def min_threshold_dist_from_shapefile(shapefile, radius=None, p=2):
"""
Kernel weights with adaptive bandwidths.
Parameters
----------
shapefile : string
shapefile name with shp suffix.
radius : float
If supplied arc_distances will be calculated
based on the given radius. p will be ignored.
p : float
Minkowski p-norm distance metric parameter:
1<=p<=infinity
2: Euclidean distance
1: Manhattan distance
Returns
-------
d : float
Maximum nearest neighbor distance between the n
observations.
Examples
--------
>>> import libpysal.api as ps
>>> import libpysal
>>> md = ps.min_threshold_dist_from_shapefile(libpysal.examples.get_path("columbus.shp"))
>>> md
0.61886415807685413
>>> ps.min_threshold_dist_from_shapefile(libpysal.examples.get_path("stl_hom.shp"), libpysal.cg.sphere.RADIUS_EARTH_MILES)
31.846942936393717
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.
"""
points = get_points_array_from_shapefile(shapefile)
if radius is not None:
kdt = cg.kdtree.Arc_KDTree(points, radius=radius)
nn = kdt.query(kdt.data, k=2)
nnd = nn[0].max(axis=0)[1]
return nnd
return min_threshold_distance(points, p)
def min_threshold_dist_from_shapefile(shapefile, radius=None, p=2):
"""
Kernel weights with adaptive bandwidths.
Parameters
----------
shapefile : string
shapefile name with shp suffix.
radius : float
If supplied arc_distances will be calculated
based on the given radius. p will be ignored.
p : float
Minkowski p-norm distance metric parameter:
1<=p<=infinity
2: Euclidean distance
1: Manhattan distance
Returns
-------
d : float
Maximum nearest neighbor distance between the n
observations.
Examples
--------
>>> md = min_threshold_dist_from_shapefile(pysal.examples.get_path("columbus.shp"))
>>> md
0.61886415807685413
>>> min_threshold_dist_from_shapefile(pysal.examples.get_path("stl_hom.shp"), pysal.cg.sphere.RADIUS_EARTH_MILES)
31.846942936393717
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.
"""
points = get_points_array_from_shapefile(shapefile)
if radius is not None:
kdt = pysal.cg.kdtree.Arc_KDTree(points, radius=radius)
nn = kdt.query(kdt.data, k=2)
nnd = nn[0].max(axis=0)[1]
return nnd
return min_threshold_distance(points, p)
def min_threshold_dist_from_shapefile(shapefile, radius=None, p=2):
"""
Kernel weights with adaptive bandwidths
Parameters
----------
shapefile : string
shapefile name with shp suffix
radius : If supplied arc_distances will be calculated
based on the given radius. p will be ignored.
p : float
Minkowski p-norm distance metric parameter:
1<=p<=infinity
2: Euclidean distance
1: Manhattan distance
Returns
-------
d : float
minimum nearest neighbor distance between the n observations
Examples
--------
>>> md = min_threshold_dist_from_shapefile(pysal.examples.get_path("columbus.shp"))
>>> md
0.61886415807685413
>>> min_threshold_dist_from_shapefile(pysal.examples.get_path("stl_hom.shp"), pysal.cg.sphere.RADIUS_EARTH_MILES)
31.846942936393717
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.
"""
points = get_points_array_from_shapefile(shapefile)
if radius is not None:
points = pysal.cg.KDTree(points, distance_metric='Arc', radius=radius)
return min_threshold_distance(points, p)