Spaces:
Sleeping
Sleeping
| import numpy as np | |
| import math | |
| import shapely | |
| from shapely.geometry import Polygon, LineString | |
| import torch | |
| from torch_geometric.utils import to_undirected, remove_self_loops | |
| from torch_geometric.data import Data | |
| def node_graph(apa_coor, apa_geo): | |
| num_op = len(apa_coor) | |
| apa_coor = apa_coor[0:-1] | |
| # apa_coor.pop(num_op-1) | |
| node_lst = [] | |
| num_p = len(apa_coor) | |
| for j in range(num_p): | |
| p = apa_coor[j] | |
| if j == 0: | |
| sindex = -1 | |
| oindex = j | |
| eindex = 1 | |
| elif j == (len(apa_coor)-1): | |
| sindex = j-1 | |
| oindex = j | |
| eindex = 0 | |
| else: | |
| sindex = j-1 | |
| oindex = j | |
| eindex = j+1 | |
| sp = apa_coor[sindex] | |
| s = np.array(sp) | |
| op = apa_coor[oindex] | |
| o = np.array(op) | |
| ox = op[0] | |
| oy = op[1] | |
| ep = apa_coor[eindex] | |
| e = np.array(ep) | |
| Area = apa_geo.area | |
| local_polygon = Polygon((sp, op, ep)) | |
| larea = (local_polygon.area) / Area | |
| se = LineString((sp, ep)) | |
| llength = se.length / math.sqrt(Area) | |
| osv = s - o | |
| oev = e - o | |
| langle = angle_between(osv, oev) | |
| if langle < 0: | |
| langle = langle + (2*math.pi) | |
| oop = (0, 0) | |
| oo = np.array(oop) | |
| regional_polygon = Polygon((sp, oop, ep)) | |
| regional_polygon_area = regional_polygon.area | |
| rarea = regional_polygon_area / Area | |
| regional_polygon_perimeter = regional_polygon.length / 2 | |
| rperimeter = regional_polygon_perimeter / math.sqrt(Area) | |
| rradius = (regional_polygon_area / regional_polygon_perimeter) / math.sqrt(Area) | |
| oosv = s - oo | |
| ooev = e - oo | |
| rangle = angle_between(oosv, ooev) | |
| if rangle < 0: | |
| rangle = rangle + (2*math.pi) | |
| #ox, oy, | |
| nl = [larea, llength, langle, rarea, rperimeter, rradius, rangle] | |
| node_lst.append(nl) | |
| ms_lst = [] | |
| me_lst = [] | |
| for k in range(num_p): | |
| if k == (num_p - 1): | |
| ms = k | |
| me = 0 | |
| else: | |
| ms = k | |
| me = k+1 | |
| ms_lst.append(ms) | |
| me_lst.append(me) | |
| mse = [ms_lst, me_lst] | |
| datasets = [] | |
| for i in range(2): | |
| node_f = torch.FloatTensor(node_lst) | |
| x = node_f | |
| edge_index = torch.tensor(mse, dtype=torch.long) | |
| edge_index, _ = remove_self_loops(edge_index) | |
| edge_index = to_undirected(edge_index=edge_index) | |
| data = Data(x=x, edge_index=edge_index) | |
| datasets.append(data) | |
| return datasets | |
| def angle_between(v1, v2): | |
| """ Returns the angle in radians between vectors 'v1' and 'v2' | |
| The sign of the angle is dependent on the order of v1 and v2 | |
| so acos(norm(dot(v1, v2))) does not work and atan2 has to be used, see: | |
| https://stackoverflow.com/questions/21483999/using-atan2-to-find-angle-between-two-vectors | |
| """ | |
| arg1 = np.cross(v1, v2) | |
| arg2 = np.dot(v1, v2) | |
| angle = np.arctan2(arg1, arg2) | |
| return angle | |