How to use the pysat.Meta function in pysat

"""

import pandas as pds
import numpy as np
import pysat

# pysat required parameters
platform = 'pysat'
name = 'testing'
# dictionary of data 'tags' and corresponding description
tags = {'': 'Regular testing data set'}
# dictionary of satellite IDs, list of corresponding tags
sat_ids = {'': ['']}
test_dates = {'': {'': pysat.datetime(2009, 1, 1)}}

meta = pysat.Meta()
meta['uts'] = {'units': 's', 'long_name': 'Universal Time', 'custom': False}
meta['mlt'] = {'units': 'hours', 'long_name': 'Magnetic Local Time'}
meta['slt'] = {'units': 'hours', 'long_name': 'Solar Local Time'}


def init(self):
    self.new_thing = True


def load(fnames, tag=None, sat_id=None, sim_multi_file_right=False,
         sim_multi_file_left=False, root_date=None):
    # create an artifical satellite data set
    parts = fnames[0].split('/')
    yr = int('20' + parts[-1][0:2])
    month = int(parts[-3])
    day = int(parts[-2])

"""

import pandas as pds
import numpy as np
import pysat

# pysat required parameters
platform = 'pysat'
name = 'testing'
# dictionary of data 'tags' and corresponding description
tags = {'': 'Regular testing data set'}
# dictionary of satellite IDs, list of corresponding tags
sat_ids = {'': ['']}
test_dates = {'': {'': pysat.datetime(2009, 1, 1)}}

meta = pysat.Meta()
meta['uts'] = {'units': 's', 'long_name': 'Universal Time', 'custom': False}
meta['mlt'] = {'units': 'hours', 'long_name': 'Magnetic Local Time'}
meta['slt'] = {'units': 'hours', 'long_name': 'Solar Local Time'}


def init(self):
    self.new_thing = True


def load(fnames, tag=None, sat_id=None, sim_multi_file_right=False,
         sim_multi_file_left=False, root_date=None):
    # create an artifical satellite data set
    parts = fnames[0].split('/')
    yr = int('20' + parts[-1][0:2])
    month = int(parts[-3])
    day = int(parts[-2])

# pysat required parameters
platform = 'pysat'
name = 'testadd1'
# dictionary of data 'tags' and corresponding description
tags = {'': 'Ascending Integers from 0 testing data set',
        'negative': 'Descending Integers from 0 testing data set',
        'plus10': 'Ascending Integers from 10 testing data set',
        'five': 'All 5s testing data set'}
# dictionary of satellite IDs, list of corresponding tags
sat_ids = {'': ['', 'negative', 'plus10', 'five']}
test_dates = {'': {'': pysat.datetime(2009, 1, 1),
                   'negative': pysat.datetime(2009, 1, 1),
                   'plus10': pysat.datetime(2009, 1, 1),
                   'five': pysat.datetime(2009, 1, 1)}}

meta = pysat.Meta()
meta['uts'] = {'units': 's', 'long_name': 'Universal Time', 'custom': False}
meta['mlt'] = {'units': 'hours', 'long_name': 'Magnetic Local Time'}
meta['slt'] = {'units': 'hours', 'long_name': 'Solar Local Time'}


def init(self):
    self.new_thing = True


def load(fnames, tag=None, sat_id=None, sim_multi_file_right=False,
         sim_multi_file_left=False, root_date=None):
    """ Loads the test files

    Parameters
    ----------
    fnames : (list)

# f.close()

            start = pds.datetime(yr[0], mo[0], day[0], ut[0])
            stop = pds.datetime(yr[-1], mo[-1], day[-1], ut[-1])
            dates = pds.date_range(start, stop, freq='H')

            new_data = pds.DataFrame(dst, index=dates, columns=['dst'])
            # pull out specific day
            new_date = pysat.datetime.strptime(filename[-10:], '%Y-%m-%d')
            idx, = np.where((new_data.index >= new_date) &
                            (new_data.index < new_date+pds.DateOffset(days=1)))
            new_data = new_data.iloc[idx, :]
            # add specific day to all data loaded for filenames
            data = pds.concat([data, new_data], sort=True, axis=0)

    return data, pysat.Meta()

if num != 0:
        # call separate load_files routine, segemented for possible
        # multiprocessor load, not included and only benefits about 20%
        output = pysat.DataFrame(load_files(cosmicFiles, tag=tag,
                                            sat_id=sat_id,
                                            altitude_bin=altitude_bin))
        utsec = output.hour * 3600. + output.minute * 60. + output.second
        output.index = \
            pysat.utils.time.create_datetime_index(year=output.year,
                                                   month=output.month,
                                                   day=output.day,
                                                   uts=utsec)
        # make sure UTS strictly increasing
        output.sort_index(inplace=True)
        # use the first available file to pick out meta information
        profile_meta = pysat.Meta()
        meta = pysat.Meta()
        ind = 0
        repeat = True
        while repeat:
            try:
                data = netcdf_file(cosmicFiles[ind], mode='r', mmap=False)
                keys = data.variables.keys()
                for key in keys:
                    profile_meta[key] = {'units': data.variables[key].units,
                                         'long_name':
                                         data.variables[key].long_name}
                # ncattrsList = data.ncattrs()
                ncattrsList = data._attributes.keys()
                for d in ncattrsList:
                    meta[d] = {'units': '', 'long_name': d}
                repeat = False

'(ram direction) of',
                                                    'satellite'),
                         'iv_xOy_Ox_angle': ' '.join('Angle between',
                                                     'projection of the ion',
                                                     'velocity on the x-y',
                                                     'plane and satellite',
                                                     'x axis'),
                         'satellite_potential': 'Satellite potential'}}

    if name not in long_inst.keys():
        print('Warning, no long-form names available for {:s}'.format(name))

        long_inst[name] = {nn: nn for nn in meta_dict['data names']}

    # Initialise the meta data
    meta = pysat.Meta()
    for cc in meta_dict['data names']:
        # Determine the long instrument name
        if cc in long_inst[name].keys():
            ll = long_inst[name][cc]
        else:
            ll = long_name[cc]

        # Assign the data units, long names, acknowledgements, and references
        meta[cc] = {'units': meta_dict['data units'][cc], 'long_name': ll}

    # Set the remaining metadata
    meta_dict['acknowledgements'] = ackn[name]
    meta_dict['reference'] = refs[name]
    mkeys = list(meta_dict.keys())
    mkeys.pop(mkeys.index('data names'))
    mkeys.pop(mkeys.index('data units'))

for d in ncattrsList:
                    meta[d] = {'units': '', 'long_name': d}
                keys = data.variables.keys()
                for key in keys:
                    profile_meta[key] = {'units': data.variables[key].units,
                                         'long_name':
                                         data.variables[key].long_name}
                repeat = False
            except RuntimeError:
                # file was empty, try the next one by incrementing ind
                ind += 1
        meta['profiles'] = profile_meta
        return output, meta
    else:
        # no data
        return pysat.DataFrame(None), pysat.Meta()

file_format = file_format.upper()

    saved_mdata = None
    running_idx = 0
    running_store = []
    two_d_keys = []
    two_d_dims = []
    three_d_keys = []
    three_d_dims = []

    for fname in fnames:
        with netCDF4.Dataset(fname, mode='r', format=file_format) as data:
            # build up dictionary with all global ncattrs
            # and add those attributes to a pysat meta object
            ncattrsList = data.ncattrs()
            mdata = pysat.Meta(units_label=units_label, name_label=name_label,
                               notes_label=notes_label, desc_label=desc_label,
                               plot_label=plot_label, axis_label=axis_label,
                               scale_label=scale_label,
                               min_label=min_label, max_label=max_label,
                               fill_label=fill_label)
            for d in ncattrsList:
                if hasattr(mdata, d):
                    mdata.__setattr__(d+'_', data.getncattr(d))
                else:
                    mdata.__setattr__(d, data.getncattr(d))

            # loadup all of the variables in the netCDF
            loadedVars = {}
            for key in data.variables.keys():
                # load up metadata.  From here group unique
                # dimensions and act accordingly, 1D, 2D, 3D

data = pds.read_csv(fnames[0][0:-11], index_col=0, parse_dates=True)
        idx, = np.where((data.index >= date) &
                        (data.index < date + pds.DateOffset(days=1)))
        result = data.iloc[idx, :]
    elif tag == 'all':
        result = pds.read_csv(fnames[0], index_col=0, parse_dates=True)
    elif tag == 'daily' or tag == 'prelim':
        result = pds.read_csv(fnames[0], index_col=0, parse_dates=True)
    elif tag == 'forecast':
        # load forecast data
        result = pds.read_csv(fnames[0], index_col=0, parse_dates=True)
    elif tag == '45day':
        # load forecast data
        result = pds.read_csv(fnames[0], index_col=0, parse_dates=True)

    meta = pysat.Meta()
    meta['f107'] = {meta.units_label: 'SFU',
                    meta.name_label: 'F10.7 cm solar index',
                    meta.desc_label:
                    'F10.7 cm radio flux in Solar Flux Units (SFU)'}

    if tag == '45day':
        meta['ap'] = {meta.name_label: 'Daily Ap index',
                      meta.desc_label: 'Daily average of 3-h ap indices'}
    elif tag == 'daily' or tag == 'prelim':
        meta['ssn'] = {meta.name_label: 'Sunspot Number',
                       meta.desc_label: 'SESC Sunspot Number',
                       meta.fill_label: -999}
        meta['ss_area'] = {meta.name_label: 'Sunspot Area',
                           meta.desc_label: 'Sunspot Area 10$^6$ Hemisphere',
                           meta.fill_label: -999}
        meta['new_reg'] = {meta.name_label: 'New Regions',