Combine read_nsrdb_psm4 and parse_nsrdb_psm4

docs/sphinx/source/reference/iotools.rst

@@ -31,7 +31,6 @@ of sources and file formats relevant to solar energy modeling.
    iotools.get_nsrdb_psm4_conus
    iotools.get_nsrdb_psm4_full_disc
    iotools.read_nsrdb_psm4
-   iotools.parse_nsrdb_psm4
    iotools.get_psm3
    iotools.read_psm3
    iotools.parse_psm3

docs/sphinx/source/whatsnew/v0.12.1.rst

@@ -23,9 +23,8 @@ Enhancements
   :py:func:`~pvlib.iotools.get_nsrdb_psm4_aggregated`,
   :py:func:`~pvlib.iotools.get_nsrdb_psm4_tmy`,
   :py:func:`~pvlib.iotools.get_nsrdb_psm4_conus`,
-  :py:func:`~pvlib.iotools.get_nsrdb_psm4_full_disc`,
-  :py:func:`~pvlib.iotools.read_nsrdb_psm4`, and
-  :py:func:`~pvlib.iotools.parse_nsrdb_psm4`. (:issue:`2326`, :pull:`2378`)
+  :py:func:`~pvlib.iotools.get_nsrdb_psm4_full_disc`, and
+  :py:func:`~pvlib.iotools.read_nsrdb_psm4`. (:issue:`2326`, :pull:`2378`)
 
 Documentation
 ~~~~~~~~~~~~~

pvlib/iotools/__init__.py

@@ -16,7 +16,6 @@
 from pvlib.iotools.psm4 import get_nsrdb_psm4_conus  # noqa: F401
 from pvlib.iotools.psm4 import get_nsrdb_psm4_full_disc  # noqa: F401
 from pvlib.iotools.psm4 import read_nsrdb_psm4  # noqa: F401
-from pvlib.iotools.psm4 import parse_nsrdb_psm4  # noqa: F401
 from pvlib.iotools.pvgis import get_pvgis_tmy, read_pvgis_tmy  # noqa: F401
 from pvlib.iotools.pvgis import read_pvgis_hourly  # noqa: F401
 from pvlib.iotools.pvgis import get_pvgis_hourly  # noqa: F401

pvlib/iotools/psm4.py

@@ -11,6 +11,7 @@
 import requests
 import pandas as pd
 from json import JSONDecodeError
+import contextlib
 
 NSRDB_API_BASE = "https://developer.nrel.gov/api/nsrdb/v2/solar/"
 PSM4_AGG_ENDPOINT = "nsrdb-GOES-aggregated-v4-0-0-download.csv"
@@ -82,7 +83,7 @@ def get_nsrdb_psm4_aggregated(latitude, longitude, api_key, email,
     Aggregated v4 API.
 
     The NSRDB is described in [1]_ and the PSM4 NSRDB GOES Aggregated v4 API is
-    described in [2]_,.
+    described in [2]_.
 
     Parameters
     ----------
@@ -132,7 +133,7 @@ def get_nsrdb_psm4_aggregated(latitude, longitude, api_key, email,
         timeseries data from NREL PSM4
     metadata : dict
         metadata from NREL PSM4 about the record, see
-        :func:`pvlib.iotools.parse_nsrdb_psm4` for fields
+        :func:`pvlib.iotools.read_nsrdb_psm4` for fields
 
     Raises
     ------
@@ -151,19 +152,15 @@ def get_nsrdb_psm4_aggregated(latitude, longitude, api_key, email,
         result in rejected requests.
 
     .. warning:: PSM4 is limited to data found in the NSRDB, please consult
-        the references below for locations with available data. Additionally,
-        querying data with < 30-minute resolution uses a different API endpoint
-        with fewer available fields (see [4]_).
+        the references below for locations with available data.
 
     See Also
     --------
     pvlib.iotools.get_nsrdb_psm4_tmy, pvlib.iotools.get_nsrdb_psm4_conus,
-    pvlib.iotools.get_nsrdb_psm4_full_disc, pvlib.iotools.read_nsrdb_psm4,
-    pvlib.iotools.parse_nsrdb_psm4
+    pvlib.iotools.get_nsrdb_psm4_full_disc, pvlib.iotools.read_nsrdb_psm4
 
     References
     ----------
-
     .. [1] `NREL National Solar Radiation Database (NSRDB)
        <https://nsrdb.nrel.gov/>`_
     .. [2] `NSRDB GOES Aggregated V4.0.0
@@ -213,7 +210,7 @@ def get_nsrdb_psm4_aggregated(latitude, longitude, api_key, email,
     # the CSV is in the response content as a UTF-8 bytestring
     # to use pandas we need to create a file buffer from the response
     fbuf = io.StringIO(response.content.decode('utf-8'))
-    return parse_nsrdb_psm4(fbuf, map_variables)
+    return read_nsrdb_psm4(fbuf, map_variables)
 
 
 def get_nsrdb_psm4_tmy(latitude, longitude, api_key, email, year='tmy',
@@ -225,7 +222,7 @@ def get_nsrdb_psm4_tmy(latitude, longitude, api_key, email, year='tmy',
     TMY v4 API.
 
     The NSRDB is described in [1]_ and the PSM4 NSRDB GOES TMY v4 API is
-    described in [2]_,.
+    described in [2]_.
 
     Parameters
     ----------
@@ -276,7 +273,7 @@ def get_nsrdb_psm4_tmy(latitude, longitude, api_key, email, year='tmy',
         timeseries data from NREL PSM4
     metadata : dict
         metadata from NREL PSM4 about the record, see
-        :func:`pvlib.iotools.parse_nsrdb_psm4` for fields
+        :func:`pvlib.iotools.read_nsrdb_psm4` for fields
 
     Raises
     ------
@@ -295,19 +292,16 @@ def get_nsrdb_psm4_tmy(latitude, longitude, api_key, email, year='tmy',
         result in rejected requests.
 
     .. warning:: PSM4 is limited to data found in the NSRDB, please consult
-        the references below for locations with available data. Additionally,
-        querying data with < 30-minute resolution uses a different API endpoint
-        with fewer available fields (see [4]_).
+        the references below for locations with available data.
 
     See Also
     --------
     pvlib.iotools.get_nsrdb_psm4_aggregated,
     pvlib.iotools.get_nsrdb_psm4_conus, pvlib.iotools.get_nsrdb_psm4_full_disc,
-    pvlib.iotools.read_nsrdb_psm4,pvlib.iotools.parse_nsrdb_psm4
+    pvlib.iotools.read_nsrdb_psm4
 
     References
     ----------
-
     .. [1] `NREL National Solar Radiation Database (NSRDB)
        <https://nsrdb.nrel.gov/>`_
     .. [2] `NSRDB GOES Tmy V4.0.0
@@ -357,7 +351,7 @@ def get_nsrdb_psm4_tmy(latitude, longitude, api_key, email, year='tmy',
     # the CSV is in the response content as a UTF-8 bytestring
     # to use pandas we need to create a file buffer from the response
     fbuf = io.StringIO(response.content.decode('utf-8'))
-    return parse_nsrdb_psm4(fbuf, map_variables)
+    return read_nsrdb_psm4(fbuf, map_variables)
 
 
 def get_nsrdb_psm4_conus(latitude, longitude, api_key, email, year='2023',
@@ -369,7 +363,7 @@ def get_nsrdb_psm4_conus(latitude, longitude, api_key, email, year='2023',
     v4 API.
 
     The NSRDB is described in [1]_ and the PSM4 NSRDB GOES CONUS v4 API is
-    described in [2]_,.
+    described in [2]_.
 
     Parameters
     ----------
@@ -418,7 +412,7 @@ def get_nsrdb_psm4_conus(latitude, longitude, api_key, email, year='2023',
         timeseries data from NREL PSM4
     metadata : dict
         metadata from NREL PSM4 about the record, see
-        :func:`pvlib.iotools.parse_nsrdb_psm4` for fields
+        :func:`pvlib.iotools.read_nsrdb_psm4` for fields
 
     Raises
     ------
@@ -437,19 +431,16 @@ def get_nsrdb_psm4_conus(latitude, longitude, api_key, email, year='2023',
         result in rejected requests.
 
     .. warning:: PSM4 is limited to data found in the NSRDB, please consult
-        the references below for locations with available data. Additionally,
-        querying data with < 30-minute resolution uses a different API endpoint
-        with fewer available fields (see [4]_).
+        the references below for locations with available data.
 
     See Also
     --------
     pvlib.iotools.get_nsrdb_psm4_aggregated,
     pvlib.iotools.get_nsrdb_psm4_tmy, pvlib.iotools.get_nsrdb_psm4_full_disc,
-    pvlib.iotools.read_nsrdb_psm4, pvlib.iotools.parse_nsrdb_psm4
+    pvlib.iotools.read_nsrdb_psm4
 
     References
     ----------
-
     .. [1] `NREL National Solar Radiation Database (NSRDB)
        <https://nsrdb.nrel.gov/>`_
     .. [2] `NSRDB GOES Conus V4.0.0
@@ -499,7 +490,7 @@ def get_nsrdb_psm4_conus(latitude, longitude, api_key, email, year='2023',
     # the CSV is in the response content as a UTF-8 bytestring
     # to use pandas we need to create a file buffer from the response
     fbuf = io.StringIO(response.content.decode('utf-8'))
-    return parse_nsrdb_psm4(fbuf, map_variables)
+    return read_nsrdb_psm4(fbuf, map_variables)
 
 
 def get_nsrdb_psm4_full_disc(latitude, longitude, api_key, email,
@@ -513,7 +504,7 @@ def get_nsrdb_psm4_full_disc(latitude, longitude, api_key, email,
     Disc v4 API.
 
     The NSRDB is described in [1]_ and the PSM4 NSRDB GOES Full Disc v4 API is
-    described in [2]_,.
+    described in [2]_.
 
     Parameters
     ----------
@@ -563,7 +554,7 @@ def get_nsrdb_psm4_full_disc(latitude, longitude, api_key, email,
         timeseries data from NREL PSM4
     metadata : dict
         metadata from NREL PSM4 about the record, see
-        :func:`pvlib.iotools.parse_nsrdb_psm4` for fields
+        :func:`pvlib.iotools.read_nsrdb_psm4` for fields
 
     Raises
     ------
@@ -582,19 +573,16 @@ def get_nsrdb_psm4_full_disc(latitude, longitude, api_key, email,
         result in rejected requests.
 
     .. warning:: PSM4 is limited to data found in the NSRDB, please consult
-        the references below for locations with available data. Additionally,
-        querying data with < 30-minute resolution uses a different API endpoint
-        with fewer available fields (see [4]_).
+        the references below for locations with available data.
 
     See Also
     --------
     pvlib.iotools.get_nsrdb_psm4_aggregated,
     pvlib.iotools.get_nsrdb_psm4_tmy, pvlib.iotools.get_nsrdb_psm4_conus,
-    pvlib.iotools.read_nsrdb_psm4, pvlib.iotools.parse_nsrdb_psm4
+    pvlib.iotools.read_nsrdb_psm4
 
     References
     ----------
-
     .. [1] `NREL National Solar Radiation Database (NSRDB)
        <https://nsrdb.nrel.gov/>`_
     .. [2] `NSRDB GOES Full Disc V4.0.0
@@ -644,19 +632,19 @@ def get_nsrdb_psm4_full_disc(latitude, longitude, api_key, email,
     # the CSV is in the response content as a UTF-8 bytestring
     # to use pandas we need to create a file buffer from the response
     fbuf = io.StringIO(response.content.decode('utf-8'))
-    return parse_nsrdb_psm4(fbuf, map_variables)
+    return read_nsrdb_psm4(fbuf, map_variables)
 
 
-def parse_nsrdb_psm4(fbuf, map_variables=True):
+def read_nsrdb_psm4(filename, map_variables=True):
     """
-    Parse an NSRDB PSM4 weather file (formatted as SAM CSV).
+    Read an NSRDB PSM4 weather file (formatted as SAM CSV).
 
     The NSRDB is described in [1]_ and the SAM CSV format is described in [2]_.
 
     Parameters
     ----------
-    fbuf: file-like object
-        File-like object containing data to read.
+    filename: str, path-like, or buffer
+        Filename or in-memory buffer of a file containing data to read.
     map_variables: bool, default True
         When true, renames columns of the Dataframe to pvlib variable names
         where applicable. See variable :const:`VARIABLE_MAP`.
@@ -726,12 +714,15 @@ def parse_nsrdb_psm4(fbuf, map_variables=True):
     Examples
     --------
     >>> # Read a local PSM4 file:
+    >>> df, metadata = iotools.read_nsrdb_psm4("data.csv")  # doctest: +SKIP
+
+    >>> # Read a file object or an in-memory buffer:
     >>> with open(filename, 'r') as f:  # doctest: +SKIP
-    ...     df, metadata = iotools.parse_nsrdb_psm4(f)  # doctest: +SKIP
+    ...     df, metadata = iotools.read_nsrdb_psm4(f)  # doctest: +SKIP
 
     See Also
     --------
-    pvlib.iotools.read_nsrdb_psm4, pvlib.iotools.get_psm4
+    pvlib.iotools.get_psm4, pvlib.iotools.read_psm3
 
     References
     ----------
@@ -740,34 +731,42 @@ def parse_nsrdb_psm4(fbuf, map_variables=True):
     .. [2] `Standard Time Series Data File Format
        <https://web.archive.org/web/20170207203107/https://sam.nrel.gov/sites/default/files/content/documents/pdf/wfcsv.pdf>`_
     """
-    # The first 2 lines of the response are headers with metadata
-    metadata_fields = fbuf.readline().split(',')
-    metadata_fields[-1] = metadata_fields[-1].strip()  # strip trailing newline
-    metadata_values = fbuf.readline().split(',')
-    metadata_values[-1] = metadata_values[-1].strip()  # strip trailing newline
-    metadata = dict(zip(metadata_fields, metadata_values))
-    # the response is all strings, so set some metadata types to numbers
-    metadata['Local Time Zone'] = int(metadata['Local Time Zone'])
-    metadata['Time Zone'] = int(metadata['Time Zone'])
-    metadata['Latitude'] = float(metadata['Latitude'])
-    metadata['Longitude'] = float(metadata['Longitude'])
-    metadata['Elevation'] = int(metadata['Elevation'])
-    # get the column names so we can set the dtypes
-    columns = fbuf.readline().split(',')
-    columns[-1] = columns[-1].strip()  # strip trailing newline
-    # Since the header has so many columns, excel saves blank cols in the
-    # data below the header lines.
-    columns = [col for col in columns if col != '']
-    dtypes = dict.fromkeys(columns, float)  # all floats except datevec
-    dtypes.update(Year=int, Month=int, Day=int, Hour=int, Minute=int)
-    dtypes['Cloud Type'] = int
-    dtypes['Fill Flag'] = int
-    data = pd.read_csv(
-        fbuf, header=None, names=columns, usecols=columns, dtype=dtypes,
-        delimiter=',', lineterminator='\n')  # skip carriage returns \r
+    if hasattr(filename, "read"):
+        # already a file-like object
+        context = contextlib.nullcontext(filename)
+    else:
+        # otherwise, assume a filename or path
+        context = open(str(filename), 'r')
+
+    with context as fbuf:
+        # The first 2 lines of the response are headers with metadata
+        metadata_fields = fbuf.readline().split(',')
+        metadata_fields[-1] = metadata_fields[-1].strip()  # trailing newline
+        metadata_values = fbuf.readline().split(',')
+        metadata_values[-1] = metadata_values[-1].strip()  # trailing newline
+        metadata = dict(zip(metadata_fields, metadata_values))
+        # the response is all strings, so set some metadata types to numbers
+        metadata['Local Time Zone'] = int(metadata['Local Time Zone'])
+        metadata['Time Zone'] = int(metadata['Time Zone'])
+        metadata['Latitude'] = float(metadata['Latitude'])
+        metadata['Longitude'] = float(metadata['Longitude'])
+        metadata['Elevation'] = int(metadata['Elevation'])
+        # get the column names so we can set the dtypes
+        columns = fbuf.readline().split(',')
+        columns[-1] = columns[-1].strip()  # strip trailing newline
+        # Since the header has so many columns, excel saves blank cols in the
+        # data below the header lines.
+        columns = [col for col in columns if col != '']
+        dtypes = dict.fromkeys(columns, float)  # all floats except datevec
+        dtypes.update(Year=int, Month=int, Day=int, Hour=int, Minute=int)
+        dtypes['Cloud Type'] = int
+        dtypes['Fill Flag'] = int
+        data = pd.read_csv(
+            fbuf, header=None, names=columns, usecols=columns, dtype=dtypes,
+            delimiter=',', lineterminator='\n')  # skip carriage returns \r
+
     # the response 1st 5 columns are a date vector, convert to datetime
-    dtidx = pd.to_datetime(
-        data[['Year', 'Month', 'Day', 'Hour', 'Minute']])
+    dtidx = pd.to_datetime(data[['Year', 'Month', 'Day', 'Hour', 'Minute']])
     # in USA all timezones are integers
     tz = 'Etc/GMT%+d' % -metadata['Time Zone']
     data.index = pd.DatetimeIndex(dtidx).tz_localize(tz)
@@ -779,41 +778,3 @@ def parse_nsrdb_psm4(fbuf, map_variables=True):
         metadata['altitude'] = metadata.pop('Elevation')
 
     return data, metadata
-
-
-def read_nsrdb_psm4(filename, map_variables=True):
-    """
-    Read an NSRDB PSM4 weather file (formatted as SAM CSV).
-
-    The NSRDB is described in [1]_ and the SAM CSV format is described in [2]_.
-
-    Parameters
-    ----------
-    filename: str or path-like
-        Filename of a file containing data to read.
-    map_variables: bool, default True
-        When true, renames columns of the Dataframe to pvlib variable names
-        where applicable. See variable :const:`VARIABLE_MAP`.
-
-    Returns
-    -------
-    data : pandas.DataFrame
-        timeseries data from NREL PSM4
-    metadata : dict
-        metadata from NREL PSM4 about the record, see
-        :func:`pvlib.iotools.parse_nsrdb_psm4` for fields
-
-    See Also
-    --------
-    pvlib.iotools.parse_nsrdb_psm4, pvlib.iotools.get_psm4
-
-    References
-    ----------
-    .. [1] `NREL National Solar Radiation Database (NSRDB)
-       <https://nsrdb.nrel.gov/>`_
-    .. [2] `Standard Time Series Data File Format
-       <https://web.archive.org/web/20170207203107/https://sam.nrel.gov/sites/default/files/content/documents/pdf/wfcsv.pdf>`_
-    """
-    with open(str(filename), 'r') as fbuf:
-        content = parse_nsrdb_psm4(fbuf, map_variables)
-    return content