src/oas-data-cleaning.py

import os
import pandas as pd
import re
from zipfile import ZipFile
import boto3
import io

data_dir = os.getcwd()
output_path = os.getcwd()

# species_list = ["rat_SD", "mouse_BALB_c", "mouse_C57BL_6", "human"]
species_list = ["mouse_BALB_c", "mouse_C57BL_6", "rat_SD"]

S3_BUCKET = "aws-hcls-ml"
S3_SRC_PREFIX = "oas-paired-sequence-data/raw"
S3_DEST_PREFIX = "oas-paired-sequence-data/processed"
s3 = boto3.client("s3")


# def calc_cdr_coordinates(row):
#     for i in range(1, 4):
#         for j in ["heavy", "light"]:
#             row[f"cdr{i}_aa_{j}_start"] = row[f"sequence_alignment_aa_{j}"].find(
#                 row[f"cdr{i}_aa_{j}"]
#             )
#             row[f"cdr{i}_aa_{j}_end"] = row[f"cdr{i}_aa_{j}_start"] + len(
#                 row[f"cdr{i}_aa_{j}"]
#             )
#     return row


for species in species_list:
    print(f"Downloading {species} files")
    list_of_df = []
    species_url_file = os.path.join(data_dir, species + "_oas_data_units.txt")
    with open(species_url_file, "r") as f:
        for csv_file in f.readlines():
            print(csv_file)
            filename = os.path.basename(csv_file)
            run_id = str(re.search(r"^(.*)_[Pp]aired", filename)[1])
            s3_key = os.path.join(S3_SRC_PREFIX, species, csv_file.strip())
            obj = s3.get_object(Bucket=S3_BUCKET, Key=s3_key)
            run_data = pd.read_csv(
                io.BytesIO(obj["Body"].read()),
                header=1,
                compression="gzip",
                on_bad_lines="warn",
                low_memory=False,
            )
            run_data = run_data[
                [
                    "sequence_alignment_aa_heavy",
                    "cdr1_aa_heavy",
                    "cdr2_aa_heavy",
                    "cdr3_aa_heavy",
                    "sequence_alignment_aa_light",
                    "cdr1_aa_light",
                    "cdr2_aa_light",
                    "cdr3_aa_light",
                ]
            ]
            run_data = run_data.dropna()
            # run_data = run_data.apply(calc_cdr_coordinates, axis=1)
            run_data.insert(
                0, "pair_id", run_id + "_" + run_data.reset_index().index.map(str)
            )
            list_of_df.append(run_data)
        species_df = pd.concat(list_of_df, ignore_index=True)
        print(f"{species} output summary:")
        print(species_df.head())
        print(species_df.shape)
        output_file_name = os.path.join(output_path, "train.csv")
        print(f"Creating {output_file_name}")
        # species_df.to_csv(output_file_name, index=False, compression="zip")
        species_df.to_csv(output_file_name, index=False)
        zip_name = species + ".zip"
        print(f"Creating {zip_name}")
        with ZipFile(zip_name, "w") as myzip:
            myzip.write("train.csv")
        print(
            f"Uploading {zip_name} to {os.path.join('s3://', S3_BUCKET, S3_DEST_PREFIX)}"
        )
        s3.upload_file(zip_name, S3_BUCKET, os.path.join(S3_DEST_PREFIX, zip_name))
        print(f"Removing {output_file_name}")
        os.remove(output_file_name)
        print(f"Removing {zip_name}")
        os.remove(zip_name)