commit a1ceeb29ef4515df76387eb8f1c5884836aa9f69
Author: raymond <iko@datacom.jp>
Date:   Mon Sep 1 23:56:24 2025 +0000

    fundamental commit
    
    APIの中にmean, variance, std, unique, count, percentile, median, mode, topn, max, min, percent-change, correlationの計算実現します。

diff --git a/server_ok.ts b/server_ok.ts
new file mode 100644
index 0000000..52b9fae
--- /dev/null
+++ b/server_ok.ts
@@ -0,0 +1,385 @@
+// package.json dependencies needed:
+// npm install express mathjs lodash
+// npm install -D @types/express @types/node @types/lodash typescript ts-node
+
+import express from 'express';
+import * as math from 'mathjs';
+import * as _ from 'lodash';
+
+const app = express();
+app.use(express.json());
+
+// Types for our data structures
+interface DataSeries {
+  values: number[];
+  labels?: string[];
+}
+
+interface Condition {
+  field: string;
+  operator: '>' | '<' | '=' | '>=' | '<=' | '!=';
+  value: number | string;
+}
+
+interface ApiResponse<T> {
+  success: boolean;
+  data?: T;
+  error?: string;
+}
+
+// Helper function for error handling
+const handleError = (error: unknown): string => {
+  return error instanceof Error ? error.message : 'Unknown error';
+};
+
+// Core statistical functions
+class AnalyticsEngine {
+  
+  // Apply conditions to filter data
+  private applyConditions(series: DataSeries, conditions: Condition[] = []): number[] {
+    if (conditions.length === 0) return series.values;
+    
+    // For now, just return all values - you'd implement condition logic here
+    // This would involve checking conditions against associated metadata
+    return series.values;
+  }
+
+  // Remove duplicates from series
+  unique(series: DataSeries): number[] {
+    return _.uniq(series.values);
+  }
+
+  // Calculate mean with optional conditions
+  mean(series: DataSeries, conditions: Condition[] = []): number {
+    const filteredValues = this.applyConditions(series, conditions);
+    if (filteredValues.length === 0) throw new Error('No data points match conditions');
+    
+    return Number(math.mean(filteredValues));
+  }
+
+  // Count values with optional conditions
+  count(series: DataSeries, conditions: Condition[] = []): number {
+    const filteredValues = this.applyConditions(series, conditions);
+    return filteredValues.length;
+  }
+
+  // Calculate variance
+  variance(series: DataSeries, conditions: Condition[] = []): number {
+    const filteredValues = this.applyConditions(series, conditions);
+    if (filteredValues.length === 0) throw new Error('No data points match conditions');
+    
+    return Number(math.variance(filteredValues));
+  }
+
+  // Calculate standard deviation
+  standardDeviation(series: DataSeries, conditions: Condition[] = []): number {
+    const filteredValues = this.applyConditions(series, conditions);
+    if (filteredValues.length === 0) throw new Error('No data points match conditions');
+    
+    return Number(math.std(filteredValues));
+  }
+
+  // Calculate percentile/quantile
+  percentile(
+    series: DataSeries, 
+    percent: number, 
+    ascending: boolean = true, 
+    conditions: Condition[] = []
+  ): number {
+    const filteredValues = this.applyConditions(series, conditions);
+    if (filteredValues.length === 0) throw new Error('No data points match conditions');
+    
+    const sorted = ascending ? 
+      _.sortBy(filteredValues) : 
+      _.sortBy(filteredValues).reverse();
+    
+    const index = (percent / 100) * (sorted.length - 1);
+    const lower = Math.floor(index);
+    const upper = Math.ceil(index);
+    const weight = index % 1;
+    
+    return sorted[lower] * (1 - weight) + sorted[upper] * weight;
+  }
+
+  // Calculate median (50th percentile)
+  median(series: DataSeries, conditions: Condition[] = []): number {
+    return this.percentile(series, 50, true, conditions);
+  }
+
+  // Calculate mode (most frequent value)
+  mode(series: DataSeries, conditions: Condition[] = []): number[] {
+    const filteredValues = this.applyConditions(series, conditions);
+    const frequency = _.countBy(filteredValues);
+    const maxFreq = Math.max(...Object.values(frequency));
+    
+    return Object.keys(frequency)
+      .filter(key => frequency[key] === maxFreq)
+      .map(Number);
+  }
+
+  // Rank values and get top N
+  topN(
+    series: DataSeries, 
+    n: number, 
+    ascending: boolean = false, 
+    conditions: Condition[] = []
+  ): number[] {
+    const filteredValues = this.applyConditions(series, conditions);
+    const sorted = ascending ? 
+      _.sortBy(filteredValues) : 
+      _.sortBy(filteredValues).reverse();
+    
+    return sorted.slice(0, n);
+  }
+
+  // Get maximum value
+  max(series: DataSeries, conditions: Condition[] = []): number {
+    const filteredValues = this.applyConditions(series, conditions);
+    if (filteredValues.length === 0) throw new Error('No data points match conditions');
+    
+    return Math.max(...filteredValues);
+  }
+
+  // Get minimum value
+  min(series: DataSeries, conditions: Condition[] = []): number {
+    const filteredValues = this.applyConditions(series, conditions);
+    if (filteredValues.length === 0) throw new Error('No data points match conditions');
+    
+    return Math.min(...filteredValues);
+  }
+
+  // Calculate percent change
+  percentChange(series: DataSeries, step: number = 1): number[] {
+    const values = series.values;
+    const changes: number[] = [];
+    
+    for (let i = step; i < values.length; i++) {
+      const change = ((values[i] - values[i - step]) / values[i - step]) * 100;
+      changes.push(change);
+    }
+    
+    return changes;
+  }
+
+  // Basic correlation between two series
+  correlation(series1: DataSeries, series2: DataSeries): number {
+    if (series1.values.length !== series2.values.length) {
+      throw new Error('Series must have same length for correlation');
+    }
+    
+    const x = series1.values;
+    const y = series2.values;
+    const n = x.length;
+    
+    const sumX = _.sum(x);
+    const sumY = _.sum(y);
+    const sumXY = _.sum(x.map((xi, i) => xi * y[i]));
+    const sumX2 = _.sum(x.map(xi => xi * xi));
+    const sumY2 = _.sum(y.map(yi => yi * yi));
+    
+    const numerator = n * sumXY - sumX * sumY;
+    const denominator = Math.sqrt((n * sumX2 - sumX * sumX) * (n * sumY2 - sumY * sumY));
+    
+    return numerator / denominator;
+  }
+}
+
+// Initialize analytics engine
+const analytics = new AnalyticsEngine();
+
+// API Routes
+app.get('/api/health', (req, res) => {
+  res.json({ status: 'OK', timestamp: new Date().toISOString() });
+});
+
+// Unique values endpoint
+app.post('/api/unique', (req, res) => {
+  try {
+    const { series }: { series: DataSeries } = req.body;
+    const result = analytics.unique(series);
+    res.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[]>);
+  }
+});
+
+// Mean calculation endpoint
+app.post('/api/mean', (req, res) => {
+  try {
+    const { series, conditions = [] }: { series: DataSeries; conditions?: Condition[] } = req.body;
+    const result = analytics.mean(series, conditions);
+    res.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>);
+  }
+});
+
+// Count endpoint
+app.post('/api/count', (req, res) => {
+  try {
+    const { series, conditions = [] }: { series: DataSeries; conditions?: Condition[] } = req.body;
+    const result = analytics.count(series, conditions);
+    res.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>);
+  }
+});
+
+// Variance endpoint
+app.post('/api/variance', (req, res) => {
+  try {
+    const { series, conditions = [] }: { series: DataSeries; conditions?: Condition[] } = req.body;
+    const result = analytics.variance(series, conditions);
+    res.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>);
+  }
+});
+
+// Standard deviation endpoint
+app.post('/api/std', (req, res) => {
+  try {
+    const { series, conditions = [] }: { series: DataSeries; conditions?: Condition[] } = req.body;
+    const result = analytics.standardDeviation(series, conditions);
+    res.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>);
+  }
+});
+
+// Percentile endpoint
+app.post('/api/percentile', (req, res) => {
+  try {
+    const { 
+      series, 
+      percent, 
+      ascending = true, 
+      conditions = [] 
+    }: { 
+      series: DataSeries; 
+      percent: number; 
+      ascending?: boolean; 
+      conditions?: Condition[] 
+    } = req.body;
+    
+    const result = analytics.percentile(series, percent, ascending, conditions);
+    res.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>);
+  }
+});
+
+// Median endpoint
+app.post('/api/median', (req, res) => {
+  try {
+    const { series, conditions = [] }: { series: DataSeries; conditions?: Condition[] } = req.body;
+    const result = analytics.median(series, conditions);
+    res.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>);
+  }
+});
+
+// Mode endpoint
+app.post('/api/mode', (req, res) => {
+  try {
+    const { series, conditions = [] }: { series: DataSeries; conditions?: Condition[] } = req.body;
+    const result = analytics.mode(series, conditions);
+    res.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[]>);
+  }
+});
+
+// Top N endpoint
+app.post('/api/topn', (req, res) => {
+  try {
+    const { 
+      series, 
+      n, 
+      ascending = false, 
+      conditions = [] 
+    }: { 
+      series: DataSeries; 
+      n: number; 
+      ascending?: boolean; 
+      conditions?: Condition[] 
+    } = req.body;
+    
+    const result = analytics.topN(series, n, ascending, conditions);
+    res.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[]>);
+  }
+});
+
+// Max/Min endpoints
+app.post('/api/max', (req, res) => {
+  try {
+    const { series, conditions = [] }: { series: DataSeries; conditions?: Condition[] } = req.body;
+    const result = analytics.max(series, conditions);
+    res.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>);
+  }
+});
+
+app.post('/api/min', (req, res) => {
+  try {
+    const { series, conditions = [] }: { series: DataSeries; conditions?: Condition[] } = req.body;
+    const result = analytics.min(series, conditions);
+    res.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>);
+  }
+});
+
+// Percent change endpoint
+app.post('/api/percent-change', (req, res) => {
+  try {
+    const { series, step = 1 }: { series: DataSeries; step?: number } = req.body;
+    const result = analytics.percentChange(series, step);
+    res.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[]>);
+  }
+});
+
+// Correlation endpoint
+app.post('/api/correlation', (req, res) => {
+  try {
+    const { series1, series2 }: { series1: DataSeries; series2: DataSeries } = req.body;
+    const result = analytics.correlation(series1, series2);
+    res.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>);
+  }
+});
+
+// Error handling middleware
+app.use((err: Error, req: express.Request, res: express.Response, next: express.NextFunction) => {
+  console.error(err.stack);
+  res.status(500).json({ success: false, error: 'Internal server error' } as ApiResponse<any>);
+});
+
+// Start server
+const PORT = process.env.PORT || 3000;
+app.listen(PORT, () => {
+  console.log(`Analytics API server running on port ${PORT}`);
+  console.log(`Health check: http://localhost:${PORT}/api/health`);
+});
+
+export default app;
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