader.Reader.__init__ –
 Returns
reader – A reader object from the Firefly, configured to output the current region selected
 Return type
Firefly.data_reader.Reader object
Examples
>>> ramses_ds = yt.load( ... "/Users/agurvich/Desktop/yt_workshop/" ... + "DICEGalaxyDisk_nonCosmological/output_00002/info_00002.txt" ... )
>>> region = ramses_ds.sphere(ramses_ds.domain_center, (1000, "kpc"))
>>> reader = region.create_firefly_object( ... "IsoGalaxyRamses", ... fields_to_include=[ ... "particle_extra_field_1", ... "particle_extra_field_2", ... ], ... fields_units=["dimensionless", "dimensionless"], ... )
>>> reader.settings["color"]["io"] = [1, 1, 0, 1] >>> reader.particleGroups[0].decimation_factor = 100 >>> reader.writeToDisk()

cut_region
(field_cuts, field_parameters=None, locals=None)¶ Return a YTCutRegion, where the a cell is identified as being inside the cut region based on the value of one or more fields. Note that in previous versions of yt the name ‘grid’ was used to represent the data object used to construct the field cut, as of yt 3.0, this has been changed to ‘obj’.
 Parameters
field_cuts (list of strings) – A list of conditionals that will be evaluated. In the namespace available, these conditionals will have access to ‘obj’ which is a data object of unknown shape, and they must generate a boolean array. For instance, conditionals = [“obj[(‘gas’, ‘temperature’)] < 1e3”]
field_parameters (dictionary) – A dictionary of field parameters to be used when applying the field cuts.
locals (dictionary) – A dictionary of local variables to use when defining the cut region.
Examples
To find the total mass of hot gas with temperature greater than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.cut_region(["obj[('gas', 'temperature')] > 1e6"]) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

deposit
(positions, fields=None, method=None, kernel_name='cubic')¶

exclude_above
(field, value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field is above a given value are masked.
 Parameters
 Returns
cut_region – The YTCutRegion with the field above the given value masked.
 Return type
Examples
To find the total mass of hot gas with temperature colder than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_above(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_below
(field, value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field is below a given value are masked.
 Parameters
 Returns
cut_region – The YTCutRegion with the field below the given value masked.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_below(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_equal
(field, value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field are equal to given value are masked.
 Parameters
 Returns
cut_region – The YTCutRegion with the field equal to the given value masked.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_equal(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_inside
(field, min_value, max_value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field are inside the interval from min_value to max_value.
 Parameters
field (string) – The field in which the conditional will be applied.
min_value (float) – The minimum value inside the interval to be excluded.
max_value (float) – The maximum value inside the interval to be excluded.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the field inside the given interval excluded.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_inside(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_nan
(field, units=None)¶ This function will return a YTCutRegion where all of the regions whose field is NaN are masked.
 Parameters
field (string) – The field in which the conditional will be applied.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the NaN entries of the field masked.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_nan(("gas", "temperature")) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_outside
(field, min_value, max_value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field are outside the interval from min_value to max_value.
 Parameters
field (string) – The field in which the conditional will be applied.
min_value (float) – The minimum value inside the interval to be excluded.
max_value (float) – The maximum value inside the interval to be excluded.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the field outside the given interval excluded.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_outside(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

extract_connected_sets
(field, num_levels, min_val, max_val, log_space=True, cumulative=True)¶ This function will create a set of contour objects, defined by having connected cell structures, which can then be studied and used to ‘paint’ their source grids, thus enabling them to be plotted.
Note that this function can return a connected set object that has no member values.

extract_isocontours
(field, value, filename=None, rescale=False, sample_values=None)¶ This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and returns the vertices of the Triangles in that isocontour.
This function simply returns the vertices of all the triangles calculated by the marching cubes algorithm; for more complex operations, such as identifying connected sets of cells above a given threshold, see the extract_connected_sets function. This is more useful for calculating, for instance, total isocontour area, or visualizing in an external program (such as MeshLab.)
 Parameters
field (string) – Any field that can be obtained in a data object. This is the field which will be isocontoured.
value (float) – The value at which the isocontour should be calculated.
filename (string, optional) – If supplied, this file will be filled with the vertices in .obj format. Suitable for loading into meshlab.
rescale (bool, optional) – If true, the vertices will be rescaled within their min/max.
sample_values (string, optional) – Any field whose value should be extracted at the center of each triangle.
 Returns
verts (array of floats) – The array of vertices, x,y,z. Taken in threes, these are the triangle vertices.
samples (array of floats) – If sample_values is specified, this will be returned and will contain the values of the field specified at the center of each triangle.
Examples
This will create a data object, find a nice value in the center, and output the vertices to “triangles.obj” after rescaling them.
>>> dd = ds.all_data() >>> rho = dd.quantities["WeightedAverageQuantity"]( ... ("gas", "density"), weight=("gas", "cell_mass") ... ) >>> verts = dd.extract_isocontours( ... ("gas", "density"), rho, "triangles.obj", True ... )

property
fcoords
¶

property
fcoords_vertex
¶

property
fwidth
¶

get_bbox
() → Tuple[unyt.array.unyt_array, unyt.array.unyt_array]¶ Return the bounding box for this data container.

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
()¶ Get the global start index of the covering grid.

has_field_parameter
(name)¶ Checks if a field parameter is set.

has_key
(key)¶ Checks if a data field already exists.

property
icoords
¶

include_above
(field, value, units=None)¶ This function will return a YTCutRegion where only the regions whose field is above a given value are included.
 Parameters
 Returns
cut_region – The YTCutRegion with the field above the given value masked.
 Return type
Examples
To find the total mass of hot gas with temperature warmer than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.include_above(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

include_below
(field, value, units=None)¶ This function will return a YTCutRegion where only the regions whose field is below a given value are included.
 Parameters
 Returns
cut_region – The YTCutRegion with only regions with the field below the given value included.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.include_below(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

include_equal
(field, value, units=None)¶ This function will return a YTCutRegion where only the regions whose field are equal to given value are included.
 Parameters
 Returns
cut_region – The YTCutRegion with the field equal to the given value included.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.include_equal(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

include_inside
(field, min_value, max_value, units=None)¶ This function will return a YTCutRegion where only the regions whose field are inside the interval from min_value to max_value are included.
 Parameters
field (string) – The field in which the conditional will be applied.
min_value (float) – The minimum value inside the interval to be excluded.
max_value (float) – The maximum value inside the interval to be excluded.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the field inside the given interval excluded.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.include_inside(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

include_outside
(field, min_value, max_value, units=None)¶ This function will return a YTCutRegion where only the regions whose field are outside the interval from min_value to max_value are included.
 Parameters
field (string) – The field in which the conditional will be applied.
min_value (float) – The minimum value inside the interval to be excluded.
max_value (float) – The maximum value inside the interval to be excluded.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the field outside the given interval excluded.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_outside(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

property
index
¶

integrate
(field, weight=None, axis=None, *, moment=1)¶ 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.
moment (integer, optional) – for a weighted projection, moment = 1 (the default) corresponds to a weighted average. moment = 2 corresponds to a weighted standard deviation.
 Returns
 Return type
YTProjection
Examples
>>> column_density = reg.integrate(("gas", "density"), axis=("index", "z"))

property
ires
¶

keys
()¶

max
(field, axis=None)¶ Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘max’ 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> max_temp = reg.max(("gas", "temperature")) >>> max_temp_proj = reg.max(("gas", "temperature"), axis=("index", "x"))

property
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 parallelaware 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.
 Returns
 Return type
Scalar or YTProjection.
Examples
>>> avg_rho = reg.mean(("gas", "density"), weight="cell_volume") >>> rho_weighted_T = reg.mean( ... ("gas", "temperature"), axis=("index", "y"), weight=("gas", "density") ... )

min
(field, axis=None)¶ Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘min’ for minimum intensity. If the min 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> min_temp = reg.min(("gas", "temperature")) >>> min_temp_proj = reg.min(("gas", "temperature"), axis=("index", "x"))

property
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.

property
pf
¶

profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field=('gas', '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") >>> ad = ds.all_data() >>> profile = ad.profile( ... ad, ... [("gas", "density")], ... [("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 parallelaware fashion, compute the “peaktopeak” of the given field.
 Parameters
field (string or tuple field name) – The field to average.
 Returns
 Return type
Scalar
Examples
>>> rho_range = reg.ptp(("gas", "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.
 Returns
filename – The name of the file that has been created.
 Return type
Examples
>>> import yt >>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046") >>> sp = ds.sphere(ds.domain_center, (10, "Mpc")) >>> fn = sp.save_as_dataset(fields=[("gas", "density"), ("gas", "temperature")]) >>> sphere_ds = yt.load(fn) >>> # the original data container is available as the data attribute >>> print(sds.data[("gas", "density")]) [ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30 3.57339907e30 2.83150720e30] g/cm**3 >>> ad = sphere_ds.all_data() >>> print(ad[("gas", "temperature")]) [ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04 4.54380547e+04 4.72560117e+04] K

property
selector
¶

set_field_parameter
(name, val)¶ Here we set up dictionaries that get passed up and down and ultimately to derived fields.

property
shape
¶

std
(field, axis=None, weight=None)¶ Compute the standard deviation of a field, optionally along an axis, with a weight.
This will, in a parallelware fashion, compute the standard deviation 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, resulting in an unweighted standard deviation.
 Parameters
field (string or tuple field name) – The field to calculate the standard deviation of
axis (string, optional) – If supplied, the axis to compute the standard deviation along (i.e., to project along)
weight (string, optional) – The field to use as a weight.
 Returns
 Return type
Scalar or YTProjection.

sum
(field, axis=None)¶ Compute the sum of a field, optionally along an axis.
This will, in a parallelaware 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> total_vol = reg.sum("cell_volume") >>> cell_count = reg.sum(("index", "ones"), axis=("index", "x"))

property
tiles
¶

to_astropy_table
(fields)¶ Export region data to a :class:~astropy.table.table.QTable, which is a Table object which is unitaware. The QTable can then be exported to an ASCII file, FITS file, etc.
See the AstroPy Table docs for more details: http://docs.astropy.org/en/stable/table/
 Parameters
fields (list of strings or tuple field names) – This is the list of fields to be exported into the QTable.
Examples
>>> sp = ds.sphere("c", (1.0, "Mpc")) >>> t = sp.to_astropy_table([("gas", "density"), ("gas", "temperature")])

to_dataframe
(fields)¶ Export a data object to a
DataFrame
.This function will take a data object and an optional list of fields and export them to a
DataFrame
object. If pandas is not importable, this will raise ImportError. Parameters
fields (list of strings or tuple field names) – This is the list of fields to be exported into the DataFrame.
 Returns
df – The data contained in the object.
 Return type
Examples
>>> dd = ds.all_data() >>> df = dd.to_dataframe([("gas", "density"), ("gas", "temperature")])

to_fits_data
(fields, length_unit=None)¶ Export a set of gridded fields to a FITS file.
This will export a set of FITS images of either the fields specified or all the fields already in the object.
 Parameters
fields (list of strings) – These fields will be pixelized and output. If “None”, the keys of the FRB will be used.
length_unit (string, optional) – the length units that the coordinates are written in. The default is to use the default length unit of the dataset.

to_glue
(fields, label='yt', data_collection=None)¶ Takes specific fields in the container and exports them to Glue (http://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.

to_xarray
(fields=None)¶ Export this fixedresolution object to an xarray Dataset
This function will take a regularized grid and optionally a list of fields and return an xarray Dataset object. If xarray 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.
 Returns
arr – The data contained in the object.
 Return type
Examples
>>> dd = ds.r[::256j, ::256j, ::256j] >>> xf1 = dd.to_xarray([("gas", "density"), ("gas", "temperature")]) >>> dd[("gas", "velocity_magnitude")] >>> xf2 = dd.to_xarray()

volume
()¶ Return the volume of the data container. This is found by adding up the volume of the cells with centers in the container, rather than using the geometric shape of the container, so this may vary very slightly from what might be expected from the geometric volume.

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.
 Raises
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"])

write_to_gdf
(gdf_path, fields, nprocs=1, field_units=None, **kwargs)¶ Write the covering grid data to a GDF file.
 Parameters
gdf_path (string) – Pathname of the GDF file to write.
fields (list of strings) – Fields to write to the GDF file.
nprocs (integer, optional) – Split the covering grid into nprocs subgrids before writing to the GDF file. Default: 1
field_units (dictionary, optional) – Dictionary of units to convert fields to. If not set, fields are in their default units.
remaining keyword arguments are passed to (All) –
yt.utilities.grid_data_format.writer.write_to_gdf. –
Examples
>>> cube.write_to_gdf( ... "clumps.h5", ... [("gas", "density"), ("gas", "temperature")], ... nprocs=16, ... overwrite=True, ... )

class
yt.data_objects.construction_data_containers.
YTCoveringGrid
(level, left_edge, dims, fields=None, ds=None, num_ghost_zones=0, use_pbar=True, field_parameters=None, *, data_source=None)[source]¶ Bases:
yt.data_objects.selection_objects.data_selection_objects.YTSelectionContainer3D
A 3D region with all data extracted to a single, specified resolution. Left edge should align with a cell boundary, but defaults to the closest cell boundary.
 Parameters
level (int) – The resolution level data to which data will be gridded. Level 0 is the root grid dx for that dataset.
left_edge (array_like) – The left edge of the region to be extracted. Specify units by supplying a YTArray, otherwise code length units are assumed.
dims (array_like) – Number of cells along each axis of resulting covering_grid
fields (array_like, optional) – A list of fields that you’d like pregenerated for your object
num_ghost_zones (integer, optional) – The number of padding ghost zones used when accessing fields.
data_source – An existing data object to intersect with the covering grid. Grid points outside the data_source will exist as empty values.
Examples
>>> cube = ds.covering_grid(2, left_edge=[0.0, 0.0, 0.0], dims=[128, 128, 128])

property
LeftEdge
¶

property
RightEdge
¶

apply_units
(arr, units)¶

argmax
(field, axis=None)¶ Return the values at which the field is maximized.
This will, in a parallelaware 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.
 Returns
 Return type
A list of YTQuantities as specified by the axis argument.
Examples
>>> temp_at_max_rho = reg.argmax( ... ("gas", "density"), axis=("gas", "temperature") ... ) >>> max_rho_xyz = reg.argmax(("gas", "density")) >>> t_mrho, v_mrho = reg.argmax( ... ("gas", "density"), ... axis=[("gas", "temperature"), ("gas", "velocity_magnitude")], ... ) >>> x, y, z = reg.argmax(("gas", "density"))

argmin
(field, axis=None)¶ Return the values at which the field is minimized.
This will, in a parallelaware 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.
 Returns
 Return type
A list of YTQuantities as specified by the axis argument.
Examples
>>> temp_at_min_rho = reg.argmin( ... ("gas", "density"), axis=("gas", "temperature") ... ) >>> min_rho_xyz = reg.argmin(("gas", "density")) >>> t_mrho, v_mrho = reg.argmin( ... ("gas", "density"), ... axis=[("gas", "temperature"), ("gas", "velocity_magnitude")], ... ) >>> x, y, z = reg.argmin(("gas", "density"))

property
blocks
¶

calculate_isocontour_flux
(field, value, field_x, field_y, field_z, fluxing_field=None)¶ This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and calculates the flux over those contours.
This function will conduct marching cubes on all the cells in a given data container (gridbygrid), and then for each identified triangular segment of an isocontour in a given cell, calculate the gradient (i.e., normal) in the isocontoured field, interpolate the local value of the “fluxing” field, the area of the triangle, and then return:
area * local_flux_value * (n dot v)
Where area, local_value, and the vector v are interpolated at the barycenter (weighted by the vertex values) of the triangle. Note that this specifically allows for the field fluxing across the surface to be different from the field being contoured. If the fluxing_field is not specified, it is assumed to be 1.0 everywhere, and the raw flux with no localweighting is returned.
Additionally, the returned flux is defined as flux into the surface, not flux out of the surface.
 Parameters
field (string) – Any field that can be obtained in a data object. This is the field which will be isocontoured and used as the “local_value” in the flux equation.
value (float) – The value at which the isocontour should be calculated.
field_x (string) – The xcomponent field
field_y (string) – The ycomponent field
field_z (string) – The zcomponent field
fluxing_field (string, optional) – The field whose passage over the surface is of interest. If not specified, assumed to be 1.0 everywhere.
 Returns
flux – The summed flux. Note that it is not currently scaled; this is simply the codeunit area times the fields.
 Return type
Examples
This will create a data object, find a nice value in the center, and calculate the metal flux over it.
>>> dd = ds.all_data() >>> rho = dd.quantities["WeightedAverageQuantity"]( ... ("gas", "density"), weight=("gas", "cell_mass") ... ) >>> flux = dd.calculate_isocontour_flux( ... ("gas", "density"), ... rho, ... ("gas", "velocity_x"), ... ("gas", "velocity_y"), ... ("gas", "velocity_z"), ... ("gas", "metallicity"), ... )

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 deeplycopied. If you modify the field parameters inplace, 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[("gas", "density")] >>> print(sp.field_data.keys()) [("gas", "density")] >>> print(sp_clone.field_data.keys()) []

comm
= None¶

create_firefly_object
(datadir=None, fields_to_include=None, fields_units=None, default_decimation_factor=100, velocity_units='km/s', coordinate_units='kpc', show_unused_fields=0, *, JSONdir=None, match_any_particle_types=True, **kwargs)¶ This function links a region of data stored in a yt dataset to the Python frontend API for [Firefly](http://github.com/ageller/Firefly), a browserbased particle visualization tool.
 Parameters
datadir (string) – Path to where any .json files should be saved. If a relative path will assume relative to ${HOME}. A value of None will default to ${HOME}/Data.
fields_to_include (array_like of strings or field tuples) – A list of fields that you want to include in your Firefly visualization for onthefly filtering and colormapping.
default_decimation_factor (integer) – The factor by which you want to decimate each particle group by (e.g. if there are 1e7 total particles in your simulation you might want to set this to 100 at first). Randomly samples your data like shuffled_data[::decimation_factor] so as to not overtax a system. This is adjustable on a per particle group basis by changing the returned reader’s reader.particleGroup[i].decimation_factor before calling reader.writeToDisk().
velocity_units (string) – The units that the velocity should be converted to in order to show streamlines in Firefly. Defaults to km/s.
coordinate_units (string) – The units that the coordinates should be converted to. Defaults to kpc.
show_unused_fields (boolean) – A flag to optionally print the fields that are available, in the dataset but were not explicitly requested to be tracked.
match_any_particle_types (boolean) – If True, when any of the fields_to_include match multiple particle groups then the field will be added for all matching particle groups. If False, an error is raised when encountering an ambiguous field. Default is True.
additional keyword arguments are passed to (Any) –
firefly.data_reader.Reader.__init__ –
 Returns
reader – A reader object from the Firefly, configured to output the current region selected
 Return type
Firefly.data_reader.Reader object
Examples
>>> ramses_ds = yt.load( ... "/Users/agurvich/Desktop/yt_workshop/" ... + "DICEGalaxyDisk_nonCosmological/output_00002/info_00002.txt" ... )
>>> region = ramses_ds.sphere(ramses_ds.domain_center, (1000, "kpc"))
>>> reader = region.create_firefly_object( ... "IsoGalaxyRamses", ... fields_to_include=[ ... "particle_extra_field_1", ... "particle_extra_field_2", ... ], ... fields_units=["dimensionless", "dimensionless"], ... )
>>> reader.settings["color"]["io"] = [1, 1, 0, 1] >>> reader.particleGroups[0].decimation_factor = 100 >>> reader.writeToDisk()

cut_region
(field_cuts, field_parameters=None, locals=None)¶ Return a YTCutRegion, where the a cell is identified as being inside the cut region based on the value of one or more fields. Note that in previous versions of yt the name ‘grid’ was used to represent the data object used to construct the field cut, as of yt 3.0, this has been changed to ‘obj’.
 Parameters
field_cuts (list of strings) – A list of conditionals that will be evaluated. In the namespace available, these conditionals will have access to ‘obj’ which is a data object of unknown shape, and they must generate a boolean array. For instance, conditionals = [“obj[(‘gas’, ‘temperature’)] < 1e3”]
field_parameters (dictionary) – A dictionary of field parameters to be used when applying the field cuts.
locals (dictionary) – A dictionary of local variables to use when defining the cut region.
Examples
To find the total mass of hot gas with temperature greater than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.cut_region(["obj[('gas', 'temperature')] > 1e6"]) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_above
(field, value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field is above a given value are masked.
 Parameters
 Returns
cut_region – The YTCutRegion with the field above the given value masked.
 Return type
Examples
To find the total mass of hot gas with temperature colder than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_above(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_below
(field, value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field is below a given value are masked.
 Parameters
 Returns
cut_region – The YTCutRegion with the field below the given value masked.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_below(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_equal
(field, value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field are equal to given value are masked.
 Parameters
 Returns
cut_region – The YTCutRegion with the field equal to the given value masked.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_equal(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_inside
(field, min_value, max_value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field are inside the interval from min_value to max_value.
 Parameters
field (string) – The field in which the conditional will be applied.
min_value (float) – The minimum value inside the interval to be excluded.
max_value (float) – The maximum value inside the interval to be excluded.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the field inside the given interval excluded.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_inside(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_nan
(field, units=None)¶ This function will return a YTCutRegion where all of the regions whose field is NaN are masked.
 Parameters
field (string) – The field in which the conditional will be applied.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the NaN entries of the field masked.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_nan(("gas", "temperature")) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_outside
(field, min_value, max_value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field are outside the interval from min_value to max_value.
 Parameters
field (string) – The field in which the conditional will be applied.
min_value (float) – The minimum value inside the interval to be excluded.
max_value (float) – The maximum value inside the interval to be excluded.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the field outside the given interval excluded.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_outside(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

extract_connected_sets
(field, num_levels, min_val, max_val, log_space=True, cumulative=True)¶ This function will create a set of contour objects, defined by having connected cell structures, which can then be studied and used to ‘paint’ their source grids, thus enabling them to be plotted.
Note that this function can return a connected set object that has no member values.

extract_isocontours
(field, value, filename=None, rescale=False, sample_values=None)¶ This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and returns the vertices of the Triangles in that isocontour.
This function simply returns the vertices of all the triangles calculated by the marching cubes algorithm; for more complex operations, such as identifying connected sets of cells above a given threshold, see the extract_connected_sets function. This is more useful for calculating, for instance, total isocontour area, or visualizing in an external program (such as MeshLab.)
 Parameters
field (string) – Any field that can be obtained in a data object. This is the field which will be isocontoured.
value (float) – The value at which the isocontour should be calculated.
filename (string, optional) – If supplied, this file will be filled with the vertices in .obj format. Suitable for loading into meshlab.
rescale (bool, optional) – If true, the vertices will be rescaled within their min/max.
sample_values (string, optional) – Any field whose value should be extracted at the center of each triangle.
 Returns
verts (array of floats) – The array of vertices, x,y,z. Taken in threes, these are the triangle vertices.
samples (array of floats) – If sample_values is specified, this will be returned and will contain the values of the field specified at the center of each triangle.
Examples
This will create a data object, find a nice value in the center, and output the vertices to “triangles.obj” after rescaling them.
>>> dd = ds.all_data() >>> rho = dd.quantities["WeightedAverageQuantity"]( ... ("gas", "density"), weight=("gas", "cell_mass") ... ) >>> verts = dd.extract_isocontours( ... ("gas", "density"), rho, "triangles.obj", True ... )

property
fcoords
¶

property
fcoords_vertex
¶

property
fwidth
¶

get_bbox
() → Tuple[unyt.array.unyt_array, unyt.array.unyt_array]¶ Return the bounding box for this data container.

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.

has_field_parameter
(name)¶ Checks if a field parameter is set.

has_key
(key)¶ Checks if a data field already exists.

property
icoords
¶

include_above
(field, value, units=None)¶ This function will return a YTCutRegion where only the regions whose field is above a given value are included.
 Parameters
 Returns
cut_region – The YTCutRegion with the field above the given value masked.
 Return type
Examples
To find the total mass of hot gas with temperature warmer than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.include_above(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

include_below
(field, value, units=None)¶ This function will return a YTCutRegion where only the regions whose field is below a given value are included.
 Parameters
 Returns
cut_region – The YTCutRegion with only regions with the field below the given value included.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.include_below(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

include_equal
(field, value, units=None)¶ This function will return a YTCutRegion where only the regions whose field are equal to given value are included.
 Parameters
 Returns
cut_region – The YTCutRegion with the field equal to the given value included.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.include_equal(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

include_inside
(field, min_value, max_value, units=None)¶ This function will return a YTCutRegion where only the regions whose field are inside the interval from min_value to max_value are included.
 Parameters
field (string) – The field in which the conditional will be applied.
min_value (float) – The minimum value inside the interval to be excluded.
max_value (float) – The maximum value inside the interval to be excluded.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the field inside the given interval excluded.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.include_inside(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

include_outside
(field, min_value, max_value, units=None)¶ This function will return a YTCutRegion where only the regions whose field are outside the interval from min_value to max_value are included.
 Parameters
field (string) – The field in which the conditional will be applied.
min_value (float) – The minimum value inside the interval to be excluded.
max_value (float) – The maximum value inside the interval to be excluded.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the field outside the given interval excluded.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_outside(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

property
index
¶

integrate
(field, weight=None, axis=None, *, moment=1)¶ 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.
moment (integer, optional) – for a weighted projection, moment = 1 (the default) corresponds to a weighted average. moment = 2 corresponds to a weighted standard deviation.
 Returns
 Return type
YTProjection
Examples
>>> column_density = reg.integrate(("gas", "density"), axis=("index", "z"))

property
ires
¶

keys
()¶

max
(field, axis=None)¶ Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘max’ 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> max_temp = reg.max(("gas", "temperature")) >>> max_temp_proj = reg.max(("gas", "temperature"), axis=("index", "x"))

property
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 parallelaware 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.
 Returns
 Return type
Scalar or YTProjection.
Examples
>>> avg_rho = reg.mean(("gas", "density"), weight="cell_volume") >>> rho_weighted_T = reg.mean( ... ("gas", "temperature"), axis=("index", "y"), weight=("gas", "density") ... )

min
(field, axis=None)¶ Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘min’ for minimum intensity. If the min 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> min_temp = reg.min(("gas", "temperature")) >>> min_temp_proj = reg.min(("gas", "temperature"), axis=("index", "x"))

property
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.

property
pf
¶

profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field=('gas', '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") >>> ad = ds.all_data() >>> profile = ad.profile( ... ad, ... [("gas", "density")], ... [("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 parallelaware fashion, compute the “peaktopeak” of the given field.
 Parameters
field (string or tuple field name) – The field to average.
 Returns
 Return type
Scalar
Examples
>>> rho_range = reg.ptp(("gas", "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.
 Returns
filename – The name of the file that has been created.
 Return type
Examples
>>> import yt >>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046") >>> sp = ds.sphere(ds.domain_center, (10, "Mpc")) >>> fn = sp.save_as_dataset(fields=[("gas", "density"), ("gas", "temperature")]) >>> sphere_ds = yt.load(fn) >>> # the original data container is available as the data attribute >>> print(sds.data[("gas", "density")]) [ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30 3.57339907e30 2.83150720e30] g/cm**3 >>> ad = sphere_ds.all_data() >>> print(ad[("gas", "temperature")]) [ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04 4.54380547e+04 4.72560117e+04] K

property
selector
¶

set_field_parameter
(name, val)[source]¶ Here we set up dictionaries that get passed up and down and ultimately to derived fields.

property
shape
¶

std
(field, axis=None, weight=None)¶ Compute the standard deviation of a field, optionally along an axis, with a weight.
This will, in a parallelware fashion, compute the standard deviation 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, resulting in an unweighted standard deviation.
 Parameters
field (string or tuple field name) – The field to calculate the standard deviation of
axis (string, optional) – If supplied, the axis to compute the standard deviation along (i.e., to project along)
weight (string, optional) – The field to use as a weight.
 Returns
 Return type
Scalar or YTProjection.

sum
(field, axis=None)¶ Compute the sum of a field, optionally along an axis.
This will, in a parallelaware 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> total_vol = reg.sum("cell_volume") >>> cell_count = reg.sum(("index", "ones"), axis=("index", "x"))

property
tiles
¶

to_astropy_table
(fields)¶ Export region data to a :class:~astropy.table.table.QTable, which is a Table object which is unitaware. The QTable can then be exported to an ASCII file, FITS file, etc.
See the AstroPy Table docs for more details: http://docs.astropy.org/en/stable/table/
 Parameters
fields (list of strings or tuple field names) – This is the list of fields to be exported into the QTable.
Examples
>>> sp = ds.sphere("c", (1.0, "Mpc")) >>> t = sp.to_astropy_table([("gas", "density"), ("gas", "temperature")])

to_dataframe
(fields)¶ Export a data object to a
DataFrame
.This function will take a data object and an optional list of fields and export them to a
DataFrame
object. If pandas is not importable, this will raise ImportError. Parameters
fields (list of strings or tuple field names) – This is the list of fields to be exported into the DataFrame.
 Returns
df – The data contained in the object.
 Return type
Examples
>>> dd = ds.all_data() >>> df = dd.to_dataframe([("gas", "density"), ("gas", "temperature")])

to_fits_data
(fields, length_unit=None)[source]¶ Export a set of gridded fields to a FITS file.
This will export a set of FITS images of either the fields specified or all the fields already in the object.
 Parameters
fields (list of strings) – These fields will be pixelized and output. If “None”, the keys of the FRB will be used.
length_unit (string, optional) – the length units that the coordinates are written in. The default is to use the default length unit of the dataset.

to_glue
(fields, label='yt', data_collection=None)¶ Takes specific fields in the container and exports them to Glue (http://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.

to_xarray
(fields=None)[source]¶ Export this fixedresolution object to an xarray Dataset
This function will take a regularized grid and optionally a list of fields and return an xarray Dataset object. If xarray 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.
 Returns
arr – The data contained in the object.
 Return type
Examples
>>> dd = ds.r[::256j, ::256j, ::256j] >>> xf1 = dd.to_xarray([("gas", "density"), ("gas", "temperature")]) >>> dd[("gas", "velocity_magnitude")] >>> xf2 = dd.to_xarray()

volume
()¶ Return the volume of the data container. This is found by adding up the volume of the cells with centers in the container, rather than using the geometric shape of the container, so this may vary very slightly from what might be expected from the geometric volume.

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.
 Raises
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"])

write_to_gdf
(gdf_path, fields, nprocs=1, field_units=None, **kwargs)[source]¶ Write the covering grid data to a GDF file.
 Parameters
gdf_path (string) – Pathname of the GDF file to write.
fields (list of strings) – Fields to write to the GDF file.
nprocs (integer, optional) – Split the covering grid into nprocs subgrids before writing to the GDF file. Default: 1
field_units (dictionary, optional) – Dictionary of units to convert fields to. If not set, fields are in their default units.
remaining keyword arguments are passed to (All) –
yt.utilities.grid_data_format.writer.write_to_gdf. –
Examples
>>> cube.write_to_gdf( ... "clumps.h5", ... [("gas", "density"), ("gas", "temperature")], ... nprocs=16, ... overwrite=True, ... )

class
yt.data_objects.construction_data_containers.
YTOctree
(left_edge=None, right_edge=None, n_ref=32, ptypes=None, ds=None, field_parameters=None)[source]¶ Bases:
yt.data_objects.selection_objects.data_selection_objects.YTSelectionContainer3D
A 3D region with all the data filled into an octree. This container will deposit particle fields onto octs using a kernel and SPH smoothing. The octree is built in a depthfirst fashion. Depthfirst search (DFS) means that tree starts refining at the root node (this is the largest node which contains every particles) and refines as far as possible along each branch before backtracking.
 Parameters
right_edge (array_like) – The right edge of the region to be extracted. Specify units by supplying a YTArray, otherwise code length units are assumed.
left_edge (array_like) – The left edge of the region to be extracted. Specify units by supplying a YTArray, otherwise code length units are assumed.
n_ref (int) – This is the maximum number of particles per leaf in the resulting octree.
ptypes (list) – This is the type of particles to include when building the tree. This will default to all particles.
Examples
octree = ds.octree(n_ref=64) x_positions_of_cells = octree[(‘index’, ‘x’)] y_positions_of_cells = octree[(‘index’, ‘y’)] z_positions_of_cells = octree[(‘index’, ‘z’)] density_of_gas_in_cells = octree[(‘gas’, ‘density’)]

apply_units
(arr, units)¶

argmax
(field, axis=None)¶ Return the values at which the field is maximized.
This will, in a parallelaware 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.
 Returns
 Return type
A list of YTQuantities as specified by the axis argument.
Examples
>>> temp_at_max_rho = reg.argmax( ... ("gas", "density"), axis=("gas", "temperature") ... ) >>> max_rho_xyz = reg.argmax(("gas", "density")) >>> t_mrho, v_mrho = reg.argmax( ... ("gas", "density"), ... axis=[("gas", "temperature"), ("gas", "velocity_magnitude")], ... ) >>> x, y, z = reg.argmax(("gas", "density"))

argmin
(field, axis=None)¶ Return the values at which the field is minimized.
This will, in a parallelaware 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.
 Returns
 Return type
A list of YTQuantities as specified by the axis argument.
Examples
>>> temp_at_min_rho = reg.argmin( ... ("gas", "density"), axis=("gas", "temperature") ... ) >>> min_rho_xyz = reg.argmin(("gas", "density")) >>> t_mrho, v_mrho = reg.argmin( ... ("gas", "density"), ... axis=[("gas", "temperature"), ("gas", "velocity_magnitude")], ... ) >>> x, y, z = reg.argmin(("gas", "density"))

property
blocks
¶

calculate_isocontour_flux
(field, value, field_x, field_y, field_z, fluxing_field=None)¶ This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and calculates the flux over those contours.
This function will conduct marching cubes on all the cells in a given data container (gridbygrid), and then for each identified triangular segment of an isocontour in a given cell, calculate the gradient (i.e., normal) in the isocontoured field, interpolate the local value of the “fluxing” field, the area of the triangle, and then return:
area * local_flux_value * (n dot v)
Where area, local_value, and the vector v are interpolated at the barycenter (weighted by the vertex values) of the triangle. Note that this specifically allows for the field fluxing across the surface to be different from the field being contoured. If the fluxing_field is not specified, it is assumed to be 1.0 everywhere, and the raw flux with no localweighting is returned.
Additionally, the returned flux is defined as flux into the surface, not flux out of the surface.
 Parameters
field (string) – Any field that can be obtained in a data object. This is the field which will be isocontoured and used as the “local_value” in the flux equation.
value (float) – The value at which the isocontour should be calculated.
field_x (string) – The xcomponent field
field_y (string) – The ycomponent field
field_z (string) – The zcomponent field
fluxing_field (string, optional) – The field whose passage over the surface is of interest. If not specified, assumed to be 1.0 everywhere.
 Returns
flux – The summed flux. Note that it is not currently scaled; this is simply the codeunit area times the fields.
 Return type
Examples
This will create a data object, find a nice value in the center, and calculate the metal flux over it.
>>> dd = ds.all_data() >>> rho = dd.quantities["WeightedAverageQuantity"]( ... ("gas", "density"), weight=("gas", "cell_mass") ... ) >>> flux = dd.calculate_isocontour_flux( ... ("gas", "density"), ... rho, ... ("gas", "velocity_x"), ... ("gas", "velocity_y"), ... ("gas", "velocity_z"), ... ("gas", "metallicity"), ... )

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 deeplycopied. If you modify the field parameters inplace, 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[("gas", "density")] >>> print(sp.field_data.keys()) [("gas", "density")] >>> print(sp_clone.field_data.keys()) []

comm
= None¶

create_firefly_object
(datadir=None, fields_to_include=None, fields_units=None, default_decimation_factor=100, velocity_units='km/s', coordinate_units='kpc', show_unused_fields=0, *, JSONdir=None, match_any_particle_types=True, **kwargs)¶ This function links a region of data stored in a yt dataset to the Python frontend API for [Firefly](http://github.com/ageller/Firefly), a browserbased particle visualization tool.
 Parameters
datadir (string) – Path to where any .json files should be saved. If a relative path will assume relative to ${HOME}. A value of None will default to ${HOME}/Data.
fields_to_include (array_like of strings or field tuples) – A list of fields that you want to include in your Firefly visualization for onthefly filtering and colormapping.
default_decimation_factor (integer) – The factor by which you want to decimate each particle group by (e.g. if there are 1e7 total particles in your simulation you might want to set this to 100 at first). Randomly samples your data like shuffled_data[::decimation_factor] so as to not overtax a system. This is adjustable on a per particle group basis by changing the returned reader’s reader.particleGroup[i].decimation_factor before calling reader.writeToDisk().
velocity_units (string) – The units that the velocity should be converted to in order to show streamlines in Firefly. Defaults to km/s.
coordinate_units (string) – The units that the coordinates should be converted to. Defaults to kpc.
show_unused_fields (boolean) – A flag to optionally print the fields that are available, in the dataset but were not explicitly requested to be tracked.
match_any_particle_types (boolean) – If True, when any of the fields_to_include match multiple particle groups then the field will be added for all matching particle groups. If False, an error is raised when encountering an ambiguous field. Default is True.
additional keyword arguments are passed to (Any) –
firefly.data_reader.Reader.__init__ –
 Returns
reader – A reader object from the Firefly, configured to output the current region selected
 Return type
Firefly.data_reader.Reader object
Examples
>>> ramses_ds = yt.load( ... "/Users/agurvich/Desktop/yt_workshop/" ... + "DICEGalaxyDisk_nonCosmological/output_00002/info_00002.txt" ... )
>>> region = ramses_ds.sphere(ramses_ds.domain_center, (1000, "kpc"))
>>> reader = region.create_firefly_object( ... "IsoGalaxyRamses", ... fields_to_include=[ ... "particle_extra_field_1", ... "particle_extra_field_2", ... ], ... fields_units=["dimensionless", "dimensionless"], ... )
>>> reader.settings["color"]["io"] = [1, 1, 0, 1] >>> reader.particleGroups[0].decimation_factor = 100 >>> reader.writeToDisk()

cut_region
(field_cuts, field_parameters=None, locals=None)¶ Return a YTCutRegion, where the a cell is identified as being inside the cut region based on the value of one or more fields. Note that in previous versions of yt the name ‘grid’ was used to represent the data object used to construct the field cut, as of yt 3.0, this has been changed to ‘obj’.
 Parameters
field_cuts (list of strings) – A list of conditionals that will be evaluated. In the namespace available, these conditionals will have access to ‘obj’ which is a data object of unknown shape, and they must generate a boolean array. For instance, conditionals = [“obj[(‘gas’, ‘temperature’)] < 1e3”]
field_parameters (dictionary) – A dictionary of field parameters to be used when applying the field cuts.
locals (dictionary) – A dictionary of local variables to use when defining the cut region.
Examples
To find the total mass of hot gas with temperature greater than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.cut_region(["obj[('gas', 'temperature')] > 1e6"]) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_above
(field, value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field is above a given value are masked.
 Parameters
 Returns
cut_region – The YTCutRegion with the field above the given value masked.
 Return type
Examples
To find the total mass of hot gas with temperature colder than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_above(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_below
(field, value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field is below a given value are masked.
 Parameters
 Returns
cut_region – The YTCutRegion with the field below the given value masked.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_below(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_equal
(field, value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field are equal to given value are masked.
 Parameters
 Returns
cut_region – The YTCutRegion with the field equal to the given value masked.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_equal(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_inside
(field, min_value, max_value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field are inside the interval from min_value to max_value.
 Parameters
field (string) – The field in which the conditional will be applied.
min_value (float) – The minimum value inside the interval to be excluded.
max_value (float) – The maximum value inside the interval to be excluded.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the field inside the given interval excluded.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_inside(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_nan
(field, units=None)¶ This function will return a YTCutRegion where all of the regions whose field is NaN are masked.
 Parameters
field (string) – The field in which the conditional will be applied.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the NaN entries of the field masked.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_nan(("gas", "temperature")) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_outside
(field, min_value, max_value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field are outside the interval from min_value to max_value.
 Parameters
field (string) – The field in which the conditional will be applied.
min_value (float) – The minimum value inside the interval to be excluded.
max_value (float) – The maximum value inside the interval to be excluded.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the field outside the given interval excluded.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_outside(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

extract_connected_sets
(field, num_levels, min_val, max_val, log_space=True, cumulative=True)¶ This function will create a set of contour objects, defined by having connected cell structures, which can then be studied and used to ‘paint’ their source grids, thus enabling them to be plotted.
Note that this function can return a connected set object that has no member values.

extract_isocontours
(field, value, filename=None, rescale=False, sample_values=None)¶ This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and returns the vertices of the Triangles in that isocontour.
This function simply returns the vertices of all the triangles calculated by the marching cubes algorithm; for more complex operations, such as identifying connected sets of cells above a given threshold, see the extract_connected_sets function. This is more useful for calculating, for instance, total isocontour area, or visualizing in an external program (such as MeshLab.)
 Parameters
field (string) – Any field that can be obtained in a data object. This is the field which will be isocontoured.
value (float) – The value at which the isocontour should be calculated.
filename (string, optional) – If supplied, this file will be filled with the vertices in .obj format. Suitable for loading into meshlab.
rescale (bool, optional) – If true, the vertices will be rescaled within their min/max.
sample_values (string, optional) – Any field whose value should be extracted at the center of each triangle.
 Returns
verts (array of floats) – The array of vertices, x,y,z. Taken in threes, these are the triangle vertices.
samples (array of floats) – If sample_values is specified, this will be returned and will contain the values of the field specified at the center of each triangle.
Examples
This will create a data object, find a nice value in the center, and output the vertices to “triangles.obj” after rescaling them.
>>> dd = ds.all_data() >>> rho = dd.quantities["WeightedAverageQuantity"]( ... ("gas", "density"), weight=("gas", "cell_mass") ... ) >>> verts = dd.extract_isocontours( ... ("gas", "density"), rho, "triangles.obj", True ... )

property
fcoords
¶

property
fcoords_vertex
¶

property
fwidth
¶

get_bbox
() → Tuple[unyt.array.unyt_array, unyt.array.unyt_array]¶ Return the bounding box for this data container.

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.

has_field_parameter
(name)¶ Checks if a field parameter is set.

has_key
(key)¶ Checks if a data field already exists.

property
icoords
¶

include_above
(field, value, units=None)¶ This function will return a YTCutRegion where only the regions whose field is above a given value are included.
 Parameters
 Returns
cut_region – The YTCutRegion with the field above the given value masked.
 Return type
Examples
To find the total mass of hot gas with temperature warmer than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.include_above(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

include_below
(field, value, units=None)¶ This function will return a YTCutRegion where only the regions whose field is below a given value are included.
 Parameters
 Returns
cut_region – The YTCutRegion with only regions with the field below the given value included.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.include_below(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

include_equal
(field, value, units=None)¶ This function will return a YTCutRegion where only the regions whose field are equal to given value are included.
 Parameters
 Returns
cut_region – The YTCutRegion with the field equal to the given value included.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.include_equal(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

include_inside
(field, min_value, max_value, units=None)¶ This function will return a YTCutRegion where only the regions whose field are inside the interval from min_value to max_value are included.
 Parameters
field (string) – The field in which the conditional will be applied.
min_value (float) – The minimum value inside the interval to be excluded.
max_value (float) – The maximum value inside the interval to be excluded.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the field inside the given interval excluded.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.include_inside(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

include_outside
(field, min_value, max_value, units=None)¶ This function will return a YTCutRegion where only the regions whose field are outside the interval from min_value to max_value are included.
 Parameters
field (string) – The field in which the conditional will be applied.
min_value (float) – The minimum value inside the interval to be excluded.
max_value (float) – The maximum value inside the interval to be excluded.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the field outside the given interval excluded.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_outside(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

property
index
¶

integrate
(field, weight=None, axis=None, *, moment=1)¶ 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.
moment (integer, optional) – for a weighted projection, moment = 1 (the default) corresponds to a weighted average. moment = 2 corresponds to a weighted standard deviation.
 Returns
 Return type
YTProjection
Examples
>>> column_density = reg.integrate(("gas", "density"), axis=("index", "z"))

property
ires
¶

keys
()¶

max
(field, axis=None)¶ Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘max’ 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> max_temp = reg.max(("gas", "temperature")) >>> max_temp_proj = reg.max(("gas", "temperature"), axis=("index", "x"))

property
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 parallelaware 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.
 Returns
 Return type
Scalar or YTProjection.
Examples
>>> avg_rho = reg.mean(("gas", "density"), weight="cell_volume") >>> rho_weighted_T = reg.mean( ... ("gas", "temperature"), axis=("index", "y"), weight=("gas", "density") ... )

min
(field, axis=None)¶ Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘min’ for minimum intensity. If the min 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> min_temp = reg.min(("gas", "temperature")) >>> min_temp_proj = reg.min(("gas", "temperature"), axis=("index", "x"))

property
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.

property
pf
¶

profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field=('gas', '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") >>> ad = ds.all_data() >>> profile = ad.profile( ... ad, ... [("gas", "density")], ... [("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 parallelaware fashion, compute the “peaktopeak” of the given field.
 Parameters
field (string or tuple field name) – The field to average.
 Returns
 Return type
Scalar
Examples
>>> rho_range = reg.ptp(("gas", "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.
 Returns
filename – The name of the file that has been created.
 Return type
Examples
>>> import yt >>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046") >>> sp = ds.sphere(ds.domain_center, (10, "Mpc")) >>> fn = sp.save_as_dataset(fields=[("gas", "density"), ("gas", "temperature")]) >>> sphere_ds = yt.load(fn) >>> # the original data container is available as the data attribute >>> print(sds.data[("gas", "density")]) [ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30 3.57339907e30 2.83150720e30] g/cm**3 >>> ad = sphere_ds.all_data() >>> print(ad[("gas", "temperature")]) [ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04 4.54380547e+04 4.72560117e+04] K

property
selector
¶

set_field_parameter
(name, val)¶ Here we set up dictionaries that get passed up and down and ultimately to derived fields.

property
sph_normalize
¶

property
sph_num_neighbors
¶

property
sph_smoothing_style
¶

std
(field, axis=None, weight=None)¶ Compute the standard deviation of a field, optionally along an axis, with a weight.
This will, in a parallelware fashion, compute the standard deviation 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, resulting in an unweighted standard deviation.
 Parameters
field (string or tuple field name) – The field to calculate the standard deviation of
axis (string, optional) – If supplied, the axis to compute the standard deviation along (i.e., to project along)
weight (string, optional) – The field to use as a weight.
 Returns
 Return type
Scalar or YTProjection.

sum
(field, axis=None)¶ Compute the sum of a field, optionally along an axis.
This will, in a parallelaware 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> total_vol = reg.sum("cell_volume") >>> cell_count = reg.sum(("index", "ones"), axis=("index", "x"))

property
tiles
¶

to_astropy_table
(fields)¶ Export region data to a :class:~astropy.table.table.QTable, which is a Table object which is unitaware. The QTable can then be exported to an ASCII file, FITS file, etc.
See the AstroPy Table docs for more details: http://docs.astropy.org/en/stable/table/
 Parameters
fields (list of strings or tuple field names) – This is the list of fields to be exported into the QTable.
Examples
>>> sp = ds.sphere("c", (1.0, "Mpc")) >>> t = sp.to_astropy_table([("gas", "density"), ("gas", "temperature")])

to_dataframe
(fields)¶ Export a data object to a
DataFrame
.This function will take a data object and an optional list of fields and export them to a
DataFrame
object. If pandas is not importable, this will raise ImportError. Parameters
fields (list of strings or tuple field names) – This is the list of fields to be exported into the DataFrame.
 Returns
df – The data contained in the object.
 Return type
Examples
>>> dd = ds.all_data() >>> df = dd.to_dataframe([("gas", "density"), ("gas", "temperature")])

to_glue
(fields, label='yt', data_collection=None)¶ Takes specific fields in the container and exports them to Glue (http://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.

property
tree
¶ The Cython+Python octree instance

volume
()¶ Return the volume of the data container. This is found by adding up the volume of the cells with centers in the container, rather than using the geometric shape of the container, so this may vary very slightly from what might be expected from the geometric volume.

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.
 Raises
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.data_objects.construction_data_containers.
YTParticleProj
(field, axis, weight_field=None, center=None, ds=None, data_source=None, method='integrate', field_parameters=None, max_level=None, *, moment=1)[source]¶ Bases:
yt.data_objects.construction_data_containers.YTProj
A projection operation optimized for SPH particle data.

apply_units
(arr, units)¶

argmax
(field, axis=None)¶ Return the values at which the field is maximized.
This will, in a parallelaware 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.
 Returns
 Return type
A list of YTQuantities as specified by the axis argument.
Examples
>>> temp_at_max_rho = reg.argmax( ... ("gas", "density"), axis=("gas", "temperature") ... ) >>> max_rho_xyz = reg.argmax(("gas", "density")) >>> t_mrho, v_mrho = reg.argmax( ... ("gas", "density"), ... axis=[("gas", "temperature"), ("gas", "velocity_magnitude")], ... ) >>> x, y, z = reg.argmax(("gas", "density"))

argmin
(field, axis=None)¶ Return the values at which the field is minimized.
This will, in a parallelaware 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.
 Returns
 Return type
A list of YTQuantities as specified by the axis argument.
Examples
>>> temp_at_min_rho = reg.argmin( ... ("gas", "density"), axis=("gas", "temperature") ... ) >>> min_rho_xyz = reg.argmin(("gas", "density")) >>> t_mrho, v_mrho = reg.argmin( ... ("gas", "density"), ... axis=[("gas", "temperature"), ("gas", "velocity_magnitude")], ... ) >>> x, y, z = reg.argmin(("gas", "density"))

property
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 deeplycopied. If you modify the field parameters inplace, 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[("gas", "density")] >>> print(sp.field_data.keys()) [("gas", "density")] >>> print(sp_clone.field_data.keys()) []

comm
= None¶

create_firefly_object
(datadir=None, fields_to_include=None, fields_units=None, default_decimation_factor=100, velocity_units='km/s', coordinate_units='kpc', show_unused_fields=0, *, JSONdir=None, match_any_particle_types=True, **kwargs)¶ This function links a region of data stored in a yt dataset to the Python frontend API for [Firefly](http://github.com/ageller/Firefly), a browserbased particle visualization tool.
 Parameters
datadir (string) – Path to where any .json files should be saved. If a relative path will assume relative to ${HOME}. A value of None will default to ${HOME}/Data.
fields_to_include (array_like of strings or field tuples) – A list of fields that you want to include in your Firefly visualization for onthefly filtering and colormapping.
default_decimation_factor (integer) – The factor by which you want to decimate each particle group by (e.g. if there are 1e7 total particles in your simulation you might want to set this to 100 at first). Randomly samples your data like shuffled_data[::decimation_factor] so as to not overtax a system. This is adjustable on a per particle group basis by changing the returned reader’s reader.particleGroup[i].decimation_factor before calling reader.writeToDisk().
velocity_units (string) – The units that the velocity should be converted to in order to show streamlines in Firefly. Defaults to km/s.
coordinate_units (string) – The units that the coordinates should be converted to. Defaults to kpc.
show_unused_fields (boolean) – A flag to optionally print the fields that are available, in the dataset but were not explicitly requested to be tracked.
match_any_particle_types (boolean) – If True, when any of the fields_to_include match multiple particle groups then the field will be added for all matching particle groups. If False, an error is raised when encountering an ambiguous field. Default is True.
additional keyword arguments are passed to (Any) –
firefly.data_reader.Reader.__init__ –
 Returns
reader – A reader object from the Firefly, configured to output the current region selected
 Return type
Firefly.data_reader.Reader object
Examples
>>> ramses_ds = yt.load( ... "/Users/agurvich/Desktop/yt_workshop/" ... + "DICEGalaxyDisk_nonCosmological/output_00002/info_00002.txt" ... )
>>> region = ramses_ds.sphere(ramses_ds.domain_center, (1000, "kpc"))
>>> reader = region.create_firefly_object( ... "IsoGalaxyRamses", ... fields_to_include=[ ... "particle_extra_field_1", ... "particle_extra_field_2", ... ], ... fields_units=["dimensionless", "dimensionless"], ... )
>>> reader.settings["color"]["io"] = [1, 1, 0, 1] >>> reader.particleGroups[0].decimation_factor = 100 >>> reader.writeToDisk()

property
fcoords
¶

property
fcoords_vertex
¶

property
field
¶

property
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.

has_field_parameter
(name)¶ Checks if a field parameter is set.

has_key
(key)¶ Checks if a data field already exists.

property
icoords
¶

property
index
¶

integrate
(field, weight=None, axis=None, *, moment=1)¶ 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.
moment (integer, optional) – for a weighted projection, moment = 1 (the default) corresponds to a weighted average. moment = 2 corresponds to a weighted standard deviation.
 Returns
 Return type
YTProjection
Examples
>>> column_density = reg.integrate(("gas", "density"), axis=("index", "z"))

property
ires
¶

keys
()¶

max
(field, axis=None)¶ Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘max’ 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> max_temp = reg.max(("gas", "temperature")) >>> max_temp_proj = reg.max(("gas", "temperature"), axis=("index", "x"))

property
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 parallelaware 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.
 Returns
 Return type
Scalar or YTProjection.
Examples
>>> avg_rho = reg.mean(("gas", "density"), weight="cell_volume") >>> rho_weighted_T = reg.mean( ... ("gas", "temperature"), axis=("index", "y"), weight=("gas", "density") ... )

min
(field, axis=None)¶ Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘min’ for minimum intensity. If the min 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> min_temp = reg.min(("gas", "temperature")) >>> min_temp_proj = reg.min(("gas", "temperature"), axis=("index", "x"))

property
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.

property
pf
¶

plot
(fields=None)¶

profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field=('gas', '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") >>> ad = ds.all_data() >>> profile = ad.profile( ... ad, ... [("gas", "density")], ... [("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 parallelaware fashion, compute the “peaktopeak” of the given field.
 Parameters
field (string or tuple field name) – The field to average.
 Returns
 Return type
Scalar
Examples
>>> rho_range = reg.ptp(("gas", "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.
 Returns
filename – The name of the file that has been created.
 Return type
Examples
>>> import yt >>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046") >>> sp = ds.sphere(ds.domain_center, (10, "Mpc")) >>> fn = sp.save_as_dataset(fields=[("gas", "density"), ("gas", "temperature")]) >>> sphere_ds = yt.load(fn) >>> # the original data container is available as the data attribute >>> print(sds.data[("gas", "density")]) [ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30 3.57339907e30 2.83150720e30] g/cm**3 >>> ad = sphere_ds.all_data() >>> print(ad[("gas", "temperature")]) [ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04 4.54380547e+04 4.72560117e+04] K

property
selector
¶

set_field_parameter
(name, val)¶ Here we set up dictionaries that get passed up and down and ultimately to derived fields.

std
(field, axis=None, weight=None)¶ Compute the standard deviation of a field, optionally along an axis, with a weight.
This will, in a parallelware fashion, compute the standard deviation 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, resulting in an unweighted standard deviation.
 Parameters
field (string or tuple field name) – The field to calculate the standard deviation of
axis (string, optional) – If supplied, the axis to compute the standard deviation along (i.e., to project along)
weight (string, optional) – The field to use as a weight.
 Returns
 Return type
Scalar or YTProjection.

sum
(field, axis=None)¶ Compute the sum of a field, optionally along an axis.
This will, in a parallelaware 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> total_vol = reg.sum("cell_volume") >>> cell_count = reg.sum(("index", "ones"), axis=("index", "x"))

property
tiles
¶

to_astropy_table
(fields)¶ Export region data to a :class:~astropy.table.table.QTable, which is a Table object which is unitaware. The QTable can then be exported to an ASCII file, FITS file, etc.
See the AstroPy Table docs for more details: http://docs.astropy.org/en/stable/table/
 Parameters
fields (list of strings or tuple field names) – This is the list of fields to be exported into the QTable.
Examples
>>> sp = ds.sphere("c", (1.0, "Mpc")) >>> t = sp.to_astropy_table([("gas", "density"), ("gas", "temperature")])

to_dataframe
(fields)¶ Export a data object to a
DataFrame
.This function will take a data object and an optional list of fields and export them to a
DataFrame
object. If pandas is not importable, this will raise ImportError. Parameters
fields (list of strings or tuple field names) – This is the list of fields to be exported into the DataFrame.
 Returns
df – The data contained in the object.
 Return type
Examples
>>> dd = ds.all_data() >>> df = dd.to_dataframe([("gas", "density"), ("gas", "temperature")])

to_frb
(width, resolution, center=None, height=None, periodic=False)¶ This function returns a FixedResolutionBuffer generated from this object.
A FixedResolutionBuffer is an object that accepts a variableresolution 2D object and transforms it into an NxM bitmap that can be plotted, examined or processed. This is a convenience function to return an FRB directly from an existing 2D data object.
 Parameters
width (width specifier) – This can either be a floating point value, in the native domain units of the simulation, or a tuple of the (value, unit) style. This will be the width of the FRB.
height (height specifier) – This will be the physical height of the FRB, by default it is equal to width. Note that this will not make any corrections to resolution for the aspect ratio.
resolution (int or tuple of ints) – The number of pixels on a side of the final FRB. If iterable, this will be the width then the height.
center (arraylike of floats, optional) – The center of the FRB. If not specified, defaults to the center of the current object.
periodic (bool) – Should the returned Fixed Resolution Buffer be periodic? (default: False).
 Returns
frb – A fixed resolution buffer, which can be queried for fields.
 Return type
Examples
>>> proj = ds.proj(("gas", "density"), 0) >>> frb = proj.to_frb((100.0, "kpc"), 1024) >>> write_image(np.log10(frb[("gas", "density")]), "density_100kpc.png")

to_glue
(fields, label='yt', data_collection=None)¶ Takes specific fields in the container and exports them to Glue (http://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.

to_pw
(fields=None, center='center', width=None, origin='centerwindow')¶ Create a
PWViewerMPL
from this object.This is a barebones mechanism of creating a plot window from this object, which can then be moved around, zoomed, and on and on. All behavior of the plot window is relegated to that routine.

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.
 Raises
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.data_objects.construction_data_containers.
YTProj
(field, axis, weight_field=None, center=None, ds=None, data_source=None, method='integrate', field_parameters=None, max_level=None, *, moment=1)[source]¶ Bases:
yt.data_objects.selection_objects.data_selection_objects.YTSelectionContainer2D

apply_units
(arr, units)¶

argmax
(field, axis=None)¶ Return the values at which the field is maximized.
This will, in a parallelaware 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.
 Returns
 Return type
A list of YTQuantities as specified by the axis argument.
Examples
>>> temp_at_max_rho = reg.argmax( ... ("gas", "density"), axis=("gas", "temperature") ... ) >>> max_rho_xyz = reg.argmax(("gas", "density")) >>> t_mrho, v_mrho = reg.argmax( ... ("gas", "density"), ... axis=[("gas", "temperature"), ("gas", "velocity_magnitude")], ... ) >>> x, y, z = reg.argmax(("gas", "density"))

argmin
(field, axis=None)¶ Return the values at which the field is minimized.
This will, in a parallelaware 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.
 Returns
 Return type
A list of YTQuantities as specified by the axis argument.
Examples
>>> temp_at_min_rho = reg.argmin( ... ("gas", "density"), axis=("gas", "temperature") ... ) >>> min_rho_xyz = reg.argmin(("gas", "density")) >>> t_mrho, v_mrho = reg.argmin( ... ("gas", "density"), ... axis=[("gas", "temperature"), ("gas", "velocity_magnitude")], ... ) >>> x, y, z = reg.argmin(("gas", "density"))

property
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 deeplycopied. If you modify the field parameters inplace, 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[("gas", "density")] >>> print(sp.field_data.keys()) [("gas", "density")] >>> print(sp_clone.field_data.keys()) []

comm
= None¶

create_firefly_object
(datadir=None, fields_to_include=None, fields_units=None, default_decimation_factor=100, velocity_units='km/s', coordinate_units='kpc', show_unused_fields=0, *, JSONdir=None, match_any_particle_types=True, **kwargs)¶ This function links a region of data stored in a yt dataset to the Python frontend API for [Firefly](http://github.com/ageller/Firefly), a browserbased particle visualization tool.
 Parameters
datadir (string) – Path to where any .json files should be saved. If a relative path will assume relative to ${HOME}. A value of None will default to ${HOME}/Data.
fields_to_include (array_like of strings or field tuples) – A list of fields that you want to include in your Firefly visualization for onthefly filtering and colormapping.
default_decimation_factor (integer) – The factor by which you want to decimate each particle group by (e.g. if there are 1e7 total particles in your simulation you might want to set this to 100 at first). Randomly samples your data like shuffled_data[::decimation_factor] so as to not overtax a system. This is adjustable on a per particle group basis by changing the returned reader’s reader.particleGroup[i].decimation_factor before calling reader.writeToDisk().
velocity_units (string) – The units that the velocity should be converted to in order to show streamlines in Firefly. Defaults to km/s.
coordinate_units (string) – The units that the coordinates should be converted to. Defaults to kpc.
show_unused_fields (boolean) – A flag to optionally print the fields that are available, in the dataset but were not explicitly requested to be tracked.
match_any_particle_types (boolean) – If True, when any of the fields_to_include match multiple particle groups then the field will be added for all matching particle groups. If False, an error is raised when encountering an ambiguous field. Default is True.
additional keyword arguments are passed to (Any) –
firefly.data_reader.Reader.__init__ –
 Returns
reader – A reader object from the Firefly, configured to output the current region selected
 Return type
Firefly.data_reader.Reader object
Examples
>>> ramses_ds = yt.load( ... "/Users/agurvich/Desktop/yt_workshop/" ... + "DICEGalaxyDisk_nonCosmological/output_00002/info_00002.txt" ... )
>>> region = ramses_ds.sphere(ramses_ds.domain_center, (1000, "kpc"))
>>> reader = region.create_firefly_object( ... "IsoGalaxyRamses", ... fields_to_include=[ ... "particle_extra_field_1", ... "particle_extra_field_2", ... ], ... fields_units=["dimensionless", "dimensionless"], ... )
>>> reader.settings["color"]["io"] = [1, 1, 0, 1] >>> reader.particleGroups[0].decimation_factor = 100 >>> reader.writeToDisk()

property
fcoords
¶

property
fcoords_vertex
¶

property
field
¶

property
fwidth
¶

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.

has_field_parameter
(name)¶ Checks if a field parameter is set.

has_key
(key)¶ Checks if a data field already exists.

property
icoords
¶

property
index
¶

integrate
(field, weight=None, axis=None, *, moment=1)¶ 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.
moment (integer, optional) – for a weighted projection, moment = 1 (the default) corresponds to a weighted average. moment = 2 corresponds to a weighted standard deviation.
 Returns
 Return type
YTProjection
Examples
>>> column_density = reg.integrate(("gas", "density"), axis=("index", "z"))

property
ires
¶

keys
()¶

max
(field, axis=None)¶ Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘max’ 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> max_temp = reg.max(("gas", "temperature")) >>> max_temp_proj = reg.max(("gas", "temperature"), axis=("index", "x"))

property
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 parallelaware 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.
 Returns
 Return type
Scalar or YTProjection.
Examples
>>> avg_rho = reg.mean(("gas", "density"), weight="cell_volume") >>> rho_weighted_T = reg.mean( ... ("gas", "temperature"), axis=("index", "y"), weight=("gas", "density") ... )

min
(field, axis=None)¶ Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘min’ for minimum intensity. If the min 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> min_temp = reg.min(("gas", "temperature")) >>> min_temp_proj = reg.min(("gas", "temperature"), axis=("index", "x"))

property
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.

property
pf
¶

profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field=('gas', '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") >>> ad = ds.all_data() >>> profile = ad.profile( ... ad, ... [("gas", "density")], ... [("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 parallelaware fashion, compute the “peaktopeak” of the given field.
 Parameters
field (string or tuple field name) – The field to average.
 Returns
 Return type
Scalar
Examples
>>> rho_range = reg.ptp(("gas", "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.
 Returns
filename – The name of the file that has been created.
 Return type
Examples
>>> import yt >>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046") >>> sp = ds.sphere(ds.domain_center, (10, "Mpc")) >>> fn = sp.save_as_dataset(fields=[("gas", "density"), ("gas", "temperature")]) >>> sphere_ds = yt.load(fn) >>> # the original data container is available as the data attribute >>> print(sds.data[("gas", "density")]) [ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30 3.57339907e30 2.83150720e30] g/cm**3 >>> ad = sphere_ds.all_data() >>> print(ad[("gas", "temperature")]) [ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04 4.54380547e+04 4.72560117e+04] K

property
selector
¶

set_field_parameter
(name, val)¶ Here we set up dictionaries that get passed up and down and ultimately to derived fields.

std
(field, axis=None, weight=None)¶ Compute the standard deviation of a field, optionally along an axis, with a weight.
This will, in a parallelware fashion, compute the standard deviation 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, resulting in an unweighted standard deviation.
 Parameters
field (string or tuple field name) – The field to calculate the standard deviation of
axis (string, optional) – If supplied, the axis to compute the standard deviation along (i.e., to project along)
weight (string, optional) – The field to use as a weight.
 Returns
 Return type
Scalar or YTProjection.

sum
(field, axis=None)¶ Compute the sum of a field, optionally along an axis.
This will, in a parallelaware 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> total_vol = reg.sum("cell_volume") >>> cell_count = reg.sum(("index", "ones"), axis=("index", "x"))

property
tiles
¶

to_astropy_table
(fields)¶ Export region data to a :class:~astropy.table.table.QTable, which is a Table object which is unitaware. The QTable can then be exported to an ASCII file, FITS file, etc.
See the AstroPy Table docs for more details: http://docs.astropy.org/en/stable/table/
 Parameters
fields (list of strings or tuple field names) – This is the list of fields to be exported into the QTable.
Examples
>>> sp = ds.sphere("c", (1.0, "Mpc")) >>> t = sp.to_astropy_table([("gas", "density"), ("gas", "temperature")])

to_dataframe
(fields)¶ Export a data object to a
DataFrame
.This function will take a data object and an optional list of fields and export them to a
DataFrame
object. If pandas is not importable, this will raise ImportError. Parameters
fields (list of strings or tuple field names) – This is the list of fields to be exported into the DataFrame.
 Returns
df – The data contained in the object.
 Return type
Examples
>>> dd = ds.all_data() >>> df = dd.to_dataframe([("gas", "density"), ("gas", "temperature")])

to_frb
(width, resolution, center=None, height=None, periodic=False)¶ This function returns a FixedResolutionBuffer generated from this object.
A FixedResolutionBuffer is an object that accepts a variableresolution 2D object and transforms it into an NxM bitmap that can be plotted, examined or processed. This is a convenience function to return an FRB directly from an existing 2D data object.
 Parameters
width (width specifier) – This can either be a floating point value, in the native domain units of the simulation, or a tuple of the (value, unit) style. This will be the width of the FRB.
height (height specifier) – This will be the physical height of the FRB, by default it is equal to width. Note that this will not make any corrections to resolution for the aspect ratio.
resolution (int or tuple of ints) – The number of pixels on a side of the final FRB. If iterable, this will be the width then the height.
center (arraylike of floats, optional) – The center of the FRB. If not specified, defaults to the center of the current object.
periodic (bool) – Should the returned Fixed Resolution Buffer be periodic? (default: False).
 Returns
frb – A fixed resolution buffer, which can be queried for fields.
 Return type
Examples
>>> proj = ds.proj(("gas", "density"), 0) >>> frb = proj.to_frb((100.0, "kpc"), 1024) >>> write_image(np.log10(frb[("gas", "density")]), "density_100kpc.png")

to_glue
(fields, label='yt', data_collection=None)¶ Takes specific fields in the container and exports them to Glue (http://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.

to_pw
(fields=None, center='center', width=None, origin='centerwindow')[source]¶ Create a
PWViewerMPL
from this object.This is a barebones mechanism of creating a plot window from this object, which can then be moved around, zoomed, and on and on. All behavior of the plot window is relegated to that routine.

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.
 Raises
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.data_objects.construction_data_containers.
YTQuadTreeProj
(field, axis, weight_field=None, center=None, ds=None, data_source=None, method='integrate', field_parameters=None, max_level=None, *, moment=1)[source]¶ Bases:
yt.data_objects.construction_data_containers.YTProj
This is a data object corresponding to a line integral through the simulation domain.
This object is typically accessed through the proj object that hangs off of index objects. YTQuadTreeProj is a projection of a field along an axis. The field can have an associated weight_field, in which case the values are multiplied by a weight before being summed, and then divided by the sum of that weight; the two fundamental modes of operating are direct line integral (no weighting) and average along a line of sight (weighting.) What makes proj different from the standard projection mechanism is that it utilizes a quadtree data structure, rather than the old mechanism for projections. It will not run in parallel, but serial runs should be substantially faster. Note also that lines of sight are integrated at every projected finestlevel cell.
 Parameters
field (string) – This is the field which will be “projected” along the axis. If multiple are specified (in a list) they will all be projected in the first pass.
axis (int) – The axis along which to slice. Can be 0, 1, or 2 for x, y, z.
weight_field (string) – If supplied, the field being projected will be multiplied by this weight value before being integrated, and at the conclusion of the projection the resultant values will be divided by the projected weight_field.
center (array_like, optional) – The ‘center’ supplied to fields that use it. Note that this does not have to have coord as one value. Strictly optional.
data_source (yt.data_objects.data_containers.YTSelectionContainer, optional) – If specified, this will be the data source used for selecting regions to project.
method (string, optional) – The method of projection to be performed. “integrate” : integration along the axis “mip” : maximum intensity projection (deprecated) “max” : maximum intensity projection “min” : minimum intensity projection “sum” : same as “integrate”, except that we don’t multiply by the path length WARNING: The “sum” option should only be used for uniform resolution grid datasets, as other datasets may result in unphysical images.
style (string, optional) – The same as the method keyword. Deprecated as of version 3.0.2. Please use method keyword instead.
field_parameters (dict of items) – Values to be passed as field parameters that can be accessed by generated fields.
moment (integer, optional) – for a weighted projection, moment = 1 (the default) corresponds to a weighted average. moment = 2 corresponds to a weighted standard deviation.
Examples
>>> ds = load("RedshiftOutput0005") >>> prj = ds.proj(("gas", "density"), 0) >>> print(proj[("gas", "density")])

apply_units
(arr, units)¶

argmax
(field, axis=None)¶ Return the values at which the field is maximized.
This will, in a parallelaware 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.
 Returns
 Return type
A list of YTQuantities as specified by the axis argument.
Examples
>>> temp_at_max_rho = reg.argmax( ... ("gas", "density"), axis=("gas", "temperature") ... ) >>> max_rho_xyz = reg.argmax(("gas", "density")) >>> t_mrho, v_mrho = reg.argmax( ... ("gas", "density"), ... axis=[("gas", "temperature"), ("gas", "velocity_magnitude")], ... ) >>> x, y, z = reg.argmax(("gas", "density"))

argmin
(field, axis=None)¶ Return the values at which the field is minimized.
This will, in a parallelaware 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.
 Returns
 Return type
A list of YTQuantities as specified by the axis argument.
Examples
>>> temp_at_min_rho = reg.argmin( ... ("gas", "density"), axis=("gas", "temperature") ... ) >>> min_rho_xyz = reg.argmin(("gas", "density")) >>> t_mrho, v_mrho = reg.argmin( ... ("gas", "density"), ... axis=[("gas", "temperature"), ("gas", "velocity_magnitude")], ... ) >>> x, y, z = reg.argmin(("gas", "density"))

property
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 deeplycopied. If you modify the field parameters inplace, 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[("gas", "density")] >>> print(sp.field_data.keys()) [("gas", "density")] >>> print(sp_clone.field_data.keys()) []

comm
= None¶

create_firefly_object
(datadir=None, fields_to_include=None, fields_units=None, default_decimation_factor=100, velocity_units='km/s', coordinate_units='kpc', show_unused_fields=0, *, JSONdir=None, match_any_particle_types=True, **kwargs)¶ This function links a region of data stored in a yt dataset to the Python frontend API for [Firefly](http://github.com/ageller/Firefly), a browserbased particle visualization tool.
 Parameters
datadir (string) – Path to where any .json files should be saved. If a relative path will assume relative to ${HOME}. A value of None will default to ${HOME}/Data.
fields_to_include (array_like of strings or field tuples) – A list of fields that you want to include in your Firefly visualization for onthefly filtering and colormapping.
default_decimation_factor (integer) – The factor by which you want to decimate each particle group by (e.g. if there are 1e7 total particles in your simulation you might want to set this to 100 at first). Randomly samples your data like shuffled_data[::decimation_factor] so as to not overtax a system. This is adjustable on a per particle group basis by changing the returned reader’s reader.particleGroup[i].decimation_factor before calling reader.writeToDisk().
velocity_units (string) – The units that the velocity should be converted to in order to show streamlines in Firefly. Defaults to km/s.
coordinate_units (string) – The units that the coordinates should be converted to. Defaults to kpc.
show_unused_fields (boolean) – A flag to optionally print the fields that are available, in the dataset but were not explicitly requested to be tracked.
match_any_particle_types (boolean) – If True, when any of the fields_to_include match multiple particle groups then the field will be added for all matching particle groups. If False, an error is raised when encountering an ambiguous field. Default is True.
additional keyword arguments are passed to (Any) –
firefly.data_reader.Reader.__init__ –
 Returns
reader – A reader object from the Firefly, configured to output the current region selected
 Return type
Firefly.data_reader.Reader object
Examples
>>> ramses_ds = yt.load( ... "/Users/agurvich/Desktop/yt_workshop/" ... + "DICEGalaxyDisk_nonCosmological/output_00002/info_00002.txt" ... )
>>> region = ramses_ds.sphere(ramses_ds.domain_center, (1000, "kpc"))
>>> reader = region.create_firefly_object( ... "IsoGalaxyRamses", ... fields_to_include=[ ... "particle_extra_field_1", ... "particle_extra_field_2", ... ], ... fields_units=["dimensionless", "dimensionless"], ... )
>>> reader.settings["color"]["io"] = [1, 1, 0, 1] >>> reader.particleGroups[0].decimation_factor = 100 >>> reader.writeToDisk()

ds
: Dataset¶

property
fcoords
¶

property
fcoords_vertex
¶

property
field
¶

property
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.

has_field_parameter
(name)¶ Checks if a field parameter is set.

has_key
(key)¶ Checks if a data field already exists.

property
icoords
¶

property
index
¶

integrate
(field, weight=None, axis=None, *, moment=1)¶ 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.
moment (integer, optional) – for a weighted projection, moment = 1 (the default) corresponds to a weighted average. moment = 2 corresponds to a weighted standard deviation.
 Returns
 Return type
YTProjection
Examples
>>> column_density = reg.integrate(("gas", "density"), axis=("index", "z"))

property
ires
¶

keys
()¶

max
(field, axis=None)¶ Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘max’ 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> max_temp = reg.max(("gas", "temperature")) >>> max_temp_proj = reg.max(("gas", "temperature"), axis=("index", "x"))

property
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 parallelaware 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.
 Returns
 Return type
Scalar or YTProjection.
Examples
>>> avg_rho = reg.mean(("gas", "density"), weight="cell_volume") >>> rho_weighted_T = reg.mean( ... ("gas", "temperature"), axis=("index", "y"), weight=("gas", "density") ... )

min
(field, axis=None)¶ Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘min’ for minimum intensity. If the min 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> min_temp = reg.min(("gas", "temperature")) >>> min_temp_proj = reg.min(("gas", "temperature"), axis=("index", "x"))

property
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.

property
pf
¶

plot
(fields=None)¶

profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field=('gas', '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") >>> ad = ds.all_data() >>> profile = ad.profile( ... ad, ... [("gas", "density")], ... [("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 parallelaware fashion, compute the “peaktopeak” of the given field.
 Parameters
field (string or tuple field name) – The field to average.
 Returns
 Return type
Scalar
Examples
>>> rho_range = reg.ptp(("gas", "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.
 Returns
filename – The name of the file that has been created.
 Return type
Examples
>>> import yt >>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046") >>> sp = ds.sphere(ds.domain_center, (10, "Mpc")) >>> fn = sp.save_as_dataset(fields=[("gas", "density"), ("gas", "temperature")]) >>> sphere_ds = yt.load(fn) >>> # the original data container is available as the data attribute >>> print(sds.data[("gas", "density")]) [ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30 3.57339907e30 2.83150720e30] g/cm**3 >>> ad = sphere_ds.all_data() >>> print(ad[("gas", "temperature")]) [ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04 4.54380547e+04 4.72560117e+04] K

property
selector
¶

set_field_parameter
(name, val)¶ Here we set up dictionaries that get passed up and down and ultimately to derived fields.

std
(field, axis=None, weight=None)¶ Compute the standard deviation of a field, optionally along an axis, with a weight.
This will, in a parallelware fashion, compute the standard deviation 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, resulting in an unweighted standard deviation.
 Parameters
field (string or tuple field name) – The field to calculate the standard deviation of
axis (string, optional) – If supplied, the axis to compute the standard deviation along (i.e., to project along)
weight (string, optional) – The field to use as a weight.
 Returns
 Return type
Scalar or YTProjection.

sum
(field, axis=None)¶ Compute the sum of a field, optionally along an axis.
This will, in a parallelaware 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> total_vol = reg.sum("cell_volume") >>> cell_count = reg.sum(("index", "ones"), axis=("index", "x"))

property
tiles
¶

to_astropy_table
(fields)¶ Export region data to a :class:~astropy.table.table.QTable, which is a Table object which is unitaware. The QTable can then be exported to an ASCII file, FITS file, etc.
See the AstroPy Table docs for more details: http://docs.astropy.org/en/stable/table/
 Parameters
fields (list of strings or tuple field names) – This is the list of fields to be exported into the QTable.
Examples
>>> sp = ds.sphere("c", (1.0, "Mpc")) >>> t = sp.to_astropy_table([("gas", "density"), ("gas", "temperature")])

to_dataframe
(fields)¶ Export a data object to a
DataFrame
.This function will take a data object and an optional list of fields and export them to a
DataFrame
object. If pandas is not importable, this will raise ImportError. Parameters
fields (list of strings or tuple field names) – This is the list of fields to be exported into the DataFrame.
 Returns
df – The data contained in the object.
 Return type
Examples
>>> dd = ds.all_data() >>> df = dd.to_dataframe([("gas", "density"), ("gas", "temperature")])

to_frb
(width, resolution, center=None, height=None, periodic=False)¶ This function returns a FixedResolutionBuffer generated from this object.
A FixedResolutionBuffer is an object that accepts a variableresolution 2D object and transforms it into an NxM bitmap that can be plotted, examined or processed. This is a convenience function to return an FRB directly from an existing 2D data object.
 Parameters
width (width specifier) – This can either be a floating point value, in the native domain units of the simulation, or a tuple of the (value, unit) style. This will be the width of the FRB.
height (height specifier) – This will be the physical height of the FRB, by default it is equal to width. Note that this will not make any corrections to resolution for the aspect ratio.
resolution (int or tuple of ints) – The number of pixels on a side of the final FRB. If iterable, this will be the width then the height.
center (arraylike of floats, optional) – The center of the FRB. If not specified, defaults to the center of the current object.
periodic (bool) – Should the returned Fixed Resolution Buffer be periodic? (default: False).
 Returns
frb – A fixed resolution buffer, which can be queried for fields.
 Return type
Examples
>>> proj = ds.proj(("gas", "density"), 0) >>> frb = proj.to_frb((100.0, "kpc"), 1024) >>> write_image(np.log10(frb[("gas", "density")]), "density_100kpc.png")

to_glue
(fields, label='yt', data_collection=None)¶ Takes specific fields in the container and exports them to Glue (http://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.

to_pw
(fields=None, center='center', width=None, origin='centerwindow')¶ Create a
PWViewerMPL
from this object.This is a barebones mechanism of creating a plot window from this object, which can then be moved around, zoomed, and on and on. All behavior of the plot window is relegated to that routine.

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.
 Raises
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.data_objects.construction_data_containers.
YTSmoothedCoveringGrid
(level, left_edge, dims, fields=None, ds=None, num_ghost_zones=0, use_pbar=True, field_parameters=None, *, data_source=None)[source]¶ Bases:
yt.data_objects.construction_data_containers.YTCoveringGrid
A 3D region with all data extracted and interpolated to a single, specified resolution. (Identical to covering_grid, except that it interpolates.)
Smoothed covering grids start at level 0, interpolating to fill the region to level 1, replacing any cells actually covered by level 1 data, and then recursively repeating this process until it reaches the specified level.
 Parameters
level (int) – The resolution level data is uniformly gridded at
left_edge (array_like) – The left edge of the region to be extracted
dims (array_like) – Number of cells along each axis of resulting covering_grid.
fields (array_like, optional) – A list of fields that you’d like pregenerated for your object
Example
cube = ds.smoothed_covering_grid(2, left_edge=[0.0, 0.0, 0.0], dims=[128, 128, 128])

property
LeftEdge
¶

property
RightEdge
¶

apply_units
(arr, units)¶

argmax
(field, axis=None)¶ Return the values at which the field is maximized.
This will, in a parallelaware 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.
 Returns
 Return type
A list of YTQuantities as specified by the axis argument.
Examples
>>> temp_at_max_rho = reg.argmax( ... ("gas", "density"), axis=("gas", "temperature") ... ) >>> max_rho_xyz = reg.argmax(("gas", "density")) >>> t_mrho, v_mrho = reg.argmax( ... ("gas", "density"), ... axis=[("gas", "temperature"), ("gas", "velocity_magnitude")], ... ) >>> x, y, z = reg.argmax(("gas", "density"))

argmin
(field, axis=None)¶ Return the values at which the field is minimized.
This will, in a parallelaware 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.
 Returns
 Return type
A list of YTQuantities as specified by the axis argument.
Examples
>>> temp_at_min_rho = reg.argmin( ... ("gas", "density"), axis=("gas", "temperature") ... ) >>> min_rho_xyz = reg.argmin(("gas", "density")) >>> t_mrho, v_mrho = reg.argmin( ... ("gas", "density"), ... axis=[("gas", "temperature"), ("gas", "velocity_magnitude")], ... ) >>> x, y, z = reg.argmin(("gas", "density"))

property
blocks
¶

calculate_isocontour_flux
(field, value, field_x, field_y, field_z, fluxing_field=None)¶ This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and calculates the flux over those contours.
This function will conduct marching cubes on all the cells in a given data container (gridbygrid), and then for each identified triangular segment of an isocontour in a given cell, calculate the gradient (i.e., normal) in the isocontoured field, interpolate the local value of the “fluxing” field, the area of the triangle, and then return:
area * local_flux_value * (n dot v)
Where area, local_value, and the vector v are interpolated at the barycenter (weighted by the vertex values) of the triangle. Note that this specifically allows for the field fluxing across the surface to be different from the field being contoured. If the fluxing_field is not specified, it is assumed to be 1.0 everywhere, and the raw flux with no localweighting is returned.
Additionally, the returned flux is defined as flux into the surface, not flux out of the surface.
 Parameters
field (string) – Any field that can be obtained in a data object. This is the field which will be isocontoured and used as the “local_value” in the flux equation.
value (float) – The value at which the isocontour should be calculated.
field_x (string) – The xcomponent field
field_y (string) – The ycomponent field
field_z (string) – The zcomponent field
fluxing_field (string, optional) – The field whose passage over the surface is of interest. If not specified, assumed to be 1.0 everywhere.
 Returns
flux – The summed flux. Note that it is not currently scaled; this is simply the codeunit area times the fields.
 Return type
Examples
This will create a data object, find a nice value in the center, and calculate the metal flux over it.
>>> dd = ds.all_data() >>> rho = dd.quantities["WeightedAverageQuantity"]( ... ("gas", "density"), weight=("gas", "cell_mass") ... ) >>> flux = dd.calculate_isocontour_flux( ... ("gas", "density"), ... rho, ... ("gas", "velocity_x"), ... ("gas", "velocity_y"), ... ("gas", "velocity_z"), ... ("gas", "metallicity"), ... )

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 deeplycopied. If you modify the field parameters inplace, 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[("gas", "density")] >>> print(sp.field_data.keys()) [("gas", "density")] >>> print(sp_clone.field_data.keys()) []

comm
= None¶

create_firefly_object
(datadir=None, fields_to_include=None, fields_units=None, default_decimation_factor=100, velocity_units='km/s', coordinate_units='kpc', show_unused_fields=0, *, JSONdir=None, match_any_particle_types=True, **kwargs)¶ This function links a region of data stored in a yt dataset to the Python frontend API for [Firefly](http://github.com/ageller/Firefly), a browserbased particle visualization tool.
 Parameters
datadir (string) – Path to where any .json files should be saved. If a relative path will assume relative to ${HOME}. A value of None will default to ${HOME}/Data.
fields_to_include (array_like of strings or field tuples) – A list of fields that you want to include in your Firefly visualization for onthefly filtering and colormapping.
default_decimation_factor (integer) – The factor by which you want to decimate each particle group by (e.g. if there are 1e7 total particles in your simulation you might want to set this to 100 at first). Randomly samples your data like shuffled_data[::decimation_factor] so as to not overtax a system. This is adjustable on a per particle group basis by changing the returned reader’s reader.particleGroup[i].decimation_factor before calling reader.writeToDisk().
velocity_units (string) – The units that the velocity should be converted to in order to show streamlines in Firefly. Defaults to km/s.
coordinate_units (string) – The units that the coordinates should be converted to. Defaults to kpc.
show_unused_fields (boolean) – A flag to optionally print the fields that are available, in the dataset but were not explicitly requested to be tracked.
match_any_particle_types (boolean) – If True, when any of the fields_to_include match multiple particle groups then the field will be added for all matching particle groups. If False, an error is raised when encountering an ambiguous field. Default is True.
additional keyword arguments are passed to (Any) –
firefly.data_reader.Reader.__init__ –
 Returns
reader – A reader object from the Firefly, configured to output the current region selected
 Return type
Firefly.data_reader.Reader object
Examples
>>> ramses_ds = yt.load( ... "/Users/agurvich/Desktop/yt_workshop/" ... + "DICEGalaxyDisk_nonCosmological/output_00002/info_00002.txt" ... )
>>> region = ramses_ds.sphere(ramses_ds.domain_center, (1000, "kpc"))
>>> reader = region.create_firefly_object( ... "IsoGalaxyRamses", ... fields_to_include=[ ... "particle_extra_field_1", ... "particle_extra_field_2", ... ], ... fields_units=["dimensionless", "dimensionless"], ... )
>>> reader.settings["color"]["io"] = [1, 1, 0, 1] >>> reader.particleGroups[0].decimation_factor = 100 >>> reader.writeToDisk()

cut_region
(field_cuts, field_parameters=None, locals=None)¶ Return a YTCutRegion, where the a cell is identified as being inside the cut region based on the value of one or more fields. Note that in previous versions of yt the name ‘grid’ was used to represent the data object used to construct the field cut, as of yt 3.0, this has been changed to ‘obj’.
 Parameters
field_cuts (list of strings) – A list of conditionals that will be evaluated. In the namespace available, these conditionals will have access to ‘obj’ which is a data object of unknown shape, and they must generate a boolean array. For instance, conditionals = [“obj[(‘gas’, ‘temperature’)] < 1e3”]
field_parameters (dictionary) – A dictionary of field parameters to be used when applying the field cuts.
locals (dictionary) – A dictionary of local variables to use when defining the cut region.
Examples
To find the total mass of hot gas with temperature greater than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.cut_region(["obj[('gas', 'temperature')] > 1e6"]) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

deposit
(positions, fields=None, method=None, kernel_name='cubic')¶

ds
: Dataset¶

exclude_above
(field, value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field is above a given value are masked.
 Parameters
 Returns
cut_region – The YTCutRegion with the field above the given value masked.
 Return type
Examples
To find the total mass of hot gas with temperature colder than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_above(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_below
(field, value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field is below a given value are masked.
 Parameters
 Returns
cut_region – The YTCutRegion with the field below the given value masked.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_below(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_equal
(field, value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field are equal to given value are masked.
 Parameters
 Returns
cut_region – The YTCutRegion with the field equal to the given value masked.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_equal(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_inside
(field, min_value, max_value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field are inside the interval from min_value to max_value.
 Parameters
field (string) – The field in which the conditional will be applied.
min_value (float) – The minimum value inside the interval to be excluded.
max_value (float) – The maximum value inside the interval to be excluded.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the field inside the given interval excluded.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_inside(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_nan
(field, units=None)¶ This function will return a YTCutRegion where all of the regions whose field is NaN are masked.
 Parameters
field (string) – The field in which the conditional will be applied.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the NaN entries of the field masked.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_nan(("gas", "temperature")) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_outside
(field, min_value, max_value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field are outside the interval from min_value to max_value.
 Parameters
field (string) – The field in which the conditional will be applied.
min_value (float) – The minimum value inside the interval to be excluded.
max_value (float) – The maximum value inside the interval to be excluded.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the field outside the given interval excluded.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_outside(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

extract_connected_sets
(field, num_levels, min_val, max_val, log_space=True, cumulative=True)¶ This function will create a set of contour objects, defined by having connected cell structures, which can then be studied and used to ‘paint’ their source grids, thus enabling them to be plotted.
Note that this function can return a connected set object that has no member values.

extract_isocontours
(field, value, filename=None, rescale=False, sample_values=None)¶ This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and returns the vertices of the Triangles in that isocontour.
This function simply returns the vertices of all the triangles calculated by the marching cubes algorithm; for more complex operations, such as identifying connected sets of cells above a given threshold, see the extract_connected_sets function. This is more useful for calculating, for instance, total isocontour area, or visualizing in an external program (such as MeshLab.)
 Parameters
field (string) – Any field that can be obtained in a data object. This is the field which will be isocontoured.
value (float) – The value at which the isocontour should be calculated.
filename (string, optional) – If supplied, this file will be filled with the vertices in .obj format. Suitable for loading into meshlab.
rescale (bool, optional) – If true, the vertices will be rescaled within their min/max.
sample_values (string, optional) – Any field whose value should be extracted at the center of each triangle.
 Returns
verts (array of floats) – The array of vertices, x,y,z. Taken in threes, these are the triangle vertices.
samples (array of floats) – If sample_values is specified, this will be returned and will contain the values of the field specified at the center of each triangle.
Examples
This will create a data object, find a nice value in the center, and output the vertices to “triangles.obj” after rescaling them.
>>> dd = ds.all_data() >>> rho = dd.quantities["WeightedAverageQuantity"]( ... ("gas", "density"), weight=("gas", "cell_mass") ... ) >>> verts = dd.extract_isocontours( ... ("gas", "density"), rho, "triangles.obj", True ... )

property
fcoords
¶

property
fcoords_vertex
¶

filename
= None¶

property
fwidth
¶

get_bbox
() → Tuple[unyt.array.unyt_array, unyt.array.unyt_array]¶ Return the bounding box for this data container.

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
()¶ Get the global start index of the covering grid.

has_field_parameter
(name)¶ Checks if a field parameter is set.

has_key
(key)¶ Checks if a data field already exists.

property
icoords
¶

include_above
(field, value, units=None)¶ This function will return a YTCutRegion where only the regions whose field is above a given value are included.
 Parameters
 Returns
cut_region – The YTCutRegion with the field above the given value masked.
 Return type
Examples
To find the total mass of hot gas with temperature warmer than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.include_above(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

include_below
(field, value, units=None)¶ This function will return a YTCutRegion where only the regions whose field is below a given value are included.
 Parameters
 Returns
cut_region – The YTCutRegion with only regions with the field below the given value included.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.include_below(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

include_equal
(field, value, units=None)¶ This function will return a YTCutRegion where only the regions whose field are equal to given value are included.
 Parameters
 Returns
cut_region – The YTCutRegion with the field equal to the given value included.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.include_equal(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

include_inside
(field, min_value, max_value, units=None)¶ This function will return a YTCutRegion where only the regions whose field are inside the interval from min_value to max_value are included.
 Parameters
field (string) – The field in which the conditional will be applied.
min_value (float) – The minimum value inside the interval to be excluded.
max_value (float) – The maximum value inside the interval to be excluded.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the field inside the given interval excluded.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.include_inside(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

include_outside
(field, min_value, max_value, units=None)¶ This function will return a YTCutRegion where only the regions whose field are outside the interval from min_value to max_value are included.
 Parameters
field (string) – The field in which the conditional will be applied.
min_value (float) – The minimum value inside the interval to be excluded.
max_value (float) – The maximum value inside the interval to be excluded.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the field outside the given interval excluded.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_outside(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

property
index
¶

integrate
(field, weight=None, axis=None, *, moment=1)¶ 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.
moment (integer, optional) – for a weighted projection, moment = 1 (the default) corresponds to a weighted average. moment = 2 corresponds to a weighted standard deviation.
 Returns
 Return type
YTProjection
Examples
>>> column_density = reg.integrate(("gas", "density"), axis=("index", "z"))

property
ires
¶

keys
()¶

max
(field, axis=None)¶ Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘max’ 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> max_temp = reg.max(("gas", "temperature")) >>> max_temp_proj = reg.max(("gas", "temperature"), axis=("index", "x"))

property
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 parallelaware 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.
 Returns
 Return type
Scalar or YTProjection.
Examples
>>> avg_rho = reg.mean(("gas", "density"), weight="cell_volume") >>> rho_weighted_T = reg.mean( ... ("gas", "temperature"), axis=("index", "y"), weight=("gas", "density") ... )

min
(field, axis=None)¶ Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘min’ for minimum intensity. If the min 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> min_temp = reg.min(("gas", "temperature")) >>> min_temp_proj = reg.min(("gas", "temperature"), axis=("index", "x"))

property
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.

property
pf
¶

profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field=('gas', '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") >>> ad = ds.all_data() >>> profile = ad.profile( ... ad, ... [("gas", "density")], ... [("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 parallelaware fashion, compute the “peaktopeak” of the given field.
 Parameters
field (string or tuple field name) – The field to average.
 Returns
 Return type
Scalar
Examples
>>> rho_range = reg.ptp(("gas", "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.
 Returns
filename – The name of the file that has been created.
 Return type
Examples
>>> import yt >>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046") >>> sp = ds.sphere(ds.domain_center, (10, "Mpc")) >>> fn = sp.save_as_dataset(fields=[("gas", "density"), ("gas", "temperature")]) >>> sphere_ds = yt.load(fn) >>> # the original data container is available as the data attribute >>> print(sds.data[("gas", "density")]) [ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30 3.57339907e30 2.83150720e30] g/cm**3 >>> ad = sphere_ds.all_data() >>> print(ad[("gas", "temperature")]) [ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04 4.54380547e+04 4.72560117e+04] K

property
selector
¶

set_field_parameter
(name, val)¶ Here we set up dictionaries that get passed up and down and ultimately to derived fields.

property
shape
¶

std
(field, axis=None, weight=None)¶ Compute the standard deviation of a field, optionally along an axis, with a weight.
This will, in a parallelware fashion, compute the standard deviation 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, resulting in an unweighted standard deviation.
 Parameters
field (string or tuple field name) – The field to calculate the standard deviation of
axis (string, optional) – If supplied, the axis to compute the standard deviation along (i.e., to project along)
weight (string, optional) – The field to use as a weight.
 Returns
 Return type
Scalar or YTProjection.

sum
(field, axis=None)¶ Compute the sum of a field, optionally along an axis.
This will, in a parallelaware 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> total_vol = reg.sum("cell_volume") >>> cell_count = reg.sum(("index", "ones"), axis=("index", "x"))

property
tiles
¶

to_astropy_table
(fields)¶ Export region data to a :class:~astropy.table.table.QTable, which is a Table object which is unitaware. The QTable can then be exported to an ASCII file, FITS file, etc.
See the AstroPy Table docs for more details: http://docs.astropy.org/en/stable/table/
 Parameters
fields (list of strings or tuple field names) – This is the list of fields to be exported into the QTable.
Examples
>>> sp = ds.sphere("c", (1.0, "Mpc")) >>> t = sp.to_astropy_table([("gas", "density"), ("gas", "temperature")])

to_dataframe
(fields)¶ Export a data object to a
DataFrame
.This function will take a data object and an optional list of fields and export them to a
DataFrame
object. If pandas is not importable, this will raise ImportError. Parameters
fields (list of strings or tuple field names) – This is the list of fields to be exported into the DataFrame.
 Returns
df – The data contained in the object.
 Return type
Examples
>>> dd = ds.all_data() >>> df = dd.to_dataframe([("gas", "density"), ("gas", "temperature")])

to_fits_data
(fields, length_unit=None)¶ Export a set of gridded fields to a FITS file.
This will export a set of FITS images of either the fields specified or all the fields already in the object.
 Parameters
fields (list of strings) – These fields will be pixelized and output. If “None”, the keys of the FRB will be used.
length_unit (string, optional) – the length units that the coordinates are written in. The default is to use the default length unit of the dataset.

to_glue
(fields, label='yt', data_collection=None)¶ Takes specific fields in the container and exports them to Glue (http://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.

to_xarray
(fields=None)¶ Export this fixedresolution object to an xarray Dataset
This function will take a regularized grid and optionally a list of fields and return an xarray Dataset object. If xarray 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.
 Returns
arr – The data contained in the object.
 Return type
Examples
>>> dd = ds.r[::256j, ::256j, ::256j] >>> xf1 = dd.to_xarray([("gas", "density"), ("gas", "temperature")]) >>> dd[("gas", "velocity_magnitude")] >>> xf2 = dd.to_xarray()

volume
()¶ Return the volume of the data container. This is found by adding up the volume of the cells with centers in the container, rather than using the geometric shape of the container, so this may vary very slightly from what might be expected from the geometric volume.

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.
 Raises
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"])

write_to_gdf
(gdf_path, fields, nprocs=1, field_units=None, **kwargs)¶ Write the covering grid data to a GDF file.
 Parameters
gdf_path (string) – Pathname of the GDF file to write.
fields (list of strings) – Fields to write to the GDF file.
nprocs (integer, optional) – Split the covering grid into nprocs subgrids before writing to the GDF file. Default: 1
field_units (dictionary, optional) – Dictionary of units to convert fields to. If not set, fields are in their default units.
remaining keyword arguments are passed to (All) –
yt.utilities.grid_data_format.writer.write_to_gdf. –
Examples
>>> cube.write_to_gdf( ... "clumps.h5", ... [("gas", "density"), ("gas", "temperature")], ... nprocs=16, ... overwrite=True, ... )

class
yt.data_objects.construction_data_containers.
YTStreamline
(positions, length=1.0, fields=None, ds=None, **kwargs)[source]¶ Bases:
yt.data_objects.selection_objects.data_selection_objects.YTSelectionContainer1D
This is a streamline, which is a set of points defined as being parallel to some vector field.
This object is typically accessed through the Streamlines.path function. The resulting arrays have their dimensionality reduced to one, and an ordered list of points at an (x,y) tuple along axis are available, as is the t field, which corresponds to a unitless measurement along the ray from start to end.
 Parameters
positions (arraylike) – List of streamline positions
length (float) – The magnitude of the distance; dts will be divided by this
fields (list of strings, optional) – If you want the object to preretrieve a set of fields, supply them here. This is not necessary.
ds (dataset object) – Passed in to access the index
kwargs (dict of items) – Any additional values are passed as field parameters that can be accessed by generated fields.
Examples
>>> from yt.visualization.api import Streamlines >>> streamlines = Streamlines(ds, [0.5] * 3) >>> streamlines.integrate_through_volume() >>> stream = streamlines.path(0) >>> fig, ax = plt.subplots() >>> ax.set_yscale("log") >>> ax.plot(stream["t"], stream[("gas", "density")], "x")

apply_units
(arr, units)¶

argmax
(field, axis=None)¶ Return the values at which the field is maximized.
This will, in a parallelaware 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.
 Returns
 Return type
A list of YTQuantities as specified by the axis argument.
Examples
>>> temp_at_max_rho = reg.argmax( ... ("gas", "density"), axis=("gas", "temperature") ... ) >>> max_rho_xyz = reg.argmax(("gas", "density")) >>> t_mrho, v_mrho = reg.argmax( ... ("gas", "density"), ... axis=[("gas", "temperature"), ("gas", "velocity_magnitude")], ... ) >>> x, y, z = reg.argmax(("gas", "density"))

argmin
(field, axis=None)¶ Return the values at which the field is minimized.
This will, in a parallelaware 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.
 Returns
 Return type
A list of YTQuantities as specified by the axis argument.
Examples
>>> temp_at_min_rho = reg.argmin( ... ("gas", "density"), axis=("gas", "temperature") ... ) >>> min_rho_xyz = reg.argmin(("gas", "density")) >>> t_mrho, v_mrho = reg.argmin( ... ("gas", "density"), ... axis=[("gas", "temperature"), ("gas", "velocity_magnitude")], ... ) >>> x, y, z = reg.argmin(("gas", "density"))

property
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 deeplycopied. If you modify the field parameters inplace, 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[("gas", "density")] >>> print(sp.field_data.keys()) [("gas", "density")] >>> print(sp_clone.field_data.keys()) []

comm
= None¶

create_firefly_object
(datadir=None, fields_to_include=None, fields_units=None, default_decimation_factor=100, velocity_units='km/s', coordinate_units='kpc', show_unused_fields=0, *, JSONdir=None, match_any_particle_types=True, **kwargs)¶ This function links a region of data stored in a yt dataset to the Python frontend API for [Firefly](http://github.com/ageller/Firefly), a browserbased particle visualization tool.
 Parameters
datadir (string) – Path to where any .json files should be saved. If a relative path will assume relative to ${HOME}. A value of None will default to ${HOME}/Data.
fields_to_include (array_like of strings or field tuples) – A list of fields that you want to include in your Firefly visualization for onthefly filtering and colormapping.
default_decimation_factor (integer) – The factor by which you want to decimate each particle group by (e.g. if there are 1e7 total particles in your simulation you might want to set this to 100 at first). Randomly samples your data like shuffled_data[::decimation_factor] so as to not overtax a system. This is adjustable on a per particle group basis by changing the returned reader’s reader.particleGroup[i].decimation_factor before calling reader.writeToDisk().
velocity_units (string) – The units that the velocity should be converted to in order to show streamlines in Firefly. Defaults to km/s.
coordinate_units (string) – The units that the coordinates should be converted to. Defaults to kpc.
show_unused_fields (boolean) – A flag to optionally print the fields that are available, in the dataset but were not explicitly requested to be tracked.
match_any_particle_types (boolean) – If True, when any of the fields_to_include match multiple particle groups then the field will be added for all matching particle groups. If False, an error is raised when encountering an ambiguous field. Default is True.
additional keyword arguments are passed to (Any) –
firefly.data_reader.Reader.__init__ –
 Returns
reader – A reader object from the Firefly, configured to output the current region selected
 Return type
Firefly.data_reader.Reader object
Examples
>>> ramses_ds = yt.load( ... "/Users/agurvich/Desktop/yt_workshop/" ... + "DICEGalaxyDisk_nonCosmological/output_00002/info_00002.txt" ... )
>>> region = ramses_ds.sphere(ramses_ds.domain_center, (1000, "kpc"))
>>> reader = region.create_firefly_object( ... "IsoGalaxyRamses", ... fields_to_include=[ ... "particle_extra_field_1", ... "particle_extra_field_2", ... ], ... fields_units=["dimensionless", "dimensionless"], ... )
>>> reader.settings["color"]["io"] = [1, 1, 0, 1] >>> reader.particleGroups[0].decimation_factor = 100 >>> reader.writeToDisk()

property
fcoords
¶

property
fcoords_vertex
¶

property
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.

has_field_parameter
(name)¶ Checks if a field parameter is set.

has_key
(key)¶ Checks if a data field already exists.

property
icoords
¶

property
index
¶

integrate
(field, weight=None, axis=None, *, moment=1)¶ 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.
moment (integer, optional) – for a weighted projection, moment = 1 (the default) corresponds to a weighted average. moment = 2 corresponds to a weighted standard deviation.
 Returns
 Return type
YTProjection
Examples
>>> column_density = reg.integrate(("gas", "density"), axis=("index", "z"))

property
ires
¶

keys
()¶

max
(field, axis=None)¶ Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘max’ 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> max_temp = reg.max(("gas", "temperature")) >>> max_temp_proj = reg.max(("gas", "temperature"), axis=("index", "x"))

property
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 parallelaware 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.
 Returns
 Return type
Scalar or YTProjection.
Examples
>>> avg_rho = reg.mean(("gas", "density"), weight="cell_volume") >>> rho_weighted_T = reg.mean( ... ("gas", "temperature"), axis=("index", "y"), weight=("gas", "density") ... )

min
(field, axis=None)¶ Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘min’ for minimum intensity. If the min 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> min_temp = reg.min(("gas", "temperature")) >>> min_temp_proj = reg.min(("gas", "temperature"), axis=("index", "x"))

property
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.

property
pf
¶

profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field=('gas', '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") >>> ad = ds.all_data() >>> profile = ad.profile( ... ad, ... [("gas", "density")], ... [("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 parallelaware fashion, compute the “peaktopeak” of the given field.
 Parameters
field (string or tuple field name) – The field to average.
 Returns
 Return type
Scalar
Examples
>>> rho_range = reg.ptp(("gas", "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.
 Returns
filename – The name of the file that has been created.
 Return type
Examples
>>> import yt >>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046") >>> sp = ds.sphere(ds.domain_center, (10, "Mpc")) >>> fn = sp.save_as_dataset(fields=[("gas", "density"), ("gas", "temperature")]) >>> sphere_ds = yt.load(fn) >>> # the original data container is available as the data attribute >>> print(sds.data[("gas", "density")]) [ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30 3.57339907e30 2.83150720e30] g/cm**3 >>> ad = sphere_ds.all_data() >>> print(ad[("gas", "temperature")]) [ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04 4.54380547e+04 4.72560117e+04] K

property
selector
¶

set_field_parameter
(name, val)¶ Here we set up dictionaries that get passed up and down and ultimately to derived fields.

sort_by
= 't'¶

std
(field, axis=None, weight=None)¶ Compute the standard deviation of a field, optionally along an axis, with a weight.
This will, in a parallelware fashion, compute the standard deviation 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, resulting in an unweighted standard deviation.
 Parameters
field (string or tuple field name) – The field to calculate the standard deviation of
axis (string, optional) – If supplied, the axis to compute the standard deviation along (i.e., to project along)
weight (string, optional) – The field to use as a weight.
 Returns
 Return type
Scalar or YTProjection.

sum
(field, axis=None)¶ Compute the sum of a field, optionally along an axis.
This will, in a parallelaware 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.
 Returns
 Return type
Either a scalar or a YTProjection.
Examples
>>> total_vol = reg.sum("cell_volume") >>> cell_count = reg.sum(("index", "ones"), axis=("index", "x"))

property
tiles
¶

to_astropy_table
(fields)¶ Export region data to a :class:~astropy.table.table.QTable, which is a Table object which is unitaware. The QTable can then be exported to an ASCII file, FITS file, etc.
See the AstroPy Table docs for more details: http://docs.astropy.org/en/stable/table/
 Parameters
fields (list of strings or tuple field names) – This is the list of fields to be exported into the QTable.
Examples
>>> sp = ds.sphere("c", (1.0, "Mpc")) >>> t = sp.to_astropy_table([("gas", "density"), ("gas", "temperature")])

to_dataframe
(fields)¶ Export a data object to a
DataFrame
.This function will take a data object and an optional list of fields and export them to a
DataFrame
object. If pandas is not importable, this will raise ImportError. Parameters
fields (list of strings or tuple field names) – This is the list of fields to be exported into the DataFrame.
 Returns
df – The data contained in the object.
 Return type
Examples
>>> dd = ds.all_data() >>> df = dd.to_dataframe([("gas", "density"), ("gas", "temperature")])

to_glue
(fields, label='yt', data_collection=None)¶ Takes specific fields in the container and exports them to Glue (http://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.
 Raises
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.data_objects.construction_data_containers.
YTSurface
(data_source, surface_field, field_value, ds=None)[source]¶ Bases:
yt.data_objects.selection_objects.data_selection_objects.YTSelectionContainer3D
This surface object identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and returns the vertices of the Triangles in that isocontour.
This object simply returns the vertices of all the triangles calculated by the marching cubes algorithm; for more complex operations, such as identifying connected sets of cells above a given threshold, see the extract_connected_sets function. This is more useful for calculating, for instance, total isocontour area, or visualizing in an external program (such as MeshLab.) The object has the properties .vertices and will sample values if a field is requested. The values are interpolated to the center of a given face.
 Parameters
data_source (YTSelectionContainer) – This is the object which will used as a source
surface_field (string) – Any field that can be obtained in a data object. This is the field which will be isocontoured.
field_value (float, YTQuantity, or unit tuple) – The value at which the isocontour should be calculated.
Examples
This will create a data object, find a nice value in the center, and output the vertices to “triangles.obj” after rescaling them.
>>> from yt.units import kpc >>> sp = ds.sphere("max", (10, "kpc")) >>> surf = ds.surface(sp, ("gas", "density"), 5e27) >>> print(surf[("gas", "temperature")]) >>> print(surf.vertices) >>> bounds = [ ... (sp.center[i]  5.0 * kpc, sp.center[i] + 5.0 * kpc) for i in range(3) ... ] >>> surf.export_ply("my_galaxy.ply", bounds=bounds)

apply_units
(arr, units)¶

argmax
(field, axis=None)¶ Return the values at which the field is maximized.
This will, in a parallelaware 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.
 Returns
 Return type
A list of YTQuantities as specified by the axis argument.
Examples
>>> temp_at_max_rho = reg.argmax( ... ("gas", "density"), axis=("gas", "temperature") ... ) >>> max_rho_xyz = reg.argmax(("gas", "density")) >>> t_mrho, v_mrho = reg.argmax( ... ("gas", "density"), ... axis=[("gas", "temperature"), ("gas", "velocity_magnitude")], ... ) >>> x, y, z = reg.argmax(("gas", "density"))

argmin
(field, axis=None)¶ Return the values at which the field is minimized.
This will, in a parallelaware 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.
 Returns
 Return type
A list of YTQuantities as specified by the axis argument.
Examples
>>> temp_at_min_rho = reg.argmin( ... ("gas", "density"), axis=("gas", "temperature") ... ) >>> min_rho_xyz = reg.argmin(("gas", "density")) >>> t_mrho, v_mrho = reg.argmin( ... ("gas", "density"), ... axis=[("gas", "temperature"), ("gas", "velocity_magnitude")], ... ) >>> x, y, z = reg.argmin(("gas", "density"))

property
blocks
¶

calculate_flux
(field_x, field_y, field_z, fluxing_field=None)[source]¶ This calculates the flux over the surface.
This function will conduct Marching Cubes on all the cells in a given data container (gridbygrid), and then for each identified triangular segment of an isocontour in a given cell, calculate the gradient (i.e., normal) in the isocontoured field, interpolate the local value of the “fluxing” field, the area of the triangle, and then return:
area * local_flux_value * (n dot v)
Where area, local_value, and the vector v are interpolated at the barycenter (weighted by the vertex values) of the triangle. Note that this specifically allows for the field fluxing across the surface to be different from the field being contoured. If the fluxing_field is not specified, it is assumed to be 1.0 everywhere, and the raw flux with no localweighting is returned.
Additionally, the returned flux is defined as flux into the surface, not flux out of the surface.
 Parameters
field_x (string) – The xcomponent field
field_y (string) – The ycomponent field
field_z (string) – The zcomponent field
fluxing_field (string, optional) – The field whose passage over the surface is of interest. If not specified, assumed to be 1.0 everywhere.
 Returns
flux – The summed flux.
 Return type
YTQuantity
References
Examples
This will create a data object, find a nice value in the center, and calculate the metal flux over it.
>>> sp = ds.sphere("max", (10, "kpc")) >>> surf = ds.surface(sp, ("gas", "density"), 5e27) >>> flux = surf.calculate_flux( ... ("gas", "velocity_x"), ... ("gas", "velocity_y"), ... ("gas", "velocity_z"), ... ("gas", "metal_density"), ... )

calculate_isocontour_flux
(field, value, field_x, field_y, field_z, fluxing_field=None)¶ This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and calculates the flux over those contours.
This function will conduct marching cubes on all the cells in a given data container (gridbygrid), and then for each identified triangular segment of an isocontour in a given cell, calculate the gradient (i.e., normal) in the isocontoured field, interpolate the local value of the “fluxing” field, the area of the triangle, and then return:
area * local_flux_value * (n dot v)
Where area, local_value, and the vector v are interpolated at the barycenter (weighted by the vertex values) of the triangle. Note that this specifically allows for the field fluxing across the surface to be different from the field being contoured. If the fluxing_field is not specified, it is assumed to be 1.0 everywhere, and the raw flux with no localweighting is returned.
Additionally, the returned flux is defined as flux into the surface, not flux out of the surface.
 Parameters
field (string) – Any field that can be obtained in a data object. This is the field which will be isocontoured and used as the “local_value” in the flux equation.
value (float) – The value at which the isocontour should be calculated.
field_x (string) – The xcomponent field
field_y (string) – The ycomponent field
field_z (string) – The zcomponent field
fluxing_field (string, optional) – The field whose passage over the surface is of interest. If not specified, assumed to be 1.0 everywhere.
 Returns
flux – The summed flux. Note that it is not currently scaled; this is simply the codeunit area times the fields.
 Return type
Examples
This will create a data object, find a nice value in the center, and calculate the metal flux over it.
>>> dd = ds.all_data() >>> rho = dd.quantities["WeightedAverageQuantity"]( ... ("gas", "density"), weight=("gas", "cell_mass") ... ) >>> flux = dd.calculate_isocontour_flux( ... ("gas", "density"), ... rho, ... ("gas", "velocity_x"), ... ("gas", "velocity_y"), ... ("gas", "velocity_z"), ... ("gas", "metallicity"), ... )

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 deeplycopied. If you modify the field parameters inplace, 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[("gas", "density")] >>> print(sp.field_data.keys()) [("gas", "density")] >>> print(sp_clone.field_data.keys()) []

comm
= None¶

create_firefly_object
(datadir=None, fields_to_include=None, fields_units=None, default_decimation_factor=100, velocity_units='km/s', coordinate_units='kpc', show_unused_fields=0, *, JSONdir=None, match_any_particle_types=True, **kwargs)¶ This function links a region of data stored in a yt dataset to the Python frontend API for [Firefly](http://github.com/ageller/Firefly), a browserbased particle visualization tool.
 Parameters
datadir (string) – Path to where any .json files should be saved. If a relative path will assume relative to ${HOME}. A value of None will default to ${HOME}/Data.
fields_to_include (array_like of strings or field tuples) – A list of fields that you want to include in your Firefly visualization for onthefly filtering and colormapping.
default_decimation_factor (integer) – The factor by which you want to decimate each particle group by (e.g. if there are 1e7 total particles in your simulation you might want to set this to 100 at first). Randomly samples your data like shuffled_data[::decimation_factor] so as to not overtax a system. This is adjustable on a per particle group basis by changing the returned reader’s reader.particleGroup[i].decimation_factor before calling reader.writeToDisk().
velocity_units (string) – The units that the velocity should be converted to in order to show streamlines in Firefly. Defaults to km/s.
coordinate_units (string) – The units that the coordinates should be converted to. Defaults to kpc.
show_unused_fields (boolean) – A flag to optionally print the fields that are available, in the dataset but were not explicitly requested to be tracked.
match_any_particle_types (boolean) – If True, when any of the fields_to_include match multiple particle groups then the field will be added for all matching particle groups. If False, an error is raised when encountering an ambiguous field. Default is True.
additional keyword arguments are passed to (Any) –
firefly.data_reader.Reader.__init__ –
 Returns
reader – A reader object from the Firefly, configured to output the current region selected
 Return type
Firefly.data_reader.Reader object
Examples
>>> ramses_ds = yt.load( ... "/Users/agurvich/Desktop/yt_workshop/" ... + "DICEGalaxyDisk_nonCosmological/output_00002/info_00002.txt" ... )
>>> region = ramses_ds.sphere(ramses_ds.domain_center, (1000, "kpc"))
>>> reader = region.create_firefly_object( ... "IsoGalaxyRamses", ... fields_to_include=[ ... "particle_extra_field_1", ... "particle_extra_field_2", ... ], ... fields_units=["dimensionless", "dimensionless"], ... )
>>> reader.settings["color"]["io"] = [1, 1, 0, 1] >>> reader.particleGroups[0].decimation_factor = 100 >>> reader.writeToDisk()

cut_region
(field_cuts, field_parameters=None, locals=None)¶ Return a YTCutRegion, where the a cell is identified as being inside the cut region based on the value of one or more fields. Note that in previous versions of yt the name ‘grid’ was used to represent the data object used to construct the field cut, as of yt 3.0, this has been changed to ‘obj’.
 Parameters
field_cuts (list of strings) – A list of conditionals that will be evaluated. In the namespace available, these conditionals will have access to ‘obj’ which is a data object of unknown shape, and they must generate a boolean array. For instance, conditionals = [“obj[(‘gas’, ‘temperature’)] < 1e3”]
field_parameters (dictionary) – A dictionary of field parameters to be used when applying the field cuts.
locals (dictionary) – A dictionary of local variables to use when defining the cut region.
Examples
To find the total mass of hot gas with temperature greater than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.cut_region(["obj[('gas', 'temperature')] > 1e6"]) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_above
(field, value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field is above a given value are masked.
 Parameters
 Returns
cut_region – The YTCutRegion with the field above the given value masked.
 Return type
Examples
To find the total mass of hot gas with temperature colder than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_above(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_below
(field, value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field is below a given value are masked.
 Parameters
 Returns
cut_region – The YTCutRegion with the field below the given value masked.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_below(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_equal
(field, value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field are equal to given value are masked.
 Parameters
 Returns
cut_region – The YTCutRegion with the field equal to the given value masked.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_equal(("gas", "temperature"), 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_inside
(field, min_value, max_value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field are inside the interval from min_value to max_value.
 Parameters
field (string) – The field in which the conditional will be applied.
min_value (float) – The minimum value inside the interval to be excluded.
max_value (float) – The maximum value inside the interval to be excluded.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the field inside the given interval excluded.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_inside(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_nan
(field, units=None)¶ This function will return a YTCutRegion where all of the regions whose field is NaN are masked.
 Parameters
field (string) – The field in which the conditional will be applied.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the NaN entries of the field masked.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_nan(("gas", "temperature")) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

exclude_outside
(field, min_value, max_value, units=None)¶ This function will return a YTCutRegion where all of the regions whose field are outside the interval from min_value to max_value.
 Parameters
field (string) – The field in which the conditional will be applied.
min_value (float) – The minimum value inside the interval to be excluded.
max_value (float) – The maximum value inside the interval to be excluded.
units (string or None) – The units of the value threshold. None will use the default units given in the field.
 Returns
cut_region – The YTCutRegion with the field outside the given interval excluded.
 Return type
Examples
>>> ds = yt.load("RedshiftOutput0005") >>> ad = ds.all_data() >>> cr = ad.exclude_outside(("gas", "temperature"), 1e5, 1e6) >>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))

export_blender
(transparency=1.0, dist_fac=None, color_field=None, emit_field=None, color_map=None, color_log=True, emit_log=True, plot_index=None, color_field_max=None, color_field_min=None, emit_field_max=None, emit_field_min=None)[source]¶ This exports the surface to the OBJ format, suitable for visualization in many different programs (e.g., Blender). NOTE: this exports an .obj file and an .mtl file, both with the general ‘filename’ as a prefix. The .obj file points to the .mtl file in its header, so if you move the 2 files, make sure you change the .obj header to account for this. ALSO NOTE: the emit_field needs to be a combination of the other 2 fields used to have the emissivity track with the color.
 Parameters
transparency (float) – This gives the transparency of the output surface plot. Values from 0.0 (invisible) to 1.0 (opaque).
dist_fac (float) – Divide the axes distances by this amount.
color_field (string) – Should a field be sample and colormapped?
emit_field (string) –
 Should we track the emissivity of a field?
NOTE: this should be a combination of the other 2 fields being used.
color_map (string) – Which color map should be applied?
color_log (bool) – Should the color field be logged before being mapped?
emit_log (bool) – Should the emitting field be logged before being mapped?
plot_index (integer) – Index of plot for multiple plots. If none, then only 1 plot.
color_field_max (float) – Maximum value of the color field across all surfaces.
color_field_min (float) – Minimum value of the color field across all surfaces.
emit_field_max (float) – Maximum value of the emitting field across all surfaces.
emit_field_min (float) – Minimum value of the emitting field across all surfaces.
Examples
>>> sp = ds.sphere("max", (10, "kpc")) >>> trans = 1.0 >>> surf = ds.surface(sp, ("gas", "density"), 5e27) >>> surf.export_obj("my_galaxy", transparency=trans)
>>> sp = ds.sphere("max", (10, "kpc")) >>> mi, ma = sp.quantities.extrema("temperature")[0] >>> rhos = [1e24, 1e25] >>> trans = [0.5, 1.0] >>> for i, r in enumerate(rhos): ... surf = ds.surface(sp, "density", r) ... surf.export_obj( ... "my_galaxy", ... transparency=trans[i], ... color_field="temperature", ... plot_index=i, ... color_field_max=ma, ... color_field_min=mi, ... )
>>> sp = ds.sphere("max", (10, "kpc")) >>> rhos = [1e24, 1e25] >>> trans = [0.5, 1.0] >>> def _Emissivity(field, data): ... return ( ... data[("gas", "density")] ... * data[("gas", "density")] ... * np.sqrt(data[("gas", "temperature")]) ... ) >>> ds.add_field(("gas", "emissivity"), function=_Emissivity, units="g / cm**6") >>> for i, r in enumerate(rhos): ... surf = ds.surface(sp, "density", r) ... surf.export_obj( ... "my_galaxy", ... transparency=trans[i], ... color_field="temperature", ... emit_field="emissivity", ... plot_index=i, ... )

export_obj
(filename, transparency=1.0, dist_fac=None, color_field=None, emit_field=None, color_map=None, color_log=True, emit_log=True, plot_index=None, color_field_max=None, color_field_min=None, emit_field_max=None, emit_field_min=None)[source]¶ Export the surface to the OBJ format
Suitable for visualization in many different programs (e.g., Blender). NOTE: this exports an .obj file and an .mtl file, both with the general ‘filename’ as a prefix. The .obj file points to the .mtl file in its header, so if you move the 2 files, make sure you change the .obj header to account for this. ALSO NOTE: the emit_field needs to be a combination of the other 2 fields used to have the emissivity track with the color.
 Parameters
filename (string) – The file this will be exported to. This cannot be a filelike object. If there are no file extensions included  both obj & mtl files are created.
transparency (float) – This gives the transparency of the output surface plot. Values from 0.0 (invisible) to 1.0 (opaque).
dist_fac (float) – Divide the axes distances by this amount.
color_field (string) – Should a field be sample and colormapped?
emit_field (string) – Should we track the emissivity of a field? This should be a combination of the other 2 fields being used.
color_map (string) – Which color map should be applied?
color_log (bool) – Should the color field be logged before being mapped?
emit_log (bool) – Should the emitting field be logged before being mapped?
plot_index (integer) – Index of plot for multiple plots. If none, then only 1 plot.
color_field_max (float) – Maximum value of the color field across all surfaces.
color_field_min (float) – Minimum value of the color field across all surfaces.
emit_field_max (float) – Maximum value of the emitting field across all surfaces.
emit_field_min (float) – Minimum value of the emitting field across all surfaces.
Examples
>>> sp = ds.sphere("max", (10, "kpc")) >>> trans = 1.0 >>> surf = ds.surface(sp, ("gas", "density"), 5e27) >>> surf.export_obj("my_galaxy", transparency=trans)
>>> sp = ds.sphere("max", (10, "kpc")) >>> mi, ma = sp.quantities.extrema("temperature") >>> rhos = [1e24, 1e25] >>> trans = [0.5, 1.0] >>> for i, r in enumerate(rhos): ... surf = ds.surface(sp, "density", r) ... surf.export_obj( ... "my_galaxy", ... transparency=trans[i], ... color_field="temperature", ... plot_index=i, ... color_field_max=ma, ... color_field_min=mi, ... )
>>> sp = ds.sphere("max", (10, "kpc")) >>> rhos = [1e24, 1e25] >>> trans = [0.5, 1.0] >>> def _Emissivity(field, data): ... return ( ... data[("gas", "density")] ... * data[("gas", "density")] ... *