{ "cells": [ { "cell_type": "markdown", "id": "8faef39e", "metadata": {}, "source": [ "# Analysing non-spinflip polarised NR data\n", "\n", "Currently (5/Aug/2021) `refnx` does not have the ability to fully analyse polarised neutron reflectometry data. However, it can be used to analyse the non-spinflip channels of a polarised neutron reflectometry measurement. Here we demonstrate how to do this using auxiliary `Parameter`. The datasets of interest have the structure:\n", "\n", "`Si | SiO2 | Permalloy | Au | 2-mercaptoethanol | D2O`" ] }, { "cell_type": "code", "execution_count": 1, "id": "c7d349ad", "metadata": {}, "outputs": [], "source": [ "# some necessary imports\n", "from importlib import resources\n", "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import refnx\n", "from refnx.analysis import Parameter, Objective, CurveFitter, GlobalObjective\n", "from refnx.reflect import SLD, Slab, Structure, ReflectModel\n", "from refnx.dataset import Data1D\n", "from refnx._lib import flatten" ] }, { "cell_type": "code", "execution_count": 2, "id": "f06c6737", "metadata": {}, "outputs": [], "source": [ "# create datasets from the NSF PNR data\n", "pth = resources.files(refnx.reflect)\n", "dd = \"c_PLP0007882.dat\"\n", "uu = \"c_PLP0007885.dat\"\n", "\n", "file_path_uu = pth / f\"tests/{uu}\"\n", "file_path_dd = pth / f\"tests/{dd}\"\n", "\n", "data_uu = Data1D(file_path_uu)\n", "data_dd = Data1D(file_path_dd)" ] }, { "cell_type": "code", "execution_count": 3, "id": "131aed72", "metadata": {}, "outputs": [], "source": [ "# create SLD (Scattering Length Density) objects for each of the materials\n", "si = SLD(2.07, name=\"Si\")\n", "sio2 = SLD(3.47, name=\"SiO2\")\n", "au = SLD(4.66, name=\"Au\")\n", "mercapto = SLD(3.49, name=\"2-mercaptoethanol\")\n", "d2o = SLD(6.35, name=\"d2o\")\n", "\n", "# to describe the Permalloy layer we're going to create parameters to describe the nuclear\n", "# and magnetic parts of the SLD. Instead of using the magnetic moment and angle we\n", "# could just use a magnetic SLD\n", "\n", "nuclear_py = Parameter(9.0, name=\"Py nuclear part\")\n", "mag_moment_py = Parameter(600, name=\"Py emu/cc\")\n", "angle = Parameter(0, name=\"angle\", bounds=(0, 90.))\n", "\n", "# Now create two SLD objects for the Permalloy layer, one for the UU channel, one for the DD channel.\n", "# don't worry that the SLD is set to zero to start with\n", "\n", "py_dd = SLD(0.0, name=\"Py DD SLD\")\n", "py_uu = SLD(0.0, name=\"Py UU SLD\")\n", "\n", "# Now we make constraints for the SLD objects. Each SLD object has two parameters,\n", "# SLD.real and SLD.imag. The conversion factor of 2.85e-3 converts the magnetic moment\n", "# from emu/cc to 10**-6 Å**-2\n", "py_dd.real.constraint = nuclear_py - mag_moment_py * 2.85e-3 * np.cos(angle*np.pi/180)\n", "py_uu.real.constraint = nuclear_py + mag_moment_py * 2.85e-3 * np.cos(angle*np.pi/180)" ] }, { "cell_type": "code", "execution_count": 4, "id": "5a4b88bb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((7.29+0j), (10.71+0j))" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# let's check on the SLDs. Observe that the SLDs obey the constraints\n", "\n", "complex(py_dd), complex(py_uu)" ] }, { "cell_type": "code", "execution_count": 5, "id": "93678e27", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((7.005+0j), (10.995000000000001+0j))" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# let's try altering the magnetic part and see if the values are updated in the SLDs.\n", "# `magnetic_Fe` is a Parameter, and Parameter values are modified like this:\n", "\n", "mag_moment_py.value = 700\n", "\n", "# note how both the SLD objects are updated.\n", "\n", "complex(py_dd), complex(py_uu)" ] }, { "cell_type": "code", "execution_count": 6, "id": "d28c798d", "metadata": {}, "outputs": [], "source": [ "# Now make Slabs that describe each layer. These can either be made from SLD objects,\n", "# or by using the `Slab` constructor directly.\n", "\n", "# sio2 slab has a thickness of 20 and roughness of 4 with the Si fronting medium\n", "sio2_l = sio2(20, 4)\n", "\n", "au_l = au(215, 4)\n", "mercapto_l = mercapto(8, 4)\n", "d2o_l = d2o(0, 4)\n", "\n", "# now make the Fe layers for each of the spin channels. Note that we create\n", "# Parameter for the thickness and roughness which will be shared over both spin channels.\n", "\n", "py_thickness = Parameter(50, name=\"Py thickness\")\n", "py_roughness = Parameter(5, name=\"Py roughness\")\n", "\n", "py_dd_l = Slab(py_thickness, py_dd, py_roughness, name=\"Py dd slab\")\n", "py_uu_l = Slab(py_thickness, py_uu, py_roughness, name=\"Py uu slab\")" ] }, { "cell_type": "code", "execution_count": 7, "id": "f18b1435", "metadata": {}, "outputs": [], "source": [ "# now we make structures for each of the spin channels\n", "# note that we use the same `sio2_l` for each of the structures. This\n", "# will share the same sio2 thickness and roughness in the structures\n", "# and will reduce parameter numbers in a fit\n", "\n", "s_dd = si | sio2_l | py_dd_l | au_l | mercapto_l | d2o_l\n", "s_uu = si | sio2_l | py_uu_l | au_l | mercapto_l | d2o_l" ] }, { "cell_type": "code", "execution_count": 8, "id": "b0454b46", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30 30\n", "36\n" ] } ], "source": [ "# The total number of parameters is reduced by sharing Parameter/SLD/Slab objects\n", "# over the two structures.\n", "\n", "# what are the number of parameters in each of the structures?\n", "print(len(list(flatten(s_dd.parameters))), len(list(flatten(s_uu.parameters))))\n", "\n", "# now what are the number of unique parameters in both parameter sets?\n", "combined_set = set(flatten(s_dd.parameters)).union(set(flatten(s_uu.parameters)))\n", "print(len(combined_set))\n", "\n", "# this shows that the unique number of parameters over both datasets is 36, reduced from 60.\n", "# i.e. there are parameters that are joint over both datasets" ] }, { "cell_type": "code", "execution_count": 9, "id": "9fe3999a", "metadata": {}, "outputs": [], "source": [ "# now place the Structures into a ReflectModel. ReflectModel applies resolution smearing, etc.\n", "\n", "model_dd = ReflectModel(s_dd)\n", "model_uu = ReflectModel(s_uu)" ] }, { "cell_type": "code", "execution_count": 10, "id": "aaec7067", "metadata": {}, "outputs": [], "source": [ "objective_dd = Objective(model_dd, data_dd, \n", " auxiliary_params=(nuclear_py, mag_moment_py, angle))\n", "objective_uu = Objective(model_uu, data_uu, \n", " auxiliary_params=(nuclear_py, mag_moment_py, angle))\n", "\n", "global_objective = GlobalObjective([objective_dd, objective_uu])" ] }, { "cell_type": "code", "execution_count": 11, "id": "9eb96e15", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "plt.scatter(data_dd.x, data_dd.y, label=\"dd\", s=4)\n", "plt.plot(data_dd.x, objective_dd.generative())\n", "\n", "plt.scatter(data_uu.x, data_uu.y, label=\"uu\", s=4)\n", "plt.plot(data_uu.x, objective_uu.generative())\n", "\n", "plt.ylabel(\"R\")\n", "plt.xlabel(\"Q / $\\\\AA^{-1}$\")\n", "plt.yscale('log')\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": 12, "id": "40bc9afe", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(*s_dd.sld_profile(), label='dd')\n", "plt.plot(*s_uu.sld_profile(), label=\"uu\")\n", "plt.ylabel(\"SLD\")\n", "plt.xlabel(\"z / $\\\\AA$\")\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": 13, "id": "2142473a", "metadata": {}, "outputs": [], "source": [ "# select the parameters to be fitted and their bounds\n", "model_uu.scale.setp(vary=True, bounds=(0.9, 1.1))\n", "model_uu.bkg.setp(vary=True, bounds=(1e-7, 5e-6))\n", "model_dd.scale.setp(vary=True, bounds=(0.9, 1.1))\n", "model_dd.bkg.setp(vary=True, bounds=(1e-7, 5e-6))\n", "\n", "sio2_l.thick.setp(vary=True, bounds=(10, 25))\n", "sio2_l.rough.setp(vary=True, bounds=(1, 8))\n", "\n", "py_thickness.setp(vary=True, bounds=(38, 55))\n", "py_roughness.setp(vary=True, bounds=(1, 8))\n", "nuclear_py.setp(vary=True, bounds=(8.5, 9.5))\n", "mag_moment_py.setp(vary=True, bounds=(500, 800))\n", "\n", "au_l.thick.setp(vary=True, bounds=(200, 240))\n", "au_l.rough.setp(vary=True, bounds=(1, 8))\n", "au.real.setp(vary=True, bounds=(4.5, 4.66))\n", "\n", "mercapto_l.thick.setp(vary=True, bounds=(5, 15))\n", "mercapto_l.rough.setp(vary=True, bounds=(1, 8))\n", "mercapto.real.setp(vary=True, bounds=(3, 4))\n", "\n", "d2o_l.rough.setp(vary=True, bounds=(1, 8))\n", "d2o.real.setp(vary=True, bounds=(6.2, 6.36))" ] }, { "cell_type": "code", "execution_count": 14, "id": "49da0113", "metadata": {}, "outputs": [], "source": [ "fitter = CurveFitter(global_objective)" ] }, { "cell_type": "code", "execution_count": 15, "id": "b7676bec", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "-1273.3106680570752: : 76it [00:11, 6.45it/s]\n" ] } ], "source": [ "fitter.fit('differential_evolution', seed=1);" ] }, { "cell_type": "code", "execution_count": 16, "id": "22e8f804", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "plt.scatter(data_dd.x, data_dd.y, label=\"dd\", s=4)\n", "plt.plot(data_dd.x, objective_dd.generative())\n", "\n", "plt.scatter(data_uu.x, data_uu.y, label=\"uu\", s=4)\n", "plt.plot(data_uu.x, objective_uu.generative())\n", "\n", "plt.ylabel(\"R\")\n", "plt.xlabel(\"Q / $\\\\AA^{-1}$\")\n", "plt.yscale('log')\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": 17, "id": "2858d05d", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(*s_dd.sld_profile(), label='dd')\n", "plt.plot(*s_uu.sld_profile(), label=\"uu\")\n", "plt.ylabel(\"SLD\")\n", "plt.xlabel(\"z / $\\\\AA$\")\n", "plt.legend();" ] }, { "cell_type": "markdown", "id": "b4730494", "metadata": {}, "source": [ "By printing out the objectives we can see what the parameters are. Here we see that the Permalloy magnetic moment is 615 emu/cc, with a nuclear SLD of $9.15\\times10^{-6}Å^{-2}$" ] }, { "cell_type": "code", "execution_count": 18, "id": "c206bbbb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Objective - 5009606032\n", "Dataset = c_PLP0007882\n", "datapoints = 94\n", "chi2 = 296.0254482329964\n", "Weighted = True\n", "Transform = None\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Py dd slab' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'Py DD SLD' \n", ">\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Au' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'Au' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '2-mercaptoethanol'\n", "\n", "________________________________________________________________________________\n", "Parameters: '2-mercaptoethanol'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'd2o' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'd2o' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: None \n", "\n", "\n", "\n" ] } ], "source": [ "print(objective_dd)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.0" } }, "nbformat": 4, "nbformat_minor": 5 }