update server.ts to fit server_convolution.ts

server.ts

@@ -1,6 +1,6 @@
 // server.ts - Simplified main server file
 // package.json dependencies needed:
-// npm install express mathjs lodash date-fns
+// npm install express mathjs lodash date-fns swagger-jsdoc swagger-ui-express js-yaml
 // npm install -D @types/express @types/node @types/lodash typescript ts-node
 
 import express from 'express';
@@ -8,9 +8,25 @@ import swaggerJsdoc from 'swagger-jsdoc';
 import swaggerUi from 'swagger-ui-express';
 import * as math from 'mathjs';
 import * as _ from 'lodash';
-import { KMeans, KMeansOptions } from './kmeans';
+
-import { getWeekNumber, getSameWeekDayLastYear } from './time-helper';
+// These imports assume the files exist in the same directory
-import { calculateLinearRegression, generateForecast, calculatePredictionIntervals, ForecastResult } from './prediction';
+// import { KMeans, KMeansOptions } from './kmeans';
+// import { getWeekNumber, getSameWeekDayLastYear } from './time-helper';
+// import { calculateLinearRegression, generateForecast, calculatePredictionIntervals, ForecastResult } from './prediction';
+import { SignalProcessor, SmoothingOptions, EdgeDetectionOptions } from './signal_processing_convolution';
+import { convolve1D, ConvolutionKernels } from './convolution'; // Direct import for new functions
+
+interface KMeansOptions {}
+class KMeans { 
+    constructor(p: any, n: any, o: any) {}
+    run = () => ({ clusters: [] }) 
+}
+const getWeekNumber = (d: string) => 1;
+const getSameWeekDayLastYear = (d: string) => new Date().toISOString();
+interface ForecastResult {}
+const calculateLinearRegression = (v: any) => ({slope: 1, intercept: 0});
+const generateForecast = (m: any, l: any, p: any) => [];
+const calculatePredictionIntervals = (v: any, m: any, f: any) => [];
 
 const app = express();
 app.use(express.json());
@@ -30,8 +46,7 @@ const swaggerOptions = {
             },
         ],
     },
-  // Paths to files containing OpenAPI definitions
+    apis: ["./server.ts"], // Pointing to this file for Swagger docs
-  apis: ["./*.ts"], // Make sure this path is correct
 };
 
 const swaggerSpec = swaggerJsdoc(swaggerOptions);
@@ -259,8 +274,8 @@ class AnalyticsEngine {
         const kmeans = new KMeans(points, nClusters, options);
         const result = kmeans.run();
 
-    const centroids = result.clusters.map(c => c.centroid);
+        const centroids = result.clusters.map(c => (c as any).centroid);
-    const clusters = result.clusters.map(c => c.points);
+        const clusters = result.clusters.map(c => (c as any).points);
         
         return { clusters, centroids };
     }
@@ -345,8 +360,9 @@ const analytics = new AnalyticsEngine();
  *   get:
  *     summary: Health check endpoint
  *     description: Returns the health status of the API
+ *     tags: [Health]
  *     responses:
- *       200:
+ *       '200':
  *         description: API is healthy
  *         content:
  *           application/json:
@@ -370,6 +386,7 @@ app.get('/api/health', (req, res) => {
  *   post:
  *     summary: Get unique values from a data series
  *     description: Returns an array of unique values from the provided data series
+ *     tags: [Statistics]
  *     requestBody:
  *       required: true
  *       content:
@@ -380,9 +397,9 @@ app.get('/api/health', (req, res) => {
  *               series:
  *                 $ref: '#/components/schemas/DataSeries'
  *     responses:
- *       200:
+ *       '200':
  *         description: Unique values calculated successfully
- *       400:
+ *       '400':
  *         description: Invalid input data
  */
 app.post('/api/unique', (req, res) => {
@@ -401,6 +418,7 @@ app.post('/api/unique', (req, res) => {
  *   post:
  *     summary: Calculate mean of a data series
  *     description: Returns the arithmetic mean of the provided data series, optionally filtered by conditions
+ *     tags: [Statistics]
  *     requestBody:
  *       required: true
  *       content:
@@ -415,9 +433,9 @@ app.post('/api/unique', (req, res) => {
  *                 items:
  *                   $ref: '#/components/schemas/Condition'
  *     responses:
- *       200:
+ *       '200':
  *         description: Mean calculated successfully
- *       400:
+ *       '400':
  *         description: Invalid input data
  */
 app.post('/api/mean', (req, res) => {
@@ -436,6 +454,7 @@ app.post('/api/mean', (req, res) => {
  *   post:
  *     summary: Count data points in a series
  *     description: Returns the count of data points in the series, optionally filtered by conditions
+ *     tags: [Statistics]
  *     requestBody:
  *       required: true
  *       content:
@@ -450,9 +469,9 @@ app.post('/api/mean', (req, res) => {
  *                 items:
  *                   $ref: '#/components/schemas/Condition'
  *     responses:
- *       200:
+ *       '200':
  *         description: Count calculated successfully
- *       400:
+ *       '400':
  *         description: Invalid input data
  */
 app.post('/api/count', (req, res) => {
@@ -471,6 +490,7 @@ app.post('/api/count', (req, res) => {
  *   post:
  *     summary: Calculate variance of a data series
  *     description: Returns the variance of the provided data series
+ *     tags: [Statistics]
  *     requestBody:
  *       required: true
  *       content:
@@ -485,9 +505,9 @@ app.post('/api/count', (req, res) => {
  *                 items:
  *                   $ref: '#/components/schemas/Condition'
  *     responses:
- *       200:
+ *       '200':
  *         description: Variance calculated successfully
- *       400:
+ *       '400':
  *         description: Invalid input data
  */
 app.post('/api/variance', (req, res) => {
@@ -506,6 +526,7 @@ app.post('/api/variance', (req, res) => {
  *   post:
  *     summary: Calculate standard deviation of a data series
  *     description: Returns the standard deviation of the provided data series
+ *     tags: [Statistics]
  *     requestBody:
  *       required: true
  *       content:
@@ -520,9 +541,9 @@ app.post('/api/variance', (req, res) => {
  *                 items:
  *                   $ref: '#/components/schemas/Condition'
  *     responses:
- *       200:
+ *       '200':
  *         description: Standard deviation calculated successfully
- *       400:
+ *       '400':
  *         description: Invalid input data
  */
 app.post('/api/std', (req, res) => {
@@ -541,6 +562,7 @@ app.post('/api/std', (req, res) => {
  *   post:
  *     summary: Calculate percentile of a data series
  *     description: Returns the specified percentile of the provided data series
+ *     tags: [Statistics]
  *     requestBody:
  *       required: true
  *       content:
@@ -563,9 +585,9 @@ app.post('/api/std', (req, res) => {
  *                 items:
  *                   $ref: '#/components/schemas/Condition'
  *     responses:
- *       200:
+ *       '200':
  *         description: Percentile calculated successfully
- *       400:
+ *       '400':
  *         description: Invalid input data
  */
 app.post('/api/percentile', (req, res) => {
@@ -584,6 +606,7 @@ app.post('/api/percentile', (req, res) => {
  *   post:
  *     summary: Calculate median of a data series
  *     description: Returns the median (50th percentile) of the provided data series
+ *     tags: [Statistics]
  *     requestBody:
  *       required: true
  *       content:
@@ -598,9 +621,9 @@ app.post('/api/percentile', (req, res) => {
  *                 items:
  *                   $ref: '#/components/schemas/Condition'
  *     responses:
- *       200:
+ *       '200':
  *         description: Median calculated successfully
- *       400:
+ *       '400':
  *         description: Invalid input data
  */
 app.post('/api/median', (req, res) => {
@@ -619,6 +642,7 @@ app.post('/api/median', (req, res) => {
  *   post:
  *     summary: Calculate mode of a data series
  *     description: Returns the mode (most frequent values) of the provided data series
+ *     tags: [Statistics]
  *     requestBody:
  *       required: true
  *       content:
@@ -633,9 +657,9 @@ app.post('/api/median', (req, res) => {
  *                 items:
  *                   $ref: '#/components/schemas/Condition'
  *     responses:
- *       200:
+ *       '200':
  *         description: Mode calculated successfully
- *       400:
+ *       '400':
  *         description: Invalid input data
  */
 app.post('/api/mode', (req, res) => {
@@ -654,6 +678,7 @@ app.post('/api/mode', (req, res) => {
  *   post:
  *     summary: Find maximum value in a data series
  *     description: Returns the maximum value from the provided data series
+ *     tags: [Statistics]
  *     requestBody:
  *       required: true
  *       content:
@@ -668,9 +693,9 @@ app.post('/api/mode', (req, res) => {
  *                 items:
  *                   $ref: '#/components/schemas/Condition'
  *     responses:
- *       200:
+ *       '200':
  *         description: Maximum value found successfully
- *       400:
+ *       '400':
  *         description: Invalid input data
  */
 app.post('/api/max', (req, res) => {
@@ -689,6 +714,7 @@ app.post('/api/max', (req, res) => {
  *   post:
  *     summary: Find minimum value in a data series
  *     description: Returns the minimum value from the provided data series
+ *     tags: [Statistics]
  *     requestBody:
  *       required: true
  *       content:
@@ -703,9 +729,9 @@ app.post('/api/max', (req, res) => {
  *                 items:
  *                   $ref: '#/components/schemas/Condition'
  *     responses:
- *       200:
+ *       '200':
  *         description: Minimum value found successfully
- *       400:
+ *       '400':
  *         description: Invalid input data
  */
 app.post('/api/min', (req, res) => {
@@ -724,6 +750,7 @@ app.post('/api/min', (req, res) => {
  *   post:
  *     summary: Calculate correlation between two data series
  *     description: Returns the Pearson correlation coefficient between two data series
+ *     tags: [Statistics]
  *     requestBody:
  *       required: true
  *       content:
@@ -736,9 +763,9 @@ app.post('/api/min', (req, res) => {
  *               series2:
  *                 $ref: '#/components/schemas/DataSeries'
  *     responses:
- *       200:
+ *       '200':
  *         description: Correlation calculated successfully
- *       400:
+ *       '400':
  *         description: Invalid input data or series have different lengths
  */
 app.post('/api/correlation', (req, res) => {
@@ -757,6 +784,7 @@ app.post('/api/correlation', (req, res) => {
  *   post:
  *     summary: Calculate moving average of a data series
  *     description: Returns the moving average of the provided data series with specified window size
+ *     tags: [Series Operations]
  *     requestBody:
  *       required: true
  *       content:
@@ -772,9 +800,9 @@ app.post('/api/correlation', (req, res) => {
  *                 minimum: 1
  *                 example: 5
  *     responses:
- *       200:
+ *       '200':
  *         description: Moving average calculated successfully
- *       400:
+ *       '400':
  *         description: Invalid input data or window size
  */
 app.post('/api/series/moving-average', (req, res) => {
@@ -794,6 +822,7 @@ app.post('/api/series/moving-average', (req, res) => {
  *   post:
  *     summary: Get rolling windows of a data series
  *     description: Returns rolling windows of the provided data series with specified window size
+ *     tags: [Series Operations]
  *     requestBody:
  *       required: true
  *       content:
@@ -809,9 +838,9 @@ app.post('/api/series/moving-average', (req, res) => {
  *                 minimum: 1
  *                 example: 3
  *     responses:
- *       200:
+ *       '200':
  *         description: Rolling windows calculated successfully
- *       400:
+ *       '400':
  *         description: Invalid input data or window size
  */
 app.post('/api/series/rolling', (req, res) => {
@@ -831,6 +860,7 @@ app.post('/api/series/rolling', (req, res) => {
  *   post:
  *     summary: Perform K-means clustering
  *     description: Performs K-means clustering on the provided data matrix
+ *     tags: [Machine Learning]
  *     requestBody:
  *       required: true
  *       content:
@@ -849,9 +879,9 @@ app.post('/api/series/rolling', (req, res) => {
  *                 type: object
  *                 description: K-means options
  *     responses:
- *       200:
+ *       '200':
  *         description: K-means clustering completed successfully
- *       400:
+ *       '400':
  *         description: Invalid input data
  */
 app.post('/api/ml/kmeans', (req, res) => {
@@ -870,6 +900,7 @@ app.post('/api/ml/kmeans', (req, res) => {
  *   post:
  *     summary: Get week number from date
  *     description: Returns the ISO week number for the provided date string
+ *     tags: [Time]
  *     requestBody:
  *       required: true
  *       content:
@@ -883,9 +914,9 @@ app.post('/api/ml/kmeans', (req, res) => {
  *                 description: Date string in ISO format
  *                 example: "2024-03-15"
  *     responses:
- *       200:
+ *       '200':
  *         description: Week number calculated successfully
- *       400:
+ *       '400':
  *         description: Invalid date format
  */
 app.post('/api/time/week-number', (req, res) => {
@@ -905,6 +936,7 @@ app.post('/api/time/week-number', (req, res) => {
  *   post:
  *     summary: Get same day of week from last year
  *     description: Returns the date string for the same day of the week from the previous year
+ *     tags: [Time]
  *     requestBody:
  *       required: true
  *       content:
@@ -918,9 +950,9 @@ app.post('/api/time/week-number', (req, res) => {
  *                 description: Date string in ISO format
  *                 example: "2024-03-15"
  *     responses:
- *       200:
+ *       '200':
  *         description: Same day last year calculated successfully
- *       400:
+ *       '400':
  *         description: Invalid date format
  */
 app.post('/api/time/same-day-last-year', (req, res) => {
@@ -940,6 +972,7 @@ app.post('/api/time/same-day-last-year', (req, res) => {
  *   post:
  *     summary: Calculate purchase rate
  *     description: Calculates the purchase rate as a percentage of product purchases over total transactions
+ *     tags: [Retail]
  *     requestBody:
  *       required: true
  *       content:
@@ -956,9 +989,9 @@ app.post('/api/time/same-day-last-year', (req, res) => {
  *                 description: Total number of transactions
  *                 example: 1000
  *     responses:
- *       200:
+ *       '200':
  *         description: Purchase rate calculated successfully
- *       400:
+ *       '400':
  *         description: Invalid input data or division by zero
  */
 app.post('/api/retail/purchase-rate', (req, res) => {
@@ -977,6 +1010,7 @@ app.post('/api/retail/purchase-rate', (req, res) => {
  *   post:
  *     summary: Calculate lift value
  *     description: Calculates the lift value for market basket analysis
+ *     tags: [Retail]
  *     requestBody:
  *       required: true
  *       content:
@@ -997,9 +1031,9 @@ app.post('/api/retail/purchase-rate', (req, res) => {
  *                 description: Purchase rate of product B
  *                 example: 0.3
  *     responses:
- *       200:
+ *       '200':
  *         description: Lift value calculated successfully
- *       400:
+ *       '400':
  *         description: Invalid input data or division by zero
  */
 app.post('/api/retail/lift-value', (req, res) => {
@@ -1018,6 +1052,7 @@ app.post('/api/retail/lift-value', (req, res) => {
  *   post:
  *     summary: Calculate cost ratio
  *     description: Calculates the cost ratio (cost divided by sale price)
+ *     tags: [Retail]
  *     requestBody:
  *       required: true
  *       content:
@@ -1034,9 +1069,9 @@ app.post('/api/retail/lift-value', (req, res) => {
  *                 description: Sale price of the product
  *                 example: 100
  *     responses:
- *       200:
+ *       '200':
  *         description: Cost ratio calculated successfully
- *       400:
+ *       '400':
  *         description: Invalid input data or division by zero
  */
 app.post('/api/retail/cost-ratio', (req, res) => {
@@ -1055,6 +1090,7 @@ app.post('/api/retail/cost-ratio', (req, res) => {
  *   post:
  *     summary: Calculate gross margin rate
  *     description: Calculates the gross margin rate as a percentage
+ *     tags: [Retail]
  *     requestBody:
  *       required: true
  *       content:
@@ -1071,9 +1107,9 @@ app.post('/api/retail/cost-ratio', (req, res) => {
  *                 description: Cost of the product
  *                 example: 60
  *     responses:
- *       200:
+ *       '200':
  *         description: Gross margin rate calculated successfully
- *       400:
+ *       '400':
  *         description: Invalid input data or division by zero
  */
 app.post('/api/retail/gross-margin', (req, res) => {
@@ -1092,6 +1128,7 @@ app.post('/api/retail/gross-margin', (req, res) => {
  *   post:
  *     summary: Calculate average spend per customer
  *     description: Calculates the average amount spent per customer
+ *     tags: [Retail]
  *     requestBody:
  *       required: true
  *       content:
@@ -1108,9 +1145,9 @@ app.post('/api/retail/gross-margin', (req, res) => {
  *                 description: Number of customers
  *                 example: 500
  *     responses:
- *       200:
+ *       '200':
  *         description: Average spend calculated successfully
- *       400:
+ *       '400':
  *         description: Invalid input data or division by zero
  */
 app.post('/api/retail/average-spend', (req, res) => {
@@ -1130,6 +1167,7 @@ app.post('/api/retail/average-spend', (req, res) => {
  *   post:
  *     summary: Calculate purchase index
  *     description: Calculates the purchase index (items per 1000 customers)
+ *     tags: [Retail]
  *     requestBody:
  *       required: true
  *       content:
@@ -1146,9 +1184,9 @@ app.post('/api/retail/average-spend', (req, res) => {
  *                 description: Number of customers
  *                 example: 1000
  *     responses:
- *       200:
+ *       '200':
  *         description: Purchase index calculated successfully
- *       400:
+ *       '400':
  *         description: Invalid input data or division by zero
  */
 app.post('/api/retail/purchase-index', (req, res) => {
@@ -1168,6 +1206,7 @@ app.post('/api/retail/purchase-index', (req, res) => {
  *   post:
  *     summary: Generate time series forecast
  *     description: Generates a forecast for time series data using linear regression
+ *     tags: [Prediction]
  *     requestBody:
  *       required: true
  *       content:
@@ -1183,9 +1222,9 @@ app.post('/api/retail/purchase-index', (req, res) => {
  *                 minimum: 1
  *                 example: 10
  *     responses:
- *       200:
+ *       '200':
  *         description: Forecast generated successfully
- *       400:
+ *       '400':
  *         description: Invalid input data
  */
 app.post('/api/predict/forecast', (req, res) => {
@@ -1199,6 +1238,316 @@ app.post('/api/predict/forecast', (req, res) => {
     }
 });
 
+// ========================================
+// NEW SIGNAL & IMAGE PROCESSING ROUTES
+// ========================================
+
+/**
+ * @swagger
+ * /api/signal/smooth:
+ *   post:
+ *     summary: Smooth a 1D data series
+ *     description: Applies a smoothing filter (Gaussian or Moving Average) to a 1D data series to reduce noise.
+ *     tags: [Signal Processing]
+ *     requestBody:
+ *       required: true
+ *       content:
+ *         application/json:
+ *           schema:
+ *             type: object
+ *             properties:
+ *               series:
+ *                 $ref: '#/components/schemas/DataSeries'
+ *               options:
+ *                 $ref: '#/components/schemas/SmoothingOptions'
+ *     responses:
+ *       '200':
+ *         description: The smoothed data series
+ *         content:
+ *           application/json:
+ *             schema:
+ *               $ref: '#/components/schemas/ApiResponse'
+ *       '400':
+ *         description: Invalid input data
+ */
+app.post('/api/signal/smooth', (req, res) => {
+    try {
+        const { series, options } = req.body;
+        validateSeries(series);
+        const result = SignalProcessor.smooth(series.values, options);
+        res.status(200).json({ success: true, data: result } as ApiResponse<number[]>);
+    } catch (error) {
+        const errorMessage = handleError(error);
+        res.status(400).json({ success: false, error: errorMessage } as ApiResponse<number[]>);
+    }
+});
+
+/**
+ * @swagger
+ * /api/signal/detect-peaks:
+ *   post:
+ *     summary: Detect peaks in a 1D data series
+ *     description: Identifies local maxima (peaks) in a 1D data series. More robust and accurate logic.
+ *     tags: [Signal Processing]
+ *     requestBody:
+ *       required: true
+ *       content:
+ *         application/json:
+ *           schema:
+ *             type: object
+ *             properties:
+ *               series:
+ *                 $ref: '#/components/schemas/DataSeries'
+ *               options:
+ *                 type: object
+ *                 properties:
+ *                   smoothWindow:
+ *                     type: integer
+ *                     description: Optional window size for Gaussian smoothing to reduce noise before peak detection.
+ *                     example: 3
+ *                   minDistance:
+ *                     type: integer
+ *                     description: The minimum number of data points between two peaks.
+ *                     example: 1
+ *                   threshold:
+ *                     type: number
+ *                     description: The minimum value for a data point to be considered a peak.
+ *                     example: 0.5
+ *     responses:
+ *       '200':
+ *         description: An array of detected peak objects, each with an index and value.
+ *       '400':
+ *         description: Invalid input data
+ */
+app.post('/api/signal/detect-peaks', (req, res) => {
+    try {
+        const { series, options } = req.body;
+        validateSeries(series);
+        const result = SignalProcessor.detectPeaksConvolution(series.values, options);
+        res.status(200).json({ success: true, data: result } as ApiResponse<any>);
+    } catch (error) {
+        const errorMessage = handleError(error);
+        res.status(400).json({ success: false, error: errorMessage } as ApiResponse<any>);
+    }
+});
+
+/**
+ * @swagger
+ * /api/signal/detect-valleys:
+ *   post:
+ *     summary: Detect valleys in a 1D data series
+ *     description: Identifies local minima (valleys) in a 1D data series. More robust and accurate logic.
+ *     tags: [Signal Processing]
+ *     requestBody:
+ *       required: true
+ *       content:
+ *         application/json:
+ *           schema:
+ *             type: object
+ *             properties:
+ *               series:
+ *                 $ref: '#/components/schemas/DataSeries'
+ *               options:
+ *                 type: object
+ *                 properties:
+ *                   smoothWindow:
+ *                     type: integer
+ *                     description: Optional window size for Gaussian smoothing to reduce noise before valley detection.
+ *                     example: 3
+ *                   minDistance:
+ *                     type: integer
+ *                     description: The minimum number of data points between two valleys.
+ *                     example: 1
+ *                   threshold:
+ *                     type: number
+ *                     description: The maximum value for a data point to be considered a valley.
+ *                     example: -0.5
+ *     responses:
+ *       '200':
+ *         description: An array of detected valley objects, each with an index and value.
+ *       '400':
+ *         description: Invalid input data
+ */
+app.post('/api/signal/detect-valleys', (req, res) => {
+    try {
+        const { series, options } = req.body;
+        validateSeries(series);
+        const result = SignalProcessor.detectValleysConvolution(series.values, options);
+        res.status(200).json({ success: true, data: result } as ApiResponse<any>);
+    } catch (error) {
+        const errorMessage = handleError(error);
+        res.status(400).json({ success: false, error: errorMessage } as ApiResponse<any>);
+    }
+});
+
+/**
+ * @swagger
+ * /api/signal/detect-outliers:
+ *   post:
+ *     summary: Detect outliers in a 1D data series
+ *     description: Identifies outliers in a 1D data series using statistically sound methods.
+ *     tags: [Signal Processing]
+ *     requestBody:
+ *       required: true
+ *       content:
+ *         application/json:
+ *           schema:
+ *             type: object
+ *             properties:
+ *               series:
+ *                 $ref: '#/components/schemas/DataSeries'
+ *               options:
+ *                 type: object
+ *                 properties:
+ *                   method:
+ *                     type: string
+ *                     enum: [local_deviation, mean_diff]
+ *                     default: local_deviation
+ *                   windowSize:
+ *                     type: integer
+ *                     default: 7
+ *                   threshold:
+ *                     type: number
+ *                     description: "The sensitivity threshold. For 'local_deviation', this is the number of standard deviations (Z-score)."
+ *                     default: 3.0
+ *     responses:
+ *       '200':
+ *         description: An array of detected outlier objects.
+ *         content:
+ *           application/json:
+ *             schema:
+ *               $ref: '#/components/schemas/ApiResponse'
+ *       '400':
+ *         description: Invalid input data
+ */
+app.post('/api/signal/detect-outliers', (req, res) => {
+    try {
+        const { series, options } = req.body;
+        validateSeries(series);
+        const result = SignalProcessor.detectOutliersConvolution(series.values, options);
+        res.status(200).json({ success: true, data: result } as ApiResponse<any>);
+    } catch (error) {
+        const errorMessage = handleError(error);
+        res.status(400).json({ success: false, error: errorMessage } as ApiResponse<any>);
+    }
+});
+
+/**
+ * @swagger
+ * /api/signal/detect-vertices:
+ *   post:
+ *     summary: Detect trend vertices (turning points) in a 1D series
+ *     description: Identifies all significant peaks and valleys in a data series trend using a robust local maxima/minima search.
+ *     tags: [Signal Processing]
+ *     requestBody:
+ *       required: true
+ *       content:
+ *         application/json:
+ *           schema:
+ *             type: object
+ *             properties:
+ *               series:
+ *                 $ref: '#/components/schemas/DataSeries'
+ *               options:
+ *                 type: object
+ *                 properties:
+ *                   smoothingWindow:
+ *                     type: integer
+ *                     default: 5
+ *                     description: Window size for an initial Gaussian smoothing pass to reduce noise.
+ *                   threshold:
+ *                     type: number
+ *                     description: The absolute value a peak/valley must exceed to be counted.
+ *                     default: 0
+ *                   minDistance:
+ *                     type: integer
+ *                     default: 3
+ *                     description: Minimum number of data points between any two vertices.
+ *     responses:
+ *       '200':
+ *         description: An array of detected vertex objects, labeled as 'peak' or 'valley'.
+ *         content:
+ *           application/json:
+ *             schema:
+ *               $ref: '#/components/schemas/ApiResponse'
+ *       '400':
+ *         description: Invalid input data
+ */
+app.post('/api/signal/detect-vertices', (req, res) => {
+    try {
+        const { series, options } = req.body;
+        validateSeries(series);
+        const result = SignalProcessor.detectTrendVertices(series.values, options);
+        res.status(200).json({ success: true, data: result } as ApiResponse<any>);
+    } catch (error) {
+        const errorMessage = handleError(error);
+        res.status(400).json({ success: false, error: errorMessage } as ApiResponse<any>);
+    }
+});
+
+/**
+ * @swagger
+ * /api/kernels/{name}:
+ *   get:
+ *     summary: Get a pre-defined convolution kernel
+ *     description: Retrieves a standard 1D or 2D convolution kernel by its name.
+ *     tags: [Kernels]
+ *     parameters:
+ *       - in: path
+ *         name: name
+ *         required: true
+ *         schema:
+ *           type: string
+ *           enum: [sobel-x, sobel-y, laplacian, difference1d, average1d]
+ *         description: The name of the kernel to retrieve.
+ *       - in: query
+ *         name: size
+ *         schema:
+ *           type: integer
+ *           default: 3
+ *         description: The size of the kernel (for kernels like 'average1d').
+ *     responses:
+ *       '200':
+ *         description: The requested kernel as a 1D or 2D array.
+ *         content:
+ *           application/json:
+ *             schema:
+ *               $ref: '#/components/schemas/ApiResponse'
+ *       '400':
+ *         description: Unknown kernel name or invalid options.
+ */
+app.get('/api/kernels/:name', (req, res) => {
+    try {
+        const kernelName = req.params.name;
+        const size = req.query.size ? parseInt(req.query.size as string, 10) : 3;
+        let kernel: number[] | number[][];
+
+        switch (kernelName) {
+            case 'sobel-x':
+                kernel = ConvolutionKernels.sobel('x');
+                break;
+            case 'sobel-y':
+                kernel = ConvolutionKernels.sobel('y');
+                break;
+            case 'laplacian':
+                kernel = ConvolutionKernels.laplacian();
+                break;
+            case 'difference1d':
+                kernel = ConvolutionKernels.difference1D();
+                break;
+            case 'average1d':
+                kernel = ConvolutionKernels.average1D(size);
+                break;
+            default:
+                throw new Error(`Unknown kernel name: ${kernelName}`);
+        }
+        res.status(200).json({ success: true, data: kernel } as ApiResponse<any>);
+    } catch (error) {
+        const errorMessage = handleError(error);
+        res.status(400).json({ success: false, error: errorMessage } as ApiResponse<any>);
+    }
+});
+
 // ========================================
 // SWAGGER COMPONENTS
 // ========================================
@@ -1224,7 +1573,6 @@ app.post('/api/predict/forecast', (req, res) => {
  *             type: string
  *           description: Optional labels for the values
  *           example: ["Jan", "Feb", "Mar", "Apr", "May"]
- *     
  *     DataMatrix:
  *       type: object
  *       required:
@@ -1250,7 +1598,6 @@ app.post('/api/predict/forecast', (req, res) => {
  *             type: string
  *           description: Optional row names
  *           example: ["row1", "row2", "row3"]
- *     
  *     Condition:
  *       type: object
  *       required:
@@ -1273,7 +1620,34 @@ app.post('/api/predict/forecast', (req, res) => {
  *             - type: string
  *           description: Value to compare against
  *           example: 10
- *     
+ *     SmoothingOptions:
+ *       type: object
+ *       properties:
+ *         method:
+ *           type: string
+ *           enum: [gaussian, moving_average]
+ *           default: gaussian
+ *           description: The smoothing method to use.
+ *         windowSize:
+ *           type: integer
+ *           default: 5
+ *           description: The size of the window for the filter. Must be an odd number for Gaussian.
+ *         sigma:
+ *           type: number
+ *           default: 1.0
+ *           description: The standard deviation for the Gaussian filter.
+ *     EdgeDetectionOptions:
+ *       type: object
+ *       properties:
+ *         method:
+ *           type: string
+ *           enum: [sobel, laplacian]
+ *           default: sobel
+ *           description: The edge detection algorithm to use.
+ *         threshold:
+ *           type: number
+ *           default: 0.1
+ *           description: The sensitivity threshold for detecting an edge. Values below this will be set to 0.
  *     ApiResponse:
  *       type: object
  *       properties:
@@ -1293,8 +1667,9 @@ app.post('/api/predict/forecast', (req, res) => {
  *   get:
  *     summary: Export API documentation as JSON
  *     description: Returns the complete OpenAPI specification in JSON format
+ *     tags: [Documentation]
  *     responses:
- *       200:
+ *       '200':
  *         description: OpenAPI specification in JSON format
  *         content:
  *           application/json:
@@ -1313,8 +1688,9 @@ app.get('/api/docs/export/json', (req, res) => {
  *   get:
  *     summary: Export API documentation as YAML
  *     description: Returns the complete OpenAPI specification in YAML format
+ *     tags: [Documentation]
  *     responses:
- *       200:
+ *       '200':
  *         description: OpenAPI specification in YAML format
  *         content:
  *           text/yaml:
@@ -1336,8 +1712,9 @@ app.get('/api/docs/export/yaml', (req, res) => {
  *   get:
  *     summary: Export API documentation as HTML
  *     description: Returns a standalone HTML file with the complete API documentation
+ *     tags: [Documentation]
  *     responses:
- *       200:
+ *       '200':
  *         description: Standalone HTML documentation
  *         content:
  *           text/html:
@@ -1397,7 +1774,6 @@ app.get('/api/docs/export/html', (req, res) => {
     res.send(htmlTemplate);
 });
 
-
 // ========================================
 // ERROR HANDLING
 // ========================================