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Adds jupyter notebooks for absorption and domains examples
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‎RATapi/examples/absorption/.ipynb_checkpoints/absorption-checkpoint.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import pathlib\n",
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"\n",
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"import numpy as np\n",
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"from IPython.display import Code\n",
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"\n",
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"import RATapi as RAT"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Absorption (imaginary SLD) - effect below the critical edge\n",
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"\n",
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"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.\n",
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"\n",
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"The example 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.\n",
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"\n",
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"For absorption with standard layers, an additional column appears in the layers block to accommodate the imagainary component of the SLD. For custom functions, we add an extra column to the output.\n",
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"\n",
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"For all calculation types, to activate this functionality it is necessary to set the 'absorption' flag when creating the project."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"problem = RAT.Project(name=\"Absorption example\", calculation=\"non polarised\", model=\"custom layers\", geometry=\"substrate/liquid\", absorption=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We now define our parameters, noting that each SLD parameter has both a real and imaginary component:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"problem.parameters.append(name=\"Alloy Thickness\", min=100.0, value=135.6, max=200.0, fit=True)\n",
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"problem.parameters.append(name=\"Alloy SLD up\", min=6.0e-6, value=9.87e-6, max=1.2e-5, fit=True)\n",
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"problem.parameters.append(name=\"Alloy SLD imaginary up\", min=1.0e-9, value=4.87e-8, max=1.0e-7, fit=True)\n",
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"problem.parameters.append(name=\"Alloy SLD down\", min=6.0e-6, value=7.05e-6, max=1.3e-5, fit=True)\n",
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"problem.parameters.append(name=\"Alloy SLD imaginary down\", min=1.0e-9, value=4.87e-8, max=1.0e-7, fit=True)\n",
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"problem.parameters.append(name=\"Alloy Roughness\", min=2.0, value=5.71, max=10.0, fit=True)\n",
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"problem.parameters.append(name=\"Gold Thickness\", min=100.0, value=154.7, max=200.0, fit=True)\n",
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"problem.parameters.append(name=\"Gold Roughness\", min=0.1, value=5.42, max=10.0, fit=True)\n",
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"problem.parameters.append(name=\"Gold SLD\", min=4.0e-6, value=4.49e-6, max=5.0e-6, fit=True)\n",
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"problem.parameters.append(name=\"Gold SLD imaginary\", min=1.0e-9, value=4.20e-8, max=1.0e-7, fit=True)\n",
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"\n",
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"problem.parameters.append(name=\"Thiol APM\", min=40.0, value=56.27, max=100.0, fit=True)\n",
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"problem.parameters.append(name=\"Thiol Head Hydration\", min=20.0, value=30.0, max=50.0, fit=True)\n",
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"problem.parameters.append(name=\"Thiol Coverage\", min=0.5, value=0.9, max=1.0, fit=True)\n",
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"\n",
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"problem.parameters.append(name=\"CW Thickness\", min=1.0, value=12.87, max=25.0, fit=True)\n",
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"problem.parameters.append(name=\"Bilayer APM\", min=48.0, value=65.86, max=90.0, fit=True)\n",
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"problem.parameters.append(name=\"Bilayer Head Hydration\", min=20.0, value=30.0, max=50.0, fit=True)\n",
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"problem.parameters.append(name=\"Bilayer Roughness\", min=1.0, value=3.87, max=10.0, fit=True)\n",
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"problem.parameters.append(name=\"Bilayer Coverage\", min=0.5, value=0.94, max=1.0, fit=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Set the bulk in and bulk out parameters:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"problem.bulk_in.set_fields(0, name=\"Silicon\", min=2.0e-6, value=2.073e-6, max=2.1e-6)\n",
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"\n",
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"problem.bulk_out.set_fields(0, name=\"D2O\", min=5.8e-06, value=6.21e-06, max=6.35e-06, fit=True)\n",
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"problem.bulk_out.append(name=\"H2O\", min=-5.6e-07, value=-3.15e-07, max=0.0, fit=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Use a different scalefactor for each dataset:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"del problem.scalefactors[0]\n",
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"problem.scalefactors.append(name=\"Scalefactor 1\", min=0.5, value=1, max=1.5, fit=True)\n",
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"problem.scalefactors.append(name=\"Scalefactor 2\", min=0.5, value=1, max=1.5, fit=True)\n",
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"problem.scalefactors.append(name=\"Scalefactor 3\", min=0.5, value=1, max=1.5, fit=True)\n",
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"problem.scalefactors.append(name=\"Scalefactor 4\", min=0.5, value=1, max=1.5, fit=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Set the backgrounds and resolutions:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"del problem.backgrounds[0]\n",
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"del problem.background_parameters[0]\n",
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"\n",
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"problem.background_parameters.append(name=\"Background parameter 1\", min=5.0e-08, value=7.88e-06, max=9.0e-05, fit=True)\n",
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"problem.background_parameters.append(name=\"Background parameter 2\", min=1.0e-08, value=5.46e-06, max=9.0e-05, fit=True)\n",
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"problem.background_parameters.append(name=\"Background parameter 3\", min=1.0e-06, value=9.01e-06, max=9.0e-05, fit=True)\n",
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"problem.background_parameters.append(name=\"Background parameter 4\", min=1.0e-06, value=5.61e-06, max=9.0e-05, fit=True)\n",
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"\n",
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"problem.backgrounds.append(name=\"Background 1\", type=\"constant\", value_1=\"Background parameter 1\")\n",
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"problem.backgrounds.append(name=\"Background 2\", type=\"constant\", value_1=\"Background parameter 2\")\n",
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"problem.backgrounds.append(name=\"Background 3\", type=\"constant\", value_1=\"Background parameter 3\")\n",
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"problem.backgrounds.append(name=\"Background 4\", type=\"constant\", value_1=\"Background parameter 4\")\n",
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"\n",
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"# Make the resolution fittable\n",
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"problem.resolution_parameters.set_fields(0, fit=True)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Add the datasets:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"data_path = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n",
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"\n",
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"data_1 = np.loadtxt(os.path.join(data_path, \"D2O_spin_down.dat\"))\n",
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"problem.data.append(name=\"D2O_dn\", data=data_1)\n",
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"\n",
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"data_2 = np.loadtxt(os.path.join(data_path, \"D2O_spin_up.dat\"))\n",
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"problem.data.append(name=\"D2O_up\", data=data_2)\n",
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"\n",
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"data_3 = np.loadtxt(os.path.join(data_path, \"H2O_spin_down.dat\"))\n",
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"problem.data.append(name=\"H2O_dn\", data=data_3)\n",
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"\n",
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"data_4 = np.loadtxt(os.path.join(data_path, \"H2O_spin_up.dat\"))\n",
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"problem.data.append(name=\"H2O_up\", data=data_4)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Add the custom file. We can see that we add an extra column for the output in our custom function."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'Code' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[8], line 7\u001b[0m\n\u001b[1;32m 1\u001b[0m problem\u001b[38;5;241m.\u001b[39mcustom_files\u001b[38;5;241m.\u001b[39mappend(\n\u001b[1;32m 2\u001b[0m name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDPPC absorption\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 3\u001b[0m filename\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvolume_thiol_bilayer.py\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 4\u001b[0m language\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 5\u001b[0m path\u001b[38;5;241m=\u001b[39mpathlib\u001b[38;5;241m.\u001b[39mPath\u001b[38;5;241m.\u001b[39mcwd()\u001b[38;5;241m.\u001b[39mresolve(),\n\u001b[1;32m 6\u001b[0m )\n\u001b[0;32m----> 7\u001b[0m \u001b[43mCode\u001b[49m(filename\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvolume_thiol_bilayer.py\u001b[39m\u001b[38;5;124m'\u001b[39m, language\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
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"\u001b[0;31mNameError\u001b[0m: name 'Code' is not defined"
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]
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}
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],
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"source": [
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"problem.custom_files.append(\n",
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" name=\"DPPC absorption\",\n",
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" filename=\"volume_thiol_bilayer.py\",\n",
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" language=\"python\",\n",
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" path=pathlib.Path.cwd().resolve(),\n",
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")\n",
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"Code(filename='volume_thiol_bilayer.py', language='python')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Finally, add the contrasts:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"problem.contrasts.append(\n",
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" name=\"D2O Down\",\n",
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" data=\"D2O_dn\",\n",
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" background=\"Background 1\",\n",
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" bulk_in=\"Silicon\",\n",
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" bulk_out=\"D2O\",\n",
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" scalefactor=\"Scalefactor 1\",\n",
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" resolution=\"Resolution 1\",\n",
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" resample=True,\n",
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" model=[\"DPPC absorption\"],\n",
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")\n",
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"\n",
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"problem.contrasts.append(\n",
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" name=\"D2O Up\",\n",
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" data=\"D2O_up\",\n",
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" background=\"Background 2\",\n",
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" bulk_in=\"Silicon\",\n",
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" bulk_out=\"D2O\",\n",
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" scalefactor=\"Scalefactor 2\",\n",
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" resolution=\"Resolution 1\",\n",
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" resample=True,\n",
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" model=[\"DPPC absorption\"],\n",
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")\n",
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"\n",
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"problem.contrasts.append(\n",
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" name=\"H2O Down\",\n",
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" data=\"H2O_dn\",\n",
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" background=\"Background 3\",\n",
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" bulk_in=\"Silicon\",\n",
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" bulk_out=\"H2O\",\n",
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" scalefactor=\"Scalefactor 3\",\n",
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" resolution=\"Resolution 1\",\n",
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" resample=True,\n",
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" model=[\"DPPC absorption\"],\n",
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")\n",
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"\n",
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"problem.contrasts.append(\n",
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" name=\"H2O Up\",\n",
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" data=\"H2O_up\",\n",
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" background=\"Background 4\",\n",
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" bulk_in=\"Silicon\",\n",
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" bulk_out=\"H2O\",\n",
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" scalefactor=\"Scalefactor 4\",\n",
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" resolution=\"Resolution 1\",\n",
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" resample=True,\n",
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" model=[\"DPPC absorption\"],\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now run RAT and plot the results."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"controls = RAT.Controls(parallel=\"contrasts\", resampleParams=[0.9, 150.0])\n",
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"problem, results = RAT.run(problem, controls)\n",
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"\n",
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"RAT.plotting.plot_ref_sld(problem, results)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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