dist_points = map(points_and_distances, nets)
return np.vstack(dist_points)
def spread_paths(nets):
return map(paths_func, nets)
#-----------------------------------------------------------------------------
# GENERATE SECOND SET OF VARIABLES AND STATES
#-----------------------------------------------------------------------------
ideal_path = 'ideal_coordinates.pickle'
#if no paths have been done before, start afresh!
if not os.path.exists(ideal_path):
#generate N kmeans cluster points from massive M number of randomly
#distributed points inside the shape file.
M = 1e5
lot_points = ps.points_in_shape(shape_path, M)
coords = cluster_points(lot_points, N)
#else import already processed coordinates if the program has already done so.
else:
f = open(name=ideal_path, mode='rb')
coords = pickle.load(f)
f.close()
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
t0 = datetime.datetime.now()
kappa = [np.vstack([[coord1[0],coord1[1],coord2[0],coord2[1]]\
use_old_path = False
searches_per_point = 3
factor = 0.05
cluster = False
N_cluster_points = False
while infinite_counter <= 1:
t0 = datetime.datetime.now()
#----------------------------------------------------------------------
# Generate N new point coordinates
#----------------------------------------------------------------------
if cluster:
new_coords = N_cluster_points
else:
new_coords = ps.points_in_shape(shape_path, N)
coords = np.append(coords, new_coords, axis=0)
coord_set = [np.vstack([[coord1[0],coord1[1],coord2[0],coord2[1]]\
for coord2 in coords]) for coord1 in coords]
t0 = datetime.datetime.now()
pool = mp.Pool()
paths = pool.map(spread_paths, coord_set)
pool.close()
pool.join()
t1 = datetime.datetime.now()
print "time to generate new paths", t1 - t0
# Append new set of paths now that old set has been deleted.
H = np.rot90(H)
H = np.flipud(H)
# Mask zeros
Hmasked = np.ma.masked_where(H == 0, H) # Mask pixels with a value of zero
return Hmasked
t_total0 = datetime.datetime.now()
t0 = datetime.datetime.now()
ideal_path = 'ideal_coordinates.pickle'
#if no paths have been done before, start afresh!
if not os.path.exists(ideal_path):
M = 1e5
many_points = ps.points_in_shape(shape_path, M)
coords = cluster_points(many_points, N)
#else import already processed coordinates if the program has already done so.
else:
f = open(name=ideal_path, mode='rb')
coords = pickle.load(f)
f.close()
#generate N kmeans cluster points from massive M number of randomly distributed
#points inside the shape file.
lonmin = np.floor(min(coords[:, 0]))
lonmax = np.ceil(max(coords[:, 0]))
latmin = np.floor(min(coords[:, 1]))
latmax = np.ceil(max(coords[:, 1]))
#sigma02 = sigma01+sigma12
lon01 = atan2(sin(alpha0)*sin(sigma01), cos(sigma01))
lon0 = lon1 - lon01
npts = max(int((np.ceil(dist) + 1)/km), 100)
all_d = np.linspace(0,dist,npts)/R
lons, lats = vlon_func(all_d, sigma01, alpha0, lon0), vlat_func(all_d, sigma01, alpha0, lon0)
return np.column_stack((lons, lats))
t_total0 = datetime.datetime.now()
number=0
while number < 50:
t0 = datetime.datetime.now()
#lat lon coordinates of random points generated within set shape file
coords = ps.points_in_shape(shape_path, N)
#lons1,lats1 = coords[:,0], coords[:,1]
#lons2,lats2 = lons1,lats1
lonmin = np.floor(min(coords[:,0]))
latmin = np.floor(min(coords[:,1]))
coords1 = [coord1 for coord1 in coords for coord2 in coords]
coords2 = [coord2 for coord1 in coords for coord2 in coords]
columns = np.column_stack((coords1, coords2))
#dists = map(haversine, columns)
#path_info = zip(coords1,coords2, dists)
sigma01, alpha0 = atan2(tan(lat1), cos(alpha1)), asin(sin(alpha1)*cos(lat1))
#sigma02 = sigma01+sigma12
lon01 = atan2(sin(alpha0)*sin(sigma01), cos(sigma01))
lon0 = lon1 - lon01
npts = max(int((np.ceil(dist) + 1)/km), 100)
all_d = np.linspace(0,dist,npts)/R
lons, lats = vlon_func(all_d, sigma01, alpha0, lon0), vlat_func(all_d, sigma01, alpha0, lon0)
return np.column_stack((lons, lats))
t_total0 = datetime.datetime.now()
t0 = datetime.datetime.now()
coords = ps.points_in_shape(shape_path, N)
lonmin = np.floor(min(coords[:,0]))
latmin = np.floor(min(coords[:,1]))
#coords1 = [coord1 for coord1 in coords for coord2 in coords]
#coords2 = [coord2 for coord1 in coords for coord2 in coords]
#columns = np.column_stack((coords1, coords2))
kappa = [np.vstack([[coord1[0],coord1[1],coord2[0],coord2[1]]\
for coord2 in coords]) for coord1 in coords]
def spread_paths(nets):