I developed an application that simulates N robots moving in a grid that try to maximize the number of visited grid cells in a limited number of steps that occur at a target point. Everything is working correctly, but terribly slow. This is currently python + numpy + mathplotlib.
Maximum robots can have a soft limit of 100 (if it can get higher, it's nice to have).
To do this, I do the following, simplified:
while steps > 0:
for robot in robots:
agent.calc(robot,steps)
A robot is an array of 1x2 numpy (x-and-y-coordinates).
The agent here decides what to do. Since I need to switch tactics and strategy on the fly, I cannot move this logic.
agent.calc updates the robot in place, one by one.
cProfiling returns. Top extraction
39014272 function calls (39010490 primitive calls) in 150.314 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
12417735 62.807 0.000 62.807 0.000 distance.py:8(taxicab_distance)
124596 36.882 0.000 36.882 0.000 {numpy.core.multiarray.array}
113657 30.204 0.000 100.800 0.001 logical_agent.py:16(choose_max_distance_to...)
12417013 6.579 0.000 69.384 0.000 squaregrid.py:30(distance)
113700 2.900 0.000 109.769 0.001 logical_agent.py:73(calc)
11652363 2.625 0.000 2.625 0.000 {method 'append' of 'list' objects}
161849 1.653 0.000 1.653 0.000 distance.py:11(euclidean_distance)
113664 1.632 0.000 1.632 0.000 {sorted}
114834 1.185 0.000 1.185 0.000 {method 'keys' of 'dict' objects}
113700 0.695 0.000 1.134 0.000 squaregrid.py:19(neighbours)
, squaregird. , , Manhattan/taxicab euclidean. distance.py, .
, taxicab_distance alot, , , .
,
def taxicab_distance(u, v):
return np.abs(u[0] - v[0]) + np.abs(u[1] - v[1])
, python , , . {Numpy.core.multiarray.array} , , , .
: → environment.distance → taxicab_distance
, , ? pythons c extensibility, cython, . ? , ?