Mahdad Jafarzadehesfahani
34 linhas
1.4 KiB
Python
34 linhas
1.4 KiB
Python
import realTimeAutoScoring
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import numpy as np
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sleepScoringModel = realTimeAutoScoring.importModel("./out_QS/train/21")
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recording = np.loadtxt("path/to_data.txt", delimiter=',')
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dataSamplesToAnalyzeBeginIndex = 0
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dataSampleCounter = 0
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predictions = []
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for row in recording:
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dataSampleCounter += 1
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if row[4] > 1:
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if dataSamplesToAnalyzeBeginIndex == 0:
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dataSamplesToAnalyzeBeginIndex = dataSampleCounter
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if dataSampleCounter == dataSamplesToAnalyzeBeginIndex+30*256:
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sig = recording[dataSamplesToAnalyzeBeginIndex:dataSamplesToAnalyzeBeginIndex+30*256]
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dataSamplesToAnalyzeBeginIndex = 0
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print(f"shape of sig: {len(sig)}")
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sigRef = [col[0] for col in sig]
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sigReq = [col[1] for col in sig]
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sigRef = np.asarray(sigRef)
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sigReq = np.asarray(sigReq)
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sigRef = sigRef.reshape((1, sigRef.shape[0]))
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sigReq = sigReq.reshape((1, sigReq.shape[0]))
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print(sigRef.shape, sigReq.shape)
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modelPrediction = realTimeAutoScoring.Predict_array(output_dir="./DataiBand/output/Fp1-Fp2_filtered",
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args_log_file="info_ch_extract.log", filtering_status=True,
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lowcut=0.3, highcut=30, fs=256, signal_req=sigReq, signal_ref=sigRef, model=sleepScoringModel)
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predictions.append(modelPrediction[0])
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