1.3.0
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@@ -527,7 +527,7 @@ class AnalysisProcedure(QObject):
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# returns indices of peaks, and dictionary of properties
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y1_peaks_pre = signal.find_peaks(self.y1_sample, height=height,
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distance=10)
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distance=self.signal_min_peak_distance)
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print(
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'peak length==>', len(
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@@ -543,7 +543,7 @@ class AnalysisProcedure(QObject):
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y1_height = min_y1_p * 0.9 # y1_peaks_avg * 0.726667
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y2_peaks_pre = signal.find_peaks(self.y2_sample, height=height,
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distance=10)
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distance=self.signal_min_peak_distance)
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##y2_peaks_avg = np.average(y2_peaks_pre[1]['peak_heights'])
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min_y2_p = np.min(y2_peaks_pre[1]['peak_heights'])
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@@ -555,9 +555,11 @@ class AnalysisProcedure(QObject):
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print(f'AVG = {y1_height}, {y2_height}', flush=True)
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y1_peaks = signal.find_peaks(
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self.y1_sample, height=y1_height, distance=10)
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self.y1_sample, height=y1_height,
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distance=self.signal_min_peak_distance)
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y2_peaks = signal.find_peaks(
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self.y2_sample, height=y2_height, distance=10)
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self.y2_sample, height=y2_height,
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distance=self.signal_min_peak_distance)
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print((f'PEAKS==> {y1_peaks}, {y2_peaks},' +
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f'{len(y1_peaks[0])}, {len(y2_peaks[0])}'), flush=True)
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print(y1_peaks[1]['peak_heights'], flush=True)
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