chg: Compatibility with new db

client.py

@@ -44,17 +44,13 @@ def main():
             algo = Wpd(fp, pre, filter, scoring, detection, post)
             getAlgoResults(algo, config)
 
-        # Return data
-        # Calculate final statistics
-        config['stats']['overall_error'] = 1 - abs(config['stats']['steps'] - config['stats']['ground_truth'] / config['stats']['ground_truth'])
-
-        # Calculate algorithm score
+        # Calculate algorithm accuracy
         score = 0
         n = 0
         for key in list(config['results'].keys()):
-            score += config['results'][key]['score']
+            score += config['results'][key]['accuracy']
             n += 1
-        config['stats']['score'] = score / n
+        config['stats']['accuracy'] = score / n
 
         res = requests.post(return_url, headers=headers, data=json.dumps(config))
 
@@ -85,10 +81,7 @@ def getAlgoResults(algorithm, config):
     # Add entry to results.
     config['results'][algorithm.filelocation] = dict()
     # Accuracy
-    config['results'][algorithm.filelocation]['error'] = 1 - abs(result[0] - result[1] / result[1])
-
-    # Calculate rating.
-    config['results'][algorithm.filelocation]['score'] = 1 - config['results'][algorithm.filelocation]['error']
+    config['results'][algorithm.filelocation]['accuracy'] = 1 - abs(result[0] - result[1]) / result[1]
 
 if __name__ == "__main__":
     main()

config.json

@@ -7,22 +7,23 @@
         "ts_factor": 1000000
       },
       "filter": {
-        "window_size": 43,
-        "type": "kaiser_bessel",
+        "window_size": 51,
+        "type": "gaussian",
+        "std": 0.3,
         "cutoff_freq": 3,
         "sample_freq": 100
       },
       "scoring": {
-        "window_size": 5,
-        "type": "max_diff"
+        "window_size": 11,
+        "type": "mean_diff"
       },
       "detection": {
         "threshold": 1.2
       },
       "post": {
-        "time_threshold": 250
+        "time_threshold": 200
       }
     }
   },
-  "file_path": "../data/Jamie_Hard_InHand_1/"
+  "file_path": "../data/Samsung_Carmelo_Hard_FrontPocket_1/"
 }

src/algorithms/peakDetection/peakFuncs.py

@@ -179,7 +179,7 @@ def __init__(self, params, smoothData, smoothDataList, peakScores):
             self.target = self.meanDiff
         elif self.typ == 'pan_tompkins':
             self.target = self.panTompkins
-        elif self.typ == 'pass':
+        elif self.typ == 'pass_through':
             self.target = self.passThrough
         else:
             raise Exception('Unknown peak scorer type: ' + self.typ)

src/main.py

@@ -38,7 +38,8 @@ def main():
     print("Algorithm complete. Running comparison.")
     result = algo.compare()
     print(result)
-    ui = UI(algo)
+    print(1 - abs((result[1] - result[0]) / result[1]))
+    #ui = UI(algo)
 
 
 # Entry point