easy_install line_profiler
cProfile
--Profiler le code entier
python -m cProfile -s cumulative chi2.py > profile
less profile
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.002 0.002 7.516 7.516 chi2.py:3(<module>)
1 0.000 0.000 6.099 6.099 chi2.py:101(chi2)
4959 0.229 0.000 3.511 0.001 chi2.py:11(detect_step)
29 0.001 0.000 3.291 0.113 chi2.py:84(calculate_border_bestfit)
435 0.008 0.000 3.289 0.008 chi2.py:35(detect_step_all_ranges)
76661 0.219 0.000 2.738 0.000 fromnumeric.py:2830(var)
1 0.000 0.000 2.538 2.538 chi2.py:53(plot)
76661 1.215 0.000 2.519 0.000 _methods.py:77(_var)
1 0.000 0.000 2.421 2.421 pyplot.py:138(show)
1 0.000 0.000 2.421 2.421 backend_bases.py:140(__call__)
1 0.000 0.000 2.417 2.417 backend_macosx.py:28(mainloop)
1 2.323 2.323 2.417 2.417 {matplotlib.backends._macosx.show}
1 0.001 0.001 1.251 1.251 pyplot.py:17(<module>)
Line profile
--Ajouter @profile à la fonction chi2
chi2.py
@profile
def chi2(trajectory, t):
s = []
border_bestfit_best = []
max_s = 0.0
for nstep in range(1, 30):
border_bestfit = calculate_border_bestfit(trajectory, nstep)
border_counter = calculate_border_counter(trajectory, border_bestfit)
s = calculate_chi2(trajectory, border_counter) / calculate_chi2(trajectory, border_bestfit)
if max_s < s:
max_s = s
border_bestfit_best = border_bestfit
# Calculate average trajectory
average_trajectory = [np.average(trajectory[border_bestfit_best[i]:border_bestfit_best[i+1]])
for i in range(0, len(border_bestfit_best) - 1)]
# Plot
plot(trajectory, border_bestfit_best, average_trajectory)
# Calculate step size
step_size = [average_trajectory[i+1] - average_trajectory[i]
for i in range(0, len(average_trajectory)-1)]
return step_size
--Créez un fichier lprof en utilisant kernprof.py
kernprof.py -l chi2.py
python -m line_profiler chi2.py.lprof
Timer unit: 1e-06 s
Total time: 6.75531 s
File: chi2.py
Function: chi2 at line 101
Line # Hits Time Per Hit % Time Line Contents
==============================================================
101 @profile
102 def chi2(trajectory, t):
103 1 3 3.0 0.0 s = []
104 1 1 1.0 0.0 border_bestfit_best = []
105 1 1 1.0 0.0 max_s = 0.0
106
107 30 23 0.8 0.0 for nstep in range(1, 30):
108 29 4188417 144428.2 62.0 border_bestfit = calculate_border_bestfit(trajectory, nstep)
109 29 310665 10712.6 4.6 border_counter = calculate_border_counter(trajectory, border_bestfit)
110 29 46667 1609.2 0.7 s = calculate_chi2(trajectory, border_counter) / calculate_chi2(trajectory, border_bestfit)
111 29 39 1.3 0.0 if max_s < s:
112 7 5 0.7 0.0 max_s = s
113 7 7 1.0 0.0 border_bestfit_best = border_bestfit
114
115 # Calculate average trajectory
116 1 1 1.0 0.0 average_trajectory = [np.average(trajectory[border_bestfit_best[i]:border_bestfit_best[i+1]])
117 12 289 24.1 0.0 for i in range(0, len(border_bestfit_best) - 1)]
118
119 # Plot
120 1 2209171 2209171.0 32.7 plot(trajectory, border_bestfit_best, average_trajectory)
121
122 # Calculate step size
123 1 2 2.0 0.0 step_size = [average_trajectory[i+1] - average_trajectory[i]
124 11 18 1.6 0.0 for i in range(0, len(average_trajectory)-1)]
125
126 1 0 0.0 0.0 return step_size
--Le temps pris pour chaque ligne est affiché --62% du temps de calcul de la fonction chi2 est utilisé pour calculer la fonction Calculate_border_bestfit
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