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Merge branch 'main' of https://github.com/pvlib/pvlib-python into pvsystem-temp-models
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.github/PULL_REQUEST_TEMPLATE.md

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- [ ] Closes #xxxx
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- [ ] I am familiar with the [contributing guidelines](https://pvlib-python.readthedocs.io/en/latest/contributing/index.html)
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- [ ] I attest that all AI-generated material has been vetted for accuracy and is in compliance with the pvlib license
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- [ ] Tests added
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- [ ] Updates entries in [`docs/sphinx/source/reference`](https://github.com/pvlib/pvlib-python/blob/main/docs/sphinx/source/reference) for API changes.
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- [ ] Adds description and name entries in the appropriate "what's new" file in [`docs/sphinx/source/whatsnew`](https://github.com/pvlib/pvlib-python/tree/main/docs/sphinx/source/whatsnew) for all changes. Includes link to the GitHub Issue with `` :issue:`num` `` or this Pull Request with `` :pull:`num` ``. Includes contributor name and/or GitHub username (link with `` :ghuser:`user` ``).

.github/workflows/welcome.yml

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name: Welcome First-Time Contributor
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on:
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pull_request_target:
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types: opened
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jobs:
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welcome:
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runs-on: ubuntu-latest
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permissions:
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pull-requests: write
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steps:
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- uses: plbstl/first-contribution@v4
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with:
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pr-opened-msg: |
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### Hey @{fc-author}! :tada:
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Thanks for opening your first pull request! We appreciate your
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contribution. Please ensure you have reviewed and understood the
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[contributing guidelines](https://pvlib-python.readthedocs.io/en/latest/contributing/index.html).
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If AI is used for any portion of this PR, you must vet the content
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for technical accuracy.

benchmarks/benchmarks/scaling.py

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lon = np.array((4.99, 5, 5.01))
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self.coordinates = np.array([(lati, loni) for
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(lati, loni) in zip(lat, lon)])
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self.times = pd.date_range('2019-01-01', freq='1T', periods=self.n)
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self.times = pd.date_range('2019-01-01', freq='1min', periods=self.n)
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self.positions = np.array([[0, 0], [100, 0], [100, 100], [0, 100]])
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self.clearsky_index = pd.Series(np.random.rand(self.n),
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index=self.times)

docs/examples/adr-pvarray/plot_fit_to_matrix.py

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25 1000 75.0 273.651
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'''
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df = pd.read_csv(StringIO(iec61853data), delim_whitespace=True)
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df = pd.read_csv(StringIO(iec61853data), sep=r"\s+")
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# %%
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#

docs/examples/agrivoltaics/plot_diffuse_PAR_Spitters_relationship.py

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solar_position = pvlib.solarposition.get_solarposition(
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# TMY timestamp is at end of hour, so shift to center of interval
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tmy.index.shift(freq="-30T"),
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tmy.index.shift(freq="-30min"),
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latitude=metadata["latitude"],
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longitude=metadata["longitude"],
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altitude=metadata["altitude"],

docs/examples/bifacial/plot_bifi_model_mc.py

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# create site location and times characteristics
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lat, lon = 36.084, -79.817
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tz = 'Etc/GMT+5'
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times = pd.date_range('2021-06-21', '2021-6-22', freq='1T', tz=tz)
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times = pd.date_range('2021-06-21', '2021-6-22', freq='1min', tz=tz)
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# create site system characteristics
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axis_tilt = 0

docs/examples/bifacial/plot_bifi_model_pvwatts.py

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# using Greensboro, NC for this example
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lat, lon = 36.084, -79.817
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tz = 'Etc/GMT+5'
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times = pd.date_range('2021-06-21', '2021-06-22', freq='1T', tz=tz)
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times = pd.date_range('2021-06-21', '2021-06-22', freq='1min', tz=tz)
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# create location object and get clearsky data
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site_location = location.Location(lat, lon, tz=tz, name='Greensboro, NC')

docs/examples/bifacial/plot_pvfactors_fixed_tilt.py

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# %%
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# First, generate the usual modeling inputs:
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times = pd.date_range('2021-06-21', '2021-06-22', freq='1T', tz='Etc/GMT+5')
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times = pd.date_range('2021-06-21', '2021-06-22', freq='1min', tz='Etc/GMT+5')
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loc = location.Location(latitude=40, longitude=-80, tz=times.tz)
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sp = loc.get_solarposition(times)
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cs = loc.get_clearsky(times)

docs/examples/floating-pv/plot_floating_pv_cell_temperature.py

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solar_position = pvlib.solarposition.get_solarposition(
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# TMY timestamp is at end of hour, so shift to center of interval
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tmy.index.shift(freq='-30T'),
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tmy.index.shift(freq='-30min'),
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latitude=metadata['latitude'],
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longitude=metadata['longitude'],
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altitude=metadata['altitude'],

docs/examples/irradiance-decomposition/plot_diffuse_fraction.py

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# NOTE: TMY3 files timestamps indicate the end of the hour, so shift indices
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# back 30-minutes to calculate solar position at center of the interval
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solpos = get_solarposition(
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greensboro.index.shift(freq="-30T"), latitude=metadata['latitude'],
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greensboro.index.shift(freq="-30min"), latitude=metadata['latitude'],
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longitude=metadata['longitude'], altitude=metadata['altitude'],
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pressure=greensboro.pressure*100, # convert from millibar to Pa
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temperature=greensboro.temp_air)

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