then in objects.base the getRange method was optimized.
It still is the main bottleneck,
but there seems to be no potential for further optimization.
Now you should modify/copy this file,
to profile your AI source code.
In some tournaments, there is a penalty for computation cost."""
from src.Wesen.loader import Loader
from cProfile import Profile
from time import perf_counter
# only in python3.3
from pstats import Stats
import sys
print("You can supply an alternative config file on the command-line")
print("You should stop Wesen by Ctrl+C to finish profiling")
sys.argv.append('--disablegui')
pr = Profile(perf_counter)
pr.run('Loader()')
pr.dump_stats('profile.stats')
# you may explore profile.stats with the pstats browser.
stats = Stats(pr)
stats.sort_stats('tottime')
# stats.print_stats('Wesen/sources',10)
stats.print_stats('Wesen/', 10)
import shutil
import os
if __name__ == '__main__':
logging.getLogger().setLevel(logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
logformat = '%(asctime)s|%(levelname)s|%(name)s|%(message)s'
ch.setFormatter(logging.Formatter(logformat))
logging.getLogger().addHandler(ch)
logger = logging.getLogger('run')
logger.info('Starting profiling')
def run_somecommand():
from ppci.api import construct
print(construct)
construct('../examples/linux64/fib/build.xml')
p = Profile()
s = p.run('run_somecommand()')
stats = pstats.Stats(p)
stats.dump_stats('results.cprofile')
stats.sort_stats('cumtime')
logger.info('Done profiling')
# Convert result profile to kcachegrind format:
if shutil.which('pyprof2calltree'):
os.system('pyprof2calltree -i results.cprofile')
cx, cy = data.shape[1] // 2, data.shape[0] // 2
idx = 0
radii = list()
for idx in range(60, 70):
frame = (convert_image(_smooth(data[idx], 1)))
y_size, x_size = frame.shape
points = StarBurst(cx + cy * 1j, 30).detect(frame)
result = fit(Conic, EllipseNormalizer(), points)
min_result = refine_pupil(frame, result)
x, y, a, b, Θ = min_result # conic2ellipse(result.coef)
radii.append(np.sqrt(a * b))
cosΘ, sinΘ = np.cos(Θ), np.sin(Θ)
x_mesh, y_mesh = np.meshgrid(np.arange(x_size), np.arange(y_size))
mask = (((x_mesh - x) * cosΘ - (y_mesh - y) * sinΘ) / a) ** 2\
+ (((x_mesh - x) * sinΘ + (y_mesh - y) * cosΘ) / b) ** 2 < 1
with MplFigure(f"sample_frame_{idx}.png", (6, 4)) as ax:
ax.imshow(frame / 2 + mask * 128)
ax.scatter(points.real, points.imag)
return radii
##
from cProfile import Profile
profile = Profile()
profile.run("test_detect(data)")
profile.dump_stats("stats.prof")
print('done')
##
radii = test_detect(data)
then in objects.base the getRange method was optimized.
It still is the main bottleneck,
but there seems to be no potential for further optimization.
Now you should modify/copy this file,
to profile your AI source code.
In some tournaments, there is a penalty for computation cost."""
from src.Wesen.loader import Loader
from cProfile import Profile
from time import perf_counter
# only in python3.3
from pstats import Stats
import sys
print("You can supply an alternative config file on the command-line")
print("You should stop Wesen by Ctrl+C to finish profiling")
sys.argv.append('--disablegui')
pr = Profile(perf_counter)
pr.run('Loader()')
pr.dump_stats('profile.stats')
# you may explore profile.stats with the pstats browser.
stats = Stats(pr)
stats.sort_stats('tottime')
# stats.print_stats('Wesen/sources',10)
stats.print_stats('Wesen/', 10)
except IndexError:
pass
return side*side
max_square = 0
for row_i, row in enumerate(matrix):
if '1' in row:
for column_i, column in enumerate(row):
if column == '1':
max_square = max(max_square,tell_square(row_i,column_i))
return max_square
profile = Profile()
s = Solution()
a = [["0","0","0","1","0","1","1","1"],["0","1","1","0","0","1","0","1"],["1","0","1","1","1","1","0","1"],["0","0","0","1","0","0","0","0"],["0","0","1","0","0","0","1","0"],["1","1","1","0","0","1","1","1"],["1","0","0","1","1","0","0","1"],["0","1","0","0","1","1","0","0"],["1","0","0","1","0","0","0","0"]]
profile.run('s.maximalSquare(a)')
stats = Stats(profile)
stats.strip_dirs()
stats.sort_stats('cumulative')
stats.print_stats()
#Excellent code
# class Solution(object):
# def maximalSquare(self, matrix):
# """
# :type matrix: List[List[str]]
# :rtype: int
# """
# if not matrix:
# return 0
# m = len(matrix);n = len(matrix[0])
times = []
worlds_data = []
game_count = 0
while game_count < num_games:
(W, goal_list, init_list, env_init_list) = random_world(size=(height, width),
wall_density=bdensity,
num_goals=2,
num_env=num_env)
print "Nominal world:"
print pretty_world(W, goal_list=goal_list, init_list=init_list,
env_init_list=env_init_list)
print "Trying to solve..."
nsprof = Profile()
nsprof.run("aut = gen_navobs_soln(init_list=init_list, goal_list=goal_list, W=W, num_obs=len(env_init_list), env_init_list=env_init_list, restrict_radius=1)")
if aut is None:
print "Nominal spec not feasible."
print "#"*60
continue
print "Resulting solution automaton M has %d nodes." % aut.size()
aut.trimDeadStates()
print "After trimming dead nodes, M has size %d" % aut.size()
ind = -1
while not hasattr(nsprof.getstats()[ind].code, "co_name") or (nsprof.getstats()[ind].code.co_name != "gen_navobs_soln"):
ind -= 1
nom_time = nsprof.getstats()[ind].totaltime
aut.writeDotFileCoord("tempsyn-ORIG.dot")
po = powerset(n)
print("make powerset")
l = 2**n-2
print("count topology")
core = 10
t=0
output = Queue()
procs= [0]*core
rancore = range(core)
for i in rancore:
procs[i] = Process(target=proce,args=(i,core,l,po,output))
for i in rancore:
procs[i].start()
print('process %d start'%i)
#process
for i in rancore:
procs[i].join()
t += output.get()
print('process %d end' % i)
output.close()
return t
if __name__=="__main__":
profile = Profile()
n = int(input('input cardinal number of topology: '))
profile.run('checkdimen(n)')
stats = Stats(profile)
stats.strip_dirs()
stats.sort_stats('cumulative')
stats.print_stats()
