Changed data normalization scaling

Author: Dmytro Trotsko

src/alternative_interface/utils.py

@@ -376,12 +376,52 @@ def series_label_of(key):
     }
 
 
-def normalize_dataset(data):
+def normalize_dataset(data, day_labels=None, initial_view_start=None, initial_view_end=None):
     """
-    Normalize a dataset to 0-100% range based on its min/max.
+    Scale a dataset so that its highest value during the displayed 2 years is set to 100.
+    Multiplies each value by a constant (100 / max_value_in_range).
     Preserves None values for missing data.
     """
-    # Filter out None values for min/max calculation
+    if not data:
+        return data
+
+    # If we have the initial view range, scale based on max value in that range
+    if day_labels and initial_view_start and initial_view_end and len(day_labels) == len(data):
+        # Find indices that fall within the initial view range (2 years)
+        view_indices = []
+        for i, day_label in enumerate(day_labels):
+            if initial_view_start <= day_label <= initial_view_end:
+                view_indices.append(i)
+
+        # Find max value only in the view range
+        view_values = [
+            data[i]
+            for i in view_indices
+            if i < len(data) and data[i] is not None and not (
+                isinstance(data[i], float) and (data[i] != data[i] or data[i] in (float("inf"), float("-inf")))
+            )
+        ]
+
+        if view_values:
+            max_val_in_range = max(view_values)
+            if max_val_in_range > 0:
+                # Scale factor: multiply by (100 / max_value_in_range)
+                scale_factor = 100.0 / max_val_in_range
+
+                # Scale all values in the dataset
+                normalized = []
+                for value in data:
+                    if value is None:
+                        normalized.append(None)
+                    elif isinstance(value, float) and (
+                        value != value or value in (float("inf"), float("-inf"))
+                    ):
+                        normalized.append(None)
+                    else:
+                        normalized.append(value * scale_factor)
+                return normalized
+
+    # Fallback: if no view range provided, scale based on max value in entire dataset
     numeric_values = [
         v
         for v in data
@@ -393,11 +433,14 @@ def normalize_dataset(data):
     if not numeric_values:
         return data  # Return as-is if no valid numeric values
 
-    min_val = min(numeric_values)
     max_val = max(numeric_values)
-    range_val = (max_val - min_val) or 1  # Avoid division by zero
+    if max_val <= 0:
+        return data  # Return as-is if max is 0 or negative
+
+    # Scale so max value = 100
+    scale_factor = 100.0 / max_val
 
-    # Normalize each value
+    # Scale each value
     normalized = []
     for value in data:
         if value is None:
@@ -407,7 +450,7 @@ def normalize_dataset(data):
         ):
             normalized.append(None)
         else:
-            normalized.append(((value - min_val) / range_val) * 100)
+            normalized.append(value * scale_factor)
 
     return normalized
 
@@ -465,18 +508,24 @@ def get_chart_data(indicators, geography):
                 series_by="signal",  # label per indicator (adjust to ("signal","geo_value") if needed)
                 time_type=indicator_time_type,
             )
+            # Initialize labels once; assume same date range for all
+            if not chart_data["labels"]:
+                chart_data["labels"] = series["labels"]
+                chart_data["dayLabels"] = series["dayLabels"]
+                chart_data["timePositions"] = series["timePositions"]
+
             # Apply readable label, color, and normalize data for each dataset
             for ds in series["datasets"]:
                 ds["label"] = title
                 ds["borderColor"] = color
                 ds["backgroundColor"] = f"{color}33"
-                # Normalize data to 0-100% range
+                # Scale data so max value in displayed 2 years = 100
                 if ds.get("data"):
-                    ds["data"] = normalize_dataset(ds["data"])
-            # Initialize labels once; assume same date range for all
-            if not chart_data["labels"]:
-                chart_data["labels"] = series["labels"]
-                chart_data["dayLabels"] = series["dayLabels"]
-                chart_data["timePositions"] = series["timePositions"]
+                    ds["data"] = normalize_dataset(
+                        ds["data"],
+                        day_labels=chart_data["dayLabels"],
+                        initial_view_start=chart_data["initialViewStart"],
+                        initial_view_end=chart_data["initialViewEnd"]
+                    )
             chart_data["datasets"].extend(series["datasets"])
     return chart_data