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alexhroomalexhroom
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Adds orsopy integration for .ort files and ORSO models (#131)
* written ort file reader * made model parsing only if the model is there * fixed union method * added orso integration notebook * added tests * added pint to requirements and added orso extras install * backwards compatibility * removed to-project and updated notebook * fixed print formatting * review fixes * fixed repeat names * fix iterator * new data * updated example data and better name resolution * removed some data * review fixes
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‎.github/workflows/run_tests.yml

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- name: Install and Test with pytest
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run: |
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export PATH="$pythonLocation:$PATH"
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python -m pip install -e .[Dev]
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python -m pip install -e .[Dev,Orso]
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pytest tests/ --cov=RATapi --cov-report=term

‎RATapi/__init__.py

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from RATapi.controls import Controls
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from RATapi.project import Project
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from RATapi.run import run
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from RATapi.utils import convert, plotting
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from RATapi.utils import convert, orso, plotting
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__all__ = ["examples", "models", "events", "ClassList", "Controls", "Project", "run", "plotting", "convert"]
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__all__ = ["examples", "models", "events", "ClassList", "Controls", "Project", "run", "plotting", "convert", "orso"]

‎RATapi/classlist.py

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@@ -296,6 +296,19 @@ def extend(self, other: Sequence[T]) -> None:
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self._check_unique_name_fields(other)
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self.data.extend(other)
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def union(self, other: Sequence[T]) -> None:
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"""Extend the ClassList by a sequence, ignoring input items with names that already exist."""
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if other and not (isinstance(other, Sequence) and not isinstance(other, str)):
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other = [other]
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self.extend(
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[
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item
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for item in other
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if hasattr(item, self.name_field) and getattr(item, self.name_field) not in self.get_names()
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]
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)
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def set_fields(self, index: Union[int, slice, str, T], **kwargs) -> None:
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"""Assign the values of an existing object's attributes using keyword arguments."""
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self._validate_name_field(kwargs)

‎RATapi/examples/data/c_PLP0011859_q.ort

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‎RATapi/examples/orso_integration/orso_integration.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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"metadata": {},
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"source": [
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"# ``orsopy`` Integration"
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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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"``python-RAT`` contains some integration with ``orsopy``, allowing for convenient interaction with the ``.ort`` file format. This integration is available through the `RATapi.utils.orso` submodule."
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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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"import RATapi.utils.orso"
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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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"## Creating models from the ORSO model description language\n",
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"\n",
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"The [ORSO model description format](https://www.reflectometry.org/advanced_and_expert_level/file_format/simple_model) allows the description of a standard slab model as a one-line string, provided that all the layer materials are defined [in the ORSO SLD database](https://slddb.esss.dk/slddb/).\n",
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"\n",
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"The function `RATapi.utils.orso.orso_model_to_rat` function can read a model and return an `ORSOSample` dataclass, which gives bulk in and bulk out parameters for the model, a list of all layers defined in the model, and all the parameters needed to define those layers as RAT models. \n",
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"\n",
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"**Note:** the ORSO format gives the thicknesses of materials in *nanometres*. When we convert them to RAT parameters, the units will be converted to Angstroms.\n",
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"\n",
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"For example, the string `air | Ni 100 | SiO2 0.5 | Si` describes a 1000 angstrom nickel film backed by a 5 angstrom silicon oxide layer. The bulk-in and bulk-out are air and silicon respectively. The roughnesses and SLDs will be calculated or taken from the ORSO SLD database."
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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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"# create the RAT parameters and layers from this model\n",
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"sample = RATapi.utils.orso.orso_model_to_rat(\"air | Ni 100 | SiO2 0.5 | Si\")\n",
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"print(sample)"
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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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"You can also set `absorption=True` and the model will account for absorption. For example if we change the nickel film for a boron carbide film and want to account for its relatively high absorption, we can add it to the output:"
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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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"sample = RATapi.utils.orso.orso_model_to_rat(\"vacuum | B4C 100 | SiO2 0.5 | Si\", absorption=True)\n",
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"print(sample)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Finally, ORSO supports defining repeated layers using parentheses. For example, if we had a polarising multilayer of 5 repetitions of 70 angstrom silicon and 70 angstrom iron, we could represent it as `air | 5 ( Si 7 | Fe 7 ) | Si`.\n",
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"\n",
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"RAT will only create the number of layers and parameters necessary, but the `ORSOSample` object's `model` attribute will give a list of layer names with the structure of the model preserved, which can be given as the layer model for a Contrast."
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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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"sample = RATapi.utils.orso.orso_model_to_rat(\"air | 5 ( Si 7 | Fe 7 ) | Si\")\n",
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"print(sample)"
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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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"## Reading in data and models from .ort files"
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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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"RAT can also load both data and model information from an .ort file. This is done through the `ORSOProject` object, which takes a file path and can also optionally account for absorption.\n",
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"\n",
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"The example data file we use here is example data for an unknown film on deposited 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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"metadata": {},
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"outputs": [],
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"source": [
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"import pathlib\n",
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"data_path = pathlib.Path(\"../data\")\n",
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"\n",
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"orso_data = RATapi.utils.orso.ORSOProject(data_path / \"c_PLP0011859_q.ort\")\n",
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"print(orso_data)"
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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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"The `ORSOProject` object contains two lists: `ORSOProject.data` and `ORSOProject.samples`. The former is a list of Data objects with each dataset defined in the file, and the latter is a list of `ORSOSample` objects (like above) with model information. Note that if the .ort file does not define a model for a dataset, that index of `ORSOProject.samples` will be None.\n",
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"\n",
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"It's then easy to access this data to create a RAT `Project` that represents our data."
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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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"from RATapi.models import Background, Contrast, Parameter, Resolution\n",
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"\n",
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"dataset = orso_data.data[0]\n",
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"sample = orso_data.samples[0]\n",
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"\n",
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"project = RATapi.Project(\n",
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" name = \"Example Project\",\n",
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" geometry = \"substrate/liquid\",\n",
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" parameters = sample.parameters,\n",
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" bulk_in = [sample.bulk_in],\n",
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" bulk_out = [sample.bulk_out],\n",
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" scalefactors = [Parameter(name=\"Scalefactor\", min=0, value=0.34, max=1.5)],\n",
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" background_parameters = [Parameter(name=\"Background Parameter\", min=0, value=2e-6, max=1)],\n",
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" backgrounds = [Background(name=\"Background\", type=\"constant\", source=\"Background Parameter\")],\n",
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" resolutions = [Resolution(name=\"Data Resolution\", type=\"data\")],\n",
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" data = [dataset],\n",
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" layers = sample.layers,\n",
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" contrasts = [Contrast(\n",
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" name = \"prist4\",\n",
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" data = dataset.name,\n",
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" background = \"Background\",\n",
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" bulk_in = sample.bulk_in.name,\n",
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" bulk_out = sample.bulk_out.name,\n",
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" scalefactor = \"Scalefactor\",\n",
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" resolution = \"Data Resolution\",\n",
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" model = sample.model,\n",
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" )]\n",
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")\n",
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"\n",
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"controls = RATapi.Controls()\n",
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"project, results = RATapi.run(project, controls)\n",
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"RATapi.plotting.plot_ref_sld(project, results)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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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": "3.11.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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