10000 update forecast RAP model variable name (#241) · SolarCity1/pvlib-python@5b02d1f · GitHub
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update forecast RAP model variable name (pvlib#241)
* try using conda-forge again * no versions * update rap total clouds field name * add note about panoply * reset doc env * switch rap to nam in doc * update whatsnew
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‎docs/sphinx/source/forecasts.rst

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@@ -17,7 +17,10 @@ state-of-the-art of solar power forecasting.
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pvlib-python uses Unidata's `Siphon
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<http://siphon.readthedocs.org/en/latest/>`_ library to simplify access
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to forecast data hosted on the Unidata `THREDDS catalog
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<http://thredds.ucar.edu/thredds/catalog.html>`_.
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<http://thredds.ucar.edu/thredds/catalog.html>`_. Siphon is great for
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programatic access of THREDDS data, but we also recommend using tools
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such as `Panoply <http://www.giss.nasa.gov/tools/panoply/>`_
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to easily browse the catalog and become more familiar with its contents.
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This document demonstrates how to use pvlib-python to create a PV power
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forecast using these tools. The `forecast
@@ -52,7 +55,7 @@ same quantity (or a very similar one), and not all variables are present
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in all models. For example, on the THREDDS server, the GFS has a field
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named
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``Total_cloud_cover_entire_atmosphere_Mixed_intervals_Average``,
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while the RAP has a field named
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while the NAM has a field named
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``Total_cloud_cover_entire_atmosphere_single_layer``, and a
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similar field in the HRRR is named
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``Total_cloud_cover_entire_atmosphere``.

‎docs/sphinx/source/whatsnew/v0.4.1.txt

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@@ -12,6 +12,7 @@ Bug fixes
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* Fixed an error in the irradiance.klucher transposition model.
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The error was introduced in version 0.4.0. (:issue:`228`)
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* Update RAP forecast model variable names. (:issue:`241`)
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Documentation
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* Mark Mikofski
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* Johannes Dollinger
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* Will Holmgren

‎pvlib/forecast.py

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@@ -1070,7 +1070,7 @@ def __init__(self, resolution='20', set_type='best'):
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self.variables = {
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'temp_air': 'Temperature_surface',
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'wind_speed_gust': 'Wind_speed_gust_surface',
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'total_clouds': 'Total_cloud_cover_entire_atmosphere_single_layer',
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'total_clouds': 'Total_cloud_cover_entire_atmosphere',
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'low_clouds': 'Low_cloud_cover_low_cloud',
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'mid_clouds': 'Medium_cloud_cover_middle_cloud',
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'high_clouds': 'High_cloud_cover_high_cloud', }

‎pvlib/test/test_forecast.py

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_start = pd.Timestamp.now(tz=_tz)
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_end = _start + pd.Timedelta(days=1)
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_modelclasses = [
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GFS, NAM, HRRR, RAP, NDFD,
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GFS, NAM, HRRR, NDFD, RAP,
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pytest.mark.xfail(HRRR_ESRL, reason="HRRR_ESRL is unreliable")]
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_working_models = []
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_variables = ['temp_air', 'wind_speed', 'total_clouds', 'low_clouds',
@@ -62,14 +62,14 @@ def test_process_data(model):
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@requires_siphon
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def test_vert_level():
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amodel = RAP()
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amodel = NAM()
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vert_level = 5000
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data = amodel.get_processed_data(_latitude, _longitude, _start, _end,
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vert_level=vert_level)
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@requires_siphon
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def test_datetime():
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amodel = RAP()
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amodel = NAM()
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start = datetime.now()
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end = start + timedelta(days=1)
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data = amodel.get_processed_data(_latitude, _longitude , start, end)

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