diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index db26440..df71251 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -29,7 +29,7 @@ jobs: timeout-minutes: 15 strategy: matrix: - environment: [test-py311, test-py312, test-py313] + environment: [test-py311, test-py312] steps: - name: Checkout uses: actions/checkout@v4 diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 0e4c7c2..3bae887 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -17,7 +17,7 @@ jobs: with: pixi-version: v0.40.2 cache: false - environments: build + environments: dist activate-environment: true - name: Build distributions run: pixi run build-dist diff --git a/.gitignore b/.gitignore index 7f977f2..c743f91 100644 --- a/.gitignore +++ b/.gitignore @@ -60,3 +60,15 @@ diff.md # Lock file *.lock + +# Tutorial required files +!tutorial_dataset.h5 + +# Tutorial generated files +configure_profile.yaml +configure_computer.yaml +configure_code.yaml +.aiida_run + +# Development test sandbox +test_dev diff --git a/LICENSE b/LICENSE index f288702..0a04128 100644 --- a/LICENSE +++ b/LICENSE @@ -1,674 +1,165 @@ - GNU GENERAL PUBLIC LICENSE + GNU LESSER GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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Such new +versions will be similar in spirit to the present version, but may +differ in detail to address new problems or concerns. + + Each version is given a distinguishing version number. If the +Library as you received it specifies that a certain numbered version +of the GNU Lesser General Public License "or any later version" +applies to it, you have the option of following the terms and +conditions either of that published version or of any later version +published by the Free Software Foundation. If the Library as you +received it does not specify a version number of the GNU Lesser +General Public License, you may choose any version of the GNU Lesser +General Public License ever published by the Free Software Foundation. + + If the Library as you received it specifies that a proxy can decide +whether future versions of the GNU Lesser General Public License shall +apply, that proxy's public statement of acceptance of any version is +permanent authorization for you to choose that version for the +Library. diff --git a/pyproject.toml b/pyproject.toml index 257beb2..81e1f08 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,23 +11,27 @@ classifiers = [ "Programming Language :: Python", "Operating System :: POSIX :: Linux", "Intended Audience :: Science/Research", - "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", + "License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)", "Development Status :: 3 - Alpha", "Framework :: AiiDA" ] keywords = ["aiida", "plugin", "fans"] requires-python = ">=3.11" dependencies = [ - "aiida-core>=2.3", + "aiida-core>=2.6", "h5py" ] # Entry Points -[project.entry-points] -"aiida.data" = { "fans" = "aiida_fans.data:FANSParameters" } -"aiida.calculations" = { "fans" = "aiida_fans.calculations:FANSCalculation" } -"aiida.parsers" = { "fans" = "aiida_fans.parsers:FANSParser" } -"aiida.cmdline.data" = { "fans" = "aiida_fans.cli:data_cli" } +# [project.entry-points."aiida.data"] +# "fans" = "aiida_fans.data:FANSParameters" +[project.entry-points."aiida.calculations"] +"fans.stashed" = "aiida_fans.calculations:FansStashedCalculation" +"fans.fragmented" = "aiida_fans.calculations:FansFragmentedCalculation" +[project.entry-points."aiida.parsers"] +"fans" = "aiida_fans.parsers:FansParser" +# [project.entry-points."aiida.cmdline.data"] +# "fans" = "aiida_fans.cli:data_cli" # Build System [build-system] @@ -44,7 +48,7 @@ platforms = ["linux-64"] ### pixi: default dependencies (in addition to aiida-core) [tool.pixi.dependencies] -fans = "*" +# None [tool.pixi.pypi-dependencies] # None @@ -52,41 +56,47 @@ fans = "*" [tool.pixi.tasks] # None -### pixi: features (in addition to fans) -[tool.pixi.feature.dev] -pypi-dependencies = {aiida-fans = { path = ".", editable = true }} -[tool.pixi.feature.prod] +### pixi: features +[tool.pixi.feature.self] pypi-dependencies = {aiida-fans = { path = ".", editable = true }} -[tool.pixi.feature.fmt] +[tool.pixi.feature.plugin] +dependencies = {aiida-fans = "==0.1.5"} +# [tool.pixi.feature.aiida] +# dependencies = {aiida-core = "2.6.*"} +[tool.pixi.feature.fans] +dependencies = {fans = "0.4.*"} +[tool.pixi.feature.ruff] dependencies = {ruff = "*"} tasks = {fmt = "ruff check", dummy = "echo dummy", my-dummy="echo my-dummy"} [tool.pixi.feature.build] pypi-dependencies = {build = "*"} tasks = {build-dist = "python -m build"} -[tool.pixi.feature.docs] +[tool.pixi.feature.sphinx] dependencies = {sphinx = "*", sphinx-book-theme = "*"} tasks = {build-docs = "sphinx-build -M html docs/source docs/build"} -[tool.pixi.feature.test] +[tool.pixi.feature.pytest] dependencies = {pytest = "*"} tasks = {test = "echo dummy test passes"} -[tool.pixi.feature.py3] -dependencies = {python = ">=3.11"} +[tool.pixi.feature.marimo] +dependencies = {marimo = "0.13.*"} +tasks = {tutorial = "marimo edit tutorial.py"} [tool.pixi.feature.py311] dependencies = {python = "3.11.*"} [tool.pixi.feature.py312] dependencies = {python = "3.12.*"} -[tool.pixi.feature.py313] -dependencies = {python = "3.13.*"} +# [tool.pixi.feature.py313] +# dependencies = {python = "3.13.*"} ### pixi: environments [tool.pixi.environments] -dev = { features = ["dev", "fmt", "test"], solve-group = "default" } -fmt = { features = ["fmt", "py3"], no-default-feature = true } -build = { features = ["build", "py3"], no-default-feature = true } -docs = { features = ["docs", "py3"], no-default-feature = true } -test-py311 = { features = ["prod", "test", "py311"], solve-group = "py311" } -test-py312 = { features = ["prod", "test", "py312"], solve-group = "py312" } -test-py313 = { features = ["prod", "test", "py313"], solve-group = "py313" } +dev = { features = ["self", "ruff", "pytest"], solve-group = "default" } +fmt = { features = ["ruff", "py312"], no-default-feature = true } +dist = { features = ["build", "py312"], no-default-feature = true } +docs = { features = ["sphinx", "py312"], no-default-feature = true } +test-py311 = { features = ["self", "fans", "pytest", "py311"], solve-group = "py311" } +test-py312 = { features = ["self", "fans", "pytest", "py312"], solve-group = "py312" } +# test-py313 = { features = ["self", "fans", "pytest", "py313"], solve-group = "py313" } +tutorial = { features = ["plugin", "fans", "marimo"], no-default-feature = true} ## Build Tools: setuptools_scm @@ -95,7 +105,10 @@ version_file = "src/aiida_fans/_version.py" ## Style Tools: ruff [tool.ruff] -extend-exclude = ["conf.py"] +extend-exclude = [ + "conf.py", + "tutorial.py" +] line-length = 120 [tool.ruff.lint] ignore = [ diff --git a/src/aiida_fans/calculations.py b/src/aiida_fans/calculations.py index 8f2575f..a53965a 100644 --- a/src/aiida_fans/calculations.py +++ b/src/aiida_fans/calculations.py @@ -1,260 +1,146 @@ -"""Calculations provided by aiida_fans.""" +"""CalcJob subclasses for aiida-fans calculations.""" from json import dump -from typing import Any, Callable +from pathlib import Path +from shutil import copyfileobj -import h5py from aiida.common.datastructures import CalcInfo, CodeInfo from aiida.common.folders import Folder from aiida.engine import CalcJob from aiida.engine.processes.process_spec import CalcJobProcessSpec -from aiida.orm import ArrayData, Dict, Float, Int, List, SinglefileData, Str -from plumpy.utils import AttributesFrozendict +from aiida.orm import Dict, Float, Int, List, SinglefileData, Str +from h5py import File as h5File -from aiida_fans.helpers import InputEncoder +from aiida_fans.helpers import make_input_dict -class FANSCalculation(CalcJob): - """AiiDA calculation plugin wrapping the FANS executable.""" - - @staticmethod - def __input_validator_selector(input: str, note: str) -> Callable[[Any, Any], str | None]: - validators: dict[str, Callable[[Any, Any], str | None]] = { - "microstructure.file": lambda _i, _p: None, - "microstructure.datasetname": lambda _i, _p: None, - "microstructure.L": lambda i, _p: note if len(i) != 3 else None, # TODO: check elements are numbers - "problem_type": lambda i, _p: note if i.value not in {"thermal", "mechanical"} else None, - "matmodel": lambda i, _p: note - if i.value - not in { - "LinearThermalIsotropic", - "LinearElasticIsotropic", - "PseudoPlasticLinearHardening", - "PseudoPlasticNonLinearHardening", - "J2ViscoPlastic_LinearIsotropicHardening", - "J2ViscoPlastic_NonLinearIsotropicHardening", - } - else None, - "material_properties": lambda _i, _p: None, # TODO: material properties - "method": lambda i, _p: note if i.value not in {"cg", "fp"} else None, - "error_parameters.measure": lambda i, _p: note if i.value not in {"Linfinity", "L1", "L2"} else None, - "error_parameters.type": lambda i, _p: note if i.value not in {"absolute", "relative"} else None, - "error_parameters.tolerance": lambda _i, _p: None, - "n_it": lambda _i, _p: None, - "macroscale_loading": lambda _i, _p: None, # TODO: macroscale loading - "results": lambda i, _p: note - if not set(i.get_list()) - <= { - "stress_average", - "strain_average", - "absolute_error", - "phase_stress_average", - "phase_strain_average", - "microstructure", - "displacement", - "stress", - "strain", - } - else None, - } - return validators[input] +class FansCalcBase(CalcJob): + """Base class of all calculations using FANS.""" @classmethod def define(cls, spec: CalcJobProcessSpec) -> None: - """Define inputs, outputs, and exit_codes of the calculation.""" + """Define inputs, outputs, and exit codes of the calculation.""" super().define(spec) - # Metadata + # Default Metadata + spec.inputs["metadata"]["label"].default = "FANS" + ## Processing Power + spec.inputs["metadata"]["options"]["withmpi"].default = True spec.inputs["metadata"]["options"]["resources"].default = { "num_machines": 1, - "num_mpiprocs_per_machine": 4, + "num_mpiprocs_per_machine": 4 } - spec.inputs["metadata"]["options"]["withmpi"].default = True - spec.inputs["metadata"]["options"]["parser_name"].default = "fans" + ## Filenames spec.inputs["metadata"]["options"]["input_filename"].default = "input.json" spec.inputs["metadata"]["options"]["output_filename"].default = "output.h5" + ## Parser + spec.inputs["metadata"]["options"]["parser_name"].default = "fans" - # New Ports: - spec.input_namespace("microstructure", help=(note := "The microstructure definition.")) - spec.input( - (input := "microstructure.file"), - valid_type=SinglefileData, - validator=cls.__input_validator_selector(input, note), - help=(note := "This specifies the path to the HDF5 file that contains the microstructure data."), - ) - spec.input( - (input := "microstructure.datasetname"), - valid_type=Str, - validator=cls.__input_validator_selector(input, note), - help=( - note - := "This is the path within the HDF5 file to the specific dataset that represents the microstructure." - ), - ) - spec.input( - (input := "microstructure.L"), - valid_type=List, - validator=cls.__input_validator_selector(input, note), - help=( - note - := "Microstructure length defines the physical dimensions of the microstructure in the x, y, and z directions." # noqa: E501 - ), - ) - - spec.input( - (input := "problem_type"), - valid_type=Str, - validator=cls.__input_validator_selector(input, note), - help=( - note - := "This defines the type of physical problem you are solving. Common options include `thermal` problems and `mechanical` problems." # noqa: E501 - ), - ) - spec.input( - (input := "matmodel"), - valid_type=Str, - validator=cls.__input_validator_selector(input, note), - help=(note := "This specifies the material model to be used in the simulation."), - ) - spec.input( - (input := "material_properties"), - valid_type=Dict, - validator=cls.__input_validator_selector(input, note), - help=(note := "This provides the necessary material parameters for the chosen material model."), - ) - spec.input( - (input := "method"), - valid_type=Str, - validator=cls.__input_validator_selector(input, note), - help=( - note - := "This indicates the numerical method to be used for solving the system of equations. `cg` stands for the Conjugate Gradient method, and `fp` stands for the Fixed Point method." # noqa: E501 - ), - ) - - spec.input_namespace( - "error_parameters", - help=( - note - := "This section defines the error parameters for the solver. Error control is applied on the finite element nodal residual of the problem." # noqa: E501 - ), - ) - spec.input( - (input := "error_parameters.measure"), - valid_type=Str, - validator=cls.__input_validator_selector(input, note), - help=(note := "Specifies the norm used to measure the error. Options include `Linfinity`, `L1`, or `L2`."), - ) - spec.input( - (input := "error_parameters.type"), - valid_type=Str, - validator=cls.__input_validator_selector(input, note), - help=(note := "Defines the type of error measurement. Options are `absolute` or `relative`."), - ) - spec.input( - (input := "error_parameters.tolerance"), - valid_type=Float, - validator=cls.__input_validator_selector(input, note), - help=( - note - := "Sets the tolerance level for the solver, defining the convergence criterion based on the chosen error measure. The solver iterates until the solution meets this tolerance." # noqa: E501 - ), - ) - - spec.input( - (input := "n_it"), - valid_type=Int, - validator=cls.__input_validator_selector(input, note), - help=(note := "Specifies the maximum number of iterations allowed for the FANS solver."), - ) - spec.input( - (input := "macroscale_loading"), - valid_type=ArrayData, - validator=cls.__input_validator_selector(input, note), - help=( - note - := "This defines the external loading applied to the microstructure. It is an array of arrays, where each sub-array represents a loading condition applied to the system. The format of the loading array depends on the problem type." # noqa: E501 - ), - ) - spec.input( - (input := "results"), - valid_type=List, - validator=cls.__input_validator_selector(input, note), - help=( - note - := "This array lists the quantities that should be stored into the results HDF5 file during the simulation." # noqa: E501 - ), - ) - - spec.output("results", valid_type=SinglefileData) - - # Exit Codes: + # Custom Metadata + spec.input("metadata.options.results_prefix", valid_type=str, default="") + spec.input("metadata.options.results", valid_type=list, default=[]) + + # Input Ports + ## Microstructure Definition + spec.input_namespace("microstructure") + spec.input("microstructure.file", valid_type=SinglefileData) + spec.input("microstructure.datasetname", valid_type=Str) + spec.input("microstructure.L", valid_type=List) + ## Problem Type and Material Model + spec.input("problem_type", valid_type=Str) + spec.input("matmodel", valid_type=Str) + spec.input("material_properties", valid_type=Dict) + ## Solver Settings + spec.input("method", valid_type=Str) + spec.input("n_it", valid_type=Int) + spec.input_namespace("error_parameters") + spec.input("error_parameters.measure", valid_type=Str) + spec.input("error_parameters.type", valid_type=Str) + spec.input("error_parameters.tolerance", valid_type=Float) + ## Macroscale Loading Conditions + spec.input("macroscale_loading", valid_type=List) + + # Output Ports + spec.output("output", valid_type=SinglefileData) + spec.output("results", valid_type=Dict, required=False) + + # Exit Codes spec.exit_code(400, "PLACEHOLDER", "This is an error code, yet to be implemented.") def prepare_for_submission(self, folder: Folder) -> CalcInfo: - """Creates the input file required by the calculation. - - Args: - folder (Folder): where the plugin should temporarily place all files needed by the calculation - - Returns: - CalcInfo: the data to be passed to the ExecManager - """ - # Write Microstructure Subset to Folder - datasetname : str = self.inputs.microstructure.datasetname.value - with folder.open("microstructure.h5","bw") as f_dest: - with h5py.File(f_dest,"w") as h5_dest: - with self.inputs.microstructure.file.open(mode="rb") as f_src: - with h5py.File(f_src,'r') as h5_src: - h5_src.copy(datasetname, h5_dest, name=datasetname) - - # Write input.json to Folder - json_to_be = dict(self.inputs) - del json_to_be["code"], json_to_be["metadata"] - to_fix = {} - for key, value in json_to_be.items(): - if isinstance(value, AttributesFrozendict): # can be moved to InputEncoder? - to_fix[key] = {} - for k, v in json_to_be[key].items(): - to_fix[key][k] = v - json_to_be = json_to_be | to_fix - - to_add = {} - for key, value in json_to_be.items(): - if key == "microstructure": - for k, v in value.items(): - if k == "file": - to_add[f"ms_{k}name"] = "microstructure.h5" - else: - to_add[f"ms_{k}"] = v - - json_to_be = to_add | json_to_be - del json_to_be["microstructure"] - - with folder.open(self.options.input_filename, "w", "utf8") as handle: - dump(json_to_be, handle, cls=InputEncoder, indent=4) - - # Specifying code info. + """Prepare the calculation for submission.""" + # Specifying the code info: codeinfo = CodeInfo() codeinfo.code_uuid = self.inputs.code.uuid - codeinfo.stdout_name = self.options.input_filename + ".log" - codeinfo.stderr_name = self.options.input_filename + ".err" + codeinfo.stdout_name = self.metadata.label + ".log" + codeinfo.stderr_name = self.metadata.label + ".err" codeinfo.cmdline_params = [self.options.input_filename, self.options.output_filename] - # Specifying calc info. + # Specifying the calc info: calcinfo = CalcInfo() calcinfo.codes_info = [codeinfo] calcinfo.local_copy_list = [] calcinfo.remote_copy_list = [] - calcinfo.retrieve_list = [ - self.options.input_filename + ".log", - self.options.input_filename + ".err", - ] + calcinfo.retrieve_list = [codeinfo.stdout_name, codeinfo.stderr_name] calcinfo.retrieve_temporary_list = [ self.options.output_filename ] - calcinfo.provenance_exclude_list = [ - "microstructure.h5" - ] return calcinfo + + +class FansStashedCalculation(FansCalcBase): + """Calculations using FANS and the "Stashed" microstructure distribution strategy.""" + + @classmethod + def define(cls, spec: CalcJobProcessSpec) -> None: + """Define inputs, outputs, and exit codes of the calculation.""" + return super().define(spec) + + def prepare_for_submission(self, folder: Folder) -> CalcInfo: + """Prepare the calculation for submission.""" + ms_filepath: Path = Path(self.inputs.code.computer.get_workdir()) / \ + "stash/microstructures" / \ + self.inputs.microstructure.file.filename + # if microstructure does not exist in stash, make it + if not ms_filepath.is_file(): + ms_filepath.parent.mkdir(parents=True, exist_ok=True) + with self.inputs.microstructure.file.open(mode='rb') as source: + with ms_filepath.open(mode='wb') as target: + copyfileobj(source, target) + + # input.json as dict + input_dict = make_input_dict(self) + input_dict["microstructure"]["filepath"] = str(ms_filepath) + # write input.json to working directory + with folder.open(self.options.input_filename, "w", "utf8") as json: + dump(input_dict, json, indent=4) + + return super().prepare_for_submission(folder) + +class FansFragmentedCalculation(FansCalcBase): + """Calculations using FANS and the "Fragmented" microstructure distribution strategy.""" + + @classmethod + def define(cls, spec: CalcJobProcessSpec) -> None: + """Define inputs, outputs, and exit codes of the calculation.""" + return super().define(spec) + + def prepare_for_submission(self, folder: Folder) -> CalcInfo: + """Prepare the calculation for submission.""" + # Write Microstructure Subset to Folder + datasetname : str = self.inputs.microstructure.datasetname.value + with folder.open("microstructure.h5","bw") as f_dest: + with h5File(f_dest,"w") as h5_dest: + with self.inputs.microstructure.file.open(mode="rb") as f_src: + with h5File(f_src,'r') as h5_src: + h5_src.copy(datasetname, h5_dest, name=datasetname) + + # input.json as dict + input_dict = make_input_dict(self) + input_dict["microstructure"]["filepath"] = "microstructure.h5" + # write input.json to working directory + with folder.open(self.options.input_filename, "w", "utf8") as json: + dump(input_dict, json, indent=4) + + return super().prepare_for_submission(folder) diff --git a/src/aiida_fans/helpers.py b/src/aiida_fans/helpers.py index 2dad749..f527c54 100644 --- a/src/aiida_fans/helpers.py +++ b/src/aiida_fans/helpers.py @@ -1,26 +1,41 @@ -"""Tools and utilities required by aiida_fans.""" +"""Tools required by aiida-fans.""" -import json +from typing import Any -from aiida.orm import ArrayData, Dict, Float, Int, List, SinglefileData, Str +from aiida.engine import CalcJob +from numpy import allclose, ndarray -class InputEncoder(json.JSONEncoder): - """Prepares a dictionary of calcjob inputs for json representation.""" +def make_input_dict(job: CalcJob) -> dict[str, Any]: + """Prepares a dictionary that maps to an input.json from calcjob inputs.""" + return { + ## Microstructure Definition + "microstructure": { + "filepath": None, # path to stashed microstructure, must be overwritten by impl + "datasetname": job.inputs.microstructure.datasetname.value, + "L": job.inputs.microstructure.L.get_list() + }, + "results_prefix": job.inputs.metadata.options.results_prefix, + ## Problem Type and Material Model + "problem_type": job.inputs.problem_type.value, + "matmodel": job.inputs.matmodel.value, + "material_properties": job.inputs.material_properties.get_dict(), + ## Solver Settings + "method": job.inputs.method.value, + "n_it": job.inputs.n_it.value, + "error_parameters": { + "measure": job.inputs.error_parameters.measure.value, + "type": job.inputs.error_parameters.type.value, + "tolerance": job.inputs.error_parameters.tolerance.value + }, + ## Macroscale Loading Conditions + "macroscale_loading": job.inputs.macroscale_loading.get_list(), + ## Results Specification + "results": job.inputs.metadata.options.results + } - def default(self, obj): - """Converts aiida datatypes to their python counterparts.""" - match obj: - case Str() | Int() | Float(): - return obj.value - case List(): - return obj.get_list() - case Dict(): - return obj.get_dict() - case ArrayData(): - return [a[1].tolist() for a in obj.get_iterarrays()] #! Caution: may be disordered - case SinglefileData(): - return obj.filename - case _: - # Let the base class default method raise the TypeError - return super().default(obj) +def arraydata_equal(first: dict[str, ndarray], second: dict[str, ndarray]) -> bool: + """Return whether two dicts of arrays are roughly equal.""" + if first.keys() != second.keys(): + return False + return all(allclose(first[key], second[key]) for key in first) diff --git a/src/aiida_fans/parsers.py b/src/aiida_fans/parsers.py index 4e01788..f755ba8 100644 --- a/src/aiida_fans/parsers.py +++ b/src/aiida_fans/parsers.py @@ -1,43 +1,56 @@ -"""Parsers provided by aiida_fans.""" +"""Parser subclass for aiida-fans calculations.""" from pathlib import Path from aiida.engine import ExitCode -from aiida.orm import SinglefileData +from aiida.orm import CalcJobNode, Dict, SinglefileData from aiida.parsers.parser import Parser -from aiida.plugins import CalculationFactory +from h5py import Dataset, Group +from h5py import File as h5File -FANSCalculation = CalculationFactory("fans") +class FansParser(Parser): + """Extracts data from FANS results.""" -class FANSParser(Parser): - """Extracts valuable data from FANS results.""" + def __init__(self, node: CalcJobNode): + """Calls `super().__init__()` then defines `self.results_dict`.""" + super().__init__(node) + self.results_dict = dict() - def parse(self, **kwargs) -> ExitCode: - """Parse outputs, store results in database. - - Returns: - ExitCode: non-zero exit code, if parsing fails - """ - retrieved_temporary_folder = Path(kwargs["retrieved_temporary_folder"]) - output_filename = self.node.get_option("output_filename") - - # Check that output_filename is valid - if (type(output_filename) is not str) or (output_filename == ""): - return self.exit_codes.ERROR_INVALID_OUTPUT - - # Check that folder content is as expected. - files_retrieved = set(self.retrieved.list_object_names()) - files_expected = set()#{output_filename} - if not files_expected <= files_retrieved: - self.logger.error(f"Found files '{files_retrieved}', expected to find '{files_expected}'") + def parse(self, **kwargs) -> ExitCode | None: + """Parse outputs and store results as nodes.""" + output_path: Path = Path(kwargs["retrieved_temporary_folder"]) / self.node.get_option("output_filename") # type: ignore + if output_path.is_file(): + self.out("output", node=SinglefileData(output_path)) + else: return self.exit_codes.ERROR_MISSING_OUTPUT - # Add output HDF5 file to repository. - output_path = retrieved_temporary_folder / output_filename - self.logger.info(f"Parsing '{output_path}'") - with output_path.open("rb") as handle: - output_node = SinglefileData(file=handle) - self.out("results", output_node) - - return ExitCode(0) + with h5File(output_path) as h5: + results = h5[ + self.node.inputs.microstructure.datasetname.value + \ + "_results/" + \ + self.node.get_option('results_prefix') + ] + results.visititems(self.parse_h5) + + if self.results_dict: + self.out("results", Dict(self.results_dict)) + + def parse_h5(self, name: str, object: Group | Dataset) -> None: + """Callable for the .visititems method of h5py Groups.""" + if isinstance(object, Group): + return + if "average" in name: + keys = name.split("/") + res = self.results_dict + data = list(object[:]) + self.nestle(res, keys, data) + + def nestle(self, bottom: dict, layers: list[str], top: list[float]) -> None: + """Recursive function to generate a nested results dictionary.""" + layer = layers.pop(0) + if len(layers) > 0: + bottom.setdefault(layer, dict()) + self.nestle(bottom[layer], layers, top) + else: + bottom[layer] = top diff --git a/src/aiida_fans/utils.py b/src/aiida_fans/utils.py new file mode 100644 index 0000000..6aa478a --- /dev/null +++ b/src/aiida_fans/utils.py @@ -0,0 +1,218 @@ +"""Utilities provided by aiida_fans.""" + +from typing import Any, Literal + +from aiida.engine import run, submit +from aiida.orm import CalcJobNode, Data, Node, QueryBuilder +from aiida.plugins import CalculationFactory, DataFactory +from numpy import ndarray + +from aiida_fans.helpers import arraydata_equal + + +def aiida_type(value: Any) -> type[Data]: + """Find the corresponding AiiDA datatype for a variable with pythonic type. + + Args: + value (Any): a python variable + + Raises: + NotImplementedError: only certain mappings are supported + + Returns: + type[Data]: an AiiDA data type + """ + match value: + case str(): + return DataFactory("core.str") # Str + case int(): + return DataFactory("core.int") # Int + case float(): + return DataFactory("core.float") # Float + case list(): + return DataFactory("core.list") # List + case dict(): + if all(map(lambda t: isinstance(t, ndarray), value.values())): + return DataFactory("core.array") # ArrayData + else: + return DataFactory("core.dict") # Dict + case _: + raise NotImplementedError(f"Received an input of value: {value} with type: {type(value)}") + +def fetch(label: str, value: Any) -> list[Node]: + """Return a list of nodes matching the label and value provided. + + Args: + label (str): the label of the node to fetch + value (Any): the value of the node to fetch + + Returns: + list[Node]: the list of nodes matching the give criteria + """ + datatype = aiida_type(value) + nodes = QueryBuilder( + ).append(cls=datatype, tag="n" + ).add_filter("n", {"label": label} + ).add_filter("n", {"attributes": {"==": datatype(value).base.attributes.all}} + ).all(flat=True) + + if datatype != DataFactory("core.array"): + return nodes # type: ignore + else: + array_nodes = [] + for array_node in nodes: + array_value = { + k: v for k, v in [ + (name, array_node.get_array(name)) for name in array_node.get_arraynames() # type: ignore + ] + } + if arraydata_equal(value, array_value): + array_nodes.append(array_node) + return array_nodes + +def generate(label: str, value: Any) -> Node: + """Return a single node with the label and value provided. + + Uses an existing node when possible, but otherwise creates one instead. + + Args: + label (str): the label of the node to generate + value (Any): the pythonic value of the node to generate + + Raises: + RuntimeError: panic if more than one node is found matching the criteria + + Returns: + Node: a stored node with label and value + """ + bone = fetch(label, value) + if len(bone) == 0: + return aiida_type(value)(value, label=label).store() + elif len(bone) == 1: + return bone.pop() + else: + raise RuntimeError + +def convert(ins: dict[str, Any], path: list[str] = []): + """Takes a dictionary of inputs and converts the values to their respective Nodes. + + Args: + ins (dict[str, Any]): a dictionary of inputs + path (list[str], optional): a list of predecessor keys for nested dictionaries. Defaults to []. + """ + for k, v in ins.items(): + if k == "metadata" or isinstance(v, Node): + continue + if k in ["microstructure", "error_parameters"]: + convert(v, path=[*path, k]) + else: + ins[k] = generate(".".join([*path, k]), v) + +def compile_query(ins: dict[str,Any], qb: QueryBuilder) -> None: + """Interate over the converted input dictionary and append to the QueryBuilder for each node. + + Args: + ins (dict[str,Any]): a dictionary of converted inputs + qb (QueryBuilder): a CalcJobNode QueryBuilder with tag='calc' + """ + for k, v in ins.items(): + if k == "metadata": + continue + if k in ["microstructure", "error_parameters"] and isinstance(v, dict): + compile_query(v, qb) + else: + qb.append( + cls=type(v), + with_outgoing="calc", + filters={"pk": v.pk} + ) + + +def execute_fans( + mode: Literal["Submit", "Run"], + inputs: dict[str, Any], + strategy: Literal["Fragmented", "Stashed"] = "Fragmented", + ): + """This utility function simplifies the process of executing aiida-fans jobs. + + The only nodes you must provide are the `code` and `microstructure` inputs. + Other inputs can be given as standard python variables. Your repository will + be automatically scanned for equivalent nodes. These will be used whenever + possible, otherwise new nodes will be created. + + The `strategy` specifies which microstructure distribution method you wish to use. + It defaults to "Fragmented". + + You must load an AiiDA profile yourself before using this function. + + **Args:** + **mode** *(Literal["Submit", "Run"])* + **inputs** *(dict[str, Any])* + **strategy** *(Literal["Fragmented", "Stashed"]), optional* + + --- + + **Example:** + ``` + from aiida import load_profile + from aiida.orm import load_code, load_node + from aiida_fans.utils import execute_fans + load_profile() + inputs = { + "code": load_code("fans"), + "microstructure": load_node(label="microstructure"), + ... + "metadata": { + "label": "an example calculation" + } + } + execute_fans("Submit", inputs, "Stashed") + ``` + """ + # update inputs with metadata.options.stash if necessary: + match strategy: + case "Stashed": + calcjob = CalculationFactory("fans.stashed") + case "Fragmented": + calcjob = CalculationFactory("fans.fragmented") + case _: + print("ERROR: Calculation strategy must be either 'Fragmented' or 'Stashed'.") + raise ValueError + + # move results_prefix and results items to metadata.options + inputs.setdefault("metadata", {}).setdefault("options", {})["results_prefix"] = inputs.pop("results_prefix", "") + inputs.setdefault("metadata", {}).setdefault("options", {})["results"] = inputs.pop("results", []) + + # fetch the inputs if possible or otherwise create them + convert(inputs) + + # check if identical calculation already exists + qb = QueryBuilder().append(cls=CalcJobNode, tag="calc", project="id") + compile_query(inputs, qb) + results = qb.all(flat=True) + if (count := len(results)) != 0: + print(f"It seems this calculation has already been performed {count} time{"s" if count > 1 else ""}. {results}") + confirmation = input("Are you sure you want to rerun it? [y/N] ").strip().lower() in ["y", "yes"] + else: + confirmation = True + + if confirmation: + match mode: + case "Run": + run(calcjob, inputs) # type: ignore + case "Submit": + submit(calcjob, inputs) # type: ignore + +def submit_fans( + inputs: dict[str, Any], + strategy: Literal["Fragmented", "Stashed"] = "Fragmented", +): + """See `execute_fans` for implementation and usage details.""" + execute_fans("Submit", inputs, strategy) + +def run_fans( + inputs: dict[str, Any], + strategy: Literal["Fragmented", "Stashed"] = "Fragmented", +): + """See `execute_fans` for implementation and usage details.""" + execute_fans("Run", inputs, strategy) diff --git a/tutorial/README.md b/tutorial/README.md new file mode 100644 index 0000000..9882601 --- /dev/null +++ b/tutorial/README.md @@ -0,0 +1,59 @@ +# aiida-fans-tutorial +Learn how to use aiida-fans in this marimo powered tutorial. + +## Usage + +Assuming you have FANS, python 3.13, venv, and pip installed on a linux system, the recommended way to use this tutorial is by creating a virtual environment in this directory with the following command: + +``` +python -m venv .venv +``` +Then activate this environment like so: + +``` +source .venv/bin/activate +``` + +You can ensure the the environment was succesfully activated with `which python` and ultimately deactivate the environment with `deactivate` when you're finished. + +You may need to install/upgrade pip now with your virtual environment activated. Run the following command: + +``` +python -m pip install --upgrade pip +``` + +Once pip is up to date, run the following command to install the tutorial's dependencies: + +``` +python -m pip install -r requirements.txt +``` + +Now you are ready to launch the notebook and begin the tutorial. Run the following command and access the marimo notebook at the port provided: + +``` +marimo run tutorial.py +``` + +## Alternative Usage + +### 1. Conda + +> [!WARNING] +> This method is a work-in-progress! + +### 2. Pixi + +> [!WARNING] +> This method is a work-in-progress! + +You can use pixi to install everything you need as defined by the pyproject.toml file. It should bundle python, FANS, AiiDA, aiida-fans, and marimo all into a virtual environment located in a .pixi directory. You can proceed to directly begin the tutorial with: + +``` +marimo run tutorial.py +``` + +Activating the environment may look something like this: + +``` +pixi shell --manifest-path ~/FANS/tutorial/pyproject.toml +``` diff --git a/tutorial/tutorial.py b/tutorial/tutorial.py new file mode 100644 index 0000000..be2abc6 --- /dev/null +++ b/tutorial/tutorial.py @@ -0,0 +1,1386 @@ + + +import marimo + +__generated_with = "0.13.0" +app = marimo.App(app_title="AiiDA-FANS Tutorial") + + +@app.cell(hide_code=True) +def _(): + import marimo as mo + from pathlib import Path + return Path, mo + + +@app.cell(hide_code=True) +def _(mo): + nav_menu = mo.nav_menu( + { + "#aiida-setup": "AiiDA Setup", + "#fans-rundown": "FANS Rundown", + "#submitting-jobs": "Submitting Jobs", + "#analysing-the-results": "Analysing the Results", + "Links": { + "https://github.com/ethan-shanahan/aiida-fans": "aiida-fans", + "https://github.com/DataAnalyticsEngineering/FANS": "FANS", + "https://www.aiida.net/": "AiiDA", + }, + } + ) + + _tip = mo.md(""" + **Requirements:** + + The rest of this tutorial assumes you have read the attached README and have installed the requirements described therein. Although not foolproof, you can run the following commands to check if AiiDA, the plugin, and FANS are installed correctly. + + ``` + verdi plugin list aiida.calculations fans + FANS + ``` + + Notice that we assume FANS is located on your PATH (or at least it is in your active environment). While this is not necessary in general practice, the tutorial will continue under this assumption. + """).callout("warn") + + mo.md(rf"""{nav_menu} + + --- + + # AiiDA-FANS Tutorial + + The goal of this tutorial is to give you an idea of how to utilise the `aiida-fans` plugin as well as an introduction to `AiiDA` and `FANS`. By the end of this tutorial, you should know how to: + + - Setup your AiiDA profile, computer, and code. + - Define FANS options and prepare a parameter space study. + - Write a `submit.py` script to run your jobs. + - Query and read the results. + + {_tip} + """) + return + + +@app.cell(hide_code=True) +def _(mo): + _note = mo.md(r""" + **Note:** _not your first profile..._ + + This section assumes you have not already set up an appropriate profile, computer, and code for using AiiDA and FANS. If you have already done this, you may wish to skip to the next section. + + However, this tutorial is designed to work with a blank profile specifically. + """).callout("info") + + mo.md(rf""" + ## AiiDA Setup + + Before we can truly begin, we must set up AiiDA on your machine. This means three things. + + 1. Create a Profile + 2. Specify a Computer + 3. Define a Code + + AiiDA has multiple user interfaces but their CLI, `verdi`, is particularly well suited to these three steps since they need to be performed only rarely. Therefore, you will need access to the terminal to proceed. + + {_note} + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md( + r""" + ### 1. Create a Profile + + By default, AiiDA stores app data at the user level. Even when AiiDA is installed in a virtual environment, it will still read and write to `.aiida` in your home directory. However, AiiDA provides users a way to seperate their data into "profiles". Let's create a profile for this tutorial. + """ + ) + return + + +@app.cell(hide_code=True) +def _(mo): + profile_settings = mo.hstack( + [ + mo.vstack([ + "Profile Name:", + "First Name:", + "Last Name:", + "Email:", + "Institution:", + ], align="start", heights="equal", gap=0.8), + mo.vstack([ + "{profile_name}", + "{first_name}", + "{last_name}", + "{email}", + "{institution}", + ], align="start", heights="equal", gap=0.5) + ], + justify="center", align="stretch", gap=2.0, + ).batch( + profile_name=mo.ui.text("aiida-fans-tutorial"), + first_name=mo.ui.text("Max"), + last_name=mo.ui.text("Mustermann"), + email=mo.ui.text("example@nomail.com"), + institution=mo.ui.text("MIB"), + ).form( + show_clear_button=True, clear_button_label="Reset", bordered=True + ) + + mo.vstack([ + mo.md("**Fill in the details below to generate your custom profile configuration.**"), + profile_settings + ], align="center") + return (profile_settings,) + + +@app.cell(hide_code=True) +def _(mo, profile_settings): + mo.stop( + profile_settings.value is None, + mo.status.spinner(title="Awaiting input above ...", remove_on_exit=False) + ) + + profile_config = \ + rf"""profile: {profile_settings.value["profile_name"]} + first_name: {profile_settings.value["first_name"]} + last_name: {profile_settings.value["last_name"]} + email: {profile_settings.value["email"]} + institution: {profile_settings.value["institution"]} + use_rabbitmq: false + set_as_default: true + non_interactive: true + """ + + with open("configure_profile.yaml", "w") as _f: + _f.write(profile_config) + + mo.md(f""" + With the values you input above, a `configure_profile.yaml` has been automatically written to the working directory. It contains the following data: + + ```yaml + {profile_config} + ``` + """).callout(kind="success") + return + + +@app.cell(hide_code=True) +def _(mo, profile_settings): + _note = mo.md(rf""" + **Note:** _on default profiles..._ + + We have made the new profile our default profile. This means that any further calls to `verdi` will implicitly use the {"" if profile_settings.value is None else profile_settings.value["profile_name"]} profile. You can change the profile on a per-call basis with the `-p/--profile` option. To change the default profile use: + + ``` + verdi profile set-default + ``` + """).callout(kind="info") + + mo.md(f""" + To create your new profile from this file run: + + ``` + verdi profile setup core.sqlite_dos --config configure_profile.yaml + ``` + + Hopefully, that completed successfully. Using these commands, you should see your new profile listed (alone if this is your first profile) and a report on it also: + + ``` + verdi profile list + verdi profile show {"" if profile_settings.value is None else profile_settings.value["profile_name"]} + ``` + + {_note} + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md( + r""" + ### 2. Specify a Computer + + Before you proceed, ensure that your local computer satisfies the following requirements: + + - it runs a Unix-like operating system (Linux distros and MacOS should work fine) + - it has `bash` installed + + AiiDA does not assume what computer you wish to run jobs on, so even if you are only using your local machine, you must tell it as much. That is what we will do here; specify the localhost computer. + """ + ) + return + + +@app.cell(hide_code=True) +def _(mo): + computer_settings = mo.hstack( + [ + mo.vstack([ + "Computer Label:", + "MPI processes:", + "Description:", + ], align="start", heights="equal", gap=0.8), + mo.vstack([ + "{label}", + "{mpiprocs}", + "{description}", + ], align="start", heights="equal", gap=0.5) + ], + justify="center", align="stretch", gap=2.0, + ).batch( + label=mo.ui.text("localhost"), + mpiprocs=mo.ui.text("2"), + description=mo.ui.text_area("This is my local machine."), + ).form( + show_clear_button=True, clear_button_label="Reset", bordered=True + ) + + mo.vstack([ + mo.md("**Fill in the details below to generate your custom computer configuration.**"), + computer_settings + ], align="center") + return (computer_settings,) + + +@app.cell(hide_code=True) +def _(Path, computer_settings, mo): + mo.stop( + computer_settings.value is None, + mo.status.spinner(title="Awaiting input above ...", remove_on_exit=False) + ) + + computer_config = \ + rf"""label: {computer_settings.value["label"]} + description: {computer_settings.value["description"]} + hostname: localhost + transport: core.local + scheduler: core.direct + shebang: #!/bin/bash + work_dir: {Path.cwd()}/.aiida_run""" + r""" + mpirun_command: mpiexec -n {tot_num_mpiprocs}""" + rf""" + mpiprocs_per_machine: {computer_settings.value["mpiprocs"]} + default_memory_per_machine: null + use_double_quotes: false + prepend_text: ' ' + append_text: ' ' + non_interactive: true + """ + + with open("configure_computer.yaml", "w") as _f: + _f.write(computer_config) + + mo.md(f""" + With the values you input above, a `configure_computer.yaml` has been automatically written to the working directory. It contains the following data: + + ```yaml + {computer_config} + ``` + """).callout(kind="success") + return + + +@app.cell(hide_code=True) +def _(computer_settings, mo): + mo.md(rf""" + To specify your new computer from this file run: + + ``` + verdi computer setup --config configure_computer.yaml + ``` + + Then you must configure the computer with the following command: + + ``` + verdi computer configure core.local {"" if computer_settings.value is None else computer_settings.value["label"]} + ``` + + The default options should be suitable. + + Hopefully, that completed successfully. Using this command, you should test that AiiDA can connect to the machine: + + ``` + verdi computer test {"" if computer_settings.value is None else computer_settings.value["label"]} + ``` + """ + ) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md( + r""" + ### 3. Define a Code + + The final step to setup AiiDA is to define the "code" you wish to utilise. Here, the "code" refers to FANS. This step is important as it tells AiiDA how to execute FANS and which plugin should handle its jobs. AiiDA provides many ways of handling the "code" of your project. Since we installed FANS in the environment, we can simply make use of it there. + """ + ) + return + + +@app.cell(hide_code=True) +def _(mo): + code_settings = mo.hstack( + [ + mo.vstack([ + mo.vstack([ + "Code Label:", + "Code Executable:", + "Description:", + ], align="start", heights="equal", gap=0.8), + "Environment Activation Script:", + ], align="start", heights="equal", gap=5.55), + mo.vstack([ + "{label}", + "{executable}", + "{description}", + "{environment}", + ], align="start", heights="equal", gap=0.5) + ], + justify="center", align="stretch", gap=2.0, + ).batch( + label=mo.ui.text("FANS"), + executable=mo.ui.text("FANS"), + description=mo.ui.text_area("The FANS executable."), + environment=mo.ui.text_area("eval \"$(conda shell.bash hook)\"\nconda activate aiida-fans-tutorial"), + ).form( + show_clear_button=True, clear_button_label="Reset", bordered=True + ) + + mo.vstack([ + mo.md("**Fill in the details below to generate your custom code configuration.**"), + code_settings + ], align="center") + return (code_settings,) + + +@app.cell(hide_code=True) +def _(code_settings, computer_settings, mo): + mo.stop( + code_settings.value is None or computer_settings.value is None, + mo.status.spinner(title="Awaiting input above ...", remove_on_exit=False) + ) + + code_config = \ + rf"""label: {code_settings.value["label"]} + description: {code_settings.value["description"]} + default_calc_job_plugin: fans + use_double_quotes: false + with_mpi: true + computer: {computer_settings.value["label"]} + filepath_executable: {code_settings.value["executable"]} + prepend_text: | + {"\n".join([f" {ln}" for ln in code_settings.value["environment"].split("\n")])} + append_text: ' ' + non_interactive: true + """ + + with open("configure_code.yaml", "w") as _f: + _f.write(code_config) + + mo.md(f""" + With the values you input above, a `configure_code.yaml` has been automatically written to the working directory. It contains the following data: + + ```yaml + {code_config} + ``` + """).callout(kind="success") + return + + +@app.cell(hide_code=True) +def _(code_settings, mo): + _note = mo.md(r""" + **Note:** _your first node..._ + + You should also note that the code is saved by AiiDA as a node, and thus we have created our first node. Any calculation jobs we perform will be connected to this code node in the provenance graph. + + To list all the nodes stored in your profile, run: + + ``` + verdi node list + ``` + """).callout(kind="info") + + mo.md(rf""" + To define your new code from this file run: + + ``` + verdi code create core.code.installed --config configure_code.yaml + ``` + + Hopefully, that completed successfully. Using these commands, you can show the details of your new code and verify that AiiDA can connect to it: + + ``` + verdi code show {"" if code_settings.value is None else code_settings.value["label"]} + verdi code test {"" if code_settings.value is None else code_settings.value["label"]} + ``` + + {_note} + """ + ) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md( + r""" + ## FANS Rundown + + FANS requires a JSON input file. The input file can be thought of in 5 sections, each specifying the various problem parameters as well as runtime settings. Each setting also notes the appropriate AiiDA datatype. This is the type of node that you must give AiiDA when running jobs, as we will see later. + + ### Microstructure Definition + + ```json + "ms_filename": "microstructures/sphere32.h5", + "ms_datasetname": "/sphere/32x32x32/ms", + "ms_L": [1.0, 1.0, 1.0] + ``` + + - `ms_filename`: This specifies the path to the HDF5 file that contains the microstructure data. (AiiDA type: `SinglefileData`) + - `ms_datasetname`: This is the path within the HDF5 file to the specific dataset that represents the microstructure. (AiiDA type: `Str`) + - `ms_L`: Microstructure length defines the physical dimensions of the microstructure in the x, y, and z directions. (AiiDA type: `List`) + + ### Problem Type and Material Model + + ```json + "problem_type": "mechanical", + "matmodel": "LinearElasticIsotropic", + "material_properties": { + "bulk_modulus": [62.5000, 222.222], + "shear_modulus": [28.8462, 166.6667] + } + ``` + + - `problem_type`: This defines the type of physical problem you are solving. Common options include "thermal" problems and "mechanical" problems. (AiiDA type: `Str`) + - `matmodel`: This specifies the material model to be used in the simulation. Examples include `LinearThermalIsotropic` for isotropic linear thermal problems, `LinearElasticIsotropic` for isotropic linear elastic mechanical problems, `PseudoPlasticLinearHardening`/`PseudoPlasticNonLinearHardening` for plasticity mimicking model with linear/nonlinear hardening, and `J2ViscoPlastic_LinearIsotropicHardening`/ `J2ViscoPlastic_NonLinearIsotropicHardening` for rate dependent J2 plasticity model with linear/nonlinear isotropic hardening. (AiiDA type: `Str`) + - `material_properties`: This provides the necessary material parameters for the chosen material model. For thermal problems, you might specify `conductivity`, while mechanical problems might require `bulk_modulus`, `shear_modulus`, and more properties for advanced material models. These properties can be defined as arrays to represent multiple phases within the microstructure. (AiiDA type: `Dict`) + + ### Solver Settings + + ```json + "method": "cg", + "error_parameters":{ + "measure": "Linfinity", + "type": "absolute", + "tolerance": 1e-10 + }, + "n_it": 100 + ``` + + - `method`: This indicates the numerical method to be used for solving the system of equations. `cg` stands for the Conjugate Gradient method, and `fp` stands for the Fixed Point method. (AiiDA type: `Str`) + - `error_parameters`: This section defines the error parameters for the solver. Error control is applied on the finite element nodal residual of the problem. + - `measure`: Specifies the norm used to measure the error. Options include `Linfinity`, `L1`, or `L2`. (AiiDA type: `Str`) + - `type`: Defines the type of error measurement. Options are `absolute` or `relative`. (AiiDA type: `Str`) + - `tolerance`: Sets the tolerance level for the solver, defining the convergence criterion based on the chosen error measure. The solver iterates until the solution meets this tolerance. (AiiDA type: `Float`) + - `n_it`: Specifies the maximum number of iterations allowed for the FANS solver. (AiiDA type: `Int`) + + + ### Macroscale Loading Conditions + + ```json + "macroscale_loading": [ + [ + [0.004, -0.002, -0.002, 0, 0, 0], + [0.008, -0.004, -0.004, 0, 0, 0], + [0.012, -0.006, -0.006, 0, 0, 0], + [0.016, -0.008, -0.008, 0, 0, 0], + ], + [ + [0, 0, 0, 0.002, 0, 0], + [0, 0, 0, 0.004, 0, 0], + [0, 0, 0, 0.006, 0, 0], + [0, 0, 0, 0.008, 0, 0], + ] + ] + ``` + + - `macroscale_loading`: This defines the external loading applied to the microstructure. It is an array of arrays, where each sub-array represents a loading condition applied to the system. The format of the loading array depends on the problem type (AiiDA type: `ArrayData`): + - For `thermal` problems, the array typically has 3 components, representing the temperature gradients in the x, y, and z directions. + - For `mechanical` problems, the array must have 6 components, corresponding to the components of the strain tensor in Mandel notation (e.g., $[[ε_{11}, ε_{22}, ε_{33}, \sqrt{2} ε_{12}, \sqrt{2} ε_{13}, \sqrt{2} ε_{23}]]$). + + In the case of path/time-dependent loading as shown, for example as in plasticity problems, the `macroscale_loading` array can include multiple steps with corresponding loading conditions. + + ### Results Specification + + ```json + "results": [ + "stress", "strain", + "stress_average", "strain_average", + "phase_stress_average", "phase_strain_average", + "microstructure", + "displacement", + "absolute_error", + ] + ``` + + - `results`: This array lists the quantities that should be stored into the results HDF5 file during the simulation. Each string in the array corresponds to a specific result (AiiDA type: `List`): + - `stress` and `strain`: The stress and strain fields at each voxel in the microstructure. + - `stress_average` and `strain_average`: Volume averaged- homogenized stress and strain over the entire microstructure. + - `phase_stress_average` and `phase_strain_average`: Volume averaged- homogenized stress and strain for each phase within the microstructure. + - `microstructure`: The original microstructure data. + - `displacement`: The displacement fluctuation field (for mechanical problems) and temperature fluctuation field (for thermal problems). + - `absolute_error`: The L-infinity error of finite element nodal residual at each iteration. + + Additional material model specific results can be included depending on the problem type and material model. + """ + ) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md( + r""" + ## Submitting Jobs + + Now that AiiDA is suitably prepared and we're familiar with the FANS parameter specifications, its time to get to work. We will conduct a mock experiment to demonstrate the simplicity and flexibility that using the plugin offers. Breaking down the submission of jobs into two steps makes for a clean workflow. + """ + ) + return + + +@app.cell(hide_code=True) +def _(mo): + import_button = mo.ui.run_button(label="RUN") + + mo.md(rf""" + ### Creating Input Parameters + + We will create all the input parameters we wish to study today at once. To begin, we import everything that will be needed and call the `load_profile()` function to activate the default profile within this script. + + Press this button only after you have created a default profile as described above in [AiiDA Setup](#aiida-setup). + + {import_button} + + ```py + from aiida.engine import run # run jobs + from aiida.plugins import CalculationFactory # generates fans calculation + from aiida.orm import ( + Group, # node organisation tool + SinglefileData, # \ + Str, # | + Float, # | + Int, # |- AiiDA datatypes + List, # | + Dict, # | + ArrayData, # / + CalcJobNode, # node type for calculation jobs + QueryBuilder, # advanced query tool + load_node, # basic query tool for nodes + load_code, # basic query tool for codes + ) + from numpy import array # numpy array + from itertools import product # for parameter space generation + from random import uniform # for parameter space generation + + from aiida import load_profile # injects profile context into script + load_profile() + ``` + """) + return (import_button,) + + +@app.cell(hide_code=True) +def imports(import_button, mo): + mo.stop(not import_button.value) # run on click + + try: + from aiida.common.exceptions import ProfileConfigurationError, ConfigurationError + + from aiida.engine import run + from aiida.plugins import CalculationFactory + from aiida.orm import ( + Group, + SinglefileData, + Str, + Float, + Int, + List, + Dict, + ArrayData, + CalcJobNode, + QueryBuilder, + load_node, + load_code, + ) + from numpy import array + from itertools import product + from random import uniform + + from aiida import load_profile + load_profile() + + except ImportError: + mo.stop(True, output=mo.md("**Imports failed to load properly!**").style(text_align="center").callout(kind="danger")) + + except ProfileConfigurationError: + mo.stop(True, output=mo.md("**Your profile failed to load properly!**").style(text_align="center").callout(kind="danger")) + + mo.md("**Success!**").style(text_align="center").callout(kind="success") + return ( + ArrayData, + CalcJobNode, + CalculationFactory, + ConfigurationError, + Dict, + Float, + Group, + Int, + List, + QueryBuilder, + SinglefileData, + Str, + array, + load_code, + product, + run, + uniform, + ) + + +@app.cell(hide_code=True) +def _(mo): + _code = r""" + groups = QueryBuilder( # we will use the advanced query method + ).append( # it consists of a series of `.append` methods + Group, filters={ + Group.fields.label: "inputs" # here we filter by groups labeled "inputs" + } + ).all( # conclude with a method to fetch the results + flat=True + ) + + if len(groups) == 0: # if the "inputs" group does not exist... + inputs = Group( # make it + label="inputs", + description="Herein are all the manually defined inputs for FANS." + ).store() # the `.store` method saves it in the database + + elif len(groups) == 1: # otherwise don't make it + inputs = groups.pop() + """ + + mo.md(rf""" + Next, we will create a "group". This is purely an organisational tool that AiiDA provides. It may come in handy later to see what nodes belong to the inputs we are creating today. + + We use the `QueryBuilder` to find all groups with label "inputs". If none exist, we create one and provide it a short description. + + ```py + {_code} + ``` + """) + return + + +@app.cell(hide_code=True) +def group(Group, QueryBuilder, import_button, mo): + mo.stop(not import_button.value) # run on click + + groups = QueryBuilder( # we will use the advanced query method + ).append( # it consists of a series of `.append` methods + Group, filters={ + Group.fields.label: "inputs" # here we filter by groups labeled "inputs" + } + ).all( # conclude with a method to fetch the results + flat=True + ) + + if len(groups) == 0: # if the "inputs" group does not exist... + inputs = Group( # make it + label="inputs", + description="Herein are all the manually defined inputs for FANS." + ).store() # the `.store` method saves it in the database + + elif len(groups) == 1: # otherwise don't make it + inputs = groups.pop() + + else: + raise + return (inputs,) + + +@app.cell +def _(Path, mo): + try: + dataset_path = Path("tutorial_dataset.h5").absolute() + except: + mo.stop(True) + return (dataset_path,) + + +@app.cell +def _(dataset_path, mo): + _code = r""" + microstructurequery = QueryBuilder( + ).append( + SinglefileData, filters={ + SinglefileData.fields.label: "microstructure" + } + ).all( + flat=True + ) + + if len(microstructurequery) == 0: + microstructurefile = SinglefileData( + Path('""" + str(dataset_path) + r"""'), + label="microstructure" + ).store() + elif len(microstructurequery) == 1: + microstructurefile = microstructurequery.pop() + else: + raise + + inputs.add_nodes(microstructurefile) # add the node to the "inputs" group + """ + + mo.md(rf""" + Next, we store the microstructure file in the database. Using a similar strategy as with the group definition, the `QueryBuilder` first searches for existing microstructures. If none are found, we define a new one in the form of a `SinglefileData` node. This built-in AiiDA datatype points to a file via a path. Finally, the microstructure node is included in our "inputs" group. + + ```py + {_code} + ``` + """) + return + + +@app.cell +def microstructure( + QueryBuilder, + SinglefileData, + dataset_path, + import_button, + inputs, + mo, +): + mo.stop(not import_button.value) # run on click + + microstructurequery = QueryBuilder( + ).append( + SinglefileData, filters={ + SinglefileData.fields.label: "microstructure" + } + ).all(flat=True) + + if len(microstructurequery) == 0: + microstructurefile = SinglefileData( + dataset_path, + label="microstructure" + ).store() + elif len(microstructurequery) == 1: + microstructurefile = microstructurequery.pop() + else: + raise + + inputs.add_nodes(microstructurefile) + return + + +@app.cell +def _(mo): + mo.md(r""" + **Note:** _more nodes..._ + + The microstructure file node we just created is saved by AiiDA as a node. Just as before, we can list all the nodes we've created thus far; and it may be helpful to do so every once in a while to ensure everything is proceeding as expected. + + To list all the nodes stored in your profile, run: + + ``` + verdi node list + ``` + """).callout(kind="info") + return + + +@app.cell +def _(mo): + def_nodes_button = mo.ui.run_button(label="RUN", kind="warn") + def_nodes_code_switch = mo.ui.switch(label="*show full code...*") + + _code = r""" + # Microstructure Definition + Str("/dset_0/image", label="ms_datasetname"), + Str("/dset_1/image", label="ms_datasetname"), + Str("/dset_2/image", label="ms_datasetname"), + List([1.0, 1.0, 1.0], label="ms_L"), + + ... + + # Problem Type and Material Model: Moduli + Dict({"bulk_modulus": bulk, "shear_modulus": shear}, label="material_properties") + for bulk, shear in product( + [[uniform(50, 75), uniform(200, 250)] for _ in range(2)], + [[uniform(25, 50), uniform(150, 200)] for _ in range(2)] + ) + + ... + """ + + mo.md(rf""" + Now, we will define the rest of our parameters. This is mostly straightforward, but we treat `ms_datasetname` and `material_properties` a little differently. + + - `ms_datasetname`: Three different datasets are chosen from the sample microstructure file provided. + - `material_properties`: A mock parameter space study is realised by randomly picking bulk and shear moduli from within a range. + + When it comes time to run our calculations, we will run the "product" of all these parameters. + + ```py + {_code} + ``` + {def_nodes_code_switch} + """) + return def_nodes_button, def_nodes_code_switch + + +@app.cell(hide_code=True) +def _(def_nodes_button, def_nodes_code_switch, mo): + def gatekeep1(): + mo.stop(not def_nodes_button.value and def_nodes_code_switch.value, output=mo.show_code()) + mo.stop(not def_nodes_button.value) + return (gatekeep1,) + + +@app.cell +def node_definition( + ArrayData, + Dict, + Float, + Int, + List, + Str, + array, + gatekeep1, + product, + uniform, +): + gatekeep1() # Ignore this line. + + nodes = [ + + # Microstructure Definition + Str("/dset_0/image", label="ms_datasetname"), + Str("/dset_1/image", label="ms_datasetname"), + Str("/dset_2/image", label="ms_datasetname"), + List([1.0, 1.0, 1.0], label="ms_L"), + + # Problem Type and Material Model + Str("mechanical", label="problem_type"), + Str("LinearElasticIsotropic", label="matmodel") + ] + [ + Dict({"bulk_modulus": bulk, "shear_modulus": shear}, label="material_properties") + for bulk, shear in product( + [[uniform(50, 75), uniform(200, 250)] for _ in range(2)], + [[uniform(25, 50), uniform(150, 200)] for _ in range(2)] + )] + [ + + # Solver Settings + Str("cg", label="method"), + Str("Linfinity", label="error_parameters.measure"), + Str("absolute", label="error_parameters.type"), + Float(1e-10, label="error_parameters.tolerance"), + Int(100, label="n_it"), + + # Macroscale Loading Conditions + ArrayData({ + "0": array([[0,0,0,0,0,0]]) + }, label="macroscale_loading"), + + # Results Specification + List(["stress", "strain", "stress_average", "strain_average", + "absolute_error", "phase_stress_average", "phase_strain_average", + "microstructure", "displacement"], label="results") + + ] + return (nodes,) + + +@app.cell +def _(def_nodes_button, mo): + mo.md(rf""" + While the cell above defined all the parameters, they still need to be stored in the database. Otherwise, they will be lost when the session ends. AiiDA automatically stores nodes when submitting them to a job, but it is good practice to handle this yourself. Moreover, you get to see your database grow step by step. After clicking the button below, try running `verdi node list` in your terminal to see all the new additions we've made so far, and `verdi node show ` for more information about specific nodes. + + It is important to note that this time we did not make any checks through the QueryBuilder to ensure that indentical nodes don't already exist. This means that if you click the button below repeatedly, you *may* cause duplicate nodes to be created. Since these are some the first nodes we're making, it is not so critical, but in practice you would want to first fetch existing nodes you want to reuse before creating the remainder of the nodes you wish to study. + + {def_nodes_button} + + ```py + for node in nodes: # iterate over the list of new node + node.store() # store each one in the database + inputs.add_nodes(node) # assign each one to the "inputs" group + ``` + """) + return + + +@app.cell +def node_storage(def_nodes_button, inputs, mo, nodes): + mo.stop(not def_nodes_button.value) # Ignore this line. + + for node in nodes: # iterate over the list of new node + node.store() # store each one in the database + inputs.add_nodes(node) # assign each one to the "inputs" group + return + + +@app.cell +def _(mo): + mk_params_code_switch = mo.ui.switch(label="*show full code...*") + + _code = r""" + some_params = [{ + "problem_type": fetch("problem_type", "mechanical"), + "matmodel": fetch("matmodel", "LinearElasticIsotropic"), + ... + }] + + ... + + ms_datasetname_params = [ + {"microstructure":{"file": ms_file,"L": ms_L, + "datasetname": fetch("ms_datasetname", "/dset_0/image"),} + }, + {"microstructure":{"file": ms_file,"L": ms_L, + "datasetname": fetch("ms_datasetname", "/dset_1/image"),} + }, + {"microstructure":{"file": ms_file,"L": ms_L, + "datasetname": fetch("ms_datasetname", "/dset_2/image"),} + } + ] + + material_properties_params = [ + {"material_properties": mp.pop()} + for mp in QueryBuilder().append( + Dict, filters={ + Dict.fields.label: "material_properties" + } + ).iterall() + ] + """ + + mo.md(rf""" + ### Executing Calculations + + Now that all the input parameters have been specified, it it time to run some calculations. We create lists of dictionaries for each set of paramaters we wish to vary. In our case, `microsctructure` needs a list, as does `material_properties`. Everything else falls into a list of length one. The keys of the dictionaries here are important and are specified by the plugin. More information is available in the documentation, but efforts are being made to synchronise these with the FANS parameter specification. + + Below, some nodes are fetched using a helper function (see [Appendix A](#appendix)) which essentially queries the database for a single node with a particular label and value. You could also use the nodes we created above instead, passing them forward as variables, but here we demonstrate how you might run calculations using a either new or old nodes at once. + + Click the button bellow when you are sure that all the nodes above have been successfully created and stored. Try `verdi node list` to see them all. + + + ```py + {_code} + ``` + + {mk_params_code_switch} + """) + return (mk_params_code_switch,) + + +@app.cell +def _(def_nodes_button, mk_params_code_switch, mo): + def gatekeep2(): + mo.stop(not def_nodes_button.value and mk_params_code_switch.value, output=mo.show_code()) + mo.stop(not def_nodes_button.value) + + def gatekeep3(): + return mo.show_code() if mk_params_code_switch.value else None + return + + +@app.cell +def parameter_definition( + ArrayData, + Dict, + QueryBuilder, + SinglefileData, + fetch, + mk_params_code_switch, + mo, +): + some_params = [{ + "problem_type": fetch("problem_type", "mechanical"), + "matmodel": fetch("matmodel", "LinearElasticIsotropic"), + "method": fetch("method", "cg"), + "error_parameters": { + "measure": fetch("error_parameters.measure", "Linfinity"), + "type": fetch("error_parameters.type", "absolute"), + "tolerance": fetch("error_parameters.tolerance", 1e-10) + }, + "n_it": fetch("n_it", 100), + "macroscale_loading": QueryBuilder().append( + ArrayData, filters={ + ArrayData.fields.label: "macroscale_loading" + } + ).first(flat=True), + "results": fetch("results", ["stress", "strain", "stress_average", "strain_average", "absolute_error", "phase_stress_average", "phase_strain_average", "microstructure", "displacement"]) + }] + + ms_file = QueryBuilder().append( + SinglefileData, filters={ + SinglefileData.fields.label: "microstructure" + } + ).first(flat=True) + + ms_L = fetch("ms_L", [1.0, 1.0, 1.0]) + + ms_datasetname_params = [ + {"microstructure":{"file": ms_file,"L": ms_L, + "datasetname": fetch("ms_datasetname", "/dset_0/image"),} + }, + {"microstructure":{"file": ms_file,"L": ms_L, + "datasetname": fetch("ms_datasetname", "/dset_1/image"),} + }, + {"microstructure":{"file": ms_file,"L": ms_L, + "datasetname": fetch("ms_datasetname", "/dset_2/image"),} + } + ] + + material_properties_params = [ + {"material_properties": mp.pop()} + for mp in QueryBuilder().append( + Dict, filters={ + Dict.fields.label: "material_properties" + } + ).iterall() + ] + + mo.show_code() if mk_params_code_switch.value else None # Ignore this line. + return material_properties_params, ms_datasetname_params, some_params + + +@app.cell +def _(code_settings, mo): + calculate_button = mo.ui.run_button(label="RUN", kind="warn") + + get_calc_state, set_calc_state = mo.state(False) + + _code = r""" + FANSCalculation = CalculationFactory("fans") # get the plugin's process class + code = {"code": load_code('""" + f"{"')}" if code_settings.value is None else code_settings.value["label"] + "')}" : <22}" + """ # get the existing code node + + for sp, dsp, mpp in product(some_params, ms_datasetname_params, material_properties_params): + all_params = sp | dsp | mpp # merge this permutation of params + run(FANSCalculation, all_params | code) # finally run the job + """ + + mo.md(rf""" + Once these lists are defined, we use the `product` function to explore every permutation of their contents. Each permutation is coupled with the code node, defined earlier, and given to the `run` function with the plugin specific `FANSCalculation` process class. + + Much like last time, we aren't checking if these calculations have already been run, so clicking the button below repeatedly will request duplicate calulations to be run and duplicate results will be generated. + + {calculate_button} + + ```py + {_code} + ``` + """) + return calculate_button, get_calc_state, set_calc_state + + +@app.cell +def calculations( + CalculationFactory, + ConfigurationError, + calculate_button, + code_settings, + load_code, + material_properties_params, + mo, + ms_datasetname_params, + product, + run, + set_calc_state, + some_params, +): + mo.stop(not calculate_button.value) + + FANSCalculation = CalculationFactory("fans") # get the plugin's process class + try: # get the existing code node + code = {"code": load_code(code_settings.value["label"])} + except ConfigurationError: + mo.stop(True, output=mo.md("**Your code failed to load properly!**\n\nPlease submit the 'Define a Code' form in the [AiiDA Setup](aiida-setup) section.").style(text_align="center").callout(kind="danger")) + + for sp, dsp, mpp in mo.status.progress_bar( + list(product(some_params, ms_datasetname_params, material_properties_params)), + title="Calculating Jobs...", completion_title="Finished!" + ): + all_params = sp | dsp | mpp # merge this permutation of params + run(FANSCalculation, all_params | code) # finally run the job + else: + set_calc_state(True) + return + + +@app.cell(hide_code=True) +def _(mo): + query_button = mo.ui.run_button(label="RUN") + + get_query_state, set_query_state = mo.state(False) + + mo.md(rf""" + ## Analysing the Results + + Once our calculations are complete, we can make use of the QueryBuilder again to find and analyse the results. + + {query_button} + """) + return get_query_state, query_button, set_query_state + + +@app.cell(hide_code=True) +def _(mo, query_button, set_query_state): + #! DO NOT DELETE + # This cell saves the query_button state to allow for user confirmation! + mo.stop(not query_button.value) # run on click + set_query_state(True) + return + + +@app.cell(hide_code=True) +def _(get_calc_state, set_calc_state): + #! DO NOT DELETE + # This cell triggers the following cell upon confirmation! + set_calc_state(get_calc_state()) + return + + +@app.cell +def _( + CalcJobNode, + Int, + QueryBuilder, + Str, + get_calc_state, + get_query_state, + mo, + set_calc_state, + set_query_state, +): + confirm = mo.ui.button(label="Are you sure?", on_click=lambda _: set_calc_state(True)) + are_you_sure = mo.md(rf""" + It seems the jobs were not calculated in this session. If you are sure that they have been completed, you may proceed. + + {confirm} + """).callout(kind="danger") + mo.stop(not get_query_state()) + mo.stop(not get_calc_state(), output=are_you_sure) + set_query_state(False) + + + # QUERY: + + calc = QueryBuilder().append(CalcJobNode).first(flat=True) + # Inputs + ins = list(calc.inputs._get_keys()) + ins = "
".join(ins) + # Microstructure Dataset Name + ms_datasetname = calc.inputs.microstructure.datasetname.value + # Material Properties + mat_props = { + "b": ( + calc.inputs.material_properties["bulk_modulus"][0], + calc.inputs.material_properties["bulk_modulus"][1] + ), + "s": ( + calc.inputs.material_properties["shear_modulus"][0], + calc.inputs.material_properties["shear_modulus"][1] + ) + } + # Outputs + outs = list(calc.outputs._get_keys()) + outs = ", ".join(outs) + # Stresses and Strains + log = calc.outputs.retrieved.get_object_content("input.json.log").split("\n") + stresses = [] + strains = [] + for ln in log: + if "Effective Stress" in ln: + stresses.append(list(map( + lambda n: round(float(n), ndigits=3), + ln.lstrip("# Effective Stress .. ") + .replace("(", "").replace(")", "").strip(" ") + .split(" ") + ))) + if "Effective Strain" in ln: + strains.append(list(map( + lambda n: round(float(n), ndigits=3), + ln.lstrip("# Effective Strain .. ") + .replace("(", "").replace(")", "").strip(" ") + .split(" ") + ))) + stress_strains = [{"stress": stress, "strain": strain} for stress, strain in zip(stresses, strains)] + # Filtered Query + filtered_calcs = \ + QueryBuilder( + ).append( # In the first `.append` we look for nodes + Str, # of the `Str` AiiDA datatype, + filters={ # then apply the filters for: + Int.fields.label: "ms_datasetname", # + Int.fields.value: {"==": "/dset_0/image"} # + }, + tag="ms_datasetname" # The `tag` is an internal reference. + + ).append( # In the second `.append` we look for nodes + CalcJobNode, # of the `CalcJobNode` AiiDA datatype, + with_incoming="ms_datasetname" # and specify required incoming nodes with + # the `tag` we defined above. + ).all(flat=True) + + + # DISPLAY: + + _code = r""" + QueryBuilder( + ).append( # In the first `.append` we look for nodes + Str, # of the `Str` AiiDA datatype, + filters={ # then apply the filters for: + Int.fields.label: "ms_datasetname", # + Int.fields.value: {"==": "dset_0"} # + }, + tag="ms_datasetname" # The `tag` is an internal reference. + + ).append( # In the second `.append` we look for nodes + CalcJobNode, # of the `CalcJobNode` AiiDA datatype, + with_incoming="ms_datasetname" # and specify required incoming nodes with + # the `tag` we defined above. + ).all(flat=True) + """ + + mo.md(rf""" + ### Fetch a single calculation... + + We will begin by querying the database for the first `CalcJobNode` present. This is the AiiDA datatype given to nodes that represent the exectution of an individual job. + + ```py + calc = QueryBuilder().append(CalcJobNode).first(flat=True) + ``` + + From this calculation job node we can gleam some identifying information, such as the type of calculation job (i.e. the process label) or its primary key in the database. Additionally, we can list the available inputs and outputs provided by this kind of job. + + | | | + |--------------------|---------------------------------------| + | **Process Label:** | {calc.process_label} | + | **Primary Key:** | {calc.pk} | + | **Inputs:** | {ins} | + | **Outputs:** | {outs} | + + ### Identify some input parameters... + + Of course, it would be helpful to know exactly what inputs were used in the calculation of this particular job. The inputs can be accessed via dot notation which provides the respective values as AiiDA datatypes. + + When it comes to the microstructure dataset name, the inputs's value is accessed through the `value` attribute. + + ```py + calc.inputs.microstructure.datasetname.value + ``` + + | | | + |-|-| + | **Microstructure Dataset Name:** | {ms_datasetname} | + + In the case of the material properties, this attribute takes the form an AiiDA `Dict` which has methods just like an ordinary `dict`. + + ```py + calc.inputs.material_properties.items() + ``` + + | | | | + |-|-|-| + |Bulk Modulus: | {mat_props["b"][0]} | {mat_props["b"][1]} | + |Shear Modulus: | {mat_props["s"][0]} | {mat_props["s"][1]} | + + ### Effective stress and strain... + + To extract the effective stress and strain per loading condition from the output of FANS, we can use the `std_out` it produces. This text is stored in the `retrieved` folder output. We can get its contents and parse it to determine our results. + + ```py + log = calc.outputs.retrieved.get_object_content("input.json.log") + for ln in log: + ... + ``` + + | Loading
Condition: | Stress: | Strain: | + |---|-------------------------------|-------------------------------| + | **1** | {stress_strains[0]["stress"]} | {stress_strains[0]["strain"]} | + + ### Perform a filtered query... + + Aside from manually examining the inputs and outputs of individual calculation jobs, the `QueryBuilder` offers the ability to filter your query based on a variety of criteria. In this instance, we query for all jobs that used the "dset_0" microstructure dataset. This time, we are given back a list of calculation job nodes to do with as we please. + + ```py + {_code} + ``` + + | | | | | | + |-|-|-|-|-| + | **Primary Keys:** | {filtered_calcs[0].pk} | {filtered_calcs[1].pk} | {filtered_calcs[2].pk} | {filtered_calcs[3].pk} | + + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r"""# Appendix""") + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md( + r""" + ## A. `fetch()` + + This is a helper function to simplify the querying of individual nodes when the label and value are known. + """ + ) + return + + +@app.cell +def _(Dict, Float, Int, List, QueryBuilder, Str, mo): + def fetch(label : str, value): + """Helper function to return a node whose label and value are known. + + Returns an error if more or less than 1 suitable node is found. + """ + match value: + case str(): + datatype = Str + case int(): + datatype = Int + case float(): + datatype = Float + case list(): + datatype = List + case dict(): + datatype = Dict + case _: + raise NotImplementedError + + bone = QueryBuilder().append( + datatype, + filters={ + datatype.fields.label: label, + "attributes.value": value + } if datatype is not List else { + datatype.fields.label: label, + "attributes.list": value + }, + ).all(flat=True) + + if len(bone) != 1: + raise RuntimeError + + return bone.pop() + + mo.show_code() + return (fetch,) + + +if __name__ == "__main__": + app.run() diff --git a/tutorial/tutorial_dataset.h5 b/tutorial/tutorial_dataset.h5 new file mode 100644 index 0000000..2811cb7 Binary files /dev/null and b/tutorial/tutorial_dataset.h5 differ