272 lines
11 KiB
Python
272 lines
11 KiB
Python
# encoding:utf-8
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import os
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import numpy as np
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import pandas as pd
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import warnings
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import matplotlib.pyplot as plt
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import matplotlib.colors as colors
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from scipy import signal
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from glob import glob
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from torch.utils.data import Dataset, DataLoader, TensorDataset
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warnings.filterwarnings("ignore")
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class BCGDataset(Dataset):
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def __init__(self, train=True):
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if train:
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self.data = np.array(pd.read_csv("./in_data/train.txt").iloc[:,np.arange(1000)])
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self.label = np.array(pd.read_csv("./in_data/train.txt").iloc[:,np.arange(1000,2000)])
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else:
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self.data = np.array(pd.read_csv("./in_data/test.txt").iloc[:, np.arange(1000)])
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self.label = np.array(pd.read_csv("./in_data/test.txt").iloc[:, np.arange(1000, 2000)])
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def __getitem__(self, index):
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return self.data[index], self.label[index]
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def __len__(self):
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return len(self.label)
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class BCG_Operation():
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def __init__(self, sample_rate=1000):
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self.sample_rate = sample_rate
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def down_sample(self,data=None, down_radio=10):
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if data is None:
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raise ValueError("data is None, please given an real value!")
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length_before = len(data)
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length_after = length_before//down_radio
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data = data[:length_after*down_radio]
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data = data.reshape(-1,down_radio)
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data = data[:,0]
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self.sample_rate = self.sample_rate/down_radio
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return data
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def Splitwin(self, data=None, len_win=None, coverage=1.0,calculate_to_end=False):
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"""
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分窗
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:param len_win: length of window
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:return: signal windows
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"""
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if ( len_win is None) or (data is None):
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raise ValueError("length of window or data is None, please given an real value!")
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else:
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length = len_win * self.sample_rate # number point of a window
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# step of split windows
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step = length*coverage
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start = 0
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Splitdata = []
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while (len(data)-start>=length):
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Splitdata.append( data[int(start):int(start+length)] )
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start += step
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if calculate_to_end and (len(data)-start>2000):
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remain = len(data)-start
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start = start - step
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step = int(remain/2000)
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start = start + step*2000
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Splitdata.append(data[int(start):int(start+length)])
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return np.array(Splitdata), step
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elif calculate_to_end :
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return np.array(Splitdata), 0
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else:
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return np.array(Splitdata)
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def Butterworth(self,data, type, low_cut = 0.0, high_cut = 0.0, order = 10):
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"""
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:param type: Type of Butter. filter, lowpass, bandpass, ...
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:param lowcut: Low cutoff frequency
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:param highcut: High cutoff frequency
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:param order: Order of filter
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:return: Signal after filtering
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"""
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if type == "lowpass": # 低通滤波处理
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b, a = signal.butter(order, low_cut / (self.sample_rate * 0.5), btype='lowpass')
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return signal.filtfilt(b, a, np.array(data))
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elif type == "bandpass": # 带通滤波处理
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low = low_cut / (self.sample_rate * 0.5)
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high = high_cut / (self.sample_rate * 0.5)
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b, a = signal.butter(order, [low, high], btype='bandpass')
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return signal.filtfilt(b, a, np.array(data))
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elif type == "highpass": # 高通滤波处理
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b, a = signal.butter(order, high_cut / (self.sample_rate * 0.5), btype='highpass')
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return signal.filtfilt(b, a, np.array(data))
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else: # 警告,滤波器类型必须有
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raise ValueError("Please choose a type of fliter")
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def AmpMovement(self, data, win_size, threshold=20, get_judge_line=False):
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"""
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基于幅值方法检测体动：
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1.将输入信号按win_size切分
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2.将每个win_size信号片段分窗，每个窗2s，步长为2s
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3.计算一分钟所有信号窗的最大峰谷值差，获取中位数和均值
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4.所有2s时间窗内，大于中位数/均值的2.2倍视为体动
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5.体动间间隔过短的信号，同样标记为体动
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:param data: Input signal
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:param win_size: Size of the win(Must be a multiple of 2)
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:return: State of signal
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"""
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Dataframe, cover_num = self.Splitwin(data, len_win=win_size, coverage=1.0, calculate_to_end=True)
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state_all = np.array([])
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Amp_list = np.array([])
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for win in range(Dataframe.shape[0]):
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state = np.array([])
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# two seconds window
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data_win = self.Splitwin(Dataframe[win], len_win=2, coverage=1.0)
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Amp = np.zeros(data_win.shape[0])
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for i in range(data_win.shape[0]):
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Amp[i] = np.max(data_win[i]) - np.min(data_win[i]) # max - min
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# 取..位数
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Median_Amp = np.percentile(Amp, 20) # 20%
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if get_judge_line:
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Amp_list = np.append(Amp_list, np.full(win_size * self.sample_rate, 2.3 * Median_Amp))
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for i in range(len(Amp)):
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if (Amp[i] > 2.1 * Median_Amp):
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state = np.append(state, "Movement")
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elif Amp[i] < threshold:
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state = np.append(state, "Nobody")
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else:
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state = np.append(state, "Sleep")
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if win == Dataframe.shape[0] - 1 and cover_num > 0:
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state = state[-int(cover_num):]
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state_all = np.append(state_all, state)
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if get_judge_line:
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return state_all, Amp_list
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else:
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return state_all
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def preprocess1(self):
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# ----------------------------------------------------------
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data_dir = "../in_data/"
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dir_list = os.listdir(data_dir)
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data_list = [data_dir + dir + "/orgData.txt" for dir in dir_list]
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label_list = [data_dir + dir + "/label.txt" for dir in dir_list]
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print(data_list)
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print(label_list)
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for i in range(len(data_list)):
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orgBCG = np.array(pd.read_csv(data_list[i], header=None)).reshape(-1)
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orgLabel = np.array(pd.read_csv(label_list[i])).reshape(-1)
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# ---------------------Movement Detection-------------------------
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operation = BCG_Operation()
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BCG = operation.Butterworth(data=orgBCG, type="bandpass", low_cut=2.5, high_cut=10, order=2)
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state_win60 = operation.AmpMovement(orgBCG, win_size=60)
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visual_state = np.array([])
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for num in range(state_win60.shape[0]):
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print("state_num/all_state: ", num, '/', state_win60.shape[0])
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if state_win60[num] == "Movement":
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visual_state = np.append(visual_state, np.full(2000, 1))
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else:
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visual_state = np.append(visual_state, np.full(2000, 0))
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# ------------------------------------------------------------------
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downBCG = operation.down_sample(data=orgBCG, down_radio=10)
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downLabel = operation.down_sample(data=orgLabel, down_radio=10)
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downState = operation.down_sample(data=visual_state, down_radio=10)
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length_before = len(downState)
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length_after = length_before // 1000
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downBCG = downBCG[:length_after * 1000]
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downLabel = downLabel[:length_after * 1000]
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downState = downState[:length_after * 1000]
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downBCG = downBCG.reshape(-1, 1000)
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downLabel = downLabel.reshape(-1, 1000)
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downState = downState.reshape(-1, 1000)
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downState = np.max(downState, axis=1)
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df_BCG = pd.DataFrame(downBCG)
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df_label = pd.DataFrame(downLabel)
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df_state = pd.DataFrame(downState, columns=["state"])
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df_BCG.to_csv()
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df_all = pd.concat([df_BCG, df_label, df_state], axis=1)
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df_all.to_csv(data_dir + "/data" + str(i + 1) + ".txt", index=False)
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def read_all_data(data_dir):
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df_all = pd.read_csv(data_dir)
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df_clean = df_all[ df_all["state"]==0.0 ]
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df_artifact = df_all[ df_all["state"]==1.0 ]
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data_clean = df_clean.iloc[:,np.arange(1000)]
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label_clean = df_clean.iloc[:,np.arange(1000,2000)]
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data_artifact = df_artifact.iloc[:,np.arange(1000)]
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label_artifact = df_artifact.iloc[:,np.arange(1000,2000)]
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return np.array(data_clean),np.array(label_clean),np.array(data_artifact),np.array(label_artifact)
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#orgBCG = np.array(pd.read_csv("../in_data/data1zuo/orgData.txt", header=None)).reshape(-1)
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#orgLabel = np.array(pd.read_csv("../in_data/data1zuo/label.txt")).reshape(-1)
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## ---------------------Movement Detection-------------------------
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#operation = BCG_Operation()
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#BCG = operation.Butterworth(data=orgBCG, type="bandpass", low_cut=2.5, high_cut=10, order=2)
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#state_win60 = operation.AmpMovement(orgBCG, win_size=60)
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#visual_state = np.array([])
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#for num in range(state_win60.shape[0]):
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# print("state_num/all_state: ", num, '/', state_win60.shape[0])
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# if state_win60[num] == "Movement":
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# visual_state = np.append(visual_state, np.full(2000, 1))
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# else:
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# visual_state = np.append(visual_state, np.full(2000, 0))
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## ------------------------------------------------------------------
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#downBCG = operation.down_sample(data=orgBCG, down_radio=10)
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#downLabel = operation.down_sample(data=orgLabel, down_radio=10)
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#downState = operation.down_sample(data=visual_state, down_radio=10)
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#length_before = len(downState)
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#length_after = length_before // 1000
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#downBCG = downBCG[:length_after * 1000]
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#downLabel = downLabel[:length_after * 1000]
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#downState = downState[:length_after * 1000]
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#downBCG = downBCG.reshape(-1, 1000)
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#downLabel = downLabel.reshape(-1, 1000)
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#downState = downState.reshape(-1, 1000)
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#downState = np.max(downState, axis=1)
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#df_BCG = pd.DataFrame(downBCG)
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#df_label = pd.DataFrame(downLabel)
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#df_state = pd.DataFrame(downState, columns=["state"])
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#df_BCG.to_csv()
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#df_all = pd.concat([df_BCG, df_label, df_state], axis=1)
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#df_all.to_csv("../in_data/data1zuo.txt", index=False)
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#data_dir = glob("../in_data/*.txt")
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#print(data_dir)
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#for num in range(len(data_dir)):
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# if num==0 :
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# all_data = pd.read_csv(data_dir[num])
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# else:
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# all_data = pd.concat([all_data,pd.read_csv(data_dir[num])],ignore_index=True,axis=0)
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#
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#all_data.to_csv("../in_data/all_data.txt",index=False)
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#data = pd.read_csv("../in_data/all_data.txt")
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#clean_data = data[data["state"]==0]
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#Movement_data = data[data["state"]==1]
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#print(data.shape)
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#print(clean_data.shape)
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#
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## -------------------- 划分训练集和测试集：7：3 ----------------------------
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#sample = clean_data.sample(int(0.3*len(clean_data)))
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#sample_index = sample.index
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#print(sample.shape)
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#print(sample_index)
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## 剩余数据
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#all_index = clean_data.index
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## 去除sample之后剩余的数据
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#residue_index = all_index.difference(sample_index)
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#print(residue_index.shape)
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#print(residue_index)
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#residue = clean_data.loc[residue_index]
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## 保存
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#test = pd.concat([sample,Movement_data],ignore_index=True)
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#test.to_csv("../in_data/test.txt",index=False)
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#residue.to_csv("../in_data/train.txt",index=False)
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