timeo 0.2.1

Terminal progress bars via decorators

⏱ timeo

Terminal progress bars for Python functions — just add a decorator.

✓ process_files   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━  100%  12.4s
  download_data   ━━━━━━━━━━━━━━━━━━━━━━━          72%   0:00:04
  compress_output ━━━━━━                           20%   0:00:31

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Why timeo?

Most progress bar libraries ask you to wrap your loops manually and manage the display yourself. timeo gets out of the way — decorate a function, iterate normally, done.

# before
for item in items:
    process(item)

# after
@timeo.track
def run(items):
    for item in timeo.iter(items):
        process(item)

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Installation

pip install timeo

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Usage

Basic progress bar

@timeo.track wraps any function with a live progress bar. The total is inferred automatically from the first argument with a len(). Use timeo.iter() to advance the bar on each iteration — no manual bookkeeping needed.

import timeo

@timeo.track
def process_files(files):
    for f in timeo.iter(files):
        do_work(f)

process_files(my_files)

Prefer manual control? Use timeo.advance() instead:

@timeo.track
def process_files(files):
    for f in files:
        do_work(f)
        timeo.advance()

---

Multiple concurrent functions

Every decorated function gets its own bar. They render together in a single live display. Finished bars collapse to a compact summary line so the output stays clean.

@timeo.track
def process_files(files):
    for f in timeo.iter(files):
        do_work(f)

@timeo.track
def download_data(urls):
    for url in timeo.iter(urls):
        fetch(url)

process_files(my_files)
download_data(my_urls)

✓ process_files   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━  100%  12.4s
  download_data   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━   72%  0:00:04

Concurrent execution (threads) is fully supported — each bar tracks its own function independently.

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Learn mode

Add learn=True and timeo will remember how long your function takes across runs, building an EMA (exponential moving average) of its runtime. Instead of counting steps, the bar fills over the expected duration.

@timeo.track(learn=True)
def run_pipeline(data):
    heavy_computation(data)
    more_work(data)

Run	Behaviour
First	Indeterminate spinner with run_pipeline (learning...) label
Subsequent	Determinate bar filling over the expected duration
After code change	Cache invalidates automatically — learning restarts

Timing data lives at ~/.cache/timeo/timings.json. The cache key is a hash of the function's bytecode — not its name — so renaming a function preserves its history, and changing its implementation resets it.

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Explicit display control

The live display starts and stops automatically in most cases. For complex scripts with branching logic or long-running setup, use timeo.live() to pin the display lifetime explicitly:

with timeo.live():
    process_files(my_files)
    download_data(my_urls)
    # display stays open until the with-block exits

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How it works

Piece	Role
@timeo.track	Wraps the function, infers total from Sized args, manages the task lifecycle
ProgressManager	Singleton owning the rich live display; reference-counted so it tears down only when all tasks finish
TrackedTask	Dataclass holding per-function progress state
timeo.iter()	Thin generator wrapper that calls advance() on each item
ContextVar	Ensures timeo.advance() always updates the right bar, even across threads

The display is built on rich.progress and rich.live.

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_{Built with rich · MIT License}