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

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：

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 pandas as pd import matplotlib.pyplot as plt %matplotlib inline import numpy as np

import pandas as pd是Python中用于数据分析和处理的库，它提供了丰富的数据结构和数据分析工具。通过使用pd别名，我们可以方便地引用pandas库中的函数和类。

matplotlib.pyplot是Python中用于绘制图表和可视化数据的库。通过使用plt别名，我们可以方便地引用matplotlib.pyplot库中的函数和类。

%matplotlib inline是Jupyter Notebook中的魔术命令，它用于在Notebook中显示matplotlib绘制的图表。这个命令告诉Notebook将图表直接嵌入到输出单元格中，而不是弹出一个新窗口显示图表。

import numpy as np是导入NumPy库，NumPy是Python中用于科学计算的库，它提供了高性能的多维数组对象和各种数学函数。

import pandas as pd import numpy as np import matplotlib.pyplot as plt file_name ='E:/liuyuan/ceshi/4cmH20_long_breaths.csv' names = ['Time', 'Flow'] data = pd.read_csv(file_name, names =names) start_index = 0 end_index = 1000 fig, ax = plt.subplots() plt.xlim(0, 10) time = np.arange(start_index, end_index) ax.set_xlabel('Time(s)') ax.set_ylabel('Flow(L/s)') ax.set_title('Breath Waveform ') plt.show()

在你的代码中，我看到你已经读取了一个csv文件到pandas DataFrame中，但是没有将数据绘制成曲线。你需要在 `plt.show()` 前面添加如下代码，将数据绘制成曲线并显示在坐标轴上：

flow = data['Flow'][start_index:end_index]
ax.plot(time, flow)

在这个代码中，我们先从DataFrame中提取出Flow列的数据，并根据start_index和end_index来截取数据，然后使用 `ax.plot()` 函数将数据绘制成曲线，并显示在坐标轴上。

完整代码如下：

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

file_name ='E:/liuyuan/ceshi/4cmH20_long_breaths.csv' 
names = ['Time', 'Flow'] 
data = pd.read_csv(file_name, names =names)   

start_index = 0 
end_index = 1000   

fig, ax = plt.subplots() 
plt.xlim(0, 10) 
time = np.arange(start_index, end_index)  

ax.set_xlabel('Time(s)') 
ax.set_ylabel('Flow(L/s)') 
ax.set_title('Breath Waveform ')  

flow = data['Flow'][start_index:end_index]
ax.plot(time, flow)

plt.show()

你可以根据需要修改 `start_index` 和 `end_index` 的值来截取不同的数据范围，并修改 `plt.xlim()` 函数的参数来调整坐标轴的显示范围。