Project: s41467-024-45642-z | Function: create_bloch_wave_calculation (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Create Bloch wave calculation setup for systematic row conditions including beam list generation and crystal parameters

**KEY TERMS:** {
  "systematic_row": "systematic row HKL indices",
  "zoneaxis_uvw": "zone axis direction",
  "wavevector": "incident electron wave vector",
  "beamlist": "selected diffracted beams",
  "max_rcpr_distance": "reciprocal space cutoff",
  "foilnormal": "specimen normal"
}

**CONTEXT CODE:**         return ()

    def other_shape(self) -> Tuple[Optional[int], ...]:
        return ()

    def other_names(self) -> Tuple[str, ...]:
        """Names of other axes"""
        return self._other_names

    def __call__(self, other: Optional[Mapping[str, int]] = None) -> DataSet:
        """Return DataSet with (index, tilt, x) dimensions for given values of other_axes."""
        # MASKED: Calculate Bloch wave amplitudes for different specimen thicknesses to generate diffraction patterns for dislocation identification implementation
        raise NotImplementedError("Function implementation has been masked")




def import_crystal(name: str, base_dir: Optional[Path] = None) -> SuperCell:
    import pyctem.iolib

    base_dir = Path(base_dir) if base_dir is not None else Path.cwd()
    load_cif = getattr(pyctem.iolib, 'load_cif', None)

    filename, dataset = name.split('?', 1)
    file = base_dir / Path(filename).expanduser()

    crystal = None
    if file.is_file():
        if file.suffix == '.tdf':
            with TemDataFile(file, "r") as tdf_file:
                crystal = tdf_file.read(dataset, cls=TemDataFile.CLASS_SUPERCELL)
        elif file.suffix == '.cif' and load_cif:
            crystal = load_cif(file)

    if crystal is None:
        from pyctem.geometry import Material
        import pyctem.data

        full_name = "%sMaterial" % name
        material_class = getattr(pyctem.data, full_name)
        if not issubclass(material_class, Material):
            raise TypeError("Expected %s to be a subclass of Material." % material_class.__name__)
        material = material_class()
        if material.name() != name:
            raise ValueError("Expected material %s's name to be %s" % (material_class.__name__, name))
        crystal = material.build_crystal()

    return crystal


def create_bloch_wave_calculation(param: Dict[str, Any], voltage: float, verbose: int = 0,
                                  base_dir: Optional[Path] = None):
    # MASKED: Create Bloch wave calculation setup for systematic row conditions including beam list generation and crystal parameters implementation
    raise NotImplementedError("Function implementation has been masked")




class QXCalculationSelector(QXCalculation):
    """
    Creates QX-plots from calculation data.

    :param amplitudes: Amplitudes data set (..., tilt, thickness, index, y, x)
    :param x_index: Name or index of axis to use for position
    :param tilt_index: Name or index of axis to use for tilt
    :param beam_index: Name or index of axis to use for beam index
    """
    def __init__(self, amplitudes: DataSet, beamlist: BeamList, x_index: Union[str, int] = 'x', tilt_index: Union[str, int] = 'tilt',
                 beam_index: Union[str, int] = 'beam'):
        self._amplitudes = amplitudes
        self._beamlist = beamlist
        axes = self._amplitudes.axes

        self._x_index = self._get_axis(x_index)
        self._tilt_index = self._get_axis(tilt_index)
        self._beam_index = self._get_axis(beam_index)

        self._q_dir_hkl = amplitudes.metadata["tilt_dir(hkl)"]
        self._other_indices = tuple(n for n in range(amplitudes.ndim) if n not in [self._x_index, self._beam_index, self._tilt_index])
        self._other_names = tuple(make_axis_name(axes[n], n) for n in self.other_indices())

        self._output_indices = (self._beam_index, self._x_index, self._tilt_index)

        order = np.argsort(self._output_indices).tolist()
        self._axes_order = tuple(order.index(n) for n in range(3))

    def _get_axis(self, index_or_name: Union[str, int]) -> int:
        if isinstance(index_or_name, str):
            return self._amplitudes.index_axis(name=index_or_name)

        index = int(index_or_name)
        if index < 0:
            index += self._amplitudes.ndim
        if index >= self._amplitudes.ndim:
            raise IndexError(f"Invalid axis: {index_or_name}")

        return index

    @property
    def x_index(self) -> int:
        """Index of X-Axis"""
        return self._x_index

    @property
    def tilt_index(self) -> int:
        """Index of Tilt-Axis"""
        return self._tilt_index

    @property
    def beam_index(self) -> int:
        """Index of Beam-Axis"""
        return self._beam_index

    @property
    def dtype(self) -> Tuple[Axis, ...]:
        """Data type of the output"""
        return self._amplitudes.dtype

    @property
    def shape(self) -> Tuple[Optional[int], ...]:
        """Shape of the output"""
        return tuple(self._amplitudes.shape[n] for n in self._output_indices)

    @property
    def axes(self) -> Tuple[Axis, ...]:
        """Axes of the output"""
        return tuple(self._amplitudes.axes[n] for n in self._output_indices)

    @property
    def beamlist(self) -> BeamList:
        return self._beamlist

    @property
    def metadata(self) -> CoreMetaData:
        return self._amplitudes.metadata

    @property
    def q_dir_hkl(self) -> Tuple[int, int, int]:
        return self._q_dir_hkl

    def other_indices(self) -> Tuple[int, ...]:
        """Tuple with other axis indices"""
        return self._other_indices

    def other_axes(self) ->Tuple[Axis, ...]:
        """Tuple with other Axis"""
        return tuple(self._amplitudes.axes[n] for n in self.other_indices())

    def other_shape(self) -> Tuple[Axis, ...]:
        """Tuple with dimension of other indices"""


Complete the function implementation replacing "raise NotImplementedError".