96755ecd21
Some checks failed
Build / Test on ubuntu-latest (push) Successful in 2m4s
Examples / Test examples (ubuntu-latest, 3.10) (push) Successful in 2m16s
Lint / lint (push) Successful in 1m25s
Build / Test on macOS-latest (push) Has been cancelled
Build / Test on windows-latest (push) Has been cancelled
CodeQL / Analyze (python) (push) Failing after 1s
89 lines
2.5 KiB
Python
89 lines
2.5 KiB
Python
"""
|
|
Lists the most common words among uncategorized events, by duration, to help in creating categories.
|
|
|
|
This might make more sense as a notebook.
|
|
"""
|
|
|
|
from collections import Counter
|
|
from datetime import datetime, timedelta, timezone
|
|
from tabulate import tabulate
|
|
from typing import Dict, List, Tuple, Any
|
|
|
|
from aw_core import Event
|
|
import aw_client
|
|
from aw_client import queries
|
|
|
|
# set up client
|
|
awc = aw_client.ActivityWatchClient("test")
|
|
|
|
|
|
def example_categories():
|
|
# TODO: Use tools in aw-research to load categories from toml file
|
|
return [
|
|
(
|
|
("Work", "ActivityWatch"),
|
|
{"type": "regex", "regex": "aw-|activitywatch", "ignore_case": True},
|
|
),
|
|
]
|
|
|
|
|
|
def get_events(categories=List[Tuple[Tuple[str], Dict[str, Any]]]):
|
|
"""
|
|
Retrieves AFK-filtered events, only returns events which are Uncategorized.
|
|
"""
|
|
|
|
start = datetime(2022, 1, 1, tzinfo=timezone.utc)
|
|
now = datetime.now(tz=timezone.utc)
|
|
timeperiods = [(start, now)]
|
|
|
|
canonicalQuery = queries.canonicalEvents(
|
|
queries.DesktopQueryParams(
|
|
bid_window="aw-watcher-window_",
|
|
bid_afk="aw-watcher-afk_",
|
|
classes=categories,
|
|
)
|
|
)
|
|
res = awc.query(
|
|
f"""
|
|
{canonicalQuery}
|
|
events = filter_keyvals(events, "$category", [["Uncategorized"]]);
|
|
duration = sum_durations(events);
|
|
RETURN = {{"events": events, "duration": duration}};
|
|
""",
|
|
timeperiods,
|
|
)
|
|
events = res[0]["events"]
|
|
print(f"Fetched {len(events)} events")
|
|
return [Event(**e) for e in events]
|
|
|
|
|
|
def events2words(events):
|
|
for e in events:
|
|
for v in e.data.values():
|
|
if isinstance(v, str):
|
|
for word in v.split():
|
|
if len(word) >= 3:
|
|
# normalize
|
|
word = word.lower()
|
|
yield (word, e.duration)
|
|
|
|
|
|
def main():
|
|
categories = example_categories()
|
|
events = get_events(categories)
|
|
|
|
# find most common words, by duration
|
|
corpus: Dict[str, timedelta] = Counter() # type: ignore
|
|
for word, duration in events2words(events):
|
|
if word not in corpus:
|
|
corpus[word] = timedelta(0)
|
|
corpus[word] += duration
|
|
|
|
# The top words are rarely useful for categorization, as they are usually browsers and other categories
|
|
# of activity which are too broad for it to make sense as a rule (except as a fallback).
|
|
print(tabulate(corpus.most_common(50), headers=["word", "duration"])) # type: ignore
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|