bspline_interpolation#

pymc_extras.utils.spline.bspline_interpolation(x, *, n=None, eval_points=None, degree=3, sparse=True)[source]#

Interpolate sparse grid to dense grid using bsplines.

Parameters:

x (Variable) – Input Variable to interpolate. 0th coordinate assumed to be mapped regularly on [0, 1] interval
n (int (optional)) – Resolution of interpolation
eval_points (vector (optional)) – Custom eval points in [0, 1] interval (or scaled properly using min/max scaling)
degree (int, optional) – BSpline degree, by default 3
sparse (bool, optional) – Use sparse operation, by default True

Returns:

The interpolated variable, interpolation is across 0th axis

Return type:

Variable

Examples

>>> import pymc as pm
>>> import numpy as np
>>> half_months = np.linspace(0, 365, 12*2)
>>> with pm.Model(coords=dict(knots_time=half_months, time=np.arange(365))) as model:
...     kernel = pm.gp.cov.ExpQuad(1, ls=365/12)
...     # ready to define gp (a latent process over parameters)
...     gp = pm.gp.gp.Latent(
...         cov_func=kernel
...     )
...     y_knots = gp.prior("y_knots", half_months[:, None], dims="knots_time")
...     y = pm.Deterministic(
...         "y",
...         bspline_interpolation(y_knots, n=365, degree=3),
...         dims="time"
...     )
...     trace = pm.sample_prior_predictive(1)

Notes

Adopted from BayesAlpha where it was written by @aseyboldt