import osimport numpy as npimport pandas as pdimport matplotlib.pyplot as plt# 1. 自动创建 图表 文件夹output_dir = "图表"os.makedirs(output_dir, exist_ok=True)# 2. 自动读取同目录下的 data.xlsxexcel_file = "data.xlsx"if not os.path.exists(excel_file):    raise FileNotFoundError(f"未在当前目录下找到 {excel_file}，请确保该 Excel 文件存在。")df = pd.read_excel(excel_file)# 3. 匹配 Excel 列名映射mapping = {    'clone': 'clone',    'age': 'age',    'menopausal_status': 'menopausal_status',    'est_der': 'estimates for der(1;16) timing',    'low_der': 'lower CI for der(1;16) timing',    'upp_der': 'upper CI for der(1;16) acquision',    'est_doub': 'estimates for der(1;16) doubling timing',    'low_doub': 'lower CI for der(1;16) doubling timing',    'upp_doub': 'upper CI for der(1;16) doubling timing',    'est_mrca': 'estimates for MRCA timing',    'low_mrca': 'lower CI for MRCA timing',    'upp_mrca': 'upper CI for MRCA timing'}# 4. 格式化克隆系名称def format_clone_name(row):    name = str(row[mapping['clone']]).strip()    if 'KU539_a' in name or 'KU539a' in name or 'clone α' in name: return 'KU539\n(clone α)'    if 'KU539_b' in name or 'KU539b' in name or 'clone β' in name: return 'KU539\n(clone β)'    if 'KU779_a' in name or 'KU779a' in name or 'clone α' in name: return 'KU779\n(clone α)'    if 'KU779_b' in name or 'KU779b' in name or 'clone β' in name: return 'KU779\n(clone β)'    if 'TMA125' in name: return 'TMA125 †'    return namedf['display_name'] = df.apply(format_clone_name, axis=1)# 为了让左侧刻度最下方是 TMA149，最上方是 KU957，将 DataFrame 颠倒df = df.iloc[::-1].reset_index(drop=True)# 5. 20种学术与商务图表配色方案字典color_palettes = {    1: { # 方案 1：原经典学术期刊配色 (默认激活)        'c_pre': '#E24A48', 'c_post': '#EBB02D', 'c_bg_bar': '#ECEFF1',        'c_der': '#F48FB1', 'c_der_dark': '#D81B60',        'c_mrca': '#b5c5e2', 'c_mrca_dark': '#1565C0',        'c_double': '#A5D6A7', 'c_double_dark': '#1B5E20',        'c_grey_overlap': '#B0BEC5', 'c_mix_overlap': '#8FBC8F',        'c_mrca_overlap': '#7f9cb5', 'c_surgery': '#7B1FA2'    }}# 提取选择的配色方案 (此处默认应用方案 1)current_palette_id = 1palette = color_palettes[current_palette_id]c_pre = palette['c_pre']c_post = palette['c_post']c_bg_bar = palette['c_bg_bar']c_der = palette['c_der']c_der_dark = palette['c_der_dark']c_mrca = palette['c_mrca']c_mrca_dark = palette['c_mrca_dark']c_double = palette['c_double']c_double_dark = palette['c_double_dark']c_grey_overlap = palette['c_grey_overlap']c_mix_overlap = palette['c_mix_overlap']c_mrca_overlap = palette['c_mrca_overlap']c_surgery = palette['c_surgery']# 6. 初始化画布fig, ax = plt.subplots(figsize=(8.0, 7.0), dpi=300)# 控制绘图视窗，放宽上方给指示标签留出独立区域ax.set_xlim(-6, 80)ax.set_ylim(-0.8, len(df) + 0.8)ax.set_xlabel('Age (years)', fontsize=10, fontname='DejaVu Sans', labelpad=5)ax.set_xticks(range(0, 90, 10))ax.set_xticklabels(range(0, 90, 10))ax.tick_params(axis='x', labelsize=9)# 隐藏上方和右侧边框ax.spines['top'].set_visible(False)ax.spines['right'].set_visible(False)# 左侧轴和下方轴彻底断开不交叉ax.spines['left'].set_visible(True)ax.spines['left'].set_bounds(0, len(df) - 1)ax.spines['left'].set_linewidth(0.8)ax.spines['bottom'].set_visible(True)ax.spines['bottom'].set_bounds(0, 80)ax.spines['bottom'].set_linewidth(0.8)# 设置刻度样式朝外突出ax.tick_params(axis='both', which='both', direction='out', length=4, width=0.8, colors='black')# 7. 核心绘图逻辑y_positions = np.arange(len(df))lw_val = 9  # 恢复标准好看的生命条粗细for i, row in df.iterrows():    y = i    max_age = row[mapping['age']]    clone_id = str(row[mapping['clone']])    # A. 基础背景生命条    ax.plot([0, max_age], [y, y], color=c_bg_bar, linewidth=lw_val, solid_capstyle='round', zorder=1)    # 提取各区间真实数据    est_der = row[mapping['est_der']]    low_der = row[mapping['low_der']]    upp_der = row[mapping['upp_der']]    est_doub = row[mapping['est_doub']]    low_doub = row[mapping['low_doub']]    upp_doub = row[mapping['upp_doub']]    est_mrca = row[mapping['est_mrca']]    low_mrca = row[mapping['low_mrca']]    upp_mrca = row[mapping['upp_mrca']]    # B. der(1;16) 区间（恢复完美的原生 round 圆头）    if not pd.isna(est_der):        ax.plot([low_der, upp_der], [y, y], color=c_der, linewidth=lw_val, solid_capstyle='round', zorder=2)        ax.scatter(est_der, y, color=c_der_dark, s=30, zorder=5)    # C. 特定的灰色重叠过渡段    if 'KU957' in clone_id or 'TMA125' in clone_id:        if not pd.isna(est_doub):            ax.plot([est_der, est_doub], [y, y], color=c_grey_overlap, linewidth=lw_val, solid_capstyle='butt',                    zorder=2.5)    # D. doubling 倍增区间（恢复完美的原生 round 圆头）    if not pd.isna(est_doub):        ax.plot([low_doub, upp_doub], [y, y], color=c_double, linewidth=lw_val, solid_capstyle='round', zorder=3)        ax.scatter(est_doub, y, color=c_double_dark, s=30, zorder=5)    # 重叠区域（含边缘圆头部分）采用完美匹配的覆盖混色    if not pd.isna(low_der) and not pd.isna(low_doub):        overlap_start = max(low_der, low_doub)        overlap_end = min(upp_der, upp_doub)        if overlap_start < overlap_end:            # 使用 round 让重叠部分的端点圆头也自然过渡变色，不会变形            ax.plot([overlap_start, overlap_end], [y, y], color=c_mix_overlap, linewidth=lw_val, solid_capstyle='round',                    zorder=3.6)    # E. MRCA occurrence 区间    if not pd.isna(est_mrca):        ax.plot([low_mrca, upp_mrca], [y, y], color=c_mrca, linewidth=lw_val, solid_capstyle='round', zorder=4)        ax.scatter(est_mrca, y, color=c_mrca_dark, s=30, zorder=5)    # MRCA 与 doubling 发生区域重叠时的完美圆头混色    if not pd.isna(low_mrca) and not pd.isna(low_doub):        overlap_start = max(low_mrca, low_doub)        overlap_end = min(upp_mrca, upp_doub)        if overlap_start < overlap_end:            ax.plot([overlap_start, overlap_end], [y, y], color=c_mrca_overlap, linewidth=lw_val,                    solid_capstyle='round', zorder=4.6)    # F. Surgery 紫色圆点    ax.scatter(max_age, y, color=c_surgery, s=30, zorder=6)# 8. 绝经状态垂直双色条status_col = df[mapping['menopausal_status']].str.lower()pre_indices = df[status_col.str.contains('pre', na=False)].indexpost_indices = df[status_col.str.contains('post', na=False)].indexif len(pre_indices) > 0:    ax.fill_betweenx([pre_indices.min() - 0.35, pre_indices.max() + 0.35], -4.5, -3.5, color=c_pre, zorder=10)if len(post_indices) > 0:    ax.fill_betweenx([post_indices.min() - 0.35, post_indices.max() + 0.35], -4.5, -3.5, color=c_post, zorder=10)ax.set_yticks(y_positions)ax.set_yticklabels(df['display_name'], fontsize=9, fontname='DejaVu Sans', va='center')ax.tick_params(axis='y', pad=6)# 9. 重要修复：文字右对齐与左对齐 顶部上方区域绘制y_top_line1 = len(df) + 0.35y_top_line2 = len(df) - 0.15# 通过减小这两个值，将整个标签区域整体向左移动（例如分别改到 0.5 和 4.0）x_text_right_align = 0.5   # 左边两行单词的右对齐基准线（值越小越靠左）x_block_left_align = 4.0   # 右边指示方块的左对齐基准线（值越小越靠左）# 1. 上方行：Menopausal (右对齐)  ->  ■ Premenopausal (左对齐)ax.text(x_text_right_align, y_top_line1, 'Menopausal', fontsize=9, ha='right', va='top', fontname='DejaVu Sans', weight='bold')ax.text(x_block_left_align, y_top_line1, '■ Premenopausal', color=c_pre, fontsize=9, ha='left', va='top', weight='bold')# 2. 下方行：status (右对齐)  ->  ■ Postmenopausal (左对齐)ax.text(x_text_right_align, y_top_line2, 'status', fontsize=9, ha='right', va='top', fontname='DejaVu Sans', weight='bold')ax.text(x_block_left_align, y_top_line2, '■ Postmenopausal', color=c_post, fontsize=9, ha='left', va='top', weight='bold')# 10. 底部的双列图例（保持左移对齐 + 优雅下移）y_leg1 = -2.4y_leg2 = -3.2ax.plot([-4.5, 2.5], [y_leg1, y_leg1], color=c_der, linewidth=9, solid_capstyle='round', clip_on=False)ax.scatter(-1.0, y_leg1, color=c_der_dark, s=30, zorder=10, clip_on=False)ax.text(4.5, y_leg1, 'der(1;16)', fontsize=9, va='center', ha='left')ax.plot([-4.5, 2.5], [y_leg2, y_leg2], color=c_mrca, linewidth=9, solid_capstyle='round', clip_on=False)ax.scatter(-1.0, y_leg2, color=c_mrca_dark, s=30, zorder=10, clip_on=False)ax.text(4.5, y_leg2, 'MRCA occurence', fontsize=9, va='center', ha='left')ax.plot([33, 41], [y_leg1, y_leg1], color=c_double, linewidth=9, solid_capstyle='round', clip_on=False)ax.scatter(37, y_leg1, color=c_double_dark, s=30, zorder=10, clip_on=False)ax.text(43, y_leg1, 'der(1;16) doubling', fontsize=9, va='center', ha='left')ax.scatter(37, y_leg2, color=c_surgery, s=30, zorder=10, clip_on=False)ax.text(43, y_leg2, 'Surgery', fontsize=9, va='center', ha='left')# 11. 将序号 'c' 置于最左上角fig.text(0.07, 0.93, 'c', fontsize=18, weight='bold', fontname='DejaVu Sans', va='top', ha='left')# 12. 保存图片plt.subplots_adjust(bottom=0.25, left=0.16, right=0.95, top=0.90)output_path = os.path.join(output_dir, "timeline_plot.png")plt.savefig(output_path, bbox_inches='tight')plt.close()print(f"Plot saved successfully to {output_path}")