Support ruff constraints

astrolabsoftware · Dec 27, 2024 · 32c0958 · 32c0958
1 parent 5056332
commit 32c0958
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Showing 3 changed files with 24 additions and 18 deletions.
diff --git a/bin/index_sso_resolver.py b/bin/index_sso_resolver.py
@@ -156,18 +156,22 @@ def main():
     sso_number_valid = sso_number[is_valid_number]
 
     # Vector contains (MPC_names, MPC_numbers, ZTF_ssnamenr)
-    index_ssodnet = np.concatenate((
-        sso_name,
-        sso_number_valid,
-        pdf.ssnamenr.to_numpy(),
-    ))
+    index_ssodnet = np.concatenate(
+        (
+            sso_name,
+            sso_number_valid,
+            pdf.ssnamenr.to_numpy(),
+        )
+    )
 
     # create index vector for Fink
-    index_fink = np.concatenate((
-        pdf.ssnamenr.to_numpy(),
-        pdf.ssnamenr[is_valid_number].to_numpy(),
-        pdf.ssnamenr.to_numpy(),
-    ))
+    index_fink = np.concatenate(
+        (
+            pdf.ssnamenr.to_numpy(),
+            pdf.ssnamenr[is_valid_number].to_numpy(),
+            pdf.ssnamenr.to_numpy(),
+        )
+    )
     index_name = np.concatenate((sso_name, sso_name[is_valid_number], sso_name))
     index_number = np.concatenate((sso_number, sso_number_valid, sso_number))
 

diff --git a/bin/stream2raw.py b/bin/stream2raw.py
@@ -96,16 +96,18 @@ def main():
     if args.producer == "sims":
         # using custom from_avro (not available for Spark 2.4.x)
         # it will be available from Spark 3.0 though
-        df_decoded = df.select([
-            from_avro(df["value"], alert_schema_json).alias("decoded")
-        ])
+        df_decoded = df.select(
+            [from_avro(df["value"], alert_schema_json).alias("decoded")]
+        )
     elif args.producer == "elasticc":
         schema = fastavro.schema.load_schema(args.schema)
         alert_schema_json = fastavro.schema.to_parsing_canonical_form(schema)
-        df_decoded = df.select([
-            from_avro(df["value"], alert_schema_json).alias("decoded"),
-            df["topic"],
-        ])
+        df_decoded = df.select(
+            [
+                from_avro(df["value"], alert_schema_json).alias("decoded"),
+                df["topic"],
+            ]
+        )
     elif args.producer == "ztf":
         # Decode on-the-fly using fastavro
         f = F.udf(lambda x: next(fastavro.reader(io.BytesIO(x))), alert_schema)

diff --git a/fink_broker/spark_utils.py b/fink_broker/spark_utils.py
@@ -340,7 +340,7 @@ def connect_to_raw_database(basepath: str, path: str, latestfirst: bool) -> Data
     while True:
         try:
             userschema = spark.read.parquet(basepath).schema
-        except Exception as e:
+        except Exception as e:  # noqa: PERF203
             _LOG.error("Error while reading %s, %s", basepath, e)
             time.sleep(wait_sec)
             wait_sec = increase_wait_time(wait_sec)