import numpy as np
import matplotlib.pyplot as plt

conda create -n plot python=3.12 -y
conda activate plot
conda install -c conda-forge matplotlib numpy pandas scipy -y

import matplotlib
import numpy as np

print("Matplotlib:", matplotlib.__version__)   # 期望 3.10.x
print("NumPy:", np.__version__)
print("Backend:", matplotlib.get_backend())    # VS Code 交互式通常是 TkAgg / QtAgg / module://...

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [1, 4, 9])
plt.xlabel("x")
plt.ylabel("y")
plt.title("pyplot 风格")
plt.show()

import matplotlib.pyplot as plt

fig, ax = plt.subplots()   # fig 是整个画布，ax 是一套坐标轴
ax.plot([1, 2, 3], [1, 4, 9])
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title("面向对象风格")
fig.savefig("oo_style.png", dpi=300)

fig, ax = plt.subplots(figsize=(6, 4), dpi=150)
ax.plot(np.linspace(0, 10, 100), np.sin(np.linspace(0, 10, 100)))
fig.tight_layout()

fig, axes = plt.subplots(2, 2, figsize=(8, 6), sharex=True, sharey=True)
# axes 是一个 shape=(2,2) 的 ndarray，可用 axes[0,1] 索引
axes = axes.flatten()               # 拍平成一维，遍历方便
for i, ax in enumerate(axes):
    x = np.linspace(0, 4 * np.pi, 200)
    ax.plot(x, np.sin(x + i * np.pi / 4))
    ax.set_title(f"子图 {i + 1}")
fig.suptitle("subplots 示例", fontsize=14)
fig.tight_layout()

fig, axd = plt.subplot_mosaic(
    [["A", "B"],
     ["A", "C"]],
    figsize=(8, 6),
    layout="constrained",   # 自动避免子图重叠
)

axd["A"].plot(np.linspace(0, 1, 100), np.random.rand(100))
axd["A"].set_title("A（跨两行）")
axd["B"].plot(np.linspace(0, 1, 100), np.random.rand(100))
axd["C"].plot(np.linspace(0, 1, 100), np.random.rand(100))

fig = plt.figure(figsize=(10, 4))
gs = fig.add_gridspec(1, 3, width_ratios=[3, 1, 1], wspace=0.15)

ax_main = fig.add_subplot(gs[0, 0])   # 主图，占 3 份
ax_dos  = fig.add_subplot(gs[0, 1], sharey=ax_main)  # 右侧小图，共享 y 轴
ax_other= fig.add_subplot(gs[0, 2], sharey=ax_main)

ax_main.plot([1, 2, 3], [1, 4, 9])
ax_dos.plot([0, 1], [0, 10])
ax_other.plot([0, 1], [0, 10])

import numpy as np

rng = np.random.default_rng(42)   # 固定随机种子，保证可复现
x = np.linspace(0, 10, 50)
y = 2.0 * x + rng.normal(0, 1.5, x.size)   # 带噪声的线性关系

fig, ax = plt.subplots(figsize=(6, 4))
x = np.linspace(0, 10, 100)
ax.plot(x, np.sin(x), color="tab:blue", linestyle="-", linewidth=1.5,
        marker="o", markersize=4, markevery=10, label="sin")
ax.plot(x, np.cos(x), color="tab:red", linestyle="--", linewidth=1.5,
        label="cos")
ax.legend()

fig, ax = plt.subplots(figsize=(6, 6))
n = 200
x = rng.uniform(-5, 5, n)
y = x + rng.normal(0, 0.4, n)          # 模拟"预测值 vs 真值"接近 y=x
err = np.abs(y - x)

sc = ax.scatter(x, y, c=err, cmap="viridis", s=30, alpha=0.8, edgecolors="k", linewidths=0.3)
ax.plot([-6, 6], [-6, 6], "r--", lw=1, label="y = x")
ax.set_xlabel("DFT 能量 / eV")
ax.set_ylabel("ML 势预测能量 / eV")
ax.set_aspect("equal")
ax.legend()
fig.colorbar(sc, ax=ax, label="绝对误差 / eV")

fig, ax = plt.subplots(figsize=(7, 4))
sites = ["Fe-N4", "Co-N4", "Ni-N4", "Cu-N4"]
overpotential = [0.42, 0.35, 0.61, 0.88]     # CO2RR 过电位 / V，越小越好

bars = ax.bar(sites, overpotential, width=0.6, color="tab:blue",
              edgecolor="k", linewidth=0.5)
ax.set_ylabel("过电位 $\eta$ / V")
ax.set_ylim(0, 1.0)
ax.axhline(0.35, color="r", linestyle="--", label="Co-N4 参考")   # 加一条参考线
ax.legend()

# 在每根柱顶标注数值
for rect in bars:
    h = rect.get_height()
    ax.text(rect.get_x() + rect.get_width() / 2, h + 0.01, f"{h:.2f}",
            ha="center", va="bottom", fontsize=9)

fig, ax = plt.subplots(figsize=(7, 4))
catalysts = ["Cu(111)", "Cu(100)", "Cu(211)", "Zn-Cu"]
E_ads = [-0.85, -0.72, -0.93, -1.05]        # CO 吸附能 / eV
err = [0.08, 0.06, 0.10, 0.07]              # 标准差

ax.errorbar(catalysts, E_ads, yerr=err, fmt="o", markersize=7,
            capsize=4, capthick=1.5, elinewidth=1.2, ecolor="tab:gray",
            color="tab:red", label="CO 吸附能")
ax.axhline(0, color="k", lw=0.8)
ax.set_ylabel("吸附能 $E_{ads}$ / eV")
ax.legend()

fig, ax = plt.subplots(figsize=(6, 4))
energies = rng.normal(-0.5, 0.2, 5000)      # 模拟 5000 个构型的吸附能

ax.hist(energies, bins=40, density=True, alpha=0.7,
        color="tab:green", edgecolor="w", histtype="stepfilled")
ax.set_xlabel("吸附能 / eV")
ax.set_ylabel("概率密度")

fig, ax = plt.subplots(figsize=(7, 4))
rng = np.random.default_rng(7)
# 四组催化剂的 CO2RR 法拉第效率(%) 分布
data = [rng.normal(78, 5, 50),
        rng.normal(65, 8, 50),
        rng.normal(52, 6, 50),
        rng.normal(88, 4, 50)]

bp = ax.boxplot(data, tick_labels=["Cat-A", "Cat-B", "Cat-C", "Cat-D"],
                patch_artist=True, showmeans=True, showfliers=True)
colors = ["tab:blue", "tab:orange", "tab:green", "tab:red"]
for patch, c in zip(bp["boxes"], colors):
    patch.set_facecolor(c)
    patch.set_alpha(0.6)
ax.set_ylabel("法拉第效率 / %")

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4))

# 构造一个双势阱势能面，模拟两个反应坐标 r1, r2 上的自由能
r1 = np.linspace(-2, 2, 200)
r2 = np.linspace(-2, 2, 200)
R1, R2 = np.meshgrid(r1, r2)
Z = (R1**2 - 1)**2 + R2**2 + 0.5 * R1 * R2   # 一个形如"双谷"的势能面

cf = ax1.contourf(R1, R2, Z, levels=20, cmap="viridis")
fig.colorbar(cf, ax=ax1, label="势能")
ax1.set_title("contourf")

cs = ax2.contour(R1, R2, Z, levels=np.arange(0, 8, 0.5), cmap="viridis")
ax2.clabel(cs, inline=True, fontsize=7, fmt="%.1f")   # 在线上标数值
ax2.set_title("contour（等值线）")

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4))

# imshow：规则矩阵，origin 决定行方向
M = rng.normal(0, 1, (20, 20))
im = ax1.imshow(M, cmap="coolwarm", origin="lower", aspect="auto")
fig.colorbar(im, ax=ax1, label="值")
ax1.set_title("imshow（矩阵）")

# pcolormesh：非均匀坐标，shading='auto' 是 3.x 推荐写法
x = np.linspace(0, 5, 30)
y = np.linspace(0, 3, 20)
X, Y = np.meshgrid(x, y)
Z = np.sin(X) * np.cos(Y)
pc = ax2.pcolormesh(X, Y, Z, cmap="viridis", shading="auto")
fig.colorbar(pc, ax=ax2, label="sin(x)cos(y)")
ax2.set_title("pcolormesh（真实坐标）")

fig, ax = plt.subplots(figsize=(6, 4))
ax.plot(x, np.sin(x))

ax.set_xlabel("反应坐标 r / Å", fontsize=13)      # 推荐带单位
ax.set_ylabel("能量 E / eV", fontsize=13)
ax.set_xlim(0, 10)        # 手动设范围；省略则自动
ax.set_ylim(-1.2, 1.2)

fig, ax = plt.subplots(figsize=(6, 4))
ax.plot(np.linspace(0, 3, 50), np.sin(np.linspace(0, 3, 50)))

high_sym = [0, 1, 2, 3]          # 高对称点的位置（k 路径累计长度）
labels = ["Γ", "X", "M", "Γ"]    # 对应符号
ax.set_xticks(high_sym)
ax.set_xticklabels(labels, fontsize=12)
# 在高对称点处画竖直虚线
for k in high_sym:
    ax.axvline(k, color="k", linestyle="--", linewidth=0.7, alpha=0.6)
ax.set_ylabel("E - E$_F$ / eV")

ax.tick_params(axis="both", direction="in", length=4, width=1.2,
               labelsize=11, top=True, right=True)
ax.minorticks_on()   # 打开次要刻度

fig, ax1 = plt.subplots(figsize=(7, 4))
potential = np.linspace(-0.2, -1.0, 50)
current = 50 * np.exp(potential / 0.3)         # 电流密度随过电位指数增长
faradaic = 100 / (1 + np.exp(-(potential + 0.6) / 0.08))  # FE 的 S 曲线

ax1.plot(potential, current, "tab:blue", label="电流密度")
ax1.set_xlabel("电位 vs. RHE / V")
ax1.set_ylabel("电流密度 / mA cm$^{-2}$", color="tab:blue")
ax1.tick_params(axis="y", labelcolor="tab:blue")

ax2 = ax1.twinx()                               # 共享 x 轴，独立右 y 轴
ax2.plot(potential, faradaic, "tab:red", label="法拉第效率")
ax2.set_ylabel("法拉第效率 / %", color="tab:red")
ax2.tick_params(axis="y", labelcolor="tab:red")

# 两条曲线合并到一个图例
lines1, labels1 = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax1.legend(lines1 + lines2, labels1 + labels2, loc="best")

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
x = np.logspace(0, 5, 50)         # 10^0 ~ 10^5

ax1.plot(x, x**2)
ax1.set_xscale("log")             # 只 x 轴取 log
ax1.set_yscale("log")             # x、y 都 log（双对数图）
ax1.set_title("log-log")

# Arrhenius：ln k = ln A - Ea/(RT) = f(1/T)
T = np.linspace(300, 600, 100)
k = 1e10 * np.exp(-50000 / (8.314 * T))     # Ea = 50 kJ/mol
ax2.plot(1000 / T, np.log(k))
ax2.set_xlabel("1000 / T  (K$^{-1}$)")
ax2.set_ylabel("ln k")
ax2.set_title("Arrhenius 图")

fig, ax = plt.subplots(figsize=(6, 4))
ax.plot(x, np.sin(x))
ax.set_title("(a) 反应势能曲线", fontsize=13, pad=10)   # pad 控制标题与图间距
fig.suptitle("图 1", fontsize=14)                        # 画布总标题（多子图时用）

ax.plot(x, np.sin(x), label="exp. data")
ax.plot(x, np.cos(x), label="DFT calc.")
ax.legend(loc="best",            # 自动挑不挡数据的位置
          frameon=True,          # 是否画边框
          framealpha=0.8,        # 边框透明度
          ncol=2,                # 分两列
          fontsize=10,
          handlelength=2.0)      # 图例中线的长度

ax.legend(loc="upper left", bbox_to_anchor=(1.02, 1.0), borderaxespad=0)

fig, ax = plt.subplots(figsize=(6, 4))
x = np.linspace(0, 3, 100)
ax.plot(x, (x - 1.2)**2)

ax.annotate("过渡态 TS",
            xy=(1.2, 0.0),            # 箭头指向的点
            xytext=(1.6, 1.5),        # 文字位置
            fontsize=11,
            arrowprops=dict(arrowstyle="->", color="k", lw=1.2))

ax.text(0.1, 2.0, "势垒 $E_a$", fontsize=11)   # 纯文字，无箭头

import matplotlib

cmap = matplotlib.colormaps["viridis"]          # 推荐：从全局注册表取 colormap
cmap = matplotlib.colormaps["RdBu_r"]
print(list(matplotlib.colormaps)[:10])          # 打印前 10 个可用名称

import matplotlib.colors as mcolors

data = rng.normal(0, 0.5, (30, 30))     # 有正有负
norm = mcolors.TwoSlopeNorm(vmin=-1.5, vcenter=0, vmax=1.5)

fig, ax = plt.subplots(figsize=(5, 5))
im = ax.imshow(data, cmap="RdBu_r", norm=norm, origin="lower")
fig.colorbar(im, ax=ax, label="Δρ (e/Å$^3$)")

import matplotlib.colors as mcolors

bounds = [0, 0.3, 0.6, 1.0]                       # 边界
cmap = mcolors.ListedColormap(["tab:green", "tab:orange", "tab:red"])
norm = mcolors.BoundaryNorm(bounds, cmap.N)

Z = rng.uniform(0, 1, (20, 20))
fig, ax = plt.subplots(figsize=(5, 5))
im = ax.imshow(Z, cmap=cmap, norm=norm, origin="lower")
fig.colorbar(im, ax=ax, ticks=[0.15, 0.45, 0.8])  # 刻度放在每个档位中间

import matplotlib
import matplotlib.pyplot as plt

matplotlib.rcParams.update({
    # ---- 中文 + 负号（Windows 下关键！）----
    "font.sans-serif": ["Microsoft YaHei", "SimHei", "SimSun", "KaiTi"],
    "axes.unicode_minus": False,   # 用 ASCII 减号，否则负号显示成方块

    # ---- 字号统一（投稿常用 7~10 pt，这里给 11 便于屏幕看）----
    "font.size": 11,
    "axes.labelsize": 12,
    "axes.titlesize": 13,
    "xtick.labelsize": 10,
    "ytick.labelsize": 10,
    "legend.fontsize": 10,

    # ---- 线条与边框 ----
    "axes.linewidth": 1.2,
    "lines.linewidth": 1.5,
    "xtick.direction": "in",
    "ytick.direction": "in",

    # ---- 分辨率 ----
    "figure.dpi": 150,
    "savefig.dpi": 300,
})

from matplotlib import font_manager
names = {f.name for f in font_manager.fontManager.ttflist}
print("SimHei" in names, "Microsoft YaHei" in names)

from matplotlib import font_manager
font_manager.fontManager.addfont(r"C:\Windows\Fonts\simhei.ttf")   # 直接指定字体文件路径
font_manager._rebuild()

ax.set_ylabel(r"能量 $E - E_F$ / eV")
ax.set_xlabel(r"反应坐标 $\xi$ / Å")
ax.set_title(r"势垒 $E_a$ = 0.45 eV")
# 下标：$E_{ads}$；上标：$^{-2}$；希腊字母：$\Delta G^{\ddagger}$

plt.style.use("seaborn-v0_8-whitegrid")   # 类似 seaborn 的网格样式
# 或叠加列表：plt.style.use(["seaborn-v0_8", "我的自定义.mplstyle"])

fig, ax = plt.subplots(figsize=(6, 4))
ax.plot(x, np.sin(x))

fig.savefig("fig.pdf", bbox_inches="tight")          # 投稿首选：矢量 PDF
fig.savefig("fig.svg", bbox_inches="tight")          # 矢量 SVG，可导入 Inkscape / PPT
fig.savefig("fig.png", dpi=300, bbox_inches="tight") # 报告 / 网页：高清位图
fig.savefig("fig.tif", dpi=600, bbox_inches="tight",
            pil_kwargs={"compression": "tiff_lzw"})   # 部分期刊要 TIFF

fig.savefig("fig.svg", bbox_inches="tight", transparent=True, pad_inches=0.05)

import numpy as np
import matplotlib.pyplot as plt

rng = np.random.default_rng(0)

# ---- 模拟数据：4 段 k 路径，每段 60 个点，5 条能带 ----
segments = [(0, 1), (1, 2), (2, 3), (3, 4)]      # 各段端点
n_per = 60
n_bands = 5

k_path = []
for a, b in segments:
    k_path.append(np.linspace(a, b, n_per))
k_path = np.concatenate(k_path)

# 用一个"正弦 + 微扰"模拟 5 条能带（价带向下、导带向上）
bands = np.zeros((n_bands, k_path.size))
for b in range(n_bands):
    bands[b] = (b - 2) * 1.5 + 0.8 * np.sin(2 * np.pi * k_path / 4 + b)

fig, ax = plt.subplots(figsize=(7, 5))

for b in range(n_bands):
    ax.plot(k_path, bands[b], color="tab:blue", lw=1.2)

ax.axhline(0, color="k", lw=0.8, ls="--")        # 费米能级 E_F = 0

# 高对称点：用竖直虚线分隔 + 下方标注符号
k_labels_pos = [0, 1, 2, 3, 4]
k_labels = ["Γ", "X", "M", "Y", "Γ"]
for k in k_labels_pos:
    ax.axvline(k, color="gray", lw=0.7, ls="--", alpha=0.6)

ax.set_xticks(k_labels_pos)
ax.set_xticklabels(k_labels, fontsize=13)
ax.set_xlim(0, 4)
ax.set_ylim(-4, 4)
ax.set_ylabel(r"$E - E_F$ / eV", fontsize=13)
ax.tick_params(direction="in", top=True, right=True)
ax.set_title("能带结构（示意）", fontsize=13)

fig.savefig("band_structure.pdf", bbox_inches="tight")

import numpy as np
import matplotlib.pyplot as plt

rng = np.random.default_rng(1)
E = np.linspace(-6, 6, 400)                      # 能量轴

def gaussian(x, mu, sigma):
    return np.exp(-(x - mu)**2 / (2 * sigma**2)) / (sigma * np.sqrt(2 * np.pi))

# 用几个高斯峰模拟 DOS（spin up 为正，spin down 为负）
dos_up = (2.0 * gaussian(E, -1.0, 0.6) + 1.2 * gaussian(E, 0.8, 0.4)
          + 0.8 * gaussian(E, 2.5, 0.5))
dos_dn = (1.8 * gaussian(E, -0.6, 0.7) + 1.0 * gaussian(E, 1.5, 0.5)
          + 0.6 * gaussian(E, 3.0, 0.4))

fig, ax = plt.subplots(figsize=(6, 6))

ax.fill_between(E, dos_up, 0, color="tab:blue", alpha=0.5, label="spin up")
ax.fill_between(E, -dos_dn, 0, color="tab:red", alpha=0.5, label="spin down")

ax.axvline(0, color="k", lw=0.8, ls="--")        # 费米能级
ax.set_xlabel(r"$E - E_F$ / eV", fontsize=13)
ax.set_ylabel("DOS / states eV$^{-1}$", fontsize=13)
ax.legend()
ax.set_title("态密度 DOS（自旋极化，示意）", fontsize=13)

fig.savefig("dos.pdf", bbox_inches="tight")

import numpy as np
import matplotlib.pyplot as plt

rng = np.random.default_rng(2)
k = np.linspace(0, 4, 200)
E = np.linspace(-6, 6, 400)

# 模拟能带
bands = np.array([(b - 2) * 1.5 + 0.8 * np.sin(2 * np.pi * k / 4 + b)
                  for b in range(5)])
# 模拟 DOS
dos = (2.0 * np.exp(-(E - 0.5)**2 / 0.5) + 1.0 * np.exp(-(E + 1.5)**2 / 0.6))

fig, axd = plt.subplot_mosaic(
    [["band", "dos"]],
    figsize=(9, 5),
    width_ratios=[3, 1],
    layout="constrained",
)

ax_band = axd["band"]
ax_dos = axd["dos"]

# 左：能带
for b in range(5):
    ax_band.plot(k, bands[b], color="tab:blue", lw=1.2)
ax_band.axhline(0, color="k", lw=0.8, ls="--")
for kp in [0, 1, 2, 3, 4]:
    ax_band.axvline(kp, color="gray", lw=0.7, ls="--", alpha=0.6)
ax_band.set_xticks([0, 1, 2, 3, 4])
ax_band.set_xticklabels(["Γ", "X", "M", "Y", "Γ"], fontsize=12)
ax_band.set_ylabel(r"$E - E_F$ / eV", fontsize=13)
ax_band.set_xlim(0, 4)
ax_band.set_ylim(-6, 6)

# 右：DOS（横着画，与能带共享 y 轴）
ax_dos.plot(dos, E, color="tab:red", lw=1.5)
ax_dos.fill_betweenx(E, dos, 0, color="tab:red", alpha=0.4)
ax_dos.axhline(0, color="k", lw=0.8, ls="--")
ax_dos.set_ylim(-6, 6)
ax_dos.set_xlabel("DOS", fontsize=12)
ax_dos.set_yticks([])          # 隐藏右侧 DOS 图的 y 轴刻度，避免重复

ax_band.set_title("能带结构 + 态密度（示意）", fontsize=13)
fig.savefig("band_dos.pdf", bbox_inches="tight")

import numpy as np
import matplotlib.pyplot as plt

catalysts = ["Cu", "Ag", "Au", "Zn"]
fe_co = [12, 5, 8, 3]     # CO 法拉第效率 / %
fe_h2 = [30, 45, 25, 60]  # H2 法拉第效率 / %
fe_hcooh = [45, 20, 55, 15]  # HCOOH 法拉第效率 / %

x = np.arange(len(catalysts))
width = 0.25

fig, ax = plt.subplots(figsize=(7, 5))
r1 = ax.bar(x - width, fe_co, width, label="CO", color="tab:blue")
r2 = ax.bar(x, fe_h2, width, label="H$_2$", color="tab:gray")
r3 = ax.bar(x + width, fe_hcooh, width, label="HCOOH", color="tab:orange")

ax.set_ylabel("法拉第效率 / %", fontsize=12)
ax.set_xticks(x)
ax.set_xticklabels(catalysts, fontsize=12)
ax.set_ylim(0, 100)
ax.legend(ncol=3)
ax.set_title("CO$_2$RR 产物选择性对比（示意）", fontsize=13)

fig.savefig("faradaic_bar.pdf", bbox_inches="tight")

import numpy as np
import matplotlib.pyplot as plt

surfaces = ["Cu(111)", "Cu(211)", "Zn(101)", "Pd(111)", "Pt(111)"]
E_CO = [-0.51, -0.78, -0.42, -1.20, -1.55]   # CO 吸附能 / eV
err = [0.05, 0.09, 0.04, 0.07, 0.06]

fig, ax = plt.subplots(figsize=(8, 5))
ax.errorbar(surfaces, E_CO, yerr=err, fmt="s", markersize=9,
            capsize=5, capthick=1.5, elinewidth=1.3,
            color="tab:red", ecolor="tab:gray", label=r"$E_{ads}$(CO)")

ax.axhline(0, color="k", lw=0.8)
# 标注"弱吸附"和"强吸附"区
ax.axhspan(-1.4, -0.9, color="tab:blue", alpha=0.12, label="强吸附区")
ax.axhspan(-0.3, 0.0, color="tab:green", alpha=0.12, label="弱吸附区")

ax.set_ylabel(r"吸附能 $E_{ads}$ / eV", fontsize=13)
ax.tick_params(direction="in", top=True, right=True)
ax.legend()
ax.set_title("CO 吸附能对比（示意）", fontsize=13)

fig.savefig("adsorption_errorbar.pdf", bbox_inches="tight")

import numpy as np
import matplotlib.pyplot as plt

# 反应坐标（离散的几个关键点）
rc = [0.0, 0.4, 1.0, 1.6, 2.0]           # 反应物 -> TS -> 中间体 -> 产物
E = [0.0, 0.85, 0.30, 1.10, -0.45]       # 相对能量 / eV（TS 是鞍点）

fig, ax = plt.subplots(figsize=(8, 5))
ax.plot(rc, E, marker="o", markersize=8, color="tab:blue", lw=2.0)

# 标注关键点
ax.annotate("反应物", xy=(0.0, 0.0), xytext=(0.05, 0.35), fontsize=11,
            arrowprops=dict(arrowstyle="->", lw=1.0))
ax.annotate("TS1", xy=(0.4, 0.85), xytext=(0.5, 1.15), fontsize=11,
            arrowprops=dict(arrowstyle="->", lw=1.0))
ax.annotate("TS2", xy=(1.6, 1.10), xytext=(1.25, 1.45), fontsize=11,
            arrowprops=dict(arrowstyle="->", lw=1.0))
ax.annotate("产物", xy=(2.0, -0.45), xytext=(1.6, -1.0), fontsize=11,
            arrowprops=dict(arrowstyle="->", lw=1.0))

# 用双箭头标注能垒 / 反应能
ax.annotate("", xy=(0.4, 0.0), xytext=(0.4, 0.85),
            arrowprops=dict(arrowstyle="<->", color="r", lw=1.5))
ax.text(0.43, 0.4, r"$E_a$ = 0.85 eV", color="r", fontsize=11)

ax.annotate("", xy=(2.0, -0.45), xytext=(2.0, 0.0),
            arrowprops=dict(arrowstyle="<->", color="g", lw=1.5))
ax.text(2.03, -0.25, r"$\Delta G$ = -0.45 eV", color="g", fontsize=11)

ax.set_xlabel("反应坐标", fontsize=13)
ax.set_ylabel("相对能量 / eV", fontsize=13)
ax.set_xticks([])          # 反应坐标没有物理刻度，隐藏数字
ax.set_ylim(-1.3, 1.6)
ax.set_title("反应势能曲线（示意）", fontsize=13)

fig.savefig("reaction_path.pdf", bbox_inches="tight")

import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt

sns.set_theme(style="ticks", context="paper", font="SimHei")  # 全局样式，font 可设中文

rng = np.random.default_rng(3)
data = {"cat": np.repeat(["A", "B", "C", "D"], 50),
        "value": rng.normal(0, 1, 200)}

fig, ax = plt.subplots(figsize=(6, 4))
sns.boxplot(data=data, x="cat", y="value", ax=ax)   # 显式传入 ax，保持 OO 风格
ax.set_ylabel("吸附能 / eV")
ax.set_title("seaborn 箱线图（底层是 Matplotlib）")

fig.savefig("sns_box.pdf", bbox_inches="tight")

# -*- coding: utf-8 -*-
"""科研出图脚本骨架：把 rcParams 设置 + 你的绘图函数组织起来。"""
import numpy as np
import matplotlib
import matplotlib.pyplot as plt

# ---------- 1. 全局样式（中文 + 负号 + 字号 + 分辨率）----------
matplotlib.rcParams.update({
    "font.sans-serif": ["Microsoft YaHei", "SimHei", "SimSun"],
    "axes.unicode_minus": False,
    "font.size": 11,
    "axes.labelsize": 12,
    "axes.titlesize": 13,
    "xtick.labelsize": 10,
    "ytick.labelsize": 10,
    "legend.fontsize": 10,
    "axes.linewidth": 1.2,
    "lines.linewidth": 1.5,
    "xtick.direction": "in",
    "ytick.direction": "in",
    "figure.dpi": 150,
    "savefig.dpi": 300,
})


# ---------- 2. 你的绘图函数（只接收 ax，便于复用 / 组合）----------
def plot_my_result(data, ax):
    """在这里写具体绘图逻辑，data 换成你的 np.loadtxt / pandas 结果。"""
    ax.plot(data[:, 0], data[:, 1], label="my data")
    ax.set_xlabel("x / unit")
    ax.set_ylabel("y / unit")
    ax.legend()
    return ax


# ---------- 3. 主流程 ----------
if __name__ == "__main__":
    x = np.linspace(0, 10, 100)
    y = np.sin(x)

    fig, ax = plt.subplots(figsize=(6, 4))
    plot_my_result(np.column_stack([x, y]), ax)
    fig.savefig("result.pdf", bbox_inches="tight")   # 投稿矢量图
    fig.savefig("result.png", dpi=300, bbox_inches="tight")  # 预览位图
    plt.show()