"Python" Pandas & matplotlib Data processing drawing surface plots

Python matplotlib module, a drawing tool library for extended Matlab, that can draw a variety of graphics

It is recommended to install Anaconda after use, integration of a lot of third library, basically meet the needs of everyone, the corresponding choice Python 2.7 or 3.5 of the can:
https://www.continuum.io/downloads#windows

脚本默认执行方式：             1.获取当前文件夹下的1.log文件             2.将数据格式化为矩阵             3.以矩阵的列索引为x坐标，行索引为y坐标，值为z坐标             4.绘制曲面图

test Data

测试所用数据： r_gain= 79.000000f,  89.000000f, 104.000000f, 120.000000f, 135.000000f, 149.000000f, 160.000000f, 172.000000f, 176.000000f, 172.000000f, 164.000000f, 159.000000f, 143.000000f, 128.000000f, 113.000000f,  97.000000f,  81.000000f, r_gain= 84.000000f, 100.000000f, 120.000000f, 136.000000f, 156.000000f, 176.000000f, 192.000000f, 204.000000f, 208.000000f, 204.000000f, 196.000000f, 180.000000f, 164.000000f, 144.000000f, 124.000000f, 108.000000f,  92.000000f, r_gain= 91.000000f, 112.000000f, 132.000000f, 156.000000f, 176.000000f, 200.000000f, 224.000000f, 240.000000f, 248.000000f, 244.000000f, 228.000000f, 208.000000f, 188.000000f, 164.000000f, 140.000000f, 120.000000f,  99.000000f, r_gain= 99.000000f, 120.000000f, 144.000000f, 172.000000f, 200.000000f, 228.000000f, 256.000000f, 276.000000f, 284.000000f, 280.000000f, 264.000000f, 240.000000f, 208.000000f, 180.000000f, 156.000000f, 132.000000f, 105.000000f, r_gain=107.000000f, 128.000000f, 156.000000f, 184.000000f, 216.000000f, 256.000000f, 288.000000f, 308.000000f, 320.000000f, 316.000000f, 296.000000f, 264.000000f, 228.000000f, 196.000000f, 164.000000f, 140.000000f, 113.000000f, r_gain=111.000000f, 132.000000f, 160.000000f, 192.000000f, 232.000000f, 272.000000f, 304.000000f, 332.000000f, 340.000000f, 336.000000f, 316.000000f, 284.000000f, 244.000000f, 204.000000f, 172.000000f, 144.000000f, 117.000000f, r_gain=109.000000f, 136.000000f, 164.000000f, 196.000000f, 232.000000f, 276.000000f, 312.000000f, 336.000000f, 348.000000f, 344.000000f, 320.000000f, 288.000000f, 248.000000f, 208.000000f, 172.000000f, 144.000000f, 117.000000f, r_gain=111.000000f, 132.000000f, 160.000000f, 192.000000f, 228.000000f, 268.000000f, 304.000000f, 328.000000f, 340.000000f, 332.000000f, 312.000000f, 280.000000f, 240.000000f, 200.000000f, 168.000000f, 140.000000f, 119.000000f, r_gain=101.000000f, 128.000000f, 152.000000f, 180.000000f, 212.000000f, 248.000000f, 280.000000f, 304.000000f, 312.000000f, 308.000000f, 288.000000f, 260.000000f, 224.000000f, 192.000000f, 160.000000f, 136.000000f, 109.000000f, r_gain= 95.000000f, 116.000000f, 140.000000f, 164.000000f, 192.000000f, 224.000000f, 248.000000f, 272.000000f, 280.000000f, 272.000000f, 256.000000f, 232.000000f, 200.000000f, 176.000000f, 152.000000f, 128.000000f, 101.000000f, r_gain= 87.000000f, 108.000000f, 128.000000f, 148.000000f, 172.000000f, 192.000000f, 216.000000f, 232.000000f, 236.000000f, 232.000000f, 220.000000f, 200.000000f, 180.000000f, 156.000000f, 136.000000f, 116.000000f,  95.000000f, r_gain= 80.000000f,  96.000000f, 112.000000f, 132.000000f, 148.000000f, 168.000000f, 180.000000f, 192.000000f, 196.000000f, 196.000000f, 184.000000f, 172.000000f, 156.000000f, 136.000000f, 120.000000f, 104.000000f,  88.000000f, r_gain= 69.000000f,  85.000000f,  96.000000f, 111.000000f, 127.000000f, 141.000000f, 153.000000f, 160.000000f, 164.000000f, 159.000000f, 157.000000f, 145.000000f, 135.000000f, 120.000000f, 104.000000f,  88.000000f,  77.000000f,

Surface Chart Scripts

# -*- coding: utf-8 -*-from matplotlib import pyplot as pltfrom mpl_toolkits.mplot3d import Axes3Dfrom pandas import DataFramedef draw(x, y, z):    ‘‘‘    采用matplolib绘制曲面图    :param x: x轴坐标数组    :param y: y轴坐标数组    :param z: z轴坐标数组    :return:    ‘‘‘    X = x    Y = y    Z = z    fig = plt.figure()    ax = fig.add_subplot(111, projection=‘3d‘)    ax.plot_trisurf(X, Y, Z)    plt.show()if __name__ == ‘__main__‘:    ‘‘‘       默认执行方式：             1.获取当前文件夹下的1.log文件             2.将数据格式化为矩阵             3.以矩阵的列索引为x坐标，行索引为y坐标，值为z坐标             4.绘制曲面图    ‘‘‘    data = {}    index_origin = 0    f = open("1.log")    line = f.readline()    while line:        data[index_origin] = line.split(‘=‘)[-1].replace(‘ ‘, ‘‘).split(‘f,‘)[0:-1]        index_origin = index_origin + 1        line = f.readline()    f.close()    df = DataFrame(data)    df = df.T    x = []    for i in range(len(df.index)):        x = x + list(df.columns)    print(x)    y = []    for i in range(len(df.index)):        for m in range(17):            y.append(i)    print(y)    z = []    for i in range(len(df.index)):        z = z + df[i:i + 1].values.tolist()[0]    z = map(float, z)    print (z)    draw(x, y, z)

"Python" Pandas & matplotlib Data processing drawing surface plots