import os import sample_path as sp import networkx as nx import numpy as np class NetworkGraph(): """ Rappresenta il grafo che contiene i nodi e gli archi presenti nell'oggetto Structure graph_struct. Ogni nodo contine la label node_id, al nodo รจ anche associato un id numerico progressivo indx che rappresenta la posizione dei sui valori nella colonna indx della traj :graph_struct: l'oggetto Structure da cui estrarre i dati per costruire il grafo graph :graph: il grafo """ def __init__(self, graph_struct): self.graph_struct = graph_struct self.graph = nx.DiGraph() self._nodes_indexes = self.graph_struct.list_of_nodes_indexes() self._nodes_labels = self.graph_struct.list_of_nodes_labels() self._fancy_indexing = None self._time_scalar_indexing_structure = [] self._transition_scalar_indexing_structure = [] self._time_filtering = [] self._transition_filtering = [] def init_graph(self): self.add_nodes(self.graph_struct.list_of_nodes_labels()) self.add_edges(self.graph_struct.list_of_edges()) self._fancy_indexing = self.build_fancy_indexing_structure(0) self.build_time_scalar_indexing_structure() self.build_time_columns_filtering_structure() self.build_transition_scalar_indexing_structure() self.build_transition_columns_filtering_structure() def add_nodes(self, list_of_nodes): for indx, id in enumerate(list_of_nodes): self.graph.add_node(id) nx.set_node_attributes(self.graph, {id:indx}, 'indx') def add_edges(self, list_of_edges): self.graph.add_edges_from(list_of_edges) def get_ordered_by_indx_set_of_parents(self, node): ordered_set = {} parents = self.get_parents_by_id(node) for n in parents: indx = self._nodes_labels.index(n) ordered_set[n] = indx ordered_set = {k: v for k, v in sorted(ordered_set.items(), key=lambda item: item[1])} return list(ordered_set.keys()) def get_ord_set_of_par_of_all_nodes(self): result = [] for node in self._nodes_labels: result.append(self.get_ordered_by_indx_set_of_parents(node)) return result def get_ordered_by_indx_parents_values(self, node): parents_values = [] parents = self.get_ordered_by_indx_set_of_parents(node) for n in parents: parents_values.append(self.graph_struct.get_states_number(n)) return parents_values def get_ordered_by_indx_parents_values_for_all_nodes(self): result = [] for node in self._nodes_labels: result.append(self.get_ordered_by_indx_parents_values(node)) return result def get_states_number_of_all_nodes_sorted(self): states_number_list = [] for node in self._nodes_labels: states_number_list.append(self.get_states_number(node)) return states_number_list def build_fancy_indexing_structure(self, start_indx): list_of_parents_list = self.get_ord_set_of_par_of_all_nodes() index_structure = [] for i, list_of_parents in enumerate(list_of_parents_list): indexes_for_a_node = [] for j, node in enumerate(list_of_parents): indexes_for_a_node.append(self.get_node_indx(node) + start_indx) index_structure.append(np.array(indexes_for_a_node, dtype=np.int)) return index_structure def build_time_scalar_indexing_structure_for_a_node(self, node_id, parents_id): #print(parents_id) T_vector = np.array([self.graph_struct.variables_frame.iloc[node_id, 1].astype(np.int)]) #print(T_vector) T_vector = np.append(T_vector, [self.graph_struct.variables_frame.iloc[x, 1] for x in parents_id]) #print(T_vector) T_vector = T_vector.cumprod().astype(np.int) return T_vector #print(T_vector) def build_time_scalar_indexing_structure(self): parents_indexes_list = self._fancy_indexing for node_indx, p_indxs in enumerate(parents_indexes_list): if p_indxs.size == 0: self._time_scalar_indexing_structure.append(np.array([self.get_states_number_by_indx(node_indx)], dtype=np.int)) else: self._time_scalar_indexing_structure.append( self.build_time_scalar_indexing_structure_for_a_node(node_indx, p_indxs)) def build_transition_scalar_indexing_structure_for_a_node(self, node_id, parents_id): M_vector = np.array([self.graph_struct.variables_frame.iloc[node_id, 1], self.graph_struct.variables_frame.iloc[node_id, 1].astype(np.int)]) M_vector = np.append(M_vector, [self.graph_struct.variables_frame.iloc[x, 1] for x in parents_id]) M_vector = M_vector.cumprod().astype(np.int) return M_vector def build_transition_scalar_indexing_structure(self): parents_indexes_list = self._fancy_indexing for node_indx, p_indxs in enumerate(parents_indexes_list): self._transition_scalar_indexing_structure.append( self.build_transition_scalar_indexing_structure_for_a_node(node_indx, p_indxs)) def build_time_columns_filtering_structure(self): parents_indexes_list = self._fancy_indexing for node_indx, p_indxs in enumerate(parents_indexes_list): if p_indxs.size == 0: self._time_filtering.append(np.append(p_indxs, np.array([node_indx], dtype=np.int))) else: self._time_filtering.append(np.append(np.array([node_indx], dtype=np.int), p_indxs)) def build_transition_columns_filtering_structure(self): parents_indexes_list = self._fancy_indexing nodes_number = len(parents_indexes_list) for node_indx, p_indxs in enumerate(parents_indexes_list): self._transition_filtering.append(np.array([node_indx + nodes_number, node_indx, *p_indxs], dtype=np.int)) def get_nodes(self): return list(self.graph.nodes) def get_nodes_sorted_by_indx(self): return self.graph_struct.list_of_nodes def get_parents_by_id(self, node_id): return list(self.graph.predecessors(node_id)) def get_states_number(self, node_id): return self.graph_struct.get_states_number(node_id) def get_states_number_by_indx(self, node_indx): return self.graph_struct.get_states_number_by_indx(node_indx) def get_node_by_index(self, node_indx): return self.graph_struct.get_node_id(node_indx) def get_node_indx(self, node_id): return nx.get_node_attributes(self.graph, 'indx')[node_id] @property def time_scalar_indexing_strucure(self): return self._time_scalar_indexing_structure @property def time_filtering(self): return self._time_filtering @property def transition_scalar_indexing_structure(self): return self._transition_scalar_indexing_structure @property def transition_filtering(self): return self._transition_filtering ######Veloci Tests####### """os.getcwd() os.chdir('..') path = os.getcwd() + '/data' s1 = sp.SamplePath(path) s1.build_trajectories() s1.build_structure() g1 = NetworkGraph(s1.structure) g1.init_graph() print(g1.transition_scalar_indexing_structure) print(g1.transition_filtering) print(g1.time_scalar_indexing_strucure) print(g1.time_filering) #print(g1.build_fancy_indexing_structure(0)) #print(g1.get_states_number_of_all_nodes_sorted()) g1.build_scalar_indexing_structure() print(g1.scalar_indexing_structure) print(g1.build_columns_filtering_structure()) g1.build_transition_scalar_indexing_structure() print(g1.transition_scalar_indexing_structure) g1.build_transition_columns_filtering_structure() print(g1.transition_filtering) [array([3, 9]), array([ 3, 9, 27]), array([ 3, 9, 27, 81])] [array([3, 0]), array([4, 1, 2]), array([5, 2, 0, 1])]"""