formatting

Author: jkmckenna

src/smftools/cli/hmm_adata.py

@@ -3,9 +3,10 @@
 import copy
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Any, List, Optional, Sequence, Tuple, Union, TYPE_CHECKING
+from typing import TYPE_CHECKING, Any, List, Optional, Sequence, Tuple, Union
 
 import numpy as np
+
 from smftools.logging_utils import get_logger
 from smftools.optional_imports import require
 

src/smftools/hmm/HMM.py

@@ -3,7 +3,7 @@
 import ast
 import json
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, TYPE_CHECKING
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple, Union
 
 import numpy as np
 from scipy.sparse import issparse

src/smftools/informatics/bam_functions.py

@@ -10,8 +10,7 @@
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from itertools import zip_longest
 from pathlib import Path
-import shutil
-from typing import Any, Dict, Iterable, List, Optional, Tuple, Union, TYPE_CHECKING
+from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Union
 
 import numpy as np
 from tqdm import tqdm
@@ -76,9 +75,10 @@ def _resolve_samtools_backend(backend: str | None) -> str:
 
 def _has_bam_index(bam_path: Path) -> bool:
     """Return True if the BAM index exists alongside the BAM."""
-    return bam_path.with_suffix(bam_path.suffix + ".bai").exists() or Path(
-        str(bam_path) + ".bai"
-    ).exists()
+    return (
+        bam_path.with_suffix(bam_path.suffix + ".bai").exists()
+        or Path(str(bam_path) + ".bai").exists()
+    )
 
 
 def _ensure_bam_index(bam_path: Path, backend: str) -> None:
@@ -215,9 +215,7 @@ def _index_bam_with_pysam(bam_path: Union[str, Path], threads: Optional[int] = N
         pysam_mod.index(bam_path)
 
 
-def _bam_to_fastq_with_samtools(
-    bam_path: Union[str, Path], fastq_path: Union[str, Path]
-) -> None:
+def _bam_to_fastq_with_samtools(bam_path: Union[str, Path], fastq_path: Union[str, Path]) -> None:
     """Convert BAM to FASTQ using samtools."""
     if not shutil.which("samtools"):
         raise RuntimeError("samtools is required but not available in PATH.")
@@ -881,7 +879,8 @@ def _to_path_pair(x) -> Tuple[Path, Path]:
                 rg_fields = [f"ID:{bc}"]
                 if rg_sample_field:
                     rg_fields.append(f"SM:{rg_sample_field}")
-                header_lines.append(f"@RG\t{'\t'.join(rg_fields)}")
+                rg_body = "\t".join(rg_fields)
+                header_lines.append(f"@RG\t{rg_body}")
         header_lines.append("@PG\tID:concat-fastq\tPN:concatenate_fastqs_to_bam\tVN:1")
         bam_out_ctx.stdin.write("\n".join(header_lines) + "\n")
 
@@ -917,7 +916,9 @@ def _clean(n: Optional[str]) -> Optional[str]:
                 )
                 if name is None:
                     name = (
-                        _clean(getattr(rec2, "name", None) if backend_choice == "python" else rec2[0])
+                        _clean(
+                            getattr(rec2, "name", None) if backend_choice == "python" else rec2[0]
+                        )
                         if rec2 is not None
                         else None
                     )
@@ -1354,9 +1355,10 @@ def extract_readnames_from_bam(aligned_BAM, samtools_backend: str | None = "auto
 
     if backend_choice == "python":
         pysam_mod = _require_pysam()
-        with pysam_mod.AlignmentFile(aligned_BAM, "rb") as bam, open(
-            txt_output, "w", encoding="utf-8"
-        ) as output_file:
+        with (
+            pysam_mod.AlignmentFile(aligned_BAM, "rb") as bam,
+            open(txt_output, "w", encoding="utf-8") as output_file,
+        ):
             for read in bam:
                 output_file.write(f"{read.query_name}\n")
         return
@@ -1370,7 +1372,7 @@ def extract_readnames_from_bam(aligned_BAM, samtools_backend: str | None = "auto
             if not line.strip():
                 continue
             qname = line.split("\t", 1)[0]
-            output_file.write(f"{qname}\n")    
+            output_file.write(f"{qname}\n")
     rc = samtools_view.wait()
     if rc != 0:
         stderr = samtools_view.stderr.read() if samtools_view.stderr else ""

src/smftools/informatics/bed_functions.py

@@ -57,13 +57,17 @@ def _require_pysam() -> "pysam_types":
     return require("pysam", extra="pysam", purpose="FASTA indexing")
 
 
-def _resolve_backend(backend: str | None, *, tool: str, python_available: bool, cli_name: str) -> str:
+def _resolve_backend(
+    backend: str | None, *, tool: str, python_available: bool, cli_name: str
+) -> str:
     choice = (backend or "auto").strip().lower()
     if choice not in {"auto", "python", "cli"}:
         raise ValueError(f"{tool}_backend must be one of: auto, python, cli")
     if choice == "python":
         if not python_available:
-            raise RuntimeError(f"{tool}_backend=python requires the Python package to be installed.")
+            raise RuntimeError(
+                f"{tool}_backend=python requires the Python package to be installed."
+            )
         return "python"
     if choice == "cli":
         if not shutil.which(cli_name):
@@ -142,7 +146,10 @@ def _bed_to_bigwig(
 
     # 1) Compute coverage → bedGraph
     bedtools_choice = _resolve_backend(
-        bedtools_backend, tool="bedtools", python_available=pybedtools is not None, cli_name="bedtools"
+        bedtools_backend,
+        tool="bedtools",
+        python_available=pybedtools is not None,
+        cli_name="bedtools",
     )
     if bedtools_choice == "python":
         logger.debug(f"[pybedtools] generating coverage bedgraph from {bed}")
@@ -171,7 +178,10 @@ def _bed_to_bigwig(
 
     # 2) Convert bedGraph → BigWig via pyBigWig
     bigwig_choice = _resolve_backend(
-        bigwig_backend, tool="bigwig", python_available=pyBigWig is not None, cli_name="bedGraphToBigWig"
+        bigwig_backend,
+        tool="bigwig",
+        python_available=pyBigWig is not None,
+        cli_name="bedGraphToBigWig",
     )
     if bigwig_choice == "python":
         logger.debug(f"[pyBigWig] converting bedgraph → bigwig: {bigwig}")

src/smftools/informatics/converted_BAM_to_adata.py

@@ -7,11 +7,12 @@
 import traceback
 from multiprocessing import Manager, Pool, current_process
 from pathlib import Path
-from typing import Iterable, Optional, Union, TYPE_CHECKING
+from typing import TYPE_CHECKING, Iterable, Optional, Union
 
 import anndata as ad
 import numpy as np
 import pandas as pd
+
 from smftools.logging_utils import get_logger
 from smftools.optional_imports import require
 

src/smftools/informatics/fasta_functions.py

@@ -6,7 +6,7 @@
 from concurrent.futures import ProcessPoolExecutor
 from importlib.util import find_spec
 from pathlib import Path
-from typing import Dict, Iterable, Tuple, TYPE_CHECKING
+from typing import TYPE_CHECKING, Dict, Iterable, Tuple
 
 import numpy as np
 from Bio import SeqIO
@@ -29,6 +29,7 @@ def _require_pysam() -> "pysam_module":
         return pysam_types
     return require("pysam", extra="pysam", purpose="FASTA access")
 
+
 pysam_types = None
 if find_spec("pysam") is not None:
     pysam_types = require("pysam", extra="pysam", purpose="FASTA access")

src/smftools/machine_learning/training/train_lightning_model.py

@@ -4,7 +4,9 @@
 
 torch = require("torch", extra="ml-base", purpose="Lightning training")
 pytorch_lightning = require("pytorch_lightning", extra="ml-extended", purpose="Lightning training")
-pl_callbacks = require("pytorch_lightning.callbacks", extra="ml-extended", purpose="Lightning training")
+pl_callbacks = require(
+    "pytorch_lightning.callbacks", extra="ml-extended", purpose="Lightning training"
+)
 
 Trainer = pytorch_lightning.Trainer
 EarlyStopping = pl_callbacks.EarlyStopping

src/smftools/plotting/classifiers.py

@@ -9,6 +9,7 @@
 plt = require("matplotlib.pyplot", extra="plotting", purpose="model plots")
 torch = require("torch", extra="ml-base", purpose="model saliency plots")
 
+
 def plot_model_performance(metrics, save_path=None):
     """Plot ROC and precision-recall curves for model metrics.
 
@@ -356,6 +357,7 @@ def plot_model_curves_from_adata_with_frequency_grid(
     import os
 
     import numpy as np
+
     sklearn_metrics = require("sklearn.metrics", extra="ml-base", purpose="model curves")
     auc = sklearn_metrics.auc
     precision_recall_curve = sklearn_metrics.precision_recall_curve

src/smftools/plotting/position_stats.py

@@ -236,6 +236,7 @@ def plot_positionwise_matrix(
 
     import numpy as np
     import pandas as pd
+
     plt = require("matplotlib.pyplot", extra="plotting", purpose="position stats plots")
     sns = require("seaborn", extra="plotting", purpose="position stats plots")
 

src/smftools/plotting/qc_plotting.py

@@ -9,6 +9,7 @@
 
 plt = require("matplotlib.pyplot", extra="plotting", purpose="QC plots")
 
+
 def plot_read_qc_histograms(
     adata,
     outdir,