NumpySegmentationExtractor#

class NumpySegmentationExtractor(image_masks, raw=None, dff=None, deconvolved=None, neuropil=None, mean_image=None, correlation_image=None, roi_ids=None, roi_locations=None, sampling_frequency=None, rejected_list=None, channel_names=None, movie_dims=None, accepted_list=None)[source]#

Bases: SegmentationExtractor

A Segmentation extractor specified by image masks and traces .npy files.

NumpySegmentationExtractor objects are built to contain all data coming from a file format for which there is currently no support. To construct this, all data must be entered manually as arguments.

Create a NumpySegmentationExtractor from a .npy file.

Parameters:

  • image_masks (np.ndarray) – Binary image for each of the regions of interest

  • raw (np.ndarray) – Fluorescence response of each of the ROI in time

  • dff (np.ndarray) – DfOverF response of each of the ROI in time

  • deconvolved (np.ndarray) – deconvolved response of each of the ROI in time

  • neuropil (np.ndarray) – neuropil response of each of the ROI in time

  • mean_image (np.ndarray) – Mean image

  • correlation_image (np.ndarray) – correlation image

  • roi_ids (int list) – Unique ids of the ROIs if any

  • roi_locations (np.ndarray) – x and y location representative of ROI mask

  • sampling_frequency (float) – Frame rate of the movie

  • rejected_list (list) – list of ROI ids that are rejected manually or via automated rejection

  • channel_names (list) – list of strings representing channel names

  • movie_dims (tuple) – height x width of the movie

property image_dims#

Return the dimensions of the image.

Returns:

image_dims – The dimensions of the image (num_rois, num_rows, num_columns).

Return type:

list

get_accepted_list() → list[source]#

Get a list of accepted ROI ids.

Returns:

accepted_list – List of accepted ROI ids.

Return type:

list

get_rejected_list() → list[source]#

Get a list of rejected ROI ids.

Returns:

rejected_list – List of rejected ROI ids.

Return type:

list

property roi_locations#

Returns the center locations (x, y) of each ROI.

get_roi_ids()[source]#

Get the list of ROI ids.

Returns:

roi_ids – List of roi ids.

Return type:

list

get_frame_shape()[source]#

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

Returns:

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

Return type:

tuple

get_num_samples()[source]#

Get the number of samples in the recording (duration of recording).

Returns:

num_samples – Number of samples in the recording.

Return type:

int

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

Get the original timestamps from the data source.

Parameters:

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

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

Returns:

timestamps – The original timestamps in seconds, or None if not available.

Return type:

np.ndarray or None