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Fix issue #370 (energy_from_power returns incorrect index for shifted hourly data) #437

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Fix issue #370 (energy_from_power returns incorrect index for shifted hourly data) #437

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ffb6150
add origin to resample
martin-springer Nov 6, 2024
fd635a0
update changelog
martin-springer Nov 6, 2024
8affd0d
fix _aggregate starting timestamp
martin-springer Nov 20, 2024
b502be4
Merge branch 'development' into fix_issue_#370
martin-springer Dec 12, 2024
3295776
move changes to pending changlog
martin-springer Dec 18, 2024
dcbf652
move changes to pending changelog 3
martin-springer Dec 18, 2024
2fc733d
move changes to pending changelog 4
martin-springer Dec 18, 2024
58eab97
add test energy from power shifted data
martin-springer Dec 18, 2024
e39e4ab
make function callable in changelog
martin-springer Dec 18, 2024
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3 changes: 2 additions & 1 deletion docs/sphinx/source/changelog/v3.0.0-beta.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@ Enhancements
* Added codecov.yml configuration file (:pull:`420`)
* Availability module no longer considered experimental (:pull:`429`)
* Add capability to seed the CircularBlockBootstrap (:pull:`429`)
* Allow sub-daily aggregation in :py:func:`~rdtools.degradation.degradation_year_on_year` (:pull:`390`)
* Allow sub-daily aggregation in :py:func:`~rdtools.degradation.degradation_year_on_year` (:pull:`390`)

Bug fixes
---------
Expand All @@ -31,6 +31,7 @@ Bug fixes
* Deploy workflow was replaced with trusted publisher workflow for pypi (:pull:`427`)
* Fix pandas 2.0.0 deprications and update syntax changes (:pull:`428`)
* Fix numpy 2.0.0 deprications and update syntax changes (:pull:`428`)
* Fix `energy_from_power`` returns incorrect index for shifted hourly data (:issue:`370`, :pull:`437`)
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Let's move this to a pending changelog section

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Done.


Tests
-----
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16 changes: 8 additions & 8 deletions rdtools/normalization.py
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Expand Up @@ -478,9 +478,9 @@ def _aggregate(time_series, target_frequency, max_timedelta, series_type):
'''

# series that has same index as desired output
output_dummy = time_series.resample(target_frequency,
closed='right',
label='right').sum()
output_dummy = time_series.resample(
target_frequency, closed="right", label="right", origin="start"
).sum()

union_index = time_series.index.union(output_dummy.index)
time_series = time_series.dropna()
Expand Down Expand Up @@ -521,13 +521,13 @@ def _aggregate(time_series, target_frequency, max_timedelta, series_type):
raise ValueError("series_type must be either 'instantaneous' or 'right_labeled', "
"not '{}'".format(series_type))

series_sum = pd.Series(data=series_sum, index=time_series.index[1:])
series_sum = pd.Series(data=np.insert(series_sum, 0, np.nan), index=time_series.index)

aggregated = series_sum.resample(target_frequency,
closed='right',
label='right').sum(min_count=1)
aggregated = series_sum.resample(
target_frequency, closed="right", label="right", origin="start"
).sum(min_count=1)

return aggregated
return aggregated[1:]


def _interpolate_series(time_series, target_index, max_timedelta=None,
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