merge_edges.py: remove old function make_new_edge()

1 files changed, 0 insertions, 34 deletions

diff --git a/Code/merge_edges.py b/Code/merge_edges.py
index be74ee6..ddd60d4 100644
--- a/Code/merge_edges.py
+++ b/Code/merge_edges.py
@@ -118,40 +118,6 @@ def compute_time_difference_in_days(t1, t2):
     return (t1 - t2).days
 
 
-def make_new_edge(lst_tuple):
-    lst = sorted(lst_tuple, reverse=True, key = lambda x: abs(float(x[2]))) # sort tuples according to absolute value of score
-    best_edge = list( lst[0] )  # use the first tuple as a basis edge
-
-    # see section 'Ranking edges using frecency' in the brain documentation 
-    F = len(lst_tuple)
-
-    RN_lst = []
-    r_lst = []
-    most_recent_edge_date = '00000000'
-    method_or_tissue = []
-    cids = ''
-    for t in lst:
-        r_lst.append( abs(float(t[2])) )
-        rids = t[4]
-        if t[5] > cids:
-            cids = t[5]
-        RN_lst.append( get_number_of_RNAseq_ids(rids) )
-        date = t[7]
-        if date > most_recent_edge_date:
-            most_recent_edge_date = date
-        method_or_tissue.append(t[9])
-    S = 365 * 10
-    curr_date = datetime.datetime.now().strftime('%Y%m%d')
-    time_diff = compute_time_difference_in_days(most_recent_edge_date, curr_date)
-    strength = sum(r_lst)/len(r_lst) * math.log(sum(RN_lst)/len(RN_lst)+1, 10) * math.log(F+1, 2) * math.exp(time_diff/S)
-    best_edge[4] = '%d' % max(RN_lst)
-    best_edge[5] = cids
-    best_edge[7] = most_recent_edge_date
-    best_edge[8] = '%.2f' % strength
-    best_edge[9] = ','.join(sorted(list(set(method_or_tissue)))) # unique methods or tissues, in string format
-    return best_edge
-
-
 def get_unique_cids(lst):
     ''' Return a list of unique, sorted ChIP-seq IDs. '''
     cids = []