Updated src/naarad/metrics for flake8 fixes

src/naarad/metrics/cluster_metric.py

@@ -1,10 +1,20 @@
 # coding=utf-8
 """
-© 2013 LinkedIn Corp. All rights reserved.
-Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at  http://www.apache.org/licenses/LICENSE-2.0
+Copyright 2013 LinkedIn Corp. All rights reserved.
 
-Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
 """
+
 from collections import defaultdict
 import datetime
 import gc
@@ -18,22 +28,23 @@
 
 logger = logging.getLogger('naarad.metrics.cluster_metric')
 
+
 class ClusterMetric(Metric):
   """
   supporting the metric of Cluster, which aggregates the performance metrics of multiple hosts
   """
 
   metrics = []   # all other non-aggregate metrics;
   aggr_metrics = []  # metrics to be aggregated
-  aggr_hosts = [] # hosts to be aggregated
+  aggr_hosts = []  # hosts to be aggregated
 
-  def __init__ (self, section, aggregate_hosts, aggregate_metrics, metrics, output_directory, resource_path, label,
-                ts_start, ts_end, rule_strings, important_sub_metrics, anomaly_detection_metrics, **other_options):
+  def __init__(self, section, aggregate_hosts, aggregate_metrics, metrics, output_directory, resource_path, label,
+               ts_start, ts_end, rule_strings, important_sub_metrics, anomaly_detection_metrics, **other_options):
     self.metrics = metrics
     self.aggr_metrics = aggregate_metrics.split()
     self.aggr_hosts = aggregate_hosts.split()
 
-    #Metric arguments take 'infile' and 'hostname', for ClusterMetric, they are invalid, so just provide empty strings.
+    # Metric arguments take 'infile' and 'hostname', for ClusterMetric, they are invalid, so just provide empty strings.
     Metric.__init__(self, section, '', '', output_directory, resource_path, label, ts_start, ts_end, rule_strings,
                     important_sub_metrics, anomaly_detection_metrics)
 
@@ -59,60 +70,60 @@ def collect(self):
       else:  # no user input of aggregate functions
         return True
 
-      cur_column = '.'.join(fields[0].split('.')[1:])    #e.g. sda.await or all.percent-sys
+      cur_column = '.'.join(fields[0].split('.')[1:])    # e.g. sda.await or all.percent-sys
 
-      #store data points of various aggregation functions
+      # Store data points of various aggregation functions
       aggr_data = {}
-      aggr_data['raw'] = []   #store all the raw values
-      aggr_data['sum'] = defaultdict(float)   #store the sum values for each timestamp
-      aggr_data['count'] = defaultdict(int) #store the count of each timestamp (i.e. qps)
+      aggr_data['raw'] = []   # Store all the raw values
+      aggr_data['sum'] = defaultdict(float)   # Store the sum values for each timestamp
+      aggr_data['count'] = defaultdict(int)  # Store the count of each timestamp (i.e. qps)
 
-      for metric in self.metrics:   # loop the list to find from all metrics to merge
+      for metric in self.metrics:   # Loop the list to find from all metrics to merge
         if metric.hostname in self.aggr_hosts and \
-          cur_column in metric.csv_column_map.values():
+           cur_column in metric.csv_column_map.values():
           file_csv = metric.get_csv(cur_column)
           timestamp_format = None
           with open(file_csv) as fh:
             for line in fh:
               aggr_data['raw'].append(line.rstrip())
               words = line.split(",")
-              ts = words[0].split('.')[0]   #in case of sub-seconds; we only want the value of seconds;
+              ts = words[0].split('.')[0]   # In case of sub-seconds; we only want the value of seconds;
               if not timestamp_format or timestamp_format == 'unknown':
                 timestamp_format = naarad.utils.detect_timestamp_format(ts)
               if timestamp_format == 'unknown':
                 continue
               ts = naarad.utils.get_standardized_timestamp(ts, timestamp_format)
               aggr_data['sum'][ts] += float(words[1])
               aggr_data['count'][ts] += 1
-      #"raw" csv file
+      # "raw" csv file
       if 'raw' in functions_aggr:
         out_csv = self.get_csv(cur_column, 'raw')
         self.csv_files.append(out_csv)
         with open(out_csv, 'w') as fh:
           fh.write("\n".join(sorted(aggr_data['raw'])))
-      
-      #"sum"  csv file
+
+      # "sum"  csv file
       if 'sum' in functions_aggr:
         out_csv = self.get_csv(cur_column, 'sum')
         self.csv_files.append(out_csv)
         with open(out_csv, 'w') as fh:
-          for k,v in sorted(aggr_data['sum'].items()):
+          for (k, v) in sorted(aggr_data['sum'].items()):
             fh.write(k + "," + str(v) + '\n')
 
       # "avg" csv file
       if 'avg' in functions_aggr:
         out_csv = self.get_csv(cur_column, 'avg')
         self.csv_files.append(out_csv)
         with open(out_csv, 'w') as fh:
-          for k,v in sorted(aggr_data['sum'].items()):
-            fh.write(k + "," + str(v/aggr_data['count'][k]) + '\n')
+          for (k, v) in sorted(aggr_data['sum'].items()):
+            fh.write(k + "," + str(v / aggr_data['count'][k]) + '\n')
 
       # "count" csv file (qps)
       if 'count' in functions_aggr:
         out_csv = self.get_csv(cur_column, 'count')
         self.csv_files.append(out_csv)
         with open(out_csv, 'w') as fh:
-          for k,v in sorted(aggr_data['count'].items()):
+          for (k, v) in sorted(aggr_data['count'].items()):
             fh.write(k + "," + str(v) + '\n')
 
       gc.collect()
@@ -124,8 +135,8 @@ def get_csv(self, column, func):
 
   def parse(self):
     """
-    merge multiple hosts' csv into one csv file. This approach has the benefit of reusing calculate_stats(), but with the penalty of reading the single csv later for calculate_stats()
-    However, since file cache will cache the newly written csv files, reading the csv file will not likely be a IO bottleneck.
+    Merge multiple hosts' csv into one csv file. This approach has the benefit of reusing calculate_stats(), but with the penalty of reading the single csv
+    later for calculate_stats(). However, since file cache will cache the newly written csv files, reading the csv file will not likely be a IO bottleneck.
     """
 
     return True