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Merge pull request #88 from FAIRmat-NFDI/bring-in-examples
Bring in NOMAD examples
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| prune * | ||
| exclude * | ||
| recursive-include src/pynxtools_xps *.py *.json | ||
| include pyproject.toml README.md dev-requirements.txt | ||
| graft src/pynxtools_xps/nomad/examples |
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| # | ||
| # Copyright The NOMAD Authors. | ||
| # | ||
| # This file is part of NOMAD. See https://nomad-lab.eu for further info. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # | ||
| """Entry points for XPS examples.""" | ||
|
|
||
| try: | ||
| from nomad.config.models.plugins import ExampleUploadEntryPoint | ||
| except ImportError as exc: | ||
| raise ImportError( | ||
| "Could not import nomad package. Please install the package 'nomad-lab'." | ||
| ) from exc | ||
|
|
||
| xps_example = ExampleUploadEntryPoint( | ||
| title="X-ray Photoelectron Spectroscopy (XPS)", | ||
| category="FAIRmat examples", | ||
| description=""" | ||
| This example presents the capabilities of the NOMAD platform to store and standardize X-ray Photoelectron Spectroscopy XPS data. | ||
| It shows the generation of a NeXus file according to the | ||
| [NXmpes](https://manual.nexusformat.org/classes/contributed_definitions/NXmpes.html#nxmpes) | ||
| application definition from an example measurement file and a subseqeuent analysis of that data set. | ||
| """, | ||
| plugin_package="pynxtools_xps", | ||
| resources=["nomad/examples/*"], | ||
| ) |
137 changes: 137 additions & 0 deletions
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src/pynxtools_xps/nomad/examples/E1 XPS data conversion to NeXus.ipynb
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| { | ||
| "cells": [ | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "38f1916e-2d0f-4f81-9b00-fedc615a96aa", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# XPS conversion example\n", | ||
| "\n", | ||
| "In this notebook a XPS measurement file from a SPECS detector (using the native SPECS .sle format) is read and converted into the [NXmpes](https://manual.nexusformat.org/classes/contributed_definitions/NXmpes.html#nxmpes) NeXus standard." | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "dc099289-78f5-4357-b7d3-715dd20179da", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Create a NeXus file from measurement data\n", | ||
| "\n", | ||
| "To convert the available files to the NeXus format we use the convert function readily supplied by [`pynxtools`](https://github.com/FAIRmat-NFDI/pynxtools)." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "7d2c1df4-1d96-4255-a18e-e323c69d32b4", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from pynxtools.dataconverter.convert import convert, logger\n", | ||
| "import logging\n", | ||
| "logger.setLevel(logging.ERROR)" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "bf441135-d4c5-4310-ae6e-39c87da1b726", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "The input parameters are defined as follows:\n", | ||
| "\n", | ||
| "**input_file**: The input files for the reader. This is a sle file from [SpecsLabProdigy](https://www.specs-group.com/nc/specs/products/detail/prodigy/) (v.63), which is the propietary format of SPECS GmbH,and a YAML ELN file containing additional information not contained in the measurement file (e.g. user name).\n", | ||
| "\n", | ||
| "**reader**: The specific reader which gets called inside `pynxtools`. This is supplied in the [`pynxtools-xps`](https://github.com/FAIRmat-NFDI/pynxtools-xps) reader plugin. For XPS data, the reader is called `xps`.\n", | ||
| "\n", | ||
| "**nxdl**: The specific NXDL application definition to which the converted file should conform. For XPS this should be `NXmpes`, the subdefinition `NXxps`, or any further subdefinitions of the form `NXxps_<name>`.\n", | ||
| " \n", | ||
| "**output**: The output filename of the NeXus file." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "8e8a76ff-918e-483d-9ed1-5417613710e1", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "convert(\n", | ||
| " input_file=[\"EX439_S718_Au_in_25_mbar_O2.sle\", \"eln_data.yaml\"],\n", | ||
| " reader='xps',\n", | ||
| " nxdl='NXmpes',\n", | ||
| " remove_align=True,\n", | ||
| " output='Au_25_mbar_O2_no_align.nxs'\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "fa5fe119-0a72-4744-a84d-4d1258f55031", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## View the data with H5Web\n", | ||
| "\n", | ||
| "H5Web is a tool for visualizing any data in the h5 data format. Since the NeXus format builds opon h5 it can be used to view this data as well. We just import the package and call H5Web with the output filename from the convert command above.\n", | ||
| "\n", | ||
| "You can also view this data with the H5Viewer or other tools from your local filesystem." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "d4ff822a-4552-49b8-ba17-9b86fd8c2ac1", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from jupyterlab_h5web import H5Web" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "f62097ae-eb0f-4572-b8d9-bebc7266b43a", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "H5Web(\"Au_25_mbar_O2_no_align.nxs\")" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "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": "3.10.10" | ||
| }, | ||
| "vscode": { | ||
| "interpreter": { | ||
| "hash": "0cbdf5d5ef28617c8bf3753ff15cd1b7b5539de5aaa68a35c3d38ca27e1ab0fa" | ||
| } | ||
| }, | ||
| "widgets": { | ||
| "application/vnd.jupyter.widget-state+json": { | ||
| "state": {}, | ||
| "version_major": 2, | ||
| "version_minor": 0 | ||
| } | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
388 changes: 388 additions & 0 deletions
388
src/pynxtools_xps/nomad/examples/E2 XPS data analysis and fitting.ipynb
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|---|---|---|
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| { | ||
| "cells": [ | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "38f1916e-2d0f-4f81-9b00-fedc615a96aa", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# XPS data analysis example\n", | ||
| "\n", | ||
| "In this notebook a XPS measurement file from a SPECS detector (using the native SPECS .sle format) that has already been converted into the [NXmpes](https://manual.nexusformat.org/classes/contributed_definitions/NXmpes.html#nxmpes) NeXus standard is read and some basic data analysis (a fit of one Au 4f spectrum) is done." | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "fa5fe119-0a72-4744-a84d-4d1258f55031", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## View the data with H5Web\n", | ||
| "\n", | ||
| "H5Web is a tool for visualizing any data in the h5 data format. Since the NeXus format builds opon h5 it can be used to view this data as well. We just import the package and call H5Web with the output filename from the convert command above.\n", | ||
| "\n", | ||
| "You can also view this data with the H5Viewer or other tools from your local filesystem." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "d4ff822a-4552-49b8-ba17-9b86fd8c2ac1", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from jupyterlab_h5web import H5Web" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "f62097ae-eb0f-4572-b8d9-bebc7266b43a", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "H5Web(\"Au_25_mbar_O2_no_align.nxs\")" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "359770eb-964f-48fd-97da-84038af10193", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Analyze data\n", | ||
| "\n", | ||
| "First, we need to import the necessarry packages. We use h5py for reading the NeXus file, lmfit for fitting and the class XPSRegion from the provided `xps_region.py` file." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "ed892838-0f76-47a8-89b6-ba8bba4f9048", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import h5py\n", | ||
| "from xps_region import XPSRegion\n", | ||
| "\n", | ||
| "from lmfit.models import GaussianModel" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "a30f1ecb-d9f9-44eb-b9d3-f61709109d6a", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Load data and plot\n", | ||
| "\n", | ||
| "We want to load the Au 4f spectrum from the Au foil from our measurement file. Feel free to adapt to different regions in the file by changing the `MEASUREMENT` variable." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "400205d5", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "MEASUREMENT = \"Au_in_vacuum__Au4f\"\n", | ||
| "\n", | ||
| "with h5py.File(\"Au_25_mbar_O2_no_align.nxs\", \"r\") as xps_file:\n", | ||
| " binding_energy = xps_file[f\"/{MEASUREMENT}/data/energy\"][:]\n", | ||
| " cps = xps_file[f\"/{MEASUREMENT}/data/data\"][:]\n", | ||
| " cps_err = xps_file[f\"/{MEASUREMENT}/data/data_errors\"][:]" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "5c88ac8c", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "There is also a convenience function in XPSRegion to directly load the data: " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "1f654ad7", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "au4f = XPSRegion.load(\"Au_25_mbar_O2_no_align.nxs\", MEASUREMENT) " | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "0f6ffc43-6e23-4f54-8a8a-0192a21368ca", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "With the loaded data we create the `au4f` `XPSRegion` containing the measurement data." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "129db648-3c47-4f81-9ee8-69788c96660e", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "au4f = XPSRegion(binding_energy=binding_energy, counts=cps, counts_err=cps_err) " | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "1c500a40-b915-44ba-a8c4-a9c37f3f7f4f", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "`XPSRegion` provides us a function to visualize the loaded data with" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "211918d9-8974-4ae2-97d7-51c6920fe6cb", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "au4f.plot()" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "cf8cb26e-df35-4ef3-bea3-e9ddcc452c2e", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Fit data\n", | ||
| "\n", | ||
| "From the preview plot we can detect two symmetric peaks which result from the spin-orbit splitting into the Au 4f5/2 and 4f3/2 regions. For illustration of the typical analysis routine, we construct two Gaussian peaks with the lmfit GaussianModel and initialize them with appropriate start values. Here we are just using initial good guesses for the start values. These, however, can eventually be deduced by data inside NOMAD as soon as enough data is available, e.g. similar to a peak detection in other XPS analysis programs. There are different peak shapes available in lmfit, such as Lorentz, Voigt, PseudoVoigt or skewed models. Please refer to the packages documentation for further details on these models and on how to use them." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "6401d0af-7830-4093-ae2e-d1c4a719ff61", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "peak_1 = GaussianModel(prefix=\"Au4f52_\")\n", | ||
| "peak_1.set_param_hint(\"amplitude\", value=3300)\n", | ||
| "peak_1.set_param_hint(\"sigma\", value=0.5)\n", | ||
| "peak_1.set_param_hint(\"center\", value=84.2)\n", | ||
| "\n", | ||
| "peak_2 = GaussianModel(prefix=\"Au4f32_\")\n", | ||
| "peak_2.set_param_hint(\"amplitude\", value=1600)\n", | ||
| "peak_2.set_param_hint(\"sigma\", value=0.5)\n", | ||
| "peak_2.set_param_hint(\"center\", value=87.2)" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "25c2b42c-3e74-435e-88c6-64e5f04a2244", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "We can simply add the two models together to create a composite model." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "62d6293b-f562-425d-b39a-22c8b3874676", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "comp = peak_1 + peak_2\n", | ||
| "params = comp.make_params()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "1c0c03fa", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "We also set a constraint, namely that the area of `peak_2` is exactly half the area of `peak_1` (since it is a photoemission doublet).\n", | ||
| "\n", | ||
| "To constrain the areas correctly, we need to set the expression for the amplitude of `peak_2` considering both the amplitude and sigma. The constraint should be:\n", | ||
| "\n", | ||
| "$$\\text{area of peak 2} = 0.5 \\times \\text{area of peak 1}$$\n", | ||
| "\n", | ||
| "Since the area $A$ of a Gaussian peak is given by:\n", | ||
| "\n", | ||
| "$$ A = \\text{amplitude} \\times \\sigma \\times \\sqrt{2\\pi}$$\n", | ||
| "\n", | ||
| "For `peak_2` to have half the area of `peak_1`:\n", | ||
| "\n", | ||
| "$$ \\text{amplitude}_2 \\times \\sigma_2 = 0.5 \\times (\\text{amplitude}_1 \\times \\sigma_1) $$\n", | ||
| "\n", | ||
| "So, the correct expression for the amplitude of `peak_2` should be:\n", | ||
| "\n", | ||
| "$$ \\text{amplitude}_2 = 0.5 \\times \\text{amplitude}_1 \\times \\frac{\\sigma_1}{\\sigma_2} $$\n", | ||
| "\n", | ||
| "Therefore, we can write:\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "b31762bd", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "params['Au4f32_amplitude'].expr = '0.5 * Au4f52_amplitude * (Au4f52_sigma / Au4f32_sigma)'" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "2aaf4749-62d6-4cec-af67-8daced4d3ed9", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "In the next step, we perform the actual fit. First, since the data in `Au_in_vacuum__Au4f` contains a very wide scan range, we only select the region with the Au 4f doublet (with `fit_region(...)`). Then, we calculate a Shirley baseline with `calc_baseline()`, set the fit model (`.fit_model(comp)`) and perform a fit (`.fit()`). All of this functions can also be used independently. The fit function takes the measurement uncertainties as weights to the fit function into account.\n", | ||
| "\n", | ||
| "Finally, the model is plotted with the previously used `plot()` method. Since we performed a fit the plot is now extended by the baseline and fits." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "c48fbeb7-1e46-4c0b-897c-3907654d033b", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "au4f.fit_region(start=80,stop=94).calc_baseline().fit_model(comp).fit(params).plot()" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "97a42467-5bbb-4c9a-a97a-5d6ed573f5f0", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "The fit result gets stored inside the `fit_result` parameter and is displayed to extract, e.g., the peak central energies. Please note that the fitting does not take the measurement uncertainties into account and the errors are simple fitting errors." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "9345ab8f-b65f-49f2-a260-bf1c7805e4a0", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "au4f.fit_result.params " | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "22d85cef-be55-402e-9083-c40a245fadb1", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "We can also extract a fitting parameter shared accross different peaks, e.g. the peak central energies. This refers to the text behind the model paramters prefix, so we select `center` here to get the central energies." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "4b7b17c0-d781-4b62-8274-c82b00f3c267", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "au4f.peak_property('center')" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "16cbedf6-0e82-43ca-909a-e826fd8df7e7", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Typically, we are also interested in the peak areas which can be calculated with `peak_areas()`" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "8aa0759b-a920-4491-97ca-54381a85d3a8", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "(areas := au4f.peak_areas())" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "f2b57a15-e95b-4dba-a14e-63031ffa3408", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "and their ratios" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "d55e8ea6-3fc9-4049-be1b-9fcec5f27ffd", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "areas / areas.max()" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "b909f5bc-446b-4c11-b5b8-1eeec79c894d", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "To assess the quality of the fit, the fit residual can be viewed with `plot_residual()`." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "cfa5fe97", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "au4f.plot_residual()" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "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": "3.10.10" | ||
| }, | ||
| "vscode": { | ||
| "interpreter": { | ||
| "hash": "0cbdf5d5ef28617c8bf3753ff15cd1b7b5539de5aaa68a35c3d38ca27e1ab0fa" | ||
| } | ||
| }, | ||
| "widgets": { | ||
| "application/vnd.jupyter.widget-state+json": { | ||
| "state": {}, | ||
| "version_major": 2, | ||
| "version_minor": 0 | ||
| } | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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|---|---|---|
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| # XPS Reader | ||
|
|
||
| ## Introduction | ||
|
|
||
| This example presents the capabilities of the NOMAD platform to store and standardize XPS data. It shows the generation of a NeXus file according to the [NXmpes](https://manual.nexusformat.org/classes/contributed_definitions/NXmpes.html#nxmpes) application definition and a successive analysis of an example data set. | ||
|
|
||
| ## Viewing uploaded data | ||
|
|
||
| Below, you find an overview of your uploaded data. | ||
| Click on the `> /` button to get a list of your data or select **FILES** from the top menu of this upload. | ||
| You may add your own files to the upload or experiment with the pre-existing electronic lab notebook (ELN) example. | ||
| The ELN follows the general structure of NOMAD ELN templates and you may refer to the [documentation](https://nomad-lab.eu/prod/v1/staging/docs/archive.html) or a [YouTube tutorial](https://youtu.be/o5ETHmGmnaI) (~1h) | ||
| for further information. | ||
| When the ELN is saved a NeXus file will be generated from the provided example data. | ||
| You may also view your supplied or generated NeXus files here with the H5Web viewer. | ||
| To do so open the **FILES** tab and just select a `.nxs` file. | ||
|
|
||
| ## Analyzing the data | ||
|
|
||
| The examples work through the use of NOMAD remote tools hub (NORTH) containers, i.e. besides using and dealing with the uploaded XPS data, an analysis container can be started. If you want to execute the examples locally you may also use your local python and jupyterlab installation. Please refer to the documentation of [pynxtools](https://github.com/FAIRmat-NFDI/pynxtools.git) and [h5web](https://github.com/silx-kit/h5web) on how to install it on your machine. | ||
|
|
||
| Note: To upload your own xps data file, you must check **"eln_data.yaml"** file to provide all the required fields. In xps some required fields or attributes do not come with xps raw data. | ||
|
|
||
| To start an analysis, note your upload id (which you find on top of this explanation) and select **ANALYZE** from the top menu, then **NOMAD Remote Tools Hub**. | ||
| In the appearing list you'll find the `xps` container, click on it and click **LAUNCH**. | ||
| After a few moments a new tab will open which displays a jupyter environment providing the required analysis tools. | ||
| To find the examples navigate to uploads inside the jupyter hub browser and select the folder with your noted upload id. | ||
| There you'll find the example `ipynb` notebook for data analysis (E2). | ||
| Double-clicking the notebook will open the example in the jupyter main window. | ||
|
|
||
| ## Where to go from here? | ||
|
|
||
| If you're interested in using this pipeline and NOMAD in general you'll find support at [FAIRmat](https://www.fairmat-nfdi.eu/fairmat/consortium). | ||
|
|
||
| For questions regarding the experiment or this specific example contact [Lukas Pielsticker](https://www.fairmat-nfdi.eu/fairmat/fairmat_/fairmatteam), [Rubel Mozumder](https://www.fairmat-nfdi.eu/fairmat/fairmat_/fairmatteam), or [Florian Dobener](https://www.fairmat-nfdi.eu/fairmat/fairmat_/fairmatteam). |
195 changes: 195 additions & 0 deletions
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src/pynxtools_xps/nomad/examples/Specs_conversion.archive.json
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| { | ||
| "data":{ | ||
| "m_def":"../upload/raw/xps.scheme.archive.yaml#/definitions/section_definitions/0", | ||
| "reader":"xps", | ||
| "nxdl":"NXmpes", | ||
| "input_files":[ | ||
| "EX439_S718_Au_in_25_mbar_O2.sle", | ||
| "eln_data.yaml" | ||
| ], | ||
| "output": "Au_25_mbar_O2_no_align.nxs", | ||
| "title":"EX439_S718_Au in 25 mbar O2", | ||
| "start_time":"2022-04-08T09:47:00.000Z", | ||
| "end_time":"2022-04-08T10:32:00.000Z", | ||
| "entry_identifier":"EX439", | ||
| "experiment_institution":"Max Planck Institute for Chemical Energy Conversion", | ||
| "experiment_facility":"Surface and Interface Analysis Group", | ||
| "experiment_laboratory":"Near-Ambient Pressure XPS Lab", | ||
| "user":{ | ||
| "name":"Lukas Pielsticker", | ||
| "affiliation":"MPI CEC", | ||
| "address":"Lukas Pielsticker", | ||
| "email":"lukas.pielsticker@cec.mpg.de", | ||
| "orcid":{ | ||
| "service":"orcid", | ||
| "identifier":"0000-0001-9361-8333" | ||
| } | ||
| }, | ||
| "instrument":{ | ||
| "device_information":{ | ||
| "vendor":"SPECS GmbH", | ||
| "model":"Custom NAP-XPS instrument", | ||
| "identifier":"null" | ||
| }, | ||
| "energy_resolution":{ | ||
| "type":"calibrated", | ||
| "resolution":0.2, | ||
| "physical_quantity":"energy" | ||
| }, | ||
| "source_probe":{ | ||
| "type":"Fixed Tube X-ray", | ||
| "probe":"photon", | ||
| "device_information":{ | ||
| "vendor":"SPECS GmbH", | ||
| "model":"µFOCUS 500", | ||
| "identifier":"null" | ||
| }, | ||
| "beam_probe":{ | ||
| "distance":0.0, | ||
| "distance/@units":"mm" | ||
| }, | ||
| "analyser":{ | ||
| "description":"hemispherical", | ||
| "device_information":{ | ||
| "vendor":"SPECS GmbH", | ||
| "model":"PHOIBOS 150 NAP", | ||
| "identifier":"null" | ||
| }, | ||
| "collectioncolumn":{ | ||
| "scheme":"angular dispersive", | ||
| "device_information":{ | ||
| "vendor":"SPECS GmbH", | ||
| "model":"PHOIBOS 150 NAP", | ||
| "identifier":"null" | ||
| } | ||
| }, | ||
| "energydispersion":{ | ||
| "scheme":"hemispherical", | ||
| "diameter":{ | ||
| "unit":"mm", | ||
| "value":150 | ||
| }, | ||
| "device_information":{ | ||
| "vendor":"SPECS GmbH", | ||
| "model":"PHOIBOS 150 NAP", | ||
| "identifier":"null" | ||
| } | ||
| }, | ||
| "detector":{ | ||
| "amplifier_type":"channeltron", | ||
| "detector_type":"Multi-anode", | ||
| "device_information":{ | ||
| "vendor":"Surface Concept GmbH", | ||
| "model":"1D-DLD detector", | ||
| "identifier":"null" | ||
| } | ||
| } | ||
| }, | ||
| "manipulator":{ | ||
| "device_information":{ | ||
| "vendor":"SPECS GmbH", | ||
| "model":"5-axis manipulator", | ||
| "identifier":"null" | ||
| }, | ||
| "temperature_sensor":{ | ||
| "name":"type K thermocouple", | ||
| "measurement":"temperature", | ||
| "attached_to":"sample", | ||
| "type":"type K thermocouple", | ||
| "value":{ | ||
| "unit":"K", | ||
| "value":298.0 | ||
| } | ||
| }, | ||
| "sample_heater":{ | ||
| "name":"Coherent Compact Evolution IR Diode LASER (DILAS)", | ||
| "physical_quantity":"temperature", | ||
| "type":"IR diode laser", | ||
| "heater_power":{ | ||
| "unit":"W", | ||
| "value":0.0 | ||
| }, | ||
| "pid":{ | ||
| "setpoint":{ | ||
| "unit":"K", | ||
| "value":298.0 | ||
| } | ||
| } | ||
| }, | ||
| "drain_current_amperemeter":{ | ||
| "name":"Amperemeter 1.0", | ||
| "measurement":"current", | ||
| "type":"wire", | ||
| "heater_power":{ | ||
| "unit":"nA", | ||
| "value":0.1 | ||
| } | ||
| }, | ||
| "sample_bias_voltmeter":{ | ||
| "name":"XPS sample voltmeter", | ||
| "measurement":"voltage", | ||
| "attached_to":"sample", | ||
| "type":"oscilloscope", | ||
| "heater_power":{ | ||
| "unit":"V", | ||
| "value":0.0 | ||
| } | ||
| }, | ||
| "sample_bias_potentiostat":{ | ||
| "name":"XPS sample potentiostat", | ||
| "physical_quantity":"voltage", | ||
| "type":"potentiostat", | ||
| "pid":{ | ||
| "setpoint":{ | ||
| "unit":"V", | ||
| "value":0.0 | ||
| } | ||
| } | ||
| } | ||
| }, | ||
| "pressure_gauge":{ | ||
| "name":"Atmion", | ||
| "measurement":"pressure", | ||
| "type":"hot-filament ionization gauge", | ||
| "value":{ | ||
| "unit":"mbar", | ||
| "value":1e-9 | ||
| } | ||
| }, | ||
| "flood_gun":{ | ||
| "name":"FG 22/35", | ||
| "physical_quantity":"current", | ||
| "type":"hot-filament ionization gauge", | ||
| "current":{ | ||
| "unit":"A", | ||
| "value":0.0 | ||
| } | ||
| } | ||
| }, | ||
| "sample":{ | ||
| "name":"Polycristalline Au foil", | ||
| "sample_id":"S718", | ||
| "atom_types":"Au", | ||
| "physical_form":"foil", | ||
| "situation":"vacuum", | ||
| "substance":{ | ||
| "name":"Au", | ||
| "molecular_mass":{ | ||
| "value":196.96657, | ||
| "unit":"g/mol" | ||
| }, | ||
| "cas_number":"7440-57-5", | ||
| "molecular_formula_hill":"Au" | ||
| }, | ||
| "history":{ | ||
| "sample_preparation":{ | ||
| "start_time":"2022-04-08T11:25:00.200Z", | ||
| "end_time":"2022-04-08T11:45:00.200Z", | ||
| "description":"sputter cleaned with Ar ions for 20 min", | ||
| "method":"Ar sputtering" | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } |
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|---|---|---|
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| title: EX439_S718_Au in 25 mbar O2 | ||
| start_time: 2022-04-08T11:47:02.0200Z | ||
| end_time: 2022-04-08T14:52:26.0400Z | ||
| entry_identifier: EX439 | ||
| experiment_institution: Max Planck Institute for Chemical Energy Conversion | ||
| experiment_facility: Surface and Interface Analysis Group | ||
| experiment_laboratory: Near-Ambient Pressure XPS Lab | ||
| program_name: SpecsLabProdigy | ||
| user: | ||
| name: Lukas Pielsticker | ||
| affiliation: Max Planck Institute for Chemical Energy Conversion | ||
| address: Lukas Pielsticker | ||
| email: lukas.pielsticker@cec.mpg.de | ||
| orcid: | ||
| service: orcid | ||
| identifier: 0000-0001-9361-8333 | ||
| instrument: | ||
| device_information: | ||
| vendor: SPECS GmbH | ||
| model: Custom NAP-XPS instrument | ||
| identifier: null | ||
| energy_resolution: | ||
| type: calibrated | ||
| resolution: | ||
| value: 0.2 | ||
| unit: eV | ||
| source_xray: | ||
| type: Fixed Tube X-ray | ||
| probe: photon | ||
| device_information: | ||
| vendor: SPECS GmbH | ||
| model: µFOCUS 500 | ||
| identifier: null | ||
| beam_xray: | ||
| distance: | ||
| value: 0.0 | ||
| unit: mm | ||
| analyser: | ||
| description: hemispherical | ||
| device_information: | ||
| vendor: SPECS GmbH | ||
| model: PHOIBOS 150 NAP | ||
| identifier: null | ||
| collectioncolumn: | ||
| scheme: angular dispersive | ||
| device_information: | ||
| vendor: SPECS GmbH | ||
| model: PHOIBOS 150 NAP | ||
| identifier: null | ||
| energydispersion: | ||
| scheme: hemispherical | ||
| diameter: | ||
| unit: mm | ||
| value: 150 | ||
| device_information: | ||
| vendor: SPECS GmbH | ||
| model: PHOIBOS 150 | ||
| identifier: null | ||
| detector: | ||
| amplifier_type: channeltron | ||
| detector_type: Multi-anode | ||
| device_information: | ||
| vendor: Surface Concept GmbH | ||
| model: 1D-DLD detector | ||
| identifier: null | ||
| manipulator: | ||
| device_information: | ||
| vendor: SPECS GmbH | ||
| model: 5-axis manipulator | ||
| identifier: v1.0 | ||
| temperature_sensor: | ||
| name: type K thermocouple | ||
| measurement: temperature | ||
| attached_to: sample | ||
| type: type K thermocouple | ||
| value: | ||
| value: 298.0 | ||
| unit: K | ||
| sample_heater: | ||
| name: Coherent Compact Evolution IR Diode LASER (DILAS) | ||
| physical_quantity: temperature | ||
| type: IR diode laser | ||
| heater_power: | ||
| value: 0.0 | ||
| unit: W | ||
| pid: | ||
| setpoint: | ||
| value: 298.0 | ||
| unit: K | ||
| cryostat: | ||
| name: null | ||
| physical_quantity: null | ||
| type: null | ||
| pid: | ||
| setpoint: null | ||
| drain_current_amperemeter: | ||
| name: Amperemeter 1.0 | ||
| measurement: current | ||
| type: wire | ||
| value: | ||
| value: 0.1 | ||
| unit: nA | ||
| sample_bias_voltmeter: | ||
| name: XPS sample voltmeter | ||
| measurement: voltage | ||
| attached_to: sample | ||
| type: oscilloscope | ||
| value: | ||
| value: 0.0 | ||
| unit: V | ||
| sample_bias_potentiostat: | ||
| name: XPS sample potentiostat | ||
| physical_quantity: voltage | ||
| type: potentiostat | ||
| pid: | ||
| setpoint: | ||
| value: 0.0 | ||
| unit: V | ||
| pressure_gauge: | ||
| name: Atmion | ||
| measurement: pressure | ||
| type: hot-filament ionization gauge | ||
| value: | ||
| value: 0.000000001 | ||
| unit: mbar | ||
| value_log: | ||
| value: | ||
| value: null | ||
| unit: null | ||
| flood_gun: | ||
| name: FG 22/35 | ||
| physical_quantity: current | ||
| type: low energy electron source | ||
| current: | ||
| value: 0.0 | ||
| unit: A | ||
| current_log: | ||
| value: | ||
| value: null | ||
| unit: null | ||
| sample: | ||
| name: Polycristalline Au foil | ||
| sample_id: S718 | ||
| atom_types: Au | ||
| physical_form: foil | ||
| situation: vacuum | ||
| substance: | ||
| name: Au | ||
| molecular_mass: | ||
| value: 196.96657 | ||
| unit: g/mol | ||
| cas_number: 7440-57-5 | ||
| molecular_formula_hill: Au | ||
| history: | ||
| sample_preparation: | ||
| start_time: 2022-04-08T11:25:00.200Z | ||
| end_time: 2022-04-08T11:45:00.200Z | ||
| description: sputter cleaned with Ar ions for 20 min | ||
| method: Ar sputtering |
887 changes: 887 additions & 0 deletions
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src/pynxtools_xps/nomad/examples/xps.scheme.archive.yaml
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| """Fitting functions for XPS spectra""" | ||
|
|
||
| from typing import Optional | ||
| from dataclasses import dataclass | ||
| import numpy as np | ||
| from numpy.linalg import norm | ||
| import plotly.graph_objects as go | ||
| import pandas as pd | ||
| from h5py import File as H5File | ||
| from lmfit import Model, CompositeModel | ||
|
|
||
|
|
||
| @dataclass | ||
| class XPSRegion: | ||
| """An XPS region representation""" | ||
|
|
||
| binding_energy: np.ndarray | ||
| counts: np.ndarray | ||
| counts_err: np.ndarray | ||
| baseline: Optional[np.ndarray] = None | ||
| _fit_region: slice = slice(None, None) | ||
| _fit_mod: Optional[Model] = None | ||
| fit_result: Optional[Model] = None | ||
|
|
||
| @staticmethod | ||
| def load(filename: str, entry: str) -> "XPSRegion": | ||
| """Load from a NeXus file. | ||
| Args: | ||
| filename (str): The NeXus file name to load. | ||
| entry (str): | ||
| The entry from which to load data. | ||
| Should be the name of an NXentry within the NeXus file. | ||
| Returns: | ||
| XPSRegion: The XPSRegion class with the loaded data. | ||
| """ | ||
| with H5File(filename, "r") as xps_file: | ||
| binding_energy = xps_file[f"/{entry}/data/energy"][:] | ||
| cps = xps_file[f"/{entry}/data/data"][:] | ||
| cps_err = xps_file[f"/{entry}/data/data_errors"][:] | ||
|
|
||
| return XPSRegion(binding_energy=binding_energy, counts=cps, counts_err=cps_err) | ||
|
|
||
| def fit_region(self, start: int, stop: int) -> "XPSRegion": | ||
| """Select a fit region within this XPSregion by x-axis value. | ||
| The fit region is always selected between start and stop, regardless of their order. | ||
| Both points are included in the region, | ||
| hence the actual selected region may be a little larger. | ||
| Args: | ||
| start (int): The start ot the region. | ||
| stop (int): The end of the region. | ||
| Returns: | ||
| XPSRegion: This class | ||
| """ | ||
| region = np.argwhere( | ||
| (self.binding_energy >= start) & (self.binding_energy <= stop) | ||
| ) | ||
|
|
||
| self._fit_region = slice(region[0, 0], region[-1, 0], 1) | ||
| return self | ||
|
|
||
| def fit_model(self, model: Model) -> "XPSRegion": | ||
| """Supply a fit model to fit this xps region. | ||
| Args: | ||
| model (lmfit.Model): The lmfit model to use. | ||
| Returns: | ||
| XPSRegion: This class | ||
| """ | ||
| self._fit_mod = model | ||
| return self | ||
|
|
||
| def fit(self, *args, **kwargs) -> "XPSRegion": | ||
| """Perform a fit of the data. You need to define a fit_model first and | ||
| execute a baseline correction before using this method. | ||
| Raises: | ||
| ValueError: If no fit model is provided or the baseline has not been corrected. | ||
| Returns: | ||
| XPSRegion: This class | ||
| """ | ||
| if self._fit_mod is None: | ||
| raise ValueError("You need to provide a fit model before performing a fit.") | ||
|
|
||
| if self.baseline is None: | ||
| raise ValueError( | ||
| "You need to perform a baseline correction before using this method." | ||
| ) | ||
|
|
||
| self.fit_result = self._fit_mod.fit( | ||
| ( | ||
| self.counts[self._fit_region] - self.baseline | ||
| if self._fit_region | ||
| else self.counts[self._fit_region] | ||
| ), | ||
| *args, | ||
| x=self.binding_energy[self._fit_region], | ||
| weights=1 / self.counts_err[self._fit_region], | ||
| **kwargs, | ||
| ) | ||
|
|
||
| return self | ||
|
|
||
| def calc_baseline(self, bg_type: str = "shirley") -> "XPSRegion": | ||
| """Calculate the baseline for this xps spectrum in the given region. | ||
| Args: | ||
| bg_type (str, optional): The background type. Defaults to "shirley". | ||
| Raises: | ||
| ValueError: If the bg_type is unsupported. | ||
| Returns: | ||
| XPSRegion: This class | ||
| """ | ||
| baselines = {"shirley": shirley_baseline} | ||
| if bg_type not in baselines: | ||
| raise ValueError(f"Unsupported baseline type {bg_type}.") | ||
|
|
||
| self.baseline = baselines[bg_type]( | ||
| self.binding_energy[self._fit_region], self.counts[self._fit_region] | ||
| ) | ||
|
|
||
| return self | ||
|
|
||
| def peak_property(self, prop: str) -> pd.DataFrame: | ||
| """Generates a dataframe with values for a property `prop` of a fitting model. | ||
| Args: | ||
| prop (str): | ||
| The name of the property to deduce for the peaks, | ||
| e.g. `center` for the peak center for gaussian or lorentzian shapes. | ||
| Raises: | ||
| ValueError: Thrown if no prior fit is performed. | ||
| Returns: | ||
| pd.DataFrame: A pandas DataFrame containing the peak property for each peak. | ||
| """ | ||
| if not self.fit_result: | ||
| raise ValueError("You need to perform a fit first.") | ||
|
|
||
| props = pd.DataFrame() | ||
| for prefix in map(lambda x: x.prefix, self.fit_result.components): | ||
| if f"{prefix}{prop}" not in self.fit_result.params: | ||
| continue | ||
|
|
||
| props = pd.concat( | ||
| [ | ||
| props, | ||
| pd.DataFrame( | ||
| {prop: self.fit_result.params.get(f"{prefix}{prop}").value}, | ||
| index=[prefix.rstrip("_")], | ||
| ), | ||
| ] | ||
| ) | ||
|
|
||
| return props | ||
|
|
||
| def peak_areas(self, region_only=False) -> pd.DataFrame: | ||
| """Calculates the peak areas of the given fit models peaks. | ||
| Args: | ||
| region_only (bool, optional): | ||
| Set true if only the area inside the set region should be consider. | ||
| Defaults to False. | ||
| Raises: | ||
| ValueError: Thrown if no prior fit is performed. | ||
| Returns: | ||
| pandas.DataFrame: A pandas DataFrame containing the peak areas. | ||
| """ | ||
| if not self.fit_result: | ||
| raise ValueError("You need to perform a fit first.") | ||
|
|
||
| areas = pd.DataFrame() | ||
| if region_only: | ||
| peaks = self.fit_result.eval_components( | ||
| x=self.binding_energy[self._fit_region] | ||
| ) | ||
| else: | ||
| peaks = self.fit_result.eval_components(x=self.binding_energy) | ||
| for prefix in peaks: | ||
| areas = pd.concat( | ||
| [ | ||
| areas, | ||
| pd.DataFrame( | ||
| {"Area": sum(peaks[prefix])}, | ||
| index=[prefix.rstrip("_")], | ||
| ), | ||
| ] | ||
| ) | ||
| return areas | ||
|
|
||
| def plot_residual(self): | ||
| """Plot the fit residual""" | ||
| if not self.fit_result: | ||
| raise ValueError("You need to perform a fit first.") | ||
|
|
||
| fig = go.Figure( | ||
| data=go.Scatter( | ||
| name="Residual", | ||
| x=self.binding_energy[self._fit_region], | ||
| y=self.fit_result.residual, | ||
| ) | ||
| ) | ||
| fig.update_xaxes(title="Binding energy (eV)") | ||
| fig.update_yaxes(title="Residual") | ||
| fig.layout.xaxis.autorange = "reversed" | ||
|
|
||
| fig.show() | ||
|
|
||
| def plot(self): | ||
| """Plot the xps region""" | ||
| fig = go.Figure( | ||
| data=go.Scatter( | ||
| name="Measurement", | ||
| x=self.binding_energy, | ||
| y=self.counts, | ||
| error_y=dict( | ||
| type="data", # value of error bar given in data coordinates | ||
| array=self.counts_err, | ||
| visible=True, | ||
| ), | ||
| ) | ||
| ) | ||
| if self._fit_region.start is not None: | ||
| fig.add_vline( | ||
| self.binding_energy[self._fit_region.start], line_color="lightgrey" | ||
| ) | ||
| if self._fit_region.stop is not None: | ||
| fig.add_vline( | ||
| self.binding_energy[self._fit_region.stop], line_color="lightgrey" | ||
| ) | ||
| if self.baseline is not None: | ||
| fig.add_trace( | ||
| go.Scatter( | ||
| name="Baseline", | ||
| x=self.binding_energy[self._fit_region], | ||
| y=self.baseline, | ||
| ), | ||
| ) | ||
| if self.fit_result is not None: | ||
| fig.add_trace( | ||
| go.Scatter( | ||
| name="Fit", | ||
| x=self.binding_energy[self._fit_region], | ||
| y=self.fit_result.best_fit + self.baseline, | ||
| ), | ||
| ) | ||
| if isinstance(self._fit_mod, CompositeModel): | ||
| peaks = self.fit_result.eval_components( | ||
| x=self.binding_energy[self._fit_region] | ||
| ) | ||
| for prefix in peaks: | ||
| fig.add_trace( | ||
| go.Scatter( | ||
| name=prefix.rstrip("_"), | ||
| x=self.binding_energy[self._fit_region], | ||
| y=peaks[prefix] + self.baseline, | ||
| ) | ||
| ) | ||
|
|
||
| fig.update_xaxes(title="Binding energy (eV)") | ||
| fig.update_yaxes(title="CPS") | ||
| fig.layout.xaxis.autorange = "reversed" | ||
|
|
||
| fig.show() | ||
|
|
||
|
|
||
| # pylint: disable=invalid-name | ||
| def shirley_baseline( | ||
| x: np.ndarray, y: np.ndarray, tol: float = 1e-5, maxit: int = 10 | ||
| ) -> np.ndarray: | ||
| """Calculate the shirley background according to the Sherwood method. | ||
| Args: | ||
| x (np.ndarray): The x-axis on which to calculate the shirley baseline. | ||
| y (np.ndarray): The y-axis on which to calculate the shirley baseline. | ||
| tol (float, optional): | ||
| The convergence tolerance at which to stop the iteration. | ||
| Defaults to 1e-5. | ||
| maxit (int, optional): | ||
| The maximum iteration after which to stop the iteration. | ||
| Defaults to 10. | ||
| Raises: | ||
| ValueError: | ||
| Is thrown when the arrays have different dimensions, | ||
| are not numpy arrays or are empty. | ||
| Is also thrown when the fit does not converge. | ||
| Returns: | ||
| np.ndarray: The shirley baseline for the x, y dataset. | ||
| """ | ||
|
|
||
| if not isinstance(x, np.ndarray) or not isinstance(y, np.ndarray): | ||
| raise ValueError( | ||
| f"Parameters x and y must be of type numpy array, not {type(x)} and {type(y)}" | ||
| ) | ||
|
|
||
| if len(x) != len(y): | ||
| raise ValueError("x and y arrays have different dimensions.") | ||
|
|
||
| if not x.any(): | ||
| raise ValueError("x-array is empty.") | ||
|
|
||
| if len(x.shape) > 1: | ||
| raise ValueError( | ||
| f"Data arrays must be one-dimensional. Found dimension {x.shape}." | ||
| ) | ||
|
|
||
| is_reversed = False | ||
| if x[0] < x[-1]: | ||
| is_reversed = True | ||
| x = x[::-1] | ||
| y = y[::-1] | ||
|
|
||
| background = np.zeros(x.shape) | ||
| background_next = background.copy() | ||
|
|
||
| iters = 0 | ||
| while True: | ||
| k = (y[0] - y[-1]) / np.trapz(y - background, x=x) | ||
|
|
||
| for energy in range(len(x)): | ||
| background_next[energy] = k * np.trapz( | ||
| y[energy:] - background[energy:], x=x[energy:] | ||
| ) | ||
|
|
||
| diff = norm(background_next - background) | ||
| background = background_next.copy() | ||
| if diff < tol: | ||
| break | ||
|
|
||
| iters += 1 | ||
| if iters == maxit: | ||
| raise ValueError( | ||
| "Maximum number of iterations exceeded before convergence." | ||
| ) | ||
|
|
||
| if is_reversed: | ||
| return (y[-1] + background)[::-1] | ||
| return y[-1] + background |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,74 @@ | ||
| # | ||
| # Copyright The NOMAD Authors. | ||
| # | ||
| # This file is part of NOMAD. See https://nomad-lab.eu for further info. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # | ||
| """Test for NOMAD examples in XPS reader plugin.""" | ||
|
|
||
| import os | ||
| import pytest | ||
|
|
||
| try: | ||
| import nomad | ||
| except ImportError: | ||
| pytest.skip( | ||
| "Skipping NOMAD example tests because nomad is not installed", | ||
| allow_module_level=True, | ||
| ) | ||
|
|
||
| from pynxtools.testing.nomad_example import ( | ||
| get_file_parameter, | ||
| parse_nomad_examples, | ||
| example_upload_entry_point_valid, | ||
| ) | ||
|
|
||
| from pynxtools_xps.nomad.entrypoints import xps_example | ||
|
|
||
|
|
||
| EXAMPLE_PATH = os.path.join( | ||
| os.path.dirname(__file__), | ||
| "..", | ||
| "src", | ||
| "pynxtools_xps", | ||
| "nomad", | ||
| "examples", | ||
| ) | ||
|
|
||
|
|
||
| @pytest.mark.parametrize( | ||
| "mainfile", | ||
| get_file_parameter(EXAMPLE_PATH), | ||
| ) | ||
| def test_parse_nomad_examples(mainfile): | ||
| """Test if NOMAD examples work.""" | ||
| archive_dict = parse_nomad_examples(mainfile) | ||
|
|
||
|
|
||
| @pytest.mark.parametrize( | ||
| ("entrypoint", "example_path"), | ||
| [ | ||
| pytest.param( | ||
| xps_example, | ||
| EXAMPLE_PATH, | ||
| id="xps_example", | ||
| ), | ||
| ], | ||
| ) | ||
| def test_example_upload_entry_point_valid(entrypoint, example_path): | ||
| """Test if NOMAD ExampleUploadEntryPoint works.""" | ||
| example_upload_entry_point_valid( | ||
| entrypoint=entrypoint, | ||
| example_path=example_path, | ||
| ) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,3 +1,4 @@ | ||
| # | ||
| # Copyright The NOMAD Authors. | ||
| # | ||
| # This file is part of NOMAD. See https://nomad-lab.eu for further info. | ||
|
|
||