net_centric_yed-ip

gistfile1.txt

#!/usr/bin/env python
# -*- coding: utf-8 -*-

'''
[
  {
    "20S301": "SHAPE",
    "20SU06": "SHAPE", 
    "77B301": "BRUNSSUM",
    "77B302": "BRUNSSUM",
    "77B402": "BRUNSSUM",
    "H13FXX": "FRANCE",
    "H21": "NETHERLANDS",
    "H22": "POLAND",
    "H23": "NETHERLANDS",
    "H24": "NETHERLANDS",
    "H31": "LUXEMBURG",
    "H47": "GERMANY",
    "H49": "CANADA",
    "H51": "U.S.A.",
    "H61": "U.S.A.",
    "H79": "ITALY",
    "H89": "ROMANIA"
  }
]


NET,OCCUPANT
NETWORK,OCCUPANT
10.0.248.0/30,20S301
10.0.248.0/30,77B301
10.135.66.0/24,20S301
10.35.166.0/24,20S301
10.88.66.0/24,77B301
10.88.166.0/24,77B302
10.0.248.4/30,77B301
10.0.248.4/30,77B302
10.0.248.8/30,20S301
10.0.248.8/30,77B302
'''

'''
ABOUT:  net centrics approach to automatise visualising IP networks in yEd
AUTHOR: bockor
DATE:   07 MAR 2020
'''

import csv
import json
import re
from collections import defaultdict
# based on the pyyed module, adapted by the author
import nra_pyyed

# SPECIFY HERE THE INPUT AND OUTPUT FILES
file_in_csv = './net_centric.csv'
file_out_graphml = './net_centric_result.graphml'

# SPECIFY THE JSON FILE THAT MAPS CAST LOCIDS TO GEO PLACE
file_in_cast = './net_centric_places.json'

# DEFINE OCCUPANT COLORS AND SHAPES
color_rtr = '#FF0000'
color_ipz = '#FFFF00'
shape_rtr = 'ellipse'
shape_fw = 'octagon'

def get_occupant_shape(occupant):
    # CHECK THE 4TH AND/OR THE 6TH CHARACTER FOR AN "F"
    # E.G. BC2F OR 20SF55
    if re.search('^.{3}F(?:$|.{2}$)', occupant):
        return shape_fw
    else:
        return shape_rtr

def get_occupant_color(occupant):
    # CHECK IF THE 4TH CHARACTER IS A "4"
    if re.search('^.{3}4', occupant):
        return color_ipz
    else:
        return color_rtr

def get_occupants_from_csv(network):
    # A SET OBJECT IS NEEDED TO GET "UNIQUE" ENTRIES
    occupants = set()
    for row in network:
        if row[0] and row[1]:
            occupants.add(row[1])
        else:
            print('[ERROR] Need 2 nodes to construct an edge')
    return (list(occupants))

def get_network_placements(network, places):
    '''
    intra networks must be placed into a GroupNode
    inter networks are standalone nodes
    '''
    inter_networks = []
    intra_networks = {} # key = network, value = geo location
    '''
    STEP 1: Group the OCCUPANTS by NETWORK

    FROM:

    NETWORK,OCCUPANT
    10.0.248.0/30,20S301
    10.0.248.0/30,77B301
    10.135.66.0/24,20S301
    10.35.166.0/24,20S301
    10.88.66.0/24,77B301
    10.88.166.0/24,77B302
    10.0.248.4/30,77B301
    10.0.248.4/30,77B302
    10.0.248.8/30,20S301
    10.0.248.8/30,77B302

    INTO:

    {'10.0.248.4/30': ['77B301', '77B302'], '10.35.166.0/24': ['20S301'], 
    '10.0.248.0/30': ['20S301', '77B301'], '10.88.66.0/24': ['77B301'], 
    '10.135.66.0/24': ['20S301'], '10.88.166.0/24': ['77B302'], 
    '10.0.248.8/30': ['20S301', '77B302']}
    '''
    occupants_by_network = defaultdict(list) 
    for net, occupant in network:
        occupants_by_network[net].append(occupant) 
    #print ("Grouped dictionary is : " + str(dict(occupants_by_network)))  

    '''
    STEP 2: Categorise the networks and append them to the relevant list or 
            dictionary

    {'10.88.166.0/24': u'BRUNSSUM', '10.0.248.4/30': u'BRUNSSUM', 
    '10.135.66.0/24': u'SHAPE', '10.35.166.0/24': u'SHAPE', 
    '10.88.66.0/24': u'BRUNSSUM'}

    ['10.0.248.0/30', '10.0.248.8/30']

    '''
    for k,v in occupants_by_network.items():
        #k=network, v=occupant
        if len(v) == 1:
            # LAN's here
            intra_networks.update({k:places[v[0]]})
        elif places[v[0]] == places[v[1]]:
            # connection within the same place
           intra_networks.update({k:places[v[0]]}) 
        else:
            inter_networks.append(k)

    '''
    STEP3
    '''    
    return (intra_networks, inter_networks)

def get_edges_from_csv(network):
    edges = []
    for row in network:
        if row[0] and row[1]:
            edge = (row[0], row[1])
            edges.append(edge)
        else:
            print('[ERROR] Need 2 nodes to construct an edge')
    return edges

def main():
    # INITIALISE THE GRAPH
    g = nra_pyyed.Graph()

    with open(file_in_csv) as fin:
        reader = csv.reader(fin)
        header = next(reader)
        network = [row for row in reader]

    with open(file_in_cast) as fcast:
        places = json.load(fcast)[0]

    # OCCUPANT PLACEMENTS
    for occupant in get_occupants_from_csv(network):
        try:
            place = places[occupant]
            if place == None:
                place = 'NAUGHT BOYS ( NO PLACE FOUND IN CAST )'
        except KeyError:
            place = 'NAUGHT BOYS ( NO LOCID FOUND IN CAST )'
        try:
            g.groups[place].add_node(occupant, \
                                    shape=get_occupant_shape(occupant), \
                                    shape_fill=get_occupant_color(occupant))
        except:
            new_group = g.add_group(place, shape="roundrectangle", \
                                    fill="#808000", edge_width="2.0")
            new_group.add_node(occupant, shape=get_occupant_shape(occupant), \
                                shape_fill=get_occupant_color(occupant))

    # NETWORK PLACEMENTS (INTER & INTRA NETWORKS)
    intra_networks, inter_networks = get_network_placements(network,places)
    
    for inter in inter_networks:
        g.add_node(inter, shape="roundrectangle", shape_fill="#FFCC00", \
                    height="30", width="100")     
    
    for k,v in intra_networks.items():
        #k=network, v=occupant
        g.groups[v].add_node(k, shape="roundrectangle", shape_fill="#FFCC00", \
                            height="30", width="100")
    

    # INSTITUTE THE EDGES (UNDIRECTED CONNECTIONS)
    for edge in get_edges_from_csv(network):
        g.add_edge(edge[0], edge[1], arrowhead='none')   

    # WRITE TO GRAPHML FILE
    with open(file_out_graphml, 'w') as fout:
        fout.write(g.get_graph())

if __name__ == '__main__':
    main()