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Added examples to from_dicts and build_knn. #704

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merged 5 commits into from
Apr 16, 2024
Merged

Added examples to from_dicts and build_knn. #704

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808ad56
added examples to from_dicts and build_knn
Kryndlea Apr 10, 2024
743251e
Update libpysal/graph/base.py
Kryndlea Apr 11, 2024
c452d26
Update libpysal/graph/base.py
Kryndlea Apr 11, 2024
4de2d52
changed knn1 and order
Kryndlea Apr 16, 2024
517b242
deleted a space
Kryndlea Apr 16, 2024
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92 changes: 92 additions & 0 deletions libpysal/graph/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -336,6 +336,51 @@ def from_dicts(cls, neighbors, weights=None):
-------
Graph
libpysal.graph.Graph based on dictionaries

Examples
--------
>>> neighbors = {
... 'Africa': ['Asia'],
... 'Asia': ['Africa', 'Europe'],
... 'Australia': [],
... 'Europe': ['Asia'],
... 'North America': ['South America'],
... 'South America': ['North America'],
... }
>>> connectivity = graph.Graph.from_dicts(neighbors)
>>> connectivity.adjacency
focal neighbor
Africa Asia 1
Asia Africa 1
Europe 1
Australia Australia 0
Europe Asia 1
North America South America 1
South America North America 1
Name: weight, dtype: float64

You can also specify weights (for example based
on the length of the shared border):

>>> weights = {
... 'Africa': [1],
... 'Asia': [0.2, 0.8],
... 'Australia': [],
... 'Europe': [1],
... 'North America': [1],
... 'South America': [1],
... }
>>> connectivity = graph.Graph.from_dicts(neighbors, weights)
>>> connectivity.adjacency
focal neighbor
Africa Asia 1.0
Asia Africa 0.2
Europe 0.8
Australia Australia 0.0
Europe Asia 1.0
North America South America 1.0
South America North America 1.0
Name: weight, dtype: float64
"""
head, tail, weight = _neighbor_dict_to_edges(neighbors, weights=weights)
return cls.from_arrays(head, tail, weight)
Expand Down Expand Up @@ -685,6 +730,53 @@ def build_knn(cls, data, k, metric="euclidean", p=2, coincident="raise"):
-------
Graph
libpysal.graph.Graph encoding KNN weights

Examples
--------
>>> import geopandas as gpd
>>> from geodatasets import get_path
>>> nybb = gpd.read_file(get_path('nybb')).set_index('BoroName')
>>> nybb
BoroCode ... geometry
BoroName ...
Staten Island 5 ... MULTIPOLYGON (((970217.022 145643.332, 970227....
Queens 4 ... MULTIPOLYGON (((1029606.077 156073.814, 102957...
Brooklyn 3 ... MULTIPOLYGON (((1021176.479 151374.797, 102100...
Manhattan 1 ... MULTIPOLYGON (((981219.056 188655.316, 980940....
Bronx 2 ... MULTIPOLYGON (((1012821.806 229228.265, 101278...

>>> knn3 = graph.Graph.build_knn(nybb.centroid, k=3)
>>> knn3.adjacency
focal neighbor
Staten Island Queens 1
Brooklyn 1
Manhattan 1
Queens Brooklyn 1
Manhattan 1
Bronx 1
Brooklyn Staten Island 1
Queens 1
Manhattan 1
Manhattan Queens 1
Brooklyn 1
Bronx 1
Bronx Queens 1
Brooklyn 1
Manhattan 1
Name: weight, dtype: int32

Specifying k=1 identifies the nearest neighbor
(note that this can be asymmetrical):

>>> knn1 = graph.Graph.build_knn(nybb.centroid, k=1)
>>> knn1.adjacency
focal neighbor
Staten Island Brooklyn 1
Queens Brooklyn 1
Brooklyn Queens 1
Manhattan Bronx 1
Bronx Manhattan 1
Name: weight, dtype: int32
"""
ids = _evaluate_index(data)

Expand Down