ExplicitTemporalWcsDopplerPositionalVectorArray

class named_arrays.ExplicitTemporalWcsDopplerPositionalVectorArray(time: 'na.AbstractScalar', wavelength_rest: 'na.AbstractScalar', crval: 'na.AbstractSpectralPositionalVectorArray', crpix: 'na.AbstractCartesianNdVectorArray', cdelt: 'na.AbstractSpectralPositionalVectorArray', pc: 'na.AbstractSpectralPositionalMatrixArray', shape_wcs: 'dict[str, int]')

Bases: AbstractImplicitTemporalDopplerPositionalVectorArray, AbstractWcsVector

Attributes

axes	A tuple of str representing the names of each dimension of this array.
axes_flattened	Combine axes into a single str.
broadcasted	if this array has multiple components, broadcast them against each other.
cartesian_nd	Convert any instance of AbstractVectorArray to an instance of AbstractCartesianNdVectorArray
cdelt
components	The vector components of this array expressed as a dict, where the keys are the names of the component.
crpix
crval
entries	The scalar entries that compose this object.
explicit	Converts this array to an instance of named_arrays.AbstractExplicitArray.
indices	Compute the index of each element of this array.
length	L2-norm of this array.
matrix	Cast this vector into its matrix representation.
ndim	Number of dimensions of the array.
normalized	Return a normalized copy of this vector, where length is unity.
pc
position	The position component of the vector.
prototype_vector	Return vector of same type with all components zeroed.
shape	The number of elements along each axis of the array.
shape_wcs
size	Total number of elements in the array.
time
type_abstract	The named_arrays.AbstractArray type corresponding to this array.
type_explicit	The named_arrays.AbstractExplicitArray type corresponding to this array.
type_matrix	The corresponding named_arrays.AbstractMatrixArray class
value	Returns a new array with its units removed, if they exist.
velocity	The line-of-sight velocity of the wave emitter.
wavelength	The wavelength component of the vector.
wavelength_rest

Methods

__init__(time, wavelength_rest, crval, ...)
add_axes(axes)	Add new singleton axes to this array.
all([axis, where])	Return True if all of the elements along the given axes are True.
any([axis, where])	Return True if any of the elements along the given axes are True.
astype(dtype[, order, casting, subok, copy])	Copy of the array cast to a specific data type.
broadcast_to(shape[, append])	A new view of this array with the specified shape.
cell_centers([axis, random])	Convert an array from cell vertices to cell centers.
combine_axes([axes, axis_new])	Combine some of the axes of the array into a single new axis.
copy()	Create a deep copy of this array.
copy_shallow()	Create a shallow copy of this array.
index(value[, axis])
index_secant(value[, axis])
interp_linear(item)	Linearly interpolate this array to find its value at the given fractional index.
max([axis, initial, where])	The maximum value of this array along the given axes.
mean([axis, where])	The mean value of this array along the given axes.
median([axis])	The median value of this array along the given axes.
min([axis, initial, where])	The minimum value of this array along the given axes.
ndindex([axis_ignored])	An iterator that yields the index of each element of this array.
percentile(q[, axis, out, overwrite_input, ...])	The requested percentile of this array along the given axes.
ptp([axis])	The peak-to-peak value of this array along the given axes.
replace(**changes)	A method version of dataclasses.replace() for named arrays.
reshape(shape)	Reorganize this array into a new shape.
rms([axis, where])	The root-mean-square of this array along the given axes.
std([axis, where])	The standard deviation of this array along the given axes.
sum([axis, where])	The sum of each element of this array along the given axes.
take_along_axis(indices, axis)	Take values from this array by matching indices along axis.
to(unit[, equivalencies, copy])	Convert this array to a new unit.
to_string([prefix, multiline])	Convert this array instance to a string representation.
to_string_array([format_value, format_unit, ...])	Convert to an array of strings where each string has an appropriately-formatted unit attached to the value.
to_value(unit[, equivalencies])	The numerical value of this array, possibly in a different unit.
transpose([axes])	Reorder the axes of this array to the given sequence.
var([axis, where])	The variance of this array along the given axes.
vmr([axis, where])	The variance-to-mean ratio of this array along the given axes.
volume_cell(axis)	Computes the n-dimensional volume of each cell formed by interpreting this array as a logically-rectangular grid of vertices.

Inheritance Diagram

Parameters:

• time (AbstractScalar)

• wavelength_rest (AbstractScalar)

• crval (AbstractSpectralPositionalVectorArray)

• crpix (AbstractCartesianNdVectorArray)

• cdelt (AbstractSpectralPositionalVectorArray)

• pc (AbstractSpectralPositionalMatrixArray)

• shape_wcs (dict[str, int])

add_axes(axes)

Add new singleton axes to this array.

Parameters:

• axes (str | Sequence[str]) – Either a single axis name or a sequence of axis names add to this array.

• self (Self)

Return type:

AbstractExplicitVectorArray

See also

named_arrays.add_axes()

A functional version of this method.

all(axis=None, where=<no value>)

Return True if all of the elements along the given axes are True.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• where (Self) – An optional mask which selects which elements to be considered.

• self (Self)

Return type:

Self

See also

numpy.all()

A functional version of this method.

any(axis=None, where=<no value>)

Return True if any of the elements along the given axes are True.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• where (Self) – An optional mask which selects which elements to be considered.

• self (Self)

Return type:

Self

See also

numpy.any()

A functional version of this method.

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

Copy of the array cast to a specific data type.

Equivalent to numpy.ndarray.astype().

Parameters:

• self (Self)

• dtype (str | dtype | Type | dict[str, str | dtype | Type])

• order (str)

• subok (bool)

• copy (bool)

Return type:

Self

broadcast_to(shape, append=False)

A new view of this array with the specified shape.

Parameters:

• shape (dict[str, int]) – The shape of the new array.

• append (bool) – If True, shape will be appended to the current shape of this array before broadcasting.

• self (Self)

Return type:

Self

See also

named_arrays.broadcast_to()

A functional version of this method.

cell_centers(axis=None, random=False)

Convert an array from cell vertices to cell centers.

Parameters:

• axis (None | str | Sequence[str]) – The axes of the array to average over.

• random (bool) – If true, select a random point within each cell instead of the geometric center.

Return type:

AbstractExplicitArray

combine_axes(axes=None, axis_new=None)

Combine some of the axes of the array into a single new axis.

Parameters:

• axes (Sequence[str]) – The axes to combine into a new axis

• axis_new (str) – The name of the new axis

• self (Self)

Return type:

Array with the specified axes combined

copy()

Create a deep copy of this array.

Parameters:

self (Self)

Return type:

Self

copy_shallow()

Create a shallow copy of this array.

Parameters:

self (Self)

Return type:

Self

index(value, axis=None)

Parameters:

• self (Self)

• value (Self)

• axis (None | str | Sequence[str])

Return type:

dict[str, Self]

index_secant(value, axis=None)

Parameters:

• self (Self)

• value (Self)

• axis (None | str | Sequence[str])

Return type:

dict[str, Self]

interp_linear(item)

Linearly interpolate this array to find its value at the given fractional index.

Parameters:

• item (dict[str, Self]) – A fractional index at which to evaluate the array.

• self (Self)

Return type:

Self

max(axis=None, initial=<no value>, where=<no value>)

The maximum value of this array along the given axes.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• initial (ArrayLike) – The initial value of the minimum, required if where provided.

• where (Self) – An optional mask which selects which elements to be considered.

• self (Self)

Return type:

Self

See also

numpy.max()

A functional version of this method.

mean(axis=None, where=<no value>)

The mean value of this array along the given axes.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• where (Self) – An optional mask which selects which elements to be considered.

• self (Self)

Return type:

Self

See also

numpy.mean()

A functional version of this method.

median(axis=None)

The median value of this array along the given axes.

Parameters:

axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

See also

numpy.median()

A functional version of this method.

min(axis=None, initial=<no value>, where=<no value>)

The minimum value of this array along the given axes.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• initial (ArrayLike) – The initial value of the minimum, required if where provided.

• where (Self) – An optional mask which selects which elements to be considered.

• self (Self)

Return type:

Self

See also

numpy.min()

A functional version of this method.

ndindex(axis_ignored=None)

An iterator that yields the index of each element of this array.

Parameters:

• axis_ignored (None | str | Sequence[str]) – The of the array to ignore when generating the iterator.

• self (Self)

Return type:

Iterator[dict[str, int]]

See also

named_arrays.ndindex()

A functional version of this method.

percentile(q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=False)

The requested percentile of this array along the given axes.

Parameters:

• q (int | float | Quantity | Self) – The percentile to compute.

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• out (None | Self) – An optional output array in which to place the result.

• overwrite_input (bool) – Whether to overwrite the input array.

• method (str) – How to interpolate the result.

• keepdims (bool) – A boolean flag indicating whether to keep the reduced dimensions.

• self (Self)

See also

numpy.percentile()

A functional version of this method.

ptp(axis=None)

The peak-to-peak value of this array along the given axes.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• self (Self)

Return type:

Self

See also

numpy.ptp()

A functional version of this method.

replace(**changes)

A method version of dataclasses.replace() for named arrays.

Parameters:

changes – The fields of the dataclass to be overwritten

Return type:

Self

reshape(shape)

Reorganize this array into a new shape.

Parameters:

• shape (dict[str, int]) – The new shape of the array, must be compatible with this array.

• self (Self)

Return type:

Self

rms(axis=None, where=<no value>)

The root-mean-square of this array along the given axes.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• where (Self) – An optional mask which selects which elements to be considered.

• self (Self)

Return type:

Self

std(axis=None, where=<no value>)

The standard deviation of this array along the given axes.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• where (Self) – An optional mask which selects which elements to be considered.

• self (Self)

Return type:

Self

See also

numpy.std()

A functional version of this method.

sum(axis=None, where=<no value>)

The sum of each element of this array along the given axes.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• where (Self) – An optional mask which selects which elements to be considered.

• self (Self)

Return type:

Self

See also

numpy.sum()

A functional version of this method.

take_along_axis(indices, axis)

Take values from this array by matching indices along axis.

Parameters:

• indices (AbstractArray) – The integer indices to take along axis.

• axis (str) – The axis of this array along which the values are taken.

• self (Self)

Return type:

Self

See also

named_arrays.take_along_axis()

A functional version of this method.

to(unit, equivalencies=[], copy=True)

Convert this array to a new unit.

Equivalent to astropy.units.Quantity.to().

Parameters:

• unit (UnitBase | dict[str, None | UnitBase]) – New unit of the returned array

• equivalencies (None | list[tuple[Unit, Unit]]) – A list of equivalence pairs to try if the units are not directly convertible.

• copy (bool) – Boolean flag controlling whether to copy the array.

• self (Self)

Return type:

AbstractExplicitVectorArray

to_string(prefix=None, multiline=None)

Convert this array instance to a string representation.

Parameters:

• prefix (None | str) – the length of this string is used to align the output

• multiline (None | bool) – flag which controls if the output should be spread over multiple lines.

Return type:

array represented as a str

to_string_array(format_value='%.2f', format_unit='latex_inline', pad_unit='$\\,$')

Convert to an array of strings where each string has an appropriately-formatted unit attached to the value.

Parameters:

• format_value (str) – The string used to format the numeric value of each element.

• format_unit (str) – The string used to format the units of each element.

• pad_unit (str) – The string used to add horizontal space between the value and unit.

Return type:

AbstractExplicitVectorArray

to_value(unit, equivalencies=[])

The numerical value of this array, possibly in a different unit.

Equivalent to astropy.units.Quantity.to_value().

Parameters:

• unit (UnitBase | dict[str, None | UnitBase]) – New unit of the returned array

• equivalencies (None | list[tuple[Unit, Unit]]) – A list of equivalence pairs to try if the units are not directly convertible.

• self (Self)

Return type:

AbstractExplicitVectorArray

transpose(axes=None)

Reorder the axes of this array to the given sequence.

Parameters:

• axes (None | Sequence[str]) – The new axis ordering of this array.

• self (Self)

Return type:

Self

See also

numpy.transpose()

The numpy version of this method.

var(axis=None, where=<no value>)

The variance of this array along the given axes.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• where (Self) – An optional mask which selects which elements to be considered.

• self (Self)

Return type:

Self

See also

numpy.var()

A functional version of this method.

vmr(axis=None, where=<no value>)

The variance-to-mean ratio of this array along the given axes.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• where (Self) – An optional mask which selects which elements to be considered.

volume_cell(axis)

Computes the n-dimensional volume of each cell formed by interpreting this array as a logically-rectangular grid of vertices.

Note that this method is usually only used for sorted arrays.

If self is a scalar, this method computes the length of each edge, and is equivalent to numpy.diff(). If self is a 2d vector, this method computes the area of each quadrilateral, and if self is a 3d vector, this method computes the volume of each cuboid.

Parameters:

axis (None | str | Sequence[str]) – The axis or axes defining the logically-rectangular grid. If self is a physical scalar, there should only be one axis. If self is a physical vector, there should be one axis for each component of the vector.

Return type:

AbstractScalar

property axes: tuple[str, ...]

A tuple of str representing the names of each dimension of this array.

Must have the same length as the number of dimensions of this array.

property axes_flattened: str

Combine axes into a single str.

This is useful for functions like numpy.flatten() which returns an array with only one dimension.

property broadcasted: AbstractExplicitArray

if this array has multiple components, broadcast them against each other.

Equivalent to a.broadcast_to(a.shape).

property cartesian_nd: AbstractCartesianNdVectorArray

Convert any instance of AbstractVectorArray to an instance of AbstractCartesianNdVectorArray

cdelt: AbstractSpectralPositionalVectorArray = <dataclasses._MISSING_TYPE object>

property components: dict[str, int | float | complex | ndarray | Quantity | AbstractArray]

The vector components of this array expressed as a dict, where the keys are the names of the component.

crpix: AbstractCartesianNdVectorArray = <dataclasses._MISSING_TYPE object>

crval: AbstractSpectralPositionalVectorArray = <dataclasses._MISSING_TYPE object>

property entries: dict[str, int | float | complex | ndarray | Quantity | AbstractArray]

The scalar entries that compose this object.

property explicit: AbstractExplicitArray

Converts this array to an instance of named_arrays.AbstractExplicitArray.

property indices: dict[str, ScalarArrayRange]

Compute the index of each element of this array.

See also

named_arrays.indices()

A functional version of this method.

property length: AbstractScalar

L2-norm of this array.

property matrix: AbstractMatrixArray

Cast this vector into its matrix representation.

property ndim: int

Number of dimensions of the array. Equivalent to numpy.ndarray.ndim.

property normalized: Self

Return a normalized copy of this vector, where length is unity.

pc: AbstractSpectralPositionalMatrixArray = <dataclasses._MISSING_TYPE object>

property position: int | float | complex | ndarray | Quantity | AbstractArray

The position component of the vector.

property prototype_vector: AbstractExplicitVectorArray

Return vector of same type with all components zeroed.

property shape: dict[str, int]

The number of elements along each axis of the array. Analogous to numpy.ndarray.shape but represented as a dict where the keys are the axis names and the values are the axis sizes.

shape_wcs: dict[str, int] = <dataclasses._MISSING_TYPE object>

property size: int

Total number of elements in the array. Equivalent to numpy.ndarray.size

time: AbstractScalar = <dataclasses._MISSING_TYPE object>

property type_abstract: Type[AbstractArray]

The named_arrays.AbstractArray type corresponding to this array.

property type_explicit: Type[AbstractExplicitArray]

The named_arrays.AbstractExplicitArray type corresponding to this array.

property type_matrix: Type[AbstractMatrixArray]

The corresponding named_arrays.AbstractMatrixArray class

property value: AbstractExplicitVectorArray

Returns a new array with its units removed, if they exist.

property velocity: int | float | complex | ndarray | Quantity | AbstractArray

The line-of-sight velocity of the wave emitter.

property wavelength: int | float | complex | ndarray | Quantity | AbstractArray

The wavelength component of the vector.

wavelength_rest: AbstractScalar = <dataclasses._MISSING_TYPE object>