import pandas as pd import matplotlib.pyplot as plt import numpy as np import pywt file_name = 'E:/liuyuan/ceshi/zhongyao/Subject_1_0cmH20_norm_breaths.csv' data = pd.read_csv(file_name, skiprows=1, usecols=[0, 2], names=['Time', 'Flow']) x = list() y = list() for i in range(len(data)): x.append(float(data.values[i][0])) y.append(float(data.values[i][1])) start_index = 0 end_index = 5372 time = np.arange(start_index, end_index) flow = np.arange(start_index, end_index) time = data['Time'][start_index:end_index] flow = data['Flow'] def wavelet_filter(data): wavelet = 'db4' # 选择小波基函数 level = 5 # 小波变换的层数 # 小波变换 coeffs = pywt.wavedec(data, wavelet, level=level) threshold = np.std(coeffs[-level]) * np.sqrt(2 * np.log(len(data))) coeffs[1:] = (pywt.threshold(c, threshold, mode='soft') for c in coeffs[1:]) filtered_data = pywt.waverec(coeffs, wavelet) return filtered_data 对Flow进行小波变换滤波 filtered_flow = wavelet_filter(flow) fig, ax = plt.subplots(figsize=(10, 5)) plt.xlim(0, 60) ax.set_ylim(-0.7, 0.7) ax.set_xlabel('Time(s)', fontsize=10) ax.set_ylabel('Flow(L/s)', fontsize=10) ax.plot(time, filtered_flow, label='Filtered Flow') ax.legend() ax.grid(True, linewidth=0.3, alpha=0.5, color='gray') plt.tight_layout() # 自动调整子图的布局 plt.show()import pandas as pd import matplotlib.pyplot as plt import numpy as np import pywt file_name = 'E:/liuyuan/ceshi/zhongyao/Subject_1_0cmH20_norm_breaths.csv' data = pd.read_csv(file_name, skiprows=1, usecols=[0, 2], names=['Time', 'Flow']) x = list() y = list() for i in range(len(data)): x.append(float(data.values[i][0])) y.append(float(data.values[i][1])) start_index = 0 end_index = 5372 time = np.arange(start_index, end_index) flow = np.arange(start_index, end_index) time = data['Time'][start_index:end_index] flow = data['Flow'] def wavelet_filter(data): wavelet = 'db4' # 选择小波基函数 level = 5 # 小波变换的层数 coeffs = pywt.wavedec(data, wavelet, level=level) threshold = np.std(coeffs[-level]) * np.sqrt(2 * np.log(len(data))) coeffs[1:] = (pywt.threshold(c, threshold, mode='soft') for c in coeffs[1:]) # 逆小波变换 filtered_data = pywt.waverec(coeffs, wavelet) return filtered_data filtered_flow = wavelet_filter(flow) fig, ax = plt.subplots(figsize=(10, 5)) plt.xlim(0, 60) ax.set_ylim(-0.7, 0.7) ax.set_xlabel('Time(s)', fontsize=10) ax.set_ylabel('Flow(L/s)', fontsize=10) ax.plot(time, filtered_flow, label='Filtered Flow') ax.legend() ax.grid(True, linewidth=0.3, alpha=0.5, color='gray') plt.tight_layout() plt.show()在此代码上添加切分代码,并按照合适窗口大小切分完准确显示
时间: 2024-04-11 14:28:37 浏览: 11
你可以在代码中添加以下切分代码,来按照合适的窗口大小进行切分并准确显示:
```python
window_size = 100 # 窗口大小
# 切分数据
num_windows = len(filtered_flow) // window_size
flow_windows = np.array_split(filtered_flow, num_windows)
# 绘制切分后的数据
fig, axs = plt.subplots(num_windows, figsize=(10, 5*num_windows), sharex=True)
for i, ax in enumerate(axs):
ax.plot(time[i*window_size:(i+1)*window_size], flow_windows[i])
ax.set_ylim(-0.7, 0.7)
ax.set_xlabel('Time(s)', fontsize=10)
ax.set_ylabel('Flow(L/s)', fontsize=10)
ax.grid(True, linewidth=0.3, alpha=0.5, color='gray')
plt.tight_layout()
plt.show()
```
这段代码将数据按照窗口大小切分,并在多个子图中分别绘制每个窗口的数据。你可以根据窗口大小调整`window_size`的值来适应你的需求。
相关问题
import numpy as np import pandas as pd import matplotlib.pyplot as plt
这三个库都是Python中常用的数据分析和可视化库。其中,NumPy是Python中用于科学计算的基础库,提供了高性能的多维数组和矩阵计算功能。Pandas是基于NumPy的一个数据处理库,提供了高效的数据结构和数据分析工具。Matplotlib是Python中最流行的数据可视化库之一,提供了各种绘图工具和图表类型,可以用于生成各种静态和动态的图表。
以下是一个简单的例子,展示如何使用这三个库绘制一个柱状图:
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# 创建数据
bar_positions = [1, 2, 3, 4]
bar_heights = [1, 2, 3, 4]
# 绘制柱状图
fig, ax = plt.subplots()
ax.bar(np.arange(len(bar_positions)), bar_heights)
# 设置x轴标签
ax.set_xticks(np.arange(len(bar_positions)))
ax.set_xticklabels(bar_positions)
# 设置y轴标签
ax.set_ylabel('Bar Heights')
# 设置图表标题
ax.set_title('A Simple Bar Chart')
# 显示图表
plt.show()
```
对下列代码进行注释:import numpy as np import pandas as pd import matplotlib.pyplot as plt
这段代码是用来导入三个Python库:NumPy、Pandas和Matplotlib.pyplot。下面是每个库的作用:
- NumPy:提供了Python的数值计算功能,尤其是多维数组对象和矩阵计算支持。
- Pandas:提供了用于数据操作和分析的工具,尤其是数据结构和数据分析函数。
- Matplotlib.pyplot:提供了用于绘图的函数和方法,使得用户可以创建各种形式的图表、图形、动画等。
在代码中,它们被分别命名为“np”、“pd”和“plt”,以后在代码中可以使用这些命名来调用每个库的相关函数和方法。