Core API¶

Spec¶

Trace `dataclass` ¶

Trace(x: ndarray, y: ndarray, label: str = '', color_value: float | None = None, style: dict[str, Any] = dict())

A single data series within a PlotSpec.

Attributes:

Name	Type	Description
`x`	`ndarray`	Array of x-axis values.
`y`	`ndarray`	Array of y-axis values.
`label`	`str`	Legend label for this trace. Empty string means no legend entry.
`color_value`	`float \| None`	Scalar used for colormap-based coloring (XYZColored plots). `None` means use the theme palette instead.
`style`	`dict[str, Any]`	Backend-specific keyword arguments passed through to the underlying draw call (e.g. `{"linestyle": "--"}` for matplotlib).

PlotSpec `dataclass` ¶

PlotSpec(traces: list[Trace | BandTrace], x_label: str = '', y_label: str = '', title: str = '', x_scale: Literal['linear', 'log'] = 'linear', y_scale: Literal['linear', 'log'] = 'linear', theme: ThemeSpec | None = None, figsize: tuple[float, float] | None = None, colormap: str | None = None, z_label: str = '', colorbar: bool = True, colorbar_pad: float | None = None, colorbar_scale: float = 1.175, latex_labels: bool = False, secondary_x_axis: SecondaryAxisSpec | None = None)

Backend-agnostic intermediate representation for a single plot panel.

PlotSpec is the decoupling boundary between Plot subclasses and backends. Plot subclasses produce it; backends consume it. Neither side imports the other.

Attributes:

Name	Type	Description
`traces`	`list[Trace \| BandTrace]`	Ordered list of data series to draw.
`x_label`	`str`	Axis label for the x-axis. May contain LaTeX when `latex_labels=True`.
`y_label`	`str`	Axis label for the y-axis.
`title`	`str`	Panel title.
`x_scale`	`Literal['linear', 'log']`	Scale type for the x-axis (`"linear"` or `"log"`).
`y_scale`	`Literal['linear', 'log']`	Scale type for the y-axis.
`theme`	`ThemeSpec \| None`	Visual theme. `None` uses each backend's own defaults.
`figsize`	`tuple[float, float] \| None`	Figure size `(width, height)` in inches. `None` uses the backend default.
`colormap`	`str \| None`	Named colormap (e.g. `"viridis"`) for XYZColored plots. `None` means use the theme palette.
`z_label`	`str`	Colorbar label, used when `colormap` is set.
`colorbar`	`bool`	Whether to draw a colorbar when `colormap` is set.
`latex_labels`	`bool`	When `True`, axis/title strings are passed raw to the backend (suitable for backends with MathJax support such as plotly HTML export). When `False`, labels are stripped to plain Unicode via :func:`~cplots.core.labels.strip_latex`.

SecondaryAxisSpec `dataclass` ¶

SecondaryAxisSpec(forward: Callable[[ndarray], ndarray], inverse: Callable[[ndarray], ndarray], label: str = '')

Secondary (twin) top x-axis specification, backend-agnostic.

Attributes:

Name	Type	Description
`forward`	`Callable[[ndarray], ndarray]`	Maps primary-axis values to secondary-axis values (e.g. k → ℓ).
`inverse`	`Callable[[ndarray], ndarray]`	Maps secondary-axis values back to primary (e.g. ℓ → k).
`label`	`str`	Label for the secondary axis.

PanelSpec `dataclass` ¶

PanelSpec(plot: PlotSpec, row: int, col: int, rowspan: int = 1, colspan: int = 1)

Position and content of one panel inside a multi-panel figure.

Attributes:

Name	Type	Description
`plot`	`PlotSpec`	The `PlotSpec` rendered in this panel.
`row`	`int`	Zero-based row index in the grid.
`col`	`int`	Zero-based column index in the grid.
`rowspan`	`int`	Number of grid rows this panel occupies.
`colspan`	`int`	Number of grid columns this panel occupies.

FigureSpec `dataclass` ¶

FigureSpec(panels: list[PanelSpec], nrows: int, ncols: int, figsize: tuple[float, float] | None = None, width_ratios: list[float] | None = None, height_ratios: list[float] | None = None, theme: ThemeSpec | None = None, sharex: bool = False, sharey: bool = False, latex_labels: bool = False, colormap: str | None = None, z_label: str = '', colorbar: bool = False, colorbar_pad: float | None = None, colorbar_scale: float = 1.175, hspace: float | None = None, wspace: float | None = None)

Backend-agnostic IR for multi-panel figures (the multi-panel PlotSpec).

Attributes:

Name	Type	Description
`panels`	`list[PanelSpec]`	List of panels that make up the figure.
`nrows`	`int`	Total number of rows in the grid.
`ncols`	`int`	Total number of columns in the grid.
`figsize`	`tuple[float, float] \| None`	Overall figure size `(width, height)` in inches.
`width_ratios`	`list[float] \| None`	Relative widths of each column. Length must equal `ncols` when provided.
`height_ratios`	`list[float] \| None`	Relative heights of each row. Length must equal `nrows` when provided.
`theme`	`ThemeSpec \| None`	Visual theme applied to all panels.
`sharex`	`bool`	Whether panels in the same column share an x-axis.
`sharey`	`bool`	Whether panels in the same row share a y-axis.
`latex_labels`	`bool`	Propagated to all panels; see :class:`PlotSpec`.

Secondary axis helpers¶

multipole_axis ¶

multipole_axis(chi: float, label: str = 'ℓ') -> SecondaryAxisSpec

Secondary axis mapping k → ℓ = k · chi for PKSpectrum plots.

Parameters:

Name	Type	Description	Default
`chi`	`float`	Comoving distance in the same units as k (e.g. Mpc/h).	required
`label`	`str`	Label for the secondary axis.	`'ℓ'`

Example

PKSpectrum(k, pk, secondary_x_axis=multipole_axis(chi=13890)).show()

wavenumber_axis ¶

wavenumber_axis(chi: float, label: str = 'k') -> SecondaryAxisSpec

Secondary axis mapping ℓ → k = ℓ / chi for ClSpectrum plots.

Parameters:

Name	Type	Description	Default
`chi`	`float`	Comoving distance in the same units as k (e.g. Mpc/h).	required
`label`	`str`	Label for the secondary axis.	`'k'`

Example

ClSpectrum(ell, cl, secondary_x_axis=wavenumber_axis(chi=13890)).show()

Theme¶

ThemeSpec `dataclass` ¶

ThemeSpec(font_family: str = 'sans-serif', font_size: int = 12, background_color: str = 'white', palette: tuple[str, ...] = ('#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd'), figsize: tuple[float, float] = (6.4, 4.8), extra: dict[str, Any] = dict())

Immutable visual theme applied across all backends.

Pass a ThemeSpec to any plot constructor or to :class:~cplots.Figure to override the backend's defaults.

Attributes:

Name	Type	Description
`font_family`	`str`	Font family name (e.g. `"serif"`, `"sans-serif"`).
`font_size`	`int`	Base font size in points.
`background_color`	`str`	Background color string. Use `"none"` for a transparent background (blends with dark slides/notebooks).
`palette`	`tuple[str, ...]`	Tuple of hex color strings cycled across traces.
`extra`	`dict[str, Any]`	Backend-specific rcParams or config overrides. The matplotlib backend applies these directly to `mpl.rcParams`; other backends may ignore unknown keys.

Data containers¶

PlotData `dataclass` ¶

PlotData()

Marker base for all typed plot data containers.

XYData `dataclass` ¶

XYData(x: ndarray, y: ndarray, x_label: str = '', y_label: str = '', uncertainties: ndarray | None = None)

Bases: PlotData

PKData `dataclass` ¶

PKData(x: ndarray, y: ndarray, x_label: str = '', y_label: str = '', uncertainties: ndarray | None = None, redshift: float = 0.0)

Bases: XYData

ClData `dataclass` ¶

ClData(x: ndarray, y: ndarray, x_label: str = '', y_label: str = '', uncertainties: ndarray | None = None)

Bases: XYData

BackgroundData `dataclass` ¶

BackgroundData(redshift: ndarray, quantities: dict[str, ndarray], labels: dict[str, str] = dict())

Bases: PlotData

TriangleData `dataclass` ¶

TriangleData(samples: ndarray, parameter_names: list[str], weights: ndarray | None = None, truths: dict[str, float] = dict())

Bases: PlotData

XYZData `dataclass` ¶

XYZData()

Bases: PlotData

Marker type for XYZColored plots.

Labels¶

strip_latex ¶

strip_latex(text: str) -> str

Convert a LaTeX-formatted label to plain Unicode text.

Handles Greek letters, common symbols, super/subscripts, and removes math-mode delimiters. Intended for backends without a LaTeX renderer.

Examples::

>>> strip_latex(r"$k$ [$h$ Mpc$^{-1}$]")
'k [h Mpc⁻¹]'
>>> strip_latex(r"$\Omega_m$")
'Ω_m'
>>> strip_latex(r"$\ell(\ell+1)C_\ell/2\pi$")
'ℓ(ℓ+1)Cℓ/2π'

Registry¶

get_backend `module-attribute` ¶

get_backend = _registry.get

Retrieve a backend by name. Alias for :meth:BackendRegistry.get.

register_backend `module-attribute` ¶

register_backend = _registry.register

Register a backend instance. Alias for :meth:BackendRegistry.register.

Backend selection¶

backend ¶

backend(name: str) -> Generator[None, None, None]

Temporarily override the active backend within a block.

set_backend ¶

set_backend(name: str) -> None

Set the process-wide default backend.

resolve_backend ¶

resolve_backend() -> str

Return the currently active backend name.

Solver adapters¶

Observables ¶

Bases: ABC

Abstract base for cosmological solver adapters.

Subclass this and implement :meth:background, :meth:pk, and :meth:cl to make any Boltzmann solver (CLASS, CAMB, custom) compatible with cplots plots without modifying any cplots internals.

The three required methods return cplots-native :class:~cplots.core.data.PlotData objects that plot constructors accept directly. Two optional methods, :meth:primordial and :meth:thermodynamics, raise :class:NotImplementedError by default; override them if your solver supports those outputs.

Example::

import numpy as np
import cplots
from cplots import Observables, BackgroundData, PKData, ClData

class MyCAMB(Observables):
    def __init__(self, results):
        self._results = results

    def background(self, **kwargs) -> BackgroundData:
        z, H = self._results.get_background()
        return BackgroundData(redshift=z, quantities={"H": H})

    def pk(self, z: float = 0.0, **kwargs) -> PKData:
        k, pk = self._results.get_matter_power(z=z)
        return PKData(x=k, y=pk, redshift=z)

    def cl(self, **kwargs) -> dict[str, ClData]:
        ell, cl_tt = self._results.get_cmb_cls()
        return {"tt": ClData(x=ell, y=cl_tt)}

out = MyCAMB(camb_results)
cplots.PKSpectrum(out.pk(z=0.0)).show()

Methods:¶

background `abstractmethod` ¶

background(**kwargs: Any) -> BackgroundData

Extract background cosmological evolution.

Returns:

Type	Description
`BackgroundData`	class:`~cplots.core.data.BackgroundData` with a redshift array
`BackgroundData`	and named quantity arrays (e.g. H(z), distances).

pk `abstractmethod` ¶

pk(z: float = 0.0, **kwargs: Any) -> PKData

Extract the matter power spectrum P(k) at redshift z.

Parameters:

Name	Type	Description	Default
`z`	`float`	Redshift at which to evaluate P(k).	`0.0`
`**kwargs`	`Any`	Solver-specific options (e.g. `k_min`, `nonlinear`, `h_units`).	`{}`

Returns:

Type	Description
`PKData`	class:`~cplots.core.data.PKData`.

cl `abstractmethod` ¶

cl(**kwargs: Any) -> dict[str, ClData]

Extract CMB angular power spectra.

Parameters:

Name	Type	Description	Default
`**kwargs`	`Any`	Solver-specific options (e.g. `lensed`, `l_max`, `spectra`, `ell_factor`).	`{}`

Returns:

Type	Description
`dict[str, ClData]`	Mapping of spectrum name (`"tt"`, `"ee"`, …) to
`dict[str, ClData]`	class:`~cplots.core.data.ClData`.

primordial ¶

primordial(**kwargs: Any) -> PKData

Extract the primordial scalar power spectrum.

Not all solvers expose this output. Override to provide an implementation; raises :class:NotImplementedError by default.

thermodynamics ¶

thermodynamics(**kwargs: Any) -> BackgroundData

Extract thermodynamics evolution (e.g. visibility function).

Not all solvers expose this output. Override to provide an implementation; raises :class:NotImplementedError by default.

ClassOutput ¶

ClassOutput(cosmo: Any)

Bases: Observables

Adapter facade for a computed classy.Class run.

Extracts cosmological quantities from a CLASS instance into cplots-native PlotData objects. The CLASS run must already be computed (cosmo.compute() called) before wrapping it here.

Example::

import cplots
from classy import Class

cosmo = Class()
cosmo.set({"output": "tCl pCl lCl mPk", "lensing": "yes"})
cosmo.compute()

out = cplots.ClassOutput(cosmo)
cplots.BackgroundEvolution(out.background()).show()
cplots.PKSpectrum(out.pk(z=0.0)).show()
cplots.ClSpectrum(out.cl(spectra=["tt", "ee"])).show()

Methods:¶

background ¶

background(quantities: Sequence[str] | None = None, labels: dict[str, str] | None = None, **kwargs: Any) -> BackgroundData

Extract background cosmological evolution.

Parameters:

Name	Type	Description	Default
`quantities`	`Sequence[str] \| None`	CLASS background dict keys to extract. Defaults to `["H [1/Mpc]", "lum. dist.", "ang.diam.dist.", "comov. dist."]`.	`None`
`labels`	`dict[str, str] \| None`	Display labels keyed by quantity name. Known quantities get sensible LaTeX defaults; unknown quantities fall back to the key.	`None`

Returns:

Type	Description
`BackgroundData`	BackgroundData with redshift and requested quantity arrays.

Raises:

Type	Description
`RuntimeError`	If the background table is unavailable.
`KeyError`	If a requested quantity key is absent from CLASS output.

pk ¶

pk(z: float = 0.0, k_min: float = 0.0001, k_max: float = 1.0, n_k: int = 200, nonlinear: bool = False, h_units: bool = True, **kwargs: Any) -> PKData

Extract matter power spectrum P(k) at a given redshift.

Parameters:

Name	Type	Description	Default
`z`	`float`	Redshift at which to evaluate P(k).	`0.0`
`k_min`	`float`	Minimum wavenumber (units set by `h_units`).	`0.0001`
`k_max`	`float`	Maximum wavenumber (units set by `h_units`).	`1.0`
`n_k`	`int`	Number of k points (log-spaced).	`200`
`nonlinear`	`bool`	Use non-linear P(k) (requires `non_linear` in CLASS params).	`False`
`h_units`	`bool`	If True (default), k is in h/Mpc and P(k) in (Mpc/h)³. If False, k is in 1/Mpc and P(k) in Mpc³.	`True`

Returns:

Type	Description
`PKData`	PKData with k array, P(k) array, redshift, and axis labels.

Raises:

Type	Description
`RuntimeError`	If P(k) is unavailable (`mPk` not in CLASS output).

cl ¶

cl(lensed: bool = True, spectra: Sequence[Literal['tt', 'ee', 'bb', 'te', 'pp']] | None = None, l_max: int = 2500, ell_factor: bool = True, units: Literal['muK2', 'K2', 'dimensionless'] = 'muK2', **kwargs: Any) -> dict[str, ClData]

Extract CMB angular power spectra.

Parameters:

Name	Type	Description	Default
`lensed`	`bool`	Use lensed (True, default) or unlensed spectra.	`True`
`spectra`	`Sequence[Literal['tt', 'ee', 'bb', 'te', 'pp']] \| None`	Which spectra to extract. Defaults to `["tt"]`. Available: `"tt"`, `"ee"`, `"bb"`, `"te"`, `"pp"`.	`None`
`l_max`	`int`	Maximum multipole to request from CLASS.	`2500`
`ell_factor`	`bool`	If True (default), apply the ℓ(ℓ+1)/2π scaling standard in CMB plotting.	`True`
`units`	`Literal['muK2', 'K2', 'dimensionless']`	Temperature unit for the output spectra. `"muK2"` (default) multiplies by T_CMB² in μK², `"K2"` gives K², and `"dimensionless"` applies no conversion (CLASS native units).	`'muK2'`

Returns:

Type	Description
`dict[str, ClData]`	Dict mapping spectrum name (`"tt"`, `"ee"`, …) to ClData.

Raises:

Type	Description
`RuntimeError`	If CMB spectra are unavailable.
`ValueError`	If `units` is not one of the accepted values.

primordial ¶

primordial(**kwargs: Any) -> PKData

Extract primordial scalar power spectrum.

Returns:

Type	Description
`PKData`	PKData with k in 1/Mpc and P_s(k).

Raises:

Type	Description
`RuntimeError`	If primordial spectrum is unavailable.

thermodynamics ¶

thermodynamics(quantities: Sequence[str] | None = None, labels: dict[str, str] | None = None, **kwargs: Any) -> BackgroundData

Extract thermodynamics evolution.

Parameters:

Name	Type	Description	Default
`quantities`	`Sequence[str] \| None`	CLASS thermodynamics dict keys to extract. Defaults to `["g [Mpc^-1]"]` (visibility function).	`None`
`labels`	`dict[str, str] \| None`	Display labels keyed by quantity name.	`None`

Returns:

Type	Description
`BackgroundData`	BackgroundData with redshift and requested quantity arrays.

Raises:

Type	Description
`RuntimeError`	If thermodynamics table is unavailable.
`KeyError`	If a requested quantity key is absent from CLASS output.

CambOutput ¶

CambOutput(results: Any)

Bases: Observables

Adapter facade for a computed CAMB results object.

Extracts cosmological quantities from a camb.CAMBdata instance into cplots-native :class:~cplots.core.data.PlotData objects. The CAMB run must already be computed (camb.get_results(pars) called) before wrapping it here.

Example::

import camb
import cplots

pars = camb.set_params(
    H0=67.4, ombh2=0.022, omch2=0.12, mnu=0.06,
    omk=0, tau=0.054, As=2.1e-9, ns=0.965,
    lmax=2500, redshifts=[0.0, 1.0],
)
results = camb.get_results(pars)

out = cplots.CambOutput(results)
cplots.BackgroundEvolution(out.background()).show()
cplots.PKSpectrum(out.pk(z=0.0)).show()
cplots.ClSpectrum(out.cl(spectra=["tt", "ee"])).show()

Methods:¶

background ¶

background(z: Any | None = None, quantities: Sequence[str] | None = None, labels: dict[str, str] | None = None, **kwargs: Any) -> BackgroundData

Extract background cosmological evolution.

Unlike CLASS, CAMB evaluates background quantities on demand over a caller-supplied redshift grid rather than from a pre-computed table.

Parameters:

Name	Type	Description	Default
`z`	`Any \| None`	Redshift array at which to evaluate quantities. Defaults to `np.linspace(0, 3, 300)`.	`None`
`quantities`	`Sequence[str] \| None`	Keys to extract. Must be a subset of `["H [km/s/Mpc]", "lum. dist.", "ang.diam.dist.", "comov. dist."]`. Defaults to all four.	`None`
`labels`	`dict[str, str] \| None`	Display labels keyed by quantity name. Known quantities get sensible LaTeX defaults; unknown quantities fall back to their key string.	`None`

Returns:

Type	Description
`BackgroundData`	class:`~cplots.core.data.BackgroundData` with z and the
`BackgroundData`	requested quantity arrays.

Raises:

Type	Description
`KeyError`	If a requested quantity key is not recognised.
`RuntimeError`	If a CAMB call fails (e.g. results not computed).

pk ¶

pk(z: float = 0.0, k_min: float = 0.0001, k_max: float = 1.0, n_k: int = 200, nonlinear: bool = False, h_units: bool = True, **kwargs: Any) -> PKData

Extract matter power spectrum P(k) at a given redshift.

Uses results.get_matter_power_interpolator() internally, so z does not need to exactly match the redshifts passed to camb.set_params(redshifts=[...]).

Parameters:

Name	Type	Description	Default
`z`	`float`	Redshift at which to evaluate P(k).	`0.0`
`k_min`	`float`	Minimum wavenumber (h/Mpc when `h_units=True`, else 1/Mpc).	`0.0001`
`k_max`	`float`	Maximum wavenumber (same units as k_min).	`1.0`
`n_k`	`int`	Number of k points (log-spaced).	`200`
`nonlinear`	`bool`	Use non-linear P(k) (requires a non-linear model configured in the CAMB params).	`False`
`h_units`	`bool`	If True (default), k is in h/Mpc and P(k) in (Mpc/h)³. If False, k is in 1/Mpc and P(k) in Mpc³.	`True`

Returns:

Type	Description
`PKData`	class:`~cplots.core.data.PKData` with k, P(k), redshift, and
`PKData`	axis labels.

Raises:

Type	Description
`RuntimeError`	If the CAMB call fails (e.g. `WantTransfer` not set or results not computed).

cl ¶

cl(lensed: bool = True, spectra: Sequence[Literal['tt', 'ee', 'bb', 'te', 'pp']] | None = None, l_max: int | None = None, ell_factor: bool = True, units: Literal['muK2', 'K2', 'dimensionless'] = 'muK2', **kwargs: Any) -> dict[str, ClData]

Extract CMB angular power spectra.

CAMB handles the ell-factor and unit conversion natively, so the implementation maps directly to results.get_cmb_power_spectra().

Parameters:

Name	Type	Description	Default
`lensed`	`bool`	Use lensed (True, default) or unlensed spectra.	`True`
`spectra`	`Sequence[Literal['tt', 'ee', 'bb', 'te', 'pp']] \| None`	Which spectra to extract. Defaults to `["tt"]`. Available: `"tt"`, `"ee"`, `"bb"`, `"te"`, `"pp"`.	`None`
`l_max`	`int \| None`	Maximum multipole to include. Defaults to the full array returned by CAMB.	`None`
`ell_factor`	`bool`	If True (default), apply the ℓ(ℓ+1)/2π scaling standard in CMB plotting.	`True`
`units`	`Literal['muK2', 'K2', 'dimensionless']`	Temperature unit. `"muK2"` (default), `"K2"`, or `"dimensionless"`.	`'muK2'`

Returns:

Type	Description
`dict[str, ClData]`	Dict mapping spectrum name to :class:`~cplots.core.data.ClData`.

Raises:

Type	Description
`ValueError`	If units is not one of the accepted values.
`RuntimeError`	If the CAMB call fails.

primordial ¶

primordial(k_min: float = 0.0001, k_max: float = 1.0, n_k: int = 200, **kwargs: Any) -> PKData

Extract primordial scalar power spectrum.

Computes the power-law spectrum analytically from the InitialPowerLaw parameters stored in results.Params.InitPower. This works for the default scalar power-law model; subclass and override for running-index or tabulated spectra.

Parameters:

Name	Type	Description	Default
`k_min`	`float`	Minimum wavenumber in 1/Mpc.	`0.0001`
`k_max`	`float`	Maximum wavenumber in 1/Mpc.	`1.0`
`n_k`	`int`	Number of k points (log-spaced).	`200`

Returns:

Type	Description
`PKData`	class:`~cplots.core.data.PKData` with k in 1/Mpc and P_s(k).

Core API¶

Spec¶

Trace dataclass ¶

PlotSpec dataclass ¶

SecondaryAxisSpec dataclass ¶

PanelSpec dataclass ¶

FigureSpec dataclass ¶

Secondary axis helpers¶

multipole_axis ¶

wavenumber_axis ¶

Theme¶

ThemeSpec dataclass ¶

Data containers¶

PlotData dataclass ¶

XYData dataclass ¶

PKData dataclass ¶

ClData dataclass ¶

BackgroundData dataclass ¶

TriangleData dataclass ¶

XYZData dataclass ¶

Labels¶

strip_latex ¶

Registry¶

get_backend module-attribute ¶

register_backend module-attribute ¶

Backend selection¶

backend ¶

set_backend ¶

resolve_backend ¶

Solver adapters¶

Observables ¶

Methods:¶

background abstractmethod ¶

pk abstractmethod ¶

cl abstractmethod ¶

primordial ¶

thermodynamics ¶

ClassOutput ¶

Methods:¶

background ¶

pk ¶

cl ¶

primordial ¶

thermodynamics ¶

CambOutput ¶

Methods:¶

background ¶

pk ¶

cl ¶

primordial ¶

Trace `dataclass` ¶

PlotSpec `dataclass` ¶

SecondaryAxisSpec `dataclass` ¶

PanelSpec `dataclass` ¶

FigureSpec `dataclass` ¶

ThemeSpec `dataclass` ¶

PlotData `dataclass` ¶

XYData `dataclass` ¶

PKData `dataclass` ¶

ClData `dataclass` ¶

BackgroundData `dataclass` ¶

TriangleData `dataclass` ¶

XYZData `dataclass` ¶

get_backend `module-attribute` ¶

register_backend `module-attribute` ¶

background `abstractmethod` ¶

pk `abstractmethod` ¶

cl `abstractmethod` ¶