oas-data-cleaning.py

# import boto3
# import io
import os
import pandas as pd
import re
import shutil
import tarfile

data_dir = os.path.join(os.getcwd(), "data_units")
# output_path = os.getcwd()

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

# S3_BUCKET = "aws-hcls-ml"
# S3_SRC_PREFIX = "oas-paired-sequence-data/raw"
# S3_DEST_PREFIX = "oas-paired-sequence-data/parquet"
# s3 = boto3.client("s3")
# BASE_URL = "https://aws-hcls-ml.s3.amazonaws.com/oas-paired-sequence-data/raw/rat_SD/SRR9179275_paired.csv.gz"
BASE_URL = "https://aws-hcls-ml.s3.amazonaws.com/oas-paired-sequence-data/raw/"

for species in species_list:
    print(f"Downloading {species} files")
    # list_of_df = []
    species_url_file = os.path.join(data_dir, species + ".txt")
    with open(species_url_file, "r") as f:
        i = 0
        os.makedirs(species, exist_ok=True)
        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])
            url = os.path.join(BASE_URL, species, csv_file)
            # 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()),
                url,
                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.insert(0, "data_unit", run_id)
            print(run_data.shape)
            output_path = os.path.join(species, "train_" + str(i) + ".parquet")
            run_data.to_parquet(output_path)
            # 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)
        # os.makedirs(species, exist_ok=True)
        # species_df.to_parquet(species, partition_cols=["data_unit"])
        zip_name = species + ".tar.gz"
        print(f"Creating {zip_name}")
        with tarfile.open(zip_name, "w:gz") as tf:
            tf.add(species, arcname="")
        # 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 {species}")
        shutil.rmtree(species)
        # print(f"Removing {zip_name}")
        # os.remove(zip_name)