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| #!/usr/bin/env python3 | |
| """ | |
| Generate an interactive HTML visualization for the gloss-to-feature alignment. | |
| This mirrors the frame_alignment.png layout but lets viewers adjust confidence thresholds. | |
| Usage: | |
| python generate_interactive_alignment.py <sample_dir> | |
| Example: | |
| python generate_interactive_alignment.py detailed_prediction_20251226_022246/sample_000 | |
| """ | |
| import sys | |
| import json | |
| import numpy as np | |
| from pathlib import Path | |
| def generate_interactive_html(sample_dir, output_path): | |
| """Create the interactive alignment HTML for the given sample directory.""" | |
| sample_dir = Path(sample_dir) | |
| # 1. Load attention weights | |
| attention_weights = np.load(sample_dir / "attention_weights.npy") | |
| # Handle both 2D (inference mode) and 3D (beam search) shapes | |
| if attention_weights.ndim == 2: | |
| attn_weights = attention_weights # [time_steps, src_len] - already 2D | |
| elif attention_weights.ndim == 3: | |
| attn_weights = attention_weights[:, :, 0] # [time_steps, src_len] - take beam 0 | |
| else: | |
| raise ValueError(f"Unexpected attention weights shape: {attention_weights.shape}") | |
| # 2. Load translation output | |
| with open(sample_dir / "translation.txt", 'r') as f: | |
| lines = f.readlines() | |
| gloss_sequence = None | |
| for line in lines: | |
| if line.startswith('Clean:'): | |
| gloss_sequence = line.replace('Clean:', '').strip() | |
| break | |
| if not gloss_sequence: | |
| print("Error: translation text not found") | |
| return | |
| glosses = gloss_sequence.split() | |
| num_glosses = len(glosses) | |
| num_features = attn_weights.shape[1] | |
| print(f"Gloss sequence: {glosses}") | |
| print(f"Feature count: {num_features}") | |
| print(f"Attention shape: {attn_weights.shape}") | |
| # 3. Convert attention weights to JSON (only keep the num_glosses rows – ignore padding) | |
| attn_data = [] | |
| for word_idx in range(min(num_glosses, attn_weights.shape[0])): | |
| weights = attn_weights[word_idx, :].tolist() | |
| attn_data.append({ | |
| 'word': glosses[word_idx], | |
| 'word_idx': word_idx, | |
| 'weights': weights | |
| }) | |
| # 4. Build the HTML payload | |
| html_content = f"""<!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>Interactive Word-Frame Alignment</title> | |
| <style> | |
| body {{ | |
| font-family: 'Arial', sans-serif; | |
| margin: 20px; | |
| background-color: #f5f5f5; | |
| }} | |
| .container {{ | |
| max-width: 1800px; | |
| margin: 0 auto; | |
| background-color: white; | |
| padding: 30px; | |
| border-radius: 8px; | |
| box-shadow: 0 2px 10px rgba(0,0,0,0.1); | |
| }} | |
| h1 {{ | |
| color: #333; | |
| border-bottom: 3px solid #4CAF50; | |
| padding-bottom: 10px; | |
| margin-bottom: 20px; | |
| }} | |
| .stats {{ | |
| background-color: #E3F2FD; | |
| padding: 15px; | |
| border-radius: 5px; | |
| margin-bottom: 20px; | |
| border-left: 4px solid #2196F3; | |
| font-size: 14px; | |
| }} | |
| .controls {{ | |
| background-color: #f9f9f9; | |
| padding: 20px; | |
| border-radius: 5px; | |
| margin-bottom: 30px; | |
| border: 1px solid #ddd; | |
| }} | |
| .control-group {{ | |
| margin-bottom: 15px; | |
| }} | |
| label {{ | |
| font-weight: bold; | |
| display: inline-block; | |
| width: 250px; | |
| color: #555; | |
| }} | |
| input[type="range"] {{ | |
| width: 400px; | |
| vertical-align: middle; | |
| }} | |
| .value-display {{ | |
| display: inline-block; | |
| width: 80px; | |
| font-family: monospace; | |
| font-size: 14px; | |
| color: #2196F3; | |
| font-weight: bold; | |
| }} | |
| .reset-btn {{ | |
| margin-top: 15px; | |
| padding: 10px 25px; | |
| background-color: #2196F3; | |
| color: white; | |
| border: none; | |
| border-radius: 5px; | |
| cursor: pointer; | |
| font-size: 14px; | |
| font-weight: bold; | |
| }} | |
| .reset-btn:hover {{ | |
| background-color: #1976D2; | |
| }} | |
| canvas {{ | |
| border: 1px solid #999; | |
| display: block; | |
| margin: 20px auto; | |
| background: white; | |
| }} | |
| .legend {{ | |
| margin-top: 20px; | |
| padding: 15px; | |
| background-color: #fff; | |
| border: 1px solid #ddd; | |
| border-radius: 5px; | |
| }} | |
| .legend-item {{ | |
| display: inline-block; | |
| margin-right: 25px; | |
| font-size: 13px; | |
| margin-bottom: 10px; | |
| }} | |
| .color-box {{ | |
| display: inline-block; | |
| width: 30px; | |
| height: 15px; | |
| margin-right: 8px; | |
| vertical-align: middle; | |
| border: 1px solid #666; | |
| }} | |
| .info-panel {{ | |
| margin-top: 20px; | |
| padding: 15px; | |
| background-color: #f9f9f9; | |
| border-radius: 5px; | |
| border: 1px solid #ddd; | |
| }} | |
| .confidence {{ | |
| display: inline-block; | |
| padding: 3px 10px; | |
| border-radius: 10px; | |
| font-weight: bold; | |
| font-size: 11px; | |
| text-transform: uppercase; | |
| }} | |
| .confidence.high {{ | |
| background-color: #4CAF50; | |
| color: white; | |
| }} | |
| .confidence.medium {{ | |
| background-color: #FF9800; | |
| color: white; | |
| }} | |
| .confidence.low {{ | |
| background-color: #f44336; | |
| color: white; | |
| }} | |
| </style> | |
| </head> | |
| <body> | |
| <div class="container"> | |
| <h1>🎯 Interactive Word-to-Frame Alignment Visualizer</h1> | |
| <div class="stats"> | |
| <strong>Translation:</strong> {' '.join(glosses)}<br> | |
| <strong>Total Words:</strong> {num_glosses} | | |
| <strong>Total Features:</strong> {num_features} | |
| </div> | |
| <div class="controls"> | |
| <h3>⚙️ Threshold Controls</h3> | |
| <div class="control-group"> | |
| <label for="peak-threshold">Peak Threshold (% of max):</label> | |
| <input type="range" id="peak-threshold" min="1" max="100" value="90" step="1"> | |
| <span class="value-display" id="peak-threshold-value">90%</span> | |
| <br> | |
| <small style="margin-left: 255px; color: #666;"> | |
| A frame is considered “significant” if its attention ≥ (peak × threshold%) | |
| </small> | |
| </div> | |
| <div class="control-group"> | |
| <label for="confidence-high">High Confidence (avg attn >):</label> | |
| <input type="range" id="confidence-high" min="0" max="100" value="50" step="1"> | |
| <span class="value-display" id="confidence-high-value">0.50</span> | |
| </div> | |
| <div class="control-group"> | |
| <label for="confidence-medium">Medium Confidence (avg attn >):</label> | |
| <input type="range" id="confidence-medium" min="0" max="100" value="20" step="1"> | |
| <span class="value-display" id="confidence-medium-value">0.20</span> | |
| </div> | |
| <button class="reset-btn" onclick="resetDefaults()"> | |
| Reset to Defaults | |
| </button> | |
| </div> | |
| <div> | |
| <h3>Word-to-Frame Alignment</h3> | |
| <p style="color: #666; font-size: 13px;"> | |
| Each word appears as a colored block. Width = frame span, ★ = peak frame, waveform = attention trace. | |
| </p> | |
| <canvas id="alignment-canvas" width="1600" height="600"></canvas> | |
| <h3 style="margin-top: 30px;">Timeline Progress Bar</h3> | |
| <canvas id="timeline-canvas" width="1600" height="100"></canvas> | |
| <div class="legend"> | |
| <strong>Legend:</strong><br><br> | |
| <div class="legend-item"> | |
| <span class="confidence high">High</span> | |
| <span class="confidence medium">Medium</span> | |
| <span class="confidence low">Low</span> | |
| Confidence Levels (opacity reflects confidence) | |
| </div> | |
| <div class="legend-item"> | |
| <span style="color: red; font-size: 20px;">★</span> | |
| Peak Frame (highest attention) | |
| </div> | |
| <div class="legend-item"> | |
| <span style="color: blue;">━</span> | |
| Attention Waveform (within word region) | |
| </div> | |
| </div> | |
| </div> | |
| <div class="info-panel"> | |
| <h3>Alignment Details</h3> | |
| <div id="alignment-details"></div> | |
| </div> | |
| </div> | |
| <script> | |
| // Attention data from Python | |
| const attentionData = {json.dumps(attn_data, ensure_ascii=False)}; | |
| const numGlosses = {num_glosses}; | |
| const numFeatures = {num_features}; | |
| // Colors for different words (matching matplotlib tab20) | |
| const colors = [ | |
| '#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', | |
| '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf', | |
| '#aec7e8', '#ffbb78', '#98df8a', '#ff9896', '#c5b0d5', | |
| '#c49c94', '#f7b6d2', '#c7c7c7', '#dbdb8d', '#9edae5' | |
| ]; | |
| // Get controls | |
| const peakThresholdSlider = document.getElementById('peak-threshold'); | |
| const peakThresholdValue = document.getElementById('peak-threshold-value'); | |
| const confidenceHighSlider = document.getElementById('confidence-high'); | |
| const confidenceHighValue = document.getElementById('confidence-high-value'); | |
| const confidenceMediumSlider = document.getElementById('confidence-medium'); | |
| const confidenceMediumValue = document.getElementById('confidence-medium-value'); | |
| const alignmentCanvas = document.getElementById('alignment-canvas'); | |
| const timelineCanvas = document.getElementById('timeline-canvas'); | |
| const alignmentCtx = alignmentCanvas.getContext('2d'); | |
| const timelineCtx = timelineCanvas.getContext('2d'); | |
| // Update displays when sliders change | |
| peakThresholdSlider.oninput = function() {{ | |
| peakThresholdValue.textContent = this.value + '%'; | |
| updateVisualization(); | |
| }}; | |
| confidenceHighSlider.oninput = function() {{ | |
| confidenceHighValue.textContent = (this.value / 100).toFixed(2); | |
| updateVisualization(); | |
| }}; | |
| confidenceMediumSlider.oninput = function() {{ | |
| confidenceMediumValue.textContent = (this.value / 100).toFixed(2); | |
| updateVisualization(); | |
| }}; | |
| function resetDefaults() {{ | |
| peakThresholdSlider.value = 90; | |
| confidenceHighSlider.value = 50; | |
| confidenceMediumSlider.value = 20; | |
| peakThresholdValue.textContent = '90%'; | |
| confidenceHighValue.textContent = '0.50'; | |
| confidenceMediumValue.textContent = '0.20'; | |
| updateVisualization(); | |
| }} | |
| function calculateAlignment(weights, peakThreshold) {{ | |
| // Find peak | |
| let peakIdx = 0; | |
| let peakWeight = weights[0]; | |
| for (let i = 1; i < weights.length; i++) {{ | |
| if (weights[i] > peakWeight) {{ | |
| peakWeight = weights[i]; | |
| peakIdx = i; | |
| }} | |
| }} | |
| // Find significant frames | |
| const threshold = peakWeight * (peakThreshold / 100); | |
| let startIdx = peakIdx; | |
| let endIdx = peakIdx; | |
| let sumWeight = 0; | |
| let count = 0; | |
| for (let i = 0; i < weights.length; i++) {{ | |
| if (weights[i] >= threshold) {{ | |
| if (i < startIdx) startIdx = i; | |
| if (i > endIdx) endIdx = i; | |
| sumWeight += weights[i]; | |
| count++; | |
| }} | |
| }} | |
| const avgWeight = count > 0 ? sumWeight / count : peakWeight; | |
| return {{ | |
| startIdx: startIdx, | |
| endIdx: endIdx, | |
| peakIdx: peakIdx, | |
| peakWeight: peakWeight, | |
| avgWeight: avgWeight, | |
| threshold: threshold | |
| }}; | |
| }} | |
| function getConfidenceLevel(avgWeight, highThreshold, mediumThreshold) {{ | |
| if (avgWeight > highThreshold) return 'high'; | |
| if (avgWeight > mediumThreshold) return 'medium'; | |
| return 'low'; | |
| }} | |
| function drawAlignmentChart() {{ | |
| const peakThreshold = parseInt(peakThresholdSlider.value); | |
| const highThreshold = parseInt(confidenceHighSlider.value) / 100; | |
| const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100; | |
| // Canvas dimensions | |
| const width = alignmentCanvas.width; | |
| const height = alignmentCanvas.height; | |
| const leftMargin = 180; | |
| const rightMargin = 50; | |
| const topMargin = 60; | |
| const bottomMargin = 80; | |
| const plotWidth = width - leftMargin - rightMargin; | |
| const plotHeight = height - topMargin - bottomMargin; | |
| const rowHeight = plotHeight / numGlosses; | |
| const featureWidth = plotWidth / numFeatures; | |
| // Clear canvas | |
| alignmentCtx.clearRect(0, 0, width, height); | |
| // Draw title | |
| alignmentCtx.fillStyle = '#333'; | |
| alignmentCtx.font = 'bold 18px Arial'; | |
| alignmentCtx.textAlign = 'center'; | |
| alignmentCtx.fillText('Word-to-Frame Alignment', width / 2, 30); | |
| alignmentCtx.font = '13px Arial'; | |
| alignmentCtx.fillText('(based on attention peaks, ★ = peak frame)', width / 2, 48); | |
| // Calculate alignments | |
| const alignments = []; | |
| for (let wordIdx = 0; wordIdx < numGlosses; wordIdx++) {{ | |
| const data = attentionData[wordIdx]; | |
| const alignment = calculateAlignment(data.weights, peakThreshold); | |
| alignment.word = data.word; | |
| alignment.wordIdx = wordIdx; | |
| alignment.weights = data.weights; | |
| alignments.push(alignment); | |
| }} | |
| // Draw grid | |
| alignmentCtx.strokeStyle = '#e0e0e0'; | |
| alignmentCtx.lineWidth = 0.5; | |
| for (let i = 0; i <= numFeatures; i++) {{ | |
| const x = leftMargin + i * featureWidth; | |
| alignmentCtx.beginPath(); | |
| alignmentCtx.moveTo(x, topMargin); | |
| alignmentCtx.lineTo(x, topMargin + plotHeight); | |
| alignmentCtx.stroke(); | |
| }} | |
| // Draw word regions | |
| for (let wordIdx = 0; wordIdx < numGlosses; wordIdx++) {{ | |
| const alignment = alignments[wordIdx]; | |
| const confidence = getConfidenceLevel(alignment.avgWeight, highThreshold, mediumThreshold); | |
| const y = topMargin + wordIdx * rowHeight; | |
| // Alpha based on confidence | |
| const alpha = confidence === 'high' ? 0.9 : confidence === 'medium' ? 0.7 : 0.5; | |
| // Draw rectangle for word region | |
| const startX = leftMargin + alignment.startIdx * featureWidth; | |
| const rectWidth = (alignment.endIdx - alignment.startIdx + 1) * featureWidth; | |
| alignmentCtx.fillStyle = colors[wordIdx % 20]; | |
| alignmentCtx.globalAlpha = alpha; | |
| alignmentCtx.fillRect(startX, y, rectWidth, rowHeight * 0.8); | |
| alignmentCtx.globalAlpha = 1.0; | |
| // Draw border | |
| alignmentCtx.strokeStyle = '#000'; | |
| alignmentCtx.lineWidth = 2; | |
| alignmentCtx.strokeRect(startX, y, rectWidth, rowHeight * 0.8); | |
| // Draw attention waveform inside rectangle | |
| alignmentCtx.strokeStyle = 'rgba(0, 0, 255, 0.8)'; | |
| alignmentCtx.lineWidth = 1.5; | |
| alignmentCtx.beginPath(); | |
| for (let i = alignment.startIdx; i <= alignment.endIdx; i++) {{ | |
| const x = leftMargin + i * featureWidth + featureWidth / 2; | |
| const weight = alignment.weights[i]; | |
| const maxWeight = alignment.peakWeight; | |
| const normalizedWeight = weight / (maxWeight * 1.2); // Scale for visibility | |
| const waveY = y + rowHeight * 0.8 - (normalizedWeight * rowHeight * 0.6); | |
| if (i === alignment.startIdx) {{ | |
| alignmentCtx.moveTo(x, waveY); | |
| }} else {{ | |
| alignmentCtx.lineTo(x, waveY); | |
| }} | |
| }} | |
| alignmentCtx.stroke(); | |
| // Draw word label | |
| const labelX = startX + rectWidth / 2; | |
| const labelY = y + rowHeight * 0.4; | |
| alignmentCtx.fillStyle = 'rgba(0, 0, 0, 0.7)'; | |
| alignmentCtx.fillRect(labelX - 60, labelY - 12, 120, 24); | |
| alignmentCtx.fillStyle = '#fff'; | |
| alignmentCtx.font = 'bold 13px Arial'; | |
| alignmentCtx.textAlign = 'center'; | |
| alignmentCtx.textBaseline = 'middle'; | |
| alignmentCtx.fillText(alignment.word, labelX, labelY); | |
| // Mark peak frame with star | |
| const peakX = leftMargin + alignment.peakIdx * featureWidth + featureWidth / 2; | |
| const peakY = y + rowHeight * 0.4; | |
| // Draw star | |
| alignmentCtx.fillStyle = '#ff0000'; | |
| alignmentCtx.strokeStyle = '#ffff00'; | |
| alignmentCtx.lineWidth = 1.5; | |
| alignmentCtx.font = '20px Arial'; | |
| alignmentCtx.textAlign = 'center'; | |
| alignmentCtx.strokeText('★', peakX, peakY); | |
| alignmentCtx.fillText('★', peakX, peakY); | |
| // Y-axis label (word names) | |
| alignmentCtx.fillStyle = '#333'; | |
| alignmentCtx.font = '12px Arial'; | |
| alignmentCtx.textAlign = 'right'; | |
| alignmentCtx.textBaseline = 'middle'; | |
| alignmentCtx.fillText(alignment.word, leftMargin - 10, y + rowHeight * 0.4); | |
| }} | |
| // Draw horizontal grid lines | |
| alignmentCtx.strokeStyle = '#ccc'; | |
| alignmentCtx.lineWidth = 0.5; | |
| for (let i = 0; i <= numGlosses; i++) {{ | |
| const y = topMargin + i * rowHeight; | |
| alignmentCtx.beginPath(); | |
| alignmentCtx.moveTo(leftMargin, y); | |
| alignmentCtx.lineTo(leftMargin + plotWidth, y); | |
| alignmentCtx.stroke(); | |
| }} | |
| // Draw axes | |
| alignmentCtx.strokeStyle = '#000'; | |
| alignmentCtx.lineWidth = 2; | |
| alignmentCtx.strokeRect(leftMargin, topMargin, plotWidth, plotHeight); | |
| // X-axis labels (frame indices) | |
| alignmentCtx.fillStyle = '#000'; | |
| alignmentCtx.font = '11px Arial'; | |
| alignmentCtx.textAlign = 'center'; | |
| alignmentCtx.textBaseline = 'top'; | |
| for (let i = 0; i < numFeatures; i++) {{ | |
| const x = leftMargin + i * featureWidth + featureWidth / 2; | |
| alignmentCtx.fillText(i.toString(), x, topMargin + plotHeight + 10); | |
| }} | |
| // Axis titles | |
| alignmentCtx.fillStyle = '#333'; | |
| alignmentCtx.font = 'bold 14px Arial'; | |
| alignmentCtx.textAlign = 'center'; | |
| alignmentCtx.fillText('Feature Frame Index', leftMargin + plotWidth / 2, height - 20); | |
| alignmentCtx.save(); | |
| alignmentCtx.translate(30, topMargin + plotHeight / 2); | |
| alignmentCtx.rotate(-Math.PI / 2); | |
| alignmentCtx.fillText('Generated Word', 0, 0); | |
| alignmentCtx.restore(); | |
| return alignments; | |
| }} | |
| function drawTimeline(alignments) {{ | |
| const highThreshold = parseInt(confidenceHighSlider.value) / 100; | |
| const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100; | |
| const width = timelineCanvas.width; | |
| const height = timelineCanvas.height; | |
| const leftMargin = 180; | |
| const rightMargin = 50; | |
| const plotWidth = width - leftMargin - rightMargin; | |
| const featureWidth = plotWidth / numFeatures; | |
| // Clear canvas | |
| timelineCtx.clearRect(0, 0, width, height); | |
| // Background bar | |
| timelineCtx.fillStyle = '#ddd'; | |
| timelineCtx.fillRect(leftMargin, 30, plotWidth, 40); | |
| timelineCtx.strokeStyle = '#000'; | |
| timelineCtx.lineWidth = 2; | |
| timelineCtx.strokeRect(leftMargin, 30, plotWidth, 40); | |
| // Draw word regions on timeline | |
| for (let wordIdx = 0; wordIdx < alignments.length; wordIdx++) {{ | |
| const alignment = alignments[wordIdx]; | |
| const confidence = getConfidenceLevel(alignment.avgWeight, highThreshold, mediumThreshold); | |
| const alpha = confidence === 'high' ? 0.9 : confidence === 'medium' ? 0.7 : 0.5; | |
| const startX = leftMargin + alignment.startIdx * featureWidth; | |
| const rectWidth = (alignment.endIdx - alignment.startIdx + 1) * featureWidth; | |
| timelineCtx.fillStyle = colors[wordIdx % 20]; | |
| timelineCtx.globalAlpha = alpha; | |
| timelineCtx.fillRect(startX, 30, rectWidth, 40); | |
| timelineCtx.globalAlpha = 1.0; | |
| timelineCtx.strokeStyle = '#000'; | |
| timelineCtx.lineWidth = 0.5; | |
| timelineCtx.strokeRect(startX, 30, rectWidth, 40); | |
| }} | |
| // Title | |
| timelineCtx.fillStyle = '#333'; | |
| timelineCtx.font = 'bold 13px Arial'; | |
| timelineCtx.textAlign = 'left'; | |
| timelineCtx.fillText('Timeline Progress Bar', leftMargin, 20); | |
| }} | |
| function updateDetailsPanel(alignments, highThreshold, mediumThreshold) {{ | |
| const panel = document.getElementById('alignment-details'); | |
| let html = '<table style="width: 100%; border-collapse: collapse;">'; | |
| html += '<tr style="background: #f0f0f0; font-weight: bold;">'; | |
| html += '<th style="padding: 8px; border: 1px solid #ddd;">Word</th>'; | |
| html += '<th style="padding: 8px; border: 1px solid #ddd;">Feature Range</th>'; | |
| html += '<th style="padding: 8px; border: 1px solid #ddd;">Peak</th>'; | |
| html += '<th style="padding: 8px; border: 1px solid #ddd;">Span</th>'; | |
| html += '<th style="padding: 8px; border: 1px solid #ddd;">Avg Attention</th>'; | |
| html += '<th style="padding: 8px; border: 1px solid #ddd;">Confidence</th>'; | |
| html += '</tr>'; | |
| for (const align of alignments) {{ | |
| const confidence = getConfidenceLevel(align.avgWeight, highThreshold, mediumThreshold); | |
| const span = align.endIdx - align.startIdx + 1; | |
| html += '<tr>'; | |
| html += `<td style="padding: 8px; border: 1px solid #ddd;"><strong>${{align.word}}</strong></td>`; | |
| html += `<td style="padding: 8px; border: 1px solid #ddd;">${{align.startIdx}} → ${{align.endIdx}}</td>`; | |
| html += `<td style="padding: 8px; border: 1px solid #ddd;">${{align.peakIdx}}</td>`; | |
| html += `<td style="padding: 8px; border: 1px solid #ddd;">${{span}}</td>`; | |
| html += `<td style="padding: 8px; border: 1px solid #ddd;">${{align.avgWeight.toFixed(4)}}</td>`; | |
| html += `<td style="padding: 8px; border: 1px solid #ddd;"><span class="confidence ${{confidence}}">${{confidence}}</span></td>`; | |
| html += '</tr>'; | |
| }} | |
| html += '</table>'; | |
| panel.innerHTML = html; | |
| }} | |
| function updateVisualization() {{ | |
| const alignments = drawAlignmentChart(); | |
| drawTimeline(alignments); | |
| const highThreshold = parseInt(confidenceHighSlider.value) / 100; | |
| const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100; | |
| updateDetailsPanel(alignments, highThreshold, mediumThreshold); | |
| }} | |
| // Event listeners for sliders | |
| peakSlider.addEventListener('input', function() {{ | |
| peakValue.textContent = peakSlider.value + '%'; | |
| updateVisualization(); | |
| }}); | |
| confidenceHighSlider.addEventListener('input', function() {{ | |
| const val = parseInt(confidenceHighSlider.value) / 100; | |
| confidenceHighValue.textContent = val.toFixed(2); | |
| updateVisualization(); | |
| }}); | |
| confidenceMediumSlider.addEventListener('input', function() {{ | |
| const val = parseInt(confidenceMediumSlider.value) / 100; | |
| confidenceMediumValue.textContent = val.toFixed(2); | |
| updateVisualization(); | |
| }}); | |
| // Initial visualization | |
| updateVisualization(); | |
| </script> | |
| </body> | |
| </html> | |
| """ | |
| # 5. Write the HTML file | |
| with open(output_path, 'w', encoding='utf-8') as f: | |
| f.write(html_content) | |
| print(f"✓ Interactive HTML generated: {output_path}") | |
| print(" Open this file in a browser and use the sliders to adjust thresholds.") | |
| if __name__ == "__main__": | |
| if len(sys.argv) != 2: | |
| print("Usage: python generate_interactive_alignment.py <sample_dir>") | |
| print("Example: python generate_interactive_alignment.py detailed_prediction_20251226_022246/sample_000") | |
| sys.exit(1) | |
| sample_dir = Path(sys.argv[1]) | |
| if not sample_dir.exists(): | |
| print(f"Error: directory not found: {sample_dir}") | |
| sys.exit(1) | |
| output_path = sample_dir / "interactive_alignment.html" | |
| generate_interactive_html(sample_dir, output_path) | |
| print("\nUsage:") | |
| print(f" Open in a browser: {output_path.absolute()}") | |
| print(" Move the sliders to preview different threshold settings in real time.") | |