Small edits

docs/installation.md

@@ -20,9 +20,10 @@ If you run into "RuntimeError: CMake must be installed to build qdldl" ensure th
 Run `conda install -c anaconda cmake` and `conda install -c conda-forge lightgbm` to do so.
 
 If you intend to run MedCAT you have to install a language model like:
-.. code-block:: console
 
-> \$ python -m spacy download en_core_web_sm
+```console
+$ python -m spacy download en_core_web_sm
+```
 
 ## From sources
 

docs/usage/usage.md

@@ -257,7 +257,7 @@ In contrast to a preprocessing function, a tool usually adds an easily interpret
 
 ## Plotting
 
-The plotting module ehrapy.pl.\* largely parallels the tl.\* and a few of the pp.\* functions.
+The plotting module `ehrapy.pl.\*` largely parallels the `tl.\*` and a few of the `pp.\*` functions.
 For most tools and for some preprocessing functions, you will find a plotting function with the same name.
 
 ### Generic

ehrapy/data/_datasets.py

@@ -116,7 +116,7 @@ def heart_failure(encoded: bool = False, columns_obs_only: dict[str, list[str]]
         columns_obs_only: Columns to include in obs only and not X.
 
     Returns:
-        :class:`~anndata.AnnData` object of the MIMIC-II dataset
+        :class:`~anndata.AnnData` object of the heart failure dataset
 
     Examples:
         >>> import ehrapy as ep

ehrapy/preprocessing/_encode.py

@@ -60,13 +60,13 @@ def encode(
         >>> # encode all autodetected (non numerical) columns using label encoding
         >>> adata_encoded = ep.pp.encode(adata, autodetect=True)
 
-        # Example using autodetect with non-default encoding mode:
+        >>> # Example using autodetect with non-default encoding mode:
         >>> import ehrapy as ep
         >>> adata = ep.dt.mimic_2()
         >>> # encode all auto-detected (non numerical) columns using one hot encoding
         >>> adata_encoded = ep.pp.encode(adata, autodetect=True, encodings='one_hot_encoding')
 
-        # Example using custom encodings per columns:
+        >>> # Example using custom encodings per columns:
         >>> import ehrapy as ep
         >>> adata = ep.dt.mimic_2()
         >>> # encode col1 and col2 using label encoding and encode col3 using one hot encoding

ehrapy/preprocessing/_normalization.py

@@ -209,7 +209,7 @@ def quantile_norm(
 ) -> AnnData | None:
     """Apply quantile normalization.
 
-    Functionality is provided by ~sklearn.preprocessing.quantile_transform,
+    Functionality is provided by :func:`~sklearn.preprocessing.quantile_transform`,
     see https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.quantile_transform.html for details.
 
     Args:

ehrapy/preprocessing/_scanpy_pp_api.py

@@ -53,10 +53,12 @@ def pca(
         chunk_size: Number of observations to include in each chunk. Required if `chunked=True` was passed.
 
     Returns:
-        X_pca: :class:`~scipy.sparse.spmatrix`, :class:`~numpy.ndarray`
-        If `data` is array-like and `return_info=False` was passed, this function only returns `X_pca`.
+        :X_pca: :class:`~scipy.sparse.spmatrix`, :class:`~numpy.ndarray`
+
+        If `data` is array-like and `return_info=False` was passed, this function only returns `X_pca`...
+
+        adata : :class:`~anndata.AnnData`
 
-        adata : anndata.AnnData
         …otherwise if `copy=True` it returns or else adds fields to `adata`:
 
         `.obsm['X_pca']`
@@ -155,7 +157,7 @@ def combat(
         inplace: Whether to replace adata.X or to return the corrected data
 
     Returns:
-        Depending on the value of `inplace`, either returns the corrected matrix or or modifies `adata.X`.
+        Depending on the value of `inplace`, either returns the corrected matrix or modifies `adata.X`.
     """
     return sc.pp.combat(adata=adata, key=key, covariates=covariates, inplace=inplace)
 
@@ -230,7 +232,7 @@ def neighbors(
         copy: Determines whether a copy of `adata` is returned.
 
     Returns:
-         Depending on `copy`, updates or returns `adata` with the following:
+         Depending on `copy`, updates or returns `adata` with the following;
          See `key_added` parameter description for the storage path of connectivities and distances.
 
          **connectivities** : sparse matrix of dtype `float32`.

ehrapy/tools/_scanpy_tl_api.py

@@ -472,7 +472,8 @@ def louvain(
         copy: Whether to copy `adata` or modify it inplace.
 
     Returns:
-        By default (``copy=False``), updates ``adata`` with the following fields:
+        By default (``copy=False``), updates ``adata`` with the following fields;
+
         ``adata.obs['louvain']`` (:class:`pandas.Series`, dtype ``category``)
         Array of dim (number of samples) that stores the subgroup id (``'0'``, ``'1'``, ...) for each observation.
 
@@ -674,10 +675,10 @@ def paga(
         copy: Copy `adata` before computation and return a copy. Otherwise, perform computation in place and return `None`.
 
     Returns:
-        **connectivities** : :class:`numpy.ndarray` (adata.uns['connectivities'])
+        **connectivities** :class:`numpy.ndarray` (adata.uns['connectivities'])
         The full adjacency matrix of the abstracted graph, weights correspond to confidence in the connectivities of partitions.
 
-       **connectivities_tree** : :class:`scipy.sparse.csr_matrix` (adata.uns['connectivities_tree'])
+       **connectivities_tree** :class:`scipy.sparse.csr_matrix` (adata.uns['connectivities_tree'])
         The adjacency matrix of the tree-like subgraph that best explains the topology.
 
     Notes: