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Kevin's suggestions (function)
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pvlib/iotools/nasa_power.py

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@@ -7,8 +7,7 @@
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URL = 'https://power.larc.nasa.gov/api/temporal/hourly/point'
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DEFAULT_PARAMETERS = [
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'ALLSKY_SFC_SW_DNI', 'ALLSKY_SFC_SW_DIFF', 'ALLSKY_SFC_SW_DWN',
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'T2M', 'WS10M'
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'dni', 'dhi', 'ghi', 'temp_air', 'wind_speed'
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]
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VARIABLE_MAP = {
@@ -48,11 +47,11 @@ def get_nasa_power(latitude, longitude, start, end,
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full list see [3]_. Note that the pvlib naming conventions can also be
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used.
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* ``ALLSKY_SFC_SW_DWN``: Global Horizontal Irradiance (GHI) [Wm⁻²]
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* ``ALLSKY_SFC_SW_DIFF``: Diffuse Horizontal Irradiance (DHI) [Wm⁻²]
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* ``ALLSKY_SFC_SW_DNI``: Direct Normal Irradiance (DNI) [Wm⁻²]
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* ``T2M``: Air temperature at 2 m [C]
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* ``WS10M``: Wind speed at 10 m [m/s]
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* Global Horizontal Irradiance (GHI) [Wm⁻²]
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* Diffuse Horizontal Irradiance (DHI) [Wm⁻²]
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* Direct Normal Irradiance (DNI) [Wm⁻²]
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* Air temperature at 2 m [C]
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* Wind speed at 10 m [m/s]
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community: str, default 're'
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Can be one of the following depending on which parameters are of
@@ -134,7 +133,6 @@ def get_nasa_power(latitude, longitude, start, end,
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hourly_data = data['properties']['parameter']
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df = pd.DataFrame(hourly_data)
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df.index = pd.to_datetime(df.index, format='%Y%m%d%H').tz_localize('UTC')
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df = df.replace(-999, np.nan)
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# Create metadata dictionary
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meta = data['header']
@@ -145,6 +143,9 @@ def get_nasa_power(latitude, longitude, start, end,
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meta['latitude'] = data['geometry']['coordinates'][1]
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meta['altitude'] = data['geometry']['coordinates'][2]
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# Replace NaN values
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df = df.replace(meta['fill_value'], np.nan)
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# Rename according to pvlib convention
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if map_variables:
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df = df.rename(columns=VARIABLE_MAP)

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