pip install whisper-pandas
Use as Python package:
# Simple to read any Whisper file
>>> from whisper_pandas import WhisperFile
>>> wsp = WhisperFile.read("data/example.wsp")
# Simple to work with metadata as objects
>>> wsp.meta.archives
[WhisperArchiveMeta(index=0, offset=52, seconds_per_point=10, points=1555200, retention=15552000),
WhisperArchiveMeta(index=1, offset=18662452, seconds_per_point=60, points=5256000, retention=315360000),
WhisperArchiveMeta(index=2, offset=81734452, seconds_per_point=3600, points=87601, retention=315363600)]
# Simple to work with data as `pandas.DataFrame` and `pandas.Series`
>>> wsp.archives[1].to_frame().set_index("timestamp")
2017-02-10 07:07:00+00:00 0.000000
2017-02-10 07:08:00+00:00 0.000000
2017-02-10 07:09:00+00:00 0.000000
2017-02-10 07:10:00+00:00 0.000000
2017-02-10 07:11:00+00:00 0.000000
...
2021-07-20 13:35:00+00:00 4.099915
2021-07-20 13:36:00+00:00 4.104024
2021-07-20 13:37:00+00:00 4.099772
2021-07-20 13:38:00+00:00 4.101358
2021-07-20 13:39:00+00:00 4.099854
Length: 2331015, dtype: float32
Use as command line tool:
whisper-pandas data/example.wsp
WhisperDB is a fixed-size time series format (see docs).
The official Python package is here: whisper
You should use it, except if you like Pandas and only need
to read (not write) Whisper files, then you should use whisper-pandas.
Why?
- Mucho simpler to use
- Mucho less likely you will shoot yourself in the foot
- Mucho speedy
A given file is always read completely into memory, partial read of metadata or archives is not supported.
Contributions are welcome via Github pull requests.
You can run the tests via pytest.
The packaging follows the recommendation in
Packaging Python Projects.
To make a release increase the version number in setup.cfg and run the commands there.