# yt.data_objects.index_subobjects.octree_subset module¶

class yt.data_objects.index_subobjects.octree_subset.OctreeSubset(base_region, domain, ds, over_refine_factor=1, num_ghost_zones=0)[source]
apply_units(arr, units)
argmax(field, axis=None)

Return the values at which the field is maximized.

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

Parameters
• field (string or tuple field name) – The field to maximize.

• axis (string or list of strings, optional) – If supplied, the fields to sample along; if not supplied, defaults to the coordinate fields. This can be the name of the coordinate fields (i.e., ‘x’, ‘y’, ‘z’) or a list of fields, but cannot be 0, 1, 2.

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

Parameters
• field (string or tuple field name) – The field to minimize.

• axis (string or list of strings, optional) – If supplied, the fields to sample along; if not supplied, defaults to the coordinate fields. This can be the name of the coordinate fields (i.e., ‘x’, ‘y’, ‘z’) or a list of fields, but cannot be 0, 1, 2.

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 deeply-copied. If you modify the field parameters in-place, it may or may not be shared between the objects, depending on the type of object that that particular field parameter is.

Notes

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

Examples

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

comm = None
count(selector)[source]
count_particles(selector, x, y, z)[source]
create_firefly_object(path_to_firefly, fields_to_include=None, fields_units=None, default_decimation_factor=100, velocity_units='km/s', coordinate_units='kpc', show_unused_fields=0, dataset_name='yt')

This function links a region of data stored in a yt dataset to the Python frontend API for [Firefly](github.com/ageller/Firefly), a browser-based particle visualization platform.

Parameters
• path_to_firefly (string) – The (ideally) absolute path to the direction containing the index.html file of Firefly.

• fields_to_include (array_like of strings) – A list of fields that you want to include in your Firefly visualization for on-the-fly 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.dumpToJSON().

• 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.

• dataset_name (string) – The name of the subdirectory the JSON files will be stored in (and the name that will appear in startup.json and in the dropdown menu at startup). e.g. yt -> json files will appear in Firefly/data/yt.

Returns

Return type

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(
...     path_to_firefly="/Users/agurvich/research/repos/Firefly",
...     fields_to_include=[
...         "particle_extra_field_1",
...         "particle_extra_field_2",
...     ],
...     fields_units=["dimensionless", "dimensionless"],
...     dataset_name="IsoGalaxyRamses",
... )

>>> reader.options["color"]["io"] = [1, 1, 0, 1]

deposit(positions, fields=None, method=None, kernel_name='cubic')[source]

Operate on the mesh, in a particle-against-mesh fashion, with exclusively local input.

This uses the octree indexing system to call a “deposition” operation (defined in yt/geometry/particle_deposit.pyx) that can take input from several particles (local to the mesh) and construct some value on the mesh. The canonical example is to sum the total mass in a mesh cell and then divide by its volume.

Parameters
• positions (array_like (Nx3)) – The positions of all of the particles to be examined. A new indexed octree will be constructed on these particles.

• fields (list of arrays) – All the necessary fields for computing the particle operation. For instance, this might include mass, velocity, etc.

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

• kernel_name (string, default 'cubic') – This is the name of the smoothing kernel to use. Current supported kernel names include cubic, quartic, quintic, wendland2, wendland4, and wendland6.

Returns

Return type

List of fortran-ordered, mesh-like arrays.

property domain_ind
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.

get_vertex_centered_data(fields)[source]
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)

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.

Returns

Return type

YTProjection

Examples

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

property ires
keys()
mask_refinement(selector)[source]
max(field, axis=None)

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

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

Parameters
• field (string or tuple field name) – The field to maximize.

• axis (string, optional) – If supplied, the axis to project the maximum along.

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

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

• axis (string, optional) – If supplied, the axis to compute the mean along (i.e., to project along)

• weight (string, optional) – The field to use as a weight.

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")
... )

mesh_sampling_particle_field(positions, mesh_field, lvlmax=None)[source]

Operate on the particles, in a mesh-against-particle fashion, with exclusively local input.

This uses the octree indexing system to call a “mesh sampling” operation (defined in yt/geometry/particle_deposit.pyx). For each particle, the function returns the value of the cell containing the particle.

Parameters
• positions (array_like (Nx3)) – The positions of all of the particles to be examined.

• mesh_field (array_like (M,)) – The value of the field to deposit.

• lvlmax (array_like (N), optional) – If provided, the maximum level where to look for cells

Returns

• List of fortran-ordered, particle-like arrays containing the

• value of the mesh at the location of the particles.

min(field, axis=None)

Compute the minimum of a field.

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

Parameters
• field (string or tuple field name) – The field to minimize.

• axis (string, optional) – If supplied, the axis to compute the minimum along.

Returns

Return type

Scalar.

Examples

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

property min_level
property nz
particle_operation(positions, fields=None, method=None, nneighbors=64, kernel_name='cubic')[source]

Operate on particles, in a particle-against-particle fashion.

This uses the octree indexing system to call a “smoothing” operation (defined in yt/geometry/particle_smooth.pyx) that expects to be called in a particle-by-particle fashion. For instance, the canonical example of this would be to compute the Nth nearest neighbor, or to compute the density for a given particle based on some kernel operation.

Many of the arguments to this are identical to those used in the smooth and deposit functions. Note that the fields argument must not be empty, as these fields will be modified in place.

Parameters
• positions (array_like (Nx3)) – The positions of all of the particles to be examined. A new indexed octree will be constructed on these particles.

• fields (list of arrays) – All the necessary fields for computing the particle operation. For instance, this might include mass, velocity, etc. One of these will likely be modified in place.

• method (string) – This is the “method name” which will be looked up in the particle_smooth namespace as methodname_smooth.

• nneighbors (int, default 64) – The number of neighbors to examine during the process.

• kernel_name (string, default 'cubic') – This is the name of the smoothing kernel to use. Current supported kernel names include cubic, quartic, quintic, wendland2, wendland4, and wendland6.

Returns

Return type

Nothing.

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")
...     [("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 parallel-aware fashion, compute the “peak-to-peak” 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

str

Examples

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

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

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

smooth(positions, fields=None, index_fields=None, method=None, create_octree=False, nneighbors=64, kernel_name='cubic')[source]

Operate on the mesh, in a particle-against-mesh fashion, with non-local input.

This uses the octree indexing system to call a “smoothing” operation (defined in yt/geometry/particle_smooth.pyx) that can take input from several (non-local) particles and construct some value on the mesh. The canonical example is to conduct a smoothing kernel operation on the mesh.

Parameters
• positions (array_like (Nx3)) – The positions of all of the particles to be examined. A new indexed octree will be constructed on these particles.

• fields (list of arrays) – All the necessary fields for computing the particle operation. For instance, this might include mass, velocity, etc.

• index_fields (list of arrays) – All of the fields defined on the mesh that may be used as input to the operation.

• method (string) – This is the “method name” which will be looked up in the particle_smooth namespace as methodname_smooth. Current methods include volume_weighted, nearest, idw, nth_neighbor, and density.

• create_octree (bool) – Should we construct a new octree for indexing the particles? In cases where we are applying an operation on a subset of the particles used to construct the mesh octree, this will ensure that we are able to find and identify all relevant particles.

• nneighbors (int, default 64) – The number of neighbors to examine during the process.

• kernel_name (string, default 'cubic') – This is the name of the smoothing kernel to use. Current supported kernel names include cubic, quartic, quintic, wendland2, wendland4, and wendland6.

Returns

Return type

List of fortran-ordered, mesh-like arrays.

std(field, weight=None)

Compute the standard deviation of a field.

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

Parameters
• field (string or tuple field name) – The field to calculate the standard deviation of

• weight (string or tuple field name) – The field to weight the standard deviation calculation by. Defaults to unweighted if unset.

Returns

Return type

Scalar

sum(field, axis=None)

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

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

Parameters
• field (string or tuple field name) – The field to sum.

• axis (string, optional) – If supplied, the axis to sum along.

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 unit-aware. 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

DataFrame

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.index_subobjects.octree_subset.OctreeSubsetBlockSlice(octree_subset, ds)[source]

Bases: object

get_vertex_centered_data(fields, smoothed=False, no_ghost=False)[source]
property octree_subset_with_gz
class yt.data_objects.index_subobjects.octree_subset.OctreeSubsetBlockSlicePosition(ind, block_slice)[source]

Bases: object

property LeftEdge
property Level
property RightEdge
clear_data()[source]
property dds
get_vertex_centered_data(fields, smoothed=False, no_ghost=False)[source]
property id
class yt.data_objects.index_subobjects.octree_subset.YTPositionArray(input_array, units=None, registry=None, dtype=None, bypass_validation=False, input_units=None, name=None)[source]

Bases: unyt.array.unyt_array

T

The transposed array.

Same as self.transpose().

Examples

>>> x = np.array([[1.,2.],[3.,4.]])
>>> x
array([[ 1.,  2.],
[ 3.,  4.]])
>>> x.T
array([[ 1.,  3.],
[ 2.,  4.]])
>>> x = np.array([1.,2.,3.,4.])
>>> x
array([ 1.,  2.,  3.,  4.])
>>> x.T
array([ 1.,  2.,  3.,  4.])

all(axis=None, out=None, keepdims=False, *, where=True)

Returns True if all elements evaluate to True.

Refer to numpy.all for full documentation.

numpy.all()

equivalent function

any(axis=None, out=None, keepdims=False, *, where=True)

Returns True if any of the elements of a evaluate to True.

Refer to numpy.any for full documentation.

numpy.any()

equivalent function

argmax(axis=None, out=None)

Return indices of the maximum values along the given axis.

Refer to numpy.argmax for full documentation.

numpy.argmax()

equivalent function

argmin(axis=None, out=None)

Return indices of the minimum values along the given axis.

Refer to numpy.argmin for detailed documentation.

numpy.argmin()

equivalent function

argpartition(kth, axis=- 1, kind='introselect', order=None)

Returns the indices that would partition this array.

Refer to numpy.argpartition for full documentation.

New in version 1.8.0.

numpy.argpartition()

equivalent function

argsort(axis=- 1, kind='quicksort', order=None)

Returns the indices that would sort the array.

See the documentation of ndarray.argsort for details about the keyword arguments.

Example

>>> from unyt import km
>>> data = [3, 8, 7]*km
>>> print(np.argsort(data))
[0 2 1]
>>> print(data.argsort())
[0 2 1]

astype(dtype, order='K', casting='unsafe', subok=True, copy=True)

Copy of the array, cast to a specified type.

Parameters
• dtype (str or dtype) – Typecode or data-type to which the array is cast.

• order ({'C', 'F', 'A', 'K'}, optional) – Controls the memory layout order of the result. ‘C’ means C order, ‘F’ means Fortran order, ‘A’ means ‘F’ order if all the arrays are Fortran contiguous, ‘C’ order otherwise, and ‘K’ means as close to the order the array elements appear in memory as possible. Default is ‘K’.

• casting ({'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional) –

Controls what kind of data casting may occur. Defaults to ‘unsafe’ for backwards compatibility.

• ’no’ means the data types should not be cast at all.

• ’equiv’ means only byte-order changes are allowed.

• ’safe’ means only casts which can preserve values are allowed.

• ’same_kind’ means only safe casts or casts within a kind, like float64 to float32, are allowed.

• ’unsafe’ means any data conversions may be done.

• subok (bool, optional) – If True, then sub-classes will be passed-through (default), otherwise the returned array will be forced to be a base-class array.

• copy (bool, optional) – By default, astype always returns a newly allocated array. If this is set to false, and the dtype, order, and subok requirements are satisfied, the input array is returned instead of a copy.

Returns

arr_t – Unless copy is False and the other conditions for returning the input array are satisfied (see description for copy input parameter), arr_t is a new array of the same shape as the input array, with dtype, order given by dtype, order.

Return type

ndarray

Notes

Changed in version 1.17.0: Casting between a simple data type and a structured one is possible only for “unsafe” casting. Casting to multiple fields is allowed, but casting from multiple fields is not.

Changed in version 1.9.0: Casting from numeric to string types in ‘safe’ casting mode requires that the string dtype length is long enough to store the max integer/float value converted.

Raises

ComplexWarning – When casting from complex to float or int. To avoid this, one should use a.real.astype(t).

Examples

>>> x = np.array([1, 2, 2.5])
>>> x
array([1. ,  2. ,  2.5])

>>> x.astype(int)
array([1, 2, 2])

base

Base object if memory is from some other object.

Examples

The base of an array that owns its memory is None:

>>> x = np.array([1,2,3,4])
>>> x.base is None
True


Slicing creates a view, whose memory is shared with x:

>>> y = x[2:]
>>> y.base is x
True

byteswap(inplace=False)

Swap the bytes of the array elements

Toggle between low-endian and big-endian data representation by returning a byteswapped array, optionally swapped in-place. Arrays of byte-strings are not swapped. The real and imaginary parts of a complex number are swapped individually.

Parameters

inplace (bool, optional) – If True, swap bytes in-place, default is False.

Returns

out – The byteswapped array. If inplace is True, this is a view to self.

Return type

ndarray

Examples

>>> A = np.array([1, 256, 8755], dtype=np.int16)
>>> list(map(hex, A))
['0x1', '0x100', '0x2233']
>>> A.byteswap(inplace=True)
array([  256,     1, 13090], dtype=int16)
>>> list(map(hex, A))
['0x100', '0x1', '0x3322']


Arrays of byte-strings are not swapped

>>> A = np.array([b'ceg', b'fac'])
>>> A.byteswap()
array([b'ceg', b'fac'], dtype='|S3')

A.newbyteorder().byteswap() produces an array with the same values

but different representation in memory

>>> A = np.array([1, 2, 3])
>>> A.view(np.uint8)
array([1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0,
0, 0], dtype=uint8)
>>> A.newbyteorder().byteswap(inplace=True)
array([1, 2, 3])
>>> A.view(np.uint8)
array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0,
0, 3], dtype=uint8)

choose(choices, out=None, mode='raise')

Use an index array to construct a new array from a set of choices.

Refer to numpy.choose for full documentation.

numpy.choose()

equivalent function

clip(min=None, max=None, out=None, **kwargs)

Return an array whose values are limited to [min, max]. One of max or min must be given.

Refer to numpy.clip for full documentation.

numpy.clip()

equivalent function

compress(condition, axis=None, out=None)

Return selected slices of this array along given axis.

Refer to numpy.compress for full documentation.

numpy.compress()

equivalent function

conj()

Complex-conjugate all elements.

Refer to numpy.conjugate for full documentation.

numpy.conjugate()

equivalent function

conjugate()

Return the complex conjugate, element-wise.

Refer to numpy.conjugate for full documentation.

numpy.conjugate()

equivalent function

convert_to_base(unit_system=None, equivalence=None, **kwargs)

Convert the array in-place to the equivalent base units in the specified unit system.

Optionally, an equivalence can be specified to convert to an equivalent quantity which is not in the same dimensions.

Parameters
• unit_system (string, optional) – The unit system to be used in the conversion. If not specified, the configured base units are used (defaults to MKS).

• equivalence (string, optional) – The equivalence you wish to use. To see which equivalencies are supported for this object, try the list_equivalencies method. Default: None

• kwargs (optional) – Any additional keyword arguments are supplied to the equivalence

Raises
• If the provided unit does not have the same dimensions as the array

• this will raise a UnitConversionError

Examples

>>> from unyt import erg, s
>>> E = 2.5*erg/s
>>> E.convert_to_base("mks")
>>> E
unyt_quantity(2.5e-07, 'W')

convert_to_cgs(equivalence=None, **kwargs)

Convert the array and in-place to the equivalent cgs units.

Optionally, an equivalence can be specified to convert to an equivalent quantity which is not in the same dimensions.

Parameters
• equivalence (string, optional) – The equivalence you wish to use. To see which equivalencies are supported for this object, try the list_equivalencies method. Default: None

• kwargs (optional) – Any additional keyword arguments are supplied to the equivalence

Raises
• If the provided unit does not have the same dimensions as the array

• this will raise a UnitConversionError

Examples

>>> from unyt import Newton
>>> data = [1., 2., 3.]*Newton
>>> data.convert_to_cgs()
>>> data
unyt_array([100000., 200000., 300000.], 'dyn')

convert_to_equivalent(unit, equivalence, **kwargs)

Convert the array in-place to the specified units, assuming the given equivalency. The dimensions of the specified units and the dimensions of the original array need not match so long as there is an appropriate conversion in the specified equivalency.

Parameters
• unit (string) – The unit that you wish to convert to.

• equivalence (string) – The equivalence you wish to use. To see which equivalencies are supported for this unitful quantity, try the list_equivalencies() method.

Examples

>>> from unyt import K
>>> a = [10, 20, 30]*(1e7*K)
>>> a.convert_to_equivalent("keV", "thermal")
>>> a
unyt_array([ 8.6173324, 17.2346648, 25.8519972], 'keV')

convert_to_mks(equivalence=None, **kwargs)

Convert the array and units to the equivalent mks units.

Optionally, an equivalence can be specified to convert to an equivalent quantity which is not in the same dimensions.

Parameters
• equivalence (string, optional) – The equivalence you wish to use. To see which equivalencies are supported for this object, try the list_equivalencies method. Default: None

• kwargs (optional) – Any additional keyword arguments are supplied to the equivalence

Raises
• If the provided unit does not have the same dimensions as the array

• this will raise a UnitConversionError

Examples

>>> from unyt import dyne, erg
>>> data = [1., 2., 3.]*erg
>>> data
unyt_array([1., 2., 3.], 'erg')
>>> data.convert_to_mks()
>>> data
unyt_array([1.e-07, 2.e-07, 3.e-07], 'J')

convert_to_units(units, equivalence=None, **kwargs)

Convert the array to the given units in-place.

Optionally, an equivalence can be specified to convert to an equivalent quantity which is not in the same dimensions.

Parameters
• units (Unit object or string) – The units you want to convert to.

• equivalence (string, optional) – The equivalence you wish to use. To see which equivalencies are supported for this object, try the list_equivalencies method. Default: None

• kwargs (optional) – Any additional keyword arguments are supplied to the equivalence

Raises
• If the provided unit does not have the same dimensions as the array

• this will raise a UnitConversionError

Examples

>>> from unyt import cm, km
>>> length = [3000, 2000, 1000]*cm
>>> length.convert_to_units('m')
>>> print(length)
[30. 20. 10.] m

copy(order='C')

Return a copy of the array.

Parameters

order ({'C', 'F', 'A', 'K'}, optional) – Controls the memory layout of the copy. ‘C’ means C-order, ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible. (Note that this function and numpy.copy() are very similar, but have different default values for their order= arguments.)

numpy.copy(), numpy.copyto()

Examples

>>> from unyt import km
>>> x = [[1,2,3],[4,5,6]] * km
>>> y = x.copy()
>>> x.fill(0)
>>> print(x)
[[0 0 0]
[0 0 0]] km

>>> print(y)
[[1 2 3]
[4 5 6]] km

ctypes

An object to simplify the interaction of the array with the ctypes module.

This attribute creates an object that makes it easier to use arrays when calling shared libraries with the ctypes module. The returned object has, among others, data, shape, and strides attributes (see Notes below) which themselves return ctypes objects that can be used as arguments to a shared library.

Parameters

None

Returns

c – Possessing attributes data, shape, strides, etc.

Return type

Python object

Notes

Below are the public attributes of this object which were documented in “Guide to NumPy” (we have omitted undocumented public attributes, as well as documented private attributes):

_ctypes.data

A pointer to the memory area of the array as a Python integer. This memory area may contain data that is not aligned, or not in correct byte-order. The memory area may not even be writeable. The array flags and data-type of this array should be respected when passing this attribute to arbitrary C-code to avoid trouble that can include Python crashing. User Beware! The value of this attribute is exactly the same as self._array_interface_['data'][0].

Note that unlike data_as, a reference will not be kept to the array: code like ctypes.c_void_p((a + b).ctypes.data) will result in a pointer to a deallocated array, and should be spelt (a + b).ctypes.data_as(ctypes.c_void_p)

_ctypes.shape

A ctypes array of length self.ndim where the basetype is the C-integer corresponding to dtype('p') on this platform. This base-type could be ctypes.c_int, ctypes.c_long, or ctypes.c_longlong depending on the platform. The c_intp type is defined accordingly in numpy.ctypeslib. The ctypes array contains the shape of the underlying array.

Type

(c_intp*self.ndim)

_ctypes.strides

A ctypes array of length self.ndim where the basetype is the same as for the shape attribute. This ctypes array contains the strides information from the underlying array. This strides information is important for showing how many bytes must be jumped to get to the next element in the array.

Type

(c_intp*self.ndim)

_ctypes.data_as(obj)

Return the data pointer cast to a particular c-types object. For example, calling self._as_parameter_ is equivalent to self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a pointer to a ctypes array of floating-point data: self.data_as(ctypes.POINTER(ctypes.c_double)).

The returned pointer will keep a reference to the array.

_ctypes.shape_as(obj)

Return the shape tuple as an array of some other c-types type. For example: self.shape_as(ctypes.c_short).

_ctypes.strides_as(obj)

Return the strides tuple as an array of some other c-types type. For example: self.strides_as(ctypes.c_longlong).

If the ctypes module is not available, then the ctypes attribute of array objects still returns something useful, but ctypes objects are not returned and errors may be raised instead. In particular, the object will still have the as_parameter attribute which will return an integer equal to the data attribute.

Examples

>>> import ctypes
>>> x = np.array([[0, 1], [2, 3]], dtype=np.int32)
>>> x
array([[0, 1],
[2, 3]], dtype=int32)
>>> x.ctypes.data
31962608 # may vary
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32))
<__main__.LP_c_uint object at 0x7ff2fc1fc200> # may vary
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)).contents
c_uint(0)
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint64)).contents
c_ulong(4294967296)
>>> x.ctypes.shape
<numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1fce60> # may vary
>>> x.ctypes.strides
<numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1ff320> # may vary

cumprod(axis=None, dtype=None, out=None)

Return the cumulative product of the elements along the given axis.

Refer to numpy.cumprod for full documentation.

numpy.cumprod()

equivalent function

cumsum(axis=None, dtype=None, out=None)

Return the cumulative sum of the elements along the given axis.

Refer to numpy.cumsum for full documentation.

numpy.cumsum()

equivalent function

property d

Returns a view into the array as a numpy array

Returns

Return type

View of this array’s data.

Example

>>> from unyt import km
>>> a = [3, 4, 5]*km
>>> a
unyt_array([3, 4, 5], 'km')
>>> a.d
array([3, 4, 5])


This function returns a view that shares the same underlying memory as the original array.

>>> b = a.d
>>> b.base is a.base
True
>>> b[2] = 4
>>> b
array([3, 4, 4])
>>> a
unyt_array([3, 4, 4], 'km')

data

Python buffer object pointing to the start of the array’s data.

diagonal(offset=0, axis1=0, axis2=1)

Return specified diagonals. In NumPy 1.9 the returned array is a read-only view instead of a copy as in previous NumPy versions. In a future version the read-only restriction will be removed.

Refer to numpy.diagonal() for full documentation.

numpy.diagonal()

equivalent function

dot(b, out=None)

dot product of two arrays.

Refer to numpy.dot for full documentation.

numpy.dot()

equivalent function

Examples

>>> from unyt import km, s
>>> a = np.eye(2)*km
>>> b = (np.ones((2, 2)) * 2)*s
>>> print(a.dot(b))
[[2. 2.]
[2. 2.]] km*s


This array method can be conveniently chained:

>>> print(a.dot(b).dot(b))
[[8. 8.]
[8. 8.]] km*s**2

dtype

Data-type of the array’s elements.

Parameters

None

Returns

d

Return type

numpy dtype object

Examples

>>> x
array([[0, 1],
[2, 3]])
>>> x.dtype
dtype('int32')
>>> type(x.dtype)
<type 'numpy.dtype'>

dump(file)

Dump a pickle of the array to the specified file. The array can be read back with pickle.load or numpy.load.

Parameters

file (str or Path) –

A string naming the dump file.

Changed in version 1.17.0: pathlib.Path objects are now accepted.

dumps()

Returns the pickle of the array as a string. pickle.loads or numpy.loads will convert the string back to an array.

Parameters

None

fill(value)

Fill the array with a scalar value.

Parameters

value (scalar) – All elements of a will be assigned this value.

Examples

>>> a = np.array([1, 2])
>>> a.fill(0)
>>> a
array([0, 0])
>>> a = np.empty(2)
>>> a.fill(1)
>>> a
array([1.,  1.])

flags

Information about the memory layout of the array.

C_CONTIGUOUS(C)

The data is in a single, C-style contiguous segment.

F_CONTIGUOUS(F)

The data is in a single, Fortran-style contiguous segment.

OWNDATA(O)

The array owns the memory it uses or borrows it from another object.

WRITEABLE(W)

The data area can be written to. Setting this to False locks the data, making it read-only. A view (slice, etc.) inherits WRITEABLE from its base array at creation time, but a view of a writeable array may be subsequently locked while the base array remains writeable. (The opposite is not true, in that a view of a locked array may not be made writeable. However, currently, locking a base object does not lock any views that already reference it, so under that circumstance it is possible to alter the contents of a locked array via a previously created writeable view onto it.) Attempting to change a non-writeable array raises a RuntimeError exception.

ALIGNED(A)

The data and all elements are aligned appropriately for the hardware.

WRITEBACKIFCOPY(X)

This array is a copy of some other array. The C-API function PyArray_ResolveWritebackIfCopy must be called before deallocating to the base array will be updated with the contents of this array.

UPDATEIFCOPY(U)

(Deprecated, use WRITEBACKIFCOPY) This array is a copy of some other array. When this array is deallocated, the base array will be updated with the contents of this array.

FNC

F_CONTIGUOUS and not C_CONTIGUOUS.

FORC

F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).

BEHAVED(B)

ALIGNED and WRITEABLE.

CARRAY(CA)

BEHAVED and C_CONTIGUOUS.

FARRAY(FA)

BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.

Notes

The flags object can be accessed dictionary-like (as in a.flags['WRITEABLE']), or by using lowercased attribute names (as in a.flags.writeable). Short flag names are only supported in dictionary access.

Only the WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by the user, via direct assignment to the attribute or dictionary entry, or by calling ndarray.setflags.

The array flags cannot be set arbitrarily:

• UPDATEIFCOPY can only be set False.

• WRITEBACKIFCOPY can only be set False.

• ALIGNED can only be set True if the data is truly aligned.

• WRITEABLE can only be set True if the array owns its own memory or the ultimate owner of the memory exposes a writeable buffer interface or is a string.

Arrays can be both C-style and Fortran-style contiguous simultaneously. This is clear for 1-dimensional arrays, but can also be true for higher dimensional arrays.

Even for contiguous arrays a stride for a given dimension arr.strides[dim] may be arbitrary if arr.shape[dim] == 1 or the array has no elements. It does not generally hold that self.strides[-1] == self.itemsize for C-style contiguous arrays or self.strides[0] == self.itemsize for Fortran-style contiguous arrays is true.

flat

A 1-D iterator over the array.

This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of, Python’s built-in iterator object.

flatten

Return a copy of the array collapsed into one dimension.

flatiter

Examples

>>> x = np.arange(1, 7).reshape(2, 3)
>>> x
array([[1, 2, 3],
[4, 5, 6]])
>>> x.flat[3]
4
>>> x.T
array([[1, 4],
[2, 5],
[3, 6]])
>>> x.T.flat[3]
5
>>> type(x.flat)
<class 'numpy.flatiter'>


An assignment example:

>>> x.flat = 3; x
array([[3, 3, 3],
[3, 3, 3]])
>>> x.flat[[1,4]] = 1; x
array([[3, 1, 3],
[3, 1, 3]])

flatten(order='C')

Return a copy of the array collapsed into one dimension.

Parameters

order ({'C', 'F', 'A', 'K'}, optional) – ‘C’ means to flatten in row-major (C-style) order. ‘F’ means to flatten in column-major (Fortran- style) order. ‘A’ means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise. ‘K’ means to flatten a in the order the elements occur in memory. The default is ‘C’.

Returns

y – A copy of the input array, flattened to one dimension.

Return type

ndarray

ravel()

Return a flattened array.

flat()

A 1-D flat iterator over the array.

Examples

>>> a = np.array([[1,2], [3,4]])
>>> a.flatten()
array([1, 2, 3, 4])
>>> a.flatten('F')
array([1, 3, 2, 4])

classmethod from_astropy(arr, unit_registry=None)

Convert an AstroPy “Quantity” to a unyt_array or unyt_quantity.

Parameters
• arr (AstroPy Quantity) – The Quantity to convert from.

• unit_registry (yt UnitRegistry, optional) – A yt unit registry to use in the conversion. If one is not supplied, the default one will be used.

Example

>>> from astropy.units import km
>>> unyt_quantity.from_astropy(km)
unyt_quantity(1., 'km')
>>> a = [1, 2, 3]*km
>>> a
<Quantity [1., 2., 3.] km>
>>> unyt_array.from_astropy(a)
unyt_array([1., 2., 3.], 'km')

classmethod from_hdf5(filename, dataset_name=None, group_name=None)

Attempts read in and convert a dataset in an hdf5 file into a unyt_array.

Parameters
• filename (string) –

• filename to of the hdf5 file. (The) –

• dataset_name (string) – The name of the dataset to read from. If the dataset has a units attribute, attempt to infer units as well.

• group_name (string) – An optional group to read the arrays from. If not specified, the arrays are datasets at the top level by default.

classmethod from_pint(arr, unit_registry=None)

Convert a Pint “Quantity” to a unyt_array or unyt_quantity.

Parameters
• arr (Pint Quantity) – The Quantity to convert from.

• unit_registry (yt UnitRegistry, optional) – A yt unit registry to use in the conversion. If one is not supplied, the default one will be used.

Examples

>>> from pint import UnitRegistry
>>> import numpy as np
>>> ureg = UnitRegistry()
>>> a = np.arange(4)
>>> b = ureg.Quantity(a, "erg/cm**3")
>>> b
<Quantity([0 1 2 3], 'erg / centimeter ** 3')>
>>> c = unyt_array.from_pint(b)
>>> c
unyt_array([0, 1, 2, 3], 'erg/cm**3')

getfield(dtype, offset=0)

Returns a field of the given array as a certain type.

A field is a view of the array data with a given data-type. The values in the view are determined by the given type and the offset into the current array in bytes. The offset needs to be such that the view dtype fits in the array dtype; for example an array of dtype complex128 has 16-byte elements. If taking a view with a 32-bit integer (4 bytes), the offset needs to be between 0 and 12 bytes.

Parameters
• dtype (str or dtype) – The data type of the view. The dtype size of the view can not be larger than that of the array itself.

• offset (int) – Number of bytes to skip before beginning the element view.

Examples

>>> x = np.diag([1.+1.j]*2)
>>> x[1, 1] = 2 + 4.j
>>> x
array([[1.+1.j,  0.+0.j],
[0.+0.j,  2.+4.j]])
>>> x.getfield(np.float64)
array([[1.,  0.],
[0.,  2.]])


By choosing an offset of 8 bytes we can select the complex part of the array for our view:

>>> x.getfield(np.float64, offset=8)
array([[1.,  0.],
[0.,  4.]])

has_equivalent(equivalence)

Check to see if this unyt_array or unyt_quantity has an equivalent unit in equiv.

Example

>>> from unyt import km, keV
>>> (1.0*km).has_equivalent('spectral')
True
>>> print((1*km).to_equivalent('MHz', equivalence='spectral'))
0.299792458 MHz
>>> print((1*keV).to_equivalent('angstrom', equivalence='spectral'))
12.39841931521966 Å

imag

The imaginary part of the array.

Examples

>>> x = np.sqrt([1+0j, 0+1j])
>>> x.imag
array([ 0.        ,  0.70710678])
>>> x.imag.dtype
dtype('float64')

in_base(unit_system=None)

Creates a copy of this array with the data in the specified unit system, and returns it in that system’s base units.

Parameters

unit_system (string, optional) – The unit system to be used in the conversion. If not specified, the configured default base units of are used (defaults to MKS).

Examples

>>> from unyt import erg, s
>>> E = 2.5*erg/s
>>> print(E.in_base("mks"))
2.5e-07 W

in_cgs()

Creates a copy of this array with the data in the equivalent cgs units, and returns it.

Returns

Return type

unyt_array object with data in this array converted to cgs units.

Example

>>> from unyt import Newton, km
>>> print((10*Newton/km).in_cgs())
10.0 g/s**2

in_mks()

Creates a copy of this array with the data in the equivalent mks units, and returns it.

Returns

Return type

unyt_array object with data in this array converted to mks units.

Example

>>> from unyt import mile
>>> print((1.*mile).in_mks())
1609.344 m

in_units(units, equivalence=None, **kwargs)

Creates a copy of this array with the data converted to the supplied units, and returns it.

Optionally, an equivalence can be specified to convert to an equivalent quantity which is not in the same dimensions.

Parameters
• units (Unit object or string) – The units you want to get a new quantity in.

• equivalence (string, optional) – The equivalence you wish to use. To see which equivalencies are supported for this object, try the list_equivalencies method. Default: None

• kwargs (optional) – Any additional keyword arguments are supplied to the equivalence

Raises
• If the provided unit does not have the same dimensions as the array

• this will raise a UnitConversionError

Examples

>>> from unyt import c, gram
>>> m = 10*gram
>>> E = m*c**2
>>> print(E.in_units('erg'))
8.987551787368176e+21 erg
>>> print(E.in_units('J'))
898755178736817.6 J

item(*args)

Copy an element of an array to a standard Python scalar and return it.

Parameters

*args (Arguments (variable number and type)) –

• none: in this case, the method only works for arrays with one element (a.size == 1), which element is copied into a standard Python scalar object and returned.

• int_type: this argument is interpreted as a flat index into the array, specifying which element to copy and return.

• tuple of int_types: functions as does a single int_type argument, except that the argument is interpreted as an nd-index into the array.

Returns

z – A copy of the specified element of the array as a suitable Python scalar

Return type

Standard Python scalar object

Notes

When the data type of a is longdouble or clongdouble, item() returns a scalar array object because there is no available Python scalar that would not lose information. Void arrays return a buffer object for item(), unless fields are defined, in which case a tuple is returned.

item is very similar to a[args], except, instead of an array scalar, a standard Python scalar is returned. This can be useful for speeding up access to elements of the array and doing arithmetic on elements of the array using Python’s optimized math.

Examples

>>> np.random.seed(123)
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[2, 2, 6],
[1, 3, 6],
[1, 0, 1]])
>>> x.item(3)
1
>>> x.item(7)
0
>>> x.item((0, 1))
2
>>> x.item((2, 2))
1

itemset(*args)

Insert scalar into an array (scalar is cast to array’s dtype, if possible)

There must be at least 1 argument, and define the last argument as item. Then, a.itemset(*args) is equivalent to but faster than a[args] = item. The item should be a scalar value and args must select a single item in the array a.

Parameters

*args (Arguments) – If one argument: a scalar, only used in case a is of size 1. If two arguments: the last argument is the value to be set and must be a scalar, the first argument specifies a single array element location. It is either an int or a tuple.

Notes

Compared to indexing syntax, itemset provides some speed increase for placing a scalar into a particular location in an ndarray, if you must do this. However, generally this is discouraged: among other problems, it complicates the appearance of the code. Also, when using itemset (and item) inside a loop, be sure to assign the methods to a local variable to avoid the attribute look-up at each loop iteration.

Examples

>>> np.random.seed(123)
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[2, 2, 6],
[1, 3, 6],
[1, 0, 1]])
>>> x.itemset(4, 0)
>>> x.itemset((2, 2), 9)
>>> x
array([[2, 2, 6],
[1, 0, 6],
[1, 0, 9]])

itemsize

Length of one array element in bytes.

Examples

>>> x = np.array([1,2,3], dtype=np.float64)
>>> x.itemsize
8
>>> x = np.array([1,2,3], dtype=np.complex128)
>>> x.itemsize
16

list_equivalencies()

Lists the possible equivalencies associated with this unyt_array or unyt_quantity.

Example

>>> from unyt import km
>>> (1.0*km).list_equivalencies()
spectral: length <-> spatial_frequency <-> frequency <-> energy
schwarzschild: mass <-> length
compton: mass <-> length

max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)

Return the maximum along a given axis.

Refer to numpy.amax for full documentation.

numpy.amax()

equivalent function

mean(axis=None, dtype=None, out=None, keepdims=False, *, where=True)

Returns the average of the array elements along given axis.

Refer to numpy.mean for full documentation.

numpy.mean()

equivalent function

min(axis=None, out=None, keepdims=False, initial=<no value>, where=True)

Return the minimum along a given axis.

Refer to numpy.amin for full documentation.

numpy.amin()

equivalent function

property morton
nbytes

Total bytes consumed by the elements of the array.

Notes

Does not include memory consumed by non-element attributes of the array object.

Examples

>>> x = np.zeros((3,5,2), dtype=np.complex128)
>>> x.nbytes
480
>>> np.prod(x.shape) * x.itemsize
480

ndarray_view()

Returns a view into the array as a numpy array

Returns

Return type

View of this array’s data.

Example

>>> from unyt import km
>>> a = [3, 4, 5]*km
>>> a
unyt_array([3, 4, 5], 'km')
>>> a.ndarray_view()
array([3, 4, 5])


This function returns a view that shares the same underlying memory as the original array.

>>> b = a.ndarray_view()
>>> b.base is a.base
True
>>> b[2] = 4
>>> b
array([3, 4, 4])
>>> a
unyt_array([3, 4, 4], 'km')

ndim

Number of array dimensions.

Examples

>>> x = np.array([1, 2, 3])
>>> x.ndim
1
>>> y = np.zeros((2, 3, 4))
>>> y.ndim
3

property ndview

Returns a view into the array as a numpy array

Returns

Return type

View of this array’s data.

Example

>>> from unyt import km
>>> a = [3, 4, 5]*km
>>> a
unyt_array([3, 4, 5], 'km')
>>> a.ndview
array([3, 4, 5])


This function returns a view that shares the same underlying memory as the original array.

>>> b = a.ndview
>>> b.base is a.base
True
>>> b[2] = 4
>>> b
array([3, 4, 4])
>>> a
unyt_array([3, 4, 4], 'km')

newbyteorder(new_order='S', /)

Return the array with the same data viewed with a different byte order.

Equivalent to:

arr.view(arr.dtype.newbytorder(new_order))


Changes are also made in all fields and sub-arrays of the array data type.

Parameters

new_order (string, optional) –

Byte order to force; a value from the byte order specifications below. new_order codes can be any of:

• ’S’ - swap dtype from current to opposite endian

• {‘<’, ‘little’} - little endian

• {‘>’, ‘big’} - big endian

• ’=’ - native order, equivalent to sys.byteorder

• {‘|’, ‘I’} - ignore (no change to byte order)

The default value (‘S’) results in swapping the current byte order.

Returns

new_arr – New array object with the dtype reflecting given change to the byte order.

Return type

array

nonzero()

Return the indices of the elements that are non-zero.

Refer to numpy.nonzero for full documentation.

numpy.nonzero()

equivalent function

partition(kth, axis=- 1, kind='introselect', order=None)

Rearranges the elements in the array in such a way that the value of the element in kth position is in the position it would be in a sorted array. All elements smaller than the kth element are moved before this element and all equal or greater are moved behind it. The ordering of the elements in the two partitions is undefined.

New in version 1.8.0.

Parameters
• kth (int or sequence of ints) – Element index to partition by. The kth element value will be in its final sorted position and all smaller elements will be moved before it and all equal or greater elements behind it. The order of all elements in the partitions is undefined. If provided with a sequence of kth it will partition all elements indexed by kth of them into their sorted position at once.

• axis (int, optional) – Axis along which to sort. Default is -1, which means sort along the last axis.

• kind ({'introselect'}, optional) – Selection algorithm. Default is ‘introselect’.

• order (str or list of str, optional) – When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need to be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties.

numpy.partition()

Return a parititioned copy of an array.

argpartition()

Indirect partition.

sort()

Full sort.

Notes

See np.partition for notes on the different algorithms.

Examples

>>> a = np.array([3, 4, 2, 1])
>>> a.partition(3)
>>> a
array([2, 1, 3, 4])

>>> a.partition((1, 3))
>>> a
array([1, 2, 3, 4])

prod(axis=None, dtype=None, out=None, keepdims=False, initial=1, where=True)

Return the product of the array elements over the given axis

Refer to numpy.prod for full documentation.

numpy.prod()

equivalent function

ptp(axis=None, out=None, keepdims=False)

Peak to peak (maximum - minimum) value along a given axis.

Refer to numpy.ptp for full documentation.

numpy.ptp()

equivalent function

put(indices, values, mode='raise')

Set a.flat[n] = values[n] for all n in indices.

Refer to numpy.put for full documentation.

numpy.put()

equivalent function

ravel([order])

Return a flattened array.

Refer to numpy.ravel for full documentation.

numpy.ravel()

equivalent function

ndarray.flat()

a flat iterator on the array.

real

The real part of the array.

Examples

>>> x = np.sqrt([1+0j, 0+1j])
>>> x.real
array([ 1.        ,  0.70710678])
>>> x.real.dtype
dtype('float64')


numpy.real

equivalent function

repeat(repeats, axis=None)

Repeat elements of an array.

Refer to numpy.repeat for full documentation.

numpy.repeat()

equivalent function

reshape(shape, order='C')

Returns an array containing the same data with a new shape.

Refer to numpy.reshape for full documentation.

numpy.reshape()

equivalent function

Notes

Unlike the free function numpy.reshape, this method on ndarray allows the elements of the shape parameter to be passed in as separate arguments. For example, a.reshape(10, 11) is equivalent to a.reshape((10, 11)).

resize(new_shape, refcheck=True)

Change shape and size of array in-place.

Parameters
• new_shape (tuple of ints, or n ints) – Shape of resized array.

• refcheck (bool, optional) – If False, reference count will not be checked. Default is True.

Returns

Return type

None

Raises
• ValueError – If a does not own its own data or references or views to it exist, and the data memory must be changed. PyPy only: will always raise if the data memory must be changed, since there is no reliable way to determine if references or views to it exist.

• SystemError – If the order keyword argument is specified. This behaviour is a bug in NumPy.

resize()

Return a new array with the specified shape.

Notes

This reallocates space for the data area if necessary.

Only contiguous arrays (data elements consecutive in memory) can be resized.

The purpose of the reference count check is to make sure you do not use this array as a buffer for another Python object and then reallocate the memory. However, reference counts can increase in other ways so if you are sure that you have not shared the memory for this array with another Python object, then you may safely set refcheck to False.

Examples

Shrinking an array: array is flattened (in the order that the data are stored in memory), resized, and reshaped:

>>> a = np.array([[0, 1], [2, 3]], order='C')
>>> a.resize((2, 1))
>>> a
array([[0],
[1]])

>>> a = np.array([[0, 1], [2, 3]], order='F')
>>> a.resize((2, 1))
>>> a
array([[0],
[2]])


Enlarging an array: as above, but missing entries are filled with zeros:

>>> b = np.array([[0, 1], [2, 3]])
>>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
>>> b
array([[0, 1, 2],
[3, 0, 0]])


Referencing an array prevents resizing…

>>> c = a
>>> a.resize((1, 1))
Traceback (most recent call last):
...
ValueError: cannot resize an array that references or is referenced ...


Unless refcheck is False:

>>> a.resize((1, 1), refcheck=False)
>>> a
array([[0]])
>>> c
array([[0]])

round(decimals=0, out=None)

Return a with each element rounded to the given number of decimals.

Refer to numpy.around for full documentation.

numpy.around()

equivalent function

searchsorted(v, side='left', sorter=None)

Find indices where elements of v should be inserted in a to maintain order.

For full documentation, see numpy.searchsorted

numpy.searchsorted()

equivalent function

setfield(val, dtype, offset=0)

Put a value into a specified place in a field defined by a data-type.

Place val into a’s field defined by dtype and beginning offset bytes into the field.

Parameters
• val (object) – Value to be placed in field.

• dtype (dtype object) – Data-type of the field in which to place val.

• offset (int, optional) – The number of bytes into the field at which to place val.

Returns

Return type

None

Examples

>>> x = np.eye(3)
>>> x.getfield(np.float64)
array([[1.,  0.,  0.],
[0.,  1.,  0.],
[0.,  0.,  1.]])
>>> x.setfield(3, np.int32)
>>> x.getfield(np.int32)
array([[3, 3, 3],
[3, 3, 3],
[3, 3, 3]], dtype=int32)
>>> x
array([[1.0e+000, 1.5e-323, 1.5e-323],
[1.5e-323, 1.0e+000, 1.5e-323],
[1.5e-323, 1.5e-323, 1.0e+000]])
>>> x.setfield(np.eye(3), np.int32)
>>> x
array([[1.,  0.,  0.],
[0.,  1.,  0.],
[0.,  0.,  1.]])

setflags(write=None, align=None, uic=None)

Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY), respectively.

These Boolean-valued flags affect how numpy interprets the memory area used by a (see Notes below). The ALIGNED flag can only be set to True if the data is actually aligned according to the type. The WRITEBACKIFCOPY and (deprecated) UPDATEIFCOPY flags can never be set to True. The flag WRITEABLE can only be set to True if the array owns its own memory, or the ultimate owner of the memory exposes a writeable buffer interface, or is a string. (The exception for string is made so that unpickling can be done without copying memory.)

Parameters
• write (bool, optional) – Describes whether or not a can be written to.

• align (bool, optional) – Describes whether or not a is aligned properly for its type.

• uic (bool, optional) – Describes whether or not a is a copy of another “base” array.

Notes

Array flags provide information about how the memory area used for the array is to be interpreted. There are 7 Boolean flags in use, only four of which can be changed by the user: WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED.

WRITEABLE (W) the data area can be written to;

ALIGNED (A) the data and strides are aligned appropriately for the hardware (as determined by the compiler);

UPDATEIFCOPY (U) (deprecated), replaced by WRITEBACKIFCOPY;

WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced by .base). When the C-API function PyArray_ResolveWritebackIfCopy is called, the base array will be updated with the contents of this array.

All flags can be accessed using the single (upper case) letter as well as the full name.

Examples

>>> y = np.array([[3, 1, 7],
...               [2, 0, 0],
...               [8, 5, 9]])
>>> y
array([[3, 1, 7],
[2, 0, 0],
[8, 5, 9]])
>>> y.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
>>> y.setflags(write=0, align=0)
>>> y.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : False
ALIGNED : False
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
>>> y.setflags(uic=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: cannot set WRITEBACKIFCOPY flag to True

shape

Tuple of array dimensions.

The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it. As with numpy.reshape, one of the new shape dimensions can be -1, in which case its value is inferred from the size of the array and the remaining dimensions. Reshaping an array in-place will fail if a copy is required.

Examples

>>> x = np.array([1, 2, 3, 4])
>>> x.shape
(4,)
>>> y = np.zeros((2, 3, 4))
>>> y.shape
(2, 3, 4)
>>> y.shape = (3, 8)
>>> y
array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])
>>> y.shape = (3, 6)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: total size of new array must be unchanged
>>> np.zeros((4,2))[::2].shape = (-1,)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: Incompatible shape for in-place modification. Use
.reshape() to make a copy with the desired shape.


numpy.reshape

similar function

ndarray.reshape

similar method

size

Number of elements in the array.

Equal to np.prod(a.shape), i.e., the product of the array’s dimensions.

Notes

a.size returns a standard arbitrary precision Python integer. This may not be the case with other methods of obtaining the same value (like the suggested np.prod(a.shape), which returns an instance of np.int_), and may be relevant if the value is used further in calculations that may overflow a fixed size integer type.

Examples

>>> x = np.zeros((3, 5, 2), dtype=np.complex128)
>>> x.size
30
>>> np.prod(x.shape)
30

sort(axis=- 1, kind=None, order=None)

Sort an array in-place. Refer to numpy.sort for full documentation.

Parameters
• axis (int, optional) – Axis along which to sort. Default is -1, which means sort along the last axis.

• kind ({'quicksort', 'mergesort', 'heapsort', 'stable'}, optional) –

Sorting algorithm. The default is ‘quicksort’. Note that both ‘stable’ and ‘mergesort’ use timsort under the covers and, in general, the actual implementation will vary with datatype. The ‘mergesort’ option is retained for backwards compatibility.

Changed in version 1.15.0: The ‘stable’ option was added.

• order (str or list of str, optional) – When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties.

numpy.sort()

Return a sorted copy of an array.

numpy.argsort()

Indirect sort.

numpy.lexsort()

Indirect stable sort on multiple keys.

numpy.searchsorted()

Find elements in sorted array.

numpy.partition()

Partial sort.

Notes

See numpy.sort for notes on the different sorting algorithms.

Examples

>>> a = np.array([[1,4], [3,1]])
>>> a.sort(axis=1)
>>> a
array([[1, 4],
[1, 3]])
>>> a.sort(axis=0)
>>> a
array([[1, 3],
[1, 4]])


Use the order keyword to specify a field to use when sorting a structured array:

>>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
>>> a.sort(order='y')
>>> a
array([(b'c', 1), (b'a', 2)],
dtype=[('x', 'S1'), ('y', '<i8')])

squeeze(axis=None)

Remove axes of length one from a.

Refer to numpy.squeeze for full documentation.

numpy.squeeze()

equivalent function

std(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)

Returns the standard deviation of the array elements along given axis.

Refer to numpy.std for full documentation.

numpy.std()

equivalent function

strides

Tuple of bytes to step in each dimension when traversing an array.

The byte offset of element (i[0], i[1], ..., i[n]) in an array a is:

offset = sum(np.array(i) * a.strides)


A more detailed explanation of strides can be found in the “ndarray.rst” file in the NumPy reference guide.

Notes

Imagine an array of 32-bit integers (each 4 bytes):

x = np.array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]], dtype=np.int32)


This array is stored in memory as 40 bytes, one after the other (known as a contiguous block of memory). The strides of an array tell us how many bytes we have to skip in memory to move to the next position along a certain axis. For example, we have to skip 4 bytes (1 value) to move to the next column, but 20 bytes (5 values) to get to the same position in the next row. As such, the strides for the array x will be (20, 4).

Examples

>>> y = np.reshape(np.arange(2*3*4), (2,3,4))
>>> y
array([[[ 0,  1,  2,  3],
[ 4,  5,  6,  7],
[ 8,  9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]])
>>> y.strides
(48, 16, 4)
>>> y[1,1,1]
17
>>> offset=sum(y.strides * np.array((1,1,1)))
>>> offset/y.itemsize
17

>>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)
>>> x.strides
(32, 4, 224, 1344)
>>> i = np.array([3,5,2,2])
>>> offset = sum(i * x.strides)
>>> x[3,5,2,2]
813
>>> offset / x.itemsize
813

sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)

Return the sum of the array elements over the given axis.

Refer to numpy.sum for full documentation.

numpy.sum()

equivalent function

swapaxes(axis1, axis2)

Return a view of the array with axis1 and axis2 interchanged.

Refer to numpy.swapaxes for full documentation.

numpy.swapaxes()

equivalent function

take(indices, axis=None, out=None, mode='raise')

Return an array formed from the elements of a at the given indices.

Refer to numpy.take for full documentation.

numpy.take()

equivalent function

to(units, equivalence=None, **kwargs)

Creates a copy of this array with the data converted to the supplied units, and returns it.

Optionally, an equivalence can be specified to convert to an equivalent quantity which is not in the same dimensions.

Note

All additional keyword arguments are passed to the equivalency, which should be used if that particular equivalency requires them.

Parameters
• units (Unit object or string) – The units you want to get a new quantity in.

• equivalence (string, optional) – The equivalence you wish to use. To see which equivalencies are supported for this unitful quantity, try the list_equivalencies() method. Default: None

• kwargs (optional) – Any additional keywoard arguments are supplied to the equivalence

Raises
• If the provided unit does not have the same dimensions as the array

• this will raise a UnitConversionError

Examples

>>> from unyt import c, gram
>>> m = 10*gram
>>> E = m*c**2
>>> print(E.to('erg'))
8.987551787368176e+21 erg
>>> print(E.to('J'))
898755178736817.6 J

to_astropy(**kwargs)

Creates a new AstroPy quantity with the same unit information.

Example

>>> from unyt import g, cm
>>> data = [3, 4, 5]*g/cm**3
>>> data.to_astropy()
<Quantity [3., 4., 5.] g / cm3>

to_equivalent(unit, equivalence, **kwargs)

Return a copy of the unyt_array in the units specified units, assuming the given equivalency. The dimensions of the specified units and the dimensions of the original array need not match so long as there is an appropriate conversion in the specified equivalency.

Parameters
• unit (string) – The unit that you wish to convert to.

• equivalence (string) – The equivalence you wish to use. To see which equivalencies are supported for this unitful quantity, try the list_equivalencies() method.

Examples

>>> from unyt import K
>>> a = 1.0e7*K
>>> print(a.to_equivalent("keV", "thermal"))
0.8617332401096504 keV

to_ndarray()

Creates a copy of this array with the unit information stripped

Example

>>> from unyt import km
>>> a = [3, 4, 5]*km
>>> a
unyt_array([3, 4, 5], 'km')
>>> b = a.to_ndarray()
>>> b
array([3, 4, 5])


The returned array will contain a copy of the data contained in the original array.

>>> a.base is not b.base
True

to_octree(over_refine_factor=1, dims=1, 1, 1, n_ref=64)[source]
to_pint(unit_registry=None)

Convert a unyt_array or unyt_quantity to a Pint Quantity.

Parameters
• arr (unyt_array or unyt_quantity) – The unitful quantity to convert from.

• unit_registry (Pint UnitRegistry, optional) – The Pint UnitRegistry to use in the conversion. If one is not supplied, the default one will be used. NOTE: This is not the same as a yt UnitRegistry object.

Examples

>>> from unyt import cm, s
>>> a = 4*cm**2/s
>>> print(a)
4 cm**2/s
>>> a.to_pint()
<Quantity(4, 'centimeter ** 2 / second')>

to_value(units=None, equivalence=None, **kwargs)

Creates a copy of this array with the data in the supplied units, and returns it without units. Output is therefore a bare NumPy array.

Optionally, an equivalence can be specified to convert to an equivalent quantity which is not in the same dimensions.

Note

All additional keyword arguments are passed to the equivalency, which should be used if that particular equivalency requires them.

Parameters
• units (Unit object or string, optional) – The units you want to get the bare quantity in. If not specified, the value will be returned in the current units.

• equivalence (string, optional) – The equivalence you wish to use. To see which equivalencies are supported for this unitful quantity, try the list_equivalencies() method. Default: None

Examples

>>> from unyt import km
>>> a = [3, 4, 5]*km
>>> print(a.to_value('cm'))
[300000. 400000. 500000.]

tobytes(order='C')

Construct Python bytes containing the raw data bytes in the array.

Constructs Python bytes showing a copy of the raw contents of data memory. The bytes object is produced in C-order by default. This behavior is controlled by the order parameter.

New in version 1.9.0.

Parameters

order ({'C', 'F', 'A'}, optional) – Controls the memory layout of the bytes object. ‘C’ means C-order, ‘F’ means F-order, ‘A’ (short for Any) means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. Default is ‘C’.

Returns

s – Python bytes exhibiting a copy of a’s raw data.

Return type

bytes

Examples

>>> x = np.array([[0, 1], [2, 3]], dtype='<u2')
>>> x.tobytes()
b'\x00\x00\x01\x00\x02\x00\x03\x00'
>>> x.tobytes('C') == x.tobytes()
True
>>> x.tobytes('F')
b'\x00\x00\x02\x00\x01\x00\x03\x00'

tofile(fid, sep='', format='%s')

Write array to a file as text or binary (default).

Data is always written in ‘C’ order, independent of the order of a. The data produced by this method can be recovered using the function fromfile().

Parameters
• fid (file or str or Path) –

An open file object, or a string containing a filename.

Changed in version 1.17.0: pathlib.Path objects are now accepted.

• sep (str) – Separator between array items for text output. If “” (empty), a binary file is written, equivalent to file.write(a.tobytes()).

• format (str) – Format string for text file output. Each entry in the array is formatted to text by first converting it to the closest Python type, and then using “format” % item.

Notes

This is a convenience function for quick storage of array data. Information on endianness and precision is lost, so this method is not a good choice for files intended to archive data or transport data between machines with different endianness. Some of these problems can be overcome by outputting the data as text files, at the expense of speed and file size.

When fid is a file object, array contents are directly written to the file, bypassing the file object’s write method. As a result, tofile cannot be used with files objects supporting compression (e.g., GzipFile) or file-like objects that do not support fileno() (e.g., BytesIO).

tolist()

Return the array as an a.ndim-levels deep nested list of Python scalars.

Return a copy of the array data as a (nested) Python list. Data items are converted to the nearest compatible builtin Python type, via the ~numpy.ndarray.item function.

If a.ndim is 0, then since the depth of the nested list is 0, it will not be a list at all, but a simple Python scalar.

Parameters

none

Returns

y – The possibly nested list of array elements.

Return type

object, or list of object, or list of list of object, or ..

Notes

The array may be recreated via a = np.array(a.tolist()), although this may sometimes lose precision.

Examples

For a 1D array, a.tolist() is almost the same as list(a), except that tolist changes numpy scalars to Python scalars:

>>> a = np.uint32([1, 2])
>>> a_list = list(a)
>>> a_list
[1, 2]
>>> type(a_list[0])
<class 'numpy.uint32'>
>>> a_tolist = a.tolist()
>>> a_tolist
[1, 2]
>>> type(a_tolist[0])
<class 'int'>


Additionally, for a 2D array, tolist applies recursively:

>>> a = np.array([[1, 2], [3, 4]])
>>> list(a)
[array([1, 2]), array([3, 4])]
>>> a.tolist()
[[1, 2], [3, 4]]


The base case for this recursion is a 0D array:

>>> a = np.array(1)
>>> list(a)
Traceback (most recent call last):
...
TypeError: iteration over a 0-d array
>>> a.tolist()
1

tostring(order='C')

A compatibility alias for tobytes, with exactly the same behavior.

Despite its name, it returns bytes not strs.

Deprecated since version 1.19.0.

trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)

Return the sum along diagonals of the array.

Refer to numpy.trace for full documentation.

numpy.trace()

equivalent function

transpose(*axes)

Returns a view of the array with axes transposed.

For a 1-D array this has no effect, as a transposed vector is simply the same vector. To convert a 1-D array into a 2D column vector, an additional dimension must be added. np.atleast2d(a).T achieves this, as does a[:, np.newaxis]. For a 2-D array, this is a standard matrix transpose. For an n-D array, if axes are given, their order indicates how the axes are permuted (see Examples). If axes are not provided and a.shape = (i[0], i[1], ... i[n-2], i[n-1]), then a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0]).

Parameters

axes (None, tuple of ints, or n ints) –

• None or no argument: reverses the order of the axes.

• tuple of ints: i in the j-th place in the tuple means a’s i-th axis becomes a.transpose()’s j-th axis.

• n ints: same as an n-tuple of the same ints (this form is intended simply as a “convenience” alternative to the tuple form)

Returns

out – View of a, with axes suitably permuted.

Return type

ndarray

transpose()

Equivalent function

ndarray.T()

Array property returning the array transposed.

ndarray.reshape()

Give a new shape to an array without changing its data.

Examples

>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
[3, 4]])
>>> a.transpose()
array([[1, 3],
[2, 4]])
>>> a.transpose((1, 0))
array([[1, 3],
[2, 4]])
>>> a.transpose(1, 0)
array([[1, 3],
[2, 4]])

property ua

Return an array filled with ones with the same units as this array

Example

>>> from unyt import km
>>> a = [4, 5, 6]*km
>>> a.unit_array
unyt_array([1, 1, 1], 'km')
>>> print(a + 7*a.unit_array)
[11 12 13] km

property unit_array

Return an array filled with ones with the same units as this array

Example

>>> from unyt import km
>>> a = [4, 5, 6]*km
>>> a.unit_array
unyt_array([1, 1, 1], 'km')
>>> print(a + 7*a.unit_array)
[11 12 13] km

property unit_quantity

Return a quantity with a value of 1 and the same units as this array

Example

>>> from unyt import km
>>> a = [4, 5, 6]*km
>>> a.unit_quantity
unyt_quantity(1, 'km')
>>> print(a + 7*a.unit_quantity)
[11 12 13] km

property uq

Return a quantity with a value of 1 and the same units as this array

Example

>>> from unyt import km
>>> a = [4, 5, 6]*km
>>> a.uq
unyt_quantity(1, 'km')
>>> print(a + 7*a.uq)
[11 12 13] km

property v

Creates a copy of this array with the unit information stripped

Example

>>> from unyt import km
>>> a = [3, 4, 5]*km
>>> a
unyt_array([3, 4, 5], 'km')
>>> b = a.v
>>> b
array([3, 4, 5])


The returned array will contain a copy of the data contained in the original array.

>>> a.base is not b.base
True

validate()[source]
property value

Creates a copy of this array with the unit information stripped

Example

>>> from unyt import km
>>> a = [3, 4, 5]*km
>>> a
unyt_array([3, 4, 5], 'km')
>>> b = a.value
>>> b
array([3, 4, 5])


The returned array will contain a copy of the data contained in the original array.

>>> a.base is not b.base
True

var(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)

Returns the variance of the array elements, along given axis.

Refer to numpy.var for full documentation.

numpy.var()

equivalent function

view([dtype][, type])

New view of array with the same data.

Note

Passing None for dtype is different from omitting the parameter, since the former invokes dtype(None) which is an alias for dtype('float_').

Parameters
• dtype (data-type or ndarray sub-class, optional) – Data-type descriptor of the returned view, e.g., float32 or int16. Omitting it results in the view having the same data-type as a. This argument can also be specified as an ndarray sub-class, which then specifies the type of the returned object (this is equivalent to setting the type parameter).

• type (Python type, optional) – Type of the returned view, e.g., ndarray or matrix. Again, omission of the parameter results in type preservation.

Notes

a.view() is used two different ways:

a.view(some_dtype) or a.view(dtype=some_dtype) constructs a view of the array’s memory with a different data-type. This can cause a reinterpretation of the bytes of memory.

a.view(ndarray_subclass) or a.view(type=ndarray_subclass) just returns an instance of ndarray_subclass that looks at the same array (same shape, dtype, etc.) This does not cause a reinterpretation of the memory.

For a.view(some_dtype), if some_dtype has a different number of bytes per entry than the previous dtype (for example, converting a regular array to a structured array), then the behavior of the view cannot be predicted just from the superficial appearance of a (shown by print(a)). It also depends on exactly how a is stored in memory. Therefore if a is C-ordered versus fortran-ordered, versus defined as a slice or transpose, etc., the view may give different results.

Examples

>>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])


Viewing array data using a different type and dtype:

>>> y = x.view(dtype=np.int16, type=np.matrix)
>>> y
matrix([[513]], dtype=int16)
>>> print(type(y))
<class 'numpy.matrix'>


Creating a view on a structured array so it can be used in calculations

>>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])
>>> xv = x.view(dtype=np.int8).reshape(-1,2)
>>> xv
array([[1, 2],
[3, 4]], dtype=int8)
>>> xv.mean(0)
array([2.,  3.])


Making changes to the view changes the underlying array

>>> xv[0,1] = 20
>>> x
array([(1, 20), (3,  4)], dtype=[('a', 'i1'), ('b', 'i1')])


Using a view to convert an array to a recarray:

>>> z = x.view(np.recarray)
>>> z.a
array([1, 3], dtype=int8)


Views share data:

>>> x[0] = (9, 10)
>>> z[0]
(9, 10)


Views that change the dtype size (bytes per entry) should normally be avoided on arrays defined by slices, transposes, fortran-ordering, etc.:

>>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)
>>> y = x[:, 0:2]
>>> y
array([[1, 2],
[4, 5]], dtype=int16)
>>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
Traceback (most recent call last):
...
ValueError: To change to a dtype of a different size, the array must be C-contiguous
>>> z = y.copy()
>>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
array([[(1, 2)],
[(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])

write_hdf5(filename, dataset_name=None, info=None, group_name=None)

Writes a unyt_array to hdf5 file.

Parameters
• filename (string) – The filename to create and write a dataset to

• dataset_name (string) – The name of the dataset to create in the file.

• info (dictionary) – A dictionary of supplementary info to write to append as attributes to the dataset.

• group_name (string) – An optional group to write the arrays to. If not specified, the arrays are datasets at the top level by default.

Examples

>>> from unyt import cm
>>> a = [1,2,3]*cm
>>> myinfo = {'field':'dinosaurs', 'type':'field_data'}
>>> a.write_hdf5('test_array_data.h5', dataset_name='dinosaurs',
...              info=myinfo)

yt.data_objects.index_subobjects.octree_subset.cell_count_cache(func)[source]