NwbImagingExtractor#

class NwbImagingExtractor(file_path: str | Path, optical_series_name: str | None = 'TwoPhotonSeries')[source]#

Bases: ImagingExtractor

An imaging extractor for NWB files.

Class used to extract data from the NWB data format. Also implements a static method to write any format specific object to NWB.

Supports both planar (2D) and volumetric (3D) imaging data from OnePhotonSeries and TwoPhotonSeries objects.

Create ImagingExtractor object from NWB file.

Parameters:

  • file_path (str) – The location of the folder containing dataset.nwb file

  • optical_series_name (string, optional) – The name of the optical series to extract data from.

__del__()[source]#

Close the NWB file.

make_nwb_metadata(nwbfile, opts)[source]#

Create metadata dictionary for NWB file.

Parameters:

  • nwbfile (pynwb.NWBFile) – The NWBFile object associated with the metadata.

  • opts (object) – The options object with name of TwoPhotonSeries as an attribute.

Notes

Metadata dictionary is stored in the nwb_metadata attribute.

Deprecated since version 0.7.3: This method is deprecated and will be removed on or after May 2026.

get_series(start_sample=None, end_sample=None) → ndarray[source]#

Get the series of samples.

Parameters:

  • start_sample (int, optional) – Start sample index (inclusive).

  • end_sample (int, optional) – End sample index (exclusive).

Returns:

series – The series of samples.

Return type:

numpy.ndarray

Notes

Importantly, we follow the convention that the dimensions of the array are returned in their matrix order, More specifically: (time, height, width)

Which is equivalent to: (samples, rows, columns)

For volumetric data, the dimensions are: (time, height, width, planes)

Which is equivalent to: (samples, rows, columns, planes)

Note that this does not match the cartesian convention: (t, x, y)

Where x is the columns width or and y is the rows or height.

get_image_shape() → tuple[int, int][source]#

Get the shape of the video frame (num_rows, num_columns).

Returns:

image_shape – Shape of the video frame (num_rows, num_columns).

Return type:

tuple

get_num_samples()[source]#

Get the number of samples in the video.

Returns:

num_samples – Number of samples in the video.

Return type:

int

get_sampling_frequency()[source]#

Get the sampling frequency in Hz.

Returns:

sampling_frequency – Sampling frequency in Hz.

Return type:

float

get_channel_names()[source]#

Get the channel names in the recoding.

Returns:

channel_names – List of strings of channel names

Return type:

list

get_native_timestamps(start_sample: int | None = None, end_sample: int | None = None) → ndarray | None[source]#

Retrieve the original unaltered timestamps for the data.

This method uses the NWB photon series get_timestamps() method, which properly handles explicit timestamps, starting_time attribute, and rate-based calculations.

Parameters:

  • start_sample (int, optional) – The starting sample index. If None, starts from the beginning.

  • end_sample (int, optional) – The ending sample index. If None, goes to the end.

Returns:

timestamps – The timestamps for the data stream.

Return type:

numpy.ndarray

get_num_planes() → int[source]#

Get the number of depth planes.

Returns:

num_planes – The number of depth planes (1 for planar data).

Return type:

int