Merge pull request #17 from ylab-hi/dev

refactor: Update CLI options in chop function

.gitignore

@@ -593,4 +593,5 @@ scripts/*.json
 *stats_*.json
 !build.rs
 .ruff_cache
-hg_deepchopper
+hg_deepchopper
+analysis_data

README.md

@@ -1,10 +1,11 @@
 # <img src="./documentation/logo.webp" alt="logo" height="100"/> **DeepChopper** [![social](https://img.shields.io/github/stars/ylab-hi/DeepChopper?style=social)](https://github.com/ylab-hi/DeepChopper/stargazers)
 
 [![pypi](https://img.shields.io/pypi/v/deepchopper.svg)](https://pypi.python.org/pypi/deepchopper)
+[![PyPI - Wheel](https://img.shields.io/pypi/wheel/deepchopper)](https://pypi.org/project/deepchopper/#files)
 [![license](https://img.shields.io/pypi/l/deepchopper.svg)](https://github.com/ylab-hi/DeepChopper/blob/main/LICENSE)
 [![pypi version](https://img.shields.io/pypi/pyversions/deepchopper.svg)](https://pypi.python.org/pypi/deepbiop)
-[![Actions status](https://github.com/ylab-hi/DeepChopper/actions/workflows/release-python.yml/badge.svg)](https://github.com/ylab-hi/DeepChopper/actions)
 [![platform](https://img.shields.io/badge/platform-linux%20%7C%20osx%20%7C%20win-blue)](https://pypi.org/project/deepchopper/#files)
+[![Actions status](https://github.com/ylab-hi/DeepChopper/actions/workflows/release-python.yml/badge.svg)](https://github.com/ylab-hi/DeepChopper/actions)
 [![Space](https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-md.svg)](https://huggingface.co/spaces/yangliz5/deepchopper)
 
 <!--toc:start-->

deepchopper/cli.py

@@ -66,6 +66,9 @@ def encode(
     if verbose:
         set_logging_level(logging.INFO)
 
+    if isinstance(fastq_path, str):
+        fastq_path = Path(fastq_path)
+
     if not fastq_path.exists():
         msg = f"Folder {fastq_path} does not exist."
         logging.error(msg)
@@ -109,6 +112,9 @@ def predict(
     if verbose:
         set_logging_level(logging.INFO)
 
+    if isinstance(data_path, str):
+        data_path = Path(data_path)
+
     tokenizer = deepchopper.models.llm.load_tokenizer_from_hyena_model(model_name="hyenadna-small-32k-seqlen")
     datamodule: LightningDataModule = deepchopper.data.fq_datamodule.FqDataModule(
         train_data_path="dummy.parquet",
@@ -129,7 +135,7 @@ def predict(
 
     trainer = lightning.pytorch.trainer.Trainer(
         accelerator=accelerator,
-        devices=gpus,
+        devices=devices,
         callbacks=callbacks,
         deterministic=False,
         logger=False,
@@ -141,19 +147,17 @@ def predict(
 def chop(
     predicts: list[Path] = typer.Argument(..., help="Paths to prediction files"),
     fq: Path = typer.Argument(..., help="Path to FASTQ file"),
-    smooth_window_size: int = typer.Option(21, "--smooth-window", "-s", help="Smooth window size"),
-    min_interval_size: int = typer.Option(13, "--min-interval", "-i", help="Minimum interval size"),
-    approved_interval_number: int = typer.Option(20, "--approved-intervals", "-a", help="Number of approved intervals"),
-    max_process_intervals: int = typer.Option(4, "--max-process", "-p", help="Maximum process intervals"),
-    min_read_length_after_chop: int = typer.Option(
-        20, "--min-read-length", "-l", help="Minimum read length after chop"
-    ),
-    output_chopped_seqs: bool = typer.Option(False, "--output-chopped", "-o", help="Output chopped sequences"),
-    chop_type: str = typer.Option("all", "--chop-type", "-t", help="Chop type"),
-    threads: int = typer.Option(2, "--threads", "-n", help="Number of threads"),
-    output_prefix: str | None = typer.Option(None, "--prefix", "-x", help="Output prefix"),
-    max_batch_size: int | None = typer.Option(None, "--max-batch", "-b", help="Maximum batch size"),
+    smooth_window_size: int = typer.Option(21, "--smooth-window", help="Smooth window size"),
+    min_interval_size: int = typer.Option(13, "--min-interval-size", help="Minimum interval size"),
+    approved_interval_number: int = typer.Option(20, "--approved-intervals", help="Number of approved intervals"),
+    max_process_intervals: int = typer.Option(4, "--max-process-intervals", help="Maximum process intervals"),
+    min_read_length_after_chop: int = typer.Option(20, "--min-read-length", help="Minimum read length after chop"),
+    output_chopped_seqs: bool = typer.Option(False, "--output-chopped", help="Output chopped sequences"),
+    chop_type: str = typer.Option("all", "--chop-type", help="Chop type"),
+    threads: int = typer.Option(2, "--threads", help="Number of threads"),
     verbose: bool = typer.Option(False, "--verbose", "-v", help="Enable verbose output"),
+    output_prefix: str | None = typer.Option(None, "--prefix", "-o", help="Output prefix"),
+    max_batch_size: int | None = typer.Option(None, "--max-batch", help="Maximum batch size"),
 ):
     """Chop sequences based on predictions."""
     if verbose:
@@ -168,37 +172,19 @@ def chop(
 
     predict_files = " ".join([f"--pdt {predict}" for predict in predicts])
 
-    commands = [
-        [
-            "deepchopper-chop",
-            predict_files,
-            "--fq",
-            fq,
-            "-t",
-            threads,
-            "-s",
-            smooth_window_size,
-            "--mis",
-            min_interval_size,
-            "-a",
-            approved_interval_number,
-            "--mpi",
-            max_process_intervals,
-            "--mcr",
-            min_read_length_after_chop,
-            "--ocq",
-            output_chopped_seqs,
-            "--ct",
-            chop_type,
-            "-o",
-            output_prefix,
-            "-m",
-            max_batch_size,
-        ],
-    ]
+    command = f"deepchopper-chop {predict_files} --fq {fq} -t {threads} -s {smooth_window_size} --mis {min_interval_size} -a {approved_interval_number} --mpi {max_process_intervals} --mcr {min_read_length_after_chop} --ct {chop_type} "
+
+    if output_chopped_seqs:
+        command += "--ocq "
+
+    if output_prefix is not None:
+        command += f"-o {output_prefix} "
+
+    if max_batch_size is not None:
+        command += f"-m {max_batch_size} "
 
     try:
-        subprocess.run(commands, check=True)
+        subprocess.run(command.split(), check=True)
     except subprocess.CalledProcessError as e:
         logging.error(f"Error: Chopping failed with exit code {e.returncode}")
         raise e