pyrqa/recurrence_analysis.py
def create_sub_matrices(self):
        """
        Create sub matrices according to the given processing order.
        Each task queue represents an execution level.
        """
        max_edge_length = math.pow(2, 16) - 1
        self.edge_length = max_edge_length if self.edge_length > max_edge_length else self.edge_length
        number_of_partitions = int(math.ceil(float(self.settings.number_of_vectors) / self.edge_length))

        self.sub_matrix_queues = []
        for partition_index_x in np.arange(number_of_partitions):
            for partition_index_y in np.arange(number_of_partitions):
                if partition_index_x == number_of_partitions - 1:
                    dim_x = self.settings.number_of_vectors - partition_index_x * self.edge_length
                    start_x = partition_index_x * self.edge_length
                else:
                    dim_x = self.edge_length
                    start_x = partition_index_x * dim_x

                if partition_index_y == number_of_partitions - 1:
                    dim_y = self.settings.number_of_vectors - partition_index_y * self.edge_length
                    start_y = partition_index_y * self.edge_length
                else:
                    dim_y = self.edge_length
                    start_y = partition_index_y * dim_y

                sub_matrix = SubMatrices.SubMatrix(partition_index_x,
                                                   partition_index_y,
                                                   dim_x,
                                                   start_x,
                                                   dim_y,
                                                   start_y)

                queue_index = None
                if self.processing_order == po.Diagonal:
                    queue_index = partition_index_x + partition_index_y

                elif self.processing_order == po.Vertical:
                    queue_index = partition_index_y

                elif self.processing_order == po.Bulk:
                    queue_index = 0

                if len(self.sub_matrix_queues) <= queue_index:
                    self.sub_matrix_queues.append(Queue.Queue())

                self.sub_matrix_queues[queue_index].put(sub_matrix)

        Create sub matrices according to the given processing order.
        Each task queue represents an execution level.
        
Create sub matrices according to the given processing order.
Each task queue represents an execution level.
Create sub matrices according to the given processing order . Each task queue represents an execution level .
def create_sub_matrices(self):
        max_edge_length = math.pow(2, 16) - 1
        self.edge_length = max_edge_length if self.edge_length > max_edge_length else self.edge_length
        number_of_partitions = int(math.ceil(float(self.settings.number_of_vectors) / self.edge_length))
        self.sub_matrix_queues = []
        for partition_index_x in np.arange(number_of_partitions):
            for partition_index_y in np.arange(number_of_partitions):
                if partition_index_x == number_of_partitions - 1:
                    dim_x = self.settings.number_of_vectors - partition_index_x * self.edge_length
                    start_x = partition_index_x * self.edge_length
                else:
                    dim_x = self.edge_length
                    start_x = partition_index_x * dim_x
                if partition_index_y == number_of_partitions - 1:
                    dim_y = self.settings.number_of_vectors - partition_index_y * self.edge_length
                    start_y = partition_index_y * self.edge_length
                else:
                    dim_y = self.edge_length
                    start_y = partition_index_y * dim_y
                sub_matrix = SubMatrices.SubMatrix(partition_index_x,
                                                   partition_index_y,
                                                   dim_x,
                                                   start_x,
                                                   dim_y,
                                                   start_y)
                queue_index = None
                if self.processing_order == po.Diagonal:
                    queue_index = partition_index_x + partition_index_y
                elif self.processing_order == po.Vertical:
                    queue_index = partition_index_y
                elif self.processing_order == po.Bulk:
                    queue_index = 0
                if len(self.sub_matrix_queues) <= queue_index:
                    self.sub_matrix_queues.append(Queue.Queue())
                self.sub_matrix_queues[queue_index].put(sub_matrix)
def create_sub_matrices ( self ) : max_edge_length = math . pow ( 2 , 16 ) - 1 self . edge_length = max_edge_length if self . edge_length > max_edge_length else self . edge_length number_of_partitions = int ( math . ceil ( float ( self . settings . number_of_vectors ) / self . edge_length ) ) self . sub_matrix_queues = [ ] for partition_index_x in np . arange ( number_of_partitions ) : for partition_index_y in np . arange ( number_of_partitions ) : if partition_index_x == number_of_partitions - 1 : dim_x = self . settings . number_of_vectors - partition_index_x * self . edge_length start_x = partition_index_x * self . edge_length else : dim_x = self . edge_length start_x = partition_index_x * dim_x if partition_index_y == number_of_partitions - 1 : dim_y = self . settings . number_of_vectors - partition_index_y * self . edge_length start_y = partition_index_y * self . edge_length else : dim_y = self . edge_length start_y = partition_index_y * dim_y sub_matrix = SubMatrices . SubMatrix ( partition_index_x , partition_index_y , dim_x , start_x , dim_y , start_y ) queue_index = None if self . processing_order == po . Diagonal : queue_index = partition_index_x + partition_index_y elif self . processing_order == po . Vertical : queue_index = partition_index_y elif self . processing_order == po . Bulk : queue_index = 0 if len ( self . sub_matrix_queues ) <= queue_index : self . sub_matrix_queues . append ( Queue . Queue ( ) ) self . sub_matrix_queues [ queue_index ] . put ( sub_matrix )
Create sub matrices according to the given processing order.
Create sub matrices according to the given processing order .
"""
        Create sub matrices according to the given processing order.
        Each task queue represents an execution level.
        """
self