from ..io.fileio import FileIO
from .weights import W, WSP
from ._contW_lists import ContiguityWeightsLists
from .util import get_ids
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']
[文档]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:`pysal.lib.weights.weights.W`
"""
[文档] def __init__(self, polygons, **kw):
criterion = 'rook'
ids = kw.pop('ids', None)
neighbors, ids = _build(polygons, criterion=criterion,
ids=ids)
W.__init__(self, neighbors, ids=ids, **kw)
@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
--------
>>> import pysal.lib
>>> wr=Rook.from_shapefile(pysal.lib.examples.get_path("columbus.shp"), "POLYID")
>>> "%.3f"%wr.pct_nonzero
'8.330'
>>> wr=Rook.from_shapefile(pysal.lib.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:`pysal.lib.weights.weights.W`
:class:`pysal.lib.weights.contiguity.Rook`
"""
sparse = kwargs.pop('sparse', False)
if idVariable is not None:
ids = get_ids(filepath, idVariable)
else:
ids = None
w = cls(FileIO(filepath), ids=ids,**kwargs)
w.set_shapefile(filepath, idVariable=idVariable, full=full)
if sparse:
w = w.to_WSP()
return w
@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:`pysal.lib.weights.weights.W`
:class:`pysal.lib.weights.contiguity.Rook`
"""
new_iterable = iter(iterable)
w = cls(new_iterable, **kwargs)
if sparse:
w = WSP.from_W(w)
return w
@classmethod
def from_dataframe(cls, df, geom_col='geometry',
idVariable=None, ids=None, id_order=None, **kwargs):
"""
Construct a weights object from a 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 `geometry`
idVariable : string
the name of the column to use as IDs. If nothing is
provided, the dataframe index is used
ids : list
a list of ids to use to index the spatial weights object.
Order is not respected from this list.
id_order : list
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.
See Also
---------
:class:`pysal.lib.weights.weights.W`
:class:`pysal.lib.weights.contiguity.Rook`
"""
if id_order is not None:
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)
elif idVariable is not None:
ids = df.get(idVariable).tolist()
elif isinstance(ids, str):
ids = df.get(ids).tolist()
return cls.from_iterable(df[geom_col].tolist(), ids=ids,
id_order=id_order, **kwargs)
[文档]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:`pysal.lib.weights.weights.W`
"""
[文档] def __init__(self, polygons, **kw):
criterion = 'queen'
ids = kw.pop('ids', None)
neighbors, ids = _build(polygons, ids=ids,
criterion=criterion)
W.__init__(self, neighbors, ids=ids, **kw)
@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
--------
>>> import pysal.lib
>>> wq=Queen.from_shapefile(pysal.lib.examples.get_path("columbus.shp"))
>>> "%.3f"%wq.pct_nonzero
'9.829'
>>> wq=Queen.from_shapefile(pysal.lib.examples.get_path("columbus.shp"),"POLYID")
>>> "%.3f"%wq.pct_nonzero
'9.829'
>>> wq=Queen.from_shapefile(pysal.lib.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:`pysal.lib.weights.weights.W`
:class:`pysal.lib.weights.contiguity.Queen`
"""
sparse = kwargs.pop('sparse', False)
if idVariable is not None:
ids = get_ids(filepath, idVariable)
else:
ids = None
w = cls(FileIO(filepath), ids=ids, **kwargs)
w.set_shapefile(filepath, idVariable=idVariable, full=full)
if sparse:
w = w.to_WSP()
return w
@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:`pysal.lib.weights.weights.W`
:class:`pysal.lib.weights.contiguiyt.Queen`
"""
new_iterable = iter(iterable)
w = cls(new_iterable, **kwargs)
if sparse:
w = WSP.from_W(w)
return w
@classmethod
def from_dataframe(cls, df, geom_col='geometry', **kwargs):
"""
Construct a weights object from a 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 `geometry`
idVariable : string
the name of the column to use as IDs. If nothing is
provided, the dataframe index is used
ids : list
a list of ids to use to index the spatial weights object.
Order is not respected from this list.
id_order : list
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.
See Also
---------
:class:`pysal.lib.weights.weights.W`
:class:`pysal.lib.weights.contiguity.Queen`
"""
idVariable = kwargs.pop('idVariable', None)
ids = kwargs.pop('ids', None)
id_order = kwargs.pop('id_order', None)
if id_order is not None:
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
ids = list(df.get(idVariable, ids))
id_order = ids
elif isinstance(id_order, str):
ids = df.get(id_order, ids)
id_order = ids
elif idVariable is not None:
ids = df.get(idVariable).tolist()
elif isinstance(ids, str):
ids = df.get(ids).tolist()
w = cls.from_iterable(df[geom_col].tolist(), ids=ids, id_order=id_order, **kwargs)
return w
[文档]def Voronoi(points):
"""
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
Returns
-------
w : W
instance of spatial weights
Examples
--------
>>> import numpy as np
>>> np.random.seed(12345)
>>> points= np.random.random((5,2))*10 + 10
>>> w = Voronoi(points)
>>> w.neighbors
{0: [1, 2, 3, 4], 1: [0, 2], 2: [0, 1, 4], 3: [0, 4], 4: [0, 2, 3]}
"""
from ..cg.voronoi import voronoi_frames
region_df, _ = voronoi_frames(points)
return Queen.from_dataframe(region_df)
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.
NOTE: 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())))))), 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 Exception('Weights criterion "{}" was not found.'.format(criterion))