DiamondLightSource / Savu

    def nOutput_datasets(self):

        return 1

    def setup(self):

        in_dataset, out_dataset = self.get_datasets()

        #=================== populate output dataset ==========================

        # Due to the reduction in dimensions, the out_dataset will have

        # different axis_labels, patterns and shape to the in_dataset and

        # these will need to be defined.

        # For more information about the syntax used here see:

        # http://savu.readthedocs.io/en/latest/api_plugin/savu.data.data_structures.data_create

        # AMEND THE PATTERNS: The output dataset will have one dimension less

        # than the in_dataset, so remove the final slice dimension from any

        # patterns you want to keep.

        rm_dim = str(in_dataset[0].get_data_patterns()

                     ['SINOGRAM']['slice_dims'][-1])

        patterns = ['SINOGRAM.' + rm_dim, 'PROJECTION.' + rm_dim]

        # AMEND THE AXIS LABELS: Find the dimensions to remove using their

        # axis_labels to ensure the plugin is as generic as possible and will

        # work for data in all orientations.

        axis_labels = copy.copy(in_dataset[0].get_axis_labels())

        rm_labels = ['detector_x', 'detector_y']

        rm_dims = sorted([in_dataset[0].get_data_dimension_by_axis_label(a)

                          for a in rm_labels])[::-1]

        for d in rm_dims:

            del axis_labels[d]

        # Add a new axis label to the list

        axis_labels.append({'Q': 'Angstrom^-1'})

        # AMEND THE SHAPE: Remove the two unrequired dimensions from the

        # original shape and add a new dimension shape.

        shape = list(in_dataset[0].get_shape())

        for d in rm_dims:

            del shape[d]

        shape += (self.get_parameters('num_bins'),)

        # populate the output dataset

        out_dataset[0].create_dataset(

                patterns={in_dataset[0]: patterns},

                axis_labels=axis_labels,

                shape=tuple(shape))

        # ASSOCIATE AN EXTRA PATTERN WITH THE DATASET: SINOGRAM and PROJECTION

        # patterns are already asssociated with the output dataset, but add 

        # another one.

        spectrum = \

            {'core_dims': (-1,), 'slice_dims': tuple(range(len(shape)-1))}

        out_dataset[0].add_pattern("SPECTRUM", **spectrum)

        #======================================================================

        #================== populate plugin datasets ==========================

        in_pData, out_pData = self.get_plugin_datasets()

        in_pData[0].plugin_data_setup('DIFFRACTION', 'single')

        out_pData[0].plugin_data_setup('SPECTRUM', 'single')

        #======================================================================

    def pre_process(self):

        self.add_axes_to_meta_data(axis, mData)

    def setup(self):

        in_dataset, out_dataset = self.get_datasets()

        # AMEND THE PATTERNS: The output dataset will have one dimension less

        # than the in_dataset, so remove the final slice dimension from any

        # patterns you want to keep.

        rm_dim = str(in_dataset[0].get_data_patterns()

                     ['SINOGRAM']['slice_dims'][-1])

        patterns = ['SINOGRAM.' + rm_dim, 'PROJECTION.' + rm_dim]

        # AMEND THE AXIS LABELS: Find the dimensions to remove using their

        # axis_labels to ensure the plugin is as generic as possible and will

        # work for data in all orientations.

        axis_labels = copy.copy(in_dataset[0].get_axis_labels())

        rm_labels = ['detector_x', 'detector_y']

        rm_dims = sorted([in_dataset[0].get_data_dimension_by_axis_label(a)

                          for a in rm_labels])[::-1]

        for d in rm_dims:

            del axis_labels[d]

        # Add a new axis label to the list

        axis_labels.append({'Q': 'Angstrom^-1'})

        # AMEND THE SHAPE: Remove the two unrequired dimensions from the

        # original shape and add a new dimension shape.

        shape = list(in_dataset[0].get_shape())

        for d in rm_dims:

            del shape[d]

        shape += (self.get_parameters('num_bins'),)

        # populate the output dataset

        out_dataset[0].create_dataset(

                patterns={in_dataset[0]: patterns},

                axis_labels=axis_labels,

                shape=tuple(shape))

        spectrum = \

            {'core_dims': (-1,), 'slice_dims': tuple(range(len(shape)-1))}

        out_dataset[0].add_pattern("SPECTRUM", **spectrum)

        # =====================================================================

        # ================== populate plugin datasets =========================

        in_pData, out_pData = self.get_plugin_datasets()

        in_pData[0].plugin_data_setup('DIFFRACTION', 'single')

        out_pData[0].plugin_data_setup('SPECTRUM', 'single')

        # =====================================================================

    def get_max_frames(self):