ClassicCUSUM

class ClassicCUSUM(settings: Optional[dict] = None)

Bases: CUSUM

Abstract base class to analyze and flag the start and end times of regions of interest in a timeseries for further analysis.

Public Methods

ClassicCUSUM.get_empty_settings(globally_available_plugins: Dict[str, List[str]] | None = None, standalone: bool = False) → Dict[str, Dict[str, Any]]

Get a dict populated with keys needed to initialize the filter if they are not set yet. This dict must have the following structure, but Min, Max, and Options can be skipped or explicitly set to None if they are not used. Type is required; Value may be omitted or set to None, both meaning there is no default and the user must supply one. All values provided must be consistent with Type. EventFinder objects MUST include a MetaReader object in settings

settings = {'Parameter 1': {'Type': <int, float, str, bool>,
                                 'Value': <value> or None,
                                 'Options': [<option_1>, <option_2>, ... ] or None,
                                 'Min': <min_value> or None,
                                 'Max': <max_value> or None
                                },
                ...
                }
Parameters:

  • globally_available_plugins (Optional[Dict[str, List[str]]]) – a dict containing all data plugins that exist to date, keyed by metaclass. Must include “MetaReader” as a key, with explicitly set Type MetaReader.

  • standalone (bool) – False if this is called as part of a GUI, True otherwise. Default False

Returns:

the dict that must be filled in to initialize the filter

Return type:

Dict[str, Dict[str, Any]]

Private Methods

ClassicCUSUM._locate_sublevel_transitions(data: ndarray[tuple[int, ...], dtype[float64]], samplerate: float, padding_before: int | None, padding_after: int | None, baseline_mean: float | None, baseline_std: float | None) → List[Any] | None

Runs the same CUSUM log-likelihood-ratio changepoint detection as CUSUM, but Step Size is already expressed in units of the local baseline standard deviation (σ) and is used directly rather than normalized against it, unlike CUSUM. Returned indices are pre-pended with 0 if 0 is not already the first entry.

Parameters:

  • data (npt.NDArray[np.float64]) – an array of data from which to extract the locations of sublevel transitions

  • samplerate (float) – the sampling rate

  • padding_before (Optional[int]) – the number of data points before the estimated start of the event in the chunk

  • padding_after (Optional[int]) – the number of data points after the estimated end of the event in the chunk

  • baseline_mean (Optional[float]) – the local mean value of the baseline current

  • baseline_std (Optional[float]) – the local standard deviation of the baseline current

Returns:

a list of entries that details sublevel transitions. Normally this would be as a list of ints, but can be a list of tuples or other entries if more info is needed. First entry must correspond to the start of the event.

Return type:

Optional[List[Any]]

Raises:

ValueError – if the event is rejected. Note that ValueError will skip and reject the event but will not stop processing of the rest of the dataset