good analytics type names

analytics/detectors/pattern_detection_model.py

@@ -97,9 +97,9 @@ class PatternDetectionModel:
         self.data_prov.synchronize()
 
     def __create_model(self, pattern):
-        if pattern == "peaks":
+        if pattern == "peak":
             return PeaksDetector()
-        if pattern == "jumps" or pattern == "drops":
+        if pattern == "jump" or pattern == "drop":
             return StepDetector(pattern)
         raise ValueError('Unknown pattern "%s"' % pattern)
 

analytics/detectors/peaks_detector.py

@@ -4,7 +4,6 @@ from scipy import signal
 import numpy as np
 
 
-
 class PeaksDetector:
     def __init__(self):
         pass

analytics/detectors/step_detector.py

@@ -1,9 +1,12 @@
-import numpy as np
-import pickle
 import scipy.signal
 from scipy.fftpack import fft
 from scipy.signal import argrelextrema
 
+import numpy as np
+import pickle
+
+
+
 def is_intersect(target_segment, segments):
     for segment in segments:
         start = max(segment['start'], target_segment[0])

analytics/worker.py

@@ -75,7 +75,7 @@ class Worker(object):
         last_prediction_time = model.learn(segments)
         # TODO: we should not do predict before labeling in all models, not just in drops
         
-        if pattern == 'drops' and len(segments) == 0:
+        if pattern == 'drop' and len(segments) == 0:
             # TODO: move result to a class which renders to json for messaging to analytics
             result = {
                 'status': 'success',
@@ -101,12 +101,12 @@ class Worker(object):
             'lastPredictionTime': last_prediction_time
         }
 
-    def get_model(self, analytic_unit_id, pattern):
+    def get_model(self, analytic_unit_id, pattern_type):
         if analytic_unit_id not in self.models_cache:
-            if pattern.find('general') != -1:
+            if pattern_type == 'general':
                 model = GeneralDetector(analytic_unit_id)
             else:
-                model = PatternDetectionModel(analytic_unit_id, pattern)
+                model = PatternDetectionModel(analytic_unit_id, pattern_type)
             self.models_cache[analytic_unit_id] = model
         return self.models_cache[analytic_unit_id]