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The power table is a more customizable version of the table component. The power table contains advanced features such as drag-and-drop rows, multi-column sorting, column filtering, and cell-spanning.

Power Table Features

• Multi-column sorting. To sort multiple columns, select the header of the first column, hold down the Control key, then select the header of the next column. Click on the header again to reverse the sort order, and click a third time to remove sorting on the column.
  
• Column filtering. Columns can be temporarily hidden from view using column filtering. Right-click on the header of the table, and uncheck columns that you would like to hide. You can disable this feature by disabling the Column Chooser Menu property on the table.
• Column reordering. You can switch the locations of columns on the table using column reordering. Drag the header of the column that you would like to move to a new location on the table. You can disable this feature by disabling the Columns Re-Orderable property on the table.
• Cell spanning. A cell can be spanned across multiple columns and rows. Keep in mind that you must explicitly define the locations of cells that must be spanned. This means that if you would like to use cell spanning, any other table features that change how the table is displayed will be disabled automatically (such as sorting, column filtering and column reordering). Click on the Cell Span Data dataset to configure spanning. Within the dataset, add a row for each new span. The "row" column controls the row in the table where the span will start. The "column" column controls the column where the span will start. The "width" column controls how many columns the span will cover. The "height" column controls how many rows the span will cover. Adding a row where "row=4, column=1, width=2, height=3" results in a span starting on the fifth row of the table and the second column (using 0-based indexing). The span will cover the second and third columns in the row and will also cover two rows below the fifth row, as shown below.
• Drag and Drop. This feature allows you to drag rows from one power table to another power table. In order to perform drag and drop, you must implement the onRowsDropped() extension function on the destination table. This is so that you can adapt the data from one table to the other within the function. You must also enable the Row Dragging Enabled property on both tables.
• Row Copying. This feature allows you to select rows and copy them to the clipboard using the standard keyboard shortcut Ctrl + C. These can then be pasted anywhere, even outside of Ignition.

Properties

Scripting

See the Vision - Power Table Scripting Functions page for the full list of scripting functions available for this component.

Event Handlers

Event handlers allow you to run a script based off specific triggers. See the full list of available event handlers on the Component Events page.

Customizers

• Vision - Power Table Customizer
• Vision Component Customizers

Examples

#Example of an onRowsDropped() extension script for two power tables with identical columns:
 
def onRowsDropped(self, sourceTable, rows, rowData, dropIndexLocation):
    if self != sourceTable:
        destDataset = self.getData()
        pyRowData = system.dataset.toPyDataSet(rowData)
        # Loop thru all the rows that have been selected and dragged to the
        # destination table.
        for row in pyRowData:
            newRow = []
            for column in row:
                newRow.append(column)
            destDataset = system.dataset.addRow(destDataset, dropIndexLocation, newRow)
        # Adds the rows to the destination table.
        self.setData(destDataset)
        # Optional. Deletes the dragged rows from the source table.
        sourceDataset = system.dataset.deleteRows(sourceTable.getData(), rows)
        sourceTable.setData(sourceDataset)
    else:
        system.gui.messageBox("Dropping on to same table not supported")
        # To drop onto the same table, the new row indices must be calculated
        # for both the dropped and deleted rows, taking changes into account.