Remove duplicate implementations in the classification app #1796

classification/admin/classification_admin.py

@@ -1,4 +1,5 @@
 import json
+from collections.abc import Callable
 from datetime import timedelta
 from typing import Optional, Union
 
@@ -738,7 +739,9 @@ def cs_to_index():
         return EvidenceKeyMap.cached_key(SpecialEKeys.CLINICAL_SIGNIFICANCE).option_dictionary_property("vg")
 
     @staticmethod
-    def _less_more_certain(summary: DiscordanceLabSummary):
+    def _describe_movement(summary: DiscordanceLabSummary, describe: Callable[[int, int], str]) -> str:
+        """ Handles the cases where the two clinical significances can't be compared (withdrawn, unchanged,
+            or one of them not on the vg scale), otherwise describe() names the direction travelled """
         cs_to_index = DiscordanceReportAdminExport.cs_to_index()
         from_value = int(cs_to_index.get(summary.clinical_significance_from, "0"))
         to_value = int(cs_to_index.get(summary.clinical_significance_to, "0"))
@@ -749,29 +752,22 @@ def _less_more_certain(summary: DiscordanceLabSummary):
             return "same"
         elif from_value == 0 or to_value == 0:
             return "?"
-        else:
-            if abs(to_value - 3) > abs(from_value - 3):
-                return "more"
-            else:
-                return "less"
+        return describe(from_value, to_value)
+
+    @staticmethod
+    def _less_more_certain(summary: DiscordanceLabSummary):
+        # VUS is the middle of the vg scale, so distance from it is how certain the call is
+        def describe(from_value: int, to_value: int) -> str:
+            return "more" if abs(to_value - 3) > abs(from_value - 3) else "less"
+
+        return DiscordanceReportAdminExport._describe_movement(summary, describe)
 
     @staticmethod
     def _up_down_for(summary: DiscordanceLabSummary):
-        cs_to_index = DiscordanceReportAdminExport.cs_to_index()
-        from_value = int(cs_to_index.get(summary.clinical_significance_from, "0"))
-        to_value = int(cs_to_index.get(summary.clinical_significance_to, "0"))
+        def describe(from_value: int, to_value: int) -> str:
+            return "upgrade" if to_value > from_value else "downgrade"
 
-        if summary.clinical_significance_to == 'withdrawn':
-            return "withdrawn"
-        elif from_value == to_value:
-            return "same"
-        elif from_value == 0 or to_value == 0:
-            return "?"
-        else:
-            if to_value > from_value:
-                return "upgrade"
-            else:
-                return "downgrade"
+        return DiscordanceReportAdminExport._describe_movement(summary, describe)
 
     def __init__(self, discordance_report: DiscordanceReport, perspective: LabPickerData):
         self.discordance_report = discordance_report

classification/models/classification.py

@@ -2028,47 +2028,6 @@ def get_visible_evidence(self, evidence, lowest_share_level: ShareLevel) -> dict
                 visible_evidence[k] = {'value': "(hidden)", 'hidden': True}
         return visible_evidence
 
-    def get_allele_info_dict(self) -> Optional[dict[str, Any]]:
-        allele_info_dict = {}
-        if allele_info := self.allele_info:
-            resolved_dict = {
-                "allele_id": allele_info.allele_id,
-                "allele_info_id": allele_info.id,
-                "allele_info_status": allele_info.status,
-                "status": allele_info.status,
-                "include": allele_info.latest_validation.include if allele_info.latest_validation else None,
-                "variant_coordinate": allele_info.variant_coordinate
-            }
-
-            if (genome_build := self.get_genome_build_opt()) and \
-                    (preferred_build := allele_info[genome_build]) and \
-                    (c_hgvs := preferred_build.c_hgvs_display):
-                resolved_dict.update(c_hgvs.to_json())
-            elif c_hgvs_raw := self.get(SpecialEKeys.C_HGVS):
-                resolved_dict.update(HGVSDisplay.parse(c_hgvs_raw).to_json())
-
-            include = False
-            if latest_validation := allele_info.latest_validation:
-                include = latest_validation.include
-
-            resolved_dict["include"] = include
-            if warning_icon := ImportedAlleleInfo.icon_for(status=allele_info.status, include=include):
-                resolved_dict.update(warning_icon.as_json())
-
-            allele_info_dict["resolved"] = resolved_dict
-
-            genome_builds = {}
-            for variant_info in allele_info.resolved_builds:
-                genome_builds[variant_info.genome_build.name] = {
-                    'variant_id': variant_info.variant_id,
-                    SpecialEKeys.C_HGVS: variant_info.c_hgvs
-                }
-
-            if genome_builds:
-                allele_info_dict["genome_builds"] = genome_builds
-
-        return allele_info_dict
-
     @staticmethod
     def get_url_for_pk(pk):
         return reverse('view_classification', kwargs={'classification_id': pk})

classification/models/classification_groups.py

@@ -9,7 +9,7 @@
 from more_itertools import first
 
 from classification.criteria_strengths import CriteriaStrength
-from classification.enums import CriteriaEvaluation, ShareLevel, SpecialEKeys
+from classification.enums import ShareLevel, SpecialEKeys
 from classification.models import (
     ClassificationModification,
     ConditionResolved,
@@ -371,25 +371,7 @@ def count(self) -> int:
 
     @cached_property
     def acmg_criteria(self) -> MultiValues[CriteriaStrength]:
-
-        def criteria_converter(cm: ClassificationModification) -> set[CriteriaStrength]:
-            strengths: set[CriteriaStrength] = set()
-            for e_key in EvidenceKeyMap.cached().criteria():
-                strength = cm.get(e_key.key)
-                if CriteriaEvaluation.is_met(strength):
-                    strengths.add(CriteriaStrength(e_key, strength))
-            for amp_level, letter in SpecialEKeys.AMP_LEVELS_TO_LEVEL.items():
-                if value := cm.get_value_list(amp_level):
-                    e_key = EvidenceKeyMap.cached_key(amp_level)
-                    for sub_value in value:
-                        sub_value_label = e_key.pretty_value(sub_value)
-                        strengths.add(CriteriaStrength(
-                            ekey=EvidenceKeyMap.cached_key(amp_level),
-                            custom_strength=f"{letter}_{sub_value_label}")
-                        )
-            return strengths
-        output = MultiValues.convert([criteria_converter(cm) for cm in self.modifications])
-        return output
+        return MultiValues.convert([cm.met_criteria_strengths() for cm in self.modifications])
 
     def _evidence_key_set(self, key: str) -> list[str]:
         all_values = set()

classification/models/evidence_mixin.py

@@ -9,7 +9,7 @@
 from annotation.models import CitationFetchRequest
 from annotation.models.models_citations import CitationFetchResponse
 from classification.criteria_strengths import CriteriaStrength, CriteriaStrengths
-from classification.enums import AlleleOriginBucket, SpecialEKeys
+from classification.enums import AlleleOriginBucket, CriteriaEvaluation, SpecialEKeys
 from genes.hgvs import HGVSComponents, PHGVS
 from library.log_utils import report_message
 from library.utils import empty_to_none
@@ -221,6 +221,24 @@ def criteria_strengths(self, e_keys: Optional['EvidenceKeyMap'] = None) -> Crite
 
         return CriteriaStrengths(strengths=criteria, is_acmg_standard=self.is_likely_acmg)
 
+    def met_criteria_strengths(self) -> set[CriteriaStrength]:
+        """ Criteria that were actually met, plus one per somatic AMP level value selected """
+        from classification.models import EvidenceKeyMap
+
+        strengths: set[CriteriaStrength] = set()
+        for e_key in EvidenceKeyMap.cached().criteria():
+            strength = self.get(e_key.key)
+            if CriteriaEvaluation.is_met(strength):
+                strengths.add(CriteriaStrength(e_key, strength))
+        for amp_level, letter in SpecialEKeys.AMP_LEVELS_TO_LEVEL.items():
+            if value := self.get_value_list(amp_level):
+                e_key = EvidenceKeyMap.cached_key(amp_level)
+                for sub_value in value:
+                    sub_value_label = e_key.pretty_value(sub_value)
+                    strengths.add(CriteriaStrength(ekey=e_key,
+                                                  custom_strength=f"{letter}_{sub_value_label}"))
+        return strengths
+
     def criteria_strength_summary(self, ekeys: Optional['EvidenceKeyMap'] = None, only_acmg: bool = False) -> str:
         strengths = self.criteria_strengths(e_keys=ekeys)
         return strengths.summary_string(acmg_only=only_acmg)

classification/models/evidence_mixin_summary_cache.py

@@ -2,8 +2,7 @@
 from functools import cached_property
 from typing import Optional, Self, TypedDict
 
-from classification.criteria_strengths import CriteriaStrength
-from classification.enums import AlleleOriginBucket, CriteriaEvaluation, SpecialEKeys
+from classification.enums import AlleleOriginBucket, SpecialEKeys
 from classification.models.evidence_key import EvidenceKeyMap
 from library.utils import strip_json
 
@@ -207,20 +206,4 @@ def somatic_sort(self) -> Optional[int]:
 
     @cached_property
     def criteria_labels(self) -> list[str]:
-        from classification.models import EvidenceKeyMap
-        cm = self.cm
-        strengths: set[CriteriaStrength] = set()
-        for e_key in EvidenceKeyMap.cached().criteria():
-            strength = cm.get(e_key.key)
-            if CriteriaEvaluation.is_met(strength):
-                strengths.add(CriteriaStrength(e_key, strength))
-        for amp_level, letter in SpecialEKeys.AMP_LEVELS_TO_LEVEL.items():
-            if value := cm.get_value_list(amp_level):
-                e_key = EvidenceKeyMap.cached_key(amp_level)
-                for sub_value in value:
-                    sub_value_label = e_key.pretty_value(sub_value)
-                    strengths.add(CriteriaStrength(
-                        ekey=EvidenceKeyMap.cached_key(amp_level),
-                        custom_strength=f"{letter}_{sub_value_label}")
-                    )
-        return list(str(x) for x in sorted(strengths))
+        return [str(x) for x in sorted(self.cm.met_criteria_strengths())]