tsflex

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:recycle: refactor indexing + :scissors: decouple stride & window + :sparkles: support segment idxs

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## :recycle: Refactor indexing * :bug: fix bug with `vectorized=True` for strided rolling - [x] vectorized support for single feature windows - [x] vectorized support for empty feature windows -...

jvdd

🎍 improving loggers as described in #66

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jonasvdd

Question: Feature extraction on time series batch

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Hello, First of all, I would like to thank you for the really nice library. I think it is much more straight forward and at the same time flexible, compared...

mbignotti

Current version:  new features: * add the columns: `duration %` -> can be directly calculated from the duration column, so does not need to be stored within the logs...

jonasvdd

Segmenter class (exposing strided-roll creation)

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Create a Segmenter class, this class exposes (in a clean interface) the creation of `stroll`s (from the StridedRolling class). Such a class enables users to create efficiently strided rolls (`stroll`s),...

jvdd

* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.resample.html. * https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html?highlight=rolling#pandas.DataFrame.rolling Maybe we can use * the same naming convention * or even re-use their underlying logic (i.e., don't reimplement the wheel)

jonasvdd

Alignment of feature windows when equal stride

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There should at least be an option to conveniently align the feature windows when there is an equal stride (or multiples of the minimal stride). This issue persists when the...

jvdd

Rethinking chunking module

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The chunking submodule is an amazing feature of `tsflex`, however it still has some flaws. It assumes: - [ ] that each series-signal is sampled at a (nearly) fixed frequency)...

jonasvdd

Add `catch_errors` flag to make robust and return the `error_val` in those cases.

jvdd

Feature extraction with an identifier

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Perform feature extraction with an identifier / group column. Some example use cases; your data is a long table format of time series from; * Multiple patients * Multiple measurements...

jvdd