def get(self, name):
"""
Return the dag of a pipeline
"""
cy_network = cy.from_networkx(
Pipeline.create_dag(pipeline_specs[name]))
return cy_network
def test_networkx_matrix(self):
print('\n---------- Matrix Test Start -----------\n')
g = nx.barabasi_albert_graph(30, 2)
nodes = g.nodes()
edges = g.edges()
print(edges)
mx1 = nx.adjacency_matrix(g)
fp = tempfile.NamedTemporaryFile()
file_name = fp.name
sp.savetxt(file_name, mx1.toarray(), fmt='%d')
# Load it back to matrix
mx2 = sp.loadtxt(file_name)
fp.close()
g2 = nx.from_numpy_matrix(mx2)
cyjs_g = util.from_networkx(g2)
#print(json.dumps(cyjs_g, indent=4))
self.assertIsNotNone(cyjs_g)
self.assertIsNotNone(cyjs_g['data'])
self.assertEqual(len(nodes), len(cyjs_g['elements']['nodes']))
self.assertEqual(len(edges), len(cyjs_g['elements']['edges']))
# Make sure all edges are reproduced
print(set(edges))
diff = compare_edge_sets(set(edges), cyjs_g['elements']['edges'])
self.assertEqual(0, len(diff))
def test_networkx_roundtrip(self):
print('\n---------- NetworkX Data Roundtrip Test Start -----------\n')
g = nx.newman_watts_strogatz_graph(100, 3, 0.5)
nodes = g.nodes()
edges = g.edges()
# Add some attributes
g.graph['name'] = 'original'
g.graph['density'] = nx.density(g)
nx.set_node_attributes(g, 'betweenness', nx.betweenness_centrality(g))
nx.set_node_attributes(g, 'degree', nx.degree(g))
nx.set_node_attributes(g, 'closeness', nx.closeness_centrality(g))
nx.set_edge_attributes(g, 'eb', nx.edge_betweenness(g))
cyjs1 = util.from_networkx(g)
g2 = util.to_networkx(cyjs1)
self.assertEqual(len(g2.nodes()), len(nodes))
self.assertEqual(len(g2.edges()), len(edges))
edge_set = set(list(map(lambda x: (int(x[0]), int(x[1])), g2.edges())))
self.assertEqual(0, len(edge_set.difference(set(edges))))
node_original = g.node[1]
node_generated = g2.node['1']
print(node_original)
print(node_generated)
self.assertEqual(node_original['degree'], node_generated['degree'])
self.assertEqual(node_original['betweenness'], node_generated['betweenness'])
self.assertEqual(node_original['closeness'], node_generated['closeness'])
def test_networkx_matrix(self):
print('\n---------- Matrix Test Start -----------\n')
g = nx.barabasi_albert_graph(30, 2)
nodes = g.nodes()
edges = g.edges()
print(edges)
mx1 = nx.adjacency_matrix(g)
fp = tempfile.NamedTemporaryFile()
file_name = fp.name
sp.savetxt(file_name, mx1.toarray(), fmt='%d')
# Load it back to matrix
mx2 = sp.loadtxt(file_name)
fp.close()
g2 = nx.from_numpy_matrix(mx2)
cyjs_g = util.from_networkx(g2)
#print(json.dumps(cyjs_g, indent=4))
self.assertIsNotNone(cyjs_g)
self.assertIsNotNone(cyjs_g['data'])
self.assertEqual(len(nodes), len(cyjs_g['elements']['nodes']))
self.assertEqual(len(edges), len(cyjs_g['elements']['edges']))
# Make sure all edges are reproduced
print(set(edges))
diff = compare_edge_sets(set(edges), cyjs_g['elements']['edges'])
self.assertEqual(0, len(diff))
def test_networkx_roundtrip(self):
print('\n---------- NetworkX Data Roundtrip Test Start -----------\n')
g = nx.newman_watts_strogatz_graph(100, 3, 0.5)
nodes = g.nodes()
edges = g.edges()
# Add some attributes
g.graph['name'] = 'original'
g.graph['density'] = nx.density(g)
nx.set_node_attributes(g, 'betweenness', nx.betweenness_centrality(g))
nx.set_node_attributes(g, 'degree', nx.degree(g))
nx.set_node_attributes(g, 'closeness', nx.closeness_centrality(g))
nx.set_edge_attributes(g, 'eb', nx.edge_betweenness(g))
cyjs1 = util.from_networkx(g)
g2 = util.to_networkx(cyjs1)
self.assertEqual(len(g2.nodes()), len(nodes))
self.assertEqual(len(g2.edges()), len(edges))
edge_set = set(list(map(lambda x: (int(x[0]), int(x[1])), g2.edges())))
self.assertEqual(0, len(edge_set.difference(set(edges))))
node_original = g.node[1]
node_generated = g2.node['1']
print(node_original)
print(node_generated)
self.assertEqual(node_original['degree'], node_generated['degree'])
self.assertEqual(node_original['betweenness'], node_generated['betweenness'])
self.assertEqual(node_original['closeness'], node_generated['closeness'])
def test_networkx_roundtrip(self):
print("\n---------- NetworkX Data Roundtrip Test Start -----------\n")
g = nx.newman_watts_strogatz_graph(100, 3, 0.5)
nodes = g.nodes()
edges = g.edges()
# Add some attributes
g.graph["name"] = "original"
g.graph["density"] = nx.density(g)
nx.set_node_attributes(g, "betweenness", nx.betweenness_centrality(g))
nx.set_node_attributes(g, "degree", nx.degree(g))
nx.set_node_attributes(g, "closeness", nx.closeness_centrality(g))
nx.set_edge_attributes(g, "eb", nx.edge_betweenness(g))
cyjs1 = util.from_networkx(g)
g2 = util.to_networkx(cyjs1)
self.assertEqual(len(g2.nodes()), len(nodes))
self.assertEqual(len(g2.edges()), len(edges))
edge_set = set(list(map(lambda x: (int(x[0]), int(x[1])), g2.edges())))
self.assertEqual(0, len(edge_set.difference(set(edges))))
node_original = g.node[1]
node_generated = g2.node["1"]
print(node_original)
print(node_generated)
self.assertEqual(node_original["degree"], node_generated["degree"])
self.assertEqual(node_original["betweenness"], node_generated["betweenness"])
self.assertEqual(node_original["closeness"], node_generated["closeness"])
def createNetwork(network,layout='hierarchical'):
cytoscapeNetwork=util.from_networkx(network)
res1=requests.post(BASE + 'networks', data=json.dumps(cytoscapeNetwork), headers=HEADERS)
res1_dict = json.loads(res1.content)
print res1.content
#print res1_dict['networkSUID']
new_suid = res1_dict['networkSUID']
requests.get(BASE + 'apply/layouts/'+layout+'/' + str(new_suid))
requests.get(BASE + 'apply/styles/SRALandscape/' + str(new_suid))
def test_networkx_emptynetwork(self):
g = nx.Graph()
cyjs_g = util.from_networkx(g)
print('\n---------- Empty Test Start -----------\n')
print(json.dumps(cyjs_g, indent=4))
self.assertIsNotNone(cyjs_g)
self.assertIsNotNone(cyjs_g['data'])
self.assertEqual(0, len(cyjs_g['elements']['nodes']))
self.assertEqual(0, len(cyjs_g['elements']['edges']))
def test_networkx_gml(self):
g = nx.read_gml('tests/data/galFiltered.gml')
cyjs_g = util.from_networkx(g)
print('\n---------- Test Start -----------\n')
print(json.dumps(cyjs_g, indent=4))
self.assertIsNotNone(cyjs_g)
self.assertIsNotNone(cyjs_g['data'])
self.assertEqual(331, len(cyjs_g['elements']['nodes']))
self.assertEqual(362, len(cyjs_g['elements']['edges']))
def test_networkx_empty_edge_attribute(self):
print('\n---------- Edge Att Test 2 Start -----------\n')
g = nx.scale_free_graph(5)
cyjs = util.from_networkx(g)
# print(json.dumps(cyjs, indent=4))
# There is only one edge, so this should be OK...
edge = cyjs['elements']['edges'][0]
print(json.dumps(edge, indent=4))
self.assertEqual(3, len(edge['data']))
def makeGraph(d):
global g
g = nx.Graph(d)
nx.write_graphml(g, 'g.xml')
authorMap = from_networkx(g)
authorNet = requests.post(server + '/networks',
data=json.dumps(authorMap),
headers={'Content-Type': 'application/json'})
net_id = authorNet.json()['networkSUID']
requests.get('%s/apply/layouts/force-directed/%d' % (server, net_id))
Image('%s/networks/%d/views/first.png' % (server, net_id))
def test_networkx_emptynetwork(self):
g = nx.Graph()
cyjs_g = util.from_networkx(g)
print('\n---------- Empty Test Start -----------\n')
print(json.dumps(cyjs_g, indent=4))
self.assertIsNotNone(cyjs_g)
self.assertIsNotNone(cyjs_g['data'])
self.assertEqual(0, len(cyjs_g['elements']['nodes']))
self.assertEqual(0, len(cyjs_g['elements']['edges']))
def test_networkx_emptynetwork(self):
g = nx.Graph()
cyjs_g = util.from_networkx(g)
print("\n---------- Empty Test Start -----------\n")
print(json.dumps(cyjs_g, indent=4))
self.assertIsNotNone(cyjs_g)
self.assertIsNotNone(cyjs_g["data"])
self.assertEqual(0, len(cyjs_g["elements"]["nodes"]))
self.assertEqual(0, len(cyjs_g["elements"]["edges"]))
def test_networkx_empty_edge_attribute(self):
print('\n---------- Edge Att Test 2 Start -----------\n')
g = nx.scale_free_graph(5)
cyjs = util.from_networkx(g)
# print(json.dumps(cyjs, indent=4))
# There is only one edge, so this should be OK...
edge = cyjs['elements']['edges'][0]
print(json.dumps(edge, indent=4))
self.assertEqual(3, len(edge['data']))
def get(self, run_id):
"""
Return the dag of the given run
"""
pipeline = db.pipelines.find_one({'run_id': run_id}, {'config':1, 'single_step':1, '_id':0})
config = json.loads(pipeline['config'])
if 'single_step' in pipeline:
dag = Pipeline.create_dag(config, one_step=True)
else:
dag = Pipeline.create_dag(config)
cy_network = cy.from_networkx(dag)
return cy_network
def test_networkx_digraph_edge_attr(self):
print('\n---------- Digraph Edge Att Test Start -----------\n')
g = nx.DiGraph()
g.add_path([0, 1, 2, 3, 4])
eb = nx.edge_betweenness(g)
nx.set_edge_attributes(g, 'eb', eb)
cyjs = util.from_networkx(g)
print(json.dumps(cyjs, indent=4))
# There is only one edge, so this should be OK...
edge = cyjs['elements']['edges'][0]
self.assertEqual(3, len(edge['data']))
def test_networkx_digraph_edge_attr(self):
print('\n---------- Digraph Edge Att Test Start -----------\n')
g = nx.DiGraph()
g.add_path([0, 1, 2, 3, 4])
eb = nx.edge_betweenness(g)
nx.set_edge_attributes(g, 'eb', eb)
cyjs = util.from_networkx(g)
print(json.dumps(cyjs, indent=4))
# There is only one edge, so this should be OK...
edge = cyjs['elements']['edges'][0]
self.assertEqual(3, len(edge['data']))
def test_networkx_edge_attribute(self):
print('\n---------- Edge Att Test Start -----------\n')
g = nx.Graph()
g.add_edge(1, 2, interaction='itr1', score=0.1)
original_edge = g[1][2]
print(original_edge.keys())
cyjs = util.from_networkx(g)
print(json.dumps(cyjs, indent=4))
# There is only one edge, so this should be OK...
edge = cyjs['elements']['edges'][0]
print(json.dumps(edge, indent=4))
self.assertEqual('itr1', cyjs['elements']['edges'][0]['data']['interaction'])
def test_networkx_edge_attribute(self):
print("\n---------- Edge Att Test Start -----------\n")
g = nx.Graph()
g.add_edge(1, 2, interaction="itr1", score=0.1)
original_edge = g[1][2]
print(original_edge.keys())
cyjs = util.from_networkx(g)
print(json.dumps(cyjs, indent=4))
# There is only one edge, so this should be OK...
edge = cyjs["elements"]["edges"][0]
print(json.dumps(edge, indent=4))
self.assertEqual("itr1", cyjs["elements"]["edges"][0]["data"]["interaction"])
def test_networkx_edge_attribute(self):
print('\n---------- Edge Att Test Start -----------\n')
g = nx.Graph()
g.add_edge(1, 2, interaction='itr1', score=0.1)
original_edge = g[1][2]
print(original_edge.keys())
cyjs = util.from_networkx(g)
print(json.dumps(cyjs, indent=4))
# There is only one edge, so this should be OK...
edge = cyjs['elements']['edges'][0]
print(json.dumps(edge, indent=4))
self.assertEqual('itr1', cyjs['elements']['edges'][0]['data']['interaction'])
def get(self, run_id):
"""
Return the dag of the given run
"""
pipeline = db.pipelines.find_one({'run_id': run_id}, {
'config': 1,
'single_step': 1,
'_id': 0
})
config = json.loads(pipeline['config'])
if 'single_step' in pipeline:
dag = Pipeline.create_dag(config, one_step=True)
else:
dag = Pipeline.create_dag(config)
cy_network = cy.from_networkx(dag)
return cy_network
def test_networkx_ba(self):
g = nx.barabasi_albert_graph(100, 3)
nodes = g.nodes()
edges = g.edges()
g.graph["name"] = "ba test"
cyjs_g = util.from_networkx(g)
print("\n---------- BA graph Test Start -----------\n")
self.assertIsNotNone(cyjs_g)
self.assertIsNotNone(cyjs_g["data"])
self.assertEqual("ba test", cyjs_g["data"]["name"])
self.assertEqual(len(nodes), len(cyjs_g["elements"]["nodes"]))
self.assertEqual(len(edges), len(cyjs_g["elements"]["edges"]))
diff = compare_edge_sets(set(edges), cyjs_g["elements"]["edges"])
self.assertEqual(0, len(diff))
def test_networkx_ba(self):
g = nx.barabasi_albert_graph(100, 3)
nodes = g.nodes()
edges = g.edges()
g.graph['name'] = 'ba test'
cyjs_g = util.from_networkx(g)
print('\n---------- BA graph Test Start -----------\n')
self.assertIsNotNone(cyjs_g)
self.assertIsNotNone(cyjs_g['data'])
self.assertEqual('ba test', cyjs_g['data']['name'])
self.assertEqual(len(nodes), len(cyjs_g['elements']['nodes']))
self.assertEqual(len(edges), len(cyjs_g['elements']['edges']))
diff = compare_edge_sets(set(edges), cyjs_g['elements']['edges'])
self.assertEqual(0, len(diff))
def export_to_cytoscape(G, export_file):
'''
Exports networkX graph to JSON file in a Cytoscape compatible format.
Inputs:
- G: networkX graph
- export_file: JSON file name to export graph to
Returns:
- None
Side Effect:
- Creates a JSON file of the name export_file.
'''
G_json = util.from_networkx(G)
with open(export_file, 'w') as outfile:
json.dump(G_json, outfile)
def test_networkx_ba(self):
g = nx.barabasi_albert_graph(100, 3)
nodes = g.nodes()
edges = g.edges()
g.graph['name'] = 'ba test'
cyjs_g = util.from_networkx(g)
print('\n---------- BA graph Test Start -----------\n')
self.assertIsNotNone(cyjs_g)
self.assertIsNotNone(cyjs_g['data'])
self.assertEqual('ba test', cyjs_g['data']['name'])
self.assertEqual(len(nodes), len(cyjs_g['elements']['nodes']))
self.assertEqual(len(edges), len(cyjs_g['elements']['edges']))
diff = compare_edge_sets(set(edges), cyjs_g['elements']['edges'])
self.assertEqual(0, len(diff))
def test_networkx_gml(self):
g = nx.read_gml(self.cur_dir + '/data/galFiltered.gml')
g.graph['name'] = 'gml_test'
cyjs_g = util.from_networkx(g)
print('\n---------- GML Test Start -----------\n')
# print(json.dumps(cyjs_g, indent=4))
self.assertIsNotNone(cyjs_g)
net_data = cyjs_g['data']
self.assertIsNotNone(net_data)
self.assertEqual('gml_test', net_data['name'])
self.assertEqual(331, len(cyjs_g['elements']['nodes']))
self.assertEqual(362, len(cyjs_g['elements']['edges']))
nodes = cyjs_g['elements']['nodes']
node0 = nodes[0]
self.assertEqual(type("1"), type(node0['data']['id']))
def test_networkx_gml(self):
g = nx.read_gml(self.cur_dir + "/data/galFiltered.gml")
g.graph["name"] = "gml_test"
cyjs_g = util.from_networkx(g)
print("\n---------- GML Test Start -----------\n")
# print(json.dumps(cyjs_g, indent=4))
self.assertIsNotNone(cyjs_g)
net_data = cyjs_g["data"]
self.assertIsNotNone(net_data)
self.assertEqual("gml_test", net_data["name"])
self.assertEqual(331, len(cyjs_g["elements"]["nodes"]))
self.assertEqual(362, len(cyjs_g["elements"]["edges"]))
nodes = cyjs_g["elements"]["nodes"]
node0 = nodes[0]
self.assertEqual(type("1"), type(node0["data"]["id"]))
def test_networkx_multidigraph_edge_attr(self):
print('\n---------- Multi-Digraph Edge Att Test Start -----------\n')
g = nx.MultiDiGraph()
g.add_node(1)
g.add_node(2)
g.add_node(3)
g.add_edge(1, 2)
g.add_edge(1, 2, attr_dict={'foo': 'bar'})
g.add_edge(1, 2)
g.add_edge(1, 3)
edges = g.edges(data=True, keys=True)
for edge in edges:
print(edge)
cyjs = util.from_networkx(g)
print(json.dumps(cyjs, indent=4))
edge = cyjs['elements']['edges'][0]
self.assertTrue(3 <= len(edge['data']))
def test_networkx_multidigraph_edge_attr(self):
print('\n---------- Multi-Digraph Edge Att Test Start -----------\n')
g = nx.MultiDiGraph()
g.add_node(1)
g.add_node(2)
g.add_node(3)
g.add_edge(1, 2)
g.add_edge(1, 2, attr_dict={'foo': 'bar'})
g.add_edge(1, 2)
g.add_edge(1, 3)
edges = g.edges(data=True, keys=True)
for edge in edges:
print(edge)
cyjs = util.from_networkx(g)
print(json.dumps(cyjs, indent=4))
edge = cyjs['elements']['edges'][0]
self.assertTrue(3 <= len(edge['data']))
def test_networkx_multidigraph_edge_attr(self):
print("\n---------- Multi-Digraph Edge Att Test Start -----------\n")
g = nx.MultiDiGraph()
g.add_node(1)
g.add_node(2)
g.add_node(3)
g.add_edge(1, 2)
g.add_edge(1, 2, attr_dict={"foo": "bar"})
g.add_edge(1, 2)
g.add_edge(1, 3)
edges = g.edges(data=True, keys=True)
for edge in edges:
print(edge)
cyjs = util.from_networkx(g)
print(json.dumps(cyjs, indent=4))
edge = cyjs["elements"]["edges"][0]
self.assertTrue(3 <= len(edge["data"]))
def test_networkx_scale_free(self):
g = nx.scale_free_graph(100)
edge_count = g.number_of_edges()
g.graph["name"] = "scale_free_test"
cyjs_g = util.from_networkx(g)
print("\n---------- Scale free network Test Start -----------\n")
print("Edge count = " + str(edge_count))
# print(json.dumps(cyjs_g, indent=4))
self.assertIsNotNone(cyjs_g)
net_data = cyjs_g["data"]
self.assertIsNotNone(net_data)
self.assertEqual("scale_free_test", net_data["name"])
self.assertEqual(100, len(cyjs_g["elements"]["nodes"]))
self.assertEqual(edge_count, len(cyjs_g["elements"]["edges"]))
nodes = cyjs_g["elements"]["nodes"]
node0 = nodes[0]
self.assertEqual(type("1"), type(node0["data"]["id"]))
def test_networkx_scale_free(self):
g = nx.scale_free_graph(100)
edge_count = g.number_of_edges()
g.graph['name'] = 'scale_free_test'
cyjs_g = util.from_networkx(g)
print('\n---------- Scale free network Test Start -----------\n')
print('Edge count = ' + str(edge_count))
# print(json.dumps(cyjs_g, indent=4))
self.assertIsNotNone(cyjs_g)
net_data = cyjs_g['data']
self.assertIsNotNone(net_data)
self.assertEqual('scale_free_test', net_data['name'])
self.assertEqual(100, len(cyjs_g['elements']['nodes']))
self.assertEqual(edge_count, len(cyjs_g['elements']['edges']))
nodes = cyjs_g['elements']['nodes']
node0 = nodes[0]
self.assertEqual(type("1"), type(node0['data']['id']))
def test_networkx_scale_free(self):
g = nx.scale_free_graph(100)
edge_count = g.number_of_edges()
g.graph['name'] = 'scale_free_test'
cyjs_g = util.from_networkx(g)
print('\n---------- Scale free network Test Start -----------\n')
print('Edge count = ' + str(edge_count))
# print(json.dumps(cyjs_g, indent=4))
self.assertIsNotNone(cyjs_g)
net_data = cyjs_g['data']
self.assertIsNotNone(net_data)
self.assertEqual('scale_free_test', net_data['name'])
self.assertEqual(100, len(cyjs_g['elements']['nodes']))
self.assertEqual(edge_count, len(cyjs_g['elements']['edges']))
nodes = cyjs_g['elements']['nodes']
node0 = nodes[0]
self.assertEqual(type("1"), type(node0['data']['id']))
def generate_genome_local(otus, loc=None):
genome_table = load_data_table([i[0] for i in otus])
genomes = list()
for otu_id, taxonomy in otus:
print(os.getpid(), otu_id)
nsti = genome_table.metadata(otu_id)['NSTI']
genome = genome_table.ids(
axis="observation")[genome_table.data(otu_id) > 0]
genome = [str(i) for i in genome]
print(os.getpid(), otu_id, "genome length", len(genome))
reactome = mna.get_reactome_local(genome, loc)
print(os.getpid(), otu_id, "reactome length", len(reactome))
rxns = mna.get_rxns_local(reactome, loc)
print(os.getpid(), otu_id, "reaction count", len(rxns))
metab_network = mna.make_metabolic_network(rxns, only_giant=True)
print(os.getpid(), otu_id, "network made")
metab_network_json = cy.from_networkx(metab_network)
genome = Genome(name=int(otu_id),
nsti=float(nsti),
metab_net=json.dumps(metab_network_json),
genome=','.join(genome),
taxonomy=taxonomy)
genomes.append(genome)
return genomes
def setUp(self):
# Generate a sample graph
self.graph = util.from_networkx(nx.barabasi_albert_graph(100, 2))
def graph_to_cytoscape(self):
miR_G = nx.Graph(self.miR_G) # unfreezing of the graph
centrality_node = self.centrality_node
rem_CC = 0
for CC in list(nx.connected_components(miR_G)):
if len(CC) < 3:
miR_G.remove_nodes_from(CC)
rem_CC += 1
print(rem_CC,
' network components with less than two nodes have been removed',
end='\n')
PORT_NUMBER = 1234
IP = 'localhost'
BASE = 'http://' + IP + ':' + str(PORT_NUMBER) + '/v1/'
requests.delete(BASE +
'session') # Delete all networks in current session
cytoscape_network = cy.from_networkx(miR_G)
cytoscape_network['data']['name'] = 'miR_Net'
res1 = requests.post(BASE + 'networks',
data=json.dumps(cytoscape_network))
res1_dict = res1.json()
new_suid = res1_dict['networkSUID']
requests.get(BASE + 'apply/layouts/force-directed/' + str(new_suid))
# load and apply style
res = requests.get(BASE + 'styles/miR_Net_Styles')
if res.status_code != 200:
with open('./options/cytoscape_styles/miR_Net_Styles.json'
) as json_file:
miR_Net_Styles = json.load(json_file)
for mapings in range(0, len(miR_Net_Styles['mappings'])):
if miR_Net_Styles['mappings'][mapings][
'visualProperty'] == 'NODE_LABEL_FONT_SIZE':
miR_Net_Styles['mappings'][mapings]['points'][1][
'value'] = max(centrality_node.values())
if miR_Net_Styles['mappings'][mapings][
'visualProperty'] == 'NODE_SIZE':
miR_Net_Styles['mappings'][mapings]['points'][1][
'value'] = max(centrality_node.values())
if miR_Net_Styles['mappings'][mapings][
'visualProperty'] == 'NODE_FILL_COLOR':
miR_Net_Styles['mappings'][mapings]['points'][1][
'value'] = max(centrality_node.values())
# Create new Visual Style
res = requests.post(BASE + "styles",
data=json.dumps(miR_Net_Styles))
# Apply it to current network
requests.get(BASE + 'apply/styles/' + 'miR_Net_Styles' + '/' +
str(new_suid)) # !Это говно почему-то не работает
def get(self, name):
"""
Return the dag of a pipeline
"""
cy_network = cy.from_networkx(Pipeline.create_dag(pipeline_specs[name]))
return cy_network
if p != q:
if distance < Graph[p][q]['distance']:
Graph[p][q]['distance'] = distance
Graph.edge[p][q]['count'] += 1
except:
Graph.add_node(p,
count=1,
refdb=value[-2],
color=COLOR[value[-2]])
Graph.add_node(q,
count=1,
refdb=value2[-2],
color=COLOR[value2[-2]])
if p != q:
if value2[-2] == value[-2]:
link_color = COLOR[value[-2]]
else:
link_color = '#000000'
Graph.add_edge(p,
q,
distance=distance,
count=1,
link_color=link_color)
#print i,j+i,value,value2,math.log(pdist([[value[-4]],[value2[-4]]])[0])
Gc = cy.from_networkx(Graph)
json.dump(Gc, open('test.json', 'w'))
def calculate(self, graph, params):
nx_graph = util.to_networkx(graph)
return util.from_networkx(nx_graph)
def test_render(self):
from py2cytoscape.cytoscapejs import viewer as cyjs
g = nx.scale_free_graph(100)
g_cyjs = from_networkx(g)
result = cyjs.render(g_cyjs, layout_algorithm='circle')
self.assertIsNone(result)
def addNetwork(self, g): """Convert a NetworkX Graph object and add it to cytoscape""" cytoscape_network = cy.from_networkx(g) res1 = requests.post(self.BASE + 'networks', data=json.dumps(cytoscape_network), headers=self.HEADERS) res1_dict = json.loads(res1.content) self.suid = res1_dict['networkSUID']
def networkx_to_cyjs(g):
return util.from_networkx(g)
def write_fixed_clustering_as_json(mcmc: BaseMCMC, output_filename: str):
'''Export the posterior fixed topology as a json usable in Cytoscape
Parameters
----------
mcmc : BaseMCMC
This is the chain that contains the traces
output_filename : str
This is the path to save the json file
'''
import networkx as nx
from py2cytoscape.util import from_networkx
import json
def clusterize(labels: np.ndarray, taxa_list: List[str]) -> Dict[int, List[str]]:
cluster = {}
for i in range(len(labels)):
if labels[i] not in cluster:
cluster[labels[i]] = []
cluster[labels[i]].append(taxa_list[i])
return cluster
def get_largest_weight(matrix: np.ndarray) -> float:
'''Returns the largest non infinite weight (value) in the matrix
'''
new_matrix = np.where(matrix == np.inf, -np.inf, matrix)
return np.amax(new_matrix)
def get_bayes_category(bf: float) -> int:
'''Classify bayes factor according to strength of evidence
Parameters
----------
bf : float
Bayes factor
Returns
-------
int
'''
category = 0
#decisive
if bf > 10 ** 2 :
category = 3
#strong
elif 10 < bf <= 10 ** 2:
category = 2
# substantial + not worth mentioning
elif 0 < bf <= 10:
category = 1
return category
clustering = mcmc.graph[STRNAMES.CLUSTERING_OBJ]
consensus_cluster_labels = generate_cluster_assignments_posthoc(clustering=clustering, set_as_value=True)
taxa_names = []
taxas = mcmc.graph.data.taxas
for taxa in taxas:
taxa_names.append(taxa.name)
consensus_cluster = clusterize(consensus_cluster_labels, taxa_names)
M = pl.summary(mcmc.graph[STRNAMES.INTERACTIONS_OBJ], set_nan_to_0=True, section='posterior')['mean']
M_condensed = condense_fixed_clustering_interaction_matrix(M, clustering=clustering)
bf = generate_interation_bayes_factors_posthoc(mcmc=mcmc, section='posterior') # (n_taxa, n_taxa)
bf_condensed = condense_fixed_clustering_interaction_matrix(bf, clustering=clustering)
columns = np.sort(list(consensus_cluster.keys()))
# Take the transpose so that rows are the srouce and columns destination
bayes_df = pd.DataFrame(bf_condensed.T, columns=columns, index=columns)
graph_bayes = nx.from_pandas_adjacency(bayes_df, create_using=nx.DiGraph())
largest = get_largest_weight(bf_condensed)
all_edges = graph_bayes.edges()
edge_attributes = {}
for edge in graph_bayes.edges(data=True):
# Column is the source and row is the destination
int_strength = M_condensed[edge[1], edge[0]]
coord = (edge[0], edge[1])
sign = 0
weight = edge[2]['weight']
if np.isint('weight'):
weight = largest
if int_strength < 0:
sign = -1
else:
sign = 1
category = get_bayes_category(weight)
bend = False
if (edge[1], edge[0]) in all_edges:
bend = True
edge_attributes[coord] = {'bayes_fac': category, 'sign': sign,
'weight': weight, 'bend': bend}
nx.set_edge_attributes(graph_bayes, edge_attributes)
nodes_attributes = {}
for keys in consensus_cluster:
nodes_attributes[keys] = {'size': len(consensus_cluster[keys])}
nx.set_node_attributes(graph_bayes, nodes_attributes)
data_json = from_networkx(graph_bayes, nodes_attributes)
if '.json' not in output_filename:
output_filename += '.json'
with open(filename, 'w') as f:
json.dump(data_json, f)
def networkx_to_cyjs(g):
return util.from_networkx(g)
def test_render(self):
from py2cytoscape.cytoscapejs import viewer as cyjs
g = nx.scale_free_graph(100)
g_cyjs = from_networkx(g)
result = cyjs.render(g_cyjs, layout_algorithm='circle')
self.assertIsNone(result)
