spiketools.spatial.target.compute_target_bins

spiketools.spatial.target.compute_target_bins(target_frs, bins, xbins, ybins=None, target_occupancy=None)

Compute binned firing based on spatial target.

Parameters:

target_frs2d array

The firing rate per target segment, organized as [n_trials, n_targets_per_trial].

binsint or list of [int, int]

The bin definition for dividing up the space. If 1d, can be integer. If 2d should be a list, defined as [number of x_bins, number of y_bins].

xbins, ybins1d array

The bin assignments per target. ybins is optional if using 1d binning. The length should equal the length of flattened target_frs.

target_occupancy1d or 2d array, optional

The number of targets per spatial target bin. If provided, used to normalize the spiking activity.

Returns:

target_bins1d or 2d array

The target related spiking activity.

Notes

For the 2d case, note that while the inputs to this function list the x-axis first, the output of this function, being a 2d array, follows the numpy convention in which columns (y-axis) are on the 0th dimension, and rows (x-axis) are on the 1th dimension.

Examples

Compute target bin firing from firing rates from 3 trials:

>>> target_frs = np.array([[2.5, 4.9, 0.2, 0.9],
...                        [5.5, 9.9, 7.7, 3.5],
...                        [1.2, 1.4, 2.1, 1.5]])
>>> bins = [2, 3]
>>> xbins, ybins = np.array([0, 1, 1, 0]), np.array([1, 1, 2, 0])
>>> compute_target_bins(target_frs, bins, xbins, ybins)
array([[0.9, 0. ],
       [2.5, 4.9],
       [0. , 0.2]])