remove napari from install (#877)

* remove napari from install * fix tox * remove qt * update pre-commit * fix mypy * cleanup pre-commit * add napari warning and release note * fix pre-commit * fixes to docs * fix mypy * fix release note * update
scverse · Aug 30, 2024 · 89ea7d2 · 89ea7d2
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diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
@@ -92,7 +92,8 @@ jobs:
                   tox -vv
 
             - name: Upload coverage to Codecov
-              uses: codecov/codecov-action@v3.1.1
+              uses: codecov/codecov-action@v4
               with:
                   name: coverage
                   verbose: true
+                  token: ${{ secrets.CODECOV_TOKEN }} # required
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -6,59 +6,21 @@ default_stages:
     - push
 minimum_pre_commit_version: 2.9.3
 repos:
-    - repo: https://github.com/pre-commit/mirrors-mypy
-      rev: v1.11.0
-      hooks:
-          - id: mypy
-            additional_dependencies: [numpy, pandas, types-requests]
-            exclude: .scripts/ci/download_data.py|squidpy/datasets/_(dataset|image).py # See https://github.com/pre-commit/mirrors-mypy/issues/33
-    - repo: https://github.com/pre-commit/mirrors-prettier
-      rev: v4.0.0-alpha.8
-      hooks:
-          - id: prettier
-    - repo: https://github.com/pre-commit/pre-commit-hooks
-      rev: v4.6.0
-      hooks:
-          - id: detect-private-key
-          - id: check-merge-conflict
-          - id: check-ast
-          - id: check-symlinks
-          - id: check-added-large-files
-          - id: check-executables-have-shebangs
-          - id: fix-encoding-pragma
-            args: [--remove]
-          - id: end-of-file-fixer
-          - id: mixed-line-ending
-            args: [--fix=lf]
-          - id: trailing-whitespace
-            exclude: ^.bumpversion.cfg$
-          - id: name-tests-test
-            args: [--django]
-          - id: check-case-conflict
-          - id: check-docstring-first
-          - id: check-yaml
-          - id: check-toml
-          - id: requirements-txt-fixer
-    - repo: https://github.com/jumanjihouse/pre-commit-hooks
-      rev: 3.0.0
-      hooks:
-          - id: script-must-have-extension
-            name: Check executable files use .sh extension
-            types: [shell, executable]
     - repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.5.4
+      rev: v0.6.2
       hooks:
           - id: ruff
             types_or: [python, pyi, jupyter]
             args: [--fix, --exit-non-zero-on-fix]
           - id: ruff-format
             types_or: [python, pyi, jupyter]
-    - repo: https://github.com/pre-commit/pygrep-hooks
-      rev: v1.10.0
+    - repo: https://github.com/asottile/blacken-docs
+      rev: 1.18.0
+      hooks:
+          - id: blacken-docs
+    - repo: https://github.com/pre-commit/mirrors-mypy
+      rev: v1.11.1
       hooks:
-          - id: python-no-eval
-          - id: python-use-type-annotations
-          - id: python-check-blanket-noqa
-          - id: rst-backticks
-          - id: rst-directive-colons
-          - id: rst-inline-touching-normal
+          - id: mypy
+            additional_dependencies: [numpy, pandas, types-requests]
+            exclude: .scripts/ci/download_data.py|squidpy/datasets/_(dataset|image).py # See https://github.com/
diff --git a/.prettierignore b/.prettierignore
diff --git a/.scripts/ci/download_data.py b/.scripts/ci/download_data.py
@@ -30,9 +30,10 @@ def _maybe_download_data(func_name: str, path: Path) -> Any:
 
 
 def main(args: argparse.Namespace) -> None:
-    import squidpy as sq
     from anndata import AnnData
 
+    import squidpy as sq
+
     all_datasets = sq.datasets._dataset.__all__ + sq.datasets._image.__all__
     all_extensions = ["h5ad"] * len(sq.datasets._dataset.__all__) + ["tiff"] * len(sq.datasets._image.__all__)
 

diff --git a/docs/classes.rst b/docs/classes.rst
@@ -7,6 +7,6 @@ Classes
     :toctree: classes
 
     im.ImageContainer
-    pl.Interactive
     im.SegmentationWatershed
     im.SegmentationCustom
+    .. pl.Interactive
diff --git a/docs/index.rst b/docs/index.rst
@@ -14,6 +14,17 @@ tissue images if available.
     :align: center
     :target: https://doi.org/10.1038/s41592-021-01358-2
 
+.. warning::
+    🚨🚨🚨 **Warning!** 🚨🚨🚨
+
+    The original napari-plugin of Squidpy has been moved to `napari-spatialdata <https://github.com/scverse/napari-spatialdata>`_.
+
+    All the functionalities previously available are also implemented in the new plugin, which also has many additional new features.
+
+    You can find a rich set of `documentation and examples <https://spatialdata.scverse.org/projects/napari/en/latest/index.html>`_, and we suggest starting with this `tutorial <https://spatialdata.scverse.org/projects/napari/en/latest/notebooks/spatialdata.html>`_.
+
+    If you are new to SpatialData, we invite you to take a look at the documentation `here <https://spatialdata.scverse.org/en/latest/tutorials/notebooks/notebooks.html>`_.
+
 Manuscript
 ----------
 Please see our manuscript :cite:`palla:22` in **Nature Methods** to learn more.

diff --git a/docs/release/notes-1.6.1.rst b/docs/release/notes-1.6.1.rst
@@ -0,0 +1,9 @@
+Squidpy 1.6.1 (2024-08-23)
+==========================
+
+Maintenance
+-----------
+
+- The Squidpy `interactive` module has been removed. Please use the ``napari-spatialdata`` plugin instead. You can find it `here <https://spatialdata.scverse.org/projects/napari/en/latest/index.html>`__  `@giovp <https://github.com/giovp>`__
+  `#877 <https://github.com/scverse/squidpy/pull/877>`__
+
diff --git a/pyproject.toml b/pyproject.toml
@@ -76,10 +76,6 @@ dependencies = [
 ]
 
 [project.optional-dependencies]
-interactive = [
-    "napari[pyqt5]==0.4.15",
-    "pydantic==1.10.13"
-]
 dev = [
     "pre-commit>=3.0.0",
     "tox>=4.0.0",
@@ -214,10 +210,11 @@ required-imports = ["from __future__ import annotations"]
 "*/__init__.py" = ["D104", "F401"]
 "tests/*"= ["D"]
 "docs/*"= ["D","B"]
-"squidpy/pl/_ligrec.py"= ["D","B"]
-"squidpy/_constants/_pkg_constants.py"= ["D101","D102","D106"]
-"squidpy/_constants/_constants.py"= ["D101"]
-"squidpy/pl/_interactive/_widgets.py"= ["D"]
+"src/squidpy/pl/_ligrec.py"= ["D","B"]
+"src/squidpy/_constants/_pkg_constants.py"= ["D101","D102","D106"]
+"src/squidpy/_constants/_constants.py"= ["D101"]
+"src/squidpy/pl/_interactive/_widgets.py"= ["D"]
+"src/squidpy/pl/_interactive/interactive.py"= ["F","E"]
 ".scripts/ci/download_data.py"= ["D","B"]
 # "squidpy/*.py"= ["RST303"]
 

diff --git a/src/squidpy/_utils.py b/src/squidpy/_utils.py
@@ -37,20 +37,18 @@ def wrapper(*args: Any, **kw: Any) -> Any:
         return wrapper
 
 
-try:
-    from numpy.typing import NDArray
+from numpy.typing import NDArray
 
-    NDArrayA = NDArray[Any]
-except (ImportError, TypeError):
-    NDArray = np.ndarray  # type: ignore[misc]
-    NDArrayA = np.ndarray  # type: ignore[misc]
+NDArrayA = NDArray[Any]
 
 
 class SigQueue(Queue["Signal"] if TYPE_CHECKING else Queue):  # type: ignore[misc]
     """Signalling queue."""
 
 
-def _unique_order_preserving(iterable: Iterable[Hashable]) -> tuple[list[Hashable], set[Hashable]]:
+def _unique_order_preserving(
+    iterable: Iterable[Hashable],
+) -> tuple[list[Hashable], set[Hashable]]:
     """Remove items from an iterable while preserving the order."""
     seen: set[Hashable] = set()
     seen_add = seen.add
@@ -124,7 +122,12 @@ def parallelize(
     else:
         tqdm = None
 
-    def runner(iterable: Iterable[Any], *args: Any, queue: SigQueue | None = None, **kwargs: Any) -> list[Any]:
+    def runner(
+        iterable: Iterable[Any],
+        *args: Any,
+        queue: SigQueue | None = None,
+        **kwargs: Any,
+    ) -> list[Any]:
         result: list[Any] = []
 
         for it in iterable:
@@ -200,7 +203,10 @@ def wrapper(*args: Any, **kwargs: Any) -> Any:
         col_len = len(collection)
         step = int(np.ceil(len(collection) / n_split))
         collections = list(
-            filter(len, (collection[i * step : (i + 1) * step] for i in range(int(np.ceil(col_len / step)))))
+            filter(
+                len,
+                (collection[i * step : (i + 1) * step] for i in range(int(np.ceil(col_len / step)))),
+            )
         )
 
     if use_runner:
@@ -289,7 +295,9 @@ def decorator(func1: Callable[..., Any]) -> Callable[..., Any]:
             def new_func1(*args: Any, **kwargs: Any) -> Any:
                 warnings.simplefilter("always", DeprecationWarning)
                 warnings.warn(
-                    fmt1.format(name=func1.__name__, reason=reason), category=DeprecationWarning, stacklevel=2
+                    fmt1.format(name=func1.__name__, reason=reason),
+                    category=DeprecationWarning,
+                    stacklevel=2,
                 )
                 warnings.simplefilter("default", DeprecationWarning)
                 return func1(*args, **kwargs)
@@ -317,7 +325,11 @@ def new_func1(*args: Any, **kwargs: Any) -> Any:
         @functools.wraps(func2)
         def new_func2(*args: Any, **kwargs: Any) -> Any:
             warnings.simplefilter("always", DeprecationWarning)
-            warnings.warn(fmt2.format(name=func2.__name__), category=DeprecationWarning, stacklevel=2)
+            warnings.warn(
+                fmt2.format(name=func2.__name__),
+                category=DeprecationWarning,
+                stacklevel=2,
+            )
             warnings.simplefilter("default", DeprecationWarning)
             return func2(*args, **kwargs)
 

diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py
@@ -233,11 +233,11 @@ def spatial_neighbors(
 
     if library_key is not None:
         mats: list[tuple[spmatrix, spmatrix]] = []
-        ixs = []  # type: ignore[var-annotated]
+        ixs: list[int] = []
         for lib in libs:
             ixs.extend(np.where(adata.obs[library_key] == lib)[0])
             mats.append(_build_fun(adata[adata.obs[library_key] == lib]))
-        ixs = np.argsort(ixs)  # type: ignore[assignment] # invert
+        ixs = np.argsort(ixs)  # type: ignore[assignment]  # invert
         Adj = block_diag([m[0] for m in mats], format="csr")[ixs, :][:, ixs]
         Dst = block_diag([m[1] for m in mats], format="csr")[ixs, :][:, ixs]
     else:

diff --git a/src/squidpy/gr/_nhood.py b/src/squidpy/gr/_nhood.py
@@ -190,7 +190,15 @@ def nhood_enrichment(
         n_jobs=n_jobs,
         backend=backend,
         show_progress_bar=show_progress_bar,
-    )(callback=_test, indices=indices, indptr=indptr, int_clust=int_clust, libraries=libraries, n_cls=n_cls, seed=seed)
+    )(
+        callback=_test,
+        indices=indices,
+        indptr=indptr,
+        int_clust=int_clust,
+        libraries=libraries,
+        n_cls=n_cls,
+        seed=seed,
+    )
     zscore = (count - perms.mean(axis=0)) / perms.std(axis=0)
 
     if copy:
@@ -293,7 +301,13 @@ def centrality_scores(
 
     if copy:
         return df
-    _save_data(adata, attr="uns", key=Key.uns.centrality_scores(cluster_key), data=df, time=start)
+    _save_data(
+        adata,
+        attr="uns",
+        key=Key.uns.centrality_scores(cluster_key),
+        data=df,
+        time=start,
+    )
 
 
 @d.dedent
@@ -345,7 +359,7 @@ def interaction_matrix(
 
     g_data = g.data if weights else np.broadcast_to(1, shape=len(g.data))
     dtype = int if pd.api.types.is_bool_dtype(g.dtype) or pd.api.types.is_integer_dtype(g.dtype) else float
-    output = np.zeros((n_cats, n_cats), dtype=dtype)  # type: ignore[var-annotated]
+    output: NDArrayA = np.zeros((n_cats, n_cats), dtype=dtype)
 
     _interaction_matrix(g_data, g.indices, g.indptr, cats.cat.codes.to_numpy(), output)
 
@@ -360,7 +374,11 @@ def interaction_matrix(
 
 @njit
 def _interaction_matrix(
-    data: NDArrayA, indices: NDArrayA, indptr: NDArrayA, cats: NDArrayA, output: NDArrayA
+    data: NDArrayA,
+    indices: NDArrayA,
+    indptr: NDArrayA,
+    cats: NDArrayA,
+    output: NDArrayA,
 ) -> NDArrayA:
     indices_list = np.split(indices, indptr[1:-1])
     data_list = np.split(data, indptr[1:-1])

diff --git a/src/squidpy/gr/_ppatterns.py b/src/squidpy/gr/_ppatterns.py
@@ -164,7 +164,7 @@ def extract_obsm(adata: AnnData, ixs: int | Sequence[int] | None) -> tuple[NDArr
         if layer not in adata.obsm:
             raise KeyError(f"Key `{layer!r}` not found in `adata.obsm`.")
         if ixs is None:
-            ixs = np.arange(adata.obsm[layer].shape[1])  # type: ignore[assignment]
+            ixs = np.arange(adata.obsm[layer].shape[1])  # type:ignore[assignment]
         ixs = list(np.ravel([ixs]))
         return adata.obsm[layer][:, ixs].T, ixs
 
@@ -180,7 +180,11 @@ def extract_obsm(adata: AnnData, ixs: int | Sequence[int] | None) -> tuple[NDArr
     mode = SpatialAutocorr(mode)  # type: ignore[assignment]
     if TYPE_CHECKING:
         assert isinstance(mode, SpatialAutocorr)
-    params = {"mode": mode.s, "transformation": transformation, "two_tailed": two_tailed}
+    params = {
+        "mode": mode.s,
+        "transformation": transformation,
+        "two_tailed": two_tailed,
+    }
 
     if mode == SpatialAutocorr.MORAN:
         params["func"] = _morans_i
@@ -425,10 +429,10 @@ def co_occurrence(
     n_splits = max(min(n_splits, n_obs), 1)
 
     # split array and labels
-    spatial_splits = tuple(s for s in np.array_split(spatial, n_splits, axis=0) if len(s))  # type: ignore[arg-type]
-    labs_splits = tuple(s for s in np.array_split(labs, n_splits, axis=0) if len(s))  # type: ignore[arg-type]
+    spatial_splits = tuple(s for s in np.array_split(spatial, n_splits, axis=0) if len(s))  # type:ignore[arg-type]
+    labs_splits = tuple(s for s in np.array_split(labs, n_splits, axis=0) if len(s))  # type:ignore[arg-type]
     # create idx array including unique combinations and self-comparison
-    x, y = np.triu_indices_from(np.empty((n_splits, n_splits)))  # type: ignore[arg-type]
+    x, y = np.triu_indices_from(np.empty((n_splits, n_splits)))  # type:ignore[arg-type]
     idx_splits = list(zip(x, y))
 
     n_jobs = _get_n_cores(n_jobs)
@@ -457,7 +461,11 @@ def co_occurrence(
         return out, interval
 
     _save_data(
-        adata, attr="uns", key=Key.uns.co_occurrence(cluster_key), data={"occ": out, "interval": interval}, time=start
+        adata,
+        attr="uns",
+        key=Key.uns.co_occurrence(cluster_key),
+        data={"occ": out, "interval": interval},
+        time=start,
     )
 
 
@@ -570,7 +578,7 @@ def _g_moments(w: spmatrix | NDArrayA) -> tuple[float, float, float]:
 
     # s1
     t = w.transpose() + w
-    t2 = t.multiply(t)  # type: ignore[union-attr]
+    t2 = t.multiply(t)  # type:ignore[union-attr]
     s1 = t2.sum() / 2.0
 
     # s2

diff --git a/src/squidpy/gr/_sepal.py b/src/squidpy/gr/_sepal.py
@@ -182,8 +182,8 @@ def _score_helper(
 
     score, sparse = [], issparse(vals)
     for i in ixs:
-        conc = vals[:, i].toarray().flatten() if sparse else vals[:, i].copy()  # type: ignore[union-attr]
-        conc = vals[:, i].toarray().flatten() if sparse else vals[:, i].copy()  # type: ignore[union-attr]
+        conc = vals[:, i].toarray().flatten() if sparse else vals[:, i].copy()  # type:ignore[union-attr]
+        conc = vals[:, i].toarray().flatten() if sparse else vals[:, i].copy()  # type:ignore[union-attr]
         time_iter = _diffusion(conc, fun, n_iter, sat, sat_idx, unsat, unsat_idx, dt=dt, thresh=thresh)
         score.append(dt * time_iter)
 
@@ -286,7 +286,7 @@ def _entropy(
 ) -> float:
     """Get entropy of an array."""
     xnz = xx[xx > 0]
-    xs = np.sum(xnz)
+    xs: np.float64 = np.sum(xnz)
     xn = xnz / xs
     xl = np.log(xn)
     return float((-xl * xn).sum())