🚨 ruff format

src/miade/concept.py

@@ -31,7 +31,6 @@ def __init__(
         meta_anns: Optional[List[MetaAnnotations]] = None,
         debug_dict: Optional[Dict] = None,
     ):
-
         self.name = name
         self.id = id
         self.category = category
@@ -54,7 +53,9 @@ def from_entity(cls, entity: [Dict]):
 
         return Concept(
             id=entity["cui"],
-            name=entity["source_value"],  # can also use detected_name which is spell checked but delimited by ~ e.g. liver~failure
+            name=entity[
+                "source_value"
+            ],  # can also use detected_name which is spell checked but delimited by ~ e.g. liver~failure
             category=None,
             start=entity["start"],
             end=entity["end"],
@@ -72,11 +73,7 @@ def __hash__(self):
         return hash((self.id, self.name, self.category))
 
     def __eq__(self, other):
-        return (
-            self.id == other.id
-            and self.name == other.name
-            and self.category == other.category
-        )
+        return self.id == other.id and self.name == other.name and self.category == other.category
 
     def __lt__(self, other):
         return int(self.id) < int(other.id)

src/miade/core.py

@@ -29,11 +29,15 @@ def create_annotator(name: str, model_factory: ModelFactory):
     """
     name = name.lower()
     if name not in model_factory.models:
-        raise ValueError(f"MedCAT model for {name} does not exist: either not configured in config.yaml or "
-                         f"missing from models directory")
+        raise ValueError(
+            f"MedCAT model for {name} does not exist: either not configured in config.yaml or "
+            f"missing from models directory"
+        )
 
     if name in model_factory.annotators.keys():
-        return model_factory.annotators[name](cat=model_factory.models.get(name), config=model_factory.configs.get(name))
+        return model_factory.annotators[name](
+            cat=model_factory.models.get(name), config=model_factory.configs.get(name)
+        )
     else:
         log.warning(f"Annotator {name} does not exist, loading generic Annotator")
         return Annotator(model_factory.models[name])
@@ -48,14 +52,15 @@ class NoteProcessor:
     :param device (str) whether inference should be run on cpu or gpu - default "cpu"
     :param custom_annotators (List[Annotators]) List of custom annotators
     """
+
     def __init__(
         self,
         model_directory: Path,
         model_config_path: Path = None,
         log_level: int = logging.INFO,
         dosage_extractor_log_level: int = logging.INFO,
         device: str = "cpu",
-        custom_annotators: Optional[List[Annotator]] = None
+        custom_annotators: Optional[List[Annotator]] = None,
     ):
         logging.getLogger("miade").setLevel(log_level)
         logging.getLogger("miade.dosageextractor").setLevel(dosage_extractor_log_level)
@@ -152,13 +157,10 @@ def _load_model_factory(self, custom_annotators: Optional[List[Annotator]] = Non
         else:
             log.warning("No general settings configured, using default settings.")
 
-        model_factory_config = {"models": mapped_models,
-                                "annotators": mapped_annotators,
-                                "configs": mapped_configs}
+        model_factory_config = {"models": mapped_models, "annotators": mapped_annotators, "configs": mapped_configs}
 
         return ModelFactory(**model_factory_config)
 
-
     def add_annotator(self, name: str) -> None:
         """
         Adds annotators to processor
@@ -167,7 +169,9 @@ def add_annotator(self, name: str) -> None:
         """
         try:
             annotator = create_annotator(name, self.model_factory)
-            log.info(f"Added {type(annotator).__name__} to processor with config {self.model_factory.configs.get(name)}")
+            log.info(
+                f"Added {type(annotator).__name__} to processor with config {self.model_factory.configs.get(name)}"
+            )
         except Exception as e:
             raise Exception(f"Error creating annotator: {e}")
 
@@ -214,11 +218,9 @@ def process(self, note: Note, record_concepts: Optional[List[Concept]] = None) -
 
         return concepts
 
-    def get_concept_dicts(self,
-                             note: Note,
-                             filter_uncategorized: bool = True,
-                             record_concepts: Optional[List[Concept]] = None
-                             ) -> List[Dict]:
+    def get_concept_dicts(
+        self, note: Note, filter_uncategorized: bool = True, record_concepts: Optional[List[Concept]] = None
+    ) -> List[Dict]:
         """
         Returns concepts in dictionary format
         :param note: (Note) note containing text to extract concepts from
@@ -233,10 +235,12 @@ def get_concept_dicts(self,
                 continue
             concept_dict = concept.__dict__
             if concept.dosage is not None:
-                concept_dict["dosage"] = {"dose": concept.dosage.dose.dict() if concept.dosage.dose else None,
-                                          "duration": concept.dosage.duration.dict() if concept.dosage.duration else None,
-                                          "frequency": concept.dosage.frequency.dict() if concept.dosage.frequency else None,
-                                          "route": concept.dosage.route.dict() if concept.dosage.route else None}
+                concept_dict["dosage"] = {
+                    "dose": concept.dosage.dose.dict() if concept.dosage.dose else None,
+                    "duration": concept.dosage.duration.dict() if concept.dosage.duration else None,
+                    "frequency": concept.dosage.frequency.dict() if concept.dosage.frequency else None,
+                    "route": concept.dosage.route.dict() if concept.dosage.route else None,
+                }
             if concept.meta is not None:
                 meta_anns = []
                 for meta in concept.meta:
@@ -249,4 +253,3 @@ def get_concept_dicts(self,
             concept_list.append(concept_dict)
 
         return concept_list
-

src/miade/dosage.py

@@ -62,9 +62,7 @@ class Route(BaseModel):
     code_system: Optional[str] = ROUTE_CODE_SYSTEM
 
 
-def parse_dose(
-    text: str, quantities: List[str], units: List[str], results: Dict
-) -> Optional[Dose]:
+def parse_dose(text: str, quantities: List[str], units: List[str], results: Dict) -> Optional[Dose]:
     """
     :param text: (str) string containing dose
     :param quantities: (list) list of quantity entities NER
@@ -119,8 +117,7 @@ def parse_dose(
         # use caliber results as backup
         if results["units"] is not None:
             log.debug(
-                f"Inconclusive dose entities {quantities}, "
-                f"using lookup results {results['qty']} {results['units']}"
+                f"Inconclusive dose entities {quantities}, " f"using lookup results {results['qty']} {results['units']}"
             )
             quantity_dosage.unit = results["units"]
             #  only autofill 1 if non-quantitative units e.g. tab, cap, puff
@@ -327,13 +324,9 @@ def from_doc(cls, doc: Doc, calculate: bool = True):
             # if duration not given in text could extract this from total dose if given
             if total_dose is not None and dose is not None and doc._.results["freq"]:
                 if dose.value is not None:
-                    daily_dose = float(dose.value) * (
-                        round(doc._.results["freq"] / doc._.results["time"])
-                    )
+                    daily_dose = float(dose.value) * (round(doc._.results["freq"] / doc._.results["time"]))
                 elif dose.high is not None:
-                    daily_dose = float(dose.high) * (
-                        round(doc._.results["freq"] / doc._.results["time"])
-                    )
+                    daily_dose = float(dose.high) * (round(doc._.results["freq"] / doc._.results["time"]))
 
         duration = parse_duration(
             text=duration_text,

src/miade/dosageextractor.py

@@ -46,17 +46,12 @@ def extract(self, text: str, calculate: bool = True) -> Optional[Dosage]:
         """
         doc = self.dosage_extractor(text)
 
-        log.debug(
-            f"NER results: {[(e.text, e.label_, e._.total_dose) for e in doc.ents]}"
-        )
+        log.debug(f"NER results: {[(e.text, e.label_, e._.total_dose) for e in doc.ents]}")
         log.debug(f"Lookup results: {doc._.results}")
 
         dosage = Dosage.from_doc(doc=doc, calculate=calculate)
 
-        if all(
-            v is None
-            for v in [dosage.dose, dosage.frequency, dosage.route, dosage.duration]
-        ):
+        if all(v is None for v in [dosage.dose, dosage.frequency, dosage.route, dosage.duration]):
             return None
 
         return dosage

src/miade/drugdoseade/entities_refiner.py

@@ -14,11 +14,7 @@ def EntitiesRefiner(doc):
     new_ents = []
     for ind, ent in enumerate(doc.ents):
         # combine consecutive labels with the same tag
-        if (
-            ent.label_ == "DURATION"
-            or ent.label_ == "FREQUENCY"
-            or ent.label_ == "DOSAGE"
-        ) and ind != 0:
+        if (ent.label_ == "DURATION" or ent.label_ == "FREQUENCY" or ent.label_ == "DOSAGE") and ind != 0:
             prev_ent = doc.ents[ind - 1]
             if prev_ent.label_ == ent.label_:
                 new_ent = Span(doc, prev_ent.start, ent.end, label=ent.label)

src/miade/drugdoseade/pattern_matcher.py

@@ -18,11 +18,7 @@
 @spacy.registry.misc("patterns_lookup_table.v1")
 def create_patterns_dict():
     patterns_data = pkgutil.get_data(__name__, "../data/patterns.csv")
-    patterns_dict = (
-        pd.read_csv(io.BytesIO(patterns_data), index_col=0)
-        .squeeze("columns")
-        .T.to_dict()
-    )
+    patterns_dict = pd.read_csv(io.BytesIO(patterns_data), index_col=0).squeeze("columns").T.to_dict()
 
     return patterns_dict
 
@@ -67,9 +63,7 @@ def __call__(self, doc: Doc) -> Doc:
         # rule-based matching based on structure of dosage - HIE medication e.g. take 2 every day, 24 tablets
         expression = r"(?P<dose_string>start [\w\s,-]+ ), (?P<total_dose>\d+) (?P<unit>[a-z]+ )?$"
         for match in re.finditer(expression, dose_string):
-            dose_string = match.group(
-                "dose_string"
-            )  # remove total dose component for lookup
+            dose_string = match.group("dose_string")  # remove total dose component for lookup
             start, end = match.span("total_dose")
             total_dose_span = doc.char_span(start, end, alignment_mode="contract")
             total_dose_span.label_ = "DOSAGE"
@@ -81,9 +75,7 @@ def __call__(self, doc: Doc) -> Doc:
                 unit_span = doc.char_span(start, end, alignment_mode="contract")
                 unit_span.label_ = "FORM"
                 unit_span._.total_dose = True
-                doc._.results[
-                    "units"
-                ] = unit_span.text  # set unit in results dict as well
+                doc._.results["units"] = unit_span.text  # set unit in results dict as well
                 new_entities.append(unit_span)
 
         # lookup patterns from CALIBERdrugdose - returns dosage results in doc._.results attribute

src/miade/drugdoseade/preprocessor.py

@@ -81,8 +81,7 @@ def __call__(self, doc: Doc) -> Doc:
 
         # remove numbers relating to strength of med e.g. aspirin 200mg tablets...
         processed_text = re.sub(
-            r" (\d+\.?\d*) (mg|ml|g|mcg|microgram|gram|%)"
-            r"(\s|/)(tab|cap|gel|cream|dose|pessaries)",
+            r" (\d+\.?\d*) (mg|ml|g|mcg|microgram|gram|%)" r"(\s|/)(tab|cap|gel|cream|dose|pessaries)",
             "",
             processed_text,
         )
@@ -102,9 +101,7 @@ def __call__(self, doc: Doc) -> Doc:
                 if replacement == " ":
                     log.debug(f"Removed multiword match '{words}'")
                 else:
-                    log.debug(
-                        f"Replaced multiword match '{words}' with '{replacement}'"
-                    )
+                    log.debug(f"Replaced multiword match '{words}' with '{replacement}'")
             processed_text = new_text
 
         # numbers replace 2

src/miade/drugdoseade/utils.py

@@ -92,9 +92,7 @@ def numbers_replace(text):
         text,
     )
     # 3 weeks...
-    text = re.sub(
-        r" ([\d.]+) (week) ", lambda m: " {:g} days ".format(int(m.group(1)) * 7), text
-    )
+    text = re.sub(r" ([\d.]+) (week) ", lambda m: " {:g} days ".format(int(m.group(1)) * 7), text)
     # 3 months ... NB assume 30 days in a month
     text = re.sub(
         r" ([\d.]+) (month) ",

src/miade/metaannotations.py

@@ -11,7 +11,7 @@
     "substance_category": SubstanceCategory,
     "reaction_pos": ReactionPos,
     "allergy_type": AllergyType,
-    "severity": Severity
+    "severity": Severity,
 }
 
 
@@ -20,7 +20,7 @@ class MetaAnnotations(BaseModel):
     value: Enum
     confidence: Optional[float]
 
-    @validator('value', pre=True)
+    @validator("value", pre=True)
     def validate_value(cls, value, values):
         enum_dict = META_ANNS_DICT
         if isinstance(value, str):
@@ -36,8 +36,4 @@ def validate_value(cls, value, values):
         return value
 
     def __eq__(self, other):
-        return (
-            self.name == other.name
-            and self.value == other.value
-        )
-
+        return self.name == other.name and self.value == other.value

src/miade/model_builders/cdbbuilder.py

@@ -60,24 +60,18 @@ def preprocess_snomed(self, output_dir: Path = Path.cwd()) -> Path:
         print("Exporting preprocessed SNOMED to csv...")
 
         if self.snomed_subset_path is not None:
-            snomed_subset = pd.read_csv(
-                str(self.snomed_subset_path), header=0, dtype={"cui": object}
-            )
+            snomed_subset = pd.read_csv(str(self.snomed_subset_path), header=0, dtype={"cui": object})
         else:
             snomed_subset = None
 
         if self.snomed_exclusions_path is not None:
-            snomed_exclusions = pd.read_csv(
-                str(self.snomed_exclusions_path), sep="\n", header=None
-            )
+            snomed_exclusions = pd.read_csv(str(self.snomed_exclusions_path), sep="\n", header=None)
             snomed_exclusions.columns = ["cui"]
         else:
             snomed_exclusions = None
 
         output_file = output_dir / Path("preprocessed_snomed.csv")
-        df = self.snomed.to_concept_df(
-            subset_list=snomed_subset, exclusion_list=snomed_exclusions
-        )
+        df = self.snomed.to_concept_df(subset_list=snomed_subset, exclusion_list=snomed_exclusions)
         df.to_csv(str(output_file), index=False)
         return output_file
 
@@ -105,7 +99,7 @@ def preprocess(self):
         if self.elg_data_path:
             self.vocab_files.append(str(self.preprocess_elg(self.temp_dir)))
         if self.custom_data_paths:
-            string_paths  = [str(path) for path in self.custom_data_paths]
+            string_paths = [str(path) for path in self.custom_data_paths]
             self.vocab_files.extend(string_paths)
 
     def create_cdb(self) -> CDB: