v0.74.1
v0.74.1 | 23 Apr 2024
This update adds the first notebooks (and tests) for outlier detection methods. Only two tests are included so far and
both tests are relatively simple, but both notebooks already show in principle how outlier removal is handled. An
important aspect is that diive single outlier methods do not remove outliers by default, but instead a flag is created
that shows where the outliers are located. The flag can then be used to remove the data points.
This update also includes the addition of a small function that creates artificial spikes in time series data and is
therefore very useful for testing outlier detection methods.
More outlier removal notebooks will be added in the future, including a notebook that shows how to combine results from
multiple outlier tests into one single overall outlier flag.
New features
- Added: new function to add impulse noise to time series (
diive.pkgs.createvar.noise.impulse)
Notebooks
- Added: new notebook for outlier detection: absolute limits, separately for daytime and nighttime
data (notebooks/OutlierDetection/AbsoluteLimitsDaytimeNighttime.ipynb) - Added: new notebook for outlier detection: absolute limits (
notebooks/OutlierDetection/AbsoluteLimits.ipynb)
Tests
- Added: test case for outlier detection: absolute limits, separately for daytime and
nighttime data (tests.test_outlierdetection.TestOutlierDetection.test_absolute_limits) - Added: test case for outlier detection: absolute
limits (tests.test_outlierdetection.TestOutlierDetection.test_absolute_limits)
What's Changed
- Outlier notebooks by @holukas in #95
- Update README.md by @inkenbrandt in #86
- Update pyproject.toml by @inkenbrandt in #85
Full Changelog: v0.74.0...v0.74.1
