I have implemented my code using Cython. It is the current bottleneck in my computations.
There are two non-numpy functions involved:
calculate_2D_dist_squared
which calculates the distance squared between two pointscalculate_2D_dist_squared_matrix
which generates the distances squared between every two distinct vertices.calculate_2D_dist_squared_matrix
organizes results so thatdist_squared_matrix[1, 2, 3, 4]
= "the distance between polygon 1, vertex 2 and polygon 3, vertex 4". All indexing starts from 0.
cdef double calculate_2D_dist_squared(self, np.ndarray[np.float64_t, ndim=1] p1, np.ndarray[np.float64_t, ndim=1] p2):
cdef np.ndarray[np.float64_t, ndim=1] relative_vector = p1 - p2
return relative_vector[0]**2 + relative_vector[1]**2
cdef np.ndarray[np.float64_t, ndim=4] calculate_2D_dist_squared_matrix(self, np.ndarray[np.float64_t, ndim=3] polygons_vertex_coords, int num_polygons, int num_vertices):
cdef:
int pi_focus
int vi_focus
int pi
int vi
# at initialization, set all dist_squared values to be -1,
# indicating that they have been initialized, but not set properly
# since by definition a dist_squared value has to be >= 0
np.ndarray[np.float64_t, ndim=4] result = -1*np.ones((num_polygons, num_vertices, num_polygons, num_vertices), dtype=np.float64)
for pi_focus in range(num_polygons):
for vi_focus in range(num_vertices):
for pi in range(num_polygons):
for vi in range(num_vertices):
# if a dist_squared < 0, then it means that it
# it has not been changed since initialization, and
# needs to be updated, this way I avoid repeating work
if result[pi_focus, vi_focus, pi, vi] < 0:
dist_squared = self.calculate_2D_dist_squared(polygons_vertex_coords[pi_focus, vi_focus], polygons_vertex_coords[pi, vi])
result[pi_focus, vi_focus, pi, vi] = dist_squared
result[pi, vi, pi_focus, vi_focus] = dist_squared
return result
What are some things I could think about in order to increase the performance of my code?
For the time being, I got a significant improvement in performance by only re-calculating updates to the dist_squared_matrix
, rather than always re-calculating the dist_squared_matrix
entirely every step.