Added option to overlap folds for Boxplot method.

Troubled_Cell_Detector.py

@@ -4,12 +4,12 @@
 
 TODO: Introduce Adjusted Outer Fence method in Boxplot using global_mean
     -> Done
-TODO: Introduce overlapping cell for adjacent folds in Boxplot
+TODO: Introduce overlapping cells for adjacent folds in Boxplot -> Done
+TODO: Extract fold computing from TC checking
 TODO: Introduce lower/upper extreme outliers in Boxplot
     (each cell is also checked for neighboring domains if existing)
 TODO: Determine max_value for Theoretical only over highest degree
 TODO: Check if indexing in wavelets is correct
-TODO: Extract fold computing from TC checking
 TODO: Add ThresholdDetector
 TODO: Add TC condition to only flag cell if left-adjacent one is flagged as
     well (remove this condition)
@@ -342,6 +342,8 @@ class Boxplot(WaveletDetector):
         Length of Boxplot whiskers.
     adjust_outer_fences : bool
         Flag whether outer fences should be adjusted using global mean.
+    num_overlapping_cells : int
+        Number of cells overlapping with adjacent folds.
 
     """
     def _reset(self, config):
@@ -359,6 +361,7 @@ class Boxplot(WaveletDetector):
         self._fold_len = config.pop('fold_len', 16)
         self._whisker_len = config.pop('whisker_len', 3)
         self._adjust_outer_fences = config.pop('adjust_outer_fences', True)
+        self._num_overlapping_cells = config.pop('num_overlapping_cells', 1)
 
     def _get_cells(self, multiwavelet_coeffs, projection):
         """Calculates troubled cells using multiwavelet coefficients.
@@ -392,8 +395,11 @@ class Boxplot(WaveletDetector):
         troubled_cells = []
 
         for fold in range(num_folds):
-            sorted_fold = sorted(indexed_coeffs[fold * self._fold_len:
-                                                (fold+1) * self._fold_len])
+            indices = np.array([i % len(indexed_coeffs) for i in range(
+                fold * self._fold_len - self._num_overlapping_cells,
+                (fold+1) * self._fold_len + self._num_overlapping_cells)])
+            sorted_fold = sorted(np.array(indexed_coeffs)[indices],
+                                 key=lambda x: x[0])
 
             boundary_index = self._fold_len//4
             balance_factor = self._fold_len/4.0 - boundary_index
@@ -422,7 +428,7 @@ class Boxplot(WaveletDetector):
                 else:
                     break
 
-            # Check for lower extreme outliers and add respective cells
+            # Check for upper extreme outliers and add respective cells
             for cell in sorted_fold[::-1][:]:
                 if cell[0] > upper_bound:
                     troubled_cells.append(cell[1])