--- name: plotting description: 科研绘图规范 —— 天文学常用图表的 Python matplotlib 绘制模板 context: fork allowed-tools: - bash - save_note - write - read --- # 科研绘图规范 ## 环境要求 - Python 3.8+ - matplotlib >= 3.7 - numpy - 可选：seaborn（统计图）、astropy（天文学单位和坐标） 安装： ```bash pip install matplotlib numpy seaborn astropy --quiet ``` ## 输出规范 | 目标 | 分辨率 | 格式 | 说明 | |------|--------|------|------| | 论文投稿 | 300+ dpi | PDF/SVG（矢量优先） | APS/A&A/MNRAS 标准 | | 演示文稿 | 150 dpi | PNG | 宽度 ≥ 1200px | | 快速预览 | 100 dpi | PNG | 屏幕查看 | ## 通用样式模板 ```python import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import numpy as np # 天文学论文标准样式 plt.rcParams.update({ 'font.family': 'serif', 'font.size': 12, 'axes.labelsize': 14, 'axes.titlesize': 14, 'xtick.labelsize': 11, 'ytick.labelsize': 11, 'legend.fontsize': 11, 'figure.figsize': (8, 6), 'figure.dpi': 300, 'savefig.dpi': 300, 'savefig.bbox': 'tight', 'savefig.pad_inches': 0.05, 'lines.linewidth': 1.5, 'axes.linewidth': 1.0, 'xtick.major.width': 0.8, 'ytick.major.width': 0.8, 'xtick.minor.width': 0.5, 'ytick.minor.width': 0.5, 'xtick.direction': 'in', 'ytick.direction': 'in', 'xtick.top': True, 'ytick.right': True, }) ``` ## 图表模板 ### 1. 光谱图 ```python def plot_spectrum(wavelength, flux, title="Spectrum", xlabel=r"Wavelength ($\AA$)", ylabel=r"Flux (erg/s/cm$^2$/$\AA$)", save_path=None): fig, ax = plt.subplots() ax.plot(wavelength, flux, 'k-', linewidth=0.8) ax.set_xlabel(xlabel) ax.set_ylabel(ylabel) ax.set_title(title) ax.minorticks_on() if save_path: fig.savefig(save_path) plt.close(fig) else: plt.show() return fig, ax ``` ### 2. 光变曲线 ```python def plot_light_curve(time, flux, time_err=None, flux_err=None, title="Light Curve", xlabel="Time (MJD)", ylabel="Flux", save_path=None): fig, ax = plt.subplots() if flux_err is not None: ax.errorbar(time, flux, yerr=flux_err, xerr=time_err, fmt='o', markersize=3, capsize=2, color='black', ecolor='gray') else: ax.plot(time, flux, 'ko', markersize=3) ax.set_xlabel(xlabel) ax.set_ylabel(ylabel) ax.set_title(title) ax.minorticks_on() if save_path: fig.savefig(save_path) plt.close(fig) else: plt.show() return fig, ax ``` ### 3. 折叠光变曲线 ```python def plot_folded_lc(phase, flux, flux_err=None, period=None, title="Folded Light Curve", save_path=None): fig, ax = plt.subplots() if flux_err is not None: ax.errorbar(phase, flux, yerr=flux_err, fmt='o', markersize=2, capsize=1, color='black', ecolor='gray', alpha=0.7) else: ax.plot(phase, flux, 'ko', markersize=2, alpha=0.7) ax.set_xlabel("Phase") ax.set_ylabel("Flux") if period: ax.set_title(f"{title} (P = {period:.4f} d)") else: ax.set_title(title) ax.set_xlim(0, 1) ax.minorticks_on() if save_path: fig.savefig(save_path) plt.close(fig) else: plt.show() return fig, ax ``` ### 4. 赫罗图（CMD） ```python def plot_hr_diagram(bp_rp, abs_g, title="HR Diagram", save_path=None, color_by_density=False): fig, ax = plt.subplots() if color_by_density and len(bp_rp) > 100: from scipy.stats import gaussian_kde xy = np.vstack([bp_rp, abs_g]) z = gaussian_kde(xy)(xy) idx = z.argsort() ax.scatter(bp_rp[idx], abs_g[idx], c=z[idx], s=5, cmap='viridis_r', edgecolors='none') cb = fig.colorbar(ax.collections[0], ax=ax, pad=0.02) cb.set_label("Stellar density") else: ax.plot(bp_rp, abs_g, 'k.', markersize=1, alpha=0.5) ax.set_xlabel(r"$G_{BP} - G_{RP}$ (mag)") ax.set_ylabel(r"$M_G$ (mag)") ax.set_title(title) ax.invert_yaxis() ax.minorticks_on() if save_path: fig.savefig(save_path) plt.close(fig) else: plt.show() return fig, ax ``` ### 5. SED 图 ```python def plot_sed(wavelength_angstrom, flux_obs, flux_err=None, flux_model=None, title="SED", save_path=None): fig, ax = plt.subplots() ax.scatter(wavelength_angstrom, flux_obs, c='black', s=30, zorder=5, label='Observed') if flux_err is not None: ax.errorbar(wavelength_angstrom, flux_obs, yerr=flux_err, fmt='none', ecolor='gray', capsize=3) if flux_model is not None: model_wave, model_flux = zip(*flux_model) if isinstance(flux_model, list) else (flux_model[0], flux_model[1]) ax.plot(model_wave, model_flux, 'r-', linewidth=1.5, label='Model', alpha=0.8) ax.set_xscale('log') ax.set_yscale('log') ax.set_xlabel(r"Wavelength ($\AA$)") ax.set_ylabel(r"Flux") ax.set_title(title) ax.legend() ax.minorticks_on() if save_path: fig.savefig(save_path) plt.close(fig) else: plt.show() return fig, ax ``` ### 6. [α/Fe] vs [Fe/H] 图 ```python def plot_abundance(feh, alpha_feh, labels=None, title="[α/Fe] vs [Fe/H]", save_path=None): fig, ax = plt.subplots() if labels is not None: from matplotlib.colors import ListedColormap colors = ['#3498db', '#e74c3c', '#2ecc71'] cmap = ListedColormap(colors[:len(set(labels))]) unique = sorted(set(labels)) for i, lab in enumerate(unique): mask = np.array(labels) == lab ax.scatter(np.array(feh)[mask], np.array(alpha_feh)[mask], s=10, alpha=0.7, label=lab, color=colors[i % len(colors)]) ax.legend() else: ax.scatter(feh, alpha_feh, s=10, alpha=0.7, c='black') ax.set_xlabel("[Fe/H] (dex)") ax.set_ylabel(r"[$\alpha$/Fe] (dex)") ax.set_title(title) ax.axhline(y=0.25, color='gray', linestyle='--', linewidth=0.8, alpha=0.5) ax.minorticks_on() if save_path: fig.savefig(save_path) plt.close(fig) else: plt.show() return fig, ax ``` ### 7. 周期图 ```python def plot_periodogram(periods, powers, best_period=None, title="Periodogram", save_path=None): fig, ax = plt.subplots() ax.plot(periods, powers, 'k-', linewidth=0.8) if best_period: ax.axvline(x=best_period, color='red', linestyle='--', linewidth=1, label=f'Best P = {best_period:.4f} d') ax.legend() ax.set_xlabel("Period (days)") ax.set_ylabel("Power") ax.set_title(title) ax.minorticks_on() if save_path: fig.savefig(save_path) plt.close(fig) else: plt.show() return fig, ax ``` ### 8. 天球投影（Mollweide） ```python def plot_skymap(ra_deg, dec_deg, values=None, title="Sky Map", save_path=None): fig, ax = plt.subplots(figsize=(10, 5), subplot_kw={'projection': 'mollweide'}) # 转换为弧度，RA 中心在 180° ra_rad = np.deg2rad(np.array(ra_deg)) dec_rad = np.deg2rad(np.array(dec_deg)) ra_rad = ra_rad - np.pi # 中心化 if values is not None: sc = ax.scatter(ra_rad, dec_rad, c=values, s=5, cmap='viridis', alpha=0.7, edgecolors='none') fig.colorbar(sc, ax=ax, pad=0.05, shrink=0.6) else: ax.scatter(ra_rad, dec_rad, c='black', s=5, alpha=0.5, edgecolors='none') ax.set_title(title) ax.grid(True, alpha=0.3) if save_path: fig.savefig(save_path, bbox_inches='tight') plt.close(fig) else: plt.show() return fig, ax ``` ### 9. 发现图（Finding Chart） ```python def plot_finding_chart(image_data, ra, dec, label=None, save_path=None): """image_data: 2D numpy array (from FITS or JPEG)""" fig, ax = plt.subplots() ax.imshow(image_data, cmap='gray_r', origin='lower') # 标注中心 h, w = image_data.shape ax.plot(w/2, h/2, 'r+', markersize=15, markeredgewidth=2) if label: ax.annotate(label, (w/2, h/2), textcoords="offset points", xytext=(10, 10), color='red', fontsize=10, fontweight='bold') ax.set_title(f"Finding Chart ({ra:.4f}, {dec:.4f})") ax.set_xlabel("X (pix)") ax.set_ylabel("Y (pix)") if save_path: fig.savefig(save_path) plt.close(fig) else: plt.show() return fig, ax ``` ## 工作流程 1. **确定图表类型**：根据数据特征选择合适的图表模板 2. **准备数据**：从文献、数据库或分析结果中提取数据 3. **生成脚本**：使用上述模板，填入实际数据 4. **执行**：`python3 script.py` 5. **验证**：检查输出图片是否正确 6. **保存**：保存到 `figures/` 目录，命名格式：`{description}_{source}.{ext}` ## 保存路径约定 - `figures/` — 项目根目录下的图表输出目录 - 文件名格式：`{描述}_{来源}_{日期}.{格式}` - 示例：`spectrum_lamost_438809089_20260710.pdf` - 示例：`hr_diagram_m31_field_20260710.png`