Docs: Minor docs tweaks for better compatibility with numpydoc.

Author: strangetom

docs/source/conf.py

@@ -26,6 +26,9 @@
     "myst_parser",
 ]
 
+numpydoc_xref_param_type = True
+numpydoc_class_members_toctree = False
+
 templates_path = ["_templates"]
 exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
 

ingredient_parser/_common.py

@@ -30,7 +30,7 @@
 
 
 def consume(iterator: Iterator, n: int | None) -> None:
-    """Advance the iterator n-steps ahead. If n is none, consume entirely.
+    """Advance the `iterator` n-steps ahead. If `n` is none, consume entirely.
 
     See consume from https://docs.python.org/3/library/itertools.html#itertools-recipes
 
@@ -126,7 +126,7 @@ def download_nltk_resources() -> None:
 
 
 def is_float(value: str) -> bool:
-    """Check if the value can be converted to a float.
+    """Check if `value` can be converted to a float.
 
     Parameters
     ----------
@@ -157,7 +157,7 @@ def is_float(value: str) -> bool:
 
 
 def is_range(value: str) -> bool:
-    """Check if the value is a range e.g. 100-200.
+    """Check if `value` is a range e.g. 100-200.
 
     Parameters
     ----------

ingredient_parser/dataclasses.py

@@ -323,7 +323,7 @@ class FoundationFood:
         Confidence of the match, between 0 and 1.
     fdc_id : int
         ID of the FDC database entry.
-    category: str
+    category : str
         Category of FDC database entry.
     data_type : str
         Food Data Central data set the entry belongs to.

ingredient_parser/en/_foundationfoods.py

@@ -701,7 +701,7 @@ def get_fuzzy_matcher() -> FuzzyEmbeddingMatcher:
 
 
 def normalise_spelling(tokens: list[str]) -> list[str]:
-    """Normalise spelling in tokens to standard spellings used in FDC ingredient
+    """Normalise spelling in `tokens` to standard spellings used in FDC ingredient
     descriptions.
 
     This also include subtitution of certain ingredients to use the FDC version e.g.

ingredient_parser/en/_structure_features.py

@@ -121,8 +121,8 @@ def detect_phrases(self, tokenized_sentence: list[Token]) -> list[list[int]]:
 
         text_pos = [(token.text, token.pos_tag) for token in self.tokenized_sentence]
         parsed = self.mip_parser.parse(text_pos)
-        for subtree in parsed.subtrees(filter=lambda t: t.label() in ["EMIP", "MIP"]):
-            indices = self._get_subtree_indices(parsed, subtree)
+        for subtree in parsed.subtrees(filter=lambda t: t.label() in ["EMIP", "MIP"]):  #  type: ignore
+            indices = self._get_subtree_indices(parsed, subtree)  #  type: ignore
             # If the conjunction is not "or", skip
             if self._cc_is_not_or(text_pos, indices):
                 continue
@@ -172,8 +172,8 @@ def detect_sentences_splits(self, tokenized_sentence: list[Token]) -> list[int]:
 
             text_pos.append((t.feat_text, pos))
         parsed = self.compound_parser.parse(text_pos)
-        for subtree in parsed.subtrees(filter=lambda t: t.label() == "CS"):
-            indices = self._get_subtree_indices(parsed, subtree)
+        for subtree in parsed.subtrees(filter=lambda t: t.label() == "CS"):  #  type: ignore
+            indices = self._get_subtree_indices(parsed, subtree)  #  type: ignore
             # If the conjunction is not "or", skip
             if self._cc_is_not_or(text_pos, indices):
                 continue

ingredient_parser/en/preprocess.py

@@ -1068,7 +1068,7 @@ def _token_features(self, token: Token) -> dict[str, str | bool]:
         return features
 
     def sentence_features(self) -> list[dict[str, str | bool]]:
-        """Return features for all tokens in sentence.
+        """Return dict of features for each token in sentence.
 
         Returns
         -------