verde.block_split

verde.block_split(coordinates, spacing, adjust='spacing', region=None)

Split a region into blocks and label points according to where they fall.

The labels are integers corresponding to the index of the block. The same index is used for the coordinates of each block.

Parameters:

coordinates : tuple of arrays

Arrays with the coordinates of each data point. Should be in the following order: (easting, northing, vertical, …). Only easting and northing will be used, all subsequent coordinates will be ignored.

spacing : float, tuple = (s_north, s_east), or None

The block size in the South-North and West-East directions, respectively. A single value means that the size is equal in both directions.

adjust : {‘spacing’, ‘region’}

Whether to adjust the spacing or the region if required. Ignored if shape is given instead of spacing. Defaults to adjusting the spacing.

region : list = [W, E, S, N]

The boundaries of a given region in Cartesian or geographic coordinates. If not region is given, will use the bounding region of the given points.

Returns:

block_coordinates : tuple of arrays

(easting, northing) arrays with the coordinates of the center of each block.

labels : array

integer label for each data point. The label is the index of the block to which that point belongs.

See also

BlockReduce

Apply a reduction operation to the data in blocks (windows).

Examples

>>> from verde import grid_coordinates
>>> coords = grid_coordinates((-5, 0, 5, 10), spacing=1)
>>> block_coords, labels = block_split(coords, spacing=2.5)
>>> for coord in block_coords:
...     print(', '.join(['{:.2f}'.format(i) for i in coord]))
-3.75, -1.25, -3.75, -1.25
6.25, 6.25, 8.75, 8.75
>>> print(labels.reshape(coords[0].shape))
[[0 0 0 1 1 1]
 [0 0 0 1 1 1]
 [0 0 0 1 1 1]
 [2 2 2 3 3 3]
 [2 2 2 3 3 3]
 [2 2 2 3 3 3]]

Note

If available, pykdtree can be installed for better performance.