DiamondLightSource / Savu

        '''

        self._ip_complete = True

    def __finalise_datasets(self):

        '''

        Inspect the two Data objects that are used to contain the input and

        output data for iterations over the course of the iterative processing

        (input/output depending on which particular iteration was being done).

        Mark one of them as the "final dataset" to be added to the output

        NeXuS file, and mark the other as "obsolete/to be removed".

        The decision between which one is kept and which one is removed

        depends on which Data object contains the OUTPUT of the very last

        iteration.

        For an odd number of iterations, this is the "original" Data object.

        For an even number of iteration, this is the "clone" Data object.

        '''

        for s1, s2 in self._ip_data_dict['iterating'].items():

            name = s1.get_name()

            name = name if 'itr_clone' not in name else s2.get_name()

            final_dataset = s1 if s1 in self.parameters['out_datasets'] else s2

            obsolete = s1 if s1 is not final_dataset else s2

            obsolete.remove = True

            # switch names if necessary

            if final_dataset.get_name() != name:

                # If this is true, then the output dataset of the last

                # iteration is the clone Data object (hence, the mismatched

                # names).

                #

                # So then:

                # - obsolete = original

                # - final_dataset = clone

                #

                # which means that the CLONED dataset needs to be set in the

                # Experiment object (self.exp) as the "out data", but under

                # the name of the ORIGINAL dataset.

                # And also, the ORIGINAL dataset is set in the Experiment

                # object, but under the name of the CLONED/OBSOLETE dataset

                temp = obsolete

                self.exp.index['out_data'][name] = final_dataset

                self.exp.index['out_data'][s2.get_name()] = temp

                # One last thing to do in this case is to set the "name"

                # inside the Data object that final_result is set to.

                #

                # This is because, in this case, the CLONED dataset is in

                # final_result, and the "name" within the Data object will

                # be some value like "itr_0".

                #

                # However, the name within the Data object needs to be the

                # name of the ORIGINAL Data object in order for the creation

                # of the output NeXuS file to work.

                final_dataset._set_name(name)

    def __set_original_datasets(self):

        '''

    def get_iteration(self):

        return self._ip_iteration

    def __finalise_datasets(self):

        '''

        Inspect the two Data objects that are used to contain the input and

        output data for iterations over the course of the iterative processing

        (input/output depending on which particular iteration was being done).

        Mark one of them as the "final dataset" to be added to the output

        NeXuS file, and mark the other as "to be removed".

        The decision between which one is kept and which one is removed

        depends on which Data object contains the OUTPUT of the very last

        iteration.

        For an odd number of iterations, this is the "original" Data object.

        For an even number of iteration, this is the "clone" Data object.

        '''

        for s1, s2 in self._ip_data_dict['iterating'].items():

            name = s1.get_name()

            name = name if 'itr_clone' not in name else s2.get_name()

            final_dataset = s1 if s1 in self.parameters['out_datasets'] else s2

            obsolete = s1 if s1 is not final_dataset else s2

            obsolete.remove = True

            # switch names if necessary

            if final_dataset.get_name() != name:

                # If this is true, then the output dataset of the last

                # iteration is the clone Data object (hence, the mismatched

                # names).

                #

                # So then:

                # - obsolete = original

                # - final_dataset = clone

                #

                # which means that the CLONED dataset needs to be set in the

                # Experiment object (self.exp) as the "out data", but under

                # the name of the ORIGINAL dataset.

                # And also, the ORIGINAL dataset is set in the Experiment

                # object, but under the name of the CLONED/OBSOLETE dataset

                temp = obsolete

                self.exp.index['out_data'][name] = final_dataset

                self.exp.index['out_data'][s2.get_name()] = temp

                # One last thing to do in this case is to set the "name"

                # inside the Data object that final_result is set to.

                #

                # This is because, in this case, the CLONED dataset is in

                # final_result, and the "name" within the Data object will

                # be some value like "itr_0".

                #

                # However, the name within the Data object needs to be the

                # name of the ORIGINAL Data object in order for the creation

                # of the output NeXuS file to work.

                final_dataset._set_name(name)

    def set_processing_complete(self):

        self._ip_complete = True

        self.end_plugin._finalise_datasets()

        self.end_plugin._finalise_plugin_datasets()

    def _finalise_iterated_datasets(self, exp):

        '''

        Inspect the two Data objects that are used to contain the input and

        output data for iterations over the course of the iterative processing

        (input/output depending on which particular iteration was being done).

        Mark one of them as the "final dataset" to be added to the output

        NeXuS file, and mark the other as "obsolete/to be removed".

        The decision between which one is kept and which one is removed

        depends on which Data object contains the OUTPUT of the very last

        iteration.

        For an odd number of iterations, this is the "original" Data object.

        For an even number of iteration, this is the "clone" Data object.

        '''

        for s1, s2 in self._ip_data_dict['iterating'].items():

            name = s1.get_name()

            name = name if 'itr_clone' not in name else s2.get_name()

            final_dataset = s1 if s1 in self.end_plugin.parameters['out_datasets'] else s2

            obsolete = s1 if s1 is not final_dataset else s2

            obsolete.remove = True

            # switch names if necessary

            if final_dataset.get_name() != name:

                # If this is true, then the output dataset of the last

                # iteration is the clone Data object (hence, the mismatched

                # names).

                #

                # So then:

                # - obsolete = original

                # - final_dataset = clone

                #

                # which means that the CLONED dataset needs to be set in the

                # Experiment object (self.exp) as the "out data", but under

                # the name of the ORIGINAL dataset.

                # And also, the ORIGINAL dataset is set in the Experiment

                # object, but under the name of the CLONED/OBSOLETE dataset

                temp = obsolete

                exp.index['out_data'][name] = final_dataset

                exp.index['out_data'][s2.get_name()] = temp

                # One last thing to do in this case is to set the "name"

                # inside the Data object that final_result is set to.

                #

                # This is because, in this case, the CLONED dataset is in

                # final_result, and the "name" within the Data object will

                # be some value like "itr_0".

                #

                # However, the name within the Data object needs to be the

                # name of the ORIGINAL Data object in order for the creation

                # of the output NeXuS file to work.

                final_dataset._set_name(name)

    def set_alternating_datasets(self):

        d1 = self.end_plugin.parameters['out_datasets'][0]