Merge pull request #744 from theislab/update/deprecate-python-3-9

Deprecation of Python 3.9

.github/workflows/test.yml

@@ -19,10 +19,10 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest]
-        python: ["3.9", "3.10"]
+        python: ["3.10", "3.11"]
         include:
           - os: macos-latest
-            python: "3.9"
+            python: "3.10"
 
     steps:
       - uses: actions/checkout@v3

docs/installation.rst

@@ -1,6 +1,6 @@
 Installation
 ============
-:mod:`moscot` requires Python version >= 3.9 to run.
+:mod:`moscot` requires Python version >= 3.10 to run.
 
 PyPI
 ----

pyproject.toml

@@ -7,7 +7,7 @@ name = "moscot"
 dynamic = ["version"]
 description = "Multi-omic single-cell optimal transport tools"
 readme = "README.rst"
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 license = {file = "LICENSE"}
 classifiers = [
     "Development Status :: 4 - Beta",
@@ -232,7 +232,7 @@ ignore_roles = [
 
 [tool.mypy]
 mypy_path = "$MYPY_CONFIG_FILE_DIR/src"
-python_version = "3.9"
+python_version = "3.10"
 plugins = "numpy.typing.mypy_plugin"
 
 ignore_errors = false
@@ -269,7 +269,7 @@ max_line_length = 120
 legacy_tox_ini = """
 [tox]
 min_version = 4.0
-env_list = lint-code,py{3.9,3.10,3.11}
+env_list = lint-code,py{3.10,3.11,3.12}
 skip_missing_interpreters = true
 
 [testenv]

src/moscot/__init__.py

@@ -4,9 +4,9 @@
 
 try:
     md = metadata.metadata(__name__)
-    __version__ = md.get("version", "")
-    __author__ = md.get("Author", "")
-    __maintainer__ = md.get("Maintainer-email", "")
+    __version__ = md.get("version", "")  # type: ignore[attr-defined]
+    __author__ = md.get("Author", "")  # type: ignore[attr-defined]
+    __maintainer__ = md.get("Maintainer-email", "")  # type: ignore[attr-defined]
 except ImportError:
     md = None
 

src/moscot/_types.py

@@ -10,8 +10,8 @@
 
     ArrayLike = NDArray[np.float64]
 except (ImportError, TypeError):
-    ArrayLike = np.ndarray
-    DTypeLike = np.dtype
+    ArrayLike = np.ndarray  # type: ignore[misc]
+    DTypeLike = np.dtype  # type: ignore[misc]
 
 ProblemKind_t = Literal["linear", "quadratic", "unknown"]
 Numeric_t = Union[int, float]  # type of `time_key` arguments

src/moscot/backends/ott/_utils.py

@@ -108,7 +108,7 @@ def densify(arr: ArrayLike) -> jax.Array:
     dense :mod:`jax` array.
     """
     if sp.issparse(arr):
-        arr = arr.toarray()
+        arr = arr.toarray()  # type: ignore[attr-defined]
     elif isinstance(arr, jesp.BCOO):
         arr = arr.todense()
     return jnp.asarray(arr)

src/moscot/backends/ott/output.py

@@ -375,8 +375,8 @@ def project_to_transport_matrix(  # type:ignore[override]
         The projected transport matrix.
         """
         src_cells, tgt_cells = jnp.asarray(src_cells), jnp.asarray(tgt_cells)
-        push: Callable[[Any], Any] = self.push if condition is None else lambda x: self.push(x, condition)  # type: ignore
-        pull: Callable[[Any], Any] = self.pull if condition is None else lambda x: self.pull(x, condition)  # type: ignore
+        push = self.push if condition is None else lambda x: self.push(x, condition)
+        pull = self.pull if condition is None else lambda x: self.pull(x, condition)
         func, src_dist, tgt_dist = (push, src_cells, tgt_cells) if forward else (pull, tgt_cells, src_cells)
         return self._project_transport_matrix(
             src_dist=src_dist,

src/moscot/backends/utils.py

@@ -42,7 +42,8 @@ def register_solver(
     return _REGISTRY.register(backend)  # type: ignore[return-value]
 
 
-@register_solver("ott")
+# TODO(@MUCDK) fix mypy error
+@register_solver("ott")  # type: ignore[arg-type]
 def _(
     problem_kind: Literal["linear", "quadratic"],
     solver_name: Optional[Literal["GENOTLinSolver"]] = None,

src/moscot/base/cost.py

@@ -53,12 +53,12 @@ def __call__(self, *args: Any, **kwargs: Any) -> ArrayLike:
         """
         cost = self._compute(*args, **kwargs)
         if np.any(np.isnan(cost)):
-            maxx = np.nanmax(cost)  # type: ignore[var-annotated]
+            maxx = np.nanmax(cost)
             logger.warning(
                 f"Cost matrix contains `{np.sum(np.isnan(cost))}` NaN values, "
                 f"setting them to the maximum value `{maxx}`."
             )
-            cost = np.nan_to_num(cost, nan=maxx)
+            cost = np.nan_to_num(cost, nan=maxx)  # type: ignore[call-overload]
         if np.any(cost < 0):
             raise ValueError(f"Cost matrix contains `{np.sum(cost < 0)}` negative values.")
         return cost

src/moscot/base/problems/_mixins.py

@@ -388,7 +388,7 @@ def _sample_from_tmap(
         account_for_unbalancedness: bool = False,
         interpolation_parameter: Optional[Numeric_t] = None,
         seed: Optional[int] = None,
-    ) -> tuple[Any, list[str]]:
+    ) -> tuple[list[Any], list[ArrayLike]]:
         rng = np.random.RandomState(seed)
         if account_for_unbalancedness and interpolation_parameter is None:
             raise ValueError("When accounting for unbalancedness, interpolation parameter must be provided.")
@@ -453,7 +453,7 @@ def _sample_from_tmap(
                 for i in range(len(rows_batch))
             ]
             all_cols_sampled.extend(cols_sampled)
-        return rows, all_cols_sampled
+        return rows, all_cols_sampled  # type: ignore[return-value]
 
     def _interpolate_transport(
         self: AnalysisMixinProtocol[K, B],

src/moscot/base/problems/_utils.py

@@ -386,7 +386,7 @@ def perm_test_extractor(res: Sequence[Tuple[ArrayLike, ArrayLike]]) -> Tuple[Arr
         corr_bs = np.concatenate(corr_bs, axis=0)
         corr_ci_low, corr_ci_high = np.quantile(corr_bs, q=ql, axis=0), np.quantile(corr_bs, q=qh, axis=0)
 
-        return pvals, corr_ci_low, corr_ci_high
+        return pvals, corr_ci_low, corr_ci_high  # type:ignore[return-value]
 
     if not (0 <= confidence_level <= 1):
         raise ValueError(f"Expected `confidence_level` to be in interval `[0, 1]`, found `{confidence_level}`.")

src/moscot/base/problems/problem.py

@@ -183,7 +183,7 @@ def _split_mass(arr: ArrayLike) -> ArrayLike:
                 if start >= adata.n_obs:
                     raise IndexError(f"Expected starting index to be smaller than `{adata.n_obs}`, found `{start}`.")
                 data = np.zeros((adata.n_obs,), dtype=float)
-                data[range(start, min(start + offset, adata.n_obs))] = 1.0  # type: ignore[index]
+                data[range(start, min(start + offset, adata.n_obs))] = 1.0
             else:
                 raise TypeError(f"Unable to interpret subset of type `{type(subset)}`.")
         elif not hasattr(data, "shape"):

src/moscot/costs/_costs.py

@@ -138,7 +138,7 @@ def _scaled_hamming_dist(x: ArrayLike, y: ArrayLike) -> float:
         raise ValueError("No shared indices.")
     b2 = y[shared_indices]
 
-    differences: ArrayLike = b1 != b2
-    double_scars: ArrayLike = differences & (b1 != 0) & (b2 != 0)
+    differences = b1 != b2
+    double_scars = differences & (b1 != 0) & (b2 != 0)
 
-    return float(float(np.sum(differences)) + np.sum(double_scars)) / len(b1)
+    return (np.sum(differences) + np.sum(double_scars)) / len(b1)

src/moscot/plotting/_utils.py

@@ -463,7 +463,7 @@ def _plot_scatter(
                 _ = kwargs.pop("palette", None)
                 if (time_points[i] == source and push) or (time_points[i] == target and not push):
                     st = f"not in {time_points[i]}"
-                    vmin, vmax = np.nanmin(tmp[mask]), np.nanmax(tmp[mask])  # type: ignore[var-annotated]
+                    vmin, vmax = np.nanmin(tmp[mask]), np.nanmax(tmp[mask])
                     column = pd.Series(tmp).fillna(st).astype("category")
                     # TODO(michalk8): check
                     if len(np.unique(column[mask.values].values)) > 2:

src/moscot/problems/generic/_generic.py

@@ -37,7 +37,7 @@ def set_quad_defaults(z: Optional[Union[str, Mapping[str, Any]]]) -> Dict[str, s
     raise TypeError("`x_attr` and `y_attr` must be of type `str` or `dict` if no callback is provided.")
 
 
-class SinkhornProblem(GenericAnalysisMixin[K, B], CompoundProblem[K, B]):
+class SinkhornProblem(GenericAnalysisMixin[K, B], CompoundProblem[K, B]):  # type: ignore[misc]
     """Class for solving a :term:`linear problem`.
 
     Parameters
@@ -264,7 +264,7 @@ def _valid_policies(self) -> Tuple[Policy_t, ...]:
         return _constants.SEQUENTIAL, _constants.EXPLICIT, _constants.STAR  # type: ignore[return-value]
 
 
-class GWProblem(GenericAnalysisMixin[K, B], CompoundProblem[K, B]):
+class GWProblem(GenericAnalysisMixin[K, B], CompoundProblem[K, B]):  # type: ignore[misc]
     """Class for solving the :term:`GW <Gromov-Wasserstein>` or :term:`FGW <fused Gromov-Wasserstein>` problems.
 
     Parameters

src/moscot/problems/space/_mapping.py

@@ -65,8 +65,8 @@ def _create_problem(
             adata_tgt=self.adata_sc,
             src_obs_mask=src_mask,
             tgt_obs_mask=None,
-            src_var_mask=self.filtered_vars,
-            tgt_var_mask=self.filtered_vars,
+            src_var_mask=self.filtered_vars,  # type: ignore[arg-type]
+            tgt_var_mask=self.filtered_vars,  # type: ignore[arg-type]
             src_key=src,
             tgt_key=tgt,
             **kwargs,

src/moscot/problems/space/_mixins.py

@@ -42,7 +42,7 @@ class SpatialAlignmentMixinProtocol(AnalysisMixinProtocol[K, B]):
     _spatial_key: Optional[str]
     batch_key: Optional[str]
 
-    def _subset_spatial(
+    def _subset_spatial(  # type:ignore[empty-body]
         self: "SpatialAlignmentMixinProtocol[K, B]",
         k: K,
         spatial_key: str,
@@ -780,13 +780,13 @@ def _compute_correspondence(
 
     def pdist(row_idx: ArrayLike, col_idx: float, feat: ArrayLike) -> Any:
         if len(row_idx) > 0:
-            return pairwise_distances(feat[row_idx, :], feat[[col_idx], :]).mean()
+            return pairwise_distances(feat[row_idx, :], feat[[col_idx], :]).mean()  # type: ignore[index]
         return np.nan
 
     # TODO(michalk8): vectorize using jax, this is just a for loop
     vpdist = np.vectorize(pdist, excluded=["feat"])
     if sp.issparse(features):
-        features = features.toarray()
+        features = features.toarray()  # type: ignore[attr-defined]
 
     feat_arr, index_arr, support_arr = [], [], []
     for ind, i in enumerate(support):

src/moscot/problems/time/_lineage.py

@@ -23,7 +23,7 @@
 __all__ = ["TemporalProblem", "LineageProblem"]
 
 
-class TemporalProblem(
+class TemporalProblem(  # type: ignore[misc]
     TemporalMixin[Numeric_t, BirthDeathProblem], BirthDeathMixin, CompoundProblem[Numeric_t, BirthDeathProblem]
 ):
     """Class for analyzing time-series single cell data based on :cite:`schiebinger:19`.

src/moscot/problems/time/_mixins.py

@@ -587,7 +587,7 @@ def cell_costs_source(self: TemporalMixinProtocol[K, B]) -> Optional[pd.DataFram
         # TODO(michalk8): `[1]` will fail if potentials is None
         df_list = [
             pd.DataFrame(
-                np.asarray(problem.solution.potentials[0]),
+                np.asarray(problem.solution.potentials[0]),  # type: ignore[union-attr,index]
                 index=problem.adata_src.obs_names,
                 columns=cols,
             )
@@ -612,7 +612,7 @@ def cell_costs_target(self: TemporalMixinProtocol[K, B]) -> Optional[pd.DataFram
         # TODO(michalk8): `[1]` will fail if potentials is None
         df_list = [
             pd.DataFrame(
-                np.array(problem.solution.potentials[1]),
+                np.array(problem.solution.potentials[1]),  # type: ignore[union-attr,index]
                 index=problem.adata_tgt.obs_names,
                 columns=cols,
             )
@@ -664,7 +664,7 @@ def _get_data(
         else:
             raise ValueError(f"No data found for `{target}` time point.")
 
-        return (
+        return (  # type:ignore[return-value]
             source_data,
             growth_rates_source,
             intermediate_data,