ndx-events Extension for NWB

This is an NWB extension for storing timestamped event data.

The latest version is 0.4.0. This is a major change from previous versions.

1. A TimestampVectorData type that extends VectorData and stores a 1D array of timestamps (float32) in seconds
   • Values are in seconds from session start time (like all other timestamps in NWB)
   • It has a scalar string attribute named "unit". The value of the attribute is fixed to "seconds".
   • It has an optional scalar float attribute named "resolution" that represents the smallest possible difference between two timestamps. This is usually 1 divided by the sampling rate for timestamps of the data acquisition system. (Alternatively, the event sampling rate could be stored.)
   • This type can be used to represent a column of timestamps in any DynamicTable, such as the NWB Units table and the new EventsTable described below.
2. A DurationVectorData type that extends VectorData and stores a 1D array of durations (float32) in seconds. It is otherwise identical to the TimestampVectorData type.
   • If this is used in a table where some events have a duration and some do not (or it is not known yet), then a value of NaN can be used for events without a duration or with a duration that is not yet specified. If the latter, the mapping should be documented in the description of the DurationVectorData.
3. A CategoricalVectorData type that extends VectorData and stores the mappings of data values (of any type) to meanings. This is an experimental type to evaluate one possible way of storing the meanings (longer descriptions) associated with different categorical values stored in a table column. This can be used to add categorical metadata values to an EventsTable. This type will be marked as experimental while the NWB team evaluates possible alternate solutions to annotating the values of a dataset, such as LinkML-based term sets, non-table based approaches, and external mapping files.
   • The type contains an object reference to a MeaningsTable named "meanings". See below. Unfortunately, because CategoricalVectorData is a dataset, it cannot contain a MeaningsTable within it, so the MeaningsTable is placed in the parent EventsTable and referenced by the CategoricalVectorData.
   • It may also contain an optional 1D attribute named "filter_values" to define missing and invalid values within a data field to be filtered out during analysis, e.g., the dataset may contain one or more of: "undefined" or "None" to signal that those values in the CategoricalVectorData dataset are missing or invalid. Due to constraints of NWB/HDMF attributes, attributes must have a dtype, so currently, only string values (not -1 or NaN) are allowed.
   • This type is similar to an EnumData, which is a VectorData of an enumerated type, except that the values stored in the column are strings that are short-hand representations of the concept, as opposed to integers. Storing strings is slightly less efficient than storing integers, but for these use cases, these tables will rarely be large and storing strings directly is more intuitive and accessible to users.
4. A MeaningsTable type that extends DynamicTable with two required columns:
   • A "value" column that contains all the possible values that could be stored in the parent CategoricalVectorData object. For example, if the CategoricalVectorData stores the port in which the subject performed a nose poke, the possible values might be "left", "center", and "right". All possible values must be listed, even if not all values are observed, e.g., if the subject does not poke in the "center" port, "center" should still be listed to signal that it was a possible option.
   • A "meaning" column with string dtype that contains a longer description of the concept. For example, for the value "left", the meaning might be "The subject performed a nosepoke in the left-most port, from the viewpoint of the subject. This is signaled by detection of the port’s infrared beam being broken."
   • Users can add custom, user-defined columns to provide additional information about the possible values, such as HED (Hierarchical Event Descriptor) tags. For HED tags, users may consider using the HedTags type, a subtype of VectorData, in the ndx-hed extension.
   • As described in CategoricalVectorData, this arrangement will be marked as experimental.
5. An EventsTable type for storing a collection of event times that have the same parameterizations/properties/metadata (i.e., they are the same type of event, such as licks, image presentations, or reward deliveries)
   • It inherits from DynamicTable and stores metadata related to each event time / instance, one per row.
   • It has a "timestamp" column of type TimestampVectorData is required.
   • It has a "duration" column of type DurationVectorData is optional.
   • Because this inherits from DynamicTable, users can add additional custom columns to store other metadata, such as parameterizations of an event, e.g., reward value in uL, image category, or tone frequency.
   • The "description" of this table should include information about how the event times were computed, especially if the times are the result of processing or filtering raw data. For example, if the experimenter is encoding different types of events using a "strobed" or "N-bit" encoding, then the "description" value should describe which channels were used and how the event time is computed, e.g., as the rise time of the first bit.
   • It contains a collection of MeaningsTable objects referenced by any CategoricalVectorData columns. These columns are placed in a subgroup of the EventsTable named "meanings". Alternatively, these MeaningsTable objects could be placed under the root NWBFile, but it is probably more useful to keep them close to the objects that they describe. As described in CategoricalVectorData, this arrangement will be marked as experimental.

The PyNWB and MatNWB APIs would provide functions to create these tables. For example, in PyNWB:

stimulus_presentation_events = EventsTable(name="stimulus_presentation_events")
stimulus_presentation_events.add_column("stimulus_type", col_cls=CategoricalVectorData)
stimulus_presentation_events.add_row(timestamp=1.0, stimulus_type="circle")
stimulus_presentation_events.add_row(timestamp=4.5, stimulus_type="square")
nwbfile.add_events_table(stimulus_presentation_events)

The APIs would also provide the following interfaces:

• nwbfile.events_tables returns a dictionary of EventsTable objects, similar to nwbfile.acquisition
• Use nwbfile.events_tables["stimulus_presentation_events"] to access an EventsTable by name
• nwbfile.merge_events_tables(tables: list[EventsTable]), which merges a selection of EventsTable objects into a read-only table, sorted by timestamp
• nwbfile.get_all_events(), which merges all the EventsTable objects into one read-only table, sorted by timestamp

This extension was developed by Ryan Ly, Oliver Rübel, the NWB Technical Advisory Board, and the NWBEP001 Review Working Group.

Information about the rationale, background, and alternative approaches to this extension can be found here: https://docs.google.com/document/d/1qcsjyFVX9oI_746RdMoDdmQPu940s0YtDjb1en1Xtdw

Installation

The latest ndx-events 0.4.0 has not yet been released on PyPI. To install it on Python, use:

pip install git+https://github.com/rly/ndx-events.git

ndx-events 0.3.0 was not released on PyPI.

To install the 0.2.0 version, use: Python:

pip install -U ndx-events

Matlab:

generateExtension('<directory path>/ndx-events/spec/ndx-events.namespace.yaml');

Usage examples

1. Example writing TTL pulses and stimulus presentations to an NWB file.

Diagram

%%{init: {'theme': 'base', 'themeVariables': {'primaryColor': '#ffffff', "primaryBorderColor': '#144E73', 'lineColor': '#D96F32'}}}%%


classDiagram
    direction LR

    class TimestampVectorData {
        <<VectorData>>

        data : NDArray[Shape["*"], Float]
        --> unit : str = "seconds"
        resolution : float, optional
    }

    class DurationVectorData {
        <<VectorData>>

        data : NDArray[Shape["*"], Float]
        --> unit : str = "seconds"
        resolution : float, optional
    }

    class CategoricalVectorData {
        <<VectorData>>

        data : NDArray[Shape["*"], Any]
        meanings : MeaningsTable
        filter_values : NDArray[Shape["*"], String], optional
    }

    class MeaningsTable {
        <<DynamicTable>>

        value : VectorData[NDArray[Shape["*"], Any]]
        meaning : VectorData[NDArray[Shape["*"], String]]
    }

    class EventsTable {
        <<DynamicTable>>

        timestamp : TimestampVectorData
        duration : DurationVectorData, optional
        meanings_tables : list[MeaningsTable]
        [additional_columns] : list[VectorData], optional
    }

    class NdxEventsNWBFile {
        <<NWBFile>>

        events : list[EventsTable]
    }

    CategoricalVectorData ..> MeaningsTable : object reference
    EventsTable "1" *--> "0..*" MeaningsTable : contains
    EventsTable "1" *--> "1..*" TimestampVectorData : contains
    EventsTable "1" *--> "0..*" DurationVectorData : contains
    EventsTable "1" *--> "0..*" CategoricalVectorData : contains
    NdxEventsNWBFile "1" *--> "0..*" EventsTable : contains

Loading

Developer installation

In a Python 3.8-3.12 environment:

pip install -r requirements-dev.txt
pip install -e .

Run tests:

pytest

Install pre-commit hooks:

pre-commit install

Style and other checks:

black .
ruff .
codespell .

This extension was created using ndx-template.