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CreateMonteCarloWorkspace v1¶

Summary ¶

Creates a randomly simulated workspace by sampling from the probability distribution of input data.

Properties ¶

Name	Direction	Type	Default	Description
InputWorkspace	Input	MatrixWorkspace	Mandatory	Input Workspace containing data to be simulated
Seed	Input	number	32	Integer seed that initialises the random-number generator, for reproducibility
MonteCarloEvents	Input	number	0	Number of Monte Carlo events to simulate. Defaults to integral of input workspace if 0.
OutputWorkspace	Output	MatrixWorkspace	Mandatory	Name of output workspace.

Description ¶

The algorithm generates a simulated workspace by sampling from the probability distribution of input data, useful for testing of fitting functions and modeling.

Usage ¶

Example - CreateMonteCarloWorkspace

# import mantid algorithms, numpy and matplotlib
from mantid.simpleapi import *
import matplotlib.pyplot as plt
import numpy as np
from mantid.api import FunctionFactory

func = FunctionWrapper(FunctionFactory.createInitialized("name=BackToBackExponential,I=25000,A=0.06,B=0.015,X0=30000,S=30;name=FlatBackground,A0=50"))
# create input workspace
x = np.linspace(29650.0, 30500.0, 201)
y = func(x)
e = np.sqrt(y)
ws = CreateWorkspace(DataX=x, DataY=y, DataE=e, UnitX="TOF")
# call algorithm
ws_mc = CreateMonteCarloWorkspace(InputWorkspace=ws, Seed=0)

fig, axes = plt.subplots(subplot_kw={'projection': 'mantid'})
axes.plot(ws, label='input', wkspIndex=0)
axes.plot(ws_mc, label='CreateMonteCarloWorkspace output', wkspIndex=0, alpha=0.75)
legend = axes.legend(fontsize=8.0).set_draggable(True).legend
fig.show()