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import os |
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from Bio import SeqIO |
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from tqdm.auto import tqdm |
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import pandas as pd |
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from huggingface_hub import HfApi |
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aav_files = [ |
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"des_mut", |
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"low_vs_high", |
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"mut_des", |
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"one_vs_many", |
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"sampled", |
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"seven_vs_many", |
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"two_vs_many", |
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] |
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meltome_files = [ |
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"human", |
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"human_cell", |
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"mixed_split", |
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] |
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sav_files = [ |
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"human", |
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"only_savs", |
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"mixed", |
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] |
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scl_files = [ |
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"balanced", |
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"human_hard", |
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"human_soft", |
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"mixed_hard", |
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"mixed_soft", |
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] |
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gb1_files = [ |
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"low_vs_high", |
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"one_vs_rest", |
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"sampled", |
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"three_vs_rest", |
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"two_vs_rest", |
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] |
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def download_wget(filename, repo): |
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if os.path.exists(f"{filename}.fasta"): |
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return |
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url = f"http://data.bioembeddings.com/public/FLIP/fasta/{repo}/{filename}.fasta" |
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os.system(f"wget {url}") |
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def upload_aav(): |
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repo = "aav" |
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for filename in aav_files: |
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download_wget(filename, repo) |
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fasta_file = f"{filename}.fasta" |
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csv_file = f"{filename}.csv" |
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db = SeqIO.index(fasta_file, "fasta") |
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output = [] |
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for _, record in tqdm(db.items()): |
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description = record.description |
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description = description.split() |
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sequence = str(record.seq) |
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seqid = description[0] |
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label = float(description[1].split("=")[1]) |
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split = description[2].split("=")[1] |
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validation = description[3].split("=")[1] |
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output.append({ |
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"seqid": seqid, |
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"label": label, |
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"sequence": sequence, |
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"split": split.lower(), |
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"validation": validation.lower(), |
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}) |
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pd.DataFrame(output).to_csv(csv_file, index=False) |
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api = HfApi() |
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api.create_repo( |
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repo_id=f"hazemessam/{repo}", |
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repo_type="dataset", |
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exist_ok=True, |
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) |
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api.upload_file( |
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path_or_fileobj=csv_file, |
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path_in_repo=csv_file, |
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repo_id=f"hazemessam/{repo}", |
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repo_type="dataset", |
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) |
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os.system(f"rm -rf {fasta_file}") |
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os.system(f"rm -rf {csv_file}") |
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def upload_meltome(): |
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repo = "meltome" |
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for filename in meltome_files: |
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download_wget(filename, repo) |
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fasta_file = f"{filename}.fasta" |
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csv_file = f"{filename}.csv" |
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db = SeqIO.index(fasta_file, "fasta") |
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output = [] |
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for _, record in tqdm(db.items()): |
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description = record.description |
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description = description.split() |
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sequence = str(record.seq) |
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seqid = description[0] |
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label = float(description[1].split("=")[1]) |
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split = description[2].split("=")[1] |
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validation = description[3].split("=")[1] |
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output.append({ |
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"seqid": seqid, |
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"label": label, |
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"sequence": sequence, |
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"split": split.lower(), |
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"validation": validation.lower(), |
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}) |
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pd.DataFrame(output).to_csv(csv_file, index=False) |
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api = HfApi() |
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api.create_repo( |
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repo_id=f"hazemessam/{repo}", |
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repo_type="dataset", |
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exist_ok=True, |
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) |
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api.upload_file( |
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path_or_fileobj=csv_file, |
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path_in_repo=csv_file, |
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repo_id=f"hazemessam/{repo}", |
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repo_type="dataset", |
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) |
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os.system(f"rm -rf {fasta_file}") |
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os.system(f"rm -rf {csv_file}") |
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def upload_sav(): |
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repo = "sav" |
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for filename in sav_files: |
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download_wget(filename, repo) |
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fasta_file = f"{filename}.fasta" |
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csv_file = f"{filename}.csv" |
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db = SeqIO.index(fasta_file, "fasta") |
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output = [] |
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for _, record in tqdm(db.items()): |
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description = record.description |
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description = description.split() |
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sequence = str(record.seq) |
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seqid = description[0] |
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label = description[1].split("=")[1] |
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split = description[2].split("=")[1] |
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validation = description[3].split("=")[1] |
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output.append({ |
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"seqid": seqid, |
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"label": label, |
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"sequence": sequence, |
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"split": split.lower(), |
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"validation": validation.lower(), |
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}) |
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pd.DataFrame(output).to_csv(csv_file, index=False) |
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api = HfApi() |
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api.create_repo( |
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repo_id=f"hazemessam/{repo}", |
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repo_type="dataset", |
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exist_ok=True, |
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) |
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api.upload_file( |
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path_or_fileobj=csv_file, |
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path_in_repo=csv_file, |
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repo_id=f"hazemessam/{repo}", |
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repo_type="dataset", |
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) |
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os.system(f"rm -rf {fasta_file}") |
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os.system(f"rm -rf {csv_file}") |
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def upload_scl(): |
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repo = "scl" |
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for filename in scl_files: |
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download_wget(filename, repo) |
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fasta_file = f"{filename}.fasta" |
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csv_file = f"{filename}.csv" |
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db = SeqIO.index(fasta_file, "fasta") |
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output = [] |
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for _, record in tqdm(db.items()): |
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description = record.description |
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description = description.split() |
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sequence = str(record.seq) |
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seqid = description[0] |
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label = description[1].split("=")[1] |
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split = description[2].split("=")[1] |
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validation = description[3].split("=")[1] |
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output.append({ |
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"seqid": seqid, |
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"label": label, |
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"sequence": sequence, |
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"split": split.lower(), |
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"validation": validation.lower(), |
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}) |
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pd.DataFrame(output).to_csv(csv_file, index=False) |
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api = HfApi() |
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api.create_repo( |
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repo_id=f"hazemessam/{repo}", |
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repo_type="dataset", |
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exist_ok=True, |
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) |
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api.upload_file( |
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path_or_fileobj=csv_file, |
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path_in_repo=csv_file, |
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repo_id=f"hazemessam/{repo}", |
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repo_type="dataset", |
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) |
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os.system(f"rm -rf {fasta_file}") |
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os.system(f"rm -rf {csv_file}") |
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def upload_gb1(): |
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repo = "gb1" |
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for filename in gb1_files: |
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download_wget(filename, repo) |
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fasta_file = f"{filename}.fasta" |
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csv_file = f"{filename}.csv" |
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db = SeqIO.index(fasta_file, "fasta") |
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output = [] |
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for _, record in tqdm(db.items()): |
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description = record.description |
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description = description.split() |
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sequence = str(record.seq) |
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seqid = description[0] |
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label = float(description[1].split("=")[1]) |
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split = description[2].split("=")[1] |
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validation = description[3].split("=")[1] |
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output.append({ |
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"seqid": seqid, |
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"label": label, |
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"sequence": sequence, |
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"split": split.lower(), |
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"validation": validation.lower(), |
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}) |
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pd.DataFrame(output).to_csv(csv_file, index=False) |
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api = HfApi() |
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api.create_repo( |
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repo_id=f"hazemessam/{repo}", |
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repo_type="dataset", |
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exist_ok=True, |
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) |
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api.upload_file( |
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path_or_fileobj=csv_file, |
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path_in_repo=csv_file, |
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repo_id=f"hazemessam/{repo}", |
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repo_type="dataset", |
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) |
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os.system(f"rm -rf {fasta_file}") |
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os.system(f"rm -rf {csv_file}") |
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if __name__ == "__main__": |
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upload_gb1() |
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upload_meltome() |
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upload_sav() |
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upload_scl() |
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upload_aav() |
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