How to use the cfgrib.dataset.Variable function in cfgrib

def test_Variable():
    res = dataset.Variable(dimensions=('lat'), data=np.array([0.0]), attributes={})

    assert res == res
    assert res != 1

'source': index.filestream.path,
        'filter_by_keys': filter_by_keys,
        'encode_cf': encode_cf,
    }
    attributes = build_dataset_attributes(index, filter_by_keys, encoding)
    return dimensions, variables, attributes, encoding


@attr.attrs()
class Dataset(object):
    """
    Map a GRIB file to the NetCDF Common Data Model with CF Conventions.
    """

    dimensions = attr.attrib(type=T.Dict[str, int])
    variables = attr.attrib(type=T.Dict[str, Variable])
    attributes = attr.attrib(type=T.Dict[str, T.Any])
    encoding = attr.attrib(type=T.Dict[str, T.Any])


def open_fileindex(
    path, grib_errors='warn', indexpath='{path}.{short_hash}.idx', index_keys=ALL_KEYS
):
    stream = messages.FileStream(path, message_class=cfmessage.CfMessage, errors=grib_errors)
    return stream.index(index_keys, indexpath=indexpath)


def open_file(
    path,
    grib_errors='warn',
    indexpath='{path}.{short_hash}.idx',
    filter_by_keys={},

offsets=offsets,
        missing_value=missing_value,
        geo_ndim=len(geo_dims),
    )

    if 'time' in coord_vars and 'step' in coord_vars:
        # add the 'valid_time' secondary coordinate
        dims, time_data = cfmessage.build_valid_time(
            coord_vars['time'].data,
            coord_vars['step'].data,
        )
        attrs = COORD_ATTRS['valid_time']
        coord_vars['valid_time'] = Variable(dimensions=dims, data=time_data, attributes=attrs)

    data_var_attrs['coordinates'] = ' '.join(coord_vars.keys())
    data_var = Variable(dimensions=dimensions, data=data, attributes=data_var_attrs)
    dims = collections.OrderedDict((d, s) for d, s in zip(dimensions, data_var.data.shape))
    return dims, data_var, coord_vars

latitudes = np.array(first['distinctLatitudes'])
        geo_coord_vars['latitude'] = Variable(
            dimensions=('latitude',), data=latitudes, attributes=COORD_ATTRS['latitude'].copy()
        )
        if latitudes[0] > latitudes[-1]:
            geo_coord_vars['latitude'].attributes['stored_direction'] = 'decreasing'
        geo_coord_vars['longitude'] = Variable(
            dimensions=('longitude',),
            data=np.array(first['distinctLongitudes']),
            attributes=COORD_ATTRS['longitude'],
        )
    elif 'geography' in encode_cf and grid_type in GRID_TYPES_2D_NON_DIMENSION_COORDS:
        geo_dims = ('y', 'x')
        geo_shape = (index.getone('Ny'), index.getone('Nx'))
        try:
            geo_coord_vars['latitude'] = Variable(
                dimensions=('y', 'x'),
                data=np.array(first['latitudes']).reshape(geo_shape),
                attributes=COORD_ATTRS['latitude'],
            )
            geo_coord_vars['longitude'] = Variable(
                dimensions=('y', 'x'),
                data=np.array(first['longitudes']).reshape(geo_shape),
                attributes=COORD_ATTRS['longitude'],
            )
        except KeyError:  # pragma: no cover
            if errors != 'ignore':
                log.warning('ecCodes provides no latitudes/longitudes for gridType=%r', grid_type)
    else:
        geo_dims = ('values',)
        geo_shape = (index.getone('numberOfPoints'),)
        # add secondary coordinates if ecCodes provides them

)
            geo_coord_vars['longitude'] = Variable(
                dimensions=('y', 'x'),
                data=np.array(first['longitudes']).reshape(geo_shape),
                attributes=COORD_ATTRS['longitude'],
            )
        except KeyError:  # pragma: no cover
            if errors != 'ignore':
                log.warning('ecCodes provides no latitudes/longitudes for gridType=%r', grid_type)
    else:
        geo_dims = ('values',)
        geo_shape = (index.getone('numberOfPoints'),)
        # add secondary coordinates if ecCodes provides them
        try:
            latitude = first['latitudes']
            geo_coord_vars['latitude'] = Variable(
                dimensions=('values',), data=np.array(latitude), attributes=COORD_ATTRS['latitude']
            )
            longitude = first['longitudes']
            geo_coord_vars['longitude'] = Variable(
                dimensions=('values',),
                data=np.array(longitude),
                attributes=COORD_ATTRS['longitude'],
            )
        except KeyError:  # pragma: no cover
            if errors != 'ignore':
                log.warning('ecCodes provides no latitudes/longitudes for gridType=%r', grid_type)
    return geo_dims, geo_shape, geo_coord_vars

data = OnDiskArray(
        stream=index.filestream,
        shape=shape,
        offsets=offsets,
        missing_value=missing_value,
        geo_ndim=len(geo_dims),
    )

    if 'time' in coord_vars and 'step' in coord_vars:
        # add the 'valid_time' secondary coordinate
        dims, time_data = cfmessage.build_valid_time(
            coord_vars['time'].data,
            coord_vars['step'].data,
        )
        attrs = COORD_ATTRS['valid_time']
        coord_vars['valid_time'] = Variable(dimensions=dims, data=time_data, attributes=attrs)

    data_var_attrs['coordinates'] = ' '.join(coord_vars.keys())
    data_var = Variable(dimensions=dimensions, data=data, attributes=data_var_attrs)
    dims = collections.OrderedDict((d, s) for d, s in zip(dimensions, data_var.data.shape))
    return dims, data_var, coord_vars

log=LOG,  # type: logging.Logger
):
    # type: (...) -> T.Tuple[T.Tuple[str, ...], T.Tuple[int, ...], T.Dict[str, Variable]]
    first = index.first()
    geo_coord_vars = collections.OrderedDict()  # type: T.Dict[str, Variable]
    grid_type = index.getone('gridType')
    if 'geography' in encode_cf and grid_type in GRID_TYPES_DIMENSION_COORDS:
        geo_dims = ('latitude', 'longitude')  # type: T.Tuple[str, ...]
        geo_shape = (index.getone('Ny'), index.getone('Nx'))  # type: T.Tuple[int, ...]
        latitudes = np.array(first['distinctLatitudes'])
        geo_coord_vars['latitude'] = Variable(
            dimensions=('latitude',), data=latitudes, attributes=COORD_ATTRS['latitude'].copy()
        )
        if latitudes[0] > latitudes[-1]:
            geo_coord_vars['latitude'].attributes['stored_direction'] = 'decreasing'
        geo_coord_vars['longitude'] = Variable(
            dimensions=('longitude',),
            data=np.array(first['distinctLongitudes']),
            attributes=COORD_ATTRS['longitude'],
        )
    elif 'geography' in encode_cf and grid_type in GRID_TYPES_2D_NON_DIMENSION_COORDS:
        geo_dims = ('y', 'x')
        geo_shape = (index.getone('Ny'), index.getone('Nx'))
        try:
            geo_coord_vars['latitude'] = Variable(
                dimensions=('y', 'x'),
                data=np.array(first['latitudes']).reshape(geo_shape),
                attributes=COORD_ATTRS['latitude'],
            )
            geo_coord_vars['longitude'] = Variable(
                dimensions=('y', 'x'),
                data=np.array(first['longitudes']).reshape(geo_shape),