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  • src/sas/sascalc/invariant/invariant.py

    rb1f20d1 r7432acb  
    610610        # Data boundaries for fitting 
    611611        qmin = self._data.x[0] 
    612         qmax = self._data.x[int(self._low_extrapolation_npts - 1)] 
     612        qmax = self._data.x[self._low_extrapolation_npts - 1] 
    613613 
    614614        # Extrapolate the low-Q data 
     
    649649        # Data boundaries for fitting 
    650650        x_len = len(self._data.x) - 1 
    651         qmin = self._data.x[int(x_len - (self._high_extrapolation_npts - 1))] 
    652         qmax = self._data.x[int(x_len)] 
     651        qmin = self._data.x[x_len - (self._high_extrapolation_npts - 1)] 
     652        qmax = self._data.x[x_len] 
    653653 
    654654        # fit the data with a model to get the appropriate parameters 
     
    688688        if npts_in is None: 
    689689            npts_in = self._low_extrapolation_npts 
    690         q_end = self._data.x[max(0, int(npts_in - 1))] 
     690        q_end = self._data.x[max(0, npts_in - 1)] 
    691691 
    692692        if q_start >= q_end: 
     
    714714        # Get extrapolation range 
    715715        if npts_in is None: 
    716             npts_in = int(self._high_extrapolation_npts) 
     716            npts_in = self._high_extrapolation_npts 
    717717        _npts = len(self._data.x) 
    718         q_start = self._data.x[min(_npts, int(_npts - npts_in))] 
     718        q_start = self._data.x[min(_npts, _npts - npts_in)] 
    719719 
    720720        if q_start >= q_end: 
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