analysis.py 文件源码

def interpolationFunction(self,data=None,spline_order=3,cval=None):
        """Returns a function F(x,y) that interpolates any values of the 2D observable.

        F = interpolationFunction(self,data=None,spline_order=3,cval=None)

        cval is set to data.mean() by default. cval cannot be chosen too large
        or too small or NaN because otherwise the spline interpolation breaks
        down near the region and produces wild oscillations.
        """
        # see http://www.scipy.org/Cookbook/Interpolation
        from scipy import ndimage

        if data is None:
            data = self.observable

        if cval is None:
            cval = data.mean()
        _data = data.filled(cval)   # fill with moderate value, not 1000 to keep spline happy

        coeffs = ndimage.spline_filter(_data,order=spline_order)
        x0,y0 = self.mNMP[0], self.mLID[0]
        dx,dy = self.mNMP[1] - x0, self.mLID[1] - y0
        def _transform(cnew, c0, dc):
            return (numpy.atleast_1d(cnew) - c0)/dc
        def interpolatedF(NMP,LID):
            """B-spline function over the 2D observable, F(NMP,LID).

            Example usage:
            >>> XX,YY = numpy.mgrid[40:75:0.5,96:150:0.5]
            >>> FF = Observable.F(XX,YY)
            >>> contourf(XX,YY,FF, numpy.linspace(-3,11,100),extend='both')
            """
            return ndimage.map_coordinates(coeffs,
                                           numpy.array([_transform(NMP,x0,dx),_transform(LID,y0,dy)]),
                                           prefilter=False,mode='constant',cval=cval)
        return interpolatedF