gemseo / uncertainty / statistics / tolerance_interval

weibull module

Computation of tolerance intervals from a data-fitted Weibull distribution.

class gemseo.uncertainty.statistics.tolerance_interval.weibull.WeibullMinToleranceInterval(size, scale, shape, location)[source]

Bases: gemseo.uncertainty.statistics.tolerance_interval.weibull.WeibullToleranceInterval

Computation of tolerance intervals from a data-fitted Weibull distribution.

The formulae come from the R library tolerance 1.

Parameters

  • size (int) – The number of samples.

  • scale (float) – The estimation of the scale of the Weibull distribution.

  • shape (float) – The estimation of the shape of the Weibull distribution.

  • location (float) – The estimation of the location of the Weibull distribution.

Return type

None

compute(coverage, confidence=0.95, side=ToleranceIntervalSide.BOTH)

Compute a tolerance interval.

Parameters

  • coverage (float) – A minimum percentage of belonging to the TI.

  • confidence (float) –

    A level of confidence in [0,1].

    By default it is set to 0.95.

  • side (gemseo.uncertainty.statistics.tolerance_interval.distribution.ToleranceIntervalSide) –

    The type of the tolerance interval characterized by its sides of interest, either a lower-sided tolerance interval \([a, +\infty[\), an upper-sided tolerance interval \(]-\infty, b]\), or a two-sided tolerance interval \([c, d]\).

    By default it is set to BOTH.

Returns

The tolerance bounds.

Return type

tuple[numpy.ndarray, numpy.ndarray]

docstring_processor(parent_doc: Optional[str], child_func: Callable) → None: Callable[[Optional[str], Callable], str] = <docstring_inheritance.processors.google.GoogleDocstringProcessor object>
Parameters

  • parent_doc (Optional[str]) –

  • child_func (Callable) –

Return type

None

class gemseo.uncertainty.statistics.tolerance_interval.weibull.WeibullToleranceInterval(size, scale, shape, location)[source]

Bases: gemseo.uncertainty.statistics.tolerance_interval.distribution.ToleranceInterval

Computation of tolerance intervals from a data-fitted Weibull distribution.

The formulae come from the R library tolerance 1.

1(1,2)

Derek S. Young, tolerance: An R Package for Estimating Tolerance Intervals, Journal of Statistical Software, 36(5), 2010

Parameters

  • size (int) – The number of samples.

  • scale (float) – The estimation of the scale of the Weibull distribution.

  • shape (float) – The estimation of the shape of the Weibull distribution.

  • location (float) – The estimation of the location of the Weibull distribution.

Return type

None

compute(coverage, confidence=0.95, side=ToleranceIntervalSide.BOTH)

Compute a tolerance interval.

Parameters

  • coverage (float) – A minimum percentage of belonging to the TI.

  • confidence (float) –

    A level of confidence in [0,1].

    By default it is set to 0.95.

  • side (gemseo.uncertainty.statistics.tolerance_interval.distribution.ToleranceIntervalSide) –

    The type of the tolerance interval characterized by its sides of interest, either a lower-sided tolerance interval \([a, +\infty[\), an upper-sided tolerance interval \(]-\infty, b]\), or a two-sided tolerance interval \([c, d]\).

    By default it is set to BOTH.

Returns

The tolerance bounds.

Return type

tuple[numpy.ndarray, numpy.ndarray]

docstring_processor(parent_doc: Optional[str], child_func: Callable) → None: Callable[[Optional[str], Callable], str] = <docstring_inheritance.processors.google.GoogleDocstringProcessor object>
Parameters

  • parent_doc (Optional[str]) –

  • child_func (Callable) –

Return type

None