Improved cli

src/capfinder/cli.py

@@ -8,21 +8,31 @@
 
 app = typer.Typer(
     help="capfinder: A Python package for decoding RNA cap types using an encoder-based deep learning model.",
-    add_completion=False,
+    add_completion=True,
     rich_markup_mode="rich",
 )
 
 
 @app.command()
-def fetch_cap_signal(
-    bam_filepath: Annotated[str, typer.Argument(help="Path to the BAM file")],
+def extract_cap_signal(
+    bam_filepath: Annotated[
+        str, typer.Option("--bam_filepath", "-b", help="Path to the BAM file")
+    ] = "",
     pod5_dir: Annotated[
-        str, typer.Argument(help="Path to directory containing POD5 files")
-    ],
-    reference: Annotated[str, typer.Argument(help="Reference Sequence (5' -> 3')")],
+        str,
+        typer.Option(
+            "--pod5_dir", "-p", help="Path to directory containing POD5 files"
+        ),
+    ] = "",
+    reference: Annotated[
+        str,
+        typer.Option("--reference", "-r", help="Reference Sequence (5' -> 3')"),
+    ] = "GCTTTCGTTCGTCTCCGGACTTATCGCACCACCTATCCATCATCAGTACTGT",
     cap_class: Annotated[
         int,
-        typer.Argument(
+        typer.Option(
+            "--cap_class",
+            "-c",
             help="""\n
     Integer-based class label for the RNA cap type. \n
     - 0 represents Cap_0 \n
@@ -33,24 +43,30 @@ def fetch_cap_signal(
     - 5 represents NAD Cap \n
     - 6 represents FAD Cap \n
     - -99 represents and unknown cap(s). \n
-    """
+    """,
         ),
-    ],
+    ] = -99,
     cap_n1_pos0: Annotated[
         int,
-        typer.Argument(
-            help="0-based index of 1st nucleotide (N1) of cap in the reference"
+        typer.Option(
+            "--cap_n1_pos0",
+            "-p",
+            help="0-based index of 1st nucleotide (N1) of cap in the reference",
         ),
-    ],
+    ] = 52,
     train_or_test: Annotated[
         str,
-        typer.Argument(
-            help="set to train or test depending on whether it is training or testing data"
+        typer.Option(
+            "--train_or_test",
+            "-t",
+            help="set to train or test depending on whether it is training or testing data",
         ),
-    ],
+    ] = "test",
     output_dir: Annotated[
         str,
-        typer.Argument(
+        typer.Option(
+            "--output_dir",
+            "-o",
             help=textwrap.dedent(
                 """
         Path to the output directory which will contain: \n
@@ -60,15 +76,14 @@ def fetch_cap_signal(
             └── (Optional) plots directory containing cap signal plots, if plot_signal is set to True.\n
             \u200B    ├── good_reads: Directory that contains the plots for the good reads.\n
             \u200B    ├── bad_reads: Directory that contains the plots for the bad reads.\n
-            \u200B    └── plotpaths.csv: CSV file containing the paths to the plots based on the read ID.\n
-    """
-            )
+            \u200B    └── plotpaths.csv: CSV file containing the paths to the plots based on the read ID.\n"""
+            ),
         ),
-    ],
+    ] = "",
     n_workers: Annotated[
         int,
         typer.Option(
-            "--n_workers", help="Number of CPUs to use for parallel processing"
+            "--n_workers", "-n", help="Number of CPUs to use for parallel processing"
         ),
     ] = 1,
     plot_signal: Annotated[
@@ -106,38 +121,56 @@ def fetch_cap_signal(
 
 
 @app.command()
-def perpare_train_dataset(
-    data_dir: Annotated[
+def make_train_dataset(
+    csv_dir: Annotated[
         str,
-        typer.Argument(
-            help="Directory containing all the cap signal data files (data__cap_x.csv)"
+        typer.Option(
+            "--csv_dir",
+            "-c",
+            help="Directory containing all the cap signal data files (data__cap_x.csv)",
         ),
-    ],
+    ] = "",
     save_dir: Annotated[
         str,
-        typer.Argument(
-            help="Directory where the processed data will be saved as csv files."
+        typer.Option(
+            "--save_dir",
+            "-s",
+            help="Directory where the processed data will be saved as csv files.",
         ),
-    ],
+    ] = "",
     target_length: Annotated[
         int,
-        typer.Argument(
-            help="Number of signal points in cap signal to consider. If the signal is shorter, it will be padded with zeros. If the signal is longer, it will be truncated."
+        typer.Option(
+            "--target_length",
+            "-t",
+            help="Number of signal points in cap signal to consider. If the signal is shorter, it will be padded with zeros. If the signal is longer, it will be truncated.",
         ),
-    ],
+    ] = 500,
     dtype: Annotated[
         str,
-        typer.Argument(
-            help="Data type to transform the dataset to Valid values are 'float16', 'float32', or 'float64'."
+        typer.Option(
+            "--dtype",
+            "-d",
+            help="Data type to transform the dataset to Valid values are 'float16', 'float32', or 'float64'.",
         ),
-    ],
+    ] = "float32",
     n_workers: Annotated[
         int,
-        typer.Argument(help="Number of CPUs to use for parallel processing"),
-    ],
+        typer.Option(
+            "--n_workers", "-n", help="Number of CPUs to use for parallel processing"
+        ),
+    ] = 1,
 ) -> None:
     """
     Prepares dataset for training the ML model.
+
+    Example command:
+    capfinder make-train-dataset \\
+        --csv_dir /path/to/csv_dir \\
+        --save_dir /path/to/save_dir \\
+        --target_length 500 \\
+        --dtype float32 \\
+        --n_workers 10
     """
     from typing import cast
 
@@ -154,7 +187,7 @@ def perpare_train_dataset(
         )
 
     train_etl(
-        data_dir=data_dir,
+        data_dir=csv_dir,
         save_dir=save_dir,
         target_length=target_length,
         dtype=dt,
@@ -165,8 +198,11 @@ def perpare_train_dataset(
 @app.command()
 def create_train_config(
     file_path: Annotated[
-        str, typer.Argument(help="File path to save the JSON configuration file")
-    ],
+        str,
+        typer.Option(
+            "--file_path", "-f", help="File path to save the JSON configuration file"
+        ),
+    ] = "",
 ) -> None:
     """Creats a dummy JSON configuration file at the specified path. Edit it to suit your needs."""
     config = {
@@ -208,12 +244,12 @@ def create_train_config(
 def train_model(
     config_file: Annotated[
         str,
-        typer.Argument(
-            help="""\n
-    Path to the JSON configuration file containing the parameters for the training pipeline. \n
-    """
+        typer.Option(
+            "--file_path",
+            "-f",
+            help="""Path to the JSON configuration file containing the parameters for the training pipeline.""",
         ),
-    ],
+    ] = "",
 ) -> None:
     """Trains the model using the parameters in the JSON configuration file."""
     from capfinder.training import run_training_pipeline