parallelkdepy.wrapper

High-level API: Functions and objects that wrap Julia calls.

Functions

initialize_dirac_sequence(data, grid, *[, ...])

Initialize a Dirac sequence on the given grid.

Classes

DensityEstimation(data, *[, grid, dims, ...])	Main API object for density estimation.
Grid([ranges, device, b32, grid_jl])	Higher level implementation of a grid to use over meshgrid.

class parallelkdepy.wrapper.DensityEstimation(data: ndarray, *, grid: Grid | bool = False, dims: Sequence | None = None, grid_bounds: Sequence | None = None, grid_padding: Sequence | None = None, device: str = 'cpu')

Bases: object

Main API object for density estimation.

property data

Numpy array of data points for density estimation.

property density

Numpy array representing the estimated density.

property device

Device type, e.g., ‘cpu’ or ‘cuda’.

estimate_density(estimation: str, **kwargs) → None

Executes the density estimation algorithm on the data.

generate_grid(dims: Sequence | None = None, grid_bounds: Sequence | None = None, grid_padding: Sequence | None = None, overwrite: bool = False) → Grid

Generates a grid based on the data and specified parameters.

Returns:

A Grid object representing the generated grid.

Return type:

Grid

get_density(**kwargs) → ndarray

Returns the estimated density as a Numpy array.

property grid

Grid used for density estimation, if any.

class parallelkdepy.wrapper.Grid(ranges: Sequence[tuple] = [], *, device: str = 'cpu', b32: bool | None = None, grid_jl=None)

Bases: object

Higher level implementation of a grid to use over meshgrid.

bounds() → list[tuple]

List of tuples of bounds for each dimension of the grid.

property device

Device type, e.g., ‘cpu’ or ‘cuda’.

fftgrid() → Grid

Returns a grid of frequency components.

property grid_jl

Underlying Julia grid object.

initial_bandwidth() → list

List of the minimum bandwidth that the grid can support in each dimension.

lower_bounds() → list

List of lower bounds for each dimension of the grid.

property shape

Shape of the grid.

step() → list

List of step sizes for each dimension of the grid.

to_meshgrid() → tuple[ndarray, ...]

Mesh grid coordinates

upper_bounds() → list

List of upper bounds for each dimension of the grid.

parallelkdepy.wrapper.initialize_dirac_sequence(data: ndarray, grid: Grid, *, bootstrap_indices: ndarray | None = None, device: str = 'cpu', method: str | None = None) → ndarray

Initialize a Dirac sequence on the given grid.

Parameters:

• data (np.ndarray) – Data points to initialize the Dirac sequence.

• grid (Grid) – The grid on which to initialize the Dirac sequence.

• bootstrap_indices (Optional[np.ndarray], optional) – Numpy array of bootstrap indices, by default None. If provided, the shape should be (n_bootstraps, n_samples).

• device (str, optional) – Device to store the array, e.g., ‘cpu’ or ‘cuda’, by default ‘cpu’.

• method (str, optional) – Method to use for initialization, e.g., ‘serial’ or ‘parallel’, by default ‘serial’.

Returns:

Numpy array representing the initialized Dirac sequence.

Return type:

np.ndarray