fix: use functional classification enum in place of old style strings

Author: bencap

src/mavedb/scripts/load_calibration_csv.py

@@ -92,7 +92,7 @@
 import csv
 import re
 from pathlib import Path
-from typing import Any, Dict, List, Literal, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple
 
 import click
 from sqlalchemy.orm import Session
@@ -101,6 +101,7 @@
 from mavedb.lib.oddspaths import oddspaths_evidence_strength_equivalent
 from mavedb.lib.score_calibrations import create_score_calibration_in_score_set
 from mavedb.models import score_calibration
+from mavedb.models.enums.functional_classification import FunctionalClassification as FunctionalClassifcationOptions
 from mavedb.models.score_set import ScoreSet
 from mavedb.models.user import User
 from mavedb.scripts.environment import with_database_session
@@ -152,23 +153,21 @@ def parse_interval(text: str) -> Tuple[Optional[float], Optional[float], bool, b
     return lower, upper, inclusive_lower, inclusive_upper
 
 
-def normalize_classification(
-    raw: Optional[str], strength: Optional[str]
-) -> Literal["normal", "abnormal", "not_specified"]:
+def normalize_classification(raw: Optional[str], strength: Optional[str]) -> FunctionalClassifcationOptions:
     if raw:
         r = raw.strip().lower()
         if r in {"normal", "abnormal", "not_specified"}:
-            return r  # type: ignore[return-value]
+            return FunctionalClassifcationOptions[r]
         if r in {"indeterminate", "uncertain", "unknown"}:
-            return "not_specified"
+            return FunctionalClassifcationOptions.not_specified
 
     if strength:
         if strength.upper().startswith("PS"):
-            return "abnormal"
+            return FunctionalClassifcationOptions.abnormal
         if strength.upper().startswith("BS"):
-            return "normal"
+            return FunctionalClassifcationOptions.normal
 
-    return "not_specified"
+    return FunctionalClassifcationOptions.not_specified
 
 
 def build_publications(

src/mavedb/scripts/load_pp_style_calibration.py

@@ -84,6 +84,7 @@
 from sqlalchemy.orm import Session
 
 from mavedb.lib.score_calibrations import create_score_calibration_in_score_set
+from mavedb.models.enums.functional_classification import FunctionalClassification as FunctionalClassifcationOptions
 from mavedb.models.score_calibration import ScoreCalibration
 from mavedb.models.score_set import ScoreSet
 from mavedb.models.user import User
@@ -214,7 +215,9 @@ def main(db: Session, archive_path: str, dataset_map: str, overwrite: bool) -> N
 
                 functional_range = score_calibration.FunctionalClassificationCreate(
                     label=f"{ps_or_bs} {strength_label} ({points})",
-                    classification="abnormal" if points > 0 else "normal",
+                    classification=FunctionalClassifcationOptions.abnormal
+                    if points > 0
+                    else FunctionalClassifcationOptions.normal,
                     range=range_data,
                     acmg_classification=acmg_classification.ACMGClassificationCreate(
                         points=int(points),

tests/helpers/constants.py

@@ -2,6 +2,7 @@
 
 from humps import camelize
 
+from mavedb.models.enums.functional_classification import FunctionalClassification as FunctionalClassificationOptions
 from mavedb.models.enums.processing_state import ProcessingState
 
 VALID_EXPERIMENT_SET_URN = "urn:mavedb:01234567"
@@ -1411,7 +1412,7 @@
 TEST_FUNCTIONAL_RANGE_NORMAL = {
     "label": "test normal functional range",
     "description": "A normal functional range",
-    "functional_classification": "normal",
+    "functional_classification": FunctionalClassificationOptions.normal.value,
     "range": [1.0, 5.0],
     "acmg_classification": TEST_ACMG_BS3_STRONG_CLASSIFICATION,
     "oddspaths_ratio": TEST_BS3_STRONG_ODDS_PATH_RATIO,
@@ -1431,7 +1432,7 @@
 TEST_FUNCTIONAL_RANGE_ABNORMAL = {
     "label": "test abnormal functional range",
     "description": "An abnormal functional range",
-    "functional_classification": "abnormal",
+    "functional_classification": FunctionalClassificationOptions.abnormal.value,
     "range": [-5.0, -1.0],
     "acmg_classification": TEST_ACMG_PS3_STRONG_CLASSIFICATION,
     "oddspaths_ratio": TEST_PS3_STRONG_ODDS_PATH_RATIO,
@@ -1450,7 +1451,7 @@
 
 TEST_FUNCTIONAL_RANGE_NOT_SPECIFIED = {
     "label": "test not specified functional range",
-    "functional_classification": "not_specified",
+    "functional_classification": FunctionalClassificationOptions.not_specified.value,
     "range": [-1.0, 1.0],
     "inclusive_lower_bound": True,
     "inclusive_upper_bound": False,
@@ -1467,7 +1468,7 @@
 TEST_FUNCTIONAL_CLASSIFICATION_NORMAL = {
     "label": "test normal functional class",
     "description": "A normal functional class",
-    "functional_classification": "normal",
+    "functional_classification": FunctionalClassificationOptions.normal.value,
     "class": "normal_class",
     "acmg_classification": TEST_ACMG_BS3_STRONG_CLASSIFICATION,
     "oddspaths_ratio": TEST_BS3_STRONG_ODDS_PATH_RATIO,
@@ -1485,7 +1486,7 @@
 TEST_FUNCTIONAL_CLASSIFICATION_ABNORMAL = {
     "label": "test abnormal functional class",
     "description": "An abnormal functional class",
-    "functional_classification": "abnormal",
+    "functional_classification": FunctionalClassificationOptions.abnormal.value,
     "class": "abnormal_class",
     "acmg_classification": TEST_ACMG_PS3_STRONG_CLASSIFICATION,
     "oddspaths_ratio": TEST_PS3_STRONG_ODDS_PATH_RATIO,
@@ -1502,7 +1503,7 @@
 
 TEST_FUNCTIONAL_CLASSIFICATION_NOT_SPECIFIED = {
     "label": "test not specified functional class",
-    "functional_classification": "not_specified",
+    "functional_classification": FunctionalClassificationOptions.not_specified.value,
     "class": "not_specified_class",
 }
 
@@ -1517,7 +1518,7 @@
 TEST_FUNCTIONAL_RANGE_INCLUDING_NEGATIVE_INFINITY = {
     "label": "test functional range including negative infinity",
     "description": "A functional range including negative infinity",
-    "functional_classification": "not_specified",
+    "functional_classification": FunctionalClassificationOptions.not_specified.value,
     "range": [None, 0.0],
     "inclusive_lower_bound": False,
     "inclusive_upper_bound": False,
@@ -1533,7 +1534,7 @@
 TEST_FUNCTIONAL_RANGE_INCLUDING_POSITIVE_INFINITY = {
     "label": "test functional range including positive infinity",
     "description": "A functional range including positive infinity",
-    "functional_classification": "not_specified",
+    "functional_classification": FunctionalClassificationOptions.not_specified.value,
     "range": [0.0, None],
     "inclusive_lower_bound": False,
     "inclusive_upper_bound": False,

tests/view_models/test_score_calibration.py

@@ -4,6 +4,7 @@
 from pydantic import ValidationError
 
 from mavedb.lib.acmg import ACMGCriterion
+from mavedb.models.enums.functional_classification import FunctionalClassification as FunctionalClassificationOptions
 from mavedb.models.enums.score_calibration_relation import ScoreCalibrationRelation
 from mavedb.view_models.score_calibration import (
     FunctionalClassificationCreate,
@@ -182,7 +183,7 @@ def test_is_contained_by_range():
     fr = FunctionalClassificationCreate.model_validate(
         {
             "label": "test range",
-            "functional_classification": "abnormal",
+            "functional_classification": FunctionalClassificationOptions.abnormal,
             "range": (0.0, 1.0),
             "inclusive_lower_bound": True,
             "inclusive_upper_bound": True,
@@ -205,7 +206,7 @@ def test_inclusive_bounds_get_default_when_unset_and_range_exists():
     fr = FunctionalClassificationCreate.model_validate(
         {
             "label": "test range",
-            "functional_classification": "abnormal",
+            "functional_classification": FunctionalClassificationOptions.abnormal,
             "range": (0.0, 1.0),
         }
     )
@@ -218,7 +219,7 @@ def test_inclusive_bounds_remain_none_when_range_is_none():
     fr = FunctionalClassificationCreate.model_validate(
         {
             "label": "test range",
-            "functional_classification": "abnormal",
+            "functional_classification": FunctionalClassificationOptions.abnormal,
             "class": "some_class",
         }
     )
@@ -245,7 +246,7 @@ def test_inclusive_bounds_remain_none_when_range_is_none():
 def test_cant_set_inclusive_bounds_when_range_is_none(bound_property, bound_value, match_text):
     invalid_data = {
         "label": "test range",
-        "functional_classification": "abnormal",
+        "functional_classification": FunctionalClassificationOptions.abnormal,
         "class": "some_class",
         bound_property: bound_value,
     }
@@ -373,7 +374,9 @@ def test_can_create_score_calibration_when_unclassified_ranges_overlap_with_clas
     # Make the first two ranges overlap, one being 'not_specified'
     valid_data["functional_classifications"][0]["range"] = [1.5, 3.0]
     valid_data["functional_classifications"][1]["range"] = [2.0, 4.0]
-    valid_data["functional_classifications"][0]["functional_classification"] = "not_specified"
+    valid_data["functional_classifications"][0]["functional_classification"] = (
+        FunctionalClassificationOptions.not_specified
+    )
     sc = ScoreCalibrationCreate.model_validate(valid_data)
     assert len(sc.functional_classifications) == len(valid_data["functional_classifications"])
 
@@ -383,8 +386,12 @@ def test_can_create_score_calibration_when_unclassified_ranges_overlap_with_each
     # Make the first two ranges overlap, both being 'not_specified'
     valid_data["functional_classifications"][0]["range"] = [1.5, 3.0]
     valid_data["functional_classifications"][1]["range"] = [2.0, 4.0]
-    valid_data["functional_classifications"][0]["functional_classification"] = "not_specified"
-    valid_data["functional_classifications"][1]["functional_classification"] = "not_specified"
+    valid_data["functional_classifications"][0]["functional_classification"] = (
+        FunctionalClassificationOptions.not_specified
+    )
+    valid_data["functional_classifications"][1]["functional_classification"] = (
+        FunctionalClassificationOptions.not_specified
+    )
     sc = ScoreCalibrationCreate.model_validate(valid_data)
     assert len(sc.functional_classifications) == len(valid_data["functional_classifications"])
 
@@ -616,7 +623,11 @@ def test_cannot_create_score_calibration_with_mixed_range_and_class_based_functi
     invalid_data = deepcopy(TEST_BRNICH_SCORE_CALIBRATION_RANGE_BASED)
     # Add a class-based functional classification to a range-based calibration
     invalid_data["functional_classifications"].append(
-        {"label": "class based classification", "functional_classification": "abnormal", "class": "some_class"}
+        {
+            "label": "class based classification",
+            "functional_classification": FunctionalClassificationOptions.abnormal,
+            "class": "some_class",
+        }
     )
 
     with pytest.raises(