Reverts "filename" parameter rename for consistency

Author: jerluc

pvlib/iotools/tmy.py

@@ -8,23 +8,23 @@
 
 # Dictionary mapping TMY3 names to pvlib names
 VARIABLE_MAP = {
-    'GHI (W/m^2)': 'ghi',
-    'ETR (W/m^2)': 'ghi_extra',
-    'DNI (W/m^2)': 'dni',
-    'ETRN (W/m^2)': 'dni_extra',
-    'DHI (W/m^2)': 'dhi',
-    'Pressure (mbar)': 'pressure',
-    'Wdir (degrees)': 'wind_direction',
-    'Wspd (m/s)': 'wind_speed',
-    'Dry-bulb (C)': 'temp_air',
-    'Dew-point (C)': 'temp_dew',
-    'RHum (%)': 'relative_humidity',
-    'Alb (unitless)': 'albedo',
-    'Pwat (cm)': 'precipitable_water'
+    "GHI (W/m^2)": "ghi",
+    "ETR (W/m^2)": "ghi_extra",
+    "DNI (W/m^2)": "dni",
+    "ETRN (W/m^2)": "dni_extra",
+    "DHI (W/m^2)": "dhi",
+    "Pressure (mbar)": "pressure",
+    "Wdir (degrees)": "wind_direction",
+    "Wspd (m/s)": "wind_speed",
+    "Dry-bulb (C)": "temp_air",
+    "Dew-point (C)": "temp_dew",
+    "RHum (%)": "relative_humidity",
+    "Alb (unitless)": "albedo",
+    "Pwat (cm)": "precipitable_water",
 }
 
 
-def read_tmy3(filename_or_obj, coerce_year=None, map_variables=True, encoding=None):
+def read_tmy3(filename, coerce_year=None, map_variables=True, encoding=None):
     """Read a TMY3 file into a pandas dataframe.
 
     Note that values contained in the metadata dictionary are unchanged
@@ -37,7 +37,7 @@ def read_tmy3(filename_or_obj, coerce_year=None, map_variables=True, encoding=No
 
     Parameters
     ----------
-    filename_or_obj : str, Path, or file-like object
+    filename : str, Path, or file-like object
         A relative file path or absolute file path.
     coerce_year : int, optional
         If supplied, the year of the index will be set to ``coerce_year``, except
@@ -186,32 +186,34 @@ def read_tmy3(filename_or_obj, coerce_year=None, map_variables=True, encoding=No
     .. [3] `SolarAnywhere file formats
        <https://www.solaranywhere.com/support/historical-data/file-formats/>`_
     """  # noqa: E501
-    head = ['USAF', 'Name', 'State', 'TZ', 'latitude', 'longitude', 'altitude']
+    head = ["USAF", "Name", "State", "TZ", "latitude", "longitude", "altitude"]
 
-    with _file_context_manager(filename_or_obj, mode="r", encoding=encoding) as fbuf:
+    with _file_context_manager(filename, mode="r", encoding=encoding) as fbuf:
         # header information on the 1st line (0 indexing)
         firstline = fbuf.readline()
         # use pandas to read the csv file buffer
         # header is actually the second line, but tell pandas to look for
         data = pd.read_csv(fbuf, header=0)
 
-    meta = dict(zip(head, firstline.rstrip('\n').split(",")))
+    meta = dict(zip(head, firstline.rstrip("\n").split(",")))
     # convert metadata strings to numeric types
-    meta['altitude'] = float(meta['altitude'])
-    meta['latitude'] = float(meta['latitude'])
-    meta['longitude'] = float(meta['longitude'])
-    meta['TZ'] = float(meta['TZ'])
-    meta['USAF'] = int(meta['USAF'])
+    meta["altitude"] = float(meta["altitude"])
+    meta["latitude"] = float(meta["latitude"])
+    meta["longitude"] = float(meta["longitude"])
+    meta["TZ"] = float(meta["TZ"])
+    meta["USAF"] = int(meta["USAF"])
 
     # get the date column as a pd.Series of numpy datetime64
-    data_ymd = pd.to_datetime(data['Date (MM/DD/YYYY)'], format='%m/%d/%Y')
+    data_ymd = pd.to_datetime(data["Date (MM/DD/YYYY)"], format="%m/%d/%Y")
     # extract minutes
-    minutes = data['Time (HH:MM)'].str.split(':').str[1].astype(int)
+    minutes = data["Time (HH:MM)"].str.split(":").str[1].astype(int)
     # shift the time column so that midnite is 00:00 instead of 24:00
-    shifted_hour = data['Time (HH:MM)'].str.split(':').str[0].astype(int) % 24
+    shifted_hour = data["Time (HH:MM)"].str.split(":").str[0].astype(int) % 24
     # shift the dates at midnight (24:00) so they correspond to the next day.
     # If midnight is specified as 00:00 do not shift date.
-    data_ymd[data['Time (HH:MM)'].str[:2] == '24'] += datetime.timedelta(days=1)  # noqa: E501
+    data_ymd[data["Time (HH:MM)"].str[:2] == "24"] += datetime.timedelta(
+        days=1
+    )  # noqa: E501
     # NOTE: as of pandas>=0.24 the pd.Series.array has a month attribute, but
     # in pandas-0.18.1, only DatetimeIndex has month, but indices are immutable
     # so we need to continue to work with the panda series of dates `data_ymd`
@@ -223,16 +225,19 @@ def read_tmy3(filename_or_obj, coerce_year=None, map_variables=True, encoding=No
     # timedeltas
     if coerce_year is not None:
         data_ymd = data_ymd.map(lambda dt: dt.replace(year=coerce_year))
-        data_ymd.iloc[-1] = data_ymd.iloc[-1].replace(year=coerce_year+1)
+        data_ymd.iloc[-1] = data_ymd.iloc[-1].replace(year=coerce_year + 1)
     # NOTE: as of pvlib-0.6.3, min req is pandas-0.18.1, so pd.to_timedelta
     # unit must be in (D,h,m,s,ms,us,ns), but pandas>=0.24 allows unit='hour'
-    data.index = data_ymd + pd.to_timedelta(shifted_hour, unit='h') \
-        + pd.to_timedelta(minutes, unit='min')
+    data.index = (
+        data_ymd
+        + pd.to_timedelta(shifted_hour, unit="h")
+        + pd.to_timedelta(minutes, unit="min")
+    )
 
     if map_variables:
         data = data.rename(columns=VARIABLE_MAP)
 
-    data = data.tz_localize(int(meta['TZ'] * 3600))
+    data = data.tz_localize(int(meta["TZ"] * 3600))
 
     return data, meta
 
@@ -367,9 +372,9 @@ def read_tmy2(filename):
        :doi:`10.2172/87130`
     """  # noqa: E501
     # paste in the column info as one long line
-    string = '%2d%2d%2d%2d%4d%4d%4d%1s%1d%4d%1s%1d%4d%1s%1d%4d%1s%1d%4d%1s%1d%4d%1s%1d%4d%1s%1d%2d%1s%1d%2d%1s%1d%4d%1s%1d%4d%1s%1d%3d%1s%1d%4d%1s%1d%3d%1s%1d%3d%1s%1d%4d%1s%1d%5d%1s%1d%10d%3d%1s%1d%3d%1s%1d%3d%1s%1d%2d%1s%1d'  # noqa: E501
-    columns = 'year,month,day,hour,ETR,ETRN,GHI,GHISource,GHIUncertainty,DNI,DNISource,DNIUncertainty,DHI,DHISource,DHIUncertainty,GHillum,GHillumSource,GHillumUncertainty,DNillum,DNillumSource,DNillumUncertainty,DHillum,DHillumSource,DHillumUncertainty,Zenithlum,ZenithlumSource,ZenithlumUncertainty,TotCld,TotCldSource,TotCldUncertainty,OpqCld,OpqCldSource,OpqCldUncertainty,DryBulb,DryBulbSource,DryBulbUncertainty,DewPoint,DewPointSource,DewPointUncertainty,RHum,RHumSource,RHumUncertainty,Pressure,PressureSource,PressureUncertainty,Wdir,WdirSource,WdirUncertainty,Wspd,WspdSource,WspdUncertainty,Hvis,HvisSource,HvisUncertainty,CeilHgt,CeilHgtSource,CeilHgtUncertainty,PresentWeather,Pwat,PwatSource,PwatUncertainty,AOD,AODSource,AODUncertainty,SnowDepth,SnowDepthSource,SnowDepthUncertainty,LastSnowfall,LastSnowfallSource,LastSnowfallUncertaint'  # noqa: E501
-    hdr_columns = 'WBAN,City,State,TZ,latitude,longitude,altitude'
+    string = "%2d%2d%2d%2d%4d%4d%4d%1s%1d%4d%1s%1d%4d%1s%1d%4d%1s%1d%4d%1s%1d%4d%1s%1d%4d%1s%1d%2d%1s%1d%2d%1s%1d%4d%1s%1d%4d%1s%1d%3d%1s%1d%4d%1s%1d%3d%1s%1d%3d%1s%1d%4d%1s%1d%5d%1s%1d%10d%3d%1s%1d%3d%1s%1d%3d%1s%1d%2d%1s%1d"  # noqa: E501
+    columns = "year,month,day,hour,ETR,ETRN,GHI,GHISource,GHIUncertainty,DNI,DNISource,DNIUncertainty,DHI,DHISource,DHIUncertainty,GHillum,GHillumSource,GHillumUncertainty,DNillum,DNillumSource,DNillumUncertainty,DHillum,DHillumSource,DHillumUncertainty,Zenithlum,ZenithlumSource,ZenithlumUncertainty,TotCld,TotCldSource,TotCldUncertainty,OpqCld,OpqCldSource,OpqCldUncertainty,DryBulb,DryBulbSource,DryBulbUncertainty,DewPoint,DewPointSource,DewPointUncertainty,RHum,RHumSource,RHumUncertainty,Pressure,PressureSource,PressureUncertainty,Wdir,WdirSource,WdirUncertainty,Wspd,WspdSource,WspdUncertainty,Hvis,HvisSource,HvisUncertainty,CeilHgt,CeilHgtSource,CeilHgtUncertainty,PresentWeather,Pwat,PwatSource,PwatUncertainty,AOD,AODSource,AODUncertainty,SnowDepth,SnowDepthSource,SnowDepthUncertainty,LastSnowfall,LastSnowfallSource,LastSnowfallUncertaint"  # noqa: E501
+    hdr_columns = "WBAN,City,State,TZ,latitude,longitude,altitude"
 
     tmy2, tmy2_meta = _read_tmy2(string, columns, hdr_columns, str(filename))
 
@@ -396,17 +401,19 @@ def _parsemeta_tmy2(columns, line):
     meta = rawmeta[:3]  # take the first string entries
     meta.append(int(rawmeta[3]))
     # Convert to decimal notation with S negative
-    longitude = (
-        float(rawmeta[5]) + float(rawmeta[6])/60) * (2*(rawmeta[4] == 'N') - 1)
+    longitude = (float(rawmeta[5]) + float(rawmeta[6]) / 60) * (
+        2 * (rawmeta[4] == "N") - 1
+    )
     # Convert to decimal notation with W negative
-    latitude = (
-        float(rawmeta[8]) + float(rawmeta[9])/60) * (2*(rawmeta[7] == 'E') - 1)
+    latitude = (float(rawmeta[8]) + float(rawmeta[9]) / 60) * (
+        2 * (rawmeta[7] == "E") - 1
+    )
     meta.append(longitude)
     meta.append(latitude)
     meta.append(float(rawmeta[10]))
 
     # Creates a dictionary of metadata
-    meta_dict = dict(zip(columns.split(','), meta))
+    meta_dict = dict(zip(columns.split(","), meta))
     return meta_dict
 
 
@@ -424,36 +431,42 @@ def _read_tmy2(string, columns, hdr_columns, fname):
             # Reset the cursor and array for each line
             cursor = 1
             part = []
-            for marker in string.split('%'):
+            for marker in string.split("%"):
                 # Skip the first line of markers
-                if marker == '':
+                if marker == "":
                     continue
 
                 # Read the next increment from the marker list
-                increment = int(re.findall(r'\d+', marker)[0])
+                increment = int(re.findall(r"\d+", marker)[0])
                 next_cursor = cursor + increment
 
                 # Extract the value from the line in the file
-                val = (line[cursor:next_cursor])
+                val = line[cursor:next_cursor]
                 # increment the cursor by the length of the read value
                 cursor = next_cursor
 
                 # Determine the datatype from the marker string
-                if marker[-1] == 'd':
+                if marker[-1] == "d":
                     try:
                         val = float(val)
                     except ValueError:
-                        raise ValueError('WARNING: In {} Read value is not an '
-                                         'integer " {} " '.format(fname, val))
-                elif marker[-1] == 's':
+                        raise ValueError(
+                            "WARNING: In {} Read value is not an "
+                            'integer " {} " '.format(fname, val)
+                        )
+                elif marker[-1] == "s":
                     try:
                         val = str(val)
                     except ValueError:
-                        raise ValueError('WARNING: In {} Read value is not a '
-                                         'string " {} " '.format(fname, val))
+                        raise ValueError(
+                            "WARNING: In {} Read value is not a "
+                            'string " {} " '.format(fname, val)
+                        )
                 else:
-                    raise Exception('WARNING: In {} Improper column DataFrame '
-                                    '" %{} " '.format(__name__, marker))
+                    raise Exception(
+                        "WARNING: In {} Improper column DataFrame "
+                        '" %{} " '.format(__name__, marker)
+                    )
 
                 part.append(val)
 
@@ -465,13 +478,17 @@ def _read_tmy2(string, columns, hdr_columns, fname):
                 axes.append(part)
 
             # Create datetime objects from read data
-            date.append(datetime.datetime(year=int(year),
-                                          month=int(part[1]),
-                                          day=int(part[2]),
-                                          hour=(int(part[3]) - 1)))
-
-    data = pd.DataFrame(
-        axes, index=date,
-        columns=columns.split(',')).tz_localize(int(meta['TZ'] * 3600))
+            date.append(
+                datetime.datetime(
+                    year=int(year),
+                    month=int(part[1]),
+                    day=int(part[2]),
+                    hour=(int(part[3]) - 1),
+                )
+            )
+
+    data = pd.DataFrame(axes, index=date, columns=columns.split(",")).tz_localize(
+        int(meta["TZ"] * 3600)
+    )
 
     return data, meta