dupin.data.spatial#
Overview
Average distribution about neighboring data points. |
Details
Spatial averaging DataMap classes.
- class dupin.data.spatial.NeighborAveraging(expected_kwarg='spatial_neighbors', remove_kwarg=True, excluded_self=True)[source]#
Average distribution about neighboring data points.
Uses neighbors for spatial averaging across a system. Neighbors can be passed in manually in an array of \((i, j)\) pairs, through a tuple of arrays, or through a
freud
neighbor list.- Parameters:
expected_kwarg (
str
, optional) – The expected key word argument passed todupin.data.base.DataModifier.__call__
to use as neighbors. Defaults to “spatial_neighbors”.remove_kwargs (
bool
, optional) – Whether the specifiedexpected_kwarg
should be removed before passing through to the composed generators. Defaults toTrue
.excluded_self (
bool
, optional) – Whether the passed neighbor lists will exclude self neighbors.True
means self neighbors will be added by the instance, andFalse
means the neighbor list provides self neighbors. Defaults toTrue
. If set incorrectly this will cause erroneous results (double or no counting).Warning – A particle should be listed as its own neighbor for purposes of the averaging. So if the passed neighbor list does not include self neighbors
excluded_self
should be true.
Note
This class can remove neighbors from the call signature for generators or maps that don’t require it through the
remove_kwarg
argument.Note
The neighbors must be passed as a keyword argument for
NeighborAveraging
to recognize it.- compute(data)[source]#
Perform spatial averaging using provided neighbors.
- Parameters:
distribution (\((N,)\) numpy.ndarray of float) – The array representing a distribution to spatially average.
- Returns:
signals – A dictionary with the key “spatially_averaged” and spatially averaged distribution as a value.
- Return type: