bletl.splines

Bases: CubicSmoothingSpline

Overrides csaps type to align its API with the scipy.interpolate splines.

Attributes:

smooth: Returns the smoothing factor
spline: Returns the spline description in SplinePPForm instance

Methods

`__call__`(x[, nu, extrapolate])	Evaluate the spline at some coordinates.
`derivative`([order, epsilon])	Returns the derivative of the spline.

derivative(order: int = 1, *, epsilon=0.001) → Callable[[ndarray], ndarray]

Returns the derivative of the spline.

Parameters:

orderint: Order of derivative.
epsilonfloat: Epsilon to calculate derivative by difference quotient.

Returns:

derivativecallable: A function that can be called with a numpy array of coordinates at which to apply the difference quotient derivative calculation.

bletl.splines.get_crossvalidated_spline(x: ndarray, y: ndarray, k_folds: int = 5, method: str = 'us', bounds: Tuple[float, float] = (0.001, 1)) → UnivariateCubicSmoothingSpline | UnivariateSpline

Returns spline with k-fold crossvalidated smoothing factor

Parameters:

xnumpy.ndarray

Time vector

ynumpy.ndarray

Value vector

k_foldsint

Number of splits used for cross-validation.

methodstr

Kind of spline.: Choices: “ucss” UnivariateCubicSmoothingSpline, “us” UnivariateSpline

boundstuple

Lower and upper bound for the smoothing factor. Must not exceed the [0, 1] interval.

Returns:

splineUnivariateCubicSmoothingSpline or scipy.interpolate.UnivariateSpline: Spline of the specified method fitted to the data. Its optimal smoothing factor is determined by k-fold crossvalidation.

bletl.splines.get_mue(bsdata: FilterTimeSeries, wells: Sequence[str] | str = 'all', blank: float | str | Dict[str, float] = 'first', k_folds: int = 5, method: str = 'us', last_cycle: int | None = None)

Approximation of specific growth rate over time via spline approximation using splines with k-fold cross validated smoothing factor

Parameters:

bsdataFilterTimeSeries: FilterTimeSeries containing timepoints and backscatter data.
wells‘all’ or list: List of wells to calculate specific growth rate for. ‘all’ calculates for all wells.
blank‘first’, float or dict: Blanks to use for specific growth rate calculation. Options: - ‘first’: Use first data point. - float: Apply this blank value to all wells. - dict: Containing well id as key and scalar or vector as blank value(s) for the respective well.
k_foldsint: “k”s for cross-validation.
methodstr: Kind of Spline. Choices: “ucss” UnivariateCubicSmoothingSpline, “us” UnivariateSpline
last_cycleint, optional: Ignores data after last_cycle

Returns:

filtertimeseriesFilterTimeSeries: FilterTimeSeries with specific growth rate over time.

Raises:

ValueError: If blanks are not provided for every well, of if an invalid blank option is passed.

bletl.splines.get_multiple_splines(fts: FilterTimeSeries, wells: Sequence[str], k_folds: int = 5, method: str = 'us', last_cycle: int | None = None) → List[Tuple[str, UnivariateCubicSmoothingSpline | UnivariateSpline]]

Returns multiple splines with k-fold crossvalidated smoothing factor

Parameters:

ftsFilterTimeSeries: FilterTimeSeries containing timepoints and observed data-
wellslist of str: Wells to fit splines to.
k_foldsint: “k”s for cross-validation
methodstr: Kind of spline. Choices: “ucss” UnivariateCubicSmoothingSpline, “us” UnivariateSpline

Returns:

splinesdict: Dict with {well:spline} for each well of “wells”