Template:Convert 3D Data to 2D Data
The Convert 3D Data to 2D Data tool is used to convert 3D cell data to a 2D UGrid with data sets. The user selects a 3D cell data set as input to the tool (the data set may have multiple time steps). Then the user may choose to output multiple data sets from the tool; the following data sets may be computed by the tool: highest active value, average, maximum, minimum, value for each layer. The 3D UGrid cells are organized into columns of cells to compute the various output data sets. Examples follow that explain how the tool works for different types of UGrids.
This tool will only work on UGrids that are made up of 3D cells and those cells must all be prisms (the side faces of the cells must be vertical). Further, the grid must have layers assigned to the cells. The tool will return an error if any of these conditions are not met.
Input parameters
• Cell data set– The input 3D data set that will be converted to 2D data sets
• Name of the output grid – Name for the newly created 2D UGrid. If this value is left blank then the name of the output grid will be set to be the name of the input cell data set.
• Compute highest active cell in column – Option to output a 2D data set where the value will come from the highest active cell in the 3D column of cells.
• Compute average value in column – Option to output a 2D data set of the average value in the 3D column of cells.
• Compute maximum value in column – Option to output a 2D data set of the maximum value in the 3D column of cells.
• Compute minimum value in column – Option to output a 2D data set of the minimum value in the 3D column of cells.
• Compute value for each layer in column - Option to output 2D data sets of the value for each layer in the 3D column of cells.
Output parameters
• The output is a 2D UGrid and data sets.
Current location in Toolbox
UGrids/Convert 3D Data to 2D Data
Examples
Files for example 1 and example 3 can be downloaded here. (Include a link to download these files.)
Example 1
The first example is a simple structured 3D Grid. The grid is made up of 4 rows, 4 columns, and 3 layers.
The data set “elev” has constant values for each layer of the grid: layer 1 – 41.6, layer 2 – 25.0, and layer 3 – 8.3. Running the tool on this data set produces the following 2D UGrid and data sets.
In this example the data sets are straight forward.
The elev_highest_active creates a data set with values from the top of the 3D UGrid. All of the values are 41.6. The elev_average data set gives the average value in each cell column and in this case that will match the values from layer 2 of the 3D UGrid (25.0). The elev_max data set matches the values of the top layer of the 3D UGrid (41.6). The elev_min data set matches the values from the bottom layer of the 3D UGrid (8.3). The elev_layer_1, elev_layer_2, and elev_layer_3 match the values from the respective layer of the 3D UGrid.
Example 2
The second example is the same 3D Grid from example 1 except in this case the 3D data set has activity. That is to say, some of the values in the data set are considered inactive. The cells with inactive values are shown in red in the following figure.
Typically, these values are not contoured on the 3D UGrid.
When the tool runs with this example the following data sets are created. First, examine the layer data sets and notice the effect that activity has on the created data sets.
The active_layer_1 data set shows the 2 cells with inactive values.
The active_layer_2 data set show the 1 cell with an inactive value.
The active_layer_3 data set shows the 1 cell with an inactive value.
The active_highest_active has values from 2 of the lower layers in the 3D UGrid.
The active_average has 3 values that are affected by the data set activity. The active_maximum and active_minimum data sets are similar to the active_average. Example 3 The third example is a 3D Grid with variable number of cells per layer and variable refinement in each layer. A figure of the entire Grid is shown, followed by figures of individual layers.
Layer 2 of the 3D UGrid.
Layer 3 of the 3D UGrid.
Layer 4 of the 3D UGrid.
Layer 5 of the 3D UGrid. When the tool is run with this example the following data sets are created.
The next figure shows the resulting 2D UGrid with the elev_highest_active data set contoured.
Notice that the 2D cells are defined by the smallest 3D cells in any column. The 2 cells that are not pink have values that came from layer 2 of the 3D Grid. The elev_average, elev_maximum, elev_minimum are computed in a straight forward manner and are not shown here. The elev_layer data sets are shown next. Notice that some layer data sets have inactive values because there were no cells defined in those cell columns.
The elev_layer_1 data set.
The elev_layer_2 data set.
The elev_layer_3 data set.
The elev_layer_4 data set.
The elev_layer_4 data set. Example 4 The fourth example uses the same 3D UGrid as example 3 and the data set now includes activity. The orange cells in the grid have a value of 5. The red cells have inactive data set values.
Layer 2 values for 3D UGrid.
When the tool is executed the following results are generated from the tool.
The active_highest_active data set result.
The active_layer_1 data set.
The active_layer_2 data set.
The active_layer_3 data set.
The active_layer_4 data set.
The active_layer_5 data set. Example 5 The fifth example is a voronoi 3D UGrid. This grid has a variable number of cells in each layer. This example comes from the MODFLOW-USG Complex Stratigraphy tutorial.
Layer 2 of 3D UGrid
Layer 3 of 3D UGrid
Layer 4 of 3D UGrid
Layer 5 of 3D UGrid. When the tool is run the following figure shows the output 2D UGrid.
