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Visualization_main.py
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Visualization_main.py
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""" 对原始的信号以及信号的特征进行监测"""
#对信号的特征进行显示
import numpy as np
from sklearn.decomposition import PCA
import pandas as pd
import pandas as pd
from User_modify import *
from Tool_Dataprocess import produce_sample_label
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.neighbors import KNeighborsClassifier
from sklearn import svm
from sklearn.ensemble import RandomForestClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.neural_network import MLPClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import StratifiedKFold
import sklearn.metrics as sm
import numpy as np
from Tool_Visualization import *
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.model_selection import LeaveOneGroupOut
from Tool_Pretreatment import *
path1 = './Feature/data23.csv'
path2 = './Feature/label23.csv'
flag=3
"""
flag=1绘制ACC特征的
"""
HZ = 200
if flag==1:
dataset = pd.read_csv(path1, header=None)
dataset = np.array(dataset)
labels_name = ['X_MA', 'X_WL', 'Y_MA', 'Y_WL', 'Z_MA', 'Z_WL']
x = np.arange(dataset.shape[0])
for i in range(6):
plt.plot(x, dataset[:, i], label=labels_name[i])
plt.legend()
plt.show()
if flag==0:
path = './data/sub11/'
movedata_0, samplingpoint_num_list = read_data(path)
init_length = 100
movedata_1 = []
for data in movedata_0:
data = pd.DataFrame(data)
data = data.iloc[0:1000,:] #把都进来的数据的长度限制在5s
data = data.dropna() # 数据清洗
#data = data.iloc[init_length:, :] # 手动去除PPG头部抖动的数据
data = np.array(data)
data = data[:, 0:-1] #不需要最后一列数据以及角速度计的数据，最后一列数据为时间戳
#movedata_1.append(data)
# 巴特沃斯滤波
filtered_data = np.zeros([data.shape[0], 9])
for filter in range(data.shape[1] - 1):
if filter <= 2:
filtered_data[:, filter] = butter_lowpass_filter(data[:, filter], 1, fs=HZ)
# filtered_data[:,filter] = butter_bandpass_filtfilt(data[:, filter], cutoff=[0.1, 1.0], fs=HZ)
else:
filtered_data[:, filter] = butter_lowpass_filter(data[:, filter], 20, fs=HZ)
filtered_data = filtered_data[init_length: data.shape[0]-init_length,:] #截断开头和结尾的数据
movedata_1.append(filtered_data)
movedata_1 = np.array(movedata_1)
movedata_2 = np.zeros((movedata_1.shape[0]*movedata_1.shape[1],movedata_1.shape[2]))
for index in range(movedata_1.shape[0]):
data = movedata_1[index,:,:]
movedata_2[movedata_1.shape[1]*index:movedata_1.shape[1]*(index+1),:] = data
dataset = movedata_2[:,0:3]
#x = np.arange(dataset.shape[0])
x1 = np.arange(24000)
x2 = np.arange(dataset.shape[0])
Visualization_PPG(x2, dataset)
for i in range(4):
Visualization_PPG(x1, dataset[i*24000:(i+1)*24000,:])
# labels_name = ['R','IR','G','X','Y','Z']
# for i in range(2,3):
# plt.plot(x, dataset[0:24000, i], label=labels_name[i])
# plt.legend()
# tick = np.arange(0,24000+8000,8000)
# plt.xticks(tick, fontsize = 6)
plt.show()
print(1)
if flag==3:
path = './data/sub11/'
movedata_0, samplingpoint_num_list = read_data(path)
init_length = 100
movedata_1 = []
for data in movedata_0[0:1]:
data = pd.DataFrame(data)
data = data.iloc[0:1000,:] #把都进来的数据的长度限制在5s
data = data.dropna() # 数据清洗
#data = data.iloc[init_length:, :] # 手动去除PPG头部抖动的数据
data = np.array(data)
data = data[:, 0:-1] #不需要最后一列数据以及角速度计的数据，最后一列数据为时间戳
#movedata_1.append(data)
Visualization_PPG(np.arange(data.shape[0]), data[:,0:3])
# 巴特沃斯滤波
filtered_data = np.zeros([data.shape[0], 9])
for filter in range(data.shape[1] - 1):
if filter <= 2:
filtered_data[:, filter] = butter_lowpass_filter(data[:, filter], 1, fs=HZ)
# filtered_data[:,filter] = butter_bandpass_filtfilt(data[:, filter], cutoff=[0.1, 1.0], fs=HZ)
else:
filtered_data[:, filter] = butter_lowpass_filter(data[:, filter], 20, fs=HZ)
filtered_data = filtered_data[init_length: data.shape[0]-init_length,:] #截断开头和结尾的数据
Visualization_PPG(np.arange(filtered_data.shape[0]), filtered_data[:,0:3])
movedata_1.append(filtered_data)