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Bayer-Group · Mar 5, 2025 · 18ed1c7 · 18ed1c7
1 parent 9740133
commit 18ed1c7
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Showing 2 changed files with 27 additions and 22 deletions.
diff --git a/phenex/phenotypes/measurement_change_phenotype.py b/phenex/phenotypes/measurement_change_phenotype.py
@@ -65,31 +65,38 @@ def _execute(self, tables) -> PhenotypeTable:
 
         # Create a self-join to compare each measurement with every other measurement
         import ibis
-        ibis.options.interactive = True     
+
+        ibis.options.interactive = True
         joined_table = phenotype_table_1.join(
-            phenotype_table_2, 
+            phenotype_table_2,
             [
                 phenotype_table_1.PERSON_ID == phenotype_table_2.PERSON_ID,
-                (phenotype_table_1.EVENT_DATE != phenotype_table_2.EVENT_DATE) | (phenotype_table_1.VALUE != phenotype_table_2.VALUE),
+                (phenotype_table_1.EVENT_DATE != phenotype_table_2.EVENT_DATE)
+                | (phenotype_table_1.VALUE != phenotype_table_2.VALUE),
             ],
-            lname='{name}_1',
-            rname='{name}_2'
+            lname="{name}_1",
+            rname="{name}_2",
         ).filter(_.EVENT_DATE_1 <= _.EVENT_DATE_2)
 
         # Calculate the change in value and the days apart
         days_between = joined_table.EVENT_DATE_2.delta(joined_table.EVENT_DATE_1, "day")
         value_change = joined_table.VALUE_2 - joined_table.VALUE_1
         joined_table = joined_table.mutate(
-            VALUE_CHANGE=value_change,
-            DAYS_BETWEEN=days_between
+            VALUE_CHANGE=value_change, DAYS_BETWEEN=days_between
         )
 
         # Filter to keep only those with at least min_change and within max_days_apart
-        value_filter = ValueFilter(min=self.min_change, max=self.max_change, column_name='VALUE_CHANGE')
+        value_filter = ValueFilter(
+            min=self.min_change, max=self.max_change, column_name="VALUE_CHANGE"
+        )
         filtered_table = value_filter.filter(joined_table)
 
-        time_filter = ValueFilter(min=self.min_days_between, max=self.max_days_between, column_name='DAYS_BETWEEN')
-        filtered_table =  time_filter.filter(filtered_table)
+        time_filter = ValueFilter(
+            min=self.min_days_between,
+            max=self.max_days_between,
+            column_name="DAYS_BETWEEN",
+        )
+        filtered_table = time_filter.filter(filtered_table)
 
         # Determine the return date based on the return_date attribute
         if self.return_date == "first":
@@ -103,13 +110,11 @@ def _execute(self, tables) -> PhenotypeTable:
 
         # Select the required columns
         filtered_table = filtered_table.mutate(
-            PERSON_ID='PERSON_ID_1',
-            VALUE='VALUE_CHANGE',
-            BOOLEAN=True            
-        )   
+            PERSON_ID="PERSON_ID_1", VALUE="VALUE_CHANGE", BOOLEAN=True
+        )
         result_table = filtered_table.select(PHENOTYPE_TABLE_COLUMNS).distinct()
-    
+
         if self.relative_time_range is not None:
             result_table = self.relative_time_range.filter(result_table)
 
-        return result_table
+        return result_table
diff --git a/phenex/test/phenotypes/test_measurement_change_phenotype.py b/phenex/test/phenotypes/test_measurement_change_phenotype.py
@@ -18,14 +18,14 @@ class MeasurementChangePhenotypeTestGenerator(PhenotypeTestGenerator):
     def define_input_tables(self):
         df = pd.DataFrame()
         N = 10
-        df["VALUE"] = \
-            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + \
-            [2, 4, 6, 8, 10, 6, 6, 6, 6, 6]
+        df["VALUE"] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + [2, 4, 6, 8, 10, 6, 6, 6, 6, 6]
         df["PERSON_ID"] = [f"P{x}" for x in range(N)] * 2
-        # change of 1, 2, 3, 4, 5, 0, -1, -2, -3, -4 
+        # change of 1, 2, 3, 4, 5, 0, -1, -2, -3, -4
         df["CODE"] = "c1"
         df["CODE_TYPE"] = "ICD10CM"
-        df["EVENT_DATE"] = [datetime.date(2022, 1, 1)] * 10 + [datetime.date(2022, 1, 1 + i) for i in range(10)]
+        df["EVENT_DATE"] = [datetime.date(2022, 1, 1)] * 10 + [
+            datetime.date(2022, 1, 1 + i) for i in range(10)
+        ]
         # days_between = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
         df.iloc[-1, 0] = None  # make a null lab value for last patient
         return [{"name": "MEASUREMENT", "df": df}]
@@ -68,7 +68,7 @@ def define_phenotype_tests(self):
 
         c3 = {
             "name": "change_leq0",
-            "persons": ["P0","P5", "P6", "P7"],
+            "persons": ["P0", "P5", "P6", "P7"],
             "phenotype": MeasurementChangePhenotype(
                 name="change_leq0",
                 phenotype=measurement_phenotype,