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import sys
sys.path.insert(0, './')
import urllib.parse
import numpy as np
import pandas as pd
from flask import Flask, g, request
from wqflask import app
from wqflask.database import database_connection
def parse_db_uri():
"""Converts a database URI to the db name, host name, user name, and password"""
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)
print(db_conn_info)
return db_conn_info
def create_dataframe(input_file):
with open(input_file) as f:
ncols = len(f.readline().split("\t"))
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
def quantileNormalize(df_input):
df = df_input.copy()
# compute rank
dic = {}
for col in df:
dic.update({col: sorted(df[col])})
sorted_df = pd.DataFrame(dic)
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]
return df
def set_data(cursor, dataset_name):
orig_file = "/home/zas1024/cfw_data/" + dataset_name + ".txt"
sample_list = []
with open(orig_file, 'r') as orig_fh, open('/home/zas1024/cfw_data/quant_norm.csv', 'r') as quant_fh:
for i, (line1, line2) in enumerate(zip(orig_fh, quant_fh)):
trait_dict = {}
sample_list = []
if i == 0:
sample_names = line1.split('\t')[1:]
else:
trait_name = line1.split('\t')[0]
for i, sample in enumerate(sample_names):
this_sample = {
"name": sample,
"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
FROM Species, InbredSet, ProbeSetFreeze, ProbeFreeze, ProbeSetXRef, ProbeSet
WHERE Species.Id = InbredSet.SpeciesId and
InbredSet.Id = ProbeFreeze.InbredSetId and
ProbeFreeze.Id = ProbeSetFreeze.ProbeFreezeId and
ProbeSetFreeze.Name = '%s' and
ProbeSetFreeze.Id = ProbeSetXRef.ProbeSetFreezeId and
ProbeSetXRef.ProbeSetId = ProbeSet.Id and
ProbeSet.Name = '%s'""" % (dataset_name, line1.split('\t')[0])
cursor.execute(query)
result_info = cursor.fetchone()
yield {
"_index": "traits",
"_type": "trait",
"_source": {
"name": trait_name,
"species": result_info[0],
"group": result_info[1],
"dataset": dataset_name,
"dataset_fullname": result_info[2],
"samples": sample_list,
"transform_types": "qnorm"
}
}
if __name__ == '__main__':
with database_connection as conn:
with conn.cursor() as cursor:
success, _ = bulk(es, set_data(cursor, sys.argv[1]))
response = es.search(
index="traits", doc_type="trait", body={
"query": {"match": {"name": "ENSMUSG00000028982"}}
}
)
print(response)
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