ax2.xaxis.set_ticks_position

ax2.xaxis.set_ticks_position('bottom') \[2\]
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相关问题

ax.xaxis.set_ticks_position

ax.xaxis.set_ticks_position是Matplotlib中的一个函数，用于设置x轴刻度的位置。具体来说，它可以设置刻度线的位置为顶部、底部或中心。例如，ax.xaxis.set_ticks_position('bottom')可以将x轴刻度线设置在底部。

将下列代码跟改为八层介质 #%%In[1] import numpy as np import matplotlib.pyplot as plt #%%In[2] def ricker(f, length, dt): t = np.arange(-length/2,(length-dt)/2, dt) #t = np.arange(0,(length-dt)/2, dt) y = (1.0 - 2.0*(np.pi2)(f2)(t2)) * np.exp(-(np.pi2)(f2)(t2)) return t,y i = 0 ; Frequency = 20;length = 0.128;dt = 0.001 t0, w0 = ricker(Frequency, length, dt) #%% rho = np.array([1.6, 2.2]) v = np.array([2000, 2500]) depth = 50 Z = rhov L = (Z[1]-Z[0])/(Z[1]+Z[0]) t1 = np.arange(0, 0.2, dt) L1 = np.zeros(np.size(t1)) t = depth2/v[0] L1[int(np.round(t/dt))] = L #%% syn = np.convolve(L1, w0, 'same') #%% fig = plt.figure(num=1, figsize=(20,15),dpi=300) ax1 = fig.add_subplot(1, 3 , 1) ax1.plot(w0, t0) ax1.xaxis.set_ticks_position('top') ax1.invert_yaxis() ax1.set_title("Amplitude", fontsize = 12) ax1.set_ylabel("Time(s)",fontsize = 12) ax2 = fig.add_subplot(1, 3, 2) ax2.plot(L1, t1) ax2.xaxis.set_ticks_position('top') ax2.invert_yaxis() ax2.set_title("Reflection coefficient", fontsize = 12) ax2.set_ylabel("Two-way travel time(s)",fontsize = 12) ax3 = fig.add_subplot(1, 3, 3) ax3.plot(syn, t1) ax3.xaxis.set_ticks_position('top') ax3.invert_yaxis() ax3.set_title("Amplitude", fontsize = 12) ax3.set_ylabel("Two-way travel time(s)",fontsize = 12) fig.suptitle('Two-layer synthetic seismogram', fontsize = 18) plt.tight_layout()

为将代码改为八层介质，需要将代码中的两层介质部分进行修改。修改后的代码如下：

import numpy as np
import matplotlib.pyplot as plt

def ricker(f, length, dt):
    t = np.arange(-length/2,(length-dt)/2, dt)
    y = (1.0 - 2.0*(np.pi*f*t)**2) * np.exp(-(np.pi*f*t)**2)
    return t,y

i = 0
Frequency = 20
length = 0.128
dt = 0.001

# 八层介质
rho = np.array([1.5, 1.8, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2])
v = np.array([1500, 1700, 2000, 2200, 2400, 2600, 2800, 3000])
depth = np.array([0, 50, 100, 150, 200, 250, 300, 350])
Z = rho * v
L = (Z[1:] - Z[:-1]) / (Z[1:] + Z[:-1])

t1 = np.arange(0, depth[-1]/v[0]*2, dt)
L1 = np.zeros(np.size(t1))

for i in range(1, np.size(depth)):
    t = depth[i]/v[i-1] + depth[i]/v[i]
    L1[int(np.round(t/dt))] = L[i-1]

t0, w0 = ricker(Frequency, length, dt)
syn = np.convolve(L1, w0, 'same')

fig = plt.figure(num=1, figsize=(20,15),dpi=300)
ax1 = fig.add_subplot(1, 3 , 1)
ax1.plot(w0, t0)
ax1.xaxis.set_ticks_position('top')
ax1.invert_yaxis()
ax1.set_title("Amplitude", fontsize = 12)
ax1.set_ylabel("Time(s)",fontsize = 12)

ax2 = fig.add_subplot(1, 3, 2)
ax2.plot(L1, t1)
ax2.xaxis.set_ticks_position('top')
ax2.invert_yaxis()
ax2.set_title("Reflection coefficient", fontsize = 12)
ax2.set_ylabel("Two-way travel time(s)",fontsize = 12)

ax3 = fig.add_subplot(1, 3, 3)
ax3.plot(syn, t1)
ax3.xaxis.set_ticks_position('top')
ax3.invert_yaxis()
ax3.set_title("Amplitude", fontsize = 12)
ax3.set_ylabel("Two-way travel time(s)",fontsize = 12)

fig.suptitle('Eight-layer synthetic seismogram', fontsize = 18)
plt.tight_layout()

修改后的代码中，我们将八层介质的密度和速度分别存储在 `rho` 和 `v` 数组中，深度存储在 `depth` 数组中。通过计算每一层的阻抗 `Z` 和反射系数 `L`，并在 `for` 循环中计算每一时刻的反射系数 `L1`。最后计算合成地震记录 `syn`，并绘制三个子图，其中第一个子图为初始波形，第二个子图为反射系数，第三个子图为合成地震记录。总体来说，代码中的计算过程和原始代码是类似的，只是修改了介质的层数和属性。