philiprehberger-task-timer
v0.1.7423 downloadsMIT
Named timers for measuring multiple operations within a function or script
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pip install philiprehberger-task-timerREADME
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()
API
| Method / Property | Description |
|---|---|
task(name) | Context manager that times the enclosed block (sync and async) |
lap(name) | Record time since the last lap or timer creation |
summary(*, file=sys.stdout) | Print formatted summary with name, duration, and percentage |
as_dict() | Return dict[str, float] of task name to seconds |
total | Property returning the total time across all tasks |
reset() | Clear all recorded tasks |
Development
pip install -e .
python -m pytest tests/ -v
Support
If you find this project useful: