Fix incompatibility with latest pandas/scipy (#149)

* Fix incompatibility with latest pandas/scipy

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.gitignore

@@ -34,3 +34,6 @@ infercnv_env/
 
 # Node.js
 node_modules/
+
+# uv
+uv.lock

README.md

@@ -28,7 +28,7 @@ except in that they look similar, but not identical, to the results of InferCNV.
 
 Please refer to the [documentation][link-docs]. In particular, the
 
--   [API documentation][link-api].
+- [API documentation][link-api].
 
 ## Installation
 

docs/contributing.md

@@ -99,11 +99,11 @@ Specify `vX.X.X` as a tag name and create a release. For more information, see [
 
 Please write documentation for new or changed features and use-cases. This project uses [sphinx][] with the following features:
 
--   the [myst][] extension allows to write documentation in markdown/Markedly Structured Text
--   [Numpy-style docstrings][numpydoc] (through the [napoloen][numpydoc-napoleon] extension).
--   Jupyter notebooks as tutorials through [myst-nb][] (See [Tutorials with myst-nb](#tutorials-with-myst-nb-and-jupyter-notebooks))
--   [Sphinx autodoc typehints][], to automatically reference annotated input and output types
--   Citations (like {cite:p}`Virshup_2023`) can be included with [sphinxcontrib-bibtex](https://sphinxcontrib-bibtex.readthedocs.io/)
+- the [myst][] extension allows to write documentation in markdown/Markedly Structured Text
+- [Numpy-style docstrings][numpydoc] (through the [napoloen][numpydoc-napoleon] extension).
+- Jupyter notebooks as tutorials through [myst-nb][] (See [Tutorials with myst-nb](#tutorials-with-myst-nb-and-jupyter-notebooks))
+- [Sphinx autodoc typehints][], to automatically reference annotated input and output types
+- Citations (like {cite:p}`Virshup_2023`) can be included with [sphinxcontrib-bibtex](https://sphinxcontrib-bibtex.readthedocs.io/)
 
 See the [scanpy developer docs](https://scanpy.readthedocs.io/en/latest/dev/documentation.html) for more information
 on how to write documentation.
@@ -120,10 +120,10 @@ repository.
 
 #### Hints
 
--   If you refer to objects from other packages, please add an entry to `intersphinx_mapping` in `docs/conf.py`. Only
-    if you do so can sphinx automatically create a link to the external documentation.
--   If building the documentation fails because of a missing link that is outside your control, you can add an entry to
-    the `nitpick_ignore` list in `docs/conf.py`
+- If you refer to objects from other packages, please add an entry to `intersphinx_mapping` in `docs/conf.py`. Only
+  if you do so can sphinx automatically create a link to the external documentation.
+- If building the documentation fails because of a missing link that is outside your control, you can add an entry to
+  the `nitpick_ignore` list in `docs/conf.py`
 
 #### Building the docs locally
 

pyproject.toml

@@ -24,7 +24,7 @@ urls.Source = "https://github.com/icbi-lab/infercnvpy"
 urls.Home-page = "https://github.com/icbi-lab/infercnvpy"
 dependencies = [
     'anndata>=0.7.3',
-    'scanpy>=1.6.0',
+    "scanpy>=1.9",
     'pandas>=1',
     'numpy>=1.20', # includes type annotations
     'tqdm>=4.63.0', # fixes tqdm.auto
@@ -36,7 +36,6 @@ dependencies = [
     'pytest-benchmark',
     # for debug logging (referenced from the issue template)
     "session-info",
-    "ipython"
 ]
 
 [project.optional-dependencies]

src/infercnvpy/pl/_chromosome_heatmap.py

@@ -147,7 +147,7 @@ def chromosome_heatmap_summary(
     tmp_obs.index = tmp_obs.index.astype(str)
 
     def _get_group_mean(group):
-        group_mean = np.mean(adata.obsm[f"X_{use_rep}"][adata.obs[groupby] == group, :], axis=0)
+        group_mean = np.mean(adata.obsm[f"X_{use_rep}"][adata.obs[groupby].values == group, :], axis=0)
         if len(group_mean.shape) == 1:
             # derived from an array instead of sparse matrix -> 1 dim instead of 2
             group_mean = group_mean[np.newaxis, :]

src/infercnvpy/tl/_infercnv.py

@@ -98,7 +98,7 @@ def infercnv(
         raise ValueError("Ensure your var_names are unique!")
     if {"chromosome", "start", "end"} - set(adata.var.columns) != set():
         raise ValueError(
-            "Genomic positions not found. There need to be `chromosome`, `start`, and " "`end` columns in `adata.var`. "
+            "Genomic positions not found. There need to be `chromosome`, `start`, and `end` columns in `adata.var`. "
         )
 
     var_mask = adata.var["chromosome"].isnull()
@@ -388,7 +388,7 @@ def _get_reference(
                     f"{reference_cat[~reference_cat_in_obs]}"
                 )
 
-            reference = np.vstack([np.mean(adata.X[obs_col == cat, :], axis=0) for cat in reference_cat])
+            reference = np.vstack([np.mean(adata.X[obs_col.values == cat, :], axis=0) for cat in reference_cat])
 
     if reference.ndim == 1:
         reference = reference[np.newaxis, :]

src/infercnvpy/tl/_scores.py

@@ -63,7 +63,7 @@ def cnv_score(
     if groupby not in adata.obs.columns and groupby == "cnv_leiden":
         raise ValueError("`cnv_leiden` not found in `adata.obs`. Did you run `tl.leiden`?")
     cluster_score = {
-        cluster: np.mean(np.abs(adata.obsm[f"X_{use_rep}"][adata.obs[groupby] == cluster, :]))
+        cluster: np.mean(np.abs(adata.obsm[f"X_{use_rep}"][adata.obs[groupby].values == cluster, :]))
         for cluster in adata.obs[groupby].unique()
     }