:py:mod:`mosum.local_prune`
===========================

.. py:module:: mosum.local_prune


Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   mosum.local_prune.multiscale_localPrune
   mosum.local_prune.local_prune
   mosum.local_prune.local_env
   mosum.local_prune.dup_merge
   mosum.local_prune.detect_interval



.. py:function:: multiscale_localPrune(x, G=None, max_unbalance=4, threshold='critical_value', alpha=0.1, threshold_function=None, criterion='eta', eta=0.4, epsilon=0.2, rule='pval', penalty='log', pen_exp=1.01, do_confint=False, level=0.05, N_reps=1000)

    Multiscale MOSUM algorithm with localised pruning

    Parameters
    ----------
    x : list
        input data
    G : int
        vector of bandwidths; given as either integers less than `len(x)/2`,
         or numbers between `0` and `0.5` describing the moving sum bandwidths relative to `len(x)`
    max_unbalance : float
       a numeric value for the maximal ratio between maximal and minimal bandwidths to be used for candidate generation,
       at least 1
    threshold : Str
        indicates which threshold should be used to determine significance.
        By default, it is chosen from the asymptotic distribution at the given significance level 'alpha`.
        Alternatively it is possible to parse a user-defined function with 'threshold_function'.
    alpha : float
        numeric value for the significance level with '0 <= alpha <= 1';
        use iff 'threshold = "critical_value"'
    threshold_function : function
        criterion : Str
       indicates how to determine whether each point 'k' at which MOSUM statistic
       exceeds the threshold is a change point; possible values are
       'eta' : there is no larger exceeding in an 'eta*G' environment of 'k'
       'epsilon' : 'k' is the maximum of its local exceeding environment, which has at least size 'epsilon*G'
   eta : float
       a positive numeric value for the minimal mutual distance of changes,
       relative to moving sum bandwidth (iff 'criterion = "eta"')
   epsilon : float
       a numeric value in (0,1] for the minimal size of exceeding environments,
       relative to moving sum bandwidth (iff 'criterion = "epsilon"')
   rule : Str
       Choice of sorting criterion for change point candidates in merging step.
       Possible values are:
       'pval' : smallest p-value
       'jump' : largest (rescaled) jump size
   penalty : Str
       Type of penalty term to be used in Schwarz criterion; possible values are:
       'log' : use 'penalty = log(len(x))**pen_exp'
       'polynomial' : use 'penalty = len(x)**pen_exp'
   pen_exp : float
       penalty exponent
   do_confint : bool
        flag indicating whether to compute the confidence intervals for change points
   level : float
        use iff 'do_confint = True'; a numeric value ('0 <= level <= 1') with which '100(1-level)%'
        confidence interval is generated
   N_reps : int
        use iff 'do.confint = True'; number of bootstrap replicates to be generated

    Returns
    -------
    multiscale_cpts object containing
    x : list
        input data
    G : int
        bandwidth vector
    threshold, alpha, threshold_function, eta
        input
    cpts : ndarray
        estimated change point
    cpts_info : DataFrame
        information on change points, including detection bandwidths, asymptotic p-values, scaled jump sizes
    pooled_cpts : ndarray
        change point candidates
    do_confint : bool
        input
    ci
        confidence intervals

    Examples
    --------
    >>> import mosum
    >>> xx = mosum.testData("mix")["x"]
    >>> xx_m  = mosum.multiscale_localPrune(xx, G = [8,15,30,70])
    >>> xx_m.summary()
    >>> xx_m.print()


.. py:function:: local_prune(x, all_cpts, rule, log_penalty, pen_exp)


.. py:function:: local_env(j, est_cpts_ind, all_cpts, current, ac)


.. py:function:: dup_merge(all_cpts)


.. py:function:: detect_interval(all_cpts, est_cpts)