claudio-iturra.github.io/filterX.html at main · claudio-iturra/claudio-iturra.github.io · GitHub

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<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Time Series Spectral Analyzer</title>
    <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
    <style>
        * {
            margin: 0;
            padding: 0;
            box-sizing: border-box;
        }

        body {
            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            min-height: 100vh;
            padding: 20px;
        }

        .container {
            max-width: 1200px;
            margin: 0 auto;
            background: white;
            border-radius: 20px;
            box-shadow: 0 20px 40px rgba(0,0,0,0.1);
            overflow: hidden;
        }

        .header {
            background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%);
            color: white;
            padding: 30px;
            text-align: center;
        }

        .header h1 {
            font-size: 2.5rem;
            margin-bottom: 10px;
            font-weight: 700;
        }

        .header p {
            font-size: 1.1rem;
            opacity: 0.9;
        }

        .main-content {
            padding: 40px;
        }

        .input-section {
            background: #f8fafc;
            border-radius: 15px;
            padding: 30px;
            margin-bottom: 30px;
            border: 2px solid #e2e8f0;
        }

        .section-title {
            font-size: 1.5rem;
            color: #2d3748;
            margin-bottom: 20px;
            font-weight: 600;
        }

        .form-group {
            margin-bottom: 25px;
        }

        label {
            display: block;
            margin-bottom: 8px;
            font-weight: 600;
            color: #4a5568;
        }

        textarea {
            width: 100%;
            height: 120px;
            padding: 15px;
            border: 2px solid #e2e8f0;
            border-radius: 10px;
            font-family: 'Courier New', monospace;
            font-size: 14px;
            resize: vertical;
            transition: border-color 0.3s ease;
        }

        textarea:focus {
            outline: none;
            border-color: #4facfe;
            box-shadow: 0 0 0 3px rgba(79, 172, 254, 0.1);
        }

        input[type="number"], select {
            width: 100%;
            padding: 12px 15px;
            border: 2px solid #e2e8f0;
            border-radius: 10px;
            font-size: 16px;
            transition: border-color 0.3s ease;
        }

        input[type="number"]:focus, select:focus {
            outline: none;
            border-color: #4facfe;
            box-shadow: 0 0 0 3px rgba(79, 172, 254, 0.1);
        }

        .filter-controls {
            display: grid;
            grid-template-columns: 1fr 1fr 1fr;
            gap: 20px;
            margin-top: 15px;
        }

        .button-group {
            display: flex;
            gap: 15px;
            margin-top: 30px;
        }

        button {
            padding: 15px 30px;
            border: none;
            border-radius: 10px;
            font-size: 16px;
            font-weight: 600;
            cursor: pointer;
            transition: all 0.3s ease;
            flex: 1;
        }

        .btn-primary {
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            color: white;
        }

        .btn-primary:hover {
            transform: translateY(-2px);
            box-shadow: 0 10px 20px rgba(102, 126, 234, 0.3);
        }

        .btn-secondary {
            background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%);
            color: white;
        }

        .btn-secondary:hover {
            transform: translateY(-2px);
            box-shadow: 0 10px 20px rgba(240, 147, 251, 0.3);
        }

        .btn-success {
            background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%);
            color: white;
        }

        .btn-success:hover {
            transform: translateY(-2px);
            box-shadow: 0 10px 20px rgba(79, 172, 254, 0.3);
        }

        .results-section {
            margin-top: 40px;
        }

        .chart-container {
            background: white;
            border-radius: 15px;
            padding: 30px;
            margin-bottom: 30px;
            box-shadow: 0 10px 30px rgba(0,0,0,0.1);
            border: 2px solid #e2e8f0;
        }

        .chart-title {
            font-size: 1.3rem;
            color: #2d3748;
            margin-bottom: 20px;
            font-weight: 600;
            text-align: center;
        }

        .info-box {
            background: linear-gradient(135deg, #ffecd2 0%, #fcb69f 100%);
            border-radius: 10px;
            padding: 20px;
            margin-bottom: 20px;
            border-left: 5px solid #f6ad55;
        }

        .info-box h3 {
            color: #744210;
            margin-bottom: 10px;
        }

        .info-box p {
            color: #744210;
            line-height: 1.6;
        }

        .hidden {
            display: none;
        }

        @media (max-width: 768px) {
            .filter-controls {
                grid-template-columns: 1fr;
            }

            .button-group {
                flex-direction: column;
            }

            .header h1 {
                font-size: 2rem;
            }

            .main-content {
                padding: 20px;
            }
        }
    </style>
</head>
<body>
    <div class="container">
        <div class="header">
            <h1>🔬 Time Series Spectral Analyzer</h1>
            <p>Advanced filtering and spectral analysis for your time series data by Claudio Iturra</p>
        </div>

        <div class="main-content">
            <div class="input-section">
                <h2 class="section-title">📊 Data Input</h2>

                <div class="form-group">
                    <label for="timeSeriesData">Time Series Data (one value per line):</label>
                    <textarea id="timeSeriesData" placeholder="Enter your time series data here, one value per line:
1.2
1.5
1.8
2.1
1.9
1.6
..."></textarea>
                    <button type="button" class="btn-secondary" onclick="loadSampleData()" style="margin-top: 10px; width: auto; padding: 8px 16px;">📊 Load Sample 30-Day Data</button>
                </div>

                <div class="form-group">
                    <label for="samplingRate">Sampling Resolution (hours):</label>
                    <input type="number" id="samplingRate" value="1" min="0.1" step="0.1">
                </div>

                <div class="form-group">
                    <label for="filterType">Filter Type:</label>
                    <select id="filterType">
                        <option value="none">No Filter</option>
                        <option value="lowpass">Low Pass</option>
                        <option value="highpass">High Pass</option>
                        <option value="bandpass">Band Pass</option>
                    </select>

                    <div class="filter-controls">
                        <div>
                            <label for="lowCutoff">Low Cutoff (hours):</label>
                            <input type="number" id="lowCutoff" value="10" min="0.1" step="0.1">
                        </div>
                        <div>
                            <label for="highCutoff">High Cutoff (hours):</label>
                            <input type="number" id="highCutoff" value="24" min="0.1" step="0.1">
                        </div>
                        <div>
                            <label for="filterOrder">Filter Order:</label>
                            <input type="number" id="filterOrder" value="4" min="1" max="10">
                        </div>
                    </div>
                </div>

                <div class="button-group">
                    <button class="btn-primary" onclick="processTimeSeries()">🔄 Process & Filter</button>
                    <button class="btn-secondary" onclick="generateSpectrum()">📈 Generate Spectrum</button>
                    <button class="btn-success" onclick="downloadResults()">💾 Download Results</button>
                </div>
            </div>

            <div class="info-box">
                <h3>💡 How to Use:</h3>
                <p>1. Paste your time series data (one value per line) • 2. Set your sampling resolution in hours • 3. Choose a filter type and parameters • 4. Click "Process & Filter" to see filtered data • 5. Click "Generate Spectrum" for frequency analysis • 6. Download your results as CSV files</p>
            </div>

            <div id="resultsSection" class="results-section hidden">
                <div class="chart-container">
                    <h3 class="chart-title">📊 Original vs Filtered Time Series</h3>
                    <canvas id="timeSeriesChart"></canvas>
                </div>

                <div class="chart-container">
                    <h3 class="chart-title">🌊 Power Spectral Density</h3>
                    <canvas id="spectrumChart"></canvas>
                </div>
            </div>
        </div>
    </div>

    <script>
        let originalData = [];
        let filteredData = [];
        let frequencies = [];
        let powerSpectrum = [];
        let timeSeriesChart = null;
        let spectrumChart = null;

        function parseTimeSeriesData() {
            const input = document.getElementById('timeSeriesData').value.trim();
            if (!input) {
                alert('Please enter time series data');
                return false;
            }

            const lines = input.split('\n');
            originalData = [];

            for (let line of lines) {
                const value = parseFloat(line.trim());
                if (!isNaN(value)) {
                    originalData.push(value);
                }
            }

            if (originalData.length < 4) {
                alert('Please provide at least 4 data points');
                return false;
            }

            return true;
        }

        function applyFilter(data, filterType, samplingRate, lowCutoff, highCutoff, order) {
            if (filterType === 'none') {
                return [...data];
            }

            let filtered = [...data];

            if (filterType === 'lowpass') {
                // Low-pass filter: keep frequencies with periods longer than highCutoff hours
                // Window size should be proportional to the cutoff period
                const windowSize = Math.max(3, Math.round(highCutoff / samplingRate));
                filtered = applyMovingAverage(data, windowSize);

                // Apply multiple passes for higher order (each pass increases attenuation)
                for (let pass = 1; pass < order; pass++) {
                    filtered = applyMovingAverage(filtered, windowSize);
                }

            } else if (filterType === 'highpass') {
                // High-pass filter: remove frequencies with periods longer than lowCutoff hours
                // Window size should be proportional to the cutoff period
                const windowSize = Math.max(5, Math.round(lowCutoff / samplingRate));
                let smoothed = applyMovingAverage(data, windowSize);

                // Apply multiple passes for smoother low-frequency component removal
                for (let pass = 1; pass < order; pass++) {
                    smoothed = applyMovingAverage(smoothed, windowSize);
                }

                // Subtract the low-frequency component to get high-frequency component
                filtered = data.map((val, i) => val - smoothed[i]);

            } else if (filterType === 'bandpass') {
                // Band-pass: keep frequencies with periods between lowCutoff and highCutoff hours

                // First apply high-pass to remove long periods (> lowCutoff)
                const highPassWindowSize = Math.max(5, Math.round(lowCutoff / samplingRate));
                let smoothedLow = applyMovingAverage(data, highPassWindowSize);

                for (let pass = 1; pass < order; pass++) {
                    smoothedLow = applyMovingAverage(smoothedLow, highPassWindowSize);
                }

                let highPassed = data.map((val, i) => val - smoothedLow[i]);

                // Then apply low-pass to remove short periods (< highCutoff)
                const lowPassWindowSize = Math.max(3, Math.round(highCutoff / samplingRate));
                filtered = applyMovingAverage(highPassed, lowPassWindowSize);

                for (let pass = 1; pass < order; pass++) {
                    filtered = applyMovingAverage(filtered, lowPassWindowSize);
                }
            }

            return filtered;
        }

        function applyMovingAverage(data, windowSize) {
            const result = [];
            for (let i = 0; i < data.length; i++) {
                let sum = 0;
                let count = 0;

                for (let j = Math.max(0, i - Math.floor(windowSize/2));
                     j <= Math.min(data.length - 1, i + Math.floor(windowSize/2)); j++) {
                    sum += data[j];
                    count++;
                }
                result.push(sum / count);
            }
            return result;
        }

        function computeFFT(data, samplingRate) {
            // Remove DC component (mean)
            const mean = data.reduce((sum, val) => sum + val, 0) / data.length;
            const centeredData = data.map(val => val - mean);

            // Apply window function (Hanning window)
            const windowedData = centeredData.map((val, i) => {
                const window = 0.5 * (1 - Math.cos(2 * Math.PI * i / (data.length - 1)));
                return val * window;
            });

            // Pad data to next power of 2 for efficient FFT
            const n = Math.pow(2, Math.ceil(Math.log2(windowedData.length)));
            const paddedData = [...windowedData];
            while (paddedData.length < n) {
                paddedData.push(0);
            }

            // Simple DFT implementation (since FFT.js might not be available)
            const power = [];
            const frequencies = [];

            for (let k = 0; k < Math.floor(n/2); k++) {
                let realSum = 0;
                let imagSum = 0;

                for (let j = 0; j < paddedData.length; j++) {
                    const angle = -2 * Math.PI * k * j / n;
                    realSum += paddedData[j] * Math.cos(angle);
                    imagSum += paddedData[j] * Math.sin(angle);
                }

                // Power spectral density
                const magnitude = Math.sqrt(realSum * realSum + imagSum * imagSum);
                power.push(magnitude * magnitude / n);

                // Frequency in cycles per hour
                frequencies.push(k / (n * samplingRate));
            }

            return { power, frequencies };
        }

        function processTimeSeries() {
            if (!parseTimeSeriesData()) return;

            const filterType = document.getElementById('filterType').value;
            const samplingRate = parseFloat(document.getElementById('samplingRate').value);
            const lowCutoff = parseFloat(document.getElementById('lowCutoff').value);
            const highCutoff = parseFloat(document.getElementById('highCutoff').value);
            const filterOrder = parseInt(document.getElementById('filterOrder').value);

            // Apply filter
            filteredData = applyFilter(originalData, filterType, samplingRate, lowCutoff, highCutoff, filterOrder);

            // Show results
            document.getElementById('resultsSection').classList.remove('hidden');

            // Create time series chart
            createTimeSeriesChart();
        }

        function generateSpectrum() {
            if (originalData.length === 0) {
                alert('Please load time series data first');
                return;
            }

            const samplingRate = parseFloat(document.getElementById('samplingRate').value);

            // Compute FFT for original data
            const originalResult = computeFFT(originalData, samplingRate);

            // Compute FFT for filtered data (if available)
            let filteredResult = null;
            if (filteredData.length > 0) {
                filteredResult = computeFFT(filteredData, samplingRate);
            }

            // Store results
            powerSpectrum = originalResult.power;
            frequencies = originalResult.frequencies;

            // Create spectrum chart with both datasets
            createSpectrumChart(originalResult, filteredResult);
        }

        function createTimeSeriesChart() {
            const ctx = document.getElementById('timeSeriesChart').getContext('2d');

            if (timeSeriesChart) {
                timeSeriesChart.destroy();
            }

            const samplingRate = parseFloat(document.getElementById('samplingRate').value);
            const timePoints = originalData.map((_, i) => i * samplingRate);

            timeSeriesChart = new Chart(ctx, {
                type: 'line',
                data: {
                    labels: timePoints,
                    datasets: [{
                        label: 'Original Data',
                        data: originalData,
                        borderColor: '#667eea',
                        backgroundColor: 'rgba(102, 126, 234, 0.1)',
                        borderWidth: 2,
                        fill: false,
                        tension: 0.1
                    }, {
                        label: 'Filtered Data',
                        data: filteredData,
                        borderColor: '#f093fb',
                        backgroundColor: 'rgba(240, 147, 251, 0.1)',
                        borderWidth: 2,
                        fill: false,
                        tension: 0.1
                    }]
                },
                options: {
                    responsive: true,
                    plugins: {
                        legend: {
                            position: 'top',
                        }
                    },
                    scales: {
                        x: {
                            display: true,
                            title: {
                                display: true,
                                text: 'Time (hours)'
                            }
                        },
                        y: {
                            display: true,
                            title: {
                                display: true,
                                text: 'Amplitude'
                            }
                        }
                    }
                }
            });
        }

        function createSpectrumChart(originalResult, filteredResult) {
            const ctx = document.getElementById('spectrumChart').getContext('2d');

            if (spectrumChart) {
                spectrumChart.destroy();
            }

            // Prepare datasets array
            const datasets = [];

            // Original data spectrum
            const originalFreqs = originalResult.frequencies.slice(1).filter(f => f > 0);
            const originalPower = originalResult.power.slice(1, originalFreqs.length + 1);
            const originalPeriods = originalFreqs.map(f => 1/f);

            const originalChartData = originalPeriods.map((period, i) => ({
                x: period,
                y: originalPower[i]
            })).filter(point => point.y > 0 && point.x > 0);

            datasets.push({
                label: 'Original Data Spectrum',
                data: originalChartData,
                borderColor: '#667eea',
                backgroundColor: 'rgba(102, 126, 234, 0.6)',
                borderWidth: 2,
                pointRadius: 1.5,
                showLine: true,
                tension: 0.1
            });

            // Filtered data spectrum (if available)
            if (filteredResult) {
                const filteredFreqs = filteredResult.frequencies.slice(1).filter(f => f > 0);
                const filteredPower = filteredResult.power.slice(1, filteredFreqs.length + 1);
                const filteredPeriods = filteredFreqs.map(f => 1/f);

                const filteredChartData = filteredPeriods.map((period, i) => ({
                    x: period,
                    y: filteredPower[i]
                })).filter(point => point.y > 0 && point.x > 0);

                datasets.push({
                    label: 'Filtered Data Spectrum',
                    data: filteredChartData,
                    borderColor: '#f093fb',
                    backgroundColor: 'rgba(240, 147, 251, 0.6)',
                    borderWidth: 2,
                    pointRadius: 1.5,
                    showLine: true,
                    tension: 0.1
                });
            }

            // Find min/max for axes
            const allPeriods = originalPeriods.concat(filteredResult ? filteredResult.frequencies.slice(1).filter(f => f > 0).map(f => 1/f) : []);
            const allPowers = originalPower.concat(filteredResult ? filteredResult.power.slice(1, filteredResult.frequencies.length) : []);

            spectrumChart = new Chart(ctx, {
                type: 'scatter',
                data: {
                    datasets: datasets
                },
                options: {
                    responsive: true,
                    plugins: {
                        legend: {
                            position: 'top',
                        }
                    },
                    scales: {
                        x: {
                            type: 'logarithmic',
                            display: true,
                            title: {
                                display: true,
                                text: 'Period (hours)'
                            },
                            min: Math.max(1, Math.min(...allPeriods.filter(p => p > 0))),
                            max: Math.max(...allPeriods.filter(p => p > 0))
                        },
                        y: {
                            type: 'logarithmic',
                            display: true,
                            title: {
                                display: true,
                                text: 'Power'
                            },
                            min: Math.min(...allPowers.filter(p => p > 0)) * 0.1
                        }
                    }
                }
            });
        }

        function downloadResults() {
            if (originalData.length === 0) {
                alert('No data to download. Please process time series first.');
                return;
            }

            // Download filtered time series
            let csvContent = "Time (hours),Original Data,Filtered Data\n";
            const samplingRate = parseFloat(document.getElementById('samplingRate').value);

            for (let i = 0; i < originalData.length; i++) {
                const time = i * samplingRate;
                const filtered = filteredData[i] || '';
                csvContent += `${time},${originalData[i]},${filtered}\n`;
            }

            downloadCSV(csvContent, 'filtered_time_series.csv');

            // Download spectrum if available
            if (powerSpectrum.length > 0) {
                // Download original spectrum
                let originalSpectrumCSV = "Frequency (1/hours),Period (hours),Original_Power\n";
                for (let i = 1; i < frequencies.length; i++) {
                    const freq = frequencies[i];
                    const period = freq > 0 ? 1/freq : 0;
                    originalSpectrumCSV += `${freq},${period},${powerSpectrum[i]}\n`;
                }
                downloadCSV(originalSpectrumCSV, 'original_spectrum.csv');

                // Download filtered spectrum if available
                if (filteredData.length > 0) {
                    const samplingRate = parseFloat(document.getElementById('samplingRate').value);
                    const filteredResult = computeFFT(filteredData, samplingRate);

                    let filteredSpectrumCSV = "Frequency (1/hours),Period (hours),Filtered_Power\n";
                    for (let i = 1; i < filteredResult.frequencies.length; i++) {
                        const freq = filteredResult.frequencies[i];
                        const period = freq > 0 ? 1/freq : 0;
                        filteredSpectrumCSV += `${freq},${period},${filteredResult.power[i]}\n`;
                    }
                    downloadCSV(filteredSpectrumCSV, 'filtered_spectrum.csv');
                }
            }
        }

        function downloadCSV(content, filename) {
            const blob = new Blob([content], { type: 'text/csv;charset=utf-8;' });
            const link = document.createElement('a');
            const url = URL.createObjectURL(blob);
            link.setAttribute('href', url);
            link.setAttribute('download', filename);
            link.style.visibility = 'hidden';
            document.body.appendChild(link);
            link.click();
            document.body.removeChild(link);
        }

        // Update filter controls based on filter type
        document.getElementById('filterType').addEventListener('change', function() {
            const filterType = this.value;
            const controls = document.querySelectorAll('.filter-controls input');

            controls.forEach(control => {
                control.disabled = filterType === 'none';
            });

            if (filterType === 'lowpass') {
                document.getElementById('lowCutoff').disabled = true;
            } else if (filterType === 'highpass') {
                document.getElementById('highCutoff').disabled = true;
            }
        });

        function loadSampleData() {
            // Generate 30 days of hourly data (720 points)
            const hours = 30 * 24; // 720 hours
            let sampleData = [];

            for (let i = 0; i < hours; i++) {
                // Create a signal with STRONG 12-hour and 24-hour frequency components:

                // 1. STRONG Daily cycle (24-hour period) - Primary component
                const dailyCycle = 5 * Math.sin(2 * Math.PI * i / 24 + Math.PI/4);

                // 2. STRONG Semi-daily cycle (12-hour period) - Secondary component
                const semiDaily = 4 * Math.sin(2 * Math.PI * i / 12);

                // 3. Weak weekly cycle (168-hour period) - Background
                const weeklyCycle = 0.5 * Math.sin(2 * Math.PI * i / 168);

                // 4. Small trend component
                const trend = 0.005 * i;

                // 5. Reduced random noise to make frequencies more visible
                const noise = 0.3 * (Math.random() - 0.5);

                // 6. Small higher frequency components (6-hour and 8-hour cycles)
                const highFreq = 0.2 * Math.sin(2 * Math.PI * i / 6) + 0.15 * Math.sin(2 * Math.PI * i / 8);

                // Combine all components with emphasis on 12h and 24h cycles
                const value = 15 + dailyCycle + semiDaily + weeklyCycle + trend + noise + highFreq;
                sampleData.push(value.toFixed(3));
            }

            // Load into textarea
            document.getElementById('timeSeriesData').value = sampleData.join('\n');

            // Set appropriate sampling rate
            document.getElementById('samplingRate').value = '1';

            alert('Sample 30-day hourly time series loaded! This data has STRONG 12-hour and 24-hour frequency components for clear spectral analysis.');
        }

        // Initialize
        document.getElementById('filterType').dispatchEvent(new Event('change'));
    </script>
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