Bump python

.github/workflows/continuous_integration.yml

@@ -7,56 +7,11 @@ on:
   workflow_dispatch:
 
 jobs:
-  format:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v5
-
-      - name: Strip Notebook Output
-        run: uv tool run --from nbconvert jupyter-nbconvert --clear-output --inplace "main/COMO.ipynb"
-
-      - name: Format Python Imports
-        uses: astral-sh/ruff-action@v3
-        with:
-          args: "check --fix --select I"
-
-      - name: Format code
-        uses: astral-sh/ruff-action@v3
-        with:
-          args: "format"
-
-      - name: Format Notebook
-        uses: astral-sh/ruff-action@v3
-        with:
-          args: "format main/COMO.ipynb"
-
-      - name: Commit Changes
-        uses: stefanzweifel/git-auto-commit-action@v5
-        with:
-          commit_message: "style: format code, Jupyter Notebook(s), and Python imports with `ruff`"
-          file_pattern: "main/como/*.py"
-
-
-  lint:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      - name: Check Lint
-        uses: astral-sh/ruff-action@v3
-        with:
-          args: "check --no-fix --verbose"
-
   test:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ "3.10", "3.11", "3.12" ]
+        python-version: [ "3.11", "3.12", "3.13" ]
     steps:
       - name: Checkout
         uses: actions/checkout@v4

main/como/combine_distributions.py

@@ -189,11 +189,13 @@ def _combine_z_distribution_for_context(
         var_name="source",
         value_name="zscore",
     )
-    combined_df = pd.DataFrame({
-        "ensembl_gene_id": z_matrix["ensembl_gene_id"],
-        "zscore": combined_z_matrix,
-        "source": "combined",
-    })
+    combined_df = pd.DataFrame(
+        {
+            "ensembl_gene_id": z_matrix["ensembl_gene_id"],
+            "zscore": combined_z_matrix,
+            "source": "combined",
+        }
+    )
     stack_df = pd.concat([stack_df, combined_df])
     # graph_zscore_distribution(
     #     df=stack_df,

main/como/rnaseq_gen.py

@@ -477,12 +477,14 @@ def zfpkm_plot(results, *, output_png_filepath: Path, plot_xfloor: int = -4):
         scale_fitted = fitted * (max_fpkm / max_fitted)
 
         to_concat.append(
-            pd.DataFrame({
-                "sample_name": [name] * len(x),
-                "log2fpkm": x,
-                "fpkm_density": y,
-                "fitted_density_scaled": scale_fitted,
-            })
+            pd.DataFrame(
+                {
+                    "sample_name": [name] * len(x),
+                    "log2fpkm": x,
+                    "fpkm_density": y,
+                    "fitted_density_scaled": scale_fitted,
+                }
+            )
         )
     mega_df = pd.concat(to_concat, ignore_index=True)
     mega_df.columns = pd.Series(data=["sample_name", "log2fpkm", "fpkm_density", "fitted_density_scaled"])
@@ -1002,14 +1004,16 @@ async def rnaseq_gen(  # noqa: C901
         )
 
     metadata_df["fragment_length"] = metadata_df["fragment_length"].astype(np.float32)
-    metadata_df = metadata_df.groupby("sample_name", as_index=False).agg({
-        "sample_name": "first",
-        "fragment_length": "mean",
-        "layout": "first",
-        "strand": "first",
-        "study": "first",
-        "library_prep": "first",
-    })
+    metadata_df = metadata_df.groupby("sample_name", as_index=False).agg(
+        {
+            "sample_name": "first",
+            "fragment_length": "mean",
+            "layout": "first",
+            "strand": "first",
+            "study": "first",
+            "library_prep": "first",
+        }
+    )
     logger.debug(f"Starting '{context_name}'")
     await _process(
         context_name=context_name,

main/como/rnaseq_preprocess.py

@@ -65,7 +65,7 @@ async def build_from_tab(cls, filepath: Path) -> _STARinformation:
             num_ambiguous = [int(i) for i in ambiguous.removesuffix("\n").split("\t")[1:]]
 
         df: pd.DataFrame = await read_file(
-            fiepath,
+            filepath,
             h5ad_as_df=True,
             sep="\t",
             header=None,
@@ -349,9 +349,9 @@ async def _write_counts_matrix(
     Returns:
         A pandas DataFrame representing the final counts matrix.
     """
-    counts: list[pd.DataFrame] = await asyncio.gather(*[
-        _create_sample_counts_matrix(metric) for metric in _organize_gene_counts_files(data_dir=como_context_dir)
-    ])
+    counts: list[pd.DataFrame] = await asyncio.gather(
+        *[_create_sample_counts_matrix(metric) for metric in _organize_gene_counts_files(data_dir=como_context_dir)]
+    )
     rna_specific_sample_names = set(config_df.loc[config_df["library_prep"] == rna.value, "sample_name"].tolist())
 
     final_matrix: pd.DataFrame = functools.reduce(lambda left, right: pd.merge(left, right, on="ensembl_gene_id", how="outer"), counts)

tests/unit/test_fisher_stats.py

@@ -1,16 +1,30 @@
+import contextlib
+from pathlib import Path
+
 import cobra
 import numpy as np
 from como.stats.fisher_exact_test import FisherExactTest
 
 
 def test_fisher_stats():
-    reference_model = cobra.io.load_matlab_model("main/data/reference_models/GeneralModelUpdatedV3.mat")
-    scenario_model = cobra.io.read_sbml_model("tests/inputs/naiveB_model.xml")
+    with (
+        contextlib.redirect_stderr(Path("/dev/null").open("w", encoding="utf-8")),
+        contextlib.redirect_stdout(Path("/dev/null").open("w", encoding="utf-8")),
+    ):
+        reference_model = cobra.io.load_matlab_model("main/data/reference_models/GeneralModelUpdatedV3.mat")
+        scenario_model = cobra.io.read_sbml_model("tests/inputs/naiveB_model.xml")
     real = FisherExactTest.run(reference=reference_model, scenario=scenario_model, pathway="Glycolysis/gluconeogenesis")
+    expected = FisherExactTest(
+        pathway="Glycolysis/gluconeogenesis",
+        statistic=np.float64(4.321708185053381),
+        pvalue=np.float64(1.2883495211648955e-05),
+        a=32,
+        b=10,
+        c=4496,
+        d=6072,
+    )
+
+    assert real == expected
+
 
-    assert real.statistic == np.float64(4.321708185053381)
-    assert real.pvalue == np.float64(1.2883495211648955e-05)
-    assert real.a == 32
-    assert real.b == 10
-    assert real.c == 4496
-    assert real.d == 6082
+test_fisher_stats()