AbstractFunctionArray#

class named_arrays.AbstractFunctionArray#

Bases: AbstractArray

Attributes

`axes`	A `tuple` of `str` representing the names of each dimension of this array.
`axes_center`	Return keys corresponding to all input axes representing bin centers
`axes_flattened`	Combine `axes` into a single `str`.
`axes_vertex`	Return keys corresponding to all input axes representing bin vertices
`broadcasted`	if this array has multiple components, broadcast them against each other.
`explicit`	Converts this array to an instance of `named_arrays.AbstractExplicitArray`.
`indices`	Compute the index of each element of this array.
`inputs`	The arrays representing the inputs to this function.
`length`	L2-norm of this array.
`ndim`	Number of dimensions of the array.
`outputs`	The arrays representing the outputs of this function.
`shape`	The number of elements along each axis of the array.
`size`	Total number of elements in the array.
`type_abstract`	The `named_arrays.AbstractArray` type corresponding to this array.
`type_explicit`	The `named_arrays.AbstractExplicitArray` type corresponding to this array.
`value`	Returns a new array with its units removed, if they exist.

Methods

`__init__`()
`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, seed])	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.
`debroadcast`([axes])	Remove redundant axes introduced by broadcasting.
`gradient`(axis[, component, edge_order])	Differentiate the outputs with respect to the inputs along `axis`.
`index`(value[, axis])
`index_secant`(value[, axis])
`integrate`(axis[, component])	Integrate the outputs over the given input `axis` or axes.
`interp_linear`(item)	Linearly interpolate this array to find its value at the given fractional index.
`isel`(**item)	Index this array along named axes given as keyword arguments.
`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.
`pcolormesh`(axs, input_component_x, ...[, ...])	Plot a `FunctionArray` via `matplotlib.pyplot.pcolormesh()`.
`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.
`regrid`(inputs[, axis, method, bounds, weights])	Resample this function array onto a new set of input coordinates using `named_arrays.regridding.regrid()`.
`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.
`weights`(inputs[, axis, method, bounds])	Compute the resampling weights of this array using `named_arrays.regridding.weights()`.

Inheritance Diagram

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.
Return type:: FunctionArray

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:

dtype (str | dtype | type)
order (str)
subok (bool)
copy (bool)

Return type:

FunctionArray

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, seed=None)#

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.
seed (None | int) – The seed of the random sampling, which has an effect only where random is true. If None (the default), the sampling differs from one call to the next, and anything computed from it cannot be reproduced.

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 (None | Sequence[str]) – The axes to combine into a new axis
axis_new (None | str) – The name of the new axis

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

debroadcast(axes=None)#

Remove redundant axes introduced by broadcasting.

Parameters:

axes (None | str | Sequence[str]) – The axes to consider removing. If None (the default), every axis of this array is considered. Axes not present in this array are ignored.
self (Self)

Return type:

Self

See also

named_arrays.debroadcast(): A functional version of this method.

gradient(axis, component=None, edge_order=1)#

Differentiate the outputs with respect to the inputs along axis.

This is the counterpart to integrate(): where that one sums the outputs against a measure computed from the inputs, this one divides the difference of the outputs by the difference of the inputs.

The result has the same inputs as this array, and its outputs are the derivative, so the units of the result are those of the outputs divided by those of the differentiation variable.

If component is None, the inputs themselves are the differentiation variable, and they must be a scalar.
If component is a str, that named sub-element of the inputs is the variable, and it must be a scalar.

A center axis, where the inputs are samples, differentiates the outputs against those samples directly. A vertex axis, where the inputs are bin edges and so has one more input than output, differentiates against the cell centers, which is where the outputs live.

Parameters:

axis (str) – The logical axis to differentiate along.
component (None | str) – The named input sub-element which is the differentiation variable. If None, the inputs themselves are the variable.
edge_order (int) – The order of the one-sided differences used at the two ends of axis, either 1 or 2. The interior always uses second-order central differences.

Raises:

ValueError – If axis is not an axis of this array, if the differentiation variable is not a scalar or does not vary along axis, or if there are too few points along axis for the given edge_order.

Return type:

FunctionArray

See also

integrate(): The counterpart of this method, which sums the outputs against the inputs rather than dividing by them.
numpy.gradient(): The numpy function this method backs.

Notes

Unlike numpy.gradient(), the differentiation variable may vary along axes other than axis, which is what a distorted grid gives, since the differences are taken elementwise.

Examples

Differentiate a parabola, whose derivative is a straight line.

Second-order edges are asked for, since the first-order ones of the default are inexact wherever the function curves.

import numpy as np
import astropy.units as u
import named_arrays as na

x = na.linspace(0, 2, axis="x", num=5) * u.mm

f = na.FunctionArray(
    inputs=x,
    outputs=np.square(x / u.mm) * u.ph,
)

f.gradient("x", edge_order=2)

FunctionArray(
    inputs=ScalarArray(
        ndarray=[0. , 0.5, 1. , 1.5, 2. ] mm,
        shape={'x': 5},
    ),
    outputs=ScalarArray(
        ndarray=[0., 1., 2., 3., 4.] ph / mm,
        shape={'x': 5},
    ),
)

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]

integrate(axis, component=None)#

Integrate the outputs over the given input axis or axes.

The differential measure is computed from the inputs via AbstractArray.volume_cell():

If component is None, the whole input is used as the integration variable and len(axis) must match the dimensionality of the inputs (one for a scalar, two for a Cartesian2dVectorArray, etc.).
If component is a str, that named sub-element of the inputs (a scalar or a sub-vector) supplies the measure, and len(axis) must match its dimensionality.

A vertex axis (where the inputs represent bin edges) is integrated with a Riemann sum, while a center axis (where the inputs represent samples) is integrated with the trapezoidal rule; mixing the two in a single call is supported. The integrated axes are removed from the outputs and collapsed in the inputs by averaging.

Parameters:

axis (str | Sequence[str]) – The logical axis or axes to integrate over.
component (None | str) – The named input sub-element supplying the integration variable. If None, the whole input is used.

Return type:

FunctionArray

Examples

Integrate a constant function over a 1D domain.

import numpy as np
import astropy.units as u
import named_arrays as na

f = na.FunctionArray(
    inputs=na.ScalarLinearSpace(0, 2, axis="x", num=101) * u.nm,
    outputs=na.ScalarArray(np.full(101, 3.0), axes=("x",)) * u.ph,
)

f.integrate("x")

FunctionArray(
    inputs=ScalarArray(
        ndarray=1. nm,
        shape={},
    ),
    outputs=ScalarArray(
        ndarray=6. nm ph,
        shape={},
    ),
)

interp_linear(item)#

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

Parameters:: item (dict[str, AbstractArray]) – A fractional index at which to evaluate the array.
Return type:: FunctionArray

isel(**item)#

Index this array along named axes given as keyword arguments.

This is a convenience wrapper around __getitem__(): a.isel(x=0) is equivalent to a[dict(x=0)].

Since keyword-argument names must be valid Python identifiers, axes whose names are not valid identifiers can only be indexed using the dict form, a[{...}].

Parameters:

item (int | slice | AbstractArray) – The index to apply along each named axis.
self (Self)

Return type:

AbstractExplicitArray

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.

pcolormesh(axs, input_component_x, input_component_y, input_component_row=None, input_component_column=None, index=None, output_component_color=None, **kwargs)#

Plot a FunctionArray via matplotlib.pyplot.pcolormesh().

FunctionArray.pcolormesh() takes in an axes object, or array of axes objects, along with components to be plotted along the x and y plot axes (input_component_x and input_component_y). Additional components can be tiled along subplot row/column and are specified in input_component_row and input_component_column.

import named_arrays as na
import numpy as np
import astropy.units as u
import matplotlib.pyplot as plt

position = na.Cartesian2dVectorLinearSpace(
    start=-10,
    stop=10,
    axis=na.Cartesian2dVectorArray(
        x='position_x',
        y='position_y',
    ),
    num=21,
) * u.m

x_width = 5 * u.m
y_width = 2 * u.m
velocity = 1 * u.m/u.s
time = na.ScalarLinearSpace(
    start=0 * u.s,
    stop=3 * u.s,
    num=4,
    axis='time'
)

intensity = np.exp(-(((position.x + velocity*time)/x_width) ** 2 + ((position.y + 2*velocity*time)/y_width)** 2))
scene = na.FunctionArray(
    inputs=position,
    outputs=intensity,
)

fig, axs = plt.subplots(
    nrows=scene.outputs.shape['time'],
    squeeze=False,
    sharex=True,
    subplot_kw=dict(aspect='equal'),
)
scene.pcolormesh(
    axs=axs,
    input_component_x='x',
    input_component_y='y',
    input_component_row='time',
)

../_images/named_arrays.AbstractFunctionArray_2_0.png

Parameters:

axs (ndarray)
input_component_x (str)
input_component_y (str)
input_component_row (None | str)
input_component_column (None | str)
index (None | dict[str, int])
output_component_color (None | str)
kwargs (Any)

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.

regrid(inputs, axis=None, method='multilinear', bounds='extrapolate', weights=None)#

Resample this function array onto a new set of input coordinates using named_arrays.regridding.regrid().

The new inputs can be another grid, or scattered points. The axes of this function which are not in axis are broadcast against the new inputs by name, as in any other operation, so the new inputs can share them, for example to give a separate set of points for each of several channels. Every other axis of the new inputs is an axis of the new grid, or an axis along which the points are listed, and takes the place of the axes in axis in the result.

If the outputs of this function are uncertain, or if the components which are resampled are uncertain, either in its inputs or in the new inputs, the nominal values and the samples are resampled separately, and the outputs of the result are uncertain.

Parameters:

inputs (float | Quantity | AbstractArray) – The new input coordinates on which to resample the outputs. If inputs is a scalar, a single point can also be a plain number or quantity. A component of a vector which is not resampled should be None, or else equal everywhere to the component of inputs, counting NaN as equal to NaN, and the result keeps the component of inputs.
axis (None | str | Sequence[str]) – The axes of inputs to resample. If None, every axis of inputs is resampled. Only the components of inputs which vary along at least one of these axes are resampled, and there should be as many of them as there are axes, so an axis along which inputs is a separate grid for each element should be left out.
method (Literal['multilinear', 'conservative']) – The resampling method to use. The multilinear method interpolates at points, so the new inputs can be scattered points along any number of axes, or a single point. The conservative method resamples cells, so the new inputs must be a grid with as many axes as axis.
bounds (Literal['extrapolate', 'nan', 'raise']) – How to treat new inputs outside of inputs. Only applies when method is multilinear. extrapolate (the default) extrapolates linearly from the nearest cell, nan sets the outputs there to numpy.nan, and raise raises a ValueError. If the components which are resampled are uncertain, each of their samples is treated on its own, so nan sets only the samples outside to numpy.nan, and raise raises if any sample is outside. See regridding.weights() for the details.
weights (None | tuple[AbstractScalar, dict[str, int], dict[str, int]]) – Optional weights which can be computed in advance using weights() to greatly speed repeated resampling of the same inputs. If given, method and bounds are ignored, since the weights already encode them. They cannot be used if the inputs which are resampled are uncertain, since each of their samples has weights of its own, but they can be used if only the outputs are uncertain.

Return type:

Self

See also

weights(): If you need to resample the same coordinates more than once.

Examples

Evaluate a function sampled on a grid at a set of scattered points.

import numpy as np
import matplotlib.pyplot as plt
import named_arrays as na

# Sample a function on a grid
inputs = na.Cartesian2dVectorLinearSpace(
    start=-1,
    stop=1,
    axis=na.Cartesian2dVectorArray("x", "y"),
    num=21,
).explicit
outputs = np.sin(3 * inputs.x) * np.cos(2 * inputs.y)
f = na.FunctionArray(inputs, outputs)

# Scatter some points along a single axis
points = na.Cartesian2dVectorArray(
    x=na.random.uniform(-1, 1, shape_random=dict(point=50), seed=1),
    y=na.random.uniform(-1, 1, shape_random=dict(point=50), seed=2),
)

# Evaluate the function at the points
g = f(points, axis=("x", "y"))

# Plot the samples of the function, and the points
fig, ax = plt.subplots(constrained_layout=True)
na.plt.scatter(
    f.inputs.x,
    f.inputs.y,
    c=f.outputs,
    s=10,
    ax=ax,
    vmin=-1,
    vmax=1,
)
na.plt.scatter(
    g.inputs.x,
    g.inputs.y,
    c=g.outputs,
    s=60,
    ax=ax,
    vmin=-1,
    vmax=1,
    edgecolors="black",
)
ax.set_aspect("equal")

../_images/named_arrays.AbstractFunctionArray_3_0.png

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) – 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.

Return type:

FunctionArray

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.

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) – 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.

Return type:

FunctionArray

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

weights(inputs, axis=None, method='multilinear', bounds='extrapolate')#

Compute the resampling weights of this array using named_arrays.regridding.weights(). The output of this method is designed to be used by regrid().

The inputs which are resampled cannot be uncertain, since each of their samples has weights of its own, but the outputs can be.

Parameters:

inputs (float | Quantity | AbstractArray) – The new input coordinates on which to resample the outputs.
axis (None | str | Sequence[str]) – The axes of inputs to resample, as in regrid().
method (Literal['multilinear', 'conservative']) – The resampling method to use.
bounds (Literal['extrapolate', 'nan', 'raise']) – How to treat new inputs outside of inputs, as in regrid().

Return type:

tuple[AbstractScalar, dict[str, int], dict[str, int]]

See also

regrid(): A method designed to use these weights.

abstract 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_center: tuple(str)#: Return keys corresponding to all input axes representing bin centers

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 axes_vertex: tuple(str)#: Return keys corresponding to all input axes representing bin vertices

property broadcasted: FunctionArray#

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

Equivalent to a.broadcast_to(a.shape).

abstract 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.

abstract property inputs: AbstractArray#: The arrays representing the inputs to this function.

property length: FunctionArray#: L2-norm of this array.

abstract property ndim: int#: Number of dimensions of the array. Equivalent to numpy.ndarray.ndim.

abstract property outputs: AbstractArray#: The arrays representing the outputs of this function.

abstract 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.

abstract property size: int#: Total number of elements in the array. Equivalent to numpy.ndarray.size

property type_abstract: Type[AbstractFunctionArray]#: The named_arrays.AbstractArray type corresponding to this array.

property type_explicit: Type[FunctionArray]#: The named_arrays.AbstractExplicitArray type corresponding to this array.

property value: FunctionArray#

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

The family is preserved, so a vector returns a vector and a function array returns a function array, but the result is always explicit: an implicit array materializes into its family’s explicit type.