[Numpy-discussion] Interpolation question
josef.pktd at gmail.com
josef.pktd at gmail.com
Sun Mar 28 17:18:56 EDT 2010
On Sun, Mar 28, 2010 at 4:47 PM, Andrea Gavana <andrea.gavana at gmail.com> wrote:
> HI All,
> On 28 March 2010 19:22, Robert Kern wrote:
>> On Sun, Mar 28, 2010 at 03:26, Anne Archibald <peridot.faceted at gmail.com> wrote:
>>> On 27 March 2010 20:24, Andrea Gavana <andrea.gavana at gmail.com> wrote:
>>>> Hi All,
>>>> I have an interpolation problem and I am having some difficulties
>>>> in tackling it. I hope I can explain myself clearly enough.
>>>> Basically, I have a whole bunch of 3D fluid flow simulations (close to
>>>> 1000), and they are a result of different combinations of parameters.
>>>> I was planning to use the Radial Basis Functions in scipy, but for the
>>>> moment let's assume, to simplify things, that I am dealing only with
>>>> one parameter (x). In 1000 simulations, this parameter x has 1000
>>>> values, obviously. The problem is, the outcome of every single
>>>> simulation is a vector of oil production over time (let's say 40
>>>> values per simulation, one per year), and I would like to be able to
>>>> interpolate my x parameter (1000 values) against all the simulations
>>>> (1000x40) and get an approximating function that, given another x
>>>> parameter (of size 1x1) will give me back an interpolated production
>>>> profile (of size 1x40).
>>> If I understand your problem correctly, you have a function taking one
>>> value as input (or one 3D vector) and returning a vector of length 40.
>>> You want to know whether there are tools in scipy to support this.
>>> I'll say first that it's not strictly necessary for there to be: you
>>> could always just build 40 different interpolators, one for each
>>> component of the output. After all, there's no interaction in the
>>> calculations between the output coordinates. This is of course
>>> awkward, in that you'd like to just call F(x) and get back a vector of
>>> length 40, but that can be remedied by writing a short wrapper
>>> function that simply calls all 40 interpolators.
>>> A problem that may be more serious is that the python loop over the 40
>>> interpolators can be slow, while a C implementation would give
>>> vector-valued results rapidly.
>> 40 is not a bad number to loop over. The thing I would be worried
>> about is the repeated calculation of the (1000, 1000) radial function
>> evaluation. I think that a small modification of Rbf could be made to
>> handle the vector-valued case. I leave that as an exercise to Andrea,
>> of course. :-)
> It seems like this whole interpolation stuff is not working as I
> thought. In particular, considering scalar-valued interpolation (i.e.,
> looking at the final oil recovery only and not the time-based oil
> production profile), interpolation with RBFs is giving
> counter-intuitive and meaningless answers. The issues I am seeing are
> basically these:
> # Interpolate the cumulative oil production
> rbf = Rbf(x1, x2, x3, x4, x5, x6, final_oil_recovery)
> # Try to use existing combination of parameters to get back
> # the original result (more or less)
> possible_recovery = rbf(x1, x2, x3, x4, x5, x6)
> 1) If I attempt to use the resulting interpolation function ("rbf"),
> inputting a single value for each x1, x2, ..., x6 that is *already
> present* in the original x1, x2, ..., x6 vectors, I get meaningless
> results (i.e., negative oil productions, 1000% error, and so on) in
> all cases with some (rare) exceptions when using the "thin-plate"
> interpolation option;
> 2) Using a combination of parameters x1, x2, ..., x6 that is *not* in
> the original set, I get non-physical (i.e., unrealistic) results: it
> appears that RBFs think that if I increase the number of production
> wells I get less oil recovered (!), sometimes by a factor of 10.
> I am pretty sure I am missing something in this whole interpolation
> thing (I am no expert at all), but honestly it is the first time I get
> such counter-intuitive results in my entire Python numerical life ;-)
The interpolation with a large number of points can be pretty erratic.
Did you use all 1000 points in the RBF?
Do see what's going on you could try 2 things:
1) Use only a few points (10 to 100)
2) Use gaussian with a large negative smooth
I had problems in the past with rbf in examples, and in one case I
switched to using neighborhood points only (e.g. with scipy.spatial,
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