It is often necessary to smoothly interpolate vector or scalar fields known as a set of discrete data points across a grid. For one- and two-dimensional problems cubic and bicubic spline implementations exist (for example, in the SciPy interpolate library [1]), but three-dimensional problems are more difficult. This software was developed for use in modelling the three-dimensional motion of paramagnetic neutral particles through Zeeman decelerators [2] and magnetic traps [3]. These fields are produced by combinations of permanent magnetic and electromagnetic elements with generally no analytic solution. The potentials are calculated, for example using finite element analysis methods, as a series of data points on a grid, which must be interpolated to return the required values for an arbitrary point within the region of interest.
The tricubic method described by Lekien and Marsden [4] implements cubic spline interpolation in three dimensions, in an efficient and accurate way. The method was originally motivated by studies of current flow in ocean dynamics [5]; high-frequency radar data give a two-dimensional vector map of the surface of the ocean as a series of discrete points, measured at regular intervals in time. The authors developed their method to smoothly interpolate the time evolution of this velocity field, and note it can equivalently operate on time-independent three-dimensional fields.
There are several commonly available implementations of this interpolator in a variety of programming languages, but none were suitable for the specific requirements of our work. ARBTools was written in Python [8], allowing easy modification of the software if needed. Unlike many tools with complex software dependencies the only additional requirement for ARBTools is NumPy [9]. The careful use of NumPy libraries has also allowed the package to be efficient, and in tests it is only moderately slower than an equivalent C implementation. The main difference in ARBTools, however, is the direct availability of the derivatives of an interpolated scalar field, knowledge of these derivatives being a prerequisite for calculating the force arising due to a potential gradient. (Unlike other interpolation methods in which the gradients are recovered via finite-differences, in the tricubic scheme the approximating polynomial function can be analytically differentiated). The software can also directly work with a vector field; for example, in the context of molecular and atomic traps this is needed when calculating the probabilities of non-adiabatic spin transitions [6], or simulating laser-cooling interactions [7].
Interpolation coefficients are calculated on-the-fly and subsequently reused where required to reduce processor time. For arbitrary points inside the interpolation volume the field magnitude, partial derivatives and vector components are readily accessible from a single query. Separate query methods are included for dealing with interpolation of a single point, or for multiple simultaneous coordinates. A fourth-order Runge-Kutta [10] algorithm is implemented for numerically solving particle motion.
Although produced for the specific application of modelling low-field-seeking neutral particles, this software has been developed to be more general. It can work directly with either a scalar or vector field input, and is suitable for a variety of applications with any field supplied across a regular, cuboid grid.
ARBTools is written in Python [8], with extensive use of NumPy [9]. ‘ARBInterp’ contains the tricubic interpolator and query methods. Any source data presented across a regular grid as either a scalar or vector field can be input into the interpolator, allowing the values of the data to be calculated for arbitrary points within the set. For scalar data the derivatives are directly accessible. Although designed for magnetic fields this software could be used with a wide variety of systems, such as modelling heat flow, or in data processing to smooth contour plots or heatmaps. Example magnetic fields, as both magnitudes and vectors, are available to download along with scripts illustrating the use of the interpolator. These example files also illustrate the expected input format of the data.
The ‘ARBTraj’ module contains functions to create a random sample of argon atoms and solve its motion through a quadrupole field. By simply changing mass and magnetic moment values this can be adapted for different atomic species, or a differently shaped magnetic potential could be specified. The included Runge-Kutta integrator can be easily modified to solve particle motion in alternative systems – for example, we have recently discussed simulating the operation of an atomic tweezer apparatus with a colleague.
To install on Linux run ‘sudo python setup.py install’. The interpolator is contained in a file called ‘ARBInterp.py’ and the command ‘from ARBTools.ARBInterp import tricubic’ will import the interpolation class.
To instantiate the class, pass it a source field – e.g. ‘interp = tricubic (sourcefield)’ will create an instance called ‘interp’. Input can be either a scalar field U(x, y, z) as an N × 4 (x, y, z, U) array or a vector field B(x, y, z) as an N × 6 (x, y, z, Bx, By, Bz) array. If an N × 4 field is passed, the interpolator will automatically default to return the magnitude and gradient of the field. If an N × 6 field is passed it will accept an optional ‘mode’ keyword argument to select one of three modes, (e.g. interp = tricubic (sourcefield, mode = ‘kw’)):
If no keyword is passed, the interpolator defaults to vector mode. Two query modes are implemented: ‘sQuery’ interpolates a single point within the volume, accepting an input in the form ([x, y, z]). ‘rQuery’ accepts a range of coordinates for simultaneous interpolation, as an array ([x_{1}, y_{1}, z_{1}]…[x_{n}, y_{n}, z_{n}]). For multiple queries rQuery is much more efficient than running sQuery in a loop.
Figure 1 shows an interpolation example. The quadrupole electric field produced by four point charges was calculated as a grid of 400^{3} data points; the left plot is a 2D slice through the central plane. A less dense grid of 40^{3} points was then calculated, and the middle image shows a plot through the centre. Lastly, the sparse grid was interpolated to reproduce the 400^{3} data, and is shown on the right.
ARBTools was written with Python 2.7.12 and NumPy 1.13.3 on Linux Mint 18.3, and has been tested with Python 3.5.2 on the same platform. It has also been tested on Enthought Canopy v2.1.9 on Microsoft Windows 7 and 10.
Example input fields and query scripts are available to download from the source repository. Performance benchmarking on 64-bit Linux with an Intel Core i7 CPU shows 100 unique interpolations for a given data set take between 20 and 50 ms, depending on which components are being returned. As expected, there is a linear relationship between number of queries and run time.
The main constraint when using ARBTools is the amount of memory required to load the source file; this is determined by the size of the input grid. For example; a cubic volume 20 mm on a side with a grid spacing of 0.5 mm contains 41^{3} = 68921 grid points, which will load in less than a second with negligible memory usage. The same data sampled at 0.25 mm intervals contains 531441 points, this may take several seconds to load and consume ≈500 MB memory. At 0.125 mm intervals we have 4173281 points, this may take up to a minute to load and consume over 5 GB of memory. Once loaded, however, querying these different datasets takes almost exactly the same amount of time.
The tricubic interpolation method values smoothness of the interpolated function and its first derivatives over absolute accuracy [4]. In order to quantify the errors in this method two types of model were considered; the quadrupole electric field produced by a series of point charges, which can be solved analytically (Figure 1), and a magnetic field produced by a permanent magnet, calculated using finite-element analysis with the ‘FEMM’ [11] software package (see Figure 2). (Of course, if an analytic solution is available there is no need to interpolate – this is simply a useful calibration tool!).
For both cases a high-resolution source dataset was created, and then a sparse subset of this data was interpolated and compared with it. Figure 3 shows the root-mean-squared errors between the interpolated and ‘true’ values of the calculated fields for a variety of grid intervals. It can be seen that for a given level of accuracy the analytic solution can tolerate a larger grid spacing – this is due to the high gradients at the interface between two materials in finite-element (or boundary volume integral [12]) analysis. In general, consideration of the nature of the data set being interpolated and its structure will inform the grid spacing chosen, which is a compromise between inaccuracy and unwieldiness. These tests were repeated with the interpolator in the ‘EQ Tools’ library [13]; although it does not provide the field derivatives, the magnitudes were found to be the same to within 1 × 10^{–6}%.
ARBTools was developed on Linux Mint 18.3, and has been tested on Windows 7 and 10.
ARBTools was developed in Python 2.7.12 and has been tested on 3.5.2. Any version of Python from 2.7 upwards should be suitable.
Several GB of RAM should be suitable for most applications. ARBTools has been used with large datasets on the Durham university supercomputer.
Written using NumPy 1.13.3. Earlier versions may work.
Name: ARBTools
Persistent identifier: https://doi.org/10.5281/zenodo.2548609
Publisher: Paul A. Walker
Version published: v1.3
Repository: GitHub
Persistent identifier: https://github.com/DurhamDecLab/ARBInterp
Licence: GPL-3.0
Date published: 15/02/2019
English
The core of ARBTools is the tricubic interpolator, which can be used with any suitably-formatted input field, for many possible tasks – for example, visualising the shape of a three-dimensional potential or extracting coherent Lagrangian structures from a time-dependent two-dimensional flow. The interpolator has been designed to be imported as a library in Python, and the output from the evaluation methods can easily be passed into third-party code, or output to file for use in non-Python systems.
As is, ARBTools can be used to model the trajectories of low-field-seeking argon atoms in a magnetic field. Simply altering the mass and magnetic moment parameters would allow other species to be modelled. If the functions defining the acceleration due to a potential are replaced, trajectories in alternative systems could easily be modelled, for example, the motion of charges in electric fields, or masses moving under gravity.
Support may be requested through the project GitHub page: (https://github.com/DurhamDecLab/ARBInterp). The source code is available and may be reused or modified at will subject to the details of the GPL-3.0 licence.
Many thanks to Dr. Lewis McArd for his invaluable advice on this and other projects.
The authors have no competing interests to declare.
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