def new_draw(self):
"""
draw the ARN using the class attributes
Args:
nothing
Returns:
a local generated web page
"""
got_net = Network(height="750px",
width="100%",
bgcolor="#222222",
font_color="white")
got_net.heading = "Nucleotids"
# set the physics layout of the network
got_net.barnes_hut()
cp = 0
for nuk in self.list_nucleotide:
for tuples in nuk.tup:
if tuples.relation in self.grps.keys():
relation_type = self.grps.get(tuples.relation)
else:
relation_type = 0
relation_type = str(nuk.paired_type)
got_net.add_node(tuples.tup[0],
label="(" + str(tuples.tup[0]) + ")" +
" -> " + str(tuples.tup[1]) + " : " +
str(relation_type),
group=relation_type)
got_net.add_node(tuples.tup[1],
label="(" + str(tuples.tup[1]) + ")" +
" -> " + str(tuples.tup[0]) + " : " +
str(relation_type),
group=relation_type)
got_net.add_edge(tuples.tup[0], tuples.tup[1])
cp += 1
# got_net.add_node(src, src, title=src)
# got_net.add_node(dst, dst, title=dst)
# got_net.add_edge(src, dst, value=w)
# neighbor_map = got_net.get_adj_list()
# add neighbor data to node hover data
'''for node in got_net.nodes:
node["title"] += " Neighbors:<br>" + \
"<br>".join(neighbor_map[node["id"]])
node["value"] = len(neighbor_map[node["id"]])'''
got_net.show("networkRNA.html")
def draw_list(self):
"""
draw the ARN using the class attributes
Args:
nothing
Returns:
a local generated web page
"""
got_net = Network(height="750px",
width="100%",
bgcolor="#222222",
font_color="white")
got_net.heading = "motif detected"
# set the physics layout of the network
got_net.barnes_hut()
cp = 0
for nuk in self.list_motif:
for tuples in nuk.tup:
if tuples.relation in self.grps.keys():
relation_type = self.grps.get(tuples.relation)
else:
relation_type = 0
relation_type = str(nuk.paired_type)
got_net.add_node(tuples.tup[0],
label="(" + str(tuples.tup[0]) + ")" +
" -> " + str(tuples.tup[1]) + " : " +
str(relation_type),
group=relation_type)
got_net.add_node(tuples.tup[1],
label="(" + str(tuples.tup[1]) + ")" +
" -> " + str(tuples.tup[0]) + " : " +
str(relation_type),
group=relation_type)
got_net.add_edge(tuples.tup[0], tuples.tup[1])
cp += 1
def draw_graph_with_pyvis(triples_df,
dataset_name,
cause_column,
treat_column,
food_column='term1',
disease_column='term2',
food_node_color='green',
disease_node_color='darkgrey',
cause_color='lightcoral',
treat_color='palegreen'):
nt = Network("800px", "100%")
nt.heading = f'Food-disease relations identified in dataset: {dataset_name}'
triples_df[food_column] = triples_df[food_column].apply(
lambda x: x.lower())
triples_df[disease_column] = triples_df[disease_column].apply(
lambda x: x.lower())
unique_pairs = triples_df.groupby([
food_column, disease_column, 'entity_id_y', 'foodon', 'snomedct',
'hansard', 'hansardClosest', 'hansardParent', 'synonyms'
]).mean()[[cause_column, treat_column]]
unique_pairs = unique_pairs[(unique_pairs[cause_column] > 0) |
(unique_pairs[treat_column] > 0)]
print(unique_pairs)
results = []
for index_columns in unique_pairs.index:
term1, term2, doid, foodon, snomedct, hansard, hansard_closest, hansard_parent, synonyms = index_columns
all_evidence = triples_df[triples_df[food_column] == term1][
triples_df[disease_column] == term2]['relation_candidates']
if len(set(all_evidence.values)) > 1:
all_evidence = '______'.join(set(all_evidence.values))
label_term1 = triples_df[triples_df[food_column] == term1][
'term1'].value_counts().idxmax()
label_term2 = triples_df[triples_df[disease_column] == term2][
'term2'].value_counts().idxmax()
nt.add_node(term1,
label=label_term1,
color=food_node_color,
size=15)
nt.add_node(term2,
label=label_term2,
color=disease_node_color,
size=15)
row = unique_pairs.loc[index_columns, :]
edge_color = cause_color if row[cause_column] > row[
treat_column] else treat_color if row[cause_column] < row[
treat_column] else 'purple'
results.append({
'term1': label_term1,
'term2': label_term2,
'evidence': all_evidence,
'treat': row[treat_column],
'cause': row[cause_column],
'doid': doid,
'foodon': foodon,
'snomedct': snomedct,
'hansard': hansard,
'hansardClosest': hansard_closest,
'hansardParent': hansard_parent,
'synonyms': synonyms
})
edge_label = f'treat: {row[treat_column]} cause: {row[cause_column]} \n{all_evidence}'
nt.add_edge(term1,
term2,
title=edge_label,
color=edge_color,
width=5)
nt.show_buttons(
filter_=['physics', 'nodes', 'edges', 'selection', 'layout'])
pd.DataFrame(results).to_csv('visualization_triples_sum.csv')
print(len(nt.edges))
# nt.enable_physics(True)
nt.show("nx.html")