def cal_grid_loc(g):
cal = Calculator()
cal.SetLayer(13)
_, c = cal.HexCellVertexesAndCenter(g)
return c.lng, c.lat
def global_transport(o_t, d_t, gridsKey, grids_to_id, k, w):
indice_count = np.zeros((_N, ), dtype=int)
for j in range(_N):
hex_cal = Calculator()
hex_cal.SetLayer(12)
key = gridsKey[j]
indices = [j]
for i in range(k):
bhex_ids = hex_cal.HexCellBoudary(key, i + 1)
inter = set(gridsKey).intersection(bhex_ids)
indices_add = [grids_to_id[x] for x in inter]
if len(indices_add) > 0:
indices = indices + indices_add
indice_count[j] = len(indices)
gamma = cvx.Variable((np.sum(indice_count), 1))
S = cvx.Variable((_N, 1))
C = np.ones((_N, 1))
A_1 = np.zeros((_N, np.sum(indice_count)))
O_1 = np.zeros((_N, np.sum(indice_count)))
cost = np.zeros((1, np.sum(indice_count)))
for j in range(_N):
hex_cal = Calculator()
hex_cal.SetLayer(12)
key = gridsKey[j]
indices = [j]
for i in range(k):
bhex_ids = hex_cal.HexCellBoudary(key, i + 1)
inter = set(gridsKey).intersection(bhex_ids)
indices_add = [grids_to_id[x] for x in inter]
if len(indices_add) > 0:
indices = indices + indices_add
A_1[j, np.sum(indice_count[:j]):np.sum(indice_count[:(j + 1)])] = 1
for p in range(int(indice_count[j])):
cost[:,
np.sum(indice_count[:j]) + p] = w * cost_norm[j, indices[p]]
O_1[j, np.sum(indice_count[:j]) + p] = w * cost_norm[j, indices[p]]
A_2 = np.zeros((_N, np.sum(indice_count)))
O_2 = np.zeros((_N, np.sum(indice_count)))
for j in range(_N):
hex_cal = Calculator()
hex_cal.SetLayer(12)
key = gridsKey[j]
indices = [j]
for i in range(k):
bhex_ids = hex_cal.HexCellBoudary(key, i + 1)
inter = set(gridsKey).intersection(bhex_ids)
indices_add = [grids_to_id[x] for x in inter]
if len(indices_add) > 0:
indices = indices + indices_add
for p in indices:
key = gridsKey[p]
indices_1 = [p]
for i in range(k):
bhex_ids = hex_cal.HexCellBoudary(key, i + 1)
inter = set(gridsKey).intersection(bhex_ids)
indices_add = [grids_to_id[x] for x in inter]
if len(indices_add) > 0:
indices_1 = indices_1 + indices_add
index = indices_1.index(j)
A_2[j, np.sum(indice_count[:p]) + index] = 1
O_2[j, np.sum(indice_count[:p]) + index] = w * cost_norm[j, p]
obj = cvx.Minimize(C.T * S + cost * gamma)
constr = [
A_2 * gamma + S >= o_t.reshape(-1, 1),
A_2 * gamma - S <= o_t.reshape(-1, 1), 0 <= gamma, 0 <= S,
A_1 * gamma == d_t.reshape(-1, 1)
]
prob = cvx.Problem(obj, constr)
d = prob.solve(solver=cvx.GLPK)
tmp = o_t.reshape(_N, 1)
ratio = np.zeros((_N, ))
tild_d = np.dot(A_2, gamma.value) - np.dot(O_2, gamma.value)
tild_d[tild_d < 0] = 0
for i in range(_N):
if (tmp[i, 0] == 0) & (tild_d[i, 0] == 0):
ratio[i] = 1
elif tild_d[i, 0] == 0:
ratio[i] = tmp[i, 0] / (tild_d[i, 0] + 1)
else:
ratio[i] = tmp[i, 0] / tild_d[i, 0]
return d, ratio
def cal_grid(lng, lat):
cal = Calculator()
cal.SetLayer(13)
return cal.HexCellKey(GeoCoord(lat, lng))