InscopixImagingExtractor#

Inscopix Imaging Extractor.

class InscopixImagingExtractor(file_path: str | Path)[source]#

Bases: ImagingExtractor

Extracts imaging data from Inscopix recordings.

Create an InscopixImagingExtractor instance from a single .isx file.

Parameters:

file_path (PathType) – Path to the Inscopix file.

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() → int[source]#

Get the number of samples in the video.

Returns:

num_samples – Number of samples in the video.

Return type:

int

get_sampling_frequency() → float[source]#

Get the sampling frequency in Hz.

Returns:

sampling_frequency – Sampling frequency in Hz.

Return type:

float

get_channel_names() → list[str][source]#

Get the channel names in the recoding.

Returns:

channel_names – List of strings of channel names

Return type:

list

get_series(start_sample: int | None = None, end_sample: int | None = 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_dtype() → dtype[source]#

Get the data type of the video.

Returns:

dtype – Data type of the video.

Return type:

dtype

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

Retrieve the original unaltered timestamps for the data in this interface.

This function should retrieve the data on-demand by re-initializing the IO. Can be overridden to return None if the extractor does not have native timestamps.

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, or None if native timestamps are not available.

Return type:

numpy.ndarray or None