Hi, Wolfram-- I'm sure there are several ways to do this, but this is what I've done. I defined a function that takes *data* and a list of field names, then I call that function elsewhere. So for instance for terms of the form v1 dot grad v2 or vi d (vj) /dxi so I have wrote the following function (which I think might be poorly named) def tensor_derivative(data,fields1,fields2): """performs (A \cdot \nabla) B, Ai di Bj where di = d/d[x,y,z] Ai in fields1, Bj in fields2 Assumes centered difference""" iM1 = slice(None,-2) iP1 = slice(2,None) all = slice(1,-1) all_all=[all]*3 out = [na.zeros(data[fields1[0]].shape)]*3 dxi=1./(2*data.dds ) for j,fj in enumerate(fields2): for i, fi in enumerate(fields1): Left,Right = [all]*3,[all]*3 Right[i] = iP1 Left[i] = iM1 out[j][all_all] += data[fi][all_all]*(data[fj][ Right ]- data[fj][ Left] )*dxi[i] return out This returns an array of results, I typically take the norm. For instance, the norm of the magnetic curvature || Bi Di Bj || as def _curvatureNorm(field,data): Bfields=['Bxhat','Byhat','Bzhat'] curvature = tensor_derivative(data,Bfields,Bfields) return na.sqrt( curvature[0]**2 + curvature[1]**2 + curvature[2]**2 ) add_field('CurvatureNorm',function=_curvatureNorm,take_log=False, validators=[ValidateSpatial(1,['Bxhat','Byhat','Bzhat'])]) and Bxhat is the unit vector along Bx, though anything can be used here. Hope that helps! d. On Mon, Sep 12, 2011 at 9:39 AM, Wolfram Schmidt <schmidt@astro.physik.uni-goettingen.de> wrote:

Hi everyone,

how can I generically define the derivatives of fields in a yt script (or is there already something to do that in yt)?

I want to define, say, second-order centered differences of a field without referring to data["particular field"] but with a place holder for just any field (like in a Fortran or C subroutine) so that I can use this single piece of code for several different fields.

Thanks, Wolfram

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