Table of Contents
Name | Direction | Type | Default | Description |
---|---|---|---|---|
Function | InOut | Function | Mandatory | Parameters defining the fitting function and its initial values |
InputWorkspace | Input | Workspace | Mandatory | Name of the input Workspace |
IgnoreInvalidData | Input | boolean | False | Flag to ignore infinities, NaNs and data with zero errors. |
DomainType | Input | string | Simple | The type of function domain to use: Simple, Sequential, or Parallel. Allowed values: [‘Simple’, ‘Sequential’, ‘Parallel’] |
OutputWorkspace | Output | Workspace | Mandatory | An output workspace. |
The algorithm will use the axes of the input workspace to evaluate a functions on them and store the result in the output workspace.
Example - 1D function and a matrix workspace.
# Load a workspace
ws = Load('MUSR00015189')
# Evaluate a function
EvaluateFunction('name=ExpDecay,Height=56', 'ws_1', StartX=0, EndX=30, OutputWorkspace='out')
Example - 2D function and a MD workspace.
# Create an empty 2D workspace (100 x 100)
nx = 100
ny = 100
ws = CreateMDHistoWorkspace(Dimensionality=2, Extents=[-10,10,-10,10], Names="x,y", Units="U,V",
NumberOfBins='%s,%s' % (nx,ny),
SignalInput=[0] * nx * ny, ErrorInput=[0] * nx * ny)
# Evaluate a function
EvaluateFunction('name=UserFunctionMD,Formula=sin(x)*sin(y)','ws', OutputWorkspace='out')
Categories: Algorithms | Optimization