nodes = res['nodes'];
edges = res['edges'];

let outgoing_edges = edges.filter(edge => nodes[edge[0]].class=='Neuron');
let incoming_edges = edges.filter(edge => !outgoing_edges.includes(edge));
let connectivity = [];
for (let edge of incoming_edges){
  let edge_id = edge[0];
  let inferred = (nodes[edge[0]].class == 'InferredSynapse') ? 1:0;
  let pre_outgoing_edge = outgoing_edges.filter(edge => edge[1] == edge_id);
  if (pre_outgoing_edge.length != 1) {
    console.error(`Cannot find outgoing edge from presynaptic neuron for edge ${edge}`);
    continue
  }
  let pre_node = pre_outgoing_edge[0][0];
  let post_node = edge[1];
  let N = nodes[edge_id].N;
  let pre_name = 'uname' in nodes[pre_node] ? nodes[pre_node].uname : nodes[pre_node].name;
  let post_name = 'uname' in nodes[post_node] ? nodes[post_node].uname : nodes[post_node].name;
  connectivity.push([pre_name, post_name, N, inferred])
  
}

csv = `PreSynaptic Neuron,PostSynaptic Neuron,N,Inferred
${connectivity.map(conn=>`${conn[0]},${conn[1]},${conn[2]},${conn[3]}\n`).join('')}`;

csv = `
# If Inferred=1, the connectivity between neurons was inferred using axonic/dendritic polarity predicted by SPIN:Skeleton-based Polarity Identification for Neurons. Please refer to
# SPIN: A Method of Skeleton-based Polarity Identification for Neurons. Neurinformatics 12:487-507. Yi-Hsuan Lee, Yen-Nan Lin, Chao-Chun Chuang and Chung-Chuan Lo (2014)
# for more details

PreSynaptic Neuron,PostSynaptic Neuron,N,Inferred
${
  connectivity.map(conn=>`${conn[0]},${conn[1]},${conn[2]},${conn[3]}\n`).join('')
}
`;