diff options
Diffstat (limited to 'wqflask/maintenance/quantile_normalize.py')
| -rw-r--r-- | wqflask/maintenance/quantile_normalize.py | 30 |
1 files changed, 15 insertions, 15 deletions
diff --git a/wqflask/maintenance/quantile_normalize.py b/wqflask/maintenance/quantile_normalize.py index 701b2b50..6751a8e5 100644 --- a/wqflask/maintenance/quantile_normalize.py +++ b/wqflask/maintenance/quantile_normalize.py @@ -20,10 +20,10 @@ def parse_db_uri(): parsed_uri = urllib.parse.urlparse(SQL_URI) db_conn_info = dict( - db = parsed_uri.path[1:], - host = parsed_uri.hostname, - user = parsed_uri.username, - passwd = parsed_uri.password) + db=parsed_uri.path[1:], + host=parsed_uri.hostname, + user=parsed_uri.username, + passwd=parsed_uri.password) print(db_conn_info) return db_conn_info @@ -35,16 +35,16 @@ def create_dataframe(input_file): input_array = np.loadtxt(open(input_file, "rb"), delimiter="\t", skiprows=1, usecols=list(range(1, ncols))) return pd.DataFrame(input_array) -#This function taken from https://github.com/ShawnLYU/Quantile_Normalize +# This function taken from https://github.com/ShawnLYU/Quantile_Normalize def quantileNormalize(df_input): df = df_input.copy() - #compute rank + # compute rank dic = {} for col in df: - dic.update({col : sorted(df[col])}) + dic.update({col: sorted(df[col])}) sorted_df = pd.DataFrame(dic) - rank = sorted_df.mean(axis = 1).tolist() - #sort + rank = sorted_df.mean(axis=1).tolist() + # sort for col in df: t = np.searchsorted(np.sort(df[col]), df[col]) df[col] = [rank[i] for i in t] @@ -65,8 +65,8 @@ def set_data(dataset_name): for i, sample in enumerate(sample_names): this_sample = { "name": sample, - "value": line1.split('\t')[i+1], - "qnorm": line2.split('\t')[i+1] + "value": line1.split('\t')[i + 1], + "qnorm": line2.split('\t')[i + 1] } sample_list.append(this_sample) query = """SELECT Species.SpeciesName, InbredSet.InbredSetName, ProbeSetFreeze.FullName @@ -99,9 +99,9 @@ if __name__ == '__main__': Conn = MySQLdb.Connect(**parse_db_uri()) Cursor = Conn.cursor() - #es = Elasticsearch([{ + # es = Elasticsearch([{ # "host": ELASTICSEARCH_HOST, "port": ELASTICSEARCH_PORT - #}], timeout=60) if (ELASTICSEARCH_HOST and ELASTICSEARCH_PORT) else None + # }], timeout=60) if (ELASTICSEARCH_HOST and ELASTICSEARCH_PORT) else None es = get_elasticsearch_connection(for_user=False) @@ -116,8 +116,8 @@ if __name__ == '__main__': success, _ = bulk(es, set_data(sys.argv[1])) response = es.search( - index = "traits", doc_type = "trait", body = { - "query": { "match": { "name": "ENSMUSG00000028982" } } + index="traits", doc_type="trait", body = { + "query": {"match": {"name": "ENSMUSG00000028982"}} } ) |