Added NonSequentialTimeSeries Model #76
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-Added NonSequentialTimeSeriesModel which can store NonSequential time steps as contrary to SingleTimeSeries which works with a fixed resolution of time steps - Updated existing modules coupled with SingleTimeSeries to be generic such that they can now accept NonSequentialTimeSeries model - Replicated tests as applicable to SingleTimeSeries - Infile storage is stored as a StructArray contrary pyarrow array as we store data and timestamps in different types
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #76 +/- ##
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- Coverage 95.77% 95.47% -0.30%
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Files 46 47 +1
Lines 3835 4179 +344
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+ Hits 3673 3990 +317
- Misses 162 189 +27 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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- updated and resolved changes from the main branch - updated the arrow storage to handle record array instead of pyarrow structure - complied with the TimeSeriesKey model for NonSequentialTimeSeries
- merged with the main branch with Chronify-related changes - resolved conflicts with incoming changes and merged changes reflecting NonSequentialTimeSeries - pytest complies with all the merged changes
daniel-thom
reviewed
Mar 5, 2025
| @@ -221,7 +221,7 @@ def to_input_output( | |||
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| xs = self.function_data.x_coords | |||
| ys = np.multiply(xs[1:], self.function_data.y_coords).tolist() | |||
| ys.insert(0, c) | |||
| ys.insert(0, c) # type:ignore | |||
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@pesap Do you know why we need this?
- added chronological checks based on comments from CADET team - merged add time series methods into one based on singleedispatchmethod func - os and path permission error on windows
daniel-thom
reviewed
Mar 5, 2025
daniel-thom
approved these changes
Mar 6, 2025
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Added NonSequentialTimeSeries Model
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-Added
NonSequentialTimeSeriesModelwhich can store non-sequential time steps as contrary toSingleTimeSerieswhich works with a fixed resolution of time stepsSingleTimeSeriesto be generic such that they can now acceptNonSequentialTimeSeriesmodelSingleTimeSeriespyarrow.StructArraycontrarypyarrow.arrayas we store data and timestamps in different types. This can also follow apandas.DataFrameapproach but I think parsing two different arrays -- one for data and one for timestamps -- should be fine for now.