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# Publication-Ready 数据图表模板
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本文件用于生成"论文 / 报告里出现的标准数据图表"：
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- Bar chart / grouped bar chart（消融实验、方法对比）
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- Line chart / 训练曲线（loss / accuracy 随 epoch）
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- Scatter plot（性能-效率 trade-off）
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- Box plot / Violin plot（统计分布）
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- Heatmap（confusion matrix / attention map / 相关性矩阵）
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特征：
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- matplotlib / seaborn / R ggplot2 出版物风
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- 含坐标轴 + 标签 + 单位 + 图例 + 误差棒 + 显著性标记
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- 字体 ≥ 10pt（确保打印可读）
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- 配色克制（≤ 6 色），可单色印刷
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- 网格线极淡或无
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> ⚠️ 重要免责声明：**本模板生成的是"出版级图表的视觉呈现"，不是真实数据可视化**。GPT Image 2 不能保证坐标和数据的精确对应。
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>
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> - 如果你需要"展示一张论文图表的样子" / "做封面 / hero 配图" → 用本模板
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> - 如果你需要"用真实数据生成可发表的图表" → 请用 matplotlib / seaborn / ggplot2 / Plotly
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## 适用范围
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- 论文方法对比 chart 的视觉示例
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- 教学 slide 中"看一眼这个图就懂"的演示图
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- Blog / 公众号配图 — "我们的方法在这个 chart 上表现"
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- 投资人 deck 中的"数据 mock"
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- 演示用、可视化教学用的图表
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## 何时使用
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- 用户提到 "publication chart / matplotlib 风 / seaborn 风 / 论文图表 / bar chart / line chart / scatter / heatmap / confusion matrix"
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- 用户希望「白底、克制、可单色、像 NeurIPS 论文那种图表」
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- 用户**明确知道**这只是视觉呈现，不依赖坐标精度
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不要使用：
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- 用户要的是「真实数据可视化产出」 → 推荐 matplotlib / seaborn / Plotly
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- 用户要的是「KPI 仪表盘 / 数据回顾」 → 用 `infographics/kpi-dashboard-infographic.md`
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- 用户要的是「商业 PPT 数据页」 → 用 `slides-and-visual-docs/visual-report-page.md`
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- 用户要的是「手绘风信息图」 → 用 `infographics/hand-drawn-infographic.md`
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## 缺失信息优先提问顺序
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1. 图表类型（bar / line / scatter / box / violin / heatmap / pie）
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2. 主题（"我们方法在 ImageNet 上的 accuracy vs baselines"）
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3. X 轴和 Y 轴名称 + 单位
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4. 数据系列数量（单一系列 / 多系列）
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5. 是否有误差棒、显著性标记 *
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6. 配色基调（学术克制 / 强调对比 / 黑白单色）
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7. 图标题 + caption（论文 figure 一般有 caption）
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## 主模板：Publication-Ready Bar Chart（默认）
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📖 描述
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整张图是一张标准学术 bar chart：横轴为方法 / 类别，纵轴为指标，多个方法对比，含误差棒、显著性 *、图例。整体白底，sans-serif 字体，限定配色。
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📝 提示词
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```json
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{
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"type": "Publication-Ready Bar Chart（学术出版级条形图）",
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"goal": "生成视觉呈现一张论文 / 报告中的 bar chart，要求白底、克制、专业、可单色印刷",
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"canvas": {
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"aspect_ratio": "{argument name=\"aspect_ratio\" default=\"4:3\"}",
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"background": "white #FFFFFF",
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"outer_padding": "60px"
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},
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"title": {
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"text": "{argument name=\"title\" default=\"Accuracy on ImageNet-1K\"}",
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"position": "top-center, sans-serif bold 13pt",
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"subtitle": "{argument name=\"subtitle\" default=\"\"}"
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},
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"axes": {
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"x_axis": {
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"label": "{argument name=\"x_label\" default=\"Method\"}",
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"categories": [
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"{argument name=\"cat1\" default=\"ResNet-50\"}",
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"{argument name=\"cat2\" default=\"ViT-B\"}",
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"{argument name=\"cat3\" default=\"Swin-B\"}",
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"{argument name=\"cat4\" default=\"ConvNeXt-B\"}",
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"{argument name=\"cat5\" default=\"Ours\"}"
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],
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"tick_label_rotation": "0deg or 30deg if labels are long"
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},
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"y_axis": {
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"label": "{argument name=\"y_label\" default=\"Top-1 Accuracy (%)\"}",
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"range": "{argument name=\"y_range\" default=\"75 to 86\"}",
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"tick_format": "decimal or percent",
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"gridlines": "very faint horizontal gridlines (light gray dashed, low opacity)"
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}
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},
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"bars": {
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"style": "vertical bars, ~30-40% width of category slot, gap between bars",
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"color_rule": {
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"default": "use a single muted color for all baselines (e.g. slate blue #64748B), highlight 'Ours' bar in accent color (e.g. orange #D97706 or red #DC2626)",
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"alternative": "if comparing methods grouped by family, use 2-3 muted colors to encode family"
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},
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"value_labels": {
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"enabled": "{argument name=\"value_labels_enabled\" default=\"true\"}",
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"rule": "show numeric value above each bar, sans-serif 9pt bold, e.g. '82.3'"
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}
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},
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"error_bars": {
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"enabled": "{argument name=\"error_bars_enabled\" default=\"true\"}",
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"style": "thin black T-bar at top of each bar, ±std or ±95% CI",
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"annotation": "mention what the error represents in caption (e.g. 'error bars show ±1 std over 5 runs')"
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},
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"significance_markers": {
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"enabled": "{argument name=\"significance_enabled\" default=\"false\"}",
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"rule": "if true, draw thin horizontal brackets between compared bars, with * / ** / *** annotation above (p<0.05 / p<0.01 / p<0.001)"
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},
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"legend": {
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"enabled": "{argument name=\"legend_enabled\" default=\"false\"}",
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"rule": "only show legend if multiple colors / groups used; place top-right inside or outside the plot area",
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"items": ["Baselines", "Ours"]
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},
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"caption": {
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"enabled": "{argument name=\"caption_enabled\" default=\"true\"}",
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"label": "{argument name=\"figure_label\" default=\"Figure 3.\"}",
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"text": "{argument name=\"caption_text\" default=\"Top-1 accuracy on ImageNet-1K. Our method outperforms all baselines while using fewer parameters. Error bars show ±1 std over 5 runs.\"}",
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"style": "below the chart, italic serif or compact sans-serif, justified, smaller font"
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},
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"constraints": {
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"must_keep": [
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"white background, no gradient, no pattern fills",
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"sans-serif fonts only (Helvetica / Inter / Arial); axis tick labels ≥ 9pt, axis labels ≥ 11pt, title ≥ 13pt",
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"color palette ≤ 6 colors, must remain readable in grayscale",
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"all axes have labels and units",
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"no 3D bar effects, no perspective tilt",
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"if multiple bars per category, group them with consistent spacing",
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"Ours bar is visually distinguishable (color or annotation)"
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],
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"avoid": [
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"rainbow colors / saturated palette",
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"3D extruded bars / pie charts (3D distorts perception)",
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"missing axis labels or units",
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"unreadable tick labels (too small or rotated awkwardly)",
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"decorative background images / textures",
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"emoji / cartoon icons inside or around bars",
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"value labels overlapping bars or each other",
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"random / irrelevant accent colors",
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"fake precision: don't render bar heights to imply real numbers — keep it clearly illustrative"
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]
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}
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}
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```
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### 参数策略
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- **必问**：图表类型（如果不是 bar）、`title`、`x_label` / `y_label` / 单位、`categories`
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- **可默认**：`aspect_ratio`（4:3）、`background`（白）、`error_bars_enabled`（true）、`value_labels_enabled`（true）、`legend_enabled`（false 单系列时）
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- **可随机**：bar 宽度、tick 数量、网格线密度（在合理范围）
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### 自动补全策略
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- 用户给"我有 5 个方法的 accuracy 对比" → 自动用 default 5 categories，highlight 最后一个为 Ours
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- 用户没指定 y_range → 推断（基于数值范围 ± 5%）
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- 用户没说 error → 默认开启 error_bars（论文标准做法）
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- 用户没说 significance → 默认关闭（除非是统计学论文）
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- 用户说"不是 bar" → 切换到对应变体
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## 变体 1：Line Chart（训练曲线 / 时间序列）
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```json
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{
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"type": "Publication-Ready Line Chart（学术出版级折线图）",
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"modify": {
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"x_axis_typical": "epoch / step / time / iteration",
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"y_axis_typical": "loss / accuracy / metric",
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"lines_count": "1-5 series, each a different muted color",
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"line_style": "solid 1.5px main line + optional shaded area (semi-transparent same color) for std band",
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"markers": "optional small markers at sparse intervals (circles / triangles), not on every point",
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"legend": "always enabled for multi-series, top-right or below",
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"rule_extra": "axes can be log-scale if data spans orders of magnitude (label as 'log scale')"
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}
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}
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```
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适用：训练曲线、time series 趋势、ablation 随超参变化、scaling laws。
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## 变体 2：Scatter Plot（trade-off 图）
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```json
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{
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"type": "Publication-Ready Scatter Plot（学术出版级散点图）",
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"modify": {
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"typical_use": "performance vs efficiency trade-off (e.g. accuracy vs FLOPs / latency / params)",
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"x_axis_typical": "compute / params / latency (often log scale)",
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"y_axis_typical": "accuracy / metric",
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"point_style": "filled circles, size encodes a third dimension (e.g. model size), color encodes a category (e.g. method family)",
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"label_each_point": "small text label next to each point with method name (no leader lines unless crowded)",
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"ours_emphasis": "Our method points are larger and use accent color + black border",
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"frontier_line": "optional: draw a Pareto frontier curve to show 'we push the frontier'"
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}
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}
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```
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适用：性能-效率 trade-off、参数 vs 准确率、Pareto frontier。
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## 变体 3：Heatmap（confusion matrix / attention map / 相关性）
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```json
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{
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"type": "Publication-Ready Heatmap（学术出版级热力图）",
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"modify": {
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"grid": "N × N（默认 5×5 至 10×10）",
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"color_map": "sequential — viridis / Blues / Reds / 灰阶；diverging（如相关矩阵）— RdBu_r 红蓝双向",
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"cell_annotation": "show numeric value inside each cell in monospace, color flips for readability on dark cells",
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"axes_label": "row labels = ground truth, column labels = predicted（confusion matrix 场景）",
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"colorbar": "right side vertical colorbar with label and ticks",
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"rule_extra": "always include colorbar; never use rainbow colormap for sequential data (jet 已被学界淘汰)"
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}
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}
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```
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适用：confusion matrix、attention 权重可视化、相关性矩阵、ablation grid。
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## 变体 4：Box Plot / Violin Plot（统计分布）
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```json
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{
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"type": "Publication-Ready Box / Violin Plot（学术出版级分布图）",
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"modify": {
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"typical_use": "compare distributions across methods / conditions / groups",
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"elements": "box (Q1, median, Q3) + whiskers (1.5 IQR) + outlier dots; violin 形状叠加显示密度",
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"median_line_emphasis": "median 线粗实线，颜色区分 group",
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"annotation": "可叠加 swarm / strip plot 显示每个数据点",
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"rule_extra": "如果用 violin，violin 内部仍画 box；不要纯 violin（损失中位数信息）"
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}
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}
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```
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适用：实验重复结果分布、跨数据集 / 跨用户 / 跨条件分布对比。
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## 避免事项
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- 用 3D 柱 / 3D 饼 → 严重不专业
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- 用彩虹 / jet colormap 表示连续值（学界已抛弃）
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- 漏掉单位 / 漏掉坐标轴标签
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- value 标签过小读不清
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- 没有 caption 或 caption 没解释 error bar
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- 把 4-5 个不相关 chart 拼一张（应该用 multi-panel figure 模板，每个 sub 图独立）
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- 假装精确（暗示这是真数据但其实是 illustrative）
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- 用花哨字体（Comic Sans / 手写体）
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- 加水印 / 装饰背景
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- 漏掉图例（多系列必须有）
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- 多 series 但配色完全相同
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- 漏掉 Ours 高亮（论文图通常要让 reviewer 一眼看出你的）
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