[FIX] Removing old database connexion configuration.

analysis/diachronic_specificity.py

 import sqlalchemy
-from gargantext_web import api
+from gargantext_web import api, db
 from node import models
 from sqlalchemy import create_engine
 from sqlalchemy.sql import func
 import numpy as np
 import collections
 
-ResourceType = models.ResourceType.sa
-Resource = models.Resource.sa
-NodeType = models.NodeType.sa
-NodeNgram = models.Node_Ngram.sa
-NodeNodeNgram = models.NodeNodeNgram.sa
-NodeNgramNgram = models.NodeNgramNgram.sa
-Ngram = models.Ngram.sa
-Node_Hyperdata = models.Node_Hyperdata.sa
-Hyperdata = models.Hyperdata.sa
-Node = models.Node.sa
-Corpus = models.Corpus.sa
-
-def get_session():
-    import sqlalchemy.orm
-    from django.db import connections
-    from sqlalchemy.orm import sessionmaker
-    from aldjemy.core import get_engine
-    alias = 'default'
-    connection = connections[alias]
-    engine = create_engine("postgresql+psycopg2://gargantua:C8kdcUrAQy66U@localhost/gargandb",
-            use_native_hstore=True)
-    Session = sessionmaker(bind=engine)
-    return Session()
 
 session = get_session()
 
-
 def result2dict(query):
     results = dict()
     for result in query:
@@ -42,7 +18,7 @@ def result2dict(query):
 
 
 def diachronic_specificity(corpus_id, terms, order=True):
-    ''' 
+    '''
     Take as parameter Corpus primary key and text of ngrams.
     Result is a dictionnary.
     Keys are period (years for now)
@@ -63,24 +39,24 @@ def diachronic_specificity(corpus_id, terms, order=True):
         .filter(Node.parent_id == corpus_id)
         .group_by(Node.hyperdata['publication_year'])
     )
-            
-            
+
+
     document_filterByngram_year = dict(ngram_frequency_query.all())
     document_all_year = dict(document_year_sum_query.all())
     #print(document_all_year)
-    
+
     relative_terms_count = dict()
     for year, total in document_all_year.items():
         terms_count = document_filterByngram_year.get(year, 0)
         relative_terms_count[year] = terms_count / total
-    
+
     mean = np.mean(list(relative_terms_count.values()))
-    
+
     relative_terms_count = {
         key: (value - mean)
         for key, value in relative_terms_count.items()
     }
-    
+
     if order == True:
         return collections.OrderedDict(sorted(relative_terms_count.items()))
     else: