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GeneratePythonFitScript v1

../_images/GeneratePythonFitScript-v1_dlg.png

GeneratePythonFitScript dialog.

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.
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’]
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    

Description

This algorithm can be used to generate a python script used for sequential fitting. The algorithm will be expanded upon in the future to allow the creation of simultaneous fit scripts.

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],
                                      Function=function, MaxIterations=500, Minimizer="Levenberg-Marquardt")

print(script_text)

Output:

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

# Dictionary { 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"

# Perform a sequential fit
output_workspaces, parameter_tables, normalised_matrices = [], [], []
for input_workspace, domain_data in input_data.items():
    fit_output = Fit(Function=function, InputWorkspace=input_workspace, WorkspaceIndex=domain_data[0],
                     StartX=domain_data[1], EndX=domain_data[2], MaxIterations=max_iterations,
                     Minimizer=minimizer, CostFunction=cost_function, EvaluationType=evaluation_type,
                     CreateOutput=True)

    output_workspaces.append(fit_output.OutputWorkspace)
    parameter_tables.append(fit_output.OutputParameters)
    normalised_matrices.append(fit_output.OutputNormalisedCovarianceMatrix)

    # 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="Sequential_Fit_Workspaces")
GroupWorkspaces(InputWorkspaces=parameter_tables, OutputWorkspace="Sequential_Fit_Parameters")
GroupWorkspaces(InputWorkspaces=normalised_matrices, OutputWorkspace="Sequential_Fit_NormalisedCovarianceMatrices")

# Plot the results of the sequential 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 in enumerate(output_workspaces):
    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 (last modified: 2021-04-16)

C++ source: GeneratePythonFitScript.cpp (last modified: 2021-04-16)