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winget install Posit.RStudio --source winget

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age = c(1, 3, 5, 2, 11, 9, 3, 9, 12, 3)
weight = c(4.4, 5.3, 7.2, 5.2, 8.5, 7.3, 6, 10.4, 10.2, 6.1)
mean(weight)
sd(weight)
cor(age,weight)
plot(age,weight)

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import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import pearsonr  # 用于计算皮尔逊相关系数

# 定义年龄和体重列表
age = [1, 3, 5, 2, 11, 9, 3, 9, 12, 3]
weight = [4.4, 5.3, 7.2, 5.2, 8.5, 7.3, 6.0, 10.4, 10.2, 6.1]

# 计算体重的平均值
x = np.mean(weight)

# 计算体重的标准差
sd_weight = np.std(weight)

# 计算年龄和体重之间的皮尔逊相关系数
correlation, _ = pearsonr(age, weight)

# 输出平均值和标准差
print(f"体重的平均值：{x}")
print(f"体重的标准差：{sd_weight}")
print(f"年龄与体重的相关系数：{correlation}")

# 绘制散点图
plt.scatter(age, weight)
plt.xlabel("年龄")
plt.ylabel("体重")
plt.title("年龄与体重的关系")
plt.show()

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# 绘制多个子图，并使用中文
import matplotlib.pyplot as plt
import scienceplots

with plt.style.context(["science", "no-latex"]):
    plt.rcParams["font.family"] = (
        " Times New Roman, SimSun"  # 设置字体族，中文为SimSun，英文为Times New Roman
    )
    plt.rcParams["mathtext.fontset"] = "stix"  # 设置数学公式字体为stix
    fig, axes = plt.subplots(figsize=(8, 6), ncols=2, nrows=2)

    axes = axes.flatten()

    for i in range(4):
        axes[i].set_xlim(9000, 11000000)
        axes[i].set_ylim(1, 100000)
        axes[i].grid(linestyle="--")

    axes[0].semilogx(
        [10000, 100000, 1000000, 10000000],
        [15.76, 36.64, 568.00, 14050.00],
        label="Index-sort-2D",
        marker="o",
        ls="--",
    )
    axes[0].semilogx(
        [10000, 100000, 1000000, 10000000],
        [21.69, 70.50, 791.20, 34160.00],
        label="Index-sort-3D",
        marker="x",
        ls="--",
    )
    axes[0].semilogx(
        [10000, 100000, 1000000, 10000000],
        [4.54, 41.12, 990.00, 14280.00],
        label="Spatial-hashing-2D",
        marker="s",
    )
    axes[0].semilogx(
        [10000, 100000, 1000000, 10000000],
        [8.33, 79.06, 2367.00, 28670.00],
        label="Spatial-hashing-3D",
        marker="^",
    )
    axes[0].set_xscale("log")
    axes[0].set_yscale("log")
    axes[0].set_xlabel("粒子数/个")
    axes[0].set_ylabel("单步耗时/ms")
    axes[0].set_title("均匀分布")

    axes[1].semilogx(
        [10000, 100000, 1000000, 1000000],
        [2.97, 43.81, 3436.00, 3436.00],
        label="Index-sort-2D",
        marker="o",
        ls="--",
    )
    axes[1].semilogx(
        [10000, 100000, 1000000, 1000000],
        [3.92, 47.99, 2503.00, 2503.00],
        label="Index-sort-3D",
        marker="x",
        ls="--",
    )
    axes[1].semilogx(
        [10000, 100000, 1000000, 1000000],
        [5.13, 54.29, 2512.00, 2512.00],
        label="Spatial-hashing-2D",
        marker="s",
    )
    axes[1].semilogx(
        [10000, 100000, 1000000, 10000000],
        [8.77, 69.27, 1478.00, 22550.00],
        label="Spatial-hashing-3D",
        marker="^",
    )
    axes[1].set_xscale("log")
    axes[1].set_yscale("log")
    axes[1].set_title("正态分布")
    axes[1].set_xlabel("粒子数/个")
    axes[1].set_ylabel("单步耗时/ms")

    axes[2].semilogx(
        [10000, 100000, 1000000, 10000000],
        [343.91, 344.75, 384.42, 2519.46],
        label="Index-sort-2D",
        marker="o",
        ls="--",
    )
    axes[2].semilogx(
        [10000, 100000, 1000000, 10000000],
        [359.96, 361.47, 411.39, 2584.77],
        label="Index-sort-3D",
        marker="x",
        ls="--",
    )
    axes[2].semilogx(
        [10000, 100000, 1000000, 10000000],
        [9.41, 31.18, 253.18, 4116.66],
        label="Spatial-hashing-2D",
        marker="s",
    )
    axes[2].semilogx(
        [10000, 100000, 1000000, 10000000],
        [9.49, 31.98, 282.29, 8533.71],
        label="Spatial-hashing-3D",
        marker="^",
    )
    axes[2].set_xscale("log")
    axes[2].set_yscale("log")
    axes[2].set_xlabel("粒子数/个")
    axes[2].set_ylabel("内存占用/MB")
    axes[2].set_title("均匀分布")

    axes[3].semilogx(
        [10000, 100000, 1000000, 1000000],
        [50.58, 83.49, 618.06, 618.06],
        label="Index-sort-2D",
        marker="o",
        ls="--",
    )
    axes[3].semilogx(
        [10000, 100000, 1000000, 1000000],
        [53.42, 58.51, 198.77, 198.77],
        label="Index-sort-3D",
        marker="x",
        ls="--",
    )
    axes[3].semilogx(
        [10000, 100000, 1000000, 10000000],
        [9.42, 48.87, 1351.06, 17735.29],
        label="Spatial-hashing-2D",
        marker="s",
    )
    axes[3].semilogx(
        [10000, 100000, 1000000, 1000000],
        [9.49, 32.61, 259.72, 259.72],
        label="Spatial-hashing-3D",
        marker="^",
    )
    axes[3].set_xscale("log")
    axes[3].set_yscale("log")
    axes[3].set_title("正态分布")
    axes[3].set_xlabel("粒子数/个")
    axes[3].set_ylabel("内存占用/MB")

    lines, labels = fig.axes[-1].get_legend_handles_labels()
    fig.legend(
        lines,
        labels,
        loc="center",
        facecolor="white",
        framealpha=0.75,
        frameon=True,
        bbox_to_anchor=(0.522, 0.506),
        ncol=2,
    )

    plt.tight_layout()
    plt.savefig("plot2.svg")
    plt.show()