Neurophotometrics (NPM) Events data conversion#

NPMEventsInterface converts discrete events from Neurophotometrics (NPM) recordings. NPM stores its events in a raw, headerless two-column stimuli CSV: the first column holds the event onset time (in the recording’s raw time base) and the second column holds the event type label (e.g. whitenoise, pinknoise, a boolean True/False annotation, or a numeric code). Each distinct label is split out and written as its own pynwb.event.EventsTable (onset timestamps) into nwbfile.events. The raw onset times are scaled to seconds by time_unit.

How the event types map onto tables is driven entirely by the editable events metadata. See How to Annotate Discrete Events Metadata for the full metadata format.

NPM events need only NeuroConv’s core dependencies, but the npm_events extra is available for a consistent install command.

pip install "neuroconv[npm_events]"

This interface targets the standalone Bonsai stimuli CSV only. NPM can also embed discrete events directly in the photometry/signal CSV, alongside the fluorescence columns: older firmware writes each digital I/O line (e.g. Stimulation, Output0/Output1, Input0/Input1) as its own 0/1-per-frame column, while newer firmware bit-packs those same lines into the Flags/LedState column. That layout does not fit this interface’s fixed headerless two columns; use the general-purpose CSV Events data conversion interface directly to select the relevant columns from the photometry CSV.

NPM recordings carry no embedded recording-start timestamp, so session_start_time must be supplied explicitly in the metadata.

>>> from datetime import datetime
>>> from zoneinfo import ZoneInfo

>>> import pandas as pd

>>> from neuroconv.datainterfaces import NPMEventsInterface

>>> # NPM events are a headerless two-column CSV: onset time (seconds) + event-type label. Here we
>>> # write a small example event file with two event types ("stim" and "noise").
>>> file_path = output_folder / "npm_events.csv"
>>> pd.DataFrame([[1.0, "stim"], [2.0, "noise"], [3.0, "stim"]]).to_csv(file_path, index=False, header=False)

>>> interface = NPMEventsInterface(file_path=file_path, verbose=False)
>>> metadata = interface.get_metadata()
>>> # NPM recordings have no embedded start time, so it must be set explicitly.
>>> metadata["NWBFile"]["session_start_time"] = datetime.now(tz=ZoneInfo("US/Pacific"))
>>> # Add subject information (required for DANDI upload)
>>> metadata["Subject"] = dict(subject_id="subject1", species="Mus musculus", sex="M", age="P30D")

>>> # Choose a path for saving the nwb file and run the conversion
>>> nwbfile_path = f"{path_to_save_nwbfile}"
>>> interface.run_conversion(nwbfile_path=nwbfile_path, metadata=metadata, overwrite=True)

Neurophotometrics (NPM) Events data conversion#

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