Feature/improve speed and limit memory (#11)

.github/workflows/tests.yml

@@ -2,9 +2,13 @@ name: tests
 
 on:
   push:
-    branches: [ main ]
+    branches:
+      - main
+      - develop
   pull_request:
-    branches: [ main ]
+    branches:
+      - main
+      - develop
 
 jobs:
   build:

.gitignore

@@ -109,3 +109,5 @@ venv.bak/
 # idea
 .idea/
 
+tests/integration_tests/run*
+

README.md

@@ -78,3 +78,4 @@ interpreter and affords greater flexibility:
 
 Check out our [documentation](https://mokapot.readthedocs.io) for more details
 and examples of mokapot in action.
+

mokapot/__init__.py

@@ -1,4 +1,5 @@
 """Initialize the mokapot package."""
+
 try:
     from importlib.metadata import version, PackageNotFoundError
 
@@ -15,11 +16,11 @@
     except DistributionNotFound:
         pass
 
-from .dataset import LinearPsmDataset
+from .dataset import LinearPsmDataset, OnDiskPsmDataset, read_file
 from .model import Model, PercolatorModel, save_model, load_model
 from .brew import brew
 from .parsers.pin import read_pin, read_percolator
 from .parsers.pepxml import read_pepxml
 from .parsers.fasta import read_fasta, make_decoys, digest
 from .writers import to_flashlfq, to_txt
-from .confidence import LinearConfidence, plot_qvalues
+from .confidence import LinearConfidence, plot_qvalues, assign_confidence

mokapot/aggregatePsmsToPeptides.py

@@ -0,0 +1,132 @@
+import os
+import sys
+import argparse
+import logging
+import warnings
+from pathlib import Path
+
+from . import __version__
+from .confidence import LinearConfidence
+from .dataset import OnDiskPsmDataset
+from .utils import get_unique_peptides_from_psms, merge_sort
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--targets_psms", type=str, required=True)
+    parser.add_argument("--decoys_psms", type=str, required=True)
+    parser.add_argument("--test_fdr", type=float, default=0.01)
+    parser.add_argument(
+        "--keep_decoys",
+        action="store_true",
+        default=False,
+    )
+    parser.add_argument("--dest_dir", type=str)
+    parser.add_argument(
+        "--verbosity", type=int, choices=[0, 1, 2, 3], default=2
+    )
+
+    args = parser.parse_args()
+
+    verbosity_dict = {
+        0: logging.ERROR,
+        1: logging.WARNING,
+        2: logging.INFO,
+        3: logging.DEBUG,
+    }
+
+    if verbosity_dict[args.verbosity] != logging.DEBUG:
+        warnings.filterwarnings("ignore")
+    logging.basicConfig(
+        format=("[{levelname}] {message}"),
+        style="{",
+        level=verbosity_dict[args.verbosity],
+    )
+
+    logging.info("mokapot version %s", str(__version__))
+    logging.info("Command issued:")
+    logging.info("%s", " ".join(sys.argv))
+    logging.info("")
+
+    psms = OnDiskPsmDataset(
+        target_column="Label",
+        peptide_column="peptide",
+        filename=None,
+        columns=None,
+        spectrum_columns=None,
+        protein_column=None,
+        group_column=None,
+        feature_columns=None,
+        metadata_columns=None,
+        filename_column=None,
+        scan_column=None,
+        specId_column=None,
+        calcmass_column=None,
+        expmass_column=None,
+        rt_column=None,
+        charge_column=None,
+        spectra_dataframe=None,
+    )
+
+    iterable = merge_sort(
+        paths=[args.decoys_psms, args.targets_psms],
+        target_column=psms.target_column,
+        col_score="score",
+    )
+    sep = "\t"
+    metadata_columns = ["PSMId", "Label", "peptide", "score", "proteinIds"]
+    output_columns = [
+        "PSMId",
+        "peptide",
+        "score",
+        "q-value",
+        "posterior_error_prob",
+        "proteinIds",
+    ]
+    peptides_path = "peptides.csv"
+    with open(peptides_path, "w") as f_peptide:
+        f_peptide.write(f"{sep.join(metadata_columns)}\n")
+    unique_peptides = get_unique_peptides_from_psms(
+        iterable=iterable,
+        peptide_col_index=2,
+        out_peptides=peptides_path,
+        sep=sep,
+    )
+    logging.info("\t- Found %i unique peptides.", unique_peptides)
+
+    out_targets, out_decoys = [
+        os.path.split(in_path)[-1].rsplit(".", 1)[0] + ".peptides"
+        for in_path in [args.targets_psms, args.decoys_psms]
+    ]
+    if args.dest_dir is not None:
+        Path(args.dest_dir).mkdir(exist_ok=True)
+        out_targets, out_decoys = [
+            os.path.join(args.dest_dir, out_path)
+            for out_path in [out_targets, out_decoys]
+        ]
+    with open(out_targets, "w") as fp:
+        fp.write(f"{sep.join(output_columns)}\n")
+    if args.keep_decoys:
+        with open(out_decoys, "w") as fp:
+            fp.write(f"{sep.join(output_columns)}\n")
+
+    LinearConfidence(
+        psms=psms,
+        levels=["peptide"],
+        level_paths=[peptides_path],
+        out_paths=[[out_targets, out_decoys]],
+        decoys=args.keep_decoys,
+        eval_fdr=args.test_fdr,
+        sep=sep,
+    )
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    except RuntimeError as e:
+        logging.error(f"[Error] {e}")
+        sys.exit(250)  # input failure
+    except ValueError as e:
+        logging.error(f"[Error] {e}")
+        sys.exit(250)  # input failure