Update app.py

app.py

@@ -40,39 +40,73 @@ def subsetEdges(city, fstyear, lstyear):
   info = taraf_info[(taraf_info['Year'] >= fstyear) & (taraf_info['City'] == city) & (taraf_info['Year'] <= lstyear)]
   narrators = edge_info[edge_info['Edge_ID'].isin(info['ID'].unique())]
   return narrators
-def splitIsnad(dataframe):
-  teacher_student =dataframe['Edge_Name'].str.split(' TO ')
-  dataframe['Teacher'] = teacher_student.apply(lambda x: x[0])
-  dataframe['Student'] = teacher_student.apply(lambda x: x[1])
-  return dataframe
 
-    
+def get_node_info(node):
+    node_info = narrator_bios[narrator_bios['Rawi ID'] == int(node)]
+    student_narrations = node_info['Number of Narrations'].to_list()
+    if len(student_narrations):
+        student_narrations = student_narrations[0]
+    else:
+        student_narrations = 1
+    student_gen = node_info['Generation'].to_list()
+    if len(student_gen):
+        student_gen = student_gen[0]
+    else:
+        student_gen = -1
+    student_rank = node_info["Narrator Rank"].to_list()
+    if len(student_rank):
+        student_rank = student_rank[0]
+    else:
+        student_rank = 'فلان'
+    node_name = node_info['Famous Name'].to_list()
+    if len(node_name):
+        node_name = node_name[0]
+    else:
+        node_name = 'فلان'
+    return node_info,student_narrations,student_gen, student_rank, node_name
+
 def network_visualizer(yaxis, city, fstyear,lastyr, num_nodes):
     edges = subsetEdges(city, fstyear, lastyr)
-    edges_split = splitIsnad(edges).reset_index()
+    
+    G = nx.from_pandas_edgelist(edges, source = 'Teacher_ID', target = 'Student_ID', create_using = nx.DiGraph())
+    nodes = list(G.nodes)
+    node_reports = [narrator_bios[narrator_bios['Rawi ID'].astype(int) == int(x)['Number of Narrations'] for x in nodes]
+    nodes_df = pd.DataFrame({'Node': nodes, 'Report': node_reports}).sort_values('Report').head(num_nodes)
+    nodes_remove = list(set(nodes) - set(nodes_df['Node'].to_list()))
+    [G.remove_nodes_from(nodes_remove)]
+    
     #.groupby(['Teacher', 'Student']).sum()
-    if edges_split.shape[0] > num_nodes:
-        edge_15 = edges_split.sort_values(by=yaxis, ascending=False).head(num_nodes)
-    else:
-        edge_15 = edges_split.copy()
-
-    net = Network(directed =True, select_menu=True, cdn_resources='remote', filter_menu=True)
-
-
+    # if edges_split.shape[0] > num_nodes:
+    #     edge_15 = edges_split.sort_values(by=yaxis, ascending=False).head(num_nodes)
+    # else:
+    #     edge_15 = edges_split.copy()
+
+    net = Network(directed =True, select_menu=True, cdn_resources='remote')
+    for node in G.nodes:
+        node_info,student_narrations,student_gen, student_rank, node_name = get_node_info(node)
+        if node == 99999:
+            net.add_node(node, font = {'size':50, 'color': 'black'}, color = '#000000', label = f'{node_name} \n ID: {node} - Gen {student_gen}', size= 70)  
+        else:
+            net.add_node(node, font = {'size':30, 'color': 'red'}, color = value_to_hex(student_narrations), label = f'{node_name} \n {student_rank} \n ID: {node} - Gen {student_gen}', size= 50)
+    for edge in G.edges:
+        row = edges[(edges['Source'] == edge[0]) & (edges['Destination'] == edge[1])].iloc[0]
+        source = row['Teacher_ID']
+        target = row['Student_ID']
+        net.add_edge(source, target, color = value_to_hex(int(row[yaxis])), label = f"{row[yaxis]}")
     
-    for _, row in edge_15.iterrows():
-        source = row['Teacher']
-        target = row['Student']
-        attribute_value = row[yaxis]
-        edge_color = value_to_hex(attribute_value)
-        teacher_info = narrator_bios[narrator_bios['Rawi ID'] == row['Teacher_ID']]
-        student_info = narrator_bios[narrator_bios['Rawi ID'] == row['Student_ID']]
-        teacher_narrations = teacher_info['Number of Narrations'].to_list()[0]
-        student_narrations = student_info['Number of Narrations'].to_list()[0]
+    # for _, row in edge_15.iterrows():
+    #     source = row['Teacher']
+    #     target = row['Student']
+    #     attribute_value = row[yaxis]
+    #     edge_color = value_to_hex(attribute_value)
+    #     teacher_info = narrator_bios[narrator_bios['Rawi ID'] == row['Teacher_ID']]
+    #     student_info = narrator_bios[narrator_bios['Rawi ID'] == row['Student_ID']]
+    #     teacher_narrations = teacher_info['Number of Narrations'].to_list()[0]
+    #     student_narrations = student_info['Number of Narrations'].to_list()[0]
         
-        net.add_node(source, color=value_to_hex(teacher_narrations), font = {'size':30, 'color': 'orange'}, label = f"{source}\n{teacher_narrations}")
-        net.add_node(target, color=value_to_hex(student_narrations), font = {'size': 20, 'color': 'red'}, label = f"{target}\n{student_narrations}")
-        net.add_edge(source, target, color=edge_color, value=attribute_value, label = f"{yaxis}:{attribute_value}")
+    #     net.add_node(source, color=value_to_hex(teacher_narrations), font = {'size':30, 'color': 'orange'}, label = f"{source}\n{teacher_narrations}")
+    #     net.add_node(target, color=value_to_hex(student_narrations), font = {'size': 20, 'color': 'red'}, label = f"{target}\n{student_narrations}")
+    #     net.add_edge(source, target, color=edge_color, value=attribute_value, label = f"{yaxis}:{attribute_value}")
 
 
     net.barnes_hut(gravity=-5000, central_gravity=0.1, spring_length=200)