DiamondLightSource / Savu

            self._ip_iteration += 1

        self.__finalise_datasets()

    def __set_datasets(self):

        '''

        Set the input and output datasets such that

        - the output dataset from the previous iteration is the input dataset of

          the current iteration that is about to be performed

        - the input dataset from the previous iteration is used to write the

          output of the urrent iteration that is about to be performed

        '''

        params = self.parameters

        # Only the 0th and 1st iterations are set in _ip_data_dicts, there is

        # NOT a key for every iteration in _ip_data_dict, hence this if/elif

        # block

        if self._ip_iteration in list(self._ip_data_dict.keys()):

            # If on the 0th or 1st iteration, set the in_datasets and

            # out_datasets according to the structure  defined in _ip_data_dict

            #

            # The body of this if statement is essentially a way to "set up" the

            # input and ouput datasets so that for iterations after the 0th and

            # 1st, the two datasets that are swapped between being used for

            # input or output (depending on the particular iteration) can be

            # swapped WITHOUT having to define a key-value pair in

            # _ip_data_dict for EVERY SINGLE ITERATION

            # this line isn't changing anything about the input datasets for

            # iteration 0, but it is setting the input dataset for iteration 1

            # to be the output dataset from iteration 0

            params['in_datasets'] = [self._ip_data_dict[self._ip_iteration][0][-1]]

            params['out_datasets'] = self._ip_data_dict[self._ip_iteration][1]

        elif self._ip_iteration > 0:

            # If on an iteration greater than 1 (since the if statement catches

            # both iteration 0 and 1), then there is some (fiddly...) logic

            # here to essentially SWAP the out dataset from the previous

            # iteration with the in dataset of the previous iteration

            #

            # Practically speaking, this means that:

            # - the out dataset from the previous iteration is used as the input

            #   for the current iteration that is about to be performed

            # - the in dataset from the previous iteration is free to be used to

            #   write the output of the current iteration that is about to be

            #   performed

            p = [params['in_datasets'], params['out_datasets']]

            for original, clone in self._ip_data_dict['iterating'].items():

                # The definition of a is based on if the "original" dataset was

                # an input or output dataset for the previous iteration.

                #

                # - the if branch is used when the "original" was the input

                # dataset for the previous iteration

                # - the else branch is used when the "original" was the output

                # dataset for the previous iteration

                a = [0, p[0].index(original)] if original in p[0] else \

                    [1, p[1].index(original)]

                # The definition of b is based on if the "clone" dataset was

                # an input or output dataset for the previous iteration

                #

                # Similarly to the definition of a:

                # - the if branch is used when the "clone" was the input

                # dataset for the previous iteration

                # - the else branch is used when the "clone" was the output

                # dataset for the previous iteration

                b = [0, p[0].index(clone)] if clone in p[0] else \

                    [1, p[1].index(clone)]

                # this is swapping the input and output datasets

                p[a[0]][a[1]], p[b[0]][b[1]] = p[b[0]][b[1]], p[a[0]][a[1]]

#    def __set_patterns(self):

#        # The pattern dict key

        # alternate a dataset with its clone

        self.set_alternating_datasets(data, clone)

    def __set_datasets(self):

        '''

        Set the input and output datasets such that

        - the output dataset from the previous iteration is the input dataset of

          the current iteration that is about to be performed

        - the input dataset from the previous iteration is used to write the

          output of the current iteration that is about to be performed

        '''

        # TODO: perhaps the pattern should be changed here, to make use of

        #  the same logic that is being used to switch the original & cloned

        #  data?

        params = self.parameters

        # Only the 0th and 1st iterations are set in _ip_data_dicts, there is

        # NOT a key for every iteration in _ip_data_dict, hence this if/elif

        # block

        if self._ip_iteration in list(self._ip_data_dict.keys()):

            # If on the 0th or 1st iteration, set the in_datasets and

            # out_datasets according to the structure  defined in _ip_data_dict

            #

            # The body of this if statement is essentially a way to "set up" the

            # input and ouput datasets so that for iterations after the 0th and

            # 1st, the two datasets that are swapped between being used for

            # input or output (depending on the particular iteration) can be

            # swapped WITHOUT having to define a key-value pair in

            # _ip_data_dict for EVERY SINGLE ITERATION

            params['in_datasets'] = self._ip_data_dict[self._ip_iteration][0]

            params['out_datasets'] = self._ip_data_dict[self._ip_iteration][1]

        elif self._ip_iteration > 0:

            # If on an iteration greater than 1 (since the if statement catches

            # both iteration 0 and 1), then there is some (fiddly...) logic

            # here to essentially SWAP the out dataset from the previous

            # iteration with the in dataset of the previous iteration

            #

            # Practically speaking, this means that:

            # - the out dataset from the previous iteration is used as the input

            #   for the current iteration that is about to be performed

            # - the in dataset from the previous iteration is free to be used to

            #   write the output of the current iteration that is about to be

            #   performed

            p = [params['in_datasets'], params['out_datasets']]

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

                a = [0, p[0].index(s1)] if s1 in p[0] else [1, p[1].index(s1)]

                b = [0, p[0].index(s2)] if s2 in p[0] else [1, p[1].index(s2)]

                p[a[0]][a[1]], p[b[0]][b[1]] = p[b[0]][b[1]], p[a[0]][a[1]]

    def set_alternating_datasets(self, d1, d2):

        names = [d1.get_name(), d2.get_name()]