aboutsummaryrefslogtreecommitdiff
path: root/gn3/computations/rust_correlation.py
blob: 831ef359840367659e8ab6cf24a279f47dd1c312 (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
"""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]

    subprocess.run(command_list, check=True)

    results = parse_correlation_output(output_file, 500)

    return results


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


def get_samples(all_samples: dict[str, str],
                base_samples: list[str],
                excluded: list[str]):
    """filter null samples and excluded samples"""

    data = {}

    if base_samples:
        fls = [
            sm for sm in base_samples if sm not in excluded]
        for sample in fls:
            if sample in all_samples:
                smp_val = all_samples[sample].strip()
                if smp_val.lower() != "x":
                    data[sample] = float(smp_val)

        return data

    return({key: float(val) for (key, val) in all_samples.items()
            if key not in excluded and val.lower().strip() != "x"})