Remove unused errors (+ update pre-commit tools)

.pre-commit-config.yaml

@@ -1,18 +1,18 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v3.1.0
+    rev: v3.2.0
     hooks:
       - id: check-merge-conflict
       - id: debug-statements
       - id: mixed-line-ending
       - id: check-case-conflict
       - id: check-yaml
   - repo: https://github.com/timothycrosley/isort
-    rev: 5.1.1
+    rev: 5.5.3
     hooks:
       - id: isort
   - repo: https://github.com/python/black
-    rev: 19.10b0
+    rev: 20.8b1
     hooks:
       - id: black
         language_version: python3

sgkit/display.py

@@ -179,7 +179,7 @@ def display_genotypes(
     ds_abbr = truncate(
         ds_calls, max_sizes={"variants": max_variants, "samples": max_samples}
     )
-    df = ds_abbr.to_dataframe().unstack(level="ploidy")  # type: ignore[no-untyped-call]
+    df = ds_abbr.to_dataframe().unstack(level="ploidy")
 
     # Convert each genotype to a string representation
     def calls_to_str(r: pd.DataFrame) -> str:
@@ -193,5 +193,8 @@ def calls_to_str(r: pd.DataFrame) -> str:
     df = df.apply(calls_to_str, axis=1).unstack("samples")
 
     return GenotypeDisplay(
-        df, (ds.sizes["variants"], ds.sizes["samples"]), max_variants, max_samples,
+        df,
+        (ds.sizes["variants"], ds.sizes["samples"]),
+        max_variants,
+        max_samples,
     )

sgkit/stats/hwe.py

@@ -123,7 +123,9 @@ def hardy_weinberg_p_value_vec(
 
 
 def hardy_weinberg_test(
-    ds: Dataset, genotype_counts: Optional[Hashable] = None, merge: bool = True,
+    ds: Dataset,
+    genotype_counts: Optional[Hashable] = None,
+    merge: bool = True,
 ) -> Dataset:
     """Exact test for HWE as described in Wigginton et al. 2005 [1].
 

sgkit/stats/popgen.py

@@ -9,7 +9,8 @@
 
 
 def diversity(
-    ds: Dataset, allele_counts: Hashable = "variant_allele_count",
+    ds: Dataset,
+    allele_counts: Hashable = "variant_allele_count",
 ) -> DataArray:
     """Compute diversity from allele counts.
 
@@ -49,7 +50,9 @@ def diversity(
 
 
 def divergence(
-    ds1: Dataset, ds2: Dataset, allele_counts: Hashable = "variant_allele_count",
+    ds1: Dataset,
+    ds2: Dataset,
+    allele_counts: Hashable = "variant_allele_count",
 ) -> DataArray:
     """Compute divergence between two genotype call datasets.
 
@@ -87,7 +90,9 @@ def divergence(
 
 
 def Fst(
-    ds1: Dataset, ds2: Dataset, allele_counts: Hashable = "variant_allele_count",
+    ds1: Dataset,
+    ds2: Dataset,
+    allele_counts: Hashable = "variant_allele_count",
 ) -> DataArray:
     """Compute Fst between two genotype call datasets.
 
@@ -112,7 +117,8 @@ def Fst(
 
 
 def Tajimas_D(
-    ds: Dataset, allele_counts: Hashable = "variant_allele_count",
+    ds: Dataset,
+    allele_counts: Hashable = "variant_allele_count",
 ) -> DataArray:
     """Compute Tajimas' D for a genotype call dataset.
 

sgkit/stats/utils.py

@@ -43,7 +43,7 @@ def concat_2d(ds: Dataset, dims: Tuple[Hashable, Hashable]) -> DataArray:
             # Add concatenation axis
             arr = arr.expand_dims(dim=dims[1], axis=1)
         arrs.append(arr)
-    return xr.concat(arrs, dim=dims[1])  # type: ignore[no-any-return,no-untyped-call]
+    return xr.concat(arrs, dim=dims[1])
 
 
 def r2_score(YP: ArrayLike, YT: ArrayLike) -> ArrayLike:

sgkit/tests/test_association.py

@@ -132,7 +132,7 @@ def _get_statistics(
         )
         res = _sm_statistics(ds, i, add_intercept)
         df_pred.append(
-            dsr.to_dataframe()  # type: ignore[no-untyped-call]
+            dsr.to_dataframe()
             .rename(columns=lambda c: c.replace("variant_", ""))
             .iloc[i]
             .to_dict()
@@ -183,9 +183,9 @@ def run(traits: Sequence[str]) -> Dataset:
 
     traits = [f"trait_{i}" for i in range(ds.attrs["n_trait"])]
     # Run regressions on individual traits and concatenate resulting statistics
-    dfr_single = xr.concat([run([t]) for t in traits], dim="traits").to_dataframe()  # type: ignore[no-untyped-call]
+    dfr_single = xr.concat([run([t]) for t in traits], dim="traits").to_dataframe()
     # Run regressions on all traits simulatenously
-    dfr_multi: DataFrame = run(traits).to_dataframe()  # type: ignore[no-untyped-call]
+    dfr_multi: DataFrame = run(traits).to_dataframe()
     pd.testing.assert_frame_equal(dfr_single, dfr_multi)
 
 

sgkit/tests/test_hwe.py

@@ -137,7 +137,7 @@ def test_hwep_dataset__precomputed_counts(ds_neq: Dataset) -> None:
     ds = ds_neq
     ac = ds["call_genotype"].sum(dim="ploidy")
     cts = [1, 0, 2]  # arg order: hets, hom1, hom2
-    gtc = xr.concat([(ac == ct).sum(dim="samples") for ct in cts], dim="counts").T  # type: ignore[no-untyped-call]
+    gtc = xr.concat([(ac == ct).sum(dim="samples") for ct in cts], dim="counts").T
     ds = ds.assign(**{"variant_genotype_counts": gtc})
     p = hwep_test(ds, genotype_counts="variant_genotype_counts", merge=False)
     assert np.all(p < 1e-8)

sgkit/tests/test_regenie.py

@@ -187,7 +187,7 @@ def prepare_stage_3_sgkit_results(
     )
     dsr = dsr.merge(ds[["variant_id"]])
     dsr = dsr.assign(outcome=xr.DataArray(df_trait.columns, dims=("outcomes")))
-    df = dsr.to_dataframe().reset_index(drop=True)  # type: ignore[no-untyped-call]
+    df = dsr.to_dataframe().reset_index(drop=True)
     return df
 
 

sgkit/tests/test_stats_utils.py

@@ -76,7 +76,11 @@ def test_concat_2d__values(n: int) -> None:
     x, y = np.arange(n), np.arange(n * n).reshape(n, n)
     z = np.copy(y)
     ds = xr.Dataset(
-        dict(x=(("dim0"), x), y=(("dim0", "dim1"), y), z=(("dim1", "dim2"), z),)
+        dict(
+            x=(("dim0"), x),
+            y=(("dim0", "dim1"), y),
+            z=(("dim1", "dim2"), z),
+        )
     )
     actual = concat_2d(ds, dims=("dim0", "dim1"))
     expected = np.concatenate([x.reshape(-1, 1), y], axis=1)