diff --git a/analysis/functions.py b/analysis/functions.py
index e36339123cd0bf662f33a42e3eb9082e3e52ea57..0d854a4be0ea5f0e257a79243f687990c24ade74 100644
--- a/analysis/functions.py
+++ b/analysis/functions.py
@@ -133,5 +133,85 @@ def create_cooc(user=None, corpus=None, whitelist=None, size=200):
cursor.execute(query_cooc)
return cooc
+def get_cooc(request=None, corpus_id=None, type="node_link"):
+ import pandas as pd
+ from copy import copy
+ import numpy as np
+ import networkx as nx
+ from networkx.readwrite import json_graph
+ from gargantext_web.api import JsonHttpResponse
+
+ from analysis.louvain import best_partition
+
+ matrix = defaultdict(lambda : defaultdict(float))
+ labels = dict()
+ weight = dict()
+
+ corpus = Node.objects.get(id=corpus_id)
+ type_cooc = NodeType.objects.get(name="Cooccurrence")
+
+ if Node.objects.filter(type=type_cooc, parent=corpus).first() is None:
+ print("Coocurrences do not exist yet, create it.")
+ whitelist = create_whitelist(request.user, corpus)
+ cooccurrence_node = create_cooc(user=request.user, corpus=corpus, whitelist=whitelist)
+ print(cooccurrence_matrix.id, "Cooc created")
+ else:
+ cooccurrence_node = Node.objects.filter(type=type_cooc, parent=corpus).first()
+
+ for cooccurrence in NodeNgramNgram.objects.filter(node=cooccurrence_node):
+ labels[cooccurrence.ngramx.id] = cooccurrence.ngramx.terms
+ labels[cooccurrence.ngramy.id] = cooccurrence.ngramy.terms
+
+ matrix[cooccurrence.ngramx.id][cooccurrence.ngramy.id] = cooccurrence.score
+ matrix[cooccurrence.ngramy.id][cooccurrence.ngramx.id] = cooccurrence.score
+
+ weight[cooccurrence.ngramy.terms] = weight.get(cooccurrence.ngramy.terms, 0) + cooccurrence.score
+ weight[cooccurrence.ngramx.terms] = weight.get(cooccurrence.ngramx.terms, 0) + cooccurrence.score
+
+
+ df = pd.DataFrame(matrix).T.fillna(0)
+ x = copy(df.values)
+ x = x / x.sum(axis=1)
+
+ # Removing unconnected nodes
+ threshold = min(x.max(axis=1))
+ matrix_filtered = np.where(x >= threshold, 1, 0)
+ #matrix_filtered = np.where(x > threshold, x, 0)
+
+ G = nx.from_numpy_matrix(matrix_filtered)
+ G = nx.relabel_nodes(G, dict(enumerate([ labels[label] for label in list(df.columns)])))
+ #G = nx.relabel_nodes(G, dict(enumerate(df.columns)))
+
+ # Removing too connected nodes (find automatic way to do it)
+# outdeg = G.degree()
+# to_remove = [n for n in outdeg if outdeg[n] >= 10]
+# G.remove_nodes_from(to_remove)
+
+ partition = best_partition(G)
+
+ for node in G.nodes():
+ try:
+ #node,type(labels[node])
+ G.node[node]['label'] = node
+ G.node[node]['size'] = weight[node]
+# G.node[node]['color'] = '19,180,300'
+ except Exception as error:
+ print(error)
+
+ data = json_graph.node_link_data(G)
+# data = json_graph.node_link_data(G, attrs={\
+# 'source':'source',\
+# 'target':'target',\
+# 'weight':'weight',\
+# #'label':'label',\
+# #'color':'color',\
+# 'id':'id',})
+ #print(data)
+ return data
+
+
+
+
+
diff --git a/gargantext_web/views.py b/gargantext_web/views.py
index 6f8d1f849ff0611201891ff85a9e184e7e17aa13..48a211aab225a0e4a7611f870541a7889d883c4b 100644
--- a/gargantext_web/views.py
+++ b/gargantext_web/views.py
@@ -475,81 +475,9 @@ def node_link(request, corpus_id):
Create the HttpResponse object with the graph dataset.
'''
- import pandas as pd
- from copy import copy
- import numpy as np
- import networkx as nx
- from networkx.readwrite import json_graph
from gargantext_web.api import JsonHttpResponse
-
- from analysis.louvain import best_partition
- from analysis.functions import create_whitelist, create_cooc
-
- matrix = defaultdict(lambda : defaultdict(float))
- labels = dict()
- weight = dict()
-
- corpus = Node.objects.get(id=corpus_id)
- type_cooc = NodeType.objects.get(name="Cooccurrence")
-
- if Node.objects.filter(type=type_cooc, parent=corpus).first() is None:
- print("Coocurrences do not exist yet, create it.")
- whitelist = create_whitelist(request.user, corpus)
- cooccurrence_node = create_cooc(user=request.user, corpus=corpus, whitelist=whitelist)
- print(cooccurrence_matrix.id, "Cooc created")
- else:
- cooccurrence_node = Node.objects.filter(type=type_cooc, parent=corpus).first()
-
- for cooccurrence in NodeNgramNgram.objects.filter(node=cooccurrence_node):
- labels[cooccurrence.ngramx.id] = cooccurrence.ngramx.terms
- labels[cooccurrence.ngramy.id] = cooccurrence.ngramy.terms
-
- matrix[cooccurrence.ngramx.id][cooccurrence.ngramy.id] = cooccurrence.score
- matrix[cooccurrence.ngramy.id][cooccurrence.ngramx.id] = cooccurrence.score
-
- weight[cooccurrence.ngramy.terms] = weight.get(cooccurrence.ngramy.terms, 0) + cooccurrence.score
- weight[cooccurrence.ngramx.terms] = weight.get(cooccurrence.ngramx.terms, 0) + cooccurrence.score
-
-
-
- df = pd.DataFrame(matrix).T.fillna(0)
- x = copy(df.values)
- x = x / x.sum(axis=1)
-
- # Removing unconnected nodes
- threshold = min(x.max(axis=1))
- matrix_filtered = np.where(x >= threshold, 1, 0)
- #matrix_filtered = np.where(x > threshold, x, 0)
-
- G = nx.from_numpy_matrix(matrix_filtered)
- G = nx.relabel_nodes(G, dict(enumerate([ labels[label] for label in list(df.columns)])))
- #G = nx.relabel_nodes(G, dict(enumerate(df.columns)))
-
- # Removing too connected nodes (find automatic way to do it)
-# outdeg = G.degree()
-# to_remove = [n for n in outdeg if outdeg[n] >= 10]
-# G.remove_nodes_from(to_remove)
-
- partition = best_partition(G)
-
- for node in G.nodes():
- try:
- #node,type(labels[node])
- G.node[node]['label'] = node
- G.node[node]['size'] = weight[node]
-# G.node[node]['color'] = '19,180,300'
- except Exception as error:
- print(error)
-
- data = json_graph.node_link_data(G)
-# data = json_graph.node_link_data(G, attrs={\
-# 'source':'source',\
-# 'target':'target',\
-# 'weight':'weight',\
-# #'label':'label',\
-# #'color':'color',\
-# 'id':'id',})
- #print(data)
+ from analysis.functions import get_cooc
+ data = get_cooc(request=request, corpus_id=corpus_id, type="node_link")
return JsonHttpResponse(data)
def graph_it(request):