--- a
+++ b/preprocessOfApneaECG/preProcessing.py
@@ -0,0 +1,185 @@
+"""
+	主要包括去燥, 计算特征信号等
+"""
+from preprocessOfApneaECG.fileIO import get_database
+from preprocessOfApneaECG.denoising import denoise_ecg
+from preprocessOfApneaECG.list2mat import list2mat
+import os
+import numpy as np
+import matlab.engine
+import scipy.io as sio
+from scipy import interpolate
+from preprocessOfApneaECG.mit2Segments import ECG_RAW_FREQUENCY
+from scipy.signal import decimate
+
+eng = matlab.engine.start_matlab()
+
+# interpolation algorithm.
+# From https://github.com/rhenanbartels/hrv/blob/develop/hrv/classical.py
+
+
+def create_time_info(rri):
+	rri_time = np.cumsum(rri) / 1000.0  # make it seconds
+	return rri_time - rri_time[0]   # force it to start at zero
+
+
+def create_interp_time(rri, fs):
+	time_rri = create_time_info(rri)
+	# print(time_rri[-1])
+	start, end = 0, 0
+	if time_rri[-1] < 60:
+		end = 60
+	else:
+		print("abnormal %s..." % time_rri[-1])
+	return np.arange(0, end, 1 / float(fs))
+
+
+def interp_cubic_spline(rri, fs):
+	time_rri = create_time_info(rri)
+	time_rri_interp = create_interp_time(rri, fs)
+	tck_rri = interpolate.splrep(time_rri, rri, s=0)
+	rri_interp = interpolate.splev(time_rri_interp, tck_rri, der=0)
+	return rri_interp
+
+
+def interp_cubic_spline_qrs(qrs_index, qrs_amp, fs):
+	time_qrs = qrs_index / float(ECG_RAW_FREQUENCY)
+	time_qrs = time_qrs - time_qrs[0]
+	time_qrs_interp = np.arange(0, 60, 1 / float(fs))
+	tck = interpolate.splrep(time_qrs, qrs_amp, s=0)
+	qrs_interp = interpolate.splev(time_qrs_interp, tck, der=0)
+	return qrs_interp
+
+
+def smooth(a, WSZ):
+	"""
+	滑动平均.
+	:param a:
+	:param WSZ:
+	:return:
+	"""
+	out0 = np.convolve(a, np.ones(WSZ, dtype=float), 'valid') / WSZ
+	r = np.arange(1, WSZ - 1, 2)
+	start = np.cumsum(a[:WSZ - 1])[::2] / r
+	stop = (np.cumsum(a[:-WSZ:-1])[::2] / r)[::-1]
+	return np.concatenate((start, out0, stop))
+
+
+
+def mat2npy(dict_data):
+	print("........")
+
+def rricheck(ecg_data, rr_intervals):
+	"""
+	# Check ECG data and RR intervals.
+	:param numpy array ecg_data: ECG signal.
+	:param numpy array rr_intervals: RR intervals.
+	:return bool:
+	"""
+	noise_flag = rr_intervals > 180
+	noise_flag1 = rr_intervals < 30
+	if len(rr_intervals) < 40 \
+			or np.sum(noise_flag) > 0 \
+			or np.sum(noise_flag1) > 0 \
+			or len(ecg_data) != 6000:
+		return False
+	else:
+		return True
+
+
+def compute_r_peak_amplitude(ecg_data, rwave):
+	"""
+	Compute R peaks amplitude based on R waves indices.
+	:param numpy array ecg_data: ECG signal.
+	:param numpy array rwave: R waves indices.
+	:return numpy array: R peak amplitude.
+	"""
+	
+	wave_amp = []
+	for peak_ind in rwave.tolist():
+		interval = 25
+		if peak_ind - interval < 0:
+			start = 0
+		else:
+			start = peak_ind - interval
+		
+		if peak_ind + interval > len(ecg_data):
+			end = len(ecg_data)
+		else:
+			end = peak_ind + interval
+		
+		amp = np.max(ecg_data[start:end])
+		wave_amp.append(amp)
+	return np.array(wave_amp)
+
+
+def pre_proc(dataset, database_name, is_debug=False):
+	"""
+	
+	:param Mit2Segment list dataset: ECG segments.
+	:return None:
+	"""
+	
+	clear_id_set, noise_id_set = [], []
+	for segment in dataset:
+		if is_debug:
+			print("now process %s	id=%s." % (segment.database_name, str(segment.global_id)))
+		# denoising and write to txt file
+		segment.denoised_ecg_data = denoise_ecg(segment.raw_ecg_data)
+		segment.write_ecg_segment(rdf=1)
+		# ecg data list to .mat
+		list2mat(segment, is_debug=True)
+		# compute RRI, RAMP and EDR.
+		eng.computeFeatures(segment.base_file_path)
+		if os.path.exists(segment.base_file_path + "/Rwave.mat"):
+			RwaveMat = sio.loadmat(segment.base_file_path + "/Rwave.mat")
+			Rwave = np.transpose(RwaveMat['Rwave'])
+			Rwave = np.reshape(Rwave, len(Rwave))
+			# RR intervals
+			RR_intervals = np.diff(Rwave)
+			# store RR intervals
+			np.save(segment.base_file_path + "/RRI.npy", RR_intervals)
+			# RRI validity check
+			rri_flag = rricheck(segment.denoised_ecg_data, RR_intervals)
+			if rri_flag:
+				clear_id_set.append(segment.global_id)
+			else:
+				noise_id_set.append(segment.global_id)
+				continue
+			# compute R peaks amplitude(RAMP)
+			RAMP = compute_r_peak_amplitude(segment.denoised_ecg_data, Rwave)
+			# smoothing filtering
+			RRI = smooth(RR_intervals, 3)
+			RAMP = smooth(RAMP, 3)
+			# spline interpolation
+			RRI = RRI / ECG_RAW_FREQUENCY * 1000.0
+			RRI = interp_cubic_spline(RRI, fs=4)
+			RAMP = interp_cubic_spline_qrs(Rwave, RAMP, fs=4)
+			# store RRI and RAMP
+			np.save(segment.base_file_path + "/RRI.npy", RRI)
+			np.save(segment.base_file_path + "/RAMP.npy", RAMP)
+			# EDR
+			EDRMat = sio.loadmat(segment.base_file_path + "/EDR.mat")
+			EDR = np.transpose(EDRMat['EDR'])
+			EDR = np.reshape(EDR, len(EDR))
+			# downsampling
+			EDR = decimate(EDR, 25)
+			np.save(segment.base_file_path + "/EDR.npy", EDR)
+			# print(".............")
+		else:
+			noise_id_set.append(segment.global_id)
+	print(len(noise_id_set))
+	print(len(clear_id_set))
+	np.save(database_name[0] + "_" + database_name[1] + "_clear_id.npy", np.array(clear_id_set))
+	np.save(database_name[0] + "_" + database_name[1] + "_noise_id.npy", np.array(noise_id_set))
+	
+
+if __name__ == '__main__':
+	# train_set = get_database(["apnea-ecg", "train"], rdf=0,is_debug=True)
+	# pre_proc(train_set, ["apnea-ecg", "train"], is_debug=True)
+	test_set = get_database(["apnea-ecg", "test"], rdf=0, is_debug=True)
+	pre_proc(test_set, ["apnea-ecg", "test"], is_debug=True)
+	
+
+
+