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Merge pull request #108 from scipp/estia
Estia McStas workflow
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‎docs/api-reference/index.md‎

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types
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tools
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workflow
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figures
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```
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## Amor
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conversions
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data
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instrument_view
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load
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orso
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resolution
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types
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utils
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figures
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normalization
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workflow
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```
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## Estia
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```{eval-rst}
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.. currentmodule:: ess.estia
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.. autosummary::
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:toctree: ../generated/modules
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:template: module-template.rst
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:recursive:
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conversions
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maskings
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corrections
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data
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mcstas
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load
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resolution
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types
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normalization
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workflow
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```

‎docs/user-guide/amor/amor-reduction.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"from ess.amor.figures import wavelength_theta_figure\n",
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"from ess.reflectometry.figures import wavelength_theta_figure\n",
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"\n",
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"wavelength_theta_figure(\n",
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" [result[ReflectivityOverZW] for result in diagnostics.values()],\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"from ess.amor.figures import q_theta_figure\n",
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"from ess.reflectometry.figures import q_theta_figure\n",
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"\n",
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"q_theta_figure(\n",
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" [res[ReflectivityOverZW] for res in diagnostics.values()],\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"from ess.amor.figures import wavelength_z_figure\n",
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"from ess.reflectometry.figures import wavelength_z_figure\n",
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"\n",
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"workflow[Filename[SampleRun]] = runs['608'][Filename[SampleRun]]\n",
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"workflow[SampleRotation[SampleRun]] = runs['608'][SampleRotation[SampleRun]]\n",

‎docs/user-guide/estia/estia-mcstas-reduction.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0",
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"metadata": {},
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"source": [
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"# Reduction of ESTIA McStas data"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1",
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"metadata": {},
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"source": [
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"This notebook demonstrates how to run the data reduction on the output of McStas simulations of the instrument.\n",
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"\n",
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"Essentially this looks very similar to how one would do data reduction on real data files from the physical instrument,\n",
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"but we replace the default loader with the `load_mcstas_events` provider."
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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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"id": "2",
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"metadata": {},
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"outputs": [],
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"source": [
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"#%matplotlib widget\n",
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"import scipp as sc\n",
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"\n",
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"from ess.estia.load import load_mcstas_events\n",
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"from ess.estia.data import estia_mcstas_example, estia_mcstas_groundtruth\n",
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"from ess.estia import EstiaWorkflow\n",
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"from ess.reflectometry.types import *\n",
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"from ess.reflectometry.figures import wavelength_z_figure, wavelength_theta_figure, q_theta_figure"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3",
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"metadata": {},
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"source": [
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"The Estia reduction workflow is created and we set parameters such as region of interest, wavelengthbins, and q-bins."
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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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"id": "4",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"wf = EstiaWorkflow()\n",
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"wf.insert(load_mcstas_events)\n",
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"wf[Filename[ReferenceRun]] = estia_mcstas_example('reference')\n",
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"\n",
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"wf[YIndexLimits] = sc.scalar(35), sc.scalar(64)\n",
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"wf[ZIndexLimits] = sc.scalar(0), sc.scalar(14 * 32)\n",
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"wf[BeamDivergenceLimits] = sc.scalar(-0.75, unit='deg'), sc.scalar(0.75, unit='deg')\n",
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"wf[WavelengthBins] = sc.geomspace('wavelength', 3.5, 12, 2001, unit='angstrom')\n",
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"wf[QBins] = 1000\n",
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"\n",
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"# There is no proton current data in the McStas files, here we just add some fake proton current\n",
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"# data to make the workflow run.\n",
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"wf[ProtonCurrent[SampleRun]] = sc.DataArray(\n",
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" sc.array(dims=('time',), values=[]),\n",
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" coords={'time': sc.array(dims=('time',), values=[], unit='s')})\n",
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"wf[ProtonCurrent[ReferenceRun]] = sc.DataArray(\n",
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" sc.array(dims=('time',), values=[]),\n",
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" coords={'time': sc.array(dims=('time',), values=[], unit='s')})\n",
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"\n",
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"\n",
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"def compute_reflectivity_curve_for_mcstas_data(wf, results):\n",
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" R, ref, da = w.compute((ReflectivityOverQ, Reference, ReducibleData[SampleRun])).values()\n",
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" # In the McStas simulation the reference has quite low intensity.\n",
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" # To make the reflectivity curve a bit more clean\n",
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" # we filter out the Q-points where the reference has too large uncertainties.\n",
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" ref = ref.hist(Q=R.coords['Q'])\n",
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" too_large_uncertainty_in_reference = sc.stddevs(ref).data > 0.3 * ref.data\n",
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" R = R.hist()\n",
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" R.data = sc.where(too_large_uncertainty_in_reference, sc.scalar(float('nan'), unit=R.unit), R.data)\n",
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" results[f\"{da.coords['sample_rotation'].value} {da.coords['sample_rotation'].unit}\"] = R\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5",
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"metadata": {},
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"source": [
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"## Ni/Ti multilayer sample\n",
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"\n",
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"Below is a comparison between the reflectivity curve obtained using the reduction workflow and the ground truth reflectivity curve that was used in the McStas simulation.\n",
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"The sample was simulated at different sample rotation settings, each settings produces a separate reflectivity curve covering a higher Q-range, and that is the angle in the legend of the figure."
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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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"id": "6",
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"metadata": {},
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"outputs": [],
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"source": [
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"results = {}\n",
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"for path in estia_mcstas_example('Ni/Ti-multilayer'):\n",
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" w = wf.copy()\n",
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" w[Filename[SampleRun]] = path\n",
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" compute_reflectivity_curve_for_mcstas_data(w, results)\n",
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"\n",
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"ground_truth = estia_mcstas_groundtruth('Ni/Ti-multilayer')\n",
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"\n",
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"sc.plot({'ground_truth': ground_truth} | results, norm='log', vmin=1e-8)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7",
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"metadata": {},
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"source": [
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"Below are a number of figures displaying different projections of the measured intensity distribution."
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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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"id": "8",
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"metadata": {},
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"outputs": [],
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"source": [
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"results = []\n",
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"for path in estia_mcstas_example('Ni/Ti-multilayer'):\n",
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" w = wf.copy()\n",
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" w[Filename[SampleRun]] = path\n",
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" results.append(w.compute(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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"id": "9",
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"metadata": {},
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"outputs": [],
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"source": [
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"wavelength_z_figure(results[3], wavelength_bins=400)"
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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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"id": "10",
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"metadata": {},
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"outputs": [],
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"source": [
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"wavelength_theta_figure(results, wavelength_bins=400, theta_bins=200, q_edges_to_display=[sc.scalar(0.016, unit='1/angstrom'), sc.scalar(0.19, unit='1/angstrom')])"
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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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"id": "11",
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"metadata": {},
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"outputs": [],
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"source": [
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"q_theta_figure(results, q_bins=300, theta_bins=200)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "12",
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"metadata": {},
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"source": [
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"## Ni on Silicon"
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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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"id": "13",
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"metadata": {},
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"outputs": [],
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"source": [
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"results = {}\n",
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"for path in estia_mcstas_example('Ni-film on silicon'):\n",
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" w = wf.copy()\n",
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" w[Filename[SampleRun]] = path\n",
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" compute_reflectivity_curve_for_mcstas_data(w, results)\n",
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"\n",
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"ground_truth = estia_mcstas_groundtruth('Ni-film on silicon')\n",
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"sc.plot({'ground_truth': ground_truth} | results, norm='log', vmin=1e-9)"
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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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"id": "14",
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"metadata": {},
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"outputs": [],
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"source": [
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"results = []\n",
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"for path in estia_mcstas_example('Ni-film on silicon'):\n",
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" w = wf.copy()\n",
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" w[Filename[SampleRun]] = path\n",
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" results.append(w.compute(ReducibleData[SampleRun]))"
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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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"id": "15",
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"metadata": {},
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"outputs": [],
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"source": [
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"wavelength_z_figure(results[3], wavelength_bins=400)"
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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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"id": "16",
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"metadata": {},
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"outputs": [],
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"source": [
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"wavelength_theta_figure(results, wavelength_bins=400, theta_bins=200, q_edges_to_display=[sc.scalar(0.016, unit='1/angstrom'), sc.scalar(0.19, unit='1/angstrom')])"
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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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"id": "17",
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"metadata": {},
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"outputs": [],
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"source": [
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"q_theta_figure(results, q_bins=300, theta_bins=200)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "18",
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"metadata": {},
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"source": [
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"## SiO2 on Silicon"
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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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"id": "19",
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"metadata": {},
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"outputs": [],
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"source": [
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"results = {}\n",
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"for path in estia_mcstas_example('Natural SiO2 on silicon'):\n",
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" w = wf.copy()\n",
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" w[Filename[SampleRun]] = path\n",
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" compute_reflectivity_curve_for_mcstas_data(w, results)\n",
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"\n",
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"ground_truth = estia_mcstas_groundtruth('Natural SiO2 on silicon')\n",
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"sc.plot({'ground_truth': ground_truth} | results, norm='log', vmin=1e-9)"
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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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"id": "20",
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"metadata": {},
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"outputs": [],
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"source": [
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"results = []\n",
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"for path in estia_mcstas_example('Natural SiO2 on silicon'):\n",
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" w = wf.copy()\n",
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" w[Filename[SampleRun]] = path\n",
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" results.append(w.compute(ReducibleData[SampleRun]))"
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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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"id": "21",
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"metadata": {},
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"outputs": [],
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"source": [
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"wavelength_z_figure(results[3], wavelength_bins=400)"
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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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"id": "22",
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"metadata": {},
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"outputs": [],
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"source": [
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"wavelength_theta_figure(results, wavelength_bins=400, theta_bins=200, q_edges_to_display=[sc.scalar(0.016, unit='1/angstrom')])"
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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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"id": "23",
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"metadata": {},
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"outputs": [],
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"source": [
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"q_theta_figure(results, q_bins=300, theta_bins=200)"
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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": "3.10.14"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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