| |
| """ |
| 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) |
|
|
| |
| attention_weights = np.load(sample_dir / "attention_weights.npy") |
| |
| if attention_weights.ndim == 2: |
| attn_weights = attention_weights |
| elif attention_weights.ndim == 3: |
| attn_weights = attention_weights[:, :, 0] |
| else: |
| raise ValueError(f"Unexpected attention weights shape: {attention_weights.shape}") |
|
|
| |
| 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}") |
|
|
| |
| 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 |
| }) |
|
|
| |
| 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> |
| """ |
|
|
| |
| 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.") |
|
|
