src.canns.data.loaders¶

Experimental data processing utilities for CANNs.

This module provides specialized functions for processing experimental data typically used in CANN analyses, including ROI data, grid cell data, and other neurophysiological _datasets.

Functions¶

`get_data_summary`(data)	Get summary statistics for experimental data.
`load_grid_data`([source, dataset_key])	Load grid cell data for 2D CANN analysis.
`load_roi_data`([source])	Load ROI data for 1D CANN analysis.
`preprocess_spike_data`(spike_data[, time_window, ...])	Preprocess spike data for analysis.
`validate_grid_data`(data)	Validate grid data format for 2D CANN analysis.
`validate_roi_data`(data)	Validate ROI data format for 1D CANN analysis.

Module Contents¶

src.canns.data.loaders.get_data_summary(data)[source]¶

Get summary statistics for experimental data.

Parameters:: data (ndarray or dict) – ROI data (ndarray) or grid data (dict).
Returns:: Summary statistics.
Return type:: dict

src.canns.data.loaders.load_grid_data(source=None, dataset_key='grid_1')[source]¶

Load grid cell data for 2D CANN analysis.

Parameters:

source (str, Path, or None) – Data source. Can be: - URL string: downloads and loads from URL - Path: loads from local file - None: uses default CANNs dataset
dataset_key (str) – Which default dataset to use (‘grid_1’ or ‘grid_2’) when source is None.

Returns:

Dictionary containing spike data and metadata if successful, None otherwise. Expected keys: ‘spike’, ‘t’, and optionally ‘x’, ‘y’ for position data.

Return type:

dict or None

Examples

>>> # Load default dataset
>>> grid_data = load_grid_data()
>>>
>>> # Load from URL
>>> grid_data = load_grid_data('https://example.com/grid_data.npz')
>>>
>>> # Load specific default dataset
>>> grid_data = load_grid_data(dataset_key='grid_2')

src.canns.data.loaders.load_roi_data(source=None)[source]¶

Load ROI data for 1D CANN analysis.

Parameters:: source (str, Path, or None) – Data source. Can be: - URL string: downloads and loads from URL - Path: loads from local file - None: uses default CANNs dataset
Returns:: ROI data array if successful, None otherwise.
Return type:: ndarray or None

Examples

>>> # Load default dataset
>>> roi_data = load_roi_data()
>>>
>>> # Load from URL
>>> roi_data = load_roi_data('https://example.com/roi_data.txt')
>>>
>>> # Load from local file
>>> roi_data = load_roi_data('./my_roi_data.txt')

src.canns.data.loaders.preprocess_spike_data(spike_data, time_window=None, min_spike_count=10)[source]¶

Preprocess spike data for analysis.

Parameters:

spike_data (list or ndarray) – Raw spike data.
time_window (tuple, optional) – (start, end) time window to filter spikes.
min_spike_count (int) – Minimum number of spikes required per neuron.

Returns:

Processed spike data, or None if processing fails.

Return type:

ndarray or None

src.canns.data.loaders.validate_grid_data(data)[source]¶

Validate grid data format for 2D CANN analysis.

Parameters:: data (dict) – Grid data dictionary.
Returns:: True if data is valid, False otherwise.
Return type:: bool

src.canns.data.loaders.validate_roi_data(data)[source]¶

Validate ROI data format for 1D CANN analysis.

Parameters:: data (ndarray) – ROI data array.
Returns:: True if data is valid, False otherwise.
Return type:: bool