diff --git a/similarities/weekly_cosine_similarities.py b/similarities/weekly_cosine_similarities.py
index 6c0aa6a..0b18dca 100755
--- a/similarities/weekly_cosine_similarities.py
+++ b/similarities/weekly_cosine_similarities.py
@@ -31,7 +31,7 @@ outfile="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/simi
 # static_tfidf = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
 # dftest = spark.read.parquet(static_tfidf)
 
-def _week_similarities(week, simfunc, tfidf_path, term_colname, included_subreddits, outdir:Path, subreddit_names, nterms, topN=None, min_df=None, max_df=None, clusters=None):
+def _week_similarities(week, simfunc, tfidf_path, term_colname, included_subreddits, outdir:Path, subreddit_names, nterms, topN=None, min_df=None, max_df=None, clusters=None, term_ids=None):
     term = term_colname
     term_id = term + '_id'
     term_id_new = term + '_id_new'
@@ -44,6 +44,10 @@ def _week_similarities(week, simfunc, tfidf_path, term_colname, included_subredd
                          week=week,
                          rescale_idf=False)
     
+    if term_ids is not None:
+        entries = duckdb.sql(f"SELECT A.{tfidf_colname}, B.{term_id} AS {term_id_new}, A.subreddit_id_new FROM entries AS A JOIN term_ids AS B ON A.{term_id_new} == B.{term_id_old}").df()
+        
+
     tfidf_colname='tf_idf'
     # if the max subreddit id we found is less than the number of subreddit names then we have to fill in 0s
     shape = (nterms,subreddit_names.shape[0])
@@ -79,7 +83,7 @@ def cosine_similarities_weekly_lsi(*args, n_components=100, lsi_model=None, **kw
     return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs)
 
 #tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf_weekly/comment_submission_terms_tfidf.parquet')
-def cosine_similarities_weekly(tfidf_path, outfile, term_colname, included_subreddits = None, topN = None, simfunc=column_similarities, min_df=0, max_df=None, static_tfidf_path=None, clusters=None, min_date=None, max_date=None, cores=1):
+def cosine_similarities_weekly(tfidf_path, outfile, term_colname, included_subreddits = None, topN = None, simfunc=column_similarities, min_df=0, max_df=None, static_tfidf_path=None, clusters=None, min_date=None, max_date=None, cores=1, term_ids=None):
     print(outfile)
     # do this step in parallel if we have the memory for it.
     # should be doable with pool.map
@@ -111,7 +115,7 @@ def cosine_similarities_weekly(tfidf_path, outfile, term_colname, included_subre
         q = q + f" WHERE CAST(week AS DATE) >= CAST('{min_date}' AS DATE)"
 
     weeks = conn.execute(q).df()
-    weeks = weeks.week.values
+    weeks = weeks.week.values 
     conn.close()
 
     if clusters is not None:
@@ -119,7 +123,7 @@ def cosine_similarities_weekly(tfidf_path, outfile, term_colname, included_subre
         clusters = duckdb.sql("SELECT A.subreddit AS sr_i, B.subreddit AS sr_j FROM clusters_raw AS A JOIN clusters_raw AS B ON A.cluster == B.cluster WHERE A.cluster != -1 AND B.cluster != -1").df()
 
     print(f"computing weekly similarities")
-    week_similarities_helper = partial(_week_similarities,simfunc=simfunc, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=None, subreddit_names=subreddit_names,nterms=nterms, clusters = clusters)
+    week_similarities_helper = partial(_week_similarities,simfunc=simfunc, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=None, subreddit_names=subreddit_names,nterms=nterms, clusters = clusters, term_ids=term_ids)
 
     if cores > 1:
         with Pool(cores) as pool: # maybe it can be done with 128 cores on the huge machine?