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GeneratePythonFitScript v1¶

Summary ¶

An algorithm to generate a Python script file for performing a sequential or simultaneous fit.

Properties ¶

Name	Direction	Type	Default	Description
InputWorkspaces	Input	str list	Mandatory	A list of workspace names to be fitted. The workspace name at index i in the list corresponds with the ‘WorkspaceIndices’, ‘StartXs’ and ‘EndXs’ properties.
WorkspaceIndices	Input	unsigned int list		A list of workspace indices to be fitted. The workspace index at index i in the list will correspond to the input workspace at index i.
StartXs	Input	dbl list		A list of start X’s to be used for the fitting. The Start X at index i will correspond to the input workspace at index i.
EndXs	Input	dbl list		A list of end X’s to be used for the fitting. The End X at index i will correspond to the input workspace at index i.
FittingType	Input	string	Sequential	The type of fitting to generate a python script for (Sequential or Simultaneous). Allowed values: [‘Sequential’, ‘Simultaneous’]
Function	Input	Function	Mandatory	The function to use for the fitting. This should be a single domain function if the Python script will be for sequential fitting, or a MultiDomainFunction if the Python script is for simultaneous fitting.
MaxIterations	Input	number	500	The MaxIterations to be passed to the Fit algorithm in the Python script.
Minimizer	Input	string	Levenberg-Marquardt	The Minimizer to be passed to the Fit algorithm in the Python script. Allowed values: [‘BFGS’, ‘Conjugate gradient (Fletcher-Reeves imp.)’, ‘Conjugate gradient (Polak-Ribiere imp.)’, ‘Damped GaussNewton’, ‘FABADA’, ‘Levenberg-Marquardt’, ‘Levenberg-MarquardtMD’, ‘Simplex’, ‘SteepestDescent’, ‘Trust Region’]
CostFunction	Input	string	Least squares	The CostFunction to be passed to the Fit algorithm in the Python script. Allowed values: [‘Least squares’, ‘Poisson’, ‘Rwp’, ‘Unweighted least squares’]
EvaluationType	Input	string	CentrePoint	The EvaluationType to be passed to the Fit algorithm in the Python script. Allowed values: [‘CentrePoint’, ‘Histogram’]
OutputBaseName	Input	string	Output_Fit	The OutputBaseName is the base output name to use for the resulting Fit workspaces.
PlotOutput	Input	boolean	True	If true, code used for plotting the results of a fit will be generated and added to the python script.
Filepath	Input	string		The name of the Python fit script which will be generated and saved in the selected location. Allowed extensions: [‘.py’]
ScriptText	Output	string

This algorithm can be used to generate a python script used for sequential or simultaneous fitting. The generated python script is intended to be a generic example for how you can perform a fit in Mantid, and can easily be adapted for specific needs. This algorithm is used by the Fit Script Generator interface.

Usage ¶

Example - generate a python script used for sequential fitting:

ws1 = CreateSampleWorkspace()
ws2 = CreateSampleWorkspace()

function = \
"name=GausOsc,A=0.2,Sigma=0.2,Frequency=1,Phi=0"

# If you want to save the python script to a file then specify the Filepath property
script_text = GeneratePythonFitScript(InputWorkspaces=["ws1", "ws1", "ws2", "ws2"], WorkspaceIndices=[0, 1, 0, 1],
                                      StartXs=[0.0, 0.0, 0.0, 0.0], EndXs=[20000.0, 20000.0, 20000.0, 20000.0],
                                      FittingType="Sequential", Function=function, MaxIterations=500,
                                      Minimizer="Levenberg-Marquardt", OutputBaseName="Output_Fit")

print(script_text)

Output:

# A python script generated to perform a sequential or simultaneous fit
from mantid.simpleapi import *
import matplotlib.pyplot as plt

# List of tuples [ (workspace_name, workspace_index, start_x, end_x) ]
input_data = [
    ("ws1", 0, 0.000000, 20000.000000),
    ("ws1", 1, 0.000000, 20000.000000),
    ("ws2", 0, 0.000000, 20000.000000),
    ("ws2", 1, 0.000000, 20000.000000)
]

# Fit function as a string
function = \
    "name=GausOsc,A=0.2,Sigma=0.2,Frequency=1,Phi=0"

# Fitting options
max_iterations = 500
minimizer = "Levenberg-Marquardt"
cost_function = "Least squares"
evaluation_type = "CentrePoint"
output_base_name = "Output_Fit"

# Perform a sequential fit
output_workspaces, parameter_tables, normalised_matrices = [], [], []
for domain_data in input_data:
    output_name = output_base_name + domain_data[0] + str(domain_data[1])

    fit_output = Fit(Function=function, InputWorkspace=domain_data[0], WorkspaceIndex=domain_data[1],
                     StartX=domain_data[2], EndX=domain_data[3], MaxIterations=max_iterations,
                     Minimizer=minimizer, CostFunction=cost_function, EvaluationType=evaluation_type,
                     Output=output_name)

    output_workspaces.append(output_name + "_Workspace")
    parameter_tables.append(output_name + "_Parameters")
    normalised_matrices.append(output_name + "_NormalisedCovarianceMatrix")

    # Use the parameters in the previous function as the start parameters of the next fit
    function = fit_output.Function

# Group the output workspaces from the sequential fit
GroupWorkspaces(InputWorkspaces=output_workspaces, OutputWorkspace=output_base_name + "Workspaces")
GroupWorkspaces(InputWorkspaces=parameter_tables, OutputWorkspace=output_base_name + "Parameters")
GroupWorkspaces(InputWorkspaces=normalised_matrices, OutputWorkspace=output_base_name + "NormalisedCovarianceMatrices")

# Plot the results of the fit
fig, axes = plt.subplots(nrows=2,
                         ncols=len(output_workspaces),
                         sharex=True,
                         gridspec_kw={"height_ratios": [2, 1]},
                         subplot_kw={"projection": "mantid"})

for i, workspace_name in enumerate(output_workspaces):
    workspace = AnalysisDataService.retrieve(workspace_name)
    axes[0, i].errorbar(workspace, "rs", wkspIndex=0, label="Data", markersize=2)
    axes[0, i].errorbar(workspace, "b-", wkspIndex=1, label="Fit")
    axes[0, i].set_title(workspace_name)
    axes[0, i].set_xlabel("")
    axes[0, i].tick_params(axis="both", direction="in")
    axes[0, i].legend()

    axes[1, i].errorbar(workspace, "ko", wkspIndex=2, markersize=2)
    axes[1, i].set_ylabel("Difference")
    axes[1, i].tick_params(axis="both", direction="in")

fig.subplots_adjust(hspace=0)
fig.show()

Example - generate a python script used for simultaneous fitting:

ws1 = CreateSampleWorkspace()
ws2 = CreateSampleWorkspace()

function = \
"composite=MultiDomainFunction,NumDeriv=true;" \
"name=GausOsc,A=0.2,Sigma=0.2,Frequency=1,Phi=0,$domains=i;" \
"name=GausOsc,A=0.2,Sigma=0.2,Frequency=1,Phi=0,$domains=i;" \
"name=GausOsc,A=0.2,Sigma=0.2,Frequency=1,Phi=0,$domains=i;" \
"name=GausOsc,A=0.2,Sigma=0.2,Frequency=1,Phi=0,$domains=i;" \
"ties=(f2.Frequency=f3.Frequency,f1.Frequency=f3.Frequency,f0.Frequency=f3.Frequency)"

# If you want to save the python script to a file then specify the Filepath property
script_text = GeneratePythonFitScript(InputWorkspaces=["ws1", "ws1", "ws2", "ws2"], WorkspaceIndices=[0, 1, 0, 1],
                                      StartXs=[0.0, 0.0, 0.0, 0.0], EndXs=[20000.0, 20000.0, 20000.0, 20000.0],
                                      FittingType="Simultaneous", Function=function, MaxIterations=500,
                                      Minimizer="Levenberg-Marquardt", OutputBaseName="Output_Fit")

print(script_text)

Output:

# A python script generated to perform a sequential or simultaneous fit
from mantid.simpleapi import *
import matplotlib.pyplot as plt

# List of tuples [ (workspace_name, workspace_index, start_x, end_x) ]
input_data = [
    ("ws1", 0, 0.000000, 20000.000000),
    ("ws1", 1, 0.000000, 20000.000000),
    ("ws2", 0, 0.000000, 20000.000000),
    ("ws2", 1, 0.000000, 20000.000000)
]

# Fit function as a string
function = \
    "composite=MultiDomainFunction,NumDeriv=true;" \
    "name=GausOsc,A=0.2,Sigma=0.2,Frequency=1,Phi=0,$domains=i;" \
    "name=GausOsc,A=0.2,Sigma=0.2,Frequency=1,Phi=0,$domains=i;" \
    "name=GausOsc,A=0.2,Sigma=0.2,Frequency=1,Phi=0,$domains=i;" \
    "name=GausOsc,A=0.2,Sigma=0.2,Frequency=1,Phi=0,$domains=i;" \
    "ties=(f2.Frequency=f3.Frequency,f1.Frequency=f3.Frequency,f0.Frequency=f3.Frequency)"

# Fitting options
max_iterations = 500
minimizer = "Levenberg-Marquardt"
cost_function = "Least squares"
evaluation_type = "CentrePoint"
output_base_name = "Output_Fit"

# Perform a simultaneous fit
input_workspaces = [domain[0] for domain in input_data]
domain_data = [domain[1:] for domain in input_data]

fit_output = \
    Fit(Function=function,
        InputWorkspace=input_workspaces[0], WorkspaceIndex=domain_data[0][0], StartX=domain_data[0][1], EndX=domain_data[0][2],
        InputWorkspace_1=input_workspaces[1], WorkspaceIndex_1=domain_data[1][0], StartX_1=domain_data[1][1], EndX_1=domain_data[1][2],
        InputWorkspace_2=input_workspaces[2], WorkspaceIndex_2=domain_data[2][0], StartX_2=domain_data[2][1], EndX_2=domain_data[2][2],
        InputWorkspace_3=input_workspaces[3], WorkspaceIndex_3=domain_data[3][0], StartX_3=domain_data[3][1], EndX_3=domain_data[3][2],
        MaxIterations=max_iterations, Minimizer=minimizer, CostFunction=cost_function,
        EvaluationType=evaluation_type, Output=output_base_name)

output_workspaces = []
for i in range(len(input_workspaces)):
    output_workspaces.append(output_base_name + "_Workspace_" + str(i))

# Plot the results of the fit
fig, axes = plt.subplots(nrows=2,
                         ncols=len(output_workspaces),
                         sharex=True,
                         gridspec_kw={"height_ratios": [2, 1]},
                         subplot_kw={"projection": "mantid"})

for i, workspace_name in enumerate(output_workspaces):
    workspace = AnalysisDataService.retrieve(workspace_name)
    axes[0, i].errorbar(workspace, "rs", wkspIndex=0, label="Data", markersize=2)
    axes[0, i].errorbar(workspace, "b-", wkspIndex=1, label="Fit")
    axes[0, i].set_title(workspace_name)
    axes[0, i].set_xlabel("")
    axes[0, i].tick_params(axis="both", direction="in")
    axes[0, i].legend()

    axes[1, i].errorbar(workspace, "ko", wkspIndex=2, markersize=2)
    axes[1, i].set_ylabel("Difference")
    axes[1, i].tick_params(axis="both", direction="in")

fig.subplots_adjust(hspace=0)
fig.show()

Categories: AlgorithmIndex | Utility\Python

Source ¶

C++ header: GeneratePythonFitScript.h

C++ source: GeneratePythonFitScript.cpp

GeneratePythonFitScript v1¶

Summary ¶

See Also ¶

Properties ¶

Description ¶

Usage ¶

Source ¶

GeneratePythonFitScript v1¶

Summary¶

See Also¶

Properties¶

Description¶

Usage¶

Source¶

Summary ¶

See Also ¶

Properties ¶

Description ¶

Usage ¶

Source ¶