[bugfix] huge bug in slope calculation
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@ -241,7 +241,7 @@ class AICPicker(AutoPicker):
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# find maximum within slope determination window
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# find maximum within slope determination window
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# 'cause slope should be calculated up to first local minimum only!
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# 'cause slope should be calculated up to first local minimum only!
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imax = np.argmax(self.Data[0].data[islope])
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imax = np.argmax(self.Data[0].data[islope])
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iislope = islope[0][0:imax]
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iislope = islope[0][0:imax+1]
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if len(iislope) <= 2:
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if len(iislope) <= 2:
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# calculate slope from initial onset to maximum of AIC function
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# calculate slope from initial onset to maximum of AIC function
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print("AICPicker: Not enough data samples left for slope calculation!")
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print("AICPicker: Not enough data samples left for slope calculation!")
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@ -269,7 +269,7 @@ class AICPicker(AutoPicker):
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raw_input()
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raw_input()
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plt.close(fig)
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plt.close(fig)
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return
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return
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iislope = islope[0][0:imax]
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iislope = islope[0][0:imax+1]
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dataslope = self.Data[0].data[iislope]
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dataslope = self.Data[0].data[iislope]
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# calculate slope as polynomal fit of order 1
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# calculate slope as polynomal fit of order 1
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xslope = np.arange(0, len(dataslope), 1)
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xslope = np.arange(0, len(dataslope), 1)
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@ -278,7 +278,7 @@ class AICPicker(AutoPicker):
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if datafit[0] >= datafit[len(datafit) - 1]:
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if datafit[0] >= datafit[len(datafit) - 1]:
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print('AICPicker: Negative slope, bad onset skipped!')
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print('AICPicker: Negative slope, bad onset skipped!')
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return
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return
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self.slope = 1 / tslope * (datafit[len(dataslope) - 1] - datafit[0])
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self.slope = 1 / len(dataslope) * (datafit[len(dataslope) - 1] - datafit[0])
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else:
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else:
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self.SNR = None
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self.SNR = None
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