Refactor clean_obs_names (#532)

* Rename input arguments from `data` to `adata`, `copy` to `inplace`,
`ID_length` to `id_length`, `base` to `alphabet`.

* Rename variables to something more meaningful and use snake as well as
lower case, consistently.

* Make use of Pandas functionality.

* Use regex expression.

scvelo/core/_anndata.py

@@ -11,16 +11,20 @@
 from scipy.sparse import csr_matrix, issparse, spmatrix
 
 from anndata import AnnData
+from scanpy._utils import deprecated_arg_names
 
 from scvelo import logging as logg
 from ._arithmetic import sum
 
 
+@deprecated_arg_names(
+    {"data": "adata", "copy": "inplace", "ID_length": "id_length", "base": "alphabet"}
+)
 def clean_obs_names(
-    data: AnnData,
-    base: str = "[AGTCBDHKMNRSVWY]",
-    ID_length: int = 12,
-    copy: bool = False,
+    adata: AnnData,
+    alphabet: str = "[AGTCBDHKMNRSVWY]",
+    id_length: int = 12,
+    inplace: bool = True,
 ) -> Optional[AnnData]:
     """Clean up the obs_names.
 
@@ -31,14 +35,14 @@ def clean_obs_names(
 
     Arguments
     ---------
-    data
+    adata
         Annotated data matrix.
-    base
+    alphabet
         Genetic code letters to be identified.
-    ID_length
+    id_length
         Length of the Genetic Codes in the samples.
-    copy
-        Return a copy instead of writing to adata.
+    inplace
+        Whether to update `adata` inplace or not.
 
     Returns
     -------
@@ -50,53 +54,53 @@ def clean_obs_names(
             names of the identified sample batches
     """
 
-    def get_base_list(name, base):
-        base_list = base
-        while re.search(base_list + base, name) is not None:
-            base_list += base
-        if len(base_list) == 0:
-            raise ValueError("Encountered an invalid ID in obs_names: ", name)
-        return base_list
-
-    adata = data.copy() if copy else data
-
-    names = adata.obs_names
-    base_list = get_base_list(names[0], base)
+    if not inplace:
+        adata = adata.copy()
 
-    if len(np.unique([len(name) for name in adata.obs_names])) == 1:
-        start, end = re.search(base_list, names[0]).span()
-        newIDs = [name[start:end] for name in names]
-        start, end = 0, len(newIDs[0])
-        for i in range(end - ID_length):
-            if np.any([ID[i] not in base for ID in newIDs]):
+    if adata.obs_names.map(len).unique().size == 1:
+        start = re.search(alphabet, adata.obs_names[0]).start()
+        end = start + re.search(f"{alphabet}*", adata.obs_names[0][start:]).end()
+        new_obs_names = [obs_name[start:end] for obs_name in adata.obs_names]
+        start, end = 0, len(new_obs_names[0])
+        for i in range(end - id_length):
+            if np.any(
+                [new_obs_name[i] not in alphabet for new_obs_name in new_obs_names]
+            ):
                 start += 1
-            if np.any([ID[::-1][i] not in base for ID in newIDs]):
+            if np.any(
+                [
+                    new_obs_name[::-1][i] not in alphabet
+                    for new_obs_name in new_obs_names
+                ]
+            ):
                 end -= 1
 
-        newIDs = [ID[start:end] for ID in newIDs]
-        prefixes = [names[i].replace(newIDs[i], "") for i in range(len(names))]
+        new_obs_names = [new_obs_name[start:end] for new_obs_name in new_obs_names]
+        prefixes = [
+            obs_name.replace(new_obs_name, "")
+            for obs_name, new_obs_name in zip(adata.obs_names, new_obs_names)
+        ]
     else:
-        prefixes, newIDs = [], []
-        for name in names:
-            match = re.search(base_list, name)
-            newID = (
-                re.search(get_base_list(name, base), name).group()
-                if match is None
-                else match.group()
-            )
-            newIDs.append(newID)
-            prefixes.append(name.replace(newID, ""))
 
-    adata.obs_names = newIDs
+        def rename_obs(obs_name):
+            start = re.search(alphabet, obs_name).start()
+            new_obs_name = re.search(f"{alphabet}*", obs_name[start:]).group()
+            return new_obs_name, obs_name.replace(new_obs_name, "")
+
+        new_obs_names, prefixes = zip(*adata.obs_names.map(rename_obs))
+
+    adata.obs_names = new_obs_names
+    adata.obs_names_make_unique()
+
     if len(prefixes[0]) > 0 and len(np.unique(prefixes)) > 1:
         adata.obs["sample_batch"] = (
             pd.Categorical(prefixes)
             if len(np.unique(prefixes)) < adata.n_obs
             else prefixes
         )
 
-    adata.obs_names_make_unique()
-    return adata if copy else None
+    if not inplace:
+        return adata
 
 
 def cleanup(