Template:Interpolate to UGrid: Difference between revisions

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This tool will interpolate both scalar and vector datasets. For vector datasets, the dataset is first converted into its x, y (and z) components. These components are interpolated to the target grid and then joined back together to make a new vector dataset.
This tool will interpolate both scalar and vector datasets. For vector datasets, the dataset is first converted into its x, y (and z) components. These components are interpolated to the target grid and then joined back together to make a new vector dataset.


The Linear and Natural neighbor interpolation methods require triangles. If the source grid has cells that are not triangles then those cells are converted to triangles using the "earcut" algorithm (ear clipping). This means that the interpolation may not exactly match the display of contours on that same grid.
The Linear and Natural neighbor interpolation methods require triangles. If the source grid has cells that are not triangles then those cells are converted to triangles using the "ear cut" algorithm. This means that the interpolation may not exactly match the display of contours on that same grid.


When the source dataset is a cell dataset and the interpolation method requires triangles (Linear and Natural neighbor), the cell centers are triangulated into a Delaunay triangulation. (This is a temporary triangulation used by the tool while interpolating. It is not visible to end users.) If this is not desired behavior then an alternative workflow is to convert the cell dataset to a point dataset prior to using the interpolation tool.
When the source dataset is a cell dataset and the interpolation method requires triangles (Linear and Natural neighbor), the cell centers are triangulated into a Delaunay triangulation. If this is not desired behavior then an alternative workflow is to convert the cell dataset to a point dataset prior to using the interpolation tool.
 
This tool allows the user to select the location (at points or cells) of the newly interpolated dataset. Unstructured Grids (UGrids) support datasets at both points and cells.
However, most other geometric entities (grid/mesh/scatter) in XMS only support dataset at points or at cells. If the user selects a dataset location that is not compatible with the geometric entity then an error will be displayed when XMS tries to load that new dataset.


====Current Location in toolbox====  
====Current Location in toolbox====  

Revision as of 17:34, 14 November 2023


Interpolate to UGrid

The Interpolate to Ugrid tool will interpolate a dataset associated with one UGrid to another UGrid within the same project.

Input Parameters

  • Source dataset – Select the dataset that will be used for interpolation.
  • Target grid – Select the grid that will be receiving the interpolated dataset.
  • Target dataset name – Name of the target dataset.
  • Target dataset location – Indicates if the dataset should be interpolated to the either the "Points" or the center of each "Cell" of the target grid.
  • Interpolation method – The following interpolation methods are supported:
    • "Linear" – Uses data points that are first triangulated to form a network of triangles.
    • "Inverse Distance Weighted (IDW)" – Based on the assumption that the interpolating surface should be influenced most by the nearby points and less by the more distant points.
    • "Natural Neighbor" – Based on the Thiessen polygon network of the point data.
  • Interpolation dimension – Set be either "2D" or "3D" interpolation. Should match the target grid's dimensions.
  • Truncate interpolated values option – The interpolated values can be limited by using one of the following truncation options:
    • "Do not truncate" – Interpolated values will not be truncated.
    • "Truncate to min/max of source dataset" – Interpolated values will be restricted to the minimum and maximum values in the source dataset.
    • "Truncate to specified min/max" – Interpolated values will be restricted to a define minimum and maximum value.
      • Truncate range minimum – Define the minimum value for truncating.
      • Truncate range maximum – Define the maximum value for truncating.
  • IDW nodal function – Available when using the IDW interpolation method. Select an IDW nodal function method using one of the following:
    • "Constant (Shepard's Method)" – The simplest form of inverse distance weighted interpolation. Includes the option to use classic weight function by enter a weighting exponent.
      • IDW constant nodal function use classic weight function – Turn on to use the classic weight function.
      • IDW constant nodal function weighting exponent – Enter a positive real number to use as the weighting exponent in the weight function used in the constant method.
    • "Gradient Plane" – Variation of Shepard's method with nodal functions or individual functions defined at each point
    • "Quadratic" – Makes use of quadratic polynomials to constrain nodal functions.
  • IDW computation of nodal coefficient option
    • "Use nearest points" – Drops distant points from consideration since they are unlikely to have a large influence on the nodal function.
      • IDW nodal coefficients number of nearest points
      • IDW nodal coefficients use nearest points in each quadrant
    • "Use all points"
  • IDW computation of interpolation weights option
    • "Use nearest points" – Drops distant points from consideration since they are unlikely to have a large influence on the interpolation weights.
      • IDW interpolation weights number of nearest points
      • IDW interpolation weights use nearest points in each quadrant
    • "Use all points"
  • Extrapolation option – Although they are referred to as interpolation schemes, most of the supported schemes perform both interpolation and extrapolation. That is, they can estimate a value at points both inside and outside the convex hull of the scatter point set. Obviously, the interpolated values are more accurate than the extrapolated values. Nevertheless, it is often necessary to perform extrapolation. Some of the schemes, however, perform interpolation but cannot be used for extrapolation. These schemes include Linear and Clough-Tocher interpolation. Both of these schemes only interpolate within the convex hull of the scatter points. Interpolation points outside the convex hull are assigned the default extrapolation value.
Select one of the following extrapolation options:
  • "No extrapolation" – Extrapolation will not be performed.
  • "Constant value" – Extrapolation will use a constant value.
    • Extrapolation constant value – Set the constant value that will be used for extrapolation.
  • "Inverse distance weighted (IDW)"
    • Extrapolation IDW interpolation weights computation option
    • Extrapolation IDW number of nearest points
    • Extrapolation IDW use nearest points in each quadrant
  • "Existing dataset" – Allows designating an existing dataset to define the extrapolation values.
    • Existing dataset – Select a dataset in the project to use for extrapolation values.
  • Clough-Tocher – Use the Clough-Tocher interpolation technique. This technique is often referred to in the literature as a finite element method because it has origins in the finite element method of numerical analysis. Before any points are interpolated, the points are first triangulated to form a network of triangles. A bivariate polynomial is defined over each triangle, creating a surface made up of a series of triangular Clough-Tocher surface patches.
  • Specify anisotropy – Sometimes the data associated with a scatter point set will have directional tendencies. The horizontal anisotropy, Azimuth, and Veritcal anistropy allows taking into account these tendencies.
  • Log interpolation – When interpolating chemical data, it is not uncommon to have a small "hot spot" somewhere in the interior of the data where the measured concentrations are many orders of magnitude higher than the majority of the other concentrations. In such cases, the large values dominate the interpolation process and details and variations in the low concentration zones are obliterated. One approach to dealing with such situations is to use log interpolation. If this option is selected, the tool takes the log of each data value in the active scatter point set prior to performing interpolation. By interpolating the log of the dataset, small values are given more weight than otherwise. Once the interpolation is finished, the tool takes the anti-log (10x) of the interpolated dataset values before assigning the dataset to the target grid or mesh.
Note that it is impossible to take the log of a zero or negative value. When the log interpolation option is turned on, a value must be entered to assign to scatter points where the current data value is less than or equal to zero. Typically, a small positive number should be used.

Output Parameters

The output will create an interpolated dataset on the target grid.

Notes

This tool will interpolate both scalar and vector datasets. For vector datasets, the dataset is first converted into its x, y (and z) components. These components are interpolated to the target grid and then joined back together to make a new vector dataset.

The Linear and Natural neighbor interpolation methods require triangles. If the source grid has cells that are not triangles then those cells are converted to triangles using the "ear cut" algorithm. This means that the interpolation may not exactly match the display of contours on that same grid.

When the source dataset is a cell dataset and the interpolation method requires triangles (Linear and Natural neighbor), the cell centers are triangulated into a Delaunay triangulation. If this is not desired behavior then an alternative workflow is to convert the cell dataset to a point dataset prior to using the interpolation tool.

Current Location in toolbox

Unstructured Grids/Interpolate to Ugrids