我正在繪制3D圖，我希望顏色少``明顯''

Question

我的代碼可以在這里看到：

import numpy as np
from matplotlib import cm
import mpl_toolkits.mplot3d.axes3d as axes3d
from matplotlib.ticker import LinearLocator, FormatStrFormatter

xlist = [+30,+20,+10,0,-10,-20,-30]
ylist = [0.0008,0.0009, 0.001, 0.0012, 0.0013]  
total_costs=[[2084.8771849999903, 17314.19051000003, 26026.73173, 65340.709810000015, 108130.0746, 143560.64033000002, 188387.24033], [2129.155209999997, 17314.301310000024, 26026.996729999984, 65341.17821, 108130.792, 143561.44293000002, 188388.11793], [6637.1766100000095, 17314.412110000034, 26027.26173, 65341.646609999996, 108131.5094, 143562.24553000001, 188388.99553], [6623.21941000002, 17314.63371000004, 26027.791729999997, 65342.58341000001, 108132.9442, 150322.81264000002, 191661.16901], [6637.240810000003, 17314.744510000033, 26028.05673000001, 65343.05181000002, 110971.15911000001, 146393.01711000002, 191661.93621]]

Z = np.array(total_costs)

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection='3d')
X, Y = np.meshgrid(xlist, ylist)
ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,
                       linewidth=0, antialiased=False,alpha=0.5, 
                        `rstride=1,cstride=1, label='skata')`

ax.set_xlabel('System-1 imbalance')
ax.set_ylabel('Penalization factor [€/MWh]')
ax.set_zlabel('Total balancing costs [€]')
#ax.set_legend('upper left', fontsize=15)
#ax.tick_params(axis='both', labelsize=15)

plt.show()

當我運行這個我得到這樣的數字：

我想要得到的數字是這樣的：

我想這與我的結果是具有離散值的列表中的列表有關。 有人知道嗎？ 先感謝您

Answer 1

我想您希望圖形上的色調逐漸變化-我知道該怎么做的方法是“簡單地”增加使用插值法繪制的點數：

import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import scipy.interpolate as interp


xlist = np.array([+30, +20, +10, 0, -10, -20, -30])
ylist = np.array([0.0008, 0.0009, 0.001, 0.0012, 0.0013])
total_costs = [[2084.8771849999903, 17314.19051000003, 26026.73173,
                65340.709810000015, 108130.0746, 143560.64033000002,
                188387.24033],
               [2129.155209999997, 17314.301310000024, 26026.996729999984,
                65341.17821, 108130.792, 143561.44293000002, 188388.11793],
               [6637.1766100000095, 17314.412110000034, 26027.26173,
                65341.646609999996, 108131.5094, 143562.24553000001,
                188388.99553],
               [6623.21941000002, 17314.63371000004, 26027.791729999997,
                65342.58341000001, 108132.9442, 150322.81264000002,
                191661.16901],
               [6637.240810000003, 17314.744510000033, 26028.05673000001,
                65343.05181000002, 110971.15911000001, 146393.01711000002,
                191661.93621]]

X, Y = np.meshgrid(xlist, ylist)
Z = np.asarray(total_costs)

Zfunc = interp.interp2d(X, Y, Z, kind='cubic', copy=False)
n_points = 100  # change this to change the "resolution"
xnew = np.linspace(start=min(xlist), stop=max(xlist), num=n_points)
ynew = np.linspace(start=min(ylist), stop=max(ylist), num=n_points)
Xnew, Ynew = np.meshgrid(xnew, ynew)
Znew = Zfunc(xnew, ynew)

fig = plt.figure(figsize=(11, 8))
ax = plt.axes([0.05, 0.05, 0.9, 0.9], projection='3d')
surface = ax.plot_surface(Xnew, Ynew, Znew, rstride=1, cstride=1,
                          cmap='coolwarm', linewidth=0.25)
fig.colorbar(surface, shrink=0.75, aspect=9)

plt.show()

線性插值： 三次插值：

Answer 2

曲面圖的面根據Z值着色。

要在面孔上獲得混合色或隨機色，可以提供帶有facecolors參數的顏色數組，而不是顏色圖。

colors=np.random.rand(X.shape[0]-1,X.shape[1]-1, 3)
ax.plot_surface(X, Y, Z, facecolors=colors,
                       linewidth=0, antialiased=False,alpha=0.5, 
                        rstride=1,cstride=1, label='skata')

產生

為了使顏色看起來更加接近，解決方案是不使用顏色圖的完整范圍。 例如，您可以在對plot_surface的調用中設置vmin=0.5*Z.min(), vmax=2*Z.max(),以便將顏色映射到比圖像中顯示的顏色大得多的范圍，從而實際值僅覆蓋顏色圖的一部分。

ax.plot_surface(X, Y, Z, cmap=cm.coolwarm, vmin=0.5*Z.min(), vmax=2*Z.max(),
                       linewidth=0, antialiased=False,alpha=0.5, 
                        rstride=1,cstride=1, label='skata')

Answer 3

你是這個意思嗎？

def stackQuestion():
    import numpy as np
    import matplotlib.pyplot as plt
    import mpl_toolkits.mplot3d 
    xlist = np.array([+30,+20,+10,0,-10,-20,-30])
    ylist = np.array([0.0008,0.0009, 0.001, 0.0012, 0.0013])
    total_costs=[[2084.8771849999903, 17314.19051000003, 26026.73173, 65340.709810000015, 108130.0746, 143560.64033000002, 188387.24033], 
                 [2129.155209999997, 17314.301310000024, 26026.996729999984, 65341.17821, 108130.792, 143561.44293000002, 188388.11793], 
                 [6637.1766100000095, 17314.412110000034, 26027.26173, 65341.646609999996, 108131.5094, 143562.24553000001, 188388.99553], 
                 [6623.21941000002, 17314.63371000004, 26027.791729999997, 65342.58341000001, 108132.9442, 150322.81264000002, 191661.16901], 
                 [6637.240810000003, 17314.744510000033, 26028.05673000001, 65343.05181000002, 110971.15911000001, 146393.01711000002, 191661.93621]]

    X, Y = np.meshgrid(xlist, ylist)
    Z = np.array(total_costs) 

    fig = plt.figure(figsize = (11, 8))
    ax = plt.axes([0.05, 0.05, 0.9, 0.9], projection = '3d')
    surface = ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1,\
        cmap = 'coolwarm', linewidth = 0.25)
    fig.colorbar(surface, shrink = 0.75, aspect = 9)

    plt.show()