spiketools.spatial.occupancy.compute_bin_counts_assgn

spiketools.spatial.occupancy.compute_bin_counts_assgn(bins, xbins, ybins=None, occupancy=None)

Compute number of counts per bin, from bin assignments.

Parameters:

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].

xbins1d array

Bin assignments for the x-dimension.

ybins1d array, optional

Bin assignments for the y-dimension.

occupancy1d or 2d array, optional

Occupancy across the spatial bins. If provided, used to normalize bin counts.

Returns:

bin_counts1d or 2d array

Amount of counts in each bin. For 2d, has shape [n_y_bins, n_x_bins] (see notes).

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 the bin counts per bin for 1d data, given precomputed x bin assignments:

>>> bins = 3
>>> xbins = [0, 2, 1, 0, 1]
>>> compute_bin_counts_assgn(bins, xbins)
array([2, 2, 1])

Compute the bin counts for 1d data, given precomputed x & y bin assignments:

>>> bins = [2, 2]
>>> xbins = [0, 0, 0, 1]
>>> ybins = [0, 0, 1, 1]
>>> compute_bin_counts_assgn(bins, xbins, ybins)
array([[2., 0.],
       [1., 1.]])