Major changes in sample_path class

main_package/classes/dynamic_graph.py

@@ -1,9 +1,10 @@
 import numpy as np
 import sample_path as sp
+import networkx as nx
 import node
 
 
-class DynamicGraph():
+class NetworkGraph():
     """
     Rappresenta un grafo dinamico con la seguente struttura:
         {Key: {Arcs:{node_object: #in_arcs.....}, Time:t, Node:node_object}.....}
@@ -20,7 +21,7 @@ class DynamicGraph():
     def __init__(self, sample_path):
         self.sample_path = sample_path
         self.graph = {}
-        self.states_number = 0
+        
 
     def initialize_graph(self, trajectory):
         """

main_package/classes/json_importer.py

@@ -15,11 +15,10 @@ class JsonImporter(AbstractImporter):
         super(JsonImporter, self).__init__(files_path)
 
     def import_data(self):
-        data = self.read_json_file()
+        raw_data = self.read_json_file()
-        self.import_trajectories(data)
+        self.import_trajectories(raw_data)
-        self.import_structure(data)
+        self.import_structure(raw_data)
-        self.import_variables(data)
+        self.import_variables(raw_data)
-
 
     def import_trajectories(self, raw_data):
         self.normalize_trajectories(raw_data, 0, 'samples')
@@ -47,6 +46,12 @@ class JsonImporter(AbstractImporter):
         for sample_indx, sample in enumerate(raw_data[indx][trajectories_key]):
             self.df_samples_list.append(pd.json_normalize(raw_data[indx][trajectories_key][sample_indx]))
 
+    def build_list_of_samples_array(self, data_frame):
+        columns_list = []
+        for column in data_frame:
+            columns_list.append(data_frame[column].to_numpy())
+        return columns_list
+
     def clear_data_frames(self):
         """
         Rimuove tutti i valori contenuti nei data_frames presenti in df_list
@@ -55,13 +60,14 @@ class JsonImporter(AbstractImporter):
         Returns:
             void
          """
-        for data_frame in self.df_list:
+        for indx in range(len(self.df_samples_list)):
-            data_frame = data_frame.iloc[0:0]
+            #data_frame = data_frame.iloc[0:0]
-
+            self.df_samples_list[indx] = self.df_samples_list[indx].iloc[0:0]
 
 
-ij = JsonImporter("../data")
+"""ij = JsonImporter("../data")
 ij.import_data()
-print(ij.df_samples_list[7])
+#print(ij.df_samples_list[7])
 print(ij.df_structure)
 print(ij.df_variables)
+print((ij.build_list_of_samples_array(0)[1].size))"""

main_package/classes/sample_path.py

@@ -1,11 +1,12 @@
 import pandas as pd
 import numpy as np
 import os
-import importer as imp
+import json_importer as imp
 import trajectory as tr
+import structure as st
 
 
-class SamplePath():
+class SamplePath:
     """
     Rappresenta l'aggregazione di una o più traiettorie.
     Ha il compito dato di costruire tutte gli oggetti Trajectory a partire
@@ -15,23 +16,35 @@ class SamplePath():
     :trajectories: lista contenente le traiettorie create
     """
 
-    def __init__(self, files_path=os.getcwd() + "/main_package/data"):
+    def __init__(self, files_path):
-        self.importer = imp.Importer(files_path)
+        print()
+        self.importer = imp.JsonImporter(files_path)
         self.trajectories = []
-
+        self.structure = None
 
     def build_trajectories(self):
-        self.importer.import_data_from_csv()
+        self.importer.import_data()
-        self.importer.merge_value_columns_in_all_frames()
+        for traj_data_frame in self.importer.df_samples_list:
-        self.importer.drop_unneccessary_columns_in_all_frames()
+            trajectory = tr.Trajectory(self.importer.build_list_of_samples_array(traj_data_frame))
-        for data_frame in self.importer.get_data_frames():
-            trajectory = tr.Trajectory(data_frame)
             self.trajectories.append(trajectory)
         self.importer.clear_data_frames()
 
+    def build_structure(self):
+        self.structure = st.Structure(self.importer.df_structure, self.importer.df_variables)
+
     def get_number_trajectories(self):
         return len(self.trajectories)
 
-
+os.getcwd()
+os.chdir('..')
+path = os.getcwd() + '/data'
+print(path)
+sp = SamplePath(path)
+sp.build_trajectories()
+sp.build_structure()
+print(sp.trajectories[7].actual_trajectory)
+print(sp.importer.df_samples_list[7])
+print(sp.get_number_trajectories())
+print(list(sp.structure.list_of_edges()))
 
 

main_package/classes/structure.py

@@ -0,0 +1,16 @@
+
+
+class Structure:
+
+    def __init__(self, structure, variables):
+        self.structure_frame = structure
+        self.variables_frame = variables
+
+    def list_of_edges(self):
+        edges_list = []
+        for indx, row in self.structure_frame.iterrows():
+           row_tuple = (row.From, row.To)
+           edges_list.append(row_tuple)
+        return edges_list
+
+

main_package/classes/trajectory.py

@@ -4,34 +4,27 @@ import numpy as np
 
 class Trajectory():
     """ 
-    Rappresenta una traiettoria come un data_frame contenente coppie (T_k,S_i) => (numpy.float64, string)
+    Rappresenta una traiettoria come un numpy_array contenente n-ple (indx, T_k,S_i,.....,Sj)
     Offre i metodi utili alla computazione sulla struttura stessa.
-    :actual_trajectory: il data_frame contenente la successione di coppie (T_k,S_i)
+    :actual_trajectory: il numpy_array contenente la successione di n-ple (indx, T_k,S_i,.....,Sj)
 
     """
 
-    def __init__(self, data_frame):
+    def __init__(self, list_of_columns):
-        self.actual_trajectory = data_frame
+        self.actual_trajectory = np.array(list_of_columns, dtype=object).T
         
     def get_trajectory(self):
         return self.actual_trajectory
 
-    def get_trajectory_as_matrix(self):
+    def merge_columns(self, list_of_cols):
-        """
+        return np.vstack(list_of_cols).T
-        Converte il data_frame actual_trajectory in formato numpy.array
+
-        Parameters:
+    """def get_states(self):
-            void
-        Returns:
-            numpy.array
-        """
-        return self.actual_trajectory[['Time','State']].to_numpy()
         
-    def get_states(self):
-        """
         Identifica gli stati visitati nella traiettoria.
         Parameters:
             void
         Returns:
             una lista contenente gli stati visitati nella traiettoria
-        """
+        
-        return self.actual_trajectory['State'].unique()
+        return self.actual_trajectory['State'].unique()"""