渲染非均匀网格，标志着等值线图

Question

我有两个问题，在matplotlib等值线图的最大值的位置：

1. 我怎样才能使任意meshgrid作为一个经常性的？

我希望两个坐标轴上刻度的位置均匀分布，同时还能反映出我节点的位置。

1. 如何使用彩色标记突出显示数据最高值的位置？

这里是我的代码：

import numpy as np 
import pylab as pl 

def plot_s(data, xlist, ylist): 

    pl.subplot(111) 
    x = np.array(xlist) 
    y = np.array(ylist) 
    X, Y = np.meshgrid(x, y) 
    CS = pl.contour(X, Y, data, colors='k') 
    pl.clabel(CS, inline = 1, fontsize=10) 
    pl.xlabel('x list') 
    pl.ylabel('y list') 
    pl.xticks(xlist) 
    pl.yticks(ylist) 
    pl.title('Contour plot') 
    pl.show() 

def main(): 

    data = np.array([[ 0.56555019, 0.57933922, 0.58266252, 0.58067285, 0.57660236, 0.57185625, 0.56711252, 0.55557035, 0.55027705, 0.54480605], 
        [ 0.55486559, 0.57349717, 0.57940478, 0.57843897, 0.57463271, 0.56963449, 0.5643922 , 0.55095598, 0.54452534, 0.53762606], 
        [ 0.53529358, 0.56254991, 0.57328105, 0.57409218, 0.57066168, 0.5654082 , 0.55956853, 0.5432474 , 0.53501127, 0.52601203], 
        [ 0.50110483, 0.54004071, 0.55800178, 0.56173719, 0.55894846, 0.55328279, 0.54642887, 0.52598388, 0.51533094, 0.50354147]]) 

    xlist = [10., 20., 30., 40., 50., 60., 70., 100., 120., 150.] 
    ylist = [50, 70, 90, 100] 
    plot_s(data, xlist, ylist) 

if __name__ == '__main__': 
    main() 

Answer 1

1. 我怎样才能使任意meshgrid作为一个经常性的？

一个建议是创建一个规则的meshgrid，首先创建最小和最大x和y之间的均匀间隔值的数组。此外，您可以使用自定义滴答来反映您的数据点不是等距的事实。在代码中查看关于我如何实现该功能的评论。

1. 如何使用彩色标记突出显示数据最高值的位置？

要检索的最高值，你可以使用np.max()，然后找到该数据阵列np.where在这个值的位置。只需在此位置绘制一个标记。

另外，使用plt.contour你可以创建一个有足够接近最高值的位置的水平轮廓，以在其上创建它周围的环，或者甚至一个观点：

epsillon = 0.0001 
levels = np.arange(max_value - epsillon, max_value + epsillon) 
CS2 = plt.contour(X,Y,data, levels, 
      origin='lower', 
      linewidths=2, 
      extent=(-3,3,-2,2)) 

注意，与第一方法中，点将在现有网格节点的顶部结束，而plt.contour会插值您的数据，并且根据所使用的插值算法，它可能会导致位置有所不同。然而在这里它似乎同意。

的代码：

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

def plot_s(data, x, y, xlist, ylist): 
    ax = plt.gca() 

    ########### create your uniform meshgrid..... ############ 
    X, Y = np.meshgrid(x, y) 
    CS = ax.contour(X, Y, data, colors='k') 

    ###### ... and let ticks indicate that your new space is not linear 
    # assign tick positions according to the regular array 
    ax.set_yticks(y) 
    # Assign the label to reflect your original nodes position 
    ax.set_yticklabels(ylist) 

    # and same for x 
    ax.set_xticks(x) 
    ax.set_xticklabels(xlist) 
    ############################################################# 


    ########### GET MAXIMUM AND MARK IT WITH A POINT ######## 
    # get maximum value in your data 
    max_value = np.max(data) 
    # get position index of this calue in your data array 
    local_max_index = np.where(data==max_value) 

    ## retrieve position of your 
    max_x = X[local_max_index[0], local_max_index[1]] 
    max_y = Y[local_max_index[0], local_max_index[1]] 

    # plot one marker on this position 
    plt.plot(max_x, max_y, color="red", marker = "o", zorder = 10, 
             markersize=15, clip_on=False) 
    ############################################################## 

    plt.title('Contour plot') 
    plt.show() 


def main(): 
    # Your data: 4 x 10 array 
    data = np.array([[ 0.56555019, 0.57933922, 0.58266252, 0.58067285, 0.57660236, 
         0.57185625, 0.56711252, 0.55557035, 0.55027705, 0.54480605], 
        [ 0.55486559, 0.57349717, 0.57940478, 0.57843897, 0.57463271, 
         0.56963449, 0.5643922 , 0.55095598, 0.54452534, 0.53762606], 
        [ 0.53529358, 0.56254991, 0.57328105, 0.57409218, 0.57066168, 
         0.5654082 , 0.55956853, 0.5432474 , 0.53501127, 0.52601203], 
        [ 0.50110483, 0.54004071, 0.55800178, 0.56173719, 0.55894846, 
         0.55328279, 0.54642887, 0.52598388, 0.51533094, 0.50354147]]) 
    # create a list values with regular interval for the mesh grid 
    x = np.array([10 + i * (150.-10.)/9 for i in range(10)]) 
    y = np.array([50 + i * (100.-50.)/4 for i in range(4)]) 

    # create arrays with values to be displayed as ticks  
    xlist = np.array([10., 20., 30., 40., 50., 60., 70., 100., 120., 150.]) 
    ylist = np.array([50, 70, 90, 100]) 

    plot_s(data, x, y, xlist, ylist) 

if __name__ == '__main__': 
    main() 

瞧：

在这里，在后台的meshgrid显示变形/映射：

Answer 2

下面是essentiall y是相同的，但是snake_charmer提出的稍微更紧凑的版本。但是，我不确定我是否正确理解您的问题。如果你的积分xlist和ylist不是太不规则的间隔，更优雅的解决方案可能是保持不规则的网格，但使用ax.grid()突出显示数据点的位置。这取决于你想要在图中显示的内容。

import numpy as np 
from matplotlib import pyplot as plt 

def plot_s(data, xlist, ylist): 

    fig, ax = plt.subplots() 
    x = np.arange(len(xlist)) 
    y = np.arange(len(ylist)) 
    X, Y = np.meshgrid(x, y) 
    CS = ax.contour(X, Y, data, colors='k') 
    ax.clabel(CS, inline = 1, fontsize=10) 
    ax.set_xlabel('x list') 
    ax.set_ylabel('y list') 
    ax.set_xticks(x) 
    ax.set_yticks(y) 
    ax.set_xticklabels(xlist) 
    ax.set_yticklabels(ylist) 

    jmax, imax = np.unravel_index(np.argmax(data), data.shape) 
    ax.plot(imax, jmax, 'ro') 

    ax.set_title('Contour plot') 
    plt.show() 

def main(): 

    data = np.array([[ 0.56555019, 0.57933922, 0.58266252, 0.58067285, 
         0.57660236, 0.57185625, 0.56711252, 0.55557035, 
         0.55027705, 0.54480605], 
        [ 0.55486559, 0.57349717, 0.57940478, 0.57843897, 
         0.57463271, 0.56963449, 0.5643922 , 0.55095598, 
         0.54452534, 0.53762606], 
        [ 0.53529358, 0.56254991, 0.57328105, 0.57409218, 
         0.57066168, 0.5654082 , 0.55956853, 0.5432474 , 
         0.53501127, 0.52601203], 
        [ 0.50110483, 0.54004071, 0.55800178, 0.56173719, 
         0.55894846, 0.55328279, 0.54642887, 0.52598388, 
         0.51533094, 0.50354147]]) 

    xlist = [10., 20., 30., 40., 50., 60., 70., 100., 120., 150.] 
    ylist = [50, 70, 90, 100] 
    plot_s(data, xlist, ylist) 

if __name__ == '__main__': 
    main()