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feat_extract_test.py

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+from tools.reader import *
+from tools.featureExtract import FeatureExtractor
+import os
+import matplotlib.pyplot as plt
+# load FilesReader and FeatureExtractor class
+path = os.getcwd()+"\data"
+print(path)
+reader = FilesReader(8,path)
+extractor = FeatureExtractor(reader.fileNumber, 60, 10)
+# init Mat:
+feats = np.array([])
+labels = np.array([])
+
+for classIndex, fileName in enumerate(reader.files):
+	# load file:
+	dataMat = reader.loadFile(fileName)
+	# gesture segmentation:
+	segMat = extractor.segmentation(dataMat, classIndex)
+	# init:
+	featMat = np.array([])
+	for segIndex, seg in enumerate(segMat):
+		# extract RMS, ZC, ARC features:
+		RMSfeat = extractor.RMSfeat(seg)
+		ZCfeat = extractor.ZCfeat(seg)
+		ARCfeat = extractor.ARCfeat(seg)
+		featVector = np.hstack((RMSfeat,ZCfeat,ARCfeat))
+		# feats splice:
+		featMat = extractor.dynamicSplice(featMat, featVector, segIndex)
+	# generate labels:
+	tempLabels = extractor.creatLabels(classIndex, len(featMat))
+	# feat array and label array splice:
+	feats = extractor.dynamicSplice(feats, featMat, classIndex)
+	labels = extractor.dynamicSplice(labels, tempLabels, classIndex, axis = 1)
+
+print(feats[0])
+print(labels[0])

feat_plot_test.py

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+# -*- coding: gbk -*-
+from tools.reader import *
+from tools.featureExtract import FeatureExtractor
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from tools.MyPlot import DataPlotter
+import os
+slice = [600,1400]
+ChanList = ['Raw-c0','Raw-c1','Raw-c2','Raw-c3','Raw-c4','Raw-c5','Raw-c6','Raw-c7']
+featList = ['RMS','ZC','ARC0','ARC1','ARC2','ARC3'] 
+path = os.getcwd()+'/data'
+channels = 8
+
+reader = FilesReader(channels, path)
+extractor = FeatureExtractor(reader.fileNumber, slice[1]-slice[0], 10, featRavel = False)
+emg = reader.loadFile('1.txt')
+print(reader.filesLength)
+sliceData = emg[slice[0]:slice[1],:]
+rawFrame = pd.DataFrame(sliceData, columns = ChanList)
+
+RMSfeat = extractor.RMSfeat(sliceData)
+ZCfeat = extractor.ZCfeat(sliceData)
+ARCfeat = extractor.ARCfeat(sliceData, False)
+zipfeat = [RMSfeat,ZCfeat]
+zipfeat.extend(ARCfeat)
+columns = extractor.creatFeatName(featList, 8)
+featplot = DataPlotter(np.hstack(zipfeat), columns, 431)
+
+
+selectFeat = ['RMS-c0', 'RMS-c5', 'RMS-c7',\
+				'ZC-c0', 'ZC-c5', 'ZC-c7',\
+				'ARC1-c0','ARC1-c5','ARC1-c7']
+selectChan = ['Raw-c0','Raw-c5','Raw-c7']
+
+
+for i in range(len(selectChan)):
+	featplot.newSubplot(431+i, rawFrame[selectChan[i]],xylabel=[u'²ÉÑùµã',selectChan[i]])
+	#plt.title('ÌâÄ¿',fontproperties='SimHei')
+
+for j in range(len(selectFeat)):
+	featplot.subplot([4,3,4+j], [selectFeat[j]], xylabel=[u'²ÉÑùµã',selectFeat[j]])
+
+plt.show()
+