# ruff: noqa: B006, N802, N803, N813
import itertools
import warnings
import numpy
from ..cg import voronoi_frames
from ..io.fileio import FileIO
from ._contW_lists import ContiguityWeightsLists
from .raster import da2W, da2WSP
from .util import get_ids, get_points_array
from .weights import WSP, W
try:
from shapely.geometry import Point as shapely_point
from ..cg.shapes import Point as pysal_point
point_type = (shapely_point, pysal_point)
except ImportError:
from ..cg.shapes import Point as point_type
WT_TYPE = {"rook": 2, "queen": 1} # for _contW_Binning
__author__ = "Sergio J. Rey <srey@asu.edu> , Levi John Wolf <levi.john.wolf@gmail.com>"
__all__ = ["Rook", "Queen", "Voronoi"]
[docs]
class Rook(W):
"""
Construct a weights object from a collection of pysal
polygons that share at least one edge.
Parameters
----------
polygons : list
a collection of PySAL shapes to build weights from
ids : list
a list of names to use to build the weights
**kw : keyword arguments
optional arguments for :class:`pysal.weights.W`
See Also
--------
:class:`libpysal.weights.weights.W`
"""
[docs]
def __init__(self, polygons, **kw):
criterion = "rook"
ids = kw.pop("ids", None)
polygons, backup = itertools.tee(polygons)
first_shape = next(iter(backup))
if isinstance(first_shape, point_type):
polygons = voronoi_frames(
get_points_array(polygons), return_input=False, as_gdf=False
)
polygons = list(polygons)
neighbors, ids = _build(polygons, criterion=criterion, ids=ids)
W.__init__(self, neighbors, ids=ids, **kw)
[docs]
@classmethod
def from_shapefile(cls, filepath, idVariable=None, full=False, **kwargs):
"""
Rook contiguity weights from a polygon shapefile.
Parameters
----------
shapefile : string
name of polygon shapefile including suffix.
sparse : boolean
If True return WSP instance
If False return W instance
Returns
-------
w : W
instance of spatial weights
Examples
--------
>>> from libpysal.weights import Rook
>>> import libpysal
>>> wr=Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"), "POLYID")
>>> "%.3f"%wr.pct_nonzero
'8.330'
>>> wr=Rook.from_shapefile(
... libpysal.examples.get_path("columbus.shp"), sparse=True
... )
>>> pct_sp = wr.sparse.nnz *1. / wr.n**2
>>> "%.3f"%pct_sp
'0.083'
Notes
-----
Rook contiguity defines as neighbors any pair of polygons that share a
common edge in their polygon definitions.
See Also
--------
:class:`libpysal.weights.weights.W`
:class:`libpysal.weights.contiguity.Rook`
"""
sparse = kwargs.pop("sparse", False)
ids = get_ids(filepath, idVariable) if idVariable is not None else None
w = cls(FileIO(filepath), ids=ids, **kwargs)
w.set_shapefile(filepath, idVariable=idVariable, full=full)
if sparse:
w = w.to_WSP()
return w
[docs]
@classmethod
def from_iterable(cls, iterable, sparse=False, **kwargs):
"""
Construct a weights object from a collection of arbitrary polygons. This
will cast the polygons to PySAL polygons, then build the W.
Parameters
----------
iterable : iterable
a collection of of shapes to be cast to PySAL shapes. Must
support iteration. Can be either Shapely or PySAL shapes.
**kw : keyword arguments
optional arguments for :class:`pysal.weights.W`
See Also
--------
:class:`libpysal.weights.weights.W`
:class:`libpysal.weights.contiguity.Rook`
"""
new_iterable = iter(iterable)
w = cls(new_iterable, **kwargs)
if sparse:
w = WSP.from_W(w)
return w
[docs]
@classmethod
def from_dataframe(
cls,
df,
geom_col=None,
idVariable=None,
ids=None,
id_order=None,
use_index=None,
**kwargs,
):
"""
Construct a weights object from a (geo)pandas dataframe with a geometry
column. This will cast the polygons to PySAL polygons, then build the W
using ids from the dataframe.
Parameters
----------
df : DataFrame
a :class: `pandas.DataFrame` containing geometries to use
for spatial weights
geom_col : string
the name of the column in `df` that contains the
geometries. Defaults to active geometry column.
idVariable : string
DEPRECATED - use `ids` instead.
the name of the column to use as IDs. If nothing is
provided, the dataframe index is used
ids : list-like, string
a list-like of ids to use to index the spatial weights object or
the name of the column to use as IDs. If nothing is
provided, the dataframe index is used if `use_index=True` or
a positional index is used if `use_index=False`.
Order of the resulting W is not respected from this list.
id_order : list
DEPRECATED - argument is deprecated and will be removed.
An ordered list of ids to use to index the spatial weights
object. If used, the resulting weights object will iterate
over results in the order of the names provided in this
argument.
use_index : bool
use index of `df` as `ids` to index the spatial weights object.
Defaults to False but in future will default to True.
See Also
--------
:class:`libpysal.weights.weights.W`
:class:`libpysal.weights.contiguity.Rook`
"""
if geom_col is None:
geom_col = df.geometry.name
if id_order is not None:
warnings.warn(
"`id_order` is deprecated and will be removed in future.",
FutureWarning,
stacklevel=2,
)
if id_order is True and ((idVariable is not None) or (ids is not None)):
# if idVariable is None, we want ids. Otherwise, we want the
# idVariable column
id_order = list(df.get(idVariable, ids))
else:
id_order = df.get(id_order, ids)
if idVariable is not None:
if ids is None:
warnings.warn(
"`idVariable` is deprecated and will be removed in future. "
"Use `ids` instead.",
FutureWarning,
stacklevel=2,
)
ids = idVariable
else:
warnings.warn(
"Both `idVariable` and `ids` passed, using `ids`.",
UserWarning,
stacklevel=2,
)
if ids is None:
if use_index is None:
warnings.warn(
"`use_index` defaults to False but will default to True in future. "
"Set True/False directly to control this behavior and silence this "
"warning",
FutureWarning,
stacklevel=2,
)
use_index = False
if use_index:
ids = df.index.tolist()
else:
if isinstance(ids, str):
ids = df[ids]
if not isinstance(ids, list):
ids = ids.tolist()
if len(ids) != len(df):
raise ValueError("The length of `ids` does not match the length of df.")
if id_order is None:
id_order = ids
return cls.from_iterable(
df[geom_col].tolist(), ids=ids, id_order=id_order, **kwargs
)
[docs]
@classmethod
def from_xarray(
cls,
da,
z_value=None,
coords_labels={},
k=1,
include_nodata=False,
n_jobs=1, # noqa: ARG003
sparse=True,
**kwargs,
):
"""
Construct a weights object from a xarray.DataArray with an additional
attribute index containing coordinate values of the raster
in the form of Pandas.Index/MultiIndex.
Parameters
----------
da : xarray.DataArray
Input 2D or 3D DataArray with shape=(z, y, x)
z_value : int/string/float
Select the z_value of 3D DataArray with multiple layers.
coords_labels : dictionary
Pass dimension labels for coordinates and layers if they do not
belong to default dimensions, which are (band/time, y/lat, x/lon)
e.g. coords_labels = {"y_label": "latitude",
"x_label": "longitude", "z_label": "year"}
Default is {} empty dictionary.
sparse : boolean
type of weight object. Default is True.
For libpysal.weights.W, sparse = False
k : int
Order of contiguity, this will select all neighbors upto kth order.
Default is 1.
include_nodata : boolean
If True, missing values will be assumed as non-missing when
selecting higher_order neighbors, Default is False
n_jobs : int
Number of cores to be used in the sparse weight construction. If -1,
all available cores are used. Default is 1.
**kwargs : keyword arguments
optional arguments passed when sparse = False
Returns
-------
w : libpysal.weights.W/libpysal.weights.WSP
instance of spatial weights class W or WSP with an index attribute
Notes
-----
1. Lower order contiguities are also selected.
2. Returned object contains `index` attribute that includes a
`Pandas.MultiIndex` object from the DataArray.
See Also
--------
:class:`libpysal.weights.weights.W`
:class:`libpysal.weights.weights.WSP`
"""
if sparse:
w = da2WSP(da, "rook", z_value, coords_labels, k, include_nodata)
else:
w = da2W(da, "rook", z_value, coords_labels, k, include_nodata, **kwargs)
return w
[docs]
class Queen(W):
"""
Construct a weights object from a collection of pysal
polygons that share at least one vertex.
Parameters
----------
polygons : list
a collection of PySAL shapes to build weights from
ids : list
a list of names to use to build the weights
**kw : keyword arguments
optional arguments for :class:`pysal.weights.W`
See Also
--------
:class:`libpysal.weights.weights.W`
"""
[docs]
def __init__(self, polygons, **kw):
criterion = "queen"
ids = kw.pop("ids", None)
polygons, backup = itertools.tee(polygons)
first_shape = next(iter(backup))
if isinstance(first_shape, point_type):
polygons = voronoi_frames(
get_points_array(polygons), return_input=False, as_gdf=False
)
polygons = list(polygons)
neighbors, ids = _build(polygons, criterion=criterion, ids=ids)
W.__init__(self, neighbors, ids=ids, **kw)
[docs]
@classmethod
def from_shapefile(cls, filepath, idVariable=None, full=False, **kwargs):
"""
Queen contiguity weights from a polygon shapefile.
Parameters
----------
shapefile : string
name of polygon shapefile including suffix.
idVariable : string
name of a column in the shapefile's DBF to use for ids.
sparse : boolean
If True return WSP instance
If False return W instance
Returns
-------
w : W
instance of spatial weights
Examples
--------
>>> from libpysal.weights import Queen
>>> import libpysal
>>> wq=Queen.from_shapefile(libpysal.examples.get_path("columbus.shp"))
>>> "%.3f"%wq.pct_nonzero
'9.829'
>>> wq=Queen.from_shapefile(libpysal.examples.get_path("columbus.shp"),"POLYID")
>>> "%.3f"%wq.pct_nonzero
'9.829'
>>> wq=Queen.from_shapefile(
... libpysal.examples.get_path("columbus.shp"), sparse=True
... )
>>> pct_sp = wq.sparse.nnz *1. / wq.n**2
>>> "%.3f"%pct_sp
'0.098'
Notes
-----
Queen contiguity defines as neighbors any pair of polygons that share at
least one vertex in their polygon definitions.
See Also
--------
:class:`libpysal.weights.weights.W`
:class:`libpysal.weights.contiguity.Queen`
"""
sparse = kwargs.pop("sparse", False)
ids = get_ids(filepath, idVariable) if idVariable is not None else None
w = cls(FileIO(filepath), ids=ids, **kwargs)
w.set_shapefile(filepath, idVariable=idVariable, full=full)
if sparse:
w = w.to_WSP()
return w
[docs]
@classmethod
def from_iterable(cls, iterable, sparse=False, **kwargs):
"""
Construct a weights object from a collection of arbitrary polygons. This
will cast the polygons to PySAL polygons, then build the W.
Parameters
----------
iterable : iterable
a collection of of shapes to be cast to PySAL shapes.
Must support iteration. Contents may either be
a shapely or PySAL shape.
**kw : keyword arguments
optional arguments for :class:`pysal.weights.W`
See Also
---------
:class:`libpysal.weights.weights.W`
:class:`libpysal.weights.contiguiyt.Queen`
"""
new_iterable = iter(iterable)
w = cls(new_iterable, **kwargs)
if sparse:
w = WSP.from_W(w)
return w
[docs]
@classmethod
def from_dataframe(
cls,
df,
geom_col=None,
idVariable=None,
ids=None,
id_order=None,
use_index=None,
**kwargs,
):
"""
Construct a weights object from a (geo)pandas dataframe with a geometry
column. This will cast the polygons to PySAL polygons, then build the W
using ids from the dataframe.
Parameters
----------
df : DataFrame
a :class: `pandas.DataFrame` containing geometries to use
for spatial weights
geom_col : string
the name of the column in `df` that contains the
geometries. Defaults to active geometry column.
idVariable : string
DEPRECATED - use `ids` instead.
the name of the column to use as IDs. If nothing is
provided, the dataframe index is used
ids : list-like, string
a list-like of ids to use to index the spatial weights object or
the name of the column to use as IDs. If nothing is
provided, the dataframe index is used if `use_index=True` or
a positional index is used if `use_index=False`.
Order of the resulting W is not respected from this list.
id_order : list
DEPRECATED - argument is deprecated and will be removed.
An ordered list of ids to use to index the spatial weights
object. If used, the resulting weights object will iterate
over results in the order of the names provided in this
argument.
use_index : bool
use index of `df` as `ids` to index the spatial weights object.
Defaults to False but in future will default to True.
See Also
--------
:class:`libpysal.weights.weights.W`
:class:`libpysal.weights.contiguity.Queen`
"""
if geom_col is None:
geom_col = df.geometry.name
if id_order is not None:
warnings.warn(
"`id_order` is deprecated and will be removed in future.",
FutureWarning,
stacklevel=2,
)
if id_order is True and ((idVariable is not None) or (ids is not None)):
# if idVariable is None, we want ids. Otherwise, we want the
# idVariable column
id_order = list(df.get(idVariable, ids))
else:
id_order = df.get(id_order, ids)
if idVariable is not None:
if ids is None:
warnings.warn(
"`idVariable` is deprecated and will be removed in future. "
"Use `ids` instead.",
FutureWarning,
stacklevel=2,
)
ids = idVariable
else:
warnings.warn(
"Both `idVariable` and `ids` passed, using `ids`.",
UserWarning,
stacklevel=2,
)
if ids is None:
if use_index is None:
warnings.warn(
"`use_index` defaults to False but will default to True in future. "
"Set True/False directly to control this behavior and silence this "
"warning",
FutureWarning,
stacklevel=2,
)
use_index = False
if use_index:
ids = df.index.tolist()
else:
if isinstance(ids, str):
ids = df[ids]
if not isinstance(ids, list):
ids = ids.tolist()
if len(ids) != len(df):
raise ValueError("The length of `ids` does not match the length of df.")
if id_order is None:
id_order = ids
return cls.from_iterable(
df[geom_col].tolist(), ids=ids, id_order=id_order, **kwargs
)
[docs]
@classmethod
def from_xarray(
cls,
da,
z_value=None,
coords_labels={},
k=1,
include_nodata=False,
n_jobs=1, # noqa: ARG003
sparse=True,
**kwargs,
):
"""
Construct a weights object from a xarray.DataArray with an additional
attribute index containing coordinate values of the raster
in the form of Pandas.Index/MultiIndex.
Parameters
----------
da : xarray.DataArray
Input 2D or 3D DataArray with shape=(z, y, x)
z_value : int/string/float
Select the z_value of 3D DataArray with multiple layers.
coords_labels : dictionary
Pass dimension labels for coordinates and layers if they do not
belong to default dimensions, which are (band/time, y/lat, x/lon)
e.g. coords_labels = {"y_label": "latitude", "x_label":
"longitude", "z_label": "year"}
Default is {} empty dictionary.
sparse : boolean
type of weight object. Default is True.
For libpysal.weights.W, sparse = False
k : int
Order of contiguity, this will select all neighbors upto kth order.
Default is 1.
include_nodata : boolean
If True, missing values will be assumed as non-missing when
selecting higher_order neighbors, Default is False
n_jobs : int
Number of cores to be used in the sparse weight construction. If -1,
all available cores are used. Default is 1.
**kwargs : keyword arguments
optional arguments passed when sparse = False
Returns
-------
w : libpysal.weights.W/libpysal.weights.WSP
instance of spatial weights class W or WSP with an index attribute
Notes
-----
1. Lower order contiguities are also selected.
2. Returned object contains `index` attribute that includes a
`Pandas.MultiIndex` object from the DataArray.
See Also
--------
:class:`libpysal.weights.weights.W`
:class:`libpysal.weights.weights.WSP`
"""
if sparse:
w = da2WSP(da, "queen", z_value, coords_labels, k, include_nodata)
else:
w = da2W(da, "queen", z_value, coords_labels, k, include_nodata, **kwargs)
return w
[docs]
def Voronoi(points, criterion="rook", clip="alpha_shape", **kwargs):
"""
Voronoi weights for a 2-d point set
Points are Voronoi neighbors if their polygons share an edge or vertex.
Parameters
----------
points : array
(n,2)
coordinates for point locations
kwargs : arguments to pass to Rook, the underlying contiguity class.
Returns
-------
w : W
instance of spatial weights
Examples
--------
>>> import numpy as np
>>> from libpysal.weights import Voronoi
>>> np.random.seed(12345)
>>> points= np.random.random((5,2))*10 + 10
>>> w = Voronoi(points)
>>> w.neighbors
{0: [2, 3, 4], 1: [2], 2: [0, 1, 4], 3: [0, 4], 4: [0, 2, 3]}
"""
from ..cg.voronoi import voronoi_frames
region_df = voronoi_frames(points, clip=clip, return_input=False, as_gdf=True)
if criterion.lower() == "queen":
cls = Queen
elif criterion.lower() == "rook":
cls = Rook
else:
raise ValueError(
f"Contiguity criterion {criterion} not supported. "
"Only 'rook' and 'queen' are supported."
)
return cls.from_dataframe(region_df, **kwargs)
def _from_dataframe(df, **kwargs):
"""
Construct a voronoi contiguity weight directly from a dataframe.
Note that if criterion='rook', this is identical to the delaunay
graph for the points if no clipping of the voronoi cells is applied.
If the input dataframe is of any other geometry type than "Point",
a value error is raised.
Parameters
----------
df : pandas.DataFrame
dataframe containing point geometries for a
voronoi diagram.
Returns
-------
w : W
instance of spatial weights.
"""
try:
x, y = df.geometry.x.values, df.geometry.y.values
except ValueError:
raise NotImplementedError(
"Voronoi weights are only"
" implemented for point geometries. "
"You may consider using df.centroid."
) from None
coords = numpy.column_stack((x, y))
return Voronoi(coords, **kwargs)
Voronoi.from_dataframe = _from_dataframe
def _build(polygons, criterion="rook", ids=None):
"""
This is a developer-facing function to construct a spatial weights object.
Parameters
----------
polygons : list
list of pysal polygons to use to build contiguity
criterion : string
option of which kind of contiguity to build.
Is either "rook" or "queen"
ids : list
list of ids to use to index the neighbor dictionary
Returns
-------
tuple containing (neighbors, ids), where neighbors is a dictionary
describing contiguity relations and ids is the list of ids used to index
that dictionary.
Notes
-----
This is different from the prior behavior of buildContiguity, which
returned an actual weights object. Since this just dispatches for the
classes above, this returns the raw ingredients for a spatial weights
object, not the object itself.
"""
if ids and len(ids) != len(set(ids)):
raise ValueError(
"The argument to the ids parameter contains duplicate entries."
)
wttype = WT_TYPE[criterion.lower()]
geo = polygons
if issubclass(type(geo), FileIO):
geo.seek(0) # Make sure we read from the beginning of the file.
neighbor_data = ContiguityWeightsLists(polygons, wttype=wttype).w
neighbors = {}
# weights={}
if ids:
for key in neighbor_data:
ida = ids[key]
if ida not in neighbors:
neighbors[ida] = set()
neighbors[ida].update([ids[x] for x in neighbor_data[key]])
for key in neighbors:
neighbors[key] = set(neighbors[key])
else:
for key in neighbor_data:
neighbors[key] = set(neighbor_data[key])
return (
dict(
list(
zip(
list(neighbors.keys()),
list(map(list, list(neighbors.values()))),
strict=True,
)
)
),
ids,
)
def buildContiguity(polygons, criterion="rook", ids=None):
"""
This is a deprecated function.
It builds a contiguity W from the polygons provided. As such, it is now
identical to calling the class constructors for Rook or Queen.
"""
# Warn('This function is deprecated. Please use the Rook or Queen classes',
# UserWarning)
if criterion.lower() == "rook":
return Rook(polygons, ids=ids)
elif criterion.lower() == "queen":
return Queen(polygons, ids=ids)
else:
raise ValueError(f"Weights criterion '{criterion}' was not found.")