Faster way to access 2D lists/arrays and do calculations in Python? -

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i running 2 nested loops (first 1 120 runs, second 1 500 runs). in of 120x500 runs need access several lists , lists-in-lists (i call them 2d arrays).

at moment 120x500 runs take 4 seconds. of time taken 3 list appends , several 2d array accesses. arrays prefilled me outside of loops.

here code:

    #range 0 119     cur_angle in range(0, __ar_angular_width-1):          #range 0 499         cur_length in range(0, int(__ar_length * range_res_scale)-1):              v_x = (auv_rot_mat_0_0*self.adjacent_dx[cur_angle][cur_length])+(auv_rot_mat_0_1*self.opposite_dy[cur_angle][cur_length])             v_y = (auv_rot_mat_1_0*self.adjacent_dx[cur_angle][cur_length])+(auv_rot_mat_1_1*self.opposite_dy[cur_angle][cur_length])              v_x_diff = (v_x+auv_trans_x) - ocp_grid_origin_x             v_y_diff = (v_y+auv_trans_y) - ocp_grid_origin_y              p_x = (m.floor(v_x_diff/ocp_grid_resolution))             p_y = (m.floor(v_y_diff/ocp_grid_resolution))              data_index = int(p_y * ocp_grid_width + p_x)              if data_index >= 0 , data_index < (len(ocp_grid.data)-1):                 probability = ocp_grid.data[data_index]                  if probability == 100:                     if not m.isnan(self.v_directions[cur_angle]):                         magnitude = m.pow(probability, 2) * self.magnitude_ab[cur_length]                          ov_1 = self.v_directions[cur_angle]                         ov_2 = magnitude                         ov_3 = self.distances[cur_length]                          obstacle_vectors.append(ov_1)                         obstacle_vectors.append(ov_2)                         obstacle_vectors.append(ov_3)

i tried figure out processing times via time.time() , building differences, didn't work reliable. calculated times fluctuating quite lot.

i not python pro, advices welcome. ideas how make code faster?

edit: initialization of arrays done code:

        self.adjacent_dx = [i[:] in [[0]*(length_iterations-1)]*(angular_iterations-1)]         self.opposite_dy = [i[:] in [[0]*(length_iterations-1)]*(angular_iterations-1)]

some general tips:

  1. try optimize algorithm best can.
  2. if it's possible, use pypy instead of regular python. doesn't require code modification if external dependencies work it.
  3. static typing , compilation c can add additional boost, requires simple code modifications. purpose can use cython.

note step 1 ofter hard , time consuming, giving small amounts of boost if have code, while steps 2 , 3 give dramatic boost without additional effort.


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