菜单
Professional sub-skill for Matplotlib focused on high-performance animations, complex multi-figure layouts (GridSpec), interactive widgets, and publication-ready typography (LaTeX/PGF).
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/tondevrel/scientific-agent-skills --skill matplotlib-pro命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
Atomic Simulation Environment - a set of tools for setting up, manipulating, running, visualizing, and analyzing atomistic simulations. Acts as a universal interface between Python and numerous quantum chemical and molecular dynamics codes. Use for building atomic structures, geometry optimization, molecular dynamics simulations, transition state searches (NEB), file format conversion (CIF, XYZ, POSCAR, PDB), electronic property calculations (DOS, band structures), and automating simulation workflows with DFT/MD codes like VASP, GPAW, Quantum ESPRESSO, LAMMPS.
The core library for Astronomy and Astrophysics in Python. Provides data structures for coordinates, time, units, FITS files, and cosmological models. Essential for observational data reduction and theoretical astrophysics. Use when working with astronomical coordinates (RA/Dec), physical units, FITS files, time scales, WCS, cosmology, or astronomical tables.
A Python package useful for chemistry (mainly physical/analytical/inorganic chemistry). Features include balancing chemical reactions, chemical kinetics (ODE integration), chemical equilibria, ionic strength calculations, and unit handling. Use when working with chemical equations, reaction balancing, kinetic modeling, equilibrium calculations, speciation, pH calculations, ionic strength, activity coefficients, or chemical formula parsing.
正在显示 SKILL.md
| name | matplotlib-pro |
| description | Professional sub-skill for Matplotlib focused on high-performance animations, complex multi-figure layouts (GridSpec), interactive widgets, and publication-ready typography (LaTeX/PGF). |
| version | 3.8 |
| license | PSF |
Beyond static plots, Matplotlib is a powerful engine for dynamic data visualization and scientific storytelling. This guide focuses on the "Pro" features: blitting for speed, Artist hierarchy for control, and LaTeX integration for papers.
Everything you see is an Artist. Figures contain Axes, Axes contain Lines, Text, Patches. Pro-level control means manipulating these objects directly instead of using high-level plt commands.
Standard animation redraws the whole figure every frame (slow). Blitting only redraws the parts that changed (e.g., the moving line), while keeping the axes and labels cached as a background image.
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
fig, ax = plt.subplots()
line, = ax.plot([], [], lw=2) # Returns the Line2D artist
def init():
ax.set_xlim(0, 2*np.pi)
ax.set_ylim(-1, 1)
return line, # Note the comma
def update(frame):
x = np.linspace(0, 2*np.pi, 100)
y = np.sin(x + frame/10.0)
line.set_data(x, y)
return line,
# blit=True is critical for performance
ani = FuncAnimation(fig, update, frames=100, init_func=init, blit=True)
plt.show()
import matplotlib as mpl
mpl.use("pgf") # Use PGF for perfect LaTeX integration
mpl.rcParams.update({
"pgf.texsystem": "pdflatex",
"font.family": "serif",
"text.usetex": True,
"pgf.rcfonts": False,
})
fig.savefig("figure.pgf") # Import this directly into your LaTeX doc
import matplotlib.gridspec as gridspec
fig = plt.figure(constrained_layout=True)
gs = gridspec.GridSpec(3, 3, figure=fig)
ax_main = fig.add_subplot(gs[0:2, :]) # Top 2/3rds
ax_hist_x = fig.add_subplot(gs[2, 0:2]) # Bottom left
ax_hist_y = fig.add_subplot(gs[2, 2]) # Bottom right
from matplotlib.widgets import Slider
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.25)
line, = ax.plot(x, np.sin(x))
ax_freq = plt.axes([0.25, 0.1, 0.65, 0.03])
slider = Slider(ax_freq, 'Freq', 0.1, 30.0, valinit=1.0)
def update(val):
line.set_ydata(np.sin(slider.val * x))
fig.canvas.draw_idle() # Optimized redraw
slider.on_changed(update)
.svg or .pdf to ensure labels don't pixelate in reports.plt.close() to prevent memory leaks in notebooks.fig.get_size_inches() and scale relative to the figure size for portability.fig.set_constrained_layout(True).Matplotlib Pro is the bridge between data and insight. Mastering the blitting engine and the PGF backend allows scientists to create dynamic evidence that is as visually compelling as it is mathematically rigorous.