python-RAT/RATapi/examples/absorption/absorption.py at b8bf460797763fac1080d3bc19322936cf795d11 · RascalSoftware/python-RAT · GitHub

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"""An example for using absorption in RAT."""

import pathlib

import numpy as np

import RATapi as RAT


def absorption():
    """Run a custom layers model including absorption.

    RAT allows the use of an imaginary, as well as real part of the SLD.
    The effect of this is usually seen below the critical edge, and must sometimes be accounted for.

    This is an example of a Custom Layers project using absorption. used here is Custom Layers.
    It analyses a bilayer sample on a permalloy / gold substrate,
    measured using polarised neutrons, against D2O and H2O, leading to 4 contrasts in total.
    Absorption (i.e. imaginary SLD) is defined for Gold and the Permalloy,
    to account for non-flat data below the critical edge.
    """
    problem = RAT.Project(
        name="Absorption example",
        calculation="normal",
        model="custom layers",
        geometry="substrate/liquid",
        absorption=True,
    )

    # Add the required parameters (substrate roughness is already there by default)
    problem.parameters.append(name="Alloy Thickness", min=100.0, value=135.6, max=200.0, fit=True)
    problem.parameters.append(name="Alloy SLD up", min=6.0e-6, value=9.87e-6, max=1.2e-5, fit=True)
    problem.parameters.append(name="Alloy SLD imaginary up", min=1.0e-9, value=4.87e-8, max=1.0e-7, fit=True)
    problem.parameters.append(name="Alloy SLD down", min=6.0e-6, value=7.05e-6, max=1.3e-5, fit=True)
    problem.parameters.append(name="Alloy SLD imaginary down", min=1.0e-9, value=4.87e-8, max=1.0e-7, fit=True)
    problem.parameters.append(name="Alloy Roughness", min=2.0, value=5.71, max=10.0, fit=True)
    problem.parameters.append(name="Gold Thickness", min=100.0, value=154.7, max=200.0, fit=True)
    problem.parameters.append(name="Gold Roughness", min=0.1, value=5.42, max=10.0, fit=True)
    problem.parameters.append(name="Gold SLD", min=4.0e-6, value=4.49e-6, max=5.0e-6, fit=True)
    problem.parameters.append(name="Gold SLD imaginary", min=1.0e-9, value=4.20e-8, max=1.0e-7, fit=True)

    problem.parameters.append(name="Thiol APM", min=40.0, value=56.27, max=100.0, fit=True)
    problem.parameters.append(name="Thiol Head Hydration", min=20.0, value=30.0, max=50.0, fit=True)
    problem.parameters.append(name="Thiol Coverage", min=0.5, value=0.9, max=1.0, fit=True)

    problem.parameters.append(name="CW Thickness", min=1.0, value=12.87, max=25.0, fit=True)
    problem.parameters.append(name="Bilayer APM", min=48.0, value=65.86, max=90.0, fit=True)
    problem.parameters.append(name="Bilayer Head Hydration", min=20.0, value=30.0, max=50.0, fit=True)
    problem.parameters.append(name="Bilayer Roughness", min=1.0, value=3.87, max=10.0, fit=True)
    problem.parameters.append(name="Bilayer Coverage", min=0.5, value=0.94, max=1.0, fit=True)

    # Change the existing Bulk In parameter to be Silicon
    problem.bulk_in.set_fields(0, name="Silicon", min=2.0e-6, value=2.073e-6, max=2.1e-6)

    # We need 2 bulk outs - D2O and H2O
    problem.bulk_out.set_fields(0, name="D2O", min=5.8e-06, value=6.21e-06, max=6.35e-06, fit=True)
    problem.bulk_out.append(name="H2O", min=-5.6e-07, value=-3.15e-07, max=0.0, fit=True)

    # Use a different scalefactor for each dataset
    del problem.scalefactors[0]
    problem.scalefactors.append(name="Scalefactor 1", min=0.5, value=1, max=1.5, fit=True)
    problem.scalefactors.append(name="Scalefactor 2", min=0.5, value=1, max=1.5, fit=True)
    problem.scalefactors.append(name="Scalefactor 3", min=0.5, value=1, max=1.5, fit=True)
    problem.scalefactors.append(name="Scalefactor 4", min=0.5, value=1, max=1.5, fit=True)

    # Similarly, use an individual background for each dataset
    del problem.backgrounds[0]
    del problem.background_parameters[0]

    problem.background_parameters.append(
        name="Background parameter 1", min=5.0e-08, value=7.88e-06, max=9.0e-05, fit=True
    )
    problem.background_parameters.append(
        name="Background parameter 2", min=1.0e-08, value=5.46e-06, max=9.0e-05, fit=True
    )
    problem.background_parameters.append(
        name="Background parameter 3", min=1.0e-06, value=9.01e-06, max=9.0e-05, fit=True
    )
    problem.background_parameters.append(
        name="Background parameter 4", min=1.0e-06, value=5.61e-06, max=9.0e-05, fit=True
    )

    problem.backgrounds.append(name="Background 1", type="constant", source="Background parameter 1")
    problem.backgrounds.append(name="Background 2", type="constant", source="Background parameter 2")
    problem.backgrounds.append(name="Background 3", type="constant", source="Background parameter 3")
    problem.backgrounds.append(name="Background 4", type="constant", source="Background parameter 4")

    # Make the resolution fittable
    problem.resolution_parameters.set_fields(0, fit=True)

    # Now add the data we need
    data_path = pathlib.Path(__file__).parents[1] / "data"

    data_1 = np.loadtxt(data_path / "D2O_spin_down.dat")
    problem.data.append(name="D2O_dn", data=data_1)

    data_2 = np.loadtxt(data_path / "D2O_spin_up.dat")
    problem.data.append(name="D2O_up", data=data_2)

    data_3 = np.loadtxt(data_path / "H2O_spin_down.dat")
    problem.data.append(name="H2O_dn", data=data_3)

    data_4 = np.loadtxt(data_path / "H2O_spin_up.dat")
    problem.data.append(name="H2O_up", data=data_4)

    # Add the custom file
    problem.custom_files.append(
        name="DPPC absorption",
        filename="volume_thiol_bilayer.py",
        language="python",
        path=pathlib.Path(__file__).parent.resolve(),
    )

    # Finally add the contrasts
    problem.contrasts.append(
        name="D2O Down",
        data="D2O_dn",
        background="Background 1",
        bulk_in="Silicon",
        bulk_out="D2O",
        scalefactor="Scalefactor 1",
        resolution="Resolution 1",
        resample=True,
        model=["DPPC absorption"],
    )

    problem.contrasts.append(
        name="D2O Up",
        data="D2O_up",
        background="Background 2",
        bulk_in="Silicon",
        bulk_out="D2O",
        scalefactor="Scalefactor 2",
        resolution="Resolution 1",
        resample=True,
        model=["DPPC absorption"],
    )

    problem.contrasts.append(
        name="H2O Down",
        data="H2O_dn",
        background="Background 3",
        bulk_in="Silicon",
        bulk_out="H2O",
        scalefactor="Scalefactor 3",
        resolution="Resolution 1",
        resample=True,
        model=["DPPC absorption"],
    )

    problem.contrasts.append(
        name="H2O Up",
        data="H2O_up",
        background="Background 4",
        bulk_in="Silicon",
        bulk_out="H2O",
        scalefactor="Scalefactor 4",
        resolution="Resolution 1",
        resample=True,
        model=["DPPC absorption"],
    )

    # Now make a controls block and run the code
    controls = RAT.Controls(parallel="contrasts", resampleNPoints=150)
    problem, results = RAT.run(problem, controls)

    return problem, results


if __name__ == "__main__":
    problem, results = absorption()
    RAT.plotting.plot_ref_sld(problem, results, True)