#GRAD = np.ma.masked_where(GRAD==0,GRAD)
H_avg1 = np.average(H)
grad_check1 = np.std(GRAD)
#-------------------------------------------------------------------------
# Find coordinates of high density within path density field.
#-------------------------------------------------------------------------
search = np.where(H > 0.50 * np.max(H))
# Scale these points with respect to the lat-lon limits!
Hmaxx, Hmaxy = search[1], search[0]
Hmaxx = (lonmax-lonmin)/(nbins) * Hmaxx + lonmin
Hmaxy = (latmax-latmin)/(nbins) * Hmaxy + latmin
# Make sure all low density coordinates ARE within shapefile!
highest_density_coords = ps.paths_in_shape(np.column_stack((Hmaxx, Hmaxy)))
WHERE = np.where(H < perc_high*H_avg1)
if 0 < rand_indicator <= 3:
#half the time move the coordinates to low density locations.
#scale these points with respect to the lat-lon limits!
Hminx, Hminy = WHERE[1], WHERE[0]
Hminx = (lonmax-lonmin)/(nbins) * Hminx + lonmin
Hminy = (latmax-latmin)/(nbins) * Hminy + latmin
# Make sure all low density coordinates ARE within shapefile!
low_density_coords = ps.paths_in_shape(np.column_stack((Hminx, Hminy)))
# Low_density_coords = many_points(shape, np.column_stack((Hminx, Hminy)))
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
# Make sure all low density coordinates ARE within shapefile!
low_density_coords = ps.paths_in_shape(np.column_stack((Hmaxx, Hmaxy)))
#N_cluster_points = kmeans(low_density_coords, N)[0]
density_coords = DENSITY.select_points()
# make sure that your density coords are within the boundary shape
density_coords = INPOLY.points_in(density_coords)
#cluster = True
if counter == 0:
grad_ideal = 1e6
avg_ideal = 0
if grad_check1 < grad_ideal and avg_ideal < H_avg1:
with open(u'ideal_coordinates.pickle', 'wb') as f: