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Update NeuroNLP.js #35

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49 changes: 24 additions & 25 deletions js/NeuroNLP.js
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Original file line number Diff line number Diff line change
Expand Up @@ -509,33 +509,32 @@ require([
$('#ui-blocker').show();
client.getConnectivity({success: function(res){
iziToast.hide({transitionOut:'fadeOut'},document.querySelector('.fetching_conn_notification'));
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 \nSPIN: 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)\nfor more details\n'
csv += 'PreSynaptic Neuron,PostSynaptic Neuron,N,Inferred'
nodes = res['nodes']
edges = res['edges']

for(e_pre in edges){
if(nodes[e_pre]['class'] == 'Neuron'){
if('uname' in nodes[e_pre])
pre = nodes[e_pre]['uname']
else
pre = nodes[e_pre]['name']
synapse_nodes = edges[e_pre]
for(synapse in synapse_nodes){
if(nodes[synapse]['class'] == 'Synapse')
inferred=0
else
inferred=1
N = nodes[synapse]['N']
post_node = nodes[Object.keys(edges[synapse])[0]]
if('uname' in post_node)
post = post_node['uname']
else
post = post_node['name']
csv += ('\n' + pre + ',' + post + ',' + N + ',' + inferred)
}
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('')}`;

var data = new Blob([csv], {type: 'text/csv'});
// If we are replacing a previously generated file we need to
// manually revoke the object URL to avoid memory leaks.
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