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continuous_timeseries.discrete_to_continuous.higher_order#

Conversion of discrete to continuous data assuming higher-order interpolation

Here, "higher-order" means quadratic or higher.

In general, this sort of interpolation is tricky and can easily go wrong. This module is intended as a convenience. However, in most cases, you will want to use the lower-level interfaces more directly so you have better control of the result.

Functions:

Name Description
discrete_to_continuous_higher_order

Convert a discrete timeseries to piecewise higher-order polynomial.

discrete_to_continuous_higher_order #

discrete_to_continuous_higher_order(
    x: PINT_NUMPY_ARRAY,
    y: PINT_NUMPY_ARRAY,
    name: str,
    order: int,
) -> TimeseriesContinuous

Convert a discrete timeseries to piecewise higher-order polynomial.

Here, higher-order means quadratic or higher. For details, see the module's docstring.

Parameters:

Name Type Description Default
x PINT_NUMPY_ARRAY

The discrete x-values from which to convert

 required
y PINT_NUMPY_ARRAY

The discrete y-values from which to convert

 required
name str

The value to use to set the result's name attribute

 required
order int

Order of the polynomial to fit and return.

 required

Returns:

Type Description
TimeseriesContinuous

Continuous version of discrete based on piecewise interpolation of order order.
Source code in src/continuous_timeseries/discrete_to_continuous/higher_order.py 
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def discrete_to_continuous_higher_order(
    x: PINT_NUMPY_ARRAY, y: PINT_NUMPY_ARRAY, name: str, order: int
) -> TimeseriesContinuous:
    """
    Convert a discrete timeseries to piecewise higher-order polynomial.

    Here, higher-order means quadratic or higher.
    For details, see
    [the module's docstring][continuous_timeseries.discrete_to_continuous.higher_order].

    Parameters
    ----------
    x
        The discrete x-values from which to convert

    y
        The discrete y-values from which to convert

    name
        The value to use to set the result's name attribute

    order
        Order of the polynomial to fit and return.

    Returns
    -------
    :
        Continuous version of `discrete`
        based on piecewise interpolation of order `order`.
    """
    try:
        import scipy.interpolate
    except ImportError as exc:
        raise MissingOptionalDependencyError(
            "discrete_to_continuous_higher_order", requirement="scipy"
        ) from exc

    # Late import to avoid circularity
    from continuous_timeseries.timeseries_continuous import (
        ContinuousFunctionScipyPPoly,
        TimeseriesContinuous,
    )

    # This is the bit that can go very wrong if done blindly.
    # Hence why this function is only a convenience.
    tck = scipy.interpolate.splrep(x=x.m, y=y.m, k=order)
    piecewise_polynomial = scipy.interpolate.PPoly.from_spline(tck)

    res = TimeseriesContinuous(
        name=name,
        time_units=x.u,
        values_units=y.u,
        function=ContinuousFunctionScipyPPoly(piecewise_polynomial),
        domain=(np.min(x), np.max(x)),
    )

    return res