Fixing KCoefficient formula.

1 files changed, 20 insertions, 9 deletions

diff --git a/src/argaze/GazeAnalysis/KCoefficient.py b/src/argaze/GazeAnalysis/KCoefficient.py
index 80fe1fd..c50bc3a 100644
--- a/src/argaze/GazeAnalysis/KCoefficient.py
+++ b/src/argaze/GazeAnalysis/KCoefficient.py
@@ -52,19 +52,24 @@ class ScanPathAnalyzer(GazeFeatures.ScanPathAnalyzer):
         duration_std = numpy.std(durations)
         amplitude_std = numpy.std(amplitudes)
 
-        Ks = []
-        for scan_step in scan_path:
+        if duration_std > 0. and amplitude_std > 0.:
+
+            Ks = []
+            for scan_step in scan_path:
+
+                Ks.append((abs(scan_step.duration - duration_mean) / duration_std) - (abs(scan_step.last_saccade.amplitude - amplitude_mean) / amplitude_std))
+
+            self.__K = numpy.array(Ks).mean()
 
-            Ks.append(((scan_step.duration - duration_mean) / duration_std) - ((scan_step.last_saccade.amplitude - amplitude_mean) / amplitude_std))
+        else:
 
-        self.__K = numpy.array(Ks).mean()
+            self.__K = 0.
 
     @property
     def K(self) -> float:
         """K coefficient."""
 
         return self.__K
-    
 
 @dataclass
 class AOIScanPathAnalyzer(GazeFeatures.AOIScanPathAnalyzer):
@@ -104,12 +109,18 @@ class AOIScanPathAnalyzer(GazeFeatures.AOIScanPathAnalyzer):
         duration_std = numpy.std(durations)
         amplitude_std = numpy.std(amplitudes)
 
-        Ks = []
-        for aoi_scan_step in aoi_scan_path:
+        if duration_std > 0. and amplitude_std > 0.:
+
+            Ks = []
+            for aoi_scan_step in aoi_scan_path:
+
+                Ks.append((abs(aoi_scan_step.duration - duration_mean) / duration_std) - (abs(aoi_scan_step.last_saccade.amplitude - amplitude_mean) / amplitude_std))
+
+            self.__K = numpy.array(Ks).mean()
 
-            Ks.append(((aoi_scan_step.duration - duration_mean) / duration_std) - ((aoi_scan_step.last_saccade.amplitude - amplitude_mean) / amplitude_std))
+        else:
 
-        self.__K = numpy.array(Ks).mean()
+            self.__K = 0.
 
     @property
     def K(self) -> float: