def test_regime_weights(self):
regimes = np.ones(25)
regimes[range(10, 20)] = 2
regimes[range(21, 25)] = 3
regimes = np.array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 1., 3., 3.,
3., 3.])
w = pysal.regime_weights(regimes)
ww0 = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
self.assertEquals(w.weights[0], ww0)
wn0 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 20]
self.assertEquals(w.neighbors[0], wn0)
regimes = ['n', 'n', 's', 's', 'e', 'e', 'w', 'w', 'e']
n = len(regimes)
w = pysal.regime_weights(regimes)
wn = {0: [1], 1: [0], 2: [3], 3: [2], 4: [5, 8], 5: [4, 8],
6: [7], 7: [6], 8: [4, 5]}
self.assertEquals(w.neighbors, wn)
def bounded_world_from_shapefile(path, n, n_as=None):
'''
Create W object for `n` agents with bounded locations assigned within
polygons of a shapefile (neighbor if in the same polygon) in proportion to
the polygon's area
...
Arguments
---------
path : str
Link to shapefile containing the geography
n : int
Number of pixels to be created within the geography
n_as : array
[Optional] Sequence with number of of agents to be assigned to
every neighborhood, in the order of the dbf accompaigning the
shapefile. If not provided, proportions are based on area.
Returns
-------
W : pysal.W
Weights object
ns : ndarray
Cardinalities for every observation to a neighborhood
xys : ndarray
Nx2 array with coordinates of agents (randomly within polygons)
'''
if not n_as:
shp = ps.open(path)
n_shares = np.array([p.area for p in shp])
shp.close()
n_shares = n_shares / n_shares.sum()
n_as = np.round(n * n_shares).astype(int)
n_as[-1] = n - n_as[:-1].sum() # Hack to get proportions to sum to n
xys = np.zeros((n, 2), dtype=int)
shp = ps.open(path)
polys = list(shp)
shp.close()
parss = [(n_a, poly) for n_a, poly in zip(n_as, polys)]
pool = mp.Pool(mp.cpu_count())
xys = pool.map(_random_pts_in_poly, parss)
xys = np.concatenate(xys)
ns = np.concatenate(
[np.array([neigh] * nn) for neigh, nn in enumerate(n_as)])
w = ps.regime_weights(ns)
return w, ns, xys
def bounded_world_from_shapefile(path, n, n_as=None):
'''
Create W object for `n` agents with bounded locations assigned within
polygons of a shapefile (neighbor if in the same polygon) in proportion to
the polygon's area
...
Arguments
---------
path : str
Link to shapefile containing the geography
n : int
Number of pixels to be created within the geography
n_as : array
[Optional] Sequence with number of of agents to be assigned to
every neighborhood, in the order of the dbf accompaigning the
shapefile. If not provided, proportions are based on area.
Returns
-------
W : pysal.W
Weights object
ns : ndarray
Cardinalities for every observation to a neighborhood
xys : ndarray
Nx2 array with coordinates of agents (randomly within polygons)
'''
if not n_as:
shp = ps.open(path)
n_shares = np.array([p.area for p in shp])
shp.close()
n_shares = n_shares / n_shares.sum()
n_as = np.round(n * n_shares).astype(int)
n_as[-1] = n - n_as[:-1].sum() # Hack to get proportions to sum to n
xys = np.zeros((n, 2), dtype=int)
shp = ps.open(path)
polys = list(shp)
shp.close()
parss = [(n_a, poly) for n_a, poly in zip(n_as, polys)]
pool = mp.Pool(mp.cpu_count())
xys = pool.map(_random_pts_in_poly, parss)
xys = np.concatenate(xys)
ns = np.concatenate([np.array([neigh]*nn) for neigh, nn in enumerate(n_as)])
w = ps.regime_weights(ns)
return w, ns, xys
