This release adds the function pytspack.hpval, which has the same arguments as the existing pytspack.hval, but uses the interpolation to evaluate the first derivative.

What's Changed

• Add hpval by @zmoon in #4

Full Changelog: v0.1.2...v0.1.3

Tweak failed tspack import messages (e8b8d35).

This release adds

• readme simplification, linking to external references (#1)
• a local build option and extension import failure messages (#1)
• installation documentation (#1)
• build dependency specification in pyproject.toml (#2)

No changes to pytspack functions.

What's Changed

• Update readme by @zmoon in #1
• Add pyproject.toml with setuptools ubound by @zmoon in #2

New Contributors

• @zmoon made their first contribution in #1

Full Changelog: v0.1...v0.1.1

pytspack

Python Wrapper around the Fortran TSPACK.

The primary purpose of TSPACK is to construct a smooth
function which interpolates a discrete set of data points.
The function may be required to have either one or two con-
tinuous derivatives, and, in the C-2 case, several options
are provided for selecting end conditions. If the accuracy
of the data does not warrant interpolation, a smoothing func-
tion (which does not pass through the data points) may be
constructed instead. The fitting method is designed to avoid
extraneous inflection points (associated with rapidly varying
data values) and preserve local shape properties of the data
(monotonicity and convexity), or to satisfy the more general
constraints of bounds on function values or first derivatives.
The package also provides a parametric representation for con-
structing general planar curves and space curves.

The fitting function h(x) (or each component h(t) in the
case of a parametric curve) is defined locally, on each
interval associated with a pair of adjacent abscissae (knots),
by its values and first derivatives at the endpoints of the
interval, along with a nonnegative tension factor SIGMA
associated with the interval (h is a Hermite interpolatory
tension spline). With SIGMA = 0, h is the cubic function
defined by the endpoint values and derivatives, and, as SIGMA
increases, h approaches the linear interpolant of the endpoint
values. Since the linear interpolant preserves positivity,
monotonicity, and convexity of the data, h can be forced to
preserve these properties by choosing SIGMA sufficiently
large. Also, since SIGMA varies with intervals, no more
tension than necessary is used in each interval, resulting in
a better fit and greater efficiency than is achieved with a
single constant tension factor.