How to use h5netcdf - 10 common examples

def _read_forcing(self, vs, var):
        with h5netcdf.File(DATA_FILES['forcing'], 'r') as infile:
            var_obj = infile.variables[var]
            return np.array(var_obj, dtype=str(var_obj.dtype)).T

def set_initial_conditions(self, vs):
        with h5netcdf.File(DATA_FILES['forcing'], 'r') as forcing_file:
            t_hor = (vs.xt[2:-2], vs.yt[2:-2])
            t_grid = (vs.xt[2:-2], vs.yt[2:-2], vs.zt)

            forc_coords = [self._get_data(vs, forcing_file, k) for k in ('xt', 'yt', 'zt')]
            forc_coords[0][...] += -360
            forc_coords[2][...] = -0.01 * forc_coords[2][::-1]

            temp_raw = self._get_data(vs, forcing_file, 'temp_ic')[..., ::-1]
            temp = veros.tools.interpolate(
                forc_coords, temp_raw, t_grid, missing_value=-1e20
            )
            vs.temp[2:-2, 2:-2, :, vs.tau] = vs.maskT[2:-2, 2:-2, :] * temp

            salt_raw = self._get_data(vs, forcing_file, 'salt_ic')[..., ::-1]
            salt = 35. + 1000 * veros.tools.interpolate(
                forc_coords, salt_raw, t_grid, missing_value=-1e20

def set_initial_conditions(self, vs):
        # initial conditions for T and S
        temp_data = self._read_forcing(vs, 'temperature')[:, :, ::-1]
        vs.temp[2:-2, 2:-2, :, :2] = temp_data[:, :, :, np.newaxis] * \
            vs.maskT[2:-2, 2:-2, :, np.newaxis]

        salt_data = self._read_forcing(vs, 'salinity')[:, :, ::-1]
        vs.salt[2:-2, 2:-2, :, :2] = salt_data[..., np.newaxis] * vs.maskT[2:-2, 2:-2, :, np.newaxis]

        # use Trenberth wind stress from MITgcm instead of ECMWF (also contained in ecmwf_4deg.cdf)
        vs.taux[2:-2, 2:-2, :] = self._read_forcing(vs, 'tau_x')
        vs.tauy[2:-2, 2:-2, :] = self._read_forcing(vs, 'tau_y')

        # heat flux
        with h5netcdf.File(DATA_FILES['ecmwf'], 'r') as ecmwf_data:
            qnec_var = ecmwf_data.variables['Q3']
            vs.qnec[2:-2, 2:-2, :] = np.array(qnec_var, dtype=str(qnec_var.dtype)).transpose()
            vs.qnec[vs.qnec <= -1e10] = 0.0

        q = self._read_forcing(vs, 'q_net')
        vs.qnet[2:-2, 2:-2, :] = -q
        vs.qnet[vs.qnet <= -1e10] = 0.0

        fxa = np.sum(vs.qnet[2:-2, 2:-2, :] * vs.area_t[2:-2, 2:-2, np.newaxis]) \
              / 12 / np.sum(vs.area_t[2:-2, 2:-2])
        print(' removing an annual mean heat flux imbalance of %e W/m^2' % fxa)
        vs.qnet[...] = (vs.qnet - fxa) * vs.maskT[:, :, -1, np.newaxis]

        # SST and SSS
        vs.sst_clim[2:-2, 2:-2, :] = self._read_forcing(vs, 'sst')
        vs.sss_clim[2:-2, 2:-2, :] = self._read_forcing(vs, 'sss')

def _read_forcing(self, vs, var):
        with h5netcdf.File(DATA_FILES['forcing'], 'r') as infile:
            var = infile.variables[var]
            return np.array(var, dtype=str(var.dtype)).T

def _read_forcing(self, vs, var):
        with h5netcdf.File(DATA_FILES['forcing'], 'r') as infile:
            var_obj = infile.variables[var]
            return np.array(var_obj, dtype=str(var_obj.dtype)).T

If using IO threads, start a new thread to write the netCDF data to disk.
    """
    import h5netcdf

    if vs.use_io_threads:
        _wait_for_disk(vs, filepath)
        _io_locks[filepath].clear()

    kwargs = {}
    if runtime_state.proc_num > 1:
        kwargs.update(
            driver='mpio',
            comm=rs.mpi_comm
        )

    nc_dataset = h5netcdf.File(filepath, mode, **kwargs)
    try:
        yield nc_dataset
    finally:
        if vs.use_io_threads:
            threading.Thread(target=_write_to_disk, args=(vs, nc_dataset, filepath)).start()
        else:
            _write_to_disk(vs, nc_dataset, filepath)

def _get_data(self, vs, var, idx=None):
        if idx is None:
            idx = Ellipsis
        else:
            idx = idx[::-1]

        kwargs = {}
        if rst.proc_num > 1:
            kwargs.update(
                driver='mpio',
                comm=rs.mpi_comm,
            )

        with h5netcdf.File(DATA_FILES['forcing'], 'r', **kwargs) as forcing_file:
            var_obj = forcing_file.variables[var]
            return np.array(var_obj[idx].astype(str(var_obj.dtype))).T

def _get_data(self, vs, var):
        with h5netcdf.File(DATA_FILES['forcing'], 'r') as forcing_file:
            var_obj = forcing_file.variables[var]
            return np.array(var_obj, dtype=str(var_obj.dtype)).T

def dtype(self):
        dt = self._h5ds.dtype
        if h5py.check_dtype(vlen=dt) is unicode:
            return str
        return dt


class Group(core.Group, HasAttributesMixin):
    _cls_name = 'h5netcdf.legacyapi.Group'
    _variable_cls = Variable

    @property
    def _group_cls(self):
        return Group

    createGroup = core.Group.create_group
    createDimension = core.Group._create_dimension

    def createVariable(self, varname, datatype, dimensions=(), zlib=False,
                       complevel=4, shuffle=True, fletcher32=False,
                       chunksizes=None, fill_value=None):
        if len(dimensions) == 0:  # it's a scalar
            # rip off chunk and filter options for consistency with netCDF4-python

            chunksizes = None
            zlib = False
            fletcher32 = False
            shuffle = False

        if datatype is str:
            datatype = h5py.special_dtype(vlen=unicode)

dt = self._h5ds.dtype
        if h5py.check_dtype(vlen=dt) is unicode:
            return str
        return dt


class Group(core.Group, HasAttributesMixin):
    _cls_name = 'h5netcdf.legacyapi.Group'
    _variable_cls = Variable

    @property
    def _group_cls(self):
        return Group

    createGroup = core.Group.create_group
    createDimension = core.Group._create_dimension

    def createVariable(self, varname, datatype, dimensions=(), zlib=False,
                       complevel=4, shuffle=True, fletcher32=False,
                       chunksizes=None, fill_value=None):
        if len(dimensions) == 0:  # it's a scalar
            # rip off chunk and filter options for consistency with netCDF4-python

            chunksizes = None
            zlib = False
            fletcher32 = False
            shuffle = False

        if datatype is str:
            datatype = h5py.special_dtype(vlen=unicode)

        kwds = {}