From 1efd4207e7c96cfb6c31e71b10628cd2ff1c8824 Mon Sep 17 00:00:00 2001 From: Yangyang Li Date: Sat, 12 Oct 2024 23:00:36 -0500 Subject: [PATCH] refactor: Update dependencies versions in pyproject.toml --- poetry.lock | 309 +++++++++++++++++++++++++------------------------ pyproject.toml | 4 +- tasks.py | 172 --------------------------- 3 files changed, 158 insertions(+), 327 deletions(-) delete mode 100644 tasks.py diff --git a/poetry.lock b/poetry.lock index 10ebb06..dd8230c 100644 --- a/poetry.lock +++ b/poetry.lock @@ -754,13 +754,13 @@ stevedore = ">=2.0.1" [[package]] name = "cloudpickle" -version = "3.0.0" +version = "3.1.0" description = "Pickler class to extend the standard pickle.Pickler functionality" optional = false python-versions = ">=3.8" files = [ - {file = "cloudpickle-3.0.0-py3-none-any.whl", hash = "sha256:246ee7d0c295602a036e86369c77fecda4ab17b506496730f2f576d9016fd9c7"}, - {file = "cloudpickle-3.0.0.tar.gz", hash = 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level = logging.INFO - - logging.basicConfig( - level=level, - format=FORMAT, - handlers=[RichHandler()], - ) - deepchopper.test_log() - - -@task -def clean(c): - Path("data.npz").unlink(missing_ok=True) - Path("kmer2idx.txt").unlink(missing_ok=True) - - -@task -def encode_parqut(c, file: Path, level="info"): - from deepchopper import encode_fq_path_to_parquet - - if level == "info": - level = logging.INFO - elif level == "debug": - level = logging.DEBUG - elif level == "warn": - level = logging.WARNING - else: - level = logging.INFO - - FORMAT = "%(message)s" - logging.basicConfig( - level=level, - format=FORMAT, - handlers=[RichHandler()], - ) - data = Path(file) - k = 3 - bases = "ACGTN" - qual_offset = 33 - - encode_fq_path_to_parquet(data, k, bases, qual_offset, vectorized_target=False) - # import pyarrow.parquet as pq - # df = pq.read_table(result_path) - # df_pd = df.to_pandas() - # assert df_pd.shape == (25, 5) - - -@task -def encode_tensor(c, file: Path, level="info"): - clean(c) - - if level == "info": - level = logging.INFO - elif level == "debug": - level = logging.DEBUG - elif level == "warn": - level = logging.WARNING - else: - level = logging.INFO - - FORMAT = "%(message)s" - logging.basicConfig( - level=level, - format=FORMAT, - handlers=[RichHandler()], - ) - - data = Path(file) - k = 3 - bases = "ACGTN" - qual_offset = 33 - - inputs, target, qual, kmer2idx = encode_fq_path(data, k, bases, qual_offset, True) - - logging.info(f"inputs.shape: {inputs.shape}") - logging.info(f"target.shape: {target.shape}") - logging.info(f"qual.shape: {qual.shape}") - logging.info(f"len(kmer2idx): {len(kmer2idx)}") - - np.savez("data.npz", inputs=inputs, target=target, qual=qual) - - with open("kmer2idx.txt", "w") as f: - for kmer, idx in kmer2idx.items(): - f.write(f"{kmer}\t{idx}\n") - - -@task -def encode_fqs(c, data_folder): - from deepchopper.data import encode_fq_files_in_folder_to_parquet - - data_folder = Path(data_folder) - encode_fq_files_in_folder_to_parquet(data_folder) - - -@task -def convert_safe(c, file): - from deepchopper.utils import save_ndarray_to_safetensor - - file = Path(file) - data = np.load(file) - - save_ndarray_to_safetensor(data, file.with_suffix(".safetensors")) - - -@task -def readq(c, file): - import pyarrow.parquet as pq - - df = pq.read_table(file) - - df_pd = df.to_pandas() - print(df_pd.shape) - print(df_pd.head()) - - from pyarrow import json - import json - - df_dict = df.to_pydict() - json.dump(df_dict, open("df.json", "w")) - - -@task -def test(c, file=None): - from datasets import load_dataset - - data_files = {"train": "tests/data/test_input.parquet"} - num_proc = 8 - train_dataset = load_dataset( - "parquet", data_files=data_files, num_proc=num_proc, split="train[:70%]" - ) - val_dataset = load_dataset( - "parquet", data_files=data_files, num_proc=num_proc, split="train[70%:90%]" - ) - test_dataset = load_dataset( - "parquet", data_files=data_files, num_proc=num_proc, split="train[90%:]" - ) - - print(f"train_dataset: {train_dataset}") - print(f"val_dataset: {val_dataset}") - print(f"test_dataset: {test_dataset}") - - -@task -def summary(c, file): - import deepchopper - import numpy as np - - file = Path(file) - len_list = deepchopper.summary_bam_record_len(file) - print(f"len_list: {len_list}") - np.save(file.with_suffix(".npy"), len_list)