whitecanvas.utils package

Submodules

whitecanvas.utils.kde module

This module was copied from the seaborn project (https://github.com/mwaskom/seaborn), which is also a copy of the scipy project (https://github.com/scipy/scipy).

Original docstring from seaborn follows:

In the process of copying, some methods were removed because they depended on other parts of scipy (especially on compiled components), allowing seaborn to have a simple and pure Python implementation. These include:

  • integrate_gaussian

  • integrate_box

  • integrate_box_1d

  • integrate_kde

  • logpdf

  • resample

Additionally, the numpy.linalg module was substituted for scipy.linalg, and the examples section (with doctests) was removed from the docstring

The original scipy license is copied below:

Copyright (c) 2001-2002 Enthought, Inc. 2003-2019, SciPy Developers. All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

  1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

  2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

  3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

class whitecanvas.utils.kde.gaussian_kde(dataset, bw_method=None, weights=None)[source]

Bases: object

Representation of a kernel-density estimate using Gaussian kernels.

Kernel density estimation is a way to estimate the probability density function (PDF) of a random variable in a non-parametric way. gaussian_kde works for both uni-variate and multi-variate data. It includes automatic bandwidth determination. The estimation works best for a unimodal distribution; bimodal or multi-modal distributions tend to be oversmoothed.

Parameters
  • dataset (array_like) – Datapoints to estimate from. In case of univariate data this is a 1-D array, otherwise a 2-D array with shape (# of dims, # of data).

  • bw_method (str, scalar or callable, optional) – The method used to calculate the estimator bandwidth. This can be ‘scott’, ‘silverman’, a scalar constant or a callable. If a scalar, this will be used directly as kde.factor. If a callable, it should take a gaussian_kde instance as only parameter and return a scalar. If None (default), ‘scott’ is used. See Notes for more details.

  • weights (array_like, optional) – weights of datapoints. This must be the same shape as dataset. If None (default), the samples are assumed to be equally weighted

dataset

The dataset with which gaussian_kde was initialized.

Type

ndarray

d

Number of dimensions.

Type

int

n

Number of datapoints.

Type

int

neff

Effective number of datapoints.

New in version 1.2.0.

Type

int

factor

The bandwidth factor, obtained from kde.covariance_factor, with which the covariance matrix is multiplied.

Type

float

covariance

The covariance matrix of dataset, scaled by the calculated bandwidth (kde.factor).

Type

ndarray

inv_cov

The inverse of covariance.

Type

ndarray

evaluate()[source]
__call__()
integrate_gaussian()
integrate_box_1d()
integrate_box()
integrate_kde()
pdf()[source]
logpdf()
resample()
set_bandwidth()[source]
covariance_factor()

Notes

Bandwidth selection strongly influences the estimate obtained from the KDE (much more so than the actual shape of the kernel). Bandwidth selection can be done by a “rule of thumb”, by cross-validation, by “plug-in methods” or by other means; see 3, 4 for reviews. gaussian_kde uses a rule of thumb, the default is Scott’s Rule.

Scott’s Rule 1, implemented as scotts_factor, is:

n**(-1./(d+4)),

with n the number of data points and d the number of dimensions. In the case of unequally weighted points, scotts_factor becomes:

neff**(-1./(d+4)),

with neff the effective number of datapoints. Silverman’s Rule 2, implemented as silverman_factor, is:

(n * (d + 2) / 4.)**(-1. / (d + 4)).

or in the case of unequally weighted points:

(neff * (d + 2) / 4.)**(-1. / (d + 4)).

Good general descriptions of kernel density estimation can be found in 1 and 2, the mathematics for this multi-dimensional implementation can be found in 1.

With a set of weighted samples, the effective number of datapoints neff is defined by:

neff = sum(weights)^2 / sum(weights^2)

as detailed in 5.

References

1(1,2,3)

D.W. Scott, “Multivariate Density Estimation: Theory, Practice, and Visualization”, John Wiley & Sons, New York, Chicester, 1992.

2(1,2)

B.W. Silverman, “Density Estimation for Statistics and Data Analysis”, Vol. 26, Monographs on Statistics and Applied Probability, Chapman and Hall, London, 1986.

3

B.A. Turlach, “Bandwidth Selection in Kernel Density Estimation: A Review”, CORE and Institut de Statistique, Vol. 19, pp. 1-33, 1993.

4

D.M. Bashtannyk and R.J. Hyndman, “Bandwidth selection for kernel conditional density estimation”, Computational Statistics & Data Analysis, Vol. 36, pp. 279-298, 2001.

5

Gray P. G., 1969, Journal of the Royal Statistical Society. Series A (General), 132, 272

covariance_factor()

Computes the coefficient (kde.factor) that multiplies the data covariance matrix to obtain the kernel covariance matrix. The default is scotts_factor. A subclass can overwrite this method to provide a different method, or set it through a call to kde.set_bandwidth.

evaluate(points)[source]

Evaluate the estimated pdf on a set of points.

Parameters

points ((# of dimensions, # of points)-array) – Alternatively, a (# of dimensions,) vector can be passed in and treated as a single point.

Returns

values – The values at each point.

Return type

(# of points,)-array

:raises ValueError : if the dimensionality of the input points is different than: the dimensionality of the KDE.

property neff
pdf(x)[source]

Evaluate the estimated pdf on a provided set of points.

Notes

This is an alias for gaussian_kde.evaluate. See the evaluate docstring for more details.

scotts_factor()[source]

Computes the coefficient (kde.factor) that multiplies the data covariance matrix to obtain the kernel covariance matrix. The default is scotts_factor. A subclass can overwrite this method to provide a different method, or set it through a call to kde.set_bandwidth.

set_bandwidth(bw_method=None)[source]

Compute the estimator bandwidth with given method.

The new bandwidth calculated after a call to set_bandwidth is used for subsequent evaluations of the estimated density.

Parameters

bw_method (str, scalar or callable, optional) – The method used to calculate the estimator bandwidth. This can be ‘scott’, ‘silverman’, a scalar constant or a callable. If a scalar, this will be used directly as kde.factor. If a callable, it should take a gaussian_kde instance as only parameter and return a scalar. If None (default), nothing happens; the current kde.covariance_factor method is kept.

Notes

New in version 0.11.

silverman_factor()[source]

Compute the Silverman factor.

Returns

s – The silverman factor.

Return type

float

property weights

whitecanvas.utils.normalize module

whitecanvas.utils.normalize.arr_color(color) numpy.ndarray[source]

Normalize a color input to a 4-element float array.

whitecanvas.utils.normalize.as_any_1d_array(x: float, size: int, dtype=None) numpy.ndarray[source]
whitecanvas.utils.normalize.as_array_1d(x: Union[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], dtype=None) numpy.ndarray[Any, numpy.dtype[numpy.number]][source]
whitecanvas.utils.normalize.as_color_array(color, size: int) numpy.ndarray[Any, numpy.dtype[numpy.float32]][source]
whitecanvas.utils.normalize.hex_color(color) str[source]

Normalize a color input to a #RRGGBBAA string.

whitecanvas.utils.normalize.normalize_xy(*args) tuple[numpy.ndarray[Any, numpy.dtype[numpy.number]], numpy.ndarray[Any, numpy.dtype[numpy.number]]][source]
whitecanvas.utils.normalize.rgba_str_color(color) str[source]

Normalize a color input to a rgba(r, g, b, a) string.

Module contents