AbstractPolynomial¶

class kgpy.function.AbstractPolynomial(input)¶

Bases: AbstractArray

Parameters:: input (InputT) –

__init__(input)¶

Parameters:: input (InputT) –
Return type:: None

Attributes

`coefficients`
`input_broadcasted`
`inverse`
`mask`
`output`
`shape`
`input`

Methods

`__init__`(input)
`copy`()	rtype: `typing.TypeVar`(`CopyableT`, bound= Copyable)
`copy_shallow`()	rtype: `typing.TypeVar`(`CopyableT`, bound= Copyable)
`design_matrix`(inp)	rtype: `kgpy.vectors.CartesianND`
`from_pickle`([path])
`pcolormesh`(axs, input_component_x, ...[, ...])	Plot this function using `matplotlib.pyplot.pcolormesh`.
`to_pickle`(path)

Inheritance Diagram

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copy()¶

Return type:: typing.TypeVar(CopyableT, bound= Copyable)
Parameters:: self (CopyableT) –

copy_shallow()¶

Return type:: typing.TypeVar(CopyableT, bound= Copyable)
Parameters:: self (CopyableT) –

design_matrix(inp)¶

Return type:

kgpy.vectors.CartesianND

Parameters:

self (PolynomialArrayT) –
inp (InputT) –

classmethod from_pickle(path=None)¶

Parameters:: path (Path | None) –

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

Plot this function using matplotlib.pyplot.pcolormesh.

Examples

import numpy as np
import matplotlib.pyplot as plt
import astropy.units as u
import kgpy.labeled
import kgpy.vectors
import kgpy.matrix
import kgpy.function

a = kgpy.labeled.LinearSpace(0 * u.rad, np.pi * u.rad, num=2, axis='a')
b = kgpy.labeled.LinearSpace(0 * u.rad, np.pi * u.rad, num=2, axis='b')
x = kgpy.labeled.LinearSpace(0 * u.rad, 2 * np.pi * u.rad, num=11, axis='x')
y = kgpy.labeled.LinearSpace(0 * u.rad, 1 * np.pi * u.rad, num=11, axis='y')

x2 = 2 * x + y / 4
y2 = x / 5 + y

inputs = kgpy.vectors.CartesianND(dict(
    a=a,
    b=b,
    x=x2,
    y=y2,
))

f = kgpy.function.Array(
    input=inputs,
    output=np.cos(a) * np.cos(b) * np.cos(x2) * np.cos(y2),
)

fig, axs = plt.subplots(
    nrows=a.num,
    ncols=b.num,
    sharex=True,
    sharey=True,
    squeeze=False,
    figsize=(12, 12),
    constrained_layout=True,
)

f.pcolormesh(
    axs=axs,
    input_component_x='x',
    input_component_y='y',
    input_component_row='a',
    input_component_column='b',
)

../_images/kgpy.function.AbstractPolynomial_0_0.png

Parameters:

axs (Array of axes that the function will be plotted on) –
input_component_x (typing.Union[str, kgpy.vectors.ComponentAxis]) –
input_component_y (typing.Union[str, kgpy.vectors.ComponentAxis]) –
input_component_row (typing.Union[str, kgpy.vectors.ComponentAxis, None]) –
input_component_column (typing.Union[str, kgpy.vectors.ComponentAxis, None]) –
output_component_color (typing.Union[str, kgpy.vectors.ComponentAxis, None]) –
index (typing.Optional[typing.Dict[str, int]]) –
kwargs –
self (AbstractArrayT) –

to_pickle(path)¶

Parameters:: path (Path | None) –

abstract property coefficients: AbstractVector¶

input: typing.TypeVar(InputT, bound= typing.Union[int, float, numpy.ndarray, astropy.units.quantity.Quantity, kgpy.labeled.AbstractArray, kgpy.uncertainty.AbstractArray, VectorInterface])¶

property input_broadcasted: InputT¶

property inverse: ArrayT¶

abstract property mask: int | float | ndarray | Quantity | AbstractArray | AbstractArray¶

abstract property output: OutputT¶

property shape: Dict[str, int]¶

Navigation

AbstractPolynomial¶