link to staged updated dsstox for ms1

dsstox_rest/batch_query.py

@@ -9,17 +9,17 @@
 from flask import jsonify
 from flask_restful import Resource, reqparse
 
-user = os.environ.get('POSTGRES_USER')
-pw = os.environ.get('AURORA_PW')
-host = os.environ.get('POSTGRES_HOST')
-dbname = os.environ.get('POSTGRES_DB')
-port = os.environ.get('POSTGRES_PORT')
+user = os.environ.get("POSTGRES_USER")
+pw = os.environ.get("AURORA_PW")
+host = os.environ.get("POSTGRES_HOST")
+dbname = os.environ.get("POSTGRES_DB")
+port = os.environ.get("POSTGRES_PORT")
 
 # request parser
 parser = reqparse.RequestParser()
-parser.add_argument('search_by')
-parser.add_argument('query', action='append')
-parser.add_argument('accuracy', type=float, required=False)
+parser.add_argument("search_by")
+parser.add_argument("query", action="append")
+parser.add_argument("accuracy", type=float, required=False)
 
 logger = logging.getLogger("nta_flask")
 logger.setLevel(logging.INFO)
@@ -35,20 +35,19 @@ def post(self, jobId="000000100000011"):
         """
         logger.info("=========== NTA batch search received ===========")
         args = parser.parse_args()
-        search_by = args['search_by']
-        query = list(args['query'])
+        search_by = args["search_by"]
+        query = list(args["query"])
         logger.info("# of queries: {}".format(len(query)))
         result = None
-        dbconn = psycopg2.connect(
-            host=host,
-            port=port,
-            user=user,
-            password=pw,
-            database=dbname)
+        dbconn = psycopg2.connect(host=host, port=port, user=user, password=pw, database=dbname)
         if search_by == "mass":
-            accuracy = args['accuracy']
+            accuracy = args["accuracy"]
             if accuracy is None:
-                return jsonify({"Error": "If searching by mass, the 'accuracy' parameter must be provided in ppm"})
+                return jsonify(
+                    {
+                        "Error": "If searching by mass, the 'accuracy' parameter must be provided in ppm"
+                    }
+                )
             result = self.mass_search(query, accuracy, dbconn)
             dbconn.close()
         elif search_by == "formula":
@@ -58,40 +57,51 @@ def post(self, jobId="000000100000011"):
             return jsonify({"Error": "search_by must be either  'mass' or 'formula'"})
         return result
 
-
     def mass_search(self, query, accuracy, dbconn):  # Note - accuracy comes in as ppm
         logger.info("=========== Searching ms1_data table ===========")
         query_list = [(float(i)) for i in query]
         results = pd.DataFrame()
         for massquery in query_list:
             max_mass = massquery + massquery * accuracy / 1000000
             min_mass = massquery - massquery * accuracy / 1000000
-            sql = """Select '""" + str(massquery) + """' as "INPUT", msr_dsstox_compound_id as "DTXCID_INDIVIDUAL_COMPONENT", 
+            sql = (
+                """Select '"""
+                + str(massquery)
+                + """' as "INPUT", msr_dsstox_compound_id as "DTXCID_INDIVIDUAL_COMPONENT", 
                 msr_monoisotopic_mass as "MONOISOTOPIC_MASS_INDIVIDUAL_COMPONENT",
                 msr_smiles as "SMILES_INDIVIDUAL_COMPONENT", dsstox_substance_id as "DTXSID", preferred_name as "PREFERRED_NAME", 
                 casrn as "CASRN", jchem_inchi_key as "INCHIKEY", acd_iupac_name as "IUPAC_NAME", mol_formula as "MOLECULAR_FORMULA",
                 monoisotopic_mass as "MONOISOTOPIC_MASS", total_median as "EXPOCAST_MEDIAN_EXPOSURE_PREDICTION_MG/KG-BW/DAY",
                 expocast_comptox_link as "EXPOCAST", nhanes_comptox_link as "NHANES", data_sources as "DATA_SOURCES", 
                 round(assay_count_active/assay_count_total*100,2) as "TOXCAST_PERCENT_ACTIVE", 
                 assay_count_active || '/' || assay_count_total as "TOXCAST_NUMBER_OF_ASSAYS/TOTAL"
-                FROM ms1_batch_search
-                where msr_monoisotopic_mass BETWEEN """ + str(min_mass) + """ AND """ + str(max_mass) + """;"""
+                FROM ms1_batch_search_new
+                where msr_monoisotopic_mass BETWEEN """
+                + str(min_mass)
+                + """ AND """
+                + str(max_mass)
+                + """;"""
+            )
             results = pd.concat([results, pd.read_sql(sql, dbconn)])
         logger.info("=========== Search complete ===========")
-        results['MASS_DIFFERENCE'] = abs(results['INPUT'].astype(float) -
-                                         results['MONOISOTOPIC_MASS_INDIVIDUAL_COMPONENT'].astype(float))
-        results['FOUND_BY'] = 'Monoisotopic Mass'
-        results = results.sort_values(by=['INPUT', 'DATA_SOURCES'], ascending=[True, False])
-        results_db_dict = results.to_dict(orient='split')
+        results["MASS_DIFFERENCE"] = abs(
+            results["INPUT"].astype(float)
+            - results["MONOISOTOPIC_MASS_INDIVIDUAL_COMPONENT"].astype(float)
+        )
+        results["FOUND_BY"] = "Monoisotopic Mass"
+        results = results.sort_values(by=["INPUT", "DATA_SOURCES"], ascending=[True, False])
+        results_db_dict = results.to_dict(orient="split")
         # logger.info(db_results)
-        return jsonify({'results': results_db_dict})
-
+        return jsonify({"results": results_db_dict})
 
     def formula_search(self, query, dbconn):
         logger.info("=========== Searching ms1_data table ===========")
         results = pd.DataFrame()
         for formulaquery in query:
-            sql = """Select '""" + formulaquery + """' as "INPUT", msr_dsstox_compound_id as "DTXCID_INDIVIDUAL_COMPONENT", 
+            sql = (
+                """Select '"""
+                + formulaquery
+                + """' as "INPUT", msr_dsstox_compound_id as "DTXCID_INDIVIDUAL_COMPONENT", 
                 msr_monoisotopic_mass as "MONOISOTOPIC_MASS_INDIVIDUAL_COMPONENT",
                 msr_smiles as "SMILES_INDIVIDUAL_COMPONENT", dsstox_substance_id as "DTXSID", preferred_name as "PREFERRED_NAME", 
                 casrn as "CASRN", jchem_inchi_key as "INCHIKEY", acd_iupac_name as "IUPAC_NAME", mol_formula as "MOLECULAR_FORMULA",
@@ -100,26 +110,24 @@ def formula_search(self, query, dbconn):
                 round(assay_count_active/assay_count_total*100,2) as "TOXCAST_PERCENT_ACTIVE", 
                 assay_count_active || '/' || assay_count_total as "TOXCAST_NUMBER_OF_ASSAYS/TOTAL"
                 FROM ms1_batch_search
-                where msr_mol_formula = '""" + formulaquery + """';"""
-            results = pd.concat([results,pd.read_sql(sql, dbconn)])
+                where msr_mol_formula = '"""
+                + formulaquery
+                + """';"""
+            )
+            results = pd.concat([results, pd.read_sql(sql, dbconn)])
         logger.info("=========== Search complete ===========")
-        results['FOUND_BY'] = 'Exact Formula'
-        results = results.sort_values(by=['INPUT', 'DATA_SOURCES'], ascending=[True, False])
-        results_db_dict = results.to_dict(orient='split')
-        return jsonify({'results': results_db_dict})
+        results["FOUND_BY"] = "Exact Formula"
+        results = results.sort_values(by=["INPUT", "DATA_SOURCES"], ascending=[True, False])
+        results_db_dict = results.to_dict(orient="split")
+        return jsonify({"results": results_db_dict})
 
 
 class DsstoxMSRFormulas(Resource):
     def get(self):
         results = pd.DataFrame()
-        dbconn = psycopg2.connect(
-            host=host,
-            port=port,
-            user=user,
-            password=pw,
-            database=dbname)
+        dbconn = psycopg2.connect(host=host, port=port, user=user, password=pw, database=dbname)
         sql = """SELECT DISTINCT msr_mol_formula AS "MS-READY MOLECULAR FORMULA" FROM ms1_batch_search;"""
-        results = pd.concat([results,pd.read_sql(sql, dbconn)])
-        results_db_dict = results.to_dict(orient='split')
-        del results_db_dict['index']
-        return jsonify({'results': results_db_dict})
+        results = pd.concat([results, pd.read_sql(sql, dbconn)])
+        results_db_dict = results.to_dict(orient="split")
+        del results_db_dict["index"]
+        return jsonify({"results": results_db_dict})