Workspaces store data that algorithms operate on. Workspaces are usually stored in-memory. An algorithm can manipulate a workspace in-place or create a new one as an output.
Workspace is as loose term that encompases a range of possible data structures. All forms of Workspace provide some common information:
Tip
In MantidPlot the data from the workspaces can be graphically viewed, inspected, and plotted in many ways.
Note
In addition to data, workspaces hold a workspace history, which details the algorithms which have been run on this workspace. This means workspaces carry all the meta-information to fully recreate themeselves.
The workspace type id identifies the type of a Workspace instance.
Tip
For C++ or Python development, these values are needed in such functions as the Analysis Data Service’s createWorkspace.
ID | Workspace Type |
---|---|
“IEventWorkspace” | IEventWorkspace |
“ITableWorkspace” | ITableWorkspace |
“WorkspaceGroup” | WorkspaceGroup |
“AbsManagedWorkspace2D” | AbsManagedWorkspace2D |
“CompressedWorkspace2D” | CompressedWorkspace2D |
“EventWorkspace” | EventWorkspace |
“ManagedWorkspace2D” | ManagedWorkspace2D |
“TableWorkspace” | TableWorkspace |
“Workspace2D” | Workspace2D |
“WorkspaceSingleValue” | WorkspaceSingleValue |
“ManagedRawFileWorkspace2D” | ManagedRawFileWorkspace2D |
“MDWorkspace” | MDWorkspace |
“MDHistoWorkspace” | MDHistoWorkspace |
This page describes how you can work with workspaces in python, including accessing their properties and history
Table Workspace is the best solution at present for customising the data structures you need. Changes beyond that are at present not trivial. For specialisation of existing data structures, or new data requirements, please contact the Mantid Team for help.
Category: Concepts