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import shapely
from geopandas import GeoDataFrame, GeoSeries
from libpysal.graph import Graph
from pandas import Series
__all__ = ["courtyards"]
def courtyards(
geometry: GeoDataFrame | GeoSeries, graph: Graph, buffer: float = 0.01
) -> Series:
"""Calculate the number of courtyards within the joined structure.
Adapted from :cite:`schirmer2015`.
Parameters
----------
geometry : GeoDataFrame
A GeoDataFrame containing objects to analyse.
graph : libpysal.graph.Graph
A spatial weights matrix for the geodataframe,
it is used to denote adjacent buildings.
buffer: float
Buffer value for the geometry. It can be used
to account for topological problems.
Returns
-------
Series
A Series containing the resulting values.
Examples
--------
>>> from libpysal import graph
>>> path = momepy.datasets.get_path("bubenec")
>>> buildings = geopandas.read_file(path, layer="buildings")
>>> buildings.head()
uID geometry
0 1 POLYGON ((1603599.221 6464369.816, 1603602.984...
1 2 POLYGON ((1603042.88 6464261.498, 1603038.961 ...
2 3 POLYGON ((1603044.65 6464178.035, 1603049.192 ...
3 4 POLYGON ((1603036.557 6464141.467, 1603036.969...
4 5 POLYGON ((1603082.387 6464142.022, 1603081.574...
>>> contiguity = graph.Graph.build_contiguity(buildings)
>>> contiguity
<Graph of 144 nodes and 248 nonzero edges indexed by
[0, 1, 2, 3, 4, ...]>
>>> momepy.courtyards(buildings, contiguity)
0 0
1 1
2 1
3 1
4 1
..
139 0
140 0
141 0
142 0
143 1
Length: 144, dtype: int32
"""
def _calculate_courtyards(group):
"""helper function to carry out the per group calculations"""
return shapely.get_num_interior_rings(
shapely.union_all(shapely.buffer(group.values, buffer))
)
# calculate per group courtyards
temp_result = (
geometry["geometry"]
.groupby(graph.component_labels.values)
.apply(_calculate_courtyards)
)
# assign group courtyard values to each building in the group
## note: this is faster than using transform directly
result = Series(
temp_result.loc[graph.component_labels.values].values, index=geometry.index
)
return result
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