Add NASA POWER to iotools

docs/sphinx/source/reference/iotools.rst

@@ -57,6 +57,17 @@ clear-sky irradiance globally.
    iotools.parse_cams
 
 
+NASA POWER
+**********
+
+Satellite-derived irradiance and weather data with global coverage.
+
+.. autosummary::
+   :toctree: generated/
+
+   iotools.get_nasa_power
+
+
 NSRDB
 *****
 

pvlib/iotools/__init__.py

@@ -39,3 +39,4 @@
 from pvlib.iotools.solcast import get_solcast_historic  # noqa: F401
 from pvlib.iotools.solcast import get_solcast_tmy  # noqa: F401
 from pvlib.iotools.solargis import get_solargis  # noqa: F401
+from pvlib.iotools.nasa_power import get_nasa_power  # noqa: F401

pvlib/iotools/nasa_power.py

@@ -0,0 +1,150 @@
+"""Function for reading and retrieving data from NASA POWER."""
+
+import pandas as pd
+import requests
+
+
+URL = 'https://power.larc.nasa.gov/api/temporal/hourly/point'
+
+VARIABLE_MAP = {
+    'ALLSKY_SFC_SW_DWN': 'ghi',
+    'ALLSKY_SFC_SW_DIFF': 'dhi',
+    'ALLSKY_SFC_SW_DNI': 'dni',
+    'CLRSKY_SFC_SW_DWN': 'ghi_clear',
+    'T2M': 'temp_air_2m',
+    'WS2M': 'wind_speed_2m',
+    'WS10M': 'wind_speed_10m',
+}
+
+
+def get_nasa_power(latitude, longitude, start, end, parameters,
+                   time_standard='utc', community='re', elevation=None,
+                   wind_height=None, wind_surface=None, map_variables=True):
+    """
+    Retrieve irradiance and weather data from NASA POWER.
+
+    A general description of NASA POWER is given in [1]_ and the API is
+    described in [2]_. A detailed list of the available parameters can be
+    found in [3]_.
+
+    Parameters
+    ----------
+    latitude: float
+        In decimal degrees, north is positive (ISO 19115).
+    longitude: float
+        In decimal degrees, east is positive (ISO 19115).
+    start: datetime like
+        First timestamp of the requested period. If a timezone is not
+        specified, UTC is assumed.
+    end: datetime like
+        Last timestamp of the requested period. If a timezone is not
+        specified, UTC is assumed.
+    parameters: str, list
+        List of parameters. Some common parameters are mentioned below; for the
+        full list see [3]_:
+
+        * ``ALLSKY_SFC_SW_DWN``: Global Horizontal Irradiance (GHI) [Wm⁻²]
+        * ``ALLSKY_SFC_SW_DIFF``: Diffuse Horizontal Irradiance (DHI) [Wm⁻²]
+        * ``ALLSKY_SFC_SW_DNI``: Direct Normal Irradiance (DNI) [Wm⁻²]
+        * ``CLRSKY_SFC_SW_DWN``: Clear-sky GHI [Wm⁻²]
+        * ``T2M``: Air temperature at 2 m [C]
+        * ``WS2M``: Wind speed at 2 m [m/s]
+        * ``WS10M``: Wind speed at 10 m [m/s]
+
+    community: str, default: ``'re'``
+        Can be one of the following depending on which parameters are of
+        interest. Note that in many cases this choice might affect the units
+        of the parameter:
+
+        * ``'re'``: renewable energy
+        * ``'sb'``: sustainable buildings
+        * ``'ag'``: agroclimatology
+
+    time_standard: str, default: ``'utc'``
+        Can be either ``'utc'`` or ``'lst'``:
+
+        * Universal Time Coordinated (utc)
+        * Local Solar Time (lst): A 15 Degrees swath that represents solar
+          noon at the middle longitude of the swath
+
+    elevation: float, optional
+        The custom site elevation in meters to produce the corrected
+        atmospheric pressure adjusted for elevation.
+    wind_height: float, optional
+        The custom wind height in meters to produce the wind speed adjusted
+        for height. Has to be between 10 and 300 m; see [4]_.
+    wind_surface: str, optional
+        The definable surface type to adjust the wind speed. For a list of the
+        surface types see [4]_.
+    map_variables: bool, optional, default: True
+        When true, renames columns of the Dataframe to pvlib variable names
+        where applicable. The default is True. See variable
+        :const:`VARIABLE_MAP`.
+
+    Raises
+    ------
+    requests.HTTPError
+        Raises an error when an incorrect request is made.
+
+    Returns
+    -------
+    data : pd.DataFrame
+        Time series data. The index corresponds to the start (left) of the
+        interval.
+
+    References
+    ----------
+    .. [1] `NASA Prediction Of Worldwide Energy Resources (POWER)
+       <https://power.larc.nasa.gov/>`_
+    .. [2] `NASA POWER API
+       <https://power.larc.nasa.gov/api/pages/>`_
+    .. [3] `NASA POWER API parameters
+       <https://power.larc.nasa.gov/parameters/>`_
+    .. [4] `NASA POWER corrected wind speed parameters
+       <https://power.larc.nasa.gov/docs/methodology/meteorology/wind/>`_
+    """
+    start = pd.Timestamp(start)
+    end = pd.Timestamp(end)
+    start = start.tz_localize('UTC') if start.tzinfo is None else start
+    end = end.tz_localize('UTC') if end.tzinfo is None else end
+
+    params = {
+        'latitude': latitude,
+        'longitude': longitude,
+        'start': start.strftime('%Y%m%d'),
+        'end': end.strftime('%Y%m%d'),
+        'community': community,
+        'parameters': ','.join(parameters),  # make parameters in a string
+        'format': 'json',
+        'user': None,
+        'header': True,
+        'time-standard': time_standard,
+        'site-elevation': elevation,
+        'wind-elevation': wind_height,
+        'wind-surface': wind_surface,
+    }
+
+    response = requests.get(URL, params=params)
+    if not response.ok:
+        # response.raise_for_status() does not give a useful error message
+        raise requests.HTTPError(response.json())
+
+    # Parse the data to dataframe
+    data = response.json()
+    hourly_data = data['properties']['parameter']
+    df = pd.DataFrame(hourly_data)
+    df.index = pd.to_datetime(df.index, format='%Y%m%d%H').tz_localize('UTC')
+
+    # Make metadata dictionary
+    meta = {key: data[key] for key in ['header', 'messages', 'parameters',
+                                       'times', 'type']}
+
+    meta['longitude'] = data['geometry']['coordinates'][0]
+    meta['latitude'] = data['geometry']['coordinates'][1]
+    meta['altitude'] = data['geometry']['coordinates'][2]
+
+    # Rename according to pvlib convention
+    if map_variables:
+        df = df.rename(columns=VARIABLE_MAP)
+
+    return df, meta