added subnetworks
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0xFugue
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@ -2,11 +2,11 @@ This repo contains scripts to generate network models (in JSON) and waku configu
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## generate_network.py
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generate_network.py generates one network and per-node configuration files. The tool is configurable with specified number of nodes, topics, network types, node types. Use with Python3. Comment out the `#draw(fname, H)` line to visualise the generated graph.
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generate_network.py generates one network and per-node configuration files. The tool is configurable with specified number of nodes, topics, network types, node types and number of subnets. Use with Python3. Comment out the `#draw(fname, H)` line to visualise the generated graph.
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> usage: $./generate_network --help
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## batch_gen.sh
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batch_gen.sh can generate given number of Waku networks and outputs them to a directory. Please make sure that the output directory does not exists; both relative and absolute paths work. The Wakunode parameters are generated at random; edit the MIN and MAX for finer control. The script requires bc & /dev/urandom.<br>
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batch_gen.sh can generate given number of Waku networks and outputs them to a directory. Please make sure that the output directory does NOT exist; both relative and absolute paths work. The Wakunode parameters are generated at random; edit the MIN and MAX for finer control. The script requires bc & /dev/urandom.<br>
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> usage: $./batch_gen.sh <output-dir> <#number of networks needed> </br>
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11
batch_gen.sh
11
batch_gen.sh
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@ -30,8 +30,8 @@ mkdir -p $path
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echo "Ok, will generate $nfiles networks & put them under '$path'."
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nwtype="NEWMANWATTSSTROGATZ"
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nodetype="DESKTOP"
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nwtype="newmanwattsstrogatz"
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nodetype="desktop"
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for i in $(seq $nfiles)
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@ -40,8 +40,11 @@ do
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n=$((RANDOM+1))
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getrand
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t=$((RANDOM+1))
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getrand
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s=`expr $((RANDOM+1)) % $n`
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dirname="$path/$i/Waku"
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mkdir "$path/$i"
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echo "Generating ./generate_network.py --dirname $dirname --num-nodes $n --num-topics $t --nw-type $nwtype --node-type $nodetype --num-partitions 1 ...."
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$(./generate_network.py --dirname $dirname --num-nodes $n --num-topics $t --nw-type $nwtype --node-type $nodetype --num-partitions 1)
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echo "Generating ./generate_network.py --dirname $dirname --num-nodes $n --num-topics $t --nw-type $nwtype --node-type $nodetype --num-partitions 1 --num-subnets $s ...."
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$(./generate_network.py --dirname $dirname --num-nodes $n --num-topics $t --nw-type $nwtype --node-type $nodetype --num-partitions 1 --num-subnets $s)
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done
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@ -35,7 +35,8 @@ class networkType(Enum):
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NW_DATA_FNAME = "network_data.json"
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PREFIX = "waku_"
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NODE_PREFIX = "waku"
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SUBNET_PREFIX = "subnetwork"
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### I/O related fns ##############################################################
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@ -53,7 +54,7 @@ def write_toml(dirname, node_name, toml):
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f.write(toml)
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# Draw the network and output the image to a file
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# Draw the network and output the image to a file; does not account for subnets yet
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def draw(dirname, H):
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nx.draw(H, pos=nx.kamada_kawai_layout(H), with_labels=True)
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fname = os.path.join(dirname, NW_DATA_FNAME)
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@ -67,7 +68,7 @@ def read_json(fname):
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jdata = json.load(f)
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return nx.node_link_graph(jdata)
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# check if the required dir can be created
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def exists_and_nonempty(dirname):
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if not os.path.exists(dirname):
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return False
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@ -151,17 +152,40 @@ networkTypeSwitch = {
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# Generate the network from nw type
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def generate_network(n, nw_type):
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return postprocess_network(networkTypeSwitch.get(nw_type)(n))
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return postprocess_network(networkTypeSwitch.get(nw_type)(n))
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# Label the generated network with prefix
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def postprocess_network(G):
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G = nx.Graph(G) # prune out parallel/multi edges
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G.remove_edges_from(nx.selfloop_edges(G)) # remove the self-loops
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mapping = {i: f"{PREFIX}{i}" for i in range(len(G))}
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mapping = {i: f"{NODE_PREFIX}_{i}" for i in range(len(G))}
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return nx.relabel_nodes(G, mapping) # label the nodes
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def generate_subnets(G, num_subnets):
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n = len(G.nodes)
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if num_subnets > n:
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raise ValueError(f"num_subnets must be <= num_nodes: num_subnets={num_subnets}, num_nodes={n}")
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if num_subnets == n: # if k == size of the network
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return {f"{NODE_PREFIX}_{i}": f"{SUBNET_PREFIX}_{i}" for i in range(n)}
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lst = list(range(n))
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random.shuffle(lst)
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offsets = sorted(random.sample(range(1, n), num_subnets - 1))
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offsets.append(n)
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start = 0
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subnets = {}
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subnet_id = 0
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for end in offsets:
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for i in range(start, end):
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subnets[f"{NODE_PREFIX}_{lst[i]}"] = f"{SUBNET_PREFIX}_{subnet_id}"
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start = end
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subnet_id += 1
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return subnets
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### file format related fns ###########################################################
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#Generate per node toml configs
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def generate_toml(topics, node_type=nodeType.DESKTOP):
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@ -170,15 +194,17 @@ def generate_toml(topics, node_type=nodeType.DESKTOP):
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# Generates network-wide json and per-node toml and writes them
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def generate_and_write_files(dirname, num_topics, H):
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def generate_and_write_files(dirname, num_topics, num_subnets, G):
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topics = generate_topics(num_topics)
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subnets = generate_subnets(G, num_subnets)
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json_dump = {}
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for node in H.nodes:
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for node in G.nodes:
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write_toml(dirname, node, generate_toml(topics)) # per node toml
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json_dump[node] = {}
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json_dump[node]["static-nodes"] = []
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for edge in H.edges(node):
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for edge in G.edges(node):
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json_dump[node]["static-nodes"].append(edge[1])
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json_dump[node][SUBNET_PREFIX] = subnets[node]
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write_json(dirname, json_dump) # network wide json
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@ -187,11 +213,15 @@ def main(
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dirname: str = "WakuNetwork", num_nodes: int = 4, num_topics: int = 1,
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nw_type: networkType = networkType.NEWMANWATTSSTROGATZ.value,
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node_type: nodeType = nodeType.DESKTOP.value,
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num_subnets: int = -1,
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num_partitions: int = 1):
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if num_partitions > 1:
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print(f"--num-partitions {num_partitions}, Sorry, we do not yet support partitions")
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sys.exit(1)
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if num_subnets == -1:
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num_subnets = num_nodes
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# Generate the network
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G = generate_network(num_nodes, nw_type)
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@ -202,8 +232,8 @@ def main(
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os.makedirs(dirname, exist_ok=True)
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# Generate file format specific data structs and write the files; optionally, draw the network
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generate_and_write_files(dirname, num_topics, G)
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draw(dirname, G)
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generate_and_write_files(dirname, num_topics, num_subnets, G)
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#draw(dirname, G)
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if __name__ == "__main__":
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