Profiler

class sorts.profiling.Profiler(distribution=False, track_memory=False, snapshot_total=True)

Bases: object

Performance profiler class.

The named performance control is especially useful for timing contents of loops.

Usage example:

p = Profiler()
p.start('list init')
p.start('program')
lst = list(range(200))
p.stop('list init')
for i in range(1000):
    p.start('list reversal')
    lst = lst[::-1]
    p.stop('list reversal')
p.stop('program')

print(p)

Methods

__init__([distribution, track_memory, …])	Initialize self.
fmt([normalize, timedelta])	Format summary of the profiler into a string.
from_txt(path)
mean([name])
memory_diff(name[, save])	Calculate a memory difference between the latest snapshot and now.
snapshot(name)	Take a tracemalloc snapshot.
start(name)	Records a start time for named call.
stop(name)	Records a time elapsed since the latest start time for named call.
to_txt(path)
total([name])