Source code for pvlib.iotools.tmy

"""
Import functions for TMY2 and TMY3 data files.
"""

import datetime
import re
import pandas as pd


[docs]def read_tmy3(filename, coerce_year=None, recolumn=True): ''' Read a TMY3 file in to a pandas dataframe. Note that values contained in the metadata dictionary are unchanged from the TMY3 file (i.e. units are retained). In the case of any discrepancies between this documentation and the TMY3 User's Manual [1]_, the TMY3 User's Manual takes precedence. The TMY3 files were updated in Jan. 2015. This function requires the use of the updated files. Parameters ---------- filename : str A relative file path or absolute file path. coerce_year : None or int, default None If supplied, the year of the index will be set to `coerce_year`, except for the last index value which will be set to the *next* year so that the index increases monotonically. recolumn : bool, default True If ``True``, apply standard names to TMY3 columns. Typically this results in stripping the units from the column name. Returns ------- Tuple of the form (data, metadata). data : DataFrame A pandas dataframe with the columns described in the table below. For more detailed descriptions of each component, please consult the TMY3 User's Manual ([1]), especially tables 1-1 through 1-6. metadata : dict The site metadata available in the file. Notes ----- The returned structures have the following fields. =============== ====== =================== key format description =============== ====== =================== altitude Float site elevation latitude Float site latitudeitude longitude Float site longitudeitude Name String site name State String state TZ Float UTC offset USAF Int USAF identifier =============== ====== =================== ============================= ====================================================================================================================================================== TMYData field description ============================= ====================================================================================================================================================== TMYData.Index A pandas datetime index. NOTE, the index is currently timezone unaware, and times are set to local standard time (daylight savings is not included) TMYData.ETR Extraterrestrial horizontal radiation recv'd during 60 minutes prior to timestamp, Wh/m^2 TMYData.ETRN Extraterrestrial normal radiation recv'd during 60 minutes prior to timestamp, Wh/m^2 TMYData.GHI Direct and diffuse horizontal radiation recv'd during 60 minutes prior to timestamp, Wh/m^2 TMYData.GHISource See [1]_, Table 1-4 TMYData.GHIUncertainty Uncertainty based on random and bias error estimates see [2]_ TMYData.DNI Amount of direct normal radiation (modeled) recv'd during 60 mintues prior to timestamp, Wh/m^2 TMYData.DNISource See [1]_, Table 1-4 TMYData.DNIUncertainty Uncertainty based on random and bias error estimates see [2]_ TMYData.DHI Amount of diffuse horizontal radiation recv'd during 60 minutes prior to timestamp, Wh/m^2 TMYData.DHISource See [1]_, Table 1-4 TMYData.DHIUncertainty Uncertainty based on random and bias error estimates see [2]_ TMYData.GHillum Avg. total horizontal illuminance recv'd during the 60 minutes prior to timestamp, lx TMYData.GHillumSource See [1]_, Table 1-4 TMYData.GHillumUncertainty Uncertainty based on random and bias error estimates see [2]_ TMYData.DNillum Avg. direct normal illuminance recv'd during the 60 minutes prior to timestamp, lx TMYData.DNillumSource See [1]_, Table 1-4 TMYData.DNillumUncertainty Uncertainty based on random and bias error estimates see [2]_ TMYData.DHillum Avg. horizontal diffuse illuminance recv'd during the 60 minutes prior to timestamp, lx TMYData.DHillumSource See [1]_, Table 1-4 TMYData.DHillumUncertainty Uncertainty based on random and bias error estimates see [2]_ TMYData.Zenithlum Avg. luminance at the sky's zenith during the 60 minutes prior to timestamp, cd/m^2 TMYData.ZenithlumSource See [1]_, Table 1-4 TMYData.ZenithlumUncertainty Uncertainty based on random and bias error estimates see [1]_ section 2.10 TMYData.TotCld Amount of sky dome covered by clouds or obscuring phenonema at time stamp, tenths of sky TMYData.TotCldSource See [1]_, Table 1-5, 8760x1 cell array of strings TMYData.TotCldUncertainty See [1]_, Table 1-6 TMYData.OpqCld Amount of sky dome covered by clouds or obscuring phenonema that prevent observing the sky at time stamp, tenths of sky TMYData.OpqCldSource See [1]_, Table 1-5, 8760x1 cell array of strings TMYData.OpqCldUncertainty See [1]_, Table 1-6 TMYData.DryBulb Dry bulb temperature at the time indicated, deg C TMYData.DryBulbSource See [1]_, Table 1-5, 8760x1 cell array of strings TMYData.DryBulbUncertainty See [1]_, Table 1-6 TMYData.DewPoint Dew-point temperature at the time indicated, deg C TMYData.DewPointSource See [1]_, Table 1-5, 8760x1 cell array of strings TMYData.DewPointUncertainty See [1]_, Table 1-6 TMYData.RHum Relatitudeive humidity at the time indicated, percent TMYData.RHumSource See [1]_, Table 1-5, 8760x1 cell array of strings TMYData.RHumUncertainty See [1]_, Table 1-6 TMYData.Pressure Station pressure at the time indicated, 1 mbar TMYData.PressureSource See [1]_, Table 1-5, 8760x1 cell array of strings TMYData.PressureUncertainty See [1]_, Table 1-6 TMYData.Wdir Wind direction at time indicated, degrees from north (360 = north; 0 = undefined,calm) TMYData.WdirSource See [1]_, Table 1-5, 8760x1 cell array of strings TMYData.WdirUncertainty See [1]_, Table 1-6 TMYData.Wspd Wind speed at the time indicated, meter/second TMYData.WspdSource See [1]_, Table 1-5, 8760x1 cell array of strings TMYData.WspdUncertainty See [1]_, Table 1-6 TMYData.Hvis Distance to discernable remote objects at time indicated (7777=unlimited), meter TMYData.HvisSource See [1]_, Table 1-5, 8760x1 cell array of strings TMYData.HvisUncertainty See [1]_, Table 1-6 TMYData.CeilHgt Height of cloud base above local terrain (7777=unlimited), meter TMYData.CeilHgtSource See [1]_, Table 1-5, 8760x1 cell array of strings TMYData.CeilHgtUncertainty See [1]_, Table 1-6 TMYData.Pwat Total precipitable water contained in a column of unit cross section from earth to top of atmosphere, cm TMYData.PwatSource See [1]_, Table 1-5, 8760x1 cell array of strings TMYData.PwatUncertainty See [1]_, Table 1-6 TMYData.AOD The broadband aerosol optical depth per unit of air mass due to extinction by aerosol component of atmosphere, unitless TMYData.AODSource See [1]_, Table 1-5, 8760x1 cell array of strings TMYData.AODUncertainty See [1]_, Table 1-6 TMYData.Alb The ratio of reflected solar irradiance to global horizontal irradiance, unitless TMYData.AlbSource See [1]_, Table 1-5, 8760x1 cell array of strings TMYData.AlbUncertainty See [1]_, Table 1-6 TMYData.Lprecipdepth The amount of liquid precipitation observed at indicated time for the period indicated in the liquid precipitation quantity field, millimeter TMYData.Lprecipquantity The period of accumulatitudeion for the liquid precipitation depth field, hour TMYData.LprecipSource See [1]_, Table 1-5, 8760x1 cell array of strings TMYData.LprecipUncertainty See [1]_, Table 1-6 TMYData.PresWth Present weather code, see [2]_. TMYData.PresWthSource Present weather code source, see [2]_. TMYData.PresWthUncertainty Present weather code uncertainty, see [2]_. ============================= ====================================================================================================================================================== .. warning:: TMY3 irradiance data corresponds to the *previous* hour, so the first index is 1AM, corresponding to the irradiance from midnight to 1AM, and the last index is midnight of the *next* year. For example, if the last index in the TMY3 file was 1988-12-31 24:00:00 this becomes 1989-01-01 00:00:00 after calling :func:`~pvlib.iotools.read_tmy3`. .. warning:: When coercing the year, the last index in the dataframe will become midnight of the *next* year. For example, if the last index in the TMY3 was 1988-12-31 24:00:00, and year is coerced to 1990 then this becomes 1991-01-01 00:00:00. References ---------- .. [1] Wilcox, S and Marion, W. "Users Manual for TMY3 Data Sets". NREL/TP-581-43156, Revised May 2008. .. [2] Wilcox, S. (2007). National Solar Radiation Database 1991 2005 Update: Users Manual. 472 pp.; NREL Report No. TP-581-41364. ''' head = ['USAF', 'Name', 'State', 'TZ', 'latitude', 'longitude', 'altitude'] with open(str(filename), 'r') as csvdata: # read in file metadata, advance buffer to second line firstline = csvdata.readline() # use pandas to read the csv file buffer # header is actually the second line, but tell pandas to look for # header information on the 1st line (0 indexing) because we've already # advanced past the true first line with the readline call above. data = pd.read_csv(csvdata, header=0) 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']) # 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') # shift the time column so that midnite is 00:00 instead of 24:00 shifted_hour = data['Time (HH:MM)'].str[:2].astype(int) % 24 # shift the dates at midnite so they correspond to the next day data_ymd[shifted_hour == 0] += datetime.timedelta(days=1) # 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` data_index = pd.DatetimeIndex(data_ymd) # use indices to check for a leap day and advance it to March 1st leapday = (data_index.month == 2) & (data_index.day == 29) data_ymd[leapday] += datetime.timedelta(days=1) # shifted_hour is a pd.Series, so use pd.to_timedelta to get a pd.Series of # 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) # 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') if recolumn: data = _recolumn(data) # rename to standard column names data = data.tz_localize(int(meta['TZ'] * 3600)) return data, meta
def _recolumn(tmy3_dataframe): """ Rename the columns of the TMY3 DataFrame. Parameters ---------- tmy3_dataframe : DataFrame inplace : bool passed to DataFrame.rename() Returns ------- Recolumned DataFrame. """ # paste in the header as one long line raw_columns = 'ETR (W/m^2),ETRN (W/m^2),GHI (W/m^2),GHI source,GHI uncert (%),DNI (W/m^2),DNI source,DNI uncert (%),DHI (W/m^2),DHI source,DHI uncert (%),GH illum (lx),GH illum source,Global illum uncert (%),DN illum (lx),DN illum source,DN illum uncert (%),DH illum (lx),DH illum source,DH illum uncert (%),Zenith lum (cd/m^2),Zenith lum source,Zenith lum uncert (%),TotCld (tenths),TotCld source,TotCld uncert (code),OpqCld (tenths),OpqCld source,OpqCld uncert (code),Dry-bulb (C),Dry-bulb source,Dry-bulb uncert (code),Dew-point (C),Dew-point source,Dew-point uncert (code),RHum (%),RHum source,RHum uncert (code),Pressure (mbar),Pressure source,Pressure uncert (code),Wdir (degrees),Wdir source,Wdir uncert (code),Wspd (m/s),Wspd source,Wspd uncert (code),Hvis (m),Hvis source,Hvis uncert (code),CeilHgt (m),CeilHgt source,CeilHgt uncert (code),Pwat (cm),Pwat source,Pwat uncert (code),AOD (unitless),AOD source,AOD uncert (code),Alb (unitless),Alb source,Alb uncert (code),Lprecip depth (mm),Lprecip quantity (hr),Lprecip source,Lprecip uncert (code),PresWth (METAR code),PresWth source,PresWth uncert (code)' # noqa: E501 new_columns = [ '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', 'Pwat', 'PwatSource', 'PwatUncertainty', 'AOD', 'AODSource', 'AODUncertainty', 'Alb', 'AlbSource', 'AlbUncertainty', 'Lprecipdepth', 'Lprecipquantity', 'LprecipSource', 'LprecipUncertainty', 'PresWth', 'PresWthSource', 'PresWthUncertainty'] mapping = dict(zip(raw_columns.split(','), new_columns)) return tmy3_dataframe.rename(columns=mapping)
[docs]def read_tmy2(filename): ''' Read a TMY2 file in to a DataFrame. Note that values contained in the DataFrame are unchanged from the TMY2 file (i.e. units are retained). Time/Date and location data imported from the TMY2 file have been modified to a "friendlier" form conforming to modern conventions (e.g. N latitude is postive, E longitude is positive, the "24th" hour of any day is technically the "0th" hour of the next day). In the case of any discrepencies between this documentation and the TMY2 User's Manual [1]_, the TMY2 User's Manual takes precedence. Parameters ---------- filename : str A relative or absolute file path. Returns ------- Tuple of the form (data, metadata). data : DataFrame A dataframe with the columns described in the table below. For a more detailed descriptions of each component, please consult the TMY2 User's Manual ([1]_), especially tables 3-1 through 3-6, and Appendix B. metadata : dict The site metadata available in the file. Notes ----- The returned structures have the following fields. ============= ================================== key description ============= ================================== WBAN Site identifier code (WBAN number) City Station name State Station state 2 letter designator TZ Hours from Greenwich latitude Latitude in decimal degrees longitude Longitude in decimal degrees altitude Site elevation in meters ============= ================================== ============================ ========================================================================================================================================================================== TMYData field description ============================ ========================================================================================================================================================================== index Pandas timeseries object containing timestamps year month day hour ETR Extraterrestrial horizontal radiation recv'd during 60 minutes prior to timestamp, Wh/m^2 ETRN Extraterrestrial normal radiation recv'd during 60 minutes prior to timestamp, Wh/m^2 GHI Direct and diffuse horizontal radiation recv'd during 60 minutes prior to timestamp, Wh/m^2 GHISource See [1]_, Table 3-3 GHIUncertainty See [1]_, Table 3-4 DNI Amount of direct normal radiation (modeled) recv'd during 60 mintues prior to timestamp, Wh/m^2 DNISource See [1]_, Table 3-3 DNIUncertainty See [1]_, Table 3-4 DHI Amount of diffuse horizontal radiation recv'd during 60 minutes prior to timestamp, Wh/m^2 DHISource See [1]_, Table 3-3 DHIUncertainty See [1]_, Table 3-4 GHillum Avg. total horizontal illuminance recv'd during the 60 minutes prior to timestamp, units of 100 lux (e.g. value of 50 = 5000 lux) GHillumSource See [1]_, Table 3-3 GHillumUncertainty See [1]_, Table 3-4 DNillum Avg. direct normal illuminance recv'd during the 60 minutes prior to timestamp, units of 100 lux DNillumSource See [1]_, Table 3-3 DNillumUncertainty See [1]_, Table 3-4 DHillum Avg. horizontal diffuse illuminance recv'd during the 60 minutes prior to timestamp, units of 100 lux DHillumSource See [1]_, Table 3-3 DHillumUncertainty See [1]_, Table 3-4 Zenithlum Avg. luminance at the sky's zenith during the 60 minutes prior to timestamp, units of 10 Cd/m^2 (e.g. value of 700 = 7,000 Cd/m^2) ZenithlumSource See [1]_, Table 3-3 ZenithlumUncertainty See [1]_, Table 3-4 TotCld Amount of sky dome covered by clouds or obscuring phenonema at time stamp, tenths of sky TotCldSource See [1]_, Table 3-5, 8760x1 cell array of strings TotCldUncertainty See [1]_, Table 3-6 OpqCld Amount of sky dome covered by clouds or obscuring phenonema that prevent observing the sky at time stamp, tenths of sky OpqCldSource See [1]_, Table 3-5, 8760x1 cell array of strings OpqCldUncertainty See [1]_, Table 3-6 DryBulb Dry bulb temperature at the time indicated, in tenths of degree C (e.g. 352 = 35.2 C). DryBulbSource See [1]_, Table 3-5, 8760x1 cell array of strings DryBulbUncertainty See [1]_, Table 3-6 DewPoint Dew-point temperature at the time indicated, in tenths of degree C (e.g. 76 = 7.6 C). DewPointSource See [1]_, Table 3-5, 8760x1 cell array of strings DewPointUncertainty See [1]_, Table 3-6 RHum Relative humidity at the time indicated, percent RHumSource See [1]_, Table 3-5, 8760x1 cell array of strings RHumUncertainty See [1]_, Table 3-6 Pressure Station pressure at the time indicated, 1 mbar PressureSource See [1]_, Table 3-5, 8760x1 cell array of strings PressureUncertainty See [1]_, Table 3-6 Wdir Wind direction at time indicated, degrees from east of north (360 = 0 = north; 90 = East; 0 = undefined,calm) WdirSource See [1]_, Table 3-5, 8760x1 cell array of strings WdirUncertainty See [1]_, Table 3-6 Wspd Wind speed at the time indicated, in tenths of meters/second (e.g. 212 = 21.2 m/s) WspdSource See [1]_, Table 3-5, 8760x1 cell array of strings WspdUncertainty See [1]_, Table 3-6 Hvis Distance to discernable remote objects at time indicated (7777=unlimited, 9999=missing data), in tenths of kilometers (e.g. 341 = 34.1 km). HvisSource See [1]_, Table 3-5, 8760x1 cell array of strings HvisUncertainty See [1]_, Table 3-6 CeilHgt Height of cloud base above local terrain (7777=unlimited, 88888=cirroform, 99999=missing data), in meters CeilHgtSource See [1]_, Table 3-5, 8760x1 cell array of strings CeilHgtUncertainty See [1]_, Table 3-6 Pwat Total precipitable water contained in a column of unit cross section from Earth to top of atmosphere, in millimeters PwatSource See [1]_, Table 3-5, 8760x1 cell array of strings PwatUncertainty See [1]_, Table 3-6 AOD The broadband aerosol optical depth (broadband turbidity) in thousandths on the day indicated (e.g. 114 = 0.114) AODSource See [1]_, Table 3-5, 8760x1 cell array of strings AODUncertainty See [1]_, Table 3-6 SnowDepth Snow depth in centimeters on the day indicated, (999 = missing data). SnowDepthSource See [1]_, Table 3-5, 8760x1 cell array of strings SnowDepthUncertainty See [1]_, Table 3-6 LastSnowfall Number of days since last snowfall (maximum value of 88, where 88 = 88 or greater days; 99 = missing data) LastSnowfallSource See [1]_, Table 3-5, 8760x1 cell array of strings LastSnowfallUncertainty See [1]_, Table 3-6 PresentWeather See [1]_, Appendix B, an 8760x1 cell array of strings. Each string contains 10 numeric values. The string can be parsed to determine each of 10 observed weather metrics. ============================ ========================================================================================================================================================================== References ---------- .. [1] Marion, W and Urban, K. "Wilcox, S and Marion, W. "User's Manual for TMY2s". NREL 1995. ''' # 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' tmy2, tmy2_meta = _read_tmy2(string, columns, hdr_columns, str(filename)) return tmy2, tmy2_meta
def _parsemeta_tmy2(columns, line): """Retrieves metadata from the top line of the tmy2 file. Parameters ---------- columns : string String of column headings in the header line : string Header string containing DataFrame Returns ------- meta : Dict of metadata contained in the header string """ # Remove duplicated spaces, and read in each element rawmeta = " ".join(line.split()).split(" ") 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) # Convert to decimal notation with W negative 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)) return meta_dict def _read_tmy2(string, columns, hdr_columns, fname): head = 1 date = [] with open(fname) as infile: fline = 0 for line in infile: # Skip the header if head != 0: meta = _parsemeta_tmy2(hdr_columns, line) head -= 1 continue # Reset the cursor and array for each line cursor = 1 part = [] for marker in string.split('%'): # Skip the first line of markers if marker == '': continue # Read the next increment from the marker list 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]) # increment the cursor by the length of the read value cursor = next_cursor # Determine the datatype from the marker string 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': try: val = str(val) except ValueError: raise ValueError('WARNING: In {} Read value is not a ' 'string " {} " '.format(fname, val)) else: raise Exception('WARNING: In {} Improper column DataFrame ' '" %{} " '.format(__name__, marker)) part.append(val) if fline == 0: axes = [part] year = part[0] + 1900 fline = 1 else: 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)) return data, meta