philiprehberger-task-timer

Named timers for measuring multiple operations within a function or script.

Installation

pip install philiprehberger-task-timer

Usage

from philiprehberger_task_timer import TaskTimer

t = TaskTimer()

with t.task("load data"):
    data = load_from_disk()

with t.task("process"):
    result = process(data)

with t.task("save"):
    save(result)

t.summary()

Output:

load data    0.52s  (26%)
process      1.23s  (62%)
save         0.24s  (12%)

Nested tasks

Tasks can be nested. Nested tasks appear indented in the summary output.

t = TaskTimer()

with t.task("pipeline"):
    with t.task("step 1"):
        do_step_1()
    with t.task("step 2"):
        do_step_2()

t.summary()

Output:

pipeline      2.05s  (100%)
  step 1      0.80s  (39%)
  step 2      1.25s  (61%)

Laps

Use lap() to record incremental time without context managers.

t = TaskTimer()
load()
t.lap("load")
process()
t.lap("process")
t.summary()

as_dict

Retrieve raw timing data as a dictionary.

t = TaskTimer()
with t.task("work"):
    do_work()

print(t.as_dict())  # {"work": 1.234}

Async support

The task() context manager works with async with as well.

t = TaskTimer()
async with t.task("fetch"):
    await fetch_data()
t.summary()

Extremes and export

Inspect the slowest and fastest recorded tasks, or persist a JSON summary to disk.

t = TaskTimer()
with t.task("load"):
    load()
with t.task("process"):
    process()

print(t.slowest())  # ("process", 1.234)
print(t.fastest())  # ("load", 0.123)

t.export_json("timings.json")

API

Development

pip install -e .
python -m pytest tests/ -v

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License

MIT