SmartTimers

SmartTimers is a collection of libraries for measuring runtime of running processes using a simple and flexible API. Time can be measured in sequential and nested code blocks.

A SmartTimer allows recording elapsed time in an arbitrary number of code blocks. Specified points in the code are marked as either the beginning of a block to measure, tic, or as the end of a measured block, toc. Times are managed internally and ordered based on tic calls. Times can be queried, operated on, and written to file.

The following schemes are supported for timing code blocks

• Series: tic('A'), toc(), ..., tic('B'), toc()
• Cascade: tic('A'), toc(), toc(), ...
• Nested: tic('A'), tic('B'), ..., toc(), toc()
• Label-paired: tic('A'), tic('B'), ..., toc('A'), toc('B')
• Mixed: arbitrary combinations of schemes

from smarttimers import SmartTimer

# Create a timer instance named 'Example'
t = SmartTimer("Example")

# Print clock details
t.tic("info")
t.print_info()
t.toc()

# Measure iterations in a loop
t.tic("loop")
for i in range(10):
    t.tic("iter " + str(i))
    sum(range(1000000))
    t.toc()
t.toc()

t.tic("sleep")
t.sleep(2)
t.toc()

# Write times to file 'Example-times.csv'
t.dump_times()

print(t["info"])
print(t.walltime)

# Print times measured in different ways
>>> print(t)
 label,  seconds,  minutes, rel_percent, cumul_sec, cumul_min,cumul_percent
  info, 0.000270, 0.000004,      0.0001,  0.000270,  0.000004,       0.0001
  loop, 0.153422, 0.002557,      0.0664,  0.153692,  0.002562,       0.0666
iter 0, 0.022840, 0.000381,      0.0099,  0.176531,  0.002942,       0.0765
iter 1, 0.023248, 0.000387,      0.0101,  0.199780,  0.003330,       0.0865
iter 2, 0.017198, 0.000287,      0.0074,  0.216977,  0.003616,       0.0940
iter 3, 0.012921, 0.000215,      0.0056,  0.229898,  0.003832,       0.0996
iter 4, 0.012754, 0.000213,      0.0055,  0.242652,  0.004044,       0.1051
iter 5, 0.012867, 0.000214,      0.0056,  0.255519,  0.004259,       0.1107
iter 6, 0.012843, 0.000214,      0.0056,  0.268361,  0.004473,       0.1162
iter 7, 0.012789, 0.000213,      0.0055,  0.281150,  0.004686,       0.1218
iter 8, 0.012818, 0.000214,      0.0056,  0.293969,  0.004899,       0.1273
iter 9, 0.012856, 0.000214,      0.0056,  0.306825,  0.005114,       0.1329
 sleep, 2.002152, 0.033369,      0.8671,  2.308977,  0.038483,       1.0000

# Print stats only for labels with keyword 'iter'
>>> print(t.stats("iter"))
namespace(avg=(0.015313280202099122, 0.00025522133670165205),
max=(0.023248409008374438, 0.0003874734834729073),
min=(0.012753532995702699, 0.00021255888326171163),
total=(0.15313280202099122, 0.0025522133670165203))