This gives access to all grid interpolation methods: linear, nearest-/next-/previous neighbor, polynomial, piecewise cubic and spline interpolation. General interpolation is performed by the interp2, interp3, and interpn interface. See here.Īll functions are specifically designed for multidimensional data. Spline interpolation methods on n-dimensional data. They are all found in the ILNumerics Interpolation Toolbox as public static members of the class: FunctionĪll interpolation methods on 2-dimensional data.ĭimension order is expected in meshgrid format (rows first).Īll interpolation methods on 3-dimensional data.Īll interpolation methods on n-dimensional data.ĭimension order is expected in natural format (columns first). The following functions allow the interpolation of gridded data. Functions for Gridded Data Interpolation on Grids It must be noted that regular grids with equal spacings are a special case of the general non-regular grids and applicable for the methods in this section, of course. The same is true here as for the known samples: the spacing between the grid lines is not required to be equal - not within one dimension and not among the dimensions. The green grid tiles above are an example of a gridded set of interpolating query points. However, these rectangles are not necessarily congruent, as can clearly be seen from the image above. All tiles formed by the grid lines are rectangles: they show all angles of 90° only. Here, the known source samples were acquired at the grid crossings of the red areas. One example of a rectilinear grid is given in the following image: However, both grids are not required to be regular, i.e.: the spacing between adjacent grid elements may vary within each dimension. Overviewīoth, the source data grid and the grid of new interpolated points are rectilinear. One dimensional data are handled in their own article. Talking about grids implies that the data are measured in $R^2\dots R^n$. Both consume the same gridded tabular data that is stored to your user home directory in the folder HOME/.CoolProp/Tables.This sections deals with the interpolation of new data from an existing grid of known data points and the special case, that the new data points are ordered in a grid themself. There are two backends implemented for tabular interpolation, BICUBIC and TTSE. Thus, this method is best suited to C , python, and the SWIG wrappers. In order to make the most effective use of the tabular interpolation methods, you must be using the low-level interface, otherwise significant overhead and slowdown will be experienced. They are approximately 4 times faster than the equivalent methods in v4 of CoolProp due to a more optimized structure. Gridded interpolation matlab full#Especially when evaluating inputs as a function of pressure and enthalpy (common in many engineering applications), evaluation of the full equation of state is simply too slow, and it is necessary to come up with some means to speed up the calculations.Īs of version 5.1 of CoolProp, the tabular interpolation methods of CoolProp v4 have been brought back from the dead, and significantly improved.
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