local_distances
Compute the distances to the neighboring objects for every object in a GeoDataFrame. and aggregate them by the specified reduction methods.
Note
Neighborhoods are defined by the spatial_weights
object, which can be created
with the fit_graph
function. The function should be applied to the input
GeoDataFrame before using this function.
Note
Option to weight the nhood values by their area before reductions.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
gdf
|
GeoDataFrame
|
The input GeoDataFrame. |
required |
spatial_weights
|
W
|
Libpysal spatial weights object. |
required |
id_col
|
str
|
The unique id column in the gdf. If None, this uses |
None
|
reductions
|
Tuple[str, ...], default=("mean",
|
A list of reduction methods for the neighborhood feature values. Allowed are "sum", "mean", "median", "min", "max", "std". |
('mean',)
|
weight_by_area
|
bool
|
Flag whether to weight the neighborhood values by the area of the object. Defaults to False. |
False
|
invert
|
bool
|
Flag whether to invert the distances. Defaults to False. |
False
|
parallel
|
bool
|
Flag whether to use parallel apply operations when computing the character. Defaults to False. |
False
|
num_processes
|
int
|
The number of processes to use when parallel=True. If -1, this will use all available cores. |
1
|
rm_nhood_cols
|
bool
|
Flag, whether to remove the extra neighborhood columns from the result gdf. Defaults to True. |
True
|
col_prefix
|
str
|
Prefix for the new column names. |
None
|
create_copy
|
bool
|
Flag whether to create a copy of the input gdf and return that. Defaults to True. |
True
|
Raises:
Type | Description |
---|---|
ValueError
|
If the |
Returns:
Type | Description |
---|---|
GeoDataFrame
|
gpd.GeoDataFrame: The input geodataframe with computed distances column added. |
Examples:
Compute the mean of eccentricity values for each neighborhood
>>> from histolytics.utils.gdf import set_uid
>>> from histolytics.data import cervix_nuclei
>>> from histolytics.spatial_graph.graph import fit_graph
>>> from histolytics.spatial_geom.shape_metrics import shape_metric
>>> from histolytics.spatial_agg.local_distances import local_distances
>>>
>>> # input data
>>> nuc = cervix_nuclei()
>>> nuc = set_uid(nuc)
>>>
>>> # Fit delaunay graph
>>> w, _ = fit_graph(nuc, "delaunay", id_col="uid", threshold=100, use_polars=True)
>>> # Compute local neighborhood distances for shape metrics
>>> nuc = local_distances(
... nuc,
... w,
... id_col="uid",
... reductions=["mean"],
... num_processes=6,
>>> )
>>> print(nuc.head(3))
geometry class_name uid uid
0 POLYGON ((940.01 5570.02, 939.01 5573, 939 559... connective 0
1 POLYGON ((906.01 5350.02, 906.01 5361, 908.01 ... connective 1
2 POLYGON ((866 5137.02, 862.77 5137.94, 860 513... squamous_epithel 2
nhood_dists_mean
uid
0 48.500637
1 55.802475
2 37.081177
Source code in src/histolytics/spatial_agg/local_distances.py
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