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Merge pull request #44 from scipp/reduce-amor-new-files
Reduce amor new files
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‎docs/examples/amor.ipynb‎

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"We will begin by importing the modules that are necessary for this notebook."
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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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"## Setup"
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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,
@@ -20,7 +27,8 @@
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"import scipp as sc\n",
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"import sciline\n",
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"from ess import amor\n",
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"from ess.reflectometry.types import *"
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"from ess.reflectometry.types import *\n",
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"from ess.amor.types import *"
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]
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},
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{
@@ -30,15 +38,48 @@
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"outputs": [],
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"source": [
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"pl = sciline.Pipeline(\n",
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" (*amor.providers, *amor.data.providers), params=amor.default_parameters\n",
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" (\n",
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" *amor.providers,\n",
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" *amor.data.providers,\n",
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" ),\n",
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" params=amor.default_parameters,\n",
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")\n",
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"\n",
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"pl[QBins] = sc.geomspace(dim='Q', start=0.008, stop=0.075, num=200, unit='1/angstrom')\n",
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"pl[SampleRotation[Sample]] = sc.scalar(0.7989, unit='deg')\n",
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"pl[PoochFilename[Sample]] = \"sample.nxs\"\n",
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"pl[SampleRotation[Reference]] = sc.scalar(0.8389, unit='deg')\n",
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"pl[PoochFilename[Reference]] = \"reference.nxs\"\n",
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"pl[WavelengthEdges] = sc.array(dims=['wavelength'], values=[2.4, 16.0], unit='angstrom')"
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"pl[SampleSize[Sample]] = sc.scalar(10.0, unit='mm')\n",
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"pl[SampleSize[Reference]] = sc.scalar(10.0, unit='mm')\n",
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"\n",
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"pl[ChopperPhase[Reference]] = sc.scalar(-7.5, unit='deg')\n",
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"pl[ChopperPhase[Sample]] = sc.scalar(-7.5, unit='deg')\n",
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"\n",
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"pl[QBins] = sc.geomspace(dim='Q', start=0.005, stop=0.3, num=391, unit='1/angstrom')\n",
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"pl[WavelengthBins] = sc.geomspace('wavelength', 2.8, 12, 301, unit='angstrom')\n",
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"\n",
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"# The YIndexLimits and ZIndexLimits define ranges on the detector where\n",
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"# data is considered to be valid signal.\n",
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"# They represent the lower and upper boundaries of a range of pixel indices.\n",
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"pl[YIndexLimits] = sc.scalar(11, unit=None), sc.scalar(41, unit=None)\n",
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"pl[ZIndexLimits] = sc.scalar(80, unit=None), sc.scalar(370, unit=None)"
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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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"pl.visualize(NormalizedIofQ, graph_attr={'rankdir': 'LR'})"
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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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"## Caching the reference result\n",
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"\n",
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"The reference result (used for normalizing the sample data) only needs to be computed once.\n",
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"It represents the intensity reflected by the super-mirror.\n",
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"\n",
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"We compute it using the pipeline and thereafter set the result back on the original pipeline."
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]
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},
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{
@@ -47,7 +88,20 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"pl.visualize((NormalizedIofQ, QResolution), graph_attr={'rankdir': 'LR'})"
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"pl[TutorialFilename[Reference]] = \"amor2023n000614.hdf\"\n",
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"# The sample rotation value in the file is slightly off, so we set it manually\n",
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"pl[SampleRotation[Reference]] = sc.scalar(0.65, unit='deg')\n",
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"\n",
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"reference_result = pl.compute(IdealReferenceIntensity)\n",
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"# Set the result back onto the pipeline to cache it\n",
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"pl[IdealReferenceIntensity] = reference_result"
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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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"If we now visualize the pipeline again, we can see that the reference is not re-computed:"
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]
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},
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{
@@ -56,8 +110,17 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# Compute I over Q and the standard deviation of Q\n",
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"ioq, qstd = pl.compute((NormalizedIofQ, QResolution)).values()"
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"pl.visualize(NormalizedIofQ, graph_attr={'rankdir': 'LR'})"
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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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"## Computing sample reflectivity\n",
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"\n",
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"We now compute the sample reflectivity from 3 runs that used different sample rotation angles.\n",
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"The different rotation angles cover different ranges in $Q$."
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"pl[TutorialFilename[Sample]] = \"amor2023n000608.hdf\"\n",
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"pl[SampleRotation[Sample]] = sc.scalar(0.85, unit='deg')\n",
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"ioq8 = pl.compute(NormalizedIofQ).hist()\n",
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"\n",
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"pl[TutorialFilename[Sample]] = \"amor2023n000609.hdf\"\n",
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"pl[SampleRotation[Sample]] = sc.scalar(2.25, unit='deg')\n",
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"ioq9 = pl.compute(NormalizedIofQ).hist()\n",
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"\n",
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"fig = plt.figure(figsize=(5, 7))\n",
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"ax1 = fig.add_axes([0, 0.55, 1.0, 0.45])\n",
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"ax2 = fig.add_axes([0, 0.0, 1.0, 0.45])\n",
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"cax = fig.add_axes([1.05, 0.55, 0.03, 0.45])\n",
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"fig1 = ioq.plot(norm='log', ax=ax1, cax=cax, grid=True)\n",
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"fig2 = ioq.mean('detector_number').plot(norm='log', ax=ax2, grid=True)\n",
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"fig1.canvas.xrange = fig2.canvas.xrange"
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"pl[TutorialFilename[Sample]] = \"amor2023n000610.hdf\"\n",
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"pl[SampleRotation[Sample]] = sc.scalar(3.65, unit='deg')\n",
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"ioq10 = pl.compute(NormalizedIofQ).hist()\n",
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"\n",
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"sc.plot({'608': ioq8, '609': ioq9, '610': ioq10}, norm='log', vmin=1e-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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"### Additional diagnostics plots"
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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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"pl[TutorialFilename[Sample]] = \"amor2023n000608.hdf\"\n",
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"pl[SampleRotation[Sample]] = sc.scalar(0.85, unit='deg')\n",
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"pl.compute(ReflectivityDiagnosticsView)"
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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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"## Make a $(\\lambda, \\theta)$ map\n",
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"A good sanity check is to create a two-dimensional map of the counts in $\\lambda$ and $\\theta$ bins. To achieve this, we request the `ThetaData` from the pipeline. In the graph above we can see that `WavelengthData` is required to compute `ThetaData`, therefore it is also present in `ThetaData` so we don't need to require it separately."
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"A good sanity check is to create a two-dimensional map of the counts in $\\lambda$ and $\\theta$ bins and make sure the triangles converge at the origin."
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"from ess.reflectometry.types import ThetaData\n",
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"\n",
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"pl.compute(ThetaData[Sample]).bins.concat('detector_number').hist(\n",
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" theta=sc.linspace(dim='theta', start=0.0, stop=1.2, num=165, unit='deg').to(\n",
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" unit='rad'\n",
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" ),\n",
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" wavelength=sc.linspace(\n",
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" dim='wavelength', start=0, stop=15.0, num=165, unit='angstrom'\n",
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" ),\n",
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").plot()"
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"pl.compute(WavelengthThetaFigure)"
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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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"This plot can be used to check if the value of the sample rotation angle $\\omega$ is correct. The bright triangles should be pointing back to the origin $\\lambda = \\theta = 0$."
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"This plot can be used to check if the value of the sample rotation angle $\\omega$ is correct. The bright triangles should be pointing back to the origin $\\lambda = \\theta = 0$. In the figure above the black lines are all passing through the origin."
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"for p in (*orso.providers, *amor.orso.providers):\n",
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" pl.insert(p)\n",
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"\n",
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"pl[orso.OrsoCreator] = orso.OrsoCreator(\n",
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" fileio.base.Person(\n",
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" name='Max Mustermann',\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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"Then, we recompute $I(Q)$ and and combine it with the ORSO metadata:"
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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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"iofq_dataset = pl.compute(orso.OrsoIofQDataset)"
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"pl.visualize(orso.OrsoIofQDataset, graph_attr={'rankdir': 'LR'})"
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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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"Unfortunately, some metadata could not be determined automatically.\n",
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"In particular, we need to specify the sample manually:"
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"We build our ORSO dataset from the computed $I(Q)$ and the ORSO metadata:"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"iofq_dataset.info.data_source.sample"
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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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"iofq_dataset.info.data_source.sample = fileio.data_source.Sample(\n",
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" name='Ni / Ti Multilayer',\n",
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" model=fileio.data_source.SampleModel(\n",
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" stack='air | (Ni | Ti) * 5 | Si',\n",
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" ),\n",
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")"
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"iofq_dataset = pl.compute(orso.OrsoIofQDataset)\n",
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"iofq_dataset"
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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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"And we also add the URL of this notebook to make it easier to reproduce the data:"
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"We also add the URL of this notebook to make it easier to reproduce the data:"
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]
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},
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{

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