Fix 2 tuple type annotations in TimeSeries #2812
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- `TimeSeries.shape` was incorrectly annotated `tuple[int]`; this is changed to `tuple[int, int, int]`. - The quantiles argument in `TimeSeries.quantiles_df` was incorrectly annotated `tuple[float]`; this is changed to `tuple[float, ...]`.
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Additional details and impacted files@@ Coverage Diff @@
## master #2812 +/- ##
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- Coverage 95.24% 95.16% -0.08%
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Files 145 145
Lines 15130 15130
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- Hits 14410 14398 -12
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dennisbader
requested changes
Jun 5, 2025
There was a problem hiding this comment.
Hi @ajyoon and thanks for this this improvement 🚀 I had one small comment, after that we can merge :)
| @@ -2019,7 +2019,9 @@ def quantile_timeseries(self, quantile=0.5, **kwargs) -> Self: | |||
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| return self.__class__(new_xa) | |||
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| def quantiles_df(self, quantiles: tuple[float] = (0.1, 0.5, 0.9)) -> pd.DataFrame: | |||
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here I think a Optional[list[float]] would actually make more sense. Could you adapt it?
E.g.
def quantiles_df(self, quantiles: Optional[list[float]] = None) -> pd.DataFrame:
quantiles = quantiles or [0.1, 0.5, 0.9]
return pd.concat(
[
self.quantile_timeseries(quantile).to_dataframe()
for quantile in quantiles
],
axis=1,
)
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Mentioned all issues that this PR fixes or addresses.(N/A)Summary
TimeSeries.shapewas incorrectly annotatedtuple[int]; this is changed totuple[int, int, int].TimeSeries.quantiles_dfwas incorrectly annotatedtuple[float]; this is changed totuple[float, ...].