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"""module contains code integration correlation implemented in rust here
https://github.com/Alexanderlacuna/correlation_rust
"""
import subprocess
import json
import os
from gn3.computations.qtlreaper import create_output_directory
from gn3.random import random_string
from gn3.settings import CORRELATION_COMMAND
from gn3.settings import TMPDIR
def generate_input_files(dataset: list[str],
output_dir: str = TMPDIR) -> tuple[str, str]:
"""function generates outputfiles and inputfiles"""
tmp_dir = f"{output_dir}/correlation"
create_output_directory(tmp_dir)
tmp_file = os.path.join(tmp_dir, f"{random_string(10)}.txt")
with open(tmp_file, "w", encoding="utf-8") as file_writer:
file_writer.write("\n".join(dataset))
return (tmp_dir, tmp_file)
def generate_json_file(tmp_dir, tmp_file, method, delimiter, x_vals) -> str:
"""generating json input file required by cargo"""
tmp_json_file = os.path.join(tmp_dir, f"{random_string(10)}.json")
output_file = os.path.join(tmp_dir, f"{random_string(10)}.txt")
correlation_args = {
"method": method,
"file_path": tmp_file,
"x_vals": x_vals,
"sample_values": "bxd1",
"output_file": output_file,
"file_delimiter": delimiter
}
with open(tmp_json_file, "w", encoding="utf-8") as outputfile:
json.dump(correlation_args, outputfile)
return (output_file, tmp_json_file)
def run_correlation(dataset, trait_vals:
list[str],
method: str,
delimiter: str):
"""entry function to call rust correlation"""
(tmp_dir, tmp_file) = generate_input_files(dataset)
(output_file, json_file) = generate_json_file(tmp_dir=tmp_dir,
tmp_file=tmp_file,
method=method,
delimiter=delimiter,
x_vals=trait_vals)
command_list = [CORRELATION_COMMAND, json_file, TMPDIR]
rls = subprocess.run(command_list, check=True)
rs = parse_correlation_output(output_file,10000)
return rs
def parse_correlation_output(result_file: str, top_n: int = 500) -> list[dict]:
"""parse file output """
corr_results = []
with open(result_file, "r", encoding="utf-8") as file_reader:
lines = [next(file_reader) for x in range(top_n)]
for line in lines:
(trait_name, corr_coeff, p_val) = line.rstrip().split(",")
corr_data = {
"num_overlap": 00, # to be later fixed
"corr_coefficient": corr_coeff,
"p_value": p_val
}
corr_results.append({trait_name: corr_data})
return corr_results
# computation specific;sample_r,lit_corr
def compute_top_n(first_run_results,init_type,dataset_1,dataset_2,dataset_type:str):
if dataset__type.lower()!= "probeset":
return first_run_results
if init_type == "sample":
# do both lit and tissue
results_a = run_correlation(dataset_1, x_vals_1,method,delimiter)
results_b = lit_correlation_for_trait(unkown)
# question how do we merge this
if init_type == "tissue":
# do sample and tissue
file_a = run_correlation(dataset_1,x_vals_1,method,delimiter)
result_b = lit_correlation_for_trait(unkown)
# merge the results
if init_type == "lit":
file_a = run_correlation()
file_b = run_correlation()
join <(file_a) <(file_b)
# do the merge here
# do both sample and tissue
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