fix: metrics for remaining segment types

.gitignore

@@ -10,3 +10,4 @@ htmlcov/
 .pytest_cache/
 tests/testtemp.py
 site/
+poetry.lock

pytrendy/io/results_pytrendy.py

@@ -35,7 +35,7 @@ def set_best(self) -> None:
             - Identifies the best trend segment based on steepness and duration.
             - The segment with the lowest `change_rank` is selected as the best.
         """
-        if len(self.segments) == 0 or not any('change_rank' in segment for segment in self.segments):
+        if len(self.segments) == 0 or not any('change_rank' in segment for segment in [s for s in self.segments if s.get('direction') in ['Up', 'Down']]):
             self.best = None
             return
         self.best = min(self.segments, key=lambda x: x.get('change_rank', math.inf))

pytrendy/post_processing/segments_analyse.py

@@ -60,6 +60,12 @@ def analyse_segments(df: pd.DataFrame, value_col: str, segments: list[dict]) ->
             segment_enhanced['pct_change'] = (
                 float(val_min / val_max - 1) if val_max != 0 else np.nan
             )
+        # I think this should capture all other segment types
+        else:
+            abs_change = float(abs(val_max - val_min))
+            segment_enhanced['change'] = abs_change
+            mean_val = df_segment[value_col].mean()
+            segment_enhanced['pct_change'] = (float(abs_change / mean_val * 100) if mean_val != 0 else np.nan)
 
         # Calculate days & cumulative total change
         days = (pd.to_datetime(segment['end']) - pd.to_datetime(segment['start'])).days
@@ -68,8 +74,7 @@ def analyse_segments(df: pd.DataFrame, value_col: str, segments: list[dict]) ->
         segment_enhanced['days'] = days # set days
 
         # Calculate cumulative total change
-        if segment['direction'] in ['Up', 'Down']:
-            segment_enhanced['total_change'] = float(df_segment[value_col].diff().sum())
+        segment_enhanced['total_change'] = float(df_segment[value_col].diff().sum())
 
         # Calculate Signal to Noise Ratio
         signal_power = np.mean(df_segment['signal']**2)
@@ -83,8 +88,7 @@ def analyse_segments(df: pd.DataFrame, value_col: str, segments: list[dict]) ->
 
     # Rank change, by steepest to shallowest change
     sorted_segments = sorted(segments_enhanced, key=lambda x: abs(x.get('total_change', 0)), reverse=True)
-    sorted_trends = [seg for seg in sorted_segments if 'total_change' in seg and abs(seg['total_change']) > 0]
-    for i, seg in enumerate(sorted_trends):
+    for i, seg in enumerate(sorted_segments):
         j = seg['time_index'] - 1
         segments_enhanced[j]['change_rank'] = int(i+1)
 

pytrendy/post_processing/segments_refine/artifact_cleanup.py

@@ -311,11 +311,11 @@ def has_partial_overlap_prev(segment: dict, segment_prev: dict) -> bool:
         # Reclassify as noise if either edge cases met
         if too_noisy or (is_abrupt_near_noise and not trend_ends_too_close) or is_small_gradual_in_noise:
             segment['direction'] = 'Noise' 
-            if 'trend_class' in segment: del segment['trend_class']
+            segment['trend_class'] = 'noise'
 
         if trend_ends_too_close or trend_too_small or trend_too_flat:
             segment['direction'] = 'Flat' 
-            if 'trend_class' in segment: del segment['trend_class']
+            segment['trend_class'] = 'flat'
 
         segments_refined.append(segment)
 

pytrendy/post_processing/segments_refine/trend_classify.py

@@ -75,4 +75,13 @@ def classify_trends(df: pd.DataFrame, value_col: str, segments: list[dict]) -> l
         if segment_length < 3:
             segments_classified[i]['trend_class'] = 'abrupt'
 
+    for segment in segments_classified:
+        if 'trend_class' not in segment:
+            if segment['direction'] == 'Flat':
+                segment['trend_class'] = 'flat'
+            elif segment['direction'] == 'Noise':
+                segment['trend_class'] = 'noise'
+            else:
+                segment['trend_class'] = 'unknown'
+
     return segments_classified