an empirical progress library
docbrown is an empirical progress library. It determines the overall duration and progression of a process by looking at the time it took in the past.
docbrown might be an option for you, if you have one long running task which parts take very different amounts of time. It is also helpful when used in environments where you can’t inform consumers about the progress of a task out-of-band (like a WSGI request/response cycle vs a WebSocket).
pip install docbrown
This repository allows you to create a deb Package for Debian and derivatives
if you’re into that kind of thing. Just run
import time from docbrown import record_progress with record_progress('process_name', ident='my_ident') as record: # ident normally is random and unique, but you can override it with # your own id. Just make sure it is not used more than once at a time. print(record.ident) # do some stuff that takes time record('loading_data') time.sleep(4) record('calculating_matrices') time.sleep(9) record('rendering_structures') time.sleep(23) record('uploading_models') time.sleep(15)
As docbrown determines progression by looking at the past every process needs
to run at least once before
get_progress can return any meaningful data. This
get_progress will just return
None on the first run of the code above.
Get the progression of our process:
from docbrown import get_progress print(get_progress('my_ident'))
There are some things that would be nice, but have not been implemented yet.
- additional storage backends apart from SQLite
- code path detection that takes optional phases into account and updates the expected duration and progress on-the-fly
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