IntraCUSUM

class IntraCUSUM(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

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

Declare the settings this event fitter exposes, on top of the base contract.

Called by poriscope when the plugin is instantiated or reconfigured, to build the settings dialog and to sanity-check whatever the user enters; the accepted values are then readable through self.settings. See get_empty_settings() for the structure of the dict and what Type, Value, Min, Max, Options and Units mean in it, and for the reserved keys the GUI builds file pickers from.

The super() call supplies the mandatory "MetaEventLoader" key, which is how this plugin is wired to its data source.

The keys this plugin adds:

  • Step Size (pA), Sensitivity, Rise Time (us) and Max Sublevels - as in CUSUM, which this fitter extends.

  • Intraevent Threshold (pA) - the depth below the event’s own carrier level at which an intra-event excursion is recognised, as opposed to a sublevel.

  • Intraevent Hysteresis (pA) - how far the signal must come back up before that excursion is considered over, so noise at the threshold does not split one excursion into several.

Parameters:

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

  • 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

IntraCUSUM._define_event_metadata_types() → Dict[str, Type[int | float | str | bool]]

Build a dict of metadata along with associated datatypes for use by the database writer downstream. Keys must match columns defined in _populate_event_metadata() All of this metadata must be populated during fitting. Options for dtypes are int, float, str, bool

Returns:

a dict of metadata keys and associated base dtypes

Return type:

Dict[str, Type[Union[int, float, str, bool]]]

IntraCUSUM._define_event_metadata_units() → Dict[str, str | None]

Build a dict of metadata units, or None if unitless. Keys must match columns defined in _populate_event_metadata() All of this metadata must be populated during fitting.

Returns:

a dict of metadata keys and associated units

Return type:

Dict[str, Optional[str]]

IntraCUSUM._populate_event_metadata(data: ndarray[tuple[int, ...], dtype[float64]], samplerate: float, baseline_mean: float | None, baseline_std: float | None, sublevel_metadata: Dict[str, List[int | float | number]]) → Dict[str, int | float | str | bool]

Assemble a list of metadata to save in the event database later. Note that keys ‘start_time_s’ and ‘index’ are already handled in the base class and should not be touched here.

Parameters:

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

  • samplerate (float) – the sampling rate

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

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

  • sublevel_metadata (Dict[str, List[Numeric]]) – the dict of sublevel metadata built by self._populate_sublevel_metadata()

Returns:

a dict of event metadata values

Return type:

Dict[str, Union[int, float, str, bool]]

Raises:

ValueError – if baseline_mean is not provided, since intra-event thresholds cannot be computed without it