#/AGOS/PROGRAMS/ANT/Versions/26.04.2015/shapefiles/aus.shp" t0 = dt.datetime.now() #----------------------------------------------------------------------------- # INITIALISE CLASS STATES #----------------------------------------------------------------------------- # Generate InShape class SHAPE = InShape(shape_path) # Create shapely polygon from imported shapefile UNIQUE_SHAPE = SHAPE.shape_poly() print type(UNIQUE_SHAPE) # Generate InPoly class INPOLY = InPoly(shape_path) GEODESIC = Geodesic() COORDS = Coordinates() INPOLY = InPoly(shape_path) POLY_NODES = INPOLY.poly_nodes() # decluster the points to desired specifications. coords = COORDS.decluster(inputs=coords, degree_dist=0.5) lonmin, lonmax = np.floor(min(coords[:, 0])), np.ceil(max(coords[:, 0])) latmin, latmax = np.floor(min(coords[:, 1])), np.ceil(max(coords[:, 1])) print lonmin, lonmax, latmin, latmax plt.figure() plt.scatter(coords[:, 0], coords[:, 1]) plt.show()
if show:
plt.figure()
plt.scatter(lons, lats)
plt.show()
#-----------------------------------------------------------------------------
# GENERATE SECOND SET OF VARIABLES AND STATES
#-----------------------------------------------------------------------------
lonmin, lonmax = np.floor(min(coords[:, 0])), np.ceil(max(coords[:, 0]))
latmin, latmax = np.floor(min(coords[:, 1])), np.ceil(max(coords[:, 1]))
print lonmin, lonmax, latmin, latmax
kappa = [np.vstack([[coord1[0],coord1[1],coord2[0],coord2[1]]\
for coord2 in coords]) for coord1 in coords]
GEODESIC = Geodesic(km_point=km_points)
def spread_paths(coord_list):
return GEODESIC.fast_paths(coord_list)
t0 = datetime.datetime.now()
pool = mp.Pool()
paths = pool.map(spread_paths, kappa)
pool.close()
pool.join()
t1 = datetime.datetime.now()
print t1 - t0
paths = GEODESIC.combine_paths(paths)
#plt.show()
#-----------------------------------------------------------------------------
# GENERATE SECOND SET OF VARIABLES AND STATES
#-----------------------------------------------------------------------------
ideal_path = 'ideal_coordinates.pickle'
#if no paths have been done before, start afresh!
#if dataless:
# coords = Dataless.locs_from_dataless(dataless_path)
# original_coords = coords
#elif os.path.exists(ideal_path):
# f = open(name=ideal_path, mode='rb')
# coords = pickle.load(f)
# f.close()
GEOD = Geodesic()
coords = cluster_points
lons, lats = coords[:,0], coords[:,1]
dists = []
for lon1, lat1 in zip(lons, lats):
for lon2, lat2 in zip(lons, lats):
dists.append(GEOD.haversine(lon1, lat1, lon2, lat2))
for i in dists:
print i
print "Min. Interstation Distance: ", np.min(dists)
coords = pickle.load(f)
f.close()
coords=cluster_points
lonmin, lonmax = np.floor(min(coords[:,0])), np.ceil(max(coords[:,0]))
latmin, latmax = np.floor(min(coords[:,1])), np.ceil(max(coords[:,1]))
print lonmin,lonmax,latmin,latmax
kappa = [np.vstack([[coord1[0],coord1[1],coord2[0],coord2[1]]\
for coord2 in coords]) for coord1 in coords]
GEODESIC = Geodesic()
def spread_paths(coord_list):
return GEODESIC.fast_paths(coord_list)
t0 = datetime.datetime.now()
pool = mp.Pool()
paths = pool.map(spread_paths, kappa)
pool.close()
pool.join()
t1 = datetime.datetime.now()
print t1-t0
counter, counter2 = 0, 0
#cd Desktop/Link\ to\ SIMULATIONS/Network_Tracks/smarter_model/
#plt.show()
#-----------------------------------------------------------------------------
# GENERATE SECOND SET OF VARIABLES AND STATES
#-----------------------------------------------------------------------------
ideal_path = 'ideal_coordinates.pickle'
#if no paths have been done before, start afresh!
#if dataless:
# coords = Dataless.locs_from_dataless(dataless_path)
# original_coords = coords
#elif os.path.exists(ideal_path):
# f = open(name=ideal_path, mode='rb')
# coords = pickle.load(f)
# f.close()
GEOD = Geodesic()
coords = cluster_points
lons, lats = coords[:, 0], coords[:, 1]
dists = []
for lon1, lat1 in zip(lons, lats):
for lon2, lat2 in zip(lons, lats):
dists.append(GEOD.haversine(lon1, lat1, lon2, lat2))
for i in dists:
print i
print "Min. Interstation Distance: ", np.min(dists)
print "Avg. Interstation Distance: ", np.average(dists)
print "Max. Interstation Distance: ", np.max(dists)
