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()