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-rw-r--r--Code/merge_edges.py34
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 = []