konigcell.static2d

konigcell.static2d(positions, mode, values=None, radii=None, pixels=None, resolution=None, xlim=None, ylim=None, executor=<class 'concurrent.futures.thread.ThreadPoolExecutor'>, max_workers=None, verbose=True)

Pixelize / rasterize static particles’ positions onto a 2D pixel grid.

This is exactly like the dynamic2d function, but particles are not considered to be moving - so they are rasterized a circles.

The input parameters are equivalent to dynamic2d - check its documentation for full details.

Examples

Compute the occupancy grid of a 3 static 2D particles of radius 0.5:

>>> import numpy as np
>>> positions = np.array([[0, 0], [1, 1], [2, 1]])
>>>
>>> import konigcell as kc
>>> pixels = kc.static2d(
>>>     positions,
>>>     kc.INTERSECTION,
>>>     radii = 0.5,
>>>     resolution = (500, 500),
>>>     xlim = [-2, 5],
>>>     ylim = [-2, 5],
>>> )

The kc.INTERSECTION pixellisation mode adds the area shaded by the particle’s movement onto the grid.

If you omit xlim and ylim, they will be computed automatically to include all particle positions.

You can reuse the same pixels grid for multiple pixellisations:

>>> kc.static2d(
>>>     positions,
>>>     kc.INTERSECTION,
>>>     radii = 0.5,
>>>     pixels = pixels,
>>> )

In this case the resolution, xlim and ylim don’t need to be set anymore; they are extracted from the Pixels grid.

You can set the maximum number of workers for the parallel pixellisation; e.g. for single-threaded execution:

>>> pixels = kc.static2d(
>>>     positions,
>>>     kc.ONE,
>>>     radii = 0.5,
>>>     resolution = (500, 500),
>>>     max_workers = 1,
>>> )

The kc.ONE pixellisation flag simply adds 1 to each pixel intersected by the moving particle.

Finally, here is a complete example computing the occupancy grid of 10,000 static 2D particles:

>>> positions = np.random.random((10_000, 2)) * 5
>>>
>>> occupancy = kc.static2d(
>>>     positions,
>>>     kc.INTERSECTION,
>>>     radii = 0.5,
>>>     resolution = (500, 500),
>>> )