new fix

utils.py

@@ -46,7 +46,7 @@ def validateData(file, timestamp_column:str=None):
 
     assert df.isna().sum().sum() == 0, "Missing values detected in the uploaded data. Please ensure your CSV file does not contain any missing values (NaNs) for accurate anomaly detection."
     assert np.isfinite(df.select_dtypes(include=[np.number])).all().all(), "Non-finite values detected in the uploaded data. Please ensure your CSV file does not contain any non-finite values (e.g., inf, -inf) for accurate anomaly detection."
-    assert 'object' not in df.columns.dtype, "Non-numeric value columns detected. Please ensure all value columns in your CSV file contain numeric data for accurate anomaly detection."
+    assert df.select_dtypes(exclude=[np.number]).columns.empty, "Non-numeric value columns detected. Please ensure all value columns in your CSV file contain numeric data for accurate anomaly detection."
 
 
 
@@ -223,7 +223,7 @@ def aggregateAnomalyScores(continuousScores: dict[str, np.ndarray], percentile:
         pd.Series: A pandas Series containing the discrete anomaly labels (1 for anomaly, 0 for normal) for each time point.
     """
     # Normalize scores per column before aggregation
-    normalized_scores = {(scores - np.median(scores)) / (np.percentile(scores, 75) - np.percentile(scores, 25) + 1e-8) for col, scores in continuousScores.items()}
+    normalized_scores = {col:(scores - np.median(scores)) / (np.percentile(scores, 75) - np.percentile(scores, 25) + 1e-8) for col, scores in continuousScores.items()}
 
     # Stack normalized scores and aggregate
     aggregated_scores = np.mean(np.column_stack(list(normalized_scores.values())), axis=1)
@@ -315,8 +315,10 @@ def plotResults(df, target_cols:list[str]=None)->plt.Figure|None:
         for col in target_cols:
             ax.plot(df['timestamp_2'], df[col], label=col)
 
-        anomalies = df[df['anomaly_label'] == 1]
-        ax.scatter(anomalies['timestamp_2'], anomalies[target_cols], color='red', label='Anomalies', marker='x')
+        for _, row in df[df['anomaly_label'] == 1].iterrows(): 
+            ax.axvspan( row['timestamp_2'] - pd.Timedelta(minutes=0.5), row['timestamp_2'] + pd.Timedelta(minutes=0.5), 
+                    color='red', alpha=0.15 )
+        
         ax.legend()
         ax.set_title('Time Series with Detected Anomalies')
         ax.set_xlabel('Timestamp')