kalepy package
Subpackages
Submodules
- kalepy.kde module
- kalepy.kernels module
- kalepy.plot module
- kalepy.sample module
- kalepy.speed_test module
- kalepy.utils module
add_cov()array_str()bins()bound_indices()centroids()cov_keep_vars()cumsum()cumtrapz()flatlen()flatten()histogram()iqrange()isinteger()isjagged()jshape()matrix_invert()meshgrid()midpoints()minmax()parse_edges()quantiles()really1d()rem_cov()run_if()run_if_notebook()run_if_script()spacing()stats()stats_str()subdivide()trapz_dens_to_mass()trapz_nd()
Module contents
Multidimensional kernel density estimation for distribution functions, resampling, and plotting.
Copyright (C) 2020 Luke Zoltan Kelley and Contributors.
- kalepy.density(data, points=None, weights=None, reflect=None, probability=False, grid=False, **kwargs)
Use a KDE to calculate the density of the given data.
This function (1) constructs a kernel-density estimate of the distribution function from which the given data were sampled; then (2) returns the values of the distribution function at points (which are automatically generated if they are not given).
- Parameters:
- datasetarray_like (N,) or (D,N,)
Dataset from which to construct the kernel-density-estimate. For multivariate data with D variables and N values, the data must be shaped (D,N). For univariate (D=1) data, this can be a single array with shape (N,).
- points([D,]M,) array_like of float, or (D,) set of array_like point specifications
The locations at which the PDF should be evaluated. The number of dimensions D must match that of the dataset that initialized this class’ instance. NOTE: If the params kwarg (see below) is given, then only those dimensions of the target parameters should be specified in points.
The meaning of points depends on the value of the grid argument:
grid=True : points must be a set of (D,) array_like objects which each give the evaluation points for the corresponding dimension to produce a grid of values. For example, for a 2D dataset, points=([0.1, 0.2, 0.3], [1, 2]), would produce a grid of points with shape (3, 2): [[0.1, 1], [0.1, 2]], [[0.2, 1], [0.2, 2]], [[0.3, 1], [0.3, 2]], and the returned values would be an array of the same shape (3, 2).
grid=False : points must be an array_like (D,M) describing the position of M sample points in each of D dimensions. For example, for a 3D dataset: points=([0.1, 0.2], [1.0, 2.0], [10, 20]), describes 2 sample points at the 3D locations, (0.1, 1.0, 10) and (0.2, 2.0, 20), and the returned values would be an array of shape (2,).
- weightsarray_like (N,), None
Weights corresponding to each dataset point. Must match the number of points N in the dataset. If None, weights are uniformly set to 1.0 for each value.
- reflect(D,) array_like, None
Locations at which reflecting boundary conditions should be imposed. For each dimension D, a pair of boundary locations (for: lower, upper) must be specified, or None. None can also be given to specify no boundary at that location. See class docstrings:Reflection for more information.
- paramsint, array_like of int, None
Only calculate the PDF for certain parameters (dimensions). See class docstrings:Projection for more information.
- gridbool,
Evaluate the KDE distribution at a grid of points specified by points. See points argument description above.
- probabilitybool, normalize the results to sum to unity
- Returns:
- pointsarray_like of scalar
Locations at which the PDF is evaluated.
- valsarray_like of scalar
PDF evaluated at the given points
- kalepy.pdf(data, points=None, weights=None, reflect=None, params=None, grid=False, **kwargs)
Use a KDE to calculate the probability-density of the given data.
Wrapper for kalepy.density(…, probability=True).
- Parameters:
- datasetarray_like (N,) or (D,N,)
Dataset from which to construct the kernel-density-estimate. For multivariate data with D variables and N values, the data must be shaped (D,N). For univariate (D=1) data, this can be a single array with shape (N,).
- points([D,]M,) array_like of float, or (D,) set of array_like point specifications
The locations at which the PDF should be evaluated. The number of dimensions D must match that of the dataset that initialized this class’ instance. NOTE: If the params kwarg (see below) is given, then only those dimensions of the target parameters should be specified in points.
The meaning of points depends on the value of the grid argument:
grid=True : points must be a set of (D,) array_like objects which each give the evaluation points for the corresponding dimension to produce a grid of values. For example, for a 2D dataset, points=([0.1, 0.2, 0.3], [1, 2]), would produce a grid of points with shape (3, 2): [[0.1, 1], [0.1, 2]], [[0.2, 1], [0.2, 2]], [[0.3, 1], [0.3, 2]], and the returned values would be an array of the same shape (3, 2).
grid=False : points must be an array_like (D,M) describing the position of M sample points in each of D dimensions. For example, for a 3D dataset: points=([0.1, 0.2], [1.0, 2.0], [10, 20]), describes 2 sample points at the 3D locations, (0.1, 1.0, 10) and (0.2, 2.0, 20), and the returned values would be an array of shape (2,).
- weightsarray_like (N,), None
Weights corresponding to each dataset point. Must match the number of points N in the dataset. If None, weights are uniformly set to 1.0 for each value.
- reflect(D,) array_like, None
Locations at which reflecting boundary conditions should be imposed. For each dimension D, a pair of boundary locations (for: lower, upper) must be specified, or None. None can also be given to specify no boundary at that location. See class docstrings:Reflection for more information.
- paramsint, array_like of int, None
Only calculate the PDF for certain parameters (dimensions). See class docstrings:Projection for more information.
- gridbool,
Evaluate the KDE distribution at a grid of points specified by points. See points argument description above.
- Returns:
- pointsarray_like of scalar
Locations at which the PDF is evaluated.
- valsarray_like of scalar
PDF evaluated at the given points
- kalepy.resample(data, size=None, weights=None, reflect=None, keep=None, **kwargs)
Use a KDE to resample from a reconstructed density function of the given data.
This function (1) constructs a kernel-density estimate of the distribution function from which the given data were sampled; then, (2) resamples size data points from that function. If size is not given, then the same number of points are returned as in the input data.
- Parameters:
- data([D,]N,) array_like of scalar, the data to be resampled,
The input data can be either: * 1D array_like (N,) with N data points, or * 2D array_like (D,N) with D parameters, and N data points
- sizeint, None (default)
The number of new data points to draw. If None, then the number of datapoints is used.
- weights(N,) array_like or None,
The weights of which each data point in data.
- reflect(D,) array_like, None (default)
Locations at which reflecting boundary conditions should be imposed. For each dimension D, a pair of boundary locations (for: lower, upper) must be specified, or None. None can also be given to specify no boundary at that location.
- keepint, array_like of int, None (default)
Parameters/dimensions where the original data-values should be drawn from, instead of from the reconstructed PDF. TODO: add more information.
- Returns:
- samples([D,]L) ndarray of float
Newly drawn samples from the PDF, where the number of points L is determined by the size argument. If squeeze is True (default), and the number of dimensions in the original dataset D is one, then the returned array will have shape (L,).