def calculate_weights(self, threshold=None, p=2, *args, **kwargs):
"""
Parameters
----------
threshold : float
distance band
p : float
Minkowski p-norm distance metric parameter:
1<=p<=infinity
2: Euclidean distance
1: Manhattan distance
"""
if threshold is None:
if hasattr(self, 'threshold'):
threshold = self.threshold
else:
raise ValueError("Must set threshold first")
logging.warning('{}: Treshold = {}'.format(self.name, threshold))
logging.info('{}: Starting weight calculation'.format(self.name))
t = time.process_time()
self.weights = pysal.DistanceBand(self.points_array,
threshold=threshold,
p=p,
*args,
**kwargs)
logging.debug('{}: Weight calculation elapsed time {}'.format(
self.name, str(timedelta(seconds=time.process_time() - t))))
return self.weights
def gpd_distanceBandW(gpdf, threshold=11.2):
"""
https://github.com/pysal/pysal/blob/master/pysal/weights/Distance.py
"""
points = gpd_points(gpdf)
w = pysal.DistanceBand(points, threshold)
return w
def test_DistanceBand_ints(self):
""" see issue #126 """
w = pysal.rook_from_shapefile(
pysal.examples.get_path("lattice10x10.shp"))
polygons = pysal.open(pysal.examples.get_path("lattice10x10.shp"),
"r").read()
points2 = [tuple(map(int, poly.vertices[0])) for poly in polygons]
w2 = pysal.DistanceBand(points2, 1)
for k in range(w.n):
self.assertEqual(w[k], w2[k])
def test_DistanceBand(self):
""" see issue #126 """
w = pysal.rook_from_shapefile(
pysal.examples.get_path("lattice10x10.shp"))
polygons = pysal.open(pysal.examples.get_path("lattice10x10.shp"),
"r").read()
points1 = [poly.centroid for poly in polygons]
w1 = pysal.DistanceBand(points1, 1)
for k in range(w.n):
self.assertEqual(w[k], w1[k])
def test_DistanceBand_arc(self):
pts = [x.centroid for x in pysal.open(self.arcShp)]
dist = pysal.cg.sphere.arcdist # default radius is Earth KM
full = np.matrix([[dist(pts[i], pts[j]) for j in xrange(
len(pts))] for i in xrange(len(pts))])
kd = pysal.cg.kdtree.KDTree(pts, distance_metric='Arc',
radius=pysal.cg.sphere.RADIUS_EARTH_KM)
w = pysal.DistanceBand(kd, full.max(), binary=False, alpha=1.0)
self.assertTrue((w.sparse.todense() == full).all())
