使用分段线性插值绘制图形

Question

我想从 CSV 数据文件中绘制带有分段线性插值的图表。目前，我只能绘制如下图表：

这是我的乳胶代码：

\documentclass{article}
\usepackage{pgfplots}
\usepackage{filecontents}

\begin{filecontents*}{data_to_plot.csv}
A,B
0.011,0.0031
0.021,0.0046
0.021,0.0075
0.022,0.0171
0.022,0.0135
0.023,0.0191
0.099,0.0287
\end{filecontents*}

\begin{document}
\begin{tikzpicture}
\begin{axis}
\addplot table [x=A, y=B, col sep=comma] {data_to_plot.csv};
\end{axis}
\end{tikzpicture}

\end{document}

但是，我想用分段线性插值绘制图形，像下面的图形（用 MS Paint 绘制）：

以下是示例数据（截断）：

A,B
0.0013,0.0061
0.0016,0.0046
0.0024,0.0057
0.0024,0.0036
0.0027,0.0064
0.0027,0.0047
0.0028,0.0068
...
...
0.9973,0.1514
0.9973,0.1421
0.9977,0.1169
0.9981,0.1289
0.9983,0.1896
0.9993,0.1335
0.9999,0.1547

所以，我的问题是：我怎样才能像这样绘图，可能还要自定义 X 值差距（第二张图显示的是0.1）？

Answer 1

我通过使用外部工具实现了这一点。就我而言，我使用了 Pythonmatplotlib.pyplot和numpy模块。我使用这些模块生成了我想要的图形并将其导出到 PDF 文件。然后我在 Latex 中将其用作figure。这是我实现目标的 Python 代码：

import matplotlib.pyplot as plt
import numpy as np

data_path = 'data_to_plot.csv'
x_title = 'A'
y_title = 'B'


def averaged_y_new(x_new, x, y):
    y_new = np.zeros(len(x_new), dtype=np.double)

    seg = x_new[1] - x_new[0]  # segment length between two adjacent x_new values
    ix_min = x_new[0] - (seg / 2.0)
    ix_max = ix_min + seg
    idx_x = 0  # iterated index of x array
    val_y = y[idx_x]  # y value of iterated index of x array
    for i in range(len(x_new)):
        sm = 0.0  # sum of y values
        nm = 0  # number of y values
        while idx_x < len(x) and ix_min <= x[idx_x] < ix_max:
            sm += y[idx_x]
            nm += 1
            idx_x += 1
        if nm != 0.0:
            y_new[i] = sm / nm  # average of y values
            val_y = y_new[i]
        else:
            y_new[i] = val_y
        ix_min = ix_max
        ix_max = ix_min + seg

    return y_new


def main():
    data = np.genfromtxt(data_path, dtype=np.double, delimiter=',', names=True)
    data.sort(order=[x_title])

    x = np.array(data[x_title])
    y = np.array(data[y_title])

    plt.xlabel(x_label)
    plt.ylabel(y_label)

    # plt.plot(x, y, 'o')

    x_new = np.linspace(0.0, 1.0, 11)
    y_new = averaged_y_new(x_new, x, y)

    plt.ylim(0, 0.5)
    plt.plot(x_new, y_new, '-bo')

    # plt.show()
    plt.savefig('graph.pdf')


if __name__ == '__main__':
    main()

上面的 Python 代码生成以下图表：