def threshold_binaryW_from_shapefile(shapefile,
threshold,
p=2,
idVariable=None,
radius=None):
"""
Threshold distance based binary weights from a shapefile.
Parameters
----------
shapefile : string
shapefile name with shp suffix
threshold : float
distance band
p : float
Minkowski p-norm distance metric parameter:
1<=p<=infinity
2: Euclidean distance
1: Manhattan distance
idVariable : string
name of a column in the shapefile's DBF to use for ids
radius : float
If supplied arc_distances will be calculated
based on the given radius. p will be ignored.
Returns
-------
w : W
instance
Weights object with binary weights
Examples
--------
>>> import libpysal.api as ps
>>> import libpysal
>>> w = ps.threshold_binaryW_from_shapefile(libpysal.examples.get_path("columbus.shp"),0.62,idVariable="POLYID")
>>> w.weights[1]
[1.0, 1.0]
Notes
-----
Supports polygon or point shapefiles. For polygon shapefiles, distance is
based on polygon centroids. Distances are defined using coordinates in
shapefile which are assumed to be projected and not geographical
coordinates.
"""
data = get_points_array_from_shapefile(shapefile)
if radius is not None:
data = cg.KDTree(data, distance_metric='Arc', radius=radius)
if idVariable:
ids = get_ids(shapefile, idVariable)
w = DistanceBand(data, threshold=threshold, p=p)
w.remap_ids(ids)
return w
return threshold_binaryW_from_array(data, threshold, p=p)
def threshold_binaryW_from_shapefile(shapefile, threshold, p=2, idVariable=None, radius=None):
"""
Threshold distance based binary weights from a shapefile.
Parameters
----------
shapefile : string
shapefile name with shp suffix
threshold : float
distance band
p : float
Minkowski p-norm distance metric parameter:
1<=p<=infinity
2: Euclidean distance
1: Manhattan distance
idVariable : string
name of a column in the shapefile's DBF to use for ids
radius : float
If supplied arc_distances will be calculated
based on the given radius. p will be ignored.
Returns
-------
w : W
instance
Weights object with binary weights
Examples
--------
>>> w = threshold_binaryW_from_shapefile(pysal.examples.get_path("columbus.shp"),0.62,idVariable="POLYID")
>>> w.weights[1]
[1, 1]
Notes
-----
Supports polygon or point shapefiles. For polygon shapefiles, distance is
based on polygon centroids. Distances are defined using coordinates in
shapefile which are assumed to be projected and not geographical
coordinates.
"""
data = get_points_array_from_shapefile(shapefile)
if radius is not None:
data = pysal.cg.KDTree(data, distance_metric='Arc', radius=radius)
if idVariable:
ids = get_ids(shapefile, idVariable)
w = DistanceBand(data, threshold=threshold, p=p)
w.remap_ids(ids)
return w
return threshold_binaryW_from_array(data, threshold, p=p)
def threshold_continuousW_from_array(array, threshold, p=2,
alpha=-1, radius=None):
"""
Continuous weights based on a distance threshold.
Parameters
----------
array : array
(n,m)
attribute data, n observations on m attributes
threshold : float
distance band
p : float
Minkowski p-norm distance metric parameter:
1<=p<=infinity
2: Euclidean distance
1: Manhattan distance
alpha : float
distance decay parameter for weight (default -1.0)
if alpha is positive the weights will not decline with
distance.
radius : If supplied arc_distances will be calculated
based on the given radius. p will be ignored.
Returns
-------
w : W
instance; Weights object with continuous weights.
Examples
--------
inverse distance weights
>>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
>>> wid=threshold_continuousW_from_array(points,11.2)
WARNING: there is one disconnected observation (no neighbors)
Island id: [2]
>>> wid.weights[0]
[0.10000000000000001, 0.089442719099991588]
gravity weights
>>> wid2=threshold_continuousW_from_array(points,11.2,alpha=-2.0)
WARNING: there is one disconnected observation (no neighbors)
Island id: [2]
>>> wid2.weights[0]
[0.01, 0.0079999999999999984]
"""
if radius is not None:
array = pysal.cg.KDTree(array, distance_metric='Arc', radius=radius)
w = DistanceBand(
array, threshold=threshold, p=p, alpha=alpha, binary=False)
return w
def threshold_continuousW_from_shapefile(shapefile, threshold, p=2,
alpha=-1, idVariable=None, radius=None):
"""
Threshold distance based continuous weights from a shapefile.
Parameters
----------
shapefile : string
shapefile name with shp suffix
threshold : float
distance band
p : float
Minkowski p-norm distance metric parameter:
1<=p<=infinity
2: Euclidean distance
1: Manhattan distance
alpha : float
distance decay parameter for weight (default -1.0)
if alpha is positive the weights will not decline with
distance.
idVariable : string
name of a column in the shapefile's DBF to use for ids
radius : float
If supplied arc_distances will be calculated
based on the given radius. p will be ignored.
Returns
-------
w : W
instance; Weights object with continuous weights.
Examples
--------
>>> w = threshold_continuousW_from_shapefile(pysal.examples.get_path("columbus.shp"),0.62,idVariable="POLYID")
>>> w.weights[1]
[1.6702346893743334, 1.7250729841938093]
Notes
-----
Supports polygon or point shapefiles. For polygon shapefiles, distance is
based on polygon centroids. Distances are defined using coordinates in
shapefile which are assumed to be projected and not geographical
coordinates.
"""
data = get_points_array_from_shapefile(shapefile)
if radius is not None:
data = pysal.cg.KDTree(data, distance_metric='Arc', radius=radius)
if idVariable:
ids = get_ids(shapefile, idVariable)
w = DistanceBand(data, threshold=threshold, p=p, alpha=alpha, binary=False)
w.remap_ids(ids)
else:
w = threshold_continuousW_from_array(data, threshold, p=p, alpha=alpha)
w.set_shapefile(shapefile,idVariable)
return w
def threshold_binaryW_from_array(array, threshold, p=2, radius=None):
"""
Binary weights based on a distance threshold.
Parameters
----------
array : array
(n,m)
attribute data, n observations on m attributes
threshold : float
distance band
p : float
Minkowski p-norm distance metric parameter:
1<=p<=infinity
2: Euclidean distance
1: Manhattan distance
radius : float
If supplied arc_distances will be calculated
based on the given radius. p will be ignored.
Returns
-------
w : W
instance
Weights object with binary weights
Examples
--------
>>> import libpysal.api as ps
>>> import libpysal
>>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
>>> wcheck = ps.W({0: [1, 3], 1: [0, 3, ], 2: [], 3: [1, 0], 4: [5], 5: [4]})
WARNING: there is one disconnected observation (no neighbors)
Island id: [2]
>>> w=ps.threshold_binaryW_from_array(points,threshold=11.2)
WARNING: there is one disconnected observation (no neighbors)
Island id: [2]
>>> libpysal.weights.util.neighbor_equality(w, wcheck)
True
>>>
"""
if radius is not None:
array = cg.KDTree(array, distance_metric='Arc', radius=radius)
return DistanceBand(array, threshold=threshold, p=p)
def threshold_binaryW_from_array(array, threshold, p=2, radius=None):
"""
Binary weights based on a distance threshold
Parameters
----------
array : array (n,m)
attribute data, n observations on m attributes
threshold : float
distance band
p : float
Minkowski p-norm distance metric parameter:
1<=p<=infinity
2: Euclidean distance
1: Manhattan distance
radius : If supplied arc_distances will be calculated
based on the given radius. p will be ignored.
Returns
-------
w : W instance
Weights object with binary weights
Examples
--------
>>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
>>> w=threshold_binaryW_from_array(points,threshold=11.2)
>>> w.weights
{0: [1, 1], 1: [1, 1], 2: [], 3: [1, 1], 4: [1], 5: [1]}
>>> w.neighbors
{0: [1, 3], 1: [0, 3], 2: [], 3: [0, 1], 4: [5], 5: [4]}
>>>
"""
if radius is not None:
array = pysal.cg.KDTree(array, distance_metric='Arc', radius=radius)
return DistanceBand(array, threshold=threshold, p=p)
