plt.plot(7, 2, i + 1)
时间: 2024-06-06 07:07:28 浏览: 21
I'm sorry, but the code you provided is incomplete and does not make sense. Can you please provide more information or context about what you are trying to accomplish with this code?
相关问题
import numpy as np import pandas as pd import matplotlib.pyplot as plt import pyhht # 读取csv文件数据 data = pd.read_csv('77.csv', header=None) time = data.iloc[:, 0].values charge = data.iloc[:, 1].values # EMD分解 decomposer = pyhht.EMD() imfs = decomposer.decompose(charge)fig, axs = plt.subplots(nrows=imfs.shape[0]+2, ncols=1, figsize=(10, 15)) axs[0].plot(time, charge, label='Original') axs[0].set_xlabel('Time') axs[0].set_ylabel('Charge') axs[0].legend() for i in range(imfs.shape[0]): axs[i+1].plot(time, imfs[i], label=f'IMF{i+1}') axs[i+1].set_xlabel('Time') axs[i+1].set_ylabel('Charge') axs[i+1].legend() residuals = charge - np.sum(imfs, axis=0) axs[imfs.shape[0]+1].plot(time, residuals, label='Residuals') axs[imfs.shape[0]+1].set_xlabel('Time') axs[imfs.shape[0]+1].set_ylabel('Charge') axs[imfs.shape[0]+1].legend() axs[0].set_title('Original Signal') for i in range(imfs.shape[0]): axs[i+1].set_title(f'IMF {i+1}') axs[imfs.shape[0]+1].set_title('Residuals') plt.tight_layout() plt.show()调整以上代码,使得子图之间间距略大
可以使用`plt.subplots_adjust()`函数来调整子图之间的间距,例如在`plt.tight_layout()`函数之前添加以下代码即可:
```python
plt.subplots_adjust(hspace=0.5)
```
其中`hspace`参数控制水平方向的间距,默认值为0.2,可以根据需要进行调整。完整代码如下:
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pyhht
# 读取csv文件数据
data = pd.read_csv('77.csv', header=None)
time = data.iloc[:, 0].values
charge = data.iloc[:, 1].values
# EMD分解
decomposer = pyhht.EMD()
imfs = decomposer.decompose(charge)
fig, axs = plt.subplots(nrows=imfs.shape[0]+2, ncols=1, figsize=(10, 15))
axs[0].plot(time, charge, label='Original')
axs[0].set_xlabel('Time')
axs[0].set_ylabel('Charge')
axs[0].legend()
for i in range(imfs.shape[0]):
axs[i+1].plot(time, imfs[i], label=f'IMF{i+1}')
axs[i+1].set_xlabel('Time')
axs[i+1].set_ylabel('Charge')
axs[i+1].legend()
residuals = charge - np.sum(imfs, axis=0)
axs[imfs.shape[0]+1].plot(time, residuals, label='Residuals')
axs[imfs.shape[0]+1].set_xlabel('Time')
axs[imfs.shape[0]+1].set_ylabel('Charge')
axs[imfs.shape[0]+1].legend()
axs[0].set_title('Original Signal')
for i in range(imfs.shape[0]):
axs[i+1].set_title(f'IMF {i+1}')
axs[imfs.shape[0]+1].set_title('Residuals')
plt.subplots_adjust(hspace=0.5)
plt.tight_layout()
plt.show()
```
修改代码 j=0 i=0 sc1=[] x1=[] while i<=10: clustering = DBSCAN(eps=0.01+j, min_samples=5).fit(X) labels = clustering.labels_ score = silhouette_score(X, labels) sc1.append(score) x1.append((j+0.01)) j=j+0.001 i=i+1 plt.plot(x1,sc1,marker='o') plt.xlabel('eps') plt.ylabel('score') plt.xticks(x1) plt.show() j=0 i=0 sc2=[] x2=[] while i<=10: clustering = DBSCAN(eps=0.01, min_samples=5+j).fit(X) labels = clustering.labels_ score = silhouette_score(X, labels) sc2.append(score) x2.append(int(j+5)) j=j+2 i=i+1 plt.plot(x2,sc2,marker='o') plt.xlabel('min_samples') plt.ylabel('score') plt.xticks(x2) plt.show() #Calinski-Harabaz 指数 j=0 i=0 sc3=[] x3=[] while i<=10: clustering = DBSCAN(eps=0.01+j, min_samples=5).fit(X) labels = clustering.labels_ score = calinski_harabasz_score(X, labels) sc3.append(score) x3.append(j+0.01) j=j+0.001 i=i+1 plt.plot(x3,sc3,marker='o') plt.xlabel('eps') plt.ylabel('score') plt.xticks(x3) plt.show() j=0 i=0 sc4=[] x4=[] while i<=10: clustering = DBSCAN(eps=0.01, min_samples=5+j).fit(X) labels = clustering.labels_ score = calinski_harabasz_score(X, labels) sc4.append(score) x4.append(int(j+5)) j=j+2 i=i+1 plt.plot(x4,sc4,marker='o') plt.xlabel('min_samples') plt.ylabel('score') plt.xticks(x4) plt.show()
这段代码使用了DBSCAN聚类算法,通过对eps和min_samples参数的调整,计算出对应的轮廓系数(silhouette_score)和Calinski-Harabasz指数(calinski_harabasz_score)。
其中,第一个while循环是对eps的调整,第二个while循环是对min_samples的调整,第三个和第四个while循环则是对应的指数的计算。每个while循环内部都有一个clustering对象,表示一个DBSCAN聚类模型,通过fit()方法对数据进行聚类,然后计算对应的指标得分,并将得分和参数的值存入对应的列表中。最后使用plt库对结果进行可视化展示。
需要注意的是,该代码在调整eps和min_samples时,只是进行了一定范围的遍历,可能并不一定能够找到最优的参数值。因此,在实际使用中,需要根据具体情况进行参数的调整和优化。
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