pool.close()
pool.join()
t1 = datetime.datetime.now()
print t1 - t0
paths = GEODESIC.combine_paths(paths)
paths = GEODESIC.remove_zeros(paths)
lons, lats = paths[:, 0], paths[:, 1]
if show:
plt.figure()
plt.scatter(lons, lats)
plt.show()
DENSITY = Density(paths=paths)
H, xedges, yedges = DENSITY.hist2d(paths=paths)
H = np.rot90(H)
H = np.flipud(H)
H = np.ma.masked_where(H == 0, H)
H_avg = np.average(H)
H_std = np.std(H)
print "The point density distribution average for {} is: {} ".format(
region_name, H_avg)
print "The point density distribution standard deviation for {} is: {} ".format(
region_name, H_std)
pool.close()
pool.join()
t1 = datetime.datetime.now()
print t1-t0
paths = GEODESIC.combine_paths(paths)
paths = GEODESIC.remove_zeros(paths)
lons, lats = paths[:,0], paths[:,1]
if show:
plt.figure()
plt.scatter(lons, lats)
plt.show()
DENSITY = Density(paths=paths)
H, xedges, yedges = DENSITY.hist2d(paths=paths)
H = np.rot90(H)
H = np.flipud(H)
H = np.ma.masked_where(H==0,H)
H_avg = np.average(H)
H_std = np.std(H)
print "The point density distribution average for {} is: {} ".format(region_name, H_avg)
print "The point density distribution standard deviation for {} is: {} ".format(region_name, H_std)
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.
#create a flattened numpy array of size 2xN from the paths created!
paths1 = GEODESIC.combine_paths(paths)
paths = list(paths)
paths1 = GEODESIC.remove_zeros(paths1)
DENSITY = Density(paths=paths1)
H, xedges, yedges = DENSITY.hist2d(paths=paths1)
grad = DENSITY.hgrad(H=H)
H_avg1 = np.average(H)
grad_check1 = np.std(grad)
H_masked = DENSITY.transform_h(H=H)
grad = DENSITY.transform_grad(grad=grad)
#search = np.where(H<0.1*np.average(H))
#Hmaxx, Hmaxy = search[1], search[0]
#Hmaxx = (lonmax-lonmin)/(nbins) * Hmaxx + lonmin
#Hmaxy = (latmax-latmin)/(nbins) * Hmaxy + latmin
# GEODESIC.fast_paths(path)
total_points.append(path)
total_points = list(it.chain(*total_points))
total_points = np.array(total_points)
total_points = np.asarray(INPOLY.points_in(total_points, poly=poly, IN=True))
plt.figure()
plt.scatter(total_points[:, 0], total_points[:, 1])
plt.scatter(coords[:, 0], coords[:, 1], c='orange')
plt.show()
DENSITY = Density(paths=total_points, nbins=nbins)
H, xedges, yedges = DENSITY.hist2d(paths=total_points)
#histogram_GIS = np.column_stack((H, xedges, yedges))
print H.shape, xedges.shape, yedges.shape
coords = np.array([[x, y] for x in xedges[:-1] for y in yedges[:-1]])
H = np.rot90(H)
H = np.flipud(H)
#H = np.rot90(H)
#H = np.rot90(H)
#plt.figure()
