Client library for ActivityWatch
Project description
aw-client
Client library for ActivityWatch in Python.
Please see the documentation for usage, and take a look at examples/
.
How to install
Install from pip: pip install aw-client
Install the latest version directly from github without cloning: pip install git+https://github.com/ActivityWatch/aw-client.git
To install from a cloned version:
- clone repo:
git clone https://github.com/ActivityWatch/aw-client.git
- cd into the directory:
cd aw-client
- run
poetry install
(will create a virtualenv, if none activated)- If you don't want to use poetry you can also use
pip install .
, but that might not get the exact version of the dependencies (due to not reading thepoetry.lock
file).
- If you don't want to use poetry you can also use
Usage
For the CLI:
$ aw-client --help
Usage: aw-client [OPTIONS] COMMAND [ARGS]...
CLI utility for aw-client to aid in interacting with the ActivityWatch
server
Options:
--host TEXT Address of host
--port INTEGER Port to use
-v, --verbose Verbosity
--testing Set to use testing ports by default
--help Show this message and exit.
Commands:
buckets List all buckets
canonical Query 'canonical events' for a single host (filtered,...
events Query events from bucket with ID `bucket_id`
heartbeat Send a heartbeat to bucket with ID `bucket_id` with JSON `data`
query Run a query in file at `path` on the server
report Generate an activity report
Debugging
- Run python with
LOG_LEVEL=debug
to get additional debugging output - If invalid events have been queued for submission, you may need to delete the file-based queues generated by this library
- To use the development version of this library use
aw-client = {path = "../aw-client" }
inpyproject.toml
Examples
The examples/
directory contains a couple of example scripts, including:
time_spent_today.py
- fetches all non-afk events and sums their duration to get the total active time for the day.working_hours.py
- computes hours worked per day (matching a "work" category rule), and exports matching events to a JSON file (for auditing).load_dataframe.py
- loads events from a host using a categorizing & AFK-filtering query, put result in a pandas dataframe, and export as CSV.merge_buckets.py
- merges two buckets with non-intersecting events by moving all events from one into the other.redact_sensitive.py
- redact sensitive events.
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