On 25/02/2008, Trond Kristiansen

I have attached the function that the FOR loop is part of as a python file. What I am trying to do is to create a set of functions that will read the output files (NetCDF) from running the ROMS model (ocean model). The output file is organized in xi (x-direction), eta (y-direction), and s (z-direction) where the s-values are vertical layers and not depth. This particular function (z_slice) will find the closest upper and lower s-layer for a given depth in meters (e.g. -100). Then values from the two selcted layers will be interpolated to create a new layer at the selected depth (-100). The problem is that the s-layers follow the bathymetry and a particular s-layer will therefore sometimes be above and sometimes below the selected depth that we want to interpolate to. That's why I need a quick script that searches all of the layers and find the upper and lower layers for a given depth value (which is negative). The z_r is a 3D array (s,eta,xi) that is created using the netcdf module.

If I understand what you're doing correctly, you have a 3D array of depths, indexed by two unproblematic coordinates (eta and xi), and by a third coordinate whose values are peculiar. For each pair (eta, xi), you want to find the value of the third coordinate that occurs at a depth closest to some given depth. Are you looking to do this for many values of depth? It seems like what you're trying to do is (approximately) invert a function - you have z as a function of s, and you want s as a function of z. Is the mapping between depths and s values monotonic? First of all, numpy.searchsorted() is the right tool for finding where depth occurs in a list of z values. It gives you the position of the next lower value; it's pretty straightforward to write down a linear interpolation (you should be able to do it without any for loop at all) and more sophisticated interpolation schemes may be straightforward as well. If you want a smoother interpolation scheme, you may want to look at scipy.interpolate. It is not always as vectorial as you might wish, but it implements splines (optionally with smoothing) quite efficiently. I believe scipy.ndimage may well have some suitable interpolation code as well. In my opinion, the value of numpy/scipy comes from the tools that allow you to express major parts of your algorithm as a line or two of code. But it is often difficult to take advantage of this by "trying to accelerate for loops". I find it really pays to step back and look at my algorithm and think how to express it as uniform operations on arrays. (Of course it helps to have a rough idea of what operations are available; the numpy documentation is sometimes sadly lacking in terms of letting you know what tools are there.) You might find useful the new http://www.scipy.org/Numpy_Functions_by_Category Good luck, Anne