Merge branch 'iss305c' into Beta_2.0.1

Author: sreeder

odmtools/odmdata/memory_database.py

@@ -194,11 +194,11 @@ def chunking(self, data):
     def updateFlag(self, ids, value):
 
 
-        flags = pd.DataFrame(columns = ['AnnotationID', 'DateTime', 'ResultID', 'ValueID'])
-        flags["DateTime"] = ids
-        flags["AnnotationID"] = value
-        flags["ResultID"] = self.series.ResultID
-        flags["ValueID"] = None
+        flags = pd.DataFrame(columns = ['annotationid', 'valuedatetime', 'resultid', 'valueid'])
+        flags["valuedatetime"] = ids
+        flags["annotationid"] = value
+        flags["resultid"] = self.series.ResultID
+        flags["valueid"] = None
 
 
         #what if the column already exists

odmtools/odmservices/edit_service.py

@@ -498,20 +498,23 @@ def restore(self):
         self.reset_filter()
 
     def save(self,  result=None):
-        values = self.memDB.getDataValuesDF()
-
-        if not result:
-            result = self.memDB.series_service.get_series(series_id = values['resultid'][0])
-        else:
-            values["resultid"] = result.ResultID
+        try:
+            values = self.memDB.getDataValuesDF()
 
-        # update result
-        result.ValueCount = 0
-        self.updateResult(result)
-        # upsert values
-        self.memDB.series_service.upsert_values(values)
-        # save new annotations
-        self.add_annotations(self.memDB.annotation_list)
+            if not result:
+                result = self.memDB.series_service.get_series(series_id = values['resultid'][0])
+            else:
+                values["resultid"] = result.ResultID
+
+            # update result
+            result.ValueCount = 0
+            self.updateResult(result)
+            # upsert values
+            self.memDB.series_service.upsert_values(values)
+            # save new annotations
+            self.add_annotations(self.memDB.annotation_list)
+        except Exception as e:
+            logger.error("Exception encountered while saving: {}".format(e))
         return result
 
     def save_existing(self, result):
@@ -651,8 +654,8 @@ def updateResult(self, result, valuecount = -10):
 
         setSchema(self.memDB.series_service._session_factory.engine)
         self.memDB.series_service.update.updateResult(result.ResultID, result.ValueCount)
-        self.memDB.series_service.update.updateAction(actionID=action.ActionID, begin=action.BeginDateTime, end=action.EndDateTime)
-
+        self.memDB.series_service.update.updateAction(actionID=action.ActionID,
+                                                      begin=action.BeginDateTime, end=action.EndDateTime)
         return result
 
     def overlapcalc(self, result, values,  overwrite):
@@ -675,16 +678,30 @@ def overlapcalc(self, result, values,  overwrite):
 
     def add_annotations(self, annolist):
         #match up with existing values and get value id
-        #get df with only ValueID and AnnotationID
-        #remove any duplicates
-        #save df to db
-        pass
 
+        print("ANNOTATIONS ARE ATTEMPTED TO ADD")
+        engine = self.memDB.series_service._session_factory.engine
+
+        q =self.memDB.series_service._session.query(TimeSeriesResultValues) \
+            .filter(TimeSeriesResultValues.ResultID == int(min(annolist["resultid"])))
 
+        query = q.statement.compile(dialect=engine.dialect)
+        # data = pd.read_sql_query(sql=query, con=self._session_factory.engine,
+        #                          params=query.params)
+        # query = "SELECT ValueID, ResultID, ValueDateTime  FROM TimeSeriesResultValues Where ResultID="+annolist["ResultID"][0]
 
+        vals = pd.read_sql_query(sql=query, con=engine,params=query.params)
+        # remove any duplicates
+        annolist.drop_duplicates(["resultid", "annotationid", "valuedatetime"], keep= 'last', inplace = True)
+        newdf = pd.merge(annolist, vals, how='left', on= ["resultid", "valuedatetime"], indicator = True)
 
 
+        #get only AnnotationID and ValueID
+        mynewdf= newdf[["valueid_y","annotationid"]]
+        mynewdf.columns = ["ValueID", "AnnotationID"]
 
+        # save df to db
+        self.memDB.series_service.add_annotations(mynewdf)