Model and experiment fitting.

[Sebastian Żurek]

Nadav Horesh napisał(a):
> 1. If at least one of your data sets to be interpulated is on a grid, 
> you can use numpy.ndimage.map function for fast interpolation for 2d (in fact for any dimensional) dataset.

I've already used a splines to interpolate a missing simulated points. 
That procedure works  great and is very fast. But I'll check the 
numpy.ndimage - I haven't used it, yet.


> 2. Isn't there an analytic expression to average the expectration values of SH over all possible orientations 
>  between B and the crystal axis? My experience shows that some analytic work can save 99% of simulation time.

Well, the simulations are already very fast. The time consumption is 
approximately ~0.3s for a single powder spectrum (2.8GHz Pentium D). The 
calculations are held by an external, very fine EPR spectra simulation 
tool. The author must have incorporated into it a lot of 
rationalizations, but this is a binary tool (unfortunately) and I do not 
know, what exactly sits inside of it... All I know, is that the 
orientations are represented by a grid (with an increment step tunable 
by a user). From library documentation: "After having computed the 
spectrum for a number of orientations specified, the simulation function 
interpolates these spectra for additional orientations before summing up 
all spectra." The interpolation is accomplish with a splines.




Thank you for your comment,

best regards

Sebastian


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