Fix/warnings

requirements.txt

@@ -3,12 +3,17 @@ dask-image>=0.5.0
 dask[array]>=2021.02.0
 docrep>=0.3.1
 leidenalg>=0.8.2
+networkx>=2.6.0
 numba>=0.52.0
+numpy>=1.18.0
 omnipath>=1.0.5
 pandas>=1.2.0
+Pillow
 scanpy>=1.8.0
 scikit-image>=0.17.1
+scikit-learn>=0.24.0
 statsmodels>=0.12.0
+tifffile
 tqdm>=4.50.2
 typing_extensions
 xarray>=0.16.1

setup.py

@@ -44,7 +44,7 @@
             for l in (Path("docs") / "requirements.txt").read_text("utf-8").splitlines()
             if not l.startswith("-r")
         ],
-        interactive=["PyQt5>=5.15.0", "napari>=0.4.15,<0.5"],
+        interactive=["PyQt5>=5.15.0", "napari>=0.4.14,<0.5"],
     ),
     classifiers=[
         "Development Status :: 5 - Production/Stable",

squidpy/gr/_ligrec.py

@@ -457,7 +457,7 @@ def _trim_data(self) -> None:
             assert isinstance(self.interactions, pd.DataFrame)
 
         logg.debug("DEBUG: Removing genes not in any interaction")
-        self._filtered_data = self._data.loc[:, set(self.interactions[SOURCE]) | set(self.interactions[TARGET])]
+        self._filtered_data = self._data.loc[:, list(set(self.interactions[SOURCE]) | set(self.interactions[TARGET]))]
 
     def _filter_interactions_by_genes(self) -> None:
         """Subset :attr:`interactions` to only those for which we have the data."""

squidpy/gr/_nhood.py

@@ -244,7 +244,7 @@ def centrality_scores(
 
     centralities = [Centrality(c) for c in centrality]
 
-    graph = nx.from_scipy_sparse_matrix(adata.obsp[connectivity_key])
+    graph = nx.Graph(adata.obsp[connectivity_key])
 
     cat = adata.obs[cluster_key].cat.categories.values
     clusters = adata.obs[cluster_key].values

squidpy/im/_container.py

@@ -576,9 +576,8 @@ def _post_process(
         **_: Any,
     ) -> xr.Dataset:
         def _rescale(arr: xr.DataArray) -> xr.DataArray:
-            # TODO(michalk8): in skimage==0.19.0, multichannel is deprecated
             scaling_fn = partial(
-                rescale, scale=[scale, scale, 1], preserve_range=True, order=1, multichannel=True, cval=cval
+                rescale, scale=[scale, scale, 1], preserve_range=True, order=1, channel_axis=-1, cval=cval
             )
             dtype = arr.dtype
 
@@ -1151,7 +1150,8 @@ def apply(
         kwargs
             Keyword arguments for :func:`dask.array.map_overlap` or :func:`dask.array.map_blocks`, depending whether
             ``depth`` is present in ``fn_kwargs``. Only used when ``chunks != None``.
-            Use ``depth`` to control boundary artifacts if ``func`` requires data from neighboring chunks.
+            Use ``depth`` to control boundary artifacts if ``func`` requires data from neighboring chunks,
+            by default, ``boundary = 'reflect`` is used.
 
         Returns
         -------
@@ -1173,6 +1173,9 @@ def apply_func(func: Callable[..., NDArrayA], arr: xr.DataArray) -> NDArrayA | d
                 else da.map_blocks(func, arr, **fn_kwargs, **kwargs, dtype=arr.dtype)
             )
 
+        if "depth" in kwargs:
+            kwargs.setdefault("boundary", "reflect")
+
         layer = self._get_layer(layer)
         if new_layer is None:
             new_layer = layer

squidpy/im/_feature_mixin.py

@@ -7,7 +7,7 @@
 import xarray as xr
 
 from skimage.util import img_as_ubyte
-from skimage.feature import greycoprops, greycomatrix
+from skimage.feature import graycoprops, graycomatrix
 import skimage.measure
 
 from squidpy._docs import d
@@ -196,12 +196,12 @@ def features_texture(
         """
         Calculate texture features.
 
-        A grey level co-occurrence matrix (`GLCM <https://en.wikipedia.org/wiki/Co-occurrence_matrix>`_) is computed
+        A gray level co-occurrence matrix (`GLCM <https://en.wikipedia.org/wiki/Co-occurrence_matrix>`_) is computed
         for different combinations of distance and angle.
 
         The distance defines the pixel difference of co-occurrence. The angle define the direction along which
-        we check for co-occurrence. The GLCM includes the number of times that grey-level :math:`j` occurs at a distance
-        :math:`d` and at an angle theta from grey-level :math:`i`.
+        we check for co-occurrence. The GLCM includes the number of times that gray-level :math:`j` occurs at a distance
+        :math:`d` and at an angle theta from gray-level :math:`i`.
 
         Parameters
         ----------
@@ -210,11 +210,11 @@ def features_texture(
         %(feature_name)s
         %(channels)s
         props
-            Texture features that are calculated, see the `prop` argument in :func:`skimage.feature.greycoprops`.
+            Texture features that are calculated, see the `prop` argument in :func:`skimage.feature.graycoprops`.
         distances
-            The `distances` argument in :func:`skimage.feature.greycomatrix`.
+            The `distances` argument in :func:`skimage.feature.graycomatrix`.
         angles
-            The `angles` argument in :func:`skimage.feature.greycomatrix`.
+            The `angles` argument in :func:`skimage.feature.graycomatrix`.
 
         Returns
         -------
@@ -244,9 +244,9 @@ def features_texture(
 
         features = {}
         for c in channels:
-            comatrix = greycomatrix(arr[..., c], distances=distances, angles=angles, levels=256)
+            comatrix = graycomatrix(arr[..., c], distances=distances, angles=angles, levels=256)
             for p in props:
-                tmp_features = greycoprops(comatrix, prop=p)
+                tmp_features = graycoprops(comatrix, prop=p)
                 for d_idx, dist in enumerate(distances):
                     for a_idx, a in enumerate(angles):
                         features[f"{feature_name}_ch-{c}_{p}_dist-{dist}_angle-{a:.2f}"] = tmp_features[d_idx, a_idx]

squidpy/im/_process.py

@@ -7,7 +7,7 @@
 from scanpy import logging as logg
 
 from dask import delayed
-from scipy.ndimage.filters import gaussian_filter as scipy_gf
+from scipy.ndimage import gaussian_filter as scipy_gf
 import numpy as np
 import dask.array as da
 

squidpy/pl/_graph.py

@@ -173,7 +173,7 @@ def interaction_matrix(
     _assert_categorical_obs(adata, key=cluster_key)
     array = _get_data(adata, cluster_key=cluster_key, func_name="interaction_matrix")
 
-    ad = AnnData(X=array, obs={cluster_key: pd.Categorical(adata.obs[cluster_key].cat.categories)})
+    ad = AnnData(X=array, obs={cluster_key: pd.Categorical(adata.obs[cluster_key].cat.categories)}, dtype=array.dtype)
     _maybe_set_colors(source=adata, target=ad, key=cluster_key, palette=palette)
     if title is None:
         title = "Interaction matrix"
@@ -236,7 +236,7 @@ def nhood_enrichment(
     _assert_categorical_obs(adata, key=cluster_key)
     array = _get_data(adata, cluster_key=cluster_key, func_name="nhood_enrichment")[mode]
 
-    ad = AnnData(X=array, obs={cluster_key: pd.Categorical(adata.obs[cluster_key].cat.categories)})
+    ad = AnnData(X=array, obs={cluster_key: pd.Categorical(adata.obs[cluster_key].cat.categories)}, dtype=array.dtype)
     _maybe_set_colors(source=adata, target=ad, key=cluster_key, palette=palette)
     if title is None:
         title = "Neighborhood enrichment"

squidpy/pl/_ligrec.py

@@ -311,7 +311,7 @@ def get_dendrogram(adata: AnnData, linkage: str = "complete") -> Mapping[str, An
     var = pd.DataFrame(pvals.columns)
     var = var.set_index(var.columns[0])
 
-    adata = AnnData(pvals.values, obs={"groups": pd.Categorical(pvals.index)}, var=var)
+    adata = AnnData(pvals.values, obs={"groups": pd.Categorical(pvals.index)}, var=var, dtype=pvals.values.dtype)
     adata.obs_names = pvals.index
     minn = np.nanmin(adata.X)
     delta = np.nanmax(adata.X) - minn

tests/conftest.py

@@ -63,7 +63,7 @@ def nhood_data(adata: AnnData) -> AnnData:
 @pytest.fixture()
 def dummy_adata() -> AnnData:
     r = np.random.RandomState(100)
-    adata = AnnData(r.rand(200, 100), obs={"cluster": r.randint(0, 3, 200)})
+    adata = AnnData(r.rand(200, 100), obs={"cluster": r.randint(0, 3, 200)}, dtype=float)
 
     adata.obsm[Key.obsm.spatial] = np.stack([r.randint(0, 500, 200), r.randint(0, 500, 200)], axis=1)
     sp.gr.spatial_neighbors(adata, spatial_key=Key.obsm.spatial, n_rings=2)
@@ -88,6 +88,7 @@ def adata_intmat() -> AnnData:
         np.zeros((5, 5)),
         obs={"cat": pd.Categorical.from_codes([0, 0, 0, 1, 1], ("a", "b"))},
         obsp={"spatial_connectivities": graph},
+        dtype=float,
     )
 
 
@@ -108,7 +109,7 @@ def adata_squaregrid() -> AnnData:
     coord = rng.integers(0, 10, size=(400, 2))
     coord = np.unique(coord, axis=0)
     counts = rng.integers(0, 10, size=(coord.shape[0], 10))
-    adata = AnnData(counts)
+    adata = AnnData(counts, dtype=counts.dtype)
     adata.obsm["spatial"] = coord
     sc.pp.scale(adata)
     return adata
@@ -275,7 +276,7 @@ def visium_adata():
             [4400, 7729],
         ]
     )
-    adata = AnnData(X=np.ones((visium_coords.shape[0], 3)))
+    adata = AnnData(X=np.ones((visium_coords.shape[0], 3)), dtype=float)
     adata.obsm[Key.obsm.spatial] = visium_coords
     adata.uns[Key.uns.spatial] = {}
     return adata
@@ -284,7 +285,7 @@ def visium_adata():
 @pytest.fixture()
 def non_visium_adata():
     non_visium_coords = np.array([[1, 0], [3, 0], [5, 6], [0, 4]])
-    adata = AnnData(X=non_visium_coords)
+    adata = AnnData(X=non_visium_coords, dtype=int)
     adata.obsm[Key.obsm.spatial] = non_visium_coords
     return adata
 

tests/datasets/test_dataset.py

@@ -2,8 +2,9 @@
 from pathlib import Path
 from http.client import RemoteDisconnected
 import pytest
+import warnings
 
-from anndata import AnnData
+from anndata import AnnData, OldFormatWarning
 
 import squidpy as sq
 
@@ -17,21 +18,26 @@ def test_import(self, func):
         assert isinstance(fn, FunctionType)
 
 
+# TODO(michalk8): parse the code and xfail iff server issue
 class TestDatasetsDownload:
     def test_download_imc(self, tmp_path: Path):
-        try:
-            adata = sq.datasets.imc(tmp_path / "foo")
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", category=OldFormatWarning)
+            try:
+                adata = sq.datasets.imc(tmp_path / "foo")
 
-            assert isinstance(adata, AnnData)
-            assert adata.shape == (4668, 34)
-        except RemoteDisconnected as e:
-            pytest.skip(str(e))
+                assert isinstance(adata, AnnData)
+                assert adata.shape == (4668, 34)
+            except RemoteDisconnected as e:
+                pytest.xfail(str(e))
 
     def test_download_visium_hne_image_crop(self, tmp_path: Path):
-        try:
-            img = sq.datasets.visium_hne_image_crop(tmp_path / "foo")
-
-            assert isinstance(img, sq.im.ImageContainer)
-            assert img.shape == (3527, 3527)
-        except RemoteDisconnected as e:
-            pytest.skip(str(e))
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", category=OldFormatWarning)
+            try:
+                img = sq.datasets.visium_hne_image_crop(tmp_path / "foo")
+
+                assert isinstance(img, sq.im.ImageContainer)
+                assert img.shape == (3527, 3527)
+            except RemoteDisconnected as e:
+                pytest.xfail(str(e))

tests/image/test_container.py

@@ -1,11 +1,11 @@
 from typing import Any, Set, List, Tuple, Union, Optional, Sequence
-from imageio import imread, imsave
 from pathlib import Path
 from itertools import permutations
 from collections import defaultdict
 from html.parser import HTMLParser
 from pytest_mock import MockerFixture
 import pytest
+import imageio
 
 from anndata import AnnData
 import anndata as ad
@@ -60,7 +60,7 @@ def test_lazy_load(self, on_init: bool, tmpdir):
         img_orig = np.random.randint(low=0, high=255, size=(100, 100, 1), dtype=np.uint8)
         if on_init:
             fname = str(tmpdir / "tmp.tiff")
-            tifffile.imsave(fname, img_orig)
+            tifffile.imwrite(fname, img_orig)
             img = ImageContainer(fname, lazy=True)
         else:
             img = ImageContainer(da.from_array(img_orig), lazy=True)
@@ -143,9 +143,9 @@ def test_add_img_invalid_zdim(self, cont: ImageContainer):
     def test_load_ext(self, shape: Tuple[int, ...], ext: str, tmpdir):
         img_orig = np.random.randint(low=0, high=255, size=shape, dtype=np.uint8)
         fname = tmpdir / f"tmp.{ext}"
-        imsave(str(fname), img_orig)
+        imageio.imsave(str(fname), img_orig)
 
-        gt = imread(str(fname))  # because of compression, we load again
+        gt = imageio.imread(str(fname))  # because of compression, we load again
         cont = ImageContainer(str(fname))
 
         np.testing.assert_array_equal(cont["image"].values.squeeze(), gt.squeeze())
@@ -154,7 +154,7 @@ def test_load_ext(self, shape: Tuple[int, ...], ext: str, tmpdir):
     def test_load_tiff(self, shape: Tuple[int, ...], tmpdir):
         img_orig = np.random.randint(low=0, high=255, size=shape, dtype=np.uint8)
         fname = tmpdir / "tmp.tiff"
-        tifffile.imsave(fname, img_orig)
+        tifffile.imwrite(fname, img_orig)
 
         cont = ImageContainer(str(fname))
 
@@ -750,8 +750,7 @@ def func(arr: Union[np.ndarray, da.Array]):
             assert isinstance(res["image"].data, da.Array)
         else:
             assert isinstance(res["image"].data, np.ndarray)
-
-        assert not np.shares_memory(cont["image"].data, res["image"].data)
+            assert not np.shares_memory(cont["image"].data, res["image"].data)
 
     def test_apply_wrong_number_of_dim(self):
         def func(arr: np.ndarray) -> float:
@@ -863,12 +862,14 @@ def test_generate_spot_crops(self):
             np.zeros((len(crop_coords), 1)),
             uns={"spatial": {"1": {"scalefactors": {"spot_diameter_fullres": 5}}}},
             obsm={"spatial": crop_coords},
+            dtype=float,
         )
         # for library_id 2 (with larger scalefactor)
         adata2 = AnnData(
             np.zeros((len(crop_coords), 1)),
             uns={"spatial": {"2": {"scalefactors": {"spot_diameter_fullres": 7}}}},
             obsm={"spatial": crop_coords},
+            dtype=float,
         )
         # concatenate
         adata = ad.concat({"1": adata1, "2": adata2}, uns_merge="unique", label="library_id")

tests/image/test_io.py

@@ -14,10 +14,12 @@
 
 
 class TestIO:
-    def _create_image(self, path: str, shape: Tuple[int, ...]):
+    @staticmethod
+    def _create_image(path: str, shape: Tuple[int, ...]):
         dtype = np.uint8 if len(shape) <= 3 else np.float32
         img = np.random.randint(0, 255, size=shape).astype(dtype)
-        tifffile.imsave(path, img)
+        # set `photometric` to remove warnings
+        tifffile.imwrite(path, img, photometric=tifffile.TIFF.PHOTOMETRIC.MINISBLACK)
 
         return img
 

tox.ini

@@ -65,6 +65,10 @@ filterwarnings =
     ignore:Using or importing the ABCs from 'collections':DeprecationWarning
     ignore:Converting `np.inexact`:DeprecationWarning
     ignore:the matrix subclass is:PendingDeprecationWarning
+    ignore:Please use:DeprecationWarning:dask_image.*
+    ignore:Auto-removal of grids by:DeprecationWarning:
+    ignore:Support for passing numbers through unit converters:DeprecationWarning
+    ignore:distutils Version classes are deprecated:DeprecationWarning
 
 [coverage:run]
 branch = true