xarray.Dataset.thin#

Dataset.thin(indexers=None, **indexers_kwargs)[source] #

Returns a new dataset with each array indexed along every n-th value for the specified dimension(s)

Parameters:

indexers (dict or int) – A dict with keys matching dimensions and integer values n or a single integer n applied over all dimensions. One of indexers or indexers_kwargs must be provided.
**indexers_kwargs ({dim: n, ...}, optional) – The keyword arguments form of indexers. One of indexers or indexers_kwargs must be provided.

Examples

>>> x_arr = np.arange(0, 26)
>>> x_arr
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
 17, 18, 19, 20, 21, 22, 23, 24, 25])
>>> x = xr.DataArray(
...  np.reshape(x_arr, (2, 13)),
...  dims=("x", "y"),
...  coords={"x": [0, 1], "y": np.arange(0, 13)},
... )
>>> x_ds = xr.Dataset({"foo": x})
>>> x_ds
<xarray.Dataset> Size: 328B
Dimensions: (x: 2, y: 13)
Coordinates:
 * x (x) int64 16B 0 1
 * y (y) int64 104B 0 1 2 3 4 5 6 7 8 9 10 11 12
Data variables:
 foo (x, y) int64 208B 0 1 2 3 4 5 6 7 8 ... 17 18 19 20 21 22 23 24 25

>>> x_ds.thin(3)
<xarray.Dataset> Size: 88B
Dimensions: (x: 1, y: 5)
Coordinates:
 * x (x) int64 8B 0
 * y (y) int64 40B 0 3 6 9 12
Data variables:
 foo (x, y) int64 40B 0 3 6 9 12
>>> x.thin({"x": 2, "y": 5})
<xarray.DataArray (x: 1, y: 3)> Size: 24B
array([[ 0, 5, 10]])
Coordinates:
 * x (x) int64 8B 0
 * y (y) int64 24B 0 5 10