Merge pull request #49 from Noble-Lab/black-fmt

Reformatted files with black

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/psf/black
-    rev: stable # Replace by any tag/version: https://github.com/psf/black/tags
+    rev: 22.6.0 # Replace by any tag/version: https://github.com/psf/black/tags
     hooks:
       - id: black
         language_version: python3 # Should be a command that runs python3.6+

casanovo/casanovo.py

@@ -5,86 +5,133 @@
 import os
 from casanovo.denovo import train, evaluate, denovo
 
-#Required options
+# Required options
 @click.command()
-@click.option("--mode", required=True, default='eval', help="Choose on a high level what the program will do. \"train\" will train a model from scratch or continue training a pre-trained model. \"eval\" will evaluate de novo sequencing performance of a pre-trained model (peptide annotations are needed for spectra). \"denovo\" will run de novo sequencing without evaluation (specificy directory path for output csv file with de novo sequences).", type=click.Choice(['train', 'eval', 'denovo']))
-@click.option("--model_path", required=True, help="Specify path to pre-trained model weights (.ckpt file) for testing or to continue to train.", type=click.Path(exists=True, dir_okay=False, file_okay=True))
-#Base options
-@click.option("--train_data_path", help="Specify path to .mgf files to be used as training data", type=click.Path(exists=True, dir_okay=True, file_okay=False))
-@click.option("--val_data_path", help="Specify path to .mgf files to be used as validation data", type=click.Path(exists=True, dir_okay=True, file_okay=False))
-@click.option("--test_data_path", help="Specify path to .mgf files to be used as test data", type=click.Path(exists=True, dir_okay=True, file_okay=False))
-@click.option("--config_path", help="Specify path to custom config file which includes data and model related options. If not included, the default config.yaml will be used.", type=click.Path(exists=True, dir_okay=False, file_okay=True))
-@click.option("--output_path", help="Specify path to output de novo sequences. Output format is .csv", type=click.Path(exists=True, dir_okay=True, file_okay=False))
-#De Novo sequencing options
-@click.option("--preprocess_spec", default=None, help="True if spectra data should be preprocessed, False if using preprocessed data.", type=click.BOOL)
-@click.option("--num_workers", default=None, help="Number of workers to use for spectra reading.", type=click.INT)
-@click.option("--gpus", default=(), help="Specify gpus for usage. For multiple gpus, use format: --gpus=0 --gpus=1 --gpus=2... etc. etc.", type=click.INT, multiple=True)
-
+@click.option(
+    "--mode",
+    required=True,
+    default="eval",
+    help='Choose on a high level what the program will do. "train" will train a model from scratch or continue training a pre-trained model. "eval" will evaluate de novo sequencing performance of a pre-trained model (peptide annotations are needed for spectra). "denovo" will run de novo sequencing without evaluation (specificy directory path for output csv file with de novo sequences).',
+    type=click.Choice(["train", "eval", "denovo"]),
+)
+@click.option(
+    "--model_path",
+    required=True,
+    help="Specify path to pre-trained model weights (.ckpt file) for testing or to continue to train.",
+    type=click.Path(exists=True, dir_okay=False, file_okay=True),
+)
+# Base options
+@click.option(
+    "--train_data_path",
+    help="Specify path to .mgf files to be used as training data",
+    type=click.Path(exists=True, dir_okay=True, file_okay=False),
+)
+@click.option(
+    "--val_data_path",
+    help="Specify path to .mgf files to be used as validation data",
+    type=click.Path(exists=True, dir_okay=True, file_okay=False),
+)
+@click.option(
+    "--test_data_path",
+    help="Specify path to .mgf files to be used as test data",
+    type=click.Path(exists=True, dir_okay=True, file_okay=False),
+)
+@click.option(
+    "--config_path",
+    help="Specify path to custom config file which includes data and model related options. If not included, the default config.yaml will be used.",
+    type=click.Path(exists=True, dir_okay=False, file_okay=True),
+)
+@click.option(
+    "--output_path",
+    help="Specify path to output de novo sequences. Output format is .csv",
+    type=click.Path(exists=True, dir_okay=True, file_okay=False),
+)
+# De Novo sequencing options
+@click.option(
+    "--preprocess_spec",
+    default=None,
+    help="True if spectra data should be preprocessed, False if using preprocessed data.",
+    type=click.BOOL,
+)
+@click.option(
+    "--num_workers",
+    default=None,
+    help="Number of workers to use for spectra reading.",
+    type=click.INT,
+)
+@click.option(
+    "--gpus",
+    default=(),
+    help="Specify gpus for usage. For multiple gpus, use format: --gpus=0 --gpus=1 --gpus=2... etc. etc.",
+    type=click.INT,
+    multiple=True,
+)
 def main(
-    #Req + base vars
+    # Req + base vars
     mode,
     model_path,
     train_data_path,
     val_data_path,
     test_data_path,
     config_path,
     output_path,
-    #De Novo vars
+    # De Novo vars
     preprocess_spec,
     num_workers,
-    gpus
+    gpus,
 ):
     """
     The command line function for casanovo. De Novo Mass Spectrometry Peptide Sequencing with a Transformer Model.
-    
+
     \b
     Training option requirements:
     mode, model_path, train_data_path, val_data_path, config_path
-    
+
     \b
     Evaluation option requirements:
     mode, model_path, test_data_path, config_path
-    
+
     \b
     De Novo option requirements:
     mode, model_path, test_data_path, config_path, output_path
     """
     logging.basicConfig(
-    level=logging.INFO, 
-    format="%(levelname)s: %(message)s",
-)
+        level=logging.INFO,
+        format="%(levelname)s: %(message)s",
+    )
     if config_path == None:
-        abs_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config.yaml')
+        abs_path = os.path.join(
+            os.path.dirname(os.path.abspath(__file__)), "config.yaml"
+        )
         with open(abs_path) as f:
             config = yaml.safe_load(f)
     else:
         with open(config_path) as f:
             config = yaml.safe_load(f)
 
-    if(preprocess_spec != None): 
-        config['preprocess_spec'] = preprocess_spec
-    if(num_workers != None): 
-            config['num_workers'] = num_workers
-    if(gpus != ()):
-            config['gpus'] = gpus
-    if mode == 'train':
+    if preprocess_spec != None:
+        config["preprocess_spec"] = preprocess_spec
+    if num_workers != None:
+        config["num_workers"] = num_workers
+    if gpus != ():
+        config["gpus"] = gpus
+    if mode == "train":
 
-        logging.info('Training Casanovo...')
+        logging.info("Training Casanovo...")
         train(train_data_path, val_data_path, model_path, config)
-        
-    elif mode == 'eval':
-        
-        logging.info('Evaluating Casanovo...')
+
+    elif mode == "eval":
+
+        logging.info("Evaluating Casanovo...")
         evaluate(test_data_path, model_path, config)
 
-    elif mode == 'denovo':
-        
-        logging.info('De novo sequencing with Casanovo...')
-        denovo(test_data_path, model_path, config, output_path)  
-        
+    elif mode == "denovo":
+
+        logging.info("De novo sequencing with Casanovo...")
+        denovo(test_data_path, model_path, config, output_path)
+
     pass
 
 
 if __name__ == "__main__":
     main()
-

casanovo/data/__init__.py

@@ -2,4 +2,4 @@
 from .datasets import (
     SpectrumDataset,
     AnnotatedSpectrumDataset,
-)
+)