Merge pull request #333 from richardhsu/flake8fixes

Fixing Flake8 Issues

MANIFEST.in

@@ -1 +1 @@
-global-include *.html *.js *.css VERSION requirements.txt
+global-include *.html *.js *.css VERSION requirements.txt

NOTICE

@@ -1,8 +1,16 @@
-© 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
-
-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.
+Copyright 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
 
+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.
 
 List of other open-source software used/depended on by Naarad:
 

bin/naarad

@@ -1,15 +1,21 @@
 #!/usr/bin/env python
 # coding=utf-8
 """
-© 2013 LinkedIn Corp. All rights reserved.
+Copyright 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
+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.
 """
+
 import logging
 import os
 import sys

lib/luminol/demo/src/rca.py

@@ -79,7 +79,8 @@ def _analyze(self):
         # Correlate with other metrics
         for entry in self.related_metrices:
           try:
-            entry_correlation_result = Correlator(self.metrix, entry, time_period=(extended_start_t, extended_end_t), use_anomaly_score=True).get_correlation_result()
+            entry_correlation_result = Correlator(self.metrix, entry, time_period=(extended_start_t, extended_end_t),
+                                                  use_anomaly_score=True).get_correlation_result()
             record = extended_start_t, extended_end_t, entry_correlation_result.__dict__, entry
             record_by_name = extended_start_t, extended_end_t, entry_correlation_result.__dict__
             output[t].append(record)

lib/luminol/demo/src/start.py

@@ -17,10 +17,12 @@
 DATA_PATH = 'static/data/'
 SCORE_FILE_PATH = 'static/'
 
+
 @app.route('/')
 def index():
   return render_template('index.html')
 
+
 @app.route('/get_selection')
 def get_selection():
   fs = list()
@@ -29,6 +31,7 @@ def get_selection():
       fs.append(f)
   return jsonify(selection=fs)
 
+
 @app.route('/detect')
 def luminoldetect():
   ts = urllib.unquote(request.args.get('ts_path')[1:])
@@ -50,7 +53,8 @@ def luminoldetect():
     for key in anom_dict:
       entry.append(anom_dict[key])
     result.append(entry)
-  return jsonify(anom = result, anom_score = anom_scores)
+  return jsonify(anom=result, anom_score=anom_scores)
+
 
 @app.route('/correlate')
 def luminolanalyze():
@@ -64,13 +68,14 @@ def luminolanalyze():
   result = myluminol.output_by_name
   return jsonify(anom=result)
 
+
 @app.route('/find_correlation_list')
 def findCorrelationListPerAnomaly():
   ts = urllib.unquote(request.args.get('ts')[1:])
   all_ts = os.listdir(DATA_PATH)
   matrices = list()
   for t in all_ts:
-    t = DATA_PATH+t
+    t = DATA_PATH + t
     if t.endswith('.csv') and t != ts:
       matrices.append(t)
   myluminol = RCA(ts, matrices)
@@ -80,16 +85,18 @@ def findCorrelationListPerAnomaly():
     l = result[t]
     data = list()
     for entry in l:
-      data.append([entry[3]]+entry[2].values())
+      data.append([entry[3]] + entry[2].values())
     data_sorted = sorted(data, key=lambda k: (-k[1], k[2], -k[3]))
     r.append([t, data_sorted])
   return jsonify(anom=r)
 
+
 def write_csv(rows, name):
   with open(SCORE_FILE_PATH + name, 'w+') as fp:
     writer = csv.writer(fp)
     writer.writerows(rows)
 
+
 def to_epoch(anom):
   r = list()
   for a in anom:
@@ -101,4 +108,4 @@ def to_epoch(anom):
 
 if __name__ == "__main__":
   app.debug = True
-  app.run(host='0.0.0.0')
+  app.run(host='0.0.0.0')

lib/luminol/setup.py

@@ -14,8 +14,9 @@
       author='Naarad Developers',
       author_email='[email protected]',
       version=luminol_version,
-      packages=['luminol', 'luminol.algorithms', 'luminol.modules', 'luminol.algorithms.anomaly_detector_algorithms', 'luminol.algorithms.correlator_algorithms'],  
-      package_dir={ '' : 'src'},
+      packages=['luminol', 'luminol.algorithms', 'luminol.modules', 'luminol.algorithms.anomaly_detector_algorithms',
+                'luminol.algorithms.correlator_algorithms'],
+      package_dir={'': 'src'},
       install_requires=required,
       license='Apache 2.0',
       )

lib/luminol/src/luminol/__init__.py

@@ -47,4 +47,4 @@ def get_root_causes(self):
     Get root causes.
     :return dict: a dict represents root causes for each anomaly.
     """
-    return getattr(self, 'causes', None)
+    return getattr(self, 'causes', None)

lib/luminol/src/luminol/algorithms/anomaly_detector_algorithms/absolute_threshold.py

@@ -15,6 +15,7 @@
 from luminol.constants import *
 from luminol.modules.time_series import TimeSeries
 
+
 class AbsoluteThreshold(AnomalyDetectorAlgorithm):
   """
   Anomalies are those data points that are above a pre-specified threshold value.
@@ -52,4 +53,3 @@ def _set_scores(self):
       if self.absolute_threshold_value_lower and value < self.absolute_threshold_value_lower:
         anom_scores[timestamp] = self.absolute_threshold_value_lower - value
     self.anom_scores = TimeSeries(self._denoise_scores(anom_scores))
-

lib/luminol/src/luminol/algorithms/anomaly_detector_algorithms/all.py

@@ -12,10 +12,10 @@
 from luminol.algorithms.anomaly_detector_algorithms import *
 
 anomaly_detector_algorithms = {
-  'bitmap_detector': bitmap_detector.BitmapDetector,
-  'default_detector': default_detector.DefaultDetector,
-  'derivative_detector': derivative_detector.DerivativeDetector,
-  'exp_avg_detector': exp_avg_detector.ExpAvgDetector,
-  'absolute_threshold': absolute_threshold.AbsoluteThreshold,
-  'diff_percent_threshold': diff_percent_threshold.DiffPercentThreshold
-}
+    'bitmap_detector': bitmap_detector.BitmapDetector,
+    'default_detector': default_detector.DefaultDetector,
+    'derivative_detector': derivative_detector.DerivativeDetector,
+    'exp_avg_detector': exp_avg_detector.ExpAvgDetector,
+    'absolute_threshold': absolute_threshold.AbsoluteThreshold,
+    'diff_percent_threshold': diff_percent_threshold.DiffPercentThreshold
+}

lib/luminol/src/luminol/algorithms/anomaly_detector_algorithms/bitmap_detector.py

@@ -137,7 +137,7 @@ def _construct_all_SAX_chunk_dict(self):
 
       else:
         # Just enter valid range.
-        if lag_dicts[i-1] is None:
+        if lag_dicts[i - 1] is None:
           lag_dict = self._construct_SAX_chunk_dict(self.sax[i - lws: i])
           lag_dicts[i] = lag_dict
           lw_leave_chunk = self.sax[0:chunk_size]
@@ -150,12 +150,12 @@ def _construct_all_SAX_chunk_dict(self):
 
         else:
           # Update dicts according to leave_chunks and enter_chunks.
-          lag_dict = copy(lag_dicts[i-1])
+          lag_dict = copy(lag_dicts[i - 1])
           lag_dict[lw_leave_chunk] -= 1
-          lag_dict[lw_enter_chunk] +=1
+          lag_dict[lw_enter_chunk] += 1
           lag_dicts[i] = lag_dict
 
-          fut_dict = copy(fut_dicts[i-1])
+          fut_dict = copy(fut_dicts[i - 1])
           fut_dict[fw_leave_chunk] -= 1
           fut_dict[fw_enter_chunk] += 1
           fut_dicts[i] = fut_dict

lib/luminol/src/luminol/algorithms/anomaly_detector_algorithms/default_detector.py

@@ -40,9 +40,9 @@ def _set_scores(self):
     anom_scores = {}
     for timestamp in anom_scores_ema.timestamps:
       # Compute a weighted anomaly score.
-      anom_scores[timestamp] = max(anom_scores_ema[timestamp], anom_scores_ema[timestamp] * DEFAULT_DETECTOR_EMA_WEIGHT \
-        + anom_scores_deri[timestamp] * (1 - DEFAULT_DETECTOR_EMA_WEIGHT))
+      anom_scores[timestamp] = max(anom_scores_ema[timestamp],
+                                   anom_scores_ema[timestamp] * DEFAULT_DETECTOR_EMA_WEIGHT + anom_scores_deri[timestamp] * (1 - DEFAULT_DETECTOR_EMA_WEIGHT))
       # If ema score is significant enough, take the bigger one of the weighted score and deri score.
       if anom_scores_ema[timestamp] > DEFAULT_DETECTOR_EMA_SIGNIFICANT:
         anom_scores[timestamp] = max(anom_scores[timestamp], anom_scores_deri[timestamp])
-    self.anom_scores = TimeSeries(self._denoise_scores(anom_scores))
+    self.anom_scores = TimeSeries(self._denoise_scores(anom_scores))

lib/luminol/src/luminol/algorithms/anomaly_detector_algorithms/diff_percent_threshold.py

@@ -15,6 +15,7 @@
 from luminol.constants import *
 from luminol.modules.time_series import TimeSeries
 
+
 class DiffPercentThreshold(AnomalyDetectorAlgorithm):
   """
   In this algorithm, anomalies are those data points that are above a percentage threshold as compared to the baseline.
@@ -35,7 +36,8 @@ def __init__(self, time_series, baseline_time_series, percent_threshold_upper=No
     self.percent_threshold_upper = percent_threshold_upper
     self.percent_threshold_lower = percent_threshold_lower
     if not self.percent_threshold_upper and not self.percent_threshold_lower:
-      raise exceptions.RequiredParametersNotPassed('luminol.algorithms.anomaly_detector_algorithms.diff_percent_threshold: Either percent_threshold_upper or percent_threshold_lower needed')
+      raise exceptions.RequiredParametersNotPassed('luminol.algorithms.anomaly_detector_algorithms.diff_percent_threshold: \
+          Either percent_threshold_upper or percent_threshold_lower needed')
 
   def _set_scores(self):
     """
@@ -60,4 +62,4 @@ def _set_scores(self):
       if self.percent_threshold_lower and diff_percent < 0 and diff_percent < self.percent_threshold_lower:
         anom_scores[timestamp] = -1 * diff_percent
 
-    self.anom_scores = TimeSeries(self._denoise_scores(anom_scores))
+    self.anom_scores = TimeSeries(self._denoise_scores(anom_scores))

lib/luminol/src/luminol/algorithms/anomaly_detector_algorithms/exp_avg_detector.py

@@ -87,4 +87,4 @@ def _set_scores(self):
     """
     if self.use_lag_window:
       self._compute_anom_data_using_window()
-    self._compute_anom_data_decay_all()
+    self._compute_anom_data_decay_all()

lib/luminol/src/luminol/algorithms/correlator_algorithms/__init__.py

@@ -49,4 +49,4 @@ def run(self):
     :return CorrelationResult: a CorrelationResult object represents the correlation result.
     """
     self._detect_correlation()
-    return self.correlation_result
+    return self.correlation_result

lib/luminol/src/luminol/algorithms/correlator_algorithms/all.py

@@ -12,5 +12,5 @@
 from luminol.algorithms.correlator_algorithms import *
 
 correlator_algorithms = {
-  'cross_correlator': cross_correlator.CrossCorrelator
+    'cross_correlator': cross_correlator.CrossCorrelator
 }

lib/luminol/src/luminol/constants.py

@@ -50,8 +50,8 @@
 DEFAULT_DERI_SMOOTHING_FACTOR = 0.2
 
 ANOMALY_THRESHOLD = {
-  'exp_avg_detector': 3,
-  'default_detector': 3
+    'exp_avg_detector': 3,
+    'default_detector': 3
 }
 
 # Percentage threshold on anomaly score below which is considered noises.
@@ -80,18 +80,18 @@
 DEFAULT_SHIFT_IMPACT = 0.05
 
 TIMESTAMP_STR_FORMATS = [
-  '%Y%m%d_%H:%M:%S',
-  '%Y-%m-%d %H:%M:%S.%f',
-  '%Y%m%d %H:%M:%S',
-  '%Y-%m-%d_%H:%M:%S',
-  '%Y-%m-%dT%H:%M:%S.%f',
-  '%H:%M:%S.%f',
-  '%Y-%m-%dT%H:%M:%S.%f%z',
-  '%Y%m%dT%H:%M:%S',
-  '%Y-%m-%d_%H:%M:%S.%f',
-  '%Y%m%d_%H:%M:%S.%f',
-  '%Y-%m-%dT%H:%M:%S',
-  '%Y-%m-%d %H:%M:%S',
-  '%Y%m%dT%H:%M:%S.%f',
-  '%H:%M:%S',
-  '%Y%m%d %H:%M:%S.%f']
+    '%Y%m%d_%H:%M:%S',
+    '%Y-%m-%d %H:%M:%S.%f',
+    '%Y%m%d %H:%M:%S',
+    '%Y-%m-%d_%H:%M:%S',
+    '%Y-%m-%dT%H:%M:%S.%f',
+    '%H:%M:%S.%f',
+    '%Y-%m-%dT%H:%M:%S.%f%z',
+    '%Y%m%dT%H:%M:%S',
+    '%Y-%m-%d_%H:%M:%S.%f',
+    '%Y%m%d_%H:%M:%S.%f',
+    '%Y-%m-%dT%H:%M:%S',
+    '%Y-%m-%d %H:%M:%S',
+    '%Y%m%dT%H:%M:%S.%f',
+    '%H:%M:%S',
+    '%Y%m%d %H:%M:%S.%f']

lib/luminol/src/luminol/correlator.py

@@ -113,4 +113,4 @@ def is_correlated(self, threshold=None):
     Compare with a threshold to determine whether two timeseries correlate to each other.
     :return: a CorrelationResult object if two time series correlate otherwise false.
     """
-    return self.correlation_result if self.correlation_result.coefficient >= threshold else False
+    return self.correlation_result if self.correlation_result.coefficient >= threshold else False

lib/luminol/src/luminol/modules/anomaly.py

@@ -41,4 +41,4 @@ def __str__(self):
     return string representation of the anomaly
     :return: string
     """
-    return "Anomaly from {0} to {1} with score {2}".format(self.start_timestamp, self.end_timestamp, self.anomaly_score)
+    return "Anomaly from {0} to {1} with score {2}".format(self.start_timestamp, self.end_timestamp, self.anomaly_score)

lib/luminol/src/luminol/modules/correlation_result.py

@@ -25,4 +25,4 @@ def __init__(self, shift, coefficient, shifted_coefficient):
     """
     self.shift = shift
     self.coefficient = coefficient
-    self.shifted_coefficient = shifted_coefficient
+    self.shifted_coefficient = shifted_coefficient

lib/luminol/src/luminol/modules/time_series.py

@@ -371,4 +371,4 @@ def sum(self, default=None):
     :param default: Value to return as a default should the calculation not be possible.
     :return: Float representing the sum or `None`.
     """
-    return numpy.asscalar(numpy.sum(self.values)) if self.values else default
+    return numpy.asscalar(numpy.sum(self.values)) if self.values else default

lib/luminol/src/luminol/tests/run_tests.py

@@ -96,8 +96,8 @@ class TestLuminol(unittest.TestCase):
   def setUp(self):
     self.anomaly = ['A', 'B']
     self.correlation = {
-      'A': ['m1', 'm2', 'm3'],
-      'B': ['m2', 'm1', 'm3']
+        'A': ['m1', 'm2', 'm3'],
+        'B': ['m2', 'm1', 'm3']
     }
     self.luminol = Luminol(self.anomaly, self.correlation)
 

lib/luminol/src/luminol/utils.py

@@ -20,6 +20,7 @@
 
 from luminol import constants, exceptions
 
+
 def compute_ema(smoothing_factor, points):
   """
   Compute exponential moving average of a list of points.
@@ -35,6 +36,7 @@ def compute_ema(smoothing_factor, points):
     ema.append(smoothing_factor * points[i] + (1 - smoothing_factor) * ema[i - 1])
   return ema
 
+
 def read_csv(csv_name):
   """
   Read data from a csv file into a dictionary.
@@ -55,6 +57,7 @@ def read_csv(csv_name):
         pass
   return data
 
+
 def to_epoch(t_str):
   """
   Covert a timestamp string to an epoch number.
@@ -68,7 +71,7 @@ def to_epoch(t_str):
     for format in constants.TIMESTAMP_STR_FORMATS:
       try:
         t = datetime.datetime.strptime(t_str, format)
-        return float(time.mktime(t.utctimetuple())*1000.0 + t.microsecond/1000.0)
+        return float(time.mktime(t.utctimetuple()) * 1000.0 + t.microsecond / 1000.0)
       except:
         pass
-  raise exceptions.InvalidDataFormat
+  raise exceptions.InvalidDataFormat

setup.cfg

@@ -1,3 +1,4 @@
 [flake8]
-ignore = E111
+ignore = E111,E128
 max-line-length = 160
+exclude = MANIFEST.in,LICENSE,NOTICE,README.md,build,env,*requirements.txt,setup.cfg